The Private Capture of Public Genius
This essay uses the 1956 AT&T antitrust consent decree—which forced the company to license its entire patent portfolio royalty-free—as a historical mirror to frame how frontier AI labs (OpenAI, Anthropic) scrape the public internet to build private models. The author argues the training corpus is a deltaic accumulation of humanity's collective expression, and compressing it into model weights constitutes a private capture of public genius. It reviews the legal landscape: the Bartz v. Anthropic and Kadrey v. Meta rulings found LLM training 'transformative' under fair use, but the market dilution question remains unresolved. The author proposes a 'Corpus Royalty'—a fixed percentage of gross revenue paid into a public fund distributed equally to every eligible American—as the only administrable remedy for unattributable collective contribution (since Shapley values are computationally infeasible at frontier scale). The piece also explores how different layers of the internet (content, discovery, attention, contribution, integrity) interact and risk collapse under AI-generated spam, and notes that Elinor Ostrom's eight conditions for governing commons are entirely unmet by today's web. Suitable for engineers and researchers interested in AI governance, data policy, intellectual property, and the social contract underlying model training.