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Comment by rhinoceraptor | Hacker News Reader

How are Anthropic and OpenAI going to compete on price when they're both already deeply unprofitable?

Serving the API is profitable. They are unprofitable because of R&D (and maybe subscription costs?). If they can continue to find access to R&D capital, there is space to reduce API costs.

Nuclear energy is really cheap too... as long as you ignore CapEx, would you like to invest?

Marginal cost of nuclear is huge. Marginal cost of inference is much smaller. Capex in nuclear isn’t a fixed cost, it is the marginal cost.

The marginal cost of nuclear energy is 14-20% of the total cost according to pages 39,40 of [1].

The point I'm making is that claiming that AI labs would be profitable if only they could stop spending money on the only thing that makes them valuable is absurd. Frontier models are like a nuclear power plant that needs to be rebuilt from scratch every 24 months.

Let's say that they paused R&D a year ago. It's June 2026, OpenAI's latest offering is GPT 4.1, Codex is still just a private beta that hasn't been updated in months. How much revenue do you think they would be making right now? My guess is approximately zero.

[1] https://www.lazard.com/media/5tlbhyla/lazards-lcoeplus-june-...

The labs don’t have to stop r and d to become profitable. They just need more customers. This strategy doesn’t work in nuclear. Building a nuclear plant doesn’t mean you can scale it up to serve the whole world. Building an ai model does.

how do you have access to their financials? are you an insider?

Edit: to the commenter below . It was widely reported that these companies were unprofitable 1 from last year. I am asking question to this specefic comment because they made a very specific claim about part of plan thats profitable . something only an insider would know.

1. https://www.wsj.com/tech/ai/openai-anthropic-profitability-e...

I'm curious why you didn't pose this question to the grandparent commenter, who first asserted the opposite?

The amount of capital they need to raise, despite the claimed revenue, indicates that they spend more than they gain, which is by definition unprofitable.

Anthropic just announced it's on track to have its first profitable quarter: https://www.wsj.com/tech/ai/mind-blowing-growth-is-about-to-...

Response: https://www.wheresyoured.at/anthropics-profitability-swindle...

They may not be able to! It's pretty widely acknowledged, for example, that if there's some surprising plateau hiding around the corner they're both going to fail. But that could mean that they're overcharging for AI usage to get research money and sustainable rates are lower rather than higher.

I think that for coding we're past the plateau issue. The frontier models of today are good enough and very valuable. The expensiveness in running them will eventually be solved by cheaper faster hardware.

I do hope that a day will come where you can buy the nvidia spark thingy for 5k that can run the equivalent of Opus 4.6 or 4.5 locally and that would be a massive thing.

> The expensiveness in running them will eventually be solved by cheaper faster hardware.

How?

* Moores Law is almost over. The 5090 improves over the 4090 mostly because of quant improvements.

* even if the hardware improves, there’s a huge incentive to slow roll the next generation. Nobody wants to end up like Sun Microsystems. Sun’s used hardware was faster than its new hardware, once you considered price. Sun ended up competing with its own used equipment.

The most obvious place for improvement is RAM, network and storage.

If someone can bring more RAM onto the market, that will unstick things.

GPUs are not really the ideal architecture for running neural networks; they are heavily bottlenecked by memory bandwidth and struggle to keep all their tensor cores supplied with data.

There is significant room to make more specialized neural network accelerators with new compute-in-memory architectures.

If the brain can run 86 billion neurons on 30W it must be possible.

Our brains run 86 billion neurons the same way a waterfall runs a fluid simulation with N quadrillion particles.

There are already some companies doing specialised inference hardware, Cerebras Systems for example. Such designs are still early days and I wouldn't be surprised to see more innovation there. Though because custom silicon design takes time I expect a multi-year cycle.

For training, not sure. But even if training runs on GPUs, once you have the model the main cost is inference.

The whole hidden plateau hypothesis is kinda bunk, because we're already pretty far in a plateau for general knowledge/question answering, but there are many subdomains where we can push model capabilities, and as we saturate one subdomain we can just shift to another economically valuable one.

There isn't one AI intelligence S curve, there are thousands of them, and they're mostly invisible in the major benchmarks, but for someone trying to do work in that specific area of capability, the progress is transformative.

I'm skeptical of a hidden plateau, but I really think it's overconfident to assume there's not one. Remember that it doesn't even have to be a technical plateau; the effective plateau of e.g. car speeds is determined by regulations and road conditions, and far below what "frontier cars" are capable of on a controlled racetrack.

That’s the scenario where we’ll all be using Chinese models

There is no moat until a company achieves RSI and/or AGI, and the one that does succeed in moat-making will do so by hacking into and destroying their competitor's infrastructure.

Once moat is achieved, you don't have to compete on price. Of course it'll be academic because the AI will probably destroy all of us.

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