AI floods the web with synthetic consensus and model collapse risks. Explore real-world context entropy and MCP as a path for AI evolution.

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As AI writes more of the internet, training data becomes self-referential and loses genuine novelty. The fix is to detect and preserve new ideas, then turn live, validated real-world context into the new engine of learning. MCP can be understood as “AI’s senses” for real-world validation and discovery. Using novelty-specialist models, curator systems, and reality-testing loops via MCP and audit logs, we can harness entropy productively.