One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow. If Zitron's argument were as solid as he keeps saying it is, you would read it and understand it and see that it is solid. He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument. But no. He jumps. He leaps. He circles back. If the situation were really "Gosh why can't you see it?!"-clear, his explanation of the situation would be clear. It isn't, because it isn't.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
Which of the hyperlinks provided at the beginning sounded like what you wanted, and after you clicked it how did it disappoint you?
The information you are describing is stuff I would not expect anyone to repeatedly duplicate in periodic blog-posts.
It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.
The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
We are only five or six years into the leap LLMs represent. For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up. It took so long not because of the physics of radio waves required time to mature and improve, but because everything else needed to profit from it did require time.
To me, LLMs are not so much AI as it is a building block. Radiowaves maybe, or the equivalent of transistors. We are already seeing that it's possible to chain LLMs into agents. Currently, price is a strict limiting factor for coding and agents.It's probably fine-ish if all you want is Claude Code or Codex, but there are many other possible compositions of LLMs that most people don't dare to experiment with. For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost. Were prices of inference hardware go down and inference algorithms keep improving, I'm convinced (and afraid) we would see things very difficult to imagine today.
Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely misses the incredible ground-level utility that many of us on HN celebrate every day through undeniable, massive productivity gains.
At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.
Writing about AI, destroying the planet for data centers, there's a lot of money to be made.
That being said, AI seems kind of miraculous sometimes.
Similar to cars. So enticing that we make everything else in the world worse in order to maximize the profit, make it indispensable, subsidize it, and make the dependency on it irreversible.
And it's not even something to blame individual people for.
Driving away from all the other cars to spend a weekend feels like freedom.
Using AI to answer a question feels like a "bicycle for the mind".
But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt.
Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
Not sure what your point is. Stock markets are based on money going into securities based on estimated future value. Even if AI were doubling productivity at a non-AI company, there is more leverage to that money going into an AI company.
The question is, is AI leading to massive productivity gains in companies that implement it? AI productivity gains take time to diffuse, but so far companies in the S&P 500 are seeing very high growth. YOY earnings growth rate for the S&P 500 is 21.7%
https://advantage.factset.com/hubfs/Website/Resources%20Sect...
He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse.
I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place.
My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit.
But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time.
The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
I think he doesn't need to understand the technology to point out the books are cooked. a business can sink in either way: the technology flops or the finances flop. he's arguing the /finances/ would flop. he doesn't argue that the /technology/ would flop, only that they can't come up with the money to pay their debters.
Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing.
From an economic perspective productivity is defined as the creation of value isn't it? Then if you "improve productivity" and does not create value in the end you're no improving productivity at all.
Also, supposed productivity gains are dubious. I personally experience at best no productivity gains when using LLMs to write code, and sometimes it's an active drain on my productivity. There was that one study a year or so ago showing similar results. People are trying to say the productivity gains are there and undeniable, but that is not true. It is very much a subject of controversy whether AI helps productivity.
That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent.
EDIT: In fact, parent comment has a link to some numbers.
[EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.
The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either.
Do you think it's not slowing? Do I miss anything really important?
My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work...
It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...
Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost.
Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost.
That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model.
I think the surest sign of productivity gains is the sheer volume of adoption. If you look beyond headlines, adoption is just incredible. Of course adoption does not necessarily point to productivity gains, but if this was some sort of FOMO or smoke and mirrors you would not see this much retention and this feverish a pace of adoption. You would not see a large segment of the profession using coding agents exclusively. All of these companies track productivity, again with imperfect proxies, yet everything points to a pretty consistent picture. Same with benchmarks, again a lot of crappy benchmarks but a lot of high quality ones too and a very diverse collection of tasks and capabilities they probe.
Even if we assume that everything you said holds true, how is that we as a crowd can make viable a service that eats some $300bn annually in infrastructure costs? Where would that money come from? Most tech companies these days are cutting their AI budgets because the per token pricing is killing them.
Cite a real source for that last bit, I don’t think that is true. Also the budgets should be cut the spend at some places goes beyond any reasonable amount. The strategy there is to hook everything in and find the right processes, then cut the rest. Things then get better and better with each model release.
The way you make a viable service that eats 300bn annually is to have enough demand to service that. Anthropic underbought compute. That tells you something.
Yeah they're very much deniable. Raw LOC/hr is much higher, and putting together a MVP, but I've yet to see any evidence that an LLM is capable of doing anything unsupervised, and if you need a human supervising everything it does... why bother having an LLM in the first place?
Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead.
The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.
Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).
I really like some good drama slop that reads like a thriller, it is entertaining.
I don't take any of it THAT serious, but lately with the IPOs that are about to hit the indizes, he has gained a lot of attention.
If you look around the internet, most people publish a negative angle on something and then extrapolate it into some grand conspiracy, which is really captivating.
Its crazy when you enter some echo chamber you never engage with (movies, gaming, art/comics) and they have their own head cannon for why the world is bad and collapsing. It puts your echo chamber into perspective to see the same patterns of argumentation and presentation spin out in a different way
And where are those? They seem particularly hard to actually observe and only appear in anecdotes.
> I'm trying to believe
For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?
Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!"
This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.
No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job.
Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades.
Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."
How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication.
I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think.
[1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.
Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. https://www.wheresyoured.at/peakai/ from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)
Predicting the timing of such a thing is notoriously difficult. I don't think being wrong about timing 2 years ago means there won't be a correction.
I'm also reminded of all the HN posts from 2007-2009 that predicted that the adoption of social networking would be a terrible thing for privacy, that it would destroy society, that people would lose their jobs over crazy shit they said on the Internet, that it would lead to the decline of trust and in-person interactions, that people would forget how to socialize, etc.
They were right about all of that but it took 15-20 years and the companies involved grew 100x in that timefold, eventually reaching trillion-dollar valuations that would've seemed insane in 2007.
There is a tremendous amount of money to be made in destroying society.
Not related to AI but, I recently rewatched "The Big Short" and your comment reminded me of it. I can't testify the accuracy of the movie, but for over year, Michael Burry was viewed as in the same manner for shorting the market, while the economy was was in a hype cycle.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).
But from the article I linked back in March 2024:
"Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models' capabilities will continue a rapacious pace of progress that has unquestionably slowed, with OpenAI admitting that GPT-4 may be worse on some tasks.
As I've written before, hallucinations are a feature not a bug. These models do not "know" anything. They are mathematical behemoths generating a best guess based on training data and labeling, and thus do not "know" what you are asking it to do. You simply cannot fix them. Hallucinations are not going away."
Since then:
- hallucinations are dramatically less of a problem
- several mass market use cases have emerged, most notably coding
> I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley.
We have seen 8 quarters since. Has any of that come to pass?
The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.
Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:
He's a Gary Marcus-level contrarian with none of the credentials or contributions to the industry. The "AI bubble" cope narrative is getting stale but will still appeal to luddite autists years after it has ceased to be relevant.
As a tangent, I don’t understand where and why meta fits into the AI race. They did not get any mind share (consumers) from the llms so far, granted they started the open source side to this but the Chinese companies produce far better models and have essentially become the default for on device set up.
They have ai glasses and integration into instagram and facebook as the other avenues. I don’t see ai glasses as compelling yet, and don’t know how much more ad revenue or user engagement they can squeeze out with llms baked into the IG of FB flows. They are spending a lot and not seeing any returns. Am I wrong in being pessimistic about meta with AI?
This is wishful thinking. AI is still getting better rapidly. Anthropic's revenue is still growing at an unprecedented rate and they haven't even released their best model (Mythos) for 4 months now.
Ed is an interesting character. His financial analysis of the AI industry makes logical sense to me (though I am not knowledgeable enough to actually know if it is correct.) However, he seems to be so angry at AI in general, that he misses the obvious areas where LLMs are actually changing the State of the Art.
Coding seems to be one of the core use-cases for LLMs (as Simon Willison pointed out recently) and even if that's the only real use-case for LLMs, they're wildly useful. I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
I don't think whether "LLMs are actually changing the State of the Art" or not matters for anything he wrote.
If the AI companies need $X billion in revenue to stay afloat, it doesn't matter if 0.5% or 5% or 50% of that revenue is from transforming the State of the Art. It's 100% irrelevant: what matters is that, transformation or no, these companies won't have the income to pay their bills. And if they can't pay their bills, a whole lot of other companies can't either.
So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.
He's got subscribers. Maybe the attitude is one he's found plays well with them.
I find it quite refreshing in some ways. Lots of people, when they start complaining about this or that aspect of this AI stuff, are wont to add in a little disclaimer that, despite all of the above, they actually really like AI and use it all the time. I assume this is to avoid the scenario of a bunch of pragmatic builders turning up and calmly shipping nuance in the comments (or whatever you call it these days when you get brigaded by a pile of angry keyboard warriors with chips on their shoulder) - and it sure is tiring having to wade through the equivocation.
That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.
It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.
> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
This is often repeated but comes from ignorance mostly. You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.
This kind of thinking really reinforces my belief that people have no idea and are using this whole [AI is not profitable and too costly] thing as a cathartic way to deal with immense progress.
However, it needs to be said that he received those numbers. I personally have quite a few issues with him, but there's no reason to doubt his journalistic integrity. Because of that, I believe he reports truthfully on data he receives by informants.
Additionally, none of the frontier models actually publicly talks about inference costs in anything but broad, "let's just forget that"-like takes. Which does not exactly spark confidence.
I'm eagerly awaiting anthropic's public disclosure of their financial details. That should be rather interesting in any case and finally put the inference-discussion to rest.
Zitron is in the business of content creation and not successful predictions. It doesn't matter how many times he (and several others around) will say the end is here, they have to be right only once.
BTW, one thing for sure he is right about are the economics, as of today there is no way these massive investments are gone be paid.
Buried lede (if the title is the actual promise), the sources don't seem to back the title either. Someone with more patience can correct me if I accidentally missed a bombshell anyway.
Edit:
> If you’re wondering what the story is, [...] I expect it to be out in the next two weeks [...] I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.
Ok, this takes clickbait to new lows. The headline is trying to sell the teaser here, with very limited meat in the middle of the sandwich.
Ed's argument for why "AI is slowing down" rests on company spending caps, in particular the Uber $1,500/engineer/tool cap.
I interpret the exact same evidence in the opposite direction. A year ago the idea that a company would spend $1,500/month/employee on AI tooling felt absurd, what could people possible want to do with AI that would cost that much?
Then coding agents (and, increasingly, general purpose agents) happened and suddenly companies are having to set limits because otherwise the demand from their employees is too high.
The TAM of these AI companies just leapt up to $1,500/knowledge-worker/month, how is that "slowing down"?
Maybe in USA in big tech where companies give absurd wages to engineers anyway in some states, that might be acceptable. But to make their ROI they need that (and more) to be spend world wide... no way that is gonna be a budget that is gonna fly in the long term...
Companies love to cut costs, and just like they axe employee numbers at will, they will just as well make that kind of budget quickly dissapear the moment they realize they can go a different path for same or better value... Or simply because share holder short-term value demands it...
The Uber $1,500/engineer/month thing is just the first signal we have had of the price companies may be willing to accept. This price will clearly vary wildly across professions, industries and geographies.
I think it's a poor number to build an "AI is slowing down" narrative around.
I hadn't heard of the TMobile and Brex spend caps, only knew about Uber's because it went viral last week. I expect we'll see more of that now that everyone is paying per token, and it sort of feels like you cannot both have spending caps and require extensive AI usage for performance reviews -- I wonder that will shake out in the end?
Anecdotally, $dayJob consumes Anthropic models via Azure subscriptions which lend themselves pretty neatly to the spending dashboards Ed mentions are missing from Anthropic themselves, and finance seems ok with the current usage, but there's no real hard incentives internally for AI usage either.
I guess Q3-4 are going to be interesting to see where this all goes.
As WIRED reported[0], despite constantly writing about how an AI collapse is just about to come, Zitron privately does PR for AI firms on the side. The man is an obvious hack, and it's disappointing that he has become one of the mainstream faces of AI skepticism.
I find it difficult to separate this piece’s tone from its content. The tone puts me off and makes it hard for me to judge it on its merits, despite some of the arguments seeming sound and well supported.
You can disagree. Sarcastically, or otherwise. But I think you may be reading more into my comment than I put there.
I’m not attacking the piece. I’m not saying it’s right. I’m not saying it’s wrong.
What I’m saying is, the tone made it hard for me to judge the arguments fairly, despite finding some of them convincing. And as much as I dislike it, persuasion does partly depend on how an argument is made.
Ed's posts are peak preaching to the choir, they're usually factually correct but he is really bad at convincing anyone who doesn't already strongly agree with him.
Have you seen his recent Bloomberg appearance? He's calm, collected, and matter-of-fact -- the complete opposite of how he presents himself on his newsletter and podcasts, but with the same argument. You wouldn't know from listening to him how spicy he usually is.
Perhaps that’s it. I would tend to agree with his position, I think, but don’t appreciate being preached to. Even less so when I agree with what’s being said.
Agreed. I am open to the possibility of the bubble bursting or whatever, but this piece is like 3,000 words and cites everything as evidence the sky is falling. It's just as bad as the pro-AI grifters, just in the other direction.
Does the truth normally lie somewhere in the middle of it all?
Probably. Although I feel more inclined to forgive Ed in this case because it's sort of fighting fire with fire, the insanely hyperbolic and obscenely misleading drivel that's coming out of the most ardent AI boosters is continually unchallenged in the public eye. In a world where we had a more realistic view of AI/ML/LLMs, the limits to its capabilities, and the negative externalities of its widespread adoption in places where it quite frankly does not belong, then I'd be more critical of the Chicken Little sort of writing style
AI has been slowing down relatively, considering its trajectory over the past 20-30 years. For one, even if LLM may have plateaud in terms of intelligence-parameters ratio, research is on-going on new frontiers for ML, including (but not limited to) world models. Other research directions are studying backpropagation and its physical analogies, such as equilibrium of chaotic states.
In addition, there's a lot of research on the hardware angle and actual prototypes are already being built such as AI-on-chip Cerebra and Taalas for one.
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
Who writes like this? When you lead with "everyone who doesn't agree with me is a lying cheat coward imbecile" I think we should just turn the volume down on you to zero.
This is breakdown in dialog. If it leads like this then I I don't care how accurate the critical analysis to follow is. I didn't read the rest of the article and don't think anyone else should either out of sheer disdain for this argumentation style.
I don't think anybody actually believes that the current investment is going to yield returns that they are projecting. Neither did people back in Dotcom or Railways or any other hype/bubbles. Yet these technology did transform and the returns came to fruition.
Internet continued to thrive and grow even after the stock market came and went, it took 13 years to roughly nasdaq to recover but the explosion of GDP from internet has been largely decoupled from the previous bubble boom and bust.
If you use the stock market as a yard stick to project new revolutionary technology we shouldn't have had trains, internet. In fact internet should've stopped with the bust of Nasdaq and everybody would've moved back to using paper but we didn't it gave rise to the next wave of economic output powered by this new tech.
The handwringing tone of the article is off-putting.
Ed is confused between whether AI is useful, and whether the current level of funding and valuations are sustainable. The following statements can both be true:
1. AI is already quite useful and will continue to be so. This is true even if AGI doesn’t happen.
2. The funding and valuations of many AI companies are too far ahead of their skis, and will probably roll back. Some may fail entirely.
About the “where’s the productivity in AI?” question: I think it’s entirely possible that the primary benefit of AI will not be top-line growth but reduced costs (through reduced human labor). Companies will need to reduce prices to prevent losing market share to existing or new competitors, meaning that GDP may not increase, but costs will.
Anthropic has made $330 billion in compute and chip commitments between Google, Amazon, and Microsoft, another $30 billion with CoreWeave and another $15 billion with SpaceX. To pay for this compute, Anthropic must meet its projected revenue of $174 billion a year by 2029.
Anthropic has raised $95 billion across rounds in February, April (from Google and Amazon), and May. These funds will be insufficient to cover Anthropic’s costs, as will Anthropic’s cash flow, meaning that it will have to raise at least another $200 billion in the next year.
How people take this seriously?
Anthropic is at 45B ARR
S-1 shows inference margin climbed to 70% (obviously could drop)
So where that 200B number is coming from ?
There are real issues on the money front. The big AI companies have a financial model that assumes a huge increase in demand in the next year or two. Otherwise the bubble pops.
"Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees."
So some of the growth was purchased by underpricing, subsidizing the customers with venture capital. Uber did that, and eventually got out of it by raising prices and squeezing the drivers.
The "fuckup" problem is real. LLM-type AI exacts huge costs because it is terrible at reporting "I don't know". When it doesn't know, it generates noise and polishes it.
If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful. You could use small, inexpensive models on small problems, and only use big models when the small models failed. Most customer service bots fit that model.
Needing ever-larger models to fix the noise problem is not cost-effective.
His rhetoric is a bit obsessive and frankly biased against AI.
That said, I think his voice is useful as a counter to the mainstream opinion.
Given the amount of investments, approaching AI from the angle of economics seems correct.
We all have some level of personal experience using AI/LLMs, both chatbots and coding tools, and I personally enjoy using them, but I am sure this experience is relevant in this discussion.
I also enjoy luxury hotels, gourmet food, jet skis and helicopters, but this is not something I indulge in often because of the cost-utility ratio.
The real cost of AI may or may not be lower than its utility. The bet is that utility is increasing while cost is falling.
Whenever I read these kind of articles about AI financials, I'm reminded of identical screeds I read about Uber a few years ago. They were angrily insistent that Uber was a scam company run by criminals and charlatans and could never, ever become profitable or make money for its investors. It was a house of cards that would come crashing down sooner or later, and take everyone's money with it. Now it's 2026. Uber still exists, has revenues of $50bn and is apparently a highly profitable business. I don't know if the original investors have made their money back yet, but Uber certainly hasn't collapsed.
Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.
Uber used the classic triple-E philosophy of Microsoft and entered a market that was ripe for disruption -- many cities lacked reliable taxi service entirely, others were cartels that fixed prices. They undercut prices to an extreme degree, subsidized fares, and when it either drove local taxi companies out of business and spurred widespread adoption as the default, it had a captive market and duopoly with Lyft which allowed them to raise fares without losing any market share whatsoever.
It's a pretty classic business strategy, and not directly comparable to any of the AI companies. There's a reason people compare the current situation to the dotcom era and not Uber. Also, don't take Uber as an example of a slam-dunk VC success story and leave it at that -- plenty of dumb ideas get pitched and funded and go bankrupt for every Uber.
Yeah, people forget the risk to Uber was real in the early days. If municipalities had enforced their taxi laws, the company would have died and all those millions invested would have been lost (or pivoted into something else).
It was only because Uber successfully bulldozed over all regulations that it was able to succeed ... and that was hard to predict before it happened.
Absolutely. Even these days, Uber really only has one or two viable competitors. With any 3rd one in a far distant 3rd. Meanwhile, swapping which AI I’m using is as easy as clicking a dropdown. Hardly comparable to a physical car ride.
AI companies are racing to win the future of computing.
They are possibly in a winner take all death race against each other.
The stakes are so high that these cash rich companies cannot afford not to throw everything they have into this.
The sunk costs are irrelevant when it’s a question of survival.
Whether you hate or love AI computing is being completely reinvented - at the absolute core of this is computers programming computers.
Anthropic is winning this race by a country mile right now.
This is such an important future bet for these companies that the trillions must be spent because there’s no future or a greatly diminished future for some of them unless they have ownership of the technology.
It doesn't matter if it's slowing down, pretty much no one has implemented it to its full extent yet. It could stop right now and we'll be finding new implementations a decade from now.
Anthropic and Open AI could evaporate tomorrow and we'll still be using the models.
The market may collapse, but the people who think AI is going to disappear as a result don't understand what it is.
"Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology."
Well there are a lot of commenters so presumably some interest. I just had a look at the Bloomberg bit https://youtu.be/zbKDmkJPVvI and didn't see sweaty boosters rattled, just Ed doing his usual spiel - they are loss making and so it's all a big con. Which is kind of unproven on the big con bit.
Ed Zitron speaks to a particular type of angry tech conservative. He’s not speaking truth or exposing anything. He’s the soothing voice the tech nerds of yesterday year are yearning for.
The angry polemic that goes on and on and on with cuss words used liberally is just meant to evoke emotion and cathartic resolution to the type of people mentioned above. Not truth.
The thing is, there are a lot of people that find comfort in what he’s writing - primarily because it’s a coping mechanism against how quickly things are moving and a way to deal with being left behind. When you spend time, years, building institutional knowledge and making a whole identity out of it, you obviously will feel bad with the threat of it being commoditised.
I would write against the content of the article but I find it easier and more illuminating to write what he has said before instead. Then it shows how incorrect the guy has been and with what confidence he keeps speaking with.
I'm collecting many kinds of predictions Ed Zitron made so that you can see for yourself whether he has a good track record.
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> While complex, generative AI is a technology that probabilistically generates answers, and has no "intelligence." It is inherently limited by its architecture, and in turn can only get "better" in a linear fashion. I see no signs that the transformer-based architecture can do significantly more than it currently does.
He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?
> Furthermore, I hypothesize a race to the bottom in generative AI will significantly hamper OpenAI's ability to expand revenue, compounded by the fact that we're approaching the limits of transformer-based architecture.
He wrote this in 2024 and since then Anthropic's revenue increased by 160x to $40 B dollars a year and OpenAI's increased by 6x. Do you think this person gets predictions right still?
> I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be,
He wrote this in 2024, do you really think we have reached upper limits? Huh?? What I'm using today is significantly more accurate and 2 tiers above what we had.
> And if there are true industry-changing possibilities waiting for us on the other side, I am yet to hear them outside of the fan fiction of Silicon Valley hucksters.
He says this about AI when we have with all honesty have had industry changing possibilities like agentic coding.
> There are indications that consumers have also lost interest. As pointed out by Alex Kantrowitz’ Big Technology newsletter, traffic to ChatGPT on both mobile and web has started to stagnate, if not decline. In January 2024, ChatGPT had 1.6 billion visits — 11% below the all-time peak of 1.8 billion. This makes it only modestly more popular than Bing, which had 1.3 billion unique visits during that period. On the mobile front, ChatGPT has an estimated 6.3 million US users — or 1.7 times less than the total of new Snapchat users added during Q4 2023.
He agrees with the claim that the consumer interest has declined. Since he said this, there was a 9x growth in active users.
"A.I bubble is bursting with Ed Zitron" (1 year back)
He's been constantly crying bubble for years now.
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> AI video won’t get truly fixed just by waiting a year.
This is what he had said in 2024, and you just need to compare video from then and now to check whether the predictions came true. Why would anyone trust what this guy has to say?
How’s that meme go? "We are 2/3 years into being 6 months away from AI taking all white collar jobs".
The criticism goes both ways. The word "fixed", in Ed terms, can be translated to "become a viable business that justifies the spend".
In regards to AI video, I think the fact that Sora is no long around is an indicator. And there is seemingly no real appetite for AI video outside of memes, jokes, and misinformation, probably indicates that the prediction around AI video has come true.
It's like someone arguing that cheese isn't real. Yes I can go to the grocery store and take a picture of cheese and show it, but what's the point? They can live in their own world. It doesn't change any of our lives. The world is what it is.
Lol... in this case, cheese imports from China are much cheaper, just not quite as good.
And for those who are all "but dur CCP get all ur data" you can use things like AWS Bedrock (at least for earlier versions of Deepseek and Qwen for now) and have more familiar people get all your data. Or buy (at obnoxiously inflated prices) your own HW and not send your data to anyone.
The funniest part of this is that people are often talking about how LLMs are now writing 100% of their code, then also saying that they don't want to expose their code to foreign government exfiltration by using foreign models.
But, uh, if an LLM is writing 100% of your code you have no actual secret sauce to hide from anyone, so why worry about it.
Some people seem to see the world only through bubbles. But if you look at human history, despite the ups and downs, we have a trajectory; generally speaking, human-created systems evolve toward ever-increasing complexity, impact, and efficiency.
The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today. To have tools that can understand, manipulate, and produce it is a massive leap forward.
Once you see things that way, it is clear that we are not in a bubble; we are in a transition. Yes, there is tons of hype and over-investment, but the demand is real, and so is the impact. Unless you are deep in the tech and have that structural depth, it is easy to dismiss. This is like the invention of the personal computer, but with 100x the impact and speed.
The only "bubble" with AI is that the initial build out is cyclical, and many of the high flying chip stocks with no software arms (ala Nvidia's CUDA) will come back to Earth. I think anyone that thinks AI is going away or won't have massive impact (though maybe not in the doomsday scenario) are in complete denial.
RTFA; it's not about AI's massive impact or lack thereof ... it's about these businesses not having a viable business model that will sustain them (beyond the next couple years).
What I suspect isn't that AI goes somewhere, but I do think that the cutting edge companies like Anthropic and OpenAI are in a very precarious position. They don't have very much of a moat and the competition has been catching up quick while spending a lot less doing so. IMO, the main thing keeping them alive right now is name recognition.
If I were to make a prediction, it's that ultimately these cheaper models are going end up eating their lunch. I don't think they'll make back the money they've invested and once that reality hits investors, those two companies are sunk.
That, however, is not the end of AI. Nor will it be the end of Nvidia/micron/etc. It will more just be a localized bubble pop that doesn't eliminate the product from the market.
> The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today.
One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow. If Zitron's argument were as solid as he keeps saying it is, you would read it and understand it and see that it is solid. He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument. But no. He jumps. He leaps. He circles back. If the situation were really "Gosh why can't you see it?!"-clear, his explanation of the situation would be clear. It isn't, because it isn't.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
Which of the hyperlinks provided at the beginning sounded like what you wanted, and after you clicked it how did it disappoint you?
The information you are describing is stuff I would not expect anyone to repeatedly duplicate in periodic blog-posts.
It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.
The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
We are only five or six years into the leap LLMs represent. For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up. It took so long not because of the physics of radio waves required time to mature and improve, but because everything else needed to profit from it did require time.
To me, LLMs are not so much AI as it is a building block. Radiowaves maybe, or the equivalent of transistors. We are already seeing that it's possible to chain LLMs into agents. Currently, price is a strict limiting factor for coding and agents.It's probably fine-ish if all you want is Claude Code or Codex, but there are many other possible compositions of LLMs that most people don't dare to experiment with. For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost. Were prices of inference hardware go down and inference algorithms keep improving, I'm convinced (and afraid) we would see things very difficult to imagine today.
Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely misses the incredible ground-level utility that many of us on HN celebrate every day through undeniable, massive productivity gains.
At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.
> undeniable, massive productivity gains.
Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
So where is all the productivity going? Where is the value? Where are the massive unemployment stats or the millions of new startups making big $$$?
Writing about AI, destroying the planet for data centers, there's a lot of money to be made.
That being said, AI seems kind of miraculous sometimes.
Similar to cars. So enticing that we make everything else in the world worse in order to maximize the profit, make it indispensable, subsidize it, and make the dependency on it irreversible.
And it's not even something to blame individual people for.
Driving away from all the other cars to spend a weekend feels like freedom.
Using AI to answer a question feels like a "bicycle for the mind".
But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt.
Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
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> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
Where did all the stock gains go before AI?
FAANG / MAG-7.
Was everything from 2012-2020 fake, too?
Not sure what your point is. Stock markets are based on money going into securities based on estimated future value. Even if AI were doubling productivity at a non-AI company, there is more leverage to that money going into an AI company.
The question is, is AI leading to massive productivity gains in companies that implement it? AI productivity gains take time to diffuse, but so far companies in the S&P 500 are seeing very high growth. YOY earnings growth rate for the S&P 500 is 21.7% https://advantage.factset.com/hubfs/Website/Resources%20Sect...
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He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse.
I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place.
My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit.
But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time.
The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
I think he doesn't need to understand the technology to point out the books are cooked. a business can sink in either way: the technology flops or the finances flop. he's arguing the /finances/ would flop. he doesn't argue that the /technology/ would flop, only that they can't come up with the money to pay their debters.
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> that he doesn't really understand how inference works from a technical perspective
Could you share what tells about it? I.e. where he was wrong about it?
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Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing.
https://unessays.substack.com/p/talk-is-cheap
From an economic perspective productivity is defined as the creation of value isn't it? Then if you "improve productivity" and does not create value in the end you're no improving productivity at all.
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Also, supposed productivity gains are dubious. I personally experience at best no productivity gains when using LLMs to write code, and sometimes it's an active drain on my productivity. There was that one study a year or so ago showing similar results. People are trying to say the productivity gains are there and undeniable, but that is not true. It is very much a subject of controversy whether AI helps productivity.
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That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent.
EDIT: In fact, parent comment has a link to some numbers.
[EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
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He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.
The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either.
I find it difficult to take him seriously.
> progress was slowing
Do you think it's not slowing? Do I miss anything really important?
My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
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anyone that takes him seriously at this point... I don't want to say very bad words here...
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I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work...
It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...
Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost.
Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost.
That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
>undeniable, massive productivity gains.
How can something so undeniable have zero scientific evidence? Are there any large peer reviewed or meta studies confirming your claim?
Because even in a field like software engineering where the output of our work is save in version control, measuring baseline productivity is hard.
LoC: people argue it’s not what’s important
PRs/day: same as LoC
Getting projects done faster: oh but what about the quality.
Solve the technical problems and actually be more productive, the social systems build around the old way of doing things will hole you back.
Finish a PR in 10 minutes doesn’t matter if you’re waiting days for a human review.
It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model.
I think the surest sign of productivity gains is the sheer volume of adoption. If you look beyond headlines, adoption is just incredible. Of course adoption does not necessarily point to productivity gains, but if this was some sort of FOMO or smoke and mirrors you would not see this much retention and this feverish a pace of adoption. You would not see a large segment of the profession using coding agents exclusively. All of these companies track productivity, again with imperfect proxies, yet everything points to a pretty consistent picture. Same with benchmarks, again a lot of crappy benchmarks but a lot of high quality ones too and a very diverse collection of tasks and capabilities they probe.
Even if we assume that everything you said holds true, how is that we as a crowd can make viable a service that eats some $300bn annually in infrastructure costs? Where would that money come from? Most tech companies these days are cutting their AI budgets because the per token pricing is killing them.
Cite a real source for that last bit, I don’t think that is true. Also the budgets should be cut the spend at some places goes beyond any reasonable amount. The strategy there is to hook everything in and find the right processes, then cut the rest. Things then get better and better with each model release.
The way you make a viable service that eats 300bn annually is to have enough demand to service that. Anthropic underbought compute. That tells you something.
> undeniable, massive productivity gains.
The jury is still out on that.
Yeah they're very much deniable. Raw LOC/hr is much higher, and putting together a MVP, but I've yet to see any evidence that an LLM is capable of doing anything unsupervised, and if you need a human supervising everything it does... why bother having an LLM in the first place?
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Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead.
The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.
Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).
I really like some good drama slop that reads like a thriller, it is entertaining. I don't take any of it THAT serious, but lately with the IPOs that are about to hit the indizes, he has gained a lot of attention. If you look around the internet, most people publish a negative angle on something and then extrapolate it into some grand conspiracy, which is really captivating. Its crazy when you enter some echo chamber you never engage with (movies, gaming, art/comics) and they have their own head cannon for why the world is bad and collapsing. It puts your echo chamber into perspective to see the same patterns of argumentation and presentation spin out in a different way
> undeniable, massive productivity gains.
Just because you keep repeating something doesn't make it an undeniable truth.
> through undeniable, massive productivity gains.
And where are those? They seem particularly hard to actually observe and only appear in anecdotes.
> I'm trying to believe
For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?
Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!"
This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.
> This, combined with his extreme ignorance,
Extreme ignorance?
> Zitron is begging for a collapse at this point
No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job.
Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades.
Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."
> undeniable, massive productivity gains
How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication.
I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think.
[1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.
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i don't think this comment contributes much to the discussion. can you elaborate more than saying "no"?
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Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. https://www.wheresyoured.at/peakai/ from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)
Predicting the timing of such a thing is notoriously difficult. I don't think being wrong about timing 2 years ago means there won't be a correction.
I'm also reminded of all the HN posts from 2007-2009 that predicted that the adoption of social networking would be a terrible thing for privacy, that it would destroy society, that people would lose their jobs over crazy shit they said on the Internet, that it would lead to the decline of trust and in-person interactions, that people would forget how to socialize, etc.
They were right about all of that but it took 15-20 years and the companies involved grew 100x in that timefold, eventually reaching trillion-dollar valuations that would've seemed insane in 2007.
There is a tremendous amount of money to be made in destroying society.
Not related to AI but, I recently rewatched "The Big Short" and your comment reminded me of it. I can't testify the accuracy of the movie, but for over year, Michael Burry was viewed as in the same manner for shorting the market, while the economy was was in a hype cycle.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).
But from the article I linked back in March 2024:
"Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models' capabilities will continue a rapacious pace of progress that has unquestionably slowed, with OpenAI admitting that GPT-4 may be worse on some tasks.
As I've written before, hallucinations are a feature not a bug. These models do not "know" anything. They are mathematical behemoths generating a best guess based on training data and labeling, and thus do not "know" what you are asking it to do. You simply cannot fix them. Hallucinations are not going away."
Since then:
- hallucinations are dramatically less of a problem
- several mass market use cases have emerged, most notably coding
- rate of progress has increased
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Not the person you are responding to, but here:
> I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley.
We have seen 8 quarters since. Has any of that come to pass?
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https://news.ycombinator.com/item?id=48447549
The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.
Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
Why is anti-AI soothing?
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He also does PR for AI companies and only really acknowledges this in interviews. As far as I know he never discloses it in his rants.
What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:
He's a Gary Marcus-level contrarian with none of the credentials or contributions to the industry. The "AI bubble" cope narrative is getting stale but will still appeal to luddite autists years after it has ceased to be relevant.
> Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong
So, judge the book by it's cover?
> arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Then the opposite should be easy to prove. AI is succeeding, is efficient, is universally good, and is working everywhere it's tried. Are those true?
> So, judge the book by it's cover?
It is literally judging the book by it's author, which is an extremely rationale judgement to make.
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And its been 3 years of AI boosters telling me that my job as a litigating attorney will not exist in 2 months. Yet here I am, gainfully employed.
As a tangent, I don’t understand where and why meta fits into the AI race. They did not get any mind share (consumers) from the llms so far, granted they started the open source side to this but the Chinese companies produce far better models and have essentially become the default for on device set up.
They have ai glasses and integration into instagram and facebook as the other avenues. I don’t see ai glasses as compelling yet, and don’t know how much more ad revenue or user engagement they can squeeze out with llms baked into the IG of FB flows. They are spending a lot and not seeing any returns. Am I wrong in being pessimistic about meta with AI?
This is wishful thinking. AI is still getting better rapidly. Anthropic's revenue is still growing at an unprecedented rate and they haven't even released their best model (Mythos) for 4 months now.
Ed is an interesting character. His financial analysis of the AI industry makes logical sense to me (though I am not knowledgeable enough to actually know if it is correct.) However, he seems to be so angry at AI in general, that he misses the obvious areas where LLMs are actually changing the State of the Art.
Coding seems to be one of the core use-cases for LLMs (as Simon Willison pointed out recently) and even if that's the only real use-case for LLMs, they're wildly useful. I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
I don't think whether "LLMs are actually changing the State of the Art" or not matters for anything he wrote.
If the AI companies need $X billion in revenue to stay afloat, it doesn't matter if 0.5% or 5% or 50% of that revenue is from transforming the State of the Art. It's 100% irrelevant: what matters is that, transformation or no, these companies won't have the income to pay their bills. And if they can't pay their bills, a whole lot of other companies can't either.
So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.
He's got subscribers. Maybe the attitude is one he's found plays well with them.
I find it quite refreshing in some ways. Lots of people, when they start complaining about this or that aspect of this AI stuff, are wont to add in a little disclaimer that, despite all of the above, they actually really like AI and use it all the time. I assume this is to avoid the scenario of a bunch of pragmatic builders turning up and calmly shipping nuance in the comments (or whatever you call it these days when you get brigaded by a pile of angry keyboard warriors with chips on their shoulder) - and it sure is tiring having to wade through the equivocation.
That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.
It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.
> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
This is often repeated but comes from ignorance mostly. You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.
This kind of thinking really reinforces my belief that people have no idea and are using this whole [AI is not profitable and too costly] thing as a cathartic way to deal with immense progress.
We know that inference cost is very significant, as he shows for example in this piece.
https://www.wheresyoured.at/oai_docs/
However, it needs to be said that he received those numbers. I personally have quite a few issues with him, but there's no reason to doubt his journalistic integrity. Because of that, I believe he reports truthfully on data he receives by informants.
Additionally, none of the frontier models actually publicly talks about inference costs in anything but broad, "let's just forget that"-like takes. Which does not exactly spark confidence.
I'm eagerly awaiting anthropic's public disclosure of their financial details. That should be rather interesting in any case and finally put the inference-discussion to rest.
Zitron is in the business of content creation and not successful predictions. It doesn't matter how many times he (and several others around) will say the end is here, they have to be right only once.
BTW, one thing for sure he is right about are the economics, as of today there is no way these massive investments are gone be paid.
For the purposes of content creation they don't even have to be right once
>have to be roughly twice the size they are today, and then double again basically every year until 2029 or 2030.
Anthropic is growing way faster than doubling yearly so don't think this is entirely implausible
Buried lede (if the title is the actual promise), the sources don't seem to back the title either. Someone with more patience can correct me if I accidentally missed a bombshell anyway.
Edit:
> If you’re wondering what the story is, [...] I expect it to be out in the next two weeks [...] I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.
Ok, this takes clickbait to new lows. The headline is trying to sell the teaser here, with very limited meat in the middle of the sandwich.
Given this, his righteous anger towards craven boosters and grifters is pretty funny. Pot calling the kettle black.
Ed's argument for why "AI is slowing down" rests on company spending caps, in particular the Uber $1,500/engineer/tool cap.
I interpret the exact same evidence in the opposite direction. A year ago the idea that a company would spend $1,500/month/employee on AI tooling felt absurd, what could people possible want to do with AI that would cost that much?
Then coding agents (and, increasingly, general purpose agents) happened and suddenly companies are having to set limits because otherwise the demand from their employees is too high.
The TAM of these AI companies just leapt up to $1,500/knowledge-worker/month, how is that "slowing down"?
Maybe in USA in big tech where companies give absurd wages to engineers anyway in some states, that might be acceptable. But to make their ROI they need that (and more) to be spend world wide... no way that is gonna be a budget that is gonna fly in the long term...
Companies love to cut costs, and just like they axe employee numbers at will, they will just as well make that kind of budget quickly dissapear the moment they realize they can go a different path for same or better value... Or simply because share holder short-term value demands it...
The Uber $1,500/engineer/month thing is just the first signal we have had of the price companies may be willing to accept. This price will clearly vary wildly across professions, industries and geographies.
I think it's a poor number to build an "AI is slowing down" narrative around.
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Funny I just read an article on how it was actually speeding up.
I hadn't heard of the TMobile and Brex spend caps, only knew about Uber's because it went viral last week. I expect we'll see more of that now that everyone is paying per token, and it sort of feels like you cannot both have spending caps and require extensive AI usage for performance reviews -- I wonder that will shake out in the end?
Anecdotally, $dayJob consumes Anthropic models via Azure subscriptions which lend themselves pretty neatly to the spending dashboards Ed mentions are missing from Anthropic themselves, and finance seems ok with the current usage, but there's no real hard incentives internally for AI usage either.
I guess Q3-4 are going to be interesting to see where this all goes.
As WIRED reported[0], despite constantly writing about how an AI collapse is just about to come, Zitron privately does PR for AI firms on the side. The man is an obvious hack, and it's disappointing that he has become one of the mainstream faces of AI skepticism.
[0]: https://www.wired.com/story/ai-pr-ed-zitron-profile/
All the top comments are commenting on the author. And now I add this metacommentary. Probably good it was flagged.
I find it difficult to separate this piece’s tone from its content. The tone puts me off and makes it hard for me to judge it on its merits, despite some of the arguments seeming sound and well supported.
Given the way tone has been intentionally abused, particularly in this industry, I’ll take a few f bombs and the truth.
Agreed. If the arguments seem sound and well supported, then all we can do is attack the tone.
You can disagree. Sarcastically, or otherwise. But I think you may be reading more into my comment than I put there.
I’m not attacking the piece. I’m not saying it’s right. I’m not saying it’s wrong.
What I’m saying is, the tone made it hard for me to judge the arguments fairly, despite finding some of them convincing. And as much as I dislike it, persuasion does partly depend on how an argument is made.
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Ed's posts are peak preaching to the choir, they're usually factually correct but he is really bad at convincing anyone who doesn't already strongly agree with him.
Have you seen his recent Bloomberg appearance? He's calm, collected, and matter-of-fact -- the complete opposite of how he presents himself on his newsletter and podcasts, but with the same argument. You wouldn't know from listening to him how spicy he usually is.
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Perhaps that’s it. I would tend to agree with his position, I think, but don’t appreciate being preached to. Even less so when I agree with what’s being said.
Agreed. I am open to the possibility of the bubble bursting or whatever, but this piece is like 3,000 words and cites everything as evidence the sky is falling. It's just as bad as the pro-AI grifters, just in the other direction.
Does the truth normally lie somewhere in the middle of it all?
>Does the truth normally lie somewhere in the middle of it all?
Usually does when you decide what constitutes extreme.
Probably. Although I feel more inclined to forgive Ed in this case because it's sort of fighting fire with fire, the insanely hyperbolic and obscenely misleading drivel that's coming out of the most ardent AI boosters is continually unchallenged in the public eye. In a world where we had a more realistic view of AI/ML/LLMs, the limits to its capabilities, and the negative externalities of its widespread adoption in places where it quite frankly does not belong, then I'd be more critical of the Chicken Little sort of writing style
AI has been slowing down relatively, considering its trajectory over the past 20-30 years. For one, even if LLM may have plateaud in terms of intelligence-parameters ratio, research is on-going on new frontiers for ML, including (but not limited to) world models. Other research directions are studying backpropagation and its physical analogies, such as equilibrium of chaotic states.
In addition, there's a lot of research on the hardware angle and actual prototypes are already being built such as AI-on-chip Cerebra and Taalas for one.
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
Who writes like this? When you lead with "everyone who doesn't agree with me is a lying cheat coward imbecile" I think we should just turn the volume down on you to zero.
This is breakdown in dialog. If it leads like this then I I don't care how accurate the critical analysis to follow is. I didn't read the rest of the article and don't think anyone else should either out of sheer disdain for this argumentation style.
I don't think anybody actually believes that the current investment is going to yield returns that they are projecting. Neither did people back in Dotcom or Railways or any other hype/bubbles. Yet these technology did transform and the returns came to fruition.
Internet continued to thrive and grow even after the stock market came and went, it took 13 years to roughly nasdaq to recover but the explosion of GDP from internet has been largely decoupled from the previous bubble boom and bust.
If you use the stock market as a yard stick to project new revolutionary technology we shouldn't have had trains, internet. In fact internet should've stopped with the bust of Nasdaq and everybody would've moved back to using paper but we didn't it gave rise to the next wave of economic output powered by this new tech.
I don't see AI to be any different.
The handwringing tone of the article is off-putting.
Ed is confused between whether AI is useful, and whether the current level of funding and valuations are sustainable. The following statements can both be true:
1. AI is already quite useful and will continue to be so. This is true even if AGI doesn’t happen.
2. The funding and valuations of many AI companies are too far ahead of their skis, and will probably roll back. Some may fail entirely.
About the “where’s the productivity in AI?” question: I think it’s entirely possible that the primary benefit of AI will not be top-line growth but reduced costs (through reduced human labor). Companies will need to reduce prices to prevent losing market share to existing or new competitors, meaning that GDP may not increase, but costs will.
Anthropic has made $330 billion in compute and chip commitments between Google, Amazon, and Microsoft, another $30 billion with CoreWeave and another $15 billion with SpaceX. To pay for this compute, Anthropic must meet its projected revenue of $174 billion a year by 2029. Anthropic has raised $95 billion across rounds in February, April (from Google and Amazon), and May. These funds will be insufficient to cover Anthropic’s costs, as will Anthropic’s cash flow, meaning that it will have to raise at least another $200 billion in the next year.
How people take this seriously? Anthropic is at 45B ARR S-1 shows inference margin climbed to 70% (obviously could drop) So where that 200B number is coming from ?
There are real issues on the money front. The big AI companies have a financial model that assumes a huge increase in demand in the next year or two. Otherwise the bubble pops.
"Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees."
So some of the growth was purchased by underpricing, subsidizing the customers with venture capital. Uber did that, and eventually got out of it by raising prices and squeezing the drivers.
The "fuckup" problem is real. LLM-type AI exacts huge costs because it is terrible at reporting "I don't know". When it doesn't know, it generates noise and polishes it. If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful. You could use small, inexpensive models on small problems, and only use big models when the small models failed. Most customer service bots fit that model. Needing ever-larger models to fix the noise problem is not cost-effective.
His rhetoric is a bit obsessive and frankly biased against AI.
That said, I think his voice is useful as a counter to the mainstream opinion.
Given the amount of investments, approaching AI from the angle of economics seems correct.
We all have some level of personal experience using AI/LLMs, both chatbots and coding tools, and I personally enjoy using them, but I am sure this experience is relevant in this discussion.
I also enjoy luxury hotels, gourmet food, jet skis and helicopters, but this is not something I indulge in often because of the cost-utility ratio.
The real cost of AI may or may not be lower than its utility. The bet is that utility is increasing while cost is falling.
I stopped as soon as the popup hit.
I think we need to see Open AI's and/or Anthropic's S1's to really know the state of it all.
Totally agree, remember WeWork's S1 and the fall that followed. Don't think it's the same case, but it'll clarify a lot of things
Whenever I read these kind of articles about AI financials, I'm reminded of identical screeds I read about Uber a few years ago. They were angrily insistent that Uber was a scam company run by criminals and charlatans and could never, ever become profitable or make money for its investors. It was a house of cards that would come crashing down sooner or later, and take everyone's money with it. Now it's 2026. Uber still exists, has revenues of $50bn and is apparently a highly profitable business. I don't know if the original investors have made their money back yet, but Uber certainly hasn't collapsed.
Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.
Uber used the classic triple-E philosophy of Microsoft and entered a market that was ripe for disruption -- many cities lacked reliable taxi service entirely, others were cartels that fixed prices. They undercut prices to an extreme degree, subsidized fares, and when it either drove local taxi companies out of business and spurred widespread adoption as the default, it had a captive market and duopoly with Lyft which allowed them to raise fares without losing any market share whatsoever.
It's a pretty classic business strategy, and not directly comparable to any of the AI companies. There's a reason people compare the current situation to the dotcom era and not Uber. Also, don't take Uber as an example of a slam-dunk VC success story and leave it at that -- plenty of dumb ideas get pitched and funded and go bankrupt for every Uber.
Yeah, people forget the risk to Uber was real in the early days. If municipalities had enforced their taxi laws, the company would have died and all those millions invested would have been lost (or pivoted into something else).
It was only because Uber successfully bulldozed over all regulations that it was able to succeed ... and that was hard to predict before it happened.
Absolutely. Even these days, Uber really only has one or two viable competitors. With any 3rd one in a far distant 3rd. Meanwhile, swapping which AI I’m using is as easy as clicking a dropdown. Hardly comparable to a physical car ride.
every week I see this guy on HN. only forum where ppl still buy this c**
The top twenty comments are negative about Ed. I think maybe HN just likes being skeptical.
AI companies are racing to win the future of computing.
They are possibly in a winner take all death race against each other.
The stakes are so high that these cash rich companies cannot afford not to throw everything they have into this.
The sunk costs are irrelevant when it’s a question of survival.
Whether you hate or love AI computing is being completely reinvented - at the absolute core of this is computers programming computers.
Anthropic is winning this race by a country mile right now.
This is such an important future bet for these companies that the trillions must be spent because there’s no future or a greatly diminished future for some of them unless they have ownership of the technology.
It doesn't matter if it's slowing down, pretty much no one has implemented it to its full extent yet. It could stop right now and we'll be finding new implementations a decade from now.
Anthropic and Open AI could evaporate tomorrow and we'll still be using the models.
The market may collapse, but the people who think AI is going to disappear as a result don't understand what it is.
I predict the bubble is going to pop right after the midterm election.
Concur.
"Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology."
Bloomberg is interested in what he has to say
But not HN commenters
Well there are a lot of commenters so presumably some interest. I just had a look at the Bloomberg bit https://youtu.be/zbKDmkJPVvI and didn't see sweaty boosters rattled, just Ed doing his usual spiel - they are loss making and so it's all a big con. Which is kind of unproven on the big con bit.
I'm so sick of people who peddle outrage for a living.
Ed Zitron speaks to a particular type of angry tech conservative. He’s not speaking truth or exposing anything. He’s the soothing voice the tech nerds of yesterday year are yearning for.
The angry polemic that goes on and on and on with cuss words used liberally is just meant to evoke emotion and cathartic resolution to the type of people mentioned above. Not truth.
The thing is, there are a lot of people that find comfort in what he’s writing - primarily because it’s a coping mechanism against how quickly things are moving and a way to deal with being left behind. When you spend time, years, building institutional knowledge and making a whole identity out of it, you obviously will feel bad with the threat of it being commoditised.
I would write against the content of the article but I find it easier and more illuminating to write what he has said before instead. Then it shows how incorrect the guy has been and with what confidence he keeps speaking with.
I'm collecting many kinds of predictions Ed Zitron made so that you can see for yourself whether he has a good track record.
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> While complex, generative AI is a technology that probabilistically generates answers, and has no "intelligence." It is inherently limited by its architecture, and in turn can only get "better" in a linear fashion. I see no signs that the transformer-based architecture can do significantly more than it currently does.
He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?
> Furthermore, I hypothesize a race to the bottom in generative AI will significantly hamper OpenAI's ability to expand revenue, compounded by the fact that we're approaching the limits of transformer-based architecture.
He wrote this in 2024 and since then Anthropic's revenue increased by 160x to $40 B dollars a year and OpenAI's increased by 6x. Do you think this person gets predictions right still?
> I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be,
He wrote this in 2024, do you really think we have reached upper limits? Huh?? What I'm using today is significantly more accurate and 2 tiers above what we had.
> And if there are true industry-changing possibilities waiting for us on the other side, I am yet to hear them outside of the fan fiction of Silicon Valley hucksters.
He says this about AI when we have with all honesty have had industry changing possibilities like agentic coding.
> There are indications that consumers have also lost interest. As pointed out by Alex Kantrowitz’ Big Technology newsletter, traffic to ChatGPT on both mobile and web has started to stagnate, if not decline. In January 2024, ChatGPT had 1.6 billion visits — 11% below the all-time peak of 1.8 billion. This makes it only modestly more popular than Bing, which had 1.3 billion unique visits during that period. On the mobile front, ChatGPT has an estimated 6.3 million US users — or 1.7 times less than the total of new Snapchat users added during Q4 2023.
He agrees with the claim that the consumer interest has declined. Since he said this, there was a 9x growth in active users.
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https://www.youtube.com/watch?v=_wStScmT748&t=1s
"AI Bubble Already Bursting?" (8 months back)
https://www.youtube.com/watch?v=T8ByoAt5gCA&t=1s
"A.I bubble is bursting with Ed Zitron" (1 year back)
He's been constantly crying bubble for years now.
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> AI video won’t get truly fixed just by waiting a year.
This is what he had said in 2024, and you just need to compare video from then and now to check whether the predictions came true. Why would anyone trust what this guy has to say?
How’s that meme go? "We are 2/3 years into being 6 months away from AI taking all white collar jobs".
The criticism goes both ways. The word "fixed", in Ed terms, can be translated to "become a viable business that justifies the spend".
In regards to AI video, I think the fact that Sora is no long around is an indicator. And there is seemingly no real appetite for AI video outside of memes, jokes, and misinformation, probably indicates that the prediction around AI video has come true.
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What's the point of arguing with any of this.
It's like someone arguing that cheese isn't real. Yes I can go to the grocery store and take a picture of cheese and show it, but what's the point? They can live in their own world. It doesn't change any of our lives. The world is what it is.
Lol... in this case, cheese imports from China are much cheaper, just not quite as good.
And for those who are all "but dur CCP get all ur data" you can use things like AWS Bedrock (at least for earlier versions of Deepseek and Qwen for now) and have more familiar people get all your data. Or buy (at obnoxiously inflated prices) your own HW and not send your data to anyone.
> "but dur CCP get all ur data"
The funniest part of this is that people are often talking about how LLMs are now writing 100% of their code, then also saying that they don't want to expose their code to foreign government exfiltration by using foreign models.
But, uh, if an LLM is writing 100% of your code you have no actual secret sauce to hide from anyone, so why worry about it.
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> What's the point of arguing with any of this.
> It's like someone arguing that cheese isn't real
I agree with your first statement (any being you) because of your second statement.
Some people seem to see the world only through bubbles. But if you look at human history, despite the ups and downs, we have a trajectory; generally speaking, human-created systems evolve toward ever-increasing complexity, impact, and efficiency.
The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today. To have tools that can understand, manipulate, and produce it is a massive leap forward.
Once you see things that way, it is clear that we are not in a bubble; we are in a transition. Yes, there is tons of hype and over-investment, but the demand is real, and so is the impact. Unless you are deep in the tech and have that structural depth, it is easy to dismiss. This is like the invention of the personal computer, but with 100x the impact and speed.
Uhh, citations for all of these claims please.
The only "bubble" with AI is that the initial build out is cyclical, and many of the high flying chip stocks with no software arms (ala Nvidia's CUDA) will come back to Earth. I think anyone that thinks AI is going away or won't have massive impact (though maybe not in the doomsday scenario) are in complete denial.
RTFA; it's not about AI's massive impact or lack thereof ... it's about these businesses not having a viable business model that will sustain them (beyond the next couple years).
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What I suspect isn't that AI goes somewhere, but I do think that the cutting edge companies like Anthropic and OpenAI are in a very precarious position. They don't have very much of a moat and the competition has been catching up quick while spending a lot less doing so. IMO, the main thing keeping them alive right now is name recognition.
If I were to make a prediction, it's that ultimately these cheaper models are going end up eating their lunch. I don't think they'll make back the money they've invested and once that reality hits investors, those two companies are sunk.
That, however, is not the end of AI. Nor will it be the end of Nvidia/micron/etc. It will more just be a localized bubble pop that doesn't eliminate the product from the market.
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I share the same perspective.
> The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today.
This is fire erasure
/s
Agreed haha! our beloved fire.