At the 50 fastest-growing startups in India, AI coding assistants now generate 46% of committed code—up from 12% a year ago. We surveyed engineering leaders to understand the productivity impact.
The Adoption Data
Code generated by AI: 46% (accepted suggestions + AI-written functions)
Developer adoption: 89% of engineers use AI assistants daily
Most used tools: GitHub Copilot (61%), Cursor (24%), Codeium (8%)
Productivity Impact
Time to ship features: 34% faster (median)
Bugs per 1,000 lines: No significant change (AI introduces new types of bugs)
Code review time: 28% longer (reviewing AI code requires more attention)
Developer satisfaction: 72% report higher job satisfaction
What’s Actually Faster
- Boilerplate code: 80% faster
- Test writing: 65% faster
- Documentation: 70% faster
- Bug fixes: 40% faster
- New feature architecture: 10% faster (minimal impact)
The Surprising Findings
Junior devs benefit most
Junior developers saw 52% productivity gains vs. 28% for seniors. AI acts as an always-available mentor.
Technical debt concerns
67% of engineering leaders worry about AI-generated technical debt. AI optimizes for “working now” not “maintainable later.”
Hiring hasn’t slowed
Despite productivity gains, 92% of companies are still hiring at the same or faster pace. AI amplifies, doesn’t replace.
Best Practices from Leaders
- Require human review for all AI-generated code
- Use AI for first drafts, humans for architecture
- Track AI code separately for quality analysis
- Invest in prompt engineering training