Devs' Top AI Tool: Cursor Outranks GitHub Copilot in New Report
A recent report from LeadDev, an organization focused on leadership in software engineering, offers a comprehensive look at how artificial intelligence is being integrated into the daily workflows of developers and their organizations. The inaugural AI Impact Report reveals a landscape of rapid adoption, with a significant majority of engineering teams already leveraging AI tools.
The findings indicate that two-thirds of developers and engineering organizations are actively using AI tools and models, signaling a widespread embrace of the technology. Another 20% are in the proof-of-concept stage, while 13% are exploring AI’s potential. Remarkably, only 2% of respondents reported no plans to use AI tools whatsoever, highlighting their status as stark outliers in an increasingly AI-driven industry. Scott Carey, editor-in-chief of LeadDev, expressed fascination with this small contingent, noting that AI is quickly becoming an integral part of modern development.
When it comes to specific tools, the report unearthed some unexpected preferences. Cursor emerged as the leading AI tool funded by organizations, utilized by 43% of respondents. GitHub Copilot, often perceived as the dominant player, followed closely at 37%. Other prominent tools, including OpenAI, Google Gemini, Windsurf, and Anthropic’s Claude, comprised a middle tier of adoption. Niche or less-mainstream options such as Amazon Q, Bedrock, Replit, and Lovable registered minimal shares, which is understandable given the survey’s focus on professional developers using organization-funded solutions.
The report also sheds light on the primary applications of AI in development. Engineers are predominantly using AI and large language models for tasks like code generation, summarizing meetings, drafting documentation and other content, and researching new concepts. However, their use of AI for more advanced or operational functions remains limited. Only 7% use AI for data analysis, 7% for testing and quality assurance, 3% for IT operations automation, and a mere 2% for code deployment. Carey observed that despite the excitement from vendors about “AI DevOps” and similar concepts, developers have yet to integrate AI deeply across the entire software development lifecycle for these areas. He noted the frustration that these are precisely the areas where AI could potentially deliver the most significant impact, yet off-the-shelf solutions are scarce and vendor focus often lies elsewhere.
A critical question addressed by the report concerns AI’s actual impact on developer productivity. While the majority of LeadDev’s respondents believe AI has made them more productive, 5% reported no change, and 10% felt less productive. A notable 26% were unsure or didn’t know, a finding Carey attributed to a widespread lack of robust systems for tracking developer productivity within organizations. This perception contrasts sharply with an earlier report from METR, which indicated that while developers thought AI improved their productivity, these tools actually slowed them down by 19%. This discrepancy highlights a potential “productivity paradox” where perceived gains don’t always align with measurable outcomes.
Looking ahead, the report also explored the implications of AI adoption for the future workforce, particularly for junior engineers. A significant majority (54%) of respondents believe their organizations will hire fewer junior developers in the long term. This shift suggests that the roles of entry-level engineers may evolve, focusing more on supervising AI agents rather than traditional coding tasks.