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
The trouble that that idea is that there comes a point where the generated AI needs machines to test the code that is generated... and it actually puts a limit on the growth of AI.
Compute hardware is ~50% of the cost of a datacenter. If we can assume a 5 year lifespan for the GPUs, that puts the ammortization timeframe closer to ~10 years.
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!
On the flip side, as an ex-physicist, politically minded academics more often than not were among the most annoying people I’ve met. Not saying this person is an academic, I can’t find out what they do at all which means they’re likely not an academic.
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
For certain values of temporarily. On human time scales, we do see bear markets which last a decade or longer, and that can have a serious impact on someone's earning ability or retirement.
Markets do include significant risk and we would do well to remember that.
Peoples' 401k will go down, but its not like they were relying on their 401k day to day (unless you retired right when LLMs got huge, i guess?). They won't directly feel it.
> 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.
Unfortunately, they are, the market (even the global market) is heavily concentrated in just a few US listed companies, and other indexes closely track US indexes. A crisis in AI would impact everyone.
For proof, look at the fall in VWRL last week
Correlated with concerns over AI.
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
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.
“A risk” is not the same as a gamble. Of course there is risk. But companies don’t have trillion dollar valuations when there isn’t a clear path to profitability.
Every one of the few companies with that kind of valuation generates tens or hundreds of billions per year in free cash flow.
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.
> 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 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.
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 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 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 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.
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.
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 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.
-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!
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 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).
>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.
I could only find these numbers in articles on something called crypto briefing dot com, not sure how the author got this number from the S-1.
When using traditional business metrics, such as operational net income, we'll see that Anthropic has been in the red for the entirety of its history, at times to the tune of $3B per quarter, except in Q2 2026.
When looking at amended S-1 (with Q3 numbers), page 23, "Consolidated Statements of Operational Data", we can see that for 2026 YTD (Q1 - Q3) net loss was $103B; operating loss was $1,260B.
Anthropic also disclosed up to $895B (multiyear) commitments for infrastructure build-up.
Basically, the company's financial results do not allow for any reasonable valuation (which would be more than 0), leave alone $2T or even $1T. If this is not an indication of a bubble I don't know what is.
If your hyper growth startup is not in the red for its entire history, it fucked up. One can argue their "profitable" Q2 was a mistake, startups at this stage should be far from profitable
While this is true, most of Anthropic's expenses involve locking up compute for the future. But they have no idea how much compute they will actually need in the future, or how much they're going to be able to charge their customers for it. Per token prices are dropping like a rock, and performance optimizations are making model serving more efficient. Combine that with the comparatively slower growth in organic AI demand (compared to debt-driven/investor subsidized demand), and you have the very real possibility of a 2000-style "dark fiber" problem.
There's no dynamic in their financial results which points to profitability with any probability at any point in the future. Yes, the revenue grows. The expenses grow even faster, AND they plan to move to building their own infrastructure instead of renting in the future (more risk as they need to be able to execute that).
One question is: can they maintain those margins? Anthropic currently is the most expensive provider by far, and it is not clear that Opus delivers enough value to justify the markup over their competitors. E.g. OpenAI charges half of Anthropic's API rates for Sol vs. Opus or Terra vs. Sonnet and appears to be more token-efficient, not to mention all the other providers.
While inference may become more efficient and drive costs down for Anthropic in the long term (though over the next decade, they have hundreds of billions in data center commitments, so it's not going to to happen soon), that goes for every provider. Commoditiziation and competition on price is likely to happen, and with models getting ever better, the value proposition of frontier models would become much less compelling.
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...
Being right at the wrong time is often worse than being wrong in the general case. Predicting bubble implosions and bear markets are the classic examples.
You assume these companies telegraph their motives, they could just as easily be feeding this narrative so they can buy more of the company at a lower price.
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.
is anyone else weirded out by folks chanting this like a mantra? imagine saying this for any other emergent tech. "NFTs are inevitable", "crypto is inevitable", "excel is inevitable", etc.
Strange comment to make. We shouldn't be honest about AI's capabilities?
How do you think we need honest deliberate action if 95% of people believe AI is a scam and a bubble and mostly just hype marketing? Just take HN's comments for example.
You're defending one extreme by claiming the other extreme is the only option. Which isn't honest behaviour even if it was well intended. Truthfully, AI will cause real problems and we need to handle it, but it's also often exaggerated. It's a rapid technological shift, but like all the other ones anyone who works hard will still find a place to be useful.
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?
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.
> How do you know we aren't already through the exponential part of the S-curve
From a recent METR report, benchmarks point towards an acceleration, LLMs are getting better at a faster rate. There's no sign that we are about to reach the top of the S curve.
> increasingly high costs of training frontier models
Somewhat surprisingly the gains do not seems to come from more compute, we have on the METR report:
> We are confident that the acceleration was not due to an acceleration in the scaling of training compute.
>From a recent METR report, benchmarks point towards an acceleration
On an internal benchmark with internal models, if you look at the public benchmark with public models there is no acceleration. That report also has a big graph labeled "fake data".
> Somewhat surprisingly the gains do not seems to come from more compute
I think this could form part of an argument that acceleration is slowing. It's only because we are not making as much improvement out of hardware gains that we're having to find the improvements in more complex architecture and more tricky to create training data.
The fact that the gains are not coming from the traditional place gains come from in technology might be a sign that a high percentage of the low hanging fruit has now been picked.
> 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).
Setting aside Standard Oil’s business practices, this is a common meme in oil exploration historically, and even today.
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 grift by a wide variety of less scrupulous early players.
I can do the hard, risky work of drilling wells / deploying AI, or I can sell you a vision of that future, today.
> Another company, which then bought the largely finished canal, cheap, they had potential profit... until tech change (railroads).
Absolutely right. Somebody may buy the DCs. The tech changes very very fast, the DCs may be entirely worthless. US has high energy costs, it can never be cost competitive.
Useful (like solar, airlines, fiber), but it won't be profitable. High capital cost, low marginal cost, subject to competition and with technical improvement --> these four factors guarantee businesses will never be profitable, and they go bust.
“When the dot-com bubble happened, it was largely driven by companies that couldn't turn a profit or had P/E ratios in the hundreds. I'm not going to pretend that I'm an expert on financial bubbles, but I've never heard of a bubble where the companies involved had such a modest valuation based on their earnings, even if their earnings (and therefore their evaluations) are enormous.”
Railroads had some of the same issue encountered by the dotcom bubble, and in particular dark fiber: Overcapacity.
Once railroads where demonstrated to be (extremely) profitable, people kept building them even if the existing demand was already met. The exact same thing happened with dark fiber (looking at you Enron).
AI is a bit different in the sense that the capacity can be used for research, and more importantly is not bound to a specific connection (between locations), and therefore a lot more fungible.
Not sure about canals, but while I think there are issue with current AI investments, I would not conflate them with the railroad bubble on this basis.
AI is a bit different in that unlike the rail tracks and the dark fiber the data centers arent actually going to be finished. It's much more of a stranded asset - there will be piles of chips and 1/4 finished construction projects, not usable assets.
No doubt somebody will use the assets for something but it's still wildly more wasteful than either railroads or dark fiber.
The UK has even higher energy costs and people seem to want to build data centers here? (Or at least they say they do - looks more like land speculation to me):
I agree with your point about rule of law, but let’s not gloat too quickly about nasty SA getting its comeuppance. It’s going to look pretty ugly in Europe when Russia follows through on its potential to disrupt or destroy critical infrastructure. We are pretty vulnerable too.
The UK has got a huge amount of wind power that can be purchased ahead of time at better rates than listed in that table, it's also got some bottlenecks with power distribution (especially between Scotland and the rest of the UK) which will take years to fix so there area areas where decent power contracts can be had for customers who can build out fairly quickly (datacentre operators with large power requirements).
People want to do all kinds of idiotic things which make no sense. We've had excess compute/gpu since 2010. People didn't know what do with all that excess and started bitcoin and tens of thousands of crypto, followed by NFT. The claim was crypto will solve all problems, which hasn't happened. Now, everyone moved on to AI, AI will do this, that and everything else and also take away all your white collar and creative jobs.
Even third world countries like India which can't provide basic services to their citizens, have no water, no power, no sanitation or cleanliness are giving hundreds of billions of dollars in incentives (land, power, water, tax subsidies) to hyperscalers, to create at most 2.6 jobs. Google clearing land to build India's largest data centre: https://www.youtube.com/watch?v=dBJPf6Pxfvs
> The UK has even higher energy costs and people seem to want to build data centers here? (Or at least they say they do - looks more like land speculation to me):
There is no meaning to what politicians and crony capitalists do. It is all lies, theft and grift.
Both our high energy prices and the current capabilities of our energy distribution network relative to the renewable generation and storage capacity we are trying to move towards are significant barriers.
> US has high energy costs, it can never be cost competitive.
A non-trivial percentage of that is artificial.
When push comes to shove and enormous profits (and anything remotely resembling national security) are at stake, the US is decent at removing artificial problems.
You've already seen a ton of that related to chips and data center build outs.
Why is solar more expensive in the US than China? Mainly tariffs. Same for batteries.
Why is nuclear more expensive in the US than South Korea? The government.
Why is SMR never going to beat conventional nuclear if you remove artificial problems? Physics. Though specialized applications will be viable with SMR.
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.
Well at least from where I'm sitting, it's working fine for us. We aren't in the midst of an oligarchy made up of the Epstein class, for one.
Could thing be better with the tech industry here? Sure, but at the same time, profits and industry leaders aren't the only things that matters. And at the very least companies here can't just exploit the public with total impunity.
We are. In the UK we have the former Prince Andrew and Peter Mandelson selling information to Epstein. There are lots of other people all around the world connected to Epstein and mostly investigations are slow or on-existent or blocked through lack of cooperation by the US.
Epstein's network is one we know about because its been exposed and because of the sexual abuse aspect of it has both drawn attention and made people less likely to cover up for those involved. Its only a fraction of the corruption that goes on though.
> We aren't in the midst of an oligarchy made up of the Epstein class, for one.
Europe even has literally hereditary monarchies; let alone an insular upper class who are famous for their debauchery. They just have much less free speech protection so it doesn't get bought up as much because of the legal risks.
Related note, but while looking up some background info on Wikipedia it appears [0] it was technically illegal to publicly insult Donald Trump in Germany at the start of his presidency which would have added a hilarious angle to a classic joke [1].
[1] In the USA, you can stand in front of the White House in Washington, DC, and yell, "Down with Ronald Reagan", and you will not be punished. Equally, you can also stand in Red Square in Moscow and yell, "Down with Ronald Reagan", and you will not be punished.
The US is great for free speech as its constitutionally protected. However it is also very corrupt. Pros and cons.
There are a lot of advantages to constitutional monarchies in terms of stability. The monarchs do not actually have much power. I on balance prefer living in the UK and would not want to move to the US. There are pros and cons, and its culture and society (e.g. unarmed police) as well as politics and some if it is about personal circumstances (as someone who is obviously ethnic minority or foreign everywhere in the world I prefer a less racist society, for example).
I can yell "down with the King" or "down with the Prime Minister" pretty much anywhere. There are some restrictions on protest in a few places like Parliament square.
I am also a European. Our oligarchy looks different for sure (and look into the history of pedophile scandals in Belgium, BTW).
I am not as optimistic as you. We seem to be stagnating our way into a stark choice between becoming an American tech vassal, a Chinese tech vassal, or losing out the remnants of our ability to compete on the world markets. Half of the continent borrows money at an unsustainable rate in order to pay millions of people to do nothing for decades.
We have made decisions that looked reasonable at the time they were made, but too much has changed in the meantime and now we are stuck in bad equilibria.
The Bismarckian pay-as-you-go pension system was stable as long as people had plenty of kids and died, on average, a few years after retiring. Now they have one kid and spend 20 years in retirement before they die. The maths of that system just does not work out.
The European highly regulated industry was king as long as the rest of the world had no other choice, but now that Asia has industrialized, they are blowing us out of the water. First with lower prices, but now also with quality. German exports are half of what they used to be, with another halving expected. And of course without exports, our current social spending cannot be sustained either - you cannot keep such a massive redistribution mechanism running on an economy based on selling each other cups of coffee in a beach bar.
We just cannot continue to live like that, something will have to yield, but many voters just don't want to hear that.
When you say “increasingly undeniable” do you mean that it’s already plainly undeniable and it’s getting more so? Or do you mean it is still a bit deniable but is threatening to become undeniable soon?
> I just wish we lived in a world without the speculative bubbles
Speculative bubbles, I.E. risk is where much of the capital comes from to develop these ideas to start. I’m not sure you can have one without the other.
> I've been saying for awhile now that the utility is increasingly undeniable.
If that was true it is strange that you would find yourself saying as much for a while now, given it sounds most appropriate in response to someone denying the utility of slop generators.
How do you know the story is like railroads and not nuclear power? The AI bet sank last week acknowledging the public hates it and could regulate it to death, not that it has no utility.
> AI models can be deployed on the same datacenter as the old one
Not if the datacenters are in space, you can upgrade the models for a little while but once you need to upgrade the silicon you need to launch new hardware.
That's not the difference. The difference is the distinction between core infra, B2B services, and B2C services.
The companies that sold core infra during dot com mostly died, because they invested in fiber etc far too quickly. Likewise with server factories like Sun.
The B2B and B2C survivors were patchy. A few did extremely well but there were a lot of casualties.
The biggest winners offered B2G - business to government - disguised as search and social media. They were actually selling individual user profiling and targeted ads, and that turned out to be the biggest money maker.
The reason Anthropic and OpenAI appear wildly overvalued is because their offering isn't AI in some abstract sense, it's the next generation of surveillance and persuasion technology. But now with corporate surveillance in addition to the traditional profiling and targeting.
You can tell a lot about what someone is doing - and the industry is doing in general - by looking at everyone's code bases. You don't need to train on them or steal keys to extract immense value.
Potentially there's also the option to eventually make claims on corporate IP developed with AI tech.
Anyone who thinks the companies who pulped and torrented whole libraries of books for their training data would never, ever, pinky promise, try to extract value from the code they're generating is being very naive.
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.
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.
>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..
> Railroads, semiconductors, plastics, networking... the utility was there for all of them, and so was the potential profit.
I wonder why so few people make the comparison to the recent VR hype. We heard the same things there about its unlimited potential, how it's inevitable that we will soon resolve all its shortcomings and that it will become ubiquitous.
I still believe VR will eventually live up to its potential, but no one can deny that bubble has popped after Meta took a huge loss on it. It took too long to resolve the hard problems so the timing didn't work for them.
> I wonder why so few people make the comparison to the recent VR hype.
AI is already a gigantically transformative technology, with a staggering amount of paid consumption, and virtually everyone I know uses it every day, where I don't know anybody who regularly uses VR in any form.
The ship has absolutely sailed on "is AI a world-changing technology", where "are the leading AI companies worth what's been paid for them" is a completely open question.
AI can maintain the multiverse and administrate it. Everyone can create just by talking.
Coding would also be a good usecase, to see the architecure in 3D and talk with Claude, point at stuff, pick him up and put him where the work needs to be done.
I think it's a match made in heaven, AI is just still way too slow an expensive for this.
Okay, but why would they? And who's going to consume?
If anyone can trivially create, every creation is meaningless. There is no billion-dollar business in abandoned Minecraft servers, so why would this suddenly change when you force people to put on an expensive VR headset to visit them?
> If anyone can trivially create, every creation is meaningless.
Everyone has always been able to trivially create. We live in an attention economy, not a creativity economy. Meaning you can create crap (or ai slop) and people will consume it if you know how to get their attention.
Attention is what gets money. Creation is just fuel for attention. Quality is a part of the equation, but much less important than people generally believe.
> With AI VR does have usecases. AI can maintain the multiverse and administrate it. Everyone can create just by talking.
Nobody cared about multiverses before AI, and it wasn't because of the lack of maintenance and administration. It's just a thing that nobody cares about, AI improve it but it's not going to make people care about it so that doesn't really change anything.
Not measurable in ROI according to reports that keep coming out every week from companies capping spending because the wow effect is not translating into growth.
Obviously AI people say it's because they don't know what they are doing, but are you going to tell me a company like Apple couldn't figure out how translate those productivity gains into something more tangible?
It didndump a lot of money into it, improving some things and headset availability. But arguably a most of it went to useless stuff no one asked for, like paying developers to build shovelware for Quest or their useless corporate Metaverse.
This argument sneaks in the fact that models are commodities which is NOT true. Claude has a different personality and performance than other models for example.
Expect more differentiation in the future and not less.
I disagree, I always reach for the best model when I’m at work. For normal questions I do use the default ChatGPT model.
Fundamentally, people underestimate the marginal increase in utility for marginal increase in “intelligence” as measured in benchmarks. Like I’d pay 5x for a model that has 2 units higher in AA intelligence.
> Claude has a different personality and performance than other models for example
This kind of thing amounts to a LoRa applied to your model. There's no reason (apart from potential IP infringement) that someone else couldn't produce a "Claude LoRa" for their model (or, more likely, given the dislike for Claude's writing style, Anthropic could offer a "non-Claude LoRa" for their own models)
I completely disagree with this. What you are saying is that performance, all they need a simple trick and they get Claude performance for free. In that case, why do we see open weight models still lagging?
Assuming you meant this for personality, I’d still disagree but not that extensively. I think personality is nuanced and complex - it can’t be simply lifted and shifted. It’s grown using a meticulous process.
> I completely disagree with this. What you are saying is that performance, all they need a simple trick and they get Claude performance for free. In that case, why do we see open weight models still lagging?
Yes, they are lagging.
But "performance of Claude's from 6 months behind" is fine for a lot of tasks. And the performance of today's Claude will be still useful for many (and more and more) tasks in 6 months.
The frontier labs are making models for smaller and smaller share of the pie, as the capabilities of open models grow. That isn't sustainable
> But "performance of Claude's from 6 months behind" is fine for a lot of tasks. And the performance of today's Claude will be still useful for many (and more and more) tasks in 6 months.
Okay, then why would one buy 6-months-ago performance for 2-3x more from an open weights lab as opposed to a closed lab?
OpenAI and Anthropic are competing heavily with GPT-6 Luna and Claude Haiku 5.5!
I don’t think GPT Astra is as good as Opus 5.5, it’s very noticeable how pedantic and poor at intuiting things it is to me. It often calls things done when Claude would keep working on them, and doesn’t seem to have as good of an eye - often leaving clear visual defects and calling things done.
> Almost everywhere I the world railways are state owned and heavily subsidised and still aren’t that attractive to customers.
That's a remnant of the past, where only a nation state could afford massive projects such as creating a railway line crisscrossing throughout a massive country.
Since the last couple of decades we see private companies investing in railway lines, including high-speed railway corridors. We also see a major liberalization push in Europe to decouple railway operators from infrastructure maintainers, and now there are plenty of private companies operating railway services across the world. In Spain alone there are 2 or 4 private companies providing high-speed railway connections between major urban centers.
Yes. That's the situation in 2026 in most of the world. But in the 19th (after the railway bubble - https://en.wikipedia.org/wiki/Railway_Mania) and early 20th century, railways were actually very profitable.
>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.
Another reason is that the way we find out what works and what not is through experimentation. Especially in a transformation like this it’s impossible to predict which configurations will work and which won’t.
Wait. Are you talking companies, or people? Companies come and go. They are replaceable virtual containers for people. I don’t think it’s appropriate to apply the term “poverty” to companies.
A large number of companies failed in the dotcom crash, but the founders did pretty well if they'd made it to an exit event. Don't forget that it was a stock market bubble imploding. The founders and early employees had already sold shares in the IPOs that put the companies on exchanges. It was the investors who bought stock that lost money.
The after effects were that it was virtually impossible to raise capital for a while afterwards, so the next round of founders struggled. But then interest rates went to practically zero and money got cheap so it was all good.
The founders doing well is not mutually exclusive to the overall crash as experienced by the public. Also, a lot of founders are doing well even before they take on a venture. 8/10 founders from successful high growth startups self report as middle to upper-middle class background and 50%+ have elite education from top 10 schools.
You're right, the broad market drags down all retirement savings and those who are about to hit the age of 60+ pay most dearly.
What on earth makes people assume there's still huge growth possible in the information sector? We've long been at the very end of the information age and even the latest attempts to squeeze out more benefits using "big data" have failed spectacularly. Especially when it comes to actually producing physical wealth i have seen no indications at all that we can increase anything drastically with a more efficient permutation of information.
For example, Let's assume AI "solves" the traveling salesman and let's assume it indeed does find a better route for the delivery guy, what kind time savings are we looking at? The 8 hour route is done in 7.5? That's not going change any society in any measurable way. The just in time industry might become even juster in time but that's about it.
It might be dated but not too long ago a report showed the effect AI had on the SAP 500 and 490 companies couldn't even find any substantial improvement. Mind you, this is during the craze, this is happening while every manager has all the incentives to use as much of the technology as possible, lest they be the one that missed out on the new steam engine. All of these companies invested heavily in cars because none of them wanted to be stuck using horses while everyone else drives off into the sunset... yet most of them didn't manage to measurably outrun the people on horses. That's not the prediction I would have made if I were switching from horses to cars.
> Railroads, semiconductors, plastics, networking... the utility was there for all of them, and so was the potential profit.
I don't see all these things as being comparable to AI... At the bottom, they were "100%" technologies. Trains were just able to transport commodities, and the internet was able to reliably connect computers. So their path to success was about gaining market.
Since they were 100% technologies, they disappeared. One could build solutions using them and forget they are building blocks, because they just worked.
LLM AI is different at the fundamental level, because it's non-deterministic. It needs human-in-the-loop. It won't ever disappear. Gaining market share doesn't fix anything.
Their point is (and I tend to agree), that LLM AI is not different at the fundamental level. The idea is that e.g. coding capability will be a standard feature of future computing hardware, not a very expensive service you rent. Anthropic is the current-gen mainframe operator selling time-shared access. In the future, that capability, too, will just be a commodity, i.e. a part of the silicon in your notebook or smartphone or whatever will replace those.
Yes, to a large extent, for current real world mass market use cases at least, the only thing keeping the biggest players relevant is that they (and their pals) have monopolised access to GPUs by buying them all and making deals to buy almost everything that Nvidia can produce (even more than they can plug in).
If SMEs were able to buy and host their own GPUs, they would not hesitate to avoid paying a subscription for access to AI tools. They're already starting to do this.
Models already are a commodity, the massive bump in access prices to profitable levels is not going to happen, because businesses will not pay it.
Are you saying current LLMs are AGI and no longer need any supervision, or that they will without a doubt be AGI at some point but we need to stop supervising them before that happens for some reason?
I also love how well this comment distills the LLM frenzy. People who talk about seeing cult behavior aren't exaggerating - we already have the short, thought-stopping chants, axioms that must be accepted, promises of salvation (GMI) if you accept the messiah (AGI) and absolute conviction in an idea that relies on faith.
Yes. And valuation. I think the leading frontier labs will be fine in the long-run and NVidia will be fine. Some of their investors who bought at too high of a valuation, expecting to flip it in too short a time frame will lose a chunk of their money when they have to sell for less.
The hyper-scalers in the middle are the most vulnerable. They borrowed billions at increasingly high rates to lock up GPU and power capacity. They are 'ground zero' of a lot of the 'circular money' deals. Big banks who financed hyper-scalers initially, expecting they could package that debt up and sell it on to diversify, are already struggling to find buyers for the debt, causing its value to drop, which then forces the banks to cover with more collateral, and that's how the snowball starts down the mountain.
Wait... with hyper-scalers, we are talking about Google, Meta, Amazon, Microsoft, are we not?
And do these not all have established business models that are ridiculously profitable, and huge cash reserves?
How the heck are THEY vulnerable, and the "bare" frontier labs like Anthropic and OpenAI with no preexisting business models or profits, but equally ridiculous borrowing are supposedly not?
I don't think they are talking about the ones you listed, but companies like CoreWeave.
Google, Meta, Amazon, MS have huge cash-producing businesses they can just fall back on. Of course the stocks will get hammered in the short term, but they are still sitting on cash volcanos.
Pure AI plays are what are most at risk. In the bad case, all the CoreWeave type companies have their assets acquired on the cheap by the existing big players. In the armageddon case, companies like Anthropic and OpenAI also end up in the discount bin.
NVIDIA will take a huge hit and we will probably end up with anti circular financing laws.
Frontier labs are losing money and are most likely to go bankrupt and be absorded by bigger companies.
Hyperscalers will take a huge beating but they are ultimately profitable and they already control the frontier labs. So after a hard crash they will be fine. A lot of their debt is actually offloaded onto pension funds, insurance companies, etc. Those risk bankruptcy.
They will be just fine. They might not see the astronomical price margins of the current bubble, but there is a lot of untapped demand that is curbed by current mem/GPU prices and if they go down the demand will rapidy grow.
I believe AI is here to stay as general concept turned into practically working implementation.
What that practicality means is we all who consume AI are learning but we're still in the early adopters phase of all that.
Of course AI companies invest a lot and try to stay on top of the traction but I also believe not all of them will survive test of time.
I personally believe that a lot of over investment in data centers and supply of energy will be burnt and will have to be reinvested somewhere else when technology advances enough to "miniaturize" AI enough so it won't require couple of football fields to provide what it does today.
I do believe cloud based AI will be always attractive to small business if price is right and to big enterprises so they don't need to invest inhouse for the scale they need, but that will be sort of alliance based enterprise B2B market.
anything between (including me for the scale I represent business wise), I can't wait until I will be able to buy appliance from 12 to 48U maximum that can do all what me and my company needs AI wise as local AI.
moment subscriptions become equally expensive as pay as you go models, I switch to mac studios with 96GB and reasonable weighted models that will be specialized in something instead of one LLM/AI to prompt everything to it and hoping it can address all sort of problems as one solution.
AI one day must fly to space, be called Mother and discover aliens. it can't be 6 football fields as physics won't let it take off. It must be one day small enough so we all can have ChatGPT 6.1 medium model at home without subscription, just as fixed price with TCO over lifecycle of the whole appliance (software + hardware together)
that may become a drama to many companies that apparently may not be worth trillions unless they diversify enough its AI offering.
Right, you're using a magic rock etched with ancient sigils to communicate instantly across the globe. But invoking Science Fiction would be ridiculous, of course. It's not like Science Fiction managed to predict any of this.
Bubbles aren't caused by betting on the technology. They're caused by betting on the wrong companies. Say you have railway, and there's 5000 railway companies. At this point no one has any idea what good looks like, and it changes every 6 months. Investors bet on all 5000, and once all the drama is over, there are 3 companies still capable of competing in the railway business. The investors in the other 4997 get screwed, but since the investors also bet on the winning companies, theres a good chance the actual successful companies themselves get screwed because their investors are broke. There is then intense consolidation, and one of the 3 to nail the technology is still left standing while somehow there are now 3-4 other companies that won by taking talent and inventions from bankrupt companies or just by peaking after the bubble burst. About 20% of the bubble companies have also survived by finding niches.
That's a bubble. It happens every time because it has to happen every time. If you stay out of the game, the people who stay in the game win and your holdings in redundant technology are worthless.
Bubbles are caused by reckless stock purchases leading to insane, unjustifiable valuations, regardless of the company's prospects.
Hysteria + widespread financially illiteracy means there are lots of people prepared to buy AI stocks at any price. They're adamant to get in (FOMO).
Suppose OpenAI will be a successful company into the long term, and you bought its stock at a $5T valuation. After several reporting seasons and subsiding hype, its true worth turns out to be closer $1T. OpenAI could still be enormously successful, but anyone you bought at much over $1T valuation is going to get hurt.
Cycles have got faster due to distribution becoming much easier. So Google and Amazon survived the internet app crash. Microsoft did just fine out of the PC.
Financiers and Entrepreneurs are playing the game better then they used to, they should both survive at higher rates.
> The reality is that the adoption is incredibly fast and that revenues of the AI labs are like doubling by the quarter.
They’re still losing gigantic amounts of money. Whether that is because they’re “flooring it” as it were and growth will eventually catch up, or because the business idea is fundamentally flawed and just temporarily alive because of investor risk appetite, is the fundamental question underpinning whether it’s a bubble or not, and nobody knows the answer to that question for sure. That’s how bubbles work.
The substack linked below only looks into one tiny sliver of the off-sheet circular financing that dwarfs even the most optimistic revenue growth, but it's a good place to start to understand why this is a bubble and that, yes, Anthropic is in fact "losing gigantic amounts of money":
"Boom and bust is not about revenue. To find that kind of risk, start by reading the balance sheet... [The 2008] bust came from off-balance-sheet instruments."
Consider the wisdom of speculating on the incredibly fast adoption of the internet and mobile technologies, and on that basis investing heavily in whatever mobile carrier had the fastest network.
The speculation that the AI investment sector depends on is not just that adoption is incredible and that the value it generates is high, it's ALSO that the companies will be able to capture a significant amount of that value generation for themselves via fees or some other mechanism.
Are the LLM vendors more comparable to Google/Apple in this scenario, or more comparable to AT&T and Bell?
Tne value gap is closing, as corporates wind back token budgets towards relative sanity and reassess what they're getting for their money.
The bubble exists because c-suite people are sheep. They follow the herd, and the frontier CEO wolf-wannabes have been trying to herd them in a profitable direction.
But herds are easily frightened, and there's always a danger they'll stampede in a different direction.
And the risk of a catastrophic AI-enhanced online blackout is increasing.
> The reality is that the adoption is incredibly fast and that revenues of the AI labs are like doubling by the quarter.
How is that compared to the speed at which money is thrown in them? And how much of the profit downstream they might indeed potentially capture with high retention rate?
Bubble can’t be assessed on mere quarter profit expansion over short period (relative to large company group life expectancy).
It is looking entirely plausible to me that Anthropic will make a profit in 2027.
Don't you think that it's less than reasonable to suggest that the technology will take on the order of 20 years to mature into widespread adoption and profitability?
> It is looking entirely plausible to me that Anthropic will make a profit in 2027.
Then you should invest heavily in their IPO, I suppose.
You'll do much better than those of us who believe there is no way their financials can ever work.
I came to my conclusion a different way, I ran different models locally and measured the amout of resources required for basic things. From that I extrapolated some napkin unit economics, and I couldn't find any way to build a profitable business from it.
I don't assume I'm the smartest guy in the room though, so for all of our sake, I hope you're right.
It looks like profitability will come when research costs drop. But that will also probably mean that the moat goes away, and the gap between frontier models and open source goes away.
In which case, the people being profitable won't be the generic big LLM companies. It'll be the people who use the LLMs, or (maybe) specialized AI companies that still have a niche and moat.
> And today's AI, for as revolutionary as it may be, is taking just as long to mature as anything in the past.
They may be as expensive to mature (in terms of equivalent duration of total economic output), but by the wall-clock we've gone from "invent Transformers to make translation less bad" to "solves Millennium maths problems" in 9 years.
After 9 years of railroads… well, those were originally an Ancient Greek way to drag ships over an otherwise annoying bit of land, and the more modern attempt had wooden rails and a rope for humans and animals to pull for around 250 years so let's say after 9 years of the steam locomotive: 1784 for the prototype, 1802 for the "first full-scale working railway steam locomotive" (according to Wikipedia), so 9 years past the invention and it was still a prototype.
Semiconductors (well, transistors, i.e. us understanding the process and doing it deliberately, rather than getting lucky with cat's whiskers before we had any clue about quantum mechanics) did at least get commercialised in less than 9 years, but AFAICT in the form of "now our radios are better, sturdier, and smaller" which is a rather smaller change than what's already happened since Transformers.
(Of course, why Transformers, rather than the other attempts at AI? Transformers are when people cared this time around, but there have been previous AI bubbles. I'd argue those are like pointing at 1515 (or the Ancient Greeks) for the railway, or cat's whiskers for semiconductors).
You should check out the fundamentals of all those businesses; quite different. AI might be closest to a railroad based on these Capex outlays, and like railroads and other utility businesses the margins are thin and paybacks long. We wouldn't have Microsofts and Googles at 3% a year, so OpenAI and Anthropic are going to need something else to support this level of investment and speculation.
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.
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.
>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.
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.
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.
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.
This is important. I think people would pay a large premium for a marginal increase in intelligence. Primarily because intelligence is correlated with exponential utility which is often ignored.
For example, I'd much rather pay for the highest intelligence model when designing something or developing something because the problems it can preempt will save me more money in the long run.
The whole issue is that intelligence (as measured in AA benchmarks for example) is not linear utility
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
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.
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.
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.
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. So yea, these H100s will last a long time in the packaging.
Meanwhile, Meta is selling their spare AI data centre capacity.
They are in Nvidia warehouses. Nvidia is selling H100s and other hardware in advance to firms who do not have data centres to put them in.
Nobody is getting fired for thinking they need to buy scarce H100s in case they aren't able to get them in the future. Nobody is even getting fired for keeping their companies out of Jensen Jail — the fear that if they don't buy the whole server stack from Nvidia they might not get future GPU allocations.
There really are analysts who think Nvidia is channel-stuffing.
If the market deflates there will be a glut of very cheap compute, both in terms of data centres (though many of them will be able to offer managed model training services instead, I suspect) and in terms of unused hardware being made available for sale.
Here's interesting question I haven't seen covered. The promises to buy hardware (not just NVIDIA but networking, servers themselves, etc etc) are NOT on the income statements of the buyers...
Are the contracts on the income statements of the sellers? If Anthropic promised to buy $200B of GPUs from NVIDIA, is it recorded on NVIDIA's income statement (other than a footnote "we have $200B orders lined up")?
'Cause if it is, that'd be $200B GDP increase _literally_ conjured out of thin air.
> 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
People are buying v100s on ebay to run LLMs and Chinese manufacturers make custom PCIE boards for them. Older cards might not be useful for training but they're perfectly capable of doing inference work.
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.
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.
> electricity is not going to get significantly cheaper
The DC push may be the thing that finally gets renewables over the finish line in the US. For example, TX is around 35% renewables right now. If that continues to rise, we may see power fall in price. It’s all about incentives.
Without a requirement for DC's to add more power to the grid I don't think the price is going down, they're essentially adding to demand and the supply isn't particularly elastic. That being said this is a good opportunity for renewables, they're the cheapest form of power.
And we just saw a pretty strong repudiation of that type of DC buildout. I have a little hope politicians can communicate it’s not DCs that are the issue, but the externalization of all the costs.
I just don't think the author really has any clue.
they seem to think that debt markets are infinite. "There's Still Plenty Of Capital To Raise"
> The ones that have issued public debt, like Alphabet and Meta, have excellent bond ratings. If they need more capital, they won't struggle to find people to give them a loan.
Yes, and that bond rating is based on the idea that they really don't have that much debt, and have a net profit of something like 30%. If they hoover up a load of debt, the rating will plunge.
> I don't think the rhetoric about the AI bubble is really about finances
Its exactly about finances. The market participants that are not bollocks deep in fake Private Equity valuations are worried about circular debt obligations that are off books. The SEC has been rather lenient about reporting of non-traditional obligations (hence why spaceX was allow to do what ever the fuck it did)
For example anthropic has about 450 billion in non-cancellable obligations. the S-1 puts it at 895 billion. all of that is someone's debt.
If openAI are doing the same thing, thats pretty close to 1.6trillion in debt, both for the same customers, at the time when customers are going "hmm this isn't giving us what we hoped for the price"
Agree. CDS on the debt of several players here is getting expensive. Debt markets are getting less promiscuous quite quickly, just when even more money is needed.
It could be that a slowing of data centre build (and hence a slowing in the finance needed) takes the heat out of the situation avoiding a dramatic burst. Even then there will be casualties. If someone knows who and when ... they could do very well. More likely, Microsoft and Google will buy up anything distressed that's worth having.
Similarly, the claim that "Once all the data centres are built, AI spending is going to plummet" feels incongruous with "it's hard for me to see AI consumption not being 10x what it is now" and "Anthropics revenue has to grow to 250 billion a year to pay off debt". Scaling up seems like a constant need, especially in a market that values growth and hype about frontier models so much. (that said, the author raises things like P/E ratios that I had no awareness of, so I'm not claiming to be an expert)
The debt thing is amazing since it ignores the interest on the debt and cherry picks Google's revenue from 2019 for some reason (which is then adjusted for inflation for the comparison).
If you have 20 years to pay off 5 trillion dollars, it doesn't come out to a neat 250 billion per year as that suggests there's 0% interest and no servicing cost. Servicing costs would probably be at least 3 times the stated figure, corresponding to 10% interest paid. Some of the debt may obviously carry less interest, but the financing is opaque (as the author admits to), we have seen interests rates creep across the economy and big tech has had hits to their credit ratings.
I hope the Liberal Party can find a better candidate than the author of this piece ...
> Im not convinced by the claims of infrastructure only being expensive to build initially.
The claims seem to be true.
> A typical one-gigawatt AI data center requires $38 billion in up-front capital expenditure (CapEx) and $0.9 billion in annual operating expenses (OpEx). If CapEx is annualized over each asset’s lifespan, the total cost of ownership equates to $8.5 billion per year. Servers dominate this cost at $5 billion per year, or 60% of the total. Operating costs are small by comparison: even energy, the largest OpEx category, costs only $0.6 billion per year.
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'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.
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.
Ok, let's say OpenAI and Anthropic are trying to be the new Google and Bing, preying on the users who value the service. Search was not an easy thing to duplicate, even at a slightly lower quality, so the market was captive. Serving slightly lower quality AI models is relatively easy though.
So it still gets back to the question of who needs or wants the truly frontier models, because the memory and user relationships will only be about that small category?
Maybe they win market share for the large category in some other way?
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.
> 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".
I don't think LLM inference is ever going to stop being a competitive market, though. Unless regulation or other factors make the existing players form a cartel.
I think it's pretty safe to say that, unless the government steps in to prevent it, they will form a cartel or some sort of oligopoly. The incentives for them to do so are very high.
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.
When the internet was just getting started, I imagine it would have sounded ludicrous to suggest that soon, practically every single software application would have some networking component.
Similarly, I think it's a good bet that in the relatively near future, pretty much all consumer software, down to piddling little phone games, will be built with the presumption of some LLM/inference layer.
One very simple reason for this is that AI could dramatically change people's expectations for UI/UX across the board. I can see a future where the expectation is to have some natural language interface at minimum, or have UI driven a great deal by inference under the hood.
In which case, applications without these features become about as appealing as command line tools to the general public.
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.
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
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.
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."
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If everyone, or even the majority, believed that there is a bubble that's going to pop with near certainty, that would tank stock prices immediately. Why would anyone hold a stock they know is going to go down precipitously? If the majority of people know it's a bubble, the bubble immediately pops. AI stocks still have worth, so most people believe there is no bubble or that prices will go up before going down.
People talk about bubbles. They worry about bubbles. For the most part, though, they go on investing as though there isn't going to be a bubble. Every bubble looked like a solid investment to most people right up until it burst, or it wouldn't have been a bubble.
So, no, most people do not think AI is a bubble. That doesn't necessarily it's not though.
> Why would anyone hold a stock they know is going to go down precipitously?
Because they believe they can make a killing selling it to someone else before that happens. Same reason anyone buys crypto despite it being intrinsically worthless. Consumers and investors alike love gambling, believing that they won't be the ones holding the bag.
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.
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
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.
Access here means being allowed to participate in the market. BYD doesn't have a problem shipping cars, it has a problem of being allowed to sell them.
People will need compute. And if the Chinese economy can transition into providing not only factory output but cheap compute, then yes - the future is indeed theirs. Every single country on this planet will need compute.
The EU is also quite skeptical of Chinese exports, though it remains to be seen how that will evolve once the EU-USA relations settle into a new normal. Certainly the EU wouldn't be happy, though, if China managed to break ASML's monopoly on EUV machines, and at seek to retaliate with economic pressure on chips.
problem is if you can just rent cheap compute from elsewhere, you'd need to ban buying compute and all that which I'm not so sure would work. maybe heavy regulations could do it buuut....
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.
And because of massive overhiring. In that scenario, the sequence of events is: Cheap money (ZIRP), companies hiring developers just to deny them to the competition (there were reports) or for empire building or as a signal to investors (etc), AI and nonzero interest rates come around, need to spend billions on AI with limited money -> lay off overhired developers and say it's because AI replaces them. It's great for companies because it allows to admit no mistake regarding the overhiring and claim to be fully in on the current thing.
My reality: Before AI Tokens I had to hire someone. External or normal.
After AI Tokens: I don't need to hire someone anymore and if, i need to know why. Because if i have an contractor for 1-2 years and explain a lot to them, i can just explain it to AI.
Also i prefered before already to not hire someone if i had the feeling they don't have potential, now i have even less incentive to do so.
Yeah absolutly. The question though will be if capitalism is able to update its gain function somehow or if the system collapses.
As long as robots are not there yet, there might be enough of buffer in the whole system for non computer jobs to catch but i'm not seeing this either.
It will increase pressure on every industry.
And it might also lead to an increase in speed of adoption because everyone getting fired, will have the tools to fill any niche.
They actually are compatible, because the idea of replacing workers with AI in large numbers isn't compatible with the kind of economic growth these valuations are predicated on. We are not living in pre-industrial revolution times, where most people are wasting huge human effort on food growth and physical tasks, having room to move into more white collar jobs.
So, if you have massive unemployment because AI really is cheaper than labor, you'll get a massive reduction in revenue for virtually every industry, collapsing all of these valuations. Even for B2B companies, Google isn't going to be making much money selling ads to unemployed people who can't afford to buy anything.
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)
> those servers have about a 5 year lifetime and then are likely to be replaced
This made sense in the past when they were not prohibitively expensive, but other than them breaking down why replace them on the 5 year schedule now? Yes, the newer stuff is better but the jumps from this point forward seem more incremental than revolutionary hardware wise. And even then, a company can simply swap out racks as parts fail.
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.
To me these are arguments _against_ the bubble popping. The market reacted to all these signals. The semis took the brunt of it but even there it wasn't really dramatic. These are signs of a relatively healthy environment.
It does very much vary with the domain though. I haven't personally noticed much improvement, they're still useful but unreliable, in my view the idea of agents, harnesses etc has been a distraction and functions to cover up the lack of progress in the underlying models.
But everyone has a different view of this, it'll be interesting to see how it plays out.
Yes, but what's interesting here is that everything didn't get better. The original "interesting" for transformers was that a translation AI turned out to also be good at generating novel prose. But if we now need to focus to train new capabilities into the models and those don't yield better behavior everywhere then we're no longer in the "free lunch" period of LLMs.
I took a six month break from AI coding after leaving my startup and just restarted this week. I feel like Opus 5.5 is a step function improvement from 4.5 or 5, which I built a large codebase with.
Opus 5.5 is capable of completing very long running tasks and managing many threads of work with subagents without getting confused in a way that simply wasn't possible before.
Competing with chinese open-weight model which can run openly or on independent providers. Once the temporary hardware shock is over and becomes more easily available, there will be tons of independent inference providers hosting open-weight models. And I suspect we will see dozens of custom, niche distilations from open-weight models being offered by smaller groups.
Anthropic has no value proposition - just being 3-6months ahead in frontier models vs. open-weight doesn't justify trilion dollar valuation.
People keep saying this about open weight models but I haven’t heard of any real companies any of my friends work at willing to switch over to them.
My company buys Claude because it’s a trusted name with a user-friendly interface and integrations with a lot of business apps.
This “open weight models will take over based on price” is analogous saying that Linux and LibreOfficr will displace Windows and Microsoft Office in the workplace because they’re drastically cheaper.
Another analogy is that people talk a lot about the resurgence of audio CDs, but if you look at sales numbers it’s about 4% of peak audio CD sales from the year 2000. Sometimes in society a trend is more that people keep saying that a trend is happening rather than a trend is really happening.
I went from using Anthropic via API daily to using Kimi K3 (now 3.1 pro) daily, only going back to Anthropic when a refactor is too much for Kimi to handle (once in a month's worth of work).
That surprised me. I didn't expect K3 to be as good as it was and now with 3.1, it's clear (imo) that the gap is closing. Anthropic still has a minor edge but from the 'feel' of these open models, that gap is negligible, not a moat.
> This “open weight models will take over based on price” is analogous saying that Linux and LibreOfficr will displace Windows and Microsoft Office in the workplace because they’re drastically cheaper.
You can't just literally drop LibreOffice into an organization and have it automatically and seamlessly work.
LLMs are, definitionally, fungible. Their input is tokens. Their output is tokens. They have minor variations in chat templates and can be trained to perform better in a particular harness (like training GPT in Codex).
Do you or your friends at work use redis? Postgres? Are they hosted at "Redis Inc" and "Postgres Inc"? No, they're commodities hosted at a huge number of places.
They don't need to "switch" over to them. They just need to stop using frontier models for everything. And I can assure you most big tech companies are already deploying open-weight models and telling their employees to use them more.
The first. There have been massive waves of price cuts in Q3. OpenAI and Anthropic keep slashing prices to take market share from each other, it seems.
So maybe it's not high risk enough for you, but it is definitely high risk enough for the market.
But the best approach is just comparing rates. Go to https://archive.epa.gov/epawaste/nonhaz/municipal/web/pdf/bo... , and you will see a huge list of companies whose bonds are classified by armies of analysts as "less risky" than these 10% Anthropic bonds. Is Anthropic less risky than AT&T, or BP, or Firestone Tire & Rubber Co? The market says "no".
currently US long term bonds have very low yield and are being issued less as usual buyers move to gold/stock -- trust in US gov. has eroded especially in fiscal matters. The budget keeps growing, interest rates are still too low to combat inflation and debt is ever-expanding, without drastic measures taken to stabilize the situation: thus, high bond yield - we pay you premium to have at least someone buy it due to trustworthiness not being the sole attractor anymore
Indices are putting high yield at about 8% right now. More importantly lenders are asking Anthropic for higher yields than they were months ago meaning they're headed in wrong direction quick.
This is what keeps me up at night. That fiber that got overbuilt in 1999 is still there spitting photons. That Vera Rubin gathering dust on a shelf will be a paperweight in 5 years.
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.
Agreed. This applies even to simple words like “velocity”. When you keep seeing the same key words/expressions used in support of the same argument, you know the person is parroting an idea as opposed to sharing an original thought.
There is no such thing as a moratorium on words. It's another phrase that people like to repeat (a meme) until the excitement or prestige of using it has waned.
That said, i agree with you that word-salad investobabble is a sign that the bubble has put many people in a raving fever. This current bubble manifests like the cryptocurrency mania.
- job loss and entry level hiring collapse --> I will out that as in the group "Not a bubble" (Not that I am thinking it myself that it's not a bubble).
It looks like the end goal of replacing labor by capital is working.
No. The biggest layoffs are happening at companies that need to free up additional capex for infrastructure buildout, because they're not getting ROI from their existing capex, and they're having trouble finding more money.
With big technological shifts like this, it's important to keep in mind that the ability to do more with less resources can pan out in two different ways: (1) Keep doing what you're already doing, and lay off the people you no longer need, or (2) Do more than you did with the same amount of resources.
The answer depends on the company, but I think many companies are still figuring out whether and how they can exercise this new capacity. We don't know where it will land yet at a societal level. It's a parameter shift, and it will take some time. Pray that you live in a place with sensible labor laws and safety nets.
The biggest metric is that anthropic make ~$2 for every $1 of compute.
Two things:
1) it requires a massive money pit to make that compute worth anything
2) it requires charging a premium price
To compare to two previous bubbles; railways in the UK and USA.
running trains was really quite cheap and engines weren't expensive per passenger served. Compared to stage coaches, the number of staff needed was < 50%. the Massive fucking problem was that it needed GDPs worth of money to make the railway in the first place. Moreover as more railways were put in it significantly altered profitably of railways near them. Lines that were there for produce moving suddenly lost profitably because the produce they were moving fell out of fashion
Anthropic's S-1 filing. I don't think the parent is necessarily posing contradicting numbers, but removing the benefit of the doubt I was giving anthropic when it comes to inference costs.
I think that's actually going to moderate the bust, by giving the big companies the moat they need.
It performs the same function as regulation, replacing "the scale to navigate and manipulate the regulatory environment successfully" with "the scale to navigate and manipulate the political environment".
It's essentially grassroots regulation instead of federal regulation.
(I don't think it will stop the bust, but moderate it, allowing more companies to survive.)
Some of these red flags aren’t real or backed up by much of anything.
- Is it uncommon or alarming for an IPO to be repriced?
- I’m not sure I see strong evidence to show that open weight models are making significant progress to catching up (and I’d argue that the model quality isn’t even where the business value is)
- What does “SpaceX disappointment” mean exactly?
- Are enterprise buyers buying raw tokens or are they buying productivity tooling for their employees? Not many companies switched to LibreOffice because it was cheaper than Microsoft Office.
- Look up BLS unemployment data, what job loss and entry level hiring collapse are you talking about?
- Does discounts on debt point to anything? Genuinely don’t know.
- What about the Chinese arms race is concerning? Isn’t an arms race evidence of value? There wouldn’t be an arms race if this technology wasn’t worth investing in. Doesn’t China’s desires to compete in this market bolster its merit?
- What is “possible downward spiral” and how do we know that’s real? Isn’t there a “possible downward spiral” with every industry at all times? This seems like pure speculation.
> - pareto frontier gap decreasing by the month between open and closed models
This is a problem for Anthropic and OpenAI, sure, but why does it matter for e.g. Nvidia (or any of its suppliers) who will use their GPUs for training?
We need to separate the model companies from the shovel-sellers in these "AI bubble" conversations.
The web of circular financing that is going on leads to higher risk. If some ventures fail, and are unable to supply compute they are obligated to, or are unable to pay for compute they are obligated to buy, this could cause a series of financial problems for other related businesses.
I also think that some businesses may find that it might not (yet) be cost effective to focus AI use on every problem.
The pushback against data centers is getting bigger too. But america at least is usually pretty industry friendly so I'm not too sure how much it'll delay planned projects. I've read the limiting factor is sometimes just finding a source of power that can consistently deliver the electricity needed for the DCs that companies want to build.
> - enterprise buyers as main consumers of closed tokens are being displaced by OS competition for pennies
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Not all tokens have equal value, and frontier labs (or at least hyperscale companies) will always be able to generate tokens at lower costs per task than enterprise companies will be able to do.
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!".
> 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
Exactly the opposite. This is good for a tech company IPO which opens up all kinds of bond options. Most dont even have a bond rating! BB+ is a get.
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.
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.
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
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.
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.
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.
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[ 234 ms ] story [ 4538 ms ] threadhttps://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
P.S: A Matlab/Physics simulation guy running for a Liberal party seat in Canada? Say more!
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
Markets do include significant risk and we would do well to remember that.
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.
For proof, look at the fall in VWRL last week Correlated with concerns over AI.
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
The lack of a clearly profitable operating model and huge (trillions) in commitments for buildout is what concerns people.
I am not convinced by this person who is not convinced there is a bubble. The bubble, by the way, specifically refers to the valuations. Is Anthropic worth 1.2 trillion when it’s not clear they will have positive cash flow in the foreseeable future?
Maybe think of it as a bimodal thing. I can easily imagine a future in which Anthropic builds a sustainable profit model, grows into new sectors, and is credibly worth 7T in ten years.
I can also easily imagine a future in which AI capabilities continue to increase but the amount that enterprises are willing to spend on Anthropic’s services to do, and in which that, plus ever-increasing competition, cause Anthropic’s revenue to grow slowly, stall, or even contract. And that Anthropic would be unable to raise sufficient capital to make up for it and would therefore forced to default on their obligations. (Or that they would be able to raise enough capital but on poor enough terms that existing investors take substantial losses as a result.)
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.
Of course it is a Gamble. Anyone who knows anything understands there's risk
Every one of the few companies with that kind of valuation generates tens or hundreds of billions per year in free cash flow.
They are down because of capex investment, but Meta is showing what’s possible with AI. They have been shrinking their workforce and steadily growing.
The internet is successful and cisco is having a blast but its valuation has still been enormously wrong during the dotcom bubble. Same goes for pets.com, etc.
So in other words, maybe the valuation of some companies is totally wrong. Maybe Anthropic is not worth two trillion, or even a hundred billion, but Nvidia is worth 20 trillion. Maybe it's the other way around. Maybe the legal framework changes and the value will be captured by the major rights holders, reddit, twitter, bertelsmann, etc.
> So, 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 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.
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.
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.
But that is the potential issue. It is valued as it will and that just isn’t proven.
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.
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.
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.
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
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.
When using traditional business metrics, such as operational net income, we'll see that Anthropic has been in the red for the entirety of its history, at times to the tune of $3B per quarter, except in Q2 2026.
When looking at amended S-1 (with Q3 numbers), page 23, "Consolidated Statements of Operational Data", we can see that for 2026 YTD (Q1 - Q3) net loss was $103B; operating loss was $1,260B.
Anthropic also disclosed up to $895B (multiyear) commitments for infrastructure build-up.
Basically, the company's financial results do not allow for any reasonable valuation (which would be more than 0), leave alone $2T or even $1T. If this is not an indication of a bubble I don't know what is.
While inference may become more efficient and drive costs down for Anthropic in the long term (though over the next decade, they have hundreds of billions in data center commitments, so it's not going to to happen soon), that goes for every provider. Commoditiziation and competition on price is likely to happen, and with models getting ever better, the value proposition of frontier models would become much less compelling.
It's almost as if they know something about the finances that the rest of us don't...
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.
The gambling around which companies, products, etc. is just the imperfect information and greed directing investment.
It's not malinvestment for the winners.
is anyone else weirded out by folks chanting this like a mantra? imagine saying this for any other emergent tech. "NFTs are inevitable", "crypto is inevitable", "excel is inevitable", etc.
inevitability != usefulness/profitability
AI is bigger than the internet, than computers, than literally any innovation or invention that has ever come before, combined.
This is not the Industrial Revolution. It's far beyond that, but that's probably the closest previous societal change
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.
Such nearly cosmic changes, obviously, come with risks.
Regardless, there is no going back.
AI can be in a bubble right now and the bubble can explode and yet all what GP said can still be true
I think civilization will have time to react and adapt, panic leads to haphazard action, we need deliberate consistent action.
How do you think we need honest deliberate action if 95% of people believe AI is a scam and a bubble and mostly just hype marketing? Just take HN's comments for example.
This..is not being honest about AI's capabilities. It's simply not specific enough to be called honest or dishonest. This is simply creating panic.
> 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.
In every bubble some say this. And it could be the exact same herd behavior (mentality) that creates a bubble.
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.
From a recent METR report, benchmarks point towards an acceleration, LLMs are getting better at a faster rate. There's no sign that we are about to reach the top of the S curve.
> increasingly high costs of training frontier models
Somewhat surprisingly the gains do not seems to come from more compute, we have on the METR report:
> We are confident that the acceleration was not due to an acceleration in the scaling of training compute.
Link to said report: https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleratio...
On an internal benchmark with internal models, if you look at the public benchmark with public models there is no acceleration. That report also has a big graph labeled "fake data".
I think this could form part of an argument that acceleration is slowing. It's only because we are not making as much improvement out of hardware gains that we're having to find the improvements in more complex architecture and more tricky to create training data.
The fact that the gains are not coming from the traditional place gains come from in technology might be a sign that a high percentage of the low hanging fruit has now been picked.
FYI Claude Code’s default is Auto Mode these days
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).
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 grift by a wide variety of less scrupulous early players.
I can do the hard, risky work of drilling wells / deploying AI, or I can sell you a vision of that future, today.
Absolutely right. Somebody may buy the DCs. The tech changes very very fast, the DCs may be entirely worthless. US has high energy costs, it can never be cost competitive.
Why Data Centres Will Change the World and Bankrupt Their Builders — John Hempton https://www.youtube.com/watch?v=GY5YyPLfqp0
Useful (like solar, airlines, fiber), but it won't be profitable. High capital cost, low marginal cost, subject to competition and with technical improvement --> these four factors guarantee businesses will never be profitable, and they go bust.
Do you know the situation with rail, canals, etc?
Once railroads where demonstrated to be (extremely) profitable, people kept building them even if the existing demand was already met. The exact same thing happened with dark fiber (looking at you Enron).
AI is a bit different in the sense that the capacity can be used for research, and more importantly is not bound to a specific connection (between locations), and therefore a lot more fungible.
Not sure about canals, but while I think there are issue with current AI investments, I would not conflate them with the railroad bubble on this basis.
No doubt somebody will use the assets for something but it's still wildly more wasteful than either railroads or dark fiber.
https://www.globalpetrolprices.com/map/electricity_industria...
Those drones hitting high-rise hotels in Dubai were rather eye opening.
Even third world countries like India which can't provide basic services to their citizens, have no water, no power, no sanitation or cleanliness are giving hundreds of billions of dollars in incentives (land, power, water, tax subsidies) to hyperscalers, to create at most 2.6 jobs. Google clearing land to build India's largest data centre: https://www.youtube.com/watch?v=dBJPf6Pxfvs
> The UK has even higher energy costs and people seem to want to build data centers here? (Or at least they say they do - looks more like land speculation to me):
There is no meaning to what politicians and crony capitalists do. It is all lies, theft and grift.
https://www.reuters.com/business/media-telecom/microsoft-pul...
Both our high energy prices and the current capabilities of our energy distribution network relative to the renewable generation and storage capacity we are trying to move towards are significant barriers.
A non-trivial percentage of that is artificial.
When push comes to shove and enormous profits (and anything remotely resembling national security) are at stake, the US is decent at removing artificial problems.
You've already seen a ton of that related to chips and data center build outs.
Why is solar more expensive in the US than China? Mainly tariffs. Same for batteries.
Why is nuclear more expensive in the US than South Korea? The government.
Why is SMR never going to beat conventional nuclear if you remove artificial problems? Physics. Though specialized applications will be viable with SMR.
This pattern repeats across many industries with large up-front costs.
https://www.japantimes.co.jp/business/2026/07/09/companies/p...
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.
Look to Europe to see how that has worked out.
Could thing be better with the tech industry here? Sure, but at the same time, profits and industry leaders aren't the only things that matters. And at the very least companies here can't just exploit the public with total impunity.
Aren't you? Or, haven't you noticed yet?
Epstein's network is one we know about because its been exposed and because of the sexual abuse aspect of it has both drawn attention and made people less likely to cover up for those involved. Its only a fraction of the corruption that goes on though.
Europe even has literally hereditary monarchies; let alone an insular upper class who are famous for their debauchery. They just have much less free speech protection so it doesn't get bought up as much because of the legal risks.
Related note, but while looking up some background info on Wikipedia it appears [0] it was technically illegal to publicly insult Donald Trump in Germany at the start of his presidency which would have added a hilarious angle to a classic joke [1].
[0] https://en.wikipedia.org/wiki/L%C3%A8se-majest%C3%A9#Germany & https://en.wikipedia.org/wiki/Freedom_of_speech_by_country#G...
[1] In the USA, you can stand in front of the White House in Washington, DC, and yell, "Down with Ronald Reagan", and you will not be punished. Equally, you can also stand in Red Square in Moscow and yell, "Down with Ronald Reagan", and you will not be punished.
Yes, America is somewhat freer then Russia. Despite aligning itself woth Russia, trying to help it as much as possible last years.
But no, America does not have more freedom or human rights defense then Europe. It has also much worst powerty.
There are a lot of advantages to constitutional monarchies in terms of stability. The monarchs do not actually have much power. I on balance prefer living in the UK and would not want to move to the US. There are pros and cons, and its culture and society (e.g. unarmed police) as well as politics and some if it is about personal circumstances (as someone who is obviously ethnic minority or foreign everywhere in the world I prefer a less racist society, for example).
I can yell "down with the King" or "down with the Prime Minister" pretty much anywhere. There are some restrictions on protest in a few places like Parliament square.
I am not as optimistic as you. We seem to be stagnating our way into a stark choice between becoming an American tech vassal, a Chinese tech vassal, or losing out the remnants of our ability to compete on the world markets. Half of the continent borrows money at an unsustainable rate in order to pay millions of people to do nothing for decades.
We have made decisions that looked reasonable at the time they were made, but too much has changed in the meantime and now we are stuck in bad equilibria.
The Bismarckian pay-as-you-go pension system was stable as long as people had plenty of kids and died, on average, a few years after retiring. Now they have one kid and spend 20 years in retirement before they die. The maths of that system just does not work out.
The European highly regulated industry was king as long as the rest of the world had no other choice, but now that Asia has industrialized, they are blowing us out of the water. First with lower prices, but now also with quality. German exports are half of what they used to be, with another halving expected. And of course without exports, our current social spending cannot be sustained either - you cannot keep such a massive redistribution mechanism running on an economy based on selling each other cups of coffee in a beach bar.
We just cannot continue to live like that, something will have to yield, but many voters just don't want to hear that.
Speculative bubbles, I.E. risk is where much of the capital comes from to develop these ideas to start. I’m not sure you can have one without the other.
If that was true it is strange that you would find yourself saying as much for a while now, given it sounds most appropriate in response to someone denying the utility of slop generators.
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.
Not if the datacenters are in space, you can upgrade the models for a little while but once you need to upgrade the silicon you need to launch new hardware.
The companies that sold core infra during dot com mostly died, because they invested in fiber etc far too quickly. Likewise with server factories like Sun.
The B2B and B2C survivors were patchy. A few did extremely well but there were a lot of casualties.
The biggest winners offered B2G - business to government - disguised as search and social media. They were actually selling individual user profiling and targeted ads, and that turned out to be the biggest money maker.
The reason Anthropic and OpenAI appear wildly overvalued is because their offering isn't AI in some abstract sense, it's the next generation of surveillance and persuasion technology. But now with corporate surveillance in addition to the traditional profiling and targeting.
You can tell a lot about what someone is doing - and the industry is doing in general - by looking at everyone's code bases. You don't need to train on them or steal keys to extract immense value.
Potentially there's also the option to eventually make claims on corporate IP developed with AI tech.
Anyone who thinks the companies who pulped and torrented whole libraries of books for their training data would never, ever, pinky promise, try to extract value from the code they're generating is being very naive.
As we all know, GE then went bankrupt and we went back to whale oil.
and, apparently, our brains https://www.media.mit.edu/publications/your-brain-on-chatgpt...
Is it?
Every single vibe-coded software I've ever used looks pretty on the outside but has massive, glaring holes in functionality that make it unusable.
It's like having food faster but then you find out it's actually full of silicone and microplastics.
I wonder why so few people make the comparison to the recent VR hype. We heard the same things there about its unlimited potential, how it's inevitable that we will soon resolve all its shortcomings and that it will become ubiquitous.
I still believe VR will eventually live up to its potential, but no one can deny that bubble has popped after Meta took a huge loss on it. It took too long to resolve the hard problems so the timing didn't work for them.
AI is already a gigantically transformative technology, with a staggering amount of paid consumption, and virtually everyone I know uses it every day, where I don't know anybody who regularly uses VR in any form.
The ship has absolutely sailed on "is AI a world-changing technology", where "are the leading AI companies worth what's been paid for them" is a completely open question.
AI can maintain the multiverse and administrate it. Everyone can create just by talking.
Coding would also be a good usecase, to see the architecure in 3D and talk with Claude, point at stuff, pick him up and put him where the work needs to be done.
I think it's a match made in heaven, AI is just still way too slow an expensive for this.
Okay, but why would they? And who's going to consume?
If anyone can trivially create, every creation is meaningless. There is no billion-dollar business in abandoned Minecraft servers, so why would this suddenly change when you force people to put on an expensive VR headset to visit them?
Everyone has always been able to trivially create. We live in an attention economy, not a creativity economy. Meaning you can create crap (or ai slop) and people will consume it if you know how to get their attention.
Attention is what gets money. Creation is just fuel for attention. Quality is a part of the equation, but much less important than people generally believe.
Here's a legacy document explaining all this since long before ai or the internet was around: https://archive.org/details/TheManualHowToHaveANumber1TheEas...
Nobody cared about multiverses before AI, and it wasn't because of the lack of maintenance and administration. It's just a thing that nobody cares about, AI improve it but it's not going to make people care about it so that doesn't really change anything.
Obviously AI people say it's because they don't know what they are doing, but are you going to tell me a company like Apple couldn't figure out how translate those productivity gains into something more tangible?
Expect more differentiation in the future and not less.
Fundamentally, people underestimate the marginal increase in utility for marginal increase in “intelligence” as measured in benchmarks. Like I’d pay 5x for a model that has 2 units higher in AA intelligence.
This kind of thing amounts to a LoRa applied to your model. There's no reason (apart from potential IP infringement) that someone else couldn't produce a "Claude LoRa" for their model (or, more likely, given the dislike for Claude's writing style, Anthropic could offer a "non-Claude LoRa" for their own models)
Assuming you meant this for personality, I’d still disagree but not that extensively. I think personality is nuanced and complex - it can’t be simply lifted and shifted. It’s grown using a meticulous process.
Yes, they are lagging.
But "performance of Claude's from 6 months behind" is fine for a lot of tasks. And the performance of today's Claude will be still useful for many (and more and more) tasks in 6 months.
The frontier labs are making models for smaller and smaller share of the pie, as the capabilities of open models grow. That isn't sustainable
Okay, then why would one buy 6-months-ago performance for 2-3x more from an open weights lab as opposed to a closed lab?
OpenAI and Anthropic are competing heavily with GPT-6 Luna and Claude Haiku 5.5!
GPT has the same performance. And performance is definitely a commodity. Why wouldn't it be?
https://en.wikipedia.org/wiki/Florida_East_Coast_Railway
That's a remnant of the past, where only a nation state could afford massive projects such as creating a railway line crisscrossing throughout a massive country.
Since the last couple of decades we see private companies investing in railway lines, including high-speed railway corridors. We also see a major liberalization push in Europe to decouple railway operators from infrastructure maintainers, and now there are plenty of private companies operating railway services across the world. In Spain alone there are 2 or 4 private companies providing high-speed railway connections between major urban centers.
Another reason is that the way we find out what works and what not is through experimentation. Especially in a transformation like this it’s impossible to predict which configurations will work and which won’t.
Almost all the "top players" in the dot-com crash died or lived on in relative poverty; only Google and Amazon turned out "very rich".
The after effects were that it was virtually impossible to raise capital for a while afterwards, so the next round of founders struggled. But then interest rates went to practically zero and money got cheap so it was all good.
You're right, the broad market drags down all retirement savings and those who are about to hit the age of 60+ pay most dearly.
It might be dated but not too long ago a report showed the effect AI had on the SAP 500 and 490 companies couldn't even find any substantial improvement. Mind you, this is during the craze, this is happening while every manager has all the incentives to use as much of the technology as possible, lest they be the one that missed out on the new steam engine. All of these companies invested heavily in cars because none of them wanted to be stuck using horses while everyone else drives off into the sunset... yet most of them didn't manage to measurably outrun the people on horses. That's not the prediction I would have made if I were switching from horses to cars.
Oh, I thought they were about unchecked greed and insufficient regulation, but I suppose that works, too
I don't see all these things as being comparable to AI... At the bottom, they were "100%" technologies. Trains were just able to transport commodities, and the internet was able to reliably connect computers. So their path to success was about gaining market.
Since they were 100% technologies, they disappeared. One could build solutions using them and forget they are building blocks, because they just worked.
LLM AI is different at the fundamental level, because it's non-deterministic. It needs human-in-the-loop. It won't ever disappear. Gaining market share doesn't fix anything.
If SMEs were able to buy and host their own GPUs, they would not hesitate to avoid paying a subscription for access to AI tools. They're already starting to do this.
Models already are a commodity, the massive bump in access prices to profitable levels is not going to happen, because businesses will not pay it.
Not AGI Pilled. NGMI
I also love how well this comment distills the LLM frenzy. People who talk about seeing cult behavior aren't exaggerating - we already have the short, thought-stopping chants, axioms that must be accepted, promises of salvation (GMI) if you accept the messiah (AGI) and absolute conviction in an idea that relies on faith.
Yes. And valuation. I think the leading frontier labs will be fine in the long-run and NVidia will be fine. Some of their investors who bought at too high of a valuation, expecting to flip it in too short a time frame will lose a chunk of their money when they have to sell for less.
The hyper-scalers in the middle are the most vulnerable. They borrowed billions at increasingly high rates to lock up GPU and power capacity. They are 'ground zero' of a lot of the 'circular money' deals. Big banks who financed hyper-scalers initially, expecting they could package that debt up and sell it on to diversify, are already struggling to find buyers for the debt, causing its value to drop, which then forces the banks to cover with more collateral, and that's how the snowball starts down the mountain.
And do these not all have established business models that are ridiculously profitable, and huge cash reserves?
How the heck are THEY vulnerable, and the "bare" frontier labs like Anthropic and OpenAI with no preexisting business models or profits, but equally ridiculous borrowing are supposedly not?
Google, Meta, Amazon, MS have huge cash-producing businesses they can just fall back on. Of course the stocks will get hammered in the short term, but they are still sitting on cash volcanos.
Pure AI plays are what are most at risk. In the bad case, all the CoreWeave type companies have their assets acquired on the cheap by the existing big players. In the armageddon case, companies like Anthropic and OpenAI also end up in the discount bin.
Hyperscalers are FAANG.
NVIDIA will take a huge hit and we will probably end up with anti circular financing laws.
Frontier labs are losing money and are most likely to go bankrupt and be absorded by bigger companies.
Hyperscalers will take a huge beating but they are ultimately profitable and they already control the frontier labs. So after a hard crash they will be fine. A lot of their debt is actually offloaded onto pension funds, insurance companies, etc. Those risk bankruptcy.
I believe AI is here to stay as general concept turned into practically working implementation.
What that practicality means is we all who consume AI are learning but we're still in the early adopters phase of all that.
Of course AI companies invest a lot and try to stay on top of the traction but I also believe not all of them will survive test of time.
I personally believe that a lot of over investment in data centers and supply of energy will be burnt and will have to be reinvested somewhere else when technology advances enough to "miniaturize" AI enough so it won't require couple of football fields to provide what it does today.
I do believe cloud based AI will be always attractive to small business if price is right and to big enterprises so they don't need to invest inhouse for the scale they need, but that will be sort of alliance based enterprise B2B market.
anything between (including me for the scale I represent business wise), I can't wait until I will be able to buy appliance from 12 to 48U maximum that can do all what me and my company needs AI wise as local AI.
moment subscriptions become equally expensive as pay as you go models, I switch to mac studios with 96GB and reasonable weighted models that will be specialized in something instead of one LLM/AI to prompt everything to it and hoping it can address all sort of problems as one solution.
AI one day must fly to space, be called Mother and discover aliens. it can't be 6 football fields as physics won't let it take off. It must be one day small enough so we all can have ChatGPT 6.1 medium model at home without subscription, just as fixed price with TCO over lifecycle of the whole appliance (software + hardware together)
that may become a drama to many companies that apparently may not be worth trillions unless they diversify enough its AI offering.
We're discussing economics, not science fiction.
If that line about space is the only line you picked up as meaningful, then you missed whole point made and your economics is science fiction, buddy
That's a bubble. It happens every time because it has to happen every time. If you stay out of the game, the people who stay in the game win and your holdings in redundant technology are worthless.
Hysteria + widespread financially illiteracy means there are lots of people prepared to buy AI stocks at any price. They're adamant to get in (FOMO).
Suppose OpenAI will be a successful company into the long term, and you bought its stock at a $5T valuation. After several reporting seasons and subsiding hype, its true worth turns out to be closer $1T. OpenAI could still be enormously successful, but anyone you bought at much over $1T valuation is going to get hurt.
Every single railroad company failed. That repeated several times.
The ones that existed on the end were created by assembling the capital brought on the bankruptcy of the original ones. Most of those failed too.
That's how a bubble is. No original investor got out ahead.
Financiers and Entrepreneurs are playing the game better then they used to, they should both survive at higher rates.
I don't think so.
The reality is that the adoption is incredibly fast and that revenues of the AI labs are like doubling by the quarter.
Further, there is the expectation that breakthroughs in applying AI to science, robotics, or medicine may unlock trillions in value.
That this will take 20 years, rather than happen before 2030, is just speculation. None of us can see the future.
They’re still losing gigantic amounts of money. Whether that is because they’re “flooring it” as it were and growth will eventually catch up, or because the business idea is fundamentally flawed and just temporarily alive because of investor risk appetite, is the fundamental question underpinning whether it’s a bubble or not, and nobody knows the answer to that question for sure. That’s how bubbles work.
They are already profitable and are investing enormous amounts into datacenter buildout to meet demand over the next years.
"Boom and bust is not about revenue. To find that kind of risk, start by reading the balance sheet... [The 2008] bust came from off-balance-sheet instruments."
https://kennyfraser.substack.com/p/anthropic-ipo-part-one-wh...
Patrick Boyle goes into a deep dive that's well worth the watch:
https://youtu.be/Ic1Jf7VlXRM?si=DjiP6LVdEykBlWwU
The speculation that the AI investment sector depends on is not just that adoption is incredible and that the value it generates is high, it's ALSO that the companies will be able to capture a significant amount of that value generation for themselves via fees or some other mechanism.
Are the LLM vendors more comparable to Google/Apple in this scenario, or more comparable to AT&T and Bell?
In this comparison they are more like Apple and Google: they are ahead in research and are able to offer better technology than their competitors.
The six month gap between the open-weight models and the frontier has not closed yet.
The bubble exists because c-suite people are sheep. They follow the herd, and the frontier CEO wolf-wannabes have been trying to herd them in a profitable direction.
But herds are easily frightened, and there's always a danger they'll stampede in a different direction.
And the risk of a catastrophic AI-enhanced online blackout is increasing.
The cheaper open-weight ones, even if they were available with our enterprise contracts, would be a huge downgrade.
How is that compared to the speed at which money is thrown in them? And how much of the profit downstream they might indeed potentially capture with high retention rate?
Bubble can’t be assessed on mere quarter profit expansion over short period (relative to large company group life expectancy).
Don't you think that it's less than reasonable to suggest that the technology will take on the order of 20 years to mature into widespread adoption and profitability?
Then you should invest heavily in their IPO, I suppose.
You'll do much better than those of us who believe there is no way their financials can ever work.
I came to my conclusion a different way, I ran different models locally and measured the amout of resources required for basic things. From that I extrapolated some napkin unit economics, and I couldn't find any way to build a profitable business from it.
I don't assume I'm the smartest guy in the room though, so for all of our sake, I hope you're right.
In which case, the people being profitable won't be the generic big LLM companies. It'll be the people who use the LLMs, or (maybe) specialized AI companies that still have a niche and moat.
> And today's AI, for as revolutionary as it may be, is taking just as long to mature as anything in the past.
They may be as expensive to mature (in terms of equivalent duration of total economic output), but by the wall-clock we've gone from "invent Transformers to make translation less bad" to "solves Millennium maths problems" in 9 years.
After 9 years of railroads… well, those were originally an Ancient Greek way to drag ships over an otherwise annoying bit of land, and the more modern attempt had wooden rails and a rope for humans and animals to pull for around 250 years so let's say after 9 years of the steam locomotive: 1784 for the prototype, 1802 for the "first full-scale working railway steam locomotive" (according to Wikipedia), so 9 years past the invention and it was still a prototype.
Semiconductors (well, transistors, i.e. us understanding the process and doing it deliberately, rather than getting lucky with cat's whiskers before we had any clue about quantum mechanics) did at least get commercialised in less than 9 years, but AFAICT in the form of "now our radios are better, sturdier, and smaller" which is a rather smaller change than what's already happened since Transformers.
(Of course, why Transformers, rather than the other attempts at AI? Transformers are when people cared this time around, but there have been previous AI bubbles. I'd argue those are like pointing at 1515 (or the Ancient Greeks) for the railway, or cat's whiskers for semiconductors).
That's an interesting claim but it needs support. How are you comparing their maturity rate? AI is arguably maturing very fast.
This is so demonstrably incorrect it's hard to even begin a counterargument to this point.
Seriously this is just missing the entire point
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.
I mention this in the article...
For example, I'd much rather pay for the highest intelligence model when designing something or developing something because the problems it can preempt will save me more money in the long run.
The whole issue is that intelligence (as measured in AA benchmarks for example) is not linear utility
I feel like this will depend on the user.
Don't the harnesses try to lock you in?
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
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.
Meanwhile, Meta is selling their spare AI data centre capacity.
Nobody is getting fired for thinking they need to buy scarce H100s in case they aren't able to get them in the future. Nobody is even getting fired for keeping their companies out of Jensen Jail — the fear that if they don't buy the whole server stack from Nvidia they might not get future GPU allocations.
There really are analysts who think Nvidia is channel-stuffing.
If the market deflates there will be a glut of very cheap compute, both in terms of data centres (though many of them will be able to offer managed model training services instead, I suspect) and in terms of unused hardware being made available for sale.
Nvidia won't lose out; everyone else is going to.
Supply and demand bring what it is means that (alleged) cheap compute won’t stay cheap or go unused for very long.
The problem is that "scarcity of GPUs and RAM" is being used as a proxy for "demand for AI".
It's a broken proxy; the scarcity of H100s is artificial.
Are the contracts on the income statements of the sellers? If Anthropic promised to buy $200B of GPUs from NVIDIA, is it recorded on NVIDIA's income statement (other than a footnote "we have $200B orders lined up")?
'Cause if it is, that'd be $200B GDP increase _literally_ conjured out of thin air.
also, power and cooling agreements are usually 10y too.
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
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 DC push may be the thing that finally gets renewables over the finish line in the US. For example, TX is around 35% renewables right now. If that continues to rise, we may see power fall in price. It’s all about incentives.
they seem to think that debt markets are infinite. "There's Still Plenty Of Capital To Raise"
> The ones that have issued public debt, like Alphabet and Meta, have excellent bond ratings. If they need more capital, they won't struggle to find people to give them a loan.
Yes, and that bond rating is based on the idea that they really don't have that much debt, and have a net profit of something like 30%. If they hoover up a load of debt, the rating will plunge.
> I don't think the rhetoric about the AI bubble is really about finances
Its exactly about finances. The market participants that are not bollocks deep in fake Private Equity valuations are worried about circular debt obligations that are off books. The SEC has been rather lenient about reporting of non-traditional obligations (hence why spaceX was allow to do what ever the fuck it did)
For example anthropic has about 450 billion in non-cancellable obligations. the S-1 puts it at 895 billion. all of that is someone's debt.
If openAI are doing the same thing, thats pretty close to 1.6trillion in debt, both for the same customers, at the time when customers are going "hmm this isn't giving us what we hoped for the price"
It could be that a slowing of data centre build (and hence a slowing in the finance needed) takes the heat out of the situation avoiding a dramatic burst. Even then there will be casualties. If someone knows who and when ... they could do very well. More likely, Microsoft and Google will buy up anything distressed that's worth having.
If you have 20 years to pay off 5 trillion dollars, it doesn't come out to a neat 250 billion per year as that suggests there's 0% interest and no servicing cost. Servicing costs would probably be at least 3 times the stated figure, corresponding to 10% interest paid. Some of the debt may obviously carry less interest, but the financing is opaque (as the author admits to), we have seen interests rates creep across the economy and big tech has had hits to their credit ratings.
I hope the Liberal Party can find a better candidate than the author of this piece ...
The claims seem to be true.
> A typical one-gigawatt AI data center requires $38 billion in up-front capital expenditure (CapEx) and $0.9 billion in annual operating expenses (OpEx). If CapEx is annualized over each asset’s lifespan, the total cost of ownership equates to $8.5 billion per year. Servers dominate this cost at $5 billion per year, or 60% of the total. Operating costs are small by comparison: even energy, the largest OpEx category, costs only $0.6 billion per year.
https://epoch.ai/data-insights/ai-datacenter-cost-breakdown
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.
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.
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.
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.
So it still gets back to the question of who needs or wants the truly frontier models, because the memory and user relationships will only be about that small category?
Maybe they win market share for the large category in some other way?
> 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.
Regulation wouldn't solve the problem, but many frightened people would be in favor of it anyway.
Similarly, I think it's a good bet that in the relatively near future, pretty much all consumer software, down to piddling little phone games, will be built with the presumption of some LLM/inference layer.
One very simple reason for this is that AI could dramatically change people's expectations for UI/UX across the board. I can see a future where the expectation is to have some natural language interface at minimum, or have UI driven a great deal by inference under the hood.
In which case, applications without these features become about as appealing as command line tools to the general public.
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.
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If everyone, or even the majority, believed that there is a bubble that's going to pop with near certainty, that would tank stock prices immediately. Why would anyone hold a stock they know is going to go down precipitously? If the majority of people know it's a bubble, the bubble immediately pops. AI stocks still have worth, so most people believe there is no bubble or that prices will go up before going down.
People talk about bubbles. They worry about bubbles. For the most part, though, they go on investing as though there isn't going to be a bubble. Every bubble looked like a solid investment to most people right up until it burst, or it wouldn't have been a bubble.
So, no, most people do not think AI is a bubble. That doesn't necessarily it's not though.
Because they believe they can make a killing selling it to someone else before that happens. Same reason anyone buys crypto despite it being intrinsically worthless. Consumers and investors alike love gambling, believing that they won't be the ones holding the bag.
- 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?
https://dealroom.co/news/169124-revolut-moves-2-000-engineer...
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.
The big issue will be access (Re: BYD)
I'm betting many countries will allow companies to buy and deploy them in their countries
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 always, reality is likely a muddled mix: there is real utility and there is wasted investment that will never pay itself back.
Simple enough, no?
After AI Tokens: I don't need to hire someone anymore and if, i need to know why. Because if i have an contractor for 1-2 years and explain a lot to them, i can just explain it to AI.
Also i prefered before already to not hire someone if i had the feeling they don't have potential, now i have even less incentive to do so.
The net result is unemployed or effectively unemployed people, who cut back on extra spending (games, patreon, subscriptions, etc.).
That puts pressure on those people/companies which then repeats as they have reduced revenue. So the cycle repeats.
As long as robots are not there yet, there might be enough of buffer in the whole system for non computer jobs to catch but i'm not seeing this either.
It will increase pressure on every industry.
And it might also lead to an increase in speed of adoption because everyone getting fired, will have the tools to fill any niche.
So, if you have massive unemployment because AI really is cheaper than labor, you'll get a massive reduction in revenue for virtually every industry, collapsing all of these valuations. Even for B2B companies, Google isn't going to be making much money selling ads to unemployed people who can't afford to buy anything.
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)
This made sense in the past when they were not prohibitively expensive, but other than them breaking down why replace them on the 5 year schedule now? Yes, the newer stuff is better but the jumps from this point forward seem more incremental than revolutionary hardware wise. And even then, a company can simply swap out racks as parts fail.
Also, power draw is a huge cost factor, and newer models are more power efficient. If you want to stay competitive, you will have to upgrade.
Same for density. Data centers take time to build and face ever increasing opposition. By increasing density, you can improve competitiveness as well.
- 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.
> I'm not convinced the AI bubble exists
You're arguing against the bubble popping. So you agree the bubble exists?
On what are you possibly basing this? The fact that there hasn't been a new model release in 6 weeks?
Opus 5 vs. 5.5 is a clear stepup.
Problem is, you need to do a diverse set of experiments to recognize it. Blender support got relevant better,t he amount of corrections got less etc.
But everyone has a different view of this, it'll be interesting to see how it plays out.
Before Opus 5.0, anything with 3d was not doable at all. Starting with Opus 5, it can build you cloth simulators and more.
Opus 5.5 is capable of completing very long running tasks and managing many threads of work with subagents without getting confused in a way that simply wasn't possible before.
Why is this? Competing on price, or compute somehow getting more expensive?
Anthropic has no value proposition - just being 3-6months ahead in frontier models vs. open-weight doesn't justify trilion dollar valuation.
My company buys Claude because it’s a trusted name with a user-friendly interface and integrations with a lot of business apps.
This “open weight models will take over based on price” is analogous saying that Linux and LibreOfficr will displace Windows and Microsoft Office in the workplace because they’re drastically cheaper.
Another analogy is that people talk a lot about the resurgence of audio CDs, but if you look at sales numbers it’s about 4% of peak audio CD sales from the year 2000. Sometimes in society a trend is more that people keep saying that a trend is happening rather than a trend is really happening.
That surprised me. I didn't expect K3 to be as good as it was and now with 3.1, it's clear (imo) that the gap is closing. Anthropic still has a minor edge but from the 'feel' of these open models, that gap is negligible, not a moat.
You can't just literally drop LibreOffice into an organization and have it automatically and seamlessly work.
LLMs are, definitionally, fungible. Their input is tokens. Their output is tokens. They have minor variations in chat templates and can be trained to perform better in a particular harness (like training GPT in Codex).
Do you or your friends at work use redis? Postgres? Are they hosted at "Redis Inc" and "Postgres Inc"? No, they're commodities hosted at a huge number of places.
In that context I don’t think I’d describe 10% as “high risk.”
So maybe it's not high risk enough for you, but it is definitely high risk enough for the market.
But the best approach is just comparing rates. Go to https://archive.epa.gov/epawaste/nonhaz/municipal/web/pdf/bo... , and you will see a huge list of companies whose bonds are classified by armies of analysts as "less risky" than these 10% Anthropic bonds. Is Anthropic less risky than AT&T, or BP, or Firestone Tire & Rubber Co? The market says "no".
currently US long term bonds have very low yield and are being issued less as usual buyers move to gold/stock -- trust in US gov. has eroded especially in fiscal matters. The budget keeps growing, interest rates are still too low to combat inflation and debt is ever-expanding, without drastic measures taken to stabilize the situation: thus, high bond yield - we pay you premium to have at least someone buy it due to trustworthiness not being the sole attractor anymore
Recently CoreWeave's bond yields spiked to 16% which represent an 18.8% probability of default per year.
You gotta be kidding. HuggingFace, a website to download files, was sold for 13B.
This is what keeps me up at night. That fiber that got overbuilt in 1999 is still there spitting photons. That Vera Rubin gathering dust on a shelf will be a paperweight in 5 years.
> 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.
There is no such thing as a moratorium on words. It's another phrase that people like to repeat (a meme) until the excitement or prestige of using it has waned.
That said, i agree with you that word-salad investobabble is a sign that the bubble has put many people in a raving fever. This current bubble manifests like the cryptocurrency mania.
It looks like the end goal of replacing labor by capital is working.
The answer depends on the company, but I think many companies are still figuring out whether and how they can exercise this new capacity. We don't know where it will land yet at a societal level. It's a parameter shift, and it will take some time. Pray that you live in a place with sensible labor laws and safety nets.
Two things:
1) it requires a massive money pit to make that compute worth anything
2) it requires charging a premium price
To compare to two previous bubbles; railways in the UK and USA.
running trains was really quite cheap and engines weren't expensive per passenger served. Compared to stage coaches, the number of staff needed was < 50%. the Massive fucking problem was that it needed GDPs worth of money to make the railway in the first place. Moreover as more railways were put in it significantly altered profitably of railways near them. Lines that were there for produce moving suddenly lost profitably because the produce they were moving fell out of fashion
That was true a quarter ago, when Elon still wasn't charging them. Now that he is they're making about $0.96 for ever $1 of compute.
Genuinely asking.
Just throwing numbers at eachother without back up isn't helpful to anyone reading
It performs the same function as regulation, replacing "the scale to navigate and manipulate the regulatory environment successfully" with "the scale to navigate and manipulate the political environment".
It's essentially grassroots regulation instead of federal regulation.
(I don't think it will stop the bust, but moderate it, allowing more companies to survive.)
- Is it uncommon or alarming for an IPO to be repriced?
- I’m not sure I see strong evidence to show that open weight models are making significant progress to catching up (and I’d argue that the model quality isn’t even where the business value is)
- What does “SpaceX disappointment” mean exactly?
- Are enterprise buyers buying raw tokens or are they buying productivity tooling for their employees? Not many companies switched to LibreOffice because it was cheaper than Microsoft Office.
- Look up BLS unemployment data, what job loss and entry level hiring collapse are you talking about?
- Does discounts on debt point to anything? Genuinely don’t know.
- What about the Chinese arms race is concerning? Isn’t an arms race evidence of value? There wouldn’t be an arms race if this technology wasn’t worth investing in. Doesn’t China’s desires to compete in this market bolster its merit?
- What is “possible downward spiral” and how do we know that’s real? Isn’t there a “possible downward spiral” with every industry at all times? This seems like pure speculation.
This is a problem for Anthropic and OpenAI, sure, but why does it matter for e.g. Nvidia (or any of its suppliers) who will use their GPUs for training?
We need to separate the model companies from the shovel-sellers in these "AI bubble" conversations.
I also think that some businesses may find that it might not (yet) be cost effective to focus AI use on every problem.
The pushback against data centers is getting bigger too. But america at least is usually pretty industry friendly so I'm not too sure how much it'll delay planned projects. I've read the limiting factor is sometimes just finding a source of power that can consistently deliver the electricity needed for the DCs that companies want to build.
- the general lack of merit with regard to most of the words in the article
- this post being front page
Oh? This is the first I've heard that.
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Not all tokens have equal value, and frontier labs (or at least hyperscale companies) will always be able to generate tokens at lower costs per task than enterprise companies will be able to do.
- 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.
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!".
Exactly the opposite. This is good for a tech company IPO which opens up all kinds of bond options. Most dont even have a bond rating! BB+ is a get.
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.
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.
Without a moat, training is an ongoing cost that they can't afford to not pay
They'll stop training when they're dead, not before.
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.
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.