According to @emollick, METR_Evals and EpochAIResearch are praised for transparent, data-accessible AI benchmarking practices, highlighting how they measure AI capability and disclose methodological challenges. According to METR_Evals, its ongoing study of AI tools in software development found an earlier 20% slowdown is now outdated, with emerging evidence of speedups, though current results are unreliable due to shifting developer behavior; the team is refining methods to address this (as reported in METR_Evals’ Feb 2026 X thread). According to EpochAIResearch’s public communications, the group similarly publishes open methodology and datasets for AI capability tracking, reinforcing reproducibility and comparability across benchmarks. For AI leaders, this transparency improves evaluation governance, procurement decisions, and model selection, and creates opportunities for vendors to align product performance with real-world developer workflows.