AI Coding Assistant Statistics: Market Data, Adoption and Trends for 2026

You are putting an AI coding assistant statistic into a budget request, board deck, or product brief. Then you find 90% adoption in one source, 62% in another, and market estimates that differ by billions of dollars. None of those figures is automatically wrong—but they often describe different people, products, and questions.

The short answer: AI coding assistant use is widespread in 2026, but there is no single defensible adoption or market-share figure. The most useful statistics separate broad AI-tool use from dedicated assistants and coding agents, vendor-reported users from paid seats, and self-reported productivity from tested code-quality results.

Key AI coding assistant statistics for 2026

• 84% of respondents to the 2025 Stack Overflow Developer Survey said they were using or planning to use AI tools in their development process. The survey reports 47.1% daily use among 33,662 all respondents and 50.6% daily use among 26,004 professional developers. This is a use-or-intent measure, not a count of daily coding-assistant users. Stack Overflow’s 2025 survey

• 62% of the 24,534 developers across 194 countries surveyed by JetBrains from April through June 2025 said they relied on at least one AI coding assistant, agent, or AI-enabled code editor. In the same survey, 85% said they regularly used AI tools for coding and development. JetBrains’ 2025 Developer Ecosystem survey

• 90% of more than 15,000 professional developers worldwide in JetBrains’ May–July 2026 fieldwork said they used AI coding agents at work at least weekly, while 68% said they used them daily. This is a coding-agent measure, not the same question as JetBrains’ 2025 assistant, agent, and editor measure. JetBrains’ 2026 coding-agent research

• 90% of the nearly 5,000 technology professionals globally surveyed for Google’s 2025 DORA report reported AI adoption, and the median respondent spent two hours per day working with AI. This population includes software roles beyond developers. Google’s 2025 DORA report summary

• Microsoft disclosed 50 million GitHub Copilot users in its FY2026 Q4 earnings call. Microsoft did not define those users as paid subscribers, monthly active users, or daily active users, so this is a vendor-reported user count—not market share. Microsoft’s FY2026 Q4 earnings call

• In a Spring 2026 task-based protocol covering 80 coding tasks, four languages, and four critical vulnerability types, Veracode reported that 55% of generation tasks produced secure code while syntax correctness exceeded 95%. This is a defined test result, not a universal rate for AI-generated code. Veracode’s Spring 2026 update

• Mordor Intelligence estimates the broader AI Code Generation and Developer Assistant Market at USD 16.13 billion in 2026. Its proprietary model forecasts USD 78.97 billion by 2031 and includes code generation, autocompletion, agentic workflow orchestration, DevOps/CI/CD, and data-science applications. Mordor Intelligence’s market estimate

• Future Market Insights estimates the narrower AI Code Assistant Market at USD 4.1 billion in 2026, forecasting USD 6.9 billion by 2036 at a 5.3% CAGR. It excludes general-purpose chat AI without code-specific features, non-AI static analysis, non-developer low-code/no-code tools, and standalone LLM API access for non-coding use. Future Market Insights’ market estimate

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Why AI coding assistant adoption has more than one answer

The adoption figure you use should match the question you are trying to answer. “Using or planning to use AI tools,” “regularly using AI for coding,” and “using a coding agent daily” describe different levels of engagement.

Stack Overflow’s 84% measure is broadest because it combines current users with people who plan to use AI tools. It is useful when you need a signal of developer interest and exposure, but it cannot tell you how many respondents depend on a dedicated assistant every day.

JetBrains’ 2025 survey gets closer to the dedicated-product question. Its 62% measure explicitly includes AI coding assistants, agents, and AI-enabled editors. Even so, it should not be treated as a direct replacement for Stack Overflow’s use-or-intent result: the populations, fieldwork, and wording differ.

DORA measures a different workplace slice again. Its nearly 5,000 respondents were technology professionals globally, spanning roles from developers to product managers. You can use its 90% figure to describe AI adoption across software-development work, but not as evidence that 90% of developers use a coding assistant.

For a presentation or planning model, keep these categories separate:

1. Broad AI-tool use or intent measures exposure and planned use.

2. Regular AI use for coding measures workflow adoption.

3. Dedicated assistant, agent, or AI-editor use measures category penetration more directly.

4. Daily agent use measures frequency, not necessarily paid adoption.

5. Vendor-reported users measure one company’s disclosed reach.

6. Commercial market estimates measure an analyst-defined revenue category.

That separation is more useful than forcing an average that has no consistent denominator.

How widely are coding agents used at work?

JetBrains’ 2026 survey offers the clearest current coding-agent measure in this source set: 90% of more than 15,000 professional developers worldwide reported using AI coding agents at work at least weekly, and 68% reported daily use. The fieldwork ran from May through July 2026.

The same survey reports workplace adoption for selected tools. These percentages are not mutually exclusive: a developer can use more than one tool. They are also not paid-seat share, revenue share, or overall market share.

Stack Overflow provides a second, narrower tool-use view. In its selected cohort of 2,689 respondents who use or develop AI agents—5.5% of the survey—81.7% reported ChatGPT, 67.9% GitHub Copilot, 47.4% Google Gemini, and 40.8% Claude Code. Those are tool-use percentages inside a selected AI-agent cohort, not a ranking of the whole developer market. See the AI-agent tool cohort in Stack Overflow’s survey

If you need a practical adjacent view of how AI-produced software changes testing needs, Quash’s vibe coding statistics and risks guide covers that distinct topic. It should not be used as evidence for category-wide coding-assistant adoption.

Vendor scale does not equal AI coding assistant market share

Microsoft’s disclosure of 50 million GitHub Copilot users is a significant vendor-scale signal. It does not establish the number of paid customers, active users, developers using Copilot daily, or GitHub Copilot’s share of all coding-assistant use.

Microsoft also said usage-based billing had been introduced and Copilot revenue accelerated by more than 60% quarter over quarter in FY2026 Q4. That is a company disclosure about Copilot’s commercial momentum, not a category revenue total.

GitHub’s Octoverse data adds platform context. GitHub reports more than 180 million developers on its platform, says 80% of new developers on GitHub used Copilot in their first week, and reports more than 1.1 million public repositories using an LLM SDK. These are GitHub platform measures, not a representative sample of all developers. GitHub’s Octoverse report

Use vendor figures for what they are: disclosed scale within a vendor’s ecosystem. Do not turn them into market share unless a source measures the total market and defines its numerator and denominator.

What productivity, trust, and code-quality data shows

High use does not mean uniform confidence. Stack Overflow found that 46% of respondents actively distrusted AI-tool accuracy in 2025, while 33% trusted it and 3% highly trusted it. Those are trust measures, not adoption measures; a developer can use an AI tool and still verify its output closely.

DORA reports a similar tension from a different population. More than 80% of its 2025 respondents said AI enhanced productivity, and 59% reported a positive influence on code quality. Both are self-reported perceptions from technology professionals, not controlled measurements of output or defect rates.

A 2026 IBM Research paper gives a more focused, but much smaller, example. Among 57 responses collected in May 2025 at one technology company, 44 respondents reported at least a 25% productivity increase from AI coding assistants and 26 reported at least a 50% increase. The evidence is self-reported and single-organization, so it cannot support a market-wide productivity claim. IBM Research’s empirical study

The IBM paper also reviewed 35 prior surveys and found that much of the evidence base emphasizes immediate productivity or single tasks rather than long-term maintainability, software quality, and organizational outcomes. That limitation matters when you are deciding whether a fast-looking prototype is ready for production.

Veracode’s Spring 2026 protocol supplies a different evidence type: task-based security testing. Across its 80 generation tasks, it reported secure-code pass rates of 62% for Python, 58% for C#, 57% for JavaScript, and 29% for Java. Its baseline setup did not include security-specific guidance. The result does not prove that AI coding assistants are inherently insecure; it shows why syntax correctness and secure behavior must be evaluated separately.

Why 2026 market-size estimates differ

The two prominent 2026 estimates in this report cannot be averaged into a single “AI coding assistant market size.” They define the category differently.

Mordor’s larger figure reflects a wider revenue universe, including code generation, autocompletion, agentic orchestration, DevOps/CI/CD, and data-science and analytics applications. Future Market Insights draws a narrower boundary around code-specific tools that help with writing, reviewing, testing, and debugging.

The conclusion is not that either estimate is “the” market size. If you are sizing a broad developer-AI opportunity, Mordor’s category may fit the question. If you are discussing tools specifically built to assist code work, FMI’s narrower definition may be more relevant. State the source’s category name alongside the number either way.

What the public record still does not measure

No first-party Quash data covers AI coding-assistant adoption, revenue, productivity, or security outcomes. Quash has no published telemetry, recurring bug-pattern dataset, customer language, or completed experiment that could produce a proprietary statistic for this report.

That absence matters because the public record cannot responsibly be combined into a Quash-derived total. Survey denominators and wording differ; Microsoft does not define what its Copilot user count represents; commercial researchers draw different revenue boundaries; DORA and IBM describe self-reported outcomes; and Veracode reports a defined testing protocol rather than everyday production behavior.

The public sources are therefore strongest when you use them to answer narrow questions: How common is AI use among a stated survey population? How often do surveyed professionals use agents? What did a vendor disclose about its product? What happened in a specified security test? Where a source cannot answer your exact question, do not extend the claim beyond its evidence.

Methodology and source notes

This report prioritizes primary survey owners, vendor disclosures, and the original research paper. Market sizing comes from commercial estimates and remains labelled as such.

The figures were selected using five rules:

1. Preserve the population. Professional developers, all respondents, and technology professionals are different groups.

2. Preserve the question. Planned use, regular use, weekly use, and daily use cannot be substituted for one another.

3. Preserve the evidence type. A self-report, a vendor disclosure, a commercial estimate, and a task-based protocol answer different questions.

4. Keep forecast periods visible. A 2031 projection and a 2036 projection are forecasts, not current 2026 market revenue.

5. Avoid reconstructed totals. This report does not add survey results, vendor users, or market estimates to manufacture a category total.

Conclusion

AI coding assistant statistics point to a clear direction: AI is embedded in many developers’ workflows, and coding-agent use is frequent among the professional developers surveyed by JetBrains in 2026. They do not support a single adoption rate, market-share figure, or productivity promise for the entire category.

When you cite a number, match it to the decision in front of you. Use a survey for a defined population, a vendor disclosure for vendor scale, a test protocol for a tested outcome, and a market estimate only with its category boundary. That choice will make your argument more useful—and much harder to misquote.