AI progress is fundamentally driven by vast amounts of data and compute, rather than improvements in sample efficiency, creating a stark contrast with human learning. This essay explores the "black hole of data" powering AIs, quantifies the massive sample-efficiency gap between humans and machines, counters common objections, and discusses the implications for white-collar automation and future AI research.
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AI's "dirty secret" is its deep reliance on human intelligence. Phelim Bradley, CEO of Prolific, explains how his platform provides the critical human data infrastructure for training and evaluating frontier models, emphasizing the shift from commoditized labor to a sophisticated marketplace of verified expertise.
Brendan Foody, CEO of Mercor, discusses the critical role of AI evaluations (evals) in model improvement, detailing how his company achieved unprecedented growth by supplying high-skilled experts to top AI labs. He explores the shift to Reinforcement Learning from AI Feedback (RLAIF), the future of work in an AI-driven economy, and why he believes the path to AGI is paved with better evals, not just more data.