[Ed. note: While we take some time to rest up over the holidays and prepare for next year, we are re-publishing our top ten posts for the year. Please enjoy our favorite work this year and we’ll see you in 2026.]

The Stack Overflow Developer Survey is full of new insights about technology, tools of the trade, community, careers, and more from 49,000+ developers from around the world, and we’re eager to share how the data stacks up this year.

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.

The effect of AI on the developer ecosystem is everywhere in this survey, from the programming languages developers are using and want to use to the influence of AI on the tools developers used this year, as well as preferences for community platforms and content.

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.

This validates our vision to be the world’s most vital source for technologists by providing trusted, human-verified knowledge everywhere developers work. The data shows a clear demand for this:

• 35% of developers use 6-10 distinct tools to get their work done, highlighting the need for seamless integration.
• 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.

The technology-focused questions got a major upgrade this year, but standard questions regarding programming languages, operating systems, and how developers learn to code stayed the same. New questions this year feature LLM models, agentic AI tools, and top frustrations with AI. From the old guard, we see the influence of AI in a few key areas:

• 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.
• Android is the preferred operating system for personal use for 29% of developers. This is an increase of 11 percentage points since last year and has boosted Android personal use above Ubuntu for the first time in the survey. While this may not be directly related to AI, Android is known for having a more open-source platform that may allow developers flexibility to cater the amount of AI and the types of AI used on this OS.
• 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%).

New questions asked this year surface technologies that have found a niche in the AI space:

• Among AI agent data storage tools, separately from databases we’ve asked developers about since 2017, respondents show a preference for Redis (43%) alongside GitHub MCP server (43%). While Redis has been on the survey as a database option since 2017, this year it shines as the top choice for AI agent data storage.
• 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.

Developer salary shows an increase in pay: This year, we have seen an increase in median pay for 20 of the roles we asked about, ranging from 5-29% compared to last year’s reported salaries per developer role.

• The US was highest for job satisfaction (29%) and Germany was lowest (19%). We know from previous analysis that job satisfaction is tied to pay and job flexibility.
• 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.
• US median salaries are higher than other countries’ median salaries. For example, compared to Germany, US cloud infrastructure engineers earned a 48% higher median salary in the past year.
• The US recorded almost twice as many remote workers (45%) as Germany (23%).

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.

The next generation of developer tools, who developers are, where they are working, and what they look for in developer communities is documented here. This data serves as a critical reminder that the future of technology will be built not just on powerful tools, but also on the trusted communities that use them.