Neil Thompson
Research Scientist, MIT Sloan School of Management and CSAIL
Dr. Neil Thompson is the Director of the FutureTech research group, and Principle Research Scientist at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), and at MIT’s Initiative on the Digital Economy (IDE) within the Sloan School of Management. Guided by Dr. Thompson’s leadership, the FutureTech group researches the cutting edge and most important trends driving progress in computing and AI, how these trends underpin scientific progress and economic prosperity, and produces rigorous insights that broaden humanity’s knowledge, and inform policy and industry decisions. Since founding MIT FutureTech in 2019, Dr. Thompson’s research group has attracted over $25M in funding, and has grown to be one of the largest research groups at MIT with over 110 researchers. Dr. Thompson maintains research partnerships with leading organizations such as Google, IBM, Amazon, Accenture, Microsoft, Los Alamos National Labs, and others.
Previous Roles
Previously, Dr. Thompson served as an Assistant Professor of Innovation and Strategy at the MIT Sloan School of Management, where he co-directed the Experimental Innovation Lab (X-Lab), and as a Visiting Professor at the Laboratory for Innovation Science at Harvard University. Prior to his academic career, Dr. Thompson has held positions for esteemed organizations such as the Broad Institute, Bain and Company, Lawrence Livermore National Laboratory, AMD, the World Bank, the United Nations, and the Canadian Parliament.
Publication, Research, and Impact
Dr. Thompson’s work has over 3000 citations with an h-index of 21 across his publication portfolio, including such well known and renowned papers as Expertise, The Computational Limits of Deep Learning, and There’s plenty of room at the Top: What will drive computer performance after Moore’s law? Dr. Thompson has been invited to present his work and recommendations to Congressional Staffers (House and Senate), the US Federal Reserve, the Pentagon, National Security Staff, the Department of Commerce, the Department of Energy, Brookings Institute, and most recently presented at a World Summit on the same program as the Prime Minister of India and Former Prime Ministers of England and Australia. With experience in 80+ countries, Dr. Thompson’s research and impact is on a global scale.
Education
Dr. Thompson has a PhD in Business & Public Policy from UC Berkeley, Haas, dual Master degrees’ in Computer Science and Statistics from UC Berkeley, and a Masters in Economics from London School of Economics and Political Science (LSE). From his undergraduate studies, Dr. Thompson has Bachelors degrees in Physics, Economics, and International Development studies from Queen’s University.
Recent Insights
Featured publications
June 15, 2026
- Peter Slattery | Research Scientist at MIT Future Tech
- Hans Gundlach | Research Assistant, CSAIL
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
Jess Graham
Michael Noetel, et al.
This paper reports results from a three-round Delphi study conducted late 2025 with 272 international AI experts who rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern.
May 22, 2026
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
- Wensu Li | Postdoctoral Associate
Maja S. Svanberg
Brian C. Goehring
This paper’s findings suggest that AI job displacement will be substantial, but also gradual – and therefore there is room for policy and retraining to mitigate unemployment impacts.
May 15, 2026
- Martin Fleming | Research Scientist, IDE
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
This paper examines technical progress in NVIDIA datacenter GPUs from the mid-2000s through 2025.
May 15, 2026
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Matthias Mertens | Research Scientist at Future Tech
Do leading LLM developers possess a proprietary “secret sauce,” or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, the authors estimate scaling-law regressions with release-date and developer fixed effects.
May 15, 2026
- Peter Slattery | Research Scientist at MIT Future Tech
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet researchers, policymakers, and technology companies lack shared terminology for discussing AI risks. This paper addresses this challenge by creating a comprehensive catalog of AI risks.
April 15, 2026
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
- Matthias Mertens | Research Scientist at Future Tech
- Brittany Harris | Data Analyst, CSAIL
- Wensu Li | Postdoctoral Associate
The authors propose that AI automation is a continuum between: crashing waves where AI capabilities surge abruptly over small sets of tasks, and rising tides where the increase in AI capabilities is more continuous and broad-based.
March 15, 2026
- Wensu Li | Postdoctoral Associate
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation.
March 15, 2026
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Jayson Lynch | Research Scientist, CSAIL
- Hans Gundlach | Research Assistant, CSAIL
This paper introduces SAGE (Self-play Adversarial Games for Enhancement), a framework for improving LLM reasoning capabilities through adversarial self-play without human-curated data.