Percy Liang, Stanford: The paradigm shift and societal effects of foundation models

Foundation models mark a fundamental shift in AI, necessitating rigorous, standardized evaluation frameworks to ensure these systems remain tethered to reality rather than becoming engines of polarization. As these models increasingly mediate human interaction with information, independent academic research serves as a vital, neutral mechanism for benchmarking capabilities and fostering transparency. Beyond technical performance, the field must address systemic challenges, including data attribution, democratic governance of model values, and the development of open-source infrastructure. By prioritizing scientific rigor over fear-based regulation, researchers can build a more accountable ecosystem that balances the need for diverse, pluralistic viewpoints with a shared understanding of objective facts. This transition requires shifting from closed-door development to a more participatory, auditable framework that treats AI as a foundational societal technology rather than a proprietary product.