AI firms push Model Context Protocol to rebuild the internet

As artificial intelligence systems race ahead in capability, a growing group of major AI companies is quietly working on a shared technical foundation they believe could reshape how software and the web interact with large language models. That effort centers on the Model Context Protocol (MCP), an emerging standard designed to let AI models talk safely and consistently to tools, apps, and data sources across the internet.

What is the Model Context Protocol?

The Model Context Protocol is a specification that defines a common way for AI models to access external “tools” — everything from databases and APIs to productivity apps and enterprise systems. Instead of every AI company inventing its own proprietary integration layer, MCP aims to become a shared protocol that any model and any tool can use.

In practical terms, MCP describes:

• How an AI model can discover what tools or data sources are available
• How it can request information or actions from those tools
• How responses are structured and passed back into the model’s context
• How permissions and security boundaries are enforced

Today, many AI tools operate in silos: chatbots connect to a few built-in services; enterprise copilots integrate with specific SaaS platforms; and browser extensions hack together custom workflows. MCP is an attempt to replace this fragmented landscape with a standardized, interoperable layer — similar to how HTTP and REST APIs standardized web communication.

Who is backing MCP and why it matters

The protocol is being pushed forward by a coalition of AI firms and developers who see interoperability as critical to the next phase of AI market growth. While Anthropic has been a key early proponent through its Claude ecosystem, MCP is intentionally framed as an open standard that any company can implement.

The motivation is straightforward:

• Reduce integration friction: Instead of building custom connectors for every AI system, developers can target one protocol.
• Enable tool reuse: A tool built for one model can, in principle, be used by others that speak MCP.
• Encourage competition on quality, not lock-in: If switching models doesn’t require rebuilding your entire stack, customers can choose based on performance, safety, or cost.
• Support safer AI deployments: A standard interface can embed clear rules about what tools can and can’t do on behalf of the model.

From an industry perspective, this fits a familiar pattern. In the early internet, companies initially built closed ecosystems and proprietary protocols, but over time, open standards like TCP/IP, HTML, and OAuth enabled an explosion of compatible services. Supporters of MCP argue that a similar shift is necessary if AI is to move from isolated chatbots to a truly integrated layer across the digital economy.

Rebuilding the internet around AI agents

Behind the technical language is a much bigger vision: an internet where AI agents are first-class participants. Instead of users manually clicking through websites and apps, AI systems could:

• Read and write data directly into SaaS tools
• Trigger workflows in project management or CRM systems
• Query internal knowledge bases and public APIs in real time
• Orchestrate complex tasks across multiple services on behalf of users

MCP is meant to provide the connective tissue that makes this possible in a structured way. Rather than scraping web pages or relying on brittle hacks, AI systems could interact with MCP-enabled services through clearly defined, machine-readable interfaces.

This has obvious implications for productivity, but also for the broader economic outlook. If AI agents can reliably plug into business software, they become much more than chat interfaces — they start to look like semi-autonomous digital workers, capable of handling back-office tasks, customer support workflows, and data analysis at scale. That, in turn, could influence labor markets, software pricing models, and how organizations think about automation.

Interoperability, safety, and control

One of the most important promises of MCP is that it can make AI systems both more powerful and more governable. Because the protocol defines a structured way to expose capabilities, organizations can:

• Limit which tools a given AI agent is allowed to access
• Specify which operations are permitted (read-only vs. write, for example)
• Log and audit what actions the AI takes through MCP tools
• Apply consistent policies across different models and vendors

This is especially important as enterprises experiment with AI across sensitive domains like finance, healthcare, and legal work. CIOs and compliance teams are less concerned with flashy demos and more focused on predictable behavior, risk management, and integration with existing systems. A standardized protocol provides a clearer foundation for that work than a patchwork of proprietary plugins.

Open standard or new kind of lock-in?

Despite the collaborative framing, the push for MCP also raises strategic questions. If one protocol becomes dominant, the companies that shape and implement it early could wield significant influence over how AI integrates with the rest of the internet. That dynamic is familiar from previous waves of technology, where control of standards or platforms has translated into long-term market power.

Supporters argue that the answer is to keep MCP open, well-documented, and governed in a way that invites broad participation — including from smaller developers, open-source projects, and non-profit organizations. The more transparent the process, the less likely the protocol becomes a de facto gatekeeper.

Still, as AI investment surges and competition intensifies, there is a tension between openness and commercial incentives. Companies want interoperability, but they also want differentiated products and defensible moats. How that tension plays out around MCP could shape the next chapter of AI infrastructure and the broader evolution of the web.

What comes next for MCP and AI infrastructure

In the near term, the success of the Model Context Protocol will depend on adoption: how many tools implement it, how many AI models support it, and how actively the developer community experiments with it. The more real-world use cases emerge — from enterprise copilots to consumer assistants — the more pressure there will be on software vendors to offer MCP-compatible integrations.

Longer term, MCP could become part of a broader stack of AI-native infrastructure, alongside vector databases, retrieval-augmented generation systems, and specialized AI chips. If that happens, the protocol might quietly underpin how billions of AI requests move across services every day, much as HTTP underpins the modern web.

For now, MCP is less about flashy features and more about plumbing — but it is precisely this kind of “boring” standard that often determines who benefits from technological shifts and how widely those benefits are distributed.

Reference Sources

The Verge – AI companies are backing a Model Context Protocol to make AI tools work together