I'm not convinced the AI bubble exists
https://matteoraso.github.io/im-not-convinced-the-ai-bubble-exists.html#im-not-convinced-the-ai-bubble-existsFatal misunderstanding.
Datacenters are not warehouses. Amazon.com being borderline-profitable owing to massive ongoing capex on warehouse expansion is not at all like AWS becoming borderline-profitable owing to massive ongoing capex on LLM datacenter build-out and SOTA model training (it's CAPEX, but carries OPEX affect because of GPU depreciation schedules and competitive hardware upgrade cycles), and opex of delivering LLM inference services, and being able to price it all competitively.
(edit: expand explanation of why it's a misunderstanding)
Everybody knew it.
But some investors don't have the patience for these "sure things" to mature into their commercial destiny and others know they can siphon off a killing by taking advantage of them as they speculate.
These bubbles aren't about potential, they're about timing.
And today's AI, for as revolutionary as it may be, is taking just as long to mature as anything in the past.
Most of the top players involved in AI right now are going to be rich. And in 20 years, the world is going to involve a lot of AI in soooo many things. But a lot of shirts are going to be lost below those top players and a lot of companies are going to fold because infrastructure, refinement, and integration take time while the FOMO speculation starts as soon as the big discovery is recognized.
Canals... where a repeating pattern was the company underestimating difficulty/costs, and going unrecoverably into debt. Another company, which then bought the largely finished canal, cheap, they had potential profit... until tech change (railroads).
Yes. And valuation. I think the leading frontier labs will be fine in the long-run and NVidia will be fine. Some of their investors who bought at too high of a valuation, expecting to flip it in too short a time frame will lose a chunk of their money when they have to sell for less.
The hyper-scalers in the middle are the most vulnerable. They borrowed billions at increasingly high rates to lock up GPU and power capacity. They are 'ground zero' of a lot of the 'circular money' deals. Big banks who financed hyper-scalers initially, expecting they could package that debt up and sell it on to diversify, are already struggling to find buyers for the debt, causing its value to drop, which then forces the banks to cover with more collateral, and that's how the snowball starts down the mountain.
And do these not all have established business models that are ridiculously profitable, and huge cash reserves?
How the heck are THEY vulnerable, and the "bare" frontier labs like Anthropic and OpenAI with no preexisting business models or profits, but equally ridiculous borrowing are supposedly not?
Nor would it have been thrust blindly into the hands of your manager, the marketing department, the CEO, and countless other folks who have no understanding of how it works, its limitations, or its risks.
AI models can be deployed on the same datacenter as the old one and be either orders of magnitude cheaper to run, or orders of magnitude more capable.
The only thing that can pop this bubble is if we're not smart enough.
> And today's AI, for as revolutionary as it may be, is taking just as long to mature as anything in the past.
I suspect this opinion may come across as naive for the myriad software engineers whose job has become pressing the enter key. Your point about timing is based on solid principles, but I don't think your comment contains a satisfactory argument that the timing is incorrect.
Expect more differentiation in the future and not less.
As we all know, GE then went bankrupt and we went back to whale oil.
I wonder why so few people make the comparison to the recent VR hype. We heard the same things there about its unlimited potential, how it's inevitable that we will soon resolve all its shortcomings and that it will become ubiquitous.
I still believe VR will eventually live up to its potential, but no one can deny that bubble has popped after Meta took a huge loss on it. It took too long to resolve the hard problems so the timing didn't work for them.
AI is bigger than the Industrial Revolution, the Internet, than computers, than literally any innovation or invention that has ever come before, combined.
Today you see AI slop and vibe coding.
Tomorrow, all of humanity will be affected, forever.
Everything about the way that we learn, that we think, and that we invent will be changed forever.
The growth slowed and operating profit has swung to a loss. The company can still make money, just not at a pace that has a chance to pay off the investments.
Also, the author conflates AI's technical potential with the market potential, naively assuming the demand will also grow as technology matures.
The author talks about revenue growth on the basis of Anthropic's S-1 filing, but ignores the third quarter results which were posted later.
As far as I know, Q3 for Anthropic wasn't the best because:
* OpenAI killed it with their models in Q3
* US Government ban on Anthropic models, including temp restricting Mythos/Fable
* They decided to dedicate more compute to training in Q3 instead of inference
* Opus 5 was a disappointment and Fable 5 was too expensive to inference
* They saturated all their available compute in Q3 and didn't have anymore to sell
Their growth rate has ramped up in Q4 since Opus 5.5 if OpenRouter data is a good proxy.
That is not the claim. Author uses Anthropic's filing as an example indicating growth of the whole industry. While questionable on its own, it is also not given in full, which I pointed out.
- anthropic IPO repricing
- pareto frontier gap decreasing by the month between open and closed models
- SpaceX disappoinment
- enterprise buyers as main consumers of closed tokens are being displaced by OS competition for pennies
- job loss and entry level hiring collapse
- massive discounts of current data center contract debt
- consumer open weights model efficiency improving constantly
- the Chinese arms race
- a possible rapid downward spiral that may force governments to step in and subsidize
Am I missing anything?
- margins shrinking at Anthropic
- Google getting thwacked by missed earnings
- OpenAI missing funding target
- price war
- Microsoft revising capex projections down
- Plateauing of model capability improvement
What's weird to me is that you don't see a lot of people making deals that seem way too good to be true but yet the total numbers are absolutely gigantic. Throw on the fact that the stranded assets will have a shelf life, it really seems terrifying. Yet everything is being priced as pretty darn risky. I think maybe there can be a big slowdown without a dramatic pop. A sort of grind down. But there's not a lot of room for error to prevent a pop so I guess it will happen? I don't know its very weird.
Also of course this is all predicated on models not making another dramatic leap. The signals out of the cutting edge labs don't really suggest they have a model that would be as dramatic of a leap as Mythos was when it came out in the hopper. Their code quality seems about as mediocre as what the publicly available models can do. The OpenAI math results are very impressive but that's ripe picking for AI. Also they keep talking about a coordinated training slowdown which is textbook antitrust so they seem to want the government to force their hands off the "spend money on R&D" button.
So I don't really know. I guess I would say purely on vibes that this doesn't feel like a bubble but the size of the build out is just so massive that there really isn't a big window for a success case.
https://dealroom.co/news/169124-revolut-moves-2-000-engineer...
> pareto frontier gap decreasing by the month between open and closed models
We need a temporary moratorium on the words "pareto frontier". Every new model is the pareto frontier by some combination of metrics. Like "Bayesian Priors", people love saying / tweeting it because it sounds Smart.
It's like watching car commercials where every single vehicle has won something from JD Power and Associates. Which, (a), who the hell are they? and (b) you can only pass out so many awards until the gluttony of awards proves the award is worthless.
This isn't targeted to you! It's a pet peeve.
The reason that is important is it will fuel the competitiveness of on premises and local open weight models.
Though maybe it's less useful in the "bubble" conversation since over four more years you could fit in various booms and busts before the supply side significantly starts changing.
As always, reality is likely a muddled mix: there is real utility and there is wasted investment that will never pay itself back.
so I think that's one big miss from this blog, or I don't understand datacenters as well as I think I do (which is always a possibility! hopefully someone will inform me if I am wrong)
If we steel-they the point in the essay, it still doesn't matter because a bubble is setup in the economics of the system, not whether the underlying tech works and/or is profitable. It can both work, be profitable, but have been overcapitalized before the knock on effects are in-place in the rest of the economy. This is a crazy analogy, but what if 1700s earth figured out, theoretically how to build a fusion reactor that could supply all the energy needs of the whole planet. But it cost in their equivalent dollars, 1 quadrillion dollars? You can't even integrate under their money curve to scrape together that many dollars. The only economy you can make about that is in the research side to push the cost down. Then when its dialed in, you can launch that rocket AND the rest of the system has had time at many different levels to integrate AI at various levels. OpenAI dry barebacked its way into the economy and literally screwed everything up. If they really were an altruistic AI safety lab, they should have kept working on ChatGPT, but demoed it to the world including government, so we could as a planet decide where we are taking this.
It is a huge bubble, AI is over capitalized, we have no idea what the knock on effects are and really the only benefit right now is in programming, which is huge and civilization changing. But it also has massive downsides in that it has given the most powerful even longer levers to move the earth.
The current-gen hardware is expected to have only 2-3 years of useful life. New tech, and especially new architecture is going to make inference much faster and more efficient in the near future, and the big vendors are going to be more limited by datacenter floor space and power/cooling, so that the hardware will be replaced very quickly by better models.
Profits are mentioned but then only revenue is discussed, Revenue != Profit. Revenue can grow yeah but experimentation/token-maxing stage is ending and price wars have started, so I'm not sure about rising profits, AI Labs won't necessary be able to capture the margin(>>China<<), it might be companies/consumers who will capture most of it, if so there is a bubble - it is in AI labs with their trillion $ valuations and they might end up in a race to the bottom, BigTech is not in a bubble for the most part, they will endure a correction and fall back on their existing business.
Meanwhile, the major tech companies first financed this build out using the cash flow from their existing revenue. Once this became more difficult to justify, they literally set up companies to keep this off their books directly, and these companies are ultimately designed to take a loss and fail. But before reaching this failure stage and defaulting on debt and obligations, they went to the criminals on Wall Street and designed a financial product structured not all that different from mortgage backed securities. Also, as the "AI" aspects of their existing businesses expand, it eats into the traditional revenue generated by their core business, which if we remember, is the original collateral being used to justify all the debt in the first place. Now these core tech companies have to win "AI", whatever that ultimately means, to not only pay the debt from the build out, but to also replace their existing collateral from their original products. Think in terms of Google Search being replaced by AI. Maybe a clean transition, maybe not. Really just depends on the technology living up to the hype. If not, the hyperscaler companies designed to take a loss a fail, will fail and with it, the circular financing will pop the very real bubble that exists.
Speaking of IPO valuations as well, it's not to say that LLMs are not a valuable technology. Just look at what they have done to computer science already. But there may be increasingly limited returns on investment if every valuable problem to be solved isn't as well defined as logical computer science problems. Without solving the hallucination problem (as a result of the next token prediction limitations), the wide application of LLMs as being sold will never happen.
They've global procurement, the franchise model means the franchisee shoulders a lot of costs, they own the land the restaurant is on in 50%+ of the locations, and they own the technology system that underpins their app + ordering.
It's a massive moat.
P.S: A Matlab/Physics simulation guy running for a Liberal party seat in Canada? Say more!
A bubble that funds all kind of crazy ideas and big investments into infrastructure work is great for laying the groundwork. But the end of the bubble won't mean the end of progress, or even necessarily a slowdown in progress
Personal assistants dealing with your email, filling out paperwork, and booking reservations don't use a lot of tokens.
If every software developer paid $200/month, every academic paid what they pay for Matlab or Mathematica licenses, every YouTuber paid for whatever video processing, and every normal person paid a subscription comparable to their TV subscriptions and cell phone plans - does that justify the valuations?
The government pays for surveillance? Companies pay for customer service replacements and target advertisements?
It seems like there are a few small categories that use a lot of tokens, and a large category that doesn't need many at all.
> The government pays for surveillance? Companies pay for customer service replacements and target advertisements?
Yes definitely. All together that's probably within striking distance of a trillion dollars in annual revenue. Just TV subscriptions + cell phone plans in the U.S. alone is about 400 billion (leaving aside any other country).
Most large, mature tech companies are at 5-10x revenue to valuation ratios (and 10-20x EBIDTA to valuation ratios).
Of course whether you get there is a separate question, but any company that fulfilled what you're asking would pretty easily justify multi-trillion dollar valuations.
That's significantly underestimating the potential. Companies typically spend $200k/year for developers (salary, benefits, taxes, equipment, office space...). If AI can make them even 25% more productive, that would justify $50k/year in tokens.
It won't be about tokens in the long run, and I think the labs know that. It will be about long-term memory and user relationships, and who gets to own that data.
The disruption and upheaval has barely even started, IMHO.
1. Revenue growth increases 2. There are more highly profitable uses cases like code.
Neither are guaranteed.
I also think the narrative that these companies are profitable on compute is erroneous. You can’t ignore the other costs and no they don’t magically go away. Anthropic can’t stop training new models now or in the foreseeable future. Gemini 4 Carbon is about to come out and is apparently competitive. Google has other revenue sources. They are in the ball game. They can wait for Anthropic to run out of money.
Actually there are two different things going on here
1. model layer companies: OpenAI, Anthropic
2. hyperscalers, data centres, hardware layer
When people say the bubble will pop, they usually mean the second one.
> I also think the narrative that these companies are profitable on compute is erroneous.
I don't think you read the Anthropic filing. It is substantially profitable if you exclude the one type of debt which was actually equity. It so happened that Amazon lent some money to Anthropic but it was tied to the valuation of Anthropic, which became a "problem" because Anthropic's valuation grew 10x. You wouldn't call this a loss in any other circumstance.
Is this the consensus view now? I'm not any kind of denier and I do recognise that motivated thinking may cause me to overestimate my own value.
But trying to be brutally neutral I'm still not sure this is true. AI doesn't make certain classes of mistakes that humans do, granted. But I'm still surprised daily even in 2026Q4 at things it does seem to make a mess of. Usually, changes that are locally good but subtly globally poor.
What is the most expensive thing in an AI datacenter? The GPU! And what it's its lifespan? 3 - 5 years!
Bubble or not, there is no way AI spending is going to plummet. It will decrease, obviously, but thinking that after 5 trillions of investment everything is profit is so naive.
Unless nobody is working on miniaturisation, then this reasoning might hold true.
As seen throughout history, any sufficiently absurd tech starts largescale (refrigerators, computer, radios) and eventually become portable.
Amortising data Center cost will take a long time. Within that time, if someone figures out how to make ai run on cheaper hardware, these data center cost will Become harder to recover.
> AI companies can also cut costs by no longer allowing people to use their products for free and temporarily suspending expensive AI research
That will just kill the industry.
Let's talk about that in 2-3 years. There is a very clear incentive to have models running locally, from data privacy to cost effectiveness. The software will pop up to easily power this, and if it is cheaper than non-open frontier models it will be used. This can be done today with a bit of hassle, and will only get easier. The consumer hardware vendors are clearly heading this way.
I feel like this will depend on the user.
For example, I'd much rather pay for the highest intelligence model when designing something or developing something because the problems it can preempt will save me more money in the long run.
The whole issue is that intelligence (as measured in AA benchmarks for example) is not linear utility
I mention this in the article...
Sorry what? There are 10x if not more Linux installation vs Windows installation in the world. Linux powers everything, including billions of smartphones, where Windows is nowhere to be seen. Same for routers. And actual proper servers.
If it's like Windows vs Linux it means open-weight models are going to be incredibly more successful and touch just about everything. All the while being cheaper and working not a bit but much better.
1) There's a huge demand for AI.
2) AI is way too expensive for the companies building it.
SW can use a ton of AI. Lots of other applications can't, and they don't need the super smart frontier versions either. We just don't need all that much compute to serve AI for these applications, and people won't need to pay the margins necessary to get access to that AI. SW alone is not going to be enough to pay for all the investment.
I don't buy brand loyalty either. If I can pay 1$/m for the same thing from someone as I pay OpenAI or Anthropic 10$/m for, I'll do that, which almost zero barrier to entry.
The buildout is paying stratospheric prices on the assumption they can charge that much on the other end, but if demand tapers off (or model efficiency doubles again), prices will start falling. Everyone talks about Jevon's, but I don't really see why I'd spend more on AI than I need to do the things I want. I already run out of stuff to ask it to do on a basic subscription, I hardly need more.
The crux is this: I can spend x for y results. If I spend more, I probably don't get 10x for 10y results - I probably get, say, 2y results. So on that equilibrium I just don't spend all that much more because the reward for all that extra spend is not worth it. And if models get better this tradeoff starts to look worse, since I get more of the super-effective benefits for cheaper. Now I'm spending 0.1x for y results, and spending even x for 2y might not make sense, let alone 10x for 3y.
My worry isn't so mcuh the large players ability to raise funding, it's the long long tail of "AI" in everything else and especially small startup companies that looks more worrying to me.
At the moment you have young grads getting $20-30 million with basically limited evaluation of their skills, all over the place. The area I am interested in is physics and engineering, and the number of Physics AI startups for where there there are not physicists or mathematicians with at least PhDs in numerical computing as founders for example looks to me like a total mess. And I keep seeing things where very clearly solvers haven't converged then getting used as training data and publicised in press releases from companies like this. I assume that most technical areas are seeing similar. This is where the dotcom bubble comparisons are most apt.
When I look at the current capacity of AI, it really feels like there's so much potential for frontier models to be used that just aren't being applied yet. Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness, and a good deal more expensive as well.
Correct me if I am wrong on this, I am only a student of mathematics and by no means a researcher.
For the programming part, I think author already says that automated solutions require a good harness, and this is indeed correct, but in my experience building good harness is hard and agents can not do this currently without human closely guiding them. And in pre-LLM programming practice we did not need harness at all, or needed only small part of it: so, solving programming problems with LLM involves more work than without LLM, even if big part of this work can be automated.
The rest of the article seems to be a speculation mostly based on the premise that LLM can really replace human in the loop, which seems to be not the case currently.
But no one really knows what the operating expenses are, Anthropics s1 does not mention them as far as I read them.
Also, great products don’t always make great money. Some companies just never figure out how to monetize and eventually go belly up. Users may not be willing to pay what you expect, and the amount they pay may not be able to pay back your debt. Not every LLM chat app is going to survive the next few years. That’s just how it is when there’s sudden growth. Our economy tends towards boom and bust cycles.
Maintenance is just as important and the cost is often overlooked. The sober reality is that an LLM is just a natural language search index. For now, Google might be the only one with the capability to maintain "training" at the pace most people expect.
I think this highlights a big issue with both sides of the "AI is/isn't a bubble" debate - the idea that there's an established pattern that economic bubbles cohere to. There really isn't, and economists haven't been able to find a consistent pattern that past bubbles have conformed too (that isn't also the case of non-bubbles). "Bubbles for Farma" is a cool paper that tries to do this[0].
Like everyone else, I have my own opinions on whether there is/isn't a current AI bubble. But we should all stay humble and try and avoid stating anything as if it's an absolute fact.
[0] https://www.nber.org/system/files/working_papers/w23191/w231...
- Anthropic is already expected to have junk bond status; this is going to make dealing with their existing debt rough while costs likely continue to rise
- Datacenters are 'cheap' once they come online, but that assumes they will. Power infrastructure hasn't been able to keep up, and several pending datacenters (I've seen estimates in the 50% range) meant for AI growth have been significantly delayed because of it.
- The US has a lot of debt coming up in 2028. The AI investments have been far more lucrative than the traditional 30-year bond. The AI bubble doesn't have to pop at all for the global market to plunge.
Exactly the opposite. This is good for a tech company IPO which opens up all kinds of bond options. Most dont even have a bond rating! BB+ is a get.
If he is not accounting for this, then he is not saying anything of value whatsoever. So many people are still operating on vibes instead of these companies' actual financial positions, and "because Claude does cool stuff for me!".
The internet is successful and cisco is having a blast but its valuation has still been enormously wrong during the dotcom bubble. Same goes for pets.com, etc.
So in other words, maybe the valuation of some companies is totally wrong. Maybe Anthropic is not worth two trillion, or even a hundred billion, but Nvidia is worth 20 trillion. Maybe it's the other way around. Maybe the legal framework changes and the value will be captured by the major rights holders, reddit, twitter, bertelsmann, etc.
Some indications in the text suggest that their insights are somewhat limited.
> In other words, once you ignore the cost of building infrastructure, AI is very profitable.
Once a provider stops such investments, they stop being "frontier" which makes them unattractive immediately. These two things are tightly connected.
> If the AI industry has to pay off 5 trillion dollars over the course of 20 years, that's 250 billion dollars a year. For context, Alphabet (parent company of Google and Youtube) reported earning almost 162 billion dollars of revenue just in 2019.
Revenue vs profit
> Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness
Anybody doing serious software engineering will wholeheartedly disagree.
> People can theoretically switch from model to model, but people tend to settle in with a product and develop brand loyalty. For example, I pretty much only use ChatGPT, not because I have any really reason to, but because it was the first LLM that I ever used
LLMs are just one piece of the tool chain, a mostly interchangeable one. Which one a concrete product uses is not of concern to the end user. This quote very much misses the role of LLMs and their providers in the big tech picture.
> In 2023, a memo from Google called "We Have No Moat, And Neither Does OpenAI" was leaked, which outlined how the open source community was keeping pace with AI labs
Labs releasing the weights to their models is not what is typically called the "open source community".
I could go on but it's just not a very well-argued opinion piece. Unclear how it made it to the HN front page.
The quality of models like Opus 5.5 are already here, smaller labs can mimick these with a little bit of a delay.
Alone the current status has massive impact.
The status quo will get just faster and cheaper.
Then we have labs like Deepmind, companies like Google and then countries fighting over it (US America vs. China). At this point its not just private VC investing in it, its also countries aka military budgets or Research Budgets.
In parallel universities are doing their research anyway and if an AI bubble would burst tomorrow, Open Source and community driven would finally have a little bit of time to coordinate.
There was just last week an article about open post-training data. I think we want this but as long as frontier is as fast as it is, its not fun to play catch up with a moving target.
To use an analogy, just like the scenario in post-apocalyptic movies where people eat each other, we are heading toward a grim end as a species.
Everyone keeps pricing this as a technology thing for some reason. This is more like a new kind of thermodynamic cycle than a new kind of software. The downstream applications are inevitable. We can't continue milling grain by using live animals walking in circles if we wish to remain competitive.
I think the bubble narrative is a necessity for some technology people. It now comes off as a fantasy that this will all blow over soon and we might go back to some semblance of normal. I'd argue that we've burned the proverbial ships at this point. There is no going back.
Think about how far we've come in such a short time. It's barely been one year of competent coding models. Some parts of tech/media seem only impressed by instant and globally successful results for some reason. Something this disruptive takes time. It took two fucking decades before I had a broadband internet connection. There are massive crow eating buffets that are still attended by those who missed the eventual consequences of the internet.
I call this “SchadenFraud”: reveling in and playing up every little negative thing that happens in the AI world to make it look like it’s all just hype or completely irrational and will come crashing down any moment now.
There are enough people aggrieved by GenAI -- traditional media, creators, artists, coders, Mathematicians, and the list is fast growing fast -- that this kind of narrative will have an audience for quite a while.
They're not looking for solutions, they're just looking for rageviews. Worse, it gives folks the hope that things have not irrevocably changed and will go back to normal after the bubble pops.
Me, I’d rather get a stark but realistic picture of what’s happening now so I can prepare better for the future.
Also Popeyes never had over $800 billion in uncancellable compute agreements. And I'm glad you're having fun with GPT, but I've moved all my projects to use DeepSeek 4.1 Flash. There are plenty of different providers. And as I use OpenRouter the change is one line.
The reason I think the PE ratios are lower than previous bubbles are because everyone already knows its a bubble. Also all these tech companies already have massive businesses and AI is not going to change that.
There is no logical contradiction even, just another race to the bottom, drying out of markets. The good ol' disruption playbook, like Amazon shrank or destroyed the markets to capture them, only now they are targeting all white collar jobs.
The conclusion starts: >I didn't write this article as some big takedown of the AI bubble theory. Even with this evidence, there are serious risks to the financial sustainability of the AI industry.
But the presented "evidence" is largely only: I don't know, nobody can tell the future. I mean there are also good reasons to be skeptic of skeptics like Ed Zitron, but Idk "modest valuation" seems like a big stretch. Also, I'd challenge the notion, that you'd only ever have to build that data centers _once_. Especially with the AI pace the chips are said to be replaced every three years, while the building itself could last 25 yrs. So even, if the run at the frontier cools down, I don't think running your data centers and models would come for free.
We can START discussing this when someone defines "worse" and we all agree on it. For SW engineering, we've been trying to define "good" for decades and we still don't know shit.
> There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for.
No, that's a total straw man. For example, I am afraid of how AI will change the world, but for that it doesn't have to stop producing slop. I'm afraid of how the slop is CURRENTLY changing the world, people and societies. Frankly, I don't even care whether someone ever pays for the slop. This isn't a "I'm going to lose my job" kind of fear, it's a "we're running education into the ground and rapidly transforming into a low-trust society" kind of fear.
“AI can't replace lawyers and doctors, but they can automate their paperwork and free up time for them to do other things”
So my question is - in software engineering (where this has bitten hardest): does anyone know _anyone_ who is less busy and has time to “do other things”? Nope, nor me.
The question is how much? If they grow 5x, then these companies are very profitable, changed entire industries,... etc. Yet, the bubble still pops because investors assumed 10x at least.
My gut feeling is that there would be such a massive demand to buy them to run our own models.
I am also not sure there is a (structural) bubble.
I do speculate whether some companies will survive, also some of the big ones.
If you ignore the cost of building cars, they are also very profitable.
>Profits Can Grow, By A Lot
They can also decline by a lot, and Anthropic's S-1 suggest that is what is happening, as their losses are widening, not narrowing.
>Google Has No Moat, But Neither Does McDonald's
That's why no one is sinking several trillion dollars into a new fast food chain.
>Alphabet, Meta, and NVIDIA are all AI companies that have seen their market capitalization explode over the past few years, but all of them have stable P/E ratios in their 20s.
The "E" in their PE ratios is circular revenue that is ultimately derived from debt and equity, not real end-user demand from profitable companies. For some of these bubble companies, if you subtract the debt-fueled private stimulus money, their actual PE ratios skyrocket.
sounds pretty good. if I made 2.08 for every dollar of my spend I would make more money. Plenty of businesses operate on much much thinner margins, and thats not utility-like at all.
It's almost as if they know something about the finances that the rest of us don't...
The trouble with Anthropic and OpenAI is, that not only do they need to get operationally profitable, they also need to be sufficiently profitable to provide a return on capital on all the money they have burned to date. Much of which has gone (and keeps going) not to assets but to expenses like the electricity bill. Thus, once the models are efficient enough to be served profitably, a relatively small amount of assets needs to be generating income for a relatively large amount of capital. It is my understanding, that currently the efficiency of the models does not grow even fast enough to outpace the need for new capital.
One more thing, that gets conveniently ignored when talking about AI cost is the cost of training the models. From one hand, there’s but a few institutions capable of building them and, from the other, it’s like the models grow on trees. Yes, you could run your open-weight model on your own hardware and pretend it’s profitable because you don’t have a comprehensive costing model and the infrastructure costs from security to electricity to risk are mixed in with your general cost base. But someone has spent astronomical amounts of money training that model you run and would probably like to get it back with some interest. Let’s hope that return is just money and not, say, the ability of a certain country to control the global software development process.
Sounds like a valid argument until you realise McDonald's is a real estate business.
There are many problems with this article. I think the bubble is real and at some point the investor money will fall off a cliff.
The value is there but the cash burn rate is extreme to say the least; and the moat is only in what you haven't released yet.
The only reason why Google search is doing well is because it's a real estate business like McDonald's. And lately their search quality has been similarly crappy.
Large companies have often changed the depreciation schedule for server hardware to 6 years rather than 3. I think it remains to be seen if this is justified as it’s not as though the pace of change in hardware has slowed down by 50% arbitrarily.
I think it’s pretty obvious to me that a data centre full of ampere GPUs is worth less than a data centre full of Blackwell GPUs. I suspect this is also why AI companies prefer to talk in terms of gigawatts of compute, as if efficiency per watt does not exist.
Data centre costs are not one-off. Some are, for buying land and building buildings and infrastructure. The actual compute however is a constant investment. I’m not familiar enough with the market to know what proportion of expenses are ongoing but I bet it’s higher than people think.
It also fails to consider the impact of 'good enough.' A lot of the build-out right now is in anticipation of trying to push the frontier level. But there comes a time, a time we're already arguably past, where models being used for a specific task will already be able to complete that task efficiently. Going way beyond the present should enable LLMs to solve ever more esoteric problems, but in economic terms if a company's goal is to harvest corn, and your corn harvester works well enough, they're not going to care whether or not that corn harvester can also solve Fugazi's 7th Great Conjecture in exomathematics.
This sounds like a nightmare but so is using windows and office everyday and everyone is already using it.
Companies are investing in scaling models. Doesn't that mean that they'll need to keep investing to stay on the edge? After all, when they lose the edge they lose customers.
> For starters, the average person isn't going to use a locally hosted model, ever
If open weights weren't a problem for these companies, ram altman wouldn't have accused the people of stealing his compute.
AGI and singularity are the Nuclear Fusion of energy.
> Outside of generative AI, self-driving cars are becoming a reality, with some evidence to suggest that they're even better than human drivers.
Haven’t they have been just around the corner for 10 years now? And the problem is not technology as such, but the fact you are inventing trains that use roadnetwork hardly large enough for cars?
> AI adoption takes time, but there's no reason to think that AI usage isn't going to massively skyrocket, even if AI doesn't get any better than it is today (and it certainly will get better). In other words, there's massive unmet demand for AI companies to profit from.
[Emphasis mine]. This might a good example of a person writing and thinking with an LLM and letting the model enchant you with its sycophantic bullshit. We might speculate endlessly about AI or any technology, but none of that is a basis on which you should throw money at, especially not as a retail investor. We can see value production of AI in software development, but the productivity increase promised is either not in the data, or it is too early to say yet. So the real valuation of AI is really difficult say still, a lot of things needs to happen, before it can be seen to produce concrete value increase. People need to stop really really fucking hating it for one.
It kinda pointless to talk whether there is a bubble or when it will pop if there is. It might happen today at market open or never. Timing it is a fools errand. Also it is kinda pointless to shout into the void how much you love/hate AI. Better use of time would be to develop the different systems and applications that will produce value for somebody. It all sounds too similar to crypto to me still…
"The AI Industry Doesn't Look Like Any Bubble We've Seen Before" - It looks exactly like the two railroad bubbles. Potential profit is there, in the future. But you have to invest in the infrastructure first. If you can't make a profit and pay back the loans fast enough, the infrastructure goes bye-bye, along with all the funds used to finance it.
"There's Still Plenty Of Capital To Raise" - IPO is not how you raise a trillion dollars. A million, sure. A billion, maybe? But a trillion puts retail investors - mom and pop - at risk. IPO is not how you raise funds for a railroad.
"Once AI companies become profitable" - there's the big red flag. This isn't Uber or Lyft that can float along on tens of millions in corporate hand-outs every quarter. They need 100x that much money, and nobody has it to hand out. So you can't wait around for profits. There's no magic cushion to float on.
"For starters, the average person isn't going to use a locally hosted model, ever" - They will in 4 years. "normal users don't want to have to deal with the hassle or expense of setting up something" - What hassle? You download a program with a fancy GUI and run it. There are a half dozen of these projects already, that provide ChatGPT on your computer from one binary. All you need is a GPU.
"Profits Can Grow, By A Lot" - Only if they remain cheaper than local hardware or open-weight inference providers. Which won't happen because they owe more money than they can make.
The sign of the bubble is all the investment made not based on stable returns, but on hype. The weird geniuses from San Francisco said the future will depend on this one technology, all work will be changed forever, and only their company can deliver it. If you don't get in now, you'll miss out on a fortune! That hype just doesn't work out. And the proof is here: the superhuman machine sucks at writing, the documents it creates are lies, and we seem to be spending more on it than we get out of it. Because it's not magic. It's just a machine.
That's just not true. Profit is there now. If the hyperscalers stopped building new infra tomorrow, they would just roll in the profits available from what exists today!
Totally unlike the railroad bubbles which built out years and years ahead with no customers and then collapsed.
> "There's Still Plenty Of Capital To Raise" - IPO is not how you raise a trillion dollars. A million, sure. A billion, maybe? But a trillion puts retail investors - mom and pop - at risk. IPO is not how you raise funds for a railroad.
You're totally confused about what an IPO is. You're confusing the valuation of Anthropic with what they will raise. They intend to offer about $100B in stock, their valuation is at 1T.
> "Once AI companies become profitable" - there's the big red flag. This isn't Uber or Lyft that can float along on tens of millions in corporate hand-outs every quarter. They need 100x that much money, and nobody has it to hand out. So you can't wait around for profits. There's no magic cushion to float on.
Most AI companies are profitable aside from expenditures to build new infrastructure to reap more profits. That's a very healthy sign.
> "For starters, the average person isn't going to use a locally hosted model, ever" - They will in 4 years. "normal users don't want to have to deal with the hassle or expense of setting up something" - What hassle? You download a program with a fancy GUI and run it. There are a half dozen of these projects already, that provide ChatGPT on your computer from one binary. All you need is a GPU.
Uhuh. A GPU. In your phone? In your laptop? The average laptop in the US is sold for about $700, desktop for about $1k. A good GPU to run a reasonable model is $10k+. It makes zero financial sense for most people to run their own inference. The issue isn't, and never was, the software, it's the hardware. And one thing we've seen from the past years of growth is that people don't look at the models from 3 years ago and say "Oh well, that's good enough, at least I can run it slowly on my $5k+ desktop". Until that happens this is a fantasy.
There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for. You can't really have it both ways here.
There are very few people in the world where when they declare that they have an opinion on it, that the declaration itself is title worthy.
And “OpenAI bubble” alone has one massive implication: the likely demise of Oracle, a blue chip business that is deeply embedded into the western world.
On the one hand, if the air is let out of the bubble before those SPVs get themselves too deep into building things that won’t be needed, that is good. But nevertheless the failures of the SPVs will harm people who are not in the exclusive billionaire club.
Also there is a considerable money-go-round; the money for everything will dry up fast when the cloud LLM businesses can’t convince people to give them money to buy Nvidia GPUs anymore l
A part of the bubble that people don’t talk about are all the small AI products and integrations that don’t do much of anything. But they took VC money to build an AI project management program and now they have to cram it in there even though it is useless and actively costs them more money. There is going to be a lot of pull back at some point with these pointless integrations and I think that is going to be felt on total AI usage.
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If everyone, or even the majority, believed that there is a bubble that's going to pop with near certainty, that would tank stock prices immediately. Why would anyone hold a stock they know is going to go down precipitously? If the majority of people know it's a bubble, the bubble immediately pops. AI stocks still have worth, so most people believe there is no bubble or that prices will go up before going down.
People talk about bubbles. They worry about bubbles. For the most part, though, they go on investing as though there isn't going to be a bubble. Every bubble looked like a solid investment to most people right up until it burst, or it wouldn't have been a bubble.
So, no, most people do not think AI is a bubble. That doesn't necessarily it's not though.
That seems to assume:
1. Those people are the invest-class and have control over what gets kept/pulled.
2. Those people don't think it'll pop later and that there's Great Fool Money to be had for just one more day at the tables.
dump n pump. day trading. long term strategies. build up for the next big thing or delusion or hysteria or lie.
if you read mainstream narratives like "AI bubble", translate it to several millions of people making lots of bucks via a lot of fraud mixed into some good, genuine, valuable work of productive people.
investors and wall street people doing anything financial is not tied to bottom up signals but to insider knowledge and top down supply chain LEGAL manipulation by people not directly involved in the actual work done.
or it's a wrap up ( made money, time to shift or prep something that will hypergrow again, maybe already re-pre-hyped ).
or it's reactions to having maxed out some part of the supply chain which had negative consequences to some other market which reduced overall gains enough to realize "oops", if we don't re-act now, future gains in other markets will be reduced almost catastrophically.
bubble talks always feels like a mix of hyped people really getting fucked over by fraud runners
the details are not too relevant for the moment as I can't have play money to invest but the narratives are interesting because they directly translate into what I read in the suggestions and advice, subliminal or direct, from people to other people.
Let’s not even talk about the second headline that costs are constrained, while one company spends few dozens off billions per quarter.
The rapid progress of multiple closed frontier models, along with the rapid progress of open models, suggests that LLMs will be like electric motors, not like social media. Are electric motors worth a huge amount of money economically? Sure. But none of the companies who invented electric motors are especially valuable today, because the technology became a commodity.
Because hardware doesn’t need updates. Newer hardware is more capable which gives competitors with newer hardware an advantage.
> Outside of these expenses, AI doesn't cost that much to run
Outside the expensive stuff it’s cheap.
But if the shoeshine boy tells you it's a bubble, it's time to buy?
If only shoeshine boys still existed, I would know what to do.
You complain that people take the bubble as a given. Yet you continue by doing what a lot of people on the other side do:
> Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness, and a good deal more expensive as well.
This isn't established. Like, at all. There's serious doubts as to whether AI-driven codebases remain maintainable, understandable and trustworthy over time. You look at a snapshot and conclude, while riding the hype wave.
“Its a bubble”
“Its a bubble”
“Its a bubble”
“Maybe its not a bubble”
*bubble pops*
every time.
One thing to keep in mind when you're "debating" things online is you're actually debating with a tag team of people, with a whole spectrum of views. And at any point people with stronger arguments (starting from different precepts) will reply while people with weaker arguments will fall silent.
That's why "the average hacker news poster" is simultaneously rich and poor, powerful and powerless.
> So, people wishcast. They tell themselves that this is all a giant bubble, and one day soon, everything will crash down and immediately go back to how it used to be.
+1 to this. I think this is why Ed Zitron gets so many readers - people want someone to tell them things will go back to “normal” someday.
I look forward to these types of posts fading in frequency much more so than AI use itself.
The lack of a clearly profitable operating model and huge (trillions) in commitments for buildout is what concerns people.
I am not convinced by this person who is not convinced there is a bubble. The bubble, by the way, specifically refers to the valuations. Is Anthropic worth 1.2 trillion when it’s not clear they will have positive cash flow in the foreseeable future?
Maybe think of it as a bimodal thing. I can easily imagine a future in which Anthropic builds a sustainable profit model, grows into new sectors, and is credibly worth 7T in ten years.
I can also easily imagine a future in which AI capabilities continue to increase but the amount that enterprises are willing to spend on Anthropic’s services to do, and in which that, plus ever-increasing competition, cause Anthropic’s revenue to grow slowly, stall, or even contract. And that Anthropic would be unable to raise sufficient capital to make up for it and would therefore forced to default on their obligations. (Or that they would be able to raise enough capital but on poor enough terms that existing investors take substantial losses as a result.)
My viewpoint also shifted around the shape of the bubble. The technology isn't going away, obviously, but the valuations of these companies like OpenAI and Anthropic feels divorced from reality. Inference continues to go down, most folks don't need frontier models for common tasks (including coding), and local models provide a competitive edge in several domains the frontier labs cannot (namely value and privacy). Combined with demand still largely subsidized and the companies being deeply unprofitable, and I just don't see the investments in Anthropic or OpenAI or the sort actually paying off. That is the bubble I'm concerned about, not the tech itself.
...now when the debts get written off and all this compute is just sitting around idle, available for bottom-dollar prices to try and eke out any sort of ROI for whichever creditor(s) inherit the assets? That's going to be pretty cool times.
Its not clear that the infrastructure will ever be paid back. Its also not clear that once models are trained and distilled that the infrastructure will be useful. Ignoring 1.9 trillion dollars of debt is probably not the greatest financial analysis.
>However, there's no reason for why this level of spending has to be maintained in the first place.
Exactly. So Anthropic and OpenAI might be holding the bags for latecomers who distill their models and get ahead. Sort of the Commodore64 and Atari of the AI space.
>Once all the data centres are built, AI spending is going to plummet.
Well data centers need maintenance, and GPUs and similar do have a very short lifespan. But even if spending plummets repayments on existing debt needs to be accounted for.
>In a pinch, AI companies can also cut costs by no longer allowing people to use their products for free and temporarily suspending expensive AI research.
Research keeps them in the headlines, see above about the latecomers.
>For context, Alphabet (parent company of Google and Youtube) reported earning almost 162 billion dollars of revenue just in 2019. Adjusted for inflation, that's an even more impressive 213 billion dollars.
Yep, so all Open AI or Anthropic need to do is get acquired by a much more profitable business to pay back their debt.
>at some point, the rate of increase has to level off, but that doesn't have to happen anytime soon.
The rate of increase of revenue in the AI sector also doesnt need to be apportioned to OpenAI or Anthropic. If it wasnt for their generous loss leading subscriptions or their rnd, theres not much there to hold on to people.
>it's hard for me to see AI consumption not being 10x what it is now.
Right AI consumption will increase very likely. But will it increase at the cost of an already subsidised subscription, or increase the use of cheap Deepseek 4.1 tokens.
>For starters, the average person isn't going to use a locally hosted model, ever.
Isnt this expressly where Apple Intelligence is heading? Building small low cost models that run on Apple hardware? If I was nVidia I would have both plans being run down at the same time, looking towards selling GPUs to hyperscalers on one side, and working with OEMs to deploy AI coprocessors in laptops on the other side.
>For example, I pretty much only use ChatGPT, not because I have any really reason to, but because it was the first LLM that I ever used.
N = 1. OpenAI is already what halfway to their trust thermocline anyway?
>A potential way for a bubble to pop is for the companies involved to run out of capital to spend while they grow, causing them to fail.
Isnt Anthropic already down to BB+?
> NVIDIA are all AI companies that have seen their market capitalization explode over the past few years
NVIDIA is a hardware company that can has so much demand on their products they will likely survive 3 AI market collapses.
>Alphabet, Meta, and NVIDIA
Alphabet, Meta and NVidia all have other sources of revenue. They are companies building AI, not AI companies.
>When the dot-com bubble happened, it was largely driven by companies that couldn't turn a profit or had P/E ratios in the hundreds.
Anthropic and OpenAI dont have "E" (as in earnings)
Anthropics Debt to Equity is 363%.
>A potential way that this could be a bubble is if AI proves to be a fad.
A potential way this could be a bubble is if the makers of consumer devices point customers at cheaper or local models as part of their onboarding experience. OpenAI and Anthropic get thoroughly zuckerberged by the people holding the primary relationship with the customer base.
Or perhaps, the funding to the top 1% of token purchasers also turns out to be loans, and that dries up too.
https://www.pymnts.com/news/artificial-intelligence/2026/ope...
>As one final note, I don't think the rhetoric about the AI bubble is really about finances.
The people bringing it up every 10 seconds are doing it to spread FUD about AI. That doesnt really change the financial picture.
Matteo's analysis is very flawed though. For instance, he projects that the outcome is "devastating the entire economy". Why would that have to be the case though?
The AI bubble can collapse partially. There are tons of examples of how this has happened in the past. Not everything is dot-com 2.0. What we see right now is a "consolidation" bordering on partial collapse of some of the companies' projected revenues. It makes sense to assume that there is not a complete collapse, and some companies will end up dominating more than others.
To say that it is (essentially) taken for granted isn’t a great start to a rebuke. You have to engage with the arguments that they have, and they have presented arguments. Just see born-again clanker Dan Luu respond to Zitron’s claims.[1]
I don’t see any engagement with skeptic’s arguments in this piece.
[1] https://danluu.com/zitron/
> In other words, there's massive unmet demand for AI companies to profit from.
Of course. There are hundreds of millions of white collar workers that are jealuously hoarding their unrightful wages.
> There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for. You can't really have it both ways here.
But you can. A bubble of this magnitude will negatively impact the world, not just AI investor cretins. You can take the extreme position that AI is useless and still be worried about the fallout.
> It's completely valid to be afraid of AI taking your job, but money is going to change hands when that happens.
The conjunction here implies a contrast which I fail to see.
> As one final note, I don't think the rhetoric about the AI bubble is really about finances. Conversation about the AI bubble is too widespread for it to be a contrarian position, and too gleeful to be driven by investors' fear. More than anything, I think the supposed AI bubble has become such a large talking point because it represents the hope that things can go back to how they used to be. People are afraid of what's going to happen with AI, [...]
The always-amazing observation that your intellectual opponents have motivated reasoning. Likewise, most AI positives have motivated reasoning, like that they can enrich themselves or that they are getting so much code out of their current subscription prices.
When I write about AI it’s clear when I argue against AI itself and when I am whining about my boo-boo thoughts about losing all of my economic prospects.
> I’ve applied to seek the Liberal Party of Canada nomination
That tracks.
>>> As one final note, I don't think the rhetoric about the AI bubble is really about finances. Conversation about the AI bubble is too widespread for it to be a contrarian position, and too gleeful to be driven by investors' fear. More than anything, I think the supposed AI bubble has become such a large talking point because it represents the hope that things can go back to how they used to be. People are afraid of what's going to happen with AI, and rightfully so. Beyond fear for the future, AI in the present has become uncomfortably common, with AI slop everywhere online and increasingly even IRL. This has all happened so fast, it's easy to forget that ChatGPT came out a little less than 4 years ago. Our world has changed in such a scary and odd way, and while I have a pretty optimistic view of AI overall, I get why people would want it all to go away. So, people wishcast. They tell themselves that this is all a giant bubble, and one day soon, everything will crash down and immediately go back to how it used to be.
And on a personal note, this probably goes against all economic theory, but I think the larger the scale gets of money, the less "real" it becomes. The US national debt is $40.5 trillion. I personally don't think a fraction of that will ever really be paid. There are rich and poor, but once you become rich, that added richness is essentially useless, and the debt we see is a mixture of money missing from the poor person's wallet, or the money missing from a billionaire's portfolio. One of these is more sensually palpable than the other.
If your product is good enough, shows potential, and has meaning, it will get funding. I have never done a startup or gotten venture capital so it's pretentious for me to say, but I think the money just comes, through effort (and I think 100's of pitches for the new startup), and we can't always know from where. It's just there. My brain is too small to consider where that money comes from. We're not valuing money at a certain point, but just sheer utility, and so investors will be there to back you up. In decades-long terms, sometimes centuries-long.
The debt will keep building up, but so will be our infrastructural changes. Early adoptees will get rich to degrees, and investors who held a conservative view on major progress like AI (will still be rich but) will be slightly less rich. Maybe there will be unpaid debt, like the 500 billion incurring a $400 billion loss during the dot-com crash. But the technology will continue growing.
After all, money doesn't mean anything anyway (in the theoretical sense). It buys us soup, clothes, and a place to pay rent to, but it is just an agreement. And with the changes in the world economy that some of us are expecting to see after the AI revolution grows even more, I really don't know what the meaning of money will become.
If large data centers cannot secure free power connections paid for by everyone else, they cannot make any of this profitable. The whole thing was a scam from the start. This is why cloud AI companies bought up so much pc and server hardware. Artificial scarcity was the only way to slow down local llm adoption and sell the false narrative that cloud AI has value.
Plucking a graphics card out of every home and centralizing the cards to a handful of large data centers that require a trillion dollars in power grid upgrades is an idea so dumb, we should all be laughing at it. They even have to use active cooling or evaporate drinking water instead of free air cooling. The consumer doesn't benefit in any way from this. Consumers would essentially be paying more just to be datamined.
LLMs are not a bubble, large data centers are a bubble. Look at how large data centers are now the first target in any war. A graphics card in every home, doesn't give someone a concentrated target. National security makes large data centers extremely bad.
At it's core, running a top-tier LLM means linear load of trillion+ weights from RAM for every token. The cost of this operation absolutely dwarfs the cost of doing the actual math. If you are only serving a single user, you can only amortize this over a handful of token streams, times like 2-4 for speculative decoding. If you are running in a datacenter, you can batch thousands of streams, and get correspondingly much more tokens out of the same expenditure of energy and hardware.
And at least currently, keeping up in that arms race means a maximal push on growth at the expense of profitability. Realistically, this will go until we hit some bottleneck.
Fair enough. But the real problem, it seems, is that people love slop.
My family is trying to sell a house in the South of France; the real estate people are sending us 500-words emails every so often, that are so obviously AI it hurts. Well written, and content free. Nobody cares. I'm the only one having issues with it.
At work, a new team let Claude do all their work and write all their status reports. Everybody seems to love it. It's so fast! And mostly correct! And so fast to correct when it's wrong! And here too, I'm the only one having issues with it.
A Canadian author wrote a book in French that turned out to be 100% AI ("C'était ça ou mourir"). Before that was revealed, every critic loved it, in Canada, in France and everywhere else (25 translations were started). The public loved it too: it's one of the top best sellers, and success continued even after the "scandal".
We are drowning in slop and nobody cares.