"A global survey of 636 software development professionals published today finds nearly two-thirds (64%) believe they are achieving at least a 25% increase in developer velocity and productivity using artificial intelligence (AI). Conducted by Jellyfish, a provider of a software engineering intelligence platform, just under a quarter (24%) report there has been a 50% to 100% increase in developer velocity and productivity, while another 6% have seen an increase of 100% or more."
"The top use cases for AI are code writing (53%), code review (49%) and code explanation (43%), with Claude Code (39%), Gemini Code Assist (35%) and GitHub Copilot (31%) being the top three tools adopted. However, only slightly more than half (53%) said AI is improving the quality of the code being developed. Other challenges include increasing cost of AI tools (42%), reluctance in adoption from senior engineers (36%) and a proliferation of tools making it difficult to select the best one (31%)."
"Despite these issues and concerns, a full 80% said AI is a positive on productivity, with three-quarters of respondents (75%) said AI has made more time available to focus on high-value activities. For example, 46% said they expect to be able to spend more time on creating roadmaps for projects. Well over half (57%) said AI increases their job satisfaction."
"In general, there is now more joy being experienced because teams are able to spend more time solving problems for the business versus having to manually complete rote tasks, he added. While it's clear AI tools are having an impact on software engineering, the level of skills and expertise that teams have is uneven. In fact, only 43% characterized their adoption of AI tools as high (33%) or very high (10%), compared to another third (33%) that described t"
A global survey of 636 software development professionals reports that 64% achieve at least a 25% increase in developer velocity and productivity using artificial intelligence. 24% report a 50% to 100% increase, and 6% report 100% or more. The most common AI use cases are code writing (53%), code review (49%), and code explanation (43%). Adopted tools include Claude Code (39%), Gemini Code Assist (35%), and GitHub Copilot (31%). 53% say AI improves code quality. Reported challenges include higher AI tool costs (42%), reluctance from senior engineers (36%), and too many tools to choose from (31%). 80% view AI as positive for productivity, 75% report more time for high-value work, 57% report increased job satisfaction, and 46% expect more time for roadmap creation.
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