In one of my previous posts, I discussed congestion in the job market caused by the surge of AI tools that scrape job descriptions and auto-apply to jobs. Since that post, another problem has emerged: progress in the capabilities of coding agents has caused a sharp rise in vibe-coded pull requests in open source repositories on GitHub. This problem can also be framed as a matching market congestion problem.

I became familiar with the problem while working in a services marketplace and solving matching-market-related problems there. That gave me direct practical experience with the typical issues. In this post, I want to share that experience and knowledge.

I explore the services marketplace, a dating platform, job search, and open source contribution through the lens of matching market design and identify a common pattern: lowering search and application costs leads to more applications, resulting in less effective matching due to reviewer overload.

I argue that just automating application screening and review with AI doesn't fully resolve the problem. In some cases, it makes it even worse by creating a self-reinforcing feedback loop: more applications → more automated filtering → even more applications. AI automation tools lack private information about applicant fit and intent.

As an alternative, I propose to redesign incentives so applicants bear more of the cost of low-value submissions and use their private knowledge to apply more carefully. The proposed solution is a reputation-credit-based system for GitHub-like platforms: non-transferable reputation credits are earned through valuable contributions and debited through low-quality pull requests and issues.