AIThe case for investing in AI benchmarks—and getting model labs to care
One post urges AI product builders to spend more than 25% of their time creating benchmarks and getting model labs to care. A quote post argues that strong benchmarks also need a clear purpose and rigorous quality checks.
TLDR
One post calls spending more than 25% of AI product-building time on benchmarks and persuading model labs to care the easiest path to accelerating a company’s progress. The post quoting it emphasizes quality: a clear thesis about which capability a benchmark measures and why it belongs in a model card, backed by methodological and task-level rigor, including expert and programmatic quality checks. That author cites Terminal-Bench, Agents’ Last Exam, OSWorld and Harvey LAB as strong examples, and says their team supported those teams through Open Benchmarks Grants.