Great post!
“All that is stopping them being even more powerful is spending on compute. Google & Microsoft are worth $1-2T each, and $10B can buy ~100x the compute used for GPT-4. Think about this: it means we are already well into hardware overhang territory[5].”
I broadly agree with the point that compute could be scaled up significantly, but I want to add a few notes about the claim that $10B buys 100x the compute of GPT-4.
Altman said “more” when asked if GPT-4 had cost $100M to train. We don’t know how much more. But PaLM seems to have only cost $9M-$23M so $100M is probably reasonable.
If OpenAI was buying up 100x the compute of GPT-4, maybe that would be a big enough spike in demand for GPUs that they would become more expensive. I’m pretty uncertain about what to expect there, but I estimated that PaLM used the equivalent of 0.01% of the world’s current GPU/TPU computing capacity for 2 months. GPT-4 seems to be bigger than PaLM, so 100x the compute used for it might be the equivalent of more than 1% of the world’s existing GPU/TPU computing capacity for 2 months.
You probably agree with me that (a) we can’t know whether it will rain on 2/10/2050 and (b) we can be pretty sure that there will be a solar eclipse on 7/22/2028. You are actively participating in a prediction market, so you seem to believe in some ability to forecast the future better than a magic 8 ball.
Where do you think the limits are to what kinds of things we can make useful predictions about, and how confident those predictions can be?