Distillation for Me, Theft for Thee?

The AI industry has been built on learning from the work of others.

Large models absorb patterns from books, websites, software code, research papers, articles, photographs, public discussions, and countless other forms of human-created knowledge. AI companies describe this process as training, learning, or innovation.

But when another laboratory learns from the outputs of a leading American model, the language suddenly changes. It becomes extraction, theft, or an attack.

Anthropic has alleged that DeepSeek, Moonshot AI, and MiniMax generated more than 16 million Claude interactions through approximately 24,000 fraudulent accounts to improve their own models. Anthropic says this violated its terms of service and regional access restrictions. Those are serious allegations, and the reported use of false accounts and circumvention of access controls should not be dismissed.

But the harder question cannot be ignored.

What principle should govern one model learning from another? And should that principle also apply when AI laboratories train on human-created information collected from the public internet?

These activities are not technically or legally identical. Public web data, licensed datasets, model outputs, unauthorised account creation, and circumvention of security controls raise different issues. A responsible discussion must recognise those distinctions.

But consistency still matters.

AI companies cannot argue that learning from publicly accessible human work is essential to innovation while implying that all machine learning from model outputs is inherently illegitimate. Nor should governments decide that the same technical method is innovative when used by an American company but threatening when used by a Chinese competitor.

The rules should be based on consent, licences, contractual obligations, access methods, deception, security circumvention, and demonstrable harm—not nationality.

This debate will shape the future of open research and AI competition. Excessively broad restrictions could allow dominant laboratories to learn from the world and then close the door behind them.