Relying on third-party LLMs is a temporary phase. The ultimate advantage will come from companies training and owning their own models, potentially on physical hardware in their office. This transforms AI from a rented tool into a core, defensible intellectual property.

Who owns an employee's personalized AI agent? If a tech giant owns this extension of an individual's intelligence, it poses a huge risk of manipulation. Companies must champion a "self-sovereign" model where individuals own their Identic AI to ensure security, autonomy, and prevent external influence on their thinking.

Beyond data privacy, enterprises are concerned that AI agents powered by frontier models will absorb their institutional knowledge. This creates a risky operational dependence where core business learnings are owned and controlled by an external AI company, not the enterprise itself.

Frontier models from giants like OpenAI force enterprises to share sensitive data, creating platform risk. The future of corporate AI lies in private, fine-tuned, open-source models that keep a company's "intelligence" in-house, preventing it from training potential competitors.

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.

Enterprises are skeptical of sharing core, differentiating data with frontier model providers. They are more comfortable using proprietary models for general functions like HR and procurement, while keeping their most sensitive business data for open-source or in-house models they can control.

Echoing crypto's "not your keys, not your crypto," a new ethos is emerging in AI: if a company's core product relies solely on another's model via an API, it has no real ownership. Startups are realizing they must control their own model weights to ensure steerability, capture their data flywheel, and build a defensible business.

Alex Karp argues that companies using third-party frontier models are inadvertently transferring their "alpha"—proprietary data, workflows, and competitive advantage—to the AI labs. He advocates for "AI sovereignty," where organizations own their compute, data, and models to protect their intellectual property.

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.