Railroads, semiconductors, plastics, networking... the utility was there for all of them, and so was the potential profit.
Everybody knew it.
But some investors don't have the patience for these "sure things" to mature into their commercial destiny and others know they can siphon off a killing by taking advantage of them as they speculate.
These bubbles aren't about potential, they're about timing.
And today's AI, for as revolutionary as it may be, is taking just as long to mature as anything in the past.
Most of the top players involved in AI right now are going to be rich. And in 20 years, the world is going to involve a lot of AI in soooo many things. But a lot of shirts are going to be lost below those top players and a lot of companies are going to fold because infrastructure, refinement, and integration take time while the FOMO speculation starts as soon as the big discovery is recognized.
> Railroads, semiconductors, plastics, networking... the utility was there for all of them, and so was the potential profit.
Canals... where a repeating pattern was the company underestimating difficulty/costs, and going unrecoverably into debt. Another company, which then bought the largely finished canal, cheap, they had potential profit... until tech change (railroads).
> 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.
Also, (setting aside Standard Oil’s business practices), this is a common meme in oil exploration historically, and even today. Early prospectors were honestly pretty stupid, and their principal differentiation was risk tolerance.
There a lot of parallels with AI…it doesn’t take a genius to see the long-term potential, but the risk is in implementation, so it’s a mad rush to the finish line by a wide variety of less scrupulous early players. Rockefeller got his big start by sweeping up failing producers who were all capital weak, and I’m sure something similar will happen here during the inevitable bust.
I can do the hard, risky work of drilling wells / deploying AI, or I can sell you a vision of that future, today. The most dangerous technologies in terms of grifting are the ones that everyone can understand.
>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.
The Yahoo founders left with never-fly-commercial money before re-joining the billionaire club by investing the money they had. Others that survived post-IPO include eBay and Dell.
There were high profile failures but we have had those recently too eg. pet.com (dot com) and color.com (post-dot com, domain has since been repurposed)
> The Yahoo founders left with never-fly-commercial money before re-joining the billionaire club by investing the money they had.
I think you're confusing people cashing out with actually creating a successful company. Yahoo is not what people would describe as flagship company, or pets.com or Ask Jeeves or myspace or Voat or whatever. The dotcom bubble made a lot of people rich, but most examples were at expense of bag holders.
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.
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.
> 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.
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..
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.
> 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.
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?
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.
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 anymore.
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.
Our most talented engineers tend to move abroad and stay there. Brain drain is one of the most serious things that can happen to a country or a continent, and we are suffering from it.
We just cannot continue to live like that, something will have to yield, but many voters just don't want to hear that.
> 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.
Indeed I am, and you're right, there is a reason for it. The tech unions don't need to capture mindshare in order to advertise VC-funded startups ;)
We shouldn't assume that YC is hosting this forum out of benevolence. However it is true that this place is also useful for things other than just advertisement, so that's a neat side effect. We just shouldn't assume that that's the primary purpose of HN.
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.
> 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.
The reasons for this are complicated and nuanced though.
Governments think they push the financial burden of transport maintenance on the people (and its a good tool to register and control people too, but thats not a strong consideration I wouldn’t wager), so they fervently cut spending on public transport and curtail cycling infrastructure in favour of cars.
But roads themselves operate at a crazy financial loss and are one of the reasons governments are in crazy debt (which nobody wants to acknowledge).
This is because road maintenance is significantly more expensive than building new roads. Which means that road use becomes heavily subsidised (much more than trains cost per user) but the cost of vehicle ownership, despite that heavy subsidy, is still enormous and one of the largest expenses an average person can have.
"The fourth power law (also known as the fourth power rule) is a rule of thumb which states that the damage to the road caused by a motor vehicle increases in proportion to the fourth power of its axle load. This law was discovered in the course of a series of scientific experiments in the United States in the late 1950s and was decisive for the development of standard construction methods in road construction."
"Example: Car (total weight 2 tonnes, 2 axles): load per axle: 1 tonnes vs Truck (total weight 30 tonnes, 3 axles): load per axle: 10 tonnes. [...] The resulting damage differential between truck and car is 15,000 to 1"
PS: this is something electric car proponents don't talk much about
- In Europe the petrol taxes pay for road maintenance. Only electric vehicles operate at a loss.
- Train capacity is much, much higher. You couldn't have the density of France, the Netherlands or even NYC without most people travelling by train. LA struggles with 1/10th of the density.
Europe is a large disparate continent, but I don’t know any country (neither Sweden, Denmark, Norway, Estonia, Finland or the UK) that covers road maintenance costs with fuel levies.
In fact in the UK you would be forced to aggregate all fuel and road tax and offset it against road maintenance because there is no direct correlation to be made, as tax is “one big bucket”.
The majority of the planet are going from one point of interest to another at a relative constant rate- but yes people are spread further and further- ironically this is helped by requiring huge parking space everywhere anyone needs to go, and needing huge houses so you can park multiple cars outside. Its a self-reinforcing problem.
(This is most evident when comparing cities like Malmö in Sweden with Orlando, FL. Malmö has a higher total population but approximately 1/20th the road surface- yet in Orlando I kept bumping into the same people everywhere I went because we’d all drive to the same Restaurants and Shops; and despite that happening we had to drive 20-25 minutes to get there! Weird coincidence or natural concentration points? Someone smarter than me would have to say).
Everything is heavily subsidized by socializing the losses and deflation of buying power for the average person.
Corporations demanding payment for everything at whatever margin they want is little different than taxation without representation and local cops shaking people down for money at random; just different legal semantics
The representation is you yourself by voting with where your money goes. At least in a market with competition.
Without competition you are talking about a monopoly and it is your government that is responsible for mitigating its effects. The government that you elected.
The mechanisms are all there, they just need to be used+embraced.
Yeah I know how the American civil religion gospel goes; don't patronize
There is inherently competition with entropy and just getting ourselves out of bed to deal with everything
Corporations and brands and tax codes are just culture war distractions on top of the raw physical competition; middle ages like words of power
The competition is with the physics that binds us to Earth; does it? Or are we just to distracted by rhetorical competition between legal constructs to embrace the levers of science
Rhetorical competition wrt to markets type blather is only seeking internal consistency with an arbitrary rhetorical framework. Not pushing the boundaries of engineering based on science.
Yall can have AI agents simulate Bloomberg terminals so actual physical scientists can focus on the necessities ground truth work you take for granted
Specifically because of massive government investment in road infrastructure in preference of railways. This would be about like the government deciding to funds DCs directly...
> 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.
>AI models can be deployed on the same datacenter as the old one
This hasn't historically been true. The clusters have been getting bigger along with the models over time. And now we may even want to have these computers have rapid access to extra compute or caches of the internet or other data stores.
Google hasn't retired any of its now ten-year-old V1 TPUs or any of the subsequent generations so it's unclear that AI infrastructure "needs" upgrades.
Of course, Google could be wrong about the economics of running old AI processors. Or maybe TPUs are special.
I do wonder how much of their inference is running on 10-year old TPUs. I’d imagine not much, if only because 10 years ago the justification for massive compute was quite different than today.
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.
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.
> 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.
>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".
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.
> 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)
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.
> 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.
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.
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.
> 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 is transformative _doesn't_ mean there isn't a bubble. Online shopping was transformative too, but there was still a dot com bubble. Amazon survived, Pets.com did not.
Because VR failed to deliver at all. The experience is narrow, isolating, and frankly pretty horrible. People get motion sickness. The set of games is limited never mind the set of non-gaming activities. AR similarly has a lot of issues like narrow FOVs, low resolution, and brightness issues.
VR is a toy with a few very narrow areas of application in industry. That does not describe the current status of AI.
Your point is a good one, but it's constructed upon the premise that AI is a single discovery or invention or even a single tech wave.
AI is bigger than the Industrial Revolution, the Internet, than computers, than literally any innovation or invention that has ever come before, combined.
Today you see AI slop and vibe coding.
Tomorrow, all of humanity will be affected, forever.
Everything about the way that we learn, that we think, and that we invent will be changed forever.
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.
We're discussing AI in general here, not individual companies (and none of those companies have a monopoly). Nobody's arguing that AI wouldn't exist if the previous revolutions hadn't occurred.
Each event was so different with different motivations. The popularity of Tulips never back. 2007 was about hiding poor risk mortgages. 2001 every company had money thrown at them and today is about a product that costs more than the market can pay.
You know what was actually a bigger invention? Language. Farming.
Imagine a world without those. Oh, wait: tomorrow people won’t know how to use language, will they? And they won’t have water to farm with, so there you go.
Humans living forever is inevitable. Galactic colonization, inevitable. Over a long-enough time scale.
AI is inevitable on the same time scale, sure. But can it actually survive as a sustainable business given what is needed to keep it relevant—will people pay what it costs to support said business—not clear. Or, at least in the next five years.
The second generation AI companies will be built on different assumptions and will likely be slower-growing, and that’s when you’ll see it truly become ubiquitous. That could be a decade though.
> Humans living forever is inevitable. Galactic colonization, inevitable. Over a long-enough time scale.
I doubt that. I think we just fizzle out. The survival of the fittest instincts that made us a successful species is destroying us as we are getting more capable. Our 'leaders' are getting ever more unhinged.
And without faster than light travel there just isn't much point in galactic colonisation. If generations have to live and die in a tin can to get to some remote star, why not just live in that tin can in the first place.
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.
Do you say that to yourself every time you stream music?
What about the labor you steal from the milkmen, butter churners, seamstresses, and elevator operators? How evil of you.
And yeah, I did it. Not you. That money would be in your bank account if you did it. But you didn't.
I personally make these things. I'm not ashamed in the least to say it, because it's true.
It's funny how I'm working around the clock using AI. Because you seem to say this isn't work. And yet I've never been so busy. So can you tell me what's going on here?
How did I manage to build so many useful things for people?
I've had over 40 million downloads of my app recently. Maybe you should think about how people value things instead of relying on your own value system. Your priors might be flawed when it comes to how the rest of society values things.
LOL what's your app then, i'm ready to see some laugh my ass off todo list AI frame work thing you've come up with from the depths of your psychosis, if you even have the guts to post and not just make some deflection excuse .
I want to live a life that makes the people I love happy, makes me feel healthy and happy, and hopefully learn some cool things and skills along the way.
Bending the universe sounds predatory, like exerting what you want onto other people without thinking about what they want
The author talks about revenue growth on the basis of Anthropic's S-1 filing, but ignores the third quarter results which were posted later.
The growth slowed and operating profit has swung to a loss. The company can still make money, just not at a pace that has a chance to pay off the investments.
Also, the author conflates AI's technical potential with the market potential, naively assuming the demand will also grow as technology matures.
> I don’t think this is naive at all; it’s quite rational that demand increases as price decreases, which does seem inevitable.
But that's not what OC is saying, isn't it? Frontier models becoming better and cheaper at the same time are not a given.
Near-frontier models that have 99% of the capabilities of frontier models, at a tenth of the cost of frontier models, might ironically be what pops the bubble.
Is there seriously a non-frontier model out there that can do what Opus 5.5 does? I can say: go look at a ticket in Jira and figure out how to fix it, right into end tests for it, make sure it aligns with previous architectural decisions we've recorded in documents, and then test it, summarize all the testing you did, and open up a PR for review?
This is just general comment to all the excitable kids over here.
Anthropic is one company in a gigantic ecosystem.
You want to understand the eco system. Got to flyover the jungle, not just stare at one elephant herd.
Read annual reports of NVIDIA, atleast one from Microsoft/Amazon/Google and atleast one representing activities of Enterprise projects (Accenture/IBM/TCS). Then look at govts budgets/programmes for AI US/EU/UAE/S.Korea.
Isn't that worse because you then get into neocloud companies like Coreweave and Nebius who have taken insane debt to build out data centers and if the demand sinks for those they'll go under
It gets worse. CoreWeave has more than $50 billion in debt. rising costs will make financing economically unfeasible. And a demand shock will push the company into bankruptcy. This will affect NVIDIA and others involved in this entire AI supply chain.
No one seems to worry about the demand of ai. That makes me wonder. My neighbors for example are normal working ppl they don’t know how to open a zip file (they call me to help them with something like this) and they are in their early 40s so not that old. Sure companies and workflows are going to become agentic but not everyone needs it or even wants it. Then there are smarter small models like Qwen 27b running on a Mac mini and this will also continue to get better and develop further. If the usage is easy I see ppl downloading a desktop app that’s dead simple instead of paying for ChatGPT. I just don’t see this growth curve going for 20 years for these companies to pay off their dept
I would say your neighbors are the perfect use case for AI. AI can understand human language. Your neighbors probably never were able to use Google for search, compare flight tickets online, Wikipedia etc.
Give your neighbors something like Alexa powered with better AI, and they are sold.
Having dealt with many family members just like this, I can tell you they certainly can use Google for search, they just don't want to. When forced to they use it fine.
If I were to guess, a lot of people, particularly older people, see computer use outside the web browser as an amateur might a chop-saw; a dangerous tool not to be used by inexperienced hands, so an AI that can also directly do the tasks for them might solve that problem (assuming it came preinstalled or someone else did that for them). Though then comes the question of whether they'd be willing to pay for it
Plus hallucinations. Most "regular people" questions aren't inherently easy to verify. And when regular people do have to verify the answers, the value of "AI" drops like a rock.
> Having dealt with many family members just like this, I can tell you they certainly can use Google for search, they just don't want to. When forced to they use it fine.
Exactly! I noticed this when I moved out of my parents' house, when suddenly my mom became an electronics whiz who can hook up VCRs and TVs just fine. I always thought I was the smart person in the house, but now I realize I was just taken advantage of.
i dont know about that, they could've adopted using google which you could already search things in pretty much natural language and even got youtube snippets how fix your problem... so i'm not convinced they will adopt
There is no "honest" AI though. It is always biased towards its prompt and ultimately its training data.
Compare flights? You have to assume it is objective and not tainted by corporate bias or backroom deals(aka ads and sponsored content), unless you selfhost and find an objective model.
Search Wikipedia? You have to constantly check that it is giving you straight facts instead of being constrained by its community and safety guidelines.
The problem with so many of these usecases is treating AI as a super intelligent Vulcan who is always logical and objective aka Spock, when we are better off treating it as if it is a salesman.
This is all true, but which travel booking assistant is really unbiased?
Online comparison sites might not be, the good old 'brick and mortar' travel assistant most likely isn't either.
Right. So my worry – inasmuch that I worry for the state of the global economy – is that this doesn't generate valuations that match the current hype.
There's so much ad money to go around. It currently fuels Meta and Google. Let's say Anthropic and OpenAI enter the field and take a slice. That's a fine business, sure. But does it justify the state of the market?
>My neighbors for example are normal working ppl they don’t know how to open a zip file (they call me to help them with something like this) and they are in their early 40s so not that old. Sure companies and workflows are going to become agentic but not everyone needs it or even wants it.
Maybe not everyone (yet) but your neighbours clearly do need it.
It's definitely an improvement. Most people would not want to depend on their neighbours for opening zip files. I know I wouldn't.
And if one of my neighbours ever asked me to help them open a zip file, I would not leave before I'm absolutely sure that they can do it themselves next time.
This year, a lot of big companies are building the agentic layer.
This has to be build and takes time.
But this is the major thing were ai will be introduced into every single layer of software we have.
The HR System which might have needed x people? The agent has solved 90% of tickets already before they hit a human. Agents will do the repetitive but not that easy to automate tasks.
Feels like collectively we are under indexing on LLMs as user interfaces. I use a CLI with an agent to things I once did in a GUI. Your neighbors should never have to worry about zip files again.
having to write "hey LLM-du-jour please unpack these files!" os strictly less efficient/more time consuming than clicking "extract" on a menu. I have hard time what usecase you have in your mind. (Unless the user is blind that is)
> companies and workflows [] going to become agentic
What makes you so sure? Some would call it "automated running scissors" and want tool firmly in responsible hands. And could assess "automated (indeterministic) reliability" as still far away in time.
Are there any contexts we may have missed that would surely, safely replace individual deliberate action with "the ANN can do it"?
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.
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.
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.
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.
Have the models become significantly more capable in the last 6 months? I'm not convinced we're still seeing massive improvements as we saw at the start. The output certainly isn't getting better, the software these companies produce is actually pretty buggy.
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, without a question. Fable 5 and Opus 5.5 are a step-change above what we had before. On the open-weights side, GLM 5.3, DeepSeek 4.1 v1017, Qwen3.8-Flash-Next are all significantly more capable in basically any task you can give them.
You can now have a model work on a task non-stop for a day or more. This was unthinkable 12 months ago, and unstable 6 months ago.
If you are searching for evidence of capability, just look at the amount of projects, blog posts and videos on game remixes, hardware reverse engineering, new compilers and runtimes that has started coming out in the last 6 months. Slop? Sure, but this kind of slop didn't exist at this level 6 or 12 months ago.
It's really very hard to pin down progress in this domain, companies are definitely targeting benchmarks.
One bit of evidence I would look at is the maths results this last year which have been impressive, that was a huge expensive brute force effort, but it is still good progress.
i am wondering in what potential world would average user or even business user require for model to work on a task for more than a couple of hours? Is assumption that models on average are used to build nextt SpaceX or something?
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
Yeah, seems like there is a GPU to engineer sweet spot with models like GLM. This could become a more popular pattern for some organizations. Likely also the reason gov. attempts to intervene
Inflation, house prices, gas prices, interest rates, huge public distrust of government and corporations, votes to restrict data center construction, endless war in Iran, etc.
- 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Profits are mentioned but then only revenue is discussed, Revenue != Profit. Revenue can grow yeah but experimentation/token-maxing stage is ending and price wars have started, so I'm not sure about rising profits, AI Labs won't necessary be able to capture the margin(>>China<<), it might be companies/consumers who will capture most of it, if so there is a bubble - it is in AI labs with their trillion $ valuations and they might end up in a race to the bottom, BigTech is not in a bubble for the most part, they will endure a correction and fall back on their existing business.
The entire premise on whether it is a bubble depends on if LLMs can be more than a next token prediction machine or not. Adding reasoning agents, agent swarms, and so on to enhance the results from what is essentially just predicting the next token still means this technology at its core only predicts the next token. It is not AGI and unless something fundamental changes, it never will be. There are certainly valuable applications for this technology as we have seen in coding, math, and other sectors. But it is still not more than taking in a lot data and predicting the next token.
Meanwhile, the major tech companies first financed this build out using the cash flow from their existing revenue. Once this became more difficult to justify, they literally set up companies to keep this off their books directly, and these companies are ultimately designed to take a loss and fail. But before reaching this failure stage and defaulting on debt and obligations, they went to the criminals on Wall Street and designed a financial product structured not all that different from mortgage backed securities. Also, as the "AI" aspects of their existing businesses expand, it eats into the traditional revenue generated by their core business, which if we remember, is the original collateral being used to justify all the debt in the first place. Now these core tech companies have to win "AI", whatever that ultimately means, to not only pay the debt from the build out, but to also replace their existing collateral from their original products. Think in terms of Google Search being replaced by AI. Maybe a clean transition, maybe not. Really just depends on the technology living up to the hype. If not, the hyperscaler companies designed to take a loss a fail, will fail and with it, the circular financing will pop the very real bubble that exists.
Speaking of IPO valuations as well, it's not to say that LLMs are not a valuable technology. Just look at what they have done to computer science already. But there may be increasingly limited returns on investment if every valuable problem to be solved isn't as well defined as logical computer science problems. Without solving the hallucination problem (as a result of the next token prediction limitations), the wide application of LLMs as being sold will never happen.
I never understand the “only a next token predictor” argument. I do share the view that something fundamental about intelligence might be missing in LLMs, but I don’t see how this argument captures anything. If you can predict the exact next word Einstein is about to write, every time, you’re as intelligent as Einstein! It seems very clear that the LLM is also “thinking” about where the output is headed after this next token.
I do hear this position quite a lot though so I’d be interested to hear more about what I might be missing.
It's a statistical machine, so you only predict a statistical Einstein, ie. something that might appear quite like Einstein and even can be fairly accurate in predicting the next word he types, but which is not him, and will be unable to predict the words that are produced not just statistically but with certain intelligence, direction and intuition.
LLMs cannot, by their core design, predict next word correctly every time.
That is the main thing you are missing.
Another thing is that I know that I am not predicting next word based on othr words in my "trainin data", there is much deeper understanding of most subjects that I am using to "predict next word".
This may not be true for everyone, I cannot know for sure, but it might be possible that there are people out there who are just biological LLMs, and in that case I am certain those people will not be able to predict next word Einstein would say either.
Is there any evidence that you yourself are more than a 'next action predictor with a multi-modal context window'?
I don't think the nature of 'predicting the next token' is the problem. One could argue that any organism works the same way. The problem is that we've had million years of evolution to stabilize the platform such that the important parts of it work without that many hallucinations -- and even then that sometimes fails and you get failure modes like e.g. schizophrenia, where the context window gets confusing and the 'next actions' don't make that much sense anymore, seen from the outside.
An LLM never experienced evolutionary pressure the same way. There's no fundamental difference between how it was incentivized to answer correctly when asked about how many Rs there are in strawberry vs whether it is safe to leave a house through the second story window, even though for any physical organism, those two would be _very_ differently weighted during training.
> Is there any evidence that you yourself are more than a 'next action predictor with a multi-modal context window'?
I love the age of LLMs because people will post stuff like “the fact that I absolutely, unqualifiedly, fundamentally do not understand two separate things indicates that I understand at least one and maybe even both of them more than you do“ and then do that cool move where they pretend they were gonna high five you but don’t
It's because of the proliferation of "insight-posting" - people thinking their little insightful "aha" thoughts are unique or have any kind of value at all.
However, insight born of knowledge ignorance is worthless. I really don't care for your great insight about nuclear fusion if you're not a nuclear engineer or physicist working on nuclear fusion, because your insight is just a hypothesis in the void that is your ignorance.
Ironically TFA falls victim to the same thing. It doesn't seem like the author is an economist or an expert of corporate finance. Their assessment of whether something is a bubble or not has absolutely no value, and carries with it the arrogant implication that the hundreds of thousands of people who are actually experts at these things never had the little 2AM epiphany that they had.
It's a smaller version of the same delusion that produces crackpots.
Predicting the next token well is indistinguishable from intelligence. The bubble is financial, as you point out, just like railroads, radio and dotcom bubbles were. The technology didn’t die, only the companies did. LLMs are in the exploit phase, that doesn’t mean there isn’t more to explore and there may be an architecture-hardware combination that takes us an order of magnitude or two higher in a step-function-like fashion just like LLMs did 2024-2026.
> entire premise on whether it is a bubble depends on if LLMs can be more than a next token prediction machine or not
I don't think current valuations need AGI to work. They just need enterprise and personal spending on AI to keep rising exponentially for a while.
We could absolutely wind up in a case where these companies are fairly close to a trillion dollars each, but not multiple trillions, at a reasonable multiple, all without AGI.
> The entire premise on whether it is a bubble depends on if LLMs can be more than a next token prediction machine or not
A theoretically perfect next token prediction machine has limitless utility. Predict the weather, stock market, lottery number, the next step of groundbreaking cancer research, etc etc. That's not the argument against current LLMs being capable of being AGI.
A theoretically perfect next token prediction machine is just an LLM with a temperature of 0, which performs worse than one with a non-zero temperature (i.e. imperfection)
The "prediction" has nothing to do with predicting the future, it just means choosing the most likely next token for existing human text
The entire premise on whether computing is a bubble depends on if computers can be more than a bit-flipping machine or not. Adding operating systems, compilers, distributed clusters, and so on to enhance the results from what is essentially just flipping bits still means this technology at its core only flips bits. It is not a general-purpose machine, and unless something fundamental changes, it never will be. There are certainly valuable applications for this technology, as we have seen in ballistic tables, census tabulation, and other sectors. But it is still not more than taking in voltages and toggling transistors between 0 and 1.
> Adding reasoning agents, agent swarms, and so on to enhance the results from what is essentially just predicting the next token still means this technology at its core only predicts the next token. It is not AGI and unless something fundamental changes, it never will be.
A lot of people have dismissed LLMs four years ago for this reason. "It cannot possibly do coding since it doesn't think, it just predicts the next token." On the other hand, Scott Alexander did say in 2019 that next token prediction is a brilliant idea and might cover a lot, including math proofs [1]. So I wouldn't say it is impossible for either 1) LLMs to be all the way to AGI or 2) LLMs to become creative enough to tell us what the AGI architecture is.
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.
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)
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.
> 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"
The current-gen hardware is expected to have only 2-3 years of useful life. New tech, and especially new architecture is going to make inference much faster and more efficient in the near future, and the big vendors are going to be more limited by datacenter floor space and power/cooling, so that the hardware will be replaced very quickly by better models.
For the time being this thesis hasn’t worked out though. H100’s are only going up in price and becoming more scarce [1]. At what point would you update if now going on 3 years into H100 lifecycle this doesn’t seem close to happen.
But they are becoming more scarce while staying on Nvidia’s shelves buy-and-hold, or being leased back in some cases. They are not being installed by customers at anything like the rate the sales figures suggest. So yeah, these H100s will last a long time in the packaging.
Meanwhile, Meta is selling their spare AI data centre capacity.
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.
Look at DeepSeek! They have done full system optimization on their model, architecting it so that it would be cheap to serve. I am proud to call DeepSeek a world class tech engineering firm in a way that 99% of the tech world is not. They are a 1% company.
> The current-gen hardware is expected to have only 2-3 years of useful life.
Expected by whom and why?
Google hasn't retired any of its now ten-year-old TPUs, or any of the subsequent generations. Maybe TPUs are special or Google doesn't understand compute economics but either argument requires data.
If your argument is depreciation, you're confusing a tax concept (depreciation) with useful life. Yes, they're supposedly related, but in practice, the relationship is rather loose. (Residential housing depreciates in 28 years yet typically lasts 2-3x as long and 4x is not rare.)
Google's V1 TPUs
It hardly seems to be a one time expense. GPUs and networking equipment may have a useful lifespan of only about five years before needing replacement, and it could be expensive to do so.
I dont know if the valuations make sense, but the usability is all there and so are the potential profits. Over a decade, that's probably all that matters. If the valuations pop or dip (anytime soon), I think that's mostly irrelevant. I get that the press probably doesn't like to hear this boring conclusion.
P.S: A Matlab/Physics simulation guy running for a Liberal party seat in Canada? Say more!
The decade after the dotcom crash brought about as many if not more long-term successful products as the dotcom boom: Youtube, Reddit, Spotify, Google Maps come to mind
A bubble that funds all kind of crazy ideas and big investments into infrastructure work is great for laying the groundwork. But the end of the bubble won't mean the end of progress, or even necessarily a slowdown in progress
In 2007, It was regular people who lost their homes and their leveraged investment properties. Regular people are not leveraged into AI startups today. If the AI bubble pops, regular feel people will feel the direct financial consequences significantly less than the '07 housing crisis.
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 which hit nasdaq.
> 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.
"Your 401k should never go down at all" is not a realistic expectation.
Hopefully, people who need to pull from their 401k's soon have wound down to less risky positions. 401k exposure is just not the same thing as your house valuation tanking.
> In 2007, It was regular people who lost their homes and their leveraged investment properties.
And the debt on which these owners defaulted had been converted into CDOs and mortgage-backed securities, it was financial institutions and investors who lost enormous amounts of money on these.
Today there are GPU-backed securities and compute futures that are helping back all the debt that is financing the datacenter boom. Maybe it’s a bubble, maybe it’s not a bubble, but either way I think its parallel to 2007.
> Most (or maybe even all) programmers [...] are worse at their respective field than an AI swarm equipped with a good harness
Is this the consensus view now? I'm not any kind of denier and I do recognise that motivated thinking may cause me to overestimate my own value.
But trying to be brutally neutral I'm still not sure this is true. AI doesn't make certain classes of mistakes that humans do, granted. But I'm still surprised daily even in 2026Q4 at things it does seem to make a mess of. Usually, changes that are locally good but subtly globally poor.
I thought the world of measuring programmer productivity by lines of code was dead in the 90s but clearly we see now that people forgot all of that when evaluating AI output.
I don’t think the question is so much of AI is a future market, it is has the capital been allocated appropriately. And no one knows. The challenge Anthropic has is they have a narrow lead on the use case that makes a lot of money today, but new models come out every month and one tiny slip or wrong decision and they are toast. There is also a reliance on the idea that two things continue to happen:
1. Revenue growth increases
2. There are more highly profitable uses cases like code.
Neither are guaranteed.
I also think the narrative that these companies are profitable on compute is erroneous. You can’t ignore the other costs and no they don’t magically go away. Anthropic can’t stop training new models now or in the foreseeable future. Gemini 4 Carbon is about to come out and is apparently competitive. Google has other revenue sources. They are in the ball game. They can wait for Anthropic to run out of money.
The amount of profit they make on compute is also not particularly attractive (at least right now). It ‘s only slightly more than 2x for Anthropic, far off from many big cloud services which sell compute at something closer to 20x.
Even if Anthropic and OpenAI go to zero, the bubble popping still won't hurt many people like it did in other recessions.
Actually there are two different things going on here
1. model layer companies: OpenAI, Anthropic
2. hyperscalers, data centres, hardware layer
When people say the bubble will pop, they usually mean the second one.
> I also think the narrative that these companies are profitable on compute is erroneous.
I don't think you read the Anthropic filing. It is substantially profitable if you exclude the one type of debt which was actually equity. It so happened that Amazon lent some money to Anthropic but it was tied to the valuation of Anthropic, which became a "problem" because Anthropic's valuation grew 10x. You wouldn't call this a loss in any other circumstance.
> I don't think you read the Anthropic filing. It is substantially profitable if you exclude the one type of debt which was actually equity.
Anthropic filling shows negative EBITDA, how is that profitable? Revenue < (computing + personal + selling expenses). That is not a business, that is a money burning furnace.
That before even taking into account the huge amount of money they promised to datacenters (i.e. "build that for us, and we would rent it for 5 years", non cancellable).
Sorry I meant in Q2 it is profitable excluding loan converted deductibles. There’s close to no chance that q3 and h2 is not profitable as well, unless you disagree and want to make that bet?
> These expenses are one-off purchases, not regular operating expenses. Once all the data centres are built, AI spending is going to plummet.
What is the most expensive thing in an AI datacenter? The GPU! And what it's its lifespan? 3 - 5 years!
Bubble or not, there is no way AI spending is going to plummet. It will decrease, obviously, but thinking that after 5 trillions of investment everything is profit is so naive.
The conclusion I've come to is that AI is still revolutionary, and the buildout is probably necessary to some degree, but it is way overestimating the demand and pricing power providers will have.
SW can use a ton of AI. Lots of other applications can't, and they don't need the super smart frontier versions either. We just don't need all that much compute to serve AI for these applications, and people won't need to pay the margins necessary to get access to that AI. SW alone is not going to be enough to pay for all the investment.
I don't buy brand loyalty either. If I can pay 1$/m for the same thing from someone as I pay OpenAI or Anthropic 10$/m for, I'll do that, which almost zero barrier to entry.
The buildout is paying stratospheric prices on the assumption they can charge that much on the other end, but if demand tapers off (or model efficiency doubles again), prices will start falling. Everyone talks about Jevon's, but I don't really see why I'd spend more on AI than I need to do the things I want. I already run out of stuff to ask it to do on a basic subscription, I hardly need more.
The crux is this: I can spend x for y results. If I spend more, I probably don't get 10x for 10y results - I probably get, say, 2y results. So on that equilibrium I just don't spend all that much more because the reward for all that extra spend is not worth it. And if models get better this tradeoff starts to look worse, since I get more of the super-effective benefits for cheaper. Now I'm spending 0.1x for y results, and spending even x for 2y might not make sense, let alone 10x for 3y.
The problem is, that its not just AI for a niche thing, its AI together with compute.
People are already using AI to build them some things with CAD. Electronics too. But now what does AI need to be very good in it without an expert? Simulation and Optimizers. This again needs more compute.
And the agentic layer will be added to all big products out there. Everything you will do will consume AI tokens
To be fair, Jevons suggests that as prices fall, novel use-cases are unlocked. The things you or others currently do with AI might not need frontier models or loads of tokens, but if prices fall or capabilities increase, new applications might arise to exploit that.
I'm not imaginative enough to see more use cases and who will pay. You can burn a lot of tokens and dollars on math proofs. Same for white hats finding vulnerabilities in all the software. Generating video.
Personal assistants dealing with your email, filling out paperwork, and booking reservations don't use a lot of tokens.
If every software developer paid $200/month, every academic paid what they pay for Matlab or Mathematica licenses, every YouTuber paid for whatever video processing, and every normal person paid a subscription comparable to their TV subscriptions and cell phone plans - does that justify the valuations?
The government pays for surveillance? Companies pay for customer service replacements and target advertisements?
It seems like there are a few small categories that use a lot of tokens, and a large category that doesn't need many at all.
> If every software developer paid $200/month, every academic paid what they pay for Matlab or Mathematica licenses, every YouTuber paid for whatever video processing, and every normal person paid a subscription comparable to their TV subscriptions and cell phone plans - does that justify the valuations?
> The government pays for surveillance? Companies pay for customer service replacements and target advertisements?
Yes definitely. All together that's probably within striking distance of a trillion dollars in annual revenue. Just TV subscriptions + cell phone plans in the U.S. alone is about 400 billion (leaving aside any other country).
Most large, mature tech companies are at 5-10x revenue to valuation ratios (and 10-20x EBIDTA to valuation ratios).
Of course whether you get there is a separate question, but any company that fulfilled what you're asking would pretty easily justify multi-trillion dollar valuations.
That $400B for video and phones though. That's like a $1k average for every adult and child in the country. It's twice what I spend, and I'm not on a family plan.
That's significantly underestimating the potential. Companies typically spend $200k/year for developers (salary, benefits, taxes, equipment, office space...). If AI can make them even 25% more productive, that would justify $50k/year in tokens.
I more than agree on the value, but not on the price. There's currently a competitive market, including free models that are pretty impressive if not "frontier".
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.
The labs probably foresee things looking more like OpenAI solving Navier-Stokes: a company identifies a strategic opportunity locked behind an extremely difficult problem, throws a huge amount of money and compute at it, solves the problem faster than their competition, snatches the opportunity.
It seems like there are a few small categories that use a lot of tokens, and a large category that doesn't need many at all.
It won't be about tokens in the long run, and I think the labs know that. It will be about long-term memory and user relationships, and who gets to own that data.
The disruption and upheaval has barely even started, IMHO.
> The reason why AI companies are spending so much money is to rollout data centres to run the AI in the first place. These expenses are one-off purchases, not regular operating expenses. Once all the data centres are built, AI spending is going to plummet
Unless nobody is working on miniaturisation, then this reasoning might hold true.
As seen throughout history, any sufficiently absurd tech starts largescale (refrigerators, computer, radios) and eventually become portable.
Amortising data Center cost will take a long time. Within that time, if someone figures out how to make ai run on cheaper hardware, these data center cost will Become harder to recover.
> AI companies can also cut costs by no longer allowing people to use their products for free and temporarily suspending expensive AI research
> Amortising data Center cost will take a long time. Within that time, if someone figures out how to make ai run on cheaper hardware, these data center cost will Become harder to recover.
Or datacenters are the old mainframes, and when they stop building them and squeezing the market, GPU prices go back to what they were before the frenzy or even lower, so anyone really interested can buy a decent GPU for a fair amount of money, and run models locally for peanuts. Meanwhile, dataframe owners go bankrupt.
Per the article, _somebody_ is overvalued. On the one hand, at some point they will stop needing new hardware, and can drastically reduce costs; in this scenario, NVIDIA will hit a wall because they no longer need to buy hardware. On the other hand, NVIDIA can keep selling; in that scenario, Anthroic &c is doomed to continue purchasing hardware, so their costs can't come down.
>For starters, the average person isn't going to use a locally hosted model, ever.
Let's talk about that in 2-3 years. There is a very clear incentive to have models running locally, from data privacy to cost effectiveness. The software will pop up to easily power this, and if it is cheaper than non-open frontier models it will be used. This can be done today with a bit of hassle, and will only get easier. The consumer hardware vendors are clearly heading this way.
Napkin math: if the 5 trillion investment is paid back by 1 billion customers, every customer pays $5000. If they want to write off the investment in 5 years, that's $1000 per year.
Even with the current AI capabilities, with the right products that seems doable.
I ain't paying 1000 USD per year (83,33 per month) for this garbage. I pay about 18 EUR per month for a Mistral subscription, and if it cost 3-4 the cost, I wouldn't pay for it.
The beauty is the open models: the margins are going to be thin on proprietary models. The only thing they got going for it, is creating artificial scarcity. But the Chinese are able to buy them from secondary markets anyway, or via Jensen Huang's girlfriend.
Even if the US proprietary models are 6 months ahead, there's going to be a time when the competition is good enough. I believe we are already well past that point.
If you don't talk about competition from DeepSeek you haven't really thought it through all the way. You might argue it's like Windows vs Linux, but operating systems have huge natural motes. Any person can just switch to a provider of an open weight model instantly. Most people don't know about it but we may very well see people learn about this broadly in the next few years. Changing models is not super complicated. Anthropic is unlikely to make $2 to the dollar when competitors are taking only pennies of profit.
The assumption here is that having "the best" model is not worth that much of a premium over a "good enough" model at a lower price. This remains to be seen, though, and people's behaviour paying for Claude Code and Codex suggests at least for now people are willing to pay.
It also assumes Anthropic can maintain the lead. If anything the competition both in the USA and in China seems to be getting closer and closer to parity with Claude.
I use Claude a lot at work and Deepseek for personal stuff, and they feel very comparable to me. I haven't found a task using Deepseek that makes me wish I had Claude - especially given how expensive Claude is.
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
>Any person can just switch to a provider of an open weight model instantly. Most people don't know about it but we may very well see people learn about this broadly in the next few years.
Sorry what? There are 10x if not more Linux installation vs Windows installation in the world. Linux powers everything, including billions of smartphones, where Windows is nowhere to be seen. Same for routers. And actual proper servers.
If it's like Windows vs Linux it means open-weight models are going to be incredibly more successful and touch just about everything. All the while being cheaper and working not a bit but much better.
When I look at the current capacity of AI, it really feels like there's so much potential for frontier models to be used that just aren't being applied yet. Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness, and a good deal more expensive as well.
This is a strange conclusion. AFAIK in mathematics it doesn't work yet like article says: there is a problem, point agent swarm on it, and mark it done. The math results from OpenAI and Anthropic were achieved by seeding the surface of known problems and randomly trying to grow solutions; while they got some good solutions indeed, it doesn't mean current LLMs will work for arbitrary math problems.
Correct me if I am wrong on this, I am only a student of mathematics and by no means a researcher.
For the programming part, I think author already says that automated solutions require a good harness, and this is indeed correct, but in my experience building good harness is hard and agents can not do this currently without human closely guiding them. And in pre-LLM programming practice we did not need harness at all, or needed only small part of it: so, solving programming problems with LLM involves more work than without LLM, even if big part of this work can be automated.
The rest of the article seems to be a speculation mostly based on the premise that LLM can really replace human in the loop, which seems to be not the case currently.
McDonald’s is considered to have one of the biggest moats of any business. They have an extremely valuable brand, excellent locations, optimized supply chains, and so on.
Their moat is only for people who feel like eating a McDonald’s. That’s what the article is saying - when analysed they don’t actually have much of a moat because there are so many other options and competitors including cooking at home. Yet they still survive and look as if they have a moat. A moat for McDonald’s would be if their customers ate there every day or even every meal, and few people actually do that.
I can pop into any McDonald across the world and be 99.9% sure that the meal will be clean and taste like what I except McDonald to taste.
I don’t love fast food but I’ve been to a lot of them, across a wide enough range of country , to say that every other fast food chain struggles hard with that.
I'm Dutch and lived near the German border. The first time eating at a German McD's was about 25 years ago, and it was an experience. Because they changed the mayonnaise to German customs. In Germany, mayonnaise is sour, not sweet and the specific McD's version (which normally is yellow, and contains, I don't know, pickles?) was rather different.
As such, your 99,9% is overrated since there's even lots of local flavours and customs, but yes it is generally good enough, and if you consider intra culture, you're flat out right.
Again that’s the point - the moat exists if you feel like a McDonald’s, because you know what you’re getting and they are pretty good at delivering on that. But if you’re hungry now and feel like a burger you’re probably not likely to walk 30 minutes across a city to go to a McDonald’s if you’re already standing outside a burger or pizza place that looks alright.
You'd be surprised. Kids want McD's, not some random burger restaurant. They want the real thing. This value proposition exists with any restaurant with franchises though. It isn't specific to McD's. According to my wife (who fancies burgers), Burger King is way better anyway. I wouldn't know. For me, it is a scam. Since what they sell as cheddar isn't cheddar (it has nothing to do with that) but processed cheese.
I enjoyed that article, thanks for sharing. It is nice to hear that people are still having a "rage against the machine" in these heady times. For me, I think a lot of us at high market cap companies think AI is going to do xyz, but most folks are employed way outside of that boundary.
Other than the fact that we’re due for one, this one is differently shaped in that there isn’t really widespread “irrational exuberance” and constant reporting about AI fortunes. I think your general investor is quite skeptical about AI and worries about exposure in their portfolio. Sure there is plenty of irrationality happening, but there is also a lot of real growth and changes in assumptions. Like the job market is way stronger than expected for even AI-threatened sectors (not entirely and I’m sorry if this affects your circles) but like we’re really just getting started here. I don’t think AI in today’s form will be the same as this time next year and that real growth is just beginning. All of the destruction of value that PE has wrought will only serve to motivate a new generation of entrepreneurs to build fundamentally better, more nimble and customer-oriented institutions.
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.
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.
I'm actually convinced that an AI Bubble doesn't matter at all.
The quality of models like Opus 5.5 are already here, smaller labs can mimick these with a little bit of a delay.
Alone the current status has massive impact.
The status quo will get just faster and cheaper.
Then we have labs like Deepmind, companies like Google and then countries fighting over it (US America vs. China). At this point its not just private VC investing in it, its also countries aka military budgets or Research Budgets.
In parallel universities are doing their research anyway and if an AI bubble would burst tomorrow, Open Source and community driven would finally have a little bit of time to coordinate.
There was just last week an article about open post-training data. I think we want this but as long as frontier is as fast as it is, its not fun to play catch up with a moving target.
You mention that training can be suspended, which is true, but don't acknowledge that puts them behind in the arms race against both other frontier companies as well as second line cheaper providers.
Without a moat, training is an ongoing cost that they can't afford to not pay
Yes, and the training data behind a lot of models is already stale and rotting fast.
Maintenance is just as important and the cost is often overlooked. The sober reality is that an LLM is just a natural language search index. For now, Google might be the only one with the capability to maintain "training" at the pace most people expect.
Some indications in the text suggest that their insights are somewhat limited.
> In other words, once you ignore the cost of building infrastructure, AI is very profitable.
Once a provider stops such investments, they stop being "frontier" which makes them unattractive immediately. These two things are tightly connected.
> If the AI industry has to pay off 5 trillion dollars over the course of 20 years, that's 250 billion dollars a year. For context, Alphabet (parent company of Google and Youtube) reported earning almost 162 billion dollars of revenue just in 2019.
Revenue vs profit
> Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness
Anybody doing serious software engineering will wholeheartedly disagree.
> People can theoretically switch from model to model, but people tend to settle in with a product and develop brand loyalty. For example, I pretty much only use ChatGPT, not because I have any really reason to, but because it was the first LLM that I ever used
LLMs are just one piece of the tool chain, a mostly interchangeable one. Which one a concrete product uses is not of concern to the end user. This quote very much misses the role of LLMs and their providers in the big tech picture.
> In 2023, a memo from Google called "We Have No Moat, And Neither Does OpenAI" was leaked, which outlined how the open source community was keeping pace with AI labs
Labs releasing the weights to their models is not what is typically called the "open source community".
I could go on but it's just not a very well-argued opinion piece. Unclear how it made it to the HN front page.
Yes, people dream of going back to the way things used to be, and irreversible steps have been taken regarding artificial intelligence. But while a certain segment of society is getting richer economically, humanity is heading toward irreversible sociological destruction.
To use an analogy, just like the scenario in post-apocalyptic movies where people eat each other, we are heading toward a grim end as a species.
There's also the fact that the US and China are in an AI arms race. So there's an inherent existential benefit to being ahead in AI regardless of where the financial feasibility/profitability discussion lands.
And at least currently, keeping up in that arms race means a maximal push on growth at the expense of profitability. Realistically, this will go until we hit some bottleneck.
>There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for.
[would -> should]
There is no logical contradiction even, just another race to the bottom, drying out of markets. The good ol' disruption playbook, like Amazon shrank or destroyed the markets to capture them, only now they are targeting all white collar jobs.
The conclusion starts:
>I didn't write this article as some big takedown of the AI bubble theory. Even with this evidence, there are serious risks to the financial sustainability of the AI industry.
But the presented "evidence" is largely only: I don't know, nobody can tell the future. I mean there are also good reasons to be skeptic of skeptics like Ed Zitron, but Idk "modest valuation" seems like a big stretch. Also, I'd challenge the notion, that you'd only ever have to build that data centers _once_. Especially with the AI pace the chips are said to be replaced every three years, while the building itself could last 25 yrs. So even, if the run at the frontier cools down, I don't think running your data centers and models would come for free.
> Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness, and a good deal more expensive as well.
We can START discussing this when someone defines "worse" and we all agree on it. For SW engineering, we've been trying to define "good" for decades and we still don't know shit.
> There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for.
No, that's a total straw man. For example, I am afraid of how AI will change the world, but for that it doesn't have to stop producing slop. I'm afraid of how the slop is CURRENTLY changing the world, people and societies. Frankly, I don't even care whether someone ever pays for the slop. This isn't a "I'm going to lose my job" kind of fear, it's a "we're running education into the ground and rapidly transforming into a low-trust society" kind of fear.
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.
“AI can't replace lawyers and doctors, but they can automate their paperwork and free up time for them to do other things”
So my question is - in software engineering (where this has bitten hardest): does anyone know _anyone_ who is less busy and has time to “do other things”? Nope, nor me.
I do many more "other" things that I previously wouldn't even dream about doing in the same time. Work that would have taken weeks now takes minutes or hours.
So yes I am doing many more "other" things. But those other things are still building features for my product.
Yeh - same. But I read “other” as “leisure things”. It’s not like we’re all having a nice time elsewhere, we’ve just compounded the amount of stuff we can deliver so we deliver even more. It’s the polar opposite of the dream where the tech does the nasty shit so we can all go chill.
The thing is, the bubble exists because even though there is no guarantee of any big profit coming from the big techs, there are billions of dollars funneling into these companies. If you look at their current numbers, they are drowning in losses. Yet they are forcing to keep this bubble alive
> As one final note, I don't think the rhetoric about the AI bubble is really about finances. Conversation about the AI bubble is too widespread for it to be a contrarian position, and too gleeful to be driven by investors' fear. More than anything, I think the supposed AI bubble has become such a large talking point because it represents the hope that things can go back to how they used to be.
I call this “SchadenFraud”: reveling in and playing up every little negative thing that happens in the AI world to make it look like it’s all just hype or completely irrational and will come crashing down any moment now.
There are enough people aggrieved by GenAI -- traditional media, creators, artists, coders, Mathematicians, and the list is fast growing fast -- that this kind of narrative will have an audience for quite a while.
They're not looking for solutions, they're just looking for rageviews. Worse, it gives folks the hope that things have not irrevocably changed and will go back to normal after the bubble pops.
Me, I’d rather get a stark but realistic picture of what’s happening now so I can prepare better for the future.
I’m not convinced that something has to be overvalued for it to be a bubble. A lot of the dotcom stocks that went bust probably could’ve taken some piece of the pie, even if small, and made a business out of it if the money hadn’t run dry. Or if they had just come along later. Turns out the internet pie is enormous and it’s still growing. And now people are much more willing to use it for payments. Even some previously bad ideas might be workable now. But investors don’t have infinite patience, and the market can spook them.
Also, great products don’t always make great money. Some companies just never figure out how to monetize and eventually go belly up. Users may not be willing to pay what you expect, and the amount they pay may not be able to pay back your debt. Not every LLM chat app is going to survive the next few years. That’s just how it is when there’s sudden growth. Our economy tends towards boom and bust cycles.
The bubble is not about whether AI is profitable or whether profits can grow.
The question is how much?
If they grow 5x, then these companies are very profitable, changed entire industries,... etc.
Yet, the bubble still pops because investors assumed 10x at least.
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.
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.
> 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.
You complain that people take the bubble as a given. Yet you continue by doing what a lot of people on the other side do:
> Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness, and a good deal more expensive as well.
This isn't established. Like, at all. There's serious doubts as to whether AI-driven codebases remain maintainable, understandable and trustworthy over time. You look at a snapshot and conclude, while riding the hype wave.
"If you check any online discussion about the financial situation of the AI industry, it's essentially taken for granted that the bubble exists and is going to pop any day now."
-------------
If everyone, or even the majority, believed that there is a bubble that's going to pop with near certainty, that would tank stock prices immediately. Why would anyone hold a stock they know is going to go down precipitously? If the majority of people know it's a bubble, the bubble immediately pops. AI stocks still have worth, so most people believe there is no bubble or that prices will go up before going down.
People talk about bubbles. They worry about bubbles. For the most part, though, they go on investing as though there isn't going to be a bubble. Every bubble looked like a solid investment to most people right up until it burst, or it wouldn't have been a bubble.
So, no, most people do not think AI is a bubble. That doesn't necessarily it's not though.
> If everyone, or even the majority, believed that there is a bubble that's going to pop with near certainty, that would tank stock prices immediately.
That seems to assume:
1. Those people are the invest-class and have control over what gets kept/pulled.
2. Those people don't think it'll pop later and that there's Great Fool Money to be had for just one more day at the tables.
I think one of the things that people analysing the finances of the AI sector often miss is that the capital expenses associated with data centre buildout involve spending a lot of money on compute hardware which depreciates and becomes obsolete very fast.
Large companies have often changed the depreciation schedule for server hardware to 6 years rather than 3. I think it remains to be seen if this is justified as it’s not as though the pace of change in hardware has slowed down by 50% arbitrarily.
I think it’s pretty obvious to me that a data centre full of ampere GPUs is worth less than a data centre full of Blackwell GPUs. I suspect this is also why AI companies prefer to talk in terms of gigawatts of compute, as if efficiency per watt does not exist.
Data centre costs are not one-off. Some are, for buying land and building buildings and infrastructure. The actual compute however is a constant investment. I’m not familiar enough with the market to know what proportion of expenses are ongoing but I bet it’s higher than people think.
> Large companies have often changed the depreciation schedule for server hardware to 6 years rather than 3. I think it remains to be seen if this is justified as it’s not as though the pace of change in hardware has slowed down by 50% arbitrarily.
Anecdotally, I bought a new desktop over 7 years ago and it still feels blazingly fast with plenty of room to grow. And the cost of a new machine is prohibitive, so a replacement at this time doesn’t feel very compelling.
>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.
That’s 52% gross margin. Not much higher than the overall S&P 500. If you break it down by sector, Real Estate, Health are, Financials (and of course Tech overall) have higher gross margins at the sector level.
>That $1 of compute is not taking into account R&D spending, infrastructure, etc
People assume this, but it is wrong. Check the S-1 for Q2, the $2.08 in revenue is for every $1.00 spent on "compute and infrastructure expenses" which also include R&D compute. (Chart is on page 137)
"Compute and infrastructure expenses primarily consist of costs attributable to operating our technology infrastructure, which supports the full range of workloads for our models, including research, model development, serving customers, and internal usage of Claude." page 146 in the S-1/A-1
It is not really clear. In the graph on page 137 does the "dollar of compute" includes only compute required for inference to provide the answer? Or does this include training as well?
It would be logical to assume that they show expense of compute required to run inference. Leaving training compute (which is huge expense) out.
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...
What a weird thing to say: a business is profitable if we ignore a key part of its cost base. That’s true of any business, you know. Even if they make a 100% coverage of every request served, it’s still a far cry from the usual internet business model where responding to a request is almost free and most of the expense is the initial investment in software and its upkeep. This is why Alphabet used to be flush with cash and we get free services.
The trouble with Anthropic and OpenAI is, that not only do they need to get operationally profitable, they also need to be sufficiently profitable to provide a return on capital on all the money they have burned to date. Much of which has gone (and keeps going) not to assets but to expenses like the electricity bill. Thus, once the models are efficient enough to be served profitably, a relatively small amount of assets needs to be generating income for a relatively large amount of capital. It is my understanding, that currently the efficiency of the models does not grow even fast enough to outpace the need for new capital.
One more thing, that gets conveniently ignored when talking about AI cost is the cost of training the models. From one hand, there’s but a few institutions capable of building them and, from the other, it’s like the models grow on trees. Yes, you could run your open-weight model on your own hardware and pretend it’s profitable because you don’t have a comprehensive costing model and the infrastructure costs from security to electricity to risk are mixed in with your general cost base. But someone has spent astronomical amounts of money training that model you run and would probably like to get it back with some interest. Let’s hope that return is just money and not, say, the ability of a certain country to control the global software development process.
When are we getting an AI_OS? I just tell the prompt what to do and the entire OS is run by an agent (likely Linux with sudo permissions). Everything including browsing and copying files and what not is an agent driven task.
This sounds like a nightmare but so is using windows and office everyday and everyone is already using it.
His analysis fails to consider trend lines in inference costs. A couple of years ago inference required a supercomputing cluster. Today, you can run quantized frontier tier models on video cards with a $1600 MSRP. At current trends we'll be running frontier models on a typical gaming computer setup in a couple of years. That is going to destroy centralization possibilities.
It also fails to consider the impact of 'good enough.' A lot of the build-out right now is in anticipation of trying to push the frontier level. But there comes a time, a time we're already arguably past, where models being used for a specific task will already be able to complete that task efficiently. Going way beyond the present should enable LLMs to solve ever more esoteric problems, but in economic terms if a company's goal is to harvest corn, and your corn harvester works well enough, they're not going to care whether or not that corn harvester can also solve Fugazi's 7th Great Conjecture in exomathematics.
I am convinced for a long time. Just the way the companies CEOs talk and move suggest that they are looking for infinite investment without delivering AGI or singularity and we are always "almost there", though it does not arrive.
AGI and singularity are the Nuclear Fusion of energy.
So is Google! They buy real estate on your phone to make themselves the default browser. They pay other browsers and phone manufacturers for integration.
The only reason why Google search is doing well is because it's a real estate business like McDonald's. And lately their search quality has been similarly crappy.
This person lives in a bubble. IPO of Antropic is literally a sign there is no more capital in private markets and it’s up to the public to take the shit bucket.
Let’s not even talk about the second headline that costs are constrained, while one company spends few dozens off billions per quarter.
> The reason why AI companies are spending so much money is to rollout data centres to run the AI in the first place. These expenses are one-off purchases, not regular operating expenses
Companies are investing in scaling models. Doesn't that mean that they'll need to keep investing to stay on the edge? After all, when they lose the edge they lose customers.
> For starters, the average person isn't going to use a locally hosted model, ever
If open weights weren't a problem for these companies, ram altman wouldn't have accused the people of stealing his compute.
Like others have said, it is not that AI/LLMs//ML is not perfectly valid technology, it is a auestion of whether the current investment into it is justifiable relative to the demand and profit. We still buy and sell tulips y’know.
> Outside of generative AI, self-driving cars are becoming a reality, with some evidence to suggest that they're even better than human drivers.
Haven’t they have been just around the corner for 10 years now? And the problem is not technology as such, but the fact you are inventing trains that use roadnetwork hardly large enough for cars?
> AI adoption takes time, but there's no reason to think that AI usage isn't going to massively skyrocket, even if AI doesn't get any better than it is today (and it certainly will get better). In other words, there's massive unmet demand for AI companies to profit from.
[Emphasis mine]. This might a good example of a person writing and thinking with an LLM and letting the model enchant you with its sycophantic bullshit. We might speculate endlessly about AI or any technology, but none of that is a basis on which you should throw money at, especially not as a retail investor. We can see value production of AI in software development, but the productivity increase promised is either not in the data, or it is too early to say yet. So the real valuation of AI is really difficult say still, a lot of things needs to happen, before it can be seen to produce concrete value increase. People need to stop really really fucking hating it for one.
It kinda pointless to talk whether there is a bubble or when it will pop if there is. It might happen today at market open or never. Timing it is a fools errand. Also it is kinda pointless to shout into the void how much you love/hate AI. Better use of time would be to develop the different systems and applications that will produce value for somebody. It all sounds too similar to crypto to me still…
Sure, the largest IPO in history was slashed by fifty percent before it came onto the market because no investor wants to underwrite its original offering, but it’s not a bubble
This analysis marginally better than Rob Ford smoking crack, I'm not convinced you should run for office, but if you succeed, please get us more Shorsey.
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
dump n pump. day trading. long term strategies. build up for the next big thing or delusion or hysteria or lie.
if you read mainstream narratives like "AI bubble", translate it to several millions of people making lots of bucks via a lot of fraud mixed into some good, genuine, valuable work of productive people.
investors and wall street people doing anything financial is not tied to bottom up signals but to insider knowledge and top down supply chain LEGAL manipulation by people not directly involved in the actual work done.
or it's a wrap up ( made money, time to shift or prep something that will hypergrow again, maybe already re-pre-hyped ).
or it's reactions to having maxed out some part of the supply chain which had negative consequences to some other market which reduced overall gains enough to realize "oops", if we don't re-act now, future gains in other markets will be reduced almost catastrophically.
bubble talks always feels like a mix of hyped people really getting fucked over by fraud runners
the details are not too relevant for the moment as I can't have play money to invest but the narratives are interesting because they directly translate into what I read in the suggestions and advice, subliminal or direct, from people to other people.
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.
The technology itself is here to stay i believe, but until they figure out a way to make it more efficient and not just burning money, a part of it does feel like a bubble
There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for. You can't really have it both ways here.
This argument only works because the author uncharitably and incorrectly added “nobody would ever pay money for” to the end of it. If you remove that sabotaging clause, you can “have it both ways”: Ai is gonna change the world, it’s gonna be slop, and you’ll have to pay for slop while grumbling that things used to better.
"The AI Industry Doesn't Look Like Any Bubble We've Seen Before" - It looks exactly like the two railroad bubbles. Potential profit is there, in the future. But you have to invest in the infrastructure first. If you can't make a profit and pay back the loans fast enough, the infrastructure goes bye-bye, along with all the funds used to finance it.
"There's Still Plenty Of Capital To Raise" - IPO is not how you raise a trillion dollars. A million, sure. A billion, maybe? But a trillion puts retail investors - mom and pop - at risk. IPO is not how you raise funds for a railroad.
"Once AI companies become profitable" - there's the big red flag. This isn't Uber or Lyft that can float along on tens of millions in corporate hand-outs every quarter. They need 100x that much money, and nobody has it to hand out. So you can't wait around for profits. There's no magic cushion to float on.
"For starters, the average person isn't going to use a locally hosted model, ever" - They will in 4 years. "normal users don't want to have to deal with the hassle or expense of setting up something" - What hassle? You download a program with a fancy GUI and run it. There are a half dozen of these projects already, that provide ChatGPT on your computer from one binary. All you need is a GPU.
"Profits Can Grow, By A Lot" - Only if they remain cheaper than local hardware or open-weight inference providers. Which won't happen because they owe more money than they can make.
The sign of the bubble is all the investment made not based on stable returns, but on hype. The weird geniuses from San Francisco said the future will depend on this one technology, all work will be changed forever, and only their company can deliver it. If you don't get in now, you'll miss out on a fortune! That hype just doesn't work out. And the proof is here: the superhuman machine sucks at writing, the documents it creates are lies, and we seem to be spending more on it than we get out of it. Because it's not magic. It's just a machine.
> It looks exactly like the two railroad bubbles. Potential profit is there, in the future. But you have to invest in the infrastructure first. If you can't make a profit and pay back the loans fast enough, the infrastructure goes bye-bye, along with all the funds used to finance it.
That's just not true. Profit is there now. If the hyperscalers stopped building new infra tomorrow, they would just roll in the profits available from what exists today!
Totally unlike the railroad bubbles which built out years and years ahead with no customers and then collapsed.
> "There's Still Plenty Of Capital To Raise" - IPO is not how you raise a trillion dollars. A million, sure. A billion, maybe? But a trillion puts retail investors - mom and pop - at risk. IPO is not how you raise funds for a railroad.
You're totally confused about what an IPO is. You're confusing the valuation of Anthropic with what they will raise. They intend to offer about $100B in stock, their valuation is at 1T.
> "Once AI companies become profitable" - there's the big red flag. This isn't Uber or Lyft that can float along on tens of millions in corporate hand-outs every quarter. They need 100x that much money, and nobody has it to hand out. So you can't wait around for profits. There's no magic cushion to float on.
Most AI companies are profitable aside from expenditures to build new infrastructure to reap more profits. That's a very healthy sign.
> "For starters, the average person isn't going to use a locally hosted model, ever" - They will in 4 years. "normal users don't want to have to deal with the hassle or expense of setting up something" - What hassle? You download a program with a fancy GUI and run it. There are a half dozen of these projects already, that provide ChatGPT on your computer from one binary. All you need is a GPU.
Uhuh. A GPU. In your phone? In your laptop? The average laptop in the US is sold for about $700, desktop for about $1k. A good GPU to run a reasonable model is $10k+. It makes zero financial sense for most people to run their own inference. The issue isn't, and never was, the software, it's the hardware. And one thing we've seen from the past years of growth is that people don't look at the models from 3 years ago and say "Oh well, that's good enough, at least I can run it slowly on my $5k+ desktop". Until that happens this is a fantasy.
> If the hyperscalers stopped building new infra tomorrow, they would just roll in the profits available from what exists today!
They owe hundreds of billions in loans on datacenters that aren't built yet and don't currently have capacity for the new users and additional tokens they promise are coming. If they don't build capacity the increase of demand would cause more outages and higher prices which would crater both their use and valuation. They have to grow or die.
> They intend to offer about $100B in stock, their valuation is at 1T
That's one company, the aggregate debt of the US AI industry approaches 1T.
> Most AI companies are profitable aside from expenditures to build new infrastructure
Which they have to complete to make the profit they promised which they need to pay off the loans they already have.
> A good GPU to run a reasonable model is $10k+
Today. Due to supply and demand. Funny thing, supply and demand. It's very reliable. High demand makes supply short for a while, but then causes supply to go up. When supply goes up, eventually demand goes down, which brings prices down. The chip factories and economies of scale that will be complete in a few years will send prices crashing, as they have before. Which might bankrupt the remaining chip companies.
"Reasonable model" isn't a thing. Many different models of different size have capacity to do many tasks. I have a laptop from 3 years ago that runs local AI models. My phone from 5 years ago runs local AI models. And any strong CPU can run models (no GPU needed). Speech to text, image/video generation, image classification, research, analysis, computer use, troubleshooting, etc, all works on tiny models. I think you don't understand the tech here. Remember that many people spend a grand every year just for a newer iPhone/Samsung. They'll spend the same for one with better AI and more RAM.
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
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.
> - 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!".
>In other words, once you ignore the cost of building infrastructure, AI is very profitable.
Its not clear that the infrastructure will ever be paid back. Its also not clear that once models are trained and distilled that the infrastructure will be useful. Ignoring 1.9 trillion dollars of debt is probably not the greatest financial analysis.
>However, there's no reason for why this level of spending has to be maintained in the first place.
Exactly. So Anthropic and OpenAI might be holding the bags for latecomers who distill their models and get ahead. Sort of the Commodore64 and Atari of the AI space.
>Once all the data centres are built, AI spending is going to plummet.
Well data centers need maintenance, and GPUs and similar do have a very short lifespan. But even if spending plummets repayments on existing debt needs to be accounted for.
>In a pinch, AI companies can also cut costs by no longer allowing people to use their products for free and temporarily suspending expensive AI research.
Research keeps them in the headlines, see above about the latecomers.
>For context, Alphabet (parent company of Google and Youtube) reported earning almost 162 billion dollars of revenue just in 2019. Adjusted for inflation, that's an even more impressive 213 billion dollars.
Yep, so all Open AI or Anthropic need to do is get acquired by a much more profitable business to pay back their debt.
>at some point, the rate of increase has to level off, but that doesn't have to happen anytime soon.
The rate of increase of revenue in the AI sector also doesnt need to be apportioned to OpenAI or Anthropic. If it wasnt for their generous loss leading subscriptions or their rnd, theres not much there to hold on to people.
>it's hard for me to see AI consumption not being 10x what it is now.
Right AI consumption will increase very likely. But will it increase at the cost of an already subsidised subscription, or increase the use of cheap Deepseek 4.1 tokens.
>For starters, the average person isn't going to use a locally hosted model, ever.
Isnt this expressly where Apple Intelligence is heading? Building small low cost models that run on Apple hardware? If I was nVidia I would have both plans being run down at the same time, looking towards selling GPUs to hyperscalers on one side, and working with OEMs to deploy AI coprocessors in laptops on the other side.
>For example, I pretty much only use ChatGPT, not because I have any really reason to, but because it was the first LLM that I ever used.
N = 1. OpenAI is already what halfway to their trust thermocline anyway?
>A potential way for a bubble to pop is for the companies involved to run out of capital to spend while they grow, causing them to fail.
Isnt Anthropic already down to BB+?
> NVIDIA are all AI companies that have seen their market capitalization explode over the past few years
NVIDIA is a hardware company that can has so much demand on their products they will likely survive 3 AI market collapses.
>Alphabet, Meta, and NVIDIA
Alphabet, Meta and NVidia all have other sources of revenue. They are companies building AI, not AI companies.
>When the dot-com bubble happened, it was largely driven by companies that couldn't turn a profit or had P/E ratios in the hundreds.
Anthropic and OpenAI dont have "E" (as in earnings)
Anthropics Debt to Equity is 363%.
>A potential way that this could be a bubble is if AI proves to be a fad.
A potential way this could be a bubble is if the makers of consumer devices point customers at cheaper or local models as part of their onboarding experience. OpenAI and Anthropic get thoroughly zuckerberged by the people holding the primary relationship with the customer base.
Or perhaps, the funding to the top 1% of token purchasers also turns out to be loans, and that dries up too.
> There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for.
One thing to keep in mind when you're "debating" things online is you're actually debating with a tag team of people, with a whole spectrum of views. And at any point people with stronger arguments (starting from different precepts) will reply while people with weaker arguments will fall silent.
That's why "the average hacker news poster" is simultaneously rich and poor, powerful and powerless.
> The reason why AI companies are spending so much money is to rollout data centres to run the AI in the first place. These expenses are one-off purchases, not regular operating expenses. Once all the data centres are built, AI spending is going to plummet.
Because hardware doesn’t need updates.
Newer hardware is more capable which gives competitors with newer hardware an advantage.
> Outside of these expenses, AI doesn't cost that much to run
The 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.
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.
> More than anything, I think the supposed AI bubble has become such a large talking point because it represents the hope that things can go back to how they used to be. People are afraid of what's going to happen with AI, and rightfully so.
> So, people wishcast. They tell themselves that this is all a giant bubble, and one day soon, everything will crash down and immediately go back to how it used to be.
+1 to this. I think this is why Ed Zitron gets so many readers - people want someone to tell them things will go back to “normal” someday.
I see it as the opposite. I think the this idea, the "seeing the LLMs as a bubble representing the hope that things can go back to normal" is itself projection from people who know their future is going to suck thanks to "AI" but really wish differently. Whether the LLM companies crash and burn, or succeed wildly, the outcome is going to be negative for the average American (and probably for the average human being).
It will eventually blow up of course but it wont be anything really expectacular, a bunch of larpers will be talking about giant loses for CEOs and surely, hopefully anthropic, all while it doesnt have a big effect on the people that know little about all of this.
But still, nothing will be back to how it was, ever
I am pretty sure the biggest thing in question is if OpenAI and Anthropic are as valuable as they say they are vs any other AI labs. I guess we will see, but I still feel it doesn’t take many ex-employees from them to build something that can wrestle them, and also it’s weird seeing other players that also have comparable AI models (Alphabet, Meta, whatever company owns Grok these days, …) do not get as much in value. There is also the “Can local models disrupt this?” question (and the Chinese labs), and there is also the question of hardware availability (will some player change this?).
I feel the projected value of both OpenAI and Anthropic requires that nothing changes in a space of technology where things seems to change a lot and reasonably suddenly.
Does Open AI or Anthropic fall behind? I think if either falls behind people are going to panic and the eventual stock price will tank. Both companies have a ton of commits so much in fact that one of them failing might take one or two other companies with them. That is what I think the big risk is.
No, no one believes that. Everyone can see that AI is going to change how many industries work completely. The parent is constructing a strawman argument "the reasoning is motivated and that motivation is nostalgia (or similar)".
Yes, quite a few people believe AI is a big scam and it’s not actually good for anything.
I know this might seem crazy to software developers who handed all their dev work over to Claude 6 months ago. But most people are not software developers.
I think a lot of software developers are convinced it must be transforming other jobs and it really isn’t and may never to the same extent. LLMs are just particularly good for technical writing and code.
But that is the potential issue. It is valued as it will and that just isn’t proven.
I have also seen this from many SWE and hobbyist programmers as well, actually, as not all workplaces have AI mandates.
Those people usually bury their head in the sand and have such strong opinions about the capability of these models as they haven't ever used anything but the free tier chatbots, if any at all.
I mean if one of the big labs collapse, then yes it mostly will? Like sure, the tech will stick around in some way or another as long as it makes money or provides a good user experience, but the forcing a garbage chatbot into every product surface, the constant flavor of the week hype, the endless doom from every CEO will all cease to exist.
No, but it is incredible for somebody to use the word “wishcasting” and then describe a group of people that would need to materialize in order for their point to make sense
I think a lot of people who post on HN don't want to admit that, apart from chatbots, AI is completely invisible to the user. A lot of users already hate chatbots to begin with and avoid using them.
So, it has already mostly disappeared, and the lack of practical applications means it will continue to bleed money. Software devs aren't using AI either for the bulk of smaller tasks that require teamwork with non-technical stakeholders.
Yes, it totally can disappear from society. The remaining use cases go with local LLMs and specialized software built around it. From the user's perspective, that's the way things always were and we'll be back to "the way it was" when the bubble is done bursting.
-1 to this. I think this is why Ed Zitron gets under the skin of AI evangelicals. They want everyone on this surrealist funny bus on a journey to whatever-the-fuck. Fingers crossed! Ignore the weird AF obfuscated financials. Ignore the crippled hardware market for everyone except data centre overlords. Nothing to see here!
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.
A fair critique. Even I, someone who started as a (righteous) angry doomer over it, have come around for the most part.
My viewpoint also shifted around the shape of the bubble. The technology isn't going away, obviously, but the valuations of these companies like OpenAI and Anthropic feels divorced from reality. Inference continues to go down, most folks don't need frontier models for common tasks (including coding), and local models provide a competitive edge in several domains the frontier labs cannot (namely value and privacy). Combined with demand still largely subsidized and the companies being deeply unprofitable, and I just don't see the investments in Anthropic or OpenAI or the sort actually paying off. That is the bubble I'm concerned about, not the tech itself.
...now when the debts get written off and all this compute is just sitting around idle, available for bottom-dollar prices to try and eke out any sort of ROI for whichever creditor(s) inherit the assets? That's going to be pretty cool times.
I stopped reading after the 10th sentence or so demonstrating AI-pilled psychosis and hand-waving. That it is obviously this or obviously that and obviously some other thing.
I look forward to these types of posts fading in frequency much more so than AI use itself.
> The AI bubble is a speculative concept that the AI industry is extremely over-valued. If true, the value of most companies involved in AI (several of whom are worth well over a trillion dollars) will collapse, devastating the entire economy. If you check any online discussion about the financial situation of the AI industry, it's essentially taken for granted that the bubble exists and is going to pop any day now. I don't think that this is a reasonable assumption, though.
Matteo's analysis is very flawed though. For instance, he projects that the
outcome is "devastating the entire economy". Why would that have to be the case though?
The AI bubble can collapse partially. There are tons of examples of how this has happened in the past. Not everything is dot-com 2.0. What we see right now is a "consolidation" bordering on partial collapse of some of the companies' projected revenues. It makes sense to assume that there is not a complete collapse, and some companies will end up dominating more than others.
> If you check any online discussion about the financial situation of the AI industry, it's essentially taken for granted that the bubble exists and is going to pop any day now. I don't think that this is a reasonable assumption, though.
To say that it is (essentially) taken for granted isn’t a great start to a rebuke. You have to engage with the arguments that they have, and they have presented arguments. Just see born-again clanker Dan Luu respond to Zitron’s claims.[1]
I don’t see any engagement with skeptic’s arguments in this piece.
> In other words, there's massive unmet demand for AI companies to profit from.
Of course. There are hundreds of millions of white collar workers that are jealuously hoarding their unrightful wages.
> There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for. You can't really have it both ways here.
But you can. A bubble of this magnitude will negatively impact the world, not just AI investor cretins. You can take the extreme position that AI is useless and still be worried about the fallout.
> It's completely valid to be afraid of AI taking your job, but money is going to change hands when that happens.
The conjunction here implies a contrast which I fail to see.
> As one final note, I don't think the rhetoric about the AI bubble is really about finances. Conversation about the AI bubble is too widespread for it to be a contrarian position, and too gleeful to be driven by investors' fear. More than anything, I think the supposed AI bubble has become such a large talking point because it represents the hope that things can go back to how they used to be. People are afraid of what's going to happen with AI, [...]
The always-amazing observation that your intellectual opponents have motivated reasoning. Likewise, most AI positives have motivated reasoning, like that they can enrich themselves or that they are getting so much code out of their current subscription prices.
When I write about AI it’s clear when I argue against AI itself and when I am whining about my boo-boo thoughts about losing all of my economic prospects.
> I’ve applied to seek the Liberal Party of Canada nomination
Large data centers never made sense. If an additional pc with a graphics card was put into every home, we wouldn't need power grid upgrades. Our power grid is designed around human distribution i.e. homes. Large data centers cherry pick where it may be possible to quickly connect power connections up. Data centers need to secure their power connections before the utility rate hikes on everyone else generate public opposition.
If large data centers cannot secure free power connections paid for by everyone else, they cannot make any of this profitable. The whole thing was a scam from the start. This is why cloud AI companies bought up so much pc and server hardware. Artificial scarcity was the only way to slow down local llm adoption and sell the false narrative that cloud AI has value.
Plucking a graphics card out of every home and centralizing the cards to a handful of large data centers that require a trillion dollars in power grid upgrades is an idea so dumb, we should all be laughing at it. They even have to use active cooling or evaporate drinking water instead of free air cooling. The consumer doesn't benefit in any way from this. Consumers would essentially be paying more just to be datamined.
LLMs are not a bubble, large data centers are a bubble. Look at how large data centers are now the first target in any war. A graphics card in every home, doesn't give someone a concentrated target. National security makes large data centers extremely bad.
Large data centers are dramatically more efficient at utilizing both the power and the hardware.
At it's core, running a top-tier LLM means linear load of trillion+ weights from RAM for every token. The cost of this operation absolutely dwarfs the cost of doing the actual math. If you are only serving a single user, you can only amortize this over a handful of token streams, times like 2-4 for speculative decoding. If you are running in a datacenter, you can batch thousands of streams, and get correspondingly much more tokens out of the same expenditure of energy and hardware.
Be slightly careful with this. Large datacenters use power to operate the facility (this is measured as PUE > 1). Datacenter-oriented hardware often contains low-diameter, high static pressure fans that consume quite impressive amounts of power that are not counted as overhead in the PUE calculation.
A home- or office-oriented workstation will have much less power-hungry fans (go compare a Noctua datasheet to a server fan datasheet - the Noctua wipes the floor with a server fan if measured by CFM/W, and the internal temperature rise is mostly controlled by airflow and not by static pressure). And in some climates and times of year the marginal facility HVAC cost associated with running a computer may be zero or even negative.
And lower utilization hardware may run in on a more efficient point on the power-vs-performance curve. [0]
That being said, your overall point is correct.
[0] For a dramatic demonstration of how much this curve matters, you can run a non-CPU-saturating, non-latency-sensitive workload on various Intel CPUs with and without turbo on. Seeing a 2x or greater difference in reported power is quite possible. (Intel is terribly at writing platform firmware and the default behavior may be wildly suboptimal.)
In addition to the batching issue, "a graphics card" is not going to cut it for the multi-trillion parameter frontier models. You're looking at a 512GB Mac Studio to support anything close to those model sizes, and that's with heavy quantization and much worse performance.
agreed, gets some mid range hardware, some click installing agents with descent customisability, and im talking boring customisability like 'help me plan what to buy this month for my home' kind of tasks. Thats what we are going towards
> There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for.
Fair enough. But the real problem, it seems, is that people love slop.
My family is trying to sell a house in the South of France; the real estate people are sending us 500-words emails every so often, that are so obviously AI it hurts. Well written, and content free. Nobody cares. I'm the only one having issues with it.
At work, a new team let Claude do all their work and write all their status reports. Everybody seems to love it. It's so fast! And mostly correct! And so fast to correct when it's wrong! And here too, I'm the only one having issues with it.
A Canadian author wrote a book in French that turned out to be 100% AI ("C'était ça ou mourir"). Before that was revealed, every critic loved it, in Canada, in France and everywhere else (25 translations were started). The public loved it too: it's one of the top best sellers, and success continued even after the "scandal".
The quote is not a contradiction at all, it can both change the world and be low quality.
If that's true for every AI output is up for discussion, but it's reasonable that if someone is skeptical about the technology, they will also be skeptical about the quality.
That the tech is taking over many areas is not a stretch at all, that's just something that can be observed. Big companies (not just big tech) have been trying to get away with lowering (cost cutting) the quality of their products for a long time, AI lends itself to the same purpose, and will (and already is) obviously be used for it. Starts with customer communication as you mentioned, and customer service, and believe it or not I don't think every company should generate their own tech stack and software for their little products.
I think the problem is calling all AI output “slop”. If people genuinely like the content then is it still “slop”?
And, playing devils advocate for a moment, does it matter if a robot or a human authored it if people like it? People
loved Magic Eye pictures in the 90s and they were computer generated too.
The problem is also with the creator. You learn nothing when AI produces things for you. When it's art, to each their own, I guess.
But when I ask my realtor questions, he doesn't know anything. I get the same answers by typing into ChatGPT.
When I ask the Claude team at work about why this or that, they don't have a clue. They "will get back to me", which means they'll forward the question to Claude and Claude will output another 500-words email with a lot of things that are obvious and not part of the question, another couple of assumptions that are plainly false, and in the middle of it, sometimes, yes, an interesting insight. But the insight doesn't come from the people sending the email, they didn't produce it, they don't see it, and they never act on it.
> But when I ask my realtor questions, he doesn't know anything. I get the same answers by typing into ChatGPT.
Not going to comment on your workplace specifically, but I’ve found this to be an issue with other industries like real estate long before ChatGPT were a thing.
The problem is the people who pickup the phone, greet you at the shop door, or take the photos, they’re the lowest paid juniors and thus know nothing.
So in most industries, you were always lucky to get through to someone with knowledge on the first attempt.
That's highly unlikely because then the company wouldn't exist. At some point in a (to use our example: a real estate business) you are going to need skilled professionals.
I think the bigger risk is what happens when they retire if we are not training the next generation of professionals.
"slop" is less so about if something is a popular product or not, it's more about the quality. You can think that AI can produce high quality outputs, but it's unreasonable to think that this applies to the majority of it's use. There has always been corner cutting, and using AI to cut corners/safe money in product design/production inevitably results already, and will more so in the future, in a ton of bad products. That does not mean that there can't be good ones too.
Slop exists because the cost of creating content has dropped to zero, which made the signal to noise ratio crater as well. Concision and density used to be a virtue. Now it's the opposite: content is measured by weight.
> "slop" is less so about if something is a popular product or not, it's more about the quality.
But we are talking about popular culture here. That was never an industry where quality was measured by artistic excellence. It's an industry that is entirely motivated by mass appeal. Thus something being popular IS the measure of success.
So if AI is slop, then so is 99% of the human-made content it's replacing.
People absolutely cared about "C'était ça ou mourir", his publisher ended up dropping him. The reason the success continued is that people denied that he used AI and had all sorts of shaky political reasons for this (e.g. claiming that Haitian writing trips up AI detectors). Even the reason it got popular in the first place is to do with the themes of the novel rather than its writing.
That's not really the point. Critics the world over LOVED the book; they only distanced themselves from it when it became clear it was AI, but when they didn't know, they couldn't stop raving about it.
Everybody knew it.
But some investors don't have the patience for these "sure things" to mature into their commercial destiny and others know they can siphon off a killing by taking advantage of them as they speculate.
These bubbles aren't about potential, they're about timing.
And today's AI, for as revolutionary as it may be, is taking just as long to mature as anything in the past.
Most of the top players involved in AI right now are going to be rich. And in 20 years, the world is going to involve a lot of AI in soooo many things. But a lot of shirts are going to be lost below those top players and a lot of companies are going to fold because infrastructure, refinement, and integration take time while the FOMO speculation starts as soon as the big discovery is recognized.
Canals... where a repeating pattern was the company underestimating difficulty/costs, and going unrecoverably into debt. Another company, which then bought the largely finished canal, cheap, they had potential profit... until tech change (railroads).
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.
There a lot of parallels with AI…it doesn’t take a genius to see the long-term potential, but the risk is in implementation, so it’s a mad rush to the finish line by a wide variety of less scrupulous early players. Rockefeller got his big start by sweeping up failing producers who were all capital weak, and I’m sure something similar will happen here during the inevitable bust.
I can do the hard, risky work of drilling wells / deploying AI, or I can sell you a vision of that future, today. The most dangerous technologies in terms of grifting are the ones that everyone can understand.
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".
There were high profile failures but we have had those recently too eg. pet.com (dot com) and color.com (post-dot com, domain has since been repurposed)
I think you're confusing people cashing out with actually creating a successful company. Yahoo is not what people would describe as flagship company, or pets.com or Ask Jeeves or myspace or Voat or whatever. The dotcom bubble made a lot of people rich, but most examples were at expense of bag holders.
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.
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.
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.
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.
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.
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.
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.
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 anymore.
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.
Our most talented engineers tend to move abroad and stay there. Brain drain is one of the most serious things that can happen to a country or a continent, and we are suffering from it.
We just cannot continue to live like that, something will have to yield, but many voters just don't want to hear that.
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.
Aren't you? Or, haven't you noticed yet?
We shouldn't assume that YC is hosting this forum out of benevolence. However it is true that this place is also useful for things other than just advertisement, so that's a neat side effect. We just shouldn't assume that that's the primary purpose of HN.
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.
The reasons for this are complicated and nuanced though.
Governments think they push the financial burden of transport maintenance on the people (and its a good tool to register and control people too, but thats not a strong consideration I wouldn’t wager), so they fervently cut spending on public transport and curtail cycling infrastructure in favour of cars.
But roads themselves operate at a crazy financial loss and are one of the reasons governments are in crazy debt (which nobody wants to acknowledge).
This is because road maintenance is significantly more expensive than building new roads. Which means that road use becomes heavily subsidised (much more than trains cost per user) but the cost of vehicle ownership, despite that heavy subsidy, is still enormous and one of the largest expenses an average person can have.
Is it? People love to have personal sovereignty over their transportation, given the choice.
"The fourth power law (also known as the fourth power rule) is a rule of thumb which states that the damage to the road caused by a motor vehicle increases in proportion to the fourth power of its axle load. This law was discovered in the course of a series of scientific experiments in the United States in the late 1950s and was decisive for the development of standard construction methods in road construction."
"Example: Car (total weight 2 tonnes, 2 axles): load per axle: 1 tonnes vs Truck (total weight 30 tonnes, 3 axles): load per axle: 10 tonnes. [...] The resulting damage differential between truck and car is 15,000 to 1"
PS: this is something electric car proponents don't talk much about
I hope trucks transporting fuel for ICEs get equal treatment.
- Train capacity is much, much higher. You couldn't have the density of France, the Netherlands or even NYC without most people travelling by train. LA struggles with 1/10th of the density.
In fact in the UK you would be forced to aggregate all fuel and road tax and offset it against road maintenance because there is no direct correlation to be made, as tax is “one big bucket”.
The majority of the planet are going from one point of interest to another at a relative constant rate- but yes people are spread further and further- ironically this is helped by requiring huge parking space everywhere anyone needs to go, and needing huge houses so you can park multiple cars outside. Its a self-reinforcing problem.
(This is most evident when comparing cities like Malmö in Sweden with Orlando, FL. Malmö has a higher total population but approximately 1/20th the road surface- yet in Orlando I kept bumping into the same people everywhere I went because we’d all drive to the same Restaurants and Shops; and despite that happening we had to drive 20-25 minutes to get there! Weird coincidence or natural concentration points? Someone smarter than me would have to say).
Corporations demanding payment for everything at whatever margin they want is little different than taxation without representation and local cops shaking people down for money at random; just different legal semantics
Without competition you are talking about a monopoly and it is your government that is responsible for mitigating its effects. The government that you elected.
The mechanisms are all there, they just need to be used+embraced.
There is inherently competition with entropy and just getting ourselves out of bed to deal with everything
Corporations and brands and tax codes are just culture war distractions on top of the raw physical competition; middle ages like words of power
The competition is with the physics that binds us to Earth; does it? Or are we just to distracted by rhetorical competition between legal constructs to embrace the levers of science
Rhetorical competition wrt to markets type blather is only seeking internal consistency with an arbitrary rhetorical framework. Not pushing the boundaries of engineering based on science.
Yall can have AI agents simulate Bloomberg terminals so actual physical scientists can focus on the necessities ground truth work you take for granted
Specifically because of massive government investment in road infrastructure in preference of railways. This would be about like the government deciding to funds DCs directly...
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.
This hasn't historically been true. The clusters have been getting bigger along with the models over time. And now we may even want to have these computers have rapid access to extra compute or caches of the internet or other data stores.
Of course, Google could be wrong about the economics of running old AI processors. Or maybe TPUs are special.
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.
> 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.
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...
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.
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".
In every bubble some say this. And it could be the exact same herd behavior (mentality) that creates a bubble.
Oh, I thought they were about unchecked greed and insufficient regulation, but I suppose that works, too
Expect more differentiation in the future and not less.
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)
GPT has the same performance. And performance is definitely a commodity. Why wouldn't it be?
As we all know, GE then went bankrupt and we went back to whale oil.
I wonder why so few people make the comparison to the recent VR hype. We heard the same things there about its unlimited potential, how it's inevitable that we will soon resolve all its shortcomings and that it will become ubiquitous.
I still believe VR will eventually live up to its potential, but no one can deny that bubble has popped after Meta took a huge loss on it. It took too long to resolve the hard problems so the timing didn't work for them.
AI 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...
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.
VR is a toy with a few very narrow areas of application in industry. That does not describe the current status of AI.
AI is bigger than the Industrial Revolution, the Internet, than computers, than literally any innovation or invention that has ever come before, combined.
Today you see AI slop and vibe coding.
Tomorrow, all of humanity will be affected, forever.
Everything about the way that we learn, that we think, and that we invent will be changed forever.
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.
Their monopolies got broken and the profits disappear in a commodity market.
If nvidia or tsmc got split up i doubt its stock is going to stay at as a high a p/e.
AI can be in a bubble right now and the bubble can explode and yet all what GP said can still be true
Imagine a world without those. Oh, wait: tomorrow people won’t know how to use language, will they? And they won’t have water to farm with, so there you go.
Humans living forever is inevitable. Galactic colonization, inevitable. Over a long-enough time scale.
AI is inevitable on the same time scale, sure. But can it actually survive as a sustainable business given what is needed to keep it relevant—will people pay what it costs to support said business—not clear. Or, at least in the next five years.
The second generation AI companies will be built on different assumptions and will likely be slower-growing, and that’s when you’ll see it truly become ubiquitous. That could be a decade though.
I doubt that. I think we just fizzle out. The survival of the fittest instincts that made us a successful species is destroying us as we are getting more capable. Our 'leaders' are getting ever more unhinged.
And without faster than light travel there just isn't much point in galactic colonisation. If generations have to live and die in a tin can to get to some remote star, why not just live in that tin can in the first place.
“IT’S THE SAME THING”
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
The "I" is doing a lot of work there, heh!
"I've stolen more lines of code ..." would be closer to honest.
What about the labor you steal from the milkmen, butter churners, seamstresses, and elevator operators? How evil of you.
And yeah, I did it. Not you. That money would be in your bank account if you did it. But you didn't.
I personally make these things. I'm not ashamed in the least to say it, because it's true.
It's funny how I'm working around the clock using AI. Because you seem to say this isn't work. And yet I've never been so busy. So can you tell me what's going on here?
How did I manage to build so many useful things for people?
I've had over 40 million downloads of my app recently. Maybe you should think about how people value things instead of relying on your own value system. Your priors might be flawed when it comes to how the rest of society values things.
In fact, I tell myself I wrote it! I've never composed more music since I started prompting streaming apps.
(Did you walk into that one by yourself, or did you have help from Gemini?)
I want to bend the universe. Maybe that's the difference.
Living the same way we did in the 2010s until retirement seems boring as hell. That was the depressing reality. This is way more fun.
I learned programming to do this stuff. Programming was the necessary evil. Now it's gone and I'm 1000x faster.
I want to live a life that makes the people I love happy, makes me feel healthy and happy, and hopefully learn some cool things and skills along the way.
Bending the universe sounds predatory, like exerting what you want onto other people without thinking about what they want
Excel was inevitable. How many trillions of hours has it saved?
AI is going to let me do your job and barely lift a finger. Therefore, inevitable.
These are thermodynamically favored technological outcomes.
The growth slowed and operating profit has swung to a loss. The company can still make money, just not at a pace that has a chance to pay off the investments.
Also, the author conflates AI's technical potential with the market potential, naively assuming the demand will also grow as technology matures.
I don’t think this is naive at all; it’s quite rational that demand increases as price decreases, which does seem inevitable.
But that's not what OC is saying, isn't it? Frontier models becoming better and cheaper at the same time are not a given.
Near-frontier models that have 99% of the capabilities of frontier models, at a tenth of the cost of frontier models, might ironically be what pops the bubble.
AFAIK GAAP Operating Income has stock compensation packages in it and is susceptible to valuation changes(?)
https://www.sec.gov/Archives/edgar/data/2130357/000162828026...
(I have not much experience with that stuff so please do not take that as reliable information and more as a question)
Anthropic is one company in a gigantic ecosystem.
You want to understand the eco system. Got to flyover the jungle, not just stare at one elephant herd.
Read annual reports of NVIDIA, atleast one from Microsoft/Amazon/Google and atleast one representing activities of Enterprise projects (Accenture/IBM/TCS). Then look at govts budgets/programmes for AI US/EU/UAE/S.Korea.
Do it with a group like a book club.
If I were to guess, a lot of people, particularly older people, see computer use outside the web browser as an amateur might a chop-saw; a dangerous tool not to be used by inexperienced hands, so an AI that can also directly do the tasks for them might solve that problem (assuming it came preinstalled or someone else did that for them). Though then comes the question of whether they'd be willing to pay for it
Exactly! I noticed this when I moved out of my parents' house, when suddenly my mom became an electronics whiz who can hook up VCRs and TVs just fine. I always thought I was the smart person in the house, but now I realize I was just taken advantage of.
Compare flights? You have to assume it is objective and not tainted by corporate bias or backroom deals(aka ads and sponsored content), unless you selfhost and find an objective model.
Search Wikipedia? You have to constantly check that it is giving you straight facts instead of being constrained by its community and safety guidelines.
The problem with so many of these usecases is treating AI as a super intelligent Vulcan who is always logical and objective aka Spock, when we are better off treating it as if it is a salesman.
There's so much ad money to go around. It currently fuels Meta and Google. Let's say Anthropic and OpenAI enter the field and take a slice. That's a fine business, sure. But does it justify the state of the market?
Maybe not everyone (yet) but your neighbours clearly do need it.
And if one of my neighbours ever asked me to help them open a zip file, I would not leave before I'm absolutely sure that they can do it themselves next time.
This has to be build and takes time.
But this is the major thing were ai will be introduced into every single layer of software we have.
The HR System which might have needed x people? The agent has solved 90% of tickets already before they hit a human. Agents will do the repetitive but not that easy to automate tasks.
How is that even possible? I thought they already put AI chats into everything. Maybe AI chat inside an AI chat.
I said it two years ago already: An LLM is the perfect user interface.
The problem though: It will make it a lot easier to do harder things which will devaluate jobs.
What makes you so sure? Some would call it "automated running scissors" and want tool firmly in responsible hands. And could assess "automated (indeterministic) reliability" as still far away in time.
Are there any contexts we may have missed that would surely, safely replace individual deliberate action with "the ANN can do it"?
- anthropic IPO repricing
- pareto frontier gap decreasing by the month between open and closed models
- SpaceX disappoinment
- enterprise buyers as main consumers of closed tokens are being displaced by OS competition for pennies
- job loss and entry level hiring collapse
- massive discounts of current data center contract debt
- consumer open weights model efficiency improving constantly
- the Chinese arms race
- a possible rapid downward spiral that may force governments to step in and subsidize
Am I missing anything?
- margins shrinking at Anthropic
- Google getting thwacked by missed earnings
- OpenAI missing funding target
- price war
- Microsoft revising capex projections down
- Plateauing of model capability improvement
What's weird to me is that you don't see a lot of people making deals that seem way too good to be true but yet the total numbers are absolutely gigantic. Throw on the fact that the stranded assets will have a shelf life, it really seems terrifying. Yet everything is being priced as pretty darn risky. I think maybe there can be a big slowdown without a dramatic pop. A sort of grind down. But there's not a lot of room for error to prevent a pop so I guess it will happen? I don't know its very weird.
Also of course this is all predicated on models not making another dramatic leap. The signals out of the cutting edge labs don't really suggest they have a model that would be as dramatic of a leap as Mythos was when it came out in the hopper. Their code quality seems about as mediocre as what the publicly available models can do. The OpenAI math results are very impressive but that's ripe picking for AI. Also they keep talking about a coordinated training slowdown which is textbook antitrust so they seem to want the government to force their hands off the "spend money on R&D" button.
So I don't really know. I guess I would say purely on vibes that this doesn't feel like a bubble but the size of the build out is just so massive that there really isn't a big window for a success case.
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.
> I'm not convinced the AI bubble exists
You're arguing against the bubble popping. So you agree the bubble exists?
You gotta be kidding. HuggingFace, a website to download files, was sold for 13B.
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.
You can now have a model work on a task non-stop for a day or more. This was unthinkable 12 months ago, and unstable 6 months ago.
If you are searching for evidence of capability, just look at the amount of projects, blog posts and videos on game remixes, hardware reverse engineering, new compilers and runtimes that has started coming out in the last 6 months. Slop? Sure, but this kind of slop didn't exist at this level 6 or 12 months ago.
https://news.ycombinator.com/item?id=50422069
It's really very hard to pin down progress in this domain, companies are definitely targeting benchmarks.
One bit of evidence I would look at is the maths results this last year which have been impressive, that was a huge expensive brute force effort, but it is still good progress.
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
https://dealroom.co/news/169124-revolut-moves-2-000-engineer...
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.
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
> pareto frontier gap decreasing by the month between open and closed models
We need a temporary moratorium on the words "pareto frontier". Every new model is the pareto frontier by some combination of metrics. Like "Bayesian Priors", people love saying / tweeting it because it sounds Smart.
It's like watching car commercials where every single vehicle has won something from JD Power and Associates. Which, (a), who the hell are they? and (b) you can only pass out so many awards until the gluttony of awards proves the award is worthless.
This isn't targeted to you! It's a pet peeve.
Also 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.
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.
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.
Simple enough, no?
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.
Profits are mentioned but then only revenue is discussed, Revenue != Profit. Revenue can grow yeah but experimentation/token-maxing stage is ending and price wars have started, so I'm not sure about rising profits, AI Labs won't necessary be able to capture the margin(>>China<<), it might be companies/consumers who will capture most of it, if so there is a bubble - it is in AI labs with their trillion $ valuations and they might end up in a race to the bottom, BigTech is not in a bubble for the most part, they will endure a correction and fall back on their existing business.
Meanwhile, the major tech companies first financed this build out using the cash flow from their existing revenue. Once this became more difficult to justify, they literally set up companies to keep this off their books directly, and these companies are ultimately designed to take a loss and fail. But before reaching this failure stage and defaulting on debt and obligations, they went to the criminals on Wall Street and designed a financial product structured not all that different from mortgage backed securities. Also, as the "AI" aspects of their existing businesses expand, it eats into the traditional revenue generated by their core business, which if we remember, is the original collateral being used to justify all the debt in the first place. Now these core tech companies have to win "AI", whatever that ultimately means, to not only pay the debt from the build out, but to also replace their existing collateral from their original products. Think in terms of Google Search being replaced by AI. Maybe a clean transition, maybe not. Really just depends on the technology living up to the hype. If not, the hyperscaler companies designed to take a loss a fail, will fail and with it, the circular financing will pop the very real bubble that exists.
Speaking of IPO valuations as well, it's not to say that LLMs are not a valuable technology. Just look at what they have done to computer science already. But there may be increasingly limited returns on investment if every valuable problem to be solved isn't as well defined as logical computer science problems. Without solving the hallucination problem (as a result of the next token prediction limitations), the wide application of LLMs as being sold will never happen.
I do hear this position quite a lot though so I’d be interested to hear more about what I might be missing.
I don't think the nature of 'predicting the next token' is the problem. One could argue that any organism works the same way. The problem is that we've had million years of evolution to stabilize the platform such that the important parts of it work without that many hallucinations -- and even then that sometimes fails and you get failure modes like e.g. schizophrenia, where the context window gets confusing and the 'next actions' don't make that much sense anymore, seen from the outside.
An LLM never experienced evolutionary pressure the same way. There's no fundamental difference between how it was incentivized to answer correctly when asked about how many Rs there are in strawberry vs whether it is safe to leave a house through the second story window, even though for any physical organism, those two would be _very_ differently weighted during training.
I love the age of LLMs because people will post stuff like “the fact that I absolutely, unqualifiedly, fundamentally do not understand two separate things indicates that I understand at least one and maybe even both of them more than you do“ and then do that cool move where they pretend they were gonna high five you but don’t
it is a golden age of Posting
However, insight born of knowledge ignorance is worthless. I really don't care for your great insight about nuclear fusion if you're not a nuclear engineer or physicist working on nuclear fusion, because your insight is just a hypothesis in the void that is your ignorance.
Ironically TFA falls victim to the same thing. It doesn't seem like the author is an economist or an expert of corporate finance. Their assessment of whether something is a bubble or not has absolutely no value, and carries with it the arrogant implication that the hundreds of thousands of people who are actually experts at these things never had the little 2AM epiphany that they had.
It's a smaller version of the same delusion that produces crackpots.
I don't think current valuations need AGI to work. They just need enterprise and personal spending on AI to keep rising exponentially for a while.
We could absolutely wind up in a case where these companies are fairly close to a trillion dollars each, but not multiple trillions, at a reasonable multiple, all without AGI.
A theoretically perfect next token prediction machine has limitless utility. Predict the weather, stock market, lottery number, the next step of groundbreaking cancer research, etc etc. That's not the argument against current LLMs being capable of being AGI.
The "prediction" has nothing to do with predicting the future, it just means choosing the most likely next token for existing human text
The best next chess move is ___
The best stock to buy now is ___
I do. And it’s not good.
A lot of people have dismissed LLMs four years ago for this reason. "It cannot possibly do coding since it doesn't think, it just predicts the next token." On the other hand, Scott Alexander did say in 2019 that next token prediction is a brilliant idea and might cover a lot, including math proofs [1]. So I wouldn't say it is impossible for either 1) LLMs to be all the way to AGI or 2) LLMs to become creative enough to tell us what the AGI architecture is.
[1] https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...
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"
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.
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
also, power and cooling agreements are usually 10y too.
P.S: A Matlab/Physics simulation guy running for a Liberal party seat in Canada? Say more!
A bubble that funds all kind of crazy ideas and big investments into infrastructure work is great for laying the groundwork. But the end of the bubble won't mean the end of progress, or even necessarily a slowdown in progress
For proof, look at the fall in VWRL last week correlated with concerns over AI which hit nasdaq.
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.
Hopefully, people who need to pull from their 401k's soon have wound down to less risky positions. 401k exposure is just not the same thing as your house valuation tanking.
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.
Is this the consensus view now? I'm not any kind of denier and I do recognise that motivated thinking may cause me to overestimate my own value.
But trying to be brutally neutral I'm still not sure this is true. AI doesn't make certain classes of mistakes that humans do, granted. But I'm still surprised daily even in 2026Q4 at things it does seem to make a mess of. Usually, changes that are locally good but subtly globally poor.
I'm only slightly joking...
1. Revenue growth increases 2. There are more highly profitable uses cases like code.
Neither are guaranteed.
I also think the narrative that these companies are profitable on compute is erroneous. You can’t ignore the other costs and no they don’t magically go away. Anthropic can’t stop training new models now or in the foreseeable future. Gemini 4 Carbon is about to come out and is apparently competitive. Google has other revenue sources. They are in the ball game. They can wait for Anthropic to run out of money.
Actually there are two different things going on here
1. model layer companies: OpenAI, Anthropic
2. hyperscalers, data centres, hardware layer
When people say the bubble will pop, they usually mean the second one.
> I also think the narrative that these companies are profitable on compute is erroneous.
I don't think you read the Anthropic filing. It is substantially profitable if you exclude the one type of debt which was actually equity. It so happened that Amazon lent some money to Anthropic but it was tied to the valuation of Anthropic, which became a "problem" because Anthropic's valuation grew 10x. You wouldn't call this a loss in any other circumstance.
Anthropic filling shows negative EBITDA, how is that profitable? Revenue < (computing + personal + selling expenses). That is not a business, that is a money burning furnace.
That before even taking into account the huge amount of money they promised to datacenters (i.e. "build that for us, and we would rent it for 5 years", non cancellable).
What is the most expensive thing in an AI datacenter? The GPU! And what it's its lifespan? 3 - 5 years!
Bubble or not, there is no way AI spending is going to plummet. It will decrease, obviously, but thinking that after 5 trillions of investment everything is profit is so naive.
SW can use a ton of AI. Lots of other applications can't, and they don't need the super smart frontier versions either. We just don't need all that much compute to serve AI for these applications, and people won't need to pay the margins necessary to get access to that AI. SW alone is not going to be enough to pay for all the investment.
I don't buy brand loyalty either. If I can pay 1$/m for the same thing from someone as I pay OpenAI or Anthropic 10$/m for, I'll do that, which almost zero barrier to entry.
The buildout is paying stratospheric prices on the assumption they can charge that much on the other end, but if demand tapers off (or model efficiency doubles again), prices will start falling. Everyone talks about Jevon's, but I don't really see why I'd spend more on AI than I need to do the things I want. I already run out of stuff to ask it to do on a basic subscription, I hardly need more.
The crux is this: I can spend x for y results. If I spend more, I probably don't get 10x for 10y results - I probably get, say, 2y results. So on that equilibrium I just don't spend all that much more because the reward for all that extra spend is not worth it. And if models get better this tradeoff starts to look worse, since I get more of the super-effective benefits for cheaper. Now I'm spending 0.1x for y results, and spending even x for 2y might not make sense, let alone 10x for 3y.
People are already using AI to build them some things with CAD. Electronics too. But now what does AI need to be very good in it without an expert? Simulation and Optimizers. This again needs more compute.
And the agentic layer will be added to all big products out there. Everything you will do will consume AI tokens
Personal assistants dealing with your email, filling out paperwork, and booking reservations don't use a lot of tokens.
If every software developer paid $200/month, every academic paid what they pay for Matlab or Mathematica licenses, every YouTuber paid for whatever video processing, and every normal person paid a subscription comparable to their TV subscriptions and cell phone plans - does that justify the valuations?
The government pays for surveillance? Companies pay for customer service replacements and target advertisements?
It seems like there are a few small categories that use a lot of tokens, and a large category that doesn't need many at all.
> The government pays for surveillance? Companies pay for customer service replacements and target advertisements?
Yes definitely. All together that's probably within striking distance of a trillion dollars in annual revenue. Just TV subscriptions + cell phone plans in the U.S. alone is about 400 billion (leaving aside any other country).
Most large, mature tech companies are at 5-10x revenue to valuation ratios (and 10-20x EBIDTA to valuation ratios).
Of course whether you get there is a separate question, but any company that fulfilled what you're asking would pretty easily justify multi-trillion dollar valuations.
That $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.
It won't be about tokens in the long run, and I think the labs know that. It will be about long-term memory and user relationships, and who gets to own that data.
The disruption and upheaval has barely even started, IMHO.
1) There's a huge demand for AI.
2) AI is way too expensive for the companies building it.
Unless nobody is working on miniaturisation, then this reasoning might hold true.
As seen throughout history, any sufficiently absurd tech starts largescale (refrigerators, computer, radios) and eventually become portable.
Amortising data Center cost will take a long time. Within that time, if someone figures out how to make ai run on cheaper hardware, these data center cost will Become harder to recover.
> AI companies can also cut costs by no longer allowing people to use their products for free and temporarily suspending expensive AI research
That will just kill the industry.
Or datacenters are the old mainframes, and when they stop building them and squeezing the market, GPU prices go back to what they were before the frenzy or even lower, so anyone really interested can buy a decent GPU for a fair amount of money, and run models locally for peanuts. Meanwhile, dataframe owners go bankrupt.
Let's talk about that in 2-3 years. There is a very clear incentive to have models running locally, from data privacy to cost effectiveness. The software will pop up to easily power this, and if it is cheaper than non-open frontier models it will be used. This can be done today with a bit of hassle, and will only get easier. The consumer hardware vendors are clearly heading this way.
The beauty is the open models: the margins are going to be thin on proprietary models. The only thing they got going for it, is creating artificial scarcity. But the Chinese are able to buy them from secondary markets anyway, or via Jensen Huang's girlfriend.
Even if the US proprietary models are 6 months ahead, there's going to be a time when the competition is good enough. I believe we are already well past that point.
For example, I'd much rather pay for the highest intelligence model when designing something or developing something because the problems it can preempt will save me more money in the long run.
The whole issue is that intelligence (as measured in AA benchmarks for example) is not linear utility
I mention this in the article...
Sorry what? There are 10x if not more Linux installation vs Windows installation in the world. Linux powers everything, including billions of smartphones, where Windows is nowhere to be seen. Same for routers. And actual proper servers.
If it's like Windows vs Linux it means open-weight models are going to be incredibly more successful and touch just about everything. All the while being cheaper and working not a bit but much better.
Correct me if I am wrong on this, I am only a student of mathematics and by no means a researcher.
For the programming part, I think author already says that automated solutions require a good harness, and this is indeed correct, but in my experience building good harness is hard and agents can not do this currently without human closely guiding them. And in pre-LLM programming practice we did not need harness at all, or needed only small part of it: so, solving programming problems with LLM involves more work than without LLM, even if big part of this work can be automated.
The rest of the article seems to be a speculation mostly based on the premise that LLM can really replace human in the loop, which seems to be not the case currently.
I think you are right here. In the end it's probably more or less a software specification? I would love to have more formal verification in coding.
Still a Master student in CS. I attended some courses about defensive programming or programming by contract and also formal verification.
I can pop into any McDonald across the world and be 99.9% sure that the meal will be clean and taste like what I except McDonald to taste.
I don’t love fast food but I’ve been to a lot of them, across a wide enough range of country , to say that every other fast food chain struggles hard with that.
'[…] expect […]'
I'm Dutch and lived near the German border. The first time eating at a German McD's was about 25 years ago, and it was an experience. Because they changed the mayonnaise to German customs. In Germany, mayonnaise is sour, not sweet and the specific McD's version (which normally is yellow, and contains, I don't know, pickles?) was rather different.
As such, your 99,9% is overrated since there's even lots of local flavours and customs, but yes it is generally good enough, and if you consider intra culture, you're flat out right.
If that’s what Ai looks like they are in trouble.
The quality of models like Opus 5.5 are already here, smaller labs can mimick these with a little bit of a delay.
Alone the current status has massive impact.
The status quo will get just faster and cheaper.
Then we have labs like Deepmind, companies like Google and then countries fighting over it (US America vs. China). At this point its not just private VC investing in it, its also countries aka military budgets or Research Budgets.
In parallel universities are doing their research anyway and if an AI bubble would burst tomorrow, Open Source and community driven would finally have a little bit of time to coordinate.
There was just last week an article about open post-training data. I think we want this but as long as frontier is as fast as it is, its not fun to play catch up with a moving target.
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.
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.
Some indications in the text suggest that their insights are somewhat limited.
> In other words, once you ignore the cost of building infrastructure, AI is very profitable.
Once a provider stops such investments, they stop being "frontier" which makes them unattractive immediately. These two things are tightly connected.
> If the AI industry has to pay off 5 trillion dollars over the course of 20 years, that's 250 billion dollars a year. For context, Alphabet (parent company of Google and Youtube) reported earning almost 162 billion dollars of revenue just in 2019.
Revenue vs profit
> Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness
Anybody doing serious software engineering will wholeheartedly disagree.
> People can theoretically switch from model to model, but people tend to settle in with a product and develop brand loyalty. For example, I pretty much only use ChatGPT, not because I have any really reason to, but because it was the first LLM that I ever used
LLMs are just one piece of the tool chain, a mostly interchangeable one. Which one a concrete product uses is not of concern to the end user. This quote very much misses the role of LLMs and their providers in the big tech picture.
> In 2023, a memo from Google called "We Have No Moat, And Neither Does OpenAI" was leaked, which outlined how the open source community was keeping pace with AI labs
Labs releasing the weights to their models is not what is typically called the "open source community".
I could go on but it's just not a very well-argued opinion piece. Unclear how it made it to the HN front page.
To use an analogy, just like the scenario in post-apocalyptic movies where people eat each other, we are heading toward a grim end as a species.
And at least currently, keeping up in that arms race means a maximal push on growth at the expense of profitability. Realistically, this will go until we hit some bottleneck.
There is no logical contradiction even, just another race to the bottom, drying out of markets. The good ol' disruption playbook, like Amazon shrank or destroyed the markets to capture them, only now they are targeting all white collar jobs.
The conclusion starts: >I didn't write this article as some big takedown of the AI bubble theory. Even with this evidence, there are serious risks to the financial sustainability of the AI industry.
But the presented "evidence" is largely only: I don't know, nobody can tell the future. I mean there are also good reasons to be skeptic of skeptics like Ed Zitron, but Idk "modest valuation" seems like a big stretch. Also, I'd challenge the notion, that you'd only ever have to build that data centers _once_. Especially with the AI pace the chips are said to be replaced every three years, while the building itself could last 25 yrs. So even, if the run at the frontier cools down, I don't think running your data centers and models would come for free.
We can START discussing this when someone defines "worse" and we all agree on it. For SW engineering, we've been trying to define "good" for decades and we still don't know shit.
> There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for.
No, that's a total straw man. For example, I am afraid of how AI will change the world, but for that it doesn't have to stop producing slop. I'm afraid of how the slop is CURRENTLY changing the world, people and societies. Frankly, I don't even care whether someone ever pays for the slop. This isn't a "I'm going to lose my job" kind of fear, it's a "we're running education into the ground and rapidly transforming into a low-trust society" kind of fear.
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.
“AI can't replace lawyers and doctors, but they can automate their paperwork and free up time for them to do other things”
So my question is - in software engineering (where this has bitten hardest): does anyone know _anyone_ who is less busy and has time to “do other things”? Nope, nor me.
So yes I am doing many more "other" things. But those other things are still building features for my product.
I call this “SchadenFraud”: reveling in and playing up every little negative thing that happens in the AI world to make it look like it’s all just hype or completely irrational and will come crashing down any moment now.
There are enough people aggrieved by GenAI -- traditional media, creators, artists, coders, Mathematicians, and the list is fast growing fast -- that this kind of narrative will have an audience for quite a while.
They're not looking for solutions, they're just looking for rageviews. Worse, it gives folks the hope that things have not irrevocably changed and will go back to normal after the bubble pops.
Me, I’d rather get a stark but realistic picture of what’s happening now so I can prepare better for the future.
Also, great products don’t always make great money. Some companies just never figure out how to monetize and eventually go belly up. Users may not be willing to pay what you expect, and the amount they pay may not be able to pay back your debt. Not every LLM chat app is going to survive the next few years. That’s just how it is when there’s sudden growth. Our economy tends towards boom and bust cycles.
The question is how much? If they grow 5x, then these companies are very profitable, changed entire industries,... etc. Yet, the bubble still pops because investors assumed 10x at least.
Also Popeyes never had over $800 billion in uncancellable compute agreements. And I'm glad you're having fun with GPT, but I've moved all my projects to use DeepSeek 4.1 Flash. There are plenty of different providers. And as I use OpenRouter the change is one line.
The reason I think the PE ratios are lower than previous bubbles are because everyone already knows its a bubble. Also all these tech companies already have massive businesses and AI is not going to change that.
You complain that people take the bubble as a given. Yet you continue by doing what a lot of people on the other side do:
> Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness, and a good deal more expensive as well.
This isn't established. Like, at all. There's serious doubts as to whether AI-driven codebases remain maintainable, understandable and trustworthy over time. You look at a snapshot and conclude, while riding the hype wave.
My gut feeling is that there would be such a massive demand to buy them to run our own models.
I am also not sure there is a (structural) bubble.
I do speculate whether some companies will survive, also some of the big ones.
-------------
If everyone, or even the majority, believed that there is a bubble that's going to pop with near certainty, that would tank stock prices immediately. Why would anyone hold a stock they know is going to go down precipitously? If the majority of people know it's a bubble, the bubble immediately pops. AI stocks still have worth, so most people believe there is no bubble or that prices will go up before going down.
People talk about bubbles. They worry about bubbles. For the most part, though, they go on investing as though there isn't going to be a bubble. Every bubble looked like a solid investment to most people right up until it burst, or it wouldn't have been a bubble.
So, no, most people do not think AI is a bubble. That doesn't necessarily it's not though.
That seems to assume:
1. Those people are the invest-class and have control over what gets kept/pulled.
2. Those people don't think it'll pop later and that there's Great Fool Money to be had for just one more day at the tables.
Large companies have often changed the depreciation schedule for server hardware to 6 years rather than 3. I think it remains to be seen if this is justified as it’s not as though the pace of change in hardware has slowed down by 50% arbitrarily.
I think it’s pretty obvious to me that a data centre full of ampere GPUs is worth less than a data centre full of Blackwell GPUs. I suspect this is also why AI companies prefer to talk in terms of gigawatts of compute, as if efficiency per watt does not exist.
Data centre costs are not one-off. Some are, for buying land and building buildings and infrastructure. The actual compute however is a constant investment. I’m not familiar enough with the market to know what proportion of expenses are ongoing but I bet it’s higher than people think.
Anecdotally, I bought a new desktop over 7 years ago and it still feels blazingly fast with plenty of room to grow. And the cost of a new machine is prohibitive, so a replacement at this time doesn’t feel very compelling.
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.
And that's their only revenue stream
People assume this, but it is wrong. Check the S-1 for Q2, the $2.08 in revenue is for every $1.00 spent on "compute and infrastructure expenses" which also include R&D compute. (Chart is on page 137)
"Compute and infrastructure expenses primarily consist of costs attributable to operating our technology infrastructure, which supports the full range of workloads for our models, including research, model development, serving customers, and internal usage of Claude." page 146 in the S-1/A-1
It would be logical to assume that they show expense of compute required to run inference. Leaving training compute (which is huge expense) out.
It's almost as if they know something about the finances that the rest of us don't...
The trouble with Anthropic and OpenAI is, that not only do they need to get operationally profitable, they also need to be sufficiently profitable to provide a return on capital on all the money they have burned to date. Much of which has gone (and keeps going) not to assets but to expenses like the electricity bill. Thus, once the models are efficient enough to be served profitably, a relatively small amount of assets needs to be generating income for a relatively large amount of capital. It is my understanding, that currently the efficiency of the models does not grow even fast enough to outpace the need for new capital.
One more thing, that gets conveniently ignored when talking about AI cost is the cost of training the models. From one hand, there’s but a few institutions capable of building them and, from the other, it’s like the models grow on trees. Yes, you could run your open-weight model on your own hardware and pretend it’s profitable because you don’t have a comprehensive costing model and the infrastructure costs from security to electricity to risk are mixed in with your general cost base. But someone has spent astronomical amounts of money training that model you run and would probably like to get it back with some interest. Let’s hope that return is just money and not, say, the ability of a certain country to control the global software development process.
This sounds like a nightmare but so is using windows and office everyday and everyone is already using it.
It also fails to consider the impact of 'good enough.' A lot of the build-out right now is in anticipation of trying to push the frontier level. But there comes a time, a time we're already arguably past, where models being used for a specific task will already be able to complete that task efficiently. Going way beyond the present should enable LLMs to solve ever more esoteric problems, but in economic terms if a company's goal is to harvest corn, and your corn harvester works well enough, they're not going to care whether or not that corn harvester can also solve Fugazi's 7th Great Conjecture in exomathematics.
AGI and singularity are the Nuclear Fusion of energy.
Sounds like a valid argument until you realise McDonald's is a real estate business.
There are many problems with this article. I think the bubble is real and at some point the investor money will fall off a cliff.
The value is there but the cash burn rate is extreme to say the least; and the moat is only in what you haven't released yet.
The only reason why Google search is doing well is because it's a real estate business like McDonald's. And lately their search quality has been similarly crappy.
Let’s not even talk about the second headline that costs are constrained, while one company spends few dozens off billions per quarter.
Companies are investing in scaling models. Doesn't that mean that they'll need to keep investing to stay on the edge? After all, when they lose the edge they lose customers.
> For starters, the average person isn't going to use a locally hosted model, ever
If open weights weren't a problem for these companies, ram altman wouldn't have accused the people of stealing his compute.
> Outside of generative AI, self-driving cars are becoming a reality, with some evidence to suggest that they're even better than human drivers.
Haven’t they have been just around the corner for 10 years now? And the problem is not technology as such, but the fact you are inventing trains that use roadnetwork hardly large enough for cars?
> AI adoption takes time, but there's no reason to think that AI usage isn't going to massively skyrocket, even if AI doesn't get any better than it is today (and it certainly will get better). In other words, there's massive unmet demand for AI companies to profit from.
[Emphasis mine]. This might a good example of a person writing and thinking with an LLM and letting the model enchant you with its sycophantic bullshit. We might speculate endlessly about AI or any technology, but none of that is a basis on which you should throw money at, especially not as a retail investor. We can see value production of AI in software development, but the productivity increase promised is either not in the data, or it is too early to say yet. So the real valuation of AI is really difficult say still, a lot of things needs to happen, before it can be seen to produce concrete value increase. People need to stop really really fucking hating it for one.
It kinda pointless to talk whether there is a bubble or when it will pop if there is. It might happen today at market open or never. Timing it is a fools errand. Also it is kinda pointless to shout into the void how much you love/hate AI. Better use of time would be to develop the different systems and applications that will produce value for somebody. It all sounds too similar to crypto to me still…
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.
But if the shoeshine boy tells you it's a bubble, it's time to buy?
If only shoeshine boys still existed, I would know what to do.
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
dump n pump. day trading. long term strategies. build up for the next big thing or delusion or hysteria or lie.
if you read mainstream narratives like "AI bubble", translate it to several millions of people making lots of bucks via a lot of fraud mixed into some good, genuine, valuable work of productive people.
investors and wall street people doing anything financial is not tied to bottom up signals but to insider knowledge and top down supply chain LEGAL manipulation by people not directly involved in the actual work done.
or it's a wrap up ( made money, time to shift or prep something that will hypergrow again, maybe already re-pre-hyped ).
or it's reactions to having maxed out some part of the supply chain which had negative consequences to some other market which reduced overall gains enough to realize "oops", if we don't re-act now, future gains in other markets will be reduced almost catastrophically.
bubble talks always feels like a mix of hyped people really getting fucked over by fraud runners
the details are not too relevant for the moment as I can't have play money to invest but the narratives are interesting because they directly translate into what I read in the suggestions and advice, subliminal or direct, from people to other people.
"The AI Industry Doesn't Look Like Any Bubble We've Seen Before" - It looks exactly like the two railroad bubbles. Potential profit is there, in the future. But you have to invest in the infrastructure first. If you can't make a profit and pay back the loans fast enough, the infrastructure goes bye-bye, along with all the funds used to finance it.
"There's Still Plenty Of Capital To Raise" - IPO is not how you raise a trillion dollars. A million, sure. A billion, maybe? But a trillion puts retail investors - mom and pop - at risk. IPO is not how you raise funds for a railroad.
"Once AI companies become profitable" - there's the big red flag. This isn't Uber or Lyft that can float along on tens of millions in corporate hand-outs every quarter. They need 100x that much money, and nobody has it to hand out. So you can't wait around for profits. There's no magic cushion to float on.
"For starters, the average person isn't going to use a locally hosted model, ever" - They will in 4 years. "normal users don't want to have to deal with the hassle or expense of setting up something" - What hassle? You download a program with a fancy GUI and run it. There are a half dozen of these projects already, that provide ChatGPT on your computer from one binary. All you need is a GPU.
"Profits Can Grow, By A Lot" - Only if they remain cheaper than local hardware or open-weight inference providers. Which won't happen because they owe more money than they can make.
The sign of the bubble is all the investment made not based on stable returns, but on hype. The weird geniuses from San Francisco said the future will depend on this one technology, all work will be changed forever, and only their company can deliver it. If you don't get in now, you'll miss out on a fortune! That hype just doesn't work out. And the proof is here: the superhuman machine sucks at writing, the documents it creates are lies, and we seem to be spending more on it than we get out of it. Because it's not magic. It's just a machine.
That's just not true. Profit is there now. If the hyperscalers stopped building new infra tomorrow, they would just roll in the profits available from what exists today!
Totally unlike the railroad bubbles which built out years and years ahead with no customers and then collapsed.
> "There's Still Plenty Of Capital To Raise" - IPO is not how you raise a trillion dollars. A million, sure. A billion, maybe? But a trillion puts retail investors - mom and pop - at risk. IPO is not how you raise funds for a railroad.
You're totally confused about what an IPO is. You're confusing the valuation of Anthropic with what they will raise. They intend to offer about $100B in stock, their valuation is at 1T.
> "Once AI companies become profitable" - there's the big red flag. This isn't Uber or Lyft that can float along on tens of millions in corporate hand-outs every quarter. They need 100x that much money, and nobody has it to hand out. So you can't wait around for profits. There's no magic cushion to float on.
Most AI companies are profitable aside from expenditures to build new infrastructure to reap more profits. That's a very healthy sign.
> "For starters, the average person isn't going to use a locally hosted model, ever" - They will in 4 years. "normal users don't want to have to deal with the hassle or expense of setting up something" - What hassle? You download a program with a fancy GUI and run it. There are a half dozen of these projects already, that provide ChatGPT on your computer from one binary. All you need is a GPU.
Uhuh. A GPU. In your phone? In your laptop? The average laptop in the US is sold for about $700, desktop for about $1k. A good GPU to run a reasonable model is $10k+. It makes zero financial sense for most people to run their own inference. The issue isn't, and never was, the software, it's the hardware. And one thing we've seen from the past years of growth is that people don't look at the models from 3 years ago and say "Oh well, that's good enough, at least I can run it slowly on my $5k+ desktop". Until that happens this is a fantasy.
They owe hundreds of billions in loans on datacenters that aren't built yet and don't currently have capacity for the new users and additional tokens they promise are coming. If they don't build capacity the increase of demand would cause more outages and higher prices which would crater both their use and valuation. They have to grow or die.
> They intend to offer about $100B in stock, their valuation is at 1T
That's one company, the aggregate debt of the US AI industry approaches 1T.
> Most AI companies are profitable aside from expenditures to build new infrastructure
Which they have to complete to make the profit they promised which they need to pay off the loans they already have.
> A good GPU to run a reasonable model is $10k+
Today. Due to supply and demand. Funny thing, supply and demand. It's very reliable. High demand makes supply short for a while, but then causes supply to go up. When supply goes up, eventually demand goes down, which brings prices down. The chip factories and economies of scale that will be complete in a few years will send prices crashing, as they have before. Which might bankrupt the remaining chip companies.
"Reasonable model" isn't a thing. Many different models of different size have capacity to do many tasks. I have a laptop from 3 years ago that runs local AI models. My phone from 5 years ago runs local AI models. And any strong CPU can run models (no GPU needed). Speech to text, image/video generation, image classification, research, analysis, computer use, troubleshooting, etc, all works on tiny models. I think you don't understand the tech here. Remember that many people spend a grand every year just for a newer iPhone/Samsung. They'll spend the same for one with better AI and more RAM.
- Anthropic is already expected to have junk bond status; this is going to make dealing with their existing debt rough while costs likely continue to rise
- Datacenters are 'cheap' once they come online, but that assumes they will. Power infrastructure hasn't been able to keep up, and several pending datacenters (I've seen estimates in the 50% range) meant for AI growth have been significantly delayed because of it.
- The US has a lot of debt coming up in 2028. The AI investments have been far more lucrative than the traditional 30-year bond. The AI bubble doesn't have to pop at all for the global market to plunge.
Exactly the opposite. This is good for a tech company IPO which opens up all kinds of bond options. Most dont even have a bond rating! BB+ is a get.
If he is not accounting for this, then he is not saying anything of value whatsoever. So many people are still operating on vibes instead of these companies' actual financial positions, and "because Claude does cool stuff for me!".
Its not clear that the infrastructure will ever be paid back. Its also not clear that once models are trained and distilled that the infrastructure will be useful. Ignoring 1.9 trillion dollars of debt is probably not the greatest financial analysis.
>However, there's no reason for why this level of spending has to be maintained in the first place.
Exactly. So Anthropic and OpenAI might be holding the bags for latecomers who distill their models and get ahead. Sort of the Commodore64 and Atari of the AI space.
>Once all the data centres are built, AI spending is going to plummet.
Well data centers need maintenance, and GPUs and similar do have a very short lifespan. But even if spending plummets repayments on existing debt needs to be accounted for.
>In a pinch, AI companies can also cut costs by no longer allowing people to use their products for free and temporarily suspending expensive AI research.
Research keeps them in the headlines, see above about the latecomers.
>For context, Alphabet (parent company of Google and Youtube) reported earning almost 162 billion dollars of revenue just in 2019. Adjusted for inflation, that's an even more impressive 213 billion dollars.
Yep, so all Open AI or Anthropic need to do is get acquired by a much more profitable business to pay back their debt.
>at some point, the rate of increase has to level off, but that doesn't have to happen anytime soon.
The rate of increase of revenue in the AI sector also doesnt need to be apportioned to OpenAI or Anthropic. If it wasnt for their generous loss leading subscriptions or their rnd, theres not much there to hold on to people.
>it's hard for me to see AI consumption not being 10x what it is now.
Right AI consumption will increase very likely. But will it increase at the cost of an already subsidised subscription, or increase the use of cheap Deepseek 4.1 tokens.
>For starters, the average person isn't going to use a locally hosted model, ever.
Isnt this expressly where Apple Intelligence is heading? Building small low cost models that run on Apple hardware? If I was nVidia I would have both plans being run down at the same time, looking towards selling GPUs to hyperscalers on one side, and working with OEMs to deploy AI coprocessors in laptops on the other side.
>For example, I pretty much only use ChatGPT, not because I have any really reason to, but because it was the first LLM that I ever used.
N = 1. OpenAI is already what halfway to their trust thermocline anyway?
>A potential way for a bubble to pop is for the companies involved to run out of capital to spend while they grow, causing them to fail.
Isnt Anthropic already down to BB+?
> NVIDIA are all AI companies that have seen their market capitalization explode over the past few years
NVIDIA is a hardware company that can has so much demand on their products they will likely survive 3 AI market collapses.
>Alphabet, Meta, and NVIDIA
Alphabet, Meta and NVidia all have other sources of revenue. They are companies building AI, not AI companies.
>When the dot-com bubble happened, it was largely driven by companies that couldn't turn a profit or had P/E ratios in the hundreds.
Anthropic and OpenAI dont have "E" (as in earnings)
Anthropics Debt to Equity is 363%.
>A potential way that this could be a bubble is if AI proves to be a fad.
A potential way this could be a bubble is if the makers of consumer devices point customers at cheaper or local models as part of their onboarding experience. OpenAI and Anthropic get thoroughly zuckerberged by the people holding the primary relationship with the customer base.
Or perhaps, the funding to the top 1% of token purchasers also turns out to be loans, and that dries up too.
https://www.pymnts.com/news/artificial-intelligence/2026/ope...
>As one final note, I don't think the rhetoric about the AI bubble is really about finances.
The people bringing it up every 10 seconds are doing it to spread FUD about AI. That doesnt really change the financial picture.
One thing to keep in mind when you're "debating" things online is you're actually debating with a tag team of people, with a whole spectrum of views. And at any point people with stronger arguments (starting from different precepts) will reply while people with weaker arguments will fall silent.
That's why "the average hacker news poster" is simultaneously rich and poor, powerful and powerless.
Because hardware doesn’t need updates. Newer hardware is more capable which gives competitors with newer hardware an advantage.
> Outside of these expenses, AI doesn't cost that much to run
Outside the expensive stuff it’s cheap.
The rapid progress of multiple closed frontier models, along with the rapid progress of open models, suggests that LLMs will be like electric motors, not like social media. Are electric motors worth a huge amount of money economically? Sure. But none of the companies who invented electric motors are especially valuable today, because the technology became a commodity.
A 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.
> 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.
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.
I guess they mean as far as AI being this utopian cure all for the ills of the world that some might have us believe is coming, its BS?
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.
But that is the potential issue. It is valued as it will and that just isn’t proven.
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.
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
So, it has already mostly disappeared, and the lack of practical applications means it will continue to bleed money. Software devs aren't using AI either for the bulk of smaller tasks that require teamwork with non-technical stakeholders.
Yes, it totally can disappear from society. The remaining use cases go with local LLMs and specialized software built around it. From the user's perspective, that's the way things always were and we'll be back to "the way it was" when the bubble is done bursting.
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?
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.
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.
“Its a bubble”
“Its a bubble”
“Its a bubble”
“Maybe its not a bubble”
*bubble pops*
every time.
My viewpoint also shifted around the shape of the bubble. The technology isn't going away, obviously, but the valuations of these companies like OpenAI and Anthropic feels divorced from reality. Inference continues to go down, most folks don't need frontier models for common tasks (including coding), and local models provide a competitive edge in several domains the frontier labs cannot (namely value and privacy). Combined with demand still largely subsidized and the companies being deeply unprofitable, and I just don't see the investments in Anthropic or OpenAI or the sort actually paying off. That is the bubble I'm concerned about, not the tech itself.
...now when the debts get written off and all this compute is just sitting around idle, available for bottom-dollar prices to try and eke out any sort of ROI for whichever creditor(s) inherit the assets? That's going to be pretty cool times.
I look forward to these types of posts fading in frequency much more so than AI use itself.
Matteo's analysis is very flawed though. For instance, he projects that the outcome is "devastating the entire economy". Why would that have to be the case though?
The AI bubble can collapse partially. There are tons of examples of how this has happened in the past. Not everything is dot-com 2.0. What we see right now is a "consolidation" bordering on partial collapse of some of the companies' projected revenues. It makes sense to assume that there is not a complete collapse, and some companies will end up dominating more than others.
To say that it is (essentially) taken for granted isn’t a great start to a rebuke. You have to engage with the arguments that they have, and they have presented arguments. Just see born-again clanker Dan Luu respond to Zitron’s claims.[1]
I don’t see any engagement with skeptic’s arguments in this piece.
[1] https://danluu.com/zitron/
> In other words, there's massive unmet demand for AI companies to profit from.
Of course. There are hundreds of millions of white collar workers that are jealuously hoarding their unrightful wages.
> There's a weird contradiction you see from AI skeptics where they're afraid of how AI will change the world, but also seem convinced that AI will only ever produce slop that nobody would ever pay money for. You can't really have it both ways here.
But you can. A bubble of this magnitude will negatively impact the world, not just AI investor cretins. You can take the extreme position that AI is useless and still be worried about the fallout.
> It's completely valid to be afraid of AI taking your job, but money is going to change hands when that happens.
The conjunction here implies a contrast which I fail to see.
> As one final note, I don't think the rhetoric about the AI bubble is really about finances. Conversation about the AI bubble is too widespread for it to be a contrarian position, and too gleeful to be driven by investors' fear. More than anything, I think the supposed AI bubble has become such a large talking point because it represents the hope that things can go back to how they used to be. People are afraid of what's going to happen with AI, [...]
The always-amazing observation that your intellectual opponents have motivated reasoning. Likewise, most AI positives have motivated reasoning, like that they can enrich themselves or that they are getting so much code out of their current subscription prices.
When I write about AI it’s clear when I argue against AI itself and when I am whining about my boo-boo thoughts about losing all of my economic prospects.
> I’ve applied to seek the Liberal Party of Canada nomination
That tracks.
If large data centers cannot secure free power connections paid for by everyone else, they cannot make any of this profitable. The whole thing was a scam from the start. This is why cloud AI companies bought up so much pc and server hardware. Artificial scarcity was the only way to slow down local llm adoption and sell the false narrative that cloud AI has value.
Plucking a graphics card out of every home and centralizing the cards to a handful of large data centers that require a trillion dollars in power grid upgrades is an idea so dumb, we should all be laughing at it. They even have to use active cooling or evaporate drinking water instead of free air cooling. The consumer doesn't benefit in any way from this. Consumers would essentially be paying more just to be datamined.
LLMs are not a bubble, large data centers are a bubble. Look at how large data centers are now the first target in any war. A graphics card in every home, doesn't give someone a concentrated target. National security makes large data centers extremely bad.
At it's core, running a top-tier LLM means linear load of trillion+ weights from RAM for every token. The cost of this operation absolutely dwarfs the cost of doing the actual math. If you are only serving a single user, you can only amortize this over a handful of token streams, times like 2-4 for speculative decoding. If you are running in a datacenter, you can batch thousands of streams, and get correspondingly much more tokens out of the same expenditure of energy and hardware.
A home- or office-oriented workstation will have much less power-hungry fans (go compare a Noctua datasheet to a server fan datasheet - the Noctua wipes the floor with a server fan if measured by CFM/W, and the internal temperature rise is mostly controlled by airflow and not by static pressure). And in some climates and times of year the marginal facility HVAC cost associated with running a computer may be zero or even negative.
And lower utilization hardware may run in on a more efficient point on the power-vs-performance curve. [0]
That being said, your overall point is correct.
[0] For a dramatic demonstration of how much this curve matters, you can run a non-CPU-saturating, non-latency-sensitive workload on various Intel CPUs with and without turbo on. Seeing a 2x or greater difference in reported power is quite possible. (Intel is terribly at writing platform firmware and the default behavior may be wildly suboptimal.)
Fair enough. But the real problem, it seems, is that people love slop.
My family is trying to sell a house in the South of France; the real estate people are sending us 500-words emails every so often, that are so obviously AI it hurts. Well written, and content free. Nobody cares. I'm the only one having issues with it.
At work, a new team let Claude do all their work and write all their status reports. Everybody seems to love it. It's so fast! And mostly correct! And so fast to correct when it's wrong! And here too, I'm the only one having issues with it.
A Canadian author wrote a book in French that turned out to be 100% AI ("C'était ça ou mourir"). Before that was revealed, every critic loved it, in Canada, in France and everywhere else (25 translations were started). The public loved it too: it's one of the top best sellers, and success continued even after the "scandal".
We are drowning in slop and nobody cares.
If that's true for every AI output is up for discussion, but it's reasonable that if someone is skeptical about the technology, they will also be skeptical about the quality.
That the tech is taking over many areas is not a stretch at all, that's just something that can be observed. Big companies (not just big tech) have been trying to get away with lowering (cost cutting) the quality of their products for a long time, AI lends itself to the same purpose, and will (and already is) obviously be used for it. Starts with customer communication as you mentioned, and customer service, and believe it or not I don't think every company should generate their own tech stack and software for their little products.
And, playing devils advocate for a moment, does it matter if a robot or a human authored it if people like it? People loved Magic Eye pictures in the 90s and they were computer generated too.
But when I ask my realtor questions, he doesn't know anything. I get the same answers by typing into ChatGPT.
When I ask the Claude team at work about why this or that, they don't have a clue. They "will get back to me", which means they'll forward the question to Claude and Claude will output another 500-words email with a lot of things that are obvious and not part of the question, another couple of assumptions that are plainly false, and in the middle of it, sometimes, yes, an interesting insight. But the insight doesn't come from the people sending the email, they didn't produce it, they don't see it, and they never act on it.
This is all just exhausting.
Not going to comment on your workplace specifically, but I’ve found this to be an issue with other industries like real estate long before ChatGPT were a thing.
The problem is the people who pickup the phone, greet you at the shop door, or take the photos, they’re the lowest paid juniors and thus know nothing.
So in most industries, you were always lucky to get through to someone with knowledge on the first attempt.
The problem with AI is that "someone" may not exist.
I think the bigger risk is what happens when they retire if we are not training the next generation of professionals.
But we are talking about popular culture here. That was never an industry where quality was measured by artistic excellence. It's an industry that is entirely motivated by mass appeal. Thus something being popular IS the measure of success.
So if AI is slop, then so is 99% of the human-made content it's replacing.
That's not really the point. Critics the world over LOVED the book; they only distanced themselves from it when it became clear it was AI, but when they didn't know, they couldn't stop raving about it.