> 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"?

> But we can still do an illustrative calculation: let’s conservatively assume that OpenAI started R&D on GPT-5 after o3’s release last April. Then there’d still be four months between then and GPT-5’s release in August,20 during which OpenAI spent around $5 billion on R&D. But that’s still higher than the $3 billion of gross profits. In other words, OpenAI spent more on R&D in the four months preceding GPT-5, than it made in gross profits during GPT-5’s four-month tenure.

These numbers actually look really good. OpenAI's revenue has been increasing 10x year-on-year since 2023, which means spending even 3x the amount of resources to produce another model next year would likely generate a healthy profit. The newer models are more efficient so inference costs tend to decrease as well.

As long as models can keep improving, and crucially if that depends on the amount of compute you put in them, OpenAI and other closed-source AI companies will succeed. If those two key assumptions stop being true, I can definitely see the whole castle of cards crumbling as open source competition will eat their lunch.

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.

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.

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?

Original is here (linked in the article):

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

The only thing you need to know about unit economics is this: https://epoch.ai/data-insights/llm-inference-price-trends

TL;DR the prices have gone down (to achieve same benchmarks) by more 9x to 400x.

This should clearly tell you that the margins are high. It would be absurd for OpenAI to be constantly be under a loss when the prices have gone down by ~50x on average. Instead of being ~50x cheaper, couldn't OpenAI be like 45x cheaper and be in profit? What's the difference?

I genuinely don't know why you need any more proof than just this statistic?

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

Does anyone have a paywall free link to inference costs for models? The article links to an estimate from the information which has a heavy subscription cost and paywalls everything

This totally glosses over the debacle that was GPT-4.5 (which possibly was GPT-5 too, btw), and the claim that it'll ever outcompete humans also totally depends on whether these systems still require human "steering" or work autonomously.

Frankly, it's real slop.

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.