Outcomes we want to see from AI at Medium Engineering

Summary

Medium Engineering defines desired outcomes and anti-goals for adopting AI, emphasizing leverage, learning, technical ambition, rigorous reasoning, fluency, and maintainable code rather than raw output. Engineers are expected to use AI for repetitive refactoring, testing, unfamiliar systems, design exploration, code analysis, and safer cross-stack work while retaining accountability for clarity, correctness, coverage, and judgment. The guidance rejects PR, commit, line, token, and model-spend metrics as success measures, allowing them only as debugging signals. It also warns against blind trust, unreviewable code volume, loss of codebase understanding, security or privacy regressions, misaligned vendors, environmental hand-waving, and performative tool adoption. The stated north star is achieving outcomes that let Medium’s humans move with the responsiveness of a much larger team while staying true to what makes the platform worth building.

Takeaways

1. 1

   AI adoption should be judged by outcomes such as meaningful test coverage, safer refactors, broader technical range, stronger design reasoning, and improved code quality—not by PRs, commits, lines changed, tokens, or model spend.

2. 2

   Engineers remain accountable for code clarity, correctness, test coverage, and their ability to reason about code, with higher expectations of human understanding for high-risk or complex systems.

3. 3

   The guidance treats security and privacy regressions, loss of shared codebase understanding, excessive review volume, misaligned vendors, environmental hand-waving, and performative AI usage as explicit anti-goals.