Read our white paper on a pragmatic approach to AI governance in America.

Source: Google Date: 2026-06-25 URL: https://blog.google/company-news/outreach-and-initiatives/public-policy/white-paper-ai-regulation/

Summary

Google published a white paper outlining its preferred approach to AI governance in the United States, rejecting both blanket regulation and a hands-off stance in favor of a tiered model. For frontier AI, the paper proposes an independent, federally overseen, industry-backed organization to set safety benchmarks and run voluntary audits — mirroring governance structures in other high-stakes sectors. For widely deployed applications, it recommends adapting existing legal frameworks rather than creating new AI-specific statutes, addressing sector-by-sector harms (child protection, IP, employment) through current law.

Implications

• Governance/regulation of AI models. The two-tier structure Google proposes — voluntary industry body for frontier models, adapted existing law for applications — is meaningfully different from the EU AI Act’s classification-and-mandate approach. If this framing gains traction in Washington, it shapes what compliance looks like for model providers: self-assessment and voluntary audit rather than pre-deployment licensing.
• Industry lobbying posture. Publishing a detailed policy white paper at this moment reads as proactive positioning ahead of anticipated federal AI legislation. Google is offering a specific framework, not just principles, which means lawmakers can adopt it as a template. Watch whether OpenAI and Anthropic publish competing frameworks or align with this one.
• Safety framing as shield. Centering “independent safety verification” in the frontier proposal lets Google argue it’s taking safety seriously while keeping the actual standard-setting inside an industry-dominated body. This is the same tension that emerged in financial sector self-regulation — voluntary audits and industry-set benchmarks can be robust or cosmetic depending on enforcement teeth that get added later.