Leading AI labs, despite intense competition, are collaborating through the Frontier Model Forum to detect and prevent Chinese firms from creating imitation models. This rare alliance is driven by the shared existential threat that 'adversarial distillation' poses to their business models and to U.S. national security.

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.

The argument that US labs must move at maximum speed to beat China is critically flawed. This frenetic pace often leads to weak cybersecurity practices, turning cutting-edge AI models into prime targets for Chinese state-sponsored theft. In this scenario, speed doesn't create a lead; it creates a vulnerability.

Despite intense domestic rivalry, top US AI labs like OpenAI, Anthropic, and Google are collaborating to detect "adversarial distillation"—where Chinese firms copy their models. This rare cooperation shows the shared commercial and national security threat from foreign competitors outweighs their direct competition.

Companies in pharma, finance, and other sectors are realizing that feeding their proprietary data to closed AI models creates a strategic risk. They fear the AI labs could become direct competitors, driving a shift towards sovereign, open-source models run on their own data.

Despite being fierce competitors, major AI labs work together behind the scenes. They share intelligence on suspicious API usage from shell companies to identify and thwart large-scale, coordinated distillation attacks from foreign adversaries, which might otherwise go undetected by a single lab.

Unlike traditional hardware where stolen plans take years to operationalize, AI competitors can incorporate leaked research or strategic directions within days or weeks. This accelerated timeline amplifies the payoff of espionage, prompting AI labs to actively train staff against social engineering and honeypot schemes.

The primary fear among large enterprises regarding frontier AI labs is not that they will become direct competitors. Instead, the core anxiety is that their sensitive, proprietary data used in prompts will leak or be used to train models that inadvertently benefit their existing rivals.

While public discourse on AI safety focuses on existential risk, for enterprises, safety means protecting proprietary knowledge ("alpha"). True enterprise AI safety is achieved by owning the compute, models, and data stack, preventing model providers from stealing trade secrets and customer data.