Q2.5 2026 Timelines Update: Uplift and Revenue

Tl;dr: Our timelines haven’t changed much (they got slightly shorter) but our modeling and evidence base have noticeably improved, so we feel somewhat more confident. Summary We intend to regularly update our AI timelines forecasts as new evidence comes in and new analyses are done. Today’s “Q2” update was delayed by the crunch to publish AI 2040: Plan A, our domestic regulation blog post, and the time needed to implement and document changes to our model. The original AI Futures Model predicted when Automated Coder (AC), an AI for which the leading AI company would rather fire its human software engineers than forego AI usage for coding, would happen using METR’s measurements of coding time horizon. (More precisely, time horizon anchors are used to set the effective compute required for AC.) While serviceable, this method has huge weaknesses, including (a) it’s unclear what time horizon corresponds to AC (it’s even unclear whether any finite value would) (b) people strongly disagree about the extent to which we should expect the time horizon trend to be superexponential as a function of effective compute, in a way that can lead to vastly different predictions. So we’ve been on the lookout for other methods for setting the effective compute required for AC, and now we have two candidates: coding uplift (i.e., how much of a speedup AIs are providing to software engineers at AGI companies) and revenue. Coding uplift is our favorite method: the basic idea is to estimate the doubling time of the quantity (uplift - 1) and extrapolate that until AC-level uplift is reached. The way we anchor the model using uplift-relevant estimates is a bit complex, so we first explain a simpler 3-parameter uplift model that gives similar results. With coding uplift, the value corresponding to AC is much less uncertain than with time horizons, and while we also expect coding uplift to be somewhat superexponential this isn’t nearly as important as with time horizons. Surprisingly, all 3 methods predict quite similar AC arrival dates.[1] We take this to be a somewhat encouraging sign about the robustness of our forecasts, though we are still very uncertain. You can explore the uplift-anchored version of the model at aifuturesmodel.com and the other options for AC forecasting via the dropdown at the top of the second graph. Each author assigned weights to the 3 AC anchoring methods which the model aggregates into an overall forecast. We also re-evaluated AI 2027’s predictions; reality seems to be going about 70-90% as fast as AI 2027 predicted. Incorporating all of the above, our latest timelines forecasts (conditional on going as fast as is technically feasible): (link) Here’s how our forecasts have shifted recently: https://www.datawrapper.de/_/4aHow/ We describe in the appendix: How our AGI forecasts have changed since 2021. We changed the modeling to take into account that training is needed to apply software improvements, which reduces the chance of very fast takeoffs. Some authors made minor adjustments to some other parameters, and we clarify that our forecasts are conditional on going as fast as is technically feasible. We made some minor changes to the model and website code. A 3-parameter uplift model for predicting when Automated Coder will arrive A simple method to predict the AC arrival date is to assume that (coding uplift - 1) grows exponentially. We think that this is the best simple model for predicting when AC will arrive. Specifically, this model takes as input 3 parameters, for which we list Daniel’s median estimates: Present day coding uplift, i.e. the speedup factor due to AI assistance: 2x. Daniel thinks 1.04x is something like a lower bound given the METR study which found 1.04x - 1.2x uplift, with METR thinking that these numbers were biased downward due to selection effects (people were less likely to participate in the study if they thought AI would be useful to them.) Otherwise, he’s integrating various sources of evidence including the Apr 2026 Anthropic internal survey having a geometric mean of 4x, and a private estimate of 1.7x AI R&D labor uplift by Ryan Greenblatt (which was an estimate for AI R&D labor as a whole, so presumably coding-only would be higher). Present day doubling time of the quantity (uplift-1): 5 months. According to Anthropic employee surveys, coding uplift has gone from 1.25x to 4x in 7 months.[2] This would be a ~2 month uplift doubling time, but correcting for Mythos being above the long-term Anthropic ECI trend gives us a ~3.5 month doubling time.[3] Now, probably their employees are biased towards overestimating coding uplift. But unless the bias has been significantly increasing over time, that’s still more than three doublings of uplift-1 in less than a year – a 3.6 month doubling time! Daniel’s median is longer, 5 months, because he’s partly deferring to the opinions of other researchers he respects (Eli and Ryan) whose subjective sense is that the doubling time is long