The impetus to continue training at the pace they are is driven by the competition. So if the money starts drying up, then they’ll naturally slow down because they’ll have to figure out how to do more with less.
I suspect that once the models hit a point of “good enough” for certain use cases companies will start putting R&D focus in other areas that may be less expensive. Like figuring out how to run more efficiently, UI/UX conventions that help users get what they’re trying to accomplish in fewer steps, various kinds of caching of requests, etc. So the cost to serve tokens over time should only come down, and will probably start coming down more rapidly as the returns to model training slow down.
That’ll probably be a while though, because each successive model tends to be a lot better than the last.
alt Hacker News
The impetus to continue training at the pace they are is driven by the competition. So if the money starts drying up, then they’ll naturally slow down because they’ll have to figure out how to do more with less.
I suspect that once the models hit a point of “good enough” for certain use cases companies will start putting R&D focus in other areas that may be less expensive. Like figuring out how to run more efficiently, UI/UX conventions that help users get what they’re trying to accomplish in fewer steps, various kinds of caching of requests, etc. So the cost to serve tokens over time should only come down, and will probably start coming down more rapidly as the returns to model training slow down.
That’ll probably be a while though, because each successive model tends to be a lot better than the last.