[Tuna-Fish]

> So why are they losing so much money?

Mostly training. Claude didn't just get to be so good at coding by magic, it was suddenly so good because they did truly staggering amounts of RLHF and RLAIF on it. They are still doing that today, on any tasks they can figure out how to evaluate it on. This is capex for them.

Their margins on inference are >90% today for tokens they sell (plans are hard to count, but still profitable). Based on what we know of it's size and architecture, running Opus is not more than 2x more expensive than running Deepseek v4 pro, for which tokens are available at under 10% of the cost of Opus. Again, the reason their margins are 50% is because they are spending so much on things that are not inference, not because inference is expensive.

> The cheap model providers have a much better chance of achieving that.

Anthropic can do it with a push of a button, once they calculate that it will provide them better profit than current pricing.

[vrighter]

you forgot that to not have a knowledge cutoff and fall behing, you need to always be training new models. It matters jack shit if inference is cheap, if you are forced to do training anyway to stay "competitive"

[tliltocatl]

Depends on the application, of course. For "google replacement" they are trying to sell it as - it's absolutely essential, but even then it works like crap. For coding… Maybe it's not so essential? Yea, it would not know about latest libraries, but maybe that's not much of a problem? Then, of course, it is a big question if LLM code generation is worth it at all.

[mikae1]

Hos much does the cutoff matter when models can JFGI?

[overfeed]

The AI companies are attempting displace Google for that sweet ad revenue. Relying on another company's search index means they can squeeze you later for a bigger slice of the pie

[CSSer]

I think people miss this because these companies exist in a space that is new in tech, and that means lots of competition through PR and marketing. When that happens, it’s easy to feel like a company is telling you about everything they’ve been working on or are openly talking about what gives them their edge when in fact the opposite is often true.

[manwithopinions]

> Their margins on inference are >90% today for tokens they sell (plans are hard to count, but still profitable).

That doesn’t make any sense, it doesn’t add up. Have you seen how much money they’re raising and burning? We know that training does not cost tens of billions.

Brockman said OpenAI expects to spend $50 billion on compute this year. OpenAI’s revenue run rate is less than $50 billion for this year! For 90% margins to be possible on inference, you are suggesting that less than $5 billion of that compute spend is inference and over $45 billion of that compute is training.

Anthropic have been desperately trying to juggle capacity by shaping user behavior through peak time usage limits because they are struggling with capacity for inference.

Plan based usage is widely acknowledged to be subsidized, you are probably the only person on earth suggesting that plans are profitable.

[winternewt]

Capacity for inference isn't a cost issue, it's an availability issue. There just isn't enough hardware out there.