About Cursorship

Find out what your AI-written code is actually costing you in review, rework and incidents.

What we do

Measurement of downstream consequences rather than adoption. It links code provenance to what happens afterwards: review time and comment volume, revert and rework rates, defect density, incident linkage, and time from first commit to production, comparing AI-assisted changes to human-authored ones within the same codebase and team. It identifies where assistance genuinely accelerates work and where it produces code that costs more to review than it saved to write, and it does this without ranking individual developers, which is stated as policy.

Who we built it for

Engineering leaders at 30 to 1,000 person companies where developers now use AI coding assistants heavily and nobody can say whether it is helping. VPs of Engineering, CTOs and platform leads.

Why this is hard to copy

The question is being asked in every engineering organisation right now and answered with vibes, and the assistant vendors have every incentive not to answer it honestly.

Refusing to produce individual developer rankings is what makes it adoptable rather than resisted, and the aggregate dataset becomes industry-defining research nobody else is positioned to produce.

How we find customers

Engineering leadership communities and CTO newsletters, plus publishing anonymised aggregate research on AI-assisted code outcomes across customers, which is a question the whole industry is arguing about with no data.