Agentic Coding and the Economics of Open Source

What It Covers

Economics professor Miklos Skoren presents research on how AI-assisted "vibe coding" disrupts open source software ecosystems. Using incentive theory and empirical data from NPM downloads and GitHub stars across 100 representative websites tested against seven AI models, the paper argues human attention — the lifeblood of open source — is being systematically redirected toward machines.

Key Questions Answered

• •Open Source Attention Economy: GitHub stars serve as a measurable proxy for human engagement with open source projects. Skoren's controlled experiment across 100 websites and seven AI models found that as AI model recommendations for a library increase, NPM downloads rise 3–5 million per week, but GitHub stars stagnate or decline — confirming machines consume code without sustaining developer communities.
• •Tailwind CSS as Canary: Tailwind CSS experienced simultaneous surges in AI-driven downloads and sharp drops in website visits, directly damaging its premium-product revenue model. Skoren's team is now testing whether this pattern holds across front-end JavaScript packages broadly, using weekly-frequency NPM and GitHub data to determine if Tailwind is the rule, not the exception.
• •Open Source Requires Scale to Survive: Harvard Business School research cited by Skoren estimates open source creates over 1,000 times more value than the labor invested in it, making direct monetization structurally impossible. This means open source projects require millions of active human users to justify maintenance — a user base that AI agents erode by consuming packages without any human-developer interaction.
• •Developer Role Shifts to Design and Requirements: With code generation effectively automated, the two remaining high-value developer functions are translating user needs into system requirements and designing component architecture. Skoren recommends treating AI as a fast coworker rather than a tool — using voice recordings or rough notes to convey ideas, then letting agents handle implementation while humans retain ownership of problem framing.
• •Comparative Advantage Applies to Human-AI Collaboration: Drawing on Ricardo's comparative advantage principle, Skoren argues humans retain a structural edge in thinking and problem framing even if AI outperforms on execution. He restructured his own scientific workflow to eliminate nearly all manual coding, replacing it with analog thinking — pen, paper, books — then converting ideas into working code via brief verbal descriptions to AI agents.