Assessing AI-assisted coding skill — a hiring-grade evaluation tool

Executive Summary

Hiring teams struggle to assess how well candidates will perform in modern, AI-augmented developer workflows—traditional algorithmic puzzles miss skills like prompt engineering, tool composition, and iterative model-driven debugging. Recruiters and managers across roughly 400K engineering teams bear high hiring costs and long time-to-hire, so they need hiring-grade signals that actually predict on-the-job performance. You could build an instrumented, project-based assessment platform that lets candidates complete realistic tasks while using LLMs and developer tools, capturing prompts, tool calls, edits, test runs and time-to-solution to produce standardized, interpretable scorecards and replayable sessions. It would integrate with GitHub/CI, offer proctoring and an API, and be sold on a team subscription model consistent with a $12K ACV benchmark. The market is attractive now because LLMs are moving into developer workflows and skills assessment is shifting from puzzles to workflow-based evaluations, creating an addressable market of about $4.8B (400K teams × $12K ACV) and strong willingness to pay to reduce bad hires and speed screening. This product’s edge would be measuring practical AI-assisted coding behavior (prompt craft, tool selection, iteration patterns) with calibrated employer-facing metrics and replayable evidence rather than static outputs. That said, preventing cheating, standardizing benchmarks across stacks, and meeting privacy/compliance requirements are real challenges that require early investment in robust instrumentation, proctoring, and validated calibration datasets.