Matt Mullenweg observes a predictable cycle where technology swings from open to proprietary and back. When proprietary systems become too profitable and user-hostile, it creates a market opportunity for open-source alternatives to emerge and capture disillusioned customers.

New technologies perceived as job-destroying, like AI, face significant public and regulatory risk. A powerful defense is to make the general public owners of the technology. When people have a financial stake in a technology's success, they are far more likely to defend it than fight against it.

The current trend toward closed, proprietary AI systems is a misguided and ultimately ineffective strategy. Ideas and talent circulate regardless of corporate walls. True, defensible innovation is fostered by openness and the rapid exchange of research, not by secrecy.

To serve both solo developers and large enterprises, GitHub focuses on creating horizontal "primitives" and APIs first. This foundational layer allows different user types to build their own specific workflows on top, avoiding the trap of creating a one-size-fits-none user experience.

Contrary to fears of chaos, allowing users to modify their software can create more stability. Users can craft a predictable, long-lasting environment tailored to their needs. This control protects them from disruptive, top-down redesigns pushed by a distant corporate office.

Vercel's CTO Malte Ubl outlines a third way for open source monetization beyond support (Red Hat) or open-core models. Vercel creates truly open libraries to grow the entire ecosystem. They find that as the overall "pie" grows, their relative slice remains constant, leading to absolute revenue growth.

The key to successful open-source AI isn't uniting everyone into a massive project. Instead, EleutherAI's model proves more effective: creating small, siloed teams with guaranteed compute and end-to-end funding for a single, specific research problem. This avoids organizational overhead and ensures completion.

The choice between open and closed-source AI is not just technical but strategic. For startups, feeding proprietary data to a closed-source provider like OpenAI, which competes across many verticals, creates long-term risk. Open-source models offer "strategic autonomy" and prevent dependency on a potential future rival.

A common misconception is that Chinese AI is fully open-source. The reality is they are often "open-weight," meaning training parameters (weights) are shared, but the underlying code and proprietary datasets are not. This provides a competitive advantage by enabling adoption while maintaining some control.