The evolution of a protocol like MCP depends on a tight feedback loop with real-world implementations. Open source clients such as Goose serve as a "reference implementation" to test and demonstrate the value of new, abstract specs like MCPUI (for user interfaces), making the protocol's benefits concrete.

While a unified data platform is non-negotiable for AI, leaders should resist standardizing AI tools and frameworks too early. Given the rapid pace of innovation, it's better to allow for experimentation and "let the flowers bloom." This dual approach—a stable data foundation with flexible tooling—enables both governance and agility.

OpenAI integrated the Model-Centric Protocol (MCP) into its agentic APIs instead of building its own. The decision was driven by Anthropic treating MCP as a truly open standard, complete with a cross-company steering committee, which fostered trust and made adoption easy and pragmatic.

The MCP protocol's primitives are not directly influenced by current model limitations. Instead, it was designed with the expectation that models would improve exponentially. For example, "progressive discovery" was built-in, anticipating that models could be trained to fetch context on-demand, solving future context bloat problems.

MCP was born from the need for a central dev team to scale its impact. By creating a protocol, they empowered individual teams at Anthropic to build and deploy their own MCP servers without being a bottleneck. This decentralized model is so successful the core team doesn't know about 90% of internal servers.

A fundamental tension within OpenAI's board was the catch-22 of safety. While some advocated for slowing down, others argued that being too cautious would allow a less scrupulous competitor to achieve AGI first, creating an even greater safety risk for humanity. This paradox fueled internal conflict and justified a rapid development pace.

OpenAI operates with a "truly bottoms-up" structure because it's impossible to create rigid long-term plans when model capabilities are advancing unpredictably. They aim fuzzily at a 1-year+ horizon but rely on empirical, rapid experimentation for short-term product development, embracing the uncertainty.

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.

Unlike traditional internet protocols that matured slowly, AI technologies are advancing at an exponential rate. An AI standards body must operate at a much higher velocity. The Agentic AI Foundation is structured to facilitate this rapid, "dog years" pace of development, which is essential to remain relevant.