Go devs lean on AI tools, but quality keeps satisfaction in check
Most Go developers are using AI coding assistants, but trust is still shaky. The 2025 Go Developer Survey shows overall satisfaction with AI tools at 55%, weighted heavily toward "Somewhat satisfied" (42%) rather than "Very satisfied" (13%).
The survey captured responses from 5,739 Go developers (September 2025) and was published January 21 on the go.dev blog.
Where AI coding tools fall short
The top complaint is blunt: 53% said these tools produced non-functional code. Another 30% said that even when the code ran, quality was weak. That's the core friction-speed without confidence.
What AI tools do well (today)
- Generating unit tests
- Writing boilerplate
- Enhanced autocompletion
- Refactoring support
- Documentation generation
These are high-leverage, low-risk tasks. Treat AI as a force multiplier for repetitive work, not as an architect.
Adoption and tool mix
53% of respondents use AI tools daily. 29% either don't use them or used them only a few times in the past month.
- ChatGPT: 45%
- GitHub Copilot: 31%
- Claude Code: 25%
- Claude: 23%
- Gemini: 20%
Go itself? Strong satisfaction
Go continues to deliver. Overall satisfaction hit 91%, with nearly two-thirds "very satisfied." One respondent summed it up: "Go is by far my favorite language; other languages feel far too complex and unhelpful. The fact that Go is comparatively small, simple, with fewer bells and whistles plays a massive role in making it such a good long-lasting foundation."
What teams are building with Go
- Command-line tools: 74%
- API/RPC services: 73%
- Libraries or frameworks: 49%
Top friction points in Go projects
- Ensuring code follows best practices/idioms: 33%
- Missing a feature they value in another language: 28%
- Finding trustworthy modules and packages: 26%
Dev environments and deployment
- Primary dev OS: macOS 60%, Linux 58%
- Deployment target: Linux-based systems 96%
- Editors: VS Code 37%, GoLand/IntelliJ 28%, Vim/NeoVim 19%
- Common deployment environments: AWS 46%, company-owned servers 44%, GCP 26%
Practical steps to boost AI-assisted code quality
- Scope AI to "safe" work: tests, boilerplate, docs, refactors. Keep protocols, core domain logic, and concurrency-heavy paths human-led.
- Require runnable examples in prompts and enforce compile/run checks in CI for AI-generated diffs.
- Pair AI with strict linters and formatters. Lock in a ruleset for idioms and style to reduce drift.
- Adopt golden tests for APIs and CLIs. If the AI changes behavior, tests will catch it.
- Create prompt playbooks for common tasks (HTTP handlers, DB repos, error patterns). Reuse what works.
- Track defects per AI-authored LOC. If quality slips, tighten usage to narrow tasks.
- Use code search and embeddings to give tools context from your repo. Less hallucination, more relevant suggestions.
The takeaway
Go is a reliable foundation. AI tools add speed, but only if you control where and how they're used. Treat them like a junior partner with guardrails, and you'll keep velocity without paying a quality tax.
If you're formalizing team skills around AI-assisted coding, this AI coding certification can help standardize workflows across the stack.
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