A global survey of 1,100 finds nearly three-quarters (72%) are now using artificial intelligence (AI) coding tools daily, with on average 42% of the code being created with the help of AI.

Conducted by Sonar, a provider of tools for reviewing code, survey respondents also project that the amount of code being generated using AI coding tools will increase to 65% by 2027.

Nearly all respondents (96%), however, said they don’t fully trust AI-generated code to be functionally correct. The challenge is that nearly half (48%) also admit they don’t always review code generated using AI tools before committing it.

Anirban Chatterjee, senior director of product marketing for Sonar, said the survey results make it clear that, at least in the short term, AI coding tools are creating more challenges for DevOps teams as the volume of code being generated continues to exponentially increase.

The most widely used AI coding tools are GitHub Copilot (75%), ChatGPT (74%), Claude (48%), Gemini/Duet AI (37%), Cursor (31%), Perplexity (21%), OpenAI Codex (21%), JetBrains AI Assistant (17%), Amazon Q Developer (12%) and Windsurf (8%). Just over a third 35% are using personal accounts to access these tools.

Additionally, nearly two-thirds of respondents (64%) have used AI agents, with a quarter (25%) using them regularly. Top use cases for AI agents are creating code documentation (68%), automating test generation (61%) and automating code review (57%).

Overall, the survey finds developers are relying on AI coding tools most often to create prototypes and experiments (88%), followed by internal, non-critical production software (83%), customer-facing applications (73%) and mission-critical services (58%).

Survey respondents also noted they perceive AI coding tools to be most effective in helping them to create documentation (74%), followed by explaining/understanding existing code (66%), generating tests (59%) and assisting in the development of new code (55%).

The top concerns developers have when using AI coding tools are AI code that looks correct but isn’t reliable (61%), exposure of sensitive data (57%) and the introduction of severe security vulnerabilities (44%). A full 88% report encountering at least one issue, such as unreliable (53%) or duplicative code (40%).

Less clear is the degree to which AI coding tools are making application developers more productive. The average amount of time allocated to tedious tasks stands at 10 hours per week, mainly because survey respondents are now spending time reviewing AI-generated code versus writing code themselves, noted Chatterjee.

Among responders with less than 10 years of experience, 40% report productivity gains from AI, but two-thirds of those respondents (66%) also said AI code looks correct but isn’t reliable, with 40% noting that reviewing AI code takes more effort than reviewing the code they wrote themselves.

It’s not likely that developers are going to rely less on AI coding tools because of these issues, but until the quality of the code generated using these tools improves, likely, the number of issues that DevOps teams will need to triage and remediate is only going to sharply increase in the New Year. The challenge now is finding the right set of AI tools to reduce all the time and effort that would otherwise be needed to review what is rapidly becoming an overwhelming amount of code.