Forecasts
This page shows our timelines and takeoff forecasts. We are highly uncertain about this, and have expressed our uncertainty as a probability distribution over the possible times when each milestone might be reached. We show the raw result of a Monte Carlo simulation of our model, as well as our subjective all-things-considered probability distributions. We plan to keep this page up to date as our predictions change.
What do we mean by "all-things-considered"?
Though we view the model's outputs as an important source of evidence about what future AI progress might look like, we don't blindly trust it. Our all-things-considered views are informed by looking at the results of the model but then making adjustments based on intuition and which factors the model doesn't include.
How have our forecasts changed since publishing the AI Futures Model?
The forecast dropdown below shows the history of how our views have changed since publication. Here is a summary of the changes:
- 2025 Dec 31: Fix infrequent bug with determining when TED-AI and ASI are achieved. Fixed an issue with the thresholds for TED-AI and ASI in the case where research taste at AC is better than the best human, i.e. >3.09 SDs. This affects a low percentage of our Monte Carlo simulations: 13% of Eli's, and 9% of Daniel's. This very slightly increases takeoff speeds: for example it increases P(AC->ASI < 1 year) from 26.3 to 27.1% for Eli, and from 36.7% to 37.1% for Daniel. It increases P(AC->ASI < 10 years) from 58.3% to 59.3% for Eli, and from 71.5% to 71.8% for Daniel.
- 2026 Jan 26: Fix minor bug in the model code, add all-things-considered forecasts for more quantities, minor updates to Eli's AC timelines and AC to ASI takeoff speeds. Fixed a numerical underflow causing incorrect coding labor calculation for certain values of the “Coding Labor Parallelization Penalty (λ)” and “Coding Automation Efficiency Improvement Factor (η)” parameters. This was formerly affecting approximately 3% of our Monte Carlo rollouts, and fixing the issue had a negligible effect on the outcome distributions. Daniel and Eli also added all-things-considered forecasts for more quantities: both added forecasts for SC, TED-AI, and ASI. Daniel also added a forecast for SAR. Eli also added a forecast for the time from AC to TED-AI. Eli also updated his all-things-considered forecasts for AC timelines and AC to ASI takeoff. He made his AC arrival median Mar 2032 instead of Jul 2032 and increased the probability of AC arrival in 2026 from 4% to 6%. These changes were due to paying a bit more respect to an Anthropic-style worldview in which we're close to AC, in part informed by Claude Code's impressiveness. He also increased the chance of fast takeoffs from AC to ASI, giving 25% to takeoff in <0.5 years as opposed to 18% before. But he also increased uncertainty at the upper end, decreasing the chance of takeoff in <10 years from 85% to 83%. These changes were due to reflecting more that perhaps the distribution should be more spread out than the model's due to outside-of-model factors.
Milestone arrival dates
The chart below shows how long we project it will take to achieve various AI milestones (toggle them on in the sidebar). The x-axis is the year the milestone is achieved, and the y-axis is the probability density at a point in time, expressed in the % chance the milestone would happen within a year at that density level.
Chart Settings
ATC shown as dashed lines
Probability densities are estimated based on 10,000 simulated trajectories.
Time from coding automation to future milestones
The chart below shows how long we project it will take to reach various milestones after achieving AC (Automated Coder). The x-axis represents years after AC achievement, and the curves show the cumulative probability for when each subsequent milestone might be reached.
Chart Settings
ATC shown as dashed lines
In our results analysis, we analyze which parameters are most important for the above forecasts. We also examine the correlation in our model between short timelines and fast takeoffs.