Shadow Derivatives: The Quiet Propertization of AI Learning
Introduction Artificial intelligence (AI) systems learn. In today’s AI markets, durable advantage comes less from any single output than from the learning that accumulates through training, fine-tuning, and downstream feedback loops.[1] Each interaction, correction, and deployment contributes incrementally to improved performance, enabling systems to generalize, adapt, and optimize over time.[2] Yet the law has struggled […]The postShadow Derivatives: The Quiet Propertization of AI Learningappeared first onTexas Law Review.
Introduction Artificial intelligence (AI) systems learn. In today’s AI markets, durable advantage comes less from any single output than from the learning that accumulates through training, fine-tuning, and downstream feedback loops.[1] Each interaction, correction, and deployment contributes incrementally to improved performance, enabling systems to generalize, adapt, and optimize over time.[2] Yet the law has struggled […]The postShadow Derivatives: The Quiet Propertization of AI Learningappeared first onTexas Law Review.
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Original: Texas Law Review
