Efficiency Debt of AI-Generated Code

Tran et al. (Google, arXiv 2608.06640): 3.52M changes over 12 months in one production C++ monorepo, with a human-written control cohort — AI-generated C++ writes ~2x the explicit loops and 30-40% fewer standard-library calls, and that source-level imperative bias shows up in production as ~5% relative compute and ~8% relative memory overhead. The reliability result splits: build failures ~1.3x and sanitizer findings ~1.3x above human, but revert rate ~0.9x BELOW. Review friction is real (blocking threads 1.92x) yet review depth does not predict which inefficiencies survive — the paper's own null, and its argument that this debt has to be caught upstream of review. Taxonomy-informed feedback cuts targeted findings 11.1%, leaving the regenerated functions still net-regressive against the human original