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AI for Documentation and Comments

Documentation is the task developers love to skip — and one of the first places they learned to trust AI. By 2025, 64% of developers reported using AI to help write documentation, making it one of the highest-adoption AI coding use cases, precisely because docs are tedious, time-consuming, and lower-stakes than shipping production logic. AI handles the full range: inline comments and docstrings (the most common and easiest to verify), API references, READMEs, architecture overviews, and plain-language explanations of unfamiliar code. The time savings are unusually clear-cut, since this is work that otherwise gets deferred or never done. But there’s a catch that AI doesn’t solve on its own: generating documentation is the easy part — keeping it accurate as the code changes is the hard part. Stale documentation, as one analysis puts it, lies with authority, and AI-generated docs still need human review for edge cases and complex logic. This page maps adoption, what AI documents, the productivity win, and the staleness trap.

4 visualizations 4 sources Last updated June 2026 Free to embed
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Source:  Google Cloud DORA 2025 State of AI-assisted Software Development (via IBM).
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Chart 1 · A top use case

Documentation is among the most common AI tasks

Documentation is among the most common AI coding tasks

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Source: Google Cloud DORA 2025 (via IBM): 64% of surveyed developers use AI to help write documentation — one of the highest-adoption AI coding tasks, because docs are tedious, time-consuming, and lower-risk than shipping logic. Documentation is where many teams first trust AI.
Chart 2 · What AI documents

Common AI documentation outputs

What AI documents (docstrings lead)

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Source: AIBI synthesis of tooling coverage 2026: AI handles four documentation jobs — inline comments & docstrings (most common, easiest to verify), API references, README/architecture overviews, and code explanations. Inline docstrings lead because they’re low-risk and instantly checkable. Illustrative weighting of use.
Chart 3 · Time saved

Documentation is a clear productivity win

Documentation is part of a 30–60% time saving

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Source: Industry productivity data 2026: developers report saving 30–60% of time on coding, testing, and documentation with AI — and documentation is often the single biggest relief, since it’s the work developers most often skip. The ROI here is unusually clear-cut. Self-reported ranges.
Chart 4 · The staleness trap

Generating docs is easy; keeping them true is hard

The staleness trap: the first draft is the easy 20%

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Source: Tembo / IBM 2026: the real problem isn’t the first draft — it’s drift. As one analysis puts it, generating docs “solved the easy 20%”; the hard 80% is keeping them accurate after the next ten PRs, because stale documentation lies with authority. AI-written docs still need human review for edge cases and complex logic. Qualitative — the key caveat.

About this data

This page compiles data on AI for code documentation, drawing on Google Cloud’s 2025 DORA State of AI-assisted Software Development report (via IBM), documentation-tooling analysis (Tembo, Index.dev), and industry productivity data.

The AI Behavior Index is the research arm of OneChat AI, an integrated multi-model AI platform. We compile and analyze data from primary research sources to make AI adoption and market trends more accessible to journalists, researchers, and decision-makers.

A note on what’s measured vs. illustrative: the 64% adoption figure is from DORA’s developer survey and is solid. The breakdown of what AI documents (inline comments vs. API refs vs. READMEs) is an illustrative synthesis of tooling coverage, not a measured task-share survey — we label it as such. The “staleness trap” is a well-documented qualitative finding, not a single statistic, but it’s the most important point on the page.

Methodology notes: the adoption figure is from DORA’s probability-based developer survey. Time-savings ranges (30–60%) are self-reported and bundle documentation with coding and testing, so the documentation-only share isn’t cleanly isolated. The “what AI documents” chart is an illustrative weighting of common capabilities, not a survey. The staleness/drift caveat is qualitative but widely echoed across independent tooling analyses.

Sources used on this page:

Corrections or suggestions: research@aibehaviorindex.org

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