A deterministic economic analyzer modeling creator labor appropriation in foundation model pre-training. Calibrated against disclosures reported by The Washington Post citing Microsoft Director of Applied Science Brent Hecht's remarks characterizing commercial LLM training datasets as the "largest theft of labor in human history."
Total billion tokens ingested into pre-training architectures (LLaMA, GPT-4, Claude grade corpuses).
Proportion of ingested text, code, and editorial works scraped without explicit licensing or creator royalties.
Fair statutory royalty benchmark per 1,000,000 validated creative tokens, based on journalistic and technical syndication rates.
Uncompensated Liability = Corpus × Labor Share % × (Rate / 1M Tokens)
Foundation models ingest billions of creative tokens, where uncompensated text and code create massive statutory liabilities. The model evaluates uncompensated volume multiplied by fair creator royalties per million tokens to determine baseline economic exposure. Adjusting the sliders or clicking preset scenarios recalculates risk ratings and rebalances statutory safe harbor comparisons immediately.