How much total investment do you think there is in AI in 2023?
My guess is total investment was around the $200B - $500B range, with about $100B of that into new startups and organizations, and around $100-$400B of that in organizations like Google and Microsoft outside of acquisitions. I have pretty high uncertainty on the upper end here, since I don’t know what fraction of Google’s revenue gets reinvested again into AI, how much Tesla is investing in AI, how much various governments are investing, etc.
How much variance do you think there is in the level of 2023 investment in AI? (Or maybe whatever other change you think is equivalent.)
Variance between different years depending on market condition and how much products take off seems like on the order of 50% to me. Like, different years have pretty hugely differing levels of investment.
My guess is about 50% of that variance is dependent on different products taking off, how much traction AI is getting in various places, and things like Chat-GPT existing vs. not existing.
So this gives around $50B - $125B of variance to be explained by product-adjacent things like Chat-GPT.
How much influence are you giving to GPT-3, GPT-3.5, GPT-4? How much to the existence of OpenAI? How much to the existence of Google? How much to Jasper? How much to good GPUs?
Existence of OpenAI is hard to disentangle from the rest. I would currently guess that in terms of total investment, GPT-2 → GPT-3 made a bigger difference than GPT-3.5 → Chat-GPT, but both made a much larger difference than GPT-3 → GPT-3.5.
I don’t think Jasper made a huge difference, since its userbase is much smaller than Chat-GPT, and also evidently the hype from it has been much lower.
Good GPUs feels kind of orthogonal. We can look at each product that makes up my 50% of the variance to be explained and see how useful/necessary good GPUs were for its development, and my sense is for Chat-GPT at least the effect of good GPUs were relatively minor since I don’t think the training to move from GPT-3.5 to Chat-GPT was very compute intensive.
I would feel fine saying expected improvements in GPUs are responsible for 25% of the 50% variance (i.e. 17.5%) if you chase things back all the way, though that again feels like it isn’t trying to add up to 100% with the impact from “Chat-GPT”. I do think it’s trying to add up to 100% with the impact from “RLHF’s effect on Chat-GPT”, which I claimed was at least 50% of the impact of Chat-GPT in-particular.
In any case, in order to make my case for $10B using these numbers I would have to argue that between 20% and 8% of the product-dependent variance in annual investment into AI is downstream of Chat-GPT, and indeed that still seems approximately right to me after crunching the numbers. It’s by far the biggest AI product of the last few years, it is directly credited with sparking an arms race between Google and Microsoft, and indeed even something as large as 40% wouldn’t seem totally crazy to me, since these kinds of things tend to be heavy-tailed, so if you select on the single biggest thing, there is a decent chance you underestimate its effect.
I didn’t realize how broadly you were defining AI investment. If you want to say that e.g ChatGPT increased investment by $10B out of $200-500B, so like +2-5%, I’m probably happy to agree (and I also think it had other accelerating effects beyond that).
I would guess that a 2-5% increase in total investment could speed up AGI timelines 1-2 weeks depending on details of the dynamics, like how fast investment was growing, how much growth is exogenous vs endogenous, diminishing returns curves, importance of human capital, etc.. If you mean +2-5% investment in a single year then I would guess the impact is < 1 week.
I haven’t thought about it much, but my all things considered estimate for the expected timelines slowdown if you just hadn’t done the ChatGPT release is probably between 1-4 weeks.
Is that the kind of effect size you are imagining here? I guess the more important dynamic is probably more people entering the space rather than timelines per se?
One thing worth pointing out in defense of your original estimate is that variance should add up to 100%, not effect sizes, so e.g. if the standard deviation is $100B then you could have 100 things each explaining ($10B)^2 of variance (and hence each responsible for +-$10B effect sizes after the fact).
I didn’t realize how broadly you were defining AI investment. If you want to say that e.g ChatGPT increased investment by $10B out of $200-500B, so like +2-5%, I’m probably happy to agree (and I also think it had other accelerating effects beyond that).
Makes sense, sorry for the miscommunication. I really didn’t feel like I was making a particularly controversial claim with the $10B, so was confused why it seemed so unreasonable to you.
I do think those $10B are going to be substantially more harmful for timelines than other money in AI, because I do think a good chunk of that money will much more directly aim at AGI than most other investment. I don’t know what my multiplier here for effect should be, but my guess is something around 3-5x in expectation (I’ve historically randomly guessed that AI applications are 10x less timelines-accelerating per dollar than full-throated AGI-research, but I sure have huge uncertainty about that number).
That, plus me thinking there is a long tail with lower probability where Chat-GPT made a huge difference in race dynamics, and thinking that this marginal increase in investment does probably translate into increases in total investment, made me think this was going to shorten timelines in-expectation by something closer to 8-16 weeks, which isn’t enormously far away from yours, though still a good bit higher.
And yeah, I do think the thing I am most worried about with Chat-GPT in addition to just shortening timelines is increasing the number of actors in the space, which also has indirect effects on timelines. A world where both Microsoft and Google are doubling down on AI is probably also a world where AI regulation has a much harder time taking off. Microsoft and Google at large also strike me as much less careful actors than the existing leaders of AGI labs which have so far had a lot of independence (which to be clear, is less of an endorsement of current AGI labs, and more of a statement about very large moral-maze like institutions with tons of momentum). In-general the dynamics of Google and Microsoft racing towards AGI sure is among my least favorite takeoff dynamics in terms of being able to somehow navigate things cautiously.
One thing worth pointing out in defense of your original estimate is that variance should add up to 100%, not effect sizes, so e.g. if the standard deviation is $100B then you could have 100 things each explaining ($10B)^2 of variance (and hence each responsible for +-$10B effect sizes after the fact).
Oh, yeah, good point. I was indeed thinking of the math a bit wrong here. I will think a bit about how this adjusts my estimates, though I think I was intuitively taking this into account.
And yeah, I do think the thing I am most worried about with Chat-GPT in addition to just shortening timelines is increasing the number of actors in the space, which also has indirect effects on timelines. A world where both Microsoft and Google are doubling down on AI is probably also a world where AI regulation has a much harder time taking off.
Maybe—but Microsoft and Google are huge organizations, and huge organizations have an incentive to push for regulation that imposes costs that they can pay while disproportionately hampering smaller competitors. It seems plausible to me that both M & G might prefer a regulatory scheme that overall slows down progress while cementing their dominance, since that would be a pretty standard regulatory-capture-driven-by-the-dominant-actors-in-the-field kind of scenario.
A sudden wave of destabilizing AI breakthroughs—with DALL-E/Midjourney/Stable Diffusion suddenly disrupting art and Chat-GPT who-knows-how-many-things—can also make people on the street concerned and both more supportive of AI regulation in general, as well as more inclined to take AGI scenarios seriously in particular. I recently saw a blog post from someone speculating that this might cause a wide variety of actors—M & G included—with a desire to slow down AI progress to join forces to push for widespread regulation.
It seems plausible to me that both M & G might prefer a regulatory scheme that overall slows down progress while cementing their dominance, since that would be a pretty standard regulatory-capture-driven-by-the-dominant-actors-in-the-field kind of scenario.
Interesting. Where did something like this happen?
I asked Chat-GPT and one of the clearest examples it came up with is patent trolling by large pharmaceutical companies. Their lobbying tends to be far more focused on securing monopoly rights to their products for as long as possible than anything related to innovation.
Other examples:
Automakers lobbying for restrictive standards for potential market disruptors like electric or self-driving vehicles
Telecoms lobbying against Net Neutrality
Taxi companies lobbying against ridesharing startups
Tech companies lobbying for intellectual property and data privacy regulations that they have better legal/compliance resources to handle
IMO it’s much easier to support high investment numbers in “AI” if you consider lots of semiconductor / AI hardware startup stuff as “AI investments”. My suspicion is that while GPUs were primarily a crypto thing for the last few years, the main growth outlook driving more investment is them being an AI thing.
I’d be interested to know how you estimate the numbers here, they seem quite inflated to me.
If 4 big tech companies were to invest $50B each in 2023 then, assuming average salary as $300k and 2:1 capital to salary then investment would be hiring about 50B/900K = 55,000 people to work on this stuff. For reference the total headcount at these orgs is roughly 100-200K.
50B/yr is also around 25-50% of the size of the total income, and greater than profits for most which again seems high.
Perhaps my capital ratio is way too low but I would find it hard to believe that these companies can meaningfully put that level of capital into action so quickly. I would guess more on the order of $50B between the major companies in 2023.
Agree with paul’s comment above that timeline shifts are the most important variable.
My guess is total investment was around the $200B - $500B range, with about $100B of that into new startups and organizations, and around $100-$400B of that in organizations like Google and Microsoft outside of acquisitions. I have pretty high uncertainty on the upper end here, since I don’t know what fraction of Google’s revenue gets reinvested again into AI, how much Tesla is investing in AI, how much various governments are investing, etc.
Variance between different years depending on market condition and how much products take off seems like on the order of 50% to me. Like, different years have pretty hugely differing levels of investment.
My guess is about 50% of that variance is dependent on different products taking off, how much traction AI is getting in various places, and things like Chat-GPT existing vs. not existing.
So this gives around $50B - $125B of variance to be explained by product-adjacent things like Chat-GPT.
Existence of OpenAI is hard to disentangle from the rest. I would currently guess that in terms of total investment, GPT-2 → GPT-3 made a bigger difference than GPT-3.5 → Chat-GPT, but both made a much larger difference than GPT-3 → GPT-3.5.
I don’t think Jasper made a huge difference, since its userbase is much smaller than Chat-GPT, and also evidently the hype from it has been much lower.
Good GPUs feels kind of orthogonal. We can look at each product that makes up my 50% of the variance to be explained and see how useful/necessary good GPUs were for its development, and my sense is for Chat-GPT at least the effect of good GPUs were relatively minor since I don’t think the training to move from GPT-3.5 to Chat-GPT was very compute intensive.
I would feel fine saying expected improvements in GPUs are responsible for 25% of the 50% variance (i.e. 17.5%) if you chase things back all the way, though that again feels like it isn’t trying to add up to 100% with the impact from “Chat-GPT”. I do think it’s trying to add up to 100% with the impact from “RLHF’s effect on Chat-GPT”, which I claimed was at least 50% of the impact of Chat-GPT in-particular.
In any case, in order to make my case for $10B using these numbers I would have to argue that between 20% and 8% of the product-dependent variance in annual investment into AI is downstream of Chat-GPT, and indeed that still seems approximately right to me after crunching the numbers. It’s by far the biggest AI product of the last few years, it is directly credited with sparking an arms race between Google and Microsoft, and indeed even something as large as 40% wouldn’t seem totally crazy to me, since these kinds of things tend to be heavy-tailed, so if you select on the single biggest thing, there is a decent chance you underestimate its effect.
I didn’t realize how broadly you were defining AI investment. If you want to say that e.g ChatGPT increased investment by $10B out of $200-500B, so like +2-5%, I’m probably happy to agree (and I also think it had other accelerating effects beyond that).
I would guess that a 2-5% increase in total investment could speed up AGI timelines 1-2 weeks depending on details of the dynamics, like how fast investment was growing, how much growth is exogenous vs endogenous, diminishing returns curves, importance of human capital, etc.. If you mean +2-5% investment in a single year then I would guess the impact is < 1 week.
I haven’t thought about it much, but my all things considered estimate for the expected timelines slowdown if you just hadn’t done the ChatGPT release is probably between 1-4 weeks.
Is that the kind of effect size you are imagining here? I guess the more important dynamic is probably more people entering the space rather than timelines per se?
One thing worth pointing out in defense of your original estimate is that variance should add up to 100%, not effect sizes, so e.g. if the standard deviation is $100B then you could have 100 things each explaining ($10B)^2 of variance (and hence each responsible for +-$10B effect sizes after the fact).
Makes sense, sorry for the miscommunication. I really didn’t feel like I was making a particularly controversial claim with the $10B, so was confused why it seemed so unreasonable to you.
I do think those $10B are going to be substantially more harmful for timelines than other money in AI, because I do think a good chunk of that money will much more directly aim at AGI than most other investment. I don’t know what my multiplier here for effect should be, but my guess is something around 3-5x in expectation (I’ve historically randomly guessed that AI applications are 10x less timelines-accelerating per dollar than full-throated AGI-research, but I sure have huge uncertainty about that number).
That, plus me thinking there is a long tail with lower probability where Chat-GPT made a huge difference in race dynamics, and thinking that this marginal increase in investment does probably translate into increases in total investment, made me think this was going to shorten timelines in-expectation by something closer to 8-16 weeks, which isn’t enormously far away from yours, though still a good bit higher.
And yeah, I do think the thing I am most worried about with Chat-GPT in addition to just shortening timelines is increasing the number of actors in the space, which also has indirect effects on timelines. A world where both Microsoft and Google are doubling down on AI is probably also a world where AI regulation has a much harder time taking off. Microsoft and Google at large also strike me as much less careful actors than the existing leaders of AGI labs which have so far had a lot of independence (which to be clear, is less of an endorsement of current AGI labs, and more of a statement about very large moral-maze like institutions with tons of momentum). In-general the dynamics of Google and Microsoft racing towards AGI sure is among my least favorite takeoff dynamics in terms of being able to somehow navigate things cautiously.
Oh, yeah, good point. I was indeed thinking of the math a bit wrong here. I will think a bit about how this adjusts my estimates, though I think I was intuitively taking this into account.
Maybe—but Microsoft and Google are huge organizations, and huge organizations have an incentive to push for regulation that imposes costs that they can pay while disproportionately hampering smaller competitors. It seems plausible to me that both M & G might prefer a regulatory scheme that overall slows down progress while cementing their dominance, since that would be a pretty standard regulatory-capture-driven-by-the-dominant-actors-in-the-field kind of scenario.
A sudden wave of destabilizing AI breakthroughs—with DALL-E/Midjourney/Stable Diffusion suddenly disrupting art and Chat-GPT who-knows-how-many-things—can also make people on the street concerned and both more supportive of AI regulation in general, as well as more inclined to take AGI scenarios seriously in particular. I recently saw a blog post from someone speculating that this might cause a wide variety of actors—M & G included—with a desire to slow down AI progress to join forces to push for widespread regulation.
Interesting. Where did something like this happen?
I asked Chat-GPT and one of the clearest examples it came up with is patent trolling by large pharmaceutical companies. Their lobbying tends to be far more focused on securing monopoly rights to their products for as long as possible than anything related to innovation.
Other examples:
Automakers lobbying for restrictive standards for potential market disruptors like electric or self-driving vehicles
Telecoms lobbying against Net Neutrality
Taxi companies lobbying against ridesharing startups
Tech companies lobbying for intellectual property and data privacy regulations that they have better legal/compliance resources to handle
IMO it’s much easier to support high investment numbers in “AI” if you consider lots of semiconductor / AI hardware startup stuff as “AI investments”. My suspicion is that while GPUs were primarily a crypto thing for the last few years, the main growth outlook driving more investment is them being an AI thing.
I’d be interested to know how you estimate the numbers here, they seem quite inflated to me.
If 4 big tech companies were to invest $50B each in 2023 then, assuming average salary as $300k and 2:1 capital to salary then investment would be hiring about 50B/900K = 55,000 people to work on this stuff. For reference the total headcount at these orgs is roughly 100-200K.
50B/yr is also around 25-50% of the size of the total income, and greater than profits for most which again seems high.
Perhaps my capital ratio is way too low but I would find it hard to believe that these companies can meaningfully put that level of capital into action so quickly. I would guess more on the order of $50B between the major companies in 2023.
Agree with paul’s comment above that timeline shifts are the most important variable.