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.
Researchers at the MIT Initiative on the Digital Economy share the latest insights about getting the most from working with AI, such as personality pairing and reorganizing job tasks.
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.
There might be some upsides in this future. Healthcare and education costs might fall because much of that expertise can be delivered, cheaper and easier, with AI-augmented tools, said principal research scientist Neil Thompson. Americans might be healthier and live longer lives thanks to AI-driven advancements in medicine. And underemployed workers might wind up with more hours of free time for creative pursuits.
Neil Thompson, principal research scientist and director of FutureTech, an interdisciplinary group at MIT Sloan and the MIT Computer Science and Artificial Intelligence Lab (CSAIL), rejects extreme views of the impact of Artificial Intelligence on work. He explains that this is one of the areas the lab has studied most extensively and deserves the attention of governments and the preparation of society, but he dismisses the apocalyptic scenario that many fear.
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."
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.
