More developers than ever are utilizing AI tools to aid and produce code.

As enterprise AI adoption gains momentum, new insights from Stack Overflow’s 2025 Developer Survey reveal a critical oversight: the increasing technical debt generated by AI tools that produce “almost right” solutions, potentially compromising the productivity gains they are expected to offer.

Stack Overflow’s annual developer survey is among the largest of its kind each year. In 2024, the report indicated that developers were not concerned about AI replacing their jobs. Somewhat ironically, Stack Overflow initially faced challenges due to the rise of generative AI, leading to decreased traffic and subsequent layoffs in 2023.

The 2025 survey, involving over 49,000 developers from 177 countries, uncovers a troubling paradox in enterprise AI adoption. AI usage continues to climb—84% of developers now use or plan to use AI tools, up from 76% in 2024. Yet, trust in these tools has significantly declined.

AI Scaling Hits Its Limits

Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to learn how top teams are:

• Turning energy into a strategic advantage

• Designing efficient inference for real throughput gains

• Unlocking competitive ROI with sustainable AI systems

Secure your spot to stay ahead: https://bit.ly/4mwGngO

“One of the most unexpected findings was a notable shift in developer preferences for AI compared to previous years. While most developers use AI, they like it less and trust it less this year,” Erin Yepis, Senior Analyst for Market Research and Insights at Stack Overflow, told VentureBeat. “This response is surprising because, with all the investment in and focus on AI in tech news, I would expect that trust would increase as the technology improves.”

The statistics tell the story. Only 33% of developers trust AI accuracy in 2025, down from 43% in 2024 and 42% in 2023. AI favorability has decreased from 77% in 2023 to 72% in 2024, and now stands at just 60% this year.

However, the survey data reveals a more pressing concern for technical decision-makers. Developers cite “AI solutions that are almost right, but not quite” as their top frustration—66% report this issue. Meanwhile, 45% say debugging AI-generated code takes more time than anticipated. While AI tools promise productivity gains, they may actually introduce new categories of technical debt.

The ‘almost right’ phenomenon disrupts developer workflows

AI tools do not just create obviously flawed code. They generate plausible solutions that require significant developer effort to make production-ready. This creates a particularly troublesome productivity issue.

“AI tools seem to universally promise to save time and increase productivity, but developers are spending time addressing unintended workflow disruptions caused by AI,” Yepis explained. “Most developers say AI tools do not tackle complexity; only 29% believed AI tools could handle complex problems this year, down from 35% last year.”

Unlike clearly broken code that developers quickly identify and discard, “almost right” solutions demand careful scrutiny. Developers must understand the issues and how to resolve them. Many report it would be faster to write the code from scratch than to debug and correct AI-generated solutions.

The workflow disruption extends beyond individual coding tasks. The survey found that 54% of developers use six or more tools to complete their tasks. This adds context-switching overhead to an already intricate development process.

Enterprise governance frameworks lag behind adoption

Rapid AI adoption has outpaced enterprise governance capabilities. Organizations now face potential security and technical debt risks that they have not fully addressed.

“Vibe coding requires a higher level of trust in the AI’s output, sacrificing confidence and potential security concerns in the code for a faster turnaround,” Ben Matthews, Senior Director of Engineering at Stack Overflow, told VentureBeat.

Developers largely reject vibe coding for professional work, with 77% noting that it is not part of their professional development process. Yet, the survey reveals gaps in how enterprises manage AI-generated code quality.

Matthews warns that AI coding tools powered by LLMs can and do produce errors. He noted that while knowledgeable developers can identify and test vulnerable code themselves, LLMs sometimes fail to even recognize any mistakes they may produce.

Security risks compound these quality issues. The survey data shows that when developers still turn to humans for coding help, 61.7% cite “ethical or security concerns about code” as a key reason. This suggests that AI tools introduce integration challenges around data access, performance, and security that organizations are still learning to manage.

Developers still use Stack Overflow and other human sources of expertise

Despite declining trust, developers are not abandoning AI tools. They are developing more sophisticated strategies for integrating them into workflows. The survey shows 69% of developers spent time learning new coding techniques or programming languages in the past year. Of these, 44% used AI-enabled tools for learning, up from 37% in 2024.

Even with the rise of vibe coding and AI, the survey data shows that developers maintain strong connections to human expertise and community resources. Stack Overflow remains the top community platform with 84% usage. GitHub follows at 67% and YouTube at 61%. Most tellingly, 89% of developers visit Stack Overflow multiple times per month. Among these, 35% turn to the platform specifically after encountering issues with AI responses.

“Although we have seen a decline in traffic, it is not as dramatic as some might suggest,” Jody Bailey, Chief Product & Technology Officer, told VentureBeat.

That said, Bailey did acknowledge that times change and the day-to-day needs of users are not the same as they were 16 years ago when Stack Overflow got started. He noted that there is not a single site or company not seeing a shift in where users come from or how they are now engaging with generative AI tools. This shift is prompting Stack Overflow to critically reassess how it measures success in the modern digital age.

“The future vitality of the internet and the broader tech ecosystem will no longer be solely defined by metrics of success outlined in the 90s or early 00s,” Bailey said. “Instead, the emphasis is increasingly on the quality of data, the reliability of information, and the incredibly vital role of expert communities and individuals in meticulously creating, sharing, and curating knowledge.”

Strategic recommendations for technical decision-makers

The Stack Overflow data suggests several key considerations for enterprise teams evaluating AI development tools.

Invest in debugging and code review capabilities: With 45% of developers reporting increased debugging time for AI code, organizations need stronger code review processes. They require debugging tools specifically designed for AI-generated solutions.

Maintain human expertise pipelines: Continued reliance on community platforms and human consultation shows that AI tools amplify rather than replace the need for experienced developers. These experts can identify and correct AI-generated code issues.

Implement staged AI adoption: Successful AI adoption requires careful integration with existing tools and processes rather than wholesale replacement of development workflows. This allows developers to leverage AI strengths while mitigating “almost right” solution risks.

Focus on AI tool literacy: Developers using AI tools daily show 88% favorability compared to 64% for weekly users. This suggests proper training and integration strategies significantly impact outcomes.

For enterprises looking to lead in AI-driven development, this data indicates that competitive advantage will come not from the speed of AI adoption, but from developing superior capabilities in AI-human workflow integration and AI-generated code quality management.

Organizations that solve the “almost right” problem, transforming AI tools into reliable productivity multipliers rather than sources of technical debt, will gain significant advantages in development speed and code quality.