Some kind of quantitative estimate of the effects on the effective pool of cognitive labour would be good.
If an H100 equivalent (part of a cluster running the latest public models) has roughly the cognitive capability of a junior engineer, and the capacity of ten of them (runs 5x as fast, for twice as long per day), then 16M H100e is a workforce increment of 160M CS grads. Next year that could be 1600M. But that "10x a CS grad" figure could be 100x too large or 10x too small, I don't know. (And I know I don't know enough to estimate it.)
(Of course over 90% of capacity is currently used for training next-gen models rather than inference, but the principle holds.)
Riffing off Dario's "country of geniuses in a data center": barring disasters, the early visible effects of AI will come from a continent of college grads in a thousand data centers. How big is that continent? Australia (30M people) or Asia (5,000M people)?
Agreed that this is _very_ hard to estimate... and in any case the ratio of human to AI productivity can't be expressed in a single number. Reason #17 (of 99999) that the future is hard to predict.
My understanding is that the percentage of frontier lab compute devoted to R&D is closer to 50% than 90%? Something like (_very_ handwavy): 10% for the training run that produces the next shipping model, 40% for other R&D activities (experiments, failed training runs, etc.), and 50% for serving customers. I don't have a specific source offhand, but to the best of my recollection, the 50/50 split is consistent with occasional reports on finances at OpenAI / Anthropic, and I've seen comments from researchers that most of the R&D compute is used for things other than final training runs. Do you have a source for the R&D share being closer to 90%?
Edit: yes, AIUI over half the R&D is not directly training, but it is testing and otherwise making the next gen fit for purpose, so not available for customers to use.
Some kind of quantitative estimate of the effects on the effective pool of cognitive labour would be good.
If an H100 equivalent (part of a cluster running the latest public models) has roughly the cognitive capability of a junior engineer, and the capacity of ten of them (runs 5x as fast, for twice as long per day), then 16M H100e is a workforce increment of 160M CS grads. Next year that could be 1600M. But that "10x a CS grad" figure could be 100x too large or 10x too small, I don't know. (And I know I don't know enough to estimate it.)
(Of course over 90% of capacity is currently used for training next-gen models rather than inference, but the principle holds.)
Riffing off Dario's "country of geniuses in a data center": barring disasters, the early visible effects of AI will come from a continent of college grads in a thousand data centers. How big is that continent? Australia (30M people) or Asia (5,000M people)?
Agreed that this is _very_ hard to estimate... and in any case the ratio of human to AI productivity can't be expressed in a single number. Reason #17 (of 99999) that the future is hard to predict.
My understanding is that the percentage of frontier lab compute devoted to R&D is closer to 50% than 90%? Something like (_very_ handwavy): 10% for the training run that produces the next shipping model, 40% for other R&D activities (experiments, failed training runs, etc.), and 50% for serving customers. I don't have a specific source offhand, but to the best of my recollection, the 50/50 split is consistent with occasional reports on finances at OpenAI / Anthropic, and I've seen comments from researchers that most of the R&D compute is used for things other than final training runs. Do you have a source for the R&D share being closer to 90%?
No, only an off-hand comment by Zvi.
Edit: yes, AIUI over half the R&D is not directly training, but it is testing and otherwise making the next gen fit for purpose, so not available for customers to use.