> 10x the subscription price, but this seems high
Inference is cheap but training is quite expensive. Plus all the money they've invested and keep investing on hardware, data centers, etc. And evidently they also need to make a profit at some point.
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> Inference is cheap
Maybe from the perspective of traditional, turn-based chat. But when you start having developers command an army of agents that work around the clock, those cheap tokens start adding up fast...
If the unit-economics work out and they can sell $0.99 of tokens for $1.00, doesn't matter how many agents you spin up. The flat rate subscriptions can't last though.
> If the unit-economics work out and they can sell $0.99 of tokens for $1.00
I think the margins have to be a lot higher than that in order to give investors the return they're expecting, to continue the never-ending training treadmill, and to build more and more datacenters to accommodate people basically DDOS'ing the GPUs in order to run their workloads.
Yes, in theory what you said makes sense. But the tightrope these companies have to walk is that the per-token costs still have to be low enough that developers and companies don't just say "ehhh I guess we can still do all this work the old-fashioned way" but ALSO high enough to cover the massive expenses AND astronomical returns everyone's expecting.
VC investment isn’t about margins, it’s about finding a unicorn. It doesn’t matter if margins are negative if your product is dominant in the market as you can fiddle with the margins after the fact. You just need to be invested long enough to see everyone else fail.
The problem with AI is that there doesn't seem to be a durable barrier to entry for a "winner take all" dynamic to work. The biggest barrier to entry seems to be the capital needed to train the models, but even free models are getting "good enough" for some uses and there's little friction to stop users from switching between models. Many frontends make this explicit by letting you pick the model you want to run inside the same environment.
If prices go up, I suspect a bunch of folks will jump to cheaper, less capable models instead of eating the added cost. The whole value proposition of AI in enterprise is around cost-cutting, so that mentality is likely to persist when choosing which model to pay for.
I imagine the calculus changes a little bit when you've invested hundreds of billions (trillions?) of dollars in a relatively short period of time. Priority number one is probably getting that money back. I think the fact that providers are RAPIDLY cutting back/jacking up prices points to this being the case.