In 2024, the United States experienced an unprecedented surge in AI legislation, with nearly 700 AI-related bills introduced across 45 states and territories, including Puerto Rico, the Virgin Islands, and Washington, D.C. This explosive growth in state-level AI governance reflects a pivotal moment in tech policy — one where lawmakers are racing to address AI's rapid advancements, ethical dilemmas, and societal risks.
But as states take the lead in crafting AI regulations, a critical tension emerges: Will America's AI future be shaped by decentralized state laws or a unified federal framework? And how will the Trump administration's deregulatory stance influence the final form of AI governance?
State versus Federal AI Policy: A Growing Divide
While the federal government under President Trump has signaled a preference for light-touch AI regulation that prioritizes innovation and economic growth, states are moving in the opposite direction. From algorithmic bias protections to deepfake bans, legislatures are enacting laws to mitigate AI risks, creating a complex legal patchwork for businesses and developers to navigate.
Key Trends in State AI Legislation
- Consumer Protection and Antidiscrimination: Colorado's AI Act imposes a "duty of reasonable care" on developers of high-risk AI systems, requiring safeguards against algorithmic discrimination in employment, healthcare, and financial services. Similarly, Utah's AI Transparency Law mandates disclosures for generative AI use, with penalties for noncompliance.
- Combatting AI-Generated Deepfakes: Tennessee's ELVIS Act (Ensuring Likeness, Voice, and Image Security) became the first US law to ban unauthorized AI voice and likeness cloning, protecting artists and public figures. California and Texas are advancing bills to criminalize deepfake election interference and nonconsensual explicit content.
- Government and Public Sector Accountability: California and Georgia are leading efforts to regulate AI use in government operations, ensuring transparency and adherence to ethical standards in public-sector AI deployments.
The Federal Stance: Deregulation
Three days after his inauguration, Trump signed Executive Order 14179, "Removing Barriers to American Leadership in Artificial Intelligence," which marked a significant pivot in federal AI policy. This order revoked several previous AI directives deemed obstacles to innovation and instructed federal agencies to develop plans promoting AI advancement free from what the administration characterized as ideological constraints.
The administration has consistently championed a hands-off
regulatory approach, arguing that extensive oversight would hamper
innovation and diminish America's competitive edge against
China and the European Union. This philosophy aligns with broader
deregulatory tendencies across various sectors, emphasizing
market-driven solutions over government mandates. However, it also
creates a vacuum that is increasingly filled by a mishmash of
state-level initiatives, leaving businesses to navigate a winding
path of disparate requirements just as artificial intelligence
begins to fulfill its promise to transform the technological
landscape. American Web3 businesses are increasingly finding
themselves at a competitive disadvantage vis-à-vis their
Chinese and European counterparts operating under a unified
regulatory regime. US companies must monitor and adapt to dozens of
potentially conflicting state regulations, which discourages
investment in cutting-edge AI applications.
A comprehensive federal framework could include components to
balance innovation with consumer protections:
- National Safety and Ethical Standards: Drawing inspiration from the EU AI Act's risk-based approach, federal legislation should establish tiered requirements based on the potential impact of an AI system. High-risk applications — including those managing financial assets or making consequential decisions — would require rigorous testing and monitoring, while lower-risk applications would face proportionally lighter requirements.
- Discrimination Safeguards: Federal AI regulation should establish baseline protections against algorithmic discrimination while creating accountability mechanisms for AI-driven decisions. This becomes particularly important in Web3 contexts where smart contracts may execute consequential transactions autonomously. The legislation should prohibit manipulative design practices ("dark patterns") in AI interfaces — a growing concern as AI chatbots and assistants become primary interfaces for digital transactions.
- Data Privacy Regulations: Clear rules
on data collection, usage, and consent are essential for the
ethical development of AI. Federal regulation should require:
– Explicit consent before using consumer data to train AI models
– Transparency regarding data sources and usage - Innovation Incentives: Balanced
regulation should include provisions to accelerate responsible AI
development:
– Research funding for breakthrough technologies
– Regulatory sandboxes allowing controlled testing of novel applications
– Tax incentives for companies investing in trustworthy AI systems
Unique Regulatory Considerations at the AI and Web3 Nexus
The void and regulatory patchwork present particular difficulties
for Web3 companies operating at the intersection of blockchain and
AI technologies. This fragmentary oversight demands strategic legal
foresight. Several key intersections deserve particular
attention:
- Decentralized AI Governance Models: Traditional regulatory frameworks struggle to address AI systems deployed on decentralized networks where responsibility is distributed across numerous participants. Who bears compliance responsibility when an AI operates on a DAO? Current state regulations rarely address these nuanced questions of distributed accountability.
- Automated Decision Systems: As smart contracts increasingly incorporate AI capabilities for adaptive decision-making, they face overlapping regulatory regimes. For instance, Colorado's requirement for human notification before AI-driven decisions conflicts with the autonomous execution principle underpinning many smart contracts.
- Data Sovereignty in AI Training: Blockchain's immutable nature creates tension with emerging AI regulations concerning data privacy and the right to be forgotten. When training data is permanently stored on a blockchain, compliance with deletion requests becomes technically challenging, if not impossible.
- Cross-Border Compliance: Web3's borderless operations face particular challenges navigating the emerging patchwork of AI regulations. Operations spanning multiple states — or internationally between the EU's strict AI Act regime and America's fragmented approach — create exponential compliance burdens.
Consolidating domestic AI regulation would acknowledge the unique technical constraints under which Web3 applications operate while maintaining equivalent safety outcomes. Transparency obligations should include documentation of datasets and model logic, with special consideration for the cryptographic verification capabilities inherent in blockchain systems. For Web3 companies, this would extend to AI-powered wallet interfaces and trading advisors that might influence cryptocurrency investment decisions. For Web3 applications, these requirements present unique technical challenges given blockchain's immutable nature. Federal legislation should acknowledge these constraints while encouraging technical solutions, such as zero-knowledge proofs, that can protect privacy without compromising blockchain integrity. These incentives should specifically target technologies addressing the unique challenges of the Web3-AI intersection, such as privacy-preserving machine learning on blockchain data or governance mechanisms for decentralized AI systems.
Strategic Compliance in an Uncertain Landscape
Until comprehensive federal legislation emerges, Web3 companies deploying AI must develop strategic approaches to navigate the current regulatory hodgepodge:
- Modular Compliance Architecture: Companies should design AI systems with regionally configurable components that can adapt to varying requirements across jurisdictions. This might include adjustable transparency levels, opt-in/opt-out mechanisms, and flexible human oversight options.
- Proactive Risk Assessment: Regular algorithmic impact assessments should identify potential harms before they manifest. These assessments should examine both AI-specific risks and those unique to blockchain implementations, such as the immutable deployment of flawed models or smart contracts with embedded biases.
- Documentation and Governance: Comprehensive documentation of AI development processes, training data, and deployment decisions creates an essential compliance foundation. Web3 companies should consider how blockchain's inherent transparency and immutability can strengthen this documentation while addressing privacy concerns.
- Cross-Sector Collaboration: The intersection of AI and Web3 technologies represents uncharted regulatory territory. Companies should actively participate in industry groups and public-private partnerships to develop workable standards that balance innovation and protection.
Preparing for the Convergence
The fragmented AI regulatory landscape presents significant challenges but also opportunities for forward-thinking Web3 companies. As federal action becomes increasingly likely, businesses that proactively adopt robust compliance frameworks will gain competitive advantages while helping shape emerging standards.
For legal practitioners advising clients in this space, maintaining vigilance on rapidly evolving state regulations while advocating for sensible federal harmonization will be essential. The convergence of blockchain, cryptocurrency, and artificial intelligence represents not only a technological frontier but a governance frontier where traditional regulatory approaches must evolve.
The rules of this new landscape are being written now by legislators, regulators, and the courts. Web3 companies that engage constructively in this process while implementing flexible compliance strategies will be best positioned to thrive in the uncertain regulatory future.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
