One thing I’m observing in these comments is a willingness of folks to project their own predictions onto Ed’s statements when validating their plausibility. Eg. “I think he’s wrong about the timing but I do expect AI companies to go to zero.”
You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning.
Those differ from Dan’s essay, which engages with the literal text of Ed’s numerous predictions during 2024 and 2025 which are demonstrably invalidated by their measurable outcomes.
>You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning.
Depends if you care about the "prediction" part or if you care about the assessment of the situation (regardless of date).
If someone in 2000 said "the subprime mortgages market is a bubble and will blow no later than 2003", they got the prediction wrong, but their assessment would be right.
The subprime market changed quite a bit in size and how much was securitized in the run up to 2007, so not sure a prediction in 2000 for a 2003 event would have been easily transferred to what happened later. Would really come down to what specifically the prediction was based on for it to be a bubble in 2000.
> If someone in 2000 said "the subprime mortgages market is a bubble and will blow no later than 2003", they got the prediction wrong, but their assessment would be right.
It doesn't make sense to split it though. Their assessment is that it's a bubble AND that it will blow no later than 2003. It's a single statement.
And it matters, because if all you're doing is saying there's an AI bubble then it's harder to prove you wrong but you also don't stand out and won't get a lot of credit for it. A very large number of people are saying the same thing as you, so who cares.
People like Zitron stand out because they go further than others and make detailed statements. Which happen to be wrong.
Zitron predicted the downfall of Oracle as someone mentioned below. He also predicted that the overhyped data center construction plans (Project Stargate, repeated vague Nvidia pledges) would not materialize.
If he got MSFT's cloud revenue growth wrong for this year, how much of that is selling shovels to OpenAI and how much is circular?
Why is Zitron’s repute evaluated entirely on the basis of failed predictions? Predictions are incredibly hard. AI enthusiasts and thought leaders have made so many demonstrably incorrect predictions it’s hard to keep track. Based on this metric, Altman and Amodei should never be taken seriously again.
Years back John C. Dvorak talked about predictions. He's was being ridiculed for his comment that there was "no evidence that computer users would want to use a mouse" (which was sort of true at the time). One of his point was that he had made a crazy amount of predictions on various topics, some came true, many didn't. People just remember the one you got right, and the ones you got horribly wrong.
Ed Zitron is just one AI crash away from being known as the guy who saw this coming. Everything else he has said can be completely wrong, he just needs to be somewhat correct on a minor crash.
Have you ever listened to Zitron speak? He's not exactly the type to hedge his predictions behind careful language about how hard predictions are. He makes every one of these predictions with absolute confidence and conviction.
I think it's fair to say predictions that Altman and Amodei make should never be taken at face value, as well as Zitron. That's fine. But that doesn't have any bearing on Dan Luu's claims. This feels like an example of what he talks about in the article when saying that people respond to his claims by pointing at something entirely different. That is to say, whether AI enthusiasts make silly predictions doesn't mean that these companies aren't going to be profitable, or that any of Zitron's predictions are any good either.
I thought in the post Ed was being evaluated based on all his predictions, and it turned out all of them were wrong.
Most people will read the post as going over all the falsifiable predictions and none of them panning out, since after the chronological prediction list it says "After this point, most further predictions that I saw were either non-falsifiable or resolve in the future".
Isn't that pretty much his whole thing, confidently telling us what will happen? Of course his reputation should suffer if his predictions are wrong, as should those of the others whose correctness : confidence ratio is too low. (But if their reputation/power/relevance mainly comes from things other than their punditry, we can't really stop 'taking them seriously' altogether.)
Because Zitron is specifically making a name for himself as a critic making bearish predictions; Altman and Amodei may have made unreasonably bullish predictions, but they've also done other relevant things like e.g. being involved in the actual development of the models.
- a list of predictions that are entirely wrong, from A-to-Z, and are not even resembling what ends up happening
- a list of predictions that are wrong, but where the underlying points are in fact interesting and have some predictive value, and it's just the "last step" that is wrong
For example, one person might say "oh it's raining in Dallas therefore I should buy some TI stock". And we'll say for sake of argument that they say that even though it's nice and sunny in Dallas at the moment.
Another person says "Oh its raining a lot in Idaho and that is going to increase potato yields and therefore I will buy McDonalds stocks cuz fries will be cheaper". In this hypothetical it turns out McDonalds buys all their potatoes from ... Kansas or something instead (and it's a specific kind of potato in a completely separate market)... but Idaho potato yields _did in fact go up_.
An even more straightforward point: the iphone 3GS comes out in 2010, people are very hyped, someone looks at how RIM _still_ hasn't gotten its shit together and declares "RIM isn't going to to be able to stay profitable 18 months from now, they're gonna have their lunch eaten".
Turns out that RIM still made a healthy profit in 2010. and 2011. And 2012. 2013 was their first loss in a while... and then it wasn't until 2014 that they really got kicked in the face.
The prediction was early, overestimated how long of a tail RIM would experience, but how wrong was it? Was the prediction of some utility?
I'm saying this... it would be helpful if _some_ more AI companies flamed out. In some sense he does himself no favors by focusing on the corps with the biggest war chest instead of the various AI companies that spend a bunch to go nowhere fast and then have just disappeared.
> The prediction was early, overestimated how long of a tail RIM would experience, but how wrong was it? Was the prediction of some utility?
Wrong enough that the utility is seriously diminished. Predicting a specific quantity dropping to a specific level at a specific date is a lot more valuable than saying “those guys are cooked”.
And even if some minuscule utility existed: why should predictors be so coddled by their observers? We should be demanding more rigour from predictors rather than looking for new and creative ways to forgive them for their folly.
Ok, I was thinking that probably they were saying that he was accidentally right, but missed the timing. It's not that. It isn't that he was accidentally right in a certain scenario also. It's that "I reinterpret the prediction to make it fit my own vision of the world"... that's not how prediction works. Hell that's not how anything would work.
Here's an actual prediction I made about a year ago: LLMs have to demonstrate that they make productivity gains that explain the costs or economics will make this problem solve itself, via higher energy cost and loss of business/productivity.
That prediction is unbound in the time horizon but it's bound by conditions that explain the triggers and how they will behave. Such prediction is useful. Hell, I could even make a prediction on why the timeline can't be bound while making a prediction on the timeline: I predict that in the next 3-5 years this will have to solve itself, because there's a limit on how much money irrational actors can pour onto something that have limited value. 7-10 years is way too much. I at least hope their coffers are that deep... if this drags on long enough, at some point people are going to want a change
This is an article that only cites Zitron’s opinions about subjective model quality. You’re tisk-tisking people to be objective about a bunch of words saying that the author’s opinions are better opinions than the guy he’s talking about OP
Some guy wrote that he’s grumpy because he couldn’t sleep and decided to dunk on an internet personality he doesn’t like, it’s not the ceremonial placement of the ur-kilogram
> This is an article that only cites Zitron’s opinions about subjective model quality. You’re tisk-tisking people to be objective about a bunch of words saying that the author’s opinions are better opinions than the guy he’s talking about OP
Oh it's not just the author's opinions. They're the opinions of a bunch of LLMs he checked, too. Much better.
What I observe is that what people don’t seem to grasp is that Zitron isn’t an AI sage. He’s simply someone who figured out that he can get a lot of attention by taking a contrarian stance when it comes to AI.
He can be 100% wrong about AI, but people will still read or listen to his next prediction. At this point, it’s mostly entertainment rather than a source of solid predictions.
He has one primary objective and that's to keep Ed Zitron in people’s minds by any means necessary.
It happens in sports, politics and, with Ed Zitron, AI.
In general I have about zero enthusiasm for trying to find defensible interpretations of things that Ed Zitron said, and I generally agree that the name of Zitron just largely needs to stop coming up in anti- and anti-anti-AI arguments since, it seems, he's just not a particularly insightful or reliable voice on the subject. That said, one or two of the specific assessments in Luu's article seem dubious as well, especially this one:
It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference. See eg. https://www.tobyord.com/writing/mostly-inference-scaling . And in fact in the quoted and linked article https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ Zitron comes up with something which looks like a recognisable explanation of this:
> Because model developers hit a wall of diminishing returns, and the only way to make their models do more was to make them burn more tokens to generate a more accurate response (this is a very simple way of describing reasoning, a thing that OpenAI launched in September 2024 and others followed).
> As a result, all the "gains" from "powerful new models" come from burning more and more tokens.
AFAICT the other drivers of recent progress in LLMs have been: ploughing in lots and lots of specialised training data custom-made at piecework websites https://www.youtube.com/watch?v=4pG3SJQPAwk ; and work on harnesses and the like. AFAICT neither of those makes false the claim that "[t]hese models have clearly hit a wall where training is hitting diminishing returns" either. Similarly, even if some big new advance does cause training or post-training to start scaling like gangbusters again in 2027 or 2028 that wouldn't make the quoted statement clearly wrong: Zitron would clearly like you to infer that there won't be any further big advances soon in LLM training, but the quoted statement doesn't clearly make that claim. (Even if he had made that claim, and it did turn out to be wrong, it would be a relatively forgivable error, more on the "cloudy crystal ball" than "misstates currently known facts" end of the spectrum.)
So: it seems that Luu took a fairly specific, objectively judgeable claim from Ed Zitron; and that claim was ... correct?; and Luu instead rated it "Wrong" without further elaboration. It seems that Luu interpreted the quoted claim as saying something like "model progress has ceased"; but it seems that's not what that specific claim (as opposed to whatever other things Zitron has said at other times and places) said.
>It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference.
I'm not an expert either, but while I do think for a bit it looked like ~all the improvement was inference-time scaling, it hasn't stayed that way. Mythos/Fable is likely a very large model (ex: it knows many things without searching) and this is probably part of its high level of capability, and the companies have started doing very large amounts of RL (which in OpenAI's case led to the HF attack).
> and the thing that is continuing to scale well or pretty well is inference
No, the models are just more intelligent. GPT 5.6 Sol can do more in fewer output tokens than any model from late 2025. Test-time compute isn't the only lever the labs have for scaling. This is among the two major things Ed has gotten laughably wrong in his technical predictions (that TTC was the last resort to make models better, and that synthetic data wouldn't help)
My understanding is that RLVR, synthetic data generation and a slew of other post-training techniques are what have driven many recent advances in models more so than manual data providers. The economics of that are for sure worse than just scaling pre-training but it is incorrect to think that test time inference scaling and manual data entry are the only ways in which models are advancing.
>> Those differ from Dan’s essay, which engages with the literal text of Ed’s numerous predictions during 2024 and 2025 which are demonstrably invalidated by their measurable outcomes.
Dan Luu did not engage on anything more, than a disorganized wall of text, ranted like a teenager using toxic personal attacks, while obsessing over calendar errors and a placeholder in a spreadsheet. If this is what passes here for a smart engineer...Lets analyze his post in a more logical and analytical way:
- His entire argument is based on the naive logic that because LLM execution speeds or benchmarks marginally improved over the last 24 months, the entire trillion dollar investment cycle is justified. A short window of venture subsidized chip buying...tells you absolutely nothing about the multi decade debt structures, physical infrastructure depreciation, and power grid constraints that dictate whether a capital heavy business model survives.
- While he whines about Zitron numbers, fails to provide a single! macro level equation to address the real financial threat. NYU finance professor Aswath Damodaran for example, explicitly warned that the current AI build out is an asset heavy, debt funded run up backed by private capital markets. Unlike the dotcom boom which was equity funded and contained to tech shareholders today AI infrastructure burdens companies with a massive $80 billion in CapEx per gigawatt, meaning a monetization correction will trigger widespread systemic debt distress and loan defaults across the real economy.
- Dismissing the AI bubble thesis, because you found a spreadsheet typo in a newsletter, and ignoring the other voices who are aligned with Zitron core premise, means you are also dismissing the research of a Nobel Laureate in economics, the Dean of Valuation, veteran hedge fund managers, Barclays, S&P Global, and Citigroup. Arguing that "the models are hitting benchmarks" while ignoring that the physical balance sheets and enterprise budgets cannot support a multi trillion dollar infrastructure build out, is exactly the type of Dunning Kruger this corner excels at....
Ed Zitron is correct, despite the clumsiness or unpleasantness of his message delivery, and this community reaction, will be an historical record of the AI bubble crowd madness.
It took years to take down Maddoff, and more to take down Bear Stearns. It will take maybe 5 - 10 years of "Ed Zitron is wrong posts here" until Anthropic and OpenAI have to be bailed out by the US government, but the day of reckoning will come. The end of this universe is all tax payers will own a piece of AI and will pay for it with increased interest rates for the next 25 years...
> the naive logic that because LLM execution speeds or benchmarks marginally improved over the last 24 months, (...)
"Marginally improved"? Are you really going to sit there tell me that an appropriate way to sum up the difference between the AI we had access to in Sep 2024 and the AI we have access to now, is "the benchmarks marginally improved"?
Btw., projections are just that - projections and I am not sure Acemoglu proved things mathematical (as in a mathematical proof) but rather within the context of a model/assumptions.
That financial markets/innovation can outpace the actual innovation is also not some new insight, but that alone doesn't necessarily make for a useful prediction.
The post is titled “How accurate have Ed Zitron's AI skeptic predictions been?” not “How accurate will Ed Zitron’s AI skeptic predictions be in the future?” or “Is the entire AI industry build out justified?”
If Patrick Boyle, Aswath Damodaran, and Daron Acemoglu have more accurate reporting and predictions about the upcoming decline of the AI industry, maybe those are voices who should be elevated over Zitron.
> It took years to take down Maddoff, and more to take down Bear Stearns. It will take maybe 5 - 10 years of "Ed Zitron is wrong posts here" until Anthropic and OpenAI have to be bailed out by the US government, but the day of reckoning will come.
Unfortunately, the market can stay irrational (far) longer than you can remain solvent.
In any case... I doubt Anthropic, OpenAI and xAI have any kind of moat that can justify a bailout. There is nothing truly unique either of these three possess, and certainly not against the free competition mostly from China or from Facebook that anyone can self-host.
Who will get the bailouts instead is the pension funds and other investment vehicles that have been force-fed crap AI stock like foie gras geese.
If Ed Zitron was merely saying that there is a AI bubble on the markets that will ultimately collapse even if we're not exactly sure when and how, then such prediction would be less interesting but also much harder to disprove.
But that's not what he's saying. He's making very specific claims that are indeed proven wrong. You can't honestly say he's correct, and the burst of an AI bubble will not be a reckoning.
On the flip side, I also see many people taking this thread as an opportunity to shit on Zitron as a person, and not "discussing his prediction". Incompetent/ blow hard/ dishonest are ones I remember off the top of my head.
AI by itself, is surprisingly polarized; Ed Zitron even more so.
Incompetent and dishonest are characteristics that follow from this professional work, adequately describing an individual who continues to make poor predictions, analyses and false statements refuted by past events.
I mean if his personal work is incompetent or dishonest...?
One of the easy ways to evaluate this is: how has he taken being incredibly wrong about his extremely confident predictions over and over and over?
Typically with people like this, they completely shrug off being super wrong. It's barely even a blip on their radar, and even bringing it up is a good way to get them to immediately attack you to deflect attention from how bad their predictions or assertions were.
If you're constantly making predictions on Topic X, and said predictions are consistently, wildly wrong, and you never actually grapple with that or acknowledge how wrong you were in the past, then that's, at the very least, intellectually dishonest.
But by all means, someone link us to his blog posts where he goes over his wrong predictions without excuses or deflections. I'd be happy to change my mind.
AI companies will never go to zero because AI is part of the Military Industrial Complex now. All the money is coming from the military and government for surveillance and power and war.
1. Revenue if anthropic and openai is unlikely to grow to the high levels they need to pay for their commitments. Many of their heavy users (coding) will eventually offset a lot of usage to more efficient and cheaper open weight models. I know of people in a company I was at that spend thousands of dollars a month on tokens. I am sure that what they're using it for can be substituted in large part by way cheaper models.
2. A lot of corporate AI usage is being pushed by management that doesn't really understand the extent of its useful, and just wants to call themselves an AI-first company.
3. If this datacenter build-out proves to be beyond the actual demand, there might be a big economic crisis as to how much of the financial system is getting tied up with it (insurance money, private credit).
Whether openai and anthropic actually die, I'm not sure. But I don't think they'll be the next big tech companies. I think eventually they'll be absorbed by others.
The whole thing I think, can be summarized as: LLMs will be commodity like. And it's price will go down and eventually will run locally, it's not at all clear that this will bring AGI and that it's worth infinite amount (or trillions) of investment ahead of the actual demand or the AGI level do-it-all-for-you AI being reached.
I feel like too many people have a binary vision of the world, i.e. you're either "pro AI" or "anti AI".
Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase. He's basically saying that (1) the current data center investments are based on unrealistic revenue projections and (2) hyperscalers are using accounting tricks to move around "money" in a circular way to make it look like more money is already flowing to AI.
You can believe all of that and still believe that AI (as in "LLM based services") work, are useful and will probably see their usefulness grow even more. Just not in the magnitude necessary to make the current investments make sense.
If you ever listen to more than a couple of Zitron interviews, it's clear that he's not the "reasonable centrist" viewpoint on AI. He's the doom-and-gloom guy.
Maybe there is someone out there positive on AI itself and calling out reasonable objections to some of the extreme things. But that's not Zitron.
> Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase
He's said variants of both of these things before! That's the point OP makes too, people say "He doesn't say that" or "But what about this" when he's said a million, sometimes contradictory things.
After looking at some of his recent interviews, he's acknowledged his previous predictions that 1. He was naive enough to think the investment won't keep coming despite lack of profitability and 2. He acknowledges that AI in it's current form is a billion dollar industry, not a trillion dollar one, but also not zero.
To be fair he is nothing more than a talking head who historically has had some pretty negative AI pieces. Sure nothing is binary but more than ever it feels like there are talking heads on both sides that have some pretty extreme views.
“there’s a once in a lifetime discount happening at the OpenAI Intelligence Depot, and I brought 15 shopping carts.”
We have to understand current usage with this behavior in mind, there are tens of thousands of people just like this author who are intentionally generating as much usage as possible on their subsidized plans because they feel compelled by some need to get free intelligence.
The only reason these expensive models are generating so much usage is because they are so heavily subsidized. Pragmatic users will shift to cheaper models which will hurt per token revenue, yes, but huge volumes of usage is going to just disappear because there isn’t the demand when it isn’t being subsidized. Less revenue per token and less tokens.
https://tokscale.ai/leaderboard a small sample of just 2k users have generated over $100m of API-equivalent usage while paying closer to just $1m.
It goes a layer up, too. Harvey (legal AI) recently tweeted that a single user query cost that firm 26k USD. This, while they have thousands of users and reportedly whole law firms paying zero. That difference is being subsidized by billions of VC dollars.
Why do everyone assume they are subsidized? When we seemingly have no idea what it costs? Maybe average subscription is breaking even and token spend is pretty much pure profit?
Case in point, claude code seems hell bent on increasing usage at all cost. Which makes sense in the growing phase (get people hooked) but it does not make sense given the hardware shortage. So, which is it?
Yeah personally I think he is right about a lot of the current state of affairs. I think he has a decent grasp of the situation despite being incredibly biased.
His predictions though? I don’t think I’ve ever read anything from him to get his read on how things will play out.
Just because one makes the wrong predictions does not mean that the data used to make them was wrong. A lot of people seem to dismiss his reporting because of his takeaways
LLMs are a national security issue? Why is China releasing their models then?
The question is not whether you should have datacenters, or the most advanced chips, or the ability to build the most capacity. But do we need all this now? Will there be enough demand? People want to make profits form this thing, and what's being pointed out is that maybe there won't be enough demand to generate profits for all this investment.
This reads to me as incredible cope. Maybe not both of these companies but certainly one of them will be enormously valuable, and there will always value in the frontier models even if much of the practical usage can be done locally
Why is it “certainly” the case that one will be enormously valuable?
Neither appears to be on track to long-term profitability specifically once you take into account depreciation on CAPEX.
On top of this the “productivity gains” from AI across the board seem to be a very mixed bag. The pitch from these companies has been huge gains in productivity and automation and although there is anecdotal evidence some people are able to do that, the broader studies seem to show marginal gains in most cases.
This critique is leaning really hard on their interpretation of "dying". They take the literal company is going to fail type of dying where as I have always taken it the same way he has presented it in his "rot-economy" context. They can remain financially "successful", but more and more people hate their products, their products are getting worse, their products are "dying". Google Search is still a good example, the old Google search is "dead" if you like, a know many people, including myself who no longer use it. More people hate and getting off Facebook. More people are jumping from Windows to macOS or Linux.
These tech giants need AI to continue to grow, and that growth at the moment seems to be coming from just two AI companies who are burning a record about of investments. OpenAI has raised nearly $200B, that is more money than Australia's tax revenue.. and they are getting further from being profitable as Chinese models are getting better and much cheaper.
The article linked seems right, but you have to take these morbid analogies in a very specific way, and assume that the only way to measure these companies is revenue/profits/money rather than their products.
I wish people would hold actual professional media economists to the same standards, along with journalists who just repeat press releases without actually challenging statements. His main argument has been the numbers don't make sense, and can't see how this won't end badly for a lot of people.
If many people hated their products, they wouldn't use them. If they didn't use them they would not be financially successful. Google is not dying just because you personally have a feeling the results are worse than before and there is no definition of dying that would be consistent with Google's current state. If Google were to die they would die the way Yahoo did, because a competitor was demonstrably better than them and everyone switched off. There is no realistic evidence that this might occur in the near future.
> If many people hated their products, they wouldn't use them
This might be true in a true, free market without monopolistic collusion and the abandonment of antitrust regulation and enforcement in the US.
In many cases people don’t switch to something else because there isn’t an alternative. Or because they don’t know how to change the defaults that come installed on their computer. Or they get a big scary warning if they figure it out and try.
I would have a hard time believing anyone who said Google was competing fairly and not juicing their numbers with Gemini. Like with search and ads, they have a lot of vested interest in profits and little regard for much else. There’s no reason to. Almost all safeguards on corporate behaviour have been taken off in the last bunch of years.
Google jumped at renaming Lake Ontario.
Of course they’re going to shove AI mode as the default on search and claim every user loves it.
> If many people hated their products, they wouldn't use them. If they didn't use them they would not be financially successful.
I think this incorrect diminishes the success of product lock-in and also doesn't consider that the world is moving more and more into concentrated wealth where consumers have less and less to offer. Google's financial success could be sustained or even continue to grow with fewer ad buyers targeting fewer people.
> Google is not dying just because you personally have a feeling the results are worse than before and there is no definition of dying that would be consistent with Google's current state.
Again, "dying" is being used as a proxy for financial success. I don't disagree that Google/Microsoft/Meta will continue to grow their revenue or even profit, but I do argue that their products are becoming worse for consumers. That may or may not lead to real competitors, but that is a whole other regulatory capture discussion.
> If Google were to die they would die the way Yahoo did, because a competitor was demonstrably better than them and everyone switched off.
I think you mean "die" here in a product/usage sense, which I think their current path seems to be going that way, but I think it will matter FAR less to Google/Alphabet than it did too Yahoo.
GP said Google Search is dead, in a way, not Google itself. Google is just to an overwhelmingly part an ad business at this point. And people hate their annoying ads. So people hate Google search, and Google ads. Basically, the majority of what Google is.
This is unfalsifiable. AI is juicing the tech majors’ growth. And in the modern economy, it may be necessary for them.
But did Disney need the internet to grow in the 1990s? No, probably not. Did streaming give it all kinds of new growth victors? Yes. And would ignoring the internet for that last three decades have probably killed it? Also yes.
This cuts both ways: you can interpret any word to be whatever you want and thus have linguistically correct criticisms for predictions that are functionally useless.
> More people hate and getting off Facebook. More people are jumping from Windows to macOS or Linux.
Might be true.
But here is another angle, thinking about the people I know that are not in tech or avid gamers, which I would say is still easily the majority of people.
Most are basically addicted to Instagram, Youtube etc.. Meta and Google owned companies, same goes with OS's, I can't think of one person that considered linux as their daily driver (other than unknowingly through phone).
You mean increase the -$2.50 lost for every $1 in revenue, or the $2Tn in debt disclosed 60 pages into the reports as a footnote.
Let us be clear, the only "growth" is in the LLM ectoparasite living rent free in peoples imaginations. The fact is when (not if) the peak of inflated LLM use-case expectations corrects, a lot of the industry won't survive.
Facebook has a founders-syndrome problem, and a product line catering to creeps. Note most normal people aren't creeps, but the ones that are creepy will buy creep-ware at a rate necessary to sustain the founder creeps ego.
Google hasn't built a successful product in decades, and acquired most of its successes like YT. There are 3 reasons this occurs, and 2 are related to corporate cult culture. One would have to fire 70% of the company to fix that problem, and one day someone will have to do just that.
>wish people would hold actual professional media economists to the same standards
OpenAI will go public soon, and the hype-cycle can finally settle down.
I should have been clearer, "tech giants need something to meet their growth expectations, AI is currently that something". I completely agree this is all going to end badly, and AI has made these tech giants grow their market cap, which is what they seem to care almost solely about. I don't think the crash will be the end of Google/Microsoft/Meta/Amazon as companies, I do think it will be the end of OpenAI cause of the insane financial commitments they have, and I think Nvidia will drastically shrink, but this will all likely take longer than I'd like.
What?? Ed Zitron has repeatedly said OpenAi or Anthropic would literally die. This keeps happening - Ed makes a prediction. It gets falsified. And people say, “no actually he meant something else”.
Ed is in public relations. He did not amateurishly claim OpenAI and Anthropic would literally die. Like any PR savvy person, he qualified his predictions by making them conditional on whether OpenAI/Anthropic could raise more money, achieve a technical breakthrough, unlock new lucrative markets, etc.
So far, OpenAI and Anthropic have raised more money. You might even be able to argue they've achieved some breakthroughs and unlocked some markets.
Regarding his predictions: The jury is still out because Ed isn't actually making the falsifiable time-bounded predictions you think he's making. He's good at making his readers think he's putting his neck out, but he's really not.
When you remove all his rhetoric and veneer, his conditional predictions are actually somewhere between bearish and cautious.
If I have a billion trillion dollars but 20% of the population hates me, but I get to live in a giant solid gold mansion with an army of servants keeping the 20% away from me, have I really failed?
It'd depend on what specifically that 20% of the population hates that hypothetical you for. Maybe their hate in this scenario turns out to be an accurate signal that you are an awful human being. In that case, you'd be a very rich, awful human being.
Have you really failed in this case? Not at making money and living a decadent, hedonistic life, if that was your goal, but yes at being a good human being, one who is good to others and is worthy of their respect, admiration, and support.
Still a failure because it is propped up by an inflated US dollar. Those trillions would mean nothing when the US economy collapses due to a cumulative effect of massive national debt, unending wars and inflation.
At least if you have the population by your side, you wouldn't have "guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur I can fly to" [1]
They are all willing to risk everything to see if their bet on achieving "singularity" fructifies. I don't see us getting anywhere close (at least with the current tech).
To even desire that you have to be so broken and impoverished I would say no, you wouldn't have failed, in the same way a dog farting didn't fail to make perfume. They don't even know what perfume is, don't know about any of the ingredients, equipment and processes. And even if they did, that wouldn't do them any good because they don't have opposable thumbs, so why be cruel and even try to explain it to them? It will either frustrate them because they don't understand, or frustrate them even more in the extremely unlikely case that they do.
But more importantly, companies aren't people, they can't be unhappy or happy. They're like fire, you don't ask what the fire wants, you ask how to make it useful.
Being thorough and accurate might make you a lot of money in the stock market, but it's not a good way to get any media presence, because that demands being in front of people quite often, and basically nobody can be insightful and well researched in all topics of the day. Once you become a pundit, whether on politics or tech, and rely on eyeballs to feed you, you are going to be throwing stinkers. And at that point, you might as well just align with an audience and not care too much about whether you are predicting anything accurately.
We have people telling us the next recession being imminent all the time. Others told us that NFTs were key to the future of art. That's the pundit way. If you want accuracy, you become like Charlie Munger, have maybe 2, 3 really good insights througout your career, and hope do to well using the insights yourself, not by being a pundit
This is exactly right. Now, that does mean something about Zitron: he’s a pundit, not an expert or a forecasting genius. You could point to any number of analogous booster types. Twitter/X somehow loves pushing these people onto my recommended feed. I remember reading breathless threads about how o3 was going to single-handedly end white collar work. There are just more of that sort of person, so none of them in particular gets the same amount of attention as Zitron, who seems to be the only person willing to go on the record against AI. Maybe his overall worldview is sound, and maybe it isn’t. But he’s not really making confident, specific predictions about the future.
Which leads me to a criticism of the piece: several assertions are described as “Wrong,” with no explanation or citation, which are not obviously wrong to my mind. For example, the assertion that “DeepSeek has commoditized the [LLM]” is at least debatable. It is a prospect that the major labs seem to have at least considered.
In my experience, people who make fun of AI's ability to eliminate white-collar work are exactly the same people who are white-knuckling it, hoping they can make another $300k next year to buy that Rivian.
People who don't need a salary look at the situation more objectively and, in general, can see that a lot of white-collar work is in peril.
> We have people telling us the next recession being imminent all the time. Others told us that NFTs were key to the future of art.
That's quite the conflation, and a very subtle way to tie a claim that's still unresolved to one that is to try and make the first one seem wrong with no evidence. Recessions are almost guaranteed in our system, they're a part of a cycle. Unless you think that the Great Recession was the last one in history, saying the next one is 'imminent' would be correct for any other viewpoint. We just don't know when it'll officially start.
...get any media presence, because that demands being in front of people quite often, and basically nobody can be insightful and well researched in all topics of the day
What? Zitron is getting a lot of media attention by repeating the same takes hundreds of times. He's not a general pundit.
> For any of his posts that I read, while there are numbers thrown around, the numbers don't actually connect to a coherent argument. In many cases, as we saw above, the numbers don't even really support his argument (such as an MAU decline in Facebook causing Meta financial problems which would then cause Meta to spuriously insert AI in places it doesn't belong). I suspect he's relying on people's eyes glazing over when they see numbers and just not thinking about what the numbers mean.
A frustrating thing about Ed Zitron is that he sometimes breaks news - gets hold of leaked numbers that nobody else has, for example - but he then wraps those numbers in his own extremely biased commentary. This makes it much harder to evaluate how credible the new information is.
>He doesn't count February 1-10, counts March 1-10 twice, counts August 21-October 21 as one month instead of two, and doesn't count October 21-November 1.
Seriously, I expected more slight wrong before I read the article. Why do people trust someone who can't add numbers correctly to make predictions?
Yes, and his post about the exact same data was noticeably more confusing and less illuminating than theirs because he was primarily concerned to point to whatever the biggest number was and go "Ooh, big number!"
There was a major material difference between the FT and Ed's post: FT explicitly noted that OpenAI has $billions in cash on hand (which is relevant if you are analyzing data about solvency), while Ed's post obfuscated that data point.
As someone else mentioned, ignore Ed’s personality and look solely at the balance sheets and capital analysis. Regardless of delivery, the math doesn’t math.
It's wild to me how much Ed is criticized while pathological liars like Musk and Altman are glossed over.
(Disclosure: I pay for Zitron's publication for the capital figures and exclusives he gets his hands on, I do not pay attention to his interviews, we are fact finding, if he wants to perform, I am not bothered, I've seen the performers on the other side, just keep the facts coming for ground truth)
> For example, when Timothy B. Lee looked at a spreadsheet that Zitron used to create a projection of Anthropic's revenue, he found, "He doesn't count February 1-10, counts March 1-10 twice, counts August 21-October 21 as one month instead of two, and doesn't count October 21-November 1. [another commenter notes that his spreadsheet also contains February 30] ... Ed claims he tried to compute Anthropic's revenue for 2025 and came up with $3.6 billion, suggesting some funny business [but the numbers work out once you fix the errors]"
As someone with a bit of understanding of accounts and finance I don't think Ed's analysis is very good. He's not someone who would pass a finance exam.
I posted here a while ago when his bubble prediction lapsed and plenty of his fans were here. Telling me how I was wrong and the Q2 reports weren't complete yet because it was still early July and blah blah
> gets hold of leaked numbers that nobody else has, for example - but he then wraps those numbers in his own extremely biased commentary.
You can think of yourself and draw your own conclusions (or we have completely lost the aptitude since "ai" started off?). You don't have to agree with Ed, or even follow his conclusions.
One of the best heuristics I've seen on whether there will be a fruitful outcome in taking someone seriously is how often they invoke labels (name calling, etc).
Even if Zitron is hopelessly wrong and a complete fool, he's an engaging writer. Like the original article itself states, it's not about the numbers, it's about catharsis. The transparent absence of AI tells in his articles is refreshing, and just rampaging against the AI boosters who lack empathy and self-awareness (which so many of them absolutely do) is cathartic. Even if it's all bunk, fake, and stupid, it's nice to have a way to self-soothe and manifest how much some of us want AI to have a reckoning.
Does that make his readers suckers? Possibly. But personally I also think it's a very human trait to seek comfort.
I'm not financially literate enough to trust my own analysis of the numbers. Ideally I'd like commentary from someone like Bloomberg's Matt Levine, a genuine expert in financial matters who is also extremely good at explaining them in terms non-finance-professionals like me can understand.
The problem is that some numbers genuinely require a critical eye. If a source tells you that the OpenAI CFO told employees July ARR exceeded the Q2 total, there are a number of serious questions to be raised on how these numbers were calculated and what the point of such a confusing comparison is supposed to be. (I think the answer has to be that ChatGPT wrote the CFO’s script, no human financial expert would think to compare an annualized figure to the sum of three specific months.) But it’s hard to analyze the facts appropriately when they’re relayed by a guy who tells you that it’s all a giant scam and infers the worst possible answer to all the questions.
No, you can't. The numbers are genuinely confusing, and Zitron actively works to make them more confusing rather than explaining what they mean because he needs them to sound as bad as possible. He also buries the numbers in thousands of words of prose!
Over the past few years, I had helped Ed with some difficult nuances around the obscure technical aspects of serving LLMs (e.g. benchmarks and model caching); he has also shouted myself and Simon Willison out positively multiple times. I stopped assisting him because he repeatedly misused said advice to the most cynical interpretation ("how can this be interpreted to make AI boosters sound crazy?) and often made it misleading at best. Nowadays I suspect he views me as one of those crazy AI boosters.
Not challenging him was a mistake in hindsight, and I own it. Going forward I will no longer be helping writers/journalists with a clear anti-AI bias.
He made a blog post, literally last month, and it said that AIs have no use outside of coding.
It just shows he's done zero research on the things he talks about all day. Radiologists are using them, ad firms, artists, translators, law firms, auditors... It's hard to think of a white collar firm not using them.
One thing I've noticed is that since LLMs came out, scientific papers with poor English have essentially disappeared. Just one of the many ways that AI is changing the world. People are using AI in all kinds of fields.
This reads slightly weird and more like a pledge of alignment than a statement from the heart.
But apart from that, I guess that's always the learning? Journalists (or people labeling themselves as such) often have their own story they want to tell, and usually do so by building it out of little blocks of reality stacked together to form the desired picture.
I would predict a similarly frustrating experience being equally probable even without the "clear anti-AI bias" attribute set.
That's an unfair characterization of tech journalists in general. When BuzzFeed News was around, I worked closely with them on technical aspects and projects to ensure that everything was correct and accurate, and there were very receptive to it; it was not a "directionally correct" thing, the journalists wanted to avoid any unintentional. That was also one of the reasons I was open-minded to helping Ed even though we ideologically disagree: the truth is what's important.
BuzzFeed News incidentally was how I first came across Ed on Twitter from his tech PR work about 9 years ago, back when he was writing guest editorials on Gizmodo about Person of Interest (which is very very funny in hindsight). It's from the heart that I'm a bit bummed out that things turned out this way.
> Given how fast these companies are growing (in terms of revenue and profit), it doesn't seem that AI is, as Zitron implied, some kind of desperation move they're reaching for because "they don't know how to grow" and are all out of ideas
Growth isn't a valid rebuttal, unless we can also sus out how much of that growth is tied up in circular financing of AI projects. We have a pretty good idea how much of Nvidia's valuation is tied up in the AI craze, it's a bit harder to tell with the megascalers....
The predictions were predictions of revenue. Circular financing of AI projects does not create revenue for OpenAI, Anthropic, Meta, or Google. Only Nvidia benefits from it.
Predicting revenue growth will stall and it does not was wrong.
A startup raises $50 million from OpenAI and Anthropic to finance API calls to OpenAI and Anthropic that they are using at a loss who in turn spend that money on compute with Microsoft and Google who in turn invest in Anthropic and OpenAI who then invest the startup using the startup’s revenue to value it… the cycle repeats.
There are multi-billion dollar valued startups invested in by OpenAI and Anthropic with hundreds of millions in ARR that are spending 90% of their revenue with Anthropic and OpenAI.
Situational Awareness, the fund that recently imploded, invested tens of billions into AI companies using their holdings in Anthropic to help finance the investments…
This could all work out fine in the long term, we’re all just speculating at this point, but the circular financing is absolutely making it to revenue because capital invested into startups is used to fund growth which is achieved by subsidizing costs incurred with OpenAI and Anthropic.
Can you provide any examples of any of the megascalers publishing any detailed financials that touch on their AI spend or revenue or profit? The only one I’m aware of that comes close is Microsoft and they have still buried it in barely related line items which still leave us making assumptions.
There’s speculation on both sides and certainly Zitron is on the extreme end of the anti-AI side with the most cynical speculation but it is indisputable that none of the megascalers are open about their AI financials. Hence, we are all speculating endlessly. If only there were published financials then the speculation could end!
The obfuscation of financials doesn’t necessarily mean something bad is happening, it could be a competitive advantage for Google to be secretive about how cost effective their TPUs are or for Microsoft to hide how much revenue uplift they’ve experienced by adding AI to 365.
Because of accounting tricks, quarterly financials don't accurately reflect the size of this fiery money pit.
The datacenter build-outs are all majority (>50% ownership) financed by other companies, with a shell company owned by the hyperscaler as a minority owner. The data center then grants the hyperscaler an exclusive leasing agreement, and because the shell company is a minority owner, legally, it's not their debt.
The only reason this has worked is because there's such a long delay taking delivery on GPUs. When these capital allocators start paying for GPUs in data centers which haven't yet broken ground, then we'll see a very visceral market reaction. Some of that has already happened, but there's enough momentum that it can be absorbed and dismissed as an anomaly. But with governments unexpectedly passing moratoriums on data centers everywhere, it's only a matter of time before there's no data center to offload those GPUs to. That's when the music stops.
I believe that was Zitron's central thesis and why he started reporting on this. It mirrors the mortgage-backed securities situation that led to the 2008 GFC, except with even fewer guard rails to prevent financial calamity.
Investors are very savvy and keenly aware of what's going to happen. There's just zero incentive to pull the fire alarm and risk being blamed for crashing the market. If you're wondering why everyone's running toward the exits instead of treating these tech companies as 10+ year investments, you have your answer.
Financial reports alone don’t paint the whole picture when it comes to valuations. For example, theoretically amazon has committed to invest 25 billion in anthropic, and anthropic has committed to spend 100 billion on aws compute. As far as we can tell, no real money has actually changed hands in either direction, but both valuations are being buoyed by their prospective investments…
Upon further thought, I realized this should be counted as a correct prediction for Zitron! Zitron did indeed “keep writing this stuff until…proven wrong”. Zitron’s prediction says nothing about what happens after being proven wrong.
> In terms of the style of reasoning, of the futurists reviewed, he's probably closest to Kurzweil, in that he uses numbers to give a kind of aura of credibility, but if you know something about the topic he's discussing or look at the numbers, the reasoning falls apart.
In one of his posts a few months ago, he went on a weird tangent about the CEO of ServiceNow talking about sales planning and whether his teams are "on plan" or not. For people who haven't spent time in or around sales, this is an extremely common shorthand for quota tracking.
Ed's rant about how impossibly weird and inhuman the framing was revealed how little he knows, and cares to know, about the mechanics of the businesses he claims to profile. Yes, something like "on plan" is jargon, but a shred of reasonable journalistic curiosity (a Google search) would explain what it means and how it's a normal statement for a career-sales CEO.
>Ed's rant about how impossibly weird and inhuman the framing was revealed how little he knows, and cares to know, about the mechanics of the businesses he claims to profile. Yes, something like "on plan" is jargon, but a shred of reasonable journalistic curiosity (a Google search) would explain what it means and how it's a normal statement for a career-sales CEO.
This is precisely the sort of feedback an LLM could provide him before he hits the publish button, funny enough.
P1. AI is useful powerful and (P1a) will continue to get more useful and powerful at the same rapid pace it's been improving
2. AI companies are very profitable, and (P2a) will be wildly profitable (eg $30T TAM) in the next few years
These are completely separate. But in practice people seem to be either proAI (both true) or anti AI (both false). P1 is clearly true and I find it hard to take anybody seriously who says otherwise. P1a... Who knows, gotta hit a wall sometime. P2 I'm way more uncertain about (especially P2a) but it seems like people like Zitron reason backwards from hating AI.
I can imagine an Ed Zitron in 1840 arguing that trains and steam engines are useless - or even a scam - because railroad investment was a bubble (which it was).
In my mind the most obvious scenario where AI gets used a lot but AI companies (exlc. NVIDIA / chip makers) aren't worth much is the hardware becomes capable of running the current state-of-the-art models locally, cheaply, and those models are basically "good enough" for 90% of tasks.
P1 is probably necessary but not sufficient for P2, which would show up as a correlation of a sorts. Maybe I should've specified even further, if the tech works (P1 and P1a) then yeah somebody is going to make a lot of money but not necessarily OpenAI/Anthropic. They could go the way of AOL/Time Warner (probably not but again who knows). Microsoft and Amazon survived the dotcom bubble and thrived, Meta came after it.
Currently they don’t seem to be. At least OpenAI is haemorrhaging money left and right. Thing is, the compute for all this is far more expensive than anyone is willing or able to pay.
For someone who falls in the middle of this a really good read is Quoth the Raven[1]. He essentially makes the argument that proposition 1 is probably true, but that before proposition 2 happens there'll be a massive bubble burst. Analogous to the dotcom boom where yes, eventually Amazon became Amazon, but before that there was a massive collapse. And he does this in a way that Dan Luu would really like because he's giving a very clear and specific time line for his prediction. I haven't been reading him long so I can't guarantee there's not going to be some goal post moving 6 months down the line though.
I found this in two minutes via a search engine, but the star blogger Dan Luu apparently cannot handle that. I'm not a regular Zitron reader, but incidentally this blog post that came up in the search is several levels above Luu's post.
> Although Zitron's past predictions have generally been wrong, maybe he'll be right about something in the future. Perhaps some of these companies will have valuations decline for some reason. But, even if there's some kind of massive AI crash and OpenAI and Anthropic go to zero, in terms of the societal impact, if on top of that, some other event occurs that prevents further progress in models beyond whatever AI labs have internally right now, that's still going to result in a fair amount of change. Which companies are successful will change who gets rich, but particular companies failing won't stop changes that fall out of current or next generation model capabilities from happening; it just moves around who benefits the most.
> If Zitron ends up being right about some company or other collapsing, that's pretty uninteresting to me compared to how capabilities have developed and will develop, where he's been wrong to date. It also happens that he's been wrong about the financial predictions he's made to date, but that doesn't really interest me, though I included a number of financial predictions for completeness.
Not sure how you can "get the big picture right" while having many egregiously wrong predictions.
Agree. Even Zitron's predictions about AI peaking are arguably correct if you step outside the silicon valley bubble. People I know who don't work in tech who use AI at work are doing the same stuff they were a couple years ago: they use it to summarize emails, generate an occasional slide deck, and mostly just send workslop to their coworkers.
And for personal use, I can count on a single hand people I know who pay for an AI subscription, and of those people nobody pays for more than the $20/month plan. And they all just use it as search or maybe to vibecode some one-off party game they use once and then throw away.
I don't know a single person who has done something like run Openclaw or leaves coding agents running on their laptop open all day.
The doctor said I was sick...but I did not die on Tuesday, so checkmate!
You are confusing an imprecise prognosis with a false diagnosis. And The funniest part is that "he keeps writing, therefore he was proven wrong" contains no actual proof that he is wrong.
Imprecise and incorrect are the same thing when it comes to an exact science. His predictions include dates and that makes them binary. You can only be right or wrong. Being close is still incorrect, and he's not even close.
It’s more like if Zitron was a doctor his prognosis would be “you’re going to die on Tuesday” and you come back on Wednesday (after not dying on Tuesday) and he defends it by saying “everybody dies”.
I've come to see Ed Zitron's dramatic predictions as reminiscent of a trend I noticed on YouTube a while back;
Whenever you'd look up anything pertaining to China's future, you'd inevitably find your screen plastered wall to wall with thumbnails of a photoshopped Xi Jinping, tears streaming down his face, next to large impact font text reading "CHINA WILL COLLAPSE IN X DAYS", with X varying from 1 to 30. Much like Ed Zitron's predictions, these events obviously never occur.
Exactly, he is monetizing the dramaslop. A sack of rice falls over and suddenly the world is on the brink of collapse. A fun exercise to see how lunatic this pattern of fear is, you can look for an echo chamber you know nothing about, maybe games or music or DIY or kitchens or whatever on youtube and suddenly you will see the same patterns play out. It is quite sad to see people do this for a living, but it is what it is. The kitchens are funny to me, because apparently there is a grand conspiracy of kitchen companies forming a monopoly funded by whoever and basically thats why you will never be able to cook like you used to be able to back in the day and basically its over. Just typing this out made me smile.
IMO Ed is entertaining and I don't believe much in LLMs, but I personally don't really care what happens next with it or what happens at all, the world will continue to spin.
Or Trump/Fox saying the Iran special military operation will be over in X days?
Or Putin/Russia saying the Ukraine special military operation will be over in X days?
Let's not pretend they didn't get it from somewhere.
If we sample from Zitron’s claims, a lot of them are wrong. Probably most of them.
I think his contribution to the discourse is the bigger picture. These companies are going into potentially economy-wrecking debt over a speculative future that looks much foggier than the last two previous technological booms.
I find it ironic, that one of the most memorable articles Dan Luu has written imo is about how futurists faked 'exponential scaling' about semiconductors, and we ran out of the 'good kind' of scaling, Dennard scaling a while ago, and people interested in maintaining the narrative have been making up marketing numbers, which are believable to outsiders, but not to those who know:
Yet, in this case, he takes the marketing numbers at face value, not being an expert in AI financing, while those who know more, can spot the sleight of hand, just like he can when it comes to semiconductors.
I can agree that the Gemini usage number is a marketing number NOT to be taken at face value. And so does Dan Luu, in the "Some reactions to Zitron" section of the article:
> BTW, a funny thing about Gemini hitting 500M users being "so unrealistic that someone at Google should have been fired, and that someone is Sundar Pichai" is that Zitron has also (incorrectly) said that Google doesn't know how to grow, and that as a result they're shoving AI everywhere. Dennis Snell pointed out that, if Zitron takes his own statement seriously, Google can make Gemini's user numbers go to any number it wants by doing the exact thing Zitron said they would do, sticking AI everywhere.
> You can't actually take Zitron's statement about Google's lack of growth leading to AI desperation seriously and also take it seriously when he says that Sundar is committing some kind of gross malpractice by naming a number like 500M users.
Other than the Gemini usage numbers, which are the marketing numbers being taken at face value?
I don’t really care of anyone’s predictions, whether that of Altman or Zitron. I can make a prediction that sun will collapse into itself and stop shining. I would probably be right as long as I don’t give any too strict timeframe to ky prediction. In terms of Zitron, his style is that of a British tabloid columnist. It’s rather refreshing when most tech journalism is just selling gadgets, stock or whatever without questioning at all what either Musk, Altman, Amodei or whoever says. It aggravates many people because, well, it is suppose to.
Regardless of whether you love LLM’s as technology, the financial realities of Anthropic and especially OpenAI really does not look good. They have a massive expenditure that they need to keep going in order to make profits, which at least in terms of OpenAI are horrendously behind. Zitron published these numbers together with Financial Times, so you gotta give him that at least. Meanwhile the CEO’s talk all kind of nonsense and give their own predictions to get more investors money to fund what might or might not be the biggest bubble in the history of finance. I certainly do not hope this happens, since the consequences would be horrific. But there is likely to be a some sort of correction in horizon, since the models will plateau and they will run out of money at some point.
Let me finish with my own prediction. I think a lot of people are going to lose a lot of money some time next couple of years.
I've been doubting him myself (his recent articles just hedge on data centers more than anything else) but then I want to ask, are there any valid critics of AI? Not a "code is bad but it'll get better" but actual criticism in the nature of the financing, politics, etc. I think separating AI as a tool and it's capabilities and AI as product that needs to make a profit is helpful, however most people are really hedging in one camp or the other in their takes.
Cal Newport has been interesting on the topic. But it’s also possible to read Zitron and skip his personal opinions and just follow the discussion of financing, that’s what I personally do. I don’t understand why anyone would take the commentary of an internet pundit as a set of predictions to evaluate as gospel
I enjoy Newport myself, he's probably the most level-headed take I've come across. Everyone else has some agenda (or product) they're trying to get at and very much ruins the messaging (ex the agent 'civilization' piece is extremely overblown especially since..that's how multi agent systems coordinate already. Nothing happened that is unprecedented and isn't how the system is designed to work.
>I think separating AI as a tool and it's capabilities and AI as product that needs to make a profit is helpful, however most people are really hedging in one camp or the other in their takes.
There are a myriad of people out there who have brought up genuine issues that AI presents, or preexisting issues that AI is exacerbating.
This series of articles is perhaps the best long form critique of AI as both a technology and an industry I've encountered [1].
> however most people are really hedging in one camp or the other in their takes.
As with most things, most people are somewhere in the middle, as the silent majority. You only see the comments of the strongly aligned, which are also the most emotionally motivated to comment.
To shamelessly shill a passion project: A friend of mine and I try to be skeptical-but-reasonable on our podcast https://kairos.fm/muckraikers/ we aggregate papers and reporting and try to contextualize it with our own (hopefully useful) perspectives and takes
The data center growth questions he raised have been where I found him interesting. I could never find any other articles to corroborate his predictions though. My question is when will the AI bubble burst?
I didn't know he'd had a long history of mispredicting AI quality and growth of companies like Microsoft and Google.
> I didn't know he'd had a long history of mispredicting AI quality and growth of companies like Microsoft and Google.
After watching this space for a long time, I think the growth angle is... untested.
All the FAANGs were slowing down after Covid. They boomed as the money printer went brrrr, then they plateaued. They've also kind of covered a lot of their potential their main total addressable markets, with the big exception of clouds, which probably have at least 5 or 10 years of growth as workloads are still being moved away from on-premise. Maybe ads, too, since TV is still around and big and there are probably a bunch of other holdouts I'm forgetting.
Then, FAANGs started reaccelerating around 2024.
Part of it was due to internal reforms, basically, layoffs shaking some things up.
But I suspect the bigger part has been AI, driven by CapEx and all sorts of other things. The problem with the AI growth is that it's highly opaque. We don't really know who the actual clients are. It is <<extremely>> likely that for Oracle (OCI), Microsoft (Azure), Google (GCP), Amazon (AWS) their direct customers are just OpenAI and Anthropic. That's it. It's likely that 70% of the AI growth is just 2 companies. That can't be healthy, it's also likely extremely risky.
Just the fact that the new cloud growth is so opaque is worrisome.
Water usage, sure. But emissions has plenty of reasonable concern.
There are the various xAI data centers have/are running using mobile gas turbines.
In general, the extreme amount of power is going to put pressure on the grids. I hope this leads to the world doubling down on renewables to offset it all, but is that going to happen? Hell, the US actively paid [0] to stop a turbine project.
I think the three valid criticisms you listed are valid, but there are other quite a few other IMO-valid criticisms of AI. Here are a few as I see them:
- AI centralizes power in the hands of capital, rendering those who can afford compute hardware vastly more capable than those who cannot and thus increasing social stratification
- AI is generally trained on the creative output of humanity without those who train it giving back proportionally (copyright "rules for thee, not for me")
- AI breaks social processes built around the idea that TRYING something is inherently a cost in time or effort, such as filing a legal claim or sending someone a threatening letter. We haven't made the social changes to punish or charge people for using every appeal/option/application, so this makes asymmetric-effort tasks like applying for a job really bad in the interim
- AI use makes it harder to develop the ability to critically think for yourself, especially among those who most need to develop that ability
- AI produces large amounts of mid-tier content, making it harder to discover exceptional human-created creative content produced after AI started to exist
- AI leads to distrust in remote communication, increasing cynicism and breaking social bonds generally. This is NOT your point about "AI spreads misinformation" - no matter whether it's true or not that AI can be used to produce misinformation, having people doubt each other is a harm
- AI demand crunches hardware and time availability for other adjacent markets, such as computer gaming, construction, 3D graphics production, etc. This harms both hobbies and professions in those fields having to cope with rising prices and lower availability of materials
- Everyone is using the same or similar AI, leading to a homogenization of culture and process across humanity. This is perhaps a mixed blessing, because humans are capricious, but less variety can be viewed as a harm
I will also say I personally hate seeing "job loss" said to mean "wealth loss" or "people starving". The goal of life isn't to have a job, it's to be well and happy. If you can be well and happy without a job, great, so it's really painful to me how people don't even see those things are not the same.
Those are two very different things, and the former should be a serious concern. If AI does indeed become a double-digit percentage of electricity usage as the AI labs themselves predict, then it becomes a significant contributor to emissions, full stop.
Basically, all criticism of issues that affect real people is invalid per you.
> environmental like emissions and water usage
There is real crisis with warming this year, yes the plan to consume staggering anounts of energy and make environment worst in the process is valid criticism.
> AI is useless and it will take the economy with it because it is a bubble
If it turns out to be true, a lot of innocent people get hurt. Valid.
> AI spreads misinformation and causes societal damage
As valid as criticism of facebook was valid the whole time. And yes, facebook made world into worst place.
> AI is trained on copyright (are we really on this side of the debate ?!)
100% valid.
> safety critics who think AI can take over the world
Not valid, that is bullshit. If you think the word is wrong suggest polite word that says the same.
I used to be a fan of Zitron after listening to an old podcast of his, and also subscribing to his newsletter.
Something weird happened after Taylor Lorenz guested on his show; a week later I received a welcome email to Taylor's own newsletter. This was especially strange because I definitely hadn't signed up to hers, and use individual masked emails in Fastmail per different service so I was sure that this was the email address originally used for Ed's newsletter. I tried to contact Ed to no avail, and since that day I never could shake the feeling that he just gave Lorenz his subscriber list. I can't prove it for certain, but there was definitely something "off".
Ed Zitron is a grifter. Period. I highly doubt he believes any of the stuff he writes himself. He plays a role in society which is the role that tells people AI is going to fail so those who don't participate in or benefit from AI feel better. They sign up to his subscriptions and pay him money to confirm their own bias.
If you look at the sub reddit r/betteroffline where these people gather and worship Zitron, most people there are economically motivated. Most of them want AI to collapse so they can invest in stocks when it's cheap or they hope AI doesn't take their jobs.
Doesn't it say something about our society that (a) it's known (at least among smart people, however 'smart' is defined) that AI is going to take away jobs, and (b) the way its treated is that people gather to online discussion forums to worship someone saying the thing that will replace their jobs isn't going to do it (but in actuality, eventually will)? In other words, who's actually doing anything to help the people out who will lose their jobs??
Side-bar: I once hired Ed for PR for my startup. He was very difficult to work with, not even willing to share how he pitched our startup to publications because he considered the pitch his IP. So it was hard to know what was resonating or not.
I share this less to take a shot at Ed but more so that you all know to ask this if you ever hire a PR person.
I don't think Zitron is wrong, exactly. He's just early. There are a lot of analysts who have faced the same criticism over the past 10-15 years. IMO the issue is that they fail to understand the staggering size and scope of the government fiscal + monetary interventions across those years. Essentially, the government has jammed all the risk into the future to repeatedly rescue near-term results.
I think something similar is happening with e.g. Amodei's predictions of mass unemployment due to AI. It won't happen in the immediate term, because deficit spending removes the economic incentive for firms to pare down their workforces.
All that said, I don't think these people are "wrong," per se. They're just early. When the sh*t hits the fan on all this, it's going to be a big problem. And, for example, companies whose primary business is collecting money for Internet ads will come face to face with the reality of how low value their products are. I have some insight into this, as I work for such a firm, and I know the true extent of the bot traffic out there.
- "artificial intelligence has three quarters to prove itself before the apocalypse comes" — Mar 2024
- "If OpenAI doesn’t either reduce their $8.5bn operating costs to $1bn or less and raise at least $5bn in the next year, they will die." — Jul 2024 2025 costs: $34B
- Generative AI “isn’t getting much more efficient” (Jul 2024 ). OpenAI’s frontier-model API price fell from $30/$60 per million input/output tokens for GPT-4 to $4/$20 for GPT-5.6 Sol, alongside major capability gains.
- “I would be shocked if [Musk’s] wealth doesn’t return to something more like he had in 2019 or 2020” (Dec 2022 ). Musk is now worth approximately $873 billion , several times his wealth when Zitron wrote this.
The actual quote and topic is far different than you’ve portrayed it. Here’s the actual quote: “ yet the company says that it expects to make $11.6 billion in 2025 and *$100 billion by 2029*, a statement so egregious that I am surprised it's not some kind of financial crime to say it out loud.” from https://www.wheresyoured.at/oai-business/
He goes on to specifically discuss how unrealistic $100B by 2029 is given that OAI is structurally unprofitable.
The fact that you’re skewing your misinterpretation of what he said so much shows your own bias I’m afraid.
I think it's fair to call his specific predictions "early." I'm saying his timelines are too short. Like almost all bearish market commentators, he doesn't understand the scope of the government stimulus. This is a systemic problem in market commentary. But, don't kid yourself. If the US stops massive deficit spending, a lot of what Zitron's saying moves forward on the timeline.
> OpenAI’s frontier-model API price fell from $30/$60 per million input/output tokens for GPT-4 to $4/$20 for GPT-5.6 Sol, alongside major capability gains.
on this specific point: tokens aren't fungible between models right? Like the argument Zitron makes is that improvements in output are due to, glibly, using more tokens to get there. We see people turn on new models and instantly use up all their tokens.
Like the actual measure is more something like "for this specific task, did it cost less now to do it than it did 3 years ago with these AI pipelines" right? The token pricing isn't actually relevant in that discussion.
If you're saying that OpenAI forecast something and you're saying it's validation that the numbers match the forecast, I don't think you're really paying attention to the problems (with annualised run rate bullshit when these are proper companies who could be publishing proper numbers), with unclear financials that are designed to look good, etc.
> I don't think Zitron is wrong, exactly. He's just early.
By that metric, so was Nostradamus. The apocalypse is coming for sure, we're just quibbling about the timeline.
Realistically, timing is everything. You don't need to get it precisely right, but you also don't get a pass if you, for example, keep predicting an imminent recession through a decade of unprecedented growth.
"I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be."
This was said in February 2024. This is months before GPT-4o released. GPT 4.5 was released a year later.
I don't know anyone holding on to models of GPT 4.5 caliber, yet know 4o.
ChatGPT 4o and 4.5 score 8 and 14 points on Artificial Analysis benchmarks. [1]
For perspective, Qwen 3.6 27B, which can be ran on single GPU setups, scores 3-5x that on modern benchmarks.
I don't know why anyone would even make a claim like that in the first place. It's like saying "Computers are never going to get faster". I feel like we could run out of sand and still have faster machines over time. Just a silly thing to say.
After looking at Nvidia's orderbook, it's a reasonable conclusion that nothing bad will happen for at least another year. In which case Ed Zitron is off by at least a year.
But there are enough signs of trouble that could happen soon: Oracle debt is junk. OpenAI might not be on a viable trajectory to IPO. One or both of those could collapse the lower quality data center companies. Zitron is probably overconfident about a crash in the short term. Probably.
> After looking at Nvidia's orderbook, it's a reasonable conclusion that nothing bad will happen for at least another year. In which case Ed Zitron is off by at least a year.
Zitron is fairly clear that nothing is going to happen for a year or so — he says himself that he thinks there's another round of funding possible for both OpenAI and Anthropic.
Oracle fits in Nvidia's pocket at this point: $67b sales, $22b op income.
Vs of course Nvidia: $302b sales, $197b op income.
Oracle was never as big of a deal as that brief market cap run implied. The herd pushed it up for no reason.
The comparison to Apple, Microsoft, Google and Amazon are pretty similar. Oracle fits in their pockets too. Who cares if their debt is junk. Ellison has nearly wrecked that ship on numerous occasions over the decades. He went on an elaborate acquisition binge in the previous epoch, buying his way to the next stage (preventing Oracle from being market-eliminated, or acquired), and that was an incredible mess that took a long time to sort. He's doing the same move now, trying to spend to stay on the board as the world rapidly changes under his feet.
You can make that claim for the general case that genAI capabilities will plateau or fishy financing in the sector will cause trouble at some point, but not for the specific talking heads that haven't constrained themselves to that sort of "broad future trajectory" prediction.
FTA:
> Note that I didn't attempt to catalogue statements that are nonsensical or were simply factually incorrect statements at the time, such as his December 2024 claim that “Generative AI's products have effectively been trapped in amber for over a year.” January 2026 claim that "[models are] basically the same as they were a year ago. They have the same efficacy". Zitron has not only made forward-looking statements that AI capabilities will not improve, he's also consistently made backwards-looking statements that capabilities have not improved which, while obviously false at the time, seem to play well to his base (along with his other false statements). If you connect all his statements together, it's implied that AI had the same capabilities in January 2026 as they did in December 2023 (and if you connect later statements, it's actually implied that capabilities in August 2026 are the same as in December 2023, though to be fair to Zitron he frequently contradicts himself and has also admitted to limited improvement at times).
Yes. I was specifically referring to Zitron's circular-financing complaints. I can't defend his separate claims that AI just isn't that useful. If he were involved in the tech industry, he'd know how preposterous those claims are.
The whole point of making predictions is timing. I can tell you the US dollar will continue to devalue (a 100% accurate prediction). But it's a worthless statement unless I can tell you when and how much.
What predictions would you say he is not wrong, but just early about?
If his issue is that he is accurate on something shady going on with the finances of AI companies but the effects are mitigated due to government intervention, he should say.
Your explanation gives him more credit than I think he deserves. He has been unambiguously incorrect many times over the past years in the time scales over which he makes predictions.
In general, claiming that a prediction is just early in an unfalsifiable claim. It allows no admission of being incorrect whether you are correct or incorrect in a given moment. If you are right, then good, you've made a correct claim. If you're wrong, just claim there are clandestine corrective forces keeping the disaster you are predicting at bay. Either way you make it out clean and still have some sense of legitimacy.
I recall a time when Google was struggling to monetize ads and then something changed and they started becoming profitable. The narrative was about improved targeting.
I think it might have helped a little but I never fully bought this argument. I don't think I've ever bought a product which was advertised to me online and I was using some of these platforms for years. I'm probably a liability to them; using up compute but not clicking on ads or buying anything.
Also when I ran social media ads many years back, I never got any users out of it. It literally seemed like mostly bot traffic back then; I can't imagine how bad the situation would be now with LLMs.
Not sure about being early when it comes to points about models reaching the peak of their capability. Honestly, I don't understand how he's qualified (in terms of knowledge) to make such claims.
Being wrong about when a financial bubble will burst is different than being wrong that there is a financial bubble. That's the question, because if AI is driving a bubble, then it will burst at some point.
"Early" works for an open-ended bubble thesis. It does not rescue dated claims that already failed.
Google's 500 million Gemini-user goal had an end-of-2025 deadline. Zitron called it so unrealistic that Sundar Pichai should be fired. Google reported more than 650 million monthly users by October.
"AI had already peaked" is also a claim about the state of the technology at that time. Agent and coding benchmarks moved sharply after it. The fact that every technology eventually peaks does not make a past claim that it already peaked correct.
The bubble may still burst. That would validate the bubble thesis. It would not retroactively fix the prediction record.
btw Zitron has the only fan base that has come after me with doxxing and death threats so far. Truly misery loves company!
Some numbers have a lot of squish to them. Gemini can't be called a failure. But Copilot is a flop and yet Microsoft can probably show you similar numbers to what Gemini has.
> The bubble may still burst. That would validate the bubble thesis. It would not retroactively fix the prediction record.
If it does not burst it will be because specific efforts have been taken to deflate it in the face of concerns like those he is raising.
The bubble may not burst for example if the securitization of AI debt really happens. Then when it happens it won't be an "AI bubble" that bursts, it will be a full-on collapse of the US economy. Like when 2008 happened it wasn't really about mortgages anymore.
Macrofinance expert Nathan Tankus has an excellent post on why the financing situation around the AI boom is just not big enough to mess up the financial system.
I agree that the specific amounts invested in AI aren't enough to cause a calamity directly.
The problem is you have the vise of a stock-market decline on one side (something basically everyone thinks is impossible), and AI-induced unemployment on the other side (something a lot of commentators, including Zitron sometimes, seem to think won't happen). Those two things in tandem would be worse than 2009 by a multiple. I doubt the US government will be able to bail it out.
Did you read the post? The author cited very clearly a plethora of very specific predictions made by Zitron that were verifiably and factually wrong. False. Incorrect. Not “early”.
What’s particularly bad about Zitron is his financial analysis. Some people have the idea in their heads that “yeah, Zitron is wrong about a lot of things, but his economic data and analysis is tight.” That couldn’t be further from the truth.
The entire AI industry, especially the vested interests, are often full of shit, sure, but the sheer amount of misunderstanding that has surrounded the economy and its relation to AI has been mind boggling. There is much bologna being accepted as reasonable or even standard by HN comment sections.
This kind of back and forth reminds me of the Dot Com bubble circa 1999. There were endless articles saying that the internet was a new golden era for mankind and that the hyperbolic company valuations were justified; the detractors cried bullocks.
I use AI every day as I assume everyone else on Hacker News does. Will the S&P 500 drop 20%? I have no idea. If you know, please let me know so I can adjust by 401K.
Eh, not to be rude/crass... but that would be inaccurate, if not hopeful. Myself and others don't, despite mandates; in fact, I aim to see this 'left behind' promise we heard years ago. Short of writing the at-will employment paperwork for HR myself, I'm not seeing it.
Escalations continue to fill my days. Turns out, people are somewhat correct: results matter. I'd say moreso than the tools we 'choose' (or skip, in this case). My null on the token scoreboard remains unnoticed/inconsequential, the work I've done has not.
All to raise a bit of timeless advice from Wu-Tang: diversify.
I’m not really convinced by any of this, in fact it seems most of the predictions were pretty correct and that Meta and Alphabet are having big issues is a shared opinion by multiple observers and pretty much every employee they have.
If they were doing so well why the layoffs? Why the tightening both in salaries and perks and work life balance? Why so many choices disrupting morale for their employees?
Help me understand where Meta and Alphabet's issues are. They seem to be doing quite well on paper -- nothing anomalous in terms of profitability or scaling recently. Layoffs are to be expected when AI can increase productivity.
The problem with judging Alphabet and Meta on aggregate performance is that their advertising businesses print so much money that they can invest everything in a boondoggle and coast when it blows up. Zuckerberg burned $100,000,000,000 on the metaverse and it didn’t make a dent.
That said, Alphabet and Meta are borrowing against their future advertising profits to fund their AI boondoggles. If the advertising businesses keep growing then they’re somewhat insulated from their own missteps but the reliability of the advertising business depends on companies having money to spend on advertising.
The metaverse was mostly self-contained and the failure had zero consequence for the broader economy. AI on the other hand, every major fund is investing everything into AI companies. Every advertiser is using subsidized AI tools to generate hyper specific adverts. If this all goes south, who knows how advertising spend will be impacted.
Google specifically are backstopping billions in data centre build out costs. They’ve committed to spending, like, all of their cash to data centres. If the market gets nervous and funding disappears, Google are deep in the hole.
Anthropic “invested” $50bn in data centers to be built by Fluidstack who raised a billion dollars from Situational Awareness who used their Anthropic ownership to raise money to fund these investments. Google are backstopping much of the Fluidstack build out, i.e: if Fluidstack goes out of business then Google is on the hook for $10bn+ in costs. And Google’s Anthropic investment makes up like $100bn on the balance sheet. So, Anthropic fails to live up to expectations and fails to pay Fluidstack who can’t pay their suppliers which puts Google on the hook to hand over tens of billions in cash while at the same time their biggest investment is going down the pan.
The top line numbers don’t really do justice to the scale of the risk. You need to look at the long term commitments they’re making.
Ah well, people love hearing what fits their world view. So if you hate LLM assisted development or vibe coding, Ed is predicting the future you want ("Don't be afraid, it's going away soon!"). As with any sudden change, there are people that tap the brake instinctively. And people like Ed thus get popular.
That, and he has a nice way of speaking like everyone's gone mad but you and him ;) Have to admit I have enjoyed his rants, and there are certainly true things about them, here's another nice one for you lovers and haters alike! [0]
Luu kinda has a reputation for knowing his shit, for over a decade now. He's not a checks notes games journalist larping as economist, but he does all right.
I don't like Ed, but it seems that Luu spends far more attention documenting failed predictions than checking other predictions that might have been correct.
Telling me that quite a few of Zitron's predictions turned out to be accurate, while many others were completely off base, means he's throwing darts on a dartboard. Why should I listen to him?
I'm sure I can make a lot of accurate predictions. It doesn't mean I'm worth listening to, if people can't distinguish the accurate from the inaccurate in the moment.
Reminds me of a tactic I've seen often amongst both critics and shills on fads. An OpenClaw fanatic on Youtube comes to mind. He makes opposing claims in different videos. One of them has to turn out to be true, and he trumpets his successes ("Look, I predicted this!"). Only a few notice he also predicted the opposite.
Just look at the other thread about Zitron and his Enron comparisons.
Everyone is always throwing darts at a dartboard when making predictions. I don’t know why you think this is a critique that applies exclusively to Zitron.
What predictions have Ed made that were correct? And how do they weigh against the incorrect ones, in terms of both quantity and quality?
That's relevant because if you make a thousand predictions, a few of them might turn out to be true. That doesn't mean you're good at making accurate predictions.
Zitron reminds me of a lot of people who got oversized reputations after the 2008 financial crash. They predicted it, or could spin past statements as predicting it, and rode off that.
But from that point on they basically assumed this role of what financial punditry call perma-bears, people who constantly predict financial doom. It gets clicks and sells subscriptions, which is probably why they do it. But from that point on their predictions weren't very good. If you constantly predict doom then every now and then you'll look like you were a genius, but it's just survivorship bias. People discount all the other times you were wrong.
I don't know a whole lot about Zitron himself. Didn't he get big calling BS on cryptocurrency stuff that actually was BS?
The problem is that AI isn't cryptocurrency. This is very real.
I do suspect there's some bubbly stuff around it. I think data center construction looks very bubbly, especially the totally ludicrous amount of permitted planned data center construction. I bet no more than 20% of that ever comes online. I'm sure there's some AI companies that won't make it, and a bunch that are overvalued. But AI as a whole is real, not just hot air.
I think lot of people including Ed are mixing 2 things the AI tech itself and the economic viability with the inflated values of companies building it currently. Me personally I am optimistic about the tech itself but don't see the current AI companies valuations being realistic or even the economic systems they are building around AI/LLM. As it will all be comoditised down to cost of compute + cost of electricity in the end.
I was skeptical of LLMs making it to where we have them today, but the test results are evident, empirical, and hold up to hard scrutiny. I remain quite skeptical of a singularity event, GAI, and anything beyond what we've seen LLMs output today. It will get faster, cheaper, but not much smarter without another breakthrough. They will not be able to save the planet from their own pollution and overconsumption.
Danluu claims Ray Kurzweil's predictions are wrong. I asked
> list 50 predictions by Ray Kurzweil that he made before 2005, the date he made the prediction, and whether to not his prediction was correct. If it was correct, give some proof.
Of the 50 that were listed, 43 were correct, 7 incorrect.
For example Kurzweil apparently predicted in 2019 that:
> Blind people wear special glasses that interpret the real world for them through speech. Sighted people also use these glasses to amplify their own abilities. Retinal and neural implants also exist, but are in limited use because they are less useful.
> No
Note that if Kurzweil makes a prediction that an event will occur before year X, and it happens in year X + 1, that still counts as a wrong prediction.
Can you get your un-named source to provide a detailed list of these predictions as Dan Luu did?
Maybe we have different interpretations of whether or not a prediction is correct or not.
If someone predicts in 1999 predicts self driving cars by 2020 and it happens in 2030. That seems different than predicting something we see no indication of ever happening. Like if they predicted 2020 and it happened in 2021 is that a fail. To me, no. The prediction is that the tech will exist at or around that date, not that the date is exact. Claiming it's a fail for being 1 second late is not a rational position and not at all in the spirit the predictions were made.
There's also issues like being directionally correct. Example: Someone says computers will be in everything. You can say "there's no computer's in fruit!, FAIL!" or you can look at the explosion of IoT devices and decide it was mostly right?
I get 17% correct if you're absolutely strict, 64% correct if you're charitable
2009
* Most books will be read on screens rather than paper.
The charitable interpretation is that most reading happens on screens, not paper. This is true today.
* Most text will be created using speech recognition technology.
False
* Intelligent roads and driverless cars will be in use, mostly on highways.
False in 2009, False in 2026 but directionally true. If you live in an area with Waymo you see them all time. I've driven down Olympic Blvd in Los Angeles and had my car surrounded by 5 Waymo cars at once. So is this false because it didn't happen by 2009 or is at least directionally true because it's happening, we see evidence of it happening, vs if we saw zero evidence then we could 100% say it's false.
* People use personal computers the size of rings, pins, credit cards and books.
rings, pins and credit cards, no, books, true. Smartphones are smaller than books. Maybe you could make the argument those are not personal computers. I think that is debatable. Even then, you can by PIs or Mini-PCs that are book size.
* Personal worn computers provide monitoring of body functions, automated identity and directions for navigation.
Arguably true. phones provide directions for navigation and are worn in pockets. Fitbits came out only a few years later. Id is not automated though.
* Cables are disappearing. Computer peripherals use wireless communication.
Arguably true. most laptops, all phones, most mice, keyboards, joypads, etc. are all wireless.
* People can talk to their computer to give commands.
False/True. Was possible was not common. That said, Siri shipped in 2011 so 2 years off.
* Computer displays built into eyeglasses for augmented reality are used.
False if you mean mainstream.
* Computers can recognize their owner's face from a picture or video.
Face ID shipped in 2017. Is that to far off?
* Three-dimensional chips are commonly used.
I'm not sure what this means.
* Sound producing speakers are being replaced with very small chip-based devices that can place high resolution sound anywhere in three-dimensional space.
False,
* A $1,000 computer can perform a trillion calculations per second.
True, happened in 2008 with the ATI Radeon HD 4850
* There is increasing interest in massively parallel neural nets, genetic algorithms and other forms of "chaotic" or complexity theory computing.
Happened in 2012 so 3 years off
* Research has been initiated on reverse engineering the brain through both destructive and non-invasive scans.
Based on the actual words, this is true and was true before the prediction.
* Autonomous nano-engineered machines have been demonstrated and include their own computational controls.
I don't really care what Zitron says or pay attention to him because it seems clear to me he has some kind of agenda.
But this article is not nearly as impartial as it claims to be. It interprets all Zitron's claims in a narrow and overly-literal way. Missing the point and refuting a technicality.
For example, looking at the last 3 year's revenue/profit growth. This tells us nothing without looking at a larger context. Has revenue growth slowed down? Have profit margins compressed? What makes up the income and has that changed? Etc.
I think anyone being intellectually honest understands that these tables alone don't refute Zitron's claims that big tech are "dying and thrashing around" which is something that could take many years and easily hide under these kind of top line / bottom line numbers. (Maybe further analysis would refute the claim, but that analysis is not present here.)
Or Zitron's claims that AI capabilities are "reaching the upper limits" back in 2024. I don't think even Zitron would disagree that AI capabilities have grown since then, but that doesn't refute his point. How long the "reaching" takes and how wide the "upper limits" are is completely up to a subjective interpretation, which this post makes no attempt to even explore.
By all means dunk on futurists. But at least steelman their positions or you look just as biased as them. (Although maybe I am off base and this bias is meant to be clear from the start by admitting to pro-AI predictions all the way back in 2015.)
It seems to me that if they'd just about reached their upper limits in early 2024 agentic coding wouldn't be eating software development like it is now, and wasn't then.
LLMs are broader than just coding. The article states this particular Zitron claim was wrong, even at the time, because you can put an LLM on a loop until it generates code that compiles. Mitigating hallucinations (in the subset of applications with verifiable output) is not really the same as solving that category of problems outright. I could quibble about a number of things in this example alone, but that's not really my point.
I don't care if the claims are correct or incorrect. I just think the tone of the article is dishonest about its own impartiality and fairness, because it doesn't even attempt to interpret the claims in any way except the least favorable. If you want to be persuasive, you should refute a claim using the most favorable interpretation of that claim possible -- this does the opposite.
Yeah, I think this is fair criticism, and I definitely felt the pointedness in the tone as well.
On the overly-literal / narrow thing, I think thats the culture around evaluating predictions overall. Like, all those posts around christmas where people make predictions and evaluate how last year went. The rigor is the norm.
I would be interested in seeing a similar list of predictions from Altman, Amodei, etc with annotations about how many have come true. Ed Zitron is a blow hard and frequently overstates things to the point where it is hard to take seriously, but so are the AI industry leaders.
I have heard multiple breathless press releases warning that the end of white collar work is "just 6 months away" and that people not using the latest Mythos/Fable/Whatever model will be hopelessly left behind.
Dan Luu's piece talks about this! He gives the example of Ray Kurzweil, who is similarly catastrophically wrong about everything, just in the opposite direction as Zitron. Luu's point isn't that anti-AI analysis is bad; it's that Zitron is bad. Zitron is bad in this analysis no matter what Altman says. It could be the case that Altman is also bad.
Altman and Zitron are like pro wrestlers. Their speech acts aren't for truth, they're for some spectacular effect on your feelings and your imagination that keeps you coming back for more.
I encourage anyone interested in this to read On Bullshit by Harry Frankfurt, the best popular philosophy work in a long time.
Kurzweil made one big claim — Singularity around 2045. Everyone at the time thought it was a total joke and hundreds of years off, if even possible. Now, that is seen as a laughably long timeline.
The rest is noise, I don't care what minor predictions he was wrong about, he called the big trend back when nobody else could make a trendline.
The fact that you know Ed Zitron's name means that, regardless of his predictions being consistently wrong (which they are), his strategy for manipulating human attention has been correct.
The question is whether he sincerely believes his own predictions or if he cynically is aware that you can create a career for yourself being a guy who says bombastic clickbait-worthy things people emotionally want to be true, instead of measured assessments of reality.
Problem is, you wouldn't know Ed Zitron's name if every piece he wrote basically said "AI might be a bit overhyped short term but will have lasting economic impacts." Booooorrrring.
The reverse is also true. OpenAI and Anthropic aren't going to get much media attention if they don't make silly claims like "all white collar jobs gone in 2 years."
And no, this isn't a new problem due to "the algorithm." Media has always been like this. Zitron is just another Peter Schiff with younger skin. The problem is human nature in general.
Hence why AI isn't going to kill media. We don't actually want sober, rational assessments of all available information from hyper-intelligent LLMs. This is unsatisfying. We want emotional validation, drama, adversarial identity and spectacle. Truth is rarely what we are seeking.
> Ed Zitron is a blow hard and frequently overstates things to the point where it is hard to take seriously, but so are the AI industry leaders.
Except that the AI industry leaders actually have skin in the game/are in the trenches and competing in the market. Ed Zitron wants you to subscribe to a newsletter so that you can read doom and gloom and...?
If you invested based on his advice, you'd have missed huge gains and/or lost money.
And I say this as a person who thinks most of the "AI industry leaders" are ethically questionable, at best, and ethically bankrupt, at worst, and that the stock market should be approached with caution due to valuations.
> multiple breathless press releases warning that the end of white collar work is "just 6 months away"
I'd like to see an example of this.
I think Altman and Amodei have been quite sober with their actual predictions. They've said things like "models can now do white collar work" but haven't yet said it is the end of white collar work.
The closest actual quote to this was in In March 2025, Anthropic CEO Dario Amodei told a Council on Foreign Relations audience that AI would be writing 90 percent of code in three to six months, and essentially all of it within twelve months.
I think that he underestimated how long it takes for technology to get uptake but in terms of capabilities he was perhaps 6 months out. I'd say that Fable class models are definitely capable of writing essentially all software, and that was released June 2026.
> In a remarkable interview with Y Combinator in November 2024, Sam Altman, CEO of OpenAI, shared a vision that could redefine the technological landscape as we know it. Altman confidently revealed that OpenAI has a clear roadmap for achieving AGI by 2025.
Note that no one ever quotes the end of Dario's line, either, where he said programmers would still be needed in that 12 months. People think the prediction was more extreme than it really was because all the clips didn't include the latter part.
> .... and in 12 months, we might be in a world where the ai is writing essentially all of the code. But the programmer still needs to specify what are the conditions of what you're doing; What is the overall app you're trying to make; What is the overall design decision; How we collaborate with other code that has been written; How do we have some common sense with whether this is a secure design or an insecure design. So as long as there are these small pieces that a programmer has to do, then I think human productivity will actually be enhanced
I think one important distinction is at least they have some humility to admit they were wrong. I think Ed Zitron has rarely, if ever acknowledged he was wrong.
Man, as a former extreme skeptic, I watched a video from Eric Schmidt in early 2025 where he said that by the end of the year nobody would be coding, and that one was dead frickin on.
Everyone who started using claude clode when it came out in early 2025, knew it was coming (not quite yet). That felt more like reporting than prophesying.
You may be living in a bubble. There are tons of developers still coding by hand, and many industries that don't trust machine generated code in general.
Dario Amodei and Eric Schmidt seem fairly well-calibrated, although a bit early. Elon Musk is constantly way, way overoptimistic (perhaps to the point of willful fraud). Zitron is hopelessly and ridiculously incompetent (and there are allegations he is willfully lying, too, but who knows).
My bet is that Amodei's claims about the dangers of AI are the most likely to come true – I'm predicticing amodei/anthropic will become the evil it was against.
No, you see, they were right all along, they just "didn't anticipate the public outcry against data center expansion", thus the obviously inevitable AI takeover of the economy is being slowed by NIMBY curmudgeons who should be ignored and punished.
The big difference is that Altman et al aren't just, or even mainly, pundits or prognosticators. Zitron's whole thing is commentary and predictions about AI and his predictions are almost all wrong.
Altman and friends are mainly actually making and delivering AI. If they also hype their timelines/valuations, they also seem to make directional progress on the goals.
Yep I distinctly remember at the beginning of 2023 that folks were predicting that we were less than two years away from there being no jobs for software developers. Pretty soon we'll reach double that timeline.
And of course it really has changed everything! But that was not all that a lot of people were prognosticating.
To be fair, Zitron's career is as a commentator. To some extent, I expect CEOs of companies to make ridiculous claims. At the end of the day, when shareholders come knocking, what they care about is whether or not your company is growing.
No such mechanism for social media personalities. You can be wrong 90% of the time, and your audience will still praise you for the 10% of the time you were right.
> To be fair, Zitron's career is as a commentator. To some extent, I expect CEOs of companies to make ridiculous claims.
This is not a comment directly at you, because I understand where you're coming from, but I think this attitude shows how far leadership and our expectations of the professional class in America have deteriorated.
My expectations are inverse to yours. I don't care if a commentator makes a claim that's wrong, if they keep being wrong people will stop listening. (In theory. Jim Cramer still has a job so who knows, really.) I certainly don't expect a commentator to have accurate internal information about a company.
CEOs on the other hand should be expected to be honest and accurate in their claims. The information a CEO shares should be accurate so that investors can make informed decisions. Instead, we seem to accept CEOs who act as salesmen first and leaders last.
If a CEO claims a product will be out later this year, and the stock goes up, then they announce actually things are delayed, and the stock still goes up, what is actually being rewarded here?
I don't think of Zitron as a journalist, I think of him as an entertainer, no different from all the columnists paid to tell people that what they already think is right.
The fact that you're holding him to a higher standard than fabulously compensated professional c-suite officers whose products are used in matters of life and death is... kinda weird?
But I think anyone who evaluates their claims understands that they’re talking their own books.
Zitron’s problems are more subtle. He benefits financially from his own claims, while also touting his impartiality and capacity for objective thought.
A person savvy enough to understand the indirect financial benefit to Dario promising that Claude is so dangerously smart it must be regulated understands Zitron’s schtick
Zitron has become the distorted reflection of the very AI boosters he criticizes and mocks.
I think the worst thing that happened to him was AI skepticism becoming a political position. This gave him a captive audience - as long as he says what they want to hear, which means that he can never ever concede that he might have been wrong or that AI might actually be progressing or having successes.
This is not conducive to good prediction long-term - rather it leads one to a state of cognitive dissonance where one's chosen enemies must be simultaneously terrifyingly powerful and incompetent dunces. The propagandist's disease.
How? AFAIK, his arguments based around the economics remains the same, he just seems increasingly dramatic and exacerbated, which is understandable when you realize industry + ecosystem (politics/reporting) is tulip mania delulu and insists 1+1=100, or 30 trillion, or whatever. His position isn't based on AI progress - it's based on AI economics, at this point he can be an obnoxious rationalist slamming flat earthers - just because he's annoying/smug doesn't mean he's less right on fundamentals which if anything is more clear now.
When you predict a company is going to fail and instead it sets revenue records you're not "dramatic and exacerbated". You're refuted.
Luu's post includes a (long!) list of specific predictions that aren't "exacerbated"; they're simply wrong.
Luu's point isn't that AI is going to succeed or that the "AI bubble" will never pop. It's that these predictions are all wrong. If you agree "directionally" with Zitron, all that means is that you're skeptical of AI. That's a totally reasonable position to have, but it has nothing to do with whether Zitron's predictions are good or bad.
> you realize industry + ecosystem (politics/reporting) is tulip mania
It could be "tulip mania" or it could be "the internet".
Luu analyzed the numbers instead of just reacting to hype.
Specifically:
>> Oct 2024: OpenAI's forecast of $3.7B revenue in 2024 and $11.6B in 2025 and $100B in 2029 are absurd, "a statement so egregious that I am surprised it's not some kind of financial crime to say it out loud"
> Wrong (2025 goal exceeded, 2029 TBD but not an egregious financial crime level of implausible)
>> February 2025: Anthropic making $34.5B in revenue 2027 is "is laughable on many levels, chief of which is that OpenAI, which made around twice as much revenue as Anthropic did in 2024, barely made a billion dollars from API calls in the same year."
> Wrong (whether or not they make that in 2027, their 2026 ARR greatly exceeding that makes the 2027 estimate non-laughable)
(edited to remove snark)
Your comment seems to miss the point that the article does not (necessarily) have a problem with Zitron being insufferable/annoying/smug. It's that his predictions are verifiably wrong. It's one thing to be annoying and right. Zitron is annoying, but not right.
Did you read the article? It paints the picture of someone who does not in fact have a good track record on the 'fundamentals', even if his broad thesis of a bubble may prove correct.
e.g. when he suggested Anthropic may be fudging their revenue numbers / projections - which was actually due to him making some careless mistakes in a spreadsheet
He has built a following of people that want to hear his extra skeptical views. And even if he changed his mind about some things, he cannot admit it, as that is not what his followers want to hear from him.
It's rare that people make the news and build a following by saying a very balanced, down to earth opinions... unfortunately.
I almost wonder if it's even possible now that so many of us get our information through algorithmic feeds.
I listened to one Zitron interview on YouTube and my home page was immediately crammed full of similarly foamy-mouthed AI critics. (As well as plenty more Zitron.) And it's suggested some people with un-nuanced pro-AI takes, too. But what the algorithm has never, not even once, given me is a sober voice that calls attention to the nuances.
>It's rare that people make the news and build a following by saying a very balanced, down to earth opinions
Humans are built for fighting and killing the rival tribe, not for pondering whether the other tribe might actually be correct. We are exceptional for even being able to overcome ourselves enough that the outcome of "fighting and killing" can be minimized in the large.
> And even if he changed his mind about some things, he cannot admit it, as that is not what his followers want to hear from him.
I don't think that's true - over the years he's changed his mind from "LLMs are useless slop machines" to "LLMs can be useful when applied wisely".
For instance in "The AI Hater's Manifesto" he says:
> It just came to me — the problem that I have with most people using LLMs is the delineation between outsourcing work and outsourcing thought. Those using LLMs to write little scripts or BQL code on a Bloomberg Terminal are inoffensive. [...] A tool being used as a tool to do tool things — in many cases involving the LLM writing a little 30-line Python script! — is not a problem, though it’s also not a trillion-dollar industry that needed to steal everybody’s art and writing.
Similarly, in "The More You Buy, The More You Lose":
> Sidenote: The only truly useful use case I’ve found is on the Bloomberg Terminal’s ASKB feature, which takes natural language and turns it into BQL code to make requests of Bloomberg’s datasets. It’s genuinely useful!
You can perhaps say that he underestimates what the technology is capable of (or, conversely, that other people overestimate LLMs) - but that's a different kind of conversation.
right, and look at current American politics. One side tried to tailor normal, routine and rational POVs.
The other...
EDIT: I do find it amusing when I write these types of comment, then suddenly realize the irrational man childs of the far right nationalist republic ethnostatists think they're the rational ones.
> I think the worst thing that happened to him was AI skepticism becoming a political position.
It is a political issue in the sense that the discussion around AI can't avoid material and cultural issues. Ed Zitron definitely has an ax to grind and I'm personally turned off by his hyperbole, but I don't think something being a political issue means it's not worth discussing. I've also noticed the pro-AI crowd pivot to accusing people of not having actual reasons for hating AI, being victims of Chinese propaganda, etc as a way to avoid actually talking about specific issues that people have about AI and DCs.
I never said it's not worth discussing, I'm just saying that it becoming a charged issue was bad for Zitron specifically because his style of content fell in demand with the most irrational parts of the crowd.
You're not wrong but for a lot of AI boosters it's also a political position - remember Anthropic and half of SV at this point is run by wide eyed effective altruists.
Ultimately one cannot separate technology or science from politics, it's inherently political
I'm not sure I have the force of will to not succumb to it. I think the only way to avoid it is to be so sure of the thing you are doing that the audience reaction is not the key metric by which you measure your success.
"Popular topic-expert" is a really cursed career to exist.
Ideally we would reward expert opinions on track-record, instead of how they make us feel. But we don't. Also ideally, the expert's conclusion wouldn't impact their ability to pay the rent (as long as it's correct), but again, that's not the world we live in.
Ed Zitron’s job is to convince people to pay him money to read what he writes, of course he’s going to preach to the choir, they’re paying him to do that and he knows it.
This is why I always consider the agenda of an author, Ed Zitron’s agenda is to make money from subscribers who read his writing.
At first I thought you were referring to Umberto Eco's laws of fascism: "Fascist societies rhetorically cast their enemies as at the same time too strong and too weak."
but it's subtly different, because incompetence is not weakness.
he makes a shit ton of money off this - in this attention economy we are doomed, people of every side find that going all in on something, no matter how intellectually dishonest, is much more profitable. Extreme AI bros or Extreme AI doomers seem dishonest. AI is an amazing technology - it is simultaneously not going to replace us in the next 3 years and it's not total garbage - the truth lies in the middle.
Its pretty clear if you listen to him now he's just (re)playing the hits for his audience. Its like MSNBC/Fox News for people who think they're too smart to fall for that.
I used to think he was just early on some of this stuff but the sheer amount of content he produces its clear he's just cashing a check.
I'm not a huge fan of his style myself but fundamentally he isn't wrong. The whole "growth" so far has been all show and no go. I see people pouring in millions only to end up bankrupt a few months later. Evangelists portray those instances as rare and simply "skill issues" and "they don't know what they are doing but I am". Anyone that's ever had several servers running at home and seen the electricity bill at the end of the month knows it - I do as well. And we are talking about servers that use power, only a fraction of a server running 4x H100s at 100% 24/7. For those of us that have - we are talking servers that have one or two xeon silvers at best, 15% load on average. And I live in a country where the electricity is veeeeeeeeeeeery affordable. I'm not taking into account training, or investment to get it going, just inference.
20 bucks subscription a month, dario, you ain't fooling anyone with above-room-temperature IQ (Celsius). Even the expensive subscriptions - we know that's not the real price. And the limits, bumping up prices every two weeks and so on, just to keep the lights on while draining investors... Zitron's claims are pretty impossible to deny: the moment those companies go public, the real prices will need to come out of the shadows and end up on your monthly bill.
At the same time I fail to see the real world benefit to this, even in software: it's like comparing Lego (before they began their anti-consumer bs) to cheap Chinese toys. The moment you look at them side-by-side, you know which one is a premium product and which one is a cheap Chinese toy. While I have been a big supporter of open source since I was a child, I was never biblical about using open source - if some proprietary piece of software does a better job than the open source one - fine, take my money. Not anymore. I see the abysmal state of cyber-security as a direct consequence of slop. Slop-written code, slop-reviewed code, tests pass(also slop), ship it. Yeah, I'm not trusting you with my data, the hell with that, I'm self-hosting everything, adios. And I sure as hell don't trust the ai-bros for anything either.
And there's another thing: Microsoft was clear about it: github is constantly down because they can't handle the load. App stores are bumping up developer fees. Anyone can slop together a todo app(which was never hard to begin with). It's still a winner-takes-all economy - no one is going to install a todo app and migrate from Google/Apple just because. So the 1000 todo apps released daily will simply be a hole in the pocked of the people who slopped them together and another reason to be paying 500 bucks for 64 gigs of ram and another 500 bucks for a 2tb nvme. Raspberry pi's started off as educational platforms for 35 bucks and their commitment was to keep them at those prices. That aged well, right? If I decide to upgrade my uconsole, I have to pull out another 350+ bucks for a raspberry pi. All thanks to the sloppification. The bubble can't pop soon enough and frankly I don't care what it takes down with it.
Looking at the refutations of Zitrons predictions in TFA, it boils down to two categories:
1. Zitron claims model capability has peaked
2. Zitron claims AI lab growth (user and revenue) has stalled.
In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.
In the second case, numbers are given showing growth, directly refuting Zitron. However, I'm giving Zitron the benefit of the doubt, given the old saying - market can remain irrational far longer than you can remain solvent. As long as people can be convinced that the sky is falling, rational predictions rarely pan out.
> 1. Zitron claims model capability has peaked … Zitron is probably right in this regard …
Is there any objective measure that shows this?
Would we use examples such as his Feb 2024 claim "I believe we're reaching the upper limits about what generative AI can do"?
> 2. Zitron claims AI lab growth (user and revenue) has stalled.… I'm giving Zitron the benefit of the doubt …
Is there some date by which you'd say it'd be fair to evaluate whether Z’s claims are true (without the benefit of the doubt)?
You mentioned revenue - would we use claims such as his 2024 claim that the companies no longer knew how to grow? But that in 2024, 2025, and 2026 both the companies revenues and profits have grown at double-digit rates each period?
You also mentioned users - would we use claims that "Sundar Pichai wants Gemini to be 'used by 500 million people before the end of 2025, 'a number so unrealistic that someone at Google should have been fired, and that someone is Sundar Pichai", where Gemini then hit 750 M users?
Or by what measures should we evaluate whether Z's claims are true?
I'll paste my response to your pre-edited questions:
I think atomic weapons peaked during the trinity test. The subsequent creation of bigger explosions by packing more fissile material does not meaningfully improve the technology.
Since LLMs are a generative technology, let's use a generative skill - painting. There are tons of brilliant painters, all with their own styles. What I think is consistent among master painters is their effortlessness in their craft. Honing a skill makes subsequent attempts less effortful.
To me, LLMs are more like atomic bombs than master artisans. To get better results, add neural nets. What would be meaningful to me, is to constrain models to a fixed amount of compute, run them on any of the copious amount of benchmarks, and see if they get the same scores at increasingly faster speeds. This, at least to me, signals mastery.
On the date for evaluation, I will set my own prediction instead, that AI labs will not become profitable, local models will drain their moat.
Taken on its own, I will concede that Zitron's predictions are wrong, but it is exactly why I bring up market irrationality. The multiple rounds of funding is propping up the unsustainable business model. Without it, user numbers and revenue can't grow.
I forsee real innovation in the AI space after the bubble pops.
Just this morning, I had to read through an LLM response about how a PC8-M5 fitting has a high flow rate because it connects to an 8mm tube, completely ignoring that the threaded M5 on the other end will only have space for a 2mm hole, so it still seems quite similar to the LLMs of 2020.
> In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.
You don't have to like them, use them, or consider them "good enough", but the idea that models haven't gotten better in the last two years is ridiculous.
> In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.
I am certain that plugging a circa 2023 model into a 2026 harness would be a pretty frustrating experience. Yes, you could code a bit with AI in 2023, but models are just much better at it than they used to be. And smaller open models are leaps and bounds better at it than they were three years ago.
Agreed, though I do think that LLMs are still more similar than we think. Sometime after the first release, AI labs found that coding sat in the niche space of lots of easily digestible data and fast feedback from error messages and compiler checks etc. This allowed models to be trained with a focus on coding tasks, but the underlying technology is still the same, the infrastructure around it changed, they are still generating via probabilistic sampling.
Don't get me wrong, I'm using local coding agents myself with varying levels of success and frustration, but the models themselves behave similarly to their siblings from 2020.
The infrastructure improved, that includes the data. I have a pet theory that if they took the earlier models and retrain it with the data they used to train the latest models, we will get a similar result.
This is kind of a "I'm not here to tell you about Jesus he either lives in your heart or he doesn't".
I am an engineer, I have about 15 years of experience. I have been using AI in my job since mid 2025. Over that time it has gone from being an interesting toy that could kind of help but would often hinder, to being an absolutely explosively powerful tool. Just from personal experience it is the thing that has improved the most of any of my tools in career. And over that time my spend on AI has sky rocketed.
You don't have to believe me, it's obviously just anecdotal, but for anyone in the same position as me (And there really are lots of us), to claim model capability peaked in 2024 is just staggeringly dumb. It'd be like claiming electric cars peaked in 2008. I don't know how further to convey this to you.
It may very well be the case that the financial side is a bubble that horribly bursts. But the technology is real and the claims Ed has made are just wrong.
I'm the same as you sans 10 years of working experience, with similar experiences using LLMs. The distinction I would like to make is that it's not the models that are improving, but rather the infrastructure around them. It started with prompt engineering, then chain-of-thought, then mixture-of-experts, now harness engineering etc. These, I think are what's driving LLM complexity, not the model.
To bring back to your electric car analogy, the electric motor peaked early on, but was not quite useful as a car until advancements in battery density, charging technology, regenerative braking were made. These are all the infrastructure needed to improve the viability of owning electric cars, just like the infrastructure was needed to make the models useful.
To be honest, I'm much more excited to see development of infrastructure around local models than seeing the arms race between AI labs. At least the former benefits the end user in a transparent way.
To be fair not predicting accurately doesn't necessarily mean they are wrong - Michael Burry is the perfect example here, a market may actually just be so fraudulent (AI companies being funded by AI companies etc) that it can defy common economics for a while.
You're right or wrong in relation to a given statement.
If your point is just that there may be an AI bubble, then yes the fact that it hasn't popped yet does not prove it wrong. But that's not what Ed Zitron is saying; he's making very specific statements that happen to be wrong time and time again.
As many said in the comments, it's common for doomers to keep predicting a crisis, every month and every year and so on, until inevitably a crisis does occur and they claim they were right. That's not how it works.
He didnt mention EZs claims about lack of data center construction compared to projected growth and valuation. I wonder if that was substanciated, since it overlaps with the bubble portrait of the "coming" wall of due service contracts hitting in 2027.
Big shout out to Zitron. Everyone knows how to monetize good predictions (stock market, etc), but he might be one of the few who successfully monetized bad predictions, making him a step ahead of the rest of us.
I think he's constantly trying to time the peak (of AI improvement, of compute capabilities, of profitability) and keeps having to readjust his predictions every time it's proven wrong. Timing the peak is a fool's errand unless you have extremely hard and unavoidable data that everyone will have to face by a deadline. But contrary to what AI boosters often imply in response to skeptics being wrong, failing to time the peak doesn't mean the peak doesn't exist. So far it has existed for every new technology.
Thank you. It's difficult to call Zitron right or wrong because he is merely grabbing the attention and clicks of AI sceptics and people afraid of any change. Patrick Boyle is another example with his constant stream of everything is broken videos, from AI to EVs.
I am not all in on AI, but the discussion requires nuance. Zitron does not have it because he is interested in attention and not discussion. The article deconstructs his antics really well. So again thank you. I really appreciated this part
> To make the case that these things are dying, he pulls on minor issues that are not positioned to cause the very large changes he suggests are about to occur. For Meta, he cited some kind of alleged MAU drop for Facebook. Rather than use Meta's own MAU figures or any kind of revenue or profit numbers, he seems to have used numbers from Similarweb. My experience with 3rd party tracking numbers like this is that they're quite inaccurate and generally useless for anything other than a rough order of magnitude comparison, making the Zitron's cited decline meaningless. FB stopped reporting MAU publicly in December 2023, but most estimates have FB MAU increasing over time and the numbers Meta does report show generally increasing usage over time for their products; Zitron cherry-picked an outlier low estimate to make his point.
He is a bit more of a PR person rage-farming disguised as a futurist. He's not optimizing for correct predictions. That's really hard and not his incentive.
There's a demand for loud and confident "AI tech will fail" and "big tech will fail", so it pays to peddle the goods.
A lot of people desperately want AI to be a nothingburger. Thus, they will seek a second opinion that just so happens to line up with their existing one. Wishful thinking at its finest.
There are an even larger amount of people who have gotten tired of the NFT levels of pushiness AI has become from a financial perspective. It's become worse than crypto bros at this point.
To commit fraud you have to lie, deceive, or conceal. All of this bonkers financing is happening in broad daylight, announced to the markets, and met with rave reviews. I tend to agree with Zitron that the whole thing is a castle made of sand - not because the tech is bad or useless but because the financing, in its scope and structure, is so outlandish - but you can build a castle made of sand as long as you don’t tell anyone it’s made of steel.
So far NVIDIA has been very careful, it’s not actual fraud, at least based on public information. It’s a very unstable and extremely risky bet, that is very likely to blow in the face face of AI companies, but it’s not fraud
Patrick Boyle had a vid specifically on whether there was Enron style fraud. The conclusion was no, though he thinks things may be a bit bubbly https://youtu.be/NufJ7g63KSY
There is no evidence of fraud that I have seen beyond complete speculation from the likes of Zitron. He basically just says "Look how big the numbers are! It's so big it must be fraud!"
I haven't read every word but I don't recall him saying AI is bad or useless ever either. His refrain is that they cannot keep up with their endless spending on training and they're not reaching new markets.
I look at the rapid rise and decline of OpenClaw and get the impression AI has not really found any foothold among average people.
1. Conversational AI - this is ubiquitous now, but not sure it's increasing at any significant rate. At least my personal usage has leveled off. Also probably not a significant driver of per token demand relative to code gen. More a driver of subscription revenue.
2. Code Generation - this is the big killer app, which I suspect has driven most if not all of the last year's revenue growth for Anthropic and to a lesser extent OpenAI. Now, I doubt we can extrapolate that growth given 1) how quickly and completely CC has been adopted by the industry and developers and 2) how corps are reigning in spend
3. Image and Video Generation - this honestly strikes me as more of a trifle/novelty than anything else. Granted I'm not a visual artist but I don't see it being a killer app anywhere to the extent of code generation.
4. Physical AI - this is the wildcard for me, but also beyond my ability to conceptualize its effects on demand. I suspect a proliferation of self driving and autonomous robotics would be a huge driver of compute demand but who knows how close we are to proliferation?
Not very. I like to always watch both sides of this issue, the people who are optimistic, pessimistic, doomers, etc... He is someone who in my opinion misunderstands the bigger picture. He often downplays the capabilities of these systems, doesnt think they will be more powerful in the very near future and also more importantly makes a fatal misunderstanding that we live in a rational world with rational actors.
Zitron sells anti-hype and AI-hate. AI is not good for a lot of people and he's telling them what they want to hear. He's great at talking and making entertaining facial expressions. That's actually a real skill I admire but it has nothing to do with predicting markets correctly. He's in the business of selling entertainment views not predicting markets. He even admits he doesn't short AI.
> No good reason, really. I got four hours of sleep and my brain wasn't good for much of anything and I saw someone posted a screenshot of a reddit post dunking on Ed Zitron's prediction record. When I wrote this review of futurist prediction accuracy, I tried to make sure that I didn't bias what I was reviewing in any way. It's not obvious from the post if the redditor who reviewed Zitron's predictions was pulling predictions in an unbiased fashion or if they were biased in some way (since AI has become a culture war issue, it wouldn't be surprising if someone pulled biased predictions), so I decided to read some Zitron in my spare time while poking at agents to get them to do an unrelated task I wanted them to do. For the futurist post, I read multiple entire books to pull predictions and generally only stopped when someone was being repetitive and kept saying the same thing over and over again. In this case, all Zitron does is be repetitive, so the methodology in the futurist review would mean that I review a few predictions and then stop immediately. To overcome this, I had ChatGPT give me a list of predictions (with no attempted tilt towards correct or incorrect predictions) and then I skimmed/read the posts that ChatGPT linked to. There were some cases where I thought ChatGPT's reading of the post was incorrect (these were generally cases where it flagged a prediction that would be incorrect if its reading was correct, but I disagreed with its reading) and (discussed further below) I also removed predictions which weren't falsifiable or seemed pointless because they were tautological (I noted something similar to this in the futurist post).
No reason really. Just very sleep deprived and want to multitask in between agentic feedback.
Some people obviously want AI to fail. Some people obviously want The Magic Machines to win.
pragh@ (Prabhakar) (yes his ldap is an anagram of graph) absolutely gutted the fire wall between search and ads.
To pick a specific piece of public information to support my claim: the previous SVP of ads quit immediately before the coup to start a subscription based search engine.
One nonpublic piece of information: basically every member of search leadership was pushed out within two years of his ascension.
Fair enough on Zitron being wrong, but I think the opening argument about Meta, Google, and Microsoft misunderstands his point.
Zitron is saying that the hyperscalers had no genuine growth opportunities, so they're using the AI bubble to achieve growth. The fact that they've continued to grow for a few years doesn't contradict his point, and Meta's steadily decreasing profit margin certainly doesn't look healthy.
Seconding that observation. The IT folks in charge of the compute direction have been quietly raising those concerns for over a decade (“and what happens to those alleged lower costs when their attention diverts to a new industry or pumping margins for shareholders?”), and the major enterprises or customers had all but migrated wholesale into public CSPs - and been eyeing the exit when they finally opened those eye-watering bills IT had kept forwarding to stakeholders. The industry wasn’t going to collapse so much as right-size, and that would turn into an inevitable cycle of churn (higher prices to drive margins, leading to more customers leaving in part or in whole, which would drive up costs higher to continue delivering positive results, ad infinitum).
The current AI build and boom has done wonders to their bottom lines in the immediate, but even Wall Street has its limits, and it sounds like there’s decreasing appetite for such CAPEX builds without associated proven revenue. That might kill some companies outright, but more likely it’ll force the major CSPs into the churn cycle that much faster.
I was a fan of his writing for a long time, but it's clear that the guy has fallen down the "build an audience that's only here for baying for blood on one particular topic" hole and won't be able to escape it.
He used to be really thoughtful and funny about the topics he took on. And I realize that's pretty standard for a critic. But now he just keeps digging deeper and deeper, getting more things wrong post after post. His posts have grown to probably 3x the frequency and 3x the length of when he was talking about stuff like Google tanking their Search business by bringing in the Ad guys.
He really used to be a "little extreme" with some really grounded viewpoints. Now it's more like he's performing the "angry british guy" in order to maintain his impression count and audience.
It reminds me a LOT of that Eli Schiff guy in the design world. A mediocre designer who couldn't get untangled from one specific design style, who figured out that anger drives clicks. Schiff became a "design critic" who eventually fell down the alt-right pathway and ended up becoming a pariah. I'm not saying Zitron is gonna turn into some right wing nutjob, but he's falling down the nutjob path right now in real time by consistently staking out the worst ungrounded takes and continually doubling down on them.
I don't know about Chegg, but the fundamental problem with Duolingo is their product does nothing to teach people a foreign language. The nasty reality of that product is if you go through their entire learning tree in a language, you might be at a CEFR level A1 for that language. And, you spent 10x the time and 10x the money you would have spent via something like Lingoda to get the same outcome.
This claim is just not true. And gets tiresome. You roughly end up where those courses claim to be in reading and listening - depending on language it can be over B1. (No course finishes B2, some do have B2 content). I ended up being able to watch some (not all) netflix series in foreign langue and I was in early B1 section. I clearly learned.
Not about Zitron specifically -- I don't read his study, but I don't know that making some statements in interviews or blog posts is the same as making a "prediction" (into which one would put much more though)
also statements can be interpreted many ways:
"Meta is dying" was countered by "Meta's revenue has increased since Zitron said that". OK, but 1) revenue/profit is only one measure of "not dying"; 2) what's the time scale? Nokia and Xerox were highly profitable companies that dominated their industries, and any prediction that they would go out of business at their height would have been laughed at, and yet, it wasn't too much later that they pretty much did.
To me and most other non-West coast Americans its obvious he's right. Which is a good explanation of why SV has the resources and success it has because it can do things no one else believes in. While the rest of the world will always be catching up.
It is nice to see an attempt of sorts to discredit, and indeed Ed is not always right. But the bigger picture is debt fueled AI buildout frenzy, regulatory capture, monopolising and gating models, tokens/compute is still losing money compared to observable revenues from customers. Otherwise we would not need inventive accounting practices, SPV's, theatrics on distillation, stopping frontier development and so on.
But taking claims from Sundar about 750M gemini users in 2025 at face value is disingenuous. This basically includes everyone who owns an android phone and opened a Gmail and Google search, translate. Congrats, you are gemini user now. Every product has received AI "feature", like your android assistant offering to listen to you while the screen is still locked by default. Dan's only sources are press releases of the AI companies. As I'm reading this, he dedicates at least 3 paraghraphs showing how Ed is wrong. And keeps coming back to it again and again. Okay so we are both refuted and proven wrong, but is that really the extent of AI use? Is this the revolution? Replacing existing function with inaccurate AI sourced from reddit posts?
I've read a number of both blogs. I'm not gonna watch Diary of CEO video. Yes, models have gotten crazy good in 2 years. Still the same failure modes. Dan's writing here comes across as same type of gish gash he rails against, minus the entertainment value.
The first section of this post responding to "Meta, Google, and Microsoft are dying" is completely misrepresenting Zitron's arguments in the linked video. Zitron is making essentially an enshitification argument concerning Google and Meta. He's not talking about whether they will be able to squeeze out more revenue in the short run, but how they are willing to abuse their users to ensure that they do. Citing increased revenue for these companies is not arguing against Zitron's point.
Luu also fixates on Zitron mentioning Prabhakar Raghavan, but then proceeds to agree with Zitron's core point that Google has intentionally degraded it's search product to maximize revenue. Maybe Raghavan is not solely responsible, but it seems fair to hold him accountable for trends that accelerated under his leadership.
Ed Zitron mostly covers the costs of data centers, circular spending, and predictions of large the market for AI has to be to justify the data center expenditures.
I was disappointed this article didn’t really cover Zitron’s main arguments.
Maybe a paid article placement? I don’t know, but I was dissapointed: I read Zitron’s material and I wanted to see good counter arguments to his rants about costs of data centers, circular spending, and predictions of large the market for AI has to be to justify the data center expenditures arguments.
Only recently came across this guy and if u ignore the standard social media hyper hype hype he has, I dont think he is wrong even his 2024 predictions.
US has 1.5x the money supply since Covid. This means everything has to go up 1.5x to reach the same parity from before. eg inflation. All corporate earnings are going to inflate as 1b yesterday is 1.5b today.
There is no whete for these mega corps to go. They dont know how to grow. meta becoming well meta with the vr crap is case and point. meta blowing billions on a few hyped AI people is the nail.
Other than cloud, microsft/google aimt doing anything. xbox? nope, hardware? nah. It is all attention economy or selling the pick axes for it.
the only mega corp that still seems to be moving the needle is apple which i think speaks volumes.
Eds point is that AI tam or expectations are insane full stop and he admits coding has a use just not as big of a tam. Gen ai in art, music, etc did not take off at all esp compared to code. this makes sense, no one pays an artist 500k/year. their time is worth little compared to coding.
Where he is wrong tho is that this is not new. there is always a hype cycle of nonsense that screws the little people. what is new is the level of polarization. it feels like ai psychosis right now in ways that remind me of crypto but worse because ai is actually useful so people are even more insufferable.
Though the bubble has not popped, I don't see the following discussed in the post: Zitron would probably point out (as have others) that many of the hyperscalers are booking valuation increases in Anthropic, OpenAI as "Other Income", which is substantially increasing their reported revenue and earnings. It's roughly:
- Hyperscalers like Goog, Meta, Msft invest cash in Anthropic, OpenAI, in exchange for equity
- The ongoing investment actually boosts the valuations in the Anthr/OpenAI (new raises are done at higher valuations), so the valuation of the Hyperscaler's existing investments in Anthr/OpenAI increases, which gets recorded as Other Income in quarterly earnings
- Much of that invested cash will itself come back (circularly) to the hyperscalers as revenue since Anthropic and OpenAI spend a lot of money via datacenters etc.
Sorry what is the punch-line here supposed to be? These investments obviously increase correlation coeffs., but these are highly correlated stocks to begin with.
valuation gains on investments are one-offs, not signs of sustained improvements in profitability that would warrant higher market caps
when those valuation gains are in turn the result of circular financing schemes (a bakery giving out money so that people buy bread from it), we're getting to a dangerous situation
The issue is simply that the posted article begins with a review of recent earnings/ revenues, but fails to discuss that a substantial part of those revenues are investment markups.
Whether it matters we don’t know yet, but it’s a fact worth noting. A better article might have tried to argue why it doesn’t matter
As far as I understand GAAP reporting standards actually require them to report gains on those positions as "earnings".
But they do report non-GAAP earnings sometimes excluding them. E.g. Google earnings per share last quater is $9.11 GAAP vs $2.85 non-GAAP (mainly because of SpaceX shares).
I feel like just saying "wrong" to some of these feels a bit unconvincing.
e.g.
>April 2025: "It also, at this point, is pretty obvious that generative AI isn't going to do much more than it does today."
>>Wrong
Is generative AI really doing much more today relative to 1.5 years ago? Sure there have been sone improvements, but i feel like nothing fundamental has shifted in that time period. Nor would i really expect it to even if the statement was false, but it seems too early to tell.
How often are people that are paid by their content attracting eyeballs correct? Isn't this a universal thing that people in these roles are constantly stating all sorts of things that never happen. This happens in sports media, politics, tech reporting, stock market predictions, etc.
Shouldn't society have a clever name for people playing these roles by now? Something catchy, insulting and based on truth might help call this out easier. I would throw influencers in there too, they are writing/video-loggin/podcasting for the same money outcome.
Ed Zitron has the economics of AI basically right. Most technologies arrive by digging an enormous financial crater, followed by a contraction period in which everyone insists the crater is actually a revolutionary new business model, before the survivors eventually buy up whatever remains.
What likely resonates is AI really does feel like a science experiment. There is clearly real value here. The problem is that the economic value has yet to catch up with the technological value. And yet the claims coming from AI companies have the unmistakable energy of a state-fair entrepreneur standing beside a suspicious knife yelling, “You have never seen anything like this before, it slices it dices...!”
I think Ed goes too far when he compares LLMs to garbage. He’s tried them, had a handful of bad experiences, and apparently decided the entire technology belongs in the round file. But much of what humans do is essentially trial and error with better PR: apply some logic, see what happens, adjust, try again, and continue until you eventually solve the problem.
If you can get an LLM to reliably do that, you can solve certain classes of problems dramatically faster.
Ed and his class of people are entertainers that get confused at experts for journalists. None of them toil at the prompt sorting things out directly. The media entertainment class frustrates me. They often spread sound bites that are off by big margin and shift markets. I find podcasters that are curious about a subject are better at finding the truth about a tech subject then finance wonks that chatter about sounds bites.
> "Sam Altman deputizing Orion from GPT-5 to GPT-4.5 suggests that OpenAI has hit a wall with making its next model, requiring him to lower expectations"
> Wrong (GPT-5 was a substantial improvement over GPT-4.5)
… I mean, this feels slightly irrelevant? Orion _does_ seem to have been demoted from 5 to 4.5; that they later released something they felt they could reasonably call gpt5 feels slightly beside the point there.
It is a curious exercise to look at the predictions of AI companies/their CEOs, too.
Frequently we'll hear from Amodei that AI is 6 months or 12 months out from replacing Software Engineers. Or we'll hear that their model is safe when regulators step in, but suddenly it's too dangerous and hacked its way towards its goal when other companies claim the same.
What they have in common with Ed Zitron is that they are both trying to sell you something.
Even though these predictions turned out mostly wrong, we should not castigate people for publicly forecasting! That is virtuous, and more people should do it.
My take would be that people have no idea what is going on today, as time expires history drifts further away from clueless to the absurd... and now you want to do the future? Whatever you say, we should at least completely ignore what your audience wants to hear. Whatever is left could be correct but only by accident. This is what the future will be like... or wait...
I have to admit, after the podcast I feel a strange sense of calming. It's for sure a more interesting approach of skepticism compared to many of the other doomsday prophets of AGI, or environmental plays.
Accurate or not, there are many risks other than those that could bring AI companies value to 0. E.g., systems not requiring passing over all the data continuously, but in small regions running at L2-L3 cache memory speed. Once someone figures out that, the need for high bandwidth memory would end.
I think the most likely way for Zitron to find vindication is through the world war we seem to be shambling into. That could easily put a stop to AI improvements, because the supply chain is so fragile. But that would have nothing to do with his ostensible reasoning.
I find it bizarre that this blogger can make sweeping claims of someone being wrong, quoting specific values that are admittedly speculative, and then just using them as gotchas for being wrong. The then ignore the conclusion that those "wrong" yet close values mean in the grand scheme of Zitron's point, even when the margin of error was inconsequential.
For example, one of his first big criticisms of Ed is Ed's claim that Meta has a dying product and its a dying company. Do I really need to look at the numbers here? Or should we look at the companies actions and history WITH the numbers included?:
- Metaverse was a complete and total disaster and forced upon the company by a CEO who is clearly completely out of touch but infallible within the company.
- Facebook is a bot riddled, AI slop haven, used only for special cases and is basically unanimously hated by the next couple generation of users. Users who are critical for revenue if the serving ads to bots scam ever implodes.
- Meta's successes seem to be solely on knowing who to buy and have failed for a decade to innovate anything.
"Dying" is not the same as "dead". As someone else has said, but I have forgotten who it was, Meta is a "mature" company trying to be young and sexy again when they should focus on their existing products.
How does anyone come away from all this with a business, where we hear all of the horrible things about their internal culture, that an AI pivot to be anything but a trend following desperation move? Then the author addresses but shrugs off entirely the fact that they now hide their Monthly Active Users. I think all of this context is pretty fucking important to think about with the numbers, especially since Meta is trending down when we get the totals for the year. Zitron's point, again, still tracks because you can list a portion of "profit" but it is too early for 2026 since they intentionally use misleading numbers. I would be very interested to see what the first half of 2024 and 2025 profit numbers were before the total year calculation. Either way, Meta's dump into AI is a huge gamble from the company that must pay off.
I don't know. I think this guy does not like Ed Zitron, which is fair. I see a man pointing fingers at someone while doing the same things he is criticising: Taking the speculative and sensational as literal and using it as some sort of gotcha to be speculative and sensational themselves.
> Now, if your CEO has never heard the phrase Ralph Loop, oh man, you are less than 30 days away from your next promotion. I'm not even exaggerating. Walk into his office, close the door, and say, hey chief, been experimenting with something. It's called Ralph Loops. And I think it could change literally everything. And he's gonna say, what's a Ralph loop? And you will say, give me $18,000 worth of API credits and I'll show you. Now you won't actually do anything, because you can't do anything. Because nobody can, because nobody knows what they're doing. But by the time he figures that out, you'll have a new title, and equity bump. [...]
> Talk about automation constantly. Nothing arouses the slumbering capitalists than the mention of automation. Drop names too, bro. Like talk about specific team members you can automate out of existence. Be like, yo, I automated Gary, bro. Tag Gary in the message. Tag him in Slack in a very public channel. Be like, yo, I just automated @Gary. His function has been Ralph Looped. And tag your CEO in the same message. You think you're getting laid off after that?
This is eerily similar to how a few people at work did some presentations about ralph loop and connecting agents to slack right when management was really pushing unrealistic AI productivity boosts. I was repeatedly asked to speed things up with AI while I was already heavily using AI to do work.
The perfect read while listening to my first podcast with the guy. Noticed a kind of shift where I previously got very defensive about my latest obsessions, but at this time and age, it was just kind of amusing.
I really like reading him. I do wish is posts were a lot shorter.
I get tired of all the 60/40 percent forecasts that have enough wiggle room the forecaster never thinks he is wrong.
I certainly dont get that from Ed!
My point is not that putting your money where your mouth is or shut up.
My point is: all analysis is contingent, there is no point in saying bulls or bears are chronically wrong or that they should beat the market to merit being listened to.
I don't see any evidence of Facebook's growth in high value regions specifically, all we see is them burning through money on failed project after failed project and getting into legal trouble.
On the off chance Dan sees this: one of the footnotes ("Some Zitron predictions" > July 2024) is broken. Right now it's showing a little 0 that doesn't actually link to anything if you click it :(
This entire genre of pundit is just a person who has figured out that you can sell copium to the masses. Once upon a time this was a decentralized "this is how Ron Paul can win" thing on Reddit, but it was inevitable that slowly it would coalesce around these kinds of engagement bait people. Every subculture has its own such figureheads who DESTROYS opponents with FACTS and REASONING or whatever, but really it's just a reality TV show. The Alex Jones, Gary Marcus, and so on of the world are mostly just selling an entertainment product.
It's just a question of what the entertainment is. Some people feel good about being told "we were being hoodwinked; there are lizard people" and others feel good about being told "you'll be 100x healthier and good looking if you take these supplements" and others feel good about being told "these people are evil demons who are stealing your water" and others feel good about being told "these idiot rich people are going to lose their shirts" and so on and so forth. It's like how I like slice of life shows and hate horror movies and my wife likes horror movies.
In a sense, the misinformation gambit of LLMs did not come fully to fruition in the West (as much as it has in 3rd world WhatsApp forward land) because the locals were already a fertile ground of poor epistemic hygiene and well served by human providers of misinformation. Hacker News itself only has some hundred thousand commenters or so and even this requires a practice of aggressive information curation to prevent unrepentant misinformation repetition nodes from polluting one's belief set.
I don’t know much about Zitron, but YouTube somehow pushes these “experts” onto my feed. This guy is full of crap, he doesn’t even care, because that doesn’t matter, what matters is view count.
Decisively declaring long-term predictions wrong only two years later seems a little premature?
E.g. a company of the size of Meta/Google/Ms may very well be dying and still producing numbers that look like growth for the next few years.
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Also, how in the world is a Jan 2025 prediction that "I believe we’re at peak AI" getting a simple "Wrong"? How are we deciding what "peak AI" is here?
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Or, "July 2025: I am not trying to be dramatic, but it's pretty easy to come to the conclusion that Cursor is going to die" -> "Wrong (Cursor gets a $60B exit)"
"August 2025: These models have clearly hit a wall where training is hitting diminishing returns" -> "Wrong"
I guess this is just trolling at this point? This is like a research paper tier question, "wrong" doesn't cut it.
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Very unimpressed with this analysis. Extremely poorly done, and pretty clearly not impartial at all. Shameful. Like, the guy does sound wrong to me, but the analysis is terrible nonetheless.
As always, shooting down commentary asking for more and more evidence is very simple.
Zitron has a somewhat ranting style. Luu's passage on Google search for example just nitpicks on the person that Zitron allegedly named as responsible for the decline of Google search.
The real issue of course is that search quality clearly has gone down. What does Luu want? A "study"? Everyone can see that. The person is not the issue.
This is a highly selective analysis of Zitron's writings that focuses on a couple of mistakes. People need to understand that any normie will understand the difference between rants, numbers and speculation in Zitron's essays. They are not written for autists who talk of "priors" and "evidence".
"This is a highly selective analysis of Zitron's writings that focuses on a couple of mistakes"
While reading this I was thinking it would be interesting to see just a few examples of Zitron predictions that he got right. Since you appear to follow his work, do you know of any?
The issue is the conspiracy. By pinning the whole problem on a single villain rather than the incentives the larger organization has created he 1. doesnt help solve the problem 2. creates an environment ripe for tribalism. Even if you ignore the racist undertones(fine its a stretch) he's still misleading people about what is happening.
I used to pay attention to Ed Zitron, exactly because he seemed to be someone who did the research, and looked at numbers. Until one day, I realized that seems to only look at numbers as long as it serves his agenda. When it doesn't, he looks away or makes the case for why the numbers are wrong, and shields himself from these "incorrect" numbers - or people who would point to anything suggesting that AI is not a total fad.
In April 2025, Zitron wrote [1] "I am sick and tired of everybody pretending that generative AI is the next big thing." This was at the time when AI coding tools had already gone "mainstream" with devs, and pretty much everyone was using tools like GH Copilot, Cursor, or something similar.
So I replied with this observation, saying how, at least within software engineers, AI is being adopted faster than any technology before [2]. To stay objective, obviously I brought receipts: sharing how, based on an older survey I ran, ~75% of devs in that survey said they used AI coding tools. Zitron made fun of the small sample size (216 people), blocked, and pretended like AI had zero PMF anywhere in the world.
I stopped taking him seriously since then. And I'm wondering ever since: does he deliberately only look at numbers and facts that he can tell the AI doomer story around? Or is it more that he finds that there's not many people who are "informed sceptics", and decided to play this role?
Looking at it from an objective/historical perspective, when he first got on his high horse agents weren’t a thing, MCP was in its infancy at best, and half the code generated didn’t compile from frontier models.
From there everything split into two factions, which I’ll dub as believers and non-believers. From there, it has entirely been a cult following despite the evidence showing that models and agents have legs for software development.
FWIW my post history would show my extreme skepticism, and to an extent I still am. I think the real power of models isn’t the model at all but the harnesses.
Either way I think he lost the plot and runs on vibes himself. I also think this whole AI movement is going to have their 2000/2008 moment before the phoenix rises from the ashes.
I don't agree with the believers vs non-believers dichotomy. That makes this look like a religious war and it shouldn't have to be like that. To me AI is a very useful tool, no more no less, it isn't a 'make a wish' machine and it isn't a silver bullet for all of the issues that have plagued software so far. But when properly applied it's quite useful. "Non-believers" would have to be people that have yet to actually use AI, just like you can probably be a non-believer in peanuts until you've seen them, and most people that I know acknowledge the reality of AI tools and their use cases.
All tools, AI included, have a business end and as long as you point that away from yourself you will be fine. But to properly apply it (rather than as a faster way to make a huge mess) takes discipline and being methodical. It was never different.
what's it with people who think a bubble is literally destined to happen because of some religious belief that history repeats itself? I find it fascinating to understand the theory of mind of such people. So much confidence.
Ed's right about OpenAI being out over its skis, he's right about the numbers for everything being wildly optimistic and he's right about the circular financing/leverage aspect of a bubble. He hates AI though so he can't acknowledge its usefulness, it was pretty clear to see on his DOAC interview, Stephen kept bringing up real wins and clear progress, and Ed wouldn't engage in a substantive way.
Yes, his thesis is pretty much correct, his personal opinions aren’t too relevant or valuable. It’s a mistake to take his predictions as the important thing, it’s pretty much irrelevant, the whole field is very dynamic and complex, and he obviously isn’t an oracle who can predict the future. The only thing that matters is the thesis
I think he deliberately looks at doomer facts. I'm not sure it's cynical so much as a genuine belief that it's all nonsense. But IMO the belief is based on a lack of understanding of AI.
He's part of the professional political / social media class now. He makes his money through engagement. I've stopped trusting people of this ilk. I listen to people who make most of their money not via the media ecosystem.
I don't know anything about this guy, but based on the discourse every time he comes up, Ed Zitron is a more polarizing figure for HNers than even Trump. Everyone here loves trying to dunk on the guy, yet he's apparently living rent free in everyone's social media feeds.
This isn't a serious analysis of the actual thesis or any of the predictions in any meaningful way. I learned almost nothing from this shallow wall of text. It seems overly focused on "predictions" instead of the ideas behind the predictions, i.e.
- the existence and severity of the financial AI bubble
- the claimed efficacy of AI in terms of its utility vs the actual observed utility
- whether the net good provided by AI outweighs its very heavy costs
I find the section of listing a bunch of selected "predictions" and just saying "Wrong" to elucidate very little. Not that a sentence is sufficient to provide explanation, but Dan stops even doing that bare minimum partway through and just saying "Wrong" full-stop. The reasoning is left up to the reader I guess?
How is it wrong? What was the actual thesis behind it? Is the underlying idea wrong or just the specifics on execution? Was there undetermined factors that mled to the wrong prediction? What can we learn from those factors in order to update our model?
We saw that even though the underlying financials in 2008 were trash and lots of people knew they were trash, things didn't quite collapse in the time frame or way we expected, because an unknown part is how much shenanigans companies can do to extend the runway.
As an example, credit ratings agencies didn't drop ratings to match reality because of customer relation incentives, which is a factor that is not easy to account for and strongly affects the timing of the collapse.
I find the positive reactions to this blog to be confusing. I feel like I learned nothing at all, which makes sense considering under "why write this?" he says "I got four hours of sleep and my brain wasn't good for much of anything and I saw someone posted a screenshot of a reddit post dunking on Ed Zitron's prediction record."
Zitron is providing a service: he's running the train for those who want to believe, or sincerely believe, AI is a bubble. His interviews are apocalyptical one sided rants that confuse what is with what Zitron thinks should be. I'm not sure he's wrong, by the way. As LLM performance is becoming more and more commoditized I fail to see how spend catches up to expectations to keep running this market as it does, but that's beside the point.
One annoying side effect is that YouTube's algorithms will always try to force feed you more of the things you last searched, to amplify your biases and send you down the doomscroll rabbit hole. So if you search for Zitron, next time you visit you'll be flooded senselessly with naysayers, contrarians and skeptics from all courses of life.
I have stopped reading Ed Zitron quite a while ago because of his long-winded and polemic style. But counting his failed predictions is maybe not that useful, in the sense that his core thesis is really just the AI Bubble. While it hasn't burst, all of his predictions remain wrong; when (if) it does, he'll have been right. Trying to guess the exact time or failure mode (whether it's open models or corporate sticker shock or a bond crisis) is a fool's errand when there are so many powerful actors all-in on keeping up appearances. Exposing the financial and corporate shenanigans of the AI world is nonetheless useful. I just wish someone would do it in a more level-headed way.
Fundamentally someone at some point needs to pay for them. And this should mean by actual end customer payments. Now to get size of those payments and how much collectively needs to be spend start to look to me rather questionable. It is big number to spend.
> I don't benefit in any particular way if AI does well, except insofar as anyone who holds broad index funds benefits, but I do care about accuracy.
Does Dan Luu still work at Nvidia?
> I had ChatGPT give me a list of predictions (with no attempted tilt towards correct or incorrect predictions) and then I skimmed/read the posts that ChatGPT linked to.
yeah alright
> If I really thought about it, I probably could've found something better to do with the time
Extremely likely.
> And yet, it would seem that my fact checking process is a lot more thorough than Zitron's.
Dan says in his post he fact checked with Chat-GPT and Claude, so: lmao.
If anyone who predicts the future was reliably right, they could easily shut the hell up and become a billionaire on stock picks. But they don't do that, because nobody can predict the future. You can say what may happen, but not for certain, and definitely not exactly when. All predictions without insider knowledge are idle musings.
Also consider that the economy isn't rational. Our economy should have tanked several times by now. AI should have fallen apart by now. Meta, Google, Microsoft should have declined. Instead it's record profits. So don't try to make predictions by being rational.
I think it would be naive to believe Zitron is unaware of what he's doing.
He's created a huge following (and is presumably making a lot of money) from pushing a hardcore AI-skeptic narrative, and I can't blame him for seeing that opportunity and running with it. We're ultimately all responsible for recognising these people and weighting their advise as necessary.
Additionally, from a public reputational perspective making bad predictions simply doesn't matter. In finance we're all aware of perma-bears who will predict the sky is about to fall, and when it doesn't just argue that the disaster is still coming, but is taking longer than expected, or that some unforeseeable thing happened which has compounded the risk but has for now kicked the can down the road.
So ultimately, it will be very hard to say Zitron is wrong unless he starts time-boxing his predictions, which I don't believe Zitron has done for obvious reasons.
That said I don't listen to him much at all. I've tried to listen to him a few times, but it's become evident very quickly that he doesn't understand the technical details enough to be making the predictions he's making and seems way too emotionally invested in the arguments he's pushing without good reason. I have strong personal filters for low-quality sources like Zitron – if someone raises enough flags I avoid them like the plague.
Ed gets hard because of the love the anti-ai people give him and seeing him as the voice of anti-ai... If you listen to his arguments though he lacks knowledge of what these companies even do. He's been on so many shows discussing the negatives but he admitted to not even using the models for anything
Cannot take profits of these companies seriously when they have been laying off employees by the droves the past couple of years. Obviously profits will increase when workforce expenses have reduced. I'll only take it seriously after a couple more years and see how the company has held up with massively reduced workforce (powered by AI™). Another point would be to see how these incumbents get challenged by new startups and if incumbents can survive this phase.
Don't you think those companies are mostly correcting for their massive covid overhiring + zero rate bonanza coming to its end?
Also, back when elon had bought twitter and had been firing people left and right, it was prophesied that with such inept management the company was going to collapse very soon. I thought it was obvious it would collapse. But that has not happened (yet, but it's been quite some years), and the service seems to be holding up after some initial shakiness.
I would partly agree with you in so far as Twitter not collapsing and working well. I'll go so far as to say it was the right move by Elon to downsize the company as you really don't need so many people to run a social media site. However, I don't think Twitter can be compared to the scale at which Alphabet/Amazon operate. Alphabet/Amazon run hundreds of businesses (on the scale of X) within their parent umbrella organization. So even if one collapses it won't affect the other as much as it should. But since they are removing workforce by the thousands, I am wondering how much of it will have an impact on their bottomline in the coming years. I mean... they will be under pressure by their own stakeholders to put their money where their mouth is and remove workforce if AI has reached levels where workforce can be replaced. If they don't do it then they are tacitly admitting that we haven't reached that technical breakthrough needed. The only way forward for these companies is to make it happen one way or the other. They are heavily invested in it and cannot back out now.
Tech companies invested in AI: AI is amazing (so good, it might kill us all, help?), and the ROI is going to be amazing.
NVIDIA specifically: We will allocate cash in strange deals that makes it looks like we're making investments but actually we're buying sales from those companies as they'll spend the money back with us, and everyone seems to think that's OK because the share price is going stinkers.
Cynics: AI is a scourge on society, and the fiscals are circular and a scourge on the economic stability of the planet.
The more nuanced argument I take is that Zitron is right about the circular finances and hyperinflation of valuations, and wrong about the utility of AI to help people. The Tech companies are just outright wrong about the finances, but right about everything else. And, NVIDIA's time will come, and it won't be pretty.
None of this requires me to believe that if Ed says something it must be true or false. It's just another data point, and he does uncover information I can't find myself that checks out. The fact he is wrong about the functional utility of AI or that he's getting super focused on hyperscalers when the real issues are in Oracle and NVIDIA, I can live with.
750 comments but I can't believe how bad this article is. The paragraph on MAU says similarweb is untrustworthy but then cites "most estimates"...which? Sorry, I can't read this without thinking you're biased against Ed Zitron.
The best part is the spreadsheet, which makes him sound like he's just sloppy, but danluu specifically says he's being deliberately misleading elsewhere, like the opposite of hanlon's razor now? So is he being deliberately sloppy in his spreadsheets or is he just incompetent? I suppose both is a better read but you need more proof for the "deliberately misleading" line and the spreadsheet frankly is danluu's best evidence beyond the list of failed predictions which is less striking in my mind.
While I think he's probably wrong on the timing, which clearly has been wrong since 2024, it doesn't really address the greater critique, which is that ai companies and nvidia and memory manufacturers need revenues collectively in the trillions to make their investors whole. That is still unsustainable such that it's been called out by other investors and financial journalists.
The first correct, detailed prediction (in print) about the collapse of Fannie Mae and Freddie Mac as a result of the subprime crisis was made by Max Keiser, of Karmabanque, in (Zac Goldsmith's) The Ecologist magazine.
In 2004.
And he'd been talking about it before then.
People thought Max Keiser was a crank; instead he made detailed predictions based on his intuitions, limited insider feedback and cold hard facts. He couldn't say exactly when it would happen; he laid out some horsemen you could expect for the apocalypse and this was one.
The thing about a bubble is everything is fine until it isn't. And it's worth observing that key parts of what Zitron is discussing has been covered in the WSJ and FT.
It's all very well posting numbers to "disprove" him when what he is pointing out is that the AI hype train is delusional and the costs are buried.
But Zitron is directionally correct, I think, particularly with regards to Oracle, where things he has said have literally come true.
Frankly as a Brit I remain amused at how much Zitron winds Americans up just by being himself — sweary, vulgar, rude, catty. And since non-Brits can't read Brits, Luu has to engage in pretty immature character assassination about it.
Zitron is an AI doomer and a clear fact-distorter. I follow him because even a broken clock is right twice a day and I do think he's a pretty smart guy. I don't read his newsletter (it's basically word vomit these days), but his interviews are marginally interesting. One of the quotes in the linked article is totally correct:
> He found a niche in anti-tech grift, and is now exploiting the niche for all he can.
I am currently surrounded by hyperventilating corporate and country level leadership who are fully swept up and captured by the AI craze.
I have seen some wild failure modes from people who are outsourcing their thinking to LLM. Amendments to clauses that don't make English sense. Multi-paragraph long replies in emails that say nothing specific to the issue at hand. Responding to questions with "AI says this" (but I asked you, not the LLM). Leadership wants us to embrace AI but there is no product for the layperson, it feels like everything is front end + generic prompts + $LLM_API_key. People want to make customer service bots that have access to personal data.
I feel like software devs are so lucky in that at least people in your field can see an LLM for what it is and harness (no pun intended) it appropriately. As a lay user, no such luck. Leadership and purse strings are far removed from IC work and don't understand why an AI product wouldn't work, they've heard otherwise in their circles, you had better make it work so that they can claim to have delivered an AI transformation this year.
Enter Zitron.
Zitron is a woo-pushing grifter (his product: his stance on AI). Even without examining his reasoning or the accuracy of his predictions, Zitron is hard to listen to. Mostly, he shouts out a constant barrage of bare assertions that his research is thorough and irrefutable and the doom is coming and ever "AI booster" who disagrees with him is an idiot, all the while without actually spending time arguing his point.
But Zitron feels like one of the few people actually pushing back against this craze.
I would very much like a better argument for a position that I support, please and thank you.
Zitron is just a rage farmer that tells angry people what they want hear while selling them subscriptions.
Maybe he is well meaning, but it's pretty common these types are just milking an audience that they dialed in on with zero regard for integrity or honesty.
zitron is a jester. He knows his audience and plays it well. It is incredibly tiring to listen to his lies, but so are the AI boosters. I think it is net zero
just give it time, anyone (even those with 115k subscribers) who predicts a crash every day will eventually always be right. might take a decade or two… :)
Everything everyone is predicting has almost zero value. Nobody owns a crystal ball. Right now there could be someone working in a garage somewhere that could shake the entire LLM world to the core with a unique insight and innovation.
Think about the world before the 2017 "Attention Is All You Need" paper.
Did anyone predict that paper, what preceded and followed it?
Nope.
Same case now.
Maybe the word "prediction" is the problem; "guessing" would be better.
OK, what the heck. I'll make a prediction too:
You better buy SpaceX stock now. The way things are going in the US, the only way we build AI data centers at scale will be in space. Politicians have turned data centers into punching bags to be used to gain votes. We can't build power plants and people are being led to believe all kinds of things. Regardless of which, if any, are true or not, the rate of construction of AI data centers in the US is and will be seriously constrained by realities on the ground.
Have you chatted with Ai about the economics of space vs terrestrial?
Even if you believe they can get cost to launch 1KG down to 100 bucks... (current falcon heavy is $1550) you still have the problem with all those solar panels.
1GW of space AI requires... 1 GW of solar to power it.
Current manufacturers of space solar is 1-2 MW &.. space solar aint cheap. $100 per watt. They estimate each spacex will be 250 kW. So.. $25M just for the solar for each satellite. Yeah you can use less efficient / cheaper solar, but then your weight goes up.
Also... Ground AI datacenters haven't nailed down how quickly GPUs depreciate. Accountants claim 6 years while CRWV says it's longer and most realists say it's shorter. It's based on how quickly GPUs will improve. Which is QUICKLY.
In space you'll have to depreciate GPU, all supporting hardware including the satellite, electronics, solar panels, and launch costs. On the ground it's just the GPU & you can even sell the old one down.
ground based AI needs power. $FRVO is an interesting ticker if you believe in their technology. Just announced a deal with google yesterday.
It isn't going to be easy, of course. Yet, I do believe they will make it work.
One correction: SpaceX makes their own solar arrays. The cost is nowhere near what you quoted. Source: I worked for SpaceX for a few years.
Also, you can generate a lot more power in space per unit area due to not having the limitations created by our atmosphere and weather. It's continuous generation at 30 to 40 percent higher per-unit-area radiation. Generation on earth is roughly an inverted parabola (on a good day without clouds and weather) that, at best, if you integrate the area under the power curve, delivers 66% of the total equivalent energy say, a nuclear reactor, could deliver during the same period. So, once you increase radiation from about 1,000 W/m2 (impossible to achieve on earth due to weather and other realities) to 1,400 W/m2, 24/7, no weather, etc. The scenario quickly becomes vastly different.
Geothermal is very interesting, thanks for the ticker, I'll look into it.
All that said, I think the US (and Europe, if they care to survive) needs to have, as a top national priority, a massive program for the construction of nuclear power plants.
A few years ago, I ran through an analysis of power generation needs to convert our entire fleet of vehicles to electric power. That required at least doubling our power generation capacity. This was the equivalent of having to build 1,200 nuclear power plants, each producing 1 GW 24/7.
This is impossible to achieve with solar, or wind, or the combination.
So, even if we decide not to care one bit about AI data centers, we need to double our power generation capacity (and infrastructure to deliver it).
Add AI data centers to that equation and the number could easily climb another 600 nuclear power plants.
Here's the problem that a naïve conversation with an LLM will not uncover: Humanity and Politics.
Could we embark on such a massive energy-generation project? Yes, absolutely.
Is it realistic? Nope. Not even close.
In other words, we can do it but it is impossible...which sounds like an oxymoron until you consider that we have lots of examples of projects that are absolutely plausible that, once they contact political and government reality, quickly become impossible. The best-worst example I always grab is the California high speed train disaster.
The author asks "How can people take this seriously?"
I would reply the same regarding this article. Nearly all of these refutations are unconvincing.
The claim: "I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be."
The rebuttal: "Zitron's argument at the time was that hallucinations were as good as they were going to get, which meant that AI performance is capped at 2024 levels. Both the overall prediction and the mechanism were wrong. This one seemed wrong at the time, in that I noted here in 2024 that you can make AI code halfway decently by just putting it in a loop and having it run until the code compiles and tests pass"
Putting an LLM in a loop, burning tokens, and thrashing against a compiler and test suite is a ridiculous way to say that hallucinations have been "solved". Please. This is absurd.
both ed zitron and dan luu are professional bloggers who earn their living from selling ai sensationalism, zitron using paywalls and luu using patreon. whether the sensationalism is positive or negative, either way it is a very clear conflict of interest.
I read all Ed's posts, and enjoy them, and I like AI as a tool and use it every day.
I think it's really, really, really important to have a contrarian opinion out there, even it's a voice howling in the wilderness. Even if most of his predictions are wrong. Even if he swears a lot and gets a bit ranty at times.
Personally, I think he's going to be mostly right in the long term about the AI bubble, but mostly wrong in the long term about the effectiveness of AI (i.e. I think it will have a net-positive effect in the long term).
I always keep in mind the Gartner Hype Cycle [0] is true, and we're still on the initial slope up to the Peak Of Inflated Expectations.
I had an interesting experience after I stopped reading HN comments for two weeks. I think it had to do with not reading the doom and gloom on here, which I tend to agree with. But after two weeks of reading neither the hype nor the doom about LLMs, I realized how amazing (though not perfect) this tool is. My point isn't whether LLMs are good or bad, but just how I didn't realize how much my consumption of other people's opinions infected my own outlook. It was like the opposite of rose-colored glasses.
BTW, the tool I used to block HN is called Foqos, which, of course, I heard about right here on HN. Based on my screen time reports, I got back about 2 hours per day average after blocking all my favorite sites.
I agree that Zitron is not honest and a denialist regarding the usefulness of AI, but the "profit" chart is just completely misleading as most of these companies have made large investments in OpenAI/Anthropic and list the gains in their share price as "profits" - so yes, while the asset prices are rising, that's typical in any financial bubble. Google is actually cash flow negative for the first time in history (which imo should be what measures profitability: income from products and services - costs to produce them) so Zitron's core thesis that there are no returns in AI is playing out as far as I can tell...
circular financing, layers of financial engineering: https://finance.yahoo.com/markets/stocks/articles/google-par...: On Aug. 6, Alphabet filed its 13F with regulators covering its second-quarter trading activity. Given that SpaceX went public on June 12, Google's parent company is now required to include its SpaceX holdings in its quarterly 13F.
I've said this before in here but Zitron is completely captured by his audience at best and a grifter at worst. A couple of months ago he was tweeting that people were crazy talking about _agents_, that they didn't _exist_ and that people talking about them were shills or bots. The responses were full of incredulous software developers saying but but but I use them every day, they're so good they're scary actually...
Zitron is just, like Dan Luu says, wrong about everything and doesn't care anymore, he's in the business of extracting money from his engagement.
I think it's a mistake to make concrete predictions about something like a stock market crash on a given timeline. As they say, the market can remain irrational for longer than you can remain solvent. However, Zitron is directionally correct about a lot.
I will say, the first part Mr. Luu says about big tech not being out of ideas is pure horse shit. Anyone who has worked in big tech knows that leadership at those companies can have no idea what they are doing and still be successful in earnings or the stock market. They are sometimes successful despite themselves.
I appreciate someone picking apart Zitron in a constructive way. He’s been an easy recommend to folks in my circles who just want to be angry, but there’s a reason I don’t read his stuff on the regular or in detail: I don’t want to be angry, I want to do something.
And I say this as someone who started as a doomer, and is increasingly a pessimistic pragmatist (“LLMs have value as tools, but not nearly as much monetary value as the main players believe they do”).
I get it though: for those of us who grew up alongside the net and tech sector, who loudly decried M$ greed for ME/Vista/8/11 but celebrated them at XP/7/10, who remembered when Google’s “Don’t Be Evil” was spoken with serious reverence, the current era of tech feels toxic and nauseating. Current AI is a prime target for that discontent, as are the companies whose motives very clearly aren’t societal progress so much as reality authoring and authoritarianism. In that vein, Zitron is magnetic because his entire position is “you’re right to be mad and they’re all going to die from hubris without you having to actually do anything”, which itself panders to the human desire for personally preferential outcomes sans individual effort.
Properly picked apart though, and he has as much substance to offer as the ardent boosters: a handful of “trust me bros” with a smattering of distractions to wind you up, but never actually address your concerns or questions.
Ed Zitron is providing his assessment and prognosis strictly on financial basis. But AI has national security and geopolitical dimensions as well. US government is fully committed to keep the AI lead for as long as possible. And they use all sorts of tricks to affect the market in AI's favor. Financial analysis alone is not enough. The government will do anything to prevent AI bubble from bursting.
Obviously Ed is a special case, but let's be honest, pretty much anybody telling you they can predict the future is selling you BS. And that includes all the people confidently predicting he was wrong. The actual situation is, there are genuine unknowns driving things with significant influence, and nobody actually knows what is going to happen. At best, people can talk about risks and likelihoods of outcomes.
I've always been suprised with the amount people take him seriously. I think he's someone who makes people who dislike AI "feel good". I don't blame someone who doesn't understand AI or have read what he's written.
But for the bloombergs and other podcasts I would have expected them to do a bit of research. I honestly think with the amount of doubling down he's been doing that he's a grifter.
I think cable news channels like CNN and CNBC like to have a "counterpoint" on to make their coverage seem balanced. There's always a guy saying the stock market is about to crash. There's always a guy saying oil will hit $200 per barrel. There's always a guy saying AI is a bubble.
Zitron is never right but he exists so that media can claim to be balanced.
How has it taken so long for this to get some proper attention?
I'm not Ed's biggest fan, but he's been pointing out some very important things for quite some time now, regarding the ludicrous over-investments going on around so many companies that are completely and utterly devoid of profits and will likely never have any.
He's dead wrong about the usefulness of the technology, but he's dead right about the uncontrolled corrupt circular financing that is driving this crazy over-expansion and market distortion. He fulfils a very useful function as a counterweight to the big tech horse-shit hosepipe that sprays us every day.
Zitron in general is representative of the conspiratorial thinking that has infected all spectra of the political space. Zitron obviously occupies more of the left space.
Good critique of LLMs & the companies behind them is hard to find, and it is harder when people gravitate towards this sort of thinking.
> For example, with a style that could be described as the opposite of clickbait, Simon Willison has written what I suspect is the most widely read blog among programmers for the past 3-4 years (in the same way that, at various times in the past, Joel Spolsky or Jeff Atwood or Steve Yegge seemed to be the most widely read programmer among programmers). Among programmers and other serious users of AI, I would guess that Willison has a larger audience than Zitron.
I have no idea Simon Willison is the most widely read blog among programmers. I truly have no idea, and I've been programming for just a decade.
A lot of Simon's blogs posted here are when new LLM models are released, on how good are these LLM models create pelican riding a bike using SVGs. Nothing particularly interesting to me.
I truly have no idea why would people be interested in blogs about LLM creating pelican riding a bike svgs every single time a new model is released. Maybe its a proof of AGI/ASI for some?
I guess to me, Simon Willison will always be the "create-a-pelican-riding-a-bike-using-svg-dude".
I used to read his posts about Django years ago (a little strange in itself as I'm not a Python guy) and it took me a while to realise he was the same person!
Thanks for this post. Zitron is a prime example of how someone with little background, expertise, or domain experience can establish a grift in the outrage economy. Unfortunately, emotion and illusion when properly marketed can still go a long way in this world.
Zitron has staked his bear position and isn't budging, so regardless if he's been wrong and wrong again, he'll be remembered for calling the bubble if/when it pops, if only because so few in the media have done so without equivocation.
I just remembered that before AI, I knew Zitron from ranting about IoT and consumer tech. He just appeared one day in my Twitter Timeline, claiming to be the "man on the ground" at CES (or so my memory at least)
So from that standpoint, I guess it would make sense that a personality like that would now have ended up at the AI topic.
I would like to see a comparison with predictions of the pro-AI bubble and their hit rates though. This writing feels as selective as it tries to paint Zitron as.
All of the grifters (and lots of capital) can't wait to jump on the bandwagon because they see it as their chance to clean up. The same happened during the last 4 tech revolutions. (in my life so far: semiconductors, the internet, mobile communications, smart phones, crypto, EVs, probably forgot a few). And of course, as always, the longer term will be more amazing than the short term hype.
Hate on him all you want (he's gotten very repetitive for the sake of subscribers and reads), but the basic premise that the modern AI industry is a circular-dealing, point-of-diminishing-returns grift still holds. The bubble is here and the longer it inflates, the worse the pop will be. Sure the goalposts have moved, but the basic numbers don't math.
The current cross-deals aren't purely circular; quite a bit of revenue is, in fact, flowing into the AI industry from the outside (you know, via customers). What's more notable is the shared risk, as the deals tie multiple companies across the chain to a set of shared bets.
If the frontier labs suddenly found themselves unable to compete with cheap open weight models running on widely available compute, then one might expect the frontier labs to be the only ones exposed to that risk, while a chip-manufacturer like nvidia could thrive in either environment. A partnership or commitment from the chip manufacturers to the frontier labs could change that. Whether that kind of inescapable connection exists is hard to predict without a lot of specific modeling, and at this point I'm inclined to think that neither chip nor datacenter demand is going to drop any time soon, and that the commitments would be unwound before a company like nvidia is threatened.
Zitron implied 2 years ago that OpenAI would collapse by now. How's that bubble popping going? All NVDA+memory co+frontier lab numbers are accelerating
The thing about bubbles is that everyone who thinks they’re going to pop look crazy until they pop.
And to be clear, a bubble popping doesn’t mean that AI goes away forever.
What it does mean is that we’ll see some kind of economic crash or recession, and we’ll probably see at least one big company fail or go bankrupt/restructure.
OpenAI is the company in most obvious peril.
I happen to think that Nvidia is in a more perilous position than they appear. Their hardware advancement pace is relatively weak and they’re in a crypto-like hardware bubble where they’re one technology breakthrough away from a complete collapse in demand for their AI data center solutions. They’re also doing a lot of sketchy hardware financing schemes.
Comparing to other bubbles in the past, this one could still have something like 2 years left. Patrick Boyle has a video on the topic, in which he's also very careful to point out he could easily be wrong.
By all means, keep betting on this being a wild success and we'll see who's right in a little while. If you don't think this is a bubble then you're in for a ride.
Look if we are going to raid pensions and 401Ks to prop up the valuation targets of Anthropic and OpenAI longer, then the bubble can stretch further and further, but eventually datacenters have to get built and powered. It's the power generation part that no one talks about. We simply don't have enough power in the United States to scale at the rate that Anthropic and OpenAI need to prop up their absurd valuations. AI is real, but these valuations are not.
TL;DR: Ed is directionally correct, but it's anyone's guess as to the exact timing.
In the meantime I'm not going to complain about subsidized credits from the big labs. :-)
Collapse of entire economies will result on a scale that will eclipse the great depression. And it wont be because AI revolutionized anything. AI will become a dirty word to never be uttered by anyone in the human race after.
i relate to zitron cuz of how much he seems to genuinely hate the people in charge of this AI bubble. he, like me, seems to wish that when this all falls apart they get proportionate harm to go.
that'll obviously never happen. it's wishful thinking and venting tbh. that's why i like listening to his stuff.
it's better than reading the wholly AI slop docs my CEO keeps sending out
I made the account to make the comment because I feel strongly about him being untrustworthy. You can take it at face value or assume I'm lying, that's your call.
It is funny to see mainstream media finally catching up to how much of a grifter this guy really is. I believe I was the first at least on HN to make a list of his horrible predictions over the time [1]. Since then Kelsey Piper [2] also wrote about it and got some traction.
I knew that somewhere in the future, something has to give because you can't just go around saying anything without losing some credibility. You still have some ardent followers in his cult of a subreddit.
But just to be clear: Ed is part of a bigger problem in tech journalism which is characterised by extreme pessimism and excessive skepticism. It is not correct to view Ed in isolation rather to see it as a part of the culture in which he can thrive.
I want to point out that Zitron is _just a guy_. He’s not a billionaire CEO. He’s not a politician. He’s allowed to say what he wants whether it’s right or wrong, and _he’s just a guy_. He’s not swinging stock values for a group of insiders by tweeting. He’s not using pension funds to pay for a jacked-up IPO. He’s blogging and making a podcast, neither of which is compulsory for you to read.
You don’t have to spend effort proving him wrong. Just don’t read it and move on with your life. Regardless of which “side” of AI you’re on it’s kinda ridiculous how much effort gets spent on screaming gotcha at this one commentator.
He charges people money for a newsletter that reinforces completely detached from reality viewpoints. If he's just making stuff up, that's called grifting, and it's good people call that out.
I think the most enjoyable part of this is to have written it in the characteristic long-form Zitron wall of text style while still managing to pack almost the whole text with meaning, which is entirely the opposite of Zitron style.
Posting revenue numbers as an example of a company not dying seems rather foolish. Microsoft's gaming side is floundering and dying and Linux has been growing at an unprecedented rate as a result of Microsoft's decisions. Not to mention the geopolitical aspects at play here as countries make a serious consideration to drop Windows.
These companies are all heavily buoyed by their investments in AI which is essentially an oroboros of money.
I do think you have to consider predictions in the context of the world around them; he's been incorrect, sure, but has he been more incorrect than the predictions of those opposing him? Being less wrong than others is the same as being more right.
Easy to claim things as wrong without prodiving any facts.
Can I try?
> But when people bring him up, they're of course not generally citing his anger
Wrong.
> Google has been increasing the relative priority of revenue over the user experience over time
Wrong.
> I'm curious what people do after being on the wrong side
Wrong.
Some of the claims categorized as "wrong" are also completely true, such as training hitting diminishing returns. New models are barely an improvement and most people I know stuck on Opus 4.6 over any newer one for example.
Exact same thing for the claim "the fact we're running out of high quality training data and we're hitting the walls of scaling laws, in the training paradigm, these models aren't getting better. What we're seeing today is pretty much what they're always gonna be like".
If anything, model performance has regressed in actual use (i.e. not benchmarks) for the past half a year.
> Some of the claims categorized as "wrong" are also completely true, such as training hitting diminishing returns. New models are barely an improvement and most people I know stuck on Opus 4.6 over any newer one for example.
OK, but the first instance of a claim of diminishing returns was in February 2024, when GPT-4 was the best model available. Do you really think improvement since then has been minimal?
One thing I’m observing in these comments is a willingness of folks to project their own predictions onto Ed’s statements when validating their plausibility. Eg. “I think he’s wrong about the timing but I do expect AI companies to go to zero.”
You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning.
Those differ from Dan’s essay, which engages with the literal text of Ed’s numerous predictions during 2024 and 2025 which are demonstrably invalidated by their measurable outcomes.
>You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning.
Depends if you care about the "prediction" part or if you care about the assessment of the situation (regardless of date).
If someone in 2000 said "the subprime mortgages market is a bubble and will blow no later than 2003", they got the prediction wrong, but their assessment would be right.
Predicting things 5 years too early is often as useless as not predicting anything.
You can say "AI will be able to _____" and be right 99.9 times out of 100, but the question is when.
You can say "The AI market will go to 0" and be at least directionally right eventually.
But none of it matters if you get the timing wrong.
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The subprime market changed quite a bit in size and how much was securitized in the run up to 2007, so not sure a prediction in 2000 for a 2003 event would have been easily transferred to what happened later. Would really come down to what specifically the prediction was based on for it to be a bubble in 2000.
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> If someone in 2000 said "the subprime mortgages market is a bubble and will blow no later than 2003", they got the prediction wrong, but their assessment would be right.
It doesn't make sense to split it though. Their assessment is that it's a bubble AND that it will blow no later than 2003. It's a single statement.
And it matters, because if all you're doing is saying there's an AI bubble then it's harder to prove you wrong but you also don't stand out and won't get a lot of credit for it. A very large number of people are saying the same thing as you, so who cares.
People like Zitron stand out because they go further than others and make detailed statements. Which happen to be wrong.
Zitron predicted the downfall of Oracle as someone mentioned below. He also predicted that the overhyped data center construction plans (Project Stargate, repeated vague Nvidia pledges) would not materialize.
If he got MSFT's cloud revenue growth wrong for this year, how much of that is selling shovels to OpenAI and how much is circular?
Why is Zitron’s repute evaluated entirely on the basis of failed predictions? Predictions are incredibly hard. AI enthusiasts and thought leaders have made so many demonstrably incorrect predictions it’s hard to keep track. Based on this metric, Altman and Amodei should never be taken seriously again.
Years back John C. Dvorak talked about predictions. He's was being ridiculed for his comment that there was "no evidence that computer users would want to use a mouse" (which was sort of true at the time). One of his point was that he had made a crazy amount of predictions on various topics, some came true, many didn't. People just remember the one you got right, and the ones you got horribly wrong.
Ed Zitron is just one AI crash away from being known as the guy who saw this coming. Everything else he has said can be completely wrong, he just needs to be somewhat correct on a minor crash.
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Have you ever listened to Zitron speak? He's not exactly the type to hedge his predictions behind careful language about how hard predictions are. He makes every one of these predictions with absolute confidence and conviction.
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I think it's fair to say predictions that Altman and Amodei make should never be taken at face value, as well as Zitron. That's fine. But that doesn't have any bearing on Dan Luu's claims. This feels like an example of what he talks about in the article when saying that people respond to his claims by pointing at something entirely different. That is to say, whether AI enthusiasts make silly predictions doesn't mean that these companies aren't going to be profitable, or that any of Zitron's predictions are any good either.
What else would his repute be based on? Maybe telling retrospective truth? Per the article, he does terribly by that metric as well.
I thought in the post Ed was being evaluated based on all his predictions, and it turned out all of them were wrong.
Most people will read the post as going over all the falsifiable predictions and none of them panning out, since after the chronological prediction list it says "After this point, most further predictions that I saw were either non-falsifiable or resolve in the future".
If Dan is reading maybe he can clarify?
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Isn't that pretty much his whole thing, confidently telling us what will happen? Of course his reputation should suffer if his predictions are wrong, as should those of the others whose correctness : confidence ratio is too low. (But if their reputation/power/relevance mainly comes from things other than their punditry, we can't really stop 'taking them seriously' altogether.)
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Because Zitron is specifically making a name for himself as a critic making bearish predictions; Altman and Amodei may have made unreasonably bullish predictions, but they've also done other relevant things like e.g. being involved in the actual development of the models.
Here's two possible set of predictions:
- a list of predictions that are entirely wrong, from A-to-Z, and are not even resembling what ends up happening
- a list of predictions that are wrong, but where the underlying points are in fact interesting and have some predictive value, and it's just the "last step" that is wrong
For example, one person might say "oh it's raining in Dallas therefore I should buy some TI stock". And we'll say for sake of argument that they say that even though it's nice and sunny in Dallas at the moment.
Another person says "Oh its raining a lot in Idaho and that is going to increase potato yields and therefore I will buy McDonalds stocks cuz fries will be cheaper". In this hypothetical it turns out McDonalds buys all their potatoes from ... Kansas or something instead (and it's a specific kind of potato in a completely separate market)... but Idaho potato yields _did in fact go up_.
An even more straightforward point: the iphone 3GS comes out in 2010, people are very hyped, someone looks at how RIM _still_ hasn't gotten its shit together and declares "RIM isn't going to to be able to stay profitable 18 months from now, they're gonna have their lunch eaten".
Turns out that RIM still made a healthy profit in 2010. and 2011. And 2012. 2013 was their first loss in a while... and then it wasn't until 2014 that they really got kicked in the face.
The prediction was early, overestimated how long of a tail RIM would experience, but how wrong was it? Was the prediction of some utility?
I'm saying this... it would be helpful if _some_ more AI companies flamed out. In some sense he does himself no favors by focusing on the corps with the biggest war chest instead of the various AI companies that spend a bunch to go nowhere fast and then have just disappeared.
> The prediction was early, overestimated how long of a tail RIM would experience, but how wrong was it? Was the prediction of some utility?
Wrong enough that the utility is seriously diminished. Predicting a specific quantity dropping to a specific level at a specific date is a lot more valuable than saying “those guys are cooked”.
And even if some minuscule utility existed: why should predictors be so coddled by their observers? We should be demanding more rigour from predictors rather than looking for new and creative ways to forgive them for their folly.
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Being early is the same as being wrong. Being lucky is the same as being right.
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Ok, I was thinking that probably they were saying that he was accidentally right, but missed the timing. It's not that. It isn't that he was accidentally right in a certain scenario also. It's that "I reinterpret the prediction to make it fit my own vision of the world"... that's not how prediction works. Hell that's not how anything would work.
Here's an actual prediction I made about a year ago: LLMs have to demonstrate that they make productivity gains that explain the costs or economics will make this problem solve itself, via higher energy cost and loss of business/productivity.
That prediction is unbound in the time horizon but it's bound by conditions that explain the triggers and how they will behave. Such prediction is useful. Hell, I could even make a prediction on why the timeline can't be bound while making a prediction on the timeline: I predict that in the next 3-5 years this will have to solve itself, because there's a limit on how much money irrational actors can pour onto something that have limited value. 7-10 years is way too much. I at least hope their coffers are that deep... if this drags on long enough, at some point people are going to want a change
This is an article that only cites Zitron’s opinions about subjective model quality. You’re tisk-tisking people to be objective about a bunch of words saying that the author’s opinions are better opinions than the guy he’s talking about OP
Some guy wrote that he’s grumpy because he couldn’t sleep and decided to dunk on an internet personality he doesn’t like, it’s not the ceremonial placement of the ur-kilogram
> This is an article that only cites Zitron’s opinions about subjective model quality. You’re tisk-tisking people to be objective about a bunch of words saying that the author’s opinions are better opinions than the guy he’s talking about OP
Oh it's not just the author's opinions. They're the opinions of a bunch of LLMs he checked, too. Much better.
What I observe is that what people don’t seem to grasp is that Zitron isn’t an AI sage. He’s simply someone who figured out that he can get a lot of attention by taking a contrarian stance when it comes to AI.
He can be 100% wrong about AI, but people will still read or listen to his next prediction. At this point, it’s mostly entertainment rather than a source of solid predictions.
He has one primary objective and that's to keep Ed Zitron in people’s minds by any means necessary.
It happens in sports, politics and, with Ed Zitron, AI.
In general I have about zero enthusiasm for trying to find defensible interpretations of things that Ed Zitron said, and I generally agree that the name of Zitron just largely needs to stop coming up in anti- and anti-anti-AI arguments since, it seems, he's just not a particularly insightful or reliable voice on the subject. That said, one or two of the specific assessments in Luu's article seem dubious as well, especially this one:
> August 2025 https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ : "These models have clearly hit a wall where training is hitting diminishing returns"
> Wrong
It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference. See eg. https://www.tobyord.com/writing/mostly-inference-scaling . And in fact in the quoted and linked article https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ Zitron comes up with something which looks like a recognisable explanation of this:
> Because model developers hit a wall of diminishing returns, and the only way to make their models do more was to make them burn more tokens to generate a more accurate response (this is a very simple way of describing reasoning, a thing that OpenAI launched in September 2024 and others followed).
> As a result, all the "gains" from "powerful new models" come from burning more and more tokens.
AFAICT the other drivers of recent progress in LLMs have been: ploughing in lots and lots of specialised training data custom-made at piecework websites https://www.youtube.com/watch?v=4pG3SJQPAwk ; and work on harnesses and the like. AFAICT neither of those makes false the claim that "[t]hese models have clearly hit a wall where training is hitting diminishing returns" either. Similarly, even if some big new advance does cause training or post-training to start scaling like gangbusters again in 2027 or 2028 that wouldn't make the quoted statement clearly wrong: Zitron would clearly like you to infer that there won't be any further big advances soon in LLM training, but the quoted statement doesn't clearly make that claim. (Even if he had made that claim, and it did turn out to be wrong, it would be a relatively forgivable error, more on the "cloudy crystal ball" than "misstates currently known facts" end of the spectrum.)
So: it seems that Luu took a fairly specific, objectively judgeable claim from Ed Zitron; and that claim was ... correct?; and Luu instead rated it "Wrong" without further elaboration. It seems that Luu interpreted the quoted claim as saying something like "model progress has ceased"; but it seems that's not what that specific claim (as opposed to whatever other things Zitron has said at other times and places) said.
>> August 2025 https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ : "These models have clearly hit a wall where training is hitting diminishing returns"
>> Wrong
>It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference.
I'm not an expert either, but while I do think for a bit it looked like ~all the improvement was inference-time scaling, it hasn't stayed that way. Mythos/Fable is likely a very large model (ex: it knows many things without searching) and this is probably part of its high level of capability, and the companies have started doing very large amounts of RL (which in OpenAI's case led to the HF attack).
> and the thing that is continuing to scale well or pretty well is inference
No, the models are just more intelligent. GPT 5.6 Sol can do more in fewer output tokens than any model from late 2025. Test-time compute isn't the only lever the labs have for scaling. This is among the two major things Ed has gotten laughably wrong in his technical predictions (that TTC was the last resort to make models better, and that synthetic data wouldn't help)
My understanding is that RLVR, synthetic data generation and a slew of other post-training techniques are what have driven many recent advances in models more so than manual data providers. The economics of that are for sure worse than just scaling pre-training but it is incorrect to think that test time inference scaling and manual data entry are the only ways in which models are advancing.
>> Those differ from Dan’s essay, which engages with the literal text of Ed’s numerous predictions during 2024 and 2025 which are demonstrably invalidated by their measurable outcomes.
Dan Luu did not engage on anything more, than a disorganized wall of text, ranted like a teenager using toxic personal attacks, while obsessing over calendar errors and a placeholder in a spreadsheet. If this is what passes here for a smart engineer...Lets analyze his post in a more logical and analytical way:
- His entire argument is based on the naive logic that because LLM execution speeds or benchmarks marginally improved over the last 24 months, the entire trillion dollar investment cycle is justified. A short window of venture subsidized chip buying...tells you absolutely nothing about the multi decade debt structures, physical infrastructure depreciation, and power grid constraints that dictate whether a capital heavy business model survives.
- While he whines about Zitron numbers, fails to provide a single! macro level equation to address the real financial threat. NYU finance professor Aswath Damodaran for example, explicitly warned that the current AI build out is an asset heavy, debt funded run up backed by private capital markets. Unlike the dotcom boom which was equity funded and contained to tech shareholders today AI infrastructure burdens companies with a massive $80 billion in CapEx per gigawatt, meaning a monetization correction will trigger widespread systemic debt distress and loan defaults across the real economy.
"Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://mitsloan.mit.edu/ideas-made-to-matter/a-new-look-eco...
- Dismissing the AI bubble thesis, because you found a spreadsheet typo in a newsletter, and ignoring the other voices who are aligned with Zitron core premise, means you are also dismissing the research of a Nobel Laureate in economics, the Dean of Valuation, veteran hedge fund managers, Barclays, S&P Global, and Citigroup. Arguing that "the models are hitting benchmarks" while ignoring that the physical balance sheets and enterprise budgets cannot support a multi trillion dollar infrastructure build out, is exactly the type of Dunning Kruger this corner excels at....
Ed Zitron is correct, despite the clumsiness or unpleasantness of his message delivery, and this community reaction, will be an historical record of the AI bubble crowd madness.
It took years to take down Maddoff, and more to take down Bear Stearns. It will take maybe 5 - 10 years of "Ed Zitron is wrong posts here" until Anthropic and OpenAI have to be bailed out by the US government, but the day of reckoning will come. The end of this universe is all tax payers will own a piece of AI and will pay for it with increased interest rates for the next 25 years...
> the naive logic that because LLM execution speeds or benchmarks marginally improved over the last 24 months, (...)
"Marginally improved"? Are you really going to sit there tell me that an appropriate way to sum up the difference between the AI we had access to in Sep 2024 and the AI we have access to now, is "the benchmarks marginally improved"?
What do you mean by "take down Bear Stearns"?
Btw., projections are just that - projections and I am not sure Acemoglu proved things mathematical (as in a mathematical proof) but rather within the context of a model/assumptions.
That financial markets/innovation can outpace the actual innovation is also not some new insight, but that alone doesn't necessarily make for a useful prediction.
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The post is titled “How accurate have Ed Zitron's AI skeptic predictions been?” not “How accurate will Ed Zitron’s AI skeptic predictions be in the future?” or “Is the entire AI industry build out justified?”
If Patrick Boyle, Aswath Damodaran, and Daron Acemoglu have more accurate reporting and predictions about the upcoming decline of the AI industry, maybe those are voices who should be elevated over Zitron.
> It took years to take down Maddoff, and more to take down Bear Stearns. It will take maybe 5 - 10 years of "Ed Zitron is wrong posts here" until Anthropic and OpenAI have to be bailed out by the US government, but the day of reckoning will come.
Unfortunately, the market can stay irrational (far) longer than you can remain solvent.
In any case... I doubt Anthropic, OpenAI and xAI have any kind of moat that can justify a bailout. There is nothing truly unique either of these three possess, and certainly not against the free competition mostly from China or from Facebook that anyone can self-host.
Who will get the bailouts instead is the pension funds and other investment vehicles that have been force-fed crap AI stock like foie gras geese.
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If Ed Zitron was merely saying that there is a AI bubble on the markets that will ultimately collapse even if we're not exactly sure when and how, then such prediction would be less interesting but also much harder to disprove.
But that's not what he's saying. He's making very specific claims that are indeed proven wrong. You can't honestly say he's correct, and the burst of an AI bubble will not be a reckoning.
You maybe right, but why don’t you put your prediction in Metacalculus or a prediction market. Thats what they are for.
On the flip side, I also see many people taking this thread as an opportunity to shit on Zitron as a person, and not "discussing his prediction". Incompetent/ blow hard/ dishonest are ones I remember off the top of my head.
AI by itself, is surprisingly polarized; Ed Zitron even more so.
Not sure why you see it as such;
Incompetent and dishonest are characteristics that follow from this professional work, adequately describing an individual who continues to make poor predictions, analyses and false statements refuted by past events.
Far from “shitting on” Zitron
I mean if his personal work is incompetent or dishonest...?
One of the easy ways to evaluate this is: how has he taken being incredibly wrong about his extremely confident predictions over and over and over?
Typically with people like this, they completely shrug off being super wrong. It's barely even a blip on their radar, and even bringing it up is a good way to get them to immediately attack you to deflect attention from how bad their predictions or assertions were.
If you're constantly making predictions on Topic X, and said predictions are consistently, wildly wrong, and you never actually grapple with that or acknowledge how wrong you were in the past, then that's, at the very least, intellectually dishonest.
But by all means, someone link us to his blog posts where he goes over his wrong predictions without excuses or deflections. I'd be happy to change my mind.
AI companies will never go to zero because AI is part of the Military Industrial Complex now. All the money is coming from the military and government for surveillance and power and war.
Speaking of wrong predictions... the entire US military budget would have to be spent on OpenAI/Anthropic to keep the bubble from bursting.
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Every conspiracy theory has an escape hatch
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Things in general I think he's right about:
1. Revenue if anthropic and openai is unlikely to grow to the high levels they need to pay for their commitments. Many of their heavy users (coding) will eventually offset a lot of usage to more efficient and cheaper open weight models. I know of people in a company I was at that spend thousands of dollars a month on tokens. I am sure that what they're using it for can be substituted in large part by way cheaper models.
2. A lot of corporate AI usage is being pushed by management that doesn't really understand the extent of its useful, and just wants to call themselves an AI-first company.
3. If this datacenter build-out proves to be beyond the actual demand, there might be a big economic crisis as to how much of the financial system is getting tied up with it (insurance money, private credit).
Whether openai and anthropic actually die, I'm not sure. But I don't think they'll be the next big tech companies. I think eventually they'll be absorbed by others.
The whole thing I think, can be summarized as: LLMs will be commodity like. And it's price will go down and eventually will run locally, it's not at all clear that this will bring AGI and that it's worth infinite amount (or trillions) of investment ahead of the actual demand or the AGI level do-it-all-for-you AI being reached.
I feel like too many people have a binary vision of the world, i.e. you're either "pro AI" or "anti AI".
Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase. He's basically saying that (1) the current data center investments are based on unrealistic revenue projections and (2) hyperscalers are using accounting tricks to move around "money" in a circular way to make it look like more money is already flowing to AI.
You can believe all of that and still believe that AI (as in "LLM based services") work, are useful and will probably see their usefulness grow even more. Just not in the magnitude necessary to make the current investments make sense.
If you ever listen to more than a couple of Zitron interviews, it's clear that he's not the "reasonable centrist" viewpoint on AI. He's the doom-and-gloom guy.
Maybe there is someone out there positive on AI itself and calling out reasonable objections to some of the extreme things. But that's not Zitron.
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> Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase
He's said variants of both of these things before! That's the point OP makes too, people say "He doesn't say that" or "But what about this" when he's said a million, sometimes contradictory things.
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After looking at some of his recent interviews, he's acknowledged his previous predictions that 1. He was naive enough to think the investment won't keep coming despite lack of profitability and 2. He acknowledges that AI in it's current form is a billion dollar industry, not a trillion dollar one, but also not zero.
Definitely less extreme than he used to be.
> Ed Zitron doesn't say that AI doesn't work
Ive seen him say it does work pretty consistently? He also talks about other things that are more interesting, but he still says it doesnt work
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To be fair he is nothing more than a talking head who historically has had some pretty negative AI pieces. Sure nothing is binary but more than ever it feels like there are talking heads on both sides that have some pretty extreme views.
> You can believe all of that
You can believe all of that (I do) and see that Zitron is a liar and an influencer.
re: substituting with cheaper models. I think some people here are oblivious to how much of Anthropic and OpenAI’s usage is artificial.
Take this one example of a user with 15 Codex subscriptions ($3k) generating $60k in API-equivalent usage per month: https://hraness.com/writing/my-girlfriend-asked-me-why-i-hav...
“there’s a once in a lifetime discount happening at the OpenAI Intelligence Depot, and I brought 15 shopping carts.”
We have to understand current usage with this behavior in mind, there are tens of thousands of people just like this author who are intentionally generating as much usage as possible on their subsidized plans because they feel compelled by some need to get free intelligence.
The only reason these expensive models are generating so much usage is because they are so heavily subsidized. Pragmatic users will shift to cheaper models which will hurt per token revenue, yes, but huge volumes of usage is going to just disappear because there isn’t the demand when it isn’t being subsidized. Less revenue per token and less tokens.
https://tokscale.ai/leaderboard a small sample of just 2k users have generated over $100m of API-equivalent usage while paying closer to just $1m.
https://aicharts.io/gpt-subsidy
It goes a layer up, too. Harvey (legal AI) recently tweeted that a single user query cost that firm 26k USD. This, while they have thousands of users and reportedly whole law firms paying zero. That difference is being subsidized by billions of VC dollars.
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Why do everyone assume they are subsidized? When we seemingly have no idea what it costs? Maybe average subscription is breaking even and token spend is pretty much pure profit?
Case in point, claude code seems hell bent on increasing usage at all cost. Which makes sense in the growing phase (get people hooked) but it does not make sense given the hardware shortage. So, which is it?
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theyre not being subsidized, and no ones using tokens just because.
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Yeah personally I think he is right about a lot of the current state of affairs. I think he has a decent grasp of the situation despite being incredibly biased.
His predictions though? I don’t think I’ve ever read anything from him to get his read on how things will play out.
Just because one makes the wrong predictions does not mean that the data used to make them was wrong. A lot of people seem to dismiss his reporting because of his takeaways
> I am sure that what they're using it for can be substituted in large part by way cheaper models.
I saw a commit in the repo at work that edited a few config files and a dictionary.txt file. "co-authored by Fable"
I have colleagues who have replaced ctrl-f on a (long) document they already have open with prompting an LLM to find things for them.
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None of this actually matters if this is a nuclear weapons-style arms race.
The data center build out will become a matter of national security, and will be backstopped by governments.
LLMs are a national security issue? Why is China releasing their models then?
The question is not whether you should have datacenters, or the most advanced chips, or the ability to build the most capacity. But do we need all this now? Will there be enough demand? People want to make profits form this thing, and what's being pointed out is that maybe there won't be enough demand to generate profits for all this investment.
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This reads to me as incredible cope. Maybe not both of these companies but certainly one of them will be enormously valuable, and there will always value in the frontier models even if much of the practical usage can be done locally
Why is it “certainly” the case that one will be enormously valuable?
Neither appears to be on track to long-term profitability specifically once you take into account depreciation on CAPEX.
On top of this the “productivity gains” from AI across the board seem to be a very mixed bag. The pitch from these companies has been huge gains in productivity and automation and although there is anecdotal evidence some people are able to do that, the broader studies seem to show marginal gains in most cases.
This critique is leaning really hard on their interpretation of "dying". They take the literal company is going to fail type of dying where as I have always taken it the same way he has presented it in his "rot-economy" context. They can remain financially "successful", but more and more people hate their products, their products are getting worse, their products are "dying". Google Search is still a good example, the old Google search is "dead" if you like, a know many people, including myself who no longer use it. More people hate and getting off Facebook. More people are jumping from Windows to macOS or Linux.
These tech giants need AI to continue to grow, and that growth at the moment seems to be coming from just two AI companies who are burning a record about of investments. OpenAI has raised nearly $200B, that is more money than Australia's tax revenue.. and they are getting further from being profitable as Chinese models are getting better and much cheaper.
The article linked seems right, but you have to take these morbid analogies in a very specific way, and assume that the only way to measure these companies is revenue/profits/money rather than their products.
I wish people would hold actual professional media economists to the same standards, along with journalists who just repeat press releases without actually challenging statements. His main argument has been the numbers don't make sense, and can't see how this won't end badly for a lot of people.
If many people hated their products, they wouldn't use them. If they didn't use them they would not be financially successful. Google is not dying just because you personally have a feeling the results are worse than before and there is no definition of dying that would be consistent with Google's current state. If Google were to die they would die the way Yahoo did, because a competitor was demonstrably better than them and everyone switched off. There is no realistic evidence that this might occur in the near future.
> If many people hated their products, they wouldn't use them
This might be true in a true, free market without monopolistic collusion and the abandonment of antitrust regulation and enforcement in the US.
In many cases people don’t switch to something else because there isn’t an alternative. Or because they don’t know how to change the defaults that come installed on their computer. Or they get a big scary warning if they figure it out and try.
I would have a hard time believing anyone who said Google was competing fairly and not juicing their numbers with Gemini. Like with search and ads, they have a lot of vested interest in profits and little regard for much else. There’s no reason to. Almost all safeguards on corporate behaviour have been taken off in the last bunch of years.
Google jumped at renaming Lake Ontario.
Of course they’re going to shove AI mode as the default on search and claim every user loves it.
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> If many people hated their products, they wouldn't use them. If they didn't use them they would not be financially successful.
I think this incorrect diminishes the success of product lock-in and also doesn't consider that the world is moving more and more into concentrated wealth where consumers have less and less to offer. Google's financial success could be sustained or even continue to grow with fewer ad buyers targeting fewer people.
> Google is not dying just because you personally have a feeling the results are worse than before and there is no definition of dying that would be consistent with Google's current state.
Again, "dying" is being used as a proxy for financial success. I don't disagree that Google/Microsoft/Meta will continue to grow their revenue or even profit, but I do argue that their products are becoming worse for consumers. That may or may not lead to real competitors, but that is a whole other regulatory capture discussion.
> If Google were to die they would die the way Yahoo did, because a competitor was demonstrably better than them and everyone switched off.
I think you mean "die" here in a product/usage sense, which I think their current path seems to be going that way, but I think it will matter FAR less to Google/Alphabet than it did too Yahoo.
> If Google were to die they would die the way Yahoo did
This might be true 20 years ago where Google had one true product, Search. Since then, they have diversified and got their fingers a million pies.
I don’t think you can get true competition with the way MS,GOOG,AMZ have grown. You’ll get a duopoly, or maybe a triopoly.
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You must not talk to a lot of retired people, for whom every tech product is a bewildering nightmare.
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GP said Google Search is dead, in a way, not Google itself. Google is just to an overwhelmingly part an ad business at this point. And people hate their annoying ads. So people hate Google search, and Google ads. Basically, the majority of what Google is.
> If many people hated their products, they wouldn't use them
You do understand how drugs work right?
> These tech giants need AI to continue to grow
This is unfalsifiable. AI is juicing the tech majors’ growth. And in the modern economy, it may be necessary for them.
But did Disney need the internet to grow in the 1990s? No, probably not. Did streaming give it all kinds of new growth victors? Yes. And would ignoring the internet for that last three decades have probably killed it? Also yes.
How much did Disney invest in backbone Internet technologies in the 1990s?
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You can define any word to be whatever you want and thus have linguistically correct predictions that are functionally useless.
This cuts both ways: you can interpret any word to be whatever you want and thus have linguistically correct criticisms for predictions that are functionally useless.
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> More people hate and getting off Facebook. More people are jumping from Windows to macOS or Linux.
Might be true.
But here is another angle, thinking about the people I know that are not in tech or avid gamers, which I would say is still easily the majority of people.
Most are basically addicted to Instagram, Youtube etc.. Meta and Google owned companies, same goes with OS's, I can't think of one person that considered linux as their daily driver (other than unknowingly through phone).
>tech giants need AI to continue to grow
You mean increase the -$2.50 lost for every $1 in revenue, or the $2Tn in debt disclosed 60 pages into the reports as a footnote.
Let us be clear, the only "growth" is in the LLM ectoparasite living rent free in peoples imaginations. The fact is when (not if) the peak of inflated LLM use-case expectations corrects, a lot of the industry won't survive.
Facebook has a founders-syndrome problem, and a product line catering to creeps. Note most normal people aren't creeps, but the ones that are creepy will buy creep-ware at a rate necessary to sustain the founder creeps ego.
https://en.wikipedia.org/wiki/Founder%27s_syndrome
Google hasn't built a successful product in decades, and acquired most of its successes like YT. There are 3 reasons this occurs, and 2 are related to corporate cult culture. One would have to fire 70% of the company to fix that problem, and one day someone will have to do just that.
>wish people would hold actual professional media economists to the same standards
OpenAI will go public soon, and the hype-cycle can finally settle down.
https://en.wikipedia.org/wiki/Gartner_hype_cycle
LLM do have basic utility in search and pattern recognition, but only the delusional believe it will hyper-scale unconstrained forever. =3
I should have been clearer, "tech giants need something to meet their growth expectations, AI is currently that something". I completely agree this is all going to end badly, and AI has made these tech giants grow their market cap, which is what they seem to care almost solely about. I don't think the crash will be the end of Google/Microsoft/Meta/Amazon as companies, I do think it will be the end of OpenAI cause of the insane financial commitments they have, and I think Nvidia will drastically shrink, but this will all likely take longer than I'd like.
What?? Ed Zitron has repeatedly said OpenAi or Anthropic would literally die. This keeps happening - Ed makes a prediction. It gets falsified. And people say, “no actually he meant something else”.
Ed is in public relations. He did not amateurishly claim OpenAI and Anthropic would literally die. Like any PR savvy person, he qualified his predictions by making them conditional on whether OpenAI/Anthropic could raise more money, achieve a technical breakthrough, unlock new lucrative markets, etc.
So far, OpenAI and Anthropic have raised more money. You might even be able to argue they've achieved some breakthroughs and unlocked some markets.
Regarding his predictions: The jury is still out because Ed isn't actually making the falsifiable time-bounded predictions you think he's making. He's good at making his readers think he's putting his neck out, but he's really not.
When you remove all his rhetoric and veneer, his conditional predictions are actually somewhere between bearish and cautious.
It's mostly theatre.
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If I have a billion trillion dollars but 20% of the population hates me, but I get to live in a giant solid gold mansion with an army of servants keeping the 20% away from me, have I really failed?
It'd depend on what specifically that 20% of the population hates that hypothetical you for. Maybe their hate in this scenario turns out to be an accurate signal that you are an awful human being. In that case, you'd be a very rich, awful human being.
Have you really failed in this case? Not at making money and living a decadent, hedonistic life, if that was your goal, but yes at being a good human being, one who is good to others and is worthy of their respect, admiration, and support.
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Still a failure because it is propped up by an inflated US dollar. Those trillions would mean nothing when the US economy collapses due to a cumulative effect of massive national debt, unending wars and inflation.
At least if you have the population by your side, you wouldn't have "guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur I can fly to" [1]
They are all willing to risk everything to see if their bet on achieving "singularity" fructifies. I don't see us getting anywhere close (at least with the current tech).
[1]: https://futurism.com/the-byte/openai-ceo-survivalist-prepper
To even desire that you have to be so broken and impoverished I would say no, you wouldn't have failed, in the same way a dog farting didn't fail to make perfume. They don't even know what perfume is, don't know about any of the ingredients, equipment and processes. And even if they did, that wouldn't do them any good because they don't have opposable thumbs, so why be cruel and even try to explain it to them? It will either frustrate them because they don't understand, or frustrate them even more in the extremely unlikely case that they do.
But more importantly, companies aren't people, they can't be unhappy or happy. They're like fire, you don't ask what the fire wants, you ask how to make it useful.
Depends on your metric for failure.
As a decent human being? Absolute failure. As a supervillain? Complete success.
Yes.
How morally bankrupt can one be?
type shit Louis the XVI would ponder about
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You should let them eat cake.
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Was Microsoft dying under Balmer? I think we’d say yes. Even if the SEC filings indicated otherwise.
Right. If we merely define "dying" to mean something other than dying, it's possible for a not-dying company to be "dying."
https://danluu.com/ballmer/
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Being thorough and accurate might make you a lot of money in the stock market, but it's not a good way to get any media presence, because that demands being in front of people quite often, and basically nobody can be insightful and well researched in all topics of the day. Once you become a pundit, whether on politics or tech, and rely on eyeballs to feed you, you are going to be throwing stinkers. And at that point, you might as well just align with an audience and not care too much about whether you are predicting anything accurately.
We have people telling us the next recession being imminent all the time. Others told us that NFTs were key to the future of art. That's the pundit way. If you want accuracy, you become like Charlie Munger, have maybe 2, 3 really good insights througout your career, and hope do to well using the insights yourself, not by being a pundit
This is exactly right. Now, that does mean something about Zitron: he’s a pundit, not an expert or a forecasting genius. You could point to any number of analogous booster types. Twitter/X somehow loves pushing these people onto my recommended feed. I remember reading breathless threads about how o3 was going to single-handedly end white collar work. There are just more of that sort of person, so none of them in particular gets the same amount of attention as Zitron, who seems to be the only person willing to go on the record against AI. Maybe his overall worldview is sound, and maybe it isn’t. But he’s not really making confident, specific predictions about the future.
Which leads me to a criticism of the piece: several assertions are described as “Wrong,” with no explanation or citation, which are not obviously wrong to my mind. For example, the assertion that “DeepSeek has commoditized the [LLM]” is at least debatable. It is a prospect that the major labs seem to have at least considered.
In my experience, people who make fun of AI's ability to eliminate white-collar work are exactly the same people who are white-knuckling it, hoping they can make another $300k next year to buy that Rivian.
People who don't need a salary look at the situation more objectively and, in general, can see that a lot of white-collar work is in peril.
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is this comment written by ai?
> We have people telling us the next recession being imminent all the time.
Yeah uhm so I'm not sure if you've like seen the world recently, but uh.
Yea
S&P 500 - All time high
DJIA - All time high
Unemployment rate - Near all time low (for last 20 years)
Number of US small businesses - All time high
US GDP - All time high
[Sources]
https://www.bls.gov/charts/employment-situation/civilian-une...
https://www.sellerscommerce.com/blog/small-business-statisti...
https://fred.stlouisfed.org/series/GDP
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I believe the saying goes:
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> We have people telling us the next recession being imminent all the time. Others told us that NFTs were key to the future of art.
That's quite the conflation, and a very subtle way to tie a claim that's still unresolved to one that is to try and make the first one seem wrong with no evidence. Recessions are almost guaranteed in our system, they're a part of a cycle. Unless you think that the Great Recession was the last one in history, saying the next one is 'imminent' would be correct for any other viewpoint. We just don't know when it'll officially start.
> Once you become a pundit, whether on politics or tech, and rely on eyeballs to feed you, you are going to be throwing stinkers.
Peter Zeihan
...get any media presence, because that demands being in front of people quite often, and basically nobody can be insightful and well researched in all topics of the day
What? Zitron is getting a lot of media attention by repeating the same takes hundreds of times. He's not a general pundit.
> Zitron is getting a lot of media attention by repeating the same takes hundreds of times
They’re not the same take. Predicting collapse thirty days from now for three years running isn’t the same take, it’s a series of wrong takes.
> For any of his posts that I read, while there are numbers thrown around, the numbers don't actually connect to a coherent argument. In many cases, as we saw above, the numbers don't even really support his argument (such as an MAU decline in Facebook causing Meta financial problems which would then cause Meta to spuriously insert AI in places it doesn't belong). I suspect he's relying on people's eyes glazing over when they see numbers and just not thinking about what the numbers mean.
A frustrating thing about Ed Zitron is that he sometimes breaks news - gets hold of leaked numbers that nobody else has, for example - but he then wraps those numbers in his own extremely biased commentary. This makes it much harder to evaluate how credible the new information is.
>He doesn't count February 1-10, counts March 1-10 twice, counts August 21-October 21 as one month instead of two, and doesn't count October 21-November 1.
Seriously, I expected more slight wrong before I read the article. Why do people trust someone who can't add numbers correctly to make predictions?
Didn’t the FT both vet his numbers and also publish on the same leak?
Yes, and his post about the exact same data was noticeably more confusing and less illuminating than theirs because he was primarily concerned to point to whatever the biggest number was and go "Ooh, big number!"
https://www.wheresyoured.at/exclusive-openai-financials/
https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb... (https://archive.ph/pAIEa)
There was a major material difference between the FT and Ed's post: FT explicitly noted that OpenAI has $billions in cash on hand (which is relevant if you are analyzing data about solvency), while Ed's post obfuscated that data point.
As someone else mentioned, ignore Ed’s personality and look solely at the balance sheets and capital analysis. Regardless of delivery, the math doesn’t math.
It's wild to me how much Ed is criticized while pathological liars like Musk and Altman are glossed over.
(Disclosure: I pay for Zitron's publication for the capital figures and exclusives he gets his hands on, I do not pay attention to his interviews, we are fact finding, if he wants to perform, I am not bothered, I've seen the performers on the other side, just keep the facts coming for ground truth)
This was mentioned in the article.
> For example, when Timothy B. Lee looked at a spreadsheet that Zitron used to create a projection of Anthropic's revenue, he found, "He doesn't count February 1-10, counts March 1-10 twice, counts August 21-October 21 as one month instead of two, and doesn't count October 21-November 1. [another commenter notes that his spreadsheet also contains February 30] ... Ed claims he tried to compute Anthropic's revenue for 2025 and came up with $3.6 billion, suggesting some funny business [but the numbers work out once you fix the errors]"
As someone with a bit of understanding of accounts and finance I don't think Ed's analysis is very good. He's not someone who would pass a finance exam.
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I posted here a while ago when his bubble prediction lapsed and plenty of his fans were here. Telling me how I was wrong and the Q2 reports weren't complete yet because it was still early July and blah blah
Are we living in the same universe? Musk and Altman are glossed over and not criticized?
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> while pathological liars like Musk and Altman are glossed over.
You seem to live in a bubble where these people are worshipped ... :-(
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> gets hold of leaked numbers that nobody else has, for example - but he then wraps those numbers in his own extremely biased commentary.
You can think of yourself and draw your own conclusions (or we have completely lost the aptitude since "ai" started off?). You don't have to agree with Ed, or even follow his conclusions.
Like I said, "This makes it much harder to evaluate how credible the new information is."
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“Because Danny had a mortgage and a boss to answer to … The guilty don't feel guilty, they learn not to“
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One of the best heuristics I've seen on whether there will be a fruitful outcome in taking someone seriously is how often they invoke labels (name calling, etc).
Hasn't failed - both in real life and online.
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Even if Zitron is hopelessly wrong and a complete fool, he's an engaging writer. Like the original article itself states, it's not about the numbers, it's about catharsis. The transparent absence of AI tells in his articles is refreshing, and just rampaging against the AI boosters who lack empathy and self-awareness (which so many of them absolutely do) is cathartic. Even if it's all bunk, fake, and stupid, it's nice to have a way to self-soothe and manifest how much some of us want AI to have a reckoning.
Does that make his readers suckers? Possibly. But personally I also think it's a very human trait to seek comfort.
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Right, you're very much his target audience.
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If you find derision and extreme cynicism "soothing", the problem may be with you, rather than those who are optimistic about AI.
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I don’t understand the issue, cannot you ignore his commentary and just look at the numbers?
I'm not financially literate enough to trust my own analysis of the numbers. Ideally I'd like commentary from someone like Bloomberg's Matt Levine, a genuine expert in financial matters who is also extremely good at explaining them in terms non-finance-professionals like me can understand.
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The problem is that some numbers genuinely require a critical eye. If a source tells you that the OpenAI CFO told employees July ARR exceeded the Q2 total, there are a number of serious questions to be raised on how these numbers were calculated and what the point of such a confusing comparison is supposed to be. (I think the answer has to be that ChatGPT wrote the CFO’s script, no human financial expert would think to compare an annualized figure to the sum of three specific months.) But it’s hard to analyze the facts appropriately when they’re relayed by a guy who tells you that it’s all a giant scam and infers the worst possible answer to all the questions.
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No, you can't. The numbers are genuinely confusing, and Zitron actively works to make them more confusing rather than explaining what they mean because he needs them to sound as bad as possible. He also buries the numbers in thousands of words of prose!
No, particularly not when he manipulates and selectively discloses them.
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Over the past few years, I had helped Ed with some difficult nuances around the obscure technical aspects of serving LLMs (e.g. benchmarks and model caching); he has also shouted myself and Simon Willison out positively multiple times. I stopped assisting him because he repeatedly misused said advice to the most cynical interpretation ("how can this be interpreted to make AI boosters sound crazy?) and often made it misleading at best. Nowadays I suspect he views me as one of those crazy AI boosters.
Not challenging him was a mistake in hindsight, and I own it. Going forward I will no longer be helping writers/journalists with a clear anti-AI bias.
He made a blog post, literally last month, and it said that AIs have no use outside of coding.
It just shows he's done zero research on the things he talks about all day. Radiologists are using them, ad firms, artists, translators, law firms, auditors... It's hard to think of a white collar firm not using them.
One thing I've noticed is that since LLMs came out, scientific papers with poor English have essentially disappeared. Just one of the many ways that AI is changing the world. People are using AI in all kinds of fields.
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>I will no longer be helping writers/journalists with a clear anti-AI bias.
Take out the 'anti-AI' qualification, and you've got a decent general principle.
This reads slightly weird and more like a pledge of alignment than a statement from the heart.
But apart from that, I guess that's always the learning? Journalists (or people labeling themselves as such) often have their own story they want to tell, and usually do so by building it out of little blocks of reality stacked together to form the desired picture.
I would predict a similarly frustrating experience being equally probable even without the "clear anti-AI bias" attribute set.
That's an unfair characterization of tech journalists in general. When BuzzFeed News was around, I worked closely with them on technical aspects and projects to ensure that everything was correct and accurate, and there were very receptive to it; it was not a "directionally correct" thing, the journalists wanted to avoid any unintentional. That was also one of the reasons I was open-minded to helping Ed even though we ideologically disagree: the truth is what's important.
BuzzFeed News incidentally was how I first came across Ed on Twitter from his tech PR work about 9 years ago, back when he was writing guest editorials on Gizmodo about Person of Interest (which is very very funny in hindsight). It's from the heart that I'm a bit bummed out that things turned out this way.
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> Given how fast these companies are growing (in terms of revenue and profit), it doesn't seem that AI is, as Zitron implied, some kind of desperation move they're reaching for because "they don't know how to grow" and are all out of ideas
Growth isn't a valid rebuttal, unless we can also sus out how much of that growth is tied up in circular financing of AI projects. We have a pretty good idea how much of Nvidia's valuation is tied up in the AI craze, it's a bit harder to tell with the megascalers....
You are confusing Capex and revenue.
The predictions were predictions of revenue. Circular financing of AI projects does not create revenue for OpenAI, Anthropic, Meta, or Google. Only Nvidia benefits from it.
Predicting revenue growth will stall and it does not was wrong.
Circular financing absolutely creates revenue.
A startup raises $50 million from OpenAI and Anthropic to finance API calls to OpenAI and Anthropic that they are using at a loss who in turn spend that money on compute with Microsoft and Google who in turn invest in Anthropic and OpenAI who then invest the startup using the startup’s revenue to value it… the cycle repeats.
There are multi-billion dollar valued startups invested in by OpenAI and Anthropic with hundreds of millions in ARR that are spending 90% of their revenue with Anthropic and OpenAI.
Situational Awareness, the fund that recently imploded, invested tens of billions into AI companies using their holdings in Anthropic to help finance the investments…
This could all work out fine in the long term, we’re all just speculating at this point, but the circular financing is absolutely making it to revenue because capital invested into startups is used to fund growth which is achieved by subsidizing costs incurred with OpenAI and Anthropic.
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As public companies, the megascalers publish pretty detailed financial reports.
Can you provide any examples of any of the megascalers publishing any detailed financials that touch on their AI spend or revenue or profit? The only one I’m aware of that comes close is Microsoft and they have still buried it in barely related line items which still leave us making assumptions.
There’s speculation on both sides and certainly Zitron is on the extreme end of the anti-AI side with the most cynical speculation but it is indisputable that none of the megascalers are open about their AI financials. Hence, we are all speculating endlessly. If only there were published financials then the speculation could end!
The obfuscation of financials doesn’t necessarily mean something bad is happening, it could be a competitive advantage for Google to be secretive about how cost effective their TPUs are or for Microsoft to hide how much revenue uplift they’ve experienced by adding AI to 365.
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Because of accounting tricks, quarterly financials don't accurately reflect the size of this fiery money pit.
The datacenter build-outs are all majority (>50% ownership) financed by other companies, with a shell company owned by the hyperscaler as a minority owner. The data center then grants the hyperscaler an exclusive leasing agreement, and because the shell company is a minority owner, legally, it's not their debt.
The only reason this has worked is because there's such a long delay taking delivery on GPUs. When these capital allocators start paying for GPUs in data centers which haven't yet broken ground, then we'll see a very visceral market reaction. Some of that has already happened, but there's enough momentum that it can be absorbed and dismissed as an anomaly. But with governments unexpectedly passing moratoriums on data centers everywhere, it's only a matter of time before there's no data center to offload those GPUs to. That's when the music stops.
I believe that was Zitron's central thesis and why he started reporting on this. It mirrors the mortgage-backed securities situation that led to the 2008 GFC, except with even fewer guard rails to prevent financial calamity.
Investors are very savvy and keenly aware of what's going to happen. There's just zero incentive to pull the fire alarm and risk being blamed for crashing the market. If you're wondering why everyone's running toward the exits instead of treating these tech companies as 10+ year investments, you have your answer.
Financial reports alone don’t paint the whole picture when it comes to valuations. For example, theoretically amazon has committed to invest 25 billion in anthropic, and anthropic has committed to spend 100 billion on aws compute. As far as we can tell, no real money has actually changed hands in either direction, but both valuations are being buoyed by their prospective investments…
They actually do not, they do not share details on their AI revenue and investments
Reports which are (un)surprisingly light on actual financial details regarding their AI ‘investments’ and any profits therein.
This is the scary part as this is not entirely true if you care to look into it.
https://youtu.be/HXlcMbxzz0U?is=XdvcNJKGJxEwlB7I
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> February 2025: "I will keep writing this stuff until I’m proven wrong." Wrong (Zitron continues to write despite repeatedly being proven wrong)
Lol, I appreciate the sprinkling of well timed humor in an article that is mostly straightforward facts and analysis.
Upon further thought, I realized this should be counted as a correct prediction for Zitron! Zitron did indeed “keep writing this stuff until…proven wrong”. Zitron’s prediction says nothing about what happens after being proven wrong.
Oh nice, I came here and made a very similar comment. I like the Easter eggs that reward readers who are paying attention.
> In terms of the style of reasoning, of the futurists reviewed, he's probably closest to Kurzweil, in that he uses numbers to give a kind of aura of credibility, but if you know something about the topic he's discussing or look at the numbers, the reasoning falls apart.
In one of his posts a few months ago, he went on a weird tangent about the CEO of ServiceNow talking about sales planning and whether his teams are "on plan" or not. For people who haven't spent time in or around sales, this is an extremely common shorthand for quota tracking.
Ed's rant about how impossibly weird and inhuman the framing was revealed how little he knows, and cares to know, about the mechanics of the businesses he claims to profile. Yes, something like "on plan" is jargon, but a shred of reasonable journalistic curiosity (a Google search) would explain what it means and how it's a normal statement for a career-sales CEO.
>Ed's rant about how impossibly weird and inhuman the framing was revealed how little he knows, and cares to know, about the mechanics of the businesses he claims to profile. Yes, something like "on plan" is jargon, but a shred of reasonable journalistic curiosity (a Google search) would explain what it means and how it's a normal statement for a career-sales CEO.
This is precisely the sort of feedback an LLM could provide him before he hits the publish button, funny enough.
This is a common issue I face on HN as well. A lot of "folk wisdom" on here is divorced from how stuff actually operates.
Consider two propositions:
P1. AI is useful powerful and (P1a) will continue to get more useful and powerful at the same rapid pace it's been improving
2. AI companies are very profitable, and (P2a) will be wildly profitable (eg $30T TAM) in the next few years
These are completely separate. But in practice people seem to be either proAI (both true) or anti AI (both false). P1 is clearly true and I find it hard to take anybody seriously who says otherwise. P1a... Who knows, gotta hit a wall sometime. P2 I'm way more uncertain about (especially P2a) but it seems like people like Zitron reason backwards from hating AI.
I can imagine an Ed Zitron in 1840 arguing that trains and steam engines are useless - or even a scam - because railroad investment was a bubble (which it was).
I can certainly see one happening without the other, but I'd expect P1 and P2 to be correlated at least some of the time?
In my mind the most obvious scenario where AI gets used a lot but AI companies (exlc. NVIDIA / chip makers) aren't worth much is the hardware becomes capable of running the current state-of-the-art models locally, cheaply, and those models are basically "good enough" for 90% of tasks.
Sounds like the dot com bubble to me. many companies went bankrupt but the technology marched on.
P1 is probably necessary but not sufficient for P2, which would show up as a correlation of a sorts. Maybe I should've specified even further, if the tech works (P1 and P1a) then yeah somebody is going to make a lot of money but not necessarily OpenAI/Anthropic. They could go the way of AOL/Time Warner (probably not but again who knows). Microsoft and Amazon survived the dotcom bubble and thrived, Meta came after it.
Currently they don’t seem to be. At least OpenAI is haemorrhaging money left and right. Thing is, the compute for all this is far more expensive than anyone is willing or able to pay.
The most successful use of AI has almost certainly been advertising, which companies were already doing before the recent AI hype.
If by AI we mean LLMs, the question looks a bit different.
For someone who falls in the middle of this a really good read is Quoth the Raven[1]. He essentially makes the argument that proposition 1 is probably true, but that before proposition 2 happens there'll be a massive bubble burst. Analogous to the dotcom boom where yes, eventually Amazon became Amazon, but before that there was a massive collapse. And he does this in a way that Dan Luu would really like because he's giving a very clear and specific time line for his prediction. I haven't been reading him long so I can't guarantee there's not going to be some goal post moving 6 months down the line though.
[1]: https://quoththeraven.substack.com/p/the-real-ai-crash-will-...
Yeah well, why not focus on Zitron's Oracle predictions? Article from May 27, 2025, when ORCL was skyrocketing.
https://www.wheresyoured.at/measures/
Now ORCL is back to $140.
I found this in two minutes via a search engine, but the star blogger Dan Luu apparently cannot handle that. I'm not a regular Zitron reader, but incidentally this blog post that came up in the search is several levels above Luu's post.
Zitron gets the big picture right.
From the blog:
> Although Zitron's past predictions have generally been wrong, maybe he'll be right about something in the future. Perhaps some of these companies will have valuations decline for some reason. But, even if there's some kind of massive AI crash and OpenAI and Anthropic go to zero, in terms of the societal impact, if on top of that, some other event occurs that prevents further progress in models beyond whatever AI labs have internally right now, that's still going to result in a fair amount of change. Which companies are successful will change who gets rich, but particular companies failing won't stop changes that fall out of current or next generation model capabilities from happening; it just moves around who benefits the most.
> If Zitron ends up being right about some company or other collapsing, that's pretty uninteresting to me compared to how capabilities have developed and will develop, where he's been wrong to date. It also happens that he's been wrong about the financial predictions he's made to date, but that doesn't really interest me, though I included a number of financial predictions for completeness.
Not sure how you can "get the big picture right" while having many egregiously wrong predictions.
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Agree. Even Zitron's predictions about AI peaking are arguably correct if you step outside the silicon valley bubble. People I know who don't work in tech who use AI at work are doing the same stuff they were a couple years ago: they use it to summarize emails, generate an occasional slide deck, and mostly just send workslop to their coworkers.
And for personal use, I can count on a single hand people I know who pay for an AI subscription, and of those people nobody pays for more than the $20/month plan. And they all just use it as search or maybe to vibecode some one-off party game they use once and then throw away.
I don't know a single person who has done something like run Openclaw or leaves coding agents running on their laptop open all day.
> February 2025: "I will keep writing this stuff until I’m proven wrong." Wrong (Zitron continues to write despite repeatedly being proven wrong)
This was a good one.
Hilarious, but he never actually said he'd stop writing, just that he'd continue writing until proven wrong.
And provably, he did. He wrote until then, and continued after. Since he never stopped writing, he met the challenge.
He's now free to stop writing whenever he wants and still not fail that statement. ;)
Are you a software tester?
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The doctor said I was sick...but I did not die on Tuesday, so checkmate!
You are confusing an imprecise prognosis with a false diagnosis. And The funniest part is that "he keeps writing, therefore he was proven wrong" contains no actual proof that he is wrong.
Imprecise and incorrect are the same thing when it comes to an exact science. His predictions include dates and that makes them binary. You can only be right or wrong. Being close is still incorrect, and he's not even close.
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It’s more like if Zitron was a doctor his prognosis would be “you’re going to die on Tuesday” and you come back on Wednesday (after not dying on Tuesday) and he defends it by saying “everybody dies”.
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I've come to see Ed Zitron's dramatic predictions as reminiscent of a trend I noticed on YouTube a while back;
Whenever you'd look up anything pertaining to China's future, you'd inevitably find your screen plastered wall to wall with thumbnails of a photoshopped Xi Jinping, tears streaming down his face, next to large impact font text reading "CHINA WILL COLLAPSE IN X DAYS", with X varying from 1 to 30. Much like Ed Zitron's predictions, these events obviously never occur.
Exactly, he is monetizing the dramaslop. A sack of rice falls over and suddenly the world is on the brink of collapse. A fun exercise to see how lunatic this pattern of fear is, you can look for an echo chamber you know nothing about, maybe games or music or DIY or kitchens or whatever on youtube and suddenly you will see the same patterns play out. It is quite sad to see people do this for a living, but it is what it is. The kitchens are funny to me, because apparently there is a grand conspiracy of kitchen companies forming a monopoly funded by whoever and basically thats why you will never be able to cook like you used to be able to back in the day and basically its over. Just typing this out made me smile.
IMO Ed is entertaining and I don't believe much in LLMs, but I personally don't really care what happens next with it or what happens at all, the world will continue to spin.
Or Trump/Fox saying the Iran special military operation will be over in X days? Or Putin/Russia saying the Ukraine special military operation will be over in X days?
Let's not pretend they didn't get it from somewhere.
If we sample from Zitron’s claims, a lot of them are wrong. Probably most of them.
I think his contribution to the discourse is the bigger picture. These companies are going into potentially economy-wrecking debt over a speculative future that looks much foggier than the last two previous technological booms.
I find it ironic, that one of the most memorable articles Dan Luu has written imo is about how futurists faked 'exponential scaling' about semiconductors, and we ran out of the 'good kind' of scaling, Dennard scaling a while ago, and people interested in maintaining the narrative have been making up marketing numbers, which are believable to outsiders, but not to those who know:
https://danluu.com/futurist-predictions/
Yet, in this case, he takes the marketing numbers at face value, not being an expert in AI financing, while those who know more, can spot the sleight of hand, just like he can when it comes to semiconductors.
I can agree that the Gemini usage number is a marketing number NOT to be taken at face value. And so does Dan Luu, in the "Some reactions to Zitron" section of the article:
> BTW, a funny thing about Gemini hitting 500M users being "so unrealistic that someone at Google should have been fired, and that someone is Sundar Pichai" is that Zitron has also (incorrectly) said that Google doesn't know how to grow, and that as a result they're shoving AI everywhere. Dennis Snell pointed out that, if Zitron takes his own statement seriously, Google can make Gemini's user numbers go to any number it wants by doing the exact thing Zitron said they would do, sticking AI everywhere.
> You can't actually take Zitron's statement about Google's lack of growth leading to AI desperation seriously and also take it seriously when he says that Sundar is committing some kind of gross malpractice by naming a number like 500M users.
Other than the Gemini usage numbers, which are the marketing numbers being taken at face value?
Who are the people who know more about AI financing and can point out the sleight of hand? I'd be interested in reading them.
I don’t really care of anyone’s predictions, whether that of Altman or Zitron. I can make a prediction that sun will collapse into itself and stop shining. I would probably be right as long as I don’t give any too strict timeframe to ky prediction. In terms of Zitron, his style is that of a British tabloid columnist. It’s rather refreshing when most tech journalism is just selling gadgets, stock or whatever without questioning at all what either Musk, Altman, Amodei or whoever says. It aggravates many people because, well, it is suppose to.
Regardless of whether you love LLM’s as technology, the financial realities of Anthropic and especially OpenAI really does not look good. They have a massive expenditure that they need to keep going in order to make profits, which at least in terms of OpenAI are horrendously behind. Zitron published these numbers together with Financial Times, so you gotta give him that at least. Meanwhile the CEO’s talk all kind of nonsense and give their own predictions to get more investors money to fund what might or might not be the biggest bubble in the history of finance. I certainly do not hope this happens, since the consequences would be horrific. But there is likely to be a some sort of correction in horizon, since the models will plateau and they will run out of money at some point.
Let me finish with my own prediction. I think a lot of people are going to lose a lot of money some time next couple of years.
I’m with Zitron on the frustration and even the analysis of the economic feasibility of AI.
What I’ve stopped doing is reading him regularly. It feels hard to parse the factual from the obviously exaggerated.
I get he’s frustrated. We all are. But I’m not sure letting it out that much helps making the very urgent case he’s making.
I've been doubting him myself (his recent articles just hedge on data centers more than anything else) but then I want to ask, are there any valid critics of AI? Not a "code is bad but it'll get better" but actual criticism in the nature of the financing, politics, etc. I think separating AI as a tool and it's capabilities and AI as product that needs to make a profit is helpful, however most people are really hedging in one camp or the other in their takes.
Cal Newport has been interesting on the topic. But it’s also possible to read Zitron and skip his personal opinions and just follow the discussion of financing, that’s what I personally do. I don’t understand why anyone would take the commentary of an internet pundit as a set of predictions to evaluate as gospel
I enjoy Newport myself, he's probably the most level-headed take I've come across. Everyone else has some agenda (or product) they're trying to get at and very much ruins the messaging (ex the agent 'civilization' piece is extremely overblown especially since..that's how multi agent systems coordinate already. Nothing happened that is unprecedented and isn't how the system is designed to work.
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>I think separating AI as a tool and it's capabilities and AI as product that needs to make a profit is helpful, however most people are really hedging in one camp or the other in their takes.
There are a myriad of people out there who have brought up genuine issues that AI presents, or preexisting issues that AI is exacerbating.
This series of articles is perhaps the best long form critique of AI as both a technology and an industry I've encountered [1].
[1] https://aphyr.com/posts/420-the-future-of-everything-is-lies...
Check out the writing of Baldur Bjarnason. There are a lot of blog posts and a book outlining the risks and tradeoffs.
https://www.baldurbjarnason.com/2023/ai-position/ https://illusion.baldurbjarnason.com/
> however most people are really hedging in one camp or the other in their takes.
As with most things, most people are somewhere in the middle, as the silent majority. You only see the comments of the strongly aligned, which are also the most emotionally motivated to comment.
The internet is not real life.
To shamelessly shill a passion project: A friend of mine and I try to be skeptical-but-reasonable on our podcast https://kairos.fm/muckraikers/ we aggregate papers and reporting and try to contextualize it with our own (hopefully useful) perspectives and takes
The data center growth questions he raised have been where I found him interesting. I could never find any other articles to corroborate his predictions though. My question is when will the AI bubble burst?
I didn't know he'd had a long history of mispredicting AI quality and growth of companies like Microsoft and Google.
I ended up on this comment after looking through the linked article and realizing the words "data center" never come up in it.
> I didn't know he'd had a long history of mispredicting AI quality and growth of companies like Microsoft and Google.
After watching this space for a long time, I think the growth angle is... untested.
All the FAANGs were slowing down after Covid. They boomed as the money printer went brrrr, then they plateaued. They've also kind of covered a lot of their potential their main total addressable markets, with the big exception of clouds, which probably have at least 5 or 10 years of growth as workloads are still being moved away from on-premise. Maybe ads, too, since TV is still around and big and there are probably a bunch of other holdouts I'm forgetting.
Then, FAANGs started reaccelerating around 2024.
Part of it was due to internal reforms, basically, layoffs shaking some things up.
But I suspect the bigger part has been AI, driven by CapEx and all sorts of other things. The problem with the AI growth is that it's highly opaque. We don't really know who the actual clients are. It is <<extremely>> likely that for Oracle (OCI), Microsoft (Azure), Google (GCP), Amazon (AWS) their direct customers are just OpenAI and Anthropic. That's it. It's likely that 70% of the AI growth is just 2 companies. That can't be healthy, it's also likely extremely risky.
Just the fact that the new cloud growth is so opaque is worrisome.
Valid criticisms of AI
- safety critics who think AI can take over the world like Yud (I find this the least credible but still valid)
- Bernie type of critics who think AI can cause widespread job losses
- Ruxandra Teslo who thinks AI can remove meaning which I feel is the most serious one [1]
What are not valid
- environmental like emissions and water usage
- AI is useless and it will take the economy with it because it is a bubble
- AI spreads misinformation and causes societal damage
- AI is trained on copyright (are we really on this side of the debate ?!)
[1] https://substack.com/@ruxandrabio/p-213699661
> - environmental like emissions and water usage
Water usage, sure. But emissions has plenty of reasonable concern.
There are the various xAI data centers have/are running using mobile gas turbines.
In general, the extreme amount of power is going to put pressure on the grids. I hope this leads to the world doubling down on renewables to offset it all, but is that going to happen? Hell, the US actively paid [0] to stop a turbine project.
[0]: https://www.bbc.com/news/articles/c1e1vg0gjl5o
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What is invalid about the environmental angle? (aside from the dubious water claims)
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Another one: data centers making electricity more expensive while not contributing much to the local economy.
I think the three valid criticisms you listed are valid, but there are other quite a few other IMO-valid criticisms of AI. Here are a few as I see them:
- AI centralizes power in the hands of capital, rendering those who can afford compute hardware vastly more capable than those who cannot and thus increasing social stratification
- AI is generally trained on the creative output of humanity without those who train it giving back proportionally (copyright "rules for thee, not for me")
- AI breaks social processes built around the idea that TRYING something is inherently a cost in time or effort, such as filing a legal claim or sending someone a threatening letter. We haven't made the social changes to punish or charge people for using every appeal/option/application, so this makes asymmetric-effort tasks like applying for a job really bad in the interim
- AI use makes it harder to develop the ability to critically think for yourself, especially among those who most need to develop that ability
- AI produces large amounts of mid-tier content, making it harder to discover exceptional human-created creative content produced after AI started to exist
- AI leads to distrust in remote communication, increasing cynicism and breaking social bonds generally. This is NOT your point about "AI spreads misinformation" - no matter whether it's true or not that AI can be used to produce misinformation, having people doubt each other is a harm
- AI demand crunches hardware and time availability for other adjacent markets, such as computer gaming, construction, 3D graphics production, etc. This harms both hobbies and professions in those fields having to cope with rising prices and lower availability of materials
- Everyone is using the same or similar AI, leading to a homogenization of culture and process across humanity. This is perhaps a mixed blessing, because humans are capricious, but less variety can be viewed as a harm
I will also say I personally hate seeing "job loss" said to mean "wealth loss" or "people starving". The goal of life isn't to have a job, it's to be well and happy. If you can be well and happy without a job, great, so it's really painful to me how people don't even see those things are not the same.
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> - environmental like emissions and water usage
Those are two very different things, and the former should be a serious concern. If AI does indeed become a double-digit percentage of electricity usage as the AI labs themselves predict, then it becomes a significant contributor to emissions, full stop.
Basically, all criticism of issues that affect real people is invalid per you.
> environmental like emissions and water usage
There is real crisis with warming this year, yes the plan to consume staggering anounts of energy and make environment worst in the process is valid criticism.
> AI is useless and it will take the economy with it because it is a bubble
If it turns out to be true, a lot of innocent people get hurt. Valid.
> AI spreads misinformation and causes societal damage
As valid as criticism of facebook was valid the whole time. And yes, facebook made world into worst place.
> AI is trained on copyright (are we really on this side of the debate ?!)
100% valid.
> safety critics who think AI can take over the world
Not valid, that is bullshit. If you think the word is wrong suggest polite word that says the same.
This sounds completely reasonable:
> - AI spreads misinformation and causes societal damage
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I used to be a fan of Zitron after listening to an old podcast of his, and also subscribing to his newsletter. Something weird happened after Taylor Lorenz guested on his show; a week later I received a welcome email to Taylor's own newsletter. This was especially strange because I definitely hadn't signed up to hers, and use individual masked emails in Fastmail per different service so I was sure that this was the email address originally used for Ed's newsletter. I tried to contact Ed to no avail, and since that day I never could shake the feeling that he just gave Lorenz his subscriber list. I can't prove it for certain, but there was definitely something "off".
Ed Zitron is a grifter. Period. I highly doubt he believes any of the stuff he writes himself. He plays a role in society which is the role that tells people AI is going to fail so those who don't participate in or benefit from AI feel better. They sign up to his subscriptions and pay him money to confirm their own bias.
If you look at the sub reddit r/betteroffline where these people gather and worship Zitron, most people there are economically motivated. Most of them want AI to collapse so they can invest in stocks when it's cheap or they hope AI doesn't take their jobs.
Doesn't it say something about our society that (a) it's known (at least among smart people, however 'smart' is defined) that AI is going to take away jobs, and (b) the way its treated is that people gather to online discussion forums to worship someone saying the thing that will replace their jobs isn't going to do it (but in actuality, eventually will)? In other words, who's actually doing anything to help the people out who will lose their jobs??
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Side-bar: I once hired Ed for PR for my startup. He was very difficult to work with, not even willing to share how he pitched our startup to publications because he considered the pitch his IP. So it was hard to know what was resonating or not.
I share this less to take a shot at Ed but more so that you all know to ask this if you ever hire a PR person.
I don't think Zitron is wrong, exactly. He's just early. There are a lot of analysts who have faced the same criticism over the past 10-15 years. IMO the issue is that they fail to understand the staggering size and scope of the government fiscal + monetary interventions across those years. Essentially, the government has jammed all the risk into the future to repeatedly rescue near-term results.
I think something similar is happening with e.g. Amodei's predictions of mass unemployment due to AI. It won't happen in the immediate term, because deficit spending removes the economic incentive for firms to pare down their workforces.
All that said, I don't think these people are "wrong," per se. They're just early. When the sh*t hits the fan on all this, it's going to be a big problem. And, for example, companies whose primary business is collecting money for Internet ads will come face to face with the reality of how low value their products are. I have some insight into this, as I work for such a firm, and I know the true extent of the bot traffic out there.
No, hes been extremely wrong multiple times, he should not be treated seriously
Its silly to say hes just early, when his predictions give specific timelines that don't work at all
- "so egregious that I am surprised it's not some kind of financial crime to say it out loud" — on OpenAI forecasting $11.6B for 2025 (https://www.wheresyoured.at/exclusive-openai-financials/) actual: $13.07B. source is his own scoop (https://www.wheresyoured.at/exclusive-openai-financials/)
- "artificial intelligence has three quarters to prove itself before the apocalypse comes" — Mar 2024
- "If OpenAI doesn’t either reduce their $8.5bn operating costs to $1bn or less and raise at least $5bn in the next year, they will die." — Jul 2024 2025 costs: $34B
- Generative AI “isn’t getting much more efficient” (Jul 2024 ). OpenAI’s frontier-model API price fell from $30/$60 per million input/output tokens for GPT-4 to $4/$20 for GPT-5.6 Sol, alongside major capability gains.
- “I would be shocked if [Musk’s] wealth doesn’t return to something more like he had in 2019 or 2020” (Dec 2022 ). Musk is now worth approximately $873 billion , several times his wealth when Zitron wrote this.
sauce https://x.com/pitdesi/status/2093783287097602052
The actual quote and topic is far different than you’ve portrayed it. Here’s the actual quote: “ yet the company says that it expects to make $11.6 billion in 2025 and *$100 billion by 2029*, a statement so egregious that I am surprised it's not some kind of financial crime to say it out loud.” from https://www.wheresyoured.at/oai-business/
He goes on to specifically discuss how unrealistic $100B by 2029 is given that OAI is structurally unprofitable.
The fact that you’re skewing your misinterpretation of what he said so much shows your own bias I’m afraid.
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I think it's fair to call his specific predictions "early." I'm saying his timelines are too short. Like almost all bearish market commentators, he doesn't understand the scope of the government stimulus. This is a systemic problem in market commentary. But, don't kid yourself. If the US stops massive deficit spending, a lot of what Zitron's saying moves forward on the timeline.
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> OpenAI’s frontier-model API price fell from $30/$60 per million input/output tokens for GPT-4 to $4/$20 for GPT-5.6 Sol, alongside major capability gains.
on this specific point: tokens aren't fungible between models right? Like the argument Zitron makes is that improvements in output are due to, glibly, using more tokens to get there. We see people turn on new models and instantly use up all their tokens.
Like the actual measure is more something like "for this specific task, did it cost less now to do it than it did 3 years ago with these AI pipelines" right? The token pricing isn't actually relevant in that discussion.
On that basis Musk and Altman should be ignored almost completely. Just because timelines are wrong doesn't make them not worth listening to. https://en.wikipedia.org/wiki/List_of_predictions_for_autono...
If you're saying that OpenAI forecast something and you're saying it's validation that the numbers match the forecast, I don't think you're really paying attention to the problems (with annualised run rate bullshit when these are proper companies who could be publishing proper numbers), with unclear financials that are designed to look good, etc.
> I don't think Zitron is wrong, exactly. He's just early.
By that metric, so was Nostradamus. The apocalypse is coming for sure, we're just quibbling about the timeline.
Realistically, timing is everything. You don't need to get it precisely right, but you also don't get a pass if you, for example, keep predicting an imminent recession through a decade of unprecedented growth.
"Being right at the wrong time is indistinguishable from being wrong” - Howard Marks.
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"I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be."
This was said in February 2024. This is months before GPT-4o released. GPT 4.5 was released a year later.
I don't know anyone holding on to models of GPT 4.5 caliber, yet know 4o. ChatGPT 4o and 4.5 score 8 and 14 points on Artificial Analysis benchmarks. [1]
For perspective, Qwen 3.6 27B, which can be ran on single GPU setups, scores 3-5x that on modern benchmarks.
I don't know why anyone would even make a claim like that in the first place. It's like saying "Computers are never going to get faster". I feel like we could run out of sand and still have faster machines over time. Just a silly thing to say.
[1] https://artificialanalysis.ai/?models=muse-spark-1-2%2Cgemin...
After looking at Nvidia's orderbook, it's a reasonable conclusion that nothing bad will happen for at least another year. In which case Ed Zitron is off by at least a year.
But there are enough signs of trouble that could happen soon: Oracle debt is junk. OpenAI might not be on a viable trajectory to IPO. One or both of those could collapse the lower quality data center companies. Zitron is probably overconfident about a crash in the short term. Probably.
> After looking at Nvidia's orderbook, it's a reasonable conclusion that nothing bad will happen for at least another year. In which case Ed Zitron is off by at least a year.
Zitron is fairly clear that nothing is going to happen for a year or so — he says himself that he thinks there's another round of funding possible for both OpenAI and Anthropic.
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Oracle fits in Nvidia's pocket at this point: $67b sales, $22b op income.
Vs of course Nvidia: $302b sales, $197b op income.
Oracle was never as big of a deal as that brief market cap run implied. The herd pushed it up for no reason.
The comparison to Apple, Microsoft, Google and Amazon are pretty similar. Oracle fits in their pockets too. Who cares if their debt is junk. Ellison has nearly wrecked that ship on numerous occasions over the decades. He went on an elaborate acquisition binge in the previous epoch, buying his way to the next stage (preventing Oracle from being market-eliminated, or acquired), and that was an incredible mess that took a long time to sort. He's doing the same move now, trying to spend to stay on the board as the world rapidly changes under his feet.
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You can make that claim for the general case that genAI capabilities will plateau or fishy financing in the sector will cause trouble at some point, but not for the specific talking heads that haven't constrained themselves to that sort of "broad future trajectory" prediction.
FTA:
> Note that I didn't attempt to catalogue statements that are nonsensical or were simply factually incorrect statements at the time, such as his December 2024 claim that “Generative AI's products have effectively been trapped in amber for over a year.” January 2026 claim that "[models are] basically the same as they were a year ago. They have the same efficacy". Zitron has not only made forward-looking statements that AI capabilities will not improve, he's also consistently made backwards-looking statements that capabilities have not improved which, while obviously false at the time, seem to play well to his base (along with his other false statements). If you connect all his statements together, it's implied that AI had the same capabilities in January 2026 as they did in December 2023 (and if you connect later statements, it's actually implied that capabilities in August 2026 are the same as in December 2023, though to be fair to Zitron he frequently contradicts himself and has also admitted to limited improvement at times).
Yes. I was specifically referring to Zitron's circular-financing complaints. I can't defend his separate claims that AI just isn't that useful. If he were involved in the tech industry, he'd know how preposterous those claims are.
I don’t think you read the article, which documents many cases of him being wrong
For example saying in 2024 that LLMs had peaked. That’s not early, that is already, definitively wrong.
> He's just early
The whole point of making predictions is timing. I can tell you the US dollar will continue to devalue (a 100% accurate prediction). But it's a worthless statement unless I can tell you when and how much.
What predictions would you say he is not wrong, but just early about?
If his issue is that he is accurate on something shady going on with the finances of AI companies but the effects are mitigated due to government intervention, he should say.
Your explanation gives him more credit than I think he deserves. He has been unambiguously incorrect many times over the past years in the time scales over which he makes predictions.
In general, claiming that a prediction is just early in an unfalsifiable claim. It allows no admission of being incorrect whether you are correct or incorrect in a given moment. If you are right, then good, you've made a correct claim. If you're wrong, just claim there are clandestine corrective forces keeping the disaster you are predicting at bay. Either way you make it out clean and still have some sense of legitimacy.
Zitron is wrong, not "early", and the post has an extremely long list of examples.
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I recall a time when Google was struggling to monetize ads and then something changed and they started becoming profitable. The narrative was about improved targeting.
I think it might have helped a little but I never fully bought this argument. I don't think I've ever bought a product which was advertised to me online and I was using some of these platforms for years. I'm probably a liability to them; using up compute but not clicking on ads or buying anything.
Also when I ran social media ads many years back, I never got any users out of it. It literally seemed like mostly bot traffic back then; I can't imagine how bad the situation would be now with LLMs.
Not sure about being early when it comes to points about models reaching the peak of their capability. Honestly, I don't understand how he's qualified (in terms of knowledge) to make such claims.
Being early is being wrong.
Being wrong about when a financial bubble will burst is different than being wrong that there is a financial bubble. That's the question, because if AI is driving a bubble, then it will burst at some point.
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"Early" works for an open-ended bubble thesis. It does not rescue dated claims that already failed.
Google's 500 million Gemini-user goal had an end-of-2025 deadline. Zitron called it so unrealistic that Sundar Pichai should be fired. Google reported more than 650 million monthly users by October.
"AI had already peaked" is also a claim about the state of the technology at that time. Agent and coding benchmarks moved sharply after it. The fact that every technology eventually peaks does not make a past claim that it already peaked correct.
The bubble may still burst. That would validate the bubble thesis. It would not retroactively fix the prediction record.
btw Zitron has the only fan base that has come after me with doxxing and death threats so far. Truly misery loves company!
Some numbers have a lot of squish to them. Gemini can't be called a failure. But Copilot is a flop and yet Microsoft can probably show you similar numbers to what Gemini has.
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> The bubble may still burst. That would validate the bubble thesis. It would not retroactively fix the prediction record.
If it does not burst it will be because specific efforts have been taken to deflate it in the face of concerns like those he is raising.
The bubble may not burst for example if the securitization of AI debt really happens. Then when it happens it won't be an "AI bubble" that bursts, it will be a full-on collapse of the US economy. Like when 2008 happened it wasn't really about mortgages anymore.
Macrofinance expert Nathan Tankus has an excellent post on why the financing situation around the AI boom is just not big enough to mess up the financial system.
https://www.crisesnotes.com/sigh-no-ed-zitron-ai-bond-issuan...
I agree that the specific amounts invested in AI aren't enough to cause a calamity directly.
The problem is you have the vise of a stock-market decline on one side (something basically everyone thinks is impossible), and AI-induced unemployment on the other side (something a lot of commentators, including Zitron sometimes, seem to think won't happen). Those two things in tandem would be worse than 2009 by a multiple. I doubt the US government will be able to bail it out.
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"Being too early is indistinguishable from being wrong"
Tim O' Reilly
in fairness, if you make the same prediction over and over again, you will probably be right eventually by sheer random chance.
Did you read the post? The author cited very clearly a plethora of very specific predictions made by Zitron that were verifiably and factually wrong. False. Incorrect. Not “early”.
What’s particularly bad about Zitron is his financial analysis. Some people have the idea in their heads that “yeah, Zitron is wrong about a lot of things, but his economic data and analysis is tight.” That couldn’t be further from the truth.
The entire AI industry, especially the vested interests, are often full of shit, sure, but the sheer amount of misunderstanding that has surrounded the economy and its relation to AI has been mind boggling. There is much bologna being accepted as reasonable or even standard by HN comment sections.
This kind of back and forth reminds me of the Dot Com bubble circa 1999. There were endless articles saying that the internet was a new golden era for mankind and that the hyperbolic company valuations were justified; the detractors cried bullocks.
I use AI every day as I assume everyone else on Hacker News does. Will the S&P 500 drop 20%? I have no idea. If you know, please let me know so I can adjust by 401K.
> assume everyone else on Hacker News does
Eh, not to be rude/crass... but that would be inaccurate, if not hopeful. Myself and others don't, despite mandates; in fact, I aim to see this 'left behind' promise we heard years ago. Short of writing the at-will employment paperwork for HR myself, I'm not seeing it.
Escalations continue to fill my days. Turns out, people are somewhat correct: results matter. I'd say moreso than the tools we 'choose' (or skip, in this case). My null on the token scoreboard remains unnoticed/inconsequential, the work I've done has not.
All to raise a bit of timeless advice from Wu-Tang: diversify.
I’m not really convinced by any of this, in fact it seems most of the predictions were pretty correct and that Meta and Alphabet are having big issues is a shared opinion by multiple observers and pretty much every employee they have.
If they were doing so well why the layoffs? Why the tightening both in salaries and perks and work life balance? Why so many choices disrupting morale for their employees?
Help me understand where Meta and Alphabet's issues are. They seem to be doing quite well on paper -- nothing anomalous in terms of profitability or scaling recently. Layoffs are to be expected when AI can increase productivity.
Meta narrowly avoided disaster earlier this year: https://www.reuters.com/investigations/mark-zuckerberg-had-b...
The problem with judging Alphabet and Meta on aggregate performance is that their advertising businesses print so much money that they can invest everything in a boondoggle and coast when it blows up. Zuckerberg burned $100,000,000,000 on the metaverse and it didn’t make a dent.
That said, Alphabet and Meta are borrowing against their future advertising profits to fund their AI boondoggles. If the advertising businesses keep growing then they’re somewhat insulated from their own missteps but the reliability of the advertising business depends on companies having money to spend on advertising.
The metaverse was mostly self-contained and the failure had zero consequence for the broader economy. AI on the other hand, every major fund is investing everything into AI companies. Every advertiser is using subsidized AI tools to generate hyper specific adverts. If this all goes south, who knows how advertising spend will be impacted.
Google specifically are backstopping billions in data centre build out costs. They’ve committed to spending, like, all of their cash to data centres. If the market gets nervous and funding disappears, Google are deep in the hole.
Anthropic “invested” $50bn in data centers to be built by Fluidstack who raised a billion dollars from Situational Awareness who used their Anthropic ownership to raise money to fund these investments. Google are backstopping much of the Fluidstack build out, i.e: if Fluidstack goes out of business then Google is on the hook for $10bn+ in costs. And Google’s Anthropic investment makes up like $100bn on the balance sheet. So, Anthropic fails to live up to expectations and fails to pay Fluidstack who can’t pay their suppliers which puts Google on the hook to hand over tens of billions in cash while at the same time their biggest investment is going down the pan.
The top line numbers don’t really do justice to the scale of the risk. You need to look at the long term commitments they’re making.
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Alphabet hasn’t had any layoffs, tightening of salaries and perks, or impacts to work life balance. Morale is quite good, imo as an employee
Yeah, the three tables just looking at revenue and profit YoY is pretty laughable financial analysis.
Ah well, people love hearing what fits their world view. So if you hate LLM assisted development or vibe coding, Ed is predicting the future you want ("Don't be afraid, it's going away soon!"). As with any sudden change, there are people that tap the brake instinctively. And people like Ed thus get popular.
That, and he has a nice way of speaking like everyone's gone mad but you and him ;) Have to admit I have enjoyed his rants, and there are certainly true things about them, here's another nice one for you lovers and haters alike! [0]
[0] https://www.linkedin.com/embed/feed/update/urn:li:ugcPost:74...
I like this version:
AI Economics for Dummies: https://www.mcsweeneys.net/articles/ai-economics-for-dummies
Examining Dan Luu's predictions would be harder since he doesn't even date his articles.
Was this hit piece prompted by Zitron being mentioned in the tech scene lately?
https://lwn.net/Articles/1091245/
I don't know. If you post walls of text and ramble on like Luu, perhaps you are sitting in a glass house?
Luu kinda has a reputation for knowing his shit, for over a decade now. He's not a checks notes games journalist larping as economist, but he does all right.
He may be a good programmer, but I have never seen any interesting article from him. Also, making predictions is different from being knowledgeable.
It is ironic that Zitron is accused of having a cult following whereas Luu clearly has one, here at least.
the dates of his articles (well, month and year) are all here: https://danluu.com/
I don't like Ed, but it seems that Luu spends far more attention documenting failed predictions than checking other predictions that might have been correct.
Telling me that quite a few of Zitron's predictions turned out to be accurate, while many others were completely off base, means he's throwing darts on a dartboard. Why should I listen to him?
I'm sure I can make a lot of accurate predictions. It doesn't mean I'm worth listening to, if people can't distinguish the accurate from the inaccurate in the moment.
Reminds me of a tactic I've seen often amongst both critics and shills on fads. An OpenClaw fanatic on Youtube comes to mind. He makes opposing claims in different videos. One of them has to turn out to be true, and he trumpets his successes ("Look, I predicted this!"). Only a few notice he also predicted the opposite.
Just look at the other thread about Zitron and his Enron comparisons.
Everyone is always throwing darts at a dartboard when making predictions. I don’t know why you think this is a critique that applies exclusively to Zitron.
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What predictions have Ed made that were correct? And how do they weigh against the incorrect ones, in terms of both quantity and quality?
That's relevant because if you make a thousand predictions, a few of them might turn out to be true. That doesn't mean you're good at making accurate predictions.
Zitron reminds me of a lot of people who got oversized reputations after the 2008 financial crash. They predicted it, or could spin past statements as predicting it, and rode off that.
But from that point on they basically assumed this role of what financial punditry call perma-bears, people who constantly predict financial doom. It gets clicks and sells subscriptions, which is probably why they do it. But from that point on their predictions weren't very good. If you constantly predict doom then every now and then you'll look like you were a genius, but it's just survivorship bias. People discount all the other times you were wrong.
I don't know a whole lot about Zitron himself. Didn't he get big calling BS on cryptocurrency stuff that actually was BS?
The problem is that AI isn't cryptocurrency. This is very real.
I do suspect there's some bubbly stuff around it. I think data center construction looks very bubbly, especially the totally ludicrous amount of permitted planned data center construction. I bet no more than 20% of that ever comes online. I'm sure there's some AI companies that won't make it, and a bunch that are overvalued. But AI as a whole is real, not just hot air.
I think lot of people including Ed are mixing 2 things the AI tech itself and the economic viability with the inflated values of companies building it currently. Me personally I am optimistic about the tech itself but don't see the current AI companies valuations being realistic or even the economic systems they are building around AI/LLM. As it will all be comoditised down to cost of compute + cost of electricity in the end.
I was skeptical of LLMs making it to where we have them today, but the test results are evident, empirical, and hold up to hard scrutiny. I remain quite skeptical of a singularity event, GAI, and anything beyond what we've seen LLMs output today. It will get faster, cheaper, but not much smarter without another breakthrough. They will not be able to save the planet from their own pollution and overconsumption.
> but the test results are evident, empirical, and hold up to hard scrutiny
Sorry which test results? The gamed benchmarks?
https://x.com/victormustar/status/2094901703468306626
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Danluu claims Ray Kurzweil's predictions are wrong. I asked
> list 50 predictions by Ray Kurzweil that he made before 2005, the date he made the prediction, and whether to not his prediction was correct. If it was correct, give some proof.
Of the 50 that were listed, 43 were correct, 7 incorrect.
You "asked"? Who (or more likely, what) did you ask?
Dan Luu's analysis of Kurzweil is in this article: https://danluu.com/futurist-predictions/
In the "Appendix: detailed information on predictions" he lists the predictions, and their truth or lack thereof. He was using a list on Wikipedia: https://web.archive.org/web/20170225013846/https://en.wikipe...
For example Kurzweil apparently predicted in 2019 that:
> Blind people wear special glasses that interpret the real world for them through speech. Sighted people also use these glasses to amplify their own abilities. Retinal and neural implants also exist, but are in limited use because they are less useful.
> No
Note that if Kurzweil makes a prediction that an event will occur before year X, and it happens in year X + 1, that still counts as a wrong prediction.
Can you get your un-named source to provide a detailed list of these predictions as Dan Luu did?
Maybe we have different interpretations of whether or not a prediction is correct or not.
If someone predicts in 1999 predicts self driving cars by 2020 and it happens in 2030. That seems different than predicting something we see no indication of ever happening. Like if they predicted 2020 and it happened in 2021 is that a fail. To me, no. The prediction is that the tech will exist at or around that date, not that the date is exact. Claiming it's a fail for being 1 second late is not a rational position and not at all in the spirit the predictions were made.
There's also issues like being directionally correct. Example: Someone says computers will be in everything. You can say "there's no computer's in fruit!, FAIL!" or you can look at the explosion of IoT devices and decide it was mostly right?
I get 17% correct if you're absolutely strict, 64% correct if you're charitable
2009
* Most books will be read on screens rather than paper.
The charitable interpretation is that most reading happens on screens, not paper. This is true today.
* Most text will be created using speech recognition technology.
False
* Intelligent roads and driverless cars will be in use, mostly on highways.
False in 2009, False in 2026 but directionally true. If you live in an area with Waymo you see them all time. I've driven down Olympic Blvd in Los Angeles and had my car surrounded by 5 Waymo cars at once. So is this false because it didn't happen by 2009 or is at least directionally true because it's happening, we see evidence of it happening, vs if we saw zero evidence then we could 100% say it's false.
* People use personal computers the size of rings, pins, credit cards and books.
rings, pins and credit cards, no, books, true. Smartphones are smaller than books. Maybe you could make the argument those are not personal computers. I think that is debatable. Even then, you can by PIs or Mini-PCs that are book size.
* Personal worn computers provide monitoring of body functions, automated identity and directions for navigation.
Arguably true. phones provide directions for navigation and are worn in pockets. Fitbits came out only a few years later. Id is not automated though.
* Cables are disappearing. Computer peripherals use wireless communication.
Arguably true. most laptops, all phones, most mice, keyboards, joypads, etc. are all wireless.
* People can talk to their computer to give commands.
False/True. Was possible was not common. That said, Siri shipped in 2011 so 2 years off.
* Computer displays built into eyeglasses for augmented reality are used.
False if you mean mainstream.
* Computers can recognize their owner's face from a picture or video.
Face ID shipped in 2017. Is that to far off?
* Three-dimensional chips are commonly used.
I'm not sure what this means.
* Sound producing speakers are being replaced with very small chip-based devices that can place high resolution sound anywhere in three-dimensional space.
False,
* A $1,000 computer can perform a trillion calculations per second.
True, happened in 2008 with the ATI Radeon HD 4850
* There is increasing interest in massively parallel neural nets, genetic algorithms and other forms of "chaotic" or complexity theory computing.
Happened in 2012 so 3 years off
* Research has been initiated on reverse engineering the brain through both destructive and non-invasive scans.
Based on the actual words, this is true and was true before the prediction.
* Autonomous nano-engineered machines have been demonstrated and include their own computational controls.
False
...continued...
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I don't really care what Zitron says or pay attention to him because it seems clear to me he has some kind of agenda.
But this article is not nearly as impartial as it claims to be. It interprets all Zitron's claims in a narrow and overly-literal way. Missing the point and refuting a technicality.
For example, looking at the last 3 year's revenue/profit growth. This tells us nothing without looking at a larger context. Has revenue growth slowed down? Have profit margins compressed? What makes up the income and has that changed? Etc.
I think anyone being intellectually honest understands that these tables alone don't refute Zitron's claims that big tech are "dying and thrashing around" which is something that could take many years and easily hide under these kind of top line / bottom line numbers. (Maybe further analysis would refute the claim, but that analysis is not present here.)
Or Zitron's claims that AI capabilities are "reaching the upper limits" back in 2024. I don't think even Zitron would disagree that AI capabilities have grown since then, but that doesn't refute his point. How long the "reaching" takes and how wide the "upper limits" are is completely up to a subjective interpretation, which this post makes no attempt to even explore.
By all means dunk on futurists. But at least steelman their positions or you look just as biased as them. (Although maybe I am off base and this bias is meant to be clear from the start by admitting to pro-AI predictions all the way back in 2015.)
It seems to me that if they'd just about reached their upper limits in early 2024 agentic coding wouldn't be eating software development like it is now, and wasn't then.
LLMs are broader than just coding. The article states this particular Zitron claim was wrong, even at the time, because you can put an LLM on a loop until it generates code that compiles. Mitigating hallucinations (in the subset of applications with verifiable output) is not really the same as solving that category of problems outright. I could quibble about a number of things in this example alone, but that's not really my point.
I don't care if the claims are correct or incorrect. I just think the tone of the article is dishonest about its own impartiality and fairness, because it doesn't even attempt to interpret the claims in any way except the least favorable. If you want to be persuasive, you should refute a claim using the most favorable interpretation of that claim possible -- this does the opposite.
Yeah, I think this is fair criticism, and I definitely felt the pointedness in the tone as well.
On the overly-literal / narrow thing, I think thats the culture around evaluating predictions overall. Like, all those posts around christmas where people make predictions and evaluate how last year went. The rigor is the norm.
I'm not familiar with the culture, but that sounds reasonable.
I'll quibble with the term rigor. I think the article contains the strictness that word implies, but not the thoroughness.
Zitron pretty much called the rise of AI content farms and the ensuing SEO spam. His takes on practical AI limitations were spot on.
I would be interested in seeing a similar list of predictions from Altman, Amodei, etc with annotations about how many have come true. Ed Zitron is a blow hard and frequently overstates things to the point where it is hard to take seriously, but so are the AI industry leaders.
I have heard multiple breathless press releases warning that the end of white collar work is "just 6 months away" and that people not using the latest Mythos/Fable/Whatever model will be hopelessly left behind.
Dan Luu's piece talks about this! He gives the example of Ray Kurzweil, who is similarly catastrophically wrong about everything, just in the opposite direction as Zitron. Luu's point isn't that anti-AI analysis is bad; it's that Zitron is bad. Zitron is bad in this analysis no matter what Altman says. It could be the case that Altman is also bad.
> He gives the example of Ray Kurzweil, who is similarly catastrophically wrong about everything
Is he, though? It seems to me that a lot of his predictions were surprisingly close to the mark, especially given how long ago they were made.
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Altman and Zitron are like pro wrestlers. Their speech acts aren't for truth, they're for some spectacular effect on your feelings and your imagination that keeps you coming back for more.
I encourage anyone interested in this to read On Bullshit by Harry Frankfurt, the best popular philosophy work in a long time.
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Kurzweil made one big claim — Singularity around 2045. Everyone at the time thought it was a total joke and hundreds of years off, if even possible. Now, that is seen as a laughably long timeline.
The rest is noise, I don't care what minor predictions he was wrong about, he called the big trend back when nobody else could make a trendline.
The fact that you know Ed Zitron's name means that, regardless of his predictions being consistently wrong (which they are), his strategy for manipulating human attention has been correct.
The question is whether he sincerely believes his own predictions or if he cynically is aware that you can create a career for yourself being a guy who says bombastic clickbait-worthy things people emotionally want to be true, instead of measured assessments of reality.
Problem is, you wouldn't know Ed Zitron's name if every piece he wrote basically said "AI might be a bit overhyped short term but will have lasting economic impacts." Booooorrrring.
The reverse is also true. OpenAI and Anthropic aren't going to get much media attention if they don't make silly claims like "all white collar jobs gone in 2 years."
And no, this isn't a new problem due to "the algorithm." Media has always been like this. Zitron is just another Peter Schiff with younger skin. The problem is human nature in general.
Hence why AI isn't going to kill media. We don't actually want sober, rational assessments of all available information from hyper-intelligent LLMs. This is unsatisfying. We want emotional validation, drama, adversarial identity and spectacle. Truth is rarely what we are seeking.
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I'm going to need some evidence that Kurzweil was wrong. AFAICT he was right in 80%+ of his predictions with several more on track.
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> Ed Zitron is a blow hard and frequently overstates things to the point where it is hard to take seriously, but so are the AI industry leaders.
Except that the AI industry leaders actually have skin in the game/are in the trenches and competing in the market. Ed Zitron wants you to subscribe to a newsletter so that you can read doom and gloom and...?
If you invested based on his advice, you'd have missed huge gains and/or lost money.
And I say this as a person who thinks most of the "AI industry leaders" are ethically questionable, at best, and ethically bankrupt, at worst, and that the stock market should be approached with caution due to valuations.
The fact is there is BullSh_t and hype on both sides.
Both Zitron and AI execs have good points, but both are trying to sell you something. The murky truth lies between the two extremes.
IMHO, having Zitron around is a counter to the AI leaders. Is he the best? No. Is he the loudest? Yes.
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> multiple breathless press releases warning that the end of white collar work is "just 6 months away"
I'd like to see an example of this.
I think Altman and Amodei have been quite sober with their actual predictions. They've said things like "models can now do white collar work" but haven't yet said it is the end of white collar work.
The closest actual quote to this was in In March 2025, Anthropic CEO Dario Amodei told a Council on Foreign Relations audience that AI would be writing 90 percent of code in three to six months, and essentially all of it within twelve months.
I think that he underestimated how long it takes for technology to get uptake but in terms of capabilities he was perhaps 6 months out. I'd say that Fable class models are definitely capable of writing essentially all software, and that was released June 2026.
You could try checking the news.
> In a remarkable interview with Y Combinator in November 2024, Sam Altman, CEO of OpenAI, shared a vision that could redefine the technological landscape as we know it. Altman confidently revealed that OpenAI has a clear roadmap for achieving AGI by 2025.
https://www.tomsguide.com/ai/chatgpt/sam-altman-claims-agi-i...
Now Altman is claiming it'll be this year for sure: https://www.msn.com/en-in/news/other/sam-altman-makes-bold-a...
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Note that no one ever quotes the end of Dario's line, either, where he said programmers would still be needed in that 12 months. People think the prediction was more extreme than it really was because all the clips didn't include the latter part.
> .... and in 12 months, we might be in a world where the ai is writing essentially all of the code. But the programmer still needs to specify what are the conditions of what you're doing; What is the overall app you're trying to make; What is the overall design decision; How we collaborate with other code that has been written; How do we have some common sense with whether this is a secure design or an insecure design. So as long as there are these small pieces that a programmer has to do, then I think human productivity will actually be enhanced
I think one important distinction is at least they have some humility to admit they were wrong. I think Ed Zitron has rarely, if ever acknowledged he was wrong.
I actually think you’ve got it backwards
I think one of Zitron’s problems is that his moral righteousness has blinded him to how embarrassingly incorrect he is about the AI space.
It’s similarly hubristic with the benefit of shielding from his adversarial framing.
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> I think one important distinction is at least they have some humility to admit they were wrong.
I'd be amazed to see a source proving that statement. There's never been a retraction around the AGI claims for example as far as I'm aware.
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Humility? I don't think they know what the word means let alone possess the capacity to demonstrate it.
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Man, as a former extreme skeptic, I watched a video from Eric Schmidt in early 2025 where he said that by the end of the year nobody would be coding, and that one was dead frickin on.
As far as I'm aware, aside from toy prototypes, all software is still built by humans coding, albeit with better auto-complete.
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I guess I don't exist...
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Everyone who started using claude clode when it came out in early 2025, knew it was coming (not quite yet). That felt more like reporting than prophesying.
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There are whole industries that still don't let LLM-generated code touch production
You may be living in a bubble. There are tons of developers still coding by hand, and many industries that don't trust machine generated code in general.
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Two problems. One, people mean different things by “coding”. Two, it’s diffused slower than folks predicted.
But the core prediction was not crazy for “coding as typing code”, which at most big tech companies is at roughly 100% automation now.
If by “coding” you mean the full SDLC then we are not automated yet.
I think failing to account for the diffusion delay was an actual prediction error, since that is a property of every technology in history.
On a second reading I'm going to take this as sarcasm.
I mean, I code?
Dario Amodei and Eric Schmidt seem fairly well-calibrated, although a bit early. Elon Musk is constantly way, way overoptimistic (perhaps to the point of willful fraud). Zitron is hopelessly and ridiculously incompetent (and there are allegations he is willfully lying, too, but who knows).
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My bet is that Amodei's claims about the dangers of AI are the most likely to come true – I'm predicticing amodei/anthropic will become the evil it was against.
Those claims are warnings if you're working class, and marketing if you're corporate.
I predict they go bankrupt.
No, you see, they were right all along, they just "didn't anticipate the public outcry against data center expansion", thus the obviously inevitable AI takeover of the economy is being slowed by NIMBY curmudgeons who should be ignored and punished.
The big difference is that Altman et al aren't just, or even mainly, pundits or prognosticators. Zitron's whole thing is commentary and predictions about AI and his predictions are almost all wrong.
Altman and friends are mainly actually making and delivering AI. If they also hype their timelines/valuations, they also seem to make directional progress on the goals.
Yep I distinctly remember at the beginning of 2023 that folks were predicting that we were less than two years away from there being no jobs for software developers. Pretty soon we'll reach double that timeline.
And of course it really has changed everything! But that was not all that a lot of people were prognosticating.
The end of white collar work mean that company can finish all jobs by new model.
Here's an example https://en.wikipedia.org/wiki/List_of_predictions_for_autono...
I'm curious about why this got downvotes for literally answering the question
To be fair, Zitron's career is as a commentator. To some extent, I expect CEOs of companies to make ridiculous claims. At the end of the day, when shareholders come knocking, what they care about is whether or not your company is growing.
No such mechanism for social media personalities. You can be wrong 90% of the time, and your audience will still praise you for the 10% of the time you were right.
> To be fair, Zitron's career is as a commentator. To some extent, I expect CEOs of companies to make ridiculous claims.
This is not a comment directly at you, because I understand where you're coming from, but I think this attitude shows how far leadership and our expectations of the professional class in America have deteriorated.
My expectations are inverse to yours. I don't care if a commentator makes a claim that's wrong, if they keep being wrong people will stop listening. (In theory. Jim Cramer still has a job so who knows, really.) I certainly don't expect a commentator to have accurate internal information about a company.
CEOs on the other hand should be expected to be honest and accurate in their claims. The information a CEO shares should be accurate so that investors can make informed decisions. Instead, we seem to accept CEOs who act as salesmen first and leaders last.
If a CEO claims a product will be out later this year, and the stock goes up, then they announce actually things are delayed, and the stock still goes up, what is actually being rewarded here?
I don't think of Zitron as a journalist, I think of him as an entertainer, no different from all the columnists paid to tell people that what they already think is right.
The fact that you're holding him to a higher standard than fabulously compensated professional c-suite officers whose products are used in matters of life and death is... kinda weird?
CEOs and leaders used to trade on credibility.
The 5090 Nvidia card evidently is selling for five grand now! It’s just a question of when the bubble burst….
But I think anyone who evaluates their claims understands that they’re talking their own books.
Zitron’s problems are more subtle. He benefits financially from his own claims, while also touting his impartiality and capacity for objective thought.
A person savvy enough to understand the indirect financial benefit to Dario promising that Claude is so dangerously smart it must be regulated understands Zitron’s schtick
Zitron has become the distorted reflection of the very AI boosters he criticizes and mocks.
I think the worst thing that happened to him was AI skepticism becoming a political position. This gave him a captive audience - as long as he says what they want to hear, which means that he can never ever concede that he might have been wrong or that AI might actually be progressing or having successes.
This is not conducive to good prediction long-term - rather it leads one to a state of cognitive dissonance where one's chosen enemies must be simultaneously terrifyingly powerful and incompetent dunces. The propagandist's disease.
How? AFAIK, his arguments based around the economics remains the same, he just seems increasingly dramatic and exacerbated, which is understandable when you realize industry + ecosystem (politics/reporting) is tulip mania delulu and insists 1+1=100, or 30 trillion, or whatever. His position isn't based on AI progress - it's based on AI economics, at this point he can be an obnoxious rationalist slamming flat earthers - just because he's annoying/smug doesn't mean he's less right on fundamentals which if anything is more clear now.
When you predict a company is going to fail and instead it sets revenue records you're not "dramatic and exacerbated". You're refuted.
Luu's post includes a (long!) list of specific predictions that aren't "exacerbated"; they're simply wrong.
Luu's point isn't that AI is going to succeed or that the "AI bubble" will never pop. It's that these predictions are all wrong. If you agree "directionally" with Zitron, all that means is that you're skeptical of AI. That's a totally reasonable position to have, but it has nothing to do with whether Zitron's predictions are good or bad.
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> you realize industry + ecosystem (politics/reporting) is tulip mania
It could be "tulip mania" or it could be "the internet".
Luu analyzed the numbers instead of just reacting to hype.
Specifically:
>> Oct 2024: OpenAI's forecast of $3.7B revenue in 2024 and $11.6B in 2025 and $100B in 2029 are absurd, "a statement so egregious that I am surprised it's not some kind of financial crime to say it out loud"
> Wrong (2025 goal exceeded, 2029 TBD but not an egregious financial crime level of implausible)
>> February 2025: Anthropic making $34.5B in revenue 2027 is "is laughable on many levels, chief of which is that OpenAI, which made around twice as much revenue as Anthropic did in 2024, barely made a billion dollars from API calls in the same year."
> Wrong (whether or not they make that in 2027, their 2026 ARR greatly exceeding that makes the 2027 estimate non-laughable)
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(edited to remove snark) Your comment seems to miss the point that the article does not (necessarily) have a problem with Zitron being insufferable/annoying/smug. It's that his predictions are verifiably wrong. It's one thing to be annoying and right. Zitron is annoying, but not right.
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Did you read the article? It paints the picture of someone who does not in fact have a good track record on the 'fundamentals', even if his broad thesis of a bubble may prove correct.
e.g. when he suggested Anthropic may be fudging their revenue numbers / projections - which was actually due to him making some careless mistakes in a spreadsheet
I have the exact same feeling about him.
He has built a following of people that want to hear his extra skeptical views. And even if he changed his mind about some things, he cannot admit it, as that is not what his followers want to hear from him.
It's rare that people make the news and build a following by saying a very balanced, down to earth opinions... unfortunately.
> It's rare that people make the news and build a following by saying a very balanced, down to earth opinions... unfortunately.
Just gets you disliked by both sides, "certainty sells".
Some folks would prefer a confident wrong/simple answer over a "well it depends/is nuanced" answer.
I almost wonder if it's even possible now that so many of us get our information through algorithmic feeds.
I listened to one Zitron interview on YouTube and my home page was immediately crammed full of similarly foamy-mouthed AI critics. (As well as plenty more Zitron.) And it's suggested some people with un-nuanced pro-AI takes, too. But what the algorithm has never, not even once, given me is a sober voice that calls attention to the nuances.
>It's rare that people make the news and build a following by saying a very balanced, down to earth opinions
Humans are built for fighting and killing the rival tribe, not for pondering whether the other tribe might actually be correct. We are exceptional for even being able to overcome ourselves enough that the outcome of "fighting and killing" can be minimized in the large.
> And even if he changed his mind about some things, he cannot admit it, as that is not what his followers want to hear from him.
I don't think that's true - over the years he's changed his mind from "LLMs are useless slop machines" to "LLMs can be useful when applied wisely".
For instance in "The AI Hater's Manifesto" he says:
> It just came to me — the problem that I have with most people using LLMs is the delineation between outsourcing work and outsourcing thought. Those using LLMs to write little scripts or BQL code on a Bloomberg Terminal are inoffensive. [...] A tool being used as a tool to do tool things — in many cases involving the LLM writing a little 30-line Python script! — is not a problem, though it’s also not a trillion-dollar industry that needed to steal everybody’s art and writing.
Similarly, in "The More You Buy, The More You Lose":
> Sidenote: The only truly useful use case I’ve found is on the Bloomberg Terminal’s ASKB feature, which takes natural language and turns it into BQL code to make requests of Bloomberg’s datasets. It’s genuinely useful!
You can perhaps say that he underestimates what the technology is capable of (or, conversely, that other people overestimate LLMs) - but that's a different kind of conversation.
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right, and look at current American politics. One side tried to tailor normal, routine and rational POVs.
The other...
EDIT: I do find it amusing when I write these types of comment, then suddenly realize the irrational man childs of the far right nationalist republic ethnostatists think they're the rational ones.
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> I think the worst thing that happened to him was AI skepticism becoming a political position.
It is a political issue in the sense that the discussion around AI can't avoid material and cultural issues. Ed Zitron definitely has an ax to grind and I'm personally turned off by his hyperbole, but I don't think something being a political issue means it's not worth discussing. I've also noticed the pro-AI crowd pivot to accusing people of not having actual reasons for hating AI, being victims of Chinese propaganda, etc as a way to avoid actually talking about specific issues that people have about AI and DCs.
> It is a political issue in the sense that the discussion around AI can't avoid material and cultural issues.
I'm not even sure it's political. I think it might be a religion now. He doesn't think that AI is bad, he believes that AI is bad.
It’s too late to worry about political repercussions everything‘s political at this point in time…
I never said it's not worth discussing, I'm just saying that it becoming a charged issue was bad for Zitron specifically because his style of content fell in demand with the most irrational parts of the crowd.
You're not wrong but for a lot of AI boosters it's also a political position - remember Anthropic and half of SV at this point is run by wide eyed effective altruists.
Ultimately one cannot separate technology or science from politics, it's inherently political
Audience capture is a terrible thing.
I'm not sure I have the force of will to not succumb to it. I think the only way to avoid it is to be so sure of the thing you are doing that the audience reaction is not the key metric by which you measure your success.
"Popular topic-expert" is a really cursed career to exist.
Ideally we would reward expert opinions on track-record, instead of how they make us feel. But we don't. Also ideally, the expert's conclusion wouldn't impact their ability to pay the rent (as long as it's correct), but again, that's not the world we live in.
"Your boos mean nothing, I've seen what makes you cheer"
Ed Zitron’s job is to convince people to pay him money to read what he writes, of course he’s going to preach to the choir, they’re paying him to do that and he knows it.
This is why I always consider the agenda of an author, Ed Zitron’s agenda is to make money from subscribers who read his writing.
That’s level 0 analysis, you’re supposed to go to the next steps and not stop here…
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>one's chosen enemies must be simultaneously terrifyingly powerful and incompetent dunces
I've got some bad news for you.
I really don't find it coherent to call someone who you keep consistently losing to stupid.
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At first I thought you were referring to Umberto Eco's laws of fascism: "Fascist societies rhetorically cast their enemies as at the same time too strong and too weak."
but it's subtly different, because incompetence is not weakness.
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Audience capture is a bastard.
he makes a shit ton of money off this - in this attention economy we are doomed, people of every side find that going all in on something, no matter how intellectually dishonest, is much more profitable. Extreme AI bros or Extreme AI doomers seem dishonest. AI is an amazing technology - it is simultaneously not going to replace us in the next 3 years and it's not total garbage - the truth lies in the middle.
"audience capture"
Its pretty clear if you listen to him now he's just (re)playing the hits for his audience. Its like MSNBC/Fox News for people who think they're too smart to fall for that.
I used to think he was just early on some of this stuff but the sheer amount of content he produces its clear he's just cashing a check.
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I'm not a huge fan of his style myself but fundamentally he isn't wrong. The whole "growth" so far has been all show and no go. I see people pouring in millions only to end up bankrupt a few months later. Evangelists portray those instances as rare and simply "skill issues" and "they don't know what they are doing but I am". Anyone that's ever had several servers running at home and seen the electricity bill at the end of the month knows it - I do as well. And we are talking about servers that use power, only a fraction of a server running 4x H100s at 100% 24/7. For those of us that have - we are talking servers that have one or two xeon silvers at best, 15% load on average. And I live in a country where the electricity is veeeeeeeeeeeery affordable. I'm not taking into account training, or investment to get it going, just inference.
20 bucks subscription a month, dario, you ain't fooling anyone with above-room-temperature IQ (Celsius). Even the expensive subscriptions - we know that's not the real price. And the limits, bumping up prices every two weeks and so on, just to keep the lights on while draining investors... Zitron's claims are pretty impossible to deny: the moment those companies go public, the real prices will need to come out of the shadows and end up on your monthly bill.
At the same time I fail to see the real world benefit to this, even in software: it's like comparing Lego (before they began their anti-consumer bs) to cheap Chinese toys. The moment you look at them side-by-side, you know which one is a premium product and which one is a cheap Chinese toy. While I have been a big supporter of open source since I was a child, I was never biblical about using open source - if some proprietary piece of software does a better job than the open source one - fine, take my money. Not anymore. I see the abysmal state of cyber-security as a direct consequence of slop. Slop-written code, slop-reviewed code, tests pass(also slop), ship it. Yeah, I'm not trusting you with my data, the hell with that, I'm self-hosting everything, adios. And I sure as hell don't trust the ai-bros for anything either.
And there's another thing: Microsoft was clear about it: github is constantly down because they can't handle the load. App stores are bumping up developer fees. Anyone can slop together a todo app(which was never hard to begin with). It's still a winner-takes-all economy - no one is going to install a todo app and migrate from Google/Apple just because. So the 1000 todo apps released daily will simply be a hole in the pocked of the people who slopped them together and another reason to be paying 500 bucks for 64 gigs of ram and another 500 bucks for a 2tb nvme. Raspberry pi's started off as educational platforms for 35 bucks and their commitment was to keep them at those prices. That aged well, right? If I decide to upgrade my uconsole, I have to pull out another 350+ bucks for a raspberry pi. All thanks to the sloppification. The bubble can't pop soon enough and frankly I don't care what it takes down with it.
No? He's been pretty spot on.
This very submission is about cases where Ed has not been pretty spot on.
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Looking at the refutations of Zitrons predictions in TFA, it boils down to two categories:
1. Zitron claims model capability has peaked 2. Zitron claims AI lab growth (user and revenue) has stalled.
In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.
In the second case, numbers are given showing growth, directly refuting Zitron. However, I'm giving Zitron the benefit of the doubt, given the old saying - market can remain irrational far longer than you can remain solvent. As long as people can be convinced that the sky is falling, rational predictions rarely pan out.
> 1. Zitron claims model capability has peaked … Zitron is probably right in this regard …
Is there any objective measure that shows this?
Would we use examples such as his Feb 2024 claim "I believe we're reaching the upper limits about what generative AI can do"?
> 2. Zitron claims AI lab growth (user and revenue) has stalled.… I'm giving Zitron the benefit of the doubt …
Is there some date by which you'd say it'd be fair to evaluate whether Z’s claims are true (without the benefit of the doubt)?
You mentioned revenue - would we use claims such as his 2024 claim that the companies no longer knew how to grow? But that in 2024, 2025, and 2026 both the companies revenues and profits have grown at double-digit rates each period?
You also mentioned users - would we use claims that "Sundar Pichai wants Gemini to be 'used by 500 million people before the end of 2025, 'a number so unrealistic that someone at Google should have been fired, and that someone is Sundar Pichai", where Gemini then hit 750 M users?
Or by what measures should we evaluate whether Z's claims are true?
I'll paste my response to your pre-edited questions:
I think atomic weapons peaked during the trinity test. The subsequent creation of bigger explosions by packing more fissile material does not meaningfully improve the technology.
Since LLMs are a generative technology, let's use a generative skill - painting. There are tons of brilliant painters, all with their own styles. What I think is consistent among master painters is their effortlessness in their craft. Honing a skill makes subsequent attempts less effortful.
To me, LLMs are more like atomic bombs than master artisans. To get better results, add neural nets. What would be meaningful to me, is to constrain models to a fixed amount of compute, run them on any of the copious amount of benchmarks, and see if they get the same scores at increasingly faster speeds. This, at least to me, signals mastery.
On the date for evaluation, I will set my own prediction instead, that AI labs will not become profitable, local models will drain their moat.
Taken on its own, I will concede that Zitron's predictions are wrong, but it is exactly why I bring up market irrationality. The multiple rounds of funding is propping up the unsustainable business model. Without it, user numbers and revenue can't grow.
I forsee real innovation in the AI space after the bubble pops.
Just this morning, I had to read through an LLM response about how a PC8-M5 fitting has a high flow rate because it connects to an 8mm tube, completely ignoring that the threaded M5 on the other end will only have space for a 2mm hole, so it still seems quite similar to the LLMs of 2020.
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> In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.
You don't have to like them, use them, or consider them "good enough", but the idea that models haven't gotten better in the last two years is ridiculous.
> In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.
I am certain that plugging a circa 2023 model into a 2026 harness would be a pretty frustrating experience. Yes, you could code a bit with AI in 2023, but models are just much better at it than they used to be. And smaller open models are leaps and bounds better at it than they were three years ago.
Agreed, though I do think that LLMs are still more similar than we think. Sometime after the first release, AI labs found that coding sat in the niche space of lots of easily digestible data and fast feedback from error messages and compiler checks etc. This allowed models to be trained with a focus on coding tasks, but the underlying technology is still the same, the infrastructure around it changed, they are still generating via probabilistic sampling.
Don't get me wrong, I'm using local coding agents myself with varying levels of success and frustration, but the models themselves behave similarly to their siblings from 2020.
The infrastructure improved, that includes the data. I have a pet theory that if they took the earlier models and retrain it with the data they used to train the latest models, we will get a similar result.
This is kind of a "I'm not here to tell you about Jesus he either lives in your heart or he doesn't".
I am an engineer, I have about 15 years of experience. I have been using AI in my job since mid 2025. Over that time it has gone from being an interesting toy that could kind of help but would often hinder, to being an absolutely explosively powerful tool. Just from personal experience it is the thing that has improved the most of any of my tools in career. And over that time my spend on AI has sky rocketed.
You don't have to believe me, it's obviously just anecdotal, but for anyone in the same position as me (And there really are lots of us), to claim model capability peaked in 2024 is just staggeringly dumb. It'd be like claiming electric cars peaked in 2008. I don't know how further to convey this to you.
It may very well be the case that the financial side is a bubble that horribly bursts. But the technology is real and the claims Ed has made are just wrong.
Don't worry about Jesus, he's fine.
I'm the same as you sans 10 years of working experience, with similar experiences using LLMs. The distinction I would like to make is that it's not the models that are improving, but rather the infrastructure around them. It started with prompt engineering, then chain-of-thought, then mixture-of-experts, now harness engineering etc. These, I think are what's driving LLM complexity, not the model.
To bring back to your electric car analogy, the electric motor peaked early on, but was not quite useful as a car until advancements in battery density, charging technology, regenerative braking were made. These are all the infrastructure needed to improve the viability of owning electric cars, just like the infrastructure was needed to make the models useful.
To be honest, I'm much more excited to see development of infrastructure around local models than seeing the arms race between AI labs. At least the former benefits the end user in a transparent way.
Model capabilities have peaked...? You need to try Fable or Sol.
To be fair not predicting accurately doesn't necessarily mean they are wrong - Michael Burry is the perfect example here, a market may actually just be so fraudulent (AI companies being funded by AI companies etc) that it can defy common economics for a while.
You're right or wrong in relation to a given statement.
If your point is just that there may be an AI bubble, then yes the fact that it hasn't popped yet does not prove it wrong. But that's not what Ed Zitron is saying; he's making very specific statements that happen to be wrong time and time again.
As many said in the comments, it's common for doomers to keep predicting a crisis, every month and every year and so on, until inevitably a crisis does occur and they claim they were right. That's not how it works.
He didnt mention EZs claims about lack of data center construction compared to projected growth and valuation. I wonder if that was substanciated, since it overlaps with the bubble portrait of the "coming" wall of due service contracts hitting in 2027.
Unless they find a way to make money on this, Zitron will eventually be right.
Big shout out to Zitron. Everyone knows how to monetize good predictions (stock market, etc), but he might be one of the few who successfully monetized bad predictions, making him a step ahead of the rest of us.
I think he's constantly trying to time the peak (of AI improvement, of compute capabilities, of profitability) and keeps having to readjust his predictions every time it's proven wrong. Timing the peak is a fool's errand unless you have extremely hard and unavoidable data that everyone will have to face by a deadline. But contrary to what AI boosters often imply in response to skeptics being wrong, failing to time the peak doesn't mean the peak doesn't exist. So far it has existed for every new technology.
Thank you. It's difficult to call Zitron right or wrong because he is merely grabbing the attention and clicks of AI sceptics and people afraid of any change. Patrick Boyle is another example with his constant stream of everything is broken videos, from AI to EVs.
I am not all in on AI, but the discussion requires nuance. Zitron does not have it because he is interested in attention and not discussion. The article deconstructs his antics really well. So again thank you. I really appreciated this part
> To make the case that these things are dying, he pulls on minor issues that are not positioned to cause the very large changes he suggests are about to occur. For Meta, he cited some kind of alleged MAU drop for Facebook. Rather than use Meta's own MAU figures or any kind of revenue or profit numbers, he seems to have used numbers from Similarweb. My experience with 3rd party tracking numbers like this is that they're quite inaccurate and generally useless for anything other than a rough order of magnitude comparison, making the Zitron's cited decline meaningless. FB stopped reporting MAU publicly in December 2023, but most estimates have FB MAU increasing over time and the numbers Meta does report show generally increasing usage over time for their products; Zitron cherry-picked an outlier low estimate to make his point.
Yes he is a classic cherry picker.
He is a bit more of a PR person rage-farming disguised as a futurist. He's not optimizing for correct predictions. That's really hard and not his incentive.
Look at his incentives. It makes more sense.
There's a demand for loud and confident "AI tech will fail" and "big tech will fail", so it pays to peddle the goods.
A lot of people desperately want AI to be a nothingburger. Thus, they will seek a second opinion that just so happens to line up with their existing one. Wishful thinking at its finest.
There are an even larger amount of people who have gotten tired of the NFT levels of pushiness AI has become from a financial perspective. It's become worse than crypto bros at this point.
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Thanks for putting this together, his writing really aggravates me and it was nice to see all of his predictions put together in one spot.
It's interesting how he has pivoted from denying that LLMs are useful to accusing frontier labs of Enron-scale fraud.
I suppose it's the natural pivot you have to make when your whole business is an anti-AI newsletter that costs $7 per month.
The fraud is there. Whether it surpasses Enron or not will be revealed in the future.
To commit fraud you have to lie, deceive, or conceal. All of this bonkers financing is happening in broad daylight, announced to the markets, and met with rave reviews. I tend to agree with Zitron that the whole thing is a castle made of sand - not because the tech is bad or useless but because the financing, in its scope and structure, is so outlandish - but you can build a castle made of sand as long as you don’t tell anyone it’s made of steel.
So far NVIDIA has been very careful, it’s not actual fraud, at least based on public information. It’s a very unstable and extremely risky bet, that is very likely to blow in the face face of AI companies, but it’s not fraud
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Patrick Boyle had a vid specifically on whether there was Enron style fraud. The conclusion was no, though he thinks things may be a bit bubbly https://youtu.be/NufJ7g63KSY
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There is no evidence of fraud that I have seen beyond complete speculation from the likes of Zitron. He basically just says "Look how big the numbers are! It's so big it must be fraud!"
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He LITERALLY said it is not like Enron. And repeated it.
I was thinking of this article where he compares OpenAI to Enron twice: https://www.wheresyoured.at/what-happens-if-openai-dies/
You're right that he doesn't explicitly say enron-scale fraud, that's just what I came away with from reading the article a while back.
I haven't read every word but I don't recall him saying AI is bad or useless ever either. His refrain is that they cannot keep up with their endless spending on training and they're not reaching new markets.
I look at the rapid rise and decline of OpenClaw and get the impression AI has not really found any foothold among average people.
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If we look at the demand side:
1. Conversational AI - this is ubiquitous now, but not sure it's increasing at any significant rate. At least my personal usage has leveled off. Also probably not a significant driver of per token demand relative to code gen. More a driver of subscription revenue.
2. Code Generation - this is the big killer app, which I suspect has driven most if not all of the last year's revenue growth for Anthropic and to a lesser extent OpenAI. Now, I doubt we can extrapolate that growth given 1) how quickly and completely CC has been adopted by the industry and developers and 2) how corps are reigning in spend
3. Image and Video Generation - this honestly strikes me as more of a trifle/novelty than anything else. Granted I'm not a visual artist but I don't see it being a killer app anywhere to the extent of code generation.
4. Physical AI - this is the wildcard for me, but also beyond my ability to conceptualize its effects on demand. I suspect a proliferation of self driving and autonomous robotics would be a huge driver of compute demand but who knows how close we are to proliferation?
Not very. I like to always watch both sides of this issue, the people who are optimistic, pessimistic, doomers, etc... He is someone who in my opinion misunderstands the bigger picture. He often downplays the capabilities of these systems, doesnt think they will be more powerful in the very near future and also more importantly makes a fatal misunderstanding that we live in a rational world with rational actors.
Zitron sells anti-hype and AI-hate. AI is not good for a lot of people and he's telling them what they want to hear. He's great at talking and making entertaining facial expressions. That's actually a real skill I admire but it has nothing to do with predicting markets correctly. He's in the business of selling entertainment views not predicting markets. He even admits he doesn't short AI.
Zitron sells AI is in a huge bubble. Which basically everyone agrees with who isn't directly incentivized to say it looks perfectly normal.
Personally, I don't bet in rigged games, which is what all the circular financing is.
> No good reason, really. I got four hours of sleep and my brain wasn't good for much of anything and I saw someone posted a screenshot of a reddit post dunking on Ed Zitron's prediction record. When I wrote this review of futurist prediction accuracy, I tried to make sure that I didn't bias what I was reviewing in any way. It's not obvious from the post if the redditor who reviewed Zitron's predictions was pulling predictions in an unbiased fashion or if they were biased in some way (since AI has become a culture war issue, it wouldn't be surprising if someone pulled biased predictions), so I decided to read some Zitron in my spare time while poking at agents to get them to do an unrelated task I wanted them to do. For the futurist post, I read multiple entire books to pull predictions and generally only stopped when someone was being repetitive and kept saying the same thing over and over again. In this case, all Zitron does is be repetitive, so the methodology in the futurist review would mean that I review a few predictions and then stop immediately. To overcome this, I had ChatGPT give me a list of predictions (with no attempted tilt towards correct or incorrect predictions) and then I skimmed/read the posts that ChatGPT linked to. There were some cases where I thought ChatGPT's reading of the post was incorrect (these were generally cases where it flagged a prediction that would be incorrect if its reading was correct, but I disagreed with its reading) and (discussed further below) I also removed predictions which weren't falsifiable or seemed pointless because they were tautological (I noted something similar to this in the futurist post).
No reason really. Just very sleep deprived and want to multitask in between agentic feedback.
Some people obviously want AI to fail. Some people obviously want The Magic Machines to win.
pragh@ (Prabhakar) (yes his ldap is an anagram of graph) absolutely gutted the fire wall between search and ads.
To pick a specific piece of public information to support my claim: the previous SVP of ads quit immediately before the coup to start a subscription based search engine.
One nonpublic piece of information: basically every member of search leadership was pushed out within two years of his ascension.
Fair enough on Zitron being wrong, but I think the opening argument about Meta, Google, and Microsoft misunderstands his point.
Zitron is saying that the hyperscalers had no genuine growth opportunities, so they're using the AI bubble to achieve growth. The fact that they've continued to grow for a few years doesn't contradict his point, and Meta's steadily decreasing profit margin certainly doesn't look healthy.
Seconding that observation. The IT folks in charge of the compute direction have been quietly raising those concerns for over a decade (“and what happens to those alleged lower costs when their attention diverts to a new industry or pumping margins for shareholders?”), and the major enterprises or customers had all but migrated wholesale into public CSPs - and been eyeing the exit when they finally opened those eye-watering bills IT had kept forwarding to stakeholders. The industry wasn’t going to collapse so much as right-size, and that would turn into an inevitable cycle of churn (higher prices to drive margins, leading to more customers leaving in part or in whole, which would drive up costs higher to continue delivering positive results, ad infinitum).
The current AI build and boom has done wonders to their bottom lines in the immediate, but even Wall Street has its limits, and it sounds like there’s decreasing appetite for such CAPEX builds without associated proven revenue. That might kill some companies outright, but more likely it’ll force the major CSPs into the churn cycle that much faster.
I was a fan of his writing for a long time, but it's clear that the guy has fallen down the "build an audience that's only here for baying for blood on one particular topic" hole and won't be able to escape it.
He used to be really thoughtful and funny about the topics he took on. And I realize that's pretty standard for a critic. But now he just keeps digging deeper and deeper, getting more things wrong post after post. His posts have grown to probably 3x the frequency and 3x the length of when he was talking about stuff like Google tanking their Search business by bringing in the Ad guys.
He really used to be a "little extreme" with some really grounded viewpoints. Now it's more like he's performing the "angry british guy" in order to maintain his impression count and audience.
It reminds me a LOT of that Eli Schiff guy in the design world. A mediocre designer who couldn't get untangled from one specific design style, who figured out that anger drives clicks. Schiff became a "design critic" who eventually fell down the alt-right pathway and ended up becoming a pariah. I'm not saying Zitron is gonna turn into some right wing nutjob, but he's falling down the nutjob path right now in real time by consistently staking out the worst ungrounded takes and continually doubling down on them.
His biggest error was the blanket rage against "AI" when it should have been focused on LLMs and data centers.
AI is a big field. Hating on AI is like hating food because you don't like broccoli.
Robotics AI that replaces high risk labor and even low risk repetitive stress labor is nothing but a win for humanity.
The simplest argument against AI is the fact no public company appears to be making money on it, minus revenue that contributes to AI infrastructure.
Would love to see broken down counter example of public company.
So far Chegg and Duolingo have been devastated. Surely they could cut costs drastically with AI?
I don't know about Chegg, but the fundamental problem with Duolingo is their product does nothing to teach people a foreign language. The nasty reality of that product is if you go through their entire learning tree in a language, you might be at a CEFR level A1 for that language. And, you spent 10x the time and 10x the money you would have spent via something like Lingoda to get the same outcome.
This claim is just not true. And gets tiresome. You roughly end up where those courses claim to be in reading and listening - depending on language it can be over B1. (No course finishes B2, some do have B2 content). I ended up being able to watch some (not all) netflix series in foreign langue and I was in early B1 section. I clearly learned.
You also dont have to pay.
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Not about Zitron specifically -- I don't read his study, but I don't know that making some statements in interviews or blog posts is the same as making a "prediction" (into which one would put much more though)
also statements can be interpreted many ways:
"Meta is dying" was countered by "Meta's revenue has increased since Zitron said that". OK, but 1) revenue/profit is only one measure of "not dying"; 2) what's the time scale? Nokia and Xerox were highly profitable companies that dominated their industries, and any prediction that they would go out of business at their height would have been laughed at, and yet, it wasn't too much later that they pretty much did.
To me and most other non-West coast Americans its obvious he's right. Which is a good explanation of why SV has the resources and success it has because it can do things no one else believes in. While the rest of the world will always be catching up.
It is nice to see an attempt of sorts to discredit, and indeed Ed is not always right. But the bigger picture is debt fueled AI buildout frenzy, regulatory capture, monopolising and gating models, tokens/compute is still losing money compared to observable revenues from customers. Otherwise we would not need inventive accounting practices, SPV's, theatrics on distillation, stopping frontier development and so on.
But taking claims from Sundar about 750M gemini users in 2025 at face value is disingenuous. This basically includes everyone who owns an android phone and opened a Gmail and Google search, translate. Congrats, you are gemini user now. Every product has received AI "feature", like your android assistant offering to listen to you while the screen is still locked by default. Dan's only sources are press releases of the AI companies. As I'm reading this, he dedicates at least 3 paraghraphs showing how Ed is wrong. And keeps coming back to it again and again. Okay so we are both refuted and proven wrong, but is that really the extent of AI use? Is this the revolution? Replacing existing function with inaccurate AI sourced from reddit posts?
I've read a number of both blogs. I'm not gonna watch Diary of CEO video. Yes, models have gotten crazy good in 2 years. Still the same failure modes. Dan's writing here comes across as same type of gish gash he rails against, minus the entertainment value.
The first section of this post responding to "Meta, Google, and Microsoft are dying" is completely misrepresenting Zitron's arguments in the linked video. Zitron is making essentially an enshitification argument concerning Google and Meta. He's not talking about whether they will be able to squeeze out more revenue in the short run, but how they are willing to abuse their users to ensure that they do. Citing increased revenue for these companies is not arguing against Zitron's point.
Luu also fixates on Zitron mentioning Prabhakar Raghavan, but then proceeds to agree with Zitron's core point that Google has intentionally degraded it's search product to maximize revenue. Maybe Raghavan is not solely responsible, but it seems fair to hold him accountable for trends that accelerated under his leadership.
This article cherry picks:
Ed Zitron mostly covers the costs of data centers, circular spending, and predictions of large the market for AI has to be to justify the data center expenditures.
I was disappointed this article didn’t really cover Zitron’s main arguments.
Maybe a paid article placement? I don’t know, but I was dissapointed: I read Zitron’s material and I wanted to see good counter arguments to his rants about costs of data centers, circular spending, and predictions of large the market for AI has to be to justify the data center expenditures arguments.
What are the predictions he has made that this article omits?
He is making predictions with specific timelines. No one is forcing him to do that. It is reasonable criticism to say he is make poor predictions.
Only recently came across this guy and if u ignore the standard social media hyper hype hype he has, I dont think he is wrong even his 2024 predictions.
US has 1.5x the money supply since Covid. This means everything has to go up 1.5x to reach the same parity from before. eg inflation. All corporate earnings are going to inflate as 1b yesterday is 1.5b today.
There is no whete for these mega corps to go. They dont know how to grow. meta becoming well meta with the vr crap is case and point. meta blowing billions on a few hyped AI people is the nail.
Other than cloud, microsft/google aimt doing anything. xbox? nope, hardware? nah. It is all attention economy or selling the pick axes for it.
the only mega corp that still seems to be moving the needle is apple which i think speaks volumes.
Eds point is that AI tam or expectations are insane full stop and he admits coding has a use just not as big of a tam. Gen ai in art, music, etc did not take off at all esp compared to code. this makes sense, no one pays an artist 500k/year. their time is worth little compared to coding.
Where he is wrong tho is that this is not new. there is always a hype cycle of nonsense that screws the little people. what is new is the level of polarization. it feels like ai psychosis right now in ways that remind me of crypto but worse because ai is actually useful so people are even more insufferable.
Though the bubble has not popped, I don't see the following discussed in the post: Zitron would probably point out (as have others) that many of the hyperscalers are booking valuation increases in Anthropic, OpenAI as "Other Income", which is substantially increasing their reported revenue and earnings. It's roughly:
- Hyperscalers like Goog, Meta, Msft invest cash in Anthropic, OpenAI, in exchange for equity
- The ongoing investment actually boosts the valuations in the Anthr/OpenAI (new raises are done at higher valuations), so the valuation of the Hyperscaler's existing investments in Anthr/OpenAI increases, which gets recorded as Other Income in quarterly earnings
- Much of that invested cash will itself come back (circularly) to the hyperscalers as revenue since Anthropic and OpenAI spend a lot of money via datacenters etc.
On Other Income phenomenon, see for example, https://www.ft.com/content/be97df0a-76b1-4cb0-9ba4-d1117d8d1...
Also, there's apparently lots of off-balance sheet debt. For example https://www.ft.com/content/a0a07cce-6d19-4b1e-a73b-9855a06ba...
Sorry what is the punch-line here supposed to be? These investments obviously increase correlation coeffs., but these are highly correlated stocks to begin with.
valuation gains on investments are one-offs, not signs of sustained improvements in profitability that would warrant higher market caps
when those valuation gains are in turn the result of circular financing schemes (a bakery giving out money so that people buy bread from it), we're getting to a dangerous situation
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The issue is simply that the posted article begins with a review of recent earnings/ revenues, but fails to discuss that a substantial part of those revenues are investment markups.
Whether it matters we don’t know yet, but it’s a fact worth noting. A better article might have tried to argue why it doesn’t matter
Wait are the hyperscalers booking unrealized gains as income? Or are they selling their positions?
As far as I understand GAAP reporting standards actually require them to report gains on those positions as "earnings". But they do report non-GAAP earnings sometimes excluding them. E.g. Google earnings per share last quater is $9.11 GAAP vs $2.85 non-GAAP (mainly because of SpaceX shares).
Useful piece on this from Owen Lamont:
https://www.acadian-asset.com/investment-insights/owenomics/...
https://finance.yahoo.com/news/warren-buffett-decries-accoun...
A recent Zitron claim is that they’re booking unrealized gains tied to these private labs. Google’s net revenues being a recent example.
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This is basically a bs line of reasoning, and it's easy to verify in 5 minutes (go read some SEC fillings).
Yes the investments do increase GAAP, but these are seperate line items from revenue which is what is listed in the article.
Alphabet is the biggest winner in this department, it's investments gain/losses for the same period as in the article was:
2023: -$1.45B
2024: +$2.24B
2025: +$24.90B
Yes thats a lot, but compared to it's seperate revenue growth of nearly $100B in the same period, it's not that much.
I feel like just saying "wrong" to some of these feels a bit unconvincing.
e.g.
>April 2025: "It also, at this point, is pretty obvious that generative AI isn't going to do much more than it does today." >>Wrong
Is generative AI really doing much more today relative to 1.5 years ago? Sure there have been sone improvements, but i feel like nothing fundamental has shifted in that time period. Nor would i really expect it to even if the statement was false, but it seems too early to tell.
> February 2025: "I will keep writing this stuff until I’m proven wrong."
> Wrong (Zitron continues to write despite repeatedly being proven wrong)
Technically he didn't say he would stop if he was proven wrong.
How often are people that are paid by their content attracting eyeballs correct? Isn't this a universal thing that people in these roles are constantly stating all sorts of things that never happen. This happens in sports media, politics, tech reporting, stock market predictions, etc.
Shouldn't society have a clever name for people playing these roles by now? Something catchy, insulting and based on truth might help call this out easier. I would throw influencers in there too, they are writing/video-loggin/podcasting for the same money outcome.
Ed Zitron has the economics of AI basically right. Most technologies arrive by digging an enormous financial crater, followed by a contraction period in which everyone insists the crater is actually a revolutionary new business model, before the survivors eventually buy up whatever remains.
What likely resonates is AI really does feel like a science experiment. There is clearly real value here. The problem is that the economic value has yet to catch up with the technological value. And yet the claims coming from AI companies have the unmistakable energy of a state-fair entrepreneur standing beside a suspicious knife yelling, “You have never seen anything like this before, it slices it dices...!”
I think Ed goes too far when he compares LLMs to garbage. He’s tried them, had a handful of bad experiences, and apparently decided the entire technology belongs in the round file. But much of what humans do is essentially trial and error with better PR: apply some logic, see what happens, adjust, try again, and continue until you eventually solve the problem.
If you can get an LLM to reliably do that, you can solve certain classes of problems dramatically faster.
How Accurate Was Hubert Dreyfus on AI Predictions in "What Computers Can't Do"? (2024) | https://www.brightworkresearch.com/how-accurate-was-dr-huber...
Ed and his class of people are entertainers that get confused at experts for journalists. None of them toil at the prompt sorting things out directly. The media entertainment class frustrates me. They often spread sound bites that are off by big margin and shift markets. I find podcasters that are curious about a subject are better at finding the truth about a tech subject then finance wonks that chatter about sounds bites.
> "Sam Altman deputizing Orion from GPT-5 to GPT-4.5 suggests that OpenAI has hit a wall with making its next model, requiring him to lower expectations" > Wrong (GPT-5 was a substantial improvement over GPT-4.5)
… I mean, this feels slightly irrelevant? Orion _does_ seem to have been demoted from 5 to 4.5; that they later released something they felt they could reasonably call gpt5 feels slightly beside the point there.
It is a curious exercise to look at the predictions of AI companies/their CEOs, too.
Frequently we'll hear from Amodei that AI is 6 months or 12 months out from replacing Software Engineers. Or we'll hear that their model is safe when regulators step in, but suddenly it's too dangerous and hacked its way towards its goal when other companies claim the same.
What they have in common with Ed Zitron is that they are both trying to sell you something.
Even though these predictions turned out mostly wrong, we should not castigate people for publicly forecasting! That is virtuous, and more people should do it.
Thank you Ed!
My take would be that people have no idea what is going on today, as time expires history drifts further away from clueless to the absurd... and now you want to do the future? Whatever you say, we should at least completely ignore what your audience wants to hear. Whatever is left could be correct but only by accident. This is what the future will be like... or wait...
Did he add the trillions of off balance debt to his revenue/profit overview?
https://247wallst.com/investing/2026/08/17/alphabet-meta-and...
I have to admit, after the podcast I feel a strange sense of calming. It's for sure a more interesting approach of skepticism compared to many of the other doomsday prophets of AGI, or environmental plays.
But for sure it's a branding play
Accurate or not, there are many risks other than those that could bring AI companies value to 0. E.g., systems not requiring passing over all the data continuously, but in small regions running at L2-L3 cache memory speed. Once someone figures out that, the need for high bandwidth memory would end.
Zitron's takes on AI hype vs. reality look pretty prescient. My coding job is definitely safe, still.
I think the most likely way for Zitron to find vindication is through the world war we seem to be shambling into. That could easily put a stop to AI improvements, because the supply chain is so fragile. But that would have nothing to do with his ostensible reasoning.
I find it bizarre that this blogger can make sweeping claims of someone being wrong, quoting specific values that are admittedly speculative, and then just using them as gotchas for being wrong. The then ignore the conclusion that those "wrong" yet close values mean in the grand scheme of Zitron's point, even when the margin of error was inconsequential.
For example, one of his first big criticisms of Ed is Ed's claim that Meta has a dying product and its a dying company. Do I really need to look at the numbers here? Or should we look at the companies actions and history WITH the numbers included?:
- Metaverse was a complete and total disaster and forced upon the company by a CEO who is clearly completely out of touch but infallible within the company.
- Facebook is a bot riddled, AI slop haven, used only for special cases and is basically unanimously hated by the next couple generation of users. Users who are critical for revenue if the serving ads to bots scam ever implodes.
- Meta's successes seem to be solely on knowing who to buy and have failed for a decade to innovate anything.
"Dying" is not the same as "dead". As someone else has said, but I have forgotten who it was, Meta is a "mature" company trying to be young and sexy again when they should focus on their existing products.
How does anyone come away from all this with a business, where we hear all of the horrible things about their internal culture, that an AI pivot to be anything but a trend following desperation move? Then the author addresses but shrugs off entirely the fact that they now hide their Monthly Active Users. I think all of this context is pretty fucking important to think about with the numbers, especially since Meta is trending down when we get the totals for the year. Zitron's point, again, still tracks because you can list a portion of "profit" but it is too early for 2026 since they intentionally use misleading numbers. I would be very interested to see what the first half of 2024 and 2025 profit numbers were before the total year calculation. Either way, Meta's dump into AI is a huge gamble from the company that must pay off.
I don't know. I think this guy does not like Ed Zitron, which is fair. I see a man pointing fingers at someone while doing the same things he is criticising: Taking the speculative and sensational as literal and using it as some sort of gotcha to be speculative and sensational themselves.
Most surprising thing to me here is tech giants growing 50%+ in 2 years. What's up with that?
Mo Bitar over on YouTube does the grumpy AI skeptic routine in a much more entertaining fashion.
His "Unethical guide to surviving AI layoffs" was solid gold: https://www.youtube.com/watch?v=JgVBqcqUGE0
> Now, if your CEO has never heard the phrase Ralph Loop, oh man, you are less than 30 days away from your next promotion. I'm not even exaggerating. Walk into his office, close the door, and say, hey chief, been experimenting with something. It's called Ralph Loops. And I think it could change literally everything. And he's gonna say, what's a Ralph loop? And you will say, give me $18,000 worth of API credits and I'll show you. Now you won't actually do anything, because you can't do anything. Because nobody can, because nobody knows what they're doing. But by the time he figures that out, you'll have a new title, and equity bump. [...]
> Talk about automation constantly. Nothing arouses the slumbering capitalists than the mention of automation. Drop names too, bro. Like talk about specific team members you can automate out of existence. Be like, yo, I automated Gary, bro. Tag Gary in the message. Tag him in Slack in a very public channel. Be like, yo, I just automated @Gary. His function has been Ralph Looped. And tag your CEO in the same message. You think you're getting laid off after that?
This is eerily similar to how a few people at work did some presentations about ralph loop and connecting agents to slack right when management was really pushing unrealistic AI productivity boosts. I was repeatedly asked to speed things up with AI while I was already heavily using AI to do work.
The perfect read while listening to my first podcast with the guy. Noticed a kind of shift where I previously got very defensive about my latest obsessions, but at this time and age, it was just kind of amusing.
I really like reading him. I do wish is posts were a lot shorter. I get tired of all the 60/40 percent forecasts that have enough wiggle room the forecaster never thinks he is wrong. I certainly dont get that from Ed!
Every transaction in the financial market is a bet with two sides with opposite beliefs.
I don't get why people make such a big deal off bears and bulls. Everyone is on one of these sides at every investment they make or liquidate.
Zitron does not seem to put his own skin in the game by going short though - He sells bearishness via his substack, podcasts and media appearances.
My point is not that putting your money where your mouth is or shut up.
My point is: all analysis is contingent, there is no point in saying bulls or bears are chronically wrong or that they should beat the market to merit being listened to.
> I suspect he's relying on people's eyes glazing over when they see numbers and just not thinking about what the numbers mean.
This is one of the most important learnings one can make from working in professional environments.
I don't see any evidence of Facebook's growth in high value regions specifically, all we see is them burning through money on failed project after failed project and getting into legal trouble.
On the off chance Dan sees this: one of the footnotes ("Some Zitron predictions" > July 2024) is broken. Right now it's showing a little 0 that doesn't actually link to anything if you click it :(
This entire genre of pundit is just a person who has figured out that you can sell copium to the masses. Once upon a time this was a decentralized "this is how Ron Paul can win" thing on Reddit, but it was inevitable that slowly it would coalesce around these kinds of engagement bait people. Every subculture has its own such figureheads who DESTROYS opponents with FACTS and REASONING or whatever, but really it's just a reality TV show. The Alex Jones, Gary Marcus, and so on of the world are mostly just selling an entertainment product.
It's just a question of what the entertainment is. Some people feel good about being told "we were being hoodwinked; there are lizard people" and others feel good about being told "you'll be 100x healthier and good looking if you take these supplements" and others feel good about being told "these people are evil demons who are stealing your water" and others feel good about being told "these idiot rich people are going to lose their shirts" and so on and so forth. It's like how I like slice of life shows and hate horror movies and my wife likes horror movies.
In a sense, the misinformation gambit of LLMs did not come fully to fruition in the West (as much as it has in 3rd world WhatsApp forward land) because the locals were already a fertile ground of poor epistemic hygiene and well served by human providers of misinformation. Hacker News itself only has some hundred thousand commenters or so and even this requires a practice of aggressive information curation to prevent unrepentant misinformation repetition nodes from polluting one's belief set.
I don’t know much about Zitron, but YouTube somehow pushes these “experts” onto my feed. This guy is full of crap, he doesn’t even care, because that doesn’t matter, what matters is view count.
The lady doth protest about the lady doth protesting too much, too much.
Decisively declaring long-term predictions wrong only two years later seems a little premature?
E.g. a company of the size of Meta/Google/Ms may very well be dying and still producing numbers that look like growth for the next few years.
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Also, how in the world is a Jan 2025 prediction that "I believe we’re at peak AI" getting a simple "Wrong"? How are we deciding what "peak AI" is here?
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Or, "July 2025: I am not trying to be dramatic, but it's pretty easy to come to the conclusion that Cursor is going to die" -> "Wrong (Cursor gets a $60B exit)"
So Cursor can't die once its been acquired? See https://news.ycombinator.com/item?id=49486172
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"August 2025: These models have clearly hit a wall where training is hitting diminishing returns" -> "Wrong"
I guess this is just trolling at this point? This is like a research paper tier question, "wrong" doesn't cut it.
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Very unimpressed with this analysis. Extremely poorly done, and pretty clearly not impartial at all. Shameful. Like, the guy does sound wrong to me, but the analysis is terrible nonetheless.
As always, shooting down commentary asking for more and more evidence is very simple.
Zitron has a somewhat ranting style. Luu's passage on Google search for example just nitpicks on the person that Zitron allegedly named as responsible for the decline of Google search.
The real issue of course is that search quality clearly has gone down. What does Luu want? A "study"? Everyone can see that. The person is not the issue.
This is a highly selective analysis of Zitron's writings that focuses on a couple of mistakes. People need to understand that any normie will understand the difference between rants, numbers and speculation in Zitron's essays. They are not written for autists who talk of "priors" and "evidence".
"This is a highly selective analysis of Zitron's writings that focuses on a couple of mistakes"
While reading this I was thinking it would be interesting to see just a few examples of Zitron predictions that he got right. Since you appear to follow his work, do you know of any?
The issue is the conspiracy. By pinning the whole problem on a single villain rather than the incentives the larger organization has created he 1. doesnt help solve the problem 2. creates an environment ripe for tribalism. Even if you ignore the racist undertones(fine its a stretch) he's still misleading people about what is happening.
> February 2025: "I will keep writing this stuff until I’m proven wrong." Wrong (Zitron continues to write despite repeatedly being proven wrong)
This did give me a good chuckle
Same mistake over and over again: zitron job is not "making successfully predictions", he is a content creator.
For him, it's enough to be right once, even in 3 years from now.
I used to pay attention to Ed Zitron, exactly because he seemed to be someone who did the research, and looked at numbers. Until one day, I realized that seems to only look at numbers as long as it serves his agenda. When it doesn't, he looks away or makes the case for why the numbers are wrong, and shields himself from these "incorrect" numbers - or people who would point to anything suggesting that AI is not a total fad.
In April 2025, Zitron wrote [1] "I am sick and tired of everybody pretending that generative AI is the next big thing." This was at the time when AI coding tools had already gone "mainstream" with devs, and pretty much everyone was using tools like GH Copilot, Cursor, or something similar.
So I replied with this observation, saying how, at least within software engineers, AI is being adopted faster than any technology before [2]. To stay objective, obviously I brought receipts: sharing how, based on an older survey I ran, ~75% of devs in that survey said they used AI coding tools. Zitron made fun of the small sample size (216 people), blocked, and pretended like AI had zero PMF anywhere in the world.
I stopped taking him seriously since then. And I'm wondering ever since: does he deliberately only look at numbers and facts that he can tell the AI doomer story around? Or is it more that he finds that there's not many people who are "informed sceptics", and decided to play this role?
[1] https://x.com/edzitron/status/1916903519594156407?s=20
[2] https://x.com/GergelyOrosz/status/1916906481686921483?s=20
Looking at it from an objective/historical perspective, when he first got on his high horse agents weren’t a thing, MCP was in its infancy at best, and half the code generated didn’t compile from frontier models.
From there everything split into two factions, which I’ll dub as believers and non-believers. From there, it has entirely been a cult following despite the evidence showing that models and agents have legs for software development.
FWIW my post history would show my extreme skepticism, and to an extent I still am. I think the real power of models isn’t the model at all but the harnesses.
Either way I think he lost the plot and runs on vibes himself. I also think this whole AI movement is going to have their 2000/2008 moment before the phoenix rises from the ashes.
I don't agree with the believers vs non-believers dichotomy. That makes this look like a religious war and it shouldn't have to be like that. To me AI is a very useful tool, no more no less, it isn't a 'make a wish' machine and it isn't a silver bullet for all of the issues that have plagued software so far. But when properly applied it's quite useful. "Non-believers" would have to be people that have yet to actually use AI, just like you can probably be a non-believer in peanuts until you've seen them, and most people that I know acknowledge the reality of AI tools and their use cases.
All tools, AI included, have a business end and as long as you point that away from yourself you will be fine. But to properly apply it (rather than as a faster way to make a huge mess) takes discipline and being methodical. It was never different.
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what's it with people who think a bubble is literally destined to happen because of some religious belief that history repeats itself? I find it fascinating to understand the theory of mind of such people. So much confidence.
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Ed's right about OpenAI being out over its skis, he's right about the numbers for everything being wildly optimistic and he's right about the circular financing/leverage aspect of a bubble. He hates AI though so he can't acknowledge its usefulness, it was pretty clear to see on his DOAC interview, Stephen kept bringing up real wins and clear progress, and Ed wouldn't engage in a substantive way.
Yes, his thesis is pretty much correct, his personal opinions aren’t too relevant or valuable. It’s a mistake to take his predictions as the important thing, it’s pretty much irrelevant, the whole field is very dynamic and complex, and he obviously isn’t an oracle who can predict the future. The only thing that matters is the thesis
I think he deliberately looks at doomer facts. I'm not sure it's cynical so much as a genuine belief that it's all nonsense. But IMO the belief is based on a lack of understanding of AI.
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He's part of the professional political / social media class now. He makes his money through engagement. I've stopped trusting people of this ilk. I listen to people who make most of their money not via the media ecosystem.
I don't know anything about this guy, but based on the discourse every time he comes up, Ed Zitron is a more polarizing figure for HNers than even Trump. Everyone here loves trying to dunk on the guy, yet he's apparently living rent free in everyone's social media feeds.
Yeah for real, an AI critic being popular is making everyone here lose their minds and try to come up with ten "Well Akshually..."
This isn't a serious analysis of the actual thesis or any of the predictions in any meaningful way. I learned almost nothing from this shallow wall of text. It seems overly focused on "predictions" instead of the ideas behind the predictions, i.e.
- the existence and severity of the financial AI bubble
- the claimed efficacy of AI in terms of its utility vs the actual observed utility
- whether the net good provided by AI outweighs its very heavy costs
I find the section of listing a bunch of selected "predictions" and just saying "Wrong" to elucidate very little. Not that a sentence is sufficient to provide explanation, but Dan stops even doing that bare minimum partway through and just saying "Wrong" full-stop. The reasoning is left up to the reader I guess?
How is it wrong? What was the actual thesis behind it? Is the underlying idea wrong or just the specifics on execution? Was there undetermined factors that mled to the wrong prediction? What can we learn from those factors in order to update our model?
We saw that even though the underlying financials in 2008 were trash and lots of people knew they were trash, things didn't quite collapse in the time frame or way we expected, because an unknown part is how much shenanigans companies can do to extend the runway.
As an example, credit ratings agencies didn't drop ratings to match reality because of customer relation incentives, which is a factor that is not easy to account for and strongly affects the timing of the collapse.
I find the positive reactions to this blog to be confusing. I feel like I learned nothing at all, which makes sense considering under "why write this?" he says "I got four hours of sleep and my brain wasn't good for much of anything and I saw someone posted a screenshot of a reddit post dunking on Ed Zitron's prediction record."
In case you find this site difficult to read:
Zitron is providing a service: he's running the train for those who want to believe, or sincerely believe, AI is a bubble. His interviews are apocalyptical one sided rants that confuse what is with what Zitron thinks should be. I'm not sure he's wrong, by the way. As LLM performance is becoming more and more commoditized I fail to see how spend catches up to expectations to keep running this market as it does, but that's beside the point.
One annoying side effect is that YouTube's algorithms will always try to force feed you more of the things you last searched, to amplify your biases and send you down the doomscroll rabbit hole. So if you search for Zitron, next time you visit you'll be flooded senselessly with naysayers, contrarians and skeptics from all courses of life.
AI is clearly of great utility for defense/military/security. That alone guarantees continued support financially.
I have stopped reading Ed Zitron quite a while ago because of his long-winded and polemic style. But counting his failed predictions is maybe not that useful, in the sense that his core thesis is really just the AI Bubble. While it hasn't burst, all of his predictions remain wrong; when (if) it does, he'll have been right. Trying to guess the exact time or failure mode (whether it's open models or corporate sticker shock or a bond crisis) is a fool's errand when there are so many powerful actors all-in on keeping up appearances. Exposing the financial and corporate shenanigans of the AI world is nonetheless useful. I just wish someone would do it in a more level-headed way.
> Among programmers and other serious users of AI, I would guess that Willison has a larger audience than Zitron.
You don't say.
Do his predictions about the datacenters being a risky and unprofitable business suffer the same fate as the others?
Fundamentally someone at some point needs to pay for them. And this should mean by actual end customer payments. Now to get size of those payments and how much collectively needs to be spend start to look to me rather questionable. It is big number to spend.
> I don't benefit in any particular way if AI does well, except insofar as anyone who holds broad index funds benefits, but I do care about accuracy.
Does Dan Luu still work at Nvidia?
> I had ChatGPT give me a list of predictions (with no attempted tilt towards correct or incorrect predictions) and then I skimmed/read the posts that ChatGPT linked to.
yeah alright
> If I really thought about it, I probably could've found something better to do with the time
Extremely likely.
> And yet, it would seem that my fact checking process is a lot more thorough than Zitron's.
Dan says in his post he fact checked with Chat-GPT and Claude, so: lmao.
If anyone who predicts the future was reliably right, they could easily shut the hell up and become a billionaire on stock picks. But they don't do that, because nobody can predict the future. You can say what may happen, but not for certain, and definitely not exactly when. All predictions without insider knowledge are idle musings.
Also consider that the economy isn't rational. Our economy should have tanked several times by now. AI should have fallen apart by now. Meta, Google, Microsoft should have declined. Instead it's record profits. So don't try to make predictions by being rational.
Ed Zitron sells subscriptions to his blog. Whether he’s right or wrong is irrelevant.
Why would any rational person want to pay to read falsehoods?
I think it would be naive to believe Zitron is unaware of what he's doing.
He's created a huge following (and is presumably making a lot of money) from pushing a hardcore AI-skeptic narrative, and I can't blame him for seeing that opportunity and running with it. We're ultimately all responsible for recognising these people and weighting their advise as necessary.
Additionally, from a public reputational perspective making bad predictions simply doesn't matter. In finance we're all aware of perma-bears who will predict the sky is about to fall, and when it doesn't just argue that the disaster is still coming, but is taking longer than expected, or that some unforeseeable thing happened which has compounded the risk but has for now kicked the can down the road.
So ultimately, it will be very hard to say Zitron is wrong unless he starts time-boxing his predictions, which I don't believe Zitron has done for obvious reasons.
That said I don't listen to him much at all. I've tried to listen to him a few times, but it's become evident very quickly that he doesn't understand the technical details enough to be making the predictions he's making and seems way too emotionally invested in the arguments he's pushing without good reason. I have strong personal filters for low-quality sources like Zitron – if someone raises enough flags I avoid them like the plague.
Ed gets hard because of the love the anti-ai people give him and seeing him as the voice of anti-ai... If you listen to his arguments though he lacks knowledge of what these companies even do. He's been on so many shows discussing the negatives but he admitted to not even using the models for anything
Cannot take profits of these companies seriously when they have been laying off employees by the droves the past couple of years. Obviously profits will increase when workforce expenses have reduced. I'll only take it seriously after a couple more years and see how the company has held up with massively reduced workforce (powered by AI™). Another point would be to see how these incumbents get challenged by new startups and if incumbents can survive this phase.
Don't you think those companies are mostly correcting for their massive covid overhiring + zero rate bonanza coming to its end?
Also, back when elon had bought twitter and had been firing people left and right, it was prophesied that with such inept management the company was going to collapse very soon. I thought it was obvious it would collapse. But that has not happened (yet, but it's been quite some years), and the service seems to be holding up after some initial shakiness.
I would partly agree with you in so far as Twitter not collapsing and working well. I'll go so far as to say it was the right move by Elon to downsize the company as you really don't need so many people to run a social media site. However, I don't think Twitter can be compared to the scale at which Alphabet/Amazon operate. Alphabet/Amazon run hundreds of businesses (on the scale of X) within their parent umbrella organization. So even if one collapses it won't affect the other as much as it should. But since they are removing workforce by the thousands, I am wondering how much of it will have an impact on their bottomline in the coming years. I mean... they will be under pressure by their own stakeholders to put their money where their mouth is and remove workforce if AI has reached levels where workforce can be replaced. If they don't do it then they are tacitly admitting that we haven't reached that technical breakthrough needed. The only way forward for these companies is to make it happen one way or the other. They are heavily invested in it and cannot back out now.
Here's where we're at:
Tech companies invested in AI: AI is amazing (so good, it might kill us all, help?), and the ROI is going to be amazing.
NVIDIA specifically: We will allocate cash in strange deals that makes it looks like we're making investments but actually we're buying sales from those companies as they'll spend the money back with us, and everyone seems to think that's OK because the share price is going stinkers.
Cynics: AI is a scourge on society, and the fiscals are circular and a scourge on the economic stability of the planet.
The more nuanced argument I take is that Zitron is right about the circular finances and hyperinflation of valuations, and wrong about the utility of AI to help people. The Tech companies are just outright wrong about the finances, but right about everything else. And, NVIDIA's time will come, and it won't be pretty.
None of this requires me to believe that if Ed says something it must be true or false. It's just another data point, and he does uncover information I can't find myself that checks out. The fact he is wrong about the functional utility of AI or that he's getting super focused on hyperscalers when the real issues are in Oracle and NVIDIA, I can live with.
I only read the left half of the text on my ultrawide screen.
750 comments but I can't believe how bad this article is. The paragraph on MAU says similarweb is untrustworthy but then cites "most estimates"...which? Sorry, I can't read this without thinking you're biased against Ed Zitron.
The best part is the spreadsheet, which makes him sound like he's just sloppy, but danluu specifically says he's being deliberately misleading elsewhere, like the opposite of hanlon's razor now? So is he being deliberately sloppy in his spreadsheets or is he just incompetent? I suppose both is a better read but you need more proof for the "deliberately misleading" line and the spreadsheet frankly is danluu's best evidence beyond the list of failed predictions which is less striking in my mind.
While I think he's probably wrong on the timing, which clearly has been wrong since 2024, it doesn't really address the greater critique, which is that ai companies and nvidia and memory manufacturers need revenues collectively in the trillions to make their investors whole. That is still unsustainable such that it's been called out by other investors and financial journalists.
The first correct, detailed prediction (in print) about the collapse of Fannie Mae and Freddie Mac as a result of the subprime crisis was made by Max Keiser, of Karmabanque, in (Zac Goldsmith's) The Ecologist magazine.
In 2004.
And he'd been talking about it before then.
People thought Max Keiser was a crank; instead he made detailed predictions based on his intuitions, limited insider feedback and cold hard facts. He couldn't say exactly when it would happen; he laid out some horsemen you could expect for the apocalypse and this was one.
The thing about a bubble is everything is fine until it isn't. And it's worth observing that key parts of what Zitron is discussing has been covered in the WSJ and FT.
It's all very well posting numbers to "disprove" him when what he is pointing out is that the AI hype train is delusional and the costs are buried.
But Zitron is directionally correct, I think, particularly with regards to Oracle, where things he has said have literally come true.
Frankly as a Brit I remain amused at how much Zitron winds Americans up just by being himself — sweary, vulgar, rude, catty. And since non-Brits can't read Brits, Luu has to engage in pretty immature character assassination about it.
Zitron is an AI doomer and a clear fact-distorter. I follow him because even a broken clock is right twice a day and I do think he's a pretty smart guy. I don't read his newsletter (it's basically word vomit these days), but his interviews are marginally interesting. One of the quotes in the linked article is totally correct:
> He found a niche in anti-tech grift, and is now exploiting the niche for all he can.
I am currently surrounded by hyperventilating corporate and country level leadership who are fully swept up and captured by the AI craze.
I have seen some wild failure modes from people who are outsourcing their thinking to LLM. Amendments to clauses that don't make English sense. Multi-paragraph long replies in emails that say nothing specific to the issue at hand. Responding to questions with "AI says this" (but I asked you, not the LLM). Leadership wants us to embrace AI but there is no product for the layperson, it feels like everything is front end + generic prompts + $LLM_API_key. People want to make customer service bots that have access to personal data.
I feel like software devs are so lucky in that at least people in your field can see an LLM for what it is and harness (no pun intended) it appropriately. As a lay user, no such luck. Leadership and purse strings are far removed from IC work and don't understand why an AI product wouldn't work, they've heard otherwise in their circles, you had better make it work so that they can claim to have delivered an AI transformation this year.
Enter Zitron.
Zitron is a woo-pushing grifter (his product: his stance on AI). Even without examining his reasoning or the accuracy of his predictions, Zitron is hard to listen to. Mostly, he shouts out a constant barrage of bare assertions that his research is thorough and irrefutable and the doom is coming and ever "AI booster" who disagrees with him is an idiot, all the while without actually spending time arguing his point.
But Zitron feels like one of the few people actually pushing back against this craze.
I would very much like a better argument for a position that I support, please and thank you.
Zitron is just a rage farmer that tells angry people what they want hear while selling them subscriptions.
Maybe he is well meaning, but it's pretty common these types are just milking an audience that they dialed in on with zero regard for integrity or honesty.
zitron is a jester. He knows his audience and plays it well. It is incredibly tiring to listen to his lies, but so are the AI boosters. I think it is net zero
just give it time, anyone (even those with 115k subscribers) who predicts a crash every day will eventually always be right. might take a decade or two… :)
Everything everyone is predicting has almost zero value. Nobody owns a crystal ball. Right now there could be someone working in a garage somewhere that could shake the entire LLM world to the core with a unique insight and innovation.
Think about the world before the 2017 "Attention Is All You Need" paper.
Did anyone predict that paper, what preceded and followed it?
Nope.
Same case now.
Maybe the word "prediction" is the problem; "guessing" would be better.
OK, what the heck. I'll make a prediction too:
You better buy SpaceX stock now. The way things are going in the US, the only way we build AI data centers at scale will be in space. Politicians have turned data centers into punching bags to be used to gain votes. We can't build power plants and people are being led to believe all kinds of things. Regardless of which, if any, are true or not, the rate of construction of AI data centers in the US is and will be seriously constrained by realities on the ground.
Hence my prediction: It's all going to space.
Have you chatted with Ai about the economics of space vs terrestrial?
Even if you believe they can get cost to launch 1KG down to 100 bucks... (current falcon heavy is $1550) you still have the problem with all those solar panels.
1GW of space AI requires... 1 GW of solar to power it.
Current manufacturers of space solar is 1-2 MW &.. space solar aint cheap. $100 per watt. They estimate each spacex will be 250 kW. So.. $25M just for the solar for each satellite. Yeah you can use less efficient / cheaper solar, but then your weight goes up.
Also... Ground AI datacenters haven't nailed down how quickly GPUs depreciate. Accountants claim 6 years while CRWV says it's longer and most realists say it's shorter. It's based on how quickly GPUs will improve. Which is QUICKLY.
In space you'll have to depreciate GPU, all supporting hardware including the satellite, electronics, solar panels, and launch costs. On the ground it's just the GPU & you can even sell the old one down.
ground based AI needs power. $FRVO is an interesting ticker if you believe in their technology. Just announced a deal with google yesterday.
It isn't going to be easy, of course. Yet, I do believe they will make it work.
One correction: SpaceX makes their own solar arrays. The cost is nowhere near what you quoted. Source: I worked for SpaceX for a few years.
Also, you can generate a lot more power in space per unit area due to not having the limitations created by our atmosphere and weather. It's continuous generation at 30 to 40 percent higher per-unit-area radiation. Generation on earth is roughly an inverted parabola (on a good day without clouds and weather) that, at best, if you integrate the area under the power curve, delivers 66% of the total equivalent energy say, a nuclear reactor, could deliver during the same period. So, once you increase radiation from about 1,000 W/m2 (impossible to achieve on earth due to weather and other realities) to 1,400 W/m2, 24/7, no weather, etc. The scenario quickly becomes vastly different.
Geothermal is very interesting, thanks for the ticker, I'll look into it.
All that said, I think the US (and Europe, if they care to survive) needs to have, as a top national priority, a massive program for the construction of nuclear power plants.
A few years ago, I ran through an analysis of power generation needs to convert our entire fleet of vehicles to electric power. That required at least doubling our power generation capacity. This was the equivalent of having to build 1,200 nuclear power plants, each producing 1 GW 24/7.
This is impossible to achieve with solar, or wind, or the combination.
So, even if we decide not to care one bit about AI data centers, we need to double our power generation capacity (and infrastructure to deliver it).
Add AI data centers to that equation and the number could easily climb another 600 nuclear power plants.
Here's the problem that a naïve conversation with an LLM will not uncover: Humanity and Politics.
Could we embark on such a massive energy-generation project? Yes, absolutely.
Is it realistic? Nope. Not even close.
In other words, we can do it but it is impossible...which sounds like an oxymoron until you consider that we have lots of examples of projects that are absolutely plausible that, once they contact political and government reality, quickly become impossible. The best-worst example I always grab is the California high speed train disaster.
The author asks "How can people take this seriously?"
I would reply the same regarding this article. Nearly all of these refutations are unconvincing.
The claim: "I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be."
The rebuttal: "Zitron's argument at the time was that hallucinations were as good as they were going to get, which meant that AI performance is capped at 2024 levels. Both the overall prediction and the mechanism were wrong. This one seemed wrong at the time, in that I noted here in 2024 that you can make AI code halfway decently by just putting it in a loop and having it run until the code compiles and tests pass"
Putting an LLM in a loop, burning tokens, and thrashing against a compiler and test suite is a ridiculous way to say that hallucinations have been "solved". Please. This is absurd.
both ed zitron and dan luu are professional bloggers who earn their living from selling ai sensationalism, zitron using paywalls and luu using patreon. whether the sensationalism is positive or negative, either way it is a very clear conflict of interest.
I’ve come this far without knowing who Ed Zitron is…
I read all Ed's posts, and enjoy them, and I like AI as a tool and use it every day.
I think it's really, really, really important to have a contrarian opinion out there, even it's a voice howling in the wilderness. Even if most of his predictions are wrong. Even if he swears a lot and gets a bit ranty at times.
Personally, I think he's going to be mostly right in the long term about the AI bubble, but mostly wrong in the long term about the effectiveness of AI (i.e. I think it will have a net-positive effect in the long term).
I always keep in mind the Gartner Hype Cycle [0] is true, and we're still on the initial slope up to the Peak Of Inflated Expectations.
[0] https://en.wikipedia.org/wiki/Gartner_hype_cycle
I had an interesting experience after I stopped reading HN comments for two weeks. I think it had to do with not reading the doom and gloom on here, which I tend to agree with. But after two weeks of reading neither the hype nor the doom about LLMs, I realized how amazing (though not perfect) this tool is. My point isn't whether LLMs are good or bad, but just how I didn't realize how much my consumption of other people's opinions infected my own outlook. It was like the opposite of rose-colored glasses.
BTW, the tool I used to block HN is called Foqos, which, of course, I heard about right here on HN. Based on my screen time reports, I got back about 2 hours per day average after blocking all my favorite sites.
I agree that Zitron is not honest and a denialist regarding the usefulness of AI, but the "profit" chart is just completely misleading as most of these companies have made large investments in OpenAI/Anthropic and list the gains in their share price as "profits" - so yes, while the asset prices are rising, that's typical in any financial bubble. Google is actually cash flow negative for the first time in history (which imo should be what measures profitability: income from products and services - costs to produce them) so Zitron's core thesis that there are no returns in AI is playing out as far as I can tell...
circular financing, layers of financial engineering: https://finance.yahoo.com/markets/stocks/articles/google-par...: On Aug. 6, Alphabet filed its 13F with regulators covering its second-quarter trading activity. Given that SpaceX went public on June 12, Google's parent company is now required to include its SpaceX holdings in its quarterly 13F.
I've said this before in here but Zitron is completely captured by his audience at best and a grifter at worst. A couple of months ago he was tweeting that people were crazy talking about _agents_, that they didn't _exist_ and that people talking about them were shills or bots. The responses were full of incredulous software developers saying but but but I use them every day, they're so good they're scary actually...
Zitron is just, like Dan Luu says, wrong about everything and doesn't care anymore, he's in the business of extracting money from his engagement.
I think it's a mistake to make concrete predictions about something like a stock market crash on a given timeline. As they say, the market can remain irrational for longer than you can remain solvent. However, Zitron is directionally correct about a lot.
I will say, the first part Mr. Luu says about big tech not being out of ideas is pure horse shit. Anyone who has worked in big tech knows that leadership at those companies can have no idea what they are doing and still be successful in earnings or the stock market. They are sometimes successful despite themselves.
Edward Benjamin Zitron (born 1986/1987) is an English author, podcaster, and public relations specialist.
So why do we carr exactly?
I appreciate someone picking apart Zitron in a constructive way. He’s been an easy recommend to folks in my circles who just want to be angry, but there’s a reason I don’t read his stuff on the regular or in detail: I don’t want to be angry, I want to do something.
And I say this as someone who started as a doomer, and is increasingly a pessimistic pragmatist (“LLMs have value as tools, but not nearly as much monetary value as the main players believe they do”).
I get it though: for those of us who grew up alongside the net and tech sector, who loudly decried M$ greed for ME/Vista/8/11 but celebrated them at XP/7/10, who remembered when Google’s “Don’t Be Evil” was spoken with serious reverence, the current era of tech feels toxic and nauseating. Current AI is a prime target for that discontent, as are the companies whose motives very clearly aren’t societal progress so much as reality authoring and authoritarianism. In that vein, Zitron is magnetic because his entire position is “you’re right to be mad and they’re all going to die from hubris without you having to actually do anything”, which itself panders to the human desire for personally preferential outcomes sans individual effort.
Properly picked apart though, and he has as much substance to offer as the ardent boosters: a handful of “trust me bros” with a smattering of distractions to wind you up, but never actually address your concerns or questions.
The whole article reeks of paid propoganda hit peice.
The author attempts to virtual signal as impartial but fails horridly.
Ed Zitron is providing his assessment and prognosis strictly on financial basis. But AI has national security and geopolitical dimensions as well. US government is fully committed to keep the AI lead for as long as possible. And they use all sorts of tricks to affect the market in AI's favor. Financial analysis alone is not enough. The government will do anything to prevent AI bubble from bursting.
Obviously Ed is a special case, but let's be honest, pretty much anybody telling you they can predict the future is selling you BS. And that includes all the people confidently predicting he was wrong. The actual situation is, there are genuine unknowns driving things with significant influence, and nobody actually knows what is going to happen. At best, people can talk about risks and likelihoods of outcomes.
This submission has citations on where he was wrong, they aren't predictions.
I've always been suprised with the amount people take him seriously. I think he's someone who makes people who dislike AI "feel good". I don't blame someone who doesn't understand AI or have read what he's written.
But for the bloombergs and other podcasts I would have expected them to do a bit of research. I honestly think with the amount of doubling down he's been doing that he's a grifter.
I think cable news channels like CNN and CNBC like to have a "counterpoint" on to make their coverage seem balanced. There's always a guy saying the stock market is about to crash. There's always a guy saying oil will hit $200 per barrel. There's always a guy saying AI is a bubble.
Zitron is never right but he exists so that media can claim to be balanced.
How has it taken so long for this to get some proper attention?
I'm not Ed's biggest fan, but he's been pointing out some very important things for quite some time now, regarding the ludicrous over-investments going on around so many companies that are completely and utterly devoid of profits and will likely never have any.
He's dead wrong about the usefulness of the technology, but he's dead right about the uncontrolled corrupt circular financing that is driving this crazy over-expansion and market distortion. He fulfils a very useful function as a counterweight to the big tech horse-shit hosepipe that sprays us every day.
what i would do for some formatting on this piece.
Zitron in general is representative of the conspiratorial thinking that has infected all spectra of the political space. Zitron obviously occupies more of the left space.
Good critique of LLMs & the companies behind them is hard to find, and it is harder when people gravitate towards this sort of thinking.
Off topic:
> For example, with a style that could be described as the opposite of clickbait, Simon Willison has written what I suspect is the most widely read blog among programmers for the past 3-4 years (in the same way that, at various times in the past, Joel Spolsky or Jeff Atwood or Steve Yegge seemed to be the most widely read programmer among programmers). Among programmers and other serious users of AI, I would guess that Willison has a larger audience than Zitron.
I have no idea Simon Willison is the most widely read blog among programmers. I truly have no idea, and I've been programming for just a decade.
A lot of Simon's blogs posted here are when new LLM models are released, on how good are these LLM models create pelican riding a bike using SVGs. Nothing particularly interesting to me.
I truly have no idea why would people be interested in blogs about LLM creating pelican riding a bike svgs every single time a new model is released. Maybe its a proof of AGI/ASI for some?
I guess to me, Simon Willison will always be the "create-a-pelican-riding-a-bike-using-svg-dude".
I used to read his posts about Django years ago (a little strange in itself as I'm not a Python guy) and it took me a while to realise he was the same person!
Thanks for this post. Zitron is a prime example of how someone with little background, expertise, or domain experience can establish a grift in the outrage economy. Unfortunately, emotion and illusion when properly marketed can still go a long way in this world.
Zitron has staked his bear position and isn't budging, so regardless if he's been wrong and wrong again, he'll be remembered for calling the bubble if/when it pops, if only because so few in the media have done so without equivocation.
I just remembered that before AI, I knew Zitron from ranting about IoT and consumer tech. He just appeared one day in my Twitter Timeline, claiming to be the "man on the ground" at CES (or so my memory at least)
So from that standpoint, I guess it would make sense that a personality like that would now have ended up at the AI topic.
I would like to see a comparison with predictions of the pro-AI bubble and their hit rates though. This writing feels as selective as it tries to paint Zitron as.
All of the grifters (and lots of capital) can't wait to jump on the bandwagon because they see it as their chance to clean up. The same happened during the last 4 tech revolutions. (in my life so far: semiconductors, the internet, mobile communications, smart phones, crypto, EVs, probably forgot a few). And of course, as always, the longer term will be more amazing than the short term hype.
He's been right on the money. People here are mad that he is because they are profiting off of the grift of the bubble.
a thorough Fisking, nicely done danluu
the analogy to Ehrlich was strikingly apt
Now let's do the same analysis for the stuff that easily flows from the mouths of AI CEOs
Hate on him all you want (he's gotten very repetitive for the sake of subscribers and reads), but the basic premise that the modern AI industry is a circular-dealing, point-of-diminishing-returns grift still holds. The bubble is here and the longer it inflates, the worse the pop will be. Sure the goalposts have moved, but the basic numbers don't math.
The current cross-deals aren't purely circular; quite a bit of revenue is, in fact, flowing into the AI industry from the outside (you know, via customers). What's more notable is the shared risk, as the deals tie multiple companies across the chain to a set of shared bets.
If the frontier labs suddenly found themselves unable to compete with cheap open weight models running on widely available compute, then one might expect the frontier labs to be the only ones exposed to that risk, while a chip-manufacturer like nvidia could thrive in either environment. A partnership or commitment from the chip manufacturers to the frontier labs could change that. Whether that kind of inescapable connection exists is hard to predict without a lot of specific modeling, and at this point I'm inclined to think that neither chip nor datacenter demand is going to drop any time soon, and that the commitments would be unwound before a company like nvidia is threatened.
> The bubble is here
Zitron implied 2 years ago that OpenAI would collapse by now. How's that bubble popping going? All NVDA+memory co+frontier lab numbers are accelerating
The thing about bubbles is that everyone who thinks they’re going to pop look crazy until they pop.
And to be clear, a bubble popping doesn’t mean that AI goes away forever.
What it does mean is that we’ll see some kind of economic crash or recession, and we’ll probably see at least one big company fail or go bankrupt/restructure.
OpenAI is the company in most obvious peril.
I happen to think that Nvidia is in a more perilous position than they appear. Their hardware advancement pace is relatively weak and they’re in a crypto-like hardware bubble where they’re one technology breakthrough away from a complete collapse in demand for their AI data center solutions. They’re also doing a lot of sketchy hardware financing schemes.
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Comparing to other bubbles in the past, this one could still have something like 2 years left. Patrick Boyle has a video on the topic, in which he's also very careful to point out he could easily be wrong.
The market can stay irrational for quite a while.
By all means, keep betting on this being a wild success and we'll see who's right in a little while. If you don't think this is a bubble then you're in for a ride.
Look if we are going to raid pensions and 401Ks to prop up the valuation targets of Anthropic and OpenAI longer, then the bubble can stretch further and further, but eventually datacenters have to get built and powered. It's the power generation part that no one talks about. We simply don't have enough power in the United States to scale at the rate that Anthropic and OpenAI need to prop up their absurd valuations. AI is real, but these valuations are not.
TL;DR: Ed is directionally correct, but it's anyone's guess as to the exact timing.
In the meantime I'm not going to complain about subsidized credits from the big labs. :-)
The AI doom is actually a happy path scenario.
Collapse of entire economies will result on a scale that will eclipse the great depression. And it wont be because AI revolutionized anything. AI will become a dirty word to never be uttered by anyone in the human race after.
i relate to zitron cuz of how much he seems to genuinely hate the people in charge of this AI bubble. he, like me, seems to wish that when this all falls apart they get proportionate harm to go.
that'll obviously never happen. it's wishful thinking and venting tbh. that's why i like listening to his stuff.
it's better than reading the wholly AI slop docs my CEO keeps sending out
C'mon anthropic. Give him a job! :-)
zitron just saw the next frontier in culture war grifting and went all in on it
Now do Sam Altman, Dario Amodei and Elon Musk.
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Zitron is the opposite of Jim Cramer. No Hype, no sales push, very cynical and conservative.
Saying someone is the opposite of Jim Cramer is basically saying they’re right.
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99% of these comments are bots.
That’s what HN sells these days. Fake bot comments to prop up wannabe celebs and startups
“You pay, we spray”
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AI slop is eating the world
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Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.
Comments should get more thoughtful and substantive, not less, as a topic gets more divisive.
When disagreeing, please reply to the argument instead of calling names. "That is idiotic; 1 + 1 is 2, not 3" can be shortened to "1 + 1 is 2, not 3."
Please don't fulminate. Please don't sneer...
https://news.ycombinator.com/newsguidelines.html
It's my fault. I'll follow the rules more carefully.
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Says anonymous internet user with a 10 minutes old account.
I made the account to make the comment because I feel strongly about him being untrustworthy. You can take it at face value or assume I'm lying, that's your call.
Great article. I was waiting for someone to eventually do an assessment on this grifter.
It is funny to see mainstream media finally catching up to how much of a grifter this guy really is. I believe I was the first at least on HN to make a list of his horrible predictions over the time [1]. Since then Kelsey Piper [2] also wrote about it and got some traction.
I knew that somewhere in the future, something has to give because you can't just go around saying anything without losing some credibility. You still have some ardent followers in his cult of a subreddit.
But just to be clear: Ed is part of a bigger problem in tech journalism which is characterised by extreme pessimism and excessive skepticism. It is not correct to view Ed in isolation rather to see it as a part of the culture in which he can thrive.
[1] https://www.theargumentmag.com/p/ais-biggest-critic-has-lost...
I want to point out that Zitron is _just a guy_. He’s not a billionaire CEO. He’s not a politician. He’s allowed to say what he wants whether it’s right or wrong, and _he’s just a guy_. He’s not swinging stock values for a group of insiders by tweeting. He’s not using pension funds to pay for a jacked-up IPO. He’s blogging and making a podcast, neither of which is compulsory for you to read.
You don’t have to spend effort proving him wrong. Just don’t read it and move on with your life. Regardless of which “side” of AI you’re on it’s kinda ridiculous how much effort gets spent on screaming gotcha at this one commentator.
He charges people money for a newsletter that reinforces completely detached from reality viewpoints. If he's just making stuff up, that's called grifting, and it's good people call that out.
> if he’s just making stuff up, that’s called grifting
So then we should be calling Dario out every time he opens his mouth, right?
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I think the most enjoyable part of this is to have written it in the characteristic long-form Zitron wall of text style while still managing to pack almost the whole text with meaning, which is entirely the opposite of Zitron style.
Posting revenue numbers as an example of a company not dying seems rather foolish. Microsoft's gaming side is floundering and dying and Linux has been growing at an unprecedented rate as a result of Microsoft's decisions. Not to mention the geopolitical aspects at play here as countries make a serious consideration to drop Windows.
These companies are all heavily buoyed by their investments in AI which is essentially an oroboros of money.
I do think you have to consider predictions in the context of the world around them; he's been incorrect, sure, but has he been more incorrect than the predictions of those opposing him? Being less wrong than others is the same as being more right.
No idea what you're trying to say... he's been directionally wrong for years as the article illustrates clearly.
Zitron's predictions have often been wrong; they've still been less wrong than those of the people who disagree with him.
Easy to claim things as wrong without prodiving any facts.
Can I try?
> But when people bring him up, they're of course not generally citing his anger
Wrong.
> Google has been increasing the relative priority of revenue over the user experience over time
Wrong.
> I'm curious what people do after being on the wrong side
Wrong.
Some of the claims categorized as "wrong" are also completely true, such as training hitting diminishing returns. New models are barely an improvement and most people I know stuck on Opus 4.6 over any newer one for example.
Exact same thing for the claim "the fact we're running out of high quality training data and we're hitting the walls of scaling laws, in the training paradigm, these models aren't getting better. What we're seeing today is pretty much what they're always gonna be like".
If anything, model performance has regressed in actual use (i.e. not benchmarks) for the past half a year.
> Some of the claims categorized as "wrong" are also completely true, such as training hitting diminishing returns. New models are barely an improvement and most people I know stuck on Opus 4.6 over any newer one for example.
OK, but the first instance of a claim of diminishing returns was in February 2024, when GPT-4 was the best model available. Do you really think improvement since then has been minimal?
Yes, since 2024 the improvement has happened only in a few contexts, and the most famous¹ LLMs have also regressed in many contexts.
1 - Their numbers have also exploded, so I have no idea of any general rule.
I personally think improvement has been minimal since ChatGPT was released actually.
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