How many developers use AI coding agents (Claude Code, Codex, Cursor, JetBrains Junie, and others)? Evidence from the Developer Ecosystem Survey 2026.
AI | 2025 Stack Overflow Developer Survey
84% of respondents are using or planning to use AI tools in their development process, an increase over last year (76%). This year we can see 51% of professional developers use AI tools daily.
Cursor: AI coding agent
Built to make you extraordinarily productive, Cursor is the best AI coding agent.
Codex for every role, tool, and workflow
Discover new Codex plugins, sites, and annotations that help analysts, marketers, designers, investors, and other teams get more done with AI.
Why Software Engineering Is Indispensable in the Age of Coding Agents
Can AI make Software Engineering (SE) -- the discipline -- obsolete? And can it make software engineers -- the professionals -- redundant? This paper argues that the rise of capable AI coding...
How Cursor is building the future of AI coding with Claude
Cline - AI Coding, Open Source and Uncompromised
Open-source AI coding agent with Plan/Act modes, MCP integration, and terminal-first workflows. Trusted by 5M+ developers worldwide.
Coding Agents Are Easy, Actually - Kara's Nonsense
Software engineering is here to stay
Codex | AI Coding Partner from OpenAI
The best way to build with agents. Codex accelerates real engineering work, from planning and building features to refactors, reviews, and releases—working seamlessly across your tools.
Usage, Effects and Requirements for AI Coding Assistants in the Enterprise: An Empirical Study
The rise of large language models (LLMs) has accelerated the development of automated techniques and tools for supporting various software engineering tasks, e.g., program understanding, code generation, software testing, and program repair. As CodeLLMs are being employed toward automating these tasks, one question that arises, especially in enterprise settings, is whether these coding assistants and the code LLMs that power them are ready for real-world projects and enterprise use cases, and how do they impact the existing software engineering process and user experience. In this paper we survey 57 developers from different domains and with varying software engineering skill about their experience with AI coding assistants and CodeLLMs. We also reviewed 35 user surveys on the usage, experience and expectations of professionals and students using AI coding assistants and CodeLLMs. Based on our study findings and analysis of existing surveys, we discuss the requirements for AI-powered coding assistants.
Git AI - Track AI Code all the way to production
Cross-agent observability from prompt to production. Track AI-generated code from Cursor, Claude Code, GitHub Copilot, Gemini, and more through the entire SDLC.
Measuring the Impact of Early-2025 AI on Experienced Open-Source...
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...
Why AI hasn’t replaced software engineers, and won’t
Coding agents as normal technology
Kilo - Pricing
Pricing for Kilo Code, the open-source AI coding agent for VS Code, JetBrains and the CLI
AI vs human code gen report: AI code creates 1.7x more issues
We analyzed 470 open-source GitHub pull requests, using CodeRabbit’s structured issue taxonomy and found that AI generated code creates 1.7x more issues.
AI Coding Agent Benchmarks & Leaderboard | Artificial Analysis
We measure real-world performance of coding agents on software engineering tasks, including cost, token usage, and execution time. We compare how performance changes across agents, models, and execution settings.