Martin Fleming
Research Scientist, IDE
Martin Fleming is a Research Scientist at the MIT CSAIL FutureTech Project and a Fellow of The Productivity Institute, a U.K. research organization exploring what productivity means for business, workers, and communities.
Martin is the former IBM Chief Economist and former IBM Chief Analytics Officer. As IBM’s Chief Economist, Martin provided regular macroeconomic insight and analysis to IBM’s senior leaders and engaged with select IBM clients providing a view of the global economic outlook. Martin shared technology industry developments, insights and forecasts with clients, industry professionals, academics, and policymakers.
Martin also led IBM’s data science profession with the mission to drive the growth and expertise of IBM’s skilled data science professionals whose expertise will ensure success in the cognitive era.
As the Chief Analytics Officer from 2010 to 2019, Martin led IBM’s business model transformation initiatives employing machine learning, artificial intelligence, natural language processing in a cloud computing environment to improve decision making and, as a result, financial performance.
Previously, within IBM Corporate Strategy, Martin led IBM’s Smarter Planet strategy development and execution with a focus on energy, climate change, transportation, water and Smarter Cities.
Martin is the former Chief Revenue Scientist, Varicent, a Toronto-based sales-performance management software provider. Martin is also a researcher at the MIT-IBM Watson AI Lab and the U.S. Bureau of Economic Analysis.
Martin is a Fellow of the National Association for Business Economics recognized for outstanding performance as a business economist, contribution to the field of business economics, and service to NABE.
Martin is a member and former chair of the Conference of Business Economists. He is also a member of the Federal Reserve Bank of Chicago’s Economist Roundtable. Martin was a member of the Federal Economic Statistics Advisory Committee and a member of the Federal Reserve Bank of New York’s Fintech Advisory Committee. Martin is a participant in the Brookings Productivity Measurement Initiative, organized by David Wessel and chaired by Janet Yellen.
Prior to joining IBM, Martin was a Principal Consultant with Abt Associates, Cambridge Massachusetts. He was also Vice President, Strategy for Reed-Elsevier, Inc., the Anglo-Dutch information company. Martin began his professional career at the System Dynamics Group, Alfred P. Sloan School of Management, Massachusetts Institute of Technology.
Martin holds a Ph.D. and an M.A. in Economics from Tufts University and a B.S. cum laude in Mathematics from University of Massachusetts Lowell.
Recent Insights
Featured publications
May 22, 2026
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
- Wensu Li | Postdoctoral Associate
Maja S. Svanberg
Brian C. Goehring
This paper’s findings suggest that AI job displacement will be substantial, but also gradual – and therefore there is room for policy and retraining to mitigate unemployment impacts.
May 15, 2026
- Martin Fleming | Research Scientist, IDE
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
This paper examines technical progress in NVIDIA datacenter GPUs from the mid-2000s through 2025.
April 15, 2026
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
- Matthias Mertens | Research Scientist at Future Tech
- Brittany Harris | Data Analyst, CSAIL
- Wensu Li | Postdoctoral Associate
The authors propose that AI automation is a continuum between: crashing waves where AI capabilities surge abruptly over small sets of tasks, and rising tides where the increase in AI capabilities is more continuous and broad-based.
March 15, 2026
- Wensu Li | Postdoctoral Associate
- Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL
- Martin Fleming | Research Scientist, IDE
This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation.