People
Updated 05/18/26Executive Director
Research Lead
Chief Operating Officer
Researcher
Research Lead
Funding Details
Updated 05/18/26- Annual Budget
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Org Details
Updated 05/18/26The AI Futures Project is a 501(c)(3) nonprofit research organization (EIN 99-4320292, previously named Artificial Intelligence Forecasting Inc) that specializes in forecasting the development and societal impact of advanced artificial intelligence. The organization was founded in 2025 by Daniel Kokotajlo, who resigned from OpenAI's governance division in April 2024 over concerns that the company was prioritizing rapid product development over AI safety. The organization's mission is to develop detailed scenario forecasts of the trajectory of advanced AI systems to inform policymakers, researchers, and the public. It operates with a small core research team and a network of advisors drawn from AI policy, forecasting, and risk analysis. The AI Futures Project's flagship publication is AI 2027, a detailed scenario forecast released in April 2025 that describes possible developments in AI between 2025 and 2027. The scenario was authored by Daniel Kokotajlo, Eli Lifland, Thomas Larsen, and Romeo Dean, with editing assistance from blogger Scott Alexander. It was created in collaboration with Lightcone Infrastructure, which built the interactive website. AI 2027 depicts rapid progress in AI capabilities, including autonomous AI systems capable of recursive self-improvement, and presents two alternative endings exploring different policy responses. Over a million people read the scenario in its first weeks, including U.S. Vice President JD Vance, who reportedly referenced its warnings in conversations about international AI coordination. Beyond written reports, the team conducts tabletop exercises and workshops based on their scenarios, involving participants from academia, technology, and public policy. These exercises simulate AI takeoff scenarios where participants take on roles such as national leaders or AI company CEOs. The organization also maintains an interactive AI forecasting model at aifuturesmodel.com, publishing probability distributions for AI capability timelines. The core team includes Daniel Kokotajlo (Executive Director), Eli Lifland (Research Lead, top-ranked on RAND Forecasting Initiative's all-time leaderboard), Thomas Larsen (Research Lead, founder of Center for AI Policy), Romeo Dean (Researcher, Harvard graduate specializing in AI chip production forecasting), and Lauren Mangla (Chief Operating Officer, managing communications, media relations, and operations). The scenario work originated through Constellation's Astra Fellowship program in Berkeley, where team members first began collaborating.
Theory of Change
Updated 05/18/26The AI Futures Project believes that detailed, concrete scenario forecasts of AI development trajectories are essential for informed decision-making about AI governance and safety. By producing rigorous forecasts and making them vivid through narrative scenarios, tabletop exercises, and interactive models, they aim to help policymakers, researchers, and the public understand the plausible speed and consequences of advanced AI development. Their theory is that if decision-makers have a clear, evidence-based picture of how AI capabilities might advance and what risks might emerge, they will be better equipped to implement appropriate safety measures, governance frameworks, and international coordination before it is too late. The causal chain runs from forecasting research to public awareness and policy engagement, ultimately leading to better-prepared institutions and reduced existential risk from advanced AI.
Grants Received
Updated 05/18/26Projects
Updated 05/18/26A long-form scenario forecast from the AI Futures Project that explores how artificial general intelligence and later superintelligence could emerge between 2025 and 2027.
A quantitative model developed by the AI Futures Project that predicts AI timelines and takeoff speeds.
Discussion
Key risk: Their impact hinges on converting scenario-driven awareness into concrete, implemented policy changes, and with substantial SFF funding already secured, the marginal value of additional dollars may be low if those conversions don’t materialize.
Case for funding: They combine credible insider knowledge (Daniel Kokotajlo) with top-tier forecasting to produce vivid, operational scenarios and tabletop exercises that have already reached senior U.S. policymakers (e.g., AI 2027), positioning them to steer governance decisions on short AI timelines.