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85% of developers use AI regularly is a real JetBrains finding, but it describes 85% of 24,534 survey respondents—not 85% of every developer worldwide. JetBrains’ State of the Developer Ecosystem 2025 was conducted from April through June 2025 and measured reported regular AI use for coding and development, not necessarily daily use, autonomous agents, or higher productivity.
The headline is therefore directionally useful but easy to overstate. AI was mainstream among the developers who answered JetBrains’ survey, while the survey design and wording do not establish that most developers use AI every day, let AI write most of their code, or have achieved measurable quality and speed improvements.
Key takeaways
- JetBrains reported that 85% of 24,534 respondents regularly used AI tools for coding and development in its 2025 ecosystem survey.
- The survey covered respondents from 194 countries and regions and was conducted between April and June 2025.
- JetBrains reported a narrower 62% figure for respondents relying on at least one AI coding assistant, agent, or AI code editor.
- A separate January 2026 JetBrains AI Pulse survey reported 90% regular workplace AI use and 74% adoption of specialized developer AI tools, but the result is not a clean year-over-year comparison with 85%.
- AI adoption measures usage, not productivity, code quality, security, developer satisfaction, or return on investment.
What does “85% of developers use AI regularly” actually mean?
The phrase means that 85% of respondents to JetBrains’ State of the Developer Ecosystem 2025 said they regularly used AI tools for coding and development tasks. The wording does not mean that 85% of all professional developers worldwide use AI every day or use autonomous coding agents.
Three parts of the statistic matter:
- “Developers” means survey respondents. The denominator is the people who answered JetBrains’ survey after its data-cleaning process, not a census of everyone employed as a developer.
- “Regularly” is a survey category. Unless the underlying question and response categories are reproduced, “regularly” should not be silently translated into “daily,” “weekly,” or “for most coding tasks.”
- “AI tools for coding and development” is broad. The category can include general-purpose chatbots, IDE assistants, inline completion, AI code editors, terminal agents, and tools for documentation, debugging, testing, or code explanation.
A developer who asks a chatbot to explain an error every few days can fit the broad usage definition without using an autonomous agent or accepting AI-generated production code. Another developer may use an agent to edit multiple files, run tests, and prepare a pull request. The 85% figure places both patterns inside a broad adoption measure.
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Which JetBrains survey produced the 85% result?
The 85% result came from JetBrains’ State of the Developer Ecosystem 2025, an annual survey of the developer community. JetBrains reported that the survey gathered 24,534 developers from 194 countries and regions during fieldwork conducted from April through June 2025. The JetBrains 2025 survey report contains the headline AI-adoption result and related findings.
JetBrains said it cleaned the data by removing incomplete or suspicious responses. The company also said it weighted the results by geography, employment, programming languages, and JetBrains product use. Those steps can improve the balance of the final dataset, but weighting does not turn a voluntary online survey into a probability sample of the global developer population.
How representative was the JetBrains sample?
The JetBrains sample was large and geographically broad, but the sample was recruited through developer-oriented online channels and JetBrains’ own communication channels. That creates a meaningful representativeness limitation: people who are interested in software tools, JetBrains products, or online developer communities may be more likely to see and complete the survey than people outside those audiences.
JetBrains listed recruitment through Google Ads, X, Facebook, Instagram, Reddit, Quora, BiliBili, MaiMai, Zhihu, dev.to, TLDR, IT Media, and JetBrains communication channels. The JetBrains developer-survey methodology describes the recruitment, cleaning, and weighting approach.
JetBrains’ 2024 methodology also warned that JetBrains users may have been more willing, on average, to complete the survey. That does not invalidate the 85% result. It changes the safest wording from “85% of developers worldwide use AI” to “85% of surveyed developers reported regular AI use.”
| Question | What the survey supports | What it does not support |
|---|---|---|
| Who used AI? | 85% of JetBrains survey respondents reported regular use for coding and development. | 85% of every developer worldwide used AI. |
| How often? | Respondents selected a “regular use” category. | 85% used AI daily or continuously. |
| What kind of AI? | AI tools used for coding and development, broadly defined. | 85% used autonomous coding agents. |
| What was the outcome? | The survey measured reported adoption. | AI improved productivity, quality, security, or delivery speed. |
Why is the 62% figure important?
JetBrains also reported that 62% of respondents relied on at least one AI coding assistant, agent, or AI code editor. The 62% figure is narrower than the 85% broad-use result, and that distinction prevents the headline from being interpreted as evidence that nearly every respondent uses a dedicated coding product.
| Measure | JetBrains result | Interpretation |
|---|---|---|
| Regular AI use for coding and development in the 2025 ecosystem survey | 85% | Broadest measure; may include chatbots and lightweight assistance. |
| No AI adoption in daily work | 15% | Complements the 85% result within the reported survey framing. |
| Reliance on at least one AI coding assistant, agent, or AI code editor | 62% | Narrower measure focused on specialist developer tools. |
| Regular use of at least one AI tool at work in the January 2026 AI Pulse survey | 90% | Newer result from a different survey context. |
| Adoption of specialized developer AI tools in the January 2026 AI Pulse survey | 74% | More focused measure of coding assistants, editors, or agents. |
The gap between 85% broad AI use and 62% specialist-tool reliance suggests that general-purpose AI assistance is more widespread than adoption of dedicated coding agents or AI-native editors. The gap does not reveal how often respondents used each category or how much code AI generated.
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What is the newer 90% JetBrains result?
A separate JetBrains AI Pulse survey reported that 90% of developers regularly used at least one AI tool at work for coding and development in January 2026. The same report said 74% had adopted specialized AI tools for developers. JetBrains defines the relevant developer population in that reporting context to include software developers, AI and machine-learning engineers, DevOps or infrastructure developers, architects, data professionals, and QA engineers involved in programming.
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The January 2026 JetBrains AI Pulse report is newer, but the 90% result should not be presented as a precise increase from 85%. The reports used different survey contexts, samples, and reporting frames. JetBrains’ 2026 report combined or compared data from September 2025 and January 2026 AI Pulse surveys with earlier Developer Ecosystem Survey waves, so the figures are useful as separate signals rather than a clean trend line.
The most accurate current summary is: JetBrains found 85% regular AI use among respondents in its April–June 2025 ecosystem survey, while a separate January 2026 AI Pulse result reported 90% regular workplace use. Both indicate widespread adoption among the surveyed populations; neither is a census of all developers.
What kinds of AI tools can count as coding and development tools?
JetBrains’ broad category can cover several substantially different workflows:
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- General-purpose chatbots: Developers can ask for error explanations, API examples, SQL queries, regular expressions, or implementation ideas.
- IDE-integrated assistants: Chat, code explanation, documentation, refactoring, test generation, and contextual suggestions appear inside an existing development environment.
- Inline completion: An assistant predicts code while a developer types. This is often much lighter-weight than repository-wide agent work.
- AI-native code editors: These editors provide project-level context and can propose or apply changes across several files.
- Terminal and repository agents: Agents can inspect a codebase, modify files, run commands, execute tests, and sometimes prepare pull requests.
- AI features around the software lifecycle: Developers may use AI for commit messages, documentation, issue analysis, pull-request review, debugging, and code translation.
Those tools are not interchangeable. A team using AI to draft documentation has a different risk profile and potential benefit from a team granting an agent permission to change production code and execute shell commands.
Why do developers use AI?
Developers commonly use AI to reduce repetitive work or shorten the time needed to understand unfamiliar material. Plausible use cases include generating boilerplate, explaining existing code, debugging, writing tests, refactoring, documenting APIs, learning frameworks, translating code between languages, generating SQL or regular expressions, reviewing pull requests, and comparing implementation approaches.
JetBrains’ qualitative research describes developers delegating unpleasant or repetitive activities while retaining responsibility for writing and understanding software. The JetBrains qualitative research on software developers and AI is useful context, but qualitative observations should not be converted into unsupported percentages for the 85% survey result.
Adoption can also reflect convenience rather than complete trust. An assistant embedded in an IDE is easy to try, and a developer can use it for a low-risk explanation while rejecting its proposed code. Frequent use therefore does not necessarily mean that a developer believes every generated answer is reliable.
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No. The 85% figure measures reported adoption, not productivity. The statistic does not measure lines of code, time saved, defect rates, delivery speed, maintainability, developer satisfaction, or business outcomes.
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JetBrains’ separate 2026 workflow research found that AI users reported perceived improvements in some areas, while behavioral analysis did not show a corresponding change in one code-readability proxy. The research also found differences between perceived code-quality changes and observed behavior. The JetBrains AI workflow-impact study reinforces the distinction between what developers feel AI changes and what behavioral data demonstrates.
A useful way to interpret any AI-adoption statistic is to separate three questions:
- Adoption: Are developers using any AI tool?
- Intensity: How often do they use it, and for which tasks?
- Effectiveness: Does AI improve speed, quality, learning, reliability, or maintainability after review and rework are included?
The JetBrains 85% result primarily answers the first question and provides only limited information about the second. It does not answer the third.
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High adoption makes governance more important, not less. AI-generated output can be plausible but incorrect, introduce security vulnerabilities, use unsuitable libraries, misunderstand requirements, or create code that passes a narrow test while failing in production.
Developers and engineering teams should account for these risks:
- Incorrect output: AI can invent APIs, misunderstand repository conventions, or produce code that appears reasonable without being correct.
- Security exposure: Generated code may contain unsafe patterns, and agents with broad permissions can make consequential changes.
- Privacy and intellectual property: Proprietary code, credentials, personal data, or customer information may leave the organization depending on the provider, product settings, and plan.
- Review burden: A fast-generated change still requires testing, code review, dependency checks, and human ownership.
- Policy conflicts: Organizations may restrict third-party cloud AI, require disclosure of AI assistance, or prohibit certain repositories from being processed externally.
- Unpredictable costs: Credit-based plans, model multipliers, API usage, and agentic workflows can make heavy usage more expensive than a simple monthly subscription suggests.
- Weak requirements: AI can accelerate a poor implementation when requirements, tests, or acceptance criteria are incomplete.
Agentic tools deserve particular caution because they may inspect repositories, edit multiple files, run tests or shell commands, and create a larger change surface than inline completion. Permission prompts, sandboxing, sensitive-file exclusions, diff review, and easy rollback are practical safeguards.
Should a developer or team buy an AI coding tool?
The survey does not identify one winning product. A sensible purchase decision starts with workflow, governance, and cost rather than the adoption headline.
| Decision question | What to evaluate | Why it matters |
|---|---|---|
| Which environment does the team already use? | JetBrains IDEs, GitHub workflows, an AI-native editor, terminal tools, or several environments. | Integration usually matters more than a feature list. |
| What level of assistance is needed? | Autocomplete, chat, explanations, test generation, repository context, or autonomous multi-file changes. | Agentic capability brings greater potential and greater permission risk. |
| Can private code be processed? | Retention, training use, enterprise controls, auditability, administration, and data-location policies. | Security and compliance requirements may rule out otherwise attractive tools. |
| How is usage billed? | Flat subscription, included AI Credits, model multipliers, API pricing, quotas, and top-up rules. | Heavy users can face materially different costs from occasional users. |
| How will output be controlled? | Diff review, permission prompts, sandboxing, tests, secret handling, and rollback. | Controls reduce the chance that a plausible error reaches production. |
| Does the workflow need provider flexibility? | Bring-your-own API keys, local models, multiple model providers, or a managed vendor plan. | Provider choice affects cost, privacy, availability, and model access. |
JetBrains AI Assistant
JetBrains AI Assistant is the natural starting point for developers already working in JetBrains IDEs. JetBrains describes AI Assistant as a collection of IDE features available through JetBrains AI or alternative activation methods, including provider API keys and external agents.
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JetBrains’ licensing documentation lists AI Free, AI Pro, AI Ultimate, AI Enterprise, and trial tiers, with usage metered through AI Credits. The documentation retrieved for this article listed individual AI Pro at $10 per month, individual AI Ultimate at $30 per month, organization AI Pro at $20 per user per month, and organization AI Ultimate at $60 per user per month. Prices, quotas, territory availability, and plan structures can change, so buyers should verify the current JetBrains AI licensing and subscription terms before purchase.
JetBrains AI Assistant is a plausible fit for IDE-native chat, completion, refactoring, documentation, and related workflows. Developers who do not use JetBrains IDEs, or heavy agent users who need highly predictable usage costs, may prefer another setup.
Junie by JetBrains
Junie is JetBrains’ agent-oriented product for repository-level work. Its official page lists a free-to-start option, bring-your-own-key support with provider-rate pricing and no JetBrains markup, and AI Pro and AI Ultimate plans. The page retrieved for this article displayed annual individual prices of $100 per year for AI Pro and $300 per year for AI Ultimate; buyers should confirm current terms on the live Junie page.
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GitHub Copilot
GitHub Copilot offers a limited free tier and paid individual and organizational plans, with integrations across development environments and GitHub-centered workflows. GitHub’s official documentation covers Copilot plans and model and usage pricing.
Copilot is a strong candidate for teams already centered on GitHub repositories, issues, and pull requests or developers who want broad editor support. Exact plan prices and included AI-credit allowances should be checked immediately before publication or purchase because the pricing and model-credit structure can change.
Cursor and other AI-first editors
Cursor is an AI-first code editor that emphasizes project-level context and multi-file development with multiple coding models. Cursor may fit developers willing to change editors and prioritize an integrated AI workflow. Cursor may be a poor fit for teams standardized on JetBrains or other conventional IDEs, or organizations with strict editor and data-governance requirements.
JetBrains also documents supported ways to connect external agents, including Cursor and GitHub Copilot, in JetBrains IDE workflows. Some agent integrations may require JetBrains IDE 2026.1 or later. The JetBrains AI page and current product documentation should be checked for compatibility before deployment.
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How should teams measure whether AI is working?
Teams should not treat adoption as success by itself. A useful evaluation compares a defined baseline with AI-assisted work while counting review and rework.
- Measure cycle time from a well-defined task start to an accepted change, not merely time to first generated code.
- Track escaped defects, security findings, test quality, rollback frequency, and review time.
- Separate autocomplete, chat, and agent use because the workflows create different costs and risks.
- Record AI-credit, API, and subscription spending by team or project where policy permits.
- Ask developers whether AI reduced effort or simply shifted effort into verification and correction.
- Test representative repositories rather than relying on vendor demos or a single small example.
- Define which code and data may be sent to external providers and audit compliance with that rule.
A pilot should include clear acceptance criteria, repository permissions, approved models, secret-handling rules, and a rollback plan. The question is not whether developers can use AI; the question is where AI produces net value without weakening security, maintainability, or accountability.
What is the most accurate conclusion about the 85% statistic?
AI is mainstream in the developer workflows represented by JetBrains’ surveys, but the strongest defensible claim is about reported adoption. The 85% result is real, the 62% specialist-tool result is narrower, and the newer 90% AI Pulse result is a separate and more current signal.
The headline should not be rewritten as “85% of developers use AI every day,” “85% use autonomous coding agents,” or “AI has made 85% of developers more productive.” The evidence supports a more careful conclusion: JetBrains found that 85% of its 2025 survey respondents regularly used AI for coding and development, while survey design and tool definitions leave open how often they used AI, what they did with it, and whether the results improved software engineering.
Frequently Asked Questions
Is the 85% JetBrains statistic accurate?
Yes. JetBrains reported that 85% of respondents to its State of the Developer Ecosystem 2025 regularly used AI tools for coding and development. The result describes 24,534 survey respondents, not every developer worldwide, and it does not mean daily use or autonomous coding.
Did 85% of developers use AI coding agents?
No. The 85% measure covered AI tools for coding and development broadly. JetBrains reported the narrower figure of 62% for respondents relying on at least one AI coding assistant, agent, or AI code editor.
Did AI make 85% of developers more productive?
No. The 85% figure measures reported adoption, not productivity. The survey does not establish improvements in delivery speed, code quality, defect rates, maintainability, or business outcomes.
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Is the newer 90% JetBrains result directly comparable with 85%?
No. A separate January 2026 JetBrains AI Pulse survey reported 90% regular workplace AI use, but it used a different survey context, sample, and reporting frame. The two percentages should not be presented as a precise year-over-year trend without methodological qualifications.
What should a team check before buying an AI coding tool?
A team should check IDE and repository integration, data-retention and training policies, enterprise controls, permissions and sandboxing, review workflows, AI-credit quotas, model pricing, API costs, and whether the tool supports the required level of assistance—from autocomplete to autonomous multi-file agents.
The Bottom Line
Bottom line: JetBrains’ 2025 survey supports the statement that 85% of its surveyed developers regularly used AI for coding and development. It does not support claims of universal daily use, widespread autonomous coding, or proven productivity gains. Treat the number as a broad adoption signal, then evaluate tool intensity, effectiveness, security, and cost separately.
