AI Coding Tools: Sean's Honest Verdict After a Year

What AI Coding Tools Actually Replaced

In Sean's workflow over the past year, AI coding tools measurably reduced time on: boilerplate code generation, documentation writing, debugging common error patterns, and explaining unfamiliar code. They did not reduce time on: system architecture decisions, code review requiring business context, debugging novel issues requiring deep system understanding, and anything requiring judgment about trade-offs. AI tools are good at code that looks like code they have seen before.

The Right Tool for the Task

Cursor: daily coding flow, inline autocomplete, quick single-file edits. Use this most of the time. Claude Code: implementing features across multiple files, codebase-wide debugging, large refactors. Use this for complex work. GitHub Copilot: teams where GitHub PR and workflow integration is valuable, or where $10/month matters versus $20/month for Cursor. The tools serve different moments. One universal answer misses the point.

The Developer Replacement Question

Adam asked Sean last month whether AI was going to replace most developers. Sean analyzed his own workflow: AI saved him approximately 8 hours per week on code-related tasks over the past year. He used those 8 hours to ship more features and take on more complex work. The AI tools expanded what he could accomplish — they did not replace the work he was doing. More output per developer, not fewer developers for the same output.

What Changed Sean's Workflow Most

The single biggest change: Claude Code plus CLAUDE.md for complex feature work. The ability to hand off a multi-file implementation with full project context, walk away, and return to tested code reduced the cognitive overhead on complex tasks more than any other tool change in the past two years. The 2-hour investment in writing CLAUDE.md for each project pays back within one week of use.