by Dev Nag, CEO & Founder of QueryPal
The US House’s proposal to impose a 10-year freeze on state-level AI regulation is more than a political maneuver. It’s a pivotal chance to unify the fragmented regulatory landscape currently challenging AI adoption at scale. For enterprises navigating the complexities of deploying artificial intelligence across multiple jurisdictions, the promise of a single, clear, national framework is long overdue.
While debates swirl around whether this freeze is a giveaway to big tech or a blow to state innovation, we’re missing a far more practical point: Regulatory clarity is the foundation for responsible deployment. Without it, AI development remains throttled by compliance costs or pushed into the gray zones of risk tolerance. In either case, consumers lose, innovation stalls, and trust erodes.
The cost of fragmentation
Today, there’s no single rulebook for AI. Instead, we’re seeing a growing tangle of state laws — from California’s SB 1047 to New York’s hiring algorithm audits to Texas’s rules on synthetic media. These efforts may be well-intentioned, but they’ve become a logistical and legal minefield for companies that operate nationally.
Engineering teams must continually adjust product behavior to comply with a patchwork of local regulations and mandates. Legal teams spend more time interpreting state statutes than preparing for upcoming federal frameworks. Compliance strategies have become more about geography than ethics or safety. That’s not sustainable.
A unified federal approach doesn’t mean no regulation, but coherent regulation. A decade-long moratorium on state-level rulemaking buys time to define what that coherence should look like, ideally in a way that prioritizes transparency, accountability, and scalability across industries.
Predictability fuels progress
One of the most powerful things a consistent framework offers is predictability. Without clear rules, companies make conservative bets by delaying deployments or shifting focus to markets where rules are easier to navigate, even if the need for ethical AI is greater elsewhere.
For example, a company designing an AI tool for healthcare may face drastically different data handling requirements in Illinois than in Florida. In response, the company might exclude certain populations or features from its platform altogether — not because it wants to, but because the compliance risk isn’t worth it.
That doesn’t lead to equity. It leads to exclusion.
A national framework would simplify this calculus. Developers could design products for broad application, knowing that one set of standards — not 50 — determines what’s acceptable. This foresight would create a more equitable deployment path, especially for underserved or lower-resourced regions that often get left out of early AI rollouts due to compliance concerns.
What the freeze actually does
Critics of the moratorium often portray it as a disguised deregulation, but that overlooks the nuance. The bill doesn’t strip away oversight. It simply centralizes it, placing the onus on federal agencies to create actionable, enforceable, and consistent rules. Doing so reduces the legal uncertainty that currently plagues cross-border deployment and helps businesses focus their compliance investments in one direction.
The freeze also gives CIOs and procurement leaders clearer guidance when evaluating vendors. Rather than chasing local optimization — tools tailored to a specific state’s AI law — they can prioritize solutions aligned with anticipated federal standards. That, in turn, encourages a more robust and secure AI vendor grounded in standard best practices.
Risks still exist
To be clear, this isn’t a get-out-of-jail-free card for enterprises. A decade-long freeze could leave certain harms unaddressed if federal regulators fail to act swiftly or comprehensively. Without thoughtful governance, gaps will emerge, especially in areas like facial recognition, election misinformation, and algorithmic discrimination.
But this isn’t a reason to reject the freeze outright. It’s a reason to treat it as a mandate for federal leadership. The real risk isn’t the pause on state laws but the potential for federal inaction during the pause.
Agencies must treat this window as a once-in-a-generation opportunity to define AI standards with durability, nuance, and public input. The timeline is generous. The work should not be slow.
State innovation
There is also a legitimate worry that the freeze suppresses the “laboratories of democracy” function that state-level innovation has historically provided. Many essential consumer protections — data privacy, anti-discrimination measures, and even clean energy laws — originated in states before entering federal code.
But we should ask, “Is that the right model for AI?” AI is not regional. A recommendation engine doesn’t care where a user lives. A biased training dataset doesn’t correct itself when crossing state lines. The ethical concerns and safety risks are global in scope, and so too should be the framework that governs them.
Instead of using states as regulatory labs, we should use pilot programs, stakeholder engagement, and structured public comment to evolve federal rules intelligently. There is still room for local experimentation — but not at the expense of national consistency.
Toward a sustainable AI future
AI is fast becoming infrastructure. It’s not a side project or an experimental trend — it’s the engine beneath hiring platforms, supply chains, legal systems, public safety, and more. Infrastructure requires standards. Standards require consensus. And consensus is hard to teach when the rules change every 300 miles.
A 10-year state regulation freeze offers something rare: the chance to step back, align nationally, and design policy for what AI is becoming, not what it has been. The real question is whether we use that time wisely.
Because if we do, we’ll end up with a framework that supports innovation, protects citizens, and gives businesses the clarity they need to build confidently. If we don’t, we’ll spend another decade building around inconsistency — and that’s not a future anyone should be coding toward.
Dev Nag is the CEO/Founder at QueryPal. He was previously on the founding team at GLMX, one of the largest electronic securities trading platforms in the money markets, with over $3 trillion in daily balances. He was also CTO/Founder at Wavefront (acquired by VMware) and a Senior Engineer at Google, where he helped develop the back-end for all financial processing of Google ad revenue.