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AI coding tools are widely used at work, but frequent use does not mean developers trust every answer. JetBrains reported that 90% of developers in its survey regularly used at least one AI tool for coding and development tasks in January 2026; in a separate 2025 survey, Stack Overflow found that 46% of respondents did not trust AI output accuracy. Those figures describe different surveys and populations, but together they capture the central tension: adoption is high, while verification still matters.
What the surveys say about AI use and trust
| Finding | Source and scope | What it does—and does not—show |
|---|---|---|
| 90% regularly used at least one AI tool for coding and development tasks at work in January 2026. | JetBrains’ 2026 AI Pulse reporting, published in its April 2026 analysis. | A high rate of self-reported regular use among the report’s defined developer roles; not a census of all developers. |
| 46% said they did not trust AI output accuracy, up from 31% in 2024. | Stack Overflow’s 2025 Developer Survey and accompanying 2025 release. | Reported skepticism about accuracy; it is not a measure of how often respondents use AI. |
| Claude Code was the most-used AI coding tool for 31% of developers. | JetBrains’ 2026 report on AI coding-agent adoption. | A survey-specific “most-used” result, not a measure of universal market share. |
| 35% visit Stack Overflow for AI-related issues at least some of the time. | Stack Overflow’s 2025 Developer Survey summary. | A finding about visiting the site for AI-related help, not a measure of AI tool adoption or answer quality. |
The percentages should not be compared as if they came from one poll. JetBrains and Stack Overflow asked different questions, in different survey cycles, and the figures capture distinct things: regular workplace use, trust in output accuracy, a tool’s status as most-used, and visits to a developer Q&A site.
Who counts as a developer in the 90% finding?
JetBrains’ definition covers respondents in roles including developer, programmer, or software engineer; AI or machine-learning engineer; DevOps or infrastructure developer; architect; data scientist, data engineer, or data analyst; and QA engineers involved in programming. The 90% figure refers to respondents in those role categories who said they regularly used at least one AI tool for coding and development tasks at work in January 2026.
That scope is broader than only people who write application code as their primary job. The result is useful as a snapshot of AI use across a defined set of technical roles, but it should not be read as a precise estimate for every developer, workplace, or country.
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Which AI coding tools do developers report using?
JetBrains’ 2026 reporting names Claude Code, Cursor, JetBrains AI Assistant, Junie, GitHub Copilot, OpenAI Codex, and Google Antigravity among tools in use. The evidence supports identifying these products as tools developers reported using; it does not establish that they are equivalent in features, reliability, privacy, or price.
Claude Code’s “most-used” result
In JetBrains’ 2026 agent-adoption reporting, 31% of developers named Claude Code as their most-used AI coding tool. “Most-used” is a respondent-level designation in that survey; it does not mean that 31% of all developers use Claude Code regularly, nor that the product holds 31% of the overall market.
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JetBrains also reported that 39% of GitHub Copilot users use Copilot, among other surfaces, in JetBrains IDEs. This is a finding about where some Copilot users work with the tool. It is not a ranking against the other named products or evidence that all Copilot use occurs inside JetBrains IDEs.
How broad is the Stack Overflow survey?
Stack Overflow says its 2025 Developer Survey received more than 49,000 responses from 177 countries and covered 314 technologies. That reach makes it a substantial survey of developer experiences and technology use, but response totals alone do not prove that every region, role, or technology community is represented equally. Its trust result is best read as what respondents reported, not as a population-wide measurement of AI accuracy.
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The finding that 35% of developers visit Stack Overflow for AI-related issues at least some of the time adds a community-support dimension. It says developers use the site for help with AI-related problems; it does not tell us whether their questions concern generated code, AI products, or another aspect of AI in software work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What workflow studies can—and cannot—tell us
Survey answers show what people say they use or trust. A different kind of evidence comes from JetBrains Research’s longitudinal work: its 2026 description says researchers analyzed two years of log data from 800 software developers alongside survey and interview responses. A related publication describes two years of fine-grained telemetry from 800 developers and a survey of 62 professionals.
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Longitudinal logs can show patterns in developer workflows over time, complementing a one-time survey response. But the accessible study summaries do not establish a single causal result that can be safely stated as “AI makes developers faster” or “AI improves code quality.” Tool adoption figures alone cannot prove those outcomes either. Speed, quality, and delivery depend on what is measured and the context in which a tool is used.
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What developers and teams should take from the numbers
- Separate use from trust. Regular use can coexist with doubts about accuracy; the JetBrains adoption finding and Stack Overflow trust finding measure different dimensions.
- Check the metric before comparing tools. “Regularly uses an AI tool” and “names a tool as most-used” are not interchangeable measures.
- Treat generated output as something to verify. The reported trust concern makes review a practical part of AI-assisted work, rather than an assumption that frequent adoption signals correctness.
- Do not infer productivity or quality gains from adoption. The cited survey findings describe reported usage and attitudes, while the study summaries describe research methods; they do not establish a universal productivity or quality effect.
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