There is currently no comprehensive federal statute specifically governing AI; however, regulatory agencies and state legislatures have issued guidance, enforcement actions, and laws that collectively form a developing patchwork of AI-related regulations. Founders face conflicting obligations from state legislatures, federal agencies, and political shifts that swing with each administration.
The Biden administration issued a non-binding “AI Bill of Rights” framework, which provided policy guidance but did not create new legal obligations. The framework emphasized safety, safeguards against discrimination, and transparency. It was advisory, but regulators treated it as a signal to act. The FTC began scrutinizing AI-generated outputs, marketing claims, and bias risks. Agencies issued warnings. Enforcement actions followed.
In 2025, the Trump administration issued the Removing Barriers Executive Order, signaling a shift toward deregulation and prioritizing innovation. While it directed agencies to review and revise guidance, it did not repeal existing statutory obligations or the authority of regulators. That order directs agencies to cut restrictions, eliminate guidance inconsistent with AI growth, and promote American dominance in the AI sector. It positions regulation as an obstacle, not a tool.
Founders cannot wait for Congress to resolve the divide. Decisions about data collection, model outputs, user disclosures, and enforcement readiness must be made now. Building for compliance under the wrong framework creates rework, exposure, and delays at launch. What a team ships in one state may trigger liability in another.
The law is not settled. But product teams are already accountable.
The Federal Patchwork: Executive Orders, Proposed Bills, and Agency Guidance
Federal AI governance is a moving target. No single statute defines permissible use, risk thresholds, or design obligations. Instead, companies face a layered system made up of executive orders, regulatory enforcement, and proposed legislation.
The Federal Trade Commission has initiated enforcement actions involving AI, including a case against Rite Aid over facial recognition use that lacked adequate safeguards, raising concerns under existing consumer protection laws.
The FTC has warned companies against deploying AI tools that generate bias, mislead users, or exaggerate performance. Claims about fairness or explainability must be defensible. If not, they trigger enforcement.
The Federal Communications Commission ruled that existing robocall laws apply to AI-generated voices. That decision extends liability to voice cloning tools marketed or used in commercial messaging.
The Biden AI Bill of Rights introduced principles around data privacy, discrimination prevention, and human fallback. While rescinded, those principles still influence agency behavior and state-level legislation.
Congress has proposed several AI-related bills, such as the SAFE Innovation Act, the NO FAKES Act, and others, although none have passed into law as of 2026. These bills reflect growing concern around AI safety, IP protection, and transparency. The NO FAKES Act targets AI-generated likenesses, criminalizing unauthorized use of voices and faces. The AI Research Innovation and Accountability Act pushes for transparency requirements, safety benchmarks, and NIST-driven sector guidance.
The political shift is real. To start with, the Biden administration favored front-loaded obligations. Conversely, the Trump administration promotes deregulation and market-led growth. The difference shapes how agencies interpret their power, and whether developers must preemptively bake compliance into design.
Founders should not wait for federal legislation, as enforcement under existing laws, such as consumer protection, anti-discrimination, and privacy statutes, is already underway and can apply to AI systems depending on the use case. The law is being made through audits, litigation, and agency actions right now.
State AI Laws Are Setting the Real Precedents
Federal law is slow, even though States are not waiting. The first enforceable AI statutes are coming from state legislatures, and they are reshaping product development more than anything coming out of Washington.
Colorado passed the first comprehensive AI statute in the United States. The Colorado AI Act, effective in 2026, imposes a duty of reasonable care on developers and deployers of “high-risk AI systems” and requires risk mitigation practices, impact assessments, and documentation, but does not apply to all AI tools.
Companies must identify foreseeable risks, implement controls, and document compliance. No revenue threshold applies. If you build or deploy AI in Colorado and your system falls under the high-risk definition, the law applies.
California followed with a suite of targeted bills. Developers of generative AI systems must disclose high-level summaries of their training datasets, including whether the data contains copyrighted or personal material. Separate laws regulate deepfake content in elections, require AI-generated content disclosures for consumer-facing tools, and impose transparency standards for healthcare and public-facing AI platforms.
The California AI Transparency Act includes penalties for non-compliance, up to $5,000 per day in some cases, particularly related to failure to disclose AI-generated content or comply with consumer-facing transparency obligations. Failing to label AI content or respond to data requests carries direct financial consequences.
Utah, Texas, and Tennessee have taken narrower approaches. Utah’s law requires disclosure when GenAI is used in professional services. Texas restricts the use of AI in government systems that could manipulate behavior or violate constitutional rights. Tennessee passed the ELVIS Act, protecting voice and likeness rights against AI-based impersonation.
These state laws impose enforceable obligations, with regulatory authority and financial penalties for non-compliance. Companies must treat them as binding law, not policy suggestions. They also signal what other states will copy. Founders building for scale must treat these as floor-level requirements, not one-off regional rules.
Key Risk Zones Emerging in U.S. AI Legislation
The themes are clear. Across both federal and state levels, four legal pressure points are emerging in AI regulation. These are not theoretical. They map directly to current litigation and draft legislation.
Bias and discrimination risks in AI systems, especially those used in hiring, lending, or healthcare, can create liability under civil rights and consumer protection laws. Companies are expected to implement documented processes to mitigate these risks, even if discrimination was unintended.
Deepfakes and Digital Likeness. Laws now protect voice, face, and image as intellectual property. If a system generates synthetic media that uses someone’s likeness without consent, that use may trigger right-of-publicity violations or criminal exposure. Developers building with video, voice, or influencer-adjacent content must map consent rights and downstream usage.
Automated Decision-Making (ADM). ADM tools that make or inform consequential decisions trigger new duties. California’s privacy agency now requires opt-outs and impact assessments. Businesses using AI to triage job applicants, approve loans, or rank medical needs must disclose that use, provide a human fallback, and explain outcomes when challenged.
Training Data Transparency. Certain states, like California, now require high-level disclosures regarding training data used in generative AI systems, especially when the data includes personal or copyrighted content. If datasets include copyrighted works, personal data, or scraped content, developers must disclose that use and be prepared to defend it. Failure to document dataset origins or data rights introduces breach risk, regulatory exposure, and IP litigation.
These risk zones now define the minimum compliance baseline for AI tools in the United States. They are enforceable, trackable, and expanding.
What Comes Next: Regulation by Category
The next wave of AI regulation will not focus on how advanced a system is. It will focus on where and how the system is used. Laws are shifting toward tiered frameworks that define compliance obligations based on risk level, not technical complexity.
Expect regulators to draw hard lines between low-risk tools, like content filters or grammar correctors, and high-risk systems used in credit scoring, hiring, or healthcare triage. The Colorado AI Act already uses this structure. The European Union’s AI Act does the same. U.S. regulators are moving in that direction.
Some legislators and regulators are exploring licensing frameworks for foundational models, which could involve registration, testing, or disclosure requirements before commercial deployment, but no such federal regime currently exists.
Lawmakers are considering frameworks that would require large model developers to register, disclose capabilities, submit to testing, and certify risk controls before commercial release.
Enforcement is moving toward civil penalties tied to harm caused by outputs, not model structure. If your product generates misleading information, false biometric matches, or discriminatory decisions, regulators will measure exposure by impact.
Congress is slow. Agencies are not. Expect rulemaking, guidance, and enforcement memos to fill the gaps while lawmakers debate. That means founders must track not just statutes but also agency positions, hearing transcripts, and informal guidance.
Regulatory Arbitrage Is Closing, and You Can’t Ignore Compliance Until IPO
Founders used to defer regulatory planning until late-stage funding. That no longer works. AI products trigger multi-jurisdictional obligations even before Series A. Privacy laws, training data rules, and state-level disclosure requirements apply from the first launch.
Product launch timelines now often depend not only on technical readiness but also on legal and compliance preparedness, especially for AI tools used in regulated industries or involving personal data. Selling to enterprise or government clients requires compliance documentation, risk scoring, and usage disclosures. If your tool touches user content or substitutes for human judgment, the procurement team will ask for your legal posture before signing.
Advertising creates exposure, too. “AI-washing,” making exaggerated claims about safety, fairness, or explainability, draws FTC scrutiny. If the output cannot be defended, the claim becomes a legal liability.
Enforcement of existing laws that intersect with AI, such as consumer protection, privacy, and discrimination statutes, is already underway, and companies must integrate legal compliance into AI product development even in the absence of a federal AI-specific statute. Traverse Legal builds scalable compliance architectures for AI from product design to public launch. Book a 15-minute AI risk assessment.
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