[uejfiweun]

I mean yeah, but that's why there are far more research avenues these days than just pure LLMs, for instance world models. The thinking is that if LLMs can achieve near-human performance in the language domain then we must be very close to achieving human performance in the "general" domain - that's the main thesis of the current AI financial bubble (see articles like AI 2027). And if that is the case, you still want as much compute as possible, both to accelerate research and to achieve greater performance on other architectures that benefit from scaling.

[rishabhaiover]

How does scaling compute does not go hand-in-hand with energy generation? To me, scaling one and not the other puts a different set of constraints on overall growth. And the energy industry works at a different pace than these hyperscalars scaling compute.

[pixl97]

The other thing here is we know the human brain learns on far less samples than LLMs in their current form. If there is any kind of learning breakthrough then the amount of compute used for learning could explode overnight