Go developers are using AI coding tools, but many are not convinced by the code they produce. In the Go team’s 2025 survey, 53% of respondents used AI-powered development tools daily, yet just 13% were very satisfied with them. The clearest divide is between AI’s usefulness for bounded, repetitive work and developers’ concerns about correctness, quality and consistency.
What the Go developer survey found
The Go team’s 2025 survey ran from September 9 to 30, 2025. It received 7,070 responses and retained 5,379 after cleaning. The respondents skewed toward experienced professionals: 87% were professional developers, 82% used Go in their primary job, and 75% had at least six years of professional development experience. These are survey results, not a probability sample of every Go developer: participation was public and self-selected, supplemented by randomized in-product invitations to VS Code and GoLand users. Percentages are rounded. Go team survey report
Use was broad, but not universal: 53% said they used AI-powered development tools daily, while 29% used them at most a few times in the past month or not at all. Overall satisfaction was 55%, but that figure conceals a modest level of enthusiasm: 42% were somewhat satisfied and 13% very satisfied. Usage findings · Satisfaction findings
Why satisfaction is lukewarm
The main complaint was output quality. In the survey, 53% identified non-functional code as their main problem, and 30% said that even code that worked was poor quality. Those are different failure modes: one demands debugging or replacement; the other may pass a basic test but still impose a review and maintenance burden.
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Respondents described inconsistency with their preferred practices and difficulty generating complex features in larger codebases. One financial-services respondent said: “I’m never satisfied with code quality or consistency, it never follows the practices I want to.” Another said tools could explain code effectively but struggled to generate complex features in codebases of 10,000 or more lines. Those comments illustrate individual experiences; they are not measured benchmarks. Go team survey responses
Where Go developers find AI useful
The survey points to practical, bounded tasks rather than hands-off software development. Developers cited generating unit tests and boilerplate, autocomplete, refactoring, documentation, and answering questions about APIs or configuration. These uses either reduce repetitive typing or help a developer find and apply information; the engineer can still check the result against the codebase and requirements. Go team survey responses
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That pattern helps explain how frequent use can coexist with reservations. A tool can save time on a first draft or locate an API detail without being reliable enough to own a feature end to end. The survey’s overall satisfaction number should therefore not be read as a verdict that AI is either broadly successful or broadly useless.
Are Go developers using AI to write code?
Views on AI-written code are divided. The survey found 66% were already using, or hoped to use, AI for writing code, while 25% did not want AI involved. These figures capture differing intentions and attitudes, not a measure of how much production code is AI-generated or how often it ships unchanged. Go team survey findings
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Agentic coding—letting an AI tool take a more active role in completing development work—was not the primary mode for most respondents. Seventeen percent said it was their primary mode, while 40% tried agentic modes occasionally. That suggests experimentation without broad reliance as the default workflow. Go team survey findings
Which AI assistants did Go developers use?
The survey’s named assistants included ChatGPT, GitHub Copilot and Claude. InfoWorld’s summary of the results reports ChatGPT at 45%, GitHub Copilot at 31%, Claude Code at 25%, Claude at 23%, and Gemini at 20%. These are reported usage shares, not a head-to-head quality ranking; the figures do not establish which assistant writes the most correct Go code or fits a particular project best. InfoWorld summary
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The Go team warns that changes in methodology make direct comparisons between assistant use in 2024 and 2025 imperfect. Treat the listed shares as a snapshot of this survey, not a clean year-over-year measure of market growth. Go team survey report
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge an AI tool for a Go project
The survey does not rank assistants or test their code. For a project-level choice, use it as a reason to evaluate the work you actually expect the tool to do, rather than assuming popularity equals quality. Compare candidates on:
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- Correctness and review burden: Can reviewers verify generated changes, and how often does code fail tests or need substantial revision?
- Task fit: Does it help more with repetitive work such as tests and boilerplate, or with the complex features your team needs?
- Information help: Are its answers about APIs and configuration relevant to your dependencies and project context?
- Editor integration: Does it work smoothly in the IDE or editor your developers already use?
- Level of autonomy: Does supervised completion suit the team better than an agentic mode that takes multiple steps?
- Fit with local conventions: Does it follow the project’s established Go practices, interfaces and patterns?
Use a small, representative task set and review the results against the same criteria. Keep human review and the project’s normal checks in place, particularly for changes that affect behavior or span several packages. The survey supports caution about quality, but does not establish that every tool or Go project has the same limitations.
The dissatisfaction is about AI tools, not Go
The contrast with developers’ views of Go itself is pronounced: 91% were satisfied with Go, including almost two-thirds who were very satisfied. By comparison, 13% were very satisfied with AI-powered development tools. The survey’s “meh” signal is therefore specific to AI assistance; it is not evidence that respondents are dissatisfied with the language. Go satisfaction findings · AI-tool satisfaction findings
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