Who is using AI to code? Global diffusion and impact of generative AI

Generative coding tools promise big productivity gains, but uneven uptake could widen skill and income gaps. We train a neural classifier to spot AI-generated Python functions in over 30 million GitHub commits by 160,000 developers, tracking how fast —and where— these tools take hold. Today, AI writes an estimated 29 % of Python functions in the US, a modest and shrinking lead over other countries. We estimate that quarterly output, measured in online code contributions, has increased by 3.6% because of this. Our evidence suggests that programmers using AI may also more readily expand into new domains of software development. However, experienced, senior-level programmers capture nearly all productivity and exploration gains, while we observe no significant benefits of AI adoption for early-career programmers. This widens, rather than closes, the skill gap.
, , , # Data from: Who is using AI to code? Global diffusion and impact of generative AI

Dataset DOI: [10.5061/dryad.3r2280gv0](10.5061/dryad.3r2280gv0)

## Description of the data and file structure

Data for replication of the paper \"Who is using AI to code? Global diffusion and impact of generative AI.\"

### Files and variables

#### File: final_data_2.zip

#### final_data directory

Reference for the inputs used by the notebooks and scripts.

All Excel workbooks have been flattened to per-sheet CSVs in `clean_csv/`, preserving values while removing formatting.

#### Top-level files

* `AI_010_data_uq.parquet` — User-quarter panel used for cross-checks in gender/experience analyses.
* `country_functions.csv` — Function-level AI share data with country codes and years (non-US sample).
* `full_countries.csv` — Country-level adoption estimates with corrected shares and standard errors by year.
* `functions.csv` — Function-level AI share data (US sample); used in diffusion/ttest and panel scr...