Comment by root-parent
2 days ago
>> 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.
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.
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.
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.
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.
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.