AI Code Volume Metrics Deemed Vanity

Hot take: Elevating AI-generated code volume as a productivity metric is a dangerous regression that prioritizes bloating codebases over building the right things, serving AI vendor marketing and cost-cutting executives instead of engineering quality.

• –**Resurrecting Failed Metrics**: Celebrating the percentage of code generated by AI is identical to using "lines of code" as a proxy for developer value, which the industry spent decades learning to avoid.
• –**The Paradox of Speed**: While AI tools help draft code faster, independent studies indicate that they also lead to higher code churn, lower comprehension scores for authors, and complex integration bottlenecks.
• –**Layoff Pretexts**: Reductions in engineering headcount at tech firms are being blamed on AI productivity gains without evidence of organizational output increases, exposing volume metrics as a convenience for cost-cutting decisions.
• –**Measure Outcomes, Not Activity**: The standard metrics of software delivery—DORA metrics, system reliability, and customer value—remain the only true measures of team success, regardless of whether the code was typed by a human or a model.