AI Futures Model: Dec 2025 Update

AI Futures Model: Dec 2025 Update AI Futures Project Subscribe Sign in AI Futures Model: Dec 2025 Update We've significantly improved our model of AI timelines and takeoff speeds! Daniel Kokotajlo , Eli Lifland , Brendan Halstead , and Alex Kastner Dec 31, 2025 176 56 17 Share We’ve significantly upgraded our timelines and takeoff model! Our new unified model predicts when AIs will reach key capability milestones: for example, Automated Coder / AC (full automation of coding) and superintelligence / ASI (much better than the best humans at virtually all cognitive tasks). This post will briefly explain how the model works, present our timelines and takeoff forecasts, and compare it to our previous ( AI 2027 ) models (spoiler: the AI Futures Model predicts longer timelines to full coding automation than our previous model by about 3-5 years, in significant part due to being less bullish on pre-full-automation AI R&D speedups). Added Jan 2026: see here for clarifications regarding how our forecasts have changed since AI 2027. If you’re interested in playing with the model yourself, the best way to do so is via this interactive website: aifuturesmodel.com . If you’d like to skip over the motivation for our model to an explanation for how it works, go here , The website has a more in-depth explanation of the model (starts here ; use the diagram on the right as a table of contents), as well as our forecasts . Why do timelines and takeoff modeling? The future is very hard to predict. We don’t think this model, or any other model, should be trusted completely. The model takes into account what we think are the most important dynamics and factors, but it doesn’t take into account everything. Also, only some of the parameter values in the model are grounded in empirical data; the rest are intuitive guesses. If you disagree with our guesses, you can change them above. Nevertheless, we think that modeling work is important. Our overall view is the result of weighing many considerations, factors, arguments, etc.; a model is a way to do this transparently and explicitly, as opposed to implicitly and all in our head. By reading about our model, you can come to understand why we have the views we do, what arguments and trends seem most important to us, etc. The future is uncertain, but we shouldn’t just wait for it to arrive. If we try to predict what will happen, if we pay attention to the trends and extrapolate them, if we build models of the underlying dynamics, then we’ll have a better sense of what is likely, and we’ll be less unprepared for what happens. We’ll also be able to better incorporate future empirical data into our forecasts. In fact, the improvements we’ve made to this model, as compared to our timelines model at the time we published AI 2027 (Apr 2025), have resulted in a roughly 3-5 year shift in our median for full coding automation. This has primarily come from improving our modeling of AI R&D automation. These modeling improvements have resu

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