I’ve been thinking a lot about how AI assistants evolve—whether they’ll keep absorbing specialized tools (like ChatGPT making search engines redundant) or if there’s another path. Model Context Protocol (MCP) feels like a turning point.
Most people talk about MCP as just a tool-calling API, but I think that misses the bigger picture. It could redefine how AI assistants work—not as standalone products but as dynamic platforms orchestrating external AI services.
In this article, I explore MCP’s implications through four angles:
- AI Competitive Dynamics – Why generalist AI models tend to absorb specialized tools.
- Economics of Complements – How AI assistants can thrive by enabling third-party tools instead of replacing them.
- MCP as a Personal Agentic Platform – The shift from chatbots to deeply integrated AI assistants.
- MCP as an Agent-to-Agent Protocol – Why AI systems should talk to each other instead of relying on humans as intermediaries.
I’d love to hear from others experimenting with MCP — does it feel like just another API, or is there something bigger here?
murmansk|11 months ago
Most people talk about MCP as just a tool-calling API, but I think that misses the bigger picture. It could redefine how AI assistants work—not as standalone products but as dynamic platforms orchestrating external AI services.
In this article, I explore MCP’s implications through four angles: - AI Competitive Dynamics – Why generalist AI models tend to absorb specialized tools. - Economics of Complements – How AI assistants can thrive by enabling third-party tools instead of replacing them. - MCP as a Personal Agentic Platform – The shift from chatbots to deeply integrated AI assistants. - MCP as an Agent-to-Agent Protocol – Why AI systems should talk to each other instead of relying on humans as intermediaries.
I’d love to hear from others experimenting with MCP — does it feel like just another API, or is there something bigger here?