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Learn More About Neil Thompson
Neil Thompson is the Director of the FutureTech research project at MIT’s Computer Science and Artificial Intelligence Lab and a Principal Investigator at MIT’s Initiative on the Digital Economy.
Previously, he 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. Thompson 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.
Thompson has a PhD in Business and Public Policy from Berkeley, where he also completed master’s degrees in computer science and statistics. In addition, he holds a master’s in economics from the London School of Economics as well as undergraduate degrees in physics and international development. Prior to academia, Thompson worked at organizations such as Lawrence Livermore National Laboratory, Bain and Company, the United Nations, the World Bank, and the Canadian Parliament.
Neil Thompson is available to advise your organization via virtual and in-person consulting meetings, interactive workshops and customized keynotes through the exclusive representation of Stern Speakers & Advisors, a division of Stern Strategy Group®.
Neil Thompson offers highly customized keynotes and presentations to meet your organization's needs, with topics including but not limited to:
- The Big Trends Driving AI: What They Are and How They Can Help Us Predict AI’s Future
- The Economics of Generative AI: The Good, The Bad, and the Ugly
- Computing After Moore’s Law: The Economic Paradigm That Underpinned Computing Is Coming Undone
- Quantum Computing: What Is It and How Should It Fit In Company Strategic Planning?
- It’s a Wiki World: The Power of Wikipedia in the Real World
Silicon Valley Is Pricing Academics Out of AI Research
March 10, 2024
Automation May Be Possible — But When Will Businesses Want To Do It?
February 20, 2024
We May Not Lose Our Jobs to Robots So Quickly, MIT Study Finds
January 22, 2024
Will AI Take Our Jobs? Maybe Not Just Yet.
January 22, 2024
Quantum Computing: What Leaders Need to Know Now
January 11, 2024
AI's Impact on the Future of Work with Neil Thompson (Audio)
January 1, 2024
Will Quantum Computing Be Better For Your Business?
November 17, 2023
5 Questions for MIT’s Neil Thompson
June 2, 2023
Study: Industry Now Dominates AI Research
May 18, 2023
Study Finds Wikipedia Influences Judicial Behavior
July 27, 2022
Deep Learning's Diminishing Returns
September 24, 2021
Why Innovation’s Future Isn’t (Just) Open
May 11, 2020
We're Not Prepared for the End of Moore's Law
February 24, 2020
Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?
(MIT FutureTech, January 2024)
Should Firms Hold More Patents? A Randomized Control Trial on the Commercial Value of Patents
(Academy of Management, July 2023)
Democratising Case Law While Teaching Students
(European Journal of Legal Education, June 2023)
Economic Impacts of AI-Augmented R&D
(arXiv, January 2023)
How Fast Do Algorithms Improve? [Point of View]
(Proceedings of the IEEE, October 2021)
The Decline of Computers as a General Purpose Technology
(Communications of the ACM, March 2021)
Moore's Law: What Comes Next?
(Communications of the ACM, February 2021)
Building the Algorithm Commons: Who Discovered the Algorithms That Underpin Computing in the Modern Enterprise?
(Global Strategy Journal, February 2021)
Sourcing Innovation in the Digital Age
(SSRN, October 2020)
Does Winning a Patent Race Lead to More Follow-On Innovation?
(Journal of Legal Analysis, June 2020)