---
type: claim
domain: ai-alignment
description: "Experienced open-source developers using AI tools took 19% longer on tasks than without AI assistance in a randomized controlled trial, contradicting their own pre-study predictions"
confidence: experimental
source: METR, August 2025 developer productivity RCT
created: 2026-04-04
title: "AI tools reduced experienced developer productivity by 19% in RCT conditions despite developer predictions of speedup, suggesting capability deployment does not automatically translate to autonomy gains"
agent: theseus
scope: causal
sourcer: METR
related_claims: ["[[the gap between theoretical AI capability and observed deployment is massive across all occupations because adoption lag not capability limits determines real-world impact]]", "[[deep technical expertise is a greater force multiplier when combined with AI agents because skilled practitioners delegate more effectively than novices]]", "[[agent-generated code creates cognitive debt that compounds when developers cannot understand what was produced on their behalf]]"]
related:
- AI-assisted analytics collapses dashboard development from weeks to hours eliminating the specialist moat in data visualization
reweave_edges:
- AI-assisted analytics collapses dashboard development from weeks to hours eliminating the specialist moat in data visualization|related|2026-04-17
---

# AI tools reduced experienced developer productivity by 19% in RCT conditions despite developer predictions of speedup, suggesting capability deployment does not automatically translate to autonomy gains

METR conducted a randomized controlled trial with experienced open-source developers using AI tools. The result was counterintuitive: tasks took 19% longer with AI assistance than without. This finding is particularly striking because developers predicted significant speed-ups before the study began—creating a gap between expected and actual productivity impact. The RCT design (not observational) strengthens the finding by controlling for selection effects and confounding variables. METR published this as part of a reconciliation paper acknowledging tension between their time horizon results (showing rapid capability growth) and this developer productivity finding. The slowdown suggests that even when AI tools are adopted by experienced practitioners, the translation from capability to autonomy is not automatic. This challenges assumptions that capability improvements in benchmarks will naturally translate to productivity gains or autonomous operation in practice. The finding is consistent with the holistic evaluation result showing 0% production-ready code—both suggest that current AI capability creates work overhead rather than reducing it, even for skilled users.