While everyone argues about which AI model is best, the smart money is building connectors that work with ALL of them. 

This week GitHub and Microsoft just made this approach inevitable - and if you're still building custom integrations for every AI tool, you're about to feel very stupid.

What is MCP and why the scary name doesn't matter:

• MCP = Model Context Protocol (sounds scarier than it is)
• Think of it as a translator that helps your AI talk to different business tools
• Like giving ChatGPT a "phone number" to call the weather service
• Once you build the bridge, ANY AI can use it - ChatGPT, Claude, Gemini, Grok

Real-world examples beyond tech companies:

• Dental practice: AI assistant checks client schedules and availability directly
• E-commerce: AI customer service checks inventory in real-time instead of manual lookups
• Any business: Connect your AI to your actual business systems, not just generic responses

The infrastructure shift that happened this week:

• GitHub launched MCP Registry - basically an "app store" for AI connectors
• OpenAI's responses API now supports remote MCP servers (no more local embedding required)
• Microsoft shipping 10+ MCP servers for developer workflows + Windows OS integration
• When Microsoft treats something as infrastructure, it's past experimental phase

Why GitHub just nuked entire startup categories:

• Companies like Glamma, MCP.io, Composio were building paid MCP directories/marketplaces
• GitHub said "here's the same thing, free, where your code already lives"
• Classic platform move - bundle emerging technology to become the default
• Don't build where the platform can roll in and give it away for free

The architectural advantage for your business:

• Built one Stripe MCP connector in 5-6 hours over two days at SimpleDirect
• Now entire team can pull billing data, check subscriptions, handle overcharges
• Models are swappable (GPT-4 to GPT-5 to Claude 4) but connectors stay the same
• Build once, works with every future AI model - no more vendor lock-in

Security warnings (don't skip this):

• MCP servers can be exploited for credential theft and remote code execution
• Start with read-only permissions only - AI agents can make edits if you allow it
• Don't put confidential information in MCP servers yet
• Expect latency issues and have backup systems if MCP goes down

Your action plan for this week:

1. Try existing connectors first - GitHub MCP Registry or built-in Claude/ChatGPT tools
2. Start with read-only permissions - audit and test before allowing write access
3. Connect two different AI agents to same connector to prove reusability
4. Document everything - future you will thank present you

Bottom line: Your business logic lives in the MCP connectors, not the models. While everyone debates which AI is best, build infrastructure that works with all of them. The companies that figure out MCP now will have AI systems that work across every platform.

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