Published on September 9, 2025 4:58 PM GMTIn response to my recent post arguing against above-trend progress from better RL environments, yet another argument for short(er) AGI timelines was raised to me:Sure, at the current rate of progress, it would take a while (e.g. 5 years) to reach AIs that can automate research engineering within AI companies while being cheap and fast (superhuman coder). But we'll get large increases in the rate of AI progress well before superhuman coder due to AIs accelerating AI R&D. Probably we'll see big enough accelerations to really cut down on timelines to superhuman coder once AIs can somewhat reliably complete tasks that take top human research engineers 8 hours. After all, such AIs would probably be capable enough to greatly change research engineering in AI companies.I'm skeptical of this argument: I think that it's unlikely (15%) that we see large speed-ups (>2x faster overall AI progress) due to AIs which are only able to complete 8-hour tasks somewhat reliably[1]. I do think we'll probably see massive speed-ups in overall AI progress due to AIs accelerating AI R&D, but I think this will require a higher level of capability. I also think that non-massive speed-ups on the way to very capable AIs will substantially shorten timelines[2] and will result in large changes to (research engineering) workflows in AI companies. I predict that these engineering workflow changes will be very salient to AI company employees and I discuss implications of this below.Concretely, let's imagine AIs which can complete 8-hour reasonably self-contained AI R&D engineering tasks[3] (within an AI developer) around 50% of the time. I'll call these "8-hour AIs".First, when would we expect to see such AIs? For this argument to go through, we need to see these 8-hour AIs relatively soon. One way of estimating this is to look at the horizon length trends on METR's task suite[4]. AIs which can do 8-hour real-world AI R&D engineering tasks would probably have a longer than 8-hour time horizon on METR's task suite because real-world tasks are probably relatively harder for AIs than benchmark tasks. To be charitable to the argument I'm responding to, I'll assume 16-hour 50% reliability time horizons on METR's task suite corresponds to 8-hour real-world AI R&D engineering tasks. (Correspondingly, note that when I say "8-hour AIs", I'm not referring to the number measured by METR! I expect that "8-hour AIs" occur at least somewhat after we see 8-hour 50% reliability time horizons on METR's task suite!) If we extrapolate out a 170-day doubling time, we'd expect 16-hour 50% reliability to happen around 1.3 years from now. (We expect to see around 3-hour 80% reliability time horizons on METR's task suite at this time.) Thus, it seems pretty likely this happens soon, meaning that if such AIs were able to greatly accelerate AI R&D this could shorten timelines considerably.Could AIs able to complete 8-hour self-contained AI R&D engineering tasks accelerate AI R&D enough to make a big dent in timelines? I'll argue that this is unlikely in two ways:First, I'll argue somewhat mechanistically that it seems hard for AIs which are only this capable to speed things up this much.Next, I'll show that some simple curve fitting also makes a high level of acceleration look unlikely.AI progress speed-ups don't seem large enoughWe'll try to guess how much these 8-hour AIs speed up research engineers within the AI company and then guess how much this research engineering acceleration would speed up overall AI progress. In practice, the effect of these 8-hour AIs wouldn't be well described as just speeding up engineers: probably the engineers would do more work in parallel and would do different kinds of work. But, we can attempt to guess an equivalent speed-up in terms of usefulness; as in, the effect of these AIs is as good as an X times speed-up to the company's research engineers (when they are doing work which is reasonably centrally research engineering work). It seems like it would be hard for these 8-hour AIs to speed up engineers by more than 4x:These AIs wouldn't be able to automate some tasks (without a human helping them) and this bottleneck would limit the speed-up due to Amdahl's law.These AIs probably wouldn't be able to help much with non-coding tasks (e.g., employees communicating to share state) and in aggregate these are a substantial fraction of the job. (AIs which fully automate research engineering could bypass the need to share a bunch of state with human engineers, though some communication skills would still be needed.)It seems like it would often be hard for the human working with the AI to quickly gain enough context to help the AI (when it gets stuck or messes up)Lack of reliability would slow things down (especially if AIs remain relatively worse than humans at understanding whether they've succeeded).AIs could compensate for these limitations by doing (much) more of the work they are particularly good at, but there isn't a strong reason to think this effect makes a huge difference at this level of capability. It's worth emphasizing that a key advantage of the AIs is that they could be much faster than humans. All considered, I expect substantially less than 4x speed-ups to research engineering from such AIs; after thinking about it some, I ended up feeling like maybe 2x is about right for 8-hour AIs after some time for adaptation from human engineers. See "Appendix: sanity check of engineering speed-up" for a sanity check which yields a similar result.How much would a 2x acceleration to research engineering boost the rate of AI progress?Research engineering is only a subset of the labor going into AI R&D, so this isn't as good as accelerating all labor by 2x. Further, labor is only one input going into AI R&D: another key input is compute for experiments. That said, engineering labor is a very important input and faster/more engineering labor doesn't just allow for implementing more experiments and making training runs more efficient: critically, it can also make experiments cheaper (use less compute) and better (see this breakdown from ai-2027 for ways accelerated engineering labor can speed up AI R&D).AI R&D progress (algorithms and software) isn't the only thing driving AI progress; AI progress is also driven by scaling up the compute used for training runs (and scaling up spending on acquiring data).Using this breakdown, I do a more involved estimate in "Appendix: How do speed-ups to engineering translate to overall AI progress speed-ups?". In short: because engineering is only one input into AI R&D, speeding up engineering a lot only speeds up AI R&D somewhat and AI R&D is around 55% of what's driving AI progress, applying a further discount. I come to an overall estimate that a 2x engineering speed-up would only (somewhat charitably) yield a 1.2x speed-up to overall AI progress while a 4x engineering speed-up would only (somewhat charitably) yield a 1.5x speed-up in overall AI progress.I think a 1.5x speed-up in overall AI progress is substantially more than I expect for 8-hour AIs (or in about 1.3 years) as getting an estimate this high required being pretty charitable in several places.(I'd estimate 1.15x overall AI progress acceleration as a central estimate from this methodology for 8-hour AIs (after some adaptation time), by guessing a 2x engineering speed-up and using somewhat less charitable constants in the conversion. My central estimate is lower (1.1x) for 1.3 years from now because I expect 8-hour AIs (as I defined above) to come somewhat later than this and there is a need for some adaptation time.)Interpolating between now and superhuman coder doesn't indicate large speed-ups within 2 yearsHere's another strategy we can apply:Let's say we're interested in analyzing the speed-up we expect in around 1.5 years (because this is around when we expect these 8-hour AIs).We have some guess at the current speed-up in AI R&D, some guess at the speed-up at the point of superhuman coder, and some guess at when we'll see superhuman coder given the current rate of AI progress.We might expect that this speed-up to AI R&D increases roughly exponentially over time, so we can get a sense of what speed-ups will be like along the way by exponentially interpolating between now and superhuman coder.Now let's apply this simple curve fitting strategy. What speed-up should we expect right now? I'd guess we're (charitably) seeing around 1.2x engineering speed-up[5] and applying the method in "Appendix: How do speed-ups to engineering translate to overall AI progress speed-ups?", we'd expect maybe around a 1.05x overall AI progress speed-up (which generally seems reasonable though a bit high to me). We'll assume 5 years until superhuman coder at the current rate of AI progress and that the AI R&D acceleration at the point of superhuman coder is 5x (the same number used in ai-2027). Technically, we want the time to superhuman coder at the unaccelerated current rate of AI progress, but current acceleration is small, so this doesn't make much of a difference either way. We have to convert this AI R&D acceleration number into an overall AI progress number which yields around 3.6x faster overall AI progress.We want to exponentially interpolate between 1.05x and 3.6x to map from the number of years of AI progress (as in, years of AI progress from the present at the current unaccelerated rate) to the level of acceleration. 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src: local('MathJax_Size4'), local('MathJax_Size4-Regular')}@font-face {font-family: MJXc-TeX-size4-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Size4-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Size4-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Size4-Regular.otf') format('opentype')}@font-face {font-family: MJXc-TeX-vec-R; src: local('MathJax_Vector'), local('MathJax_Vector-Regular')}@font-face {font-family: MJXc-TeX-vec-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Regular.otf') format('opentype')}@font-face {font-family: MJXc-TeX-vec-B; src: local('MathJax_Vector Bold'), local('MathJax_Vector-Bold')}@font-face {font-family: MJXc-TeX-vec-Bx; src: local('MathJax_Vector'); font-weight: bold}@font-face {font-family: MJXc-TeX-vec-Bw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Bold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Bold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Bold.otf') format('opentype')} where Y is the number of years of AI progress at the current (unaccelerated) rate.[6] If we plug in 1.3 years, we get a 1.14x overall AI progress speed-up. At 2 years, we get 1.24x overall AI progress speed-up.Thus, this interpolation also doesn't predict large speed-ups in two years and the speed-ups it predicts roughly line up with the estimate from the prior section.[7] Of course, this is just a simple model to get a sense for what might happen.One objection you might have is that by the time 2 years have passed we'll actually be further along than just extrapolating out the current rate of progress because we're already seeing non-trivial overall AI progress speed-ups at that point, so the numbers I've given are underestimates for what we'll actually see at that point. In other words, 2 years at the current rate of progress will get us to a point where we have 1.24x overall AI progress speed-up (which is non-trivial), so in 2 actual calendar years, we'll get substantially further than 2 default years of progress due to this non-trivial speed-up.To model this, we can set up a differential equation:dYdt=1+0.05⋅(2.6/0.05)Y/5Y is the "number of years of AI progress at the (unaccelerated) current rate" while t is the number of actual calendar years from the present.If we run this, we get:The overall AI progress speed-ups at 1.3 and 2 years produced by the model are only slightly higher at 1.15x and 1.30x respectively. Superhuman coder does arrive substantially faster (around 3.5 years instead of 5 years), but it still takes 3 years before we see larger than 2x speed-ups to overall AI progress.You could disagree with this model because:You think the current speed-up to overall AI progress is much higher (e.g. 1.2x rather than 1.05x)You think the overall AI progress speed-up at the point of superhuman coder is much higher (e.g. 8x rather than 3.6x)You expect progress multipliers to follow a very different curve than exponential.Though note that the first two of these adjustments each only make a moderate difference to the bottom line (setting the current overall AI progress speed-up to 1.2x or setting the superhuman coder overall speed-up to 8x each only shorten the timeline to superhuman coder by half a year[8]).What about speedups from mechanisms other than accelerating engineering?Above, I discuss AIs accelerating R&D work that's similar to the sorts of work that human employees might otherwise do (though my discussion was mostly specific to engineering). One alternative story is that AIs will be able to accelerate AI progress via doing something very different from adding labor similar to the labor humans do. For instance, AIs might generate huge amounts of higher quality data (e.g. RL environments) to train future AIs on and this could result in a feedback loop that accelerates AI progress. I'm currently skeptical that the data generation feedback loop story will result in substantially above trend progress as I discuss here, but it's worth emphasizing that the arguments I discuss above only apply to AIs accelerating AI R&D via labor which is somewhat similar to the labor that humans do for AI R&D.I think speedups from mechanisms other than AIs doing labor similar to what humans do is a major reason we might see large accelerations (>2x overall AI progress acceleration) from 8-hour AIs.(Concretely, I think accelerations this large are maybe like 15% likely and about 7% of this is due to mechanisms other than AIs accelerating labor humans might do.)Other reasons to expect very short timelinesI obviously don't address all the reasons you might expect very short (