AI Coding Tools Boost Code Output, But Effective Retention Rate Is Only 10%-30%

The popularity of AI coding tools has significantly increased code output, but metrics for measuring developer productivity have become unclear. In the past, lines of code were used as a measure; now, Token quotas have become a status symbol among Silicon Valley developers, but they are considered a strange input metric that does little to improve efficiency.

Data from multiple analytics companies shows that while the adoption rate of AI-generated code is as high as 80%-90%, subsequent rework leads to an actual effective retention rate of only 10%-30%. GitClear found that AI users have a 9.4x higher code modification rate than non-AI users, and Jellyfish noted that when Token costs increase 10x, throughput only doubles—tools bring quantity rather than value.

Although engineers enjoy the convenience of tools, issues like code review backlogs and technical debt accumulation are prominent. The industry is exploring ways to use AI efficiently, and companies are forced to adapt to changes in this new era.