# Measuring AI Coding Productivity

Whether AI coding tools make developers more productive is an empirical question, and almost every published answer is compromised by how it was measured. The failures aren't exotic — they're the standard problems the human sciences catalogued decades ago, showing up because software engineering mostly builds its own study designs without borrowing that discipline. [[twelve-ways-wrong-ai-coding]] (Greg Wilson) is the fullest enumeration; this page pulls the recurring failure types into one place and pins the strongest evidence to each.

Computing has had its own version of that discipline since 1991: Raj Jain's [[art-of-computer-systems-performance-analysis]] covers experimental design, workload selection, and the statistical treatment of measurement results, and the AI-coding literature largely rediscovers its chapters the hard way.

## The failure types

**Proxy-metric / Goodhart failures.** Lines of code, commit counts, PRs closed, and suggestion-acceptance rate are all easy to count and all measure activity rather than value. A 40% rise in lines per developer measures verbosity; deleting tangled code and replacing it with less is progress that reads as a loss. Once any of these becomes a target it stops tracking the thing you cared about — developers make more, smaller commits, or split tickets. Acceptance rate is the sharpest case: an enterprise study of 400 developers found a 33% acceptance rate with high satisfaction and *no* measure of whether accepted code was correct or secure.

**Missing control group / no counterfactual.** Before/after comparisons attribute every change over the period to the tool, when you also hired, refactored CI, and changed vendors. Comparing AI-assisted developers against a control using "nothing" is worse — that baseline doesn't exist, because real developers without an LLM still have documentation, colleagues, and thinking time. Internal validity requires a credible counterfactual, which most industry reports lack.

**Novelty and Hawthorne effects.** New tools feel fast, and observed people work differently. A four-week study that finds a boost has found a four-week boost; the effects that decide whether the tool was worth it — skill atrophy ([[dont-outsource-learning]]), debt accumulation, changed collaboration — surface over months. Self-report surveys stack a third contaminant on top: social-desirability bias, strongest when management chose the tool.

**Selection bias.** Comparing volunteers against non-volunteers compares two populations, not two conditions. Early adopters are already more motivated and often higher-performing; one longitudinal study found Copilot users were more active than non-users before the tool was introduced. It's the most common flaw in industry reports because it's the cheapest study to run.

**Individual vs system.** Writing code 30% faster changes nothing if ticket-to-production time is flat — writing code wasn't the bottleneck, and more generated code means more to review. This is where the local win becomes a global loss.

## The strongest evidence

Four results carry most of the weight because they avoid the failures above:

- **METR RCT (+19% time).** A randomized controlled trial with experienced open-source developers found AI tools *increased* completion time by 19% — the opposite of what participants predicted for themselves. A real control group on real (not greenfield) tasks, which is why it outranks the "55% faster" Copilot figure from a ninety-minute from-scratch exercise.
- **Xu 2025 (senior −19% / +6.5% review).** Junior contributors gained output while senior developers lost 19% of their own productivity absorbing a 6.5% increase in review load from AI-generated code — the individual-vs-system failure measured directly.
- **He 2026 (807 repos).** Across 807 open-source repos adopting Cursor, a large but transient velocity increase alongside a persistent rise in code complexity and static-analysis warnings — the novelty effect and the deferred cost, separated by measuring over time.
- **Liu 2026 (300k+ commits, >15% flawed).** Over 15% of more than 300,000 AI-authored commits introduced at least one quality issue, and nearly a quarter of those persisted long-term — the "easy half" (generation) looking good while the hard half (correctness, maintenance) degrades.

## Relation to other pages

[[danluu-ai-coding-testing]] argues the same point from the bench: single summary numbers hide run-to-run variance wide enough that a few runs support any conclusion, so "show me the distribution." [[no-silver-bullet-llms]] supplies the DORA/CircleCI instability data that the individual-vs-system failure predicts at the delivery level. [[reviewing-ai-code]] is the individual-vs-system failure restated as a hard throughput cap — review tops out near 400 LOC/hour, so faster generation just moves the bottleneck. [[constraint-decay]] explains why greenfield-task timings mislead: agents look strong on loosely specified work and degrade as production constraints accumulate. [[dont-outsource-learning]] is the long-run cost that novelty-window studies structurally cannot see.