We 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.

METR 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 .

This 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]

Now, 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] .

If 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]

We 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 .

We 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 )

In this post, we forecast SAR and TED-AI using the following approaches:

Filtered 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:

We 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:

Here’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]

After discussing some other considerations , we share our all-things-considered forecasts (conditional on AGI projects/companies not slowing down):

Note 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.

In 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]

Both 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.

The 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.

This 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:

The 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.

Regarding AIs’ contributions over time, METR says:

We believe AI’s contribution has grown relatively more important over time.

The 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.

Given 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.

In 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:

Our 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.

It 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.

METR 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.

(See also this Thomas Kwa tweet , this Ryan Greenblatt tweet , and Ajeya Cotra’s Substack for similar points.)

As an analogy, imagine AI progress as a car driving uphill.

To 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]

AI 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.

So 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.

See 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):

We 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 .

We 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 :

A 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.]

It’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.

We 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.

(You may wish to skip this section and go to the next 2 if you’re interested in the simplest extrapolations.)

Here’s a simple method for forecasting when SAR will arrive:

This 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.”