MIT FutureTech is an interdisciplinary research group at MIT CSAIL studying the economic and technical foundations of progress in computing and AI. The group produces rigorous insights on AI trends, risks, and impacts to inform policy, industry, and scientific funding decisions.
MIT FutureTech is an interdisciplinary research group at MIT CSAIL studying the economic and technical foundations of progress in computing and AI. The group produces rigorous insights on AI trends, risks, and impacts to inform policy, industry, and scientific funding decisions.
People
Updated 05/18/26Director, MIT FutureTech research project
AI Risk Management Leader, MIT FutureTech
Funding Details
Updated 05/18/26- Annual Budget
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- Current Runway
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- Funding Goal
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- Funding Raised to Date
- $25,000,000
Org Details
Updated 05/18/26MIT FutureTech is an interdisciplinary research group based at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), with additional affiliation at MIT Sloan's Initiative on the Digital Economy. Founded in 2019 by Neil Thompson, who serves as its Director and Principal Research Scientist, the group studies the foundations of progress in computing: identifying the most important technical and economic trends, understanding how they underpin economic prosperity, and assessing how society can harness them to sustain productivity growth. The group draws on computer science, economics, and management to analyze trends that create opportunities for — or pose risks to — sustained economic growth. Key research pillars include post-Moore's Law computing performance, algorithmic progress, AI's effect on scientific discovery, labor market effects of automation, and the landscape of AI safety risks. FutureTech maintains several public tools and databases, including the Processor Database (tracking hardware performance), the Algorithm Wiki, and the AI Risk Repository. The AI Risk Repository, released in August 2024 and widely covered by media including MIT Technology Review and TechCrunch, catalogs over 700 AI risks extracted from 74 existing frameworks and classifications. It organizes risks into seven domains including discrimination and toxicity, privacy and security, misinformation, malicious misuse, human-computer interaction, socioeconomic and environmental harms, and AI system safety and failures. FutureTech has attracted over $25 million in funding since its founding, including grants from Open Philanthropy totaling approximately $16.7 million across three awards (2020, 2022, and 2023), as well as support from the National Science Foundation, Accenture, IBM, the MIT-Air Force AI Accelerator, and MIT Lincoln Laboratory. The group has grown to over 110 researchers, making it one of the largest research groups at MIT. Its research has been published in Science, Nature Communications, Communications of the ACM, and IEEE Spectrum, and has been covered by The Washington Post, The Economist, Wired, Nature, and The Guardian. Beyond publishing research, FutureTech advises governments, nonprofits, and industry leaders, and Thompson has presented findings to Congressional staffers, the Federal Reserve, and the Pentagon. The group organizes conferences and workshops — including the FutureTech Conference 2025 on AI automation timelines — and engages with policymakers and funders across more than 80 countries.
Theory of Change
Updated 05/18/26MIT FutureTech believes that rigorous, interdisciplinary research on AI and computing trends is a crucial input to sound decision-making by policymakers, funders, and industry leaders. By building the empirical foundation for understanding how fast AI is advancing, what risks it introduces, and how those risks are distributed across society, the group aims to help key decision-makers navigate technological transitions in ways that sustain economic prosperity and reduce the chance of catastrophic or poorly-managed AI developments. The AI Risk Repository specifically attempts to close gaps in collective understanding of AI risks so that researchers and policymakers can address the full landscape of hazards, not just those already well-studied.
Grants Received– no grants recorded
Updated 05/18/26Projects
Updated 05/18/26A comprehensive, regularly updated MIT database of 1,700+ AI risks, organized by causal and domain taxonomies to support research, audits, and policy on AI risk management.
An MIT project building a comprehensive database of CPUs, GPUs, FPGAs, and other chips worldwide to support systematic analysis of technical and economic trends in computer hardware.
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