Developers and teams making decisions about AI coding tool adoption have no access to standardized, reproducible benchmarks measuring actual downstream outcomes — code quality, maintainability, defect rates, or performance — compared to manual approaches. Adoption and rejection decisions are therefore driven by narrative and identity rather than evidence, preventing rational tooling choices at scale. A neutral, multi-team benchmarking marketplace could create shared ground truth that benefits both tool vendors and practitioners.

Teams adopting or rejecting AI coding tools have zero empirical evidence on real-world outcomes like defect rates, maintainability, and velocity — decisions are vibes-based, costing orgs millions in wrong tooling bets.

Engineering leaders and DevTool procurement teams at mid-to-large companies evaluating Copilot, Cursor, Devin, and similar AI coding tools.

Tool vendors already spend heavily on marketing unverified claims; they'd pay for credible third-party validation, while enterprises would pay for decision-grade data — analogous to how Gartner/Forrester monetize analyst reports but with reproducible empirical data instead of opinions.

MVP: open-source benchmark harness that runs identical coding tasks (bug fixes, feature builds, refactors) across AI tools and manual baselines in sandboxed repos, measuring DORA-style metrics plus static analysis scores; marketplace layer lets teams contribute anonymized results and access aggregate dashboards, with vendors paying for certified benchmark profiles.

AI developer tools market is $30B+ and growing; even capturing 0.5% as the neutral benchmarking layer is $150M — comparable to software testing/QA analytics markets.