Thanks for this—I’m in a more peripheral part of the industry (consumer/industrial LLM usage, not directly at an AI lab), and my timelines are somewhat longer (5 years for 50% chance), but I may be using a different criterion for “automate virtually all remote workers”. It’ll be a fair bit of time (in AI frame—a year or ten) between “labs show generality sufficient to automate most remote work” and “most remote work is actually performed by AI”.
A key dynamic is that I think massive acceleration in AI is likely after the point when AIs can accelerate labor working on AI R&D. (Due to all of: the direct effects of accelerating AI software progress, this acceleration rolling out to hardware R&D and scaling up chip production, and potentially greatly increased investment.) See also here and here.
So, you might very quickly (1-2 years) go from “the AIs are great, fast, and cheap software engineers speeding up AI R&D” to “wildly superhuman AI that can achieve massive technical accomplishments”.
I think massive acceleration in AI is likely after the point when AIs can accelerate labor working on AI R&D.
Fully agreed. And the trickle-down from AI-for-AI-R&D to AI-for-tool-R&D to AI-for-managers-to-replace-workers (and -replace-middle-managers) is still likely to be a bit extended. And the path is required—just like self-driving cars: the bar for adoption isn’t “better than the median human” or even “better than the best affordable human”, but “enough better that the decision-makers can’t find a reason to delay”.
Thanks for this—I’m in a more peripheral part of the industry (consumer/industrial LLM usage, not directly at an AI lab), and my timelines are somewhat longer (5 years for 50% chance), but I may be using a different criterion for “automate virtually all remote workers”. It’ll be a fair bit of time (in AI frame—a year or ten) between “labs show generality sufficient to automate most remote work” and “most remote work is actually performed by AI”.
A key dynamic is that I think massive acceleration in AI is likely after the point when AIs can accelerate labor working on AI R&D. (Due to all of: the direct effects of accelerating AI software progress, this acceleration rolling out to hardware R&D and scaling up chip production, and potentially greatly increased investment.) See also here and here.
So, you might very quickly (1-2 years) go from “the AIs are great, fast, and cheap software engineers speeding up AI R&D” to “wildly superhuman AI that can achieve massive technical accomplishments”.
Fully agreed. And the trickle-down from AI-for-AI-R&D to AI-for-tool-R&D to AI-for-managers-to-replace-workers (and -replace-middle-managers) is still likely to be a bit extended. And the path is required—just like self-driving cars: the bar for adoption isn’t “better than the median human” or even “better than the best affordable human”, but “enough better that the decision-makers can’t find a reason to delay”.