When a company distills knowledge from a competitor's AI, it's not just scraping pre-training data. It's a highly efficient process of extracting the model's intelligence, reasoning patterns, and skills. This is more akin to an apprentice directly interacting with and learning from a world-class expert than simply reading the same textbooks the expert used.

Despite creating supposedly superintelligent models, leading AI labs still rely on crude access restrictions to prevent 'distillation'—an existential threat where competitors replicate their models. This reveals a critical capability gap: their AI is not yet smart enough to detect and prevent its own theft.

Large, centralized AI models are vulnerable to 'distillation attacks,' where a smaller model can be trained cheaply by querying the larger one. This technical reality, combined with the moral hypocrisy of creators restricting copying after scraping the internet, strongly suggests a future dominated by decentralized, open-source models.

As more of the internet and code repositories are generated by leading AI models, any new model trained on this public data inadvertently "distills" the knowledge and quirks of those proprietary systems. This blurs the line between original training and outright copying.

As enterprises replace expensive proprietary models with cheaper open-source alternatives, frontier labs like OpenAI and Anthropic face an existential threat. Their strategic response could be to lobby for regulations that effectively make open-source models illegal, creating a protective moat.

Companies like Anthropic and OpenAI are shifting from being API providers to building first-party "super apps." This creates a conflict where they might reserve their most powerful models for internal use, giving smaller, distilled versions to API customers, thus undermining the third-party ecosystem they helped create.

In his trial against OpenAI, Elon Musk admitted under oath that using one AI model to train another—a practice known as distillation—is something 'all the companies do.' This confirms that a legally and ethically gray practice is widespread across the industry.

Frontier AI labs are restricting API access not just for security, but to prevent competitors from using 'distillation' to create cheap copies of their models. This practice makes it impossible to recoup massive R&D investments, forcing a move towards more restrictive, geopolitically motivated access.

The push for AI regulation, often led by companies like Anthropic, is likely leading toward an attempt to ban open-source models. The justification will be that open models lack guardrails and are therefore dangerous, effectively cementing the power of a few closed-source providers.