Humanity’s Last Exam

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1. Oct 8
   Inside the AI data boom: from labeling to RL environments

   In this Silicon Valley 101 podcast episode, He Yunzhong, who works on post-training and evaluation research at Scale AI, and Sun Yiyou, a UC Berkeley postdoc on the Agents’ Last Exam (ALE) project, unpack the fast-growing but opaque market for AI training data. They note AfterQuery was valued at $300 million in its April A round and, per media reports, $3.2 billion by September, while Bespoke Labs announced $40 million in combined seed and A funding in July, and trace the shift from crowd labeling and pre-training toward rubrics, verifiable signals and RL environments bundling tasks, tools and verification. They also discuss procurement bottlenecks in vertical domains, the blurred line between benchmarks and data sales — including a claim that some data firms sold evaluation data — expert fabrication and contamination, and OpenAI's February decision to stop reporting SWE-bench Verified scores. Guest views are personal.

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