Neil Thompson

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Neil Thompson

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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.

www.neil-t.com

Publications

"How to Measure and Draw Causal Inferences with Patent Scope."

Kuhn, Jeffrey M., and Neil Thompson. International Journal of the Economics of Business. Forthcoming.

"Science is Shaped by Wikipedia: Evidence From a Randomized Control Trial."

Thompson, Neil C., and Douglas Hanley, MIT Sloan Working Paper 5238-17. Cambridge, MA: MIT Sloan School of Management, September 2017.

"Firm Software Parallelism: Building a Measure of how Firms will be Impacted by the Changeover to Multicore Chips."

Thompson, Neil. 2012.

"Intellectual Property and Academic Science."

Thompson, Neil. 2012.

"The Statistics of a Fundamental Change in how Computers Work and its Impact on Firm Productivity."

Thompson, Neil. 2012.

Recent Insights

Ideas Made to Matter

The top 10 MIT Sloan articles of 2024

Once again, AI was everywhere. But research about federal spending leads the list.

Dec 3, 2024
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Ideas Made to Matter

New database details AI risks

The AI Risk Repository, a database of over 700 risks posed by AI, aims to provide a shared framework for monitoring and maintaining AI risk oversight.

Nov 26, 2024
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Media Highlights

Press Financial Times

Whose job is safe from AI?

The natural worry for anyone hoping to have a job in five years' time is what AI might do to that job. MIT professor David Autor and research scientist Neil Thompson's research suggests a clarifying question: Does AI look like it is going to do the most highly skilled part of your job or the low-skill rump that you've not been able to get rid of? The answer to that question may help to predict whether your job is about to get more fun or more annoying — and whether your salary is likely to rise, or fall.

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