If AI Trains Mostly on AI Text, Where Does New Knowledge Come From?
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If AI Trains Mostly on AI Text, Where Does New Knowledge Come From?
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This story was originally published on HackerNoon at: https://hackernoon.com/if-ai-trains-mostly-on-ai-text-where-does-new-knowledge-come-from.
AI floods the web with synthetic consensus and model collapse risks. Explore real-world context entropy and MCP as a path for AI evolution.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #future-of-ai, #ai-model-collapse, #ai-evolution, #context-engineering, #synthetic-data, #model-context-protocol, #ai-learning-loops, #hackernoon-top-story, and more.
This story was written by: @sebastianmartinez. Learn more about this writer by checking @sebastianmartinez's about page, and for more stories, please visit hackernoon.com.
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.