Strategy and Forecasting
The MATS Strategy & Forecasting Track supports research on the long-horizon questions advanced AI raises, from AI timelines and geopolitical competition to institutional futures in a post-AGI world. As capabilities accelerate, key decisions about advanced AI are being made under deeper uncertainty, often before strong empirical evidence or broad consensus has emerged. Addressing these challenges requires structured forecasting, scenario analysis, geopolitical modeling, and macro-strategic thinking.
- Initial application: Submit track-specific short response questions
- Stream applications & follow-up: Apply to individual streams; follow-up includes interviews or additional assessments depending on the stream.
This track is focused on macro-strategy: understanding how the transition to advanced AI will unfold, how institutions and societies may adapt to highly capable AI systems, and what actions taken today can most improve outcomes in the long run. Some streams center on structured forecasting, particularly quantitative work on capabilities, timelines, compute, and economic impact. Others emphasize scenario analysis and modeling, including frontier lab dynamics, geopolitical competition, state behavior, transition scenarios, and tabletop exercises.
These forecasts and models may be used to support analysis of what policy options are available to steer the path to AGI or the post-AGI future, to describe the costs and benefits of these options, and to raise awareness of how choices being made today could expand or narrow the range of options available to future policymakers. Research in this track might explore how, why, when, and where advanced AI will prompt rapid changes in industrial production, military tactics, and general scientific research, as well as the second-order effect of these changes on geopolitics, democracy, and capitalism.
Fellows in this track need to be comfortable with uncertain inference, probabilistic claims, and writing clearly about questions where the evidence base is limited. Experience with or interest in interdisciplinary research is helpful, as many of the research questions in this track ask how changes in one area of society will affect behavior in other fields. Strong candidates have come from forecasting, economics, history, philosophy, political science, international relations, computer science, security studies, and quantitative social science, among other backgrounds.
Fellows are matched to mentors based on fit, and projects are scoped to produce concrete artifacts by program end, e.g., forecasting reports, scenarios, policy memos, strategic analyses, and peer-reviewed research. Target audiences include lab strategy and policy teams, AISI staff, national security analysts, the funders and policymakers making long-horizon decisions about advanced AI, and the broader forecasting, governance, and AI safety communities.
We are interested in mentoring projects in AI forecasting and governance. This work would build on the AI 2027 report to either do more scenario forecasting or explore how to positively affect key decision points, informed by our scenario.
We are interested in mentoring projects in AI forecasting and governance. This work would build on the AI 2027 report to either do more scenario forecasting or explore how to positively affect key decision points, informed by our scenario.
This mentor also has a stream in the Biosecurity track.
This stream focuses on how advanced AI could enable new and dangerous physical technologies, and on assessing when risks become tractable or urgent as those capabilities arrive.
Forethought
AI macrostrategy: strategic questions about how the transition to advanced AI will happen, and what we can do now to prepare for it.
Topics of interest include better futures, power concentration, takeoff speeds, deals with AIs, space governance, and acausal trade.