What fraction of economically-valuable cognitive labor is already being automated today? How has that changed over time, especially recently?
I notice I’m confused about these ostensibly extremely basic questions, which arose in reading Open Phil’s old CCF-takeoff report, whose main metric is “time from AI that could readily[2] automate 20% of cognitive tasks to AI that could readily automate 100% of cognitive tasks”. A cursory search of Epoch’s data, Metaculus, and this forum didn’t turn up anything, but I didn’t spend much time at all doing so.
I was originally motivated by wanting to empirically understand recursive AI self-improvement better, which led to me stumbling upon the CAIS paper Examples of AI Improving AI, but I don’t have any sense whatsoever of how the paper’s 39 examples as of Oct-2023 translate to OP’s main metric even after constraining “cognitive tasks” in its operational definition to just AI R&D.
A survey was administered to attendees of three AI conferences during the summer of 2018 (ICML, IJCAI and the HLAI conference). The survey included questions for estimating AI capabilities over the next decade, questions for forecasting five scenarios of transformative AI and questions concerning the impact of computational resources in AI research. Respondents indicated a median of 21.5% of human tasks (i.e., all tasks that humans are currently paid to do) can be feasibly automated now, and that this figure would rise to 40% in 5 years and 60% in 10 years
which would suggest that OP’s clock should’ve started ticking in 2018, so that incorporating CCF-takeoff author Tom Davidson’s “~50% to a <3 year takeoff and ~80% to <10 year i.e. time from 20%-AI to 100%-AI, for cognitive tasks in the global economy” means takeoff should’ve already occurred… so I’m dismissing this survey’s relevance to my question (sorry).
I’m mainly wondering how Open Phil, and really anyone who uses fraction of economically-valuable cognitive labor automated / automatable (e.g. the respondents to that 2018 survey; some folks on the forum) as a useful proxy for thinking about takeoff, tracks this proxy as a way to empirically ground their takeoff-related reasoning. If you’re one of them, I’m curious if you’d answer your own question in the affirmative?
I am not one of them—I was wondering the same thing, and was hoping you had a good answer.
If I was trying to answer this question, I would probably try to figure out what fraction of all economically-valuable labor each year was cognitive, the breakdown of which tasks comprise that labor, and the year-on-year productivity increases on those task, then use that to compute the percentage of economically-valuable labor that is being automated that year.
Concretely, to get a number for the US in 1900 I might use a weighted average of productivity increases across cognitive tasks in 1900, in an approach similar to how CPI is computed
Look at the occupations listed in the 1900 census records
Figure out which ones are common, and then sample some common ones and make wild guesses about what those jobs looked like in 1900
Classify those tasks as cognitive or non-cognitive
Come to estimate that record-keeping tasks are around a quarter to a half of all cognitive labor
Notice that typewriters were starting to become more popular - about 100,000 typewriters sold per year
Note that those 100k typewriters were going to the people who would save the most time by using them
As such, estimate 1-2% productivity growth in record-keeping tasks in 1900
Multiply the productivity growth for record-keeping tasks by the fraction of time (technically actually 1-1/productivity increase but when productivity increase is small it’s not a major factor)
Estimate that 0.5% of cognitive labor was automated by specifically typewriters in 1900
Figure that’s about half of all cognitive labor automation in 1900
and thus I would estimate ~1% of all cognitive labor was automated in 1900. By the same methodology I would probably estimate closer to 5% for 2024.
Again, though, I am not associated with Open Phil and am not sure if they think about cognitive task automation in the same way.
What fraction of economically-valuable cognitive labor is already being automated today? How has that changed over time, especially recently?
I notice I’m confused about these ostensibly extremely basic questions, which arose in reading Open Phil’s old CCF-takeoff report, whose main metric is “time from AI that could readily[2] automate 20% of cognitive tasks to AI that could readily automate 100% of cognitive tasks”. A cursory search of Epoch’s data, Metaculus, and this forum didn’t turn up anything, but I didn’t spend much time at all doing so.
I was originally motivated by wanting to empirically understand recursive AI self-improvement better, which led to me stumbling upon the CAIS paper Examples of AI Improving AI, but I don’t have any sense whatsoever of how the paper’s 39 examples as of Oct-2023 translate to OP’s main metric even after constraining “cognitive tasks” in its operational definition to just AI R&D.
I did find this 2018 survey of expert opinion
which would suggest that OP’s clock should’ve started ticking in 2018, so that incorporating CCF-takeoff author Tom Davidson’s “~50% to a <3 year takeoff and ~80% to <10 year i.e. time from 20%-AI to 100%-AI, for cognitive tasks in the global economy” means takeoff should’ve already occurred… so I’m dismissing this survey’s relevance to my question (sorry).
Did e.g. a telephone operator in 1910 perform cognitive labor, by the definition we want to use here?
I’m mainly wondering how Open Phil, and really anyone who uses fraction of economically-valuable cognitive labor automated / automatable (e.g. the respondents to that 2018 survey; some folks on the forum) as a useful proxy for thinking about takeoff, tracks this proxy as a way to empirically ground their takeoff-related reasoning. If you’re one of them, I’m curious if you’d answer your own question in the affirmative?
I am not one of them—I was wondering the same thing, and was hoping you had a good answer.
If I was trying to answer this question, I would probably try to figure out what fraction of all economically-valuable labor each year was cognitive, the breakdown of which tasks comprise that labor, and the year-on-year productivity increases on those task, then use that to compute the percentage of economically-valuable labor that is being automated that year.
Concretely, to get a number for the US in 1900 I might use a weighted average of productivity increases across cognitive tasks in 1900, in an approach similar to how CPI is computed
Look at the occupations listed in the 1900 census records
Figure out which ones are common, and then sample some common ones and make wild guesses about what those jobs looked like in 1900
Classify those tasks as cognitive or non-cognitive
Come to estimate that record-keeping tasks are around a quarter to a half of all cognitive labor
Notice that typewriters were starting to become more popular - about 100,000 typewriters sold per year
Note that those 100k typewriters were going to the people who would save the most time by using them
As such, estimate 1-2% productivity growth in record-keeping tasks in 1900
Multiply the productivity growth for record-keeping tasks by the fraction of time (technically actually 1-1/productivity increase but when productivity increase is small it’s not a major factor)
Estimate that 0.5% of cognitive labor was automated by specifically typewriters in 1900
Figure that’s about half of all cognitive labor automation in 1900
and thus I would estimate ~1% of all cognitive labor was automated in 1900. By the same methodology I would probably estimate closer to 5% for 2024.
Again, though, I am not associated with Open Phil and am not sure if they think about cognitive task automation in the same way.