US AI regulation enters deep water: industry risks and responses under a fragmented landscape

Introduction: When Every State Has Its Own AI Laws

In 2025, the global AI industry's focus is shifting from technological capability to governance rules. Unlike the EU, which has established a unified regulatory framework through the AI Act, the United States has yet to enact comprehensive AI legislation at the federal level. According to the U.S. section of White & Case's *AI Watch: Global regulatory tracker*, current U.S. AI regulation is effectively a patchwork of federal executive orders, state legislation, and industry self-regulation, forming a complex compliance map.

This fragmentation is not a temporary transition, but an inevitable outcome of the U.S. political system and industrial landscape. For corporate decision-makers, understanding this landscape is no longer a matter reserved for legal departments, but a strategic issue concerning product roadmaps, market access, and capital valuation.

Industry Context: Why Has Regulation Become Fragmented?

The fragmentation of U.S. AI regulation stems from the interaction of three forces:

Federal legislative stagnation. Despite multiple AI-related bills having been introduced in Congress, the two parties have significant disagreements on core issues such as privacy, liability, and fairness, making it difficult to reach a majority consensus. The executive branch has therefore turned to existing laws and executive orders to promote regulation, such as the White House's 2023 Executive Order on AI, which required federal agencies to develop safety standards for AI use. However, the order has limited legal binding force and may change with shifts in administration.

States racing to legislate. California, Colorado, New York, and other technology and financial hubs have taken the lead in advancing AI-specific laws, covering algorithmic transparency, impact assessments for automated decision-making, and content labeling for generative AI. Taking Colorado's AI Act, passed in 2024, as an example, the law imposes affirmative obligations on developers and deployers of high-risk AI systems, making it a benchmark for U.S. state-level regulation—but it has also raised industry concerns about cross-state compliance costs.

Regulators taking the initiative. Enforcement agencies such as the Federal Trade Commission (FTC) and the Equal Employment Opportunity Commission (EEOC) are using existing consumer protection and civil rights laws to file lawsuits or issue guidance against AI discrimination, false advertising, and other practices. This "enforcement-first" model means that rules are gradually accumulated through case-by-case precedents, yet it fails to provide companies with forward-looking certainty.

White & Case's tracking report systematically organizes these developments. Its value lies not only in the compilation of legal texts, but also in revealing a shift in U.S. regulation from "technology neutrality" to "scenario implementation"—regulators are beginning to focus on the consequences of AI applications in specific industries such as hiring, healthcare, and finance.

Market Impact: Who Pays for the Uncertainty?

Regulatory fragmentation has had a direct and quantifiable impact on the U.S. AI market. Companies, customers, and investors all need to reassess their risk exposure.Compliance costs for enterprises have soared. A company operating nationwide must simultaneously track proposed regulations from at least dozens of states. Each law differs in its definition of "high-risk AI," notification obligations, and audit standards, meaning enterprises need to establish a matrix-style compliance system. According to preliminary industry estimates, mid-sized enterprises could see annual compliance costs increase by millions of dollars to meet the fragmented regulatory requirements across states, while large enterprises would need to double the size of their global compliance teams.

Customer decision-making has turned conservative. When procuring AI solutions, enterprise clients are beginning to include compliance capability as a core evaluation metric. Regulated industries such as healthcare, finance, and human resources are particularly sensitive—they not only ask about vendors' model performance, but also demand proof that their systems can satisfy legal requirements in multiple states. This shift has turned compliance capability into a new selling point for AI products, rather than a cost item.

Investment logic has shifted. Venture capital firms are adding dedicated AI compliance reviews to their due diligence processes, conducting in-depth examinations of target companies' data governance, model transparency, and discrimination audit capabilities. Startups that fail to build a governance framework early may face valuation discounts as early as their Series A round, or even be asked by investors to replace management. Meanwhile, startups focused on AI compliance auditing and model testing are beginning to attract capital, forming a new niche track.

Competitive Landscape: Beneficiaries and Those Under Pressure

Regulatory fragmentation is redistributing competitive advantages across the AI industry chain, with companies in different roles facing varying circumstances.

Large tech companies have room for "regulatory arbitrage." Leading players such as OpenAI, Google DeepMind, and Microsoft AI leverage substantial legal resources to simultaneously handle federal lobbying, state-level compliance, and international coordination. They turn regulatory documents into product differentiation—for example, proactively publishing model information cards, which both responds to regulatory initiatives and builds consumer trust barriers. For these companies, regulatory pressure instead becomes a moat that squeezes out smaller competitors.

AI startups bear the greatest pressure. Early-stage founding teams often focus on technological breakthroughs and lack dedicated legal staff. A seemingly simple state-level transparency requirement could force rework of the model training process. More seriously, independent third-party testing required for compliance audits often costs hundreds of thousands of dollars, directly eroding startups' burn rate. This could raise the barrier to entry for AI application-layer entrepreneurship, with outstanding teams choosing to merge into large companies to gain access to compliance infrastructure.合规咨询与技术服务商直接受益。 顶级律师事务所、四大会计师事务所和新兴AI治理平台迎来业务高峰。Wachtell、White & Case等律所纷纷设立AI监管专项团队，为企业提供追踪、解读和落地服务。同时，提供模型可解释性工具、歧视检测软件的创业公司（如Credo AI、Fiddler）订单激增，成为碎片化监管下的隐形赢家。

国际生态出现绕行效应。 部分全球企业因美国各州合规复杂，优先将AI研发部署于欧盟或英国等监管框架统一的市场。这并非放弃美国市场，而是将其作为准入门槛更高的“合规高地”，待规则明朗后以合规产品打入。这种策略反过来促使美国政策制定者反思碎片化的成本，可能加速联邦层面协调立法。

Enterprise Implications：企业如何从合规威胁转向治理优势

面对碎片化监管，企业不应被动应付，而应将其纳入AI战略顶层设计。以下四项措施具有优先实施价值：

建立动态监管雷达。 单靠季度法律顾问报告已无法应对变化速度。企业应成立跨部门AI治理委员会，联动法务、技术、风控和业务团队，使用White & Case这类追踪工具保持周度监控，并对所有AI应用实施影响评估分级。

将合规嵌入产品研发流程。 在模型开发阶段即引入公平性测试、文档化设计和透明度记录，而非在市场监管后补救。这要求MLOps流程与合规管理深度耦合，形成“合规即代码”的自动化检查点。

重新定位合规价值主张。 面向客户和投资者，主动披露治理结构、审计结果和监管应对策略。与监管机构保持沟通，参与规则制定听证。企业不妨将合规作为全球市场扩展的先发优势，尤其是当欧盟AI法案全面生效后，一套完善的治理体系可同时满足多个司法辖区要求。

预留监管应对预算。 将合规成本作为固定运营支出，而非项目性偶发费用。预测未来24个月可能落地的各州法律，提前进行沙盒测试和系统改造。对关键系统建立“监管开关”，在特定州法律生效时快速调整算法逻辑。

Outlook：未来12至36个月的趋势预判Within 12 months: Federal agencies intensify enforcement, state-level legislation accelerates. From the second half of 2025 to 2026, the FTC and EEOC are expected to announce more AI enforcement cases, particularly in consumer credit, job advertisements, and healthcare decision-making. At least 10 states will advance substantive AI legislation, and some states may attempt to unify model laws, but the overall fragmented landscape will remain unchanged.

Within 24 months: A federal framework takes shape, industry standards rise. Congress may pass a foundational AI bill focusing on safety research, federal procurement, and algorithmic transparency, but it will not completely replace state laws. Meanwhile, technical standards organizations (such as NIST and IEEE) will issue actionable guidelines, effectively forming a de facto industry baseline for compliance. Companies should shift their focus from legal provisions to the implementation of standards.

Within 36 months: Global regulatory coordination becomes the main theme, and the US model moves toward pragmatism. The EU, US, UK, and major Asia-Pacific economies will form a soft coordination mechanism characterized by "convergent principles, divergent details." US regulation will place greater emphasis on a risk-based approach, in contrast to the EU's strict horizontal regulation. Companies with global compliance capabilities will reap the greatest benefits, as regulatory convergence reduces the cost of adapting across multiple markets.

Ultimately, regulatory fragmentation is a hurdle that the US AI industry must overcome on its path to maturity. Companies that view regulation as a nuisance today may find tomorrow that governance systems have become hard currency for participating in global competition. Now is the moment to adjust course.