Overview
- AI coding tools are described as strong at first drafts of common patterns, repetitive setup code, and simple unit tests.
- The article says generated code often looks clean but skips project rules like required error handling or safe library use, which can plant hidden bugs and security flaws.
- The author reports that engineers now spend more time reviewing suggestions, fixing gaps, and fitting AI output into complex systems with many moving parts.
- The piece argues that the highest value shifts to system design, turning vague goals into concrete plans, deep debugging across services, and clear communication with teams.
- The author advises treating AI like a very fast junior engineer by giving small, clear tasks and checking every line against style guides, dependencies, and performance needs.