ai

A client connected 11 MCP servers to their development environment in three months. When I reviewed the OAuth scopes, two of them had write access to production infrastructure. Nobody had checked.

A client's engineering team adopted AI coding tools across the board. Individual velocity soared. But their DORA metrics flatlined — and the reasons had nothing to do with the tools themselves.

The SpaceX-Cursor deal forced a lot of teams to rethink their AI tooling strategy overnight. But the real problem isn't the acquisition — it's that most teams never had a strategy to begin with.

Last quarter I tracked my time across three consulting engagements. I spent 11 hours a week reviewing AI-generated code and under 8 writing my own. The ratio surprised me, and it changed how I think about developer productivity.

A client's platform had 23 AI agents built by a team of 8. Nobody could tell me what half of them did. Agent sprawl is the new microservices sprawl, and the cleanup looks depressingly similar.

A client's AI features were burning through their OpenAI budget 3x faster than projected. Adding OpenTelemetry's GenAI semantic conventions revealed the problem wasn't what anyone expected.

A startup founder built their MVP almost entirely with AI coding agents. It worked. Then they hired a team, and within two months nobody could ship anything. I got called in to figure out why.

A client's team tripled their PR output with coding agents. Six weeks later, their cycle time had doubled and nobody could explain why.

AI code review tools are everywhere now. After rolling one out on a real project, I learned more about our team than about the tool itself.

The latest DORA report confirms what I've seen on consulting engagements — AI tools amplify existing conditions. If your processes are solid, AI accelerates you. If they're not, you just create technical debt faster.

Practical insights from months of using AI-assisted coding in real client projects — what works, what doesn't, and how it changed my workflow.