Slow takeoff for AI R&D, fast takeoff for everything else
Why is AI progress so much more apparent in coding than everywhere else?
Among people who have "AGI timelines", most do not set their timelines based on data, but rather update them based on their own day-to-day experiences and social signals.
As of 2025, my guess is that individual perception of AI progress correlates with how closely someone's daily activities resemble how an AI researcher spends their time. The reason why users of coding agents feel a higher rate of automation in their bones, whereas people in most other occupations don't, is because automating engineering has been the focus of the industry for a while now. Despite the expectations for 2025 to be the year of the AI agent, it turns out the industry is small and cannot have too many priorities, hence basically the only competent agents we got in 2025 so far are coding agents.
Everyone serious about winning the AI race is trying to automate one job: AI R&D.
To a first approximation, there is no point yet in automating anything else, except to raise capital (human or investment), or to earn money. Until you are hitting diminishing returns on your rate of acceleration, unrelated capabilities are not a priority. This means that a lot of pressure is being applied to AI research tasks at all times; and that all delays in automation of AI R&D are, in a sense, real in a way that's not necessarily the case for tasks unrelated to AI R&D. It would be odd if there were easy gains to be made in accelerating the work of AI researchers on frontier models in addition to what is already being done across the industry.
I don't know whether automating AI research is going to be smooth all the way there or not; my understanding is that slow vs fast takeoff hinges significantly on how bottlenecked we become by non-R&D factors over time. Nonetheless, the above suggests a baseline expectation: AI research automation will advance more steadily compared to auto