You might expect the labor force of NormalCorp to be roughly in equilibrium where they gain equally from spending more on compute as they gain from spending on salaries (to get more/better employees).
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However, I'm quite skeptical of this type of consideration making a big difference because the ML industry has already varied the compute input massively, with over 7 OOMs of compute difference between research now (in 2025) vs at the time of AlexNet 12 years ago, (invalidating the view that there is some relatively narrow range of inputs in which neither input is bottlenecking) and AI companies effectively can't pay more to get faster or much better employees, so we're not at a particularly privileged point in human AI R&D capabilities.
SlowCorp has 625K H100s per researcher. What do you even do with that much compute if you drop it into this world? Is every researcher just sweeping hyperparameters on the biggest pretraining runs? I'd normally say "scale up pretraining another factor of 100" and then expect that SlowCorp could plausibly outperform NormalCorp, except you've limited them to 1 week and a similar amount of total compute, so they don't even have that option (and in fact they can't even run normal pretraining runs, since those take longer than 1 week to complete).
The quality and amount of labor isn't the primary problem here. The problem is that the current practices for AI development are specialized to the current labor:compute ratio, and can't just be changed on a dime if you drastically change the ratio. Sure, the compute input has varied massively over 7 OOMs; importantly this did not happen all at once, the ecosystem adapted to it.
SlowCorp would be in a much better position if it was in a world where AI development had evolved with these kinds of bottlenecks existing all along. Frontier pretraining runs would be massively more parallel, and would complete in a day. There would be dramatically more investment in automation of hyperparameter sweeps and scaling analyses, rather than depending on human labor to do that. The inference-time compute paradigm would have started 1-2 years earlier, and would be significantly more mature. How fast would AI progress be in that world if you are SlowCorp? I agree it would still be slower than current AI progress, but it is really hard to guess how much slower, and it's definitely drastically faster than if you just impute a SlowCorp in today's world (which mostly seems like it will flounder and die immediately).
So we can break down the impacts into two categories:
If you want to pump your intuition for what AutomatedCorp should be capable of, the relevant SlowCorp is the one that only faces the first problem, that is, you want to consider the SlowCorp that evolved in a world with those constraints in place all along, not the SlowCorp thrown into a research ecosystem not designed for the constraints it faces. Personally, once I try to imagine that I just run into a wall of "who even knows what that world looks like" and fail to have my intuition pumped.
This seems mostly right, except that it's often hard to parallelize work and manage large projects - which seems like it slows thing importantly. And, of course, some things are strongly serialized using time that can't be sped up via more compute or more people. (See: PM hires 9 women to have baby in one month.)
Similarly, running 1,000 AI research groups in parallel might get you the same 20 insights 50 times, rather than generating far more insights. And managing and integrating the research, and deciding where to allocate research time, plausibly gets harder at more than a linear rate with more groups.
So overall, the model seems correct, but I think the 10x speed up is more likely than the 20x speed up.
I agree parallelization penalties might bite hard in practice. But it's worth noting that the AIs in the AutomatedCorp hypothetical also run 50x faster and are more capable.
(A strong marginal parallelization penalty exponent of 0.4 would render the 50x additional workers equivalent to a 5x improvement in labor speed, substantially smaller than the 50x speed improvement.)
Maybe it would be helpful to start using some toy models of DAGs/tech trees to get an idea of how wide/deep ratios affect the relevant speedups. It sounds like so far that much of this is just people having warring intuitions about 'no, the tree is deep and narrow and so slowing down/speeding up workers doesn't have that much effect because Amdahl's law so I handwave it at ~1x speed' vs 'no, I think it's wide and lots of work-arounds to any slow node if you can pay for the compute to bypass them and I will handwave it at 5x speed'.
This isn't that important, but I think the idea of using an exponential parallelization penalty is common in the economics literature. I specifically used 0.4 as around the harshest penalty I've heard of. I believe this number comes from some studies on software engineering where they found something like this.
I'm currently skeptical that toy models of DAGs/tech trees will add much value over:
(Separately AIs might be notably better at coordinating than humans are which might change things substantially. Toy models of this might be helpful.)
How much should we expect AI progress to speed up after fully automating AI R&D? This post presents an intuition pump for reasoning about the level of acceleration by talking about different hypothetical companies with different labor forces, amounts of serial time, and compute. Essentially, if you'd expect an AI research lab with substantially less serial time and fewer researchers than current labs (but the same cumulative compute) to make substantially less algorithmic progress, you should also expect a research lab with an army of automated researchers running at much higher serial speed to get correspondingly more done. (And if you'd expect the company with less serial time to make similar amounts of progress, the same reasoning would also imply limited acceleration.) We also discuss potential sources of asymmetry which could break this correspondence and implications of this intuition pump.
The intuition pump
Imagine theoretical AI companies with the following properties:
NormalCorp is similar to a future frontier AI company. SlowCorp is like NormalCorp except with 50x less serial time, a 5x smaller workforce, and lacking above median researchers/engineers.[2] How much less would SlowCorp accomplish than NormalCorp, i.e. what fraction of NormalCorp's time does it take to achieve the amount of algorithmic progress that SlowCorp would get in a week?
SlowCorp has 50x less serial labor, 5x less parallel labor, as well as reduced labor quality. Intuitively, it seems like it should make much less progress than NormalCorp. My guess is that we should expect NormalCorp to achieve SlowCorp's total progress in at most roughly 1/10th of its time.
Now let's consider an additional corporation, AutomatedCorp, which is an analog for a company sped up by AI R&D automation.
AutomatedCorp is like NormalCorp except with 50x more serial time, a 50x larger workforce, and only world-class researchers and engineers. The jump from NormalCorp to AutomatedCorp is like the jump from SlowCorp to NormalCorp but with 10x more employees, and with the structure of the increase in labor quality being a bit different.
It seems like the speedup from NormalCorp to AutomatedCorp should be at least similar to the jump from SlowCorp to NormalCorp, i.e. at least roughly 10x. My best guess is around 20x.
AutomatedCorp is an analogy for a hypothetical AI company with AI researchers that match the best human researcher while having 200k copies that are each 50x faster than humans.[5] If you have the intuition that a downgrade to SlowCorp would be very hobbling while this level of AI R&D automation wouldn't vastly speed up progress, consider how to reconcile this.
That's the basic argument. Below I will go over some clarifications, a few reasons the jumps between the corps might be asymmetric, and the implications of high speedups from AutomatedCorp.
Clarifications
There are a few potentially important details which aren't clear in the analogy, written in the context of the jump from NormalCorp to AutomatedCorp:
Asymmetries
Why would there be any particular reason why the current regime was special such that scaling up labor (including quality and speed) is highly asymmetric from scaling down labor?
Here I'll cover asymmetries between the jumps from SlowCorp to NormalCorp and NormalCorp to AutomatedCorp.
There are some reasons you might eventually see asymmetry between improving vs. degrading labor quality, speed, and quantity. In particular, in some extreme limit you might e.g. just figure out the best experiments to run from an ex-ante perspective after doing all the possibly useful theoretical work etc. But, it's very unclear where we are relative to various absolute limits and there isn't any particular reason to expect we're very close. One way to think about this is to imagine some aliens which are actually 50x slower than us and which have ML researchers/engineers only as good as our median AI researchers/engineers (while having a similar absolute amount of compute in terms of FLOP/s). These aliens could consider the exact same hypothetical, but for them, the move from NormalCorp to AutomatedCorp is very similar to our move from SlowCorp to NormalCorp. So, if we're uncertain about whether we are these slow aliens in the hypothetical, we should think the situation is symmetric and our guesses for the SlowCorp vs. NormalCorp and NormalCorp vs. AutomatedCorp multipliers should be basically the same.
(That is, if we can't do some absolute analysis of our quantity/quality/speed of labor which implies that (e.g.) returns diminish right around now or some absolute analysis of the relationship between labor and compute. Such an analysis would presumably need to be mechanistic (aka inside view) or utilize actual experiments (like I discuss in the one of the items in the list above) because analysis which just looks at reference classes (aka outside view) would apply just as well to the aliens and doesn't take into account the amount of compute we have in practice. I don't know how you'd do this mechanistic analysis reliably, though actual experiments could work.)
Implications
I've now introduced some intuition pumps with AutomatedCorp, NormalCorp, and SlowCorp. Why do I think these intuition pumps are useful? I think the biggest crux about the plausibility of a bunch of faster AI progress due to AI automation of AI R&D is how much acceleration you'd see in something like the AutomatedCorp scenario (relative to the NormalCorp scenario). This doesn't have to be the crux: you could think the initial acceleration is high, but that this progress will very quickly slow due to diminishing returns on AI R&D effort biting harder than how much improved algorithms yield faster progress due to smarter, faster, and cheaper AI researchers which can accelerate things further. But, I think it is somewhat hard for the returns (and other factors) to look so bad that we won't at least have the equivalent of 3 years of overall AI progress (not just algorithms) within 1 year of seeing AIs matching the description of AutomatedCorp if we condition on these AIs yielding an AI R&D acceleration multiplier of >20x.[7]
Another potential crux for downstream implications is how big of a deal >4 years of overall AI progress is. Notably, if we see 4 year timelines (e.g. to the level of AIs I've discussed), then 4 years of AI progress brought us from the systems we have now (e.g. o3) to full AI R&D automation, so another 4 years of progress feels intuitively very large.[8] Also, if we see higher returns to some period of AI progress (in terms of ability to accelerate AI R&D), then this makes a super-exponential loop where smarter AIs build ever smarter AI systems faster and faster more likely. Overall, shorter timelines tend to imply faster takeoff (at least evidentially, the causal story is much more complex). I think sometimes disagreements about takeoff would be resolved if we condition on timelines and what the run up to a given level of capability looks like, because the disagreement is really about the returns to a given amount of AI progress.
These employees were the best that NormalCorp could find while hiring aggressively over a few years as well as a smaller core of more experienced researchers and engineers (around 300) who've worked in AI for longer. They have some number of the best employees working in AI (perhaps they have 1/5 of the best 1000 people on earth), but most of their employees are more like typical tech employees: what NormalCorp could hire in a few years with high salaries and an aim to recruit rapidly. ↩︎
And below median, but that shouldn't have as big of an effect as removing the above median employees. ↩︎
These employees were the best that NormalCorp could find while hiring aggressively over a few years as well as a smaller core of more experienced researchers and engineers (around 300) who've worked in AI for longer. They have some number of the best employees working in AI (perhaps they have 1/5 of the best 1000 people on earth), but most of their employees are more like typical tech employees: what NormalCorp could hire in a few years with high salaries and an aim to recruit rapidly. ↩︎
Roughly 1.5-3x smaller than OpenAI's current computational resources ↩︎
These are basically just the estimates for the number of copies and speed at the point of superhuman AI researchers in AI 2027, but I get similar numbers if I do the estimate myself. Note that (at least for my estimates) the 50x speed includes accounting for AIs working 24/7 (a factor of 3) and being better at coordinating and sharing state with weaker models so they can easily complete some tasks faster. It's plausible that heavy inference time compute use implies that we'll initially have a smaller number of slower AI researchers, but we should still expect that quantity and speed will quickly increase after this is initially achieved. So, you can think about this scenario as being what happens after allowing for some time for costs to drop. This scenario occurring a bit after initial automation doesn't massively alter the bottom line takeaways. (That said, if inference time compute allows for greatly boosting capabilities, then at the time when we have huge numbers of fast AI researchers matching the best humans, we might also be able to run a smaller number of researchers which are substantially qualitatively superhuman.) ↩︎
Interestingly, this implies that AI runtime compute use is comparable to human. Producing a second of cognition from a human takes perhaps 1e14 to 1e15 FLOP or between 1/10 to 1 H100 seconds. We're imagining that AI inference takes 1/5 of an H100 second to produce a second of cognition. While inference requirements are similar in this scenario, I'm imagining that training requirements start substantially higher than human lifetime FLOP. (I'm imagining the AI was trained for roughly 1e28 flop while human lifetime FLOP is more like 1e24.) This seems roughly right as I think we should expect faster inference but bigger training requirements, at least after a bit of adaptation time etc., based on how historical AI progress goes. But this is not super clear cut. ↩︎
And we condition on reaching this level of capability prior to 2032 so that it is easier to understand the relevant regime, and on the relevant AI company going full steam ahead without external blockers. ↩︎
The picture is a bit messy because I expect AI progress will start slowing due to slowed compute scaling by around 2030 or so (if we don't achieve very impressive AI by this point). This is partially due to continued compute scaling requiring very extreme quantities of investment by this point and partially due to fab capacity running out as ML chips eat up a larger and larger share of fab capacity. In such a regime, I expect a somewhat higher fraction of the progress will be algorithmic (rather than from scaling compute or from finding additional data), though not by that much as algorithmic progress is driven by additional compute instead of additional data. Also, the rate of algorithmic progress will be slower at an absolute level. So, 20x faster algorithmic progress will yield a higher overall progress multiplier, but progress will also be generally slower. So, you'll maybe get a lower number of 2024-equivalent years of progress, but a higher number of 2031-equivalent years of progress. ↩︎