🔗 AI’s impact on software engineers in 2026: key trends, Part 2

What if AI tools not only boost productivity but also lead to decreased code quality? In this concluding part of the series, Gergely Orosz and Elin Nilsson delve into the findings from over 900 survey responses regarding AI's effects on software engineers by 2026. They reveal that while AI can reduce the time spent on routine tasks, it introduces new challenges like unrealistic expectations and quality issues.

Many companies struggle with large-scale adoption, finding that effective AI use depends heavily on existing engineering practices. Engineers report a decline in codebase quality, with more bugs sneaking into projects and less experienced programmers grappling with AI-generated code. The piece emphasizes the need for robust guidelines and highlights the complex influence of AI on collaboration, highlighting varying impacts based on individual adaptability and team culture.