{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"If AI Trains Mostly on AI Text, Where Does New Knowledge Come From?","html":"","width":"100%","height":180,"duration":1190,"description":"\n This story was originally published on HackerNoon at: https://hackernoon.com/if-ai-trains-mostly-on-ai-text-where-does-new-knowledge-come-from.\n AI floods the web with synthetic consensus and model collapse risks. Explore real-world context entropy and MCP as a path for AI evolution. \n Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n 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.\n \n \n This story was written by: @sebastianmartinez. Learn more about this writer by checking @sebastianmartinez's about page,\n and for more stories, please visit hackernoon.com.\n \n \n \n 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.\n \n ","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}