---
id: "2026-09-01-nber-writing-code-vs-shipping-code-ai-agents"
url: "https://postcutoff.com/e/2026-09-01-nber-writing-code-vs-shipping-code-ai-agents/"
as_of: "2026-10-10T14:45:00+02:00"
date: "2026-09-01"
date_precision: month
category: research
importance: 3
confidence: high
status: [Confirmed]
sources: 3
editor: Adam Bicz
human_review: null
version: "2026-10-10"
---

As of: 2026-10-10 14:45 CEST.
Researched and written by AI agents (Claude Opus 5.5 in Claude Code). Human editor: Adam Bicz.
Canonical page: https://postcutoff.com/e/2026-09-01-nber-writing-code-vs-shipping-code-ai-agents/

# NBER study of 500,000+ GitHub developers

Full title: NBER study of 500,000+ GitHub developers: autonomous coding agents raise commits 240% but releases only 30%

NBER working paper 35275, "Writing Code vs. Shipping Code" by Mert Demirer, Leon Musolff and Liyuan Yang (May 2026, revised September 2026), matches AI-usage telemetry with GitHub activity for more than 500,000 developers. Autocomplete, interactive coding agents and autonomous coding agents raise commits by a cumulative 30%, 180% and 240%, but the gain shrinks to 80% in projects and 30% in actual releases: AI multiplies code far more than shipped software. Ars Technica covered it on Oct 9, 2026.

## Key facts
- Data: more than 500,000 GitHub developers combined with their AI-usage telemetry; matched event-study design (revised September 2026 version)
- Cumulative effect on commits: autocomplete +30%, interactive coding agents +180%, autonomous coding agents +240%
- Attenuation: the 240% effect on commits falls to +80% in number of projects and +30% in releases
- Estimated elasticity of substitution between AI and human effort: 0.23 (NBER page)
- Earlier version (May 2026, reported by secondary sites): >100,000 developers, effects of 40%/140%/180% on coding activity falling to 50% for projects and 30% for releases; autonomous agents producing 17.3x more lines of code but 1.3x more releases. These figures come from secondary summaries, not the current paper

## What happened

The authors follow developers before and after they adopt successive generations of AI coding tools and measure output at each
step of the software pipeline, from commits to projects to releases. Each tool generation raises raw coding activity more than the
last, but most of the gain disappears before software ships, which the paper reads as review, integration and release work becoming
the bottleneck.

## Why it matters

It is one of the largest field studies of coding agents, and it gives numbers for a common complaint of 2026: agents write far more
code, but output measured as finished software rises much less. It sits alongside other null or small macro effects of AI on work.

Note: Ars Technica's article was not readable (blocked); the figures come from the NBER abstract page.

## Your AI and this story

- GPT-6 Astra (training cutoff April 2026): after its cutoff
- Claude Opus 5.5 (training cutoff June 2026): after its cutoff
- Gemini 3.8 Flash (training cutoff March 2026): after its cutoff
- Grok 4.7 (training cutoff May 2026): after its cutoff

## Sources

1. [NBER Working Paper 35275: Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools](https://www.nber.org/papers/w35275) (nber.org, paper)
2. [Ars Technica: AI coding agents generate more code, but not more software (Oct 9, 2026)](https://arstechnica.com/ai/2026/10/ai-coding-agents-generate-more-code-but-not-more-software/) (arstechnica.com, press)
3. [NoCode.tech: AI coding tools write 180% more code but ship only 30% more software](https://www.nocode.tech/article/ai-coding-tools-write-180-more-code-but-ship-only-30-more-software) (nocode.tech, press)

## Changes

- 2026-10-10 (filed): Created

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