Last year Anthropic gave away one of the most successful things it has ever built. And, no, I’m not talking about Claude. I’m referring to MCP, the now ubiquitous Model Context Protocol, which Anthropic donated to the Linux Foundation’s new Agentic AI Foundation. At the time, MCP was pulling nearly 100 million monthly SDK downloads across more than 10,000 active servers, prompting the question as to why any company would give up such a popular piece of technology.
Google did much the same months earlier, handing its Agent2Agent (A2A) protocol to the Linux Foundation with AWS, Cisco, Microsoft, Salesforce, SAP, and ServiceNow signing on as founding members. OpenAI, not to be outdone, supports remote MCP servers in its Responses API, sits on the MCP steering committee, and contributed AGENTS.md to that same foundation alongside its fiercest rival’s protocol.
It’s like Game of Thrones, except the principal AI powers seek regime change through seeming acts of beneficence rather than violence. For those who have been around for a while, it’s also entirely predictable, following a similar script we’ve seen in the cloud, on-premises servers, and more. Platform companies don’t give away technologies they’ve stopped caring about. They give away technologies they no longer need to own because competitive advantage has shifted to new ground.
What does this mean for AI?
Google has long been an exceptionally active contributor to open source. As I wrote in 2017, Google wasn’t open sourcing TensorFlow and Kubernetes out of generosity but rather turning these open source assets into on-ramps for Google Cloud. Google was playing catch-up to AWS and Microsoft. As then Google product manager Martin Buhr said, the company hoped to “create a gravity well in the market for container-based apps [so] that a significant percentage of them will end up with us.”
In other words, platform companies routinely commoditize one layer of the stack so they can compete somewhere they hold a stronger hand.
GitHub may be an even better example. Git is open. Anyone can host a Git repository and, once upon a time, different companies did just that. Yet GitHub became the default place software development happens for millions of developers. Nobody pays for Git, but lots of people pay for GitHub. We’re seeing this same phenomenon play out in AI.
Anthrophic and OpenAI have both pretended at being all for humanity’s good, but that’s not a good explanation for why they’re racing to give away things like MCP. The deeper reason is that the model itself has turned out to be a poor place to build a lasting moat, and they’re trying to figure out what’s next. As I pointed out recently, the frontier model leaderboards change almost weekly. As such, enterprises shouldn’t build their AI strategy around the assumption that any one vendor will remain permanently ahead on model quality. Instead, as I suggested, AI may be sexy, but the “dull reality” is connecting those models to enterprise data, workflows, etc.
The AI companies understand this better than anyone. Sure, they’ll continue spending billions training ever more capable models because frontier models attract developers, generate headlines, and open enterprise doors. But they’re also quietly acknowledging that benchmark leadership alone doesn’t create a durable platform.
Developers return to the places where their tools, workflows, teammates, and accumulated work already live. Enterprises double down on the systems where their data, permissions, governance, and business processes are already connected. Every new integration makes that destination a little harder to leave, and every new workflow increases its pull. That’s what MCP, A2A, etc., are all about: increasing gravity around the models.
Every major AI company wants to become the place where AI-assisted work naturally happens, and they’re now amassing armies of forward deployed engineers and trying other means to get legacy infrastructure to tie back to their frontier models. The enterprise incumbents want the same thing, but from the opposite direction. They don’t need to own the frontier; instead they need to connect the frontier to the systems that already safely run the business.
That’s why I’m skeptical whenever someone confidently predicts that AI will sweep away enterprise software. I’ve seen this movie before. Developers absolutely live on the frontier, but enterprises don’t. Enterprises create value by connecting new capabilities to decades of accumulated applications, data, policies, and business processes. The newest model matters, and so does the newest agent framework. But neither creates much business value until it’s connected to customer records, financial systems, supply chains, HR data, and everything else enterprises already depend on.
That’s where incumbents still possess enormous gravitational pull. My employer, Oracle, certainly believes so, just as Microsoft, SAP, Salesforce, and ServiceNow do. (Disclosure: I run developer relations at Oracle, which participates in the Agentic AI Foundation.) Ironically, open protocols strengthen that position rather than weaken it. If every model can speak MCP and every agent can interoperate through common standards, enterprises gain the freedom to adopt whichever frontier technology looks best without rebuilding every integration. The protocol becomes interchangeable.
None of this diminishes the importance of open standards. MCP succeeded because it solves a genuine problem. Developers shouldn’t have to build a custom connector every time an AI application needs access to a database or other business system. Neutral governance also matters because nobody wants foundational infrastructure controlled by a direct competitor. But we shouldn’t confuse open interfaces with open markets.
An enterprise may find it easy to swap one MCP-compatible model for another while still remaining deeply dependent on the place where its prompts, evaluations, security policies, and employee habits have accumulated. Again, we’ve seen this before. Kubernetes made workloads dramatically more portable without making AWS, Azure, and Google Cloud interchangeable. SQL has been standardized for decades, yet databases remain fiercely differentiated businesses. Standards reduce friction, but they rarely eliminate competitive advantage. They simply move it.
In like manner, Anthropic, Google, OpenAI, and others are happily standardizing how models, agents, tools, and enterprise systems communicate because they don’t expect the connection itself to determine the winner. Instead they expect to win by becoming the place where AI-assisted work naturally accumulates. Along the way, we’re going to see copious quantities of code given away, increasing developer productivity for all and outsized financial bonanzas for a few. Game on.