Use is universal, but structure and full-lifecycle integration trail adoption
Productivity depends on discipline; review and oversight are now embedded in how developers work.
Differences in experience, company scale, and ecosystem readiness shape who scales fastest.
Optimism is high for the long-term impact of AI, with 75% expressing a positive outlook. Efficiency leads adoption, teams want fewer repetitive tasks and faster coding. The top concern is output consistency, cited by 79 %, while only 11% say AI already matches a mid-level developer’s quality.
General assistants lead early use. ChatGPT leads with 87% having used it in the past six months, followed by Cursor (70%) and Copilot (68%). IDEs feel most AI-ready. Collaboration, documentation, and monitoring tools require clearer usage patterns.
AI shows up across the lifecycle, anchored in coding. Most outputs still pass through human review. The top blocker is output quality. 44% cite unreliable output as the biggest blocker; 86% prioritize accuracy over speed. 73% say AI improves quality, but most edit outputs before production.
93% say AI makes them faster. The gains are steady rather than dramatic, with improvements felt first at the individual level. Team practices are catching up, and review workflows are being adapted to handle AI-generated changes.
87% are self-training on AI. Hiring expectations are rising, and many organizations are still formalizing training and policy. Developers are moving ahead quickly; companies are working to match that pace.
