A new Google Cloud report reveals that AI adoption in software development has reached 90%, marking a 14% increase from the previous year. The findings, based on a survey of 5,000 global technology professionals and over 100 hours of in-depth interviews, show that developers now spend an average of two hours per day using AI tools in their core workflows. Nathen Harvey, head of Google Cloud’s DevOps Research and Assessment team, said the data indicates AI is now as ubiquitous in software work as computers themselves. “It’s almost to the point where we could have asked these technologists, ‘Are you using a computer at work?’” Harvey noted. The report highlights how AI is reshaping the role of software engineers. Tasks such as writing code, generating documentation, creating test cases, and analyzing data are increasingly being supported or automated by AI. Ryan J. Salva, senior director of product management at Google, explained that this shift is enabling more people to become builders and creators, not just coders. Traditionally, product managers defined features and specifications. Now, with AI, they can rapidly prototype and test ideas, bringing them closer to actual software deployment. Salva expects a growing number of professionals to engage directly in building and delivering software, not just writing code. Google CEO Sundar Pichai has also acknowledged the impact of AI, noting a 10% increase in engineering velocity and plans to hire more engineers in the coming year. “We plan to hire more engineers next year because the opportunity space of what we can do is expanding,” Pichai said. Despite AI’s growing role, foundational programming skills remain essential. Surprisingly, the report found that the perceived importance of knowing programming syntax—understanding the rules and structure of coding languages—has increased. This counters the assumption that AI will make coding knowledge obsolete. Still, trust in AI remains a challenge. Thirty percent of respondents said they trust AI “a little” or “not at all.” Harvey emphasized that while AI can speed up development, engineers must still understand the code they work with. “You are going to be entirely unsuccessful if you cannot read the language, at the very least,” Salva said. He added that with hundreds of programming languages in use, the ability to read and interpret code is more important than ever. “The end product was never just code,” Salva said. “It’s about understanding the logic, the intent, and the system as a whole.” As AI tools become more embedded in software development, engineers are shifting focus from writing lines of code to thinking strategically about product design, architecture, and problem-solving. The future of software engineering, according to Google, lies in combining human creativity with AI-powered efficiency.