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  "url": "https://blog.aifutures.org/p/q35-timelines-update-looks-like-the",
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{"ok":true,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","text":"Q3.5 Timelines Update: Looks Like the Intelligence Explosion Will Happen in 2027 After All\nAI Futures Project\nSubscribe Sign in\nQ3.5 Timelines Update: Looks Like the Intelligence Explosion Will Happen in 2027 After All\nOur timelines have shortened\n+2\nEli Lifland , Brendan Halstead , Daniel Kokotajlo , and 3 others\nNov 10, 2026\n415\n128\n98\nShare\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.\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 .\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\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 .\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\nTimelines/Takeoff Update Summary\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 .\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 )\nIn this post, we forecast SAR and TED-AI using the following approaches:\nSimple models:\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.\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.\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.\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).\nTom Davidson’s toy model: Predicts superintelligence in around a year conditional on a software-only singularity, so SAR sometime in 2027.\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:\nModeling how automation changes the rate of progress, allowing forecasting reasonably beyond SAR.\nInforming what parameter settings in the simple research taste extrapolation are plausible.\nThe results of these methods:\nHow to think about these results:\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.\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.\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.\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:\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.\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.\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.\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\nAfter discussing some other considerations , we share our overall forecasts (conditional on AGI projects/companies not slowing down):\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.\nMETR’s estimates imply that progress is currently sped up by 1.7x due to AI\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), which implies a 1.7x acceleration due to AI (1.67–1.74x depending on how the 80% is read; see appendix ). 9\nBoth of these estimates rely significantly on evidence METR wasn’t able to share: METR says that “Anthropic redacted a majority of the evidence we found most informative for these conclusions”, which makes it difficult to know exactly what the 2x and 80% estimates are based on.\nThe 2x acceleration figure is partially based on evidence METR was allowed to share: the following graph, depicting benchmark scores recently growing at roughly twice the historical rate. The 80%/1.7x estimates have no comparable public support.\nThis estimate is produced by fitting trendlines to the performance of models as judged by AECI, Anthropic’s internal version of Epoch’s Capabilities Index ( ECI ). The trend fitted from data points after Oct 2025 has 1.9x the slope of the trend fitted from data points before Oct 2025. This was the best-fitting breakpoint (i.e., it was chosen to have the best fit to the data and it matched data better than fitting a one-time jump ). Since the accelerated trendline is an average of the progress over the last year, and we should expect AI R&D acceleration to increase over time, it’s plausible that the instantaneous acceleration right now is even faster. Regarding the acceleration getting faster over time, METR says:\nThe rate of growth could be undergoing continuous acceleration, e.g. hyperbolic growth. While we do not see clear evidence of this in the AECI trends, there is other evidence that makes this seem more plausible which Anthropic redacted. If growth is hyperbolic, we are likely underestimating instantaneous acceleration and substantially understating acceleration in the near future.\nRegarding AIs’ contributions over time, METR says:\nWe believe AI’s contribution has grown relatively more important over time.\nThe AECI data points that had previously been released by Anthropic seemed best explained by a one-time jump at Mythos rather than a change in slope. However, with the new AECI data points from the METR report, it looks like the post-Mythos slope is now more clearly higher than the pre-Mythos one.\nGiven the redactions, how much to update on METR’s report depends heavily on how much one trusts the redacted analysis by METR’s researchers (more discussion of this below ). This is a very unfortunate situation. We aren’t sure what reasons Anthropic gave internally for redacting all of this important information, but we are very skeptical that Anthropic is fairly assessing the costs and benefits from an impartial perspective. We strongly urge Anthropic to release enough information such that external observers can make a complete assessment.\nIn the Fable 5.2 system card , Anthropic have not added much clarity, with the following quote being ambiguous as to whether they agree with the METR report’s estimate; they only specifically claim that “Claude Mythos 5.2” (which does not seem like the internal frontier Model 4) “ does not double the internal rate of overall aggregate progress in AI research and development.” Full quote here:\nOur estimates of both the degree of acceleration and the attribution to AI are uncertain, so our determination that Claude Mythos 5.2 does not cross the RSP threshold reflects a holistic judgment that, given its shortcomings and limitations, it does not double the internal rate of overall aggregate progress in AI research and development (relative to an unassisted counterfactual). We will discuss overall trends in acceleration in a future report.\n1.7x acceleration of overall progress is a much bigger deal than a 1.7x labor uplift\nIt seems that many people aren’t tracking how big of a deal 1.7x AI-attributable pace of progress acceleration is, often conflating it with a weaker form of uplift.\nMETR describes a 1.7x acceleration of overall AI progress (due to AI uplift). This is saying the entire company is making 1.7x as much progress as it was before. Software R&D and research labor are only parts of the company, and inputs into overall progress. Because AI progress also depends on other factors, like compute, you should expect a 1.7x acceleration in individual inputs like software R&D or research labor to translate into less than a 1.7x acceleration in overall progress.\nLabor uplift is how much faster researchers are getting work done due to AI assistance.\nIt’s as if the researchers read, write, talk, and think that much faster.\nAccording to the METR report, an informal poll of Anthropic researchers yielded an estimate of 12x – though that’s just self-reports, which may be biased.\nAI software R&D uplift is how much faster software progress is going with the AIs in present day than it would be without them.\nIt is much lower than labor uplift because compute is an important input to software R&D.\nImagine, for example, that your researchers get work done 12x faster, but you only have the same amount of GPUs as before. You can’t do 12x as many experiments, at least not with the same compute budget; you have to economize.\nPace of progress acceleration is how much faster AI progress is advancing in present day, compared to a baseline reference period ( in Anthropic’s case , the baseline is “the fastest rate of extended progress we’ve observed in the absence of significant AI contributions to AI R&D”). It is usually much lower than AI software R&D uplift because (a) it is subject to diminishing returns to more software R&D and (b) it includes compute scaling.\nThe compute scaling point is simple: Part of the reason why modern AIs are smarter than older AIs is literally that they are bigger, trained longer, etc. More compute goes in, more capability comes out. As AI companies build more datacenters they unlock the ability to make larger AIs and train them longer. Since overall progress comes partly from software R&D and partly from compute scaling, a doubling in software R&D will cause less than a doubling in overall progress.\nTo expand on the diminishing returns point: AI software progress in general is subject to diminishing returns; the low-hanging fruit gets picked first, and to keep finding better and better algorithms requires exponentially more investment (more and bigger experiments, more and smarter researchers thinking for longer, etc.). Historically, the AI industry has scaled up R&D investments in a way that’s roughly cancelled out these diminishing returns. But the point here is, to get two years of research progress in one year, you have to more than double the AI software R&D uplift.\n(See also this Thomas Kwa tweet , this Ryan Greenblatt tweet , and Ajeya Cotra’s Substack for similar points.)\nAs an analogy, imagine AI progress as a car driving uphill.\nThe speed of the car is analogous to the speed of overall AI progress. Right now it’s roughly twice as fast as before.\nThe engine is research effort. AI software R&D uplift means that there is more effective labor going into making algorithmic progress.\nThe hill gets steeper as you go. As the easy algorithmic improvements get used up, it takes more research effort to continue progress at the same rate.\nTo make the car go twice as fast, the engine has to work more than twice as fast, because of the hill getting steeper. On top of this, to make the engine work twice as fast, the AI needs to make humans more than twice as productive, because progress is also dependent on compute, which hasn’t significantly accelerated. 10\nA 1.7x pace of progress is 5 months ahead of the AI 2027 scenario\nAI 2027 predicted 2x AI R&D uplift in Dec 2026; as argued above , this would imply a substantially lower pace of progress than the 1.7x AI-attributable pace of progress that METR’s estimates imply for Nov.\nSo when exactly did AI 2027 predict a 1.7x pace of progress over baseline? We created this trajectory by modifying a few key parameters until it roughly matched AI 2027. In that trajectory, 1.7x acceleration over the 2024.0–2025.5 rate of progress happens in roughly April 2027. 11 So by this metric, we are ~5 months ahead of schedule.\nSee below for a graph tracking these 2 quantities throughout the trajectory, as well as coding labor uplift (coding labor uplift is higher than overall AI R&D labor uplift, which unfortunately isn’t tracked in the AIFM):\nModeling\nOperationalization\nWe condition this section on there being a genuine 1.7x acceleration over the 2024-2025.5 rate of progress (we also test 1.5x and 2x), as of Nov 1. We discuss uncertainty about whether it might be slower or faster below .\nWe focus primarily on forecasting the median arrival date of Superhuman AI Researcher (SAR), and the chance of it arriving by the end of 2027. In the AI Futures Model (AIFM) and in this writeup, a SAR is operationalized as :\nA SAR is an AI that, if dropped into an AGI project in present day, would be as good at research taste as the project would be if there were only human researchers, who were each made as competent at research taste as the top researcher. 12 The SAR must also qualify as an Automated Coder (AC), i.e., be able to fully automate coding in the AGI project. [By research taste, we mean the ability to select research directions and experiments, and interpret their results.]\nIt’s a somewhat stronger condition than AI R&D parity , the point at which the company would rather fire their human researchers than fire their AIs, because the research taste must be at the level of the top human rather than the average. 13 If one wishes not to rely on our abstraction of research taste, then you can think of our SAR arrival dates as upper bounds for when AI R&D parity will be reached.\nWe discuss forecasts of capability levels above SAR to some extent, but we focus primarily on SAR because (a) we think SAR might already represent a “point of no return” (more below ) and (b) forecasting post-SAR milestones involves positing a translation between AI R&D and general capabilities, which introduces more complexity.\nSimple models\nResearch taste linear extrapolation\n(You may wish to skip this section and go to the next 2 if you’re interested in the simplest extrapolations.)\nBasic method\nHere’s a simple method for forecasting when SAR will arrive:\nDetermine how good AIs’ research taste is currently.\nDetermine how quickly research taste improves as AIs get generally more capable (where general capabilities are measured by Anthropic’s internal version of ECI , the AECI).\nLinearly extrapolate the recent, accelerated AECI trend until top-human-level research taste is reached.\nThis is a conservative method, because it assumes the recent trend merely continues, rather than bending upward due to increasing AI R&D acceleration. In fact, increasing AI R&D acceleration seems plausible: METR’s bottom-line view is that there is “significant risk that further capabilities progress could lead to even faster acceleration over the coming year.”\nThis method doesn't depend on modeling future AI R&D acceleration, so on that dimension it errs late. This forecasting method does not require that the acceleration has been AI-attributable, just that the acceleration persists. 14\nWe’ll treat “Model 4,” internally deployed in Sep 2026, as the starting point. 15 We’ll use METR’s estimates of AECI for Model 4 and recent AECI slope, and convert this trend to research taste using Daniel’s median parameters for the AI Futures Model from Aug 2026.\nThis predicts SAR arrives in Nov 2027, about 14 months after Model 4. Daniel’s parameters predict Model 4’s research taste is about −3.5 SD. SAR requires +3.09 SD, or 99.9th percentile research taste. With Daniel’s taste slope, each point of AECI converts to 0.2 SD of research taste (3 SDs per year of 2025 progress ÷ 15 AECI points per such year). That converts the recent trend of 29 AECI points per year to 5.8 SD of research taste per year. That covers the gap from −3.5 SD to the +3.09 SD threshold for SAR in 1.1 years.\nOne-at-a-time para","chars":20000,"total_chars":112874,"offset":0,"truncated":true,"next_offset":20000}

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  "url": "https://forecastingresearch.substack.com/p/how-close-is-recursive-self-improvement",
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{"ok":true,"url":"https://forecastingresearch.substack.com/p/how-close-is-recursive-self-improvement","text":"How Close Is Recursive Self-Improvement?\nSubscribe Sign in\nHow Close Is Recursive Self-Improvement?\nOur new report reviews the evidence, turns key disagreements into forecasting questions, and gives initial LLM forecasts.\nForecasting Research Institute\nNov 10, 2026\n25\n6\n4\nShare\nPost written by Simas Kučinskas and Josh Rosenberg\nAI companies increasingly say that they may soon achieve recursive self-improvement (RSI). With RSI, current AI systems help develop future AI systems in a recursive loop. RSI could dramatically accelerate AI capability progress. Importantly, this acceleration could happen inside AI companies before outsiders can notice.\nAI companies now say that this loop is starting. In an update to its IPO filing, Anthropic claimed that, based on expected future models, it now meets its definition of “dramatically accelerated progress attributable to AI.” OpenAI said that it reached its goal of an “automated AI research intern” in September 2026, and it targets an automated AI researcher by March 2028.\nOf course, AI companies are not necessarily neutral observers. So how close is RSI really? In our new report , we provide a comprehensive overview of AI companies’ reported data, public benchmarks, and existing forecasts . 1 We also turn the key disagreements into forecasting questions and provide initial forecasts from an ensemble of frontier LLMs .\nThis post summarizes what we found.\nAI is a highly capable research assistant, but humans still supply most of the research judgment\nThe data reported by AI companies show that AI does a large and fast-growing share of AI R&D.\nAt Anthropic, Claude wrote over 90% of contributions to the codebase in September 2026. At OpenAI, AI agents did 3.1 workdays of work for each human workday in August 2026, up from about 0.5 in May 2026.\nHowever, humans still supply most of the research judgment, such as choosing which research areas to focus on. For instance, no measured part of Anthropic’s AI R&D is fully autonomous . Anthropic also states that “large performance gaps persist when it comes to Claude exercising judgement in choosing goals in both engineering and research.” At OpenAI , AI completed 57% of 2–4-hour tasks with no human intervention in July 2026 (up from 28% in January) and 35% of 8–16-hour tasks (up from 10%).\nPublic evidence points the same way\nAI agents do well on well-defined machine learning (ML) benchmarks.\nOn PostTrainBench , where agents turn small base models into useful assistants, the best agent scores 45.6, against 51.1 for the official instruct models. On WeirdML , where agents build ML models for small, unusual problems, GPT-6 Astra scored 42.2 (out of 100) in September 2026, up from 15.0 for GPT-5.6 Sol in July 2026. On TasteVal , where a model chooses and interprets AI R&D experiments, Claude Opus 5.5 reaches the score of human experts with 2.3× less compute.\nHowever, AI agents struggle with open-ended research. On InnovationEval , where agents try to reinvent a published ML innovation they have not seen, GPT-5.6 Sol recovered 15% of the innovation’s gain. (GPT-6 Astra scored 63% but had likely memorized the original paper.) In the CRUX 2 open-world evaluation, AI agents got the research question of an unpublished AI paper and had to write their own paper on it. The original paper’s authors rejected all four AI-written papers, including two from a follow-up study with newer models, more time, and a larger budget.\nThere is some internal evidence of a potential acceleration\nAI capability progress has not clearly accelerated on public measures. Since September 2024, the frontier of the Epoch Capabilities Index (ECI) has risen by about 14 points a year . Here, the ECI frontier is the highest ECI score of any publicly available model by a given date.\nThat said, there is some internal evidence from frontier AI companies of a potential acceleration. In the Claude Opus 5.5 system card , a preliminary METR assessment estimated “~1.5X overall acceleration in capabilities due to AI” inside Anthropic, with “perhaps 30% chance of 2X.” At OpenAI , experiments per active experimenter hit an all-time high in August 2026, about 1.6× the 2025 level. However, OpenAI notes that “our available compute has also grown significantly since 2025.”\nIn a report published after our November 3 data cutoff, METR now estimates that capability progress at Anthropic is about 2× its earlier rate, and that around 80% of this acceleration is due to AI. This estimate applies to Anthropic only and includes unreleased models. In addition, part of the acceleration may reflect Anthropic catching up to the frontier, and Anthropic redacted much of the evidence that METR found most informative.\nBroad acceleration needs three contested steps\nExisting forecasts about the likely trajectory of RSI diverge widely.\nOn a simple extrapolation of current trends, mostly using company-reported metrics, AI could take over most AI R&D tasks by 2027–2028, in line with OpenAI’s internal goal of an automated AI researcher by March 2028. A key uncertainty is whether these trends continue, as the remaining non-automated tasks increasingly require research judgment. Forecasts of full automation of AI R&D range from 2027 (insiders at AI companies and the AI Futures Project’s AI 2027 scenario) to 2052 (the AI Impacts survey of ML researchers).\nIn our report, we cover the key points of debate, including the views of researchers who expect RSI very soon and those who are more skeptical. For example, the AI Futures Project’s AI 2027 scenario depicts a superhuman AI researcher in 2027. In contrast, Arvind Narayanan and Sayash Kapoor expect gradual automation of AI development instead of “a singular, discontinuous moment when recursive self-improvement is achieved.”\nWhy do people disagree? Broad acceleration through automated AI R&D needs three steps to happen in turn , as shown in the diagram below:\nAutomation: Is AI R&D automated?\nAcceleration: Does AI capability progress accelerate sharply?\nBreadth: Are capability gains broad?\nEach step is contested. The main cruxes are whether AI develops research taste, whether diminishing returns or bottlenecks such as compute, data, or regulation cap the acceleration, and whether gains spread beyond AI R&D and easily verified tasks.\nFrontier LLMs forecast about a 40% chance of a “fast year” by 2030\nWe turned these cruxes into 18 forecasting questions.\nTo measure acceleration, we propose a forecasting question on the likelihood of a “fast year”: any 12 months in which the ECI frontier rises by 30 points or more. This increase is about 2× ECI’s pace since September 2024. The largest rise in any 12-month window (as of our data cutoff of November 3, 2026) is 21 points.\nTo get an initial set of forecasts, we asked an ensemble of four frontier LLMs: Claude Opus 5.5, Claude Sonnet 5.5, Claude Fable 5.1, and GPT-6 Astra. Each LLM did its own web research and answered every question five times. We used the forecasting pipeline of our Automated AI Risk Outlook (AIRO) dashboard.\nThe ensemble forecasts about a 40% chance of a fast year by 2030. If a fast year happens, the ensemble gives about a 70% chance that AI automation of AI R&D caused it. The four LLMs disagree substantially on the probability of a fast year. For a fast year by the end of 2030, their forecasts range from 35% (Claude Sonnet 5.5) to almost 56% (GPT-6 Astra).\nThe LLMs made these forecasts on October 29, 2026, before the new METR report was released. If Anthropic’s internal 2× pace holds and reaches public models, that would roughly match the pace of a fast year.\nWith a fast year, the LLM forecast of an AI catastrophe is almost 3× higher\nWe also asked how a fast year would change the chances of two outcomes by 2050. Both definitions come from our Longitudinal Expert AI Panel (LEAP).\nThe first outcome is expert artificial general intelligence (AGI) : more than 50% of LEAP panelists agree that AGI exists. Here, AGI is a commercially available AI system that meets two conditions: (i) in every primarily non-physical occupation, it can outperform a 90th-percentile human professional (based on 2025 performance) on at least 90% of the economically useful non-physical tasks in that occupation; (ii) its inference cost is no more than 5× the cost of equivalent human labor.\nThe second outcome is an AI catastrophe : a catastrophic event caused primarily by AI systems in which more than 10% of the people alive at the start of a five-year period die by the end of that period.\nConditional on a fast year by 2030, the LLM ensemble forecasts:\nAn 84.3% chance of expert AGI by 2050 (vs. 55.7% without a fast year).\nAn 8.4% chance of an AI catastrophe by 2050 (vs. 2.9% without a fast year).\nIf a fast year happens by 2030, the ensemble’s forecast of an AI catastrophe by 2050 is almost 3× higher. For comparison, the median expert in LEAP gave 2% to an AI catastrophe by 2050.\nLimitations\nWe note a few key limitations of our report:\nLLM forecasts. Several LLMs are now statistically indistinguishable from superforecasters on ForecastBench , our forecasting benchmark. For this reason, we think that LLM forecasts provide an informative first benchmark. However, we know less about their accuracy on complex questions with little base-rate information, such as the fast-year question. Also, we elicited the LLM forecasts before the new METR report was published.\nData cutoff. Our data cutoff is November 3, 2026. The evidence is moving rapidly, so the report does not fully cover, for example, the new METR report, Epoch’s Bayesian update to the ECI, or Claude Mythos 5.2 and Fable 5.2.\nWhat comes next\nWe are currently collecting forecasts from top human forecasters and AI experts. We plan to add them, together with evidence released after our current data cutoff, to an updated version of the report.\nThe full report collects the definitions of automated AI R&D, the complete set of evidence, existing projections and forecasts, and all 18 forecasting questions.\nRead the full report here .\n1 For this report, we reviewed data up to November 3, 2026 (our data cutoff).\n25\n6\n4\nShare\nDiscussion about this post\nComments Restacks\nYupanqui\n1d\nmost of the numbers here come from the labs grading their own loop. outsiders need their own meter\nReply\nShare\nTim Urista\n3d\nFor the next forecast update, would you consider a paired run that keeps the October 29 model versions and prompts fixed, where those versions remain available, and gives them the new evidence? Alongside the current-model forecast, that would help separate the effect of the METR result from changes in the forecasters themselves. Saving the retrieved sources and dates would make the comparison auditable. Five answers per model show variation within a setup, but may still lean on the same underlying evidence; a source-overlap check would help interpret the apparent agreement.\nReply\nShare\n4 more comments...\nTop Latest Discussions\nReady for more?\nSubscribe\n© 2026 Forecasting Research Institute · Privacy ∙ Terms ∙ Collection notice\nStart your Substack Get the app\nSubstack is the home for writing, podcasting, and media founders.","chars":11148,"total_chars":11148,"offset":0,"truncated":false,"next_offset":null}