Despite 90% adoption, most developers still see A.I. as an assistant, not a partner. Unsplash
For decades, software was built line by line by human hands. That process is changing fast because of A.I. According to Google’s latest annual DORA: State of A.I.-assisted Software Development report, released today (Sept. 23), 90 percent of technology professionals now use A.I. in their workflows, representing a 14 percent jump from last year. The survey of more than 5,000 software professionals and IT specialists found that developers rely on A.I. for tasks ranging from writing code snippets to running tests and reviewing security.
Despite higher A.I. adoption, however, trust in the technology remains low. While most say A.I. makes them faster and more productive, only 24 percent say they trust it “a lot” or “a great deal.” Nearly a third admit they trust it “a little” or not at all.
“Boardroom-level prioritization shows that this change is likely here to stay. Every organization is facing pressure to improve software performance even in the face of broad economic pressures and constraints,” Nathen Harvey, the study’s lead researcher and a developer advocate at Google Cloud, told Observer. “On an individual level, A.I. has captured the human imagination and inspired developers to find ways to drive both top and bottom-line improvements for businesses.”
The study found that 85 percent of professionals say A.I. has made them more productive, though 41 percent call the improvement only “slight.” Fewer than 10 percent reported any decline in productivity. Developers now spend a median of two hours a day using these A.I. tools, and top-performing organizations report that A.I. is boosting throughput, allowing them to deliver applications faster and more reliably.
Code quality is where A.I.’s impact is most evident. Much of the software it helps create ends up running in production systems far longer than developers ever anticipated. That longevity shows A.I.–generated code can be more useful than expected, but it also raises the stakes. Readability and adaptability matter far more than quick fixes when judging code quality.
Software relies on constant code changes, such as tweaks, fixes and new features, to stay alive. Feedback loops from automated tools or users act like vital signs, signaling the system’s health. But Harvey cautioned that while A.I. speeds development, it can also make software delivery more unstable. “Even with the help of A.I., teams will still need ways to get fast feedback on the code changes that are being made,” he said.
For now, developers are hesitant to give up control. Only a quarter in the survey say they have high trust in A.I.’s coding abilities. Google researchers call this the “trust paradox”: A.I. is a useful assistant, but not yet a true partner. That skepticism could slow progress toward advanced uses like autonomous testing or fully automated release management.
Harvey noted that developers treat A.I. output with the same healthy skepticism they apply to go-to resources, like coding solutions found on Stack Overflow—useful but never blindly trusted. “A.I. is only as good as the data it has access to,” he said. “If your company’s internal data is messy, siloed, or hard to reach, your A.I. tools will give generic, unhelpful answers, holding you back instead of helping.”
Harvey also noted that A.I. hasn’t eased burnout or reduced friction. While it boosts individual productivity, those challenges often stem from company culture, leadership and processes—problems technology alone can’t fix. “If leaders start expecting more because A.I. makes developers faster, it could even add to the pressure,” he added.
To address this gap, Google introduced the DORA A.I. Capabilities Model, a framework of seven technical and cultural practices aimed at amplifying A.I.’s impact. The model emphasizes user focus, clear communication and small-batch workflows—underscoring that success requires more than just new tools.
“Culture and mindset continue to be huge influences on helping teams achieve and sustain top performance. A climate for learning, fast flow, fast feedback, and a practice of continuous improvement are what drive sustainable success. A.I. amplifies the necessity for all of these and provides the catalyst to transform along the way,” said Harvey.
Ultimately, Google’s 2025 report argues the biggest barrier isn’t adoption but trust. Without stronger confidence in A.I.’s reliability, the future of software development will depend as much on winning developer faith as on improving the technology itself.
