The 2025 Stack Overflow Developer Survey, which gathered insights from over 49,000 developers across 160 countries, reveals significant trends in the adoption and trust of AI tools in development workflows.

Trust in AI accuracy has notably declined, dropping to 33% in 2025 from 43% in the previous year, while overall favorability towards AI tools has decreased from 77% in 2023 to 60% this year.

Despite 80% of developers using AI tools, frustrations are rising due to the frequent inaccuracy and incompleteness of AI-generated code, leading to a significant drop in trust from 40% to 29% this year.

While about 60% of developers view AI tools favorably, there remains a notable divide, with 20% feeling indifferent or unsure, and another 20% expressing unfavorable views.

The survey highlights a divide in the developer community regarding AI coding tools, with craft-focused developers valuing the coding process and delivery-focused developers prioritizing product shipping.

To maximize the benefits of AI coding tools, the industry emphasizes the importance of proper training and adaptation, as resistance to these tools remains prevalent.

Among developers who have tried AI agents, 69% reported increased productivity, although concerns about job displacement from AI are low, with 64% not viewing it as a threat.

The AI coding tools market is projected to grow significantly, from $5.5 billion in 2024 to $47.3 billion by 2034, driven by productivity gains.

Median salaries for developers have risen across various roles, with the U.S. maintaining the highest job satisfaction and salaries compared to countries like Germany.

Community reliance remains crucial, with Stack Overflow, GitHub, and YouTube being the most used platforms for developer support, emphasizing the importance of human interaction.

Younger developers show a preference for more social and interactive content formats, with 37% of those aged 18-24 favoring 'Chat (people)' compared to only 20% of those aged 55-64.

Strategic recommendations for enterprises include investing in debugging capabilities and focusing on AI tool literacy to enhance the integration of AI in development processes.