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Inside the Race to Measure Frontier Intelligence
Why it mattersThis discussion examines frontier AI model evaluation as public benchmarks saturate and models learn to optimize for tests. It covers continuously refreshed private evals, pre-release testing, long-horizon agentic tasks, cost and latency, reward hacking, enterprise ROI, and the policy implications of whose values benchmarks encode.
Discuss: Can continuously refreshed private AI evaluations measure long-horizon agentic systems without creating new opportunities for reward hacking or embedding the evaluator’s values?
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