How Much Data Is Enough? Why the Riemann Zeta Function Predicts the Future of AI & Discovery

In biomedical AI, we constantly ask: Do we need more data, or do we need a smarter model?In this episode, we break down The Zeta Law of Discoverability—a theoretical framework linking sample size complexity to the Riemann zeta function. We explore how signal-to-noise accumulates across spectral modes like a "Tower of Hanoi" puzzle, why certain diseases (like Alzheimer’s) can be detected with small datasets while others (like psychiatric conditions) need massive samples, and the counterintuitive "three-modality paradox"—how adding seemingly redundant data (like text) can dramatically boost sample efficiency by steepening spectral decay.