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.
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The dynamic between tech and government is not a simple decline but a cycle of alignment (post-WWII), hostility (2000s-2010s), and a recent return to collaboration. This "back to the future" trend is driven by geopolitical needs and cultural shifts, suggesting the current alignment is a return to a historical norm.
In an AI-driven ecosystem, data and content need to be fluidly accessible to various systems and agents. Any SaaS platform that feels like a "walled garden," locking content away, will be rejected by power users. The winning platforms will prioritize open, interoperable access to user data.
History shows that transformative innovations like airlines, vaccines, and PCs, while beneficial to society, often fail to create sustained, concentrated shareholder value as they become commoditized. This suggests the massive valuations in AI may be misplaced, with the technology's benefits accruing more to users than investors in the long run.
There appears to be a predictable 5-10 year lag between a startup's innovation gaining traction (e.g., Calendly) and a tech giant commoditizing it as a feature (e.g., Google Calendar's scheduling). This "commoditization window" is the crucial timeframe for a startup to build a brand, network effects, and a durable moat.
The idea of a truly "open web" was a brief historical moment. Powerful, proprietary "organizing layers" like search engines and app stores inevitably emerge to centralize ecosystems and capture value. Today's AI chatbots are simply the newest form of these organizing layers.
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.
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 history of AI tools shows that products launching with fewer restrictions to empower individual developers (e.g., Stable Diffusion) tend to capture mindshare and adoption faster than cautious, locked-down competitors (e.g., DALL-E). Early-stage velocity trumps enterprise-grade caution.
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.
While modern UIs are essential, the backend IBM i (AS/400) platform remains entrenched in many businesses. The reason is its extreme reliability and stability, which would require massive, difficult, and expensive custom software development to achieve on open systems like Linux.
