I don't know how to make sense of this level of investment. I feel that I lack the proper conceptual framework to make sense of the purchasing power of half a trillion USD in this context.
1) reasoning capabilities in latest models are rapidly approaching superhuman levels and continue to scale with compute.
2) intelligence at a certain level is easier to achieve algorithmically when the hardware improves. There's also a larger path to intelligence and often simpler mechanisms
3) most current generation reasoning AI models leverage test time compute and RL in training--both of which can make use of more compute readily. For example RL on coding against compilers proofs against verifiers.
All of this points to compute now being basically the only bottleneck to massively superhuman AIs in domains like math and coding--rest no comment (idk what superhuman is in a domain with no objective evals)
A calculator is superhuman if you're prepared to put up with it's foibles.
One, capable of replacing some large proportion of global gdp (this definition has a lot of obstructions: organizational, bureaucratic, robotic)...
two, difficult to find problems in which average human can solve but model cannot. The problem with this definition is that the distinct nature of intelligence of AI and the broadness of tasks is such that this metric is probably only achievable after AI is already in reality massively superhuman intelligence in aggregate. Compare this with Go AIs which were massively superhuman and often still failing to count ladders correctly--which was also fixed by more scaling.
All in all I avoid the term AGI because for me AGI is comparing average intelligence on broad tasks rel humans and I'm already not sure if it's achieved by current models whereas superhuman research math is clearly not achieved because humans are still making all of progress of new results.