survey.stackoverflow.co
AI | 2025 Stack Overflow Developer Survey
More developers actively distrust the accuracy of AI tools (46%) than trust it (33%), and only a fraction (3%) report "highly trusting" the output. Experienced developers are the most cautious, with the lowest "highly trust" rate (2.6%) and the highest "highly distrust" rate (20%), indicating a widespread need for human verification for those in roles with accountability. In 2024, 35% of professional developers already believed that AI tools struggled with complex tasks. This year, that number has dropped to 29% among professional developers and is consistent amongst experience levels. Complex tasks carry too much risk to spend extra time proving out the efficacy of AI tools. Developers show the most resistance to using AI for high-responsibility, systemic tasks like Deployment and monitoring (76% don't plan to) and Project planning (69% don't plan to). … Is it a learning curve, or is the tech not there yet? 87% of all respondents agree they are concerned about the accuracy, and 81% agree they have concerns about the security and privacy of data. When it comes to data management for agents, traditional, developer-friendly tools like Redis (43%) are being repurposed for AI, alongside emerging vector-native databases like ChromaDB (20%) and pgvector (18%).
Related Pain Points4件
AI agent security and blast radius management
9Production incidents show AI agents leaking internal data, shipping ransomware through plugins, and executing destructive actions (deleting repos). Security shifted from prompt injection to actual agent capabilities and operational risk.
AI Agent Hallucination and Factuality Failures
9AI agents confidently generate false information with hallucination rates up to 79% in reasoning models and ~70% error rates in real deployments. These failures cause business-critical issues including data loss, liability exposure, and broken user trust.
Building RAG systems for AI chatbots requires massive engineering investment
8Raw GPT models have no knowledge of a company's specific business, products, or policies. Developers must build complex Retrieval-Augmented Generation (RAG) systems to dynamically fetch and feed the right information from help centers, tickets, and documentation in real-time, requiring significant ongoing maintenance.
Developers avoid AI for high-responsibility tasks due to accuracy concerns
776% of developers won't use AI for deployment/monitoring, and 69% avoid it for project planning. High-responsibility, systemic tasks carry too much risk for unverified AI outputs. This reflects both capability and trust gaps.