2025 Stack Overflow Developer Survey: developers remain willing but reluctant to use AI

Paper · Source
AI at Work

Source: Stack Overflow · 2025-07-29

No need to bury the lede: while the adoption of AI tools continues to increase, so does developers’ lack of trust in the output of those tools.

Trust but verify? Developers are frustrated, and this year’s results demonstrate that the future of code is about trust, not just tools. AI tool adoption continues to climb, with 80% of developers now using them in their workflows.

Yet this widespread use has not translated into confidence. In fact, trust in the accuracy of AI has fallen from 40% in previous years to just 29% this year. We’ve also seen positive favorability in AI decrease from 72% to 60% year over year. The cause for this shift can be found in the related data:

The number-one frustration, cited by 45% of respondents, is dealing with "AI solutions that are almost right, but not quite," which often makes debugging more time-consuming. In fact, 66% of developers say they are spending more time fixing "almost-right" AI-generated code. When the code gets complicated and the stakes are high, developers turn to people. An overwhelming 75% said they would still ask another person for help when they don’t trust AI’s answers.

69% of developers have spent time in the last year learning new coding techniques or a new programming language; 44% learned with the help of AI-enabled tools, up from 37% in 2024.

36% of developers learned to code specifically for AI in the last year; developers of all experience levels are just starting to invest time in AI programming.

The adoption of AI agents is far from universal. We asked if the AI agent revolution was here, and the answer is a definitive "not yet." While 52% of developers say agents have affected how they complete their work, the primary benefit is personal productivity: 69% agree they've seen an increase. When asked about "vibe coding"—generating entire applications from prompts—nearly 72% said it is not part of their professional work, and an additional 5% emphatically do not participate in vibe coding. This aligns with the fact that most developers (64%) do not see AI as a threat to their jobs, but they are less confident about that compared to last year (when 68% believed AI was not a threat to their job).

In an era of AI-generated answers, the need for real human connection has never been more apparent. For the first time we asked about community platforms, and the results show that developers rely on a portfolio of resources, with Stack Overflow (84%), GitHub (67%), and YouTube (61%) leading the pack.

When developers visit Stack Overflow, their top-ranked activity is reading comments, showing a deep interest in human-to-human context. It’s why we’re investing in features that create more ways to cultivate community and power learning.

There is an emerging role for Stack Overflow: Serving as the human-verified source of truth for AI-generated code. About 35% of developers report that some of their visits to Stack Overflow are a result of AI-related issues.

Programming languages that are growing in popularity are also known to be AI-compatible: Python usage is up 7 percentage points, followed by Rust and Go (+2 percentage points), all of which are used in AI development and infrastructure now.

Developers learning to code in the past year are continuing to use technical documentation more than other resources (68%) and are using AI tools more than they were last year (44%).

Developers are adapting their existing monitoring tools for agentic AI monitoring and observability with tools like Sentry (32%) and New Relic (13%), which have both been around for 20+ years.

For the first time this year we asked about specific LLMs instead of asking about AI search and development tools generally. We see OpenAI chat models still retain the most usage among developers (81%). Anthropic’s Claude Sonnet models are used more by professional developers (45%) than by those learning to code (30%).

Developers are not just learning to code; they are learning to code for AI: 67% of developers indicated they were learning to code for AI in the workplace or on personal projects.

The survey reveals a developer workforce that is largely staying put, but not necessarily content. 46% of developers are "not looking" for a new job, but of those who are in a role, a combined 75% describe themselves as "complacent" or "not happy at work." Overall, there is an increase in happy developers compared to last year (24% vs. 20%).

What contributes to job satisfaction? It's not just about the tech. The top drivers are "autonomy and trust," "competitive pay," and "solving real-world problems." This focus on fundamentals is also reflected in what makes developers endorse a new technology. A "reputation for quality" and a "robust and complete API" rank far higher than "AI integration," which came in second to last. The message is clear: Developers value tools that are reliable, functional, and solve real problems over those that simply ride the latest technology wave.

Autonomy and trust at work were ranked highest for reasons to be happy at work, but competitive pay, ranked second, was frequently ranked first, too.

This year’s survey paints a picture of a community navigating the complexities of a new technological era. Developers are ready to push back on enterprise AI through a nuanced conversation about trust, reliability, and the enduring value of human expertise.

Lines of inquiry this paper opens 24

Research framings built by reading the notes related to this paper — the questions it feeds into.

Do AI coding tools measurably improve developer productivity and code quality? How does AI adoption reshape collaboration patterns in knowledge work? Does AI deployment reduce or exacerbate workplace inequality and income instability? How do hallucinated citations emerge in AI scholarly output? Why do standard evaluation practices obscure safety-critical AI failures? Does AI assistance erode cognitive skills while inflating perceived competence? Why do confident AI outputs mislead human trust calibration? How do users confuse explanation quality with actual system accuracy? Why do people trust AI chatbots with sensitive information? How do educators verify student capability when AI can produce indistinguishable work?