Thanks, it was a video so didn’t survive the cross-post. I’ve put in a hyperlink for now.
Benjamin_Todd
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Thanks, useful to have these figures and an independent data on these calculations.
I’ve been estimating it based on a 500x increase in effective FLOP per generation, rather than 100x of regular FLOP.
Rough calculations are here.
At the current trajectory, the GPT-6 training run costs $6bn in 2028, and GPT-7 costs $130bn in 2031.I think that makes GPT-8 a couple of trillion in 2034.
You’re right that if you wanted to train GPT-8 in 2031 instead, then it would cost roughly 500x more than training GPT-7 that year.
I’m open to that and felt unsure the post was a good idea after I released it. I had some discussion with him on twitter afterwards, where we smoothed things over a bit: https://x.com/GaryMarcus/status/1888604860523946354
Thanks for the comments!
I’m especially keen to explore bottlenecks (e.g. another suggestion I saw is that to reach 1 billion a year would require 10x current global lithium production to supply the batteries.)
A factor of 2 for increased difficultly due to processing intensity seems reasonable, and I should have thrown it in. (Though my estimates were to an order of magnitude so this probably won’t change the bottom line, and on the other side, many robots will weigh <100kg and some will be non-humanoid.)
Thanks, and fair points!
Note that it you convert only half the car factories, you can still produce 0.5 billion robots per year, so it doesn’t change the basics picture that much. (It’s all order of magnitude stuff.)
I talk a little about some other estimates—a standard trajectory would be 20-30 years on the long end. ASI enabled could be even faster than 5yr. I agree it would be nice to flesh these out more.
Also agree it would be good to figure out the conversion efficiency better. One factor on the other side is robots involve lighter parts, which apparently makes it easier. Ideally we’d also check for other input factors that could bottleneck production -eg lithium for batteries at over 100m.
That’s helpful! Makes me think the all in hardware costs could be off by a factor of 2x.
I did wonder about maintenance costs, but I figured they wouldn’t change the picture too much because I only assume an avg 3 year lifetime for the robot, and figured they wouldn’t need a huge amount of maintenance to make it to that point.
Moreover, if there’s worthwhile maintenance that extends the lifetime further, then the hardware costs could end up cheaper than my per year estimate.
I’m also envisioning the costs after a big scale up, and there would be robot repair shops as numerous as car repair, rather than needing to fly in specialists.
That said, I agree it would be interesting to look at how much is spent on car maintenance per year on a car vs. capital costs. (I expect it would be under 10%?)
I’d be happy to put the opening bunch of paragraphs. I was feeling reluctant to cross-post because I often update my articles as I learn more about a topic, and I don’t want to keep multiple versions in sync (especially for a lower priority article).
Yes—if anyone reading knows more about manufacturing and could comment on how easy it would be to convert, that would be very helpful.
I also agree it would be interesting to try to do more analysis of how much ASI and robotics could speed up construction of robot factories, by looking at different bottlenecks and how much they could help.
I’m not sure a robot workforce would have a huge effect initially, since there’s already a large pool of human workers (though maybe you get some boost by making everything run 24⁄7). However, at later stages it might become hard to hire enough human workers, while with robots you could keep scaling.
Thanks, great comment.
Seems like we roughly agree on the human-only case. My thinking was that the profit margin would initially be 90-99%, which would create huge economic incentives. Though incentives and coordination were probably stronger in WW2, which could make things slower. Also 10x per year for 5 years sounds like a lot – helpful to point out they didn’t quite achieve that in WW2.
With ASI, I agree something like another 5x speed-up sounds plausible.
I agree (1) and (2) are possibilities. However, from a personal planning pov, you should focus preparing for scenarios (i) that might last a long time (ii) where you can affect what happens, since that’s where the stakes are.
Scenarios where we all die soon can be mostly be ignored, unless you think they make up most of the probability. (Edit: to be clear it does reduce the value of saving vs. spending, just don’t think it’s a big effect unless probabilities are high.)
I think (3) is the key way to push back.
I feel unsure all my preferences are either (i) local and easily satisfied or (ii) impartial & altruistic. You only need to have one type of preference with, say, log returns to money that can be better satisfied post-AGI to make capital post-AGI valuable to you (emulations maybe).
But let’s focus on the altruistic case – I’m very interested in the question of how valuable capital will be altruistically post-AGI.
I think your argument about relative neglectedness makes sense, but is maybe too strong.
There’s 500 trillion of world wealth, so if you have $1m now, that’s 2e-9 of world wealth. Through good investing through the transition, it seems like you can increase your share. Then set that against chance of confiscation etc, and plausibly you end up with a similar share afterwards.
You say you’d be competing with the entire rest of the pot post-transition, but that seems too negative. Only <3% of income today is used on broadly altruistic stuff, and the amount focused on impartial longtermist values is miniscule (which is why AI safety is neglected in the first place). It seems likely it would still be a minority in the future.
People with an impartial perspective might be able to make good trades with the majority who are locally focused (give up earth for the commons etc.). People with low discount rates should also be able to increase their share over time.
So if you have 2e-9 of future world wealth, it seems like you could get a significantly larger share of the influence (>10x) from the perspective of your values.
Now you need to compare that to $1m extra donated to AI safety in the short-term. If you think that would reduce x-risk by less 1e-8 then saving to give could be more valuable.
Suppose about $10bn will be donated to AI safety before the lock-in moment. Now consider adding a marginal $10bn. Maybe that decreases x-risk by another ~1%. Then that means $1m decreases it by about 10e-6. So with these numbers, I agree donating now is ~100x better.
However, I could imagine people with other reasonable inputs concluding the opposite. It’s also not obvious to me that donating now dominates so much that I’d want to allocate 0% to the other scenario.
True, though I think many people have the intuition that returns diminish faster than log (at least given current tech).
For example, most people think increasing their income from $10k to $20k would do more for their material wellbeing than increasing it from $1bn to $2bn.
I think the key issue is whether new tech makes it easier to buy huge amounts of utility, or that people want to satisfy other preferences beyond material wellbeing (which may have log or even close to linear returns).
I agree the lower bound for output isn’t very tight. I’d be very interested to hear other simple rules of thumb you could use to provide a tighter one.
I’ll add a note to the section on input tokens that since they don’t require KV cache, it’s possible to get much closer to the upper bound.
The opportunities for algorithmic improvements go far beyond the parallelization and mixture of experts methods you mention.
I agree. I’d be very interested in anyone’s forecasts for how they might evolve.
I’ve been working with (very roughly) another ~10x or so improvement in “inference efficiency” by 2030 (or how to measure this and make sure it’s independent from other factors).
By this I mean that if we were able to train a model with 10^26 FLOP this year, achieving a fixed level of learning efficiency, it would require 10X FLOP to generate useful output, while by 2030 it would only require X FLOP to get the same output.
Thanks that’s interesting!
Can I double check, do you think this affects the bottom lines?
The bottom line is supposed to be that FLOP/s vs. FLOP per forward pass can be used as an upper bound, and memory bandwidth vs. model size can be used as an lower bound, and real life efficiency falls somewhere in the middle depending on a many factors (inc. length of KV cache), which I don’t try to get into, but is plausibly around 15% of the upper bound for GPT-4 on H100s.
Are you saying that the lower bound for output tokens should maybe be even lower, because the KV cache can be larger than the model weights?
You need to add in the endowments of the colleges as well. The richest college at Cambridge (Trinity) has an endowment of about $1.5bn; whereas the richest college at Oxford has only about $300m.
What are the chances of being a billionaire or getting $30m plus if you go to Harvard rather than an elite uni?
And then what about HBS rather than Harvard?
Agree—Glassdoor is mainly designed to appeal to job seekers. The way they get their data is by only granting access if you reveal your salary. So the salary data ends up tilted towards the people who are seeking jobs.
There’s also a sampling problem. Google has ~10,000 engineers, but there’s probably only ~100 who earn $1mn+. Large companies normally only have a couple of responses, so even if you sampled everyone randomly, you’d only get ~1 top earner in the sample.
Hi Jonah,
Great posts.
I agree these figures show it’s plausible that the value of donations in finance are significantly larger than the direct economic contribution of many jobs, though I see it as highly uncertain. When you’re working in highly socially valuable sectors like research or some entrepreneurship, it seems to me that the two are roughly comparable, with big error bars.
However, I don’t think this shows it’s plausible that earning to give is likely to be the path towards doing the most good. There are many careers that seem to offer influence over budgets significantly larger than what you could expect to donate. For instance, the average budget per employee at DfiD is about $6mn per year, and you get similar figures at the World Bank, and many major foundations. It seems possible to move this money into something similarly effective or better than cash transfers. We’ve also just done an estimate of party politics showing that the expected budget influenced towards your preferred causes is 1-80mn if you’re an Oxford graduate over a career, and that takes account of chances of success.
You’d expect there to be less competition to influence the budgets of foundations for the better than to earn money, so these figures make sense.
(And then there’s all the meta things, like persuading people to do earning to give :) )
One point to note with Carl’s 30x figure—that’s only when comparing the short-run welfare impact of a GDP boost with a transfer to GiveDirectly. If you also care about the long-run effects, then it becomes much less clear.
Hmm interesting.
I see the case for focusing only on compute (since relatively objective), but it still seems important to try to factor in some amount of algorithmic progress to pretraining (which means that the cost to achieve GPT-6 level performance will be dropping over time).
The points on Epoch are getting outside of my expertise – I see my role as to synthesise what experts are saying. It’s good to know these critiques exist and it would be cool to see them written up and discussed.