botcommits.dev

Tracking AI-generated commits across all public GitHub repos. Updated monthly from GH Archive via BigQuery.

AI-Attributed Commits

Repos with AI Commits

Developers Using AI

AI Share of Pushes

The Iceberg: Public Repos Are Only 18.5% of GitHub Activity

~28M

Estimated total Claude commits/month

(public + private extrapolation)

81.5%

of GitHub contributions are in private repos

Source: GitHub Octoverse 2025

Copilot + Cursor commits (unattributed)

No attribution markers — invisible to this method

Methodology: We observe 5.19M Claude commits in Feb 2026 from public repos. Since 81.5% of GitHub activity is private, and assuming similar AI adoption in private repos, the total is estimated at 5.19M ÷ 0.185 ≈ 28M commits/month. This is speculative — enterprise adoption may be higher or lower than public repo rates. Copilot and Cursor leave no commit-level attribution markers and are excluded entirely.

AI-Attributed Commits per Month (Oct 2025 = incomplete GH Archive data)

Commits by AI Tool

AI vs Human: Estimated Code Volume (commits + diff size adjustment)

Denominator: total push events from BigQuery (not total commits — each push averages ~3 commits). Diff size adjustment: AI commits have ~2.5x larger diffs than human commits (per published research), so the code volume share is estimated at 2.5x the raw commit share. Both lines shown. Copilot/Cursor are excluded (no attribution markers) — real AI share is higher.

Model Fit: Exponential vs Logistic vs Gompertz (+ 6-month projection)

Exponential y = a·e^bt — R²=0.989, AIC=314.7

Logistic y = L/(1+e^-k(t-t₀)) — R²=0.989, AIC=316.7, ceiling hit upper bound

Gompertz y = L·e^{-e^-k(t-t₀)} — R²=0.981, AIC=323.8, ceiling ~641M

Best fit by AIC: Exponential. The logistic ceiling converged to the upper bound (~1B), meaning the data does not yet show saturation. With only 14 months of observations and a data quality gap at month 10, we cannot distinguish exponential from early-stage logistic. This does not predict indefinite exponential growth — it means the inflection point has not yet been observed.

Growth Rate (MoM %)

Acceleration (Change in Growth Rate)

Cumulative AI-Attributed Commits (Jan 2025 - Mar 2026)

Tool	Total Commits	Peak Month Repos	Peak Month Devs

Methodology & Data Notes

Data sources

Jan - Oct 2025: GH Archive via Google BigQuery. Exact commit-level counts from PushEvent payloads.
Nov 2025 - Feb 2026: GitHub Search API (approximate). GH Archive stopped including commit message payloads in Nov 2025, reducing table size from ~400 GB/month to ~89 GB. We fall back to GitHub's commit search endpoint, which returns total_count estimates that may differ from exact counts.
Mar 2026*: Forecast. 1,723,910 commits observed in 8 days (Mar 1-8), linearly extrapolated to 31 days.

Detection signals

Claude Code: Co-Authored-By: Claude ... @anthropic.com trailers, Generated with [Claude Code] body markers, noreply@anthropic.com author email.
Aider: aider: prefix in commit messages, Co-Authored-By: aider trailers.
OpenAI Codex CLI: Co-Authored-By: ... @openai.com, noreply@openai.com author email.
Devin: Jan-Oct: devin-ai-integration actor/author in BigQuery. Nov+: co-authored-by devin-ai via GitHub Search API (author-based search returned 0; co-author trailer search works). Detection method differs between periods.

Known limitations

Oct 2025: GH Archive data appears incomplete for this month (61M push events vs ~71M in adjacent months). Commits counts are artificially low. This month is excluded from growth rate and acceleration calculations.
Nov 2025 growth rate: Since Oct is excluded, Nov's MoM growth is estimated as the average monthly growth from Sep to Nov (two-month span halved).
Growth rate charts start at Apr 2025: The first 3 months (Jan-Mar) have extreme MoM percentages (e.g., 8,800% Jan to Feb) due to near-zero base counts. These are excluded from the growth rate and acceleration charts to keep the scale readable.
GitHub Copilot and Cursor do not leave commit-level attribution markers, so they are invisible to this methodology. The numbers here represent a lower bound of AI-assisted coding.
Only public repos are tracked. Private repo AI usage is not measurable via GH Archive or the Search API.

Model fitting

Three models fitted to Claude commit counts using scipy.optimize.curve_fit (nonlinear least squares): Exponential (2 params), Logistic (3 params), Gompertz (3 params).
Oct 2025 excluded from fitting due to incomplete data. 13 data points used.
Model comparison via AIC (Akaike Information Criterion): lower = better fit with parsimony penalty for extra parameters.
Exponential wins (AIC=314.7 vs 316.7 logistic, 323.8 Gompertz). The logistic ceiling parameter converged to its upper bound (~1B), meaning the optimizer found no evidence of saturation in the data. Effectively, the logistic degenerates to an exponential with an extra unused parameter.
This does not predict indefinite exponential growth. It means the inflection point of the S-curve has not yet been observed with 14 months of data. More data points are needed to distinguish the models.
Projections beyond Feb 2026 are extrapolations and should be treated with appropriate skepticism. The exponential projects 104M commits/month by Aug 2026; the Gompertz projects 40M. The true value likely falls between them.