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.
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.
Which AI risks could cause the most harm in the next five years? New MIT research shows that businesses should be aware of threats like competitive pressure and dangerous AI capabilities.
Turning a company into an agentic enterprise isn't just a tech upgrade — it's a massive economic bet in compute, talent, and time. Should businesses go all in from day one, or build it out in stages? On this podcast episode, principal research scientist Neil Thompson discussed the smartest way to place that bet.
According to a paper by principal research scientist Neil Thompson, research scientist Peter Slattery and co-authors, 272 international AI experts rated AI-enabled weapons and cyberattacks among the five most dangerous risks between 2025 and 2030. The report highlights information, national security, and finance as the areas most exposed to the risk from AI. "Coding and hacking are some of the areas where we're seeing the fastest growth in AI capability," said Slattery.
Principal research scientist Neil Thompson, research scientist Martin Fleming, and co-author wrote: "Many corporate leaders are still assessing the risks of AI adoption. As time passes without a major mishap, they will become less cautious, the pace will pick up, and the financial rewards will reinforce further AI deployment. This is because AI has a 'J-curve' effect on the bottom line, with those in the later stages of adoption enjoying more profitability than those in the earlier stages."
Principal research scientist Neil Thompson wrote: "AI can increasingly provide information. Technical professionals will continue to provide something harder to automate: judgment, context, and the ability to help people turn knowledge into decisions. When this happens, decision friction decreases."
