alt Hacker NewsI'm not convinced the AI bubble exists
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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.
The logic here is so flawed it suggests to me a significant lack of financial understanding. For example:
>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. Adjusted for inflation, that's an even more impressive 213 billion dollars.
Alphabet's REVENUE was $162B, not earnings! You can't use all your revenue to pay off your debts because you have to use some of your revenue to pay operating and other expenses. So even if AI generates as much revenue as Alphabet did in 2019 (adjusted for inflation), there is no chance it could pay back $250B/year.
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.
Big red flags that I think point the other way:
- anthropic IPO repricing
- pareto frontier gap decreasing by the month between open and closed models
- SpaceX disappoinment
- enterprise buyers as main consumers of closed tokens are being displaced by OS competition for pennies
- job loss and entry level hiring collapse
- massive discounts of current data center contract debt
- consumer open weights model efficiency improving constantly
- the Chinese arms race
- a possible rapid downward spiral that may force governments to step in and subsidize
Am I missing anything?
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
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.
> Profits Can Grow, By A Lot
Profits are mentioned but then only revenue is discussed, Revenue != Profit. Revenue can grow yeah but experimentation/token-maxing stage is ending and price wars have started, so I'm not sure about rising profits, AI Labs won't necessary be able to capture the margin(>>China<<), it might be companies/consumers who will capture most of it, if so there is a bubble - it is in AI labs with their trillion $ valuations and they might end up in a race to the bottom, BigTech is not in a bubble for the most part, they will endure a correction and fall back on their existing business.
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.
McDonalds doesn't have a moat? Eh - yes they do.
They've global procurement, the franchise model means the franchisee shoulders a lot of costs, they own the land the restaurant is on in 50%+ of the locations, and they own the technology system that underpins their app + ordering.
It's a massive moat.
I dont know if the valuations make sense, but the usability is all there and so are the potential profits. Over a decade, that's probably all that matters. If the valuations pop or dip (anytime soon), I think that's mostly irrelevant. I get that the press probably doesn't like to hear this boring conclusion.
P.S: A Matlab/Physics simulation guy running for a Liberal party seat in Canada? Say more!
I'm not imaginative enough to see more use cases and who will pay. You can burn a lot of tokens and dollars on math proofs. Same for white hats finding vulnerabilities in all the software. Generating video.
Personal assistants dealing with your email, filling out paperwork, and booking reservations don't use a lot of tokens.
If every software developer paid $200/month, every academic paid what they pay for Matlab or Mathematica licenses, every YouTuber paid for whatever video processing, and every normal person paid a subscription comparable to their TV subscriptions and cell phone plans - does that justify the valuations?
The government pays for surveillance? Companies pay for customer service replacements and target advertisements?
It seems like there are a few small categories that use a lot of tokens, and a large category that doesn't need many at all.
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.
I think there's just some core arguments here that you can totally reasonably disagree with
>In theory, an AI company has to be worried about their users switching over to a competitor or using an open source model instead. In practice, this isn't a major concern. For starters, the average person...
There's two things to disagree with here. First, this isn't theory, I work at a company and use AI heavily, switching model is as simple as changing a line in a json file. And would I do it? I've done it like 5 times this year. Claude was good, then openai was for a while, then there were times gemini was great for quick trivial tasks, now I'm mainly back on opus-5-5 with astra for some more complex tasks. Secondly, I'm not the average person, no, I'm the person openai and anthropic actually make money from. Sure, a consumer might be stickier, but so far they're not really making money from consumers, they're mainly doing B2B.
The core thing that bugs me is not so much that AI is going to be massively unprofitable, I think there's definitely going to be profitable companies. I just think if you're Google, the best case scenario is you're transitioning from fantastic economies of scale in a monopoly business to a business with costs roughly proportional to revenue and no moat. That is really bad for Google and it's not unreasonable to think it's motivation enough for them to try and undercut Anthropic and OpenAI until they're bankrupt.
> 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.
> 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 reason why AI companies are spending so much money is to rollout data centres to run the AI in the first place. These expenses are one-off purchases, not regular operating expenses. Once all the data centres are built, AI spending is going to plummet
Unless nobody is working on miniaturisation, then this reasoning might hold true.
As seen throughout history, any sufficiently absurd tech starts largescale (refrigerators, computer, radios) and eventually become portable.
Amortising data Center cost will take a long time. Within that time, if someone figures out how to make ai run on cheaper hardware, these data center cost will Become harder to recover.
> AI companies can also cut costs by no longer allowing people to use their products for free and temporarily suspending expensive AI research
That will just kill the industry.
>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.
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 $2 to the dollar when competitors are taking only pennies of profit.
Two things can be true at the same time:
1) There's a huge demand for AI.
2) AI is way too expensive for the companies building it.
The conclusion I've come to is that AI is still revolutionary, and the buildout is probably necessary to some degree, but it is way overestimating the demand and pricing power providers will have.
SW can use a ton of AI. Lots of other applications can't, and they don't need the super smart frontier versions either. We just don't need all that much compute to serve AI for these applications, and people won't need to pay the margins necessary to get access to that AI. SW alone is not going to be enough to pay for all the investment.
I don't buy brand loyalty either. If I can pay 1$/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.
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.
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.
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.
> When a bubble happens, it's usually preceded by the companies involved reaching obviously inflated evaluations, which makes the burst obvious in hindsight. With public AI companies, we don't see that happening. 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. If the increase in their valuations were irrational (or even just in expectation of future growth as opposed to current earnings), you would expect the P/E ratios to go up. Instead, their valuations look exactly what we would expect from a healthy trillion dollar company. When the dot-com bubble happened, it was largely driven by companies that couldn't turn a profit or had P/E ratios in the hundreds. I'm not going to pretend that I'm an expert on financial bubbles, but I've never heard of a bubble where the companies involved had such a modest valuation based on their earnings, even if their earnings (and therefore their evaluations) are enormous.
My worry isn't so mcuh the large players ability to raise funding, it's the long long tail of "AI" in everything else and especially small startup companies that looks more worrying to me.
At the moment you have young grads getting $20-30 million with basically limited evaluation of their skills, all over the place. The area I am interested in is physics and engineering, and the number of Physics AI startups for where there there are not physicists or mathematicians with at least PhDs in numerical computing as founders for example looks to me like a total mess. And I keep seeing things where very clearly solvers haven't converged then getting used as training data and publicised in press releases from companies like this. I assume that most technical areas are seeing similar. This is where the dotcom bubble comparisons are most apt.
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.
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.
Your post repeatedly confuses evidence that the AI industry is commercially viable with evidence that the AI industry's current valuations and investment levels are economically sustainable.
When I look at the current capacity of AI, it really feels like there's so much potential for frontier models to be used that just aren't being applied yet. Most (or maybe even all) programmers and mathematicians are worse at their respective field than an AI swarm equipped with a good harness, and a good deal more expensive as well.
Correct me if I am wrong on this, I am only a student of mathematics and by no means a researcher.
For the programming part, I think author already says that automated solutions require a good harness, and this is indeed correct, but in my experience building good harness is hard and agents can not do this currently without human closely guiding them. And in pre-LLM programming practice we did not need harness at all, or needed only small part of it: so, solving programming problems with LLM involves more work than without LLM, even if big part of this work can be automated.
The rest of the article seems to be a speculation mostly based on the premise that LLM can really replace human in the loop, which seems to be not the case currently.
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.
> These expenses are one-off purchases, not regular operating expenses.
But no one really knows what the operating expenses are, Anthropics s1 does not mention them as far as I read them.
I mostly use Chinese models now. Last 30 days the average token price has been 98% lower than OpenAI. This from my dashboard today. So I now use 10x the tokens on the Chinese models. More people will discover this. I can’t understand how OpenAI can recapture what they have spent.
>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.
First mistake. No. Data centers are depreciating. And they're one or two hardware changes away from having most of the hardware in them worthless (because even if you're contend to keep running those, your competitors would move to the next tech).
>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. This partially offsets the risk of losing customers to a different product.
Never helped browser vendors maintain their base. Not even social networks, where the graph serves as an incentive to stay. And there's also generational churn. Facebook is a boomer wasteland now, for example.
Unless I misread it, this article simply doesn’t mention training or the cost of it.
Is it me or is this a lot of extremely dense information?
Is this worth reading? I have to ask because of the anime avatar, which strongly discredits the author all by itself.
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.
> When a bubble happens, it's usually preceded by the companies involved reaching obviously inflated evaluations, which makes the burst obvious in hindsight.
I think this highlights a big issue with both sides of the "AI is/isn't a bubble" debate - the idea that there's an established pattern that economic bubbles cohere to. There really isn't, and economists haven't been able to find a consistent pattern that past bubbles have conformed too (that isn't also the case of non-bubbles). "Bubbles for Farma" is a cool paper that tries to do this[0].
Like everyone else, I have my own opinions on whether there is/isn't a current AI bubble. But we should all stay humble and try and avoid stating anything as if it's an absolute fact.
[0] https://www.nber.org/system/files/working_papers/w23191/w231...
Just because a technology is going to be successful in the future doesn't mean that the current beliefs are accurate of which company is going to capture the most profits from that technology.
The internet is successful and cisco is having a blast but its valuation has still been enormously wrong during the dotcom bubble. Same goes for pets.com, etc.
So in other words, maybe the valuation of some companies is totally wrong. Maybe Anthropic is not worth two trillion, or even a hundred billion, but Nvidia is worth 20 trillion. Maybe it's the other way around. Maybe the legal framework changes and the value will be captured by the major rights holders, reddit, twitter, bertelsmann, etc.
I think the biggest problem is people are conflating current AI being useful with current AI companies being financially viable. Dot com bubble had its winners and losers but the biggest thing is that many of the current AI companies are valued at prices the winners were decades after the dot com bubble. My prediction the big 3 infrastructure AMZ, MSFT, GOOG will lead openai and anthropic to a point where they owe them trillions in payments and use it to take them over.
What exactly are the author's credentials?
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.
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.
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.
What people don't realize is the exponential runaway growth when AI can make its own buyers.
I am increasingly open to the possibility that AI is undervalued. There have been recent applications of GPT 6.1 to the client business that genuinely threaten to fully eliminate their development team and put the business in the captain's chair again. I'm inches away from convincing them they have what it takes to prompt the robot to manage their codebase 100%. AI adoption is entirely a people problem now. It's a much better developer than me in most ways the business cares about.
Everyone keeps pricing this as a technology thing for some reason. This is more like a new kind of thermodynamic cycle than a new kind of software. The downstream applications are inevitable. We can't continue milling grain by using live animals walking in circles if we wish to remain competitive.
I think the bubble narrative is a necessity for some technology people. It now comes off as a fantasy that this will all blow over soon and we might go back to some semblance of normal. I'd argue that we've burned the proverbial ships at this point. There is no going back.
Think about how far we've come in such a short time. It's barely been one year of competent coding models. Some parts of tech/media seem only impressed by instant and globally successful results for some reason. Something this disruptive takes time. It took two fucking decades before I had a broadband internet connection. There are massive crow eating buffets that are still attended by those who missed the eventual consequences of the internet.
> 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.
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.
Great post. Entirely agree
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.
>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.
> 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.
Fatal misunderstanding.
Datacenters are not warehouses. Amazon.com being borderline-profitable owing to massive ongoing capex on warehouse expansion is not at all like AWS becoming borderline-profitable owing to massive ongoing capex on LLM datacenter build-out and SOTA model training (it's CAPEX, but carries OPEX affect because of GPU depreciation schedules and competitive hardware upgrade cycles), and opex of delivering LLM inference services, and being able to price it all competitively.
(edit: expand explanation of why it's a misunderstanding)