Both OpenAI’s and Anthropic’s revenue has increased massively in one year: roughly 3½-fold for OpenAI and 9-fold for Anthropic.
Their product is in demand, they lose money on each customer, so they take in a lot of money to grow their customer base and lose more money.
They need to transition to making money. To do so they need something like network effects (social media, Uber/Lyft to some extent), returns to scale, or some massive first mover advantage. I don’t see that yet.
As you say, one area where they are already starting to be genuinely useful is some more routine forms of coding. A leading indicator I think you should be looking at is that, according to Google, they’re recently reached “50% of code by character count was generated by LLMs”.
That’s less than I was expecting. And my personal experience of coding with LLMs (and speaking with others who do) is that it takes a lot of work to make it function—the LLM will write most of the code, but it’s often a long process from there to a working program—and a much longer process to a working, interpretable program. And much longer to get a working program that fits well into a codebase.
Now, I feel that coding algorithms are better than they were in that study, especially for routine tasks.
So my median expectation is that moving 50% of coding might increase google productivity by 10%. But 25% or −5% are also possible.
In general, something growing via an exponential or logistic-curve process looks small until shortly before it isn’t — and that’s even more true when it’s competing with an established alternative.
Shipping finished code is a process involving a lot of steps, only some of which are automated. So (Amdahl’s Law) the time to finished coding will be determined by those parts of the process that aren’t easily automated. If time to write code falls to zero but time to review code stays the same or even increases, then we’ll only get a mild speedup.
The other problem is that logistic curves close to their inflection point, logistic curves way before their inflection points, and true exponentials—all look the same (see our paper https://arxiv.org/abs/2109.08065 ). Ok, we might be on the verge of great LLM-based improvements—but these have been promised for a few years now. And (this is entirely my personal feeling) they feel further away now than they did in the GPT 3.5 era.
In simple economic terms, other than Tesla, the other six of the “magnificent seven” have not (so far) reached the Price/Earnings levels characteristic of bubbles just before they burst — they look more typical of those for a historically-fast-growing company.
The magnificent seven have strong non-AI income streams. I expect them to survive a bubble burst. If OpenAI had stocks, their P/E ratio would be… interesting. Well, actually, it would be quite boring, because it would be negative.
One viewpoint that I haven’t seen used much to look at foundation lab economics is ROI: they spend a ton of money training a model (including compute costs and researcher costs), they then deploy it. After allowing for inference and other costs of serving it, does the revenue they make on serving that model (before it becomes obsolete) pay for its training costs (plus interest), or not? (Another way to look at this is that a newly trained SOTA model is a form of – rapidly depreciating – capital.) I.e. would they be making a profit or a loss steady-state, if it weren’t the case that the next model is far more expensive to train? I think this is actually a fairly reasonable economic model (making movies is rather similar). Note that it there’s a built in improvement if progress in AI slows — models then stay SOTA for longer after you trained them, thus depreciate slower, so (as long as you are charging more than their inference serving cost) can make more money; and presumably the speed of increase of model training costs drops, so your actual balance sheet profit and loss get closer to the ROI analysis.
FWIW, I asked Claude Opus 4.5 in research mode to attempt to do this per-model-ROI analysis for OpenAI, and then for Anthropic, from what public materials it could locate, and it seemed to think that even in this framework OpenAI’s ROI is deeply negative: primarily because a) training run investment includes not only the final successful run but also failed runs (the same issue as in the numbers DeepSeek released) b) revenue earnings are depressed by competition so are not much above serving costs, and c) model depreciation cycles are viciously short, generally less than 6 months.
So, even on a per-model ROI basis, OpenAI are still in a “burning VC money to gain market share and intellectual capital” mode.
Of Anthropic, it seemed to think their per-model ROI was also still negative, but less so for a variety of reasons (fewer failed training runs, slower model obscolescence), and was improving. It found their predictions of profitability by 2028 plausible. (I didn’t ask it whether it might be biased.)
However, in an AI slowdown, factor c) automatically improves, and there are fairly obvious levers OpenAI could pull to improve a) and b) — some of which apparently Anthropic are already pulling.
For both companies, it mentioned that users in their highest individual subscription tiers often have usage so high that they lose them money. So I expect we’ll eventually see tighter usage caps and even higher subscription tiers.
Why would factor c automatically improve if half a year is more than enough for the Chinese companies to catch up by distilling frontier models and release their open-weight models which then will be served almost at the cost of inference? If anything, in this scenario the models themselves will be fully commodified and margins in AI will be determined by the product characteristics
I guess I’m implicitly assuming any AI slowdown is for technical reasons, so also affects Chinese companies. If it were, say, a loss of investor confidence in LLM foundation labs, then it might not affect Chinese companies if they had sufficient CCCP state funding.
If it’s for technical reasons, then it should hit Chinese companies only as soon as they catch up, doesn’t it? I’m not sure I understand your argument.
Also, I don’t think Chinese companies have any viable business model for future scaling anyway since no one outside China wants to send their data on Chinese servers. Hence they are forced to economize as much as possible, and it’s possible they are supported by Chinese authorities for political reasons.
I take your point. I think I was assuming as slowdown, not a wall, and that the Western companies had more money so got further. But yes, it’s quite sensitive to circumstantial details.
Their product is in demand, they lose money on each customer, so they take in a lot of money to grow their customer base and lose more money.
They need to transition to making money. To do so they need something like network effects (social media, Uber/Lyft to some extent), returns to scale, or some massive first mover advantage. I don’t see that yet.
That’s less than I was expecting. And my personal experience of coding with LLMs (and speaking with others who do) is that it takes a lot of work to make it function—the LLM will write most of the code, but it’s often a long process from there to a working program—and a much longer process to a working, interpretable program. And much longer to get a working program that fits well into a codebase.
When you code with LLMs, it feels like you’re really productive, because you’re always doing stuff—but often it actually slows you down. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
Now, I feel that coding algorithms are better than they were in that study, especially for routine tasks.
So my median expectation is that moving 50% of coding might increase google productivity by 10%. But 25% or −5% are also possible.
Shipping finished code is a process involving a lot of steps, only some of which are automated. So (Amdahl’s Law) the time to finished coding will be determined by those parts of the process that aren’t easily automated. If time to write code falls to zero but time to review code stays the same or even increases, then we’ll only get a mild speedup.
The other problem is that logistic curves close to their inflection point, logistic curves way before their inflection points, and true exponentials—all look the same (see our paper https://arxiv.org/abs/2109.08065 ). Ok, we might be on the verge of great LLM-based improvements—but these have been promised for a few years now. And (this is entirely my personal feeling) they feel further away now than they did in the GPT 3.5 era.
The magnificent seven have strong non-AI income streams. I expect them to survive a bubble burst. If OpenAI had stocks, their P/E ratio would be… interesting. Well, actually, it would be quite boring, because it would be negative.
Generally, I agree.
One viewpoint that I haven’t seen used much to look at foundation lab economics is ROI: they spend a ton of money training a model (including compute costs and researcher costs), they then deploy it. After allowing for inference and other costs of serving it, does the revenue they make on serving that model (before it becomes obsolete) pay for its training costs (plus interest), or not? (Another way to look at this is that a newly trained SOTA model is a form of – rapidly depreciating – capital.) I.e. would they be making a profit or a loss steady-state, if it weren’t the case that the next model is far more expensive to train? I think this is actually a fairly reasonable economic model (making movies is rather similar). Note that it there’s a built in improvement if progress in AI slows — models then stay SOTA for longer after you trained them, thus depreciate slower, so (as long as you are charging more than their inference serving cost) can make more money; and presumably the speed of increase of model training costs drops, so your actual balance sheet profit and loss get closer to the ROI analysis.
FWIW, I asked Claude Opus 4.5 in research mode to attempt to do this per-model-ROI analysis for OpenAI, and then for Anthropic, from what public materials it could locate, and it seemed to think that even in this framework OpenAI’s ROI is deeply negative: primarily because a) training run investment includes not only the final successful run but also failed runs (the same issue as in the numbers DeepSeek released) b) revenue earnings are depressed by competition so are not much above serving costs, and c) model depreciation cycles are viciously short, generally less than 6 months.
So, even on a per-model ROI basis, OpenAI are still in a “burning VC money to gain market share and intellectual capital” mode.
Of Anthropic, it seemed to think their per-model ROI was also still negative, but less so for a variety of reasons (fewer failed training runs, slower model obscolescence), and was improving. It found their predictions of profitability by 2028 plausible. (I didn’t ask it whether it might be biased.)
However, in an AI slowdown, factor c) automatically improves, and there are fairly obvious levers OpenAI could pull to improve a) and b) — some of which apparently Anthropic are already pulling.
For both companies, it mentioned that users in their highest individual subscription tiers often have usage so high that they lose them money. So I expect we’ll eventually see tighter usage caps and even higher subscription tiers.
Why would factor c automatically improve if half a year is more than enough for the Chinese companies to catch up by distilling frontier models and release their open-weight models which then will be served almost at the cost of inference? If anything, in this scenario the models themselves will be fully commodified and margins in AI will be determined by the product characteristics
I guess I’m implicitly assuming any AI slowdown is for technical reasons, so also affects Chinese companies. If it were, say, a loss of investor confidence in LLM foundation labs, then it might not affect Chinese companies if they had sufficient CCCP state funding.
If it’s for technical reasons, then it should hit Chinese companies only as soon as they catch up, doesn’t it? I’m not sure I understand your argument.
Also, I don’t think Chinese companies have any viable business model for future scaling anyway since no one outside China wants to send their data on Chinese servers. Hence they are forced to economize as much as possible, and it’s possible they are supported by Chinese authorities for political reasons.
I take your point. I think I was assuming as slowdown, not a wall, and that the Western companies had more money so got further. But yes, it’s quite sensitive to circumstantial details.