Introduction to AI in Software Development
AI tools are widely used by software developers, but they and their managers are still grappling with figuring out how exactly to best put them to use, with growing pains emerging along the way. That’s the takeaway from the latest survey of 49,000 professional developers by community and information hub StackOverflow, which itself has been heavily impacted by the addition of large language models (LLMs) to developer workflows.
The Growth of AI Tool Usage
The survey found that four in five developers use AI tools in their workflow in 2025—a portion that has been rapidly growing in recent years. This shows that developers are becoming more open to using AI tools, but there are still some issues that need to be addressed. For example, "trust in the accuracy of AI has fallen from 40 percent in previous years to just 29 percent this year." This discrepancy highlights the evolving and complex impact of AI tools like GitHub Copilot or Cursor on the profession.
Challenges with AI Tools
When asked what their top frustration with AI tools was, 45 percent of respondents said they struggled with "AI solutions that are almost right, but not quite"—the single largest reported problem. This is because unlike outputs that are clearly wrong, these can introduce insidious bugs or other problems that are difficult to immediately identify and relatively time-consuming to troubleshoot, especially for junior developers who approached the work with a false sense of confidence thanks to their reliance on AI.
The Impact on Developers
As a result, more than a third of the developers in the survey "report that some of their visits to Stack Overflow are a result of AI-related issues." That is to say, code suggestions they accepted from an LLM-based tool introduced problems they then had to turn to other people to solve. This shows that while AI tools can be helpful, they can also create new problems that developers need to deal with.
Limitations of AI Tools
Even as major improvements have recently come via reasoning-optimized models, that close-but-not-quite unreliability is unlikely to ever vanish completely; it’s endemic to the very nature of how the predictive technology works. This means that developers will need to continue to be cautious when using AI tools and make sure to thoroughly test and review any code suggestions they receive.
Conclusion
In conclusion, while AI tools are becoming increasingly popular among software developers, there are still some challenges that need to be addressed. Developers need to be aware of the potential limitations and pitfalls of AI tools and take steps to mitigate them. By doing so, they can harness the power of AI to improve their workflow and create better software.
FAQs
Q: What percentage of developers use AI tools in their workflow?
A: Four in five developers use AI tools in their workflow in 2025.
Q: What is the top frustration with AI tools among developers?
A: The top frustration is "AI solutions that are almost right, but not quite."
Q: Why do developers struggle with AI tools?
A: Developers struggle with AI tools because they can introduce insidious bugs or other problems that are difficult to identify and troubleshoot.
Q: How do AI tools affect junior developers?
A: Junior developers may approach their work with a false sense of confidence thanks to their reliance on AI, which can lead to problems down the line.
Q: What can developers do to mitigate the limitations of AI tools?
A: Developers can thoroughly test and review any code suggestions they receive from AI tools and be cautious when using them.
