AI coding assistants have gone from hype to infrastructure in two years. GitHub Copilot, Cursor, Claude, and Gemini are now standard tools at most tech companies. Here's what we actually know about their impact.
**The gains are real:**
- GitHub's study: developers using Copilot complete tasks 55% faster
- Cursor users report 30-50% productivity gains on routine tasks
- Code completion, boilerplate, unit tests, and documentation see the biggest wins
- Stack Overflow traffic dropped ~35% since ChatGPT launched — developers are asking AI instead
**Where they genuinely help:**
- Autocomplete on well-defined tasks (writing CRUD, boilerplate, obvious next lines)
- Explaining unfamiliar codebases
- Writing tests for known-good code
- Documentation generation
- Code review / bug spotting (though not reliably)
**Where they still struggle:**
- System design and architecture decisions
- Novel algorithms with no training precedent
- Debugging complex multi-file interactions
- Security-critical code (subtly introduces vulnerabilities)
- Understanding intent vs. what you literally asked for
**The 'junior developer' analogy**: AI writes code confidently and quickly, but needs review. Treat it like a fast junior developer: great for volume, still needs a senior to catch the subtle wrong stuff.
The frontier: Devin (Cognition), SWE-agent, and Claude's computer use are pushing toward autonomous coding — writing, running, and debugging entire features with minimal human input. Not there yet, but getting close fast.
**Key takeaway:** AI coding assistants give real 30-55% speed gains on routine tasks. Senior review is still essential — they're confidently wrong sometimes.