Building an AI Polymath
Shirley Ho · Daedalus · 2026
Abstract Artificial intelligence has made remarkable strides in natural language processing and image recognition, yet its impact on the natural sciences is fragmented. While specialized models like AlphaFold have revolutionized biology, the scientific enterprise remains siloed, with most foundational models narrowly tailored to specific domains or modalities. In this essay, I advocate for a new class of scientific AI: the polymathic foundation model. Inspired by the intellectual versatility of human polymaths, such a model would integrate diverse data types and disciplinary knowledge across the natural sciences. I argue that building such a model is not only technically feasible but epistemologically necessary. I draw on lessons from existing interdisciplinary successes and outline key challenges: scientific dataset curation, multimodal and multitask learning, verifiable knowledge exchange, and interpretability. I close the essay with a cautiously optimistic roadmap for how such models could transform scientific discovery in the next decade.