CAL.COM CLOSES SOURCE CODE, MISSES OPEN SOURCE LESSON

Cal.com's decision to restrict access to its source code marks a significant shift for a company that built its reputation on openness. The scheduling startup cited concerns about AI companies scraping and training models on publicly available code as justification for the closure. The move generated substantial discussion in developer communities, with 143 comments on Hacker News where the story reached 253 points. Critics argue Cal.com conflates two separate problems: protecting proprietary business logic and preventing AI training. The Core Issue Open source licenses already provide mechanisms for controlling how code is used. Cal.com could have adopted stricter licensing terms—such as prohibiting commercial AI model training—without abandoning open source entirely. This approach would address legitimate concerns while preserving the collaborative benefits the model offers. Instead, closing the code entirely eliminates the transparency and community contributions that defined Cal.com's early competitive advantage. Developers who invested time understanding and improving the codebase lose those opportunities. Broader Context The tension between open source and AI training reflects genuine concerns across the industry. However, the solution lies in licensing evolution, not abandoning open source principles. Projects like Elasticsearch, Redis, and others have implemented license changes to restrict commercial AI use without reverting to closed-source models. Cal.com's decision suggests a misunderstanding of open source value. The model thrives on community trust, transparency, and distributed innovation. Closing code may offer short-term protection but risks alienating the developer base that contributed to the platform's growth. What's Next The scheduling market remains competitive. Whether Cal.com's code closure strengthens or weakens its position depends on execution. Competitors maintaining open source approaches may attract developers seeking transparency and community participation. The broader lesson: open source isn't threatened by AI—it's threatened by projects that abandon core principles without exploring alternatives. Better solutions exist between full openness and complete closure.