Agreed that we should expect the performance difference between high- and low-context human engineers to diminish as task sizes increase. Also agreed that the right way to account for that might be to simply discount the 5-18x multiplier when projecting forwards, but I’m not entirely sure. I did think about this before writing the post, and I kept coming back to the view that when we measure Claude 3.7 as having a 50% success rate at 50-minute tasks, or o3 at 1.5-hour tasks, we should substantially discount those timings. On reflection, I suppose the counterargument is that this makes the measured doubling times look more impressive, because (plausibly) if we look at a pair of tasks that take low-context people 10 and 20 minutes respectively, the time ratio for realistically high-context people might be more than 2x. But I could imagine this playing out in other ways as well (e.g. maybe we aren’t yet looking at task sizes where people have time to absorb a significant amount of context, and so as the models climb from 1 to 4 to 16 to 64 minute tasks, the humans they’re being compared against aren’t yet benefiting from context-learning effects).
One always wishes for more data – in this case, more measurements of human task completion times with high and low context, on more problem types and a wider range of time horizons...
Agreed that we should expect the performance difference between high- and low-context human engineers to diminish as task sizes increase. Also agreed that the right way to account for that might be to simply discount the 5-18x multiplier when projecting forwards, but I’m not entirely sure. I did think about this before writing the post, and I kept coming back to the view that when we measure Claude 3.7 as having a 50% success rate at 50-minute tasks, or o3 at 1.5-hour tasks, we should substantially discount those timings. On reflection, I suppose the counterargument is that this makes the measured doubling times look more impressive, because (plausibly) if we look at a pair of tasks that take low-context people 10 and 20 minutes respectively, the time ratio for realistically high-context people might be more than 2x. But I could imagine this playing out in other ways as well (e.g. maybe we aren’t yet looking at task sizes where people have time to absorb a significant amount of context, and so as the models climb from 1 to 4 to 16 to 64 minute tasks, the humans they’re being compared against aren’t yet benefiting from context-learning effects).
One always wishes for more data – in this case, more measurements of human task completion times with high and low context, on more problem types and a wider range of time horizons...