AI firms face criticism over unpaid use of human creativity
The article argues that major AI companies depend on vast amounts of human-created content to train their models while resisting payment, licensing obligations or revenue-sharing with its creators. It says recently unsealed court documents in The New York Times’ case against OpenAI and Microsoft suggest that some insiders recognised this as potentially large-scale exploitation, raising concerns about the long-term damage to journalism, fiction, software and other creative industries.
Microsoft documents reportedly warned that generative AI could undermine the livelihoods of the people who produce its training data, creating a “doom loop” in which AI weakens the supply of valuable human content. The companies dispute the allegations and rely on a fair-use defence, while testimony cited in the filings allegedly indicated that OpenAI made no known effort to exclude paywalled material from its training datasets; the judge has not yet ruled.
- AI relies on human work while resisting payment and licensing.
- Internal documents reportedly acknowledge serious economic risks.
- The legal dispute remains unresolved.
Both sides, in good faith
The strongest fair case each way — we don't pick a winner.
The case for
AI companies rely on enormous quantities of human-created work, and creators can reasonably argue that using such material without payment or meaningful consent appropriates economic value while competing directly with its original producers. Licensing or revenue-sharing could help sustain journalism, fiction, software and other creative fields, and prevent a cycle in which AI reduces the resources needed to produce the high-quality content on which future systems depend.
The case against
AI developers can argue that training models involves learning from patterns in publicly accessible material rather than republishing or substituting for each individual work, making it analogous in important respects to human learning and potentially protected by fair-use principles. Broad licensing requirements could make advanced AI prohibitively expensive or entrench the largest incumbents, while innovation may generate new markets, tools and income for creators even if existing business models are disrupted.
Read the full article at the source →
Originally published by The Register as “Big AI’s content problem: Take the work, keep the money”.