It’s quite clear that labs both want more high-quality researchers—top talent has very high salaries, reflecting large marginal value-add.
Three objections, one obvious. I’ll state them strongly, a bit devil’s advocate; not sure where I actually land on these things.
Obvious: salaries aren’t that high.
Also, I model a large part of the value to companies of legible credentialed talent being the marketing value to VCs and investors, who (even if lab leadership can) can’t tell talent apart except by (rare) legible signs. This is actually a way to get more compute (and other capital). (The legible signs are rare because compute is a bottleneck! So a Matthew effect pertains.)
Finally, the utility of labs is very convex in the production of AI: the actual profit comes from time spent selling a non-commoditised frontier offering at large margin. So small AI production speed gains translate into large profit gains.
I also noticed this! And wondered how much evidence it is (and of what). I don’t think of Meta as especially rational in its AI-related behaviour. Maybe this is Zuckerberg trying to make up for Le Cun’s years of bad judgement.
Hmm, I actually kind of lean towards it being rational, and labs just underspending on labor vs. capital for contigent historical/cultural reasons. I do think a lot of the talent juice is in “banal” progress like efficiently running lots of experiments, and iterating on existing ideas straightforwardly (as opposed to something like “only a few people have the deep brilliance/insight to make progress”), but that doesn’t change the upshot IMO.
Three objections, one obvious. I’ll state them strongly, a bit devil’s advocate; not sure where I actually land on these things.
Obvious: salaries aren’t that high.
Also, I model a large part of the value to companies of legible credentialed talent being the marketing value to VCs and investors, who (even if lab leadership can) can’t tell talent apart except by (rare) legible signs. This is actually a way to get more compute (and other capital). (The legible signs are rare because compute is a bottleneck! So a Matthew effect pertains.)
Finally, the utility of labs is very convex in the production of AI: the actual profit comes from time spent selling a non-commoditised frontier offering at large margin. So small AI production speed gains translate into large profit gains.
Salaries have indeed now gotten pretty high—it seems like they’re within an OOM of compute spend (at least at Meta).
I also noticed this! And wondered how much evidence it is (and of what). I don’t think of Meta as especially rational in its AI-related behaviour. Maybe this is Zuckerberg trying to make up for Le Cun’s years of bad judgement.
Hmm, I actually kind of lean towards it being rational, and labs just underspending on labor vs. capital for contigent historical/cultural reasons. I do think a lot of the talent juice is in “banal” progress like efficiently running lots of experiments, and iterating on existing ideas straightforwardly (as opposed to something like “only a few people have the deep brilliance/insight to make progress”), but that doesn’t change the upshot IMO.