Google’s 10,000x Label Leap

Google claims 10,000x training data reduction via high‑fidelity labels, making data quality a dominant lever over sheer volume. This undercuts compute/data‑quantity assumptions in AI policy. — If label fidelity can substitute for data scale, compute‑based control regimes and data hoarding arguments weaken, shifting governance toward provenance, labeling standards, and auditability.