4.8 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.1 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 Paper Examines How First Query Shapes Agentic Deep Search — 1 src1.3 Conformance-Driven Iterative Refinement for Natural-Language to SysMLv2 Translation — 1 src1.3 Neural Operators for Nonlinear Functionals on RKHS — 1 src4.8 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.1 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 Paper Examines How First Query Shapes Agentic Deep Search — 1 src1.3 Conformance-Driven Iterative Refinement for Natural-Language to SysMLv2 Translation — 1 src1.3 Neural Operators for Nonlinear Functionals on RKHS — 1 src
OpenAI on Measuring Goodhart's Law in AI Development
OpenAI discusses the implications of Goodhart's law, which states that when a metric becomes a target, it loses its effectiveness as a measure. The company explains how this economic principle applies to AI development, particularly when optimizing objectives that are hard to quantify. They outline their approach to addressing this challenge.
WHY IT MATTERS ↘Most AI teams optimize proxy metrics — benchmark scores, reward-model outputs, human-approval rates — that degrade once made into explicit targets, so unmanaged Goodhart effects silently convert training gains into capability or safety regressions that only surface after deployment. Because regulators and enterprise buyers increasingly treat those same benchmarks as evidence of compliance or quality, the choice of proxy becomes a competitive and governance liability, not just a technical detail.
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