Comment by yousif_123123
1 month ago
Things in general I think he's right about:
1. Revenue if anthropic and openai is unlikely to grow to the high levels they need to pay for their commitments. Many of their heavy users (coding) will eventually offset a lot of usage to more efficient and cheaper open weight models. I know of people in a company I was at that spend thousands of dollars a month on tokens. I am sure that what they're using it for can be substituted in large part by way cheaper models.
2. A lot of corporate AI usage is being pushed by management that doesn't really understand the extent of its useful, and just wants to call themselves an AI-first company.
3. If this datacenter build-out proves to be beyond the actual demand, there might be a big economic crisis as to how much of the financial system is getting tied up with it (insurance money, private credit).
Whether openai and anthropic actually die, I'm not sure. But I don't think they'll be the next big tech companies. I think eventually they'll be absorbed by others.
The whole thing I think, can be summarized as: LLMs will be commodity like. And it's price will go down and eventually will run locally, it's not at all clear that this will bring AGI and that it's worth infinite amount (or trillions) of investment ahead of the actual demand or the AGI level do-it-all-for-you AI being reached.
I feel like too many people have a binary vision of the world, i.e. you're either "pro AI" or "anti AI".
Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase. He's basically saying that (1) the current data center investments are based on unrealistic revenue projections and (2) hyperscalers are using accounting tricks to move around "money" in a circular way to make it look like more money is already flowing to AI.
You can believe all of that and still believe that AI (as in "LLM based services") work, are useful and will probably see their usefulness grow even more. Just not in the magnitude necessary to make the current investments make sense.
If you ever listen to more than a couple of Zitron interviews, it's clear that he's not the "reasonable centrist" viewpoint on AI. He's the doom-and-gloom guy.
Maybe there is someone out there positive on AI itself and calling out reasonable objections to some of the extreme things. But that's not Zitron.
The center is not always the reasonable viewpoint. And sometimes the doom-and-gloom guy is correct.
3 replies →
As someone who's strongly anti-AI, i find Zitron to be absolutely too much pro-AI for my taste. He defends AI that's not LLMs. He barely touches on ecological impacts of AI, and barely mentions the enshittification of things. He's only really against the LLM craze, OpenAI/Anthropic over-evaluation (because Deepseek and local models will do the same for nearly free), and the crazy speculative bubble around datacenters.
If you find him very negative about AI, and i find him very positive about AI, he might just be this reasonable centrist.
8 replies →
> Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase
He's said variants of both of these things before! That's the point OP makes too, people say "He doesn't say that" or "But what about this" when he's said a million, sometimes contradictory things.
Ed's whole cornerstone thesis is that "AI doesn't work." Like, if I had to sum him up in a short sentence, that's what it'd be
that or "Angry but doesn't know much"
9 replies →
After looking at some of his recent interviews, he's acknowledged his previous predictions that 1. He was naive enough to think the investment won't keep coming despite lack of profitability and 2. He acknowledges that AI in it's current form is a billion dollar industry, not a trillion dollar one, but also not zero.
Definitely less extreme than he used to be.
> Ed Zitron doesn't say that AI doesn't work
Ive seen him say it does work pretty consistently? He also talks about other things that are more interesting, but he still says it doesnt work
If you watch his latest interviews you can see him talking about how he finds it useful for simple cases like in the Bloomberg terminal for generating code for queries, but it still has fundamental limitations for doing anything autonomously.
As someone who has used LLMs heavily at work and at home in various serious experiments, I agree. It still requires heavy babysitting and a lot of its limitations wrt context length are fundamental, not something that’s going to be easy to overcome.
1 reply →
From anything i read/watched of him, the shorthand is "it doesn't work". When taking more than a few words to explain, it means it's not replacing humans altogether, because the output is low-quality and needs human quality control. In the end, he's not arguing LLMs are not working (as in returning 500 in your browser), but that the promise of autonomous agents replacing humans for increased productivity is a lie and doesn't work.
I overall agree with his interpretation, but i'm afraid he might be wrong in the conclusion that noone is going to replace human labor with shittier agent slop. As long as we humans don't really have regulations and choice for proper quality of service, companies might just in fact enshittify everything with slop and make more profits while we suffer from lower-quality products and support.
To be fair he is nothing more than a talking head who historically has had some pretty negative AI pieces. Sure nothing is binary but more than ever it feels like there are talking heads on both sides that have some pretty extreme views.
> You can believe all of that
You can believe all of that (I do) and see that Zitron is a liar and an influencer.
re: substituting with cheaper models. I think some people here are oblivious to how much of Anthropic and OpenAI’s usage is artificial.
Take this one example of a user with 15 Codex subscriptions ($3k) generating $60k in API-equivalent usage per month: https://hraness.com/writing/my-girlfriend-asked-me-why-i-hav...
“there’s a once in a lifetime discount happening at the OpenAI Intelligence Depot, and I brought 15 shopping carts.”
We have to understand current usage with this behavior in mind, there are tens of thousands of people just like this author who are intentionally generating as much usage as possible on their subsidized plans because they feel compelled by some need to get free intelligence.
The only reason these expensive models are generating so much usage is because they are so heavily subsidized. Pragmatic users will shift to cheaper models which will hurt per token revenue, yes, but huge volumes of usage is going to just disappear because there isn’t the demand when it isn’t being subsidized. Less revenue per token and less tokens.
https://tokscale.ai/leaderboard a small sample of just 2k users have generated over $100m of API-equivalent usage while paying closer to just $1m.
https://aicharts.io/gpt-subsidy
It goes a layer up, too. Harvey (legal AI) recently tweeted that a single user query cost that firm 26k USD. This, while they have thousands of users and reportedly whole law firms paying zero. That difference is being subsidized by billions of VC dollars.
A bit of a collective action problem, but if everyone leverages this services as hard as they can without cost concern, better capital decisions will eventually be made. Like the Federal Reserve draining the M2 money supply, AI users must drain the capital supply of subsidized tokens. This pulls forward the future of "Does this tech have value at actual unsubsidized costs?" If it does, tremendous, if it doesn't, also a reasonable outcome to the grand experiment. The current pain comes from the valley of uncertainty we find ourselves in at the moment.
> That difference is being subsidized by billions of VC dollars.
"History never repeats itself, but it rhymes." -- Twain
Doordash and Pizza Arbitrage - https://news.ycombinator.com/item?id=23216852 - May 2020 (514 comments)
> I cut this deal with my neighborhood Italian restaurant! I texted the owner about being miffed they hadn’t told me they were on DoorDash. He replied. They aren’t. We compared pricing, and found the prices advertised are way off from what the restaurant charges. So I placed a $5,000 order to the neighbourhood homeless shelter. DoorDash paid him over $20,000, and I get free pasta for the rest of the year. (My neighbours have also partaken.) Glad to know it’s scaling. SoftBank has assembled a unique concentration of stupidity for itself.
3 replies →
Why do everyone assume they are subsidized? When we seemingly have no idea what it costs? Maybe average subscription is breaking even and token spend is pretty much pure profit?
Case in point, claude code seems hell bent on increasing usage at all cost. Which makes sense in the growing phase (get people hooked) but it does not make sense given the hardware shortage. So, which is it?
Anthropic admitted last year to losing money on inference, it had negative margins. The margins have improved and are now positive but there’s still a significant cost. If plans aren’t being subsidized it would mean that the margin on inference is ~99%+ which would mean OpenAI and Anthropic should be wildly profitable but both are still losing money. So, it’s mathematically impossible that they’re not subsidizing plans.
The most widely accepted estimates (though I disagree with them) are that Anthropic’s margin on API inference is ~70% from which people extrapolate what their token usage would cost via the API and compare that to what their plan costs.
https://newsletter.semianalysis.com/p/anthropic-3q26-profit-...
(edit: better link https://newsletter.semianalysis.com/p/anthropic-growth-and-b...)
re: increasing usage with resets, it’s because they’ve overblown usage and need to show that usage is growing ahead of the IPO. They’re increasing usage on fixed price plans without increasing the cost, the only plausible explanation is they have unused capacity. If they were capacity constrained then the last thing they would do is give away more usage for free.
4 replies →
> Why do everyone assume they are subsidized?
It's not an assumption when the companies hosting the models publicly announce that they're subsidizing costs.
2 replies →
You can compare the subscription cost to the API per token cost for similar queries.
2 replies →
theyre not being subsidized, and no ones using tokens just because.
[dead]
Yeah personally I think he is right about a lot of the current state of affairs. I think he has a decent grasp of the situation despite being incredibly biased.
His predictions though? I don’t think I’ve ever read anything from him to get his read on how things will play out.
Just because one makes the wrong predictions does not mean that the data used to make them was wrong. A lot of people seem to dismiss his reporting because of his takeaways
> I am sure that what they're using it for can be substituted in large part by way cheaper models.
I saw a commit in the repo at work that edited a few config files and a dictionary.txt file. "co-authored by Fable"
I have colleagues who have replaced ctrl-f on a (long) document they already have open with prompting an LLM to find things for them.
It never ceases to amaze the lengths people will go to avoid using grep
thats me
None of this actually matters if this is a nuclear weapons-style arms race.
The data center build out will become a matter of national security, and will be backstopped by governments.
LLMs are a national security issue? Why is China releasing their models then?
The question is not whether you should have datacenters, or the most advanced chips, or the ability to build the most capacity. But do we need all this now? Will there be enough demand? People want to make profits form this thing, and what's being pointed out is that maybe there won't be enough demand to generate profits for all this investment.
I would guess there is probably a capability ceiling beyond which China will not open source their weights. Maybe there is a scenario where they do so or threaten to do so if the US is so far ahead or something as a deterrent.
>But do we need all this now?
Did people want to make profits from the nuclear arms race? I'm sure some did, but it was not really a requirement.
The bottom line is there is a theoretical capability of AI which is effectively a super weapon that can be deployed against your competitor. If you think that is a possibility, even with a low probability, you should probably have a giant AI-related build if you are the US or China.
This reads to me as incredible cope. Maybe not both of these companies but certainly one of them will be enormously valuable, and there will always value in the frontier models even if much of the practical usage can be done locally
Why is it “certainly” the case that one will be enormously valuable?
Neither appears to be on track to long-term profitability specifically once you take into account depreciation on CAPEX.
On top of this the “productivity gains” from AI across the board seem to be a very mixed bag. The pitch from these companies has been huge gains in productivity and automation and although there is anecdotal evidence some people are able to do that, the broader studies seem to show marginal gains in most cases.