Benjamin Laufer (Cornell)- AI Ecosystems: Structure, Strategy, Risk and Regulation

Abstract: Machine learning (ML) and artificial intelligence (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. I begin 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. I then connect 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, zooming in from the aggregate structure of the ecosystem to the design of the algorithms themselves, I describe my work in algorithmic fairness, framing the identification of less discriminatory algorithms as a search problem with provable statistical guarantees. I close by outlining a forward-looking research agenda aimed at building both the technical infrastructure and policy mechanisms required to steer AI ecosystems toward robust, accountable and democratic outcomes.