Faculty
Neil Thompson is an Innovation Scholar at MIT’s Computer Science and Artificial Intelligence Lab and the Initiative on the Digital Economy. He is also an Associate Member of the Broad Institute.
Previously, he was an Assistant Professor of Innovation and Strategy at the MIT Sloan School of Management, where he codirected the Experimental Innovation Lab (X-Lab), and a Visiting Professor at the Laboratory for Innovation Science at Harvard University. He has advised businesses and government on the future of Moore’s Law and Machine Learning, and has been on National Academies panels on transformational technologies and scientific reliability.
He did his PhD in business and public policy at UC Berkeley, where he also did Master's degrees in computer science and statistics. He has a Master's in economics from the London School of Economics, and undergraduate degrees in physics and international development. Prior to academia, he worked at organizations including Lawrence Livermore National Laboratories, Bain and Company, The United Nations, the World Bank, and the Canadian Parliament.
Kuhn, Jeffrey M., and Neil Thompson. International Journal of the Economics of Business. Forthcoming.
Lashkari, Danial, Wensu Li, Christina Qiu, and Neil Thompson, MIT Sloan Working Paper 7381-26. Cambridge, MA: MIT Sloan School of Management, July 2026.
Thompson, Neil C., and Douglas Hanley, MIT Sloan Working Paper 5238-17. Cambridge, MA: MIT Sloan School of Management, September 2017.
Thompson, Neil. 2012.
Thompson, Neil. 2012.
Thompson, Neil. 2012.
New research from MIT Sloan School of Management and MIT FutureTech suggests that the public, investors, and regulators lack information about whether companies are prepared for AI risks.
AI performance is improving across many workplace tasks broadly, not in sudden shocks, new MIT research finds. That cadence gives companies and workers time to prepare for task-level displacements.
A recent study by principal research scientist Neil Thompson and co-authors showed that between 2024 and 2026, the best AI models went from being able to solve about 2 percent of theoretical problems in probability, statistics theory, economic theory, high-energy theory, gravitation and cosmology, and quantum physics to about 35 percent of such problems. "Within just a few years, we expect a lot of theory questions to be totally transformed in terms of AI's ability to do them," said Thompson.
Principal research scientist Neil Thompson said that fears about AI are putting significant pressure on policymakers. "For all the time we've been studying these issues, this is the moment with the most popular appreciation of these problems, and I think this does mean there is an opportunity now for both people to voice this to their representatives and for representatives to say this is not something we can keep putting off," he said.
A report out this week from principal research scientist Neil Thompson and co-authors set out to measure how AI is changing the economics of science in a variety of fields and, in the process, pointed to places where it still stalls. About 44 percent of scientists surveyed said that, over the past two years, their main research bottleneck had shifted downstream, toward later stages such as physical experimentation and data collection. Forty-one percent said their backlog of untested hypotheses had grown.
"All of them know that they're playing a bad game," said principal research scientist Neil Thompson. "This is a game where they know that if everybody continues to race as fast as they can, we're going to continue to accumulate these dangerous capabilities in a way that is a problem. But they also know that unilaterally they can't change that game."
