Researchers tracked 112 seasoned developers using AI agents in real work and found they never hand off vague prompts and trust outputs blindfolded. Instead they plan architecture, review every diff, limit tasks to small scopes, and supervise the AI like a junior dev. Letting go led to a 92% failure rate in production and a 19% drop in productivity.

Non-programming leaders starting to contribute to code with LLMs can increase iteration speed and introduce diverse perspectives, but this also risks compromising the implicit architecture of the codebase. As more non-engineers make changes, maintaining design intent and code maintainability becomes a challenge, requiring developers to adapt their roles to focus on architectural oversight. Despite these risks, democratizing coding could lead to better solutions as more perspectives are included in the development process.