alt Hacker NewsTheir inference margins are already high, the thing bringing their net down isn't training per se, it's the fact that they have to train the next iteration right away due to competition, otherwise an extended inference cycle would've eventually recouped training costs.
The bet is that cost per intelligence will keep going down and demand for intelligence will grow at the same time, if this bet holds, a user could get the same or more done with a cheaper model (running-cost wise) while paying the same subscription and the lab gets improved margins.
Open source is a challenge but their current moat is the fact that they're ahead, and as long as they stay ahead they'll be able to charge a premium.
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No doubt that if they wanted to they could be profitable.
But profitable enough to justify the ludicrous valuation?
The rush towards open weight models defeats this thesis. There’s no prize for being “ahead.” That’s academically interesting but a terrible business plan. 99% of AI use cases don’t need the latest and greatest model, and businesses increasingly understand this.
>> Their inference margins are already high
So they make a profit on each burger they sell you, but however they keep limiting how many they are willing to sell you?
> Open source is a challenge but their current moat is the fact that they're ahead, and as long as they stay ahead they'll be able to charge a premium.
Being ahead isn't much of a moat in a game where playing catch-up is significantly easier than being ahead. Also, how much ahead of Chinese labs are they at best? A year? Two? That's a moat for ants!
> it's the fact that they have to train the next iteration right away due to competition
That it would be profitable if only there were no competition is not a really strong starting point for an IPO, though, is it?
(It is, however, the winner-takes-all world view Anthropic still seems to operate with)