Framing GPT 5 as a loss because of its short run like that is a bit weird. They say "the R&D that went into GPT-5 likely informs future models like GPT-6". but that really understates what is happening here.

Barring solid evidence otherwise you would think that GPT 5.2 was built largely on GPT 5, enough that possibly the majority of the cost of 5.2 was in developing GPT 5.

It would be like if you shipped something v1.0 on day one and discovered a bug and shipped something v1.01 the next day. Then at the end of the year reported that v1.0 massively lost money but you wouldn't believe the profit we made on v1.01 it was the single largest return on a single day of development we've ever seen.

> And there are reasons to even be really bullish about AI’s long-run profitability — most notably, the sheer scale of value that AI could create. Many higher-ups at AI companies expect AI systems to outcompete humans across virtually all economically valuable tasks. If you truly believe that in your heart of hearts, that means potentially capturing trillions of dollars from labor automation. The resulting revenue growth could dwarf development costs even with thin margins and short model lifespans.

We keep seeing estimates like this repeated by AI companies and such. There is something that really irks me with it though, which is that it assumes companies that are replacing labor with LLMs are willing to pay as much as (or at least a significant fraction of) the labor costs they are replacing.

In practice, I haven't seen that to be true anywhere. If Claude Code (for example) can replace 30% of a developers job, you would expect companies to be willing to pay tens of thousands of dollars per seat for it. Anecdotally at $WORK, we get nickel and dimed on dev tools (better for AI tools somewhat). I don't expect corporate to suddenly accept to pay Anthropic 50k$ per developer even if they can lay off 1/3 of us. Will anyone pay enough to realize the capture of "trillion dollar"?

The Uber comparison is funny because they burned $32B over 14 years before profitability. OpenAI alone is burning something like $10B/year and growing, so the US AI labs are probably close to 10x Ubers burn rate.

Given the scale of AI lab burn rates being so high that the AI Capex shows up in nationwide economic stats, it clearly cannot burn for that long.

So what happens first - labs figure out how to get compute costs down by an order of magnitude, they add enough value to raise prices an order of magnitude (Uber), or some labs begin imploding?

Keep in mind another aspect of the comparison is that there wasn't an entire supply chain spend effect triggered by Uber. That is - you didn't have new car companies building new factories to produce 10x more cars, new roads being built, new gas stations being built, etc the way you have for AI.

It's like the entire economy has taken 1 giant correlated bet here.

Original is here (linked in the article):

https://epoch.ai/gradient-updates/can-ai-companies-become-pr...

What I dont understand is, why would a company pay $10,000s a month to anthropic for Claude in a situation where a Chinese LLM is 99% as good, is open weight and runs on US servers and is 5% the price?

I think the question is as it matures will, the value of the model become more stable and then what will happen to price?

If you can compare phones or pcs, there was a time when each new version was a huge upgrade to the last version, but eventually these ai models are gonna mature and something else is gonna happen

The discussion about Chinese vs US models misses a key enterprise reality: switching costs are enormous once you've built production systems around a specific API. Companies aren't just buying the model - they're buying reliability, compliance guarantees, and the ecosystem that reduces integration risk. Price matters, but in enterprise AI the "last mile" of trust and operational certainty often justifies significant premiums.