My tinfoil hat theory, which may not be that crazy, is that providers are sandbagging their models in the days leading up to a new release, so that the next model "feels" like a bigger improvement than it is.

An important aspect of AI is that it needs to be seen as moving forward all the time. Plateaus are the death of the hype cycle, and would tether people's expectations closer to reality.

▲

cousinbryce 5 hours ago | parent [-]

Possibly due to moving compute from inference to training

	▲	dluxem 4 hours ago \| parent [-]
		My purely unfounded, gut reaction to Opus 4.7 being released today was "Oh, that explains the recent 4.6 performance - they were spinning up inference on 4.7." Of course, I have no information on how they manage the deployment of their models across their infra.