Here is my current favorite version of this (though you could do better and this isn’t that careful):
AI company revenue will be perhaps ~$20 billion this year and has roughly 3x’d per year over the last 2.5 years. Let’s say 1⁄2 of this is in the US for $10 billion this year (maybe somewhat of an underestimate, but whatever). Maybe GDP impacts are 10x higher than revenue due to AI companies not internalizing the value of all of their revenue (they might be somewhat lower due to AI just displacing other revenue without adding that much value), so to hit 20% of US GDP (~$6 trillion) AI companies would need around $600 billion in revenue. The naive exponential extrapolation indicates we hit this level of annualized revenue in 4 years in the early/middle 2029. Notably, most of this revenue would be in the last year, so we’d be seeing ~10% GDP growth.
Exponential could be too aggressive, e.g., maybe capabilities progress stalls out, there isn’t sufficient investment, fab capacity runs out (this should happen just around 2029 or so, but could happen early), or the historical exponential growth is mostly due to adoption in a small and fixed number of industries with limited demand.
Historically, revenue growth tends to slow over time as the revenue follows an adoption sigmoid. I don’t think this will apply to AI as much as the revenue growth is mostly due to the technology improving unlike other sectors and I don’t see a particular reason for this to slow (other than investment + compute running out), though of course it could.
But, exponential could also be insufficiently aggressive: AI capabilities might get increasingly useful triggering more and more adoption and revenue. I think OpenAI’s revenue is probably somewhat superexponential since the release of the GPT-3 API, but I’m not sure about this and couldn’t find numbers. Or AIs might be able to accelerate their own adoption. I also think that there could be phase changes around full automation which are important and could trigger one time increases in revenue.
These parameters are somewhat debatable, e.g., maybe you think that AI companies will internalize more than 10% of the value or that GDP generally does a poor job accounting for value (in most/all sectors not just AI), so the multiplier will be more like 3x or 5x (adding another year to the exponential forecast).
My overall sense is that AI company revenue growth will probably slow, but going at least exponential all the way to 20% of US GDP with >$600 billion in revenue before 2030 seems plausible (maybe I think ~35%, with some chance of very powerful AI that doesn’t yet trigger massive revenue). So, my overall sense is that naive revenue extrapolations imply a pretty bullish view on AI, but don’t rule out medians which are ~arbitrarily far into the future as progress might slow.
I’m not sure what economic milestone is most interesting to forecast to, so I was tempted by “the economy is substantially higher due to AI, likely meaning the growth rate is substantially higher”.
I do a similar estimate for full remote work automation here. The results are pretty similar as ~20% of US GDP and remote work automation will probably hit around the same time on the naive revenue extrapolation picture.
Overall, I agree with Eli’s perspective here: revenue extrapolations probably don’t get at the main crux except via suggesting that quite short timelines to crazy stuff (<4 years) are at least plausible.
I don’t particularly like extrapolating revenue as a methodology for estimating timelines to when AI is (e.g.) a substantial fraction of US GDP (say 20%)[1], but I do think it would be worth doing a more detailed version of this timelines methodology. This is in response to Ege’s blog post with his version of this forecasting approach.
Here is my current favorite version of this (though you could do better and this isn’t that careful):
AI company revenue will be perhaps ~$20 billion this year and has roughly 3x’d per year over the last 2.5 years. Let’s say 1⁄2 of this is in the US for $10 billion this year (maybe somewhat of an underestimate, but whatever). Maybe GDP impacts are 10x higher than revenue due to AI companies not internalizing the value of all of their revenue (they might be somewhat lower due to AI just displacing other revenue without adding that much value), so to hit 20% of US GDP (~$6 trillion) AI companies would need around $600 billion in revenue. The naive exponential extrapolation indicates we hit this level of annualized revenue in 4 years in the early/middle 2029. Notably, most of this revenue would be in the last year, so we’d be seeing ~10% GDP growth.
Exponential could be too aggressive, e.g., maybe capabilities progress stalls out, there isn’t sufficient investment, fab capacity runs out (this should happen just around 2029 or so, but could happen early), or the historical exponential growth is mostly due to adoption in a small and fixed number of industries with limited demand.
Historically, revenue growth tends to slow over time as the revenue follows an adoption sigmoid. I don’t think this will apply to AI as much as the revenue growth is mostly due to the technology improving unlike other sectors and I don’t see a particular reason for this to slow (other than investment + compute running out), though of course it could.
But, exponential could also be insufficiently aggressive: AI capabilities might get increasingly useful triggering more and more adoption and revenue. I think OpenAI’s revenue is probably somewhat superexponential since the release of the GPT-3 API, but I’m not sure about this and couldn’t find numbers. Or AIs might be able to accelerate their own adoption. I also think that there could be phase changes around full automation which are important and could trigger one time increases in revenue.
These parameters are somewhat debatable, e.g., maybe you think that AI companies will internalize more than 10% of the value or that GDP generally does a poor job accounting for value (in most/all sectors not just AI), so the multiplier will be more like 3x or 5x (adding another year to the exponential forecast).
My overall sense is that AI company revenue growth will probably slow, but going at least exponential all the way to 20% of US GDP with >$600 billion in revenue before 2030 seems plausible (maybe I think ~35%, with some chance of very powerful AI that doesn’t yet trigger massive revenue). So, my overall sense is that naive revenue extrapolations imply a pretty bullish view on AI, but don’t rule out medians which are ~arbitrarily far into the future as progress might slow.
I’m not sure what economic milestone is most interesting to forecast to, so I was tempted by “the economy is substantially higher due to AI, likely meaning the growth rate is substantially higher”.
I do a similar estimate for full remote work automation here. The results are pretty similar as ~20% of US GDP and remote work automation will probably hit around the same time on the naive revenue extrapolation picture.
Overall, I agree with Eli’s perspective here: revenue extrapolations probably don’t get at the main crux except via suggesting that quite short timelines to crazy stuff (<4 years) are at least plausible.