These are the most popular AI coding tools among engineers

In an exciting development for the tech industry, a recent survey has unveiled a significant rise in the use of AI coding tools among engineers. Conducted by Jellyfish in May, the survey included insights from 645 full-time engineering professionals, ranging from individual contributors to executives, across diverse company sizes—from small startups to large enterprises. The findings highlight the remarkable growth of AI tools in software development, with a striking 90% of engineering teams now incorporating AI into their workflows, a substantial increase from 61% just a year prior. Nearly a third of these teams have fully embraced and supported AI tools, while an additional 39% are in the experimental phase. Only a minuscule 3% of respondents reported no usage of AI tools and no intentions to adopt them. The survey also revealed that 48% of engineers are utilizing two or more AI coding tools, indicating a trend towards a diversified approach in evaluating various solutions rather than committing to a single option. Leading the pack, GitHub Copilot from Microsoft emerged as the most favored tool, chosen by 42% of those surveyed. Following closely were Google’s Gemini Code Assist and Amazon Q (previously known as CodeWhisperer), tied for third place along with Cursor. Notably, the study focused solely on specialized AI coding tools, excluding general-purpose generative AI platforms such as ChatGPT. This distinction emphasizes the increasing specialization of AI solutions tailored specifically for software development. According to the results, 62% of engineers reported experiencing a productivity boost of at least 25% due to these AI coding tools, while 8% noted a doubling of their output. Conversely, fewer than 1% felt that AI was hindering their progress. Looking to the future, a significant 81% of respondents anticipate that at least a quarter of current engineering tasks will be automated by AI within the next five years. However, experts suggest that this shift will prioritize collaboration over complete automation. As one engineering leader remarked, "While AI can assist creatives, it is not inherently creative itself. The true magic occurs when knowledgeable individuals leverage AI to enhance their understanding of the problems they are addressing." With productivity gains already evident and adoption rates climbing, the current landscape of AI coding tools, led by GitHub Copilot, Gemini, Amazon Q, and Cursor, is paving the way for a future where software engineers and AI systems collaborate to innovate and develop the next wave of digital solutions.