But I’ve been increasingly starting to wonder if software engineering might not be surprisingly easy to automate when the right data/environments are used at much larger scale
I’ve had similar thoughts: I think there’s still low-hanging fruit in RL, and in scaffolding and further scaling of inference compute. But my general take is that the recent faster trend of doubling every ~4 months is already the result of picking the low-hanging RL fruit for coding and SWE, and fast inference scaling. So this kind of thing will probably lead to a continuation of the fast trend, not another acceleration.
Another source of shorter timelines, depending on what timeline you mean, is the uncertainty from translating time horizon to real-world AI research productivity. Maybe models with an 80% time horizon of 1 month or less are already enough for a huge acceleration of AI R&D, with the right scaffold/unhobbling/bureaucracy that can take advantage of lots of parallel small experiments or other work, or good complementarities between AI and human labor,
I’ve had similar thoughts: I think there’s still low-hanging fruit in RL, and in scaffolding and further scaling of inference compute. But my general take is that the recent faster trend of doubling every ~4 months is already the result of picking the low-hanging RL fruit for coding and SWE, and fast inference scaling. So this kind of thing will probably lead to a continuation of the fast trend, not another acceleration.
Another source of shorter timelines, depending on what timeline you mean, is the uncertainty from translating time horizon to real-world AI research productivity. Maybe models with an 80% time horizon of 1 month or less are already enough for a huge acceleration of AI R&D, with the right scaffold/unhobbling/bureaucracy that can take advantage of lots of parallel small experiments or other work, or good complementarities between AI and human labor,