The AI bubble is popping; we just don't know it yet
theregister.com
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It appears very unlikely we will ever see an IPO of OpenAI. Anthropic appears less doomed, but still iffy at best. Tons of other large, but little discussed, AI startups are just dead-companies-walking at this point.
The likes of AWS are showing good headline numbers but are taking out massive debt to build infrastructure that looks increasingly unneeded. Those with capacity are looking to offload it, quickly. Yes AWS has “committed contracts” for this capacity but if those commitments are with shaky AI startups then it’s mostly just fluff PR and these hyperscalers will get left holding the bag on all this debt.
On the plus side, our interest rates aren't 0% right now, so there's some room there.
On the down side, our national debt generation now exceeds 125% of GDP and bond rates are shooting up because nobody wants to buy our debt.
https://overweightskepticism.substack.com/p/ais-cash-cushion...
Yes the overall market will take a hit but, like a forest fire we need a healthy burn to just wipe out the weaker players so the older more mature trees can get on with it. Yes the big trees will get burned a bit but they’ll be fine in the long run.
We need a good brush fire to just wipe out all the iffy startups and investors that over-indexed here. Thats what people want with “let it burn.”
If you are market-corrected out of a job that situation most software engineers will be out of a job for years and wages will not recover for decades, or just never.
So: nope. Quite the opposite. Feudalism was a very slow increase in rights for normal people over time.
But also, don't count out OpenAI and Anthropic, no matter how shaky the numbers. The market has also demonstrated how to price SPCX so if they IPO, they will see their true FMV and I'm sure it's >0 and much much less than what they believe. For even in the worst case scenarios, they have valuable personnel, experience, and IP deploying AI at scale for what is to come, even if it's based on Chinese open weight models. RIP lightcone of all future value though.
As for the hyperscalers embedded in FAANNG, they've been using profitable divisions to cover the expense of growing but unprofitable divisions for a while now. In fact, that's business as usual. They're all going to land on their feet with PE ratios of ~20 or higher. Now take your favorite tech stock you prefer to scapegoat and figure out its approximate bottom relative to today. You'll be glad you did.
You know what I'd like to see? OpenAI going bankrupt, wiping out its investors, then being bought up by IBM for a nickel. Ditto with Anthropic, but ending up with Oracle (if it's still around after running up so much debt).
So you're saying we want to keep this bubble going forever?
If's it's a bubble, it's gonna pop, and better sooner than later.
If models are becoming commodities, and the main bottleneck is actually serving them at scale, Anthropic does not appear particularly well positioned.
If AWS had a ~60-80% margin for decades, I see no reason why inference can't have a ~60-80% margin for quite some time.
The problem is, if costs continue to drop ~90% for the same level of quality every 18 months, demand is unlikely to grow 10x to keep the revenue stable.
Who knows. Jevon's paradox. But the cost/quality is dropping too fast that it's hard for me to imagine demand keeps up long term to keep revenues (and profits) GROWING.
Transistor count has increased exponentially for decades and so did demand for compute. I think we will see a similar phenomenon with AI.
Same as anything with low margin: Viable competition and low switching costs.
Many sysadmins lazily do 100% AWS because they don't know any alternatives. CFO's might enforce using more economical inference providers if the cost is even 10% less.
So why didn't this happen to AWS?
Disadvantages: Top model is slightly behind the frontier for now. Bias against using their products because it's not cool/trendy.
That has been my thesis ever since Dario went on about how difficult it is to forecast capacity on the Dwarkesh podcast right about the time Claude started having 9's comparable to GitHub's.
Even as an outsider, seeing all the cloud providers bemoan their lack of capacity and their growing backlogs, I could tell compute capacity was already the biggest constraint on industry growth and that it would be very hard to come by in the future... which also probably made it the only moat.
And now we see Anthropic making very expensive deals with competitors to secure the necessary capacity. Claude likely still has enough momentum to make it worthwhile. But I can't believe Dario, probably the most AI-pilled of them all, would under-estimate demand so severely.
To be fair, this mess up actually increases my respect for him, as he focused on making sure that they could continue to run the business under reasonable assumptions rather than YOLOing compute into existence like OpenAI.
My guess is OpenAI, having the first mover advantage and the bulk of AI traffic for a very long time, could gauge the trends much better than anyone else.
This is the crux that needs to be substantiated. Without substantiation none of your other arguments hold up.
Frankly it’s the opposite scenario (that’s there’s all this demand) which is struggling for any hard evidence.
Meta tanked chip stocks by saying it was considering the same.
Worries about compute overcapacity, via folks saying they want to offload capacity, is literally the thing that nuked the “situational awareness” fund last week.
It’s long term demand that matters, not just “price.” High prices without true demand is the literal definition of a bubble.
Meta said it was considering doing the same because they saw how valuable excess capacity was.
You're misinterpreting what actually happened.
That, by itself, doesn't have to mean anything.
AWS is far more compute capacity than Amazon needs, but that's not because Amazon misjudged how much capacity they need. They built it out to sell it to others, leveraging know-how and economies of scale to do so very profitably.
How hard can that be?
In what way/shape/form is the infrastructure showing signs that are contrary to growth in need/demand? I'd really like to know if there's something I'm missing that I should keep my eye on, thanks
What do we have in the decade ahead? Robotics in every household, models 10x+ faster and more intelligent than today.
Really no significant impact in life sciences, R&D, and 'offline' world / robotics today as of yet, which is where most of the value will live.
If you want someone else to do household chores, hire someone. You can pay someone to do your house chores for years for the cost that these things will have initially.
Which are pretty useless for a lot of home layouts and degree of putting cords etc. away. I took a look a few years back and got a stick vac instead. (And have a monthly housekeeper who does a lot more than a robo-vac would.)
Wasnt AI science fiction (research) for decades but just took couple of years after Chatgpt to become mainstream. Why do you that wont happen to robotics?
Current frontier models are absolutely sci-fi AI by any measure that existed up until the late 2010s. If you showed a current frontier agentic AI, with bidirectional speech, tool use etc. to a person from 2010, they'd say it can't be real, there must be a person inside that mechanical turk.
Sci-fi is Lt. Cmdr. Data, HAL, Skynet, Culture Minds...
> Current frontier models are absolutely sci-fi AI by any measure that existed up until the late 2010s. If you showed a current frontier agentic AI, with bidirectional speech, tool use etc. to a person from 2010, they'd say it can't be real, there must be a person inside that mechanical turk.
It wasn't a natural language chat interface that makes stupid mistakes a noticeable fraction of the time and can execute command line programs.
The current state of things is closer (not close to by a long shot) to Fred Pohl's "AI" programs from his novel Gateway[0] than it is to the above examples.
But even those are decades (at least) beyond what we have now. What we have now is the foreseeable (from at least 30 or 40 years ago) evolution of what was once called "expert systems."[1]
I guess you had to be there. I certainly saw it coming.
>Expert systems worked on quite different principles.
Actually, not so much. The inference engine has changed, but otherwise it's same shit, different day.
They did interviews with experts and tried to do domain modeling, mapping the expertise to a formal symbolic logical description, ontologies, relations etc.
Both the knowledge base and the inference engine changed. They both changed to a unified machine learning paradigm. It's like you throw away your axe's handle, then the head, and then you grab a chainsaw. It's "the same thing" to that same degree.
The Internet was a 'bubble' at one point, and after it crashed in 2000, it didn't go away, it continued to build out. We're still using the Internet after the Internet bubble popped.
Over 40 years, that's 7-20 hardware changes, that turns into between $420B and $1.4T of ongoing investment (not accounting for inflation). The $30B that is called "infrastructure" only accounts for 2-7% of the overall bill.
This is NOTHING like fiber buildouts because the fiber lasts the whole 40 years with ZERO replacements and very close to 100% of the cost is infrastructure rather than a tiny percentage.
Conversely, for the data centers we're talking about, the cost of the things that need replacing every five years is one of the primary costs.
I'm not arguing that the current build out isn't crazy, just that AI isn't going away. Just like everyone thought the internet was a fad, but then it continued to grow (remember web 2.0). AI will morph into something useful and will be sticking around, even if we can't envision what it will look like.
We see a similar paradigm with lots of revolutionary technology. The initial promise is high, people get very excited, lots of money pours in, and.... 15-30 years go by before we start seeing real impact across the economy at large.
It's just a real slog to actually implement and roll out new tech.
So take robots: I can promise you that you won't see robotics in every household in the next decade (especially so if we exclude the current market of robot vacuums). Even if a company makes an incredibly capable robot "today" (and to be clear - they are not) it won't have time to scale out production, reduce costs, generate a used market that's accessible to less wealthy consumers, deal with regulatory hurdles and quality problems that only pop up in real-world usage, etc...
It's just slower than you're implying.
The change very well will happen (I'm inclined to agree that things are going to shift). That doesn't mean that the current investment is sane and will pay off.
So many historical examples of this, just two here real quick:
- Ford built his first automobile in 1896, founded a company in 1901, went out of business, got sued by ALAM, didn't build more than 10k Model T's until 1910, then only finally hit real scale (of low hundred of thousands of units) in 1913: More than a decade to "basic scale". Household ownership didn't hit 60% until 1929... 30+ years later.
- The initial web enthusiasm, followed by the dot-com crash in early 2000s...
People in 2016 were adamant that the current frontier LLM capabilities would not be achievable in the next decade.
Modern home robots prototypes look awkward and mostly useless the same way GPT-2 was looking like a curious but mostly useless thing in 2016.
I can totally see a capable and affordable home robots that are worth buying for the majority of the population being a reality by 2036. Maybe not _every_ household, but a good number of them.
Actually bought and distributed? No.
Again, hardware just doesn't roll out that fast, and you're talking about trying to distribute expensive hardware to a population with a 47k median yearly wage.
I've built robots. Even if the software expense is ~$0... you're not going to escape the fact that a robot that can do anything remotely similar to household work is just going to be expensive. Like, several thousand dollars in actuators and sensors alone, not counting any compute.
LLMs are a SaaS - Robots are delivered hardware. The graveyard is littered with software folks who thought they could deliver hardware like it was software.
To be useful they’re gonna have to be capable and powerful. If they’re good enough to be useful, they will be dangerous.
Advanced household robots are closer to fully autonomous cars and airplanes than roombas. The code is safety critical. Imagine something capable of flooding or burning down a building with hundreds of people if left to the vibe coders?
I'll even bet < majority of the US
Isn't that superintelligence?
Go to OpenRouter and look at all of the unsubsidized providers.
Is this range just Silicon Valley or what is this? Even including just Europe, you're looking at a lower bracket of 10k. If you expand to the rest of the world... Or do you think rich cities in the USA, where developers make 100k+ per year, can alone sustain this industry?
€57-114k p.a. is well within the order of magnitude of yearly gross developer salaries in Western Europe (e.g. Germany).
How are plungers comparable to LLMs?
You can't only look at how much "value" the buyer gets from the product. That's the mistake made by the original comment.
You might ask "why don't all of the LLM providers just raise their prices?". That's called price fixing or collusion, and is generally illegal.
And I'm sorry to you and the parent poster for being needlessly snarky, I wasn't in a great mood when I wrote my original comment. I'll avoid bothered-posting in the future ;).
"A.I." == "Artificially Inexpensive"
That is insane if that is true, is that even legal?
Companies can go from looking really good to a complete financial mess almost overnight when all that leverage and self-reinforcing stuff unwinds. See last weeks headlines for one such scenario.
As far as accounting of revenue is concerned, it would have been counted as appropriate. Else, like you, I would guess it's not very legal.
I think the size of their commitments is predicated on demand. Anthropic's annualized revenue run rate is now close to $50 billion, a fivefold increase from a year before [1]. They are making big investments, like $200 billion on Google's TPUs over the next five years [2], but those numbers seem justified by their expected revenue this year alone. If Anthropic cannot capture that revenue, someone else will.
Stock market valuations are a different beast, I personally think we have been due for a correction for ages now. But criticism of AI investment and particularly betting that it will all come crashing soon appears misguided to me. I can see a future where AI expenditures shifts around, not a future where everyone simply stops spending in AI all of a sudden.
[1] https://www.marketscale.com/industries/software-and-technolo...
[2] https://www.resultsense.com/news/2026-05-06-anthropic-200bn-...
What's very real is the rapidly growing amount of revenue for both OpenAI and Anthropic. That's already tens of billions per year and growing quite rapidly. Investments against that kind of revenue aren't completely horrible. To a point. But at the multi trillion dollar valuation level, of course there are going to be issues with living up to those expectations.
In my view some of the base assumptions are looking not so solid currently. It's not a given that OpenAI and Anthropic will end up with most of the revenue. The Chinese trust Silicon Valley just about as much as vice versa. Which is why they are doing their own models, chips, and data centers. This is driving a rapid commoditization for things like frontier models, open model weights, and chips. This in turn gives countries outside the US a lot of options to stay independent. Which burst the bubble that all that global revenue was going to flow towards Silicon Valley. Some of that still might. But that will have to happen based on cost and merit.
There are also geopolitical circumstances that cause most data center plans to be bottle necked on permitting, chip shortages, grid connectivity, availability of gas turbines, gas, solar panels, inverters, batteries, water, and other resources. As it turns out, you can't just willy nilly plan for hundreds of GW of data centers and expect those to pop into existence overnight along with all the needed infrastructure. Most of the announced/planned capacity for this will likely not be realized. Certainly not this decade. 5-10% by 2035 would be a lot given all the constraints and scarcity. No amount of reality distortion can change the physical constraints on this topic.
The good news is that most of the money needed for this hasn't been spent yet. And what has been spent won't be going to waste. Up and running data centers are a hot commodity right now. They won't be running idle if a bubble bursts. But probably investors dreaming of multi trillion dollar IPOs might be a bit more cautious now that SpaceX stock is trading well below its IPO value.
I'm sure AI will still come but it is too disruptive now. It needs a slowdown. As usual all the greedy investors are to blame.
There is sadly ample fiscal headroom in mundane drone-like work that was being outsourced (still cheaply, I might add) that AI can replace and even do a marginally better job of. I suspect AI prices can even increase and it will still be profitable for enterprises.
Companies like this will certainly keep expanding their AI use, and once they commit to that, there's little stopping them from moving to open models or local inference if need be.
My concern is less over the bubble and more over the social cost of AI. Call centres and the like provide a tremendous number of jobs. As AI moves into enterprises more and more, where are all these people supposed to work? Become baristas? They certainly won't be "learning to code"... What sorts of social and other unrests will this cause?
These forces I think will muddy the waters and make predictions difficult. Say what you want about the AI bubble, but if it pops, it will be different than previous ones. The bubble doesn't even need to burst because of the insane economic model, if enough people are economically devastated by it, it will cause ripple effects of its own.
Microsoft spiked on earnings, and is now actually about ~24% above it's pre-earning level.
Alphabet did lose about 7% the day of the earnings, but it recovered and now, after MSFT earnings, is actually 9% above the pre-earnings level.
Meta dropped 10% on earnings but is now back to its pre-earnings level.
Apple dropped ~10% and has not recovered (yet) but it's also famously "sitting out the AI bubble", so not sure why it's included here other than a "tech stock that went down."
Oracle has been dropping forever but after Microsoft's earnings it's climbing again.
If you zoom out, the stories change, and as you keep zooming out, they keep changing all over again.
My point is, 1) reading stocks in isolation is like reading tea leaves, and 2) if you want to point to any stocks, you should make sure they support your narrative.
There's nothing really groundbreaking at all in there, just "chips are expensive, and open weights models hosted locally in enterprise could displace Claude/GPT"
> Yeah, they dropped the price, but count the number of tokens you're tossing into it and see if it's actually cheaper. It's good for marketing, but the rub is how much you're actually using.
The level of discourse is so horrible now, I don't have words. Are these the ones making predictions on AI bubble?
People completely lack imagination about this stuff. The main problem right now with AI isn't even AI successfully producing code at a reasonable cost, it's human coordination and review that is the bottleneck.
Cost isn't just token price though - it's (number-of-tokens-used x price-per-token), and there are large differences in token efficiency between different models and harnesses. Increasingly we're seeing benchmark sites focusing on "cost per completed task" as a cost metric, and it's not always the cheapest tokens that win.
I agree that ultimately AI/coding cost is just part of the picture - at the end of the day it's about software development cost, which for time being involves humans.
If a car company releases a new car with higher mileage, would you suggest that cars are getting costlier?
I assume cheap. Then why would you call AI more expensive?
> This is poorly stated in the podcast, but the underlying point is correct: while cost-per-token is going down, overall token use is way up. This is causing the cost of using LLMs in corporations to skyrocket.
This is literally what the guy says in podcast. So the underlying point is not correct. He’s specifically talking about per task cost. There’s no “but actually they meant something else”.
I don’t discount your point about overall cost increasing but that’s not relevant here.
Let’s agree that the podcast is fundamentally wrong in their main claim.
I want to show that the podcasters should be discredited because they don’t understand a fundamental aspect of the economics so you can’t trust the main thesis.
You gotta watch something, because harnesses have changed how you can look at the cost structure of these things. If you look at cost per million tokens, it might look lower, but if the model consumes four times as many tokens in order to deliver a meaningful result, it's not cheaper. And so, Tom Claburn, one of our senior software reporters, had an excellent piece looking at how Anthropic's latest models use a tremendous number of tokens in order to deliver the result. So, sure, OpenAI's latest models might look less expensive from an API standpoint, which is great for marketing, but if it's using twice as many tokens, that's not the same thing. And that's somewhat dependent on the harness, but it's also dependent on how much reasoning effort is put into it, how they're routing the models.
He literally says “but if it’s using twice as many tokens that’s not the same thing”. Why would he bring up tokens?
I genuinely don’t know how you can conclude that he still thinks overall price per fixed task reduced.
If you continue reading, he explains it in the next sentence.
I'm not sure if you're not aware of what he's referencing there, but the specific example he brings up is that Anthropic ostensibly kept pricing identical with new models, but changed the tokenizer, which made the model use more tokens for the same input and output text. So that's a case where, prima facie, the cost per task increased. Of course, this also depends on how verbose the model is and what harness you use, and so on.
Correct me if I'm wrong; I think you believe that he makes an argument like "OpenAI decreased API pricing, but this decrease was actually secretly an increase in cost." But he does not. He's saying that even though cost-per-token is going down overall, the actual cost of using LLMs is in many cases going up, because there isn't a direct causal relationship between cost-per-token and the total cost of using LLMs.
And he's entirely correct about that.
UNLESS openAI/etc actually succeeds in making AGI that never hallucinates and goes over the current LLM limitations
as for google... well they own the web
(+google has plenty of other revenue sources, so it can just pay out its AI survival)
if you're a website owner, would you welcome chatgpt/etc's data-collection bots?
but... as for google's bots... you need your website to be on the Google search results...
Also, possible Apple since they haven't gone into deep debt to finance 'AI buildout'.
If not, I'd say they don't even count in the "everyone"-
https://blog.google/company-news/inside-google/company-annou...
I saw it put quite well in a comment on reddit:
> In 2020, the gaming segment was 47% of revenue at around 8 billion. Today it's doubled to 16 billion, or 7% of revenue. That's right, data center went from 6 billion 2020 to around 198 billion today.
Even if gaming revenue doubles again when the AI bubble pops, their total revenue will still drop by something like 80%. I'm neither smart nor dumb enough to be confident about whether that's something Nvidia can survive.
plus, even old gpus run games fine
Now their market cap is most likely destroyed for forever... Still I little doubts about them continuing to exist.
The problem is that local inference machines won't allow anywhere close to their current margins or gross sales figures. At the same time, the huge overbuild of GPUs is going to crash server sales and prices for around a half-decade as companies try to avoid hardware upgrades or buy cheap, used equipment to save costs.
I think Nvidia will survive, but they'll be back to something like a 1T (or lower) valuation.
Why? Depends totally on what kind of website you are running.
1. money from advertisements (from views, visits): you need to be on google search results at the bear minimum.
2. money from non-advertisements, mostly-offline (eg. you sell preminum stuff/service): you CAN hand out pamphlets to your service website IF your ROI per visit is so good to be true...
but even in the 2nd case, it really helps to be on the google's search result
Movements of AI stocks shouldn’t be confused with “AI as a technology” and “AI as a business”. Market valuation is a different game.
Although yes, AI as a technology is still in its early stages, and I believe it's a sound technology for us to work with.
I also think we're solving the wrong problem (removing office work) versus solving problems in the sciences, like running and monitoring biotech labs.