AI Bubble Comparisons

Drawing parallels to dotcom bubble, biotech booms, and questions about whether current AI investment levels are sustainable or represent irrational exuberance

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The debate over an impending AI bubble centers on whether massive capital expenditures represent a sustainable shift to "Computing 2.0" or an irrational frenzy destined to leave investors with "digital dust." Drawing parallels to the dotcom and biotech booms, some argue that even a market crash would benefit humanity by leaving behind permanent infrastructure and research breakthroughs that—much like generic drugs—can be repurposed even if the original providers collapse. However, skeptics warn that current token pricing is heavily subsidized and that the math for trillion-dollar valuations fails to add up, especially if hyper-specialized hardware becomes obsolete before the massive debt used to build it is serviced. Ultimately, the community remains divided on whether AI’s tangible productivity gains can outrun the "circular financing" and "irrational exuberance" currently driving the market.

65 comments tagged with this topic

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"So you have on one end the token revenue trending down, on the other end the training cost going up for the next frontier models, and you need to pay back your 10y debt." Not necessarily, the bond holders could simply take a massive hair cut and lose shitloads of money. On the topic of bubbles and exuberance, Jeff Bezos made the salient point that there was a massive over-invested biotech boom in the 1990s and tons of sophisticated investors ended up losing lots of money. But humanity still kept the medical advancements made by the boom. Stocks going down didn't un-research drugs, and it won't un-research new GPUs or un-build datacenters.
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> Stocks going down didn't un-research drugs Drugs cost pennies to manufacture after they are researched and make their way through the approval pipeline. There are many generic drug manufacturers who can work off the existing formulas. The more apt comparison is that LLMs won't be un-trained. Opus 4.8 now exists. Even if Anthropic somehow went bankrupt, that particular asset could, at the very least, be sold for proverbial pennies on the dollar to a "generic" inference provider.
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Research does get lost over time. The whole point of the patent system is keeping that from happening; if the drug company goes bankrupt, even if they lose all their internal documentation in the process, hopefully the patents and other public paperwork provides enough information for an unrelated company -- either having acquired the patent rights, or after the patent period ends -- to reconstruct the processes with less investment then the original research. If a bankrupt AI company maintains enough of a skeleton crew to consolidate and archive its intellectual property it could be sold off to another company, but there are also timelines where it all ends up digital dust in the wind.
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Or locked away in litigation for decades… See what became of the Amiga
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Those data centers are specifically for AI workloads. Let’s say everything crashes and we now have all the data centers, what do you do with them? GPU are pretty specialized hardware, without AI a data center full of outdated graphics cards isn’t really too valuable. It’s really not obvious the infrastructure we are building for AI stuff is something that will benefit humanity over time. Without talking about the fact that bubbles are extremely destructive. Bezos is obviously someone who came out ok from the dotcom bubble but we are talking about something that destroys a lot of value globally. That has real, direct consequences, not just investors losing some money. The US economy is currently only growing because of the AI bet
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AI data centers are being already used at max capacity, aren't they? I have a hard time imagining people would suddenly use AI less than they do as of today, let alone collectively drop it altogether. So the worst case scenario is that they'd need to be auctioned off way under what they'd be worth now, but still for someone to use them for AI. Dotcom infra buildup was completely different, in that it wasn't even close to being all utilized.
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But the parent comment was that one of the bigger costs in these data centers was the interest expense on the borrowed money. A restructuring removes or heavily reduces that amount. The fiber laid during the dotcom bubble never paid back the investors or lenders, but it's still profitably connecting customers all these years later.
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It’s true once built the data center can operate right up to a financed data center value of zero. The investors will loose money but the costs of AI will go down as they do
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Isn't something like 90% of the fiber laid during the dot com bubble still dark?
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Yup, that is the real economic benefit of bankruptcy - a reset.
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> Jeff Bezos made the salient point... Big AI investor tells us that investing in AI is good. Oh, the surprise! Does that invalidate this point? Yes. Because it makes no sense. The big money is not going to R&D but to build infrastructure that will be outdated in 5 years.
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Current AI datacenter/model development investment rate is roughly 1T/year. That's a lot. But the US economy is 33T/year. So the investment pays back (roughly) over ten years if, each year, the AI investments increase overall productivity by 0.6%, assuming the AI companies can capture half of the value of that productivity gain. > „[AI vendors are] paying for a fixed cost with a depreciating commodity“ That's just a confusing way to say you don't think future models will be worth the development costs. Because if future models are significantly better, why would the price of tokens to access those models deprecate?
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These are similar numbers to the dotcom bubble. With GDP growth and the percentage of productivity AI contributes staying the same in this scenario this requires regular gains in revenue or growth. If things just stumble, like with most datacenters going unbuilt the bubble will pop.
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The $1T number seems more promises than reality, which is closer to the $300B to $500B level. Still a big number, but between a third and a half of the value used in the popular media.
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I really wouldn’t be surprised if we saw some of these data centers scrapped in the next few years
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Seriously, they’re trying to justify trillion+ IPO’s while setting piles of money on fire, prices aren’t going DOWN.
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They aren't going down, but in the meantime they'll cover their ass by bribing their way into the S&P 500 and then use your 60 year old mother's 401k and teacher's pension to fund their risky capital expenditure.
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> Do we know that AI providers are going to keep these per-token prices, or eventually lower them because of competition from China? Raise, they are going to raise the prices. We will spend more on AI infrastructure in 2026 and 2027 than the gross sales of the entire global software and services sector. Current pricing is at a major loss for current providers.
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They're going to need to bring in a few trillion dollars fast to meet wall street expectations. Expect prices to rise.
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> Do we know that AI providers are going to keep these per-token prices, or eventually lower them because of competition from China? I genuinely do not know how prices can get lower from the current major providers in NA without the whole market collapsing. Everyone is spending copious amounts of money to presumably make more money back.
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I agree with all of this. So my question remains the same: How are the players investing 100s of billions in buildout going to hope to make this back? Market capture looks bleak, inference looks like a race to the bottom. End users look like they could be beneficiaries. Where do the big boys go?
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I agree, outside of the AI bubble, there's a lot of wait-and-see happening in the B2B world right now, I'd say we're currently 6-8 months into that 14 months.
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It’s also worth noting that’s the peak benefit. Expect most engineers to not hit those limits on the regular (if at all, since limiting this puts skills in focus again), and that limit to come down over time as the easy processes are automated and humans are re-tasked with harder problems relative to their TC. This is not a good bellwether for the AI industry, including its adherents. Their growth assumed a level of indispensability that’s not being reflected in hard numbers and real costs, which lends credence to the notion that these IPOs being fast-tracked are meant to try and cash out before the bubble really pops in earnest. There’s no way consuming enterprises are going to pay such insane costs for such minimal uplift in the long run, and the AI companies can’t keep offering subsidized tokens via subscription plans at their current pricing.
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Why there are so many people that still believe that AI coding is a fad? It's something that started less than two years ago and companies are already paying thousands per seat. I know one that gives you 5k per month. Which other tool went from nothing to this level of acceptance so quickly?
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That's just a non sequitur. "companies are already paying thousands per seat" has zero correlation with something being a fad or not. There are much more reasonable rationales explaining why companies are acting the way they are than "because AI coding is not a fad"
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Can you name a service that charged companies thousands/seat/month that turned out to be almost or completely useless? There's lots of random services sold to corporates that are not very useful (all the random benefits besides health care, life insurance, and other big-ticket items), but the per-seat charge of those is much smaller.
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Google Jam Board (and other digital whiteboards) had high upfront capex and lowish opex. Probably close to the price for how often they were used before being killed off. Same with the MS surface(?) tables (not tablets). I saw load of companies buy into the hype and then discard.
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So you think AWS is a fad?
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Not a service, but do you remember Scrum Masters? We had them as full time employees not so long ago. Pure fad.
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Hah. Great example actually. But far less common than AI afaict.
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> Can you name a service that charged companies thousands/seat/month that turned out to be almost or completely useless? The Concorde turned out to be fad (not "useless" - which was your reframing.) Touted as the future of travel, each seat cost about $20,000 of today's dollars, but it turned out even at those high prices people and companies were willing to pay per-passenger, supersonic trans-Atlantic air travel is not economically viable, and was discontinued.
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It's just silly to claim it has zero correlation.
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Between AI and the stock market (which of course relates directly to AI), I’ve lost count of the number of times I’ve heard lately another variation of “this time is different.” Sometimes so close to those words that I wonder why the person speaking them doesn’t feel a bit tingly. Great big warning signs all around.
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I would use these exact facts as a sign that it's maybe not what it seems. It's much too big and too fast to feel stable. It might keep at that level, increase even more, or drop down to a saner level of use / allocation.
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> It might keep at that level, increase even more, or drop down Bold prediction. :) I think anyone predicting a drop or near-term flattening is not thinking beyond the online bubbles where these tools are discussed. In a local tech meetup a lot of the normal companies are barely coming online with AI tools at their company, and even then with very low limits.
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Fear of loss to competitors embracing a technology creates a fear driven adoption. Let me ask you this: is any technology worth so much break-neck adoption without first seeing clear evidence of ROI? No. The adoption is irrational.
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There is a whole spectrum between "ai coding is a fad" and "unlimited tokens for every employees we don't even care if it actually ends up being a net positive financially"
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Oh, it won't get any better. LLMs already trained on every bit of code ever published, they won't get any more material.
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What about that means AI coding is a fad?
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perhaps the personal computer? Companies were spending 3-5k (10-15k inflation adjusted) on every employee for just hardware. everyone making comparisons to the dotcom bubble seems misguided. this is clearly computing 2.0 imo
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The Dotcom bubble is an interesting comparison. The general thrust that everything would be online was correct, it was just that the market mistimed and misallocated of capital by a decade or more. There was massive spending on infrastructure capacity that we wouldn't end up needing until the 2010s. There were hype driven valuations completely disconnected from business fundamentals just because a company was an 'internet' company. Things were going from cutting edge to obsolete in less than a year. There were breathless promises that this was business 2.0! Of course, none of that sounds remotely like what is going on today... I'm optimistic about AI, but I also don't think that it is going to change everything as fast as promised.
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Two things can be true at the same time. It can be true that this is here to stay. It can also be true that companies are grossly overvalued right now and that the market is irrationally exuberant. This would mean we could both have a crash and also see AI coding be the new future.
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I think the right comparison is the invention of the microprocessor. At that time people were grappling with a lot of the same things we are today - would it automate jobs away, would it transform education and the work place, etc.
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> Which other tool went from nothing to this level of acceptance so quickly? NFTs? My company had nothing to do with blockchain but I ended up working on NFT integration regardless.
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Also, a bucket for VC to put all that NFT, IoT, blockchain, VR investment into. VCs gonna VC and the last 15 years of bets failed so the last few years have been a transition away from those toward "the next thing".
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It's cope. People desperately want to believe that AI coding is going away so that they can go back to partying like it's 2020. So there's a huge number of HN posters claiming that the price of tokens will go UP over time rather than down (that's how Moore's Law works, right???) or that code bases that AI contributes to will spontaneously combust, or something.
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I mean, there's an "enormous incentive" for people to run their own data centers rather than using AWS. And yet, cloud is growing and on-premise is shrinking. While I hope local AI continues to exist, I'm skeptical that it will take over, for the same reason running your own servers hasn't taken over. It's just hard, and involves spending huge sums of money up front. It's also not really clear how much tokens are being subsidized. The discussion reminds me of Uber. For years people on HN claimed that Uber was going to collapse once they ran out of VC money. Then... that never happened, and everyone just moved on to discussing other things.
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It's a mix. If the current wave of LLM businesses crater, demand for LLM specific hardware (and related hardware) will crater. GPUs were propped up by crypto currencies and now by LLMs. They're still great at doing fundamental math operations, but for their value to stay up another massive business opportunity involving matrix multiplication and the like would need to rise as soon as the current business cycle winds down. Not impossible, not unlikely, probably 50-50.
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I'd think for most companies the pace of change is too high at the moment. Give it a few years, a bit of a plateau in the improvements in frontier models and I can't see how many of these companies don't implode under the weight of competition on inference prices.
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> You can ask the same for the median 330k salary in the US for Uber Engineering People DO . It's well known that most tech companies are ran incompetently. As you say, it's not the engineers' fault. But most projects and hiring in these companies exists to juice promotion criteria. And that, depending on perspective, these companies are either massively overstaffed or massively underproductive. The comparison to AI spending being wasteful holds up pretty well, these are companies that readily piss away billions in pointless spending.
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Your last question is really important. What did they accomplish with all that spend? I suspect there’s some mass delusion with respect to actual accomplishments as a result of LLM use. Sure, things are moving faster, but does it matter?
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Just to put this in context. If every company did this, all over the world, with that same limit, we are talking about something around $45B monthly in revenue for all AI companies to share.
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One could hire a competent developer here in Brazil for that amount. I know because my workplace has hired competent developers for that amount. You can even call them senior developers, but you can't get "non-startup seniors" with actual experience, those expect a bit more. I just wanted to take their number at face value. It's not like it needs more real information to make AI a bubble.
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World bank says there are 3.7B employed humans. Putting the total addressable market at around 67T if all of us spend USD 1.5k on tokens every month. This lines up well with current forecasts from the major AI labs
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> Putting the total addressable market at around 67T if all of us spend USD 1.5k on tokens every month However, that's an absurd scenario.
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well, you couldn't justify the cost if you still employed all 3.7B
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There’s probably plenty of money to be made in LLMs as a service - but not enough time has passed for the commodification to occur. I’m with you in that when the dust settles I don’t think any of the frontier model providers will have a moat. Just like during the dotcom boom a catchy URL and a webpage that could accept payments wasn’t a moat, either.
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These are still at currently subsidized prices. We'll see if they think they're getting $1500/month of value when that buys significantly fewer tokens.
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The evidence that per-token inference _is_ subsidized is (a) competition is a bloodbath (b) these companies are raising more money than any company has raised ever (c) a maybe-profitable quarter is maybe-coming for Anthropic after maybe-signing a compute deal with SpaceX that legitimizes both companies. The evidence that per-token inference _is not_ subsidized is... a quote or two from Dario and Sam Altman
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This is market introductory pricing that hasn't factored in cost recovery. Most of it has been run on early investment with the assumption they will recover costs in the long run. The prices are subsidized across the board and they will need to go up signficantly to recover them.
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For claude shops this was a huge hit. But lets back this up. There are some companies that haven't even built a break-even model at this price because they are funded by investment. As soon as those investors lose patience the first dominos will fall. For those who have somewhat of a business model, will it survive a price increase? The bigger question is do the base model providers have enough runway and have a way to keep going as they need to recover costs.
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And $1500 a month is on the very high end of where most companies will land. When you run the numbers there isn’t a realistic path that connects the dots between likely market size and the claimed valuation of the AI companies. The math simply does not add up.
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It's among a wave of fresh "non-insane" takes on AI in the enterprise. Maybe we can reel things in to a sustainable level before a giant bubble bursts.
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Token costs rising because data center build costs must be paid down.. is not the whole picture. It is actually possible for token costs to fall despite the spending frenzy. Naively you’d expect to always keep paying more - but growth in token usage is what changes the equation. Amortizing debt over an exponentially growing amount of spend across a growing customer base (not per customer) lets the debt be paid off & costs covered even as each individual’s spend stays steady or even goes down - but it only works if there’s growth beyond some threshold that makes the whole thing hang together. No one on the outside knows how much growth that is, and everyone chases maximum growth. Jevons Paradox ends up being your friend as well as the friend of the inference providers as well as the friend of the inference financiers. If it’s a strong enough effect, it has potential to cancel out all the circular financing too, and let everyone ride out the bursting of the bubble.
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They want to replace employees with AI, then replace paid AI with unpaid AI. Their wet dream was never automation. It was zero marginal cost labor. And that dream is starting to rot.