Uploaded January 2026 | Updated September 2026, 1 week ago
Computer Science Seminar Series
January 29, 2026
“AI Ecosystems: Structure, Strategy, Risk, and Regulation”
Benjamin Laufer, Cornell Tech
Machine learning and AI are not standalone artifacts: They are ecosystems where foundation models are adapted and deployed through layered pipelines spanning developers, platforms, users, and regulators. This talk explores how the structure of these ecosystems shapes the distribution of value and risk, and determines system-level properties like safety and fairness. Benjamin Laufer begins with a game-theoretic model of the interaction between general-purpose producers and domain specialists, using it to examine how regulatory design shapes incentives and equilibrium behaviors. He then connects these formal insights to empirical measurements from 1.86 million open-source AI models, reconstructing lineage networks to quantify how behaviors and failures propagate through fine-tuning. Finally, turning from the descriptive structure of the ecosystem to the design of the algorithms themselves, Laufer describes his work in algorithmic fairness, framing the identification of less discriminatory algorithms as a search problem with provable statistical guarantees. He closes by outlining a forward-looking research agenda aimed at building technical infrastructure and policy mechanisms for steering AI ecosystems toward robust, accountable, and democratic outcomes.
Benjamin Laufer is a PhD candidate at Cornell Tech, advised by Jon Kleinberg and Helen Nissenbaum. A recipient of a LinkedIn PhD Fellowship and three Rising Stars awards, Laufer researches how data-driven and AI technologies behave and interact with society. He previously worked as a research intern at Microsoft Research and a data scientist at Lime, and holds a BSE in operations research and financial engineering from Princeton University.
Computer Science Seminar Series
January 29, 2026
“AI Ecosystems: Structure, Strategy, Risk, and Regulation”
Benjamin Laufer, Cornell Tech
Machine learning and AI are not standalone artifacts: They are ecosystems where foundation models are adapted and deployed through layered pipelines spanning developers, platforms, users, and regulators. This talk explores how the structure of these ecosystems shapes the distribution of value and risk, and determines system-level properties like safety and fairness. Benjamin Laufer begins with a game-theoretic model of the interaction between general-purpose producers and domain specialists, using it to examine how regulatory design shapes incentives and equilibrium behaviors. He then connects these formal insights to empirical measurements from 1.86 million open-source AI models, reconstructing lineage networks to quantify how behaviors and failures propagate through fine-tuning. Finally, turning from the descriptive structure of the ecosystem to the design of the algorithms themselves, Laufer describes his work in algorithmic fairness, framing the identification of less discriminatory algorithms as a search problem with provable statistical guarantees. He closes by outlining a forward-looking research agenda aimed at building technical infrastructure and policy mechanisms for steering AI ecosystems toward robust, accountable, and democratic outcomes.
Benjamin Laufer is a PhD candidate at Cornell Tech, advised by Jon Kleinberg and Helen Nissenbaum. A recipient of a LinkedIn PhD Fellowship and three Rising Stars awards, Laufer researches how data-driven and AI technologies behave and interact with society. He previously worked as a research intern at Microsoft Research and a data scientist at Lime, and holds a BSE in operations research and financial engineering from Princeton University.
