Do data and compute have diminishing marginal utility?
The economics of artificial intelligence are fundamentally distinct from previous technological shifts. Traditional rules governing industrial production, and even early digital products, break down when applied to large language models. This exploration tracks a series of interconnected structural realities: how diminishing returns apply to the foundational inputs of machine learning, whether base model training can ever be truly finalized, the trajectory of synthetic data, and the political economy of user data compensation. These insights represent a record of thinking in progress for an unmapped economic landscape. I. Diminishing Marginal Utility in Machine Learning Data: A Classic Case of Diminishing Returns When evaluating data and compute, data represents a classic textbook resource governed by diminishing marginal utility. The first thousand training examples provide massive learning gains, whereas subsequent millions yield progressively less. Error rates in neural netw...