[CuriouslyC]

> It would be better for governments to buy and own their own datacenters, maybe as a coalition, and dedicate their operation to the public good. I believe that is what we actually have to do.

100% agree. The US government basically has to nationalize AI and capture an outsize portion of the revenue from it in order to fix the economy, as the combination of debt burden and interest rate pressure from de-dollarization/global realignment is going to push us into a death spiral, and even if AI is a smash hit, the ~19% federal capture of corporate revenue isn't nearly enough to pull us out of it. The people owning the compute infrastructure and capturing more profit from AI at that layer is the safest, cleanest way to increase revenue capture, a sovereign wealth fund is a mediocre idea because it's possible to play shell game with stocks and redirect profit/debt (venture capital is quite good at this!).

[root-parent]

>> The US government basically has to nationalize AI and capture an outsize portion of the revenue from it

Currently AI has generated no profit. And as it sits, is a non viable business.

I refuse to include the sellers of shovels as AI revenue.

If the companies buying the shovels are still losing money, then the tool supplier fortunes have nothing to do with the economics of the AI application layer, who is losing money on every prompt.

[aspenmartin]

It's the most naive opinion that keeps getting shoveled around. You have a product that is viewed as essential by businesses, with revenue growing by 10x a year and geopolitical ramifications that have continued to rear their heads and your opinion is "this is all an unprofitable shill". It is extraordinary to me that people really believe this. Whether or not labs run at a loss today is absolutely irrelevant. There is of course steady state economics that make sense, and its currently not well known what the profitability picture is right now, so to say "Currently AI has generated no profit" is also just speculation and not a very insightful one at that.

[root-parent]

That businesses view it as essential...is not a profitability argument.

Businesses also bought dot com infrastructure, telecom fiber, crypto platforms, metaverse tools, and overbuilt SaaS. The question is whether the AI application layer can charge more than its full cost and the costs are inference, infrastructure, depreciation, R&D, customer acquisition, support, compliance, security, and error remediation.

The numbers so far do not inspire confidence. OpenAI reportedly did $4.3B in revenue in the first half of 2025 while burning $2.5B, and Microsoft said OpenAI related losses reduced its own quarterly net income by $3.1B. An MIT 2025 enterprise AI study found $30 to 40B spent on GenAI with 95% of organizations seeing zero return.

One of the core technical reason is that hallucination destroy enterprise economics. If SAP hallucinated 2% of invoices, or Oracle returned fake rows 2% of the time, nobody would call that early stage friction. They would call it unusable for core operations.

In legal AI, even specialized tools have been measured hallucinating 30% of the time. The problem is that as AI gets better it is confidently, plausibly wrong. That forces humans to verify it.

So the cost does not disappear. It moves from doing the work to checking the work. AI coding has the same issue. If an autopilot got you there faster but one flight in ten became unstable unless the pilot constantly supervised it, that is not productivity.

For the bull case to work, the usage must explode, the quality must improve, prices must fall, reliability must rise, legal risk must shrink, and margins must expand and all this at once. I would say that instead of a business model, this is five miracles stacked on top of each other.

[aspenmartin]

> That businesses view it as essential...is not a profitability argument.

What do you mean, as in it doesn't imply profitability (and profitability of what?) today?

> Businesses also bought dot com infrastructure, telecom fiber, crypto platforms, metaverse tools, and overbuilt SaaS.

Yes, and like I said, there is absolutely a stable state where token economics are profitable.

> The question is whether the AI application layer can charge more than its full cost and the costs are inference, infrastructure, depreciation, R&D, customer acquisition, support, compliance, security, and error remediation.

Of course they can. If they couldn't, they wouldn't, and then someone would come in and charge the correct price for it because there is a tremendous amount of demand that will spur supply. I'm not sure what the mystery is here.

> The numbers so far do not inspire confidence. OpenAI reportedly did $4.3B in revenue in the first half of 2025 while burning $2.5B, and Microsoft said OpenAI related losses reduced its own quarterly net income by $3.1B. An MIT 2025 enterprise AI study found $30 to 40B spent on GenAI with 95% of organizations seeing zero return.

I do not find this particular argument inspires any confidence either -- everyone is trying to capture market share. How on earth, on a blog with ycombinator literally in the URL, do people not understand that lack of profitability now says absolutely nothing about profitability in the end state, after this growth phase?

You seem to imply by your MIT study citation that there is "evidence" that AI is not even profitable. Reading between the lines here, your theory is: the entire market is wrong, AI is a net negative ROI (and in your mind, for how long? forever?), and is inherently unprofitable. That is a crazy thing to think in light of all of the datapoints we have.

> One of the core technical reason is that hallucination destroy enterprise economics. If SAP hallucinated 2% of invoices, or Oracle returned fake rows 2% of the time, nobody would call that early stage friction. They would call it unusable for core operations.

Yet we see absolutely incredible adoption? In your view we'd see a spike and then all of those dumb CEOs will say "oh oops we were dumb" and adoption will go down. Hallucination does not destroy enterprise economics. Hallucination rates, by the way, have continued to decline after every model generation. Believe it or not there are incredibly valuable, smart, sustainable ways to incorporate AI. Even if it's just coding agent licenses, that alone is a powerful enough revenue driver.

> In legal AI, even specialized tools have been measured hallucinating 30% of the time. The problem is that as AI gets better it is confidently, plausibly wrong. That forces humans to verify it.

You seem to be conflating two things: one is that there is an inevitable eventuality where everyone will finally shut up about AI being unprofitable because we exit this growth phase and reach a more steady state market economics. The other thing is that we are not yet there. I'm not arguing about us not yet being there, though I do think the evidence even there is a bit cherry picked.

> So the cost does not disappear. It moves from doing the work to checking the work. AI coding has the same issue. If an autopilot got you there faster but one flight in ten became unstable unless the pilot constantly supervised it, that is not productivity.

That's just not going to be true for long. The reason you see coding agent adoption and other step changes in adoption + capability is that error ("hallucination" and other error modes) get to a low enough point where they are acceptable for a given application. This will eventually eat most applications. Yes we have to check things today, but this will be less and less important once we reach a point where humans are even less reliable than AI at _checking_ output from either themselves or other AI systems.

> For the bull case to work, the usage must explode, the quality must improve, prices must fall, reliability must rise, legal risk must shrink, and margins must expand and all this at once.

What??! this is all happening _right now_ right in front of you. Usage _is_ exploding in ways we didn't even dream about 8 months ago. Quality _is_ improving in very predictable ways (scaling laws anyone? benchmark trends?). Reliability _is_ rising _and thats why you see the adoption level you see for coding agents_.

[CuriouslyC]

I've heard that the API calls by themselves are ~60% profit if you ignore capital expenditures. The labs haven't generated profit because they're constantly sinking money into the next generation of larger models to stay relevant. Dario has talked about the economics of this a lot, and I do believe him there.

There's clearly also a lot of pent up demand in the corporate world for inference, the problem is that it's currently expensive enough that enterprises are balking at the cost before they've had a chance to refine processes and see projects through to fruition. That's a tractable problem to solve though.

[bombcar]

The number of capital-heavy businesses that are wildly profitable “if you ignore capital expenses” is too many to list.

Airlines, for example, which are so profitable they continually go bankrupt.

[CuriouslyC]

That's true, but if the frontier doesn't advance there's no depreciation or ongoing capital expenditure. If all the frontier labs agreed to stop making stronger AI and just try to sell what they've already trained today, their books would turn green in a hurry.

[aspenmartin]

> The US government basically has to nationalize AI and capture an outsize portion of the revenue from it in order to fix the economy, as the combination of debt burden and interest rate pressure from de-dollarization/global realignment is going to push us into a death spiral, and even if AI is a smash hit, the ~19% federal capture of corporate revenue isn't nearly enough to pull us out of it.

Any actual numbers to back this up? I don't see how nationalizing a very cutting edge technology outside of wartime is going to go super well. The leverage that these companies have is the same leverage that TSMC has: you can't just take over and expect things to rocket at the pace its going

[AtlasBarfed]

Like a system of heavily funded institutions dedicated to higher learning?