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Comment by utopiah | Hacker News Reader

It's like witnessing a rocket using the most powerful engine on Earth then once it escaped orbit turn off the engine and said "It is flying without power!".

Yes, sure, right now it is ... but that's NOT how it got here.

There are trillions invested to recoup and at most billions in sales. It doesn't add up to tokens making a profit any time soon.

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The problem is, people see "they're not profitable once you account for training" and equate that to "AI will go away soon"

But if all the AI companies stopped training new models, they would all instantly become profitable (and stick around)

The thing that makes them unprofitable, is having to compete (which means training models). If / when enough companies exit the market, the cost to compete goes down and you end up in an equilibrium

Sure, but if companies don't exit the market and FOSS alternatives don't end up being unable to get near them in quality, they have to keep spending on training. And conversely, if the market becomes uncompetitive and FOSS sucks, the winners of the AI arms race are very strongly incentivised to stick their prices up anyway...

> if companies don't exit the market and FOSS alternatives don't end up being unable to get near them in quality, they have to keep spending on training

Eh, the AI companies still have lots of datacentres. For the guys who funded with equity, they could collapse down to just running those as utilities. (For the guys who funded with debt, they'd have to restructure.)

From the customer's perspective, this situation shouldn't result in a cost spike. (Consolidation, on the other hand, would. But that's a separate argument from the one the article attemptes to make.)

How often do VC funded unicorns collectively decide to stop scaling up, shut down all their departments targeting growth and reach breakeven point by becoming low margin utilities that will never justify their valuation?

That's all true, but that ends badly for us either way. If there's competition, training must continue, which must eventually be reflected in pricing.

But if there's no more competition, there's no more incentive to keep prices low, which will also be reflected in pricing.

> If / when enough companies exit the market

That will only happen when the bubble bursts and those companies will exit by going bankrupt

> There are trillions invested to recoup and at most billions in sales. It doesn't add up to tokens making a profit any time soon

But this isn't "a ticking time bomb for enterprise." It's an issue for the AI companies' investors.

Good thing the entire nation's economic growth outlook isn't tied to these companies then. For a second I thought we had a potentially dangerous situation on how we misappropriated trillions of capital.

For anyone doubting this, total private investments in the US grew 2% in 2025 relative to the prior year, adjusted for inflation.

But within that big pie, the "IT-related" investments grew 15.7% whereas non-IT actually shrank 2.0%.

Not really, because investors will sooner or later want to see real returns on what they invested. Tokens are suddenly not dirt cheap and enterprises are screwed.

It's like selling dope, once they're addicted, a dealer could turn the screw on them

That's why it's an issue for investors. Their investment may not payout. But the things that were built will still have been built and available to sell for related purposes, the models that were trained will still be trained, and so on.

If things don't end up working out a lot of people have already been (and in the future will be) paid. It's the investors that will lose out, not the subscriber.

When I compare different foundational models on the problems I solve with AI, the differences are not that large to prevent a switch if the price gets too high. I do this like each 6 months, just to assess what is the risk of getting dependent on one provider. It's not yet worring, at least for my use-cases.

Not if they IPO and some other sucker buys the stock.

That's not an excuse to pillage the commons.

Steal from, you know, people who actually work.

Certainly not trillions. The models costing tens of billions to train are a very new development.

OK hundreds of billions with more than ~200B disclosed for OpenAI, more than ~50B for Anthropic and I have no idea how much in terms of infrastructure from Azure, other neoclouds, NVIDIA, etc. It's honestly hard to keep track of "kind of IoUs-ish" from each other but my points is order of magnitude more than few billions than has be recouped so far with tokens and large contracts.

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