US AI regulation "fragmentation" becomes a new variable in enterprise-level AI deployment: Baibi AI Watch tracks policy trends.

Industry Background: Regulatory Tracking Becomes AI Industry "Infrastructure"

White & Case LLP's recently updated "AI Watch: Global Regulatory Tracker" has drawn attention from the industry. The tool covers multiple jurisdictions, including the African Union, Australia, Brazil, Canada, China, Colombia, and the European Commission, with the U.S. page specifically mapping AI regulatory developments at both the federal and state levels.

The value of this tracker lies in its reflection of how AI regulation has evolved from a fringe issue into a core variable in corporate AI strategy. Over the past three years, the explosive growth of generative AI has prompted regulators across countries to accelerate their actions, and as the U.S. serves as a high ground for AI technology, its policy direction directly affects the global AI industry chain layout.

However, in contrast to the U.S.'s leading position in AI technology, its regulatory framework has still not formed a unified federal legislation. Instead, U.S. AI regulation presents a fragmented landscape of "executive orders and agency guidance at the federal level, and active legislation at the state level." This pattern is reshaping the rollout pace and cost structure of enterprise-level AI applications.

Market Impact: Rising Compliance Costs, Pressure on Deployment Pace

For corporate decision-makers, the fragmentation of U.S. AI regulation brings the most direct financial impact — a significant rise in compliance costs.

An AI company headquartered in California with business spanning multiple states may need to simultaneously comply with California privacy regulations, Colorado's AI consumer protection rules, New York City's AI hiring audit law, and the NIST AI Risk Management Framework recommendations at the federal level. Different jurisdictions have different definitions of "high-risk AI systems," transparency requirements, and accountability mechanisms, forcing companies to establish multiple sets of compliance processes.

More critically, regulatory uncertainty is suppressing the expected return on investment for AI projects. According to the White & Case tracker, in "high-risk" AI scenarios such as workforce management, credit assessment, and medical diagnosis, the proportion of companies delaying decisions due to legal concerns has risen notably. Investment institutions, when conducting due diligence on AI startups, have also made regulatory compliance capability a core evaluation criterion, putting valuation pressure on some early-stage AI companies.

For large multinational corporations, the situation is even more complex. They must not only deal with fragmented rules within the U.S. but also align with extraterritorial regulations such as the EU AI Act, forming a cross-compliance matrix. These multiple constraints are driving up the total cost of ownership for enterprise AI and prompting companies to reassess where AI projects are positioned — whether to build in-house, procure, or choose a deployment environment with looser regulation.

Competitive Landscape: State-Level Legislative Race Creates Regulatory Arbitrage Window

The fragmentation of U.S. AI regulation is also reshaping the competitive landscape of the industry.## Competitive Landscape: State-Level Legislative Race Creates Regulatory Arbitrage Window

The fragmentation of U.S. AI regulation is also reshaping the industry competitive landscape.

On the one hand, tech giants, backed by strong legal teams and resources, can maintain competitive advantages under multi-state compliance pressures. For example, companies such as Microsoft, Google, and Amazon actively lobby state legislatures and proactively publish AI governance white papers, attempting to shape their own practices into industry standards. These giants tend to favor a unified federal framework to reduce compliance complexity, while leveraging their influence to affect rule-making.

On the other hand, small and mid-sized AI startups bear greater compliance pressure. Lacking legal resources, they may be forced to abandon certain high-risk application scenarios or choose to restrict their products to states with looser regulations. This kind of "regulatory arbitrage" may cause AI innovation to cluster in regulatory troughs, which in turn intensifies policy competition among states.

Notably, pioneering legislation in places such as Colorado, Illinois, and California is creating a de facto "regulatory export." Because large enterprises often adopt nationwide unified product strategies, they tend to align their compliance with the strictest state standards, which indirectly raises the industry-wide entry threshold. Therefore, the impact of state-level legislation may extend beyond its jurisdictional boundaries and become a de facto national standard.

The Baibi Tracker shows that this "states first, federal lagging" pattern will not change in the short term. As more states advance AI bills in 2025-2026, companies will face an increasingly complex compliance map.

Implications for Enterprises: Build a Dynamic AI Governance Framework, Not One-Time Compliance Projects

Facing a fragmented regulatory environment, enterprises should abandon the "one-time compliance" mindset and shift toward building a dynamic AI governance system.

First, enterprises need to incorporate regulatory monitoring into their regular mechanisms. The value of tracking tools such as Baibi AI Watch lies not only in providing summaries of legal texts, but also in helping enterprises anticipate changes during the early stages of policy formation. Enterprises should designate dedicated personnel to track federal executive orders, agency guidance, and state legislative developments, and report to the board of directors on a regular basis.

Second, enterprises should build a risk-based AI classification and grading system. Referring to the NIST AI Risk Management Framework, they can classify AI applications into categories such as "low-risk" and "high-risk" based on risk levels, and allocate different compliance resources accordingly. For high-risk scenarios, algorithm audits, data governance, and model explainability should be developed in advance.

Furthermore, enterprises should pay attention to compliance transmission in the AI supply chain. Many enterprises purchase AI models from third-party vendors, but regulatory responsibility often falls on the deployer. Therefore, enterprises should clearly define AI compliance responsibility boundaries in procurement contracts and require suppliers to provide transparency documentation and impact assessment reports.

Finally, enterprises should actively participate in policy feedback. Trackers from law firms such as Baibi also remind us that regulatory rules are often formed through stakeholder bargaining. By expressing industry perspectives through industry associations, public comment periods, and other channels, enterprises can help reduce unrealistic regulatory demands and lower future compliance costs.

Outlook: Over the Next 12-24 Months, U.S. Federal AI Legislation Remains Difficult to Materialize, but Rules Will Gradually Converge In the next 12 months, the likelihood of comprehensive AI legislation passing at the U.S. federal level remains very low. Divisions between the two parties over AI regulatory approaches, compounded by the impact of election cycles, have kept the legislative process slow. However, we can expect federal administrative agencies to continue strengthening AI oversight through their existing authorities—for example, the Federal Trade Commission (FTC) enforcing rules against AI-related false advertising, and the Federal Communications Commission (FCC) regulating AI voice fraud.

In the next 24 months, more states will advance AI legislation, but state rules are likely to show a degree of convergence. Industry coalitions, professional associations, and consulting firms will push for the standardization of "best practices," and corporate compliance experience will in turn influence lawmakers. Therefore, although the form remains fragmented, substantive regulatory requirements may gradually converge around a few core themes: transparency, non-discrimination, safety accountability, and data privacy.

Looking three years ahead, the United States is highly likely to introduce some form of AI framework legislation at the federal level, but it will more likely be a combination of "framework legislation plus industry self-regulation." In this process, global regulatory trackers from institutions such as Baibi will continue to provide key intelligence to the market. For enterprises, embedding dynamic regulatory monitoring into strategic decision-making as early as possible will become a core capability for gaining long-term advantages in the AI race.

After all, the endgame of the AI industry is not only a technology race, but also a competition in institutional adaptability.