The world is being quietly rearranged by people who write very long documents.


The title they went with Knowledge Compounding: An Empirical Economic Analysis of Self-Evolving Knowledge Wikis under the Agentic ROI Framework Noisy translates that to

AI agents can now learn from their own answers, cutting costs by 80%


AI systems can now build a persistent memory of their own answers, which drastically cuts the cost of asking them follow-up questions. This means companies can run AI agents for complex tasks much more cheaply over time.
Until now, every time you asked an AI a question, it was like starting from scratch. This paper shows that if an AI can remember and organize its past answers, it becomes much more efficient. This changes how companies will think about the cost of AI, shifting it from a one-time expense per query to an investment in a growing knowledge base.
Watch for companies to start building and selling 'knowledge wikis' for AI agents, and for the cost of complex AI workflows to drop significantly over the next year.

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