Physical bottlenecks still exist, but is it really that implausible that the capabilities workforce would stumble upon huge algorithmic efficiency improvements? Recall that current algorithms are much less efficient than the human brain. There’s lots of room to go.
I don’t understand the reasoning here. It seems like you’re saying “Well, there might be compute bottlenecks, but we have so much room left to go in algorithmic improvements!” But the room to improve point is already the case right now, and seems orthogonal to the compute bottlenecks point.
E.g. if compute bottlenecks are theoretically enough to turn the 5x cognitive labor into only 1.1x overall research productivity, it will still be the case that there is lots of room for improvement but the point doesn’t really matter as research productivity hasn’t sped up much. So to argue that the situation has changed dramatically you need to argue something about how big of a deal the compute bottlenecks will in fact be.
I was more making the point that, if we enter a regime where AI can do 10 hour SWE tasks, then this will result in big algorithmic improvements, but at some point pretty quickly effective compute improvements will level out because of physical compute bottlenecks. My claim is that the point at which it will level out will be after multiple years worth of current algorithmic progress had been “squeezed out” of the available compute.
Interesting, thanks for clarifying. It’s not clear to me that this is the right primary frame to think about what would happen, as opposed to just thinking first about how big compute bottlenecks are and then adjusting the research pace for that (and then accounting for diminishing returns to more research).
I think a combination of both perspectives is best, as the argument in your favor for your frame is that there will be some low-hanging fruit from changing your workflow to adapt to the new cognitive labor.
I don’t understand the reasoning here. It seems like you’re saying “Well, there might be compute bottlenecks, but we have so much room left to go in algorithmic improvements!” But the room to improve point is already the case right now, and seems orthogonal to the compute bottlenecks point.
E.g. if compute bottlenecks are theoretically enough to turn the 5x cognitive labor into only 1.1x overall research productivity, it will still be the case that there is lots of room for improvement but the point doesn’t really matter as research productivity hasn’t sped up much. So to argue that the situation has changed dramatically you need to argue something about how big of a deal the compute bottlenecks will in fact be.
I was more making the point that, if we enter a regime where AI can do 10 hour SWE tasks, then this will result in big algorithmic improvements, but at some point pretty quickly effective compute improvements will level out because of physical compute bottlenecks. My claim is that the point at which it will level out will be after multiple years worth of current algorithmic progress had been “squeezed out” of the available compute.
Interesting, thanks for clarifying. It’s not clear to me that this is the right primary frame to think about what would happen, as opposed to just thinking first about how big compute bottlenecks are and then adjusting the research pace for that (and then accounting for diminishing returns to more research).
I think a combination of both perspectives is best, as the argument in your favor for your frame is that there will be some low-hanging fruit from changing your workflow to adapt to the new cognitive labor.