but Opus 5.5 is the best product ever made in the history of the world (yes I'm serious). There's gotta be some consideration for that.
but Opus 5.5 is the best product ever made in the history of the world (yes I'm serious). There's gotta be some consideration for that.
It has zero network effects and barely any economies of scale. It's a good product without a moat which will get replaced by any company with their own compute and cheaper energy.
I think the only way out for Anthropic is to buy a few neoclouds as they go under, but even then it's a coin flip as to if they survive the next 3 years.
Or they partially crash and get bought by Oracle. The Sun route
The market is a cold beast and doesn’t care. If things hit the fan it will happily take ownership Opus 5.5 in bankruptcy court and slit everything else out on the street.
If people paid a profitable price for Opus 5.5 it would not be the best product in the world and would not be slotted into so many things where it's benefiting from ubiquity. That circle needs squaring. There will be an Opus 5.5 that's a 10th of the price in a few months.
I personally don't really believe the meme that Anthropic and OpenAI are charging less than the marginal cost (cost of inference on requests). Open weight models have surpassed the abilities of their closed weight models when such claims started, and the inference costs on said open weights models are provably not so high.
Where they quite reasonably might be making a loss is their marginal costs + amortised fixed costs (training).
I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.
The driver for the training is an arms race between providers and open weights labs. But several dynamics could play out:
* Models will be seen as good enough, with only fine-tuning required, for most use cases, and demand for expensive training of foundation models drops off, stopping the arms race. * Most of the participants cannot raise capital due to lack of market confidence in them, and there is a 'winner takes all' dynamic with the remaining provider, who can then raise costs.
I personally think the first end game is more likely than the second.
You are absolutely entitled to call it a meme but that is also their own internal reporting/panicked emails. Subscriptions are offered at a large subsidy with the benefit of more training data and adoption.
> I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.
The big labs can't do this that's their edge over open models. The stickiness is critical for them to keep up. They might try again but it's going to finish badly.