While the spotlight in AI remains fixed on model benchmarks, a quieter - and arguably more consequential - shift is underway. A new standard is emerging that could define how AI systems actually DO things in the real world.
Last week, OpenAI committed to MCP. It’s a rare moment of alignment in AI - a shared interface layer gaining traction across rival labs. Everyone from Anthropic (who created it) to OpenAI (its fiercest competitor) is backing it.
So what is MCP? Model Context Protocol - is an open standard that lets AI models connect to apps, tools, and data sources in a consistent, composable way.📦 Think: USB-C for AI integrations, or one universal plug that fits everywhere. MCP is the plumbing that turns a model into an agent.
It’s not about making models smarter. It’s about making them more usable, agentic, and swappable. It’s the missing layer between intelligence and action. In short: What TCP/IP did for the internet, MCP might do for model-based software.
What does it unlock?
🧠For proprietary models like GPT-4 or Claude: Streamlined workflows, plugin ecosystems, and enterprise integrations
🧩 For developers and infra providers: Reusable connectors, less glue code, faster experimentation
🤖 For users: More capable AI agents - ones that don’t just chat, but reason, retrieve, and act
What happens next?
👉 Standardization creates surface area. Expect a wave of agent-specific infra, orchestration platforms, and marketplaces - just as Docker and Kubernetes did for containerized workloads.
👉 It shifts the moat. As models become swappable, value flows to the surrounding ecosystem: deployment, governance, interfaces.
👉 It forces composability. Enterprises no longer have to place one big bet. They can mix, match, and evolve their model stack with less friction.
If LLMs are the engine, MCP is the wiring. The frontier isn't just smarter models - it's interoperable systems.