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.
> Railroads, semiconductors, plastics, networking... the utility was there for all of them, and so was the potential profit.
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).
Also, (setting aside Standard Oil’s business practices), this is a common meme in oil exploration historically, and even today. Early prospectors were honestly pretty stupid, and their principal differentiation was risk tolerance.
There a lot of parallels with AI…it doesn’t take a genius to see the long-term potential, but the risk is in implementation, so it’s a mad rush to the finish line by a wide variety of less scrupulous early players. Rockefeller got his big start by sweeping up failing producers who were all capital weak, and I’m sure something similar will happen here during the inevitable bust.
I can do the hard, risky work of drilling wells / deploying AI, or I can sell you a vision of that future, today. The most dangerous technologies in terms of grifting are the ones that everyone can understand.
I wonder if physical technological progress is also the same as information treatment progress. Everybody is happy having food and materials faster. But AI bringing you solutions faster is also taking away your job, passion.. the negative side seems larger.
>AI models can be deployed on the same datacenter as the old one
This hasn't historically been true. The clusters have been getting bigger along with the models over time. And now we may even want to have these computers have rapid access to extra compute or caches of the internet or other data stores.
Google hasn't retired any of its now ten-year-old V1 TPUs or any of the subsequent generations so it's unclear that AI infrastructure "needs" upgrades.
Of course, Google could be wrong about the economics of running old AI processors. Or maybe TPUs are special.
I do wonder how much of their inference is running on 10-year old TPUs. I’d imagine not much, if only because 10 years ago the justification for massive compute was quite different than today.
I've been saying for awhile now that the utility is increasingly undeniable. It's amazing technology and for better or worse, is transforming the tech industry (in particular). I just wish we lived in a world without the speculative bubbles, because if this had been developed slowly, deliberately, over time, there would be no necessity to cast aside troublesome details like safety, suitability, and ethics...and we could enjoy the fruits as it was implemented gradually, responsibly, as it matures. As opposed to going "wow, this is really cool, but gosh I feel shitty about using it..."
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.
A tech workers union or a stronger regulatory state could help with this but neither party has been that interested in either of those things for the last 50 years.
Your point is a good one, but it's constructed upon the premise that AI is a single discovery or invention or even a single tech wave.
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.
We're discussing AI in general here, not individual companies (and none of those companies have a monopoly). Nobody's arguing that AI wouldn't exist if the previous revolutions hadn't occurred.
Each event was so different with different motivations. The popularity of Tulips never back. 2007 was about hiding poor risk mortgages. 2001 every company had money thrown at them and today is about a product that costs more than the market can pay.
You know what was actually a bigger invention? Language. Farming.
Imagine a world without those. Oh, wait: tomorrow people won’t know how to use language, will they? And they won’t have water to farm with, so there you go.
This reads less like a bulwark against sensationalism and more like an attempt to remain ideologically safe in what appears from many angles to be an extremely steep exponential start of what we can probably agree is an S-curve.
> 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.
How do you know we aren't already through the exponential part of the S-curve and about to enter the AGI winter (i.e. AGI will require a fundamentally different technology than LLMs that is still decades off)?
If no one is willing to pay increasingly high costs of training frontier models, we'll still have the models that we have now that can be fine tuned for incremental improvements. Still tons of utility in that, but little investor upside.
In 1893, GE nearly went bust as the market got cold feet that electrification was too costly, and the uses too abstract and niche. People were also hugely worried about circular financing, as they were guaranteeing bonds and taking equity stakes in their clients.
As we all know, GE then went bankrupt and we went back to whale oil.
> 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
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.
Yeah, seems like there is a GPU to engineer sweet spot with models like GLM. This could become a more popular pattern for some organizations. Likely also the reason gov. attempts to intervene
Inflation, house prices, gas prices, interest rates, huge public distrust of government and corporations, votes to restrict data center construction, endless war in Iran, etc.
Maybe this is what you meant by "Chinese arms race" but there is, though not quickly, a massive wave of increased hardware supply incoming. Again, let me mention this isn't coming soon in terms of the AI bubble conversation. But China is pushing for a home grown EUV and fabs that should start coming online and having an impact in 2030.
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.
Still useful. Especially if Chinese GPUs operating on Nvidia software become performant and accessible, thereby undercutting the price gouging that we're seeing with western chips.
I generally agree with you, but have to get this off my chest.
> 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.
Also - all the gee-whiz AI companies are headed by people who don't have great business backgrounds, either by people from tech backgrounds (chasing the last of the VC), or by people with the kind of "interesting" background who you wouldn't lend your own money!
As the friendly article points out, the "AI is a bubble" hypothesis and "entry level hiring is collapsing because AI is replacing their jobs" hypothesis are not really compatible.
As always, reality is likely a muddled mix: there is real utility and there is wasted investment that will never pay itself back.
unless I am mistaken, you missed that the datacenter infrastructure being a one-time cost. nope. those servers have about a 5 year lifetime and then are likely to be replaced. The buildings aren't the expensive part. The GPUs are.
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)
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.
If new hardware comes online that doesn't allow you to payback your billions in hardware, what then? The level of rigor in the article is low.
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.
For the time being this thesis hasn’t worked out though. H100’s are only going up in price and becoming more scarce [1]. At what point would you update if now going on 3 years into H100 lifecycle this doesn’t seem close to happen.
But they are becoming more scarce while staying on Nvidia’s shelves buy-and-hold, or being leased back in some cases. They are not being installed by customers at anything like the rate the sales figures suggest. So yeah, these H100s will last a long time in the packaging.
Meanwhile, Meta is selling their spare AI data centre capacity.
Look at DeepSeek! They have done full system optimization on their model, architecting it so that it would be cheap to serve. I am proud to call DeepSeek a world class tech engineering firm in a way that 99% of the tech world is not. They are a 1% company.
> The current-gen hardware is expected to have only 2-3 years of useful life.
Expected by whom and why?
Google hasn't retired any of its now ten-year-old TPUs, or any of the subsequent generations. Maybe TPUs are special or Google doesn't understand compute economics but either argument requires data.
If your argument is depreciation, you're confusing a tax concept (depreciation) with useful life. Yes, they're supposedly related, but in practice, the relationship is rather loose. (Residential housing depreciates in 28 years yet typically lasts 2-3x as long and 4x is not rare.)
Google's V1 TPUs
It hardly seems to be a one time expense. GPUs and networking equipment may have a useful lifespan of only about five years before needing replacement, and it could be expensive to do so.
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.
The amount of profit they make on compute is also not particularly attractive (at least right now). It ‘s only slightly more than 2x for Anthropic, far off from many big cloud services which sell compute at something closer to 20x.
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!
The decade after the dotcom crash brought about as many if not more long-term successful products as the dotcom boom: Youtube, Reddit, Spotify, Google Maps come to mind
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
In 2007, It was regular people who lost their homes and their leveraged investment properties. Regular people are not leveraged into AI startups today. If the AI bubble pops, regular feel people will feel the direct financial consequences significantly less than the '07 housing crisis.
> Regular people are not leveraged into AI startups today
this isn't accurate. as an example, anyone with a 401k will be affected by the stock market and the stock market is mostly tech, and tech has bet the house on ai
"Your 401k should never go down at all" is not a realistic expectation.
Hopefully, people who need to pull from their 401k's soon have wound down to less risky positions. 401k exposure is just not the same thing as your house valuation tanking.
> In 2007, It was regular people who lost their homes and their leveraged investment properties.
And the debt on which these owners defaulted had been converted into CDOs and mortgage-backed securities, it was financial institutions and investors who lost enormous amounts of money on these.
Today there are GPU-backed securities and compute futures that are helping back all the debt that is financing the datacenter boom. Maybe it’s a bubble, maybe it’s not a bubble, but either way I think its parallel to 2007.
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.
> 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?
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 $400B for video and phones though. That's like a $1k average for every adult and child in the country. It's twice what I spend, and I'm not on a family plan.
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.
I more than agree on the value, but not on the price. There's currently a competitive market, including free models that are pretty impressive if not "frontier".
Yeah, we need a lot more kinds compilers like LEAN. Give the accountants, physicists, chemists, biologists, and so on the tools where they can verify the results.
The labs probably foresee things looking more like OpenAI solving Navier-Stokes: a company identifies a strategic opportunity locked behind an extremely difficult problem, throws a huge amount of money and compute at it, solves the problem faster than their competition, snatches the opportunity.
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.
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.
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.
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.
The assumption here is that having "the best" model is not worth that much of a premium over a "good enough" model at a lower price. This remains to be seen, though, and people's behaviour paying for Claude Code and Codex suggests at least for now people are willing to pay.
It also assumes Anthropic can maintain the lead. If anything the competition both in the USA and in China seems to be getting closer and closer to parity with Claude.
I use Claude a lot at work and Deepseek for personal stuff, and they feel very comparable to me. I haven't found a task using Deepseek that makes me wish I had Claude - especially given how expensive Claude is.
>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.
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.
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.
Well, if you buy a bunch of GPUs and don't have buildings to put them in (a very real situation right now), then you're just .... holding hardware and not making a return off it and *the building (and related power infrastructure)* becomes much more important than how many GPUs you have.
It is called market collusion, and Jensen Wong confessed on air to "advising" SK Hynix CEO to align with his corporate projects a year before the "AI" supply chain take over.
Technically against the law, even if folks reject the fact AMD & Nvidia are Monopolistic competitive firms in the GPU markets. Thankfully the LLM crowd are rapidly moving away from the CUDA ecosystem. =3
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.
> 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.
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.
You mention that training can be suspended, which is true, but don't acknowledge that puts them behind in the arms race against both other frontier companies as well as second line cheaper providers.
Without a moat, training is an ongoing cost that they can't afford to not pay
Yes, and the training data behind a lot of models is already stale and rotting fast.
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.
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.
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.
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.
> 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.
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.
>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.
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.
That’s 52% gross margin. Not much higher than the overall S&P 500. If you break it down by sector, Real Estate, Health are, Financials (and of course Tech overall) have higher gross margins at the sector level.
>That $1 of compute is not taking into account R&D spending, infrastructure, etc
People assume this, but it is wrong. Check the S-1 for Q2, the $2.08 in revenue is for every $1.00 spent on "compute and infrastructure expenses" which also include R&D compute. (Chart is on page 137)
"Compute and infrastructure expenses primarily consist of costs attributable to operating our technology infrastructure, which supports the full range of workloads for our models, including research, model development, serving customers, and internal usage of Claude." page 146 in the S-1/A-1
It's damning that out of the only two companies in the world that absolutely must dogfood Anthropic's products, one of them, the investment bank behind Anthropic's IPO (Goldman Sachs), didn't find enough of a use case for to justify its continued use by most of the bank. Now LLM use is heavily restricted to specific departments and even in those departments spend must be approved. They've also repeatedly dropped the IPO target by almost half in just 4 weeks. That's WeWork levels of adjustment right there.
It's almost as if they know something about the finances that the rest of us don't...
> 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.
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.
Im in China, and scared. Everyone is broke, losing their jobs while on the other hand there are these giant datacenter campuses being build in the warmest and most remote places. if the bubble bursts (there is 1000% a bubble, its the same like mass maker and innovation, sharing bikes and now ai policies)… it will be bigger than any financial crisis before. The only shit I see coming out of ai hackathons and competitions here is ai tools for marketing (ai). The rest is ai slop microdramas.
I think this is correct except that it ignores the hidden bubble in the SPVs: a lot of the firms who look only partially at risk have simply moved debt off their balance sheets- but to whom? Pension funds, sovereign wealth funds etc.
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
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.
This argument only works because the author uncharitably and incorrectly added “nobody would ever pay money for” to the end of it. If you remove that sabotaging clause, you can “have it both ways”: Ai is gonna change the world, it’s gonna be slop, and you’ll have to pay for slop while grumbling that things used to better.
"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.
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?
> 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.)
> Maybe think of it as a bimodal thing.
And how does that justify the valuation that they are planning to IPO at?
It's just a total gamble, is that the claim?
> 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 easily imagine a future in which Anthropic fails to find a market fit that results in the kinds of profit that will cover their current spending commitments, and where the company is another casualty of progress.
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.
> - 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
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 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
The first reason I became suspicious of the AI bubble is it is all the same people hyping it up. The grift just switched to the new thing.
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.
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.
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.
> 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.
I see it as the opposite. I think the this idea, the "seeing the LLMs as a bubble representing the hope that things can go back to normal" is itself projection from people who know their future is going to suck thanks to "AI" but really wish differently. Whether the LLM companies crash and burn, or succeed wildly, the outcome is going to be negative for the average American (and probably for the average human being).
It will eventually blow up of course but it wont be anything really expectacular, a bunch of larpers will be talking about giant loses for CEOs and surely, hopefully anthropic, all while it doesnt have a big effect on the people that know little about all of this.
But still, nothing will be back to how it was, ever
I am pretty sure the biggest thing in question is if OpenAI and Anthropic are as valuable as they say they are vs any other AI labs. I guess we will see, but I still feel it doesn’t take many ex-employees from them to build something that can wrestle them, and also it’s weird seeing other players that also have comparable AI models (Alphabet, Meta, whatever company owns Grok these days, …) do not get as much in value. There is also the “Can local models disrupt this?” question (and the Chinese labs), and there is also the question of hardware availability (will some player change this?).
I feel the projected value of both OpenAI and Anthropic requires that nothing changes in a space of technology where things seems to change a lot and reasonably suddenly.
Does Open AI or Anthropic fall behind? I think if either falls behind people are going to panic and the eventual stock price will tank. Both companies have a ton of commits so much in fact that one of them failing might take one or two other companies with them. That is what I think the big risk is.
No, no one believes that. Everyone can see that AI is going to change how many industries work completely. The parent is constructing a strawman argument "the reasoning is motivated and that motivation is nostalgia (or similar)".
Yes, quite a few people believe AI is a big scam and it’s not actually good for anything.
I know this might seem crazy to software developers who handed all their dev work over to Claude 6 months ago. But most people are not software developers.
I think a lot of software developers are convinced it must be transforming other jobs and it really isn’t and may never to the same extent. LLMs are just particularly good for technical writing and code.
But that is the potential issue. It is valued as it will and that just isn’t proven.
I have also seen this from many SWE and hobbyist programmers as well, actually, as not all workplaces have AI mandates.
Those people usually bury their head in the sand and have such strong opinions about the capability of these models as they haven't ever used anything but the free tier chatbots, if any at all.
I mean if one of the big labs collapse, then yes it mostly will? Like sure, the tech will stick around in some way or another as long as it makes money or provides a good user experience, but the forcing a garbage chatbot into every product surface, the constant flavor of the week hype, the endless doom from every CEO will all cease to exist.
No, but it is incredible for somebody to use the word “wishcasting” and then describe a group of people that would need to materialize in order for their point to make sense
I think a lot of people who post on HN don't want to admit that, apart from chatbots, AI is completely invisible to the user. A lot of users already hate chatbots to begin with and avoid using them.
So, it has already mostly disappeared, and the lack of practical applications means it will continue to bleed money. Software devs aren't using AI either for the bulk of smaller tasks that require teamwork with non-technical stakeholders.
Yes, it totally can disappear from society. The remaining use cases go with local LLMs and specialized software built around it. From the user's perspective, that's the way things always were and we'll be back to "the way it was" when the bubble is done bursting.
-1 to this. I think this is why Ed Zitron gets under the skin of AI evangelicals. They want everyone on this surrealist funny bus on a journey to whatever-the-fuck. Fingers crossed! Ignore the weird AF obfuscated financials. Ignore the crippled hardware market for everyone except data centre overlords. Nothing to see here!
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.
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.
Large data centers are dramatically more efficient at utilizing both the power and the hardware.
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.
Be slightly careful with this. Large datacenters use power to operate the facility (this is measured as PUE > 1). Datacenter-oriented hardware often contains low-diameter, high static pressure fans that consume quite impressive amounts of power that are not counted as overhead in the PUE calculation.
A home- or office-oriented workstation will have much less power-hungry fans (go compare a Noctua datasheet to a server fan datasheet - the Noctua wipes the floor with a server fan if measured by CFM/W, and the internal temperature rise is mostly controlled by airflow and not by static pressure). And in some climates and times of year the marginal facility HVAC cost associated with running a computer may be zero or even negative.
And lower utilization hardware may run in on a more efficient point on the power-vs-performance curve. [0]
That being said, your overall point is correct.
[0] For a dramatic demonstration of how much this curve matters, you can run a non-CPU-saturating, non-latency-sensitive workload on various Intel CPUs with and without turbo on. Seeing a 2x or greater difference in reported power is quite possible. (Intel is terribly at writing platform firmware and the default behavior may be wildly suboptimal.)
In addition to the batching issue, "a graphics card" is not going to cut it for the multi-trillion parameter frontier models. You're looking at a 512GB Mac Studio to support anything close to those model sizes, and that's with heavy quantization and much worse performance.
agreed, gets some mid range hardware, some click installing agents with descent customisability, and im talking boring customisability like 'help me plan what to buy this month for my home' kind of tasks. Thats what we are going towards
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.
> Railroads, semiconductors, plastics, networking... the utility was there for all of them, and so was the potential profit.
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).
Also, (setting aside Standard Oil’s business practices), this is a common meme in oil exploration historically, and even today. Early prospectors were honestly pretty stupid, and their principal differentiation was risk tolerance.
There a lot of parallels with AI…it doesn’t take a genius to see the long-term potential, but the risk is in implementation, so it’s a mad rush to the finish line by a wide variety of less scrupulous early players. Rockefeller got his big start by sweeping up failing producers who were all capital weak, and I’m sure something similar will happen here during the inevitable bust.
I can do the hard, risky work of drilling wells / deploying AI, or I can sell you a vision of that future, today. The most dangerous technologies in terms of grifting are the ones that everyone can understand.
Capital cycle theory: https://www.youtube.com/watch?v=2J2Fb1bBufA&t=577s
I wonder if physical technological progress is also the same as information treatment progress. Everybody is happy having food and materials faster. But AI bringing you solutions faster is also taking away your job, passion.. the negative side seems larger.
The difference is that all those things needed physical infrastructure to be linearly build out.
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.
>AI models can be deployed on the same datacenter as the old one
This hasn't historically been true. The clusters have been getting bigger along with the models over time. And now we may even want to have these computers have rapid access to extra compute or caches of the internet or other data stores.
And data centers are practically free to build, are welcomed everywhere, cost little to run, and never need to have their infrastructure upgraded!
Google hasn't retired any of its now ten-year-old V1 TPUs or any of the subsequent generations so it's unclear that AI infrastructure "needs" upgrades.
Of course, Google could be wrong about the economics of running old AI processors. Or maybe TPUs are special.
I do wonder how much of their inference is running on 10-year old TPUs. I’d imagine not much, if only because 10 years ago the justification for massive compute was quite different than today.
I've been saying for awhile now that the utility is increasingly undeniable. It's amazing technology and for better or worse, is transforming the tech industry (in particular). I just wish we lived in a world without the speculative bubbles, because if this had been developed slowly, deliberately, over time, there would be no necessity to cast aside troublesome details like safety, suitability, and ethics...and we could enjoy the fruits as it was implemented gradually, responsibly, as it matures. As opposed to going "wow, this is really cool, but gosh I feel shitty about using it..."
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.
A tech workers union or a stronger regulatory state could help with this but neither party has been that interested in either of those things for the last 50 years.
> A tech workers union or a stronger regulatory state could help with this
Look to Europe to see how that has worked out.
Your point is a good one, but it's constructed upon the premise that AI is a single discovery or invention or even a single tech wave.
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.
AI wouldn’t work without any of that above stuff though.
Their monopolies got broken and the profits disappear in a commodity market.
If nvidia or tsmc got split up i doubt its stock is going to stay at as a high a p/e.
We're discussing AI in general here, not individual companies (and none of those companies have a monopoly). Nobody's arguing that AI wouldn't exist if the previous revolutions hadn't occurred.
I know where you're coming from, but we probably should not be talking about AI in this way. I don't think it's helping or serving its purpose.
I think civilization will have time to react and adapt, panic leads to haphazard action, we need deliberate consistent action.
This is exactly the kind of talk that led to all the other bubbles. Amazing how infected people get.
That's a fair point, but tulips are not AI.
I'm not even talking about tulips. I'm talking about The Information Superhighway in 1999, I'm talking about real estate investing in 2007.
Each event was so different with different motivations. The popularity of Tulips never back. 2007 was about hiding poor risk mortgages. 2001 every company had money thrown at them and today is about a product that costs more than the market can pay.
You know what was actually a bigger invention? Language. Farming.
Imagine a world without those. Oh, wait: tomorrow people won’t know how to use language, will they? And they won’t have water to farm with, so there you go.
This reads less like a bulwark against sensationalism and more like an attempt to remain ideologically safe in what appears from many angles to be an extremely steep exponential start of what we can probably agree is an S-curve.
> 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.
How do you know we aren't already through the exponential part of the S-curve and about to enter the AGI winter (i.e. AGI will require a fundamentally different technology than LLMs that is still decades off)?
If no one is willing to pay increasingly high costs of training frontier models, we'll still have the models that we have now that can be fine tuned for incremental improvements. Still tons of utility in that, but little investor upside.
> appears from many angles to be an extremely steep exponential start of what we can probably agree is an S-curve.
In every bubble some say this. And it could be the exact same herd behavior (mentality) that creates a bubble.
Congratulations on having authored a comment whose meter is indistinguishable from that of a post written by claude trained on linkedin posts.
In 1893, GE nearly went bust as the market got cold feet that electrification was too costly, and the uses too abstract and niche. People were also hugely worried about circular financing, as they were guaranteeing bonds and taking equity stakes in their clients.
As we all know, GE then went bankrupt and we went back to whale oil.
Something something than you can remain solvent
The market can remain 3.14159...
> 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.
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?
- revenue growth at Anthropic slowing
- 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.
Big enough companies who could be the best customers can run models on their infra for a fraction of the price. Eg: Revolut.
https://dealroom.co/news/169124-revolut-moves-2-000-engineer...
Yeah, seems like there is a GPU to engineer sweet spot with models like GLM. This could become a more popular pattern for some organizations. Likely also the reason gov. attempts to intervene
Inflation, house prices, gas prices, interest rates, huge public distrust of government and corporations, votes to restrict data center construction, endless war in Iran, etc.
100%, great additions. Someone should make a tracker site for this.
Maybe this is what you meant by "Chinese arms race" but there is, though not quickly, a massive wave of increased hardware supply incoming. Again, let me mention this isn't coming soon in terms of the AI bubble conversation. But China is pushing for a home grown EUV and fabs that should start coming online and having an impact in 2030.
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.
Still useful. Especially if Chinese GPUs operating on Nvidia software become performant and accessible, thereby undercutting the price gouging that we're seeing with western chips.
The big issue will be access (Re: BYD)
I generally agree with you, but have to get this off my chest.
> 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.
Also - all the gee-whiz AI companies are headed by people who don't have great business backgrounds, either by people from tech backgrounds (chasing the last of the VC), or by people with the kind of "interesting" background who you wouldn't lend your own money!
Agreed.
Also the sadder slice is all the X people token maxxing with no hope of distribution. Cool things are cool, but getting buyers is getting harder.
As the friendly article points out, the "AI is a bubble" hypothesis and "entry level hiring is collapsing because AI is replacing their jobs" hypothesis are not really compatible.
As always, reality is likely a muddled mix: there is real utility and there is wasted investment that will never pay itself back.
They're compatible because they are both driven by hype, not reality.
unless I am mistaken, you missed that the datacenter infrastructure being a one-time cost. nope. those servers have about a 5 year lifetime and then are likely to be replaced. The buildings aren't the expensive part. The GPUs are.
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)
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.
If new hardware comes online that doesn't allow you to payback your billions in hardware, what then? The level of rigor in the article is low.
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.
So much this.
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.
For the time being this thesis hasn’t worked out though. H100’s are only going up in price and becoming more scarce [1]. At what point would you update if now going on 3 years into H100 lifecycle this doesn’t seem close to happen.
But they are becoming more scarce while staying on Nvidia’s shelves buy-and-hold, or being leased back in some cases. They are not being installed by customers at anything like the rate the sales figures suggest. So yeah, these H100s will last a long time in the packaging.
Meanwhile, Meta is selling their spare AI data centre capacity.
Isn’t that what SpaceX is doing too? Selling spare compute while they work on their rocket side hustle.
Jevons' Paradox. If it becomes more efficient, cost goes down, demand goes up, use rises, revenue rises.
no, it's 10y. you just repurpose.
also, power and cooling agreements are usually 10y too.
Look at DeepSeek! They have done full system optimization on their model, architecting it so that it would be cheap to serve. I am proud to call DeepSeek a world class tech engineering firm in a way that 99% of the tech world is not. They are a 1% company.
> The current-gen hardware is expected to have only 2-3 years of useful life.
Expected by whom and why?
Google hasn't retired any of its now ten-year-old TPUs, or any of the subsequent generations. Maybe TPUs are special or Google doesn't understand compute economics but either argument requires data.
If your argument is depreciation, you're confusing a tax concept (depreciation) with useful life. Yes, they're supposedly related, but in practice, the relationship is rather loose. (Residential housing depreciates in 28 years yet typically lasts 2-3x as long and 4x is not rare.) Google's V1 TPUs
It hardly seems to be a one time expense. GPUs and networking equipment may have a useful lifespan of only about five years before needing replacement, and it could be expensive to do so.
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.
The amount of profit they make on compute is also not particularly attractive (at least right now). It ‘s only slightly more than 2x for Anthropic, far off from many big cloud services which sell compute at something closer to 20x.
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!
The decade after the dotcom crash brought about as many if not more long-term successful products as the dotcom boom: Youtube, Reddit, Spotify, Google Maps come to mind
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
indeed, very happy to see scientifically minded folks running for office.
The usability and potential profits of housing were there in 2007 and 2008, too.
In 2007, It was regular people who lost their homes and their leveraged investment properties. Regular people are not leveraged into AI startups today. If the AI bubble pops, regular feel people will feel the direct financial consequences significantly less than the '07 housing crisis.
> Regular people are not leveraged into AI startups today
this isn't accurate. as an example, anyone with a 401k will be affected by the stock market and the stock market is mostly tech, and tech has bet the house on ai
"Your 401k should never go down at all" is not a realistic expectation.
Hopefully, people who need to pull from their 401k's soon have wound down to less risky positions. 401k exposure is just not the same thing as your house valuation tanking.
i used 401ks here as one example. but if your company has to downsize due to exposure, that means you're out of a job.
> In 2007, It was regular people who lost their homes and their leveraged investment properties.
And the debt on which these owners defaulted had been converted into CDOs and mortgage-backed securities, it was financial institutions and investors who lost enormous amounts of money on these.
Today there are GPU-backed securities and compute futures that are helping back all the debt that is financing the datacenter boom. Maybe it’s a bubble, maybe it’s not a bubble, but either way I think its parallel to 2007.
And the valuations made no sense in 10 yea....oh.
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.
> 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?
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.
You make it sounds reasonable then.
That $400B for video and phones though. That's like a $1k average for every adult and child in the country. It's twice what I spend, and I'm not on a family plan.
If every software developer paid $200/month
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.
I more than agree on the value, but not on the price. There's currently a competitive market, including free models that are pretty impressive if not "frontier".
If they do for material sciences what they did for math proofs, the world is going to get weirder!
Yeah, we need a lot more kinds compilers like LEAN. Give the accountants, physicists, chemists, biologists, and so on the tools where they can verify the results.
The labs probably foresee things looking more like OpenAI solving Navier-Stokes: a company identifies a strategic opportunity locked behind an extremely difficult problem, throws a huge amount of money and compute at it, solves the problem faster than their competition, snatches the opportunity.
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.
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.
You mean they hope to be the next Facebook/Google that knows everything about you and call sell that data to advertisers? Yay :-|
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.
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.
The assumption here is that having "the best" model is not worth that much of a premium over a "good enough" model at a lower price. This remains to be seen, though, and people's behaviour paying for Claude Code and Codex suggests at least for now people are willing to pay.
It also assumes Anthropic can maintain the lead. If anything the competition both in the USA and in China seems to be getting closer and closer to parity with Claude.
I use Claude a lot at work and Deepseek for personal stuff, and they feel very comparable to me. I haven't found a task using Deepseek that makes me wish I had Claude - especially given how expensive Claude is.
>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.
I mention this in the article...
> You might argue it's like Windows vs Linux ...
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.
>Shameless Self-Plug I’ve applied to seek the Liberal Party of Canada nomination in Sudbury East—Manitoulin—Nickel Belt
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.
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.
Well, if you buy a bunch of GPUs and don't have buildings to put them in (a very real situation right now), then you're just .... holding hardware and not making a return off it and *the building (and related power infrastructure)* becomes much more important than how many GPUs you have.
Another weird trick (that seems to be in play) - don't unpack them yet, so they are not yet depreciating assets! Listen to https://open.spotify.com/episode/1QjM5Ud5kjHzMIY5G7hjhW?si=o...
There's one weird trick to preventing your GPUs from losing value. Buy all the ram.
It is called market collusion, and Jensen Wong confessed on air to "advising" SK Hynix CEO to align with his corporate projects a year before the "AI" supply chain take over.
https://en.wikipedia.org/wiki/Collusion
Technically against the law, even if folks reject the fact AMD & Nvidia are Monopolistic competitive firms in the GPU markets. Thankfully the LLM crowd are rapidly moving away from the CUDA ecosystem. =3
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.
> 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.
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.
Unless I misread it, this article simply doesn’t mention training or the cost of it.
Training would fall under "compute and infrastructure investments", which I mention in the 3rd paragraph.
You mention that training can be suspended, which is true, but don't acknowledge that puts them behind in the arms race against both other frontier companies as well as second line cheaper providers.
Without a moat, training is an ongoing cost that they can't afford to not pay
Imagine if Apple decided to "stop developing new iPhones". That's what "stop training" sounds like.
They'll stop training when they're dead, not before.
Yes, and the training data behind a lot of models is already stale and rotting fast.
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.
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.
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.
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.
> 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.
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.
>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.
> $2.08 for every $1 of compute.
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.
That’s 52% gross margin. Not much higher than the overall S&P 500. If you break it down by sector, Real Estate, Health are, Financials (and of course Tech overall) have higher gross margins at the sector level.
That $1 of compute is not taking into account R&D spending, infrastructure, etc.
And that's their only revenue stream
>That $1 of compute is not taking into account R&D spending, infrastructure, etc
People assume this, but it is wrong. Check the S-1 for Q2, the $2.08 in revenue is for every $1.00 spent on "compute and infrastructure expenses" which also include R&D compute. (Chart is on page 137)
"Compute and infrastructure expenses primarily consist of costs attributable to operating our technology infrastructure, which supports the full range of workloads for our models, including research, model development, serving customers, and internal usage of Claude." page 146 in the S-1/A-1
It's damning that out of the only two companies in the world that absolutely must dogfood Anthropic's products, one of them, the investment bank behind Anthropic's IPO (Goldman Sachs), didn't find enough of a use case for to justify its continued use by most of the bank. Now LLM use is heavily restricted to specific departments and even in those departments spend must be approved. They've also repeatedly dropped the IPO target by almost half in just 4 weeks. That's WeWork levels of adjustment right there.
It's almost as if they know something about the finances that the rest of us don't...
This should be a top level comment
Or it's like no one wanted to believe Ed Zitron, who has been telling everyone everything they need to know about this real-life, actual AI bubble....
Along with the previous 34 AI bubbles.
> 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.
> 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.
The 16% yield on Coreweave bonds probably indicates otherwise.
and those CDS...
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.
Yeah, this "just my opinion" humbragging style is an unfortunate antipattern.
Talking this way is a big part of the "tech bro" smell.
tech bro is leading indicator, they are emulated. we gotta change course.
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.
Im in China, and scared. Everyone is broke, losing their jobs while on the other hand there are these giant datacenter campuses being build in the warmest and most remote places. if the bubble bursts (there is 1000% a bubble, its the same like mass maker and innovation, sharing bikes and now ai policies)… it will be bigger than any financial crisis before. The only shit I see coming out of ai hackathons and competitions here is ai tools for marketing (ai). The rest is ai slop microdramas.
To what extent do people “on the street” as it were relate this to the real estate bubble?
I think this is correct except that it ignores the hidden bubble in the SPVs: a lot of the firms who look only partially at risk have simply moved debt off their balance sheets- but to whom? Pension funds, sovereign wealth funds etc.
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
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.
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.
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?
> 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.)
> Maybe think of it as a bimodal thing. And how does that justify the valuation that they are planning to IPO at?
It's just a total gamble, is that the claim?
> 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 easily imagine a future in which Anthropic fails to find a market fit that results in the kinds of profit that will cover their current spending commitments, and where the company is another casualty of progress.
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.
Datacenters being delayed/canceled is actually good because it reduces overinvestment.
> - 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
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 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.
Putting data and rational analysis aside, the style of this post is so similar to what I was seeing during the crypto bubble.
The first reason I became suspicious of the AI bubble is it is all the same people hyping it up. The grift just switched to the new thing.
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.
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.
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.
That is not true. The dot com bubble was widely discussed a year+ in advance of it popping.
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.
> 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.
I see it as the opposite. I think the this idea, the "seeing the LLMs as a bubble representing the hope that things can go back to normal" is itself projection from people who know their future is going to suck thanks to "AI" but really wish differently. Whether the LLM companies crash and burn, or succeed wildly, the outcome is going to be negative for the average American (and probably for the average human being).
It will eventually blow up of course but it wont be anything really expectacular, a bunch of larpers will be talking about giant loses for CEOs and surely, hopefully anthropic, all while it doesnt have a big effect on the people that know little about all of this. But still, nothing will be back to how it was, ever
Do people believe that? If AI is indeed a giant bubble and indeed the bubble pops, do people believe that AI will just... disappear from society?
I am pretty sure the biggest thing in question is if OpenAI and Anthropic are as valuable as they say they are vs any other AI labs. I guess we will see, but I still feel it doesn’t take many ex-employees from them to build something that can wrestle them, and also it’s weird seeing other players that also have comparable AI models (Alphabet, Meta, whatever company owns Grok these days, …) do not get as much in value. There is also the “Can local models disrupt this?” question (and the Chinese labs), and there is also the question of hardware availability (will some player change this?).
I feel the projected value of both OpenAI and Anthropic requires that nothing changes in a space of technology where things seems to change a lot and reasonably suddenly.
Does Open AI or Anthropic fall behind? I think if either falls behind people are going to panic and the eventual stock price will tank. Both companies have a ton of commits so much in fact that one of them failing might take one or two other companies with them. That is what I think the big risk is.
Not people on here or who use it regularly, but on other parts of the net.
Maybe they know on some level it won’t go away, but the “wishcasting” leads to rooting for the bubble in the hope that maybe it actually will.
No, no one believes that. Everyone can see that AI is going to change how many industries work completely. The parent is constructing a strawman argument "the reasoning is motivated and that motivation is nostalgia (or similar)".
Even Ed Zitron rarely talks about AI outside financials. I have no idea what OP is talking about.
I guess they mean as far as AI being this utopian cure all for the ills of the world that some might have us believe is coming, its BS?
Yes, quite a few people believe AI is a big scam and it’s not actually good for anything.
I know this might seem crazy to software developers who handed all their dev work over to Claude 6 months ago. But most people are not software developers.
I think a lot of software developers are convinced it must be transforming other jobs and it really isn’t and may never to the same extent. LLMs are just particularly good for technical writing and code.
But that is the potential issue. It is valued as it will and that just isn’t proven.
I have also seen this from many SWE and hobbyist programmers as well, actually, as not all workplaces have AI mandates.
Those people usually bury their head in the sand and have such strong opinions about the capability of these models as they haven't ever used anything but the free tier chatbots, if any at all.
I mean if one of the big labs collapse, then yes it mostly will? Like sure, the tech will stick around in some way or another as long as it makes money or provides a good user experience, but the forcing a garbage chatbot into every product surface, the constant flavor of the week hype, the endless doom from every CEO will all cease to exist.
> Do people believe that?
No, but it is incredible for somebody to use the word “wishcasting” and then describe a group of people that would need to materialize in order for their point to make sense
I think a lot of people who post on HN don't want to admit that, apart from chatbots, AI is completely invisible to the user. A lot of users already hate chatbots to begin with and avoid using them.
So, it has already mostly disappeared, and the lack of practical applications means it will continue to bleed money. Software devs aren't using AI either for the bulk of smaller tasks that require teamwork with non-technical stakeholders.
Yes, it totally can disappear from society. The remaining use cases go with local LLMs and specialized software built around it. From the user's perspective, that's the way things always were and we'll be back to "the way it was" when the bubble is done bursting.
-1 to this. I think this is why Ed Zitron gets under the skin of AI evangelicals. They want everyone on this surrealist funny bus on a journey to whatever-the-fuck. Fingers crossed! Ignore the weird AF obfuscated financials. Ignore the crippled hardware market for everyone except data centre overlords. Nothing to see here!
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.
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.
Large data centers are dramatically more efficient at utilizing both the power and the hardware.
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.
Be slightly careful with this. Large datacenters use power to operate the facility (this is measured as PUE > 1). Datacenter-oriented hardware often contains low-diameter, high static pressure fans that consume quite impressive amounts of power that are not counted as overhead in the PUE calculation.
A home- or office-oriented workstation will have much less power-hungry fans (go compare a Noctua datasheet to a server fan datasheet - the Noctua wipes the floor with a server fan if measured by CFM/W, and the internal temperature rise is mostly controlled by airflow and not by static pressure). And in some climates and times of year the marginal facility HVAC cost associated with running a computer may be zero or even negative.
And lower utilization hardware may run in on a more efficient point on the power-vs-performance curve. [0]
That being said, your overall point is correct.
[0] For a dramatic demonstration of how much this curve matters, you can run a non-CPU-saturating, non-latency-sensitive workload on various Intel CPUs with and without turbo on. Seeing a 2x or greater difference in reported power is quite possible. (Intel is terribly at writing platform firmware and the default behavior may be wildly suboptimal.)
None of that matters when there is literally a 100x multiplier on efficiency because of batching. It's all a rounding error.
In addition to the batching issue, "a graphics card" is not going to cut it for the multi-trillion parameter frontier models. You're looking at a 512GB Mac Studio to support anything close to those model sizes, and that's with heavy quantization and much worse performance.
agreed, gets some mid range hardware, some click installing agents with descent customisability, and im talking boring customisability like 'help me plan what to buy this month for my home' kind of tasks. Thats what we are going towards