Generative AI brings rapid business value, but the growing reliance on token-based pricing risks inflating long-term costs as AI becomes embedded in core enterprise operations.

Boards should act now to differentiate experimentation from dependency and invest in architectures that control cost, data, and flexibility over the long term.

The proposed solution is AI sovereignty: building and operating enterprise-controlled, self-hosted models to manage long-term costs, security, and governance, especially where frontier capabilities aren’t needed.

Treat AI architecture as a strategic concern, balancing external models with sovereign capabilities to avoid overreliance on external pricing models.

Agentic AI can amplify token costs through multi-step workflows, making costs compound rather than scale linearly and increasing financial risk.

Current market subsidies keep token prices low for now, but consolidation and rising profitability demands may push token costs higher and shift pricing power to providers.

In enterprise use, tokens become the economic dependency, as every prompt, retrieval, tool use, and agent decision consumes tokens and drives costs beyond initial expectations.

Leadership should consider whether a sovereign AI model can reliably, securely, and economically solve core problems over time, and whether owning capability is better than perpetual renting for critical workloads.