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.
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.
In a new study from MIT FutureTech and the University of Queensland, researchers engaged 272 specialists from 37 countries to prioritize AI risks and identify who is most vulnerable.
David Autor and Neil Thompson on the importance of using AI tools to collaborate with humans and the nuances of how AI affects productivity and replaces workers.
AI may also be causing workers to spend more time on the job because they're still learning how to integrate it into their workflows, said principal research scientist Neil Thompson. "Usually there's a transition period where you have to modify the processes the organization has," he said. "Initially, you become less efficient."
According to a study by principal research scientist Neil Thompson and co-authors, leading AI models in mid-2024 successfully completed 50 percent of white-collar tasks that would have taken a human three to four hours to complete; just over a year later, they completed 65 percent. The authors estimate AI systems will be able to complete 80 to 95 percent of text-based tasks by 2029. "This pace of improvement isn't quite as fast as what we've seen with AI and coding," research scientist and co-author Matthias Mertens said. "But it's still really, really fast."
In principal research scientist Neil Thompson's research — which evaluated 40 AI models across thousands of real-world job tasks, each assessed by practitioners in the relevant field — he and his colleagues found that automation doesn't affect all parts of a job equally. The critical variable is whether the tasks being automated are the expert parts of a role or the administrative scaffolding around them.
Moore's law, the observation that the number of transistors in an integrated circuit doubles about every two years, may be hitting its limit. Transistors are getting so small that experts say the laws of physics are slowing the reliable pace of progress. "During the heyday of Moore's law miniaturization gave us chips with more transistors, and it also meant that each transistor used less power," principal research scientist Neil Thompson said. "Today, miniaturization is giving us much smaller reductions in power, and so trying to cram in too many transistors produces a lot of heat and can melt a chip."
This in-person course, led by MIT Professor Andrew W. Lo, provides a practical, executive-level exploration of how AI and machine learning are reshaping the financial industry. Participants will gain a foundational understanding of AI’s evolution—from early machine learning to the current LLM era—before diving into real-world applications across the buy side, sell side, banking, insurance, and risk management sectors. Through interactive sessions, case studies, and guest lectures from leading practitioners and researchers, executives will examine the capabilities and limitations of today’s AI tools and consider how emerging innovations will forge the next generation of FinTech.
