Neil Thompson
Research Scientist, MIT Sloan School of Management and CSAIL
Neil Thompson is the Director of the FutureTech research project, where his group studies the economic and technical foundations of progress in computing, and is cross-appointed at MIT’s Computer Science and AI Lab and MIT’s Initiative on the Digital Economy.
Previously, Neil was 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 a Visiting Professor at the Laboratory for Innovation Science at Harvard. He has advised businesses and government on the future of Moore’s Law, has been on National Academies panels on transformational technologies and scientific reliability, and is part of the Council on Competitiveness’ National Commission on Innovation & Competitiveness Frontiers.
He has a PhD in Business and Public Policy from Berkeley, where he also did Masters degrees in Computer Science and Statistics. He also has a masters in Economics from the London School of Economics, and undergraduate degrees in Physics and International Development. Prior to academia, He worked at organizations such as Lawrence Livermore National Laboratory, Bain and Company, the United Nations, the World Bank, and the Canadian Parliament.
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.