The primary barrier to a general-purpose biological simulator, (a model that can actually predict the phenotypic level response of the human body to any intervention),  is not a lack of compute, but the absence of causal, human-relevant datasets. In this talk, Matt Osman outlines emerging approaches to address this gap through iPSC-derived tissues that function as both therapeutic platforms and scalable engines for data generation. Polyphron explores why high-fidelity human tissue is uniquely capable of capturing emergence, where molecular interactions translate into functional outcomes, and why generating this data across diverse genotypes is essential to building true ground-truth datasets. By closing the loop between lab-grown tissue and clinical outcomes, this approach points toward a shift from sparse, mechanism-limited data to a more predictive and programmable framework for human health.