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.
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."
Principal research scientist Neil Thompson said that even at tech companies leading on AI development, it is unlikely that workers are being told to simply deploy the tools that are supposed to do some of their work and move on. "It leads to a situation where even if there were real-time savings, it would be sucked up by the changes, and implementing them, and making sure they worked. People assume that 20% less work means four-day weeks. But new work emerges," Thompson said.
A recent MIT FutureTech report on AI risk drew responses from 272 specialists, including professor Jackson Lu, research scientist Peter Slattery, and principal research scientist Neil Thompson. "These findings are a call to arms for those building and governing AI to make sure the worst outcomes do not occur," said Thompson.
