A small nonprofit research organization studying global catastrophic risks, best known for its insight-based AI timelines model and research on the feasibility of training AGI via deep reinforcement learning.
A small nonprofit research organization studying global catastrophic risks, best known for its insight-based AI timelines model and research on the feasibility of training AGI via deep reinforcement learning.
People
Updated 05/18/26Team member / researcher
Director and Secretary
Team member
Director
President and Director
Team member / researcher
Team member / researcher
Team member / contributor
Funding Details
Updated 05/18/26- Annual Budget
- $192,781
- Current Runway
- -
- Funding Goal
- -
- Funding Raised to Date
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Org Details
Updated 05/18/26Median Group (legally Median Foundation, EIN 82-5421772) is a 501(c)(3) nonprofit research organization founded in early 2018 in the San Francisco Bay Area. The organization was co-founded by Jessica Taylor, Ben Hoffman, Baeo Maltinsky, Jack Gallagher, Colleen McKenzie, and Bryce Hidysmith. Its stated mission is research on global catastrophic risks, with a focus on technological and geopolitical changes over the next century and how their most detrimental effects might be mitigated or averted. The founding team brings backgrounds from several prominent organizations in the AI safety and effective altruism ecosystem. Jessica Taylor (President) previously worked as a mathematical researcher at the Machine Intelligence Research Institute (MIRI) and holds an MS in Computer Science from Stanford University. She led MIRI's Alignment for Advanced Machine Learning Systems program. Ben Hoffman (Secretary) worked as a researcher at GiveWell and the Open Philanthropy Project and holds an MS in Mathematics and Statistics from Georgetown University. Jack Gallagher also worked at MIRI on type theory and decision theory. Other team members include Baeo Maltinsky (BA Mathematics, UC Berkeley, former analyst at EnChroma), Colleen McKenzie (BA Computer Science and Neuroscience, Columbia, former Google product manager), Bryce Hidysmith (designer and strategist, former Numerai), and Patrick Mellor (philosophy instructor at San Francisco State University). Median Group's most well-known work is its insight-based AI timelines model, developed by Jessica Taylor, Jack Gallagher, and Baeo Maltinsky. This interactive tool estimates when AI research might reach critical milestones by tracking the accumulation of major technical insights in AI history, calibrated against the significance of the LSTM discovery. The model assumes insights increase roughly linearly over time and allows users to adjust parameters based on their priors about the total number of insights required. Other notable research includes a rough computational estimate of the feasibility of training AGI via deep reinforcement learning, analysis of GPU price-performance improvement rates, studies of how declining renewable energy costs may destabilize oil-dependent states, and explorations of neuroscience topics including brain computation. The organization has presented research at the Center for Human-Compatible AI at UC Berkeley. More recent research directions have included studies of antinormative behavior in institutions, investigations of rTMS for cognitive enhancement, and development of ML tools for detecting conversational derailing patterns. The organization received grants from the Survival and Flourishing Fund, including $98,000 in 2020 and $250,000 in 2022, both recommended by Jaan Tallinn for general operational support. Recent financial filings show declining revenue, with FY2024 reporting approximately $18,000 in revenue against $193,000 in expenses, while maintaining approximately $451,000 in net assets.
Theory of Change
Updated 05/18/26Median Group believes that by rigorously modeling technological and geopolitical trends, particularly in AI development and climate change, it can help identify trajectories that lead to global catastrophic outcomes and inform strategies for mitigation. Their insight-based AI timelines approach aims to provide better-calibrated forecasts of AI progress by grounding estimates in the historical rate of major technical discoveries, rather than relying on expert intuition alone. By making these models interactive and publicly available, they seek to improve the epistemic commons around critical questions about AI development timelines and feasibility, helping the broader research community and decision-makers allocate resources more effectively.
Grants Received
Updated 05/18/26Projects
Updated 05/18/26A rough quantitative model that analyzes a proposed scenario for achieving artificial general intelligence via deep reinforcement learning by estimating the computational resources required and the plausibility of the approach.
An interactive model that uses a historical dataset of major AI technical insights to estimate progress toward human-level AI by extrapolating the rate of insight discovery.
Ongoing conceptual and empirical work on normativity, vice-signaling, and antinormativity, including analyses such as The Debtors’ Revolt and related writing on zero-sum games and institutional dysfunction.
Discussion
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