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{"success":true,"source":{"id":"a002b1fe-529f-408f-b209-3455cb652171","url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","title":"Q3.5 Timelines Update: Looks Like the Intelligence Explosion Will Happen in 2027 After All","source_type":"blog_post"},"origin":"stored","total_chars":113075,"offset":0,"content":"AI Futures Project\n\nSubscribe Sign in\n\nQ3.5 Timelines Update: Looks Like the Intelligence Explosion Will Happen in 2027 After All\n\nOur timelines have shortened\n\n+2\n\nEli Lifland , Brendan Halstead , Daniel Kokotajlo , and 3 others\n\nNov 10, 2026\n\n415\n\n128\n\n98\n\nShare\n\nWe want to get this post out quickly given the urgency of the topic. It’s possible that there are some errors in the modeling, but we think it’s very unlikely that they undermine the high level conclusions. We would love to be corrected on any mistakes we made.\n\nMETR recently estimated that Anthropic is now making AI progress about twice as fast as its historical rate (they would not be surprised by anything from 1.3x to 3x), and guessed that “perhaps ≈80%” of the current acceleration is due to AI. If they’re right, that implies an AI-driven acceleration that is 5 months ahead of the AI 2027 scenario .\n\nThis level of acceleration due to AI is very unexpected. Previously, Daniel, the most bullish of us, assigned a 50% chance of Superhuman AI Researcher (SAR) 1 before 2029 . SAR is roughly the point at which a company would rather fire all their human researchers and rely solely on automated AI researchers than have a team of the best human researchers in the world with no AIs. 2\n\nNow, all of us forecast that Superhuman AI Researcher (SAR) will likely arrive at some point in 2027 (assuming no substantial deliberate slowdown). 3 We also predict a 35-65% chance that Top-Expert-Dominating AI (TED-AI), AIs that can automate all white-collar work, will arrive before 2028. While there were many factors and sources of evidence that influenced us, the single biggest source was the METR report. 4 .\n\nIf SAR is coming in 2027, it is incredibly urgent to pause or substantially ‘pace’ frontier AI development, with the ideal being an industry-wide pause followed by an international agreement . Companies are not on track to align a SAR, and using a misaligned SAR to do recursive self-improvement (RSI) will put us in an extremely bad position. Additionally, if we train SAR in the near future, China will probably be able to steal the training recipe and maybe even the model weights ( more ). 5\n\nTimelines/Takeoff Update Summary\n\nWe estimate that METR’s report implies a 1.7x AI-attributable acceleration in the pace of progress today, relative to historical levels. This surprises our model; we made our most recent timelines forecasts less than three months ago, and even the most aggressive model-based distribution (Daniel’s) assigned less than 4% to the pace of progress being sped up by at least 1.7x in November 2026. 6 1.7x AI-attributable acceleration is a bigger deal than it may seem; e.g., it’s a much bigger deal than making employees 1.7x more productive .\n\nWe focus in this post primarily on forecasting when Superhuman AI Researcher (SAR) will arrive. We also discuss Top-Expert-Dominating-AI (TED-AI): AIs that are better than top human experts at virtually all tasks. ( more )\n\nIn this post, we forecast SAR and TED-AI using the following approaches:\n\nSimple models:\n\nResearch taste linear extrapolation : Even assuming that the recent pace of progress doesn’t accelerate further due to automation, the fact that the recent trend is above historical rates is a big update to our timelines; a linear extrapolation of the recent rate of AI progress predicts that SAR will likely be achieved by the end of 2027.\n\nLabor uplift extrapolation : An exponential extrapolation of self-reported labor uplift predicts SAR in mid-2028. In addition to not modeling automation, there are reasons to think that an exponential is too conservative. However, self-reports may be too bullish.\n\nExtrapolating Anthropic automation levels : Anthropic recently shared a graph showing how the percentage of R&D tasks at various automation levels has changed over time. Extrapolating this predicts SAR in late 2027; this method also doesn’t model speedup from automation.\n\nExtrapolating software R&D uplift per unit of ECI : AI software R&D uplift must have recently been increasing by a larger factor with each ECI point. If we linearly extrapolate the rate at which this “factor of extra uplift per ECI point” is increasing, we get SAR in Dec 2027 (if we assume that the self-reported AI labor uplift chart is accurate) or July 2027 (if we assume that there was no meaningful automation 12 months ago and all the speed-up has happened after then).\n\nTom Davidson’s toy model: Predicts superintelligence in around a year conditional on a software-only singularity, so SAR sometime in 2027.\n\nFiltered AI Futures Model (AIFM) : Keeping the existing AIFM (including parameter distributions), and filtering for trajectories that match METR’s AI-attributable pace of progress acceleration estimates. This serves 2 purposes on top of the simple extrapolations:\n\nModeling how automation changes the rate of progress, allowing forecasting reasonably beyond SAR.\n\nInforming what parameter settings in the simple research taste extrapolation are plausible.\n\nThe results of these methods:\n\nHow to think about these results:\n\nThe simple research taste extrapolation, which is a simplified version of the AIFM, gives July or September 2027, depending on whether you account for parameter uncertainty. Other simple models predict late 2027 to mid-2028.\n\nWhen conditioned on 1.7x Nov 2026 AI-attributed acceleration, the AIFM predicts a SAR median of Feb 2027. This is faster than the simple research taste extrapolation because it usually has a higher level of Sep 2026 research taste (in the median case, matching a median researcher), 7 and it accounts for further automation.\n\nWhen additionally conditioning on a 1.2x May 2026 AI-attributed acceleration, predictions get slower because it indicates a lower rate of growth than is typical when only conditioning on 1.7x in Nov.\n\nWe then explore hypotheses for why the AI Futures Model was wrong , including parameter adjustments and modeling changes. The analysis is highly preliminary, and we were unable to come to strong conclusions. Our top candidates for what explains where we went wrong are:\n\nNear-AC coding skills result in more AI R&D uplift than we anticipated . This could explain the combination of ~1.7x AI-attributable acceleration in Nov and (very roughly) ~1.2x in May, without AIs already having high enough research taste to contribute to the acceleration. In this case, the gradual acceleration no longer implies a low research taste slope, so steeper slopes and faster taste-driven takeoffs are more likely.\n\nSkills besides coding and research taste (the two included in the AIFM), e.g., data generation, are driving acceleration. This potentially would have similar implications to the above bullet. But it’s unclear.\n\nConversion of uplift to pace of progress is too conservative. I.e., we modeled diminishing returns to AI research effort too harshly. This would point toward a faster takeoff.\n\nHere’s an example trajectory from the AIFM showing how things might go if the cause of our prediction error was underestimating AI R&D uplift from near-AC coding skills: 8\n\nAfter discussing some other considerations , we share our overall forecasts (conditional on AGI projects/companies not slowing down):\n\nNote that the gap between a person’s SAR median and TED-AI median is not their median time from SAR to TED-AI. Because the distributions of the time until SAR and of the time from SAR to TED-AI both have long right tails, the difference of the medians is typically larger than the median of the difference, so subtracting the two columns tends to overstate the median time between the two milestones.\n\nMETR’s estimates imply that progress is currently sped up by 1.7x due to AI\n\nIn METR’s recent assessment of AI R&D acceleration at Anthropic , they estimate that, over the past year, AI R&D progress has accelerated by ~2x relative to the trend before Oct 2025. METR estimates that ~80% of current acceleration is downstream of AI (as opposed to other factors), wh","truncated":true,"next_offset":8000,"facts":{"authors":["Eli Lifland"],"publisher":null,"published_date":null,"doi":null,"archived_url":null,"events":[],"versions":[],"copy_of":[],"interests":[{"party":"AI Futures Project","stake":"The authors use the 1.7x figure as the main basis for shortening their published AI timelines and for advocating a pause in frontier AI development, so a higher figure supports positions they publicly hold.","claim_id":"72048c9d-d8b0-458a-86fc-eaae8f83193a","recorded_at":"2026-11-14T02:36:04.198Z"}]},"note":"More text follows; call again with offset 8000 to continue."}