I'm not convinced the AI bubble exists

Railroads, semiconductors, plastics, networking... the utility was there for all of them, and so was the potential profit.

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.

> Profits Can Grow, By A Lot

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.

Big red flags that I think point the other way:

- 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?

Im not convinced by the claims of infrastructure only being expensive to build initially. Hardware doesn't last forever especially in a data center, maintenance isn't free, electricity is not going to get significantly cheaper. If models get more efficient we won't need super expensive and expansive hardware build outs, if models keep getting more powerful and needy and are hardware limited, each new hardware generation will stomp over all this expensive and soon to be old hardware in electrical efficiency requiring another massive round of funding that players already holding debt will struggle to afford.

I dont know if the valuations make sense, but the usability is all there and so are the potential profits. Over a decade, that's probably all that matters. If the valuations pop or dip (anytime soon), I think that's mostly irrelevant. I get that the press probably doesn't like to hear this boring conclusion.

P.S: A Matlab/Physics simulation guy running for a Liberal party seat in Canada? Say more!

I'm not imaginative enough to see more use cases and who will pay. You can burn a lot of tokens and dollars on math proofs. Same for white hats finding vulnerabilities in all the software. Generating video.

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 reason why AI companies are spending so much money is to rollout data centres to run the AI in the first place. These expenses are one-off purchases, not regular operating expenses. Once all the data centres are built, AI spending is going to plummet

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.

I enjoyed that article, thanks for sharing. It is nice to hear that people are still having a "rage against the machine" in these heady times. For me, I think a lot of us at high market cap companies think AI is going to do xyz, but most folks are employed way outside of that boundary.

McDonald’s is considered to have one of the biggest moats of any business. They have an extremely valuable brand, excellent locations, optimized supply chains, and so on.

I don’t think the question is so much of AI is a future market, it is has the capital been allocated appropriately. And no one knows. The challenge Anthropic has is they have a narrow lead on the use case that makes a lot of money today, but new models come out every month and one tiny slip or wrong decision and they are toast. There is also a reliance on the idea that two things continue to happen:

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.

If you don't talk about competition from DeepSeek you haven't really thought it through all the way. You might argue it's like Windows vs Linux, but operating systems have huge natural motes. Any person can just switch to a provider of an open weight model instantly. Most people don't know about it but we may very well see people learn about this broadly in the next few years. Changing models is not super complicated. Anthropic is unlikely to make $2 to the dollar when competitors are taking only pennies of profit.

Other than the fact that we’re due for one, this one is differently shaped in that there isn’t really widespread “irrational exuberance” and constant reporting about AI fortunes. I think your general investor is quite skeptical about AI and worries about exposure in their portfolio. Sure there is plenty of irrationality happening, but there is also a lot of real growth and changes in assumptions. Like the job market is way stronger than expected for even AI-threatened sectors (not entirely and I’m sorry if this affects your circles) but like we’re really just getting started here. I don’t think AI in today’s form will be the same as this time next year and that real growth is just beginning. All of the destruction of value that PE has wrought will only serve to motivate a new generation of entrepreneurs to build fundamentally better, more nimble and customer-oriented institutions.

There's also the fact that the US and China are in an AI arms race. So there's an inherent existential benefit to being ahead in AI regardless of where the financial feasibility/profitability discussion lands.

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.

The only modification I'd make is to amortize the investments over 5-6 years instead of 20 because most of the cost is GPUs not the buildings. This increases the required revenue by 4x.

>Shameless Self-Plug I’ve applied to seek the Liberal Party of Canada nomination in Sudbury East—Manitoulin—Nickel Belt

> 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.

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.

Unless I misread it, this article simply doesn’t mention training or the cost of it.

Well, first of all, these are not one off purchases. GPUs do break and newer generations come out rendering the old GPUs outdated. I don't know if the 2.08 figure is very valid. This is based on costs of leasing GPUs from companies that may not be making money on the lease deal. What I do know is that Anthropic weren't profitable in Q3 and I'm hearing rumblings they are losing even more money in Q4.

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.

Just because a technology is going to be successful in the future doesn't mean that the current beliefs are accurate of which company is going to capture the most profits from that technology.

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.

so many variables here. Do anthropic and openai as "AI" companies last longer term? If LLMs curve off in terms of what they can do, and local hardware becomes capable of running local models, seems like they won't last that long. But if they keep improving such that massive amounts of compute will always be needed beyond what can be done locally? Then sure. But then does it just become a commodity market where all the margins get stripped right down? Do they invent AI tech well beyond what LLMs are capable of at the moment? I would imagine, no matter which way it goes, hardware companies will win.

My thoughts has been: what would happen if GPU prices fell 50% over the next weeks?

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.

I’m not convinced that something has to be overvalued for it to be a bubble. A lot of the dotcom stocks that went bust probably could’ve taken some piece of the pie, even if small, and made a business out of it if the money hadn’t run dry. Or if they had just come along later. Turns out the internet pie is enormous and it’s still growing. And now people are much more willing to use it for payments. Even some previously bad ideas might be workable now. But investors don’t have infinite patience, and the market can spook 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.

I think the biggest problem is people are conflating current AI being useful with current AI companies being financially viable. Dot com bubble had its winners and losers but the biggest thing is that many of the current AI companies are valued at prices the winners were decades after the dot com bubble. My prediction the big 3 infrastructure AMZ, MSFT, GOOG will lead openai and anthropic to a point where they owe them trillions in payments and use it to take them over.

"If you check any online discussion about the financial situation of the AI industry, it's essentially taken for granted that the bubble exists and is going to pop any day now."

-------------

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.

Meta’s margins are apparently getting crushed as a result of AI investments.

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?

>Anthropic's S-1 filing says that they make $2.08 for every $1 of compute. In other words, once you ignore the cost of building infrastructure, AI is very profitable.

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.

His analysis fails to consider trend lines in inference costs. A couple of years ago inference required a supercomputing cluster. Today, you can run quantized frontier tier models on video cards with a $1600 MSRP. At current trends we'll be running frontier models on a typical gaming computer setup in a couple of years. That is going to destroy centralization possibilities.

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.

What a weird thing to say: a business is profitable if we ignore a key part of its cost base. That’s true of any business, you know. Even if they make a 100% coverage of every request served, it’s still a far cry from the usual internet business model where responding to a request is almost free and most of the expense is the initial investment in software and its upkeep. This is why Alphabet used to be flush with cash and we get free services.

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.

I love the AI doomerism in this thread

> The reason why AI companies are spending so much money is to rollout data centres to run the AI in the first place. These expenses are one-off purchases, not regular operating expenses

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.

The 16% yield on Coreweave bonds probably indicates otherwise.

> Google Has No Moat, But Neither Does McDonald's

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.

This analysis marginally better than Rob Ford smoking crack, I'm not convinced you should run for office, but if you succeed, please get us more Shorsey.

if i plug my ears and close my eyes the 16% neocloud bond yields suddenly don't look so bad

Meta, I have constructive feedback on your choice of titles with this post https://news.ycombinator.com/item?id=50415422

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.

It's more accurate to say that there's an OpenAI-Anthropic-X bubble. Financially sustainable investment in LLMs is useful and not going anywhere, as China shows. Dumping trillions of dollars into companies that make models that are 1.2x better than Chinese models at 100~1000x the cost, ehhh... good luck with that.

most of the infrastructure will be garbage in 4 years. As before, it will become a monopoly of 1-2 big players and everyone else's datacenters will become ... crypto miners or something.

  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.

“Its a bubble”

“Its a bubble”

“Its a bubble”

“Its a bubble”

“Maybe its not a bubble”

*bubble pops*

every time.

I like the sentiment, but the author doesn't appear to be accounting for some harsh truths:

- 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.

The discussion of the “AI bubble” often conflates two different things: (1) is AI going to justify the valuations we are seeing? and (2) are the specific AI companies we have going to justify their own valuations.

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.

Putting data and rational analysis aside, the style of this post is so similar to what I was seeing during the crypto bubble.

> If you check any online discussion about the financial situation of the AI industry, it's essentially taken for granted that the bubble exists and is going to pop any day now. I don't think that this is a reasonable assumption, though.

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.

Going in reverse:

"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.

> The reason why AI companies are spending so much money is to rollout data centres to run the AI in the first place. These expenses are one-off purchases, not regular operating expenses. Once all the data centres are built, AI spending is going to plummet.

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.

A bubble can't pop as long as most people, or at least most dollars, still think it's a bubble. Bubble pops can only take the market by surprise.

> 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.

> 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.

A fair critique. Even I, someone who started as a (righteous) angry doomer over it, have come around for the most part.

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.

I stopped reading after the 10th sentence or so demonstrating AI-pilled psychosis and hand-waving. That it is obviously this or obviously that and obviously some other thing.

I look forward to these types of posts fading in frequency much more so than AI use itself.

Large data centers never made sense. If an additional pc with a graphics card was put into every home, we wouldn't need power grid upgrades. Our power grid is designed around human distribution i.e. homes. Large data centers cherry pick where it may be possible to quickly connect power connections up. Data centers need to secure their power connections before the utility rate hikes on everyone else generate public opposition.

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.