As legal advisors, we’re seeing companies built in ways that look nothing like three years ago and the legal infrastructure most founders rely on hasn’t caught up. Below are our high-priority insights you should consider before you build your company:
- Your AI-Generated Code Might Not Be Yours. The U.S. Copyright Office requires sufficient human authorship for copyright protection, while the USPTO requires a natural person inventor for patent protection. For many AI-native startups, trade secrets will be the most valuable intellectual property: proprietary training data, fine-tuning methods, and model architectures are protectable under applicable law, but only with reasonable measures to maintain secrecy. Open source license review and vetting is also critical in this environment: AI coding tools may pull in open source components without the developer’s knowledge, and if any of that code carries a “copyleft” license, it could require disclosure of your proprietary code or trigger other obligations that undermine your IP position. Additionally, the AI tools themselves come with terms of service that may affect ownership of outputs and impose restrictions on commercial use of generated content.
Legal Considerations: AI-native startups can build protectable IP, but should document the human contributions underlying potentially protectable works and inventions. Before beginning work, each individual or service provider (whether employee or contractor) should execute an enforceable (i) nondisclosure agreement protecting the company’s nonpublic information, data, methods, and processes, and (ii) intellectual property assignment transferring their ownership rights in created works to the company. Take precautions when using AI-assisted code development: use appropriate tools and review processes to identify potentially problematic open-source components that could trigger further disclosure of your proprietary code or methods, and document the licensing (and comply with it) for any permissive open source code used. Review the terms of service for any AI tools used in development to confirm that output ownership and usage rights align with your business model.
- Your Data Use May Make You a Target, or Worse. If your AI product collects personal data to train or refine models, know which state privacy laws apply. The list of states that require data protection assessments for high-risk processing is growing. Consider using the NIST AI Risk Management Framework as a voluntary framework for identifying and managing AI-specific risks. Also, if your technology will use customer-owned data or information to train or refine its models, consider whether your customer agreement or applicable laws allow you to do so.
Legal Considerations: If your business will operate in or process the personal information of individuals, review what state-specific laws may apply: 24 states have enacted consumer data privacy laws as of the date of this post, and a smaller number of jurisdictions have legislation governing certain AI system applications. Confirm your ownership of and/or ability to use any data that is used to train or calibrate your AI models; using data without appropriate rights or permissions can create significant legal risk with respect to your rights to the model or output, even if you use it in an aggregated or de-identified manner.
- The New Startup Doesn't Have Employees. Solo-founded startups are increasingly prevalent, and the median time before a first hire continues to stretch. AI agents are filling roles historically held by a technical or business-minded co-founder, developers, and other early hires. When the remaining human contributions are narrower and project-based, founders have a natural incentive to engage contract service providers rather than take on the overhead of traditional employment, raising important questions about how those workers are classified.
- Legal Considerations: Because worker classification is determined by legal standards rather than company preference alone, two distinct risks apply. First, the federal framework for independent-contractor classification is in flux. The Department of Labor adopted an economic-reality test in 2024 and has since proposed replacing that rule with a streamlined analysis grounded in federal judicial precedent. Worker classification is also jurisdiction-specific: the federal FLSA test is only part of the analysis, and some states apply different standards. Misclassification can stall or kill a financing round. Second, for workers who are employees, founders must determine whether each role qualifies for an FLSA overtime exemption. AI-assisted roles also deserve careful exemption analysis: a position involving AI orchestration or implementation may not qualify for an exemption simply because it sits within a technology organization and availability turns on the employee's actual duties. Think carefully about both contractor classification and exemption analysis from the outset.
TL;DR: If you’re building an AI-native company, put these at the top of your list:
- Get your IP house in order. Initiate a policy of documenting human contributions, cataloging open-source dependencies, and addressing IP ownership and trade secret protection as critical first steps.
- Build your data governance framework now. Map your data flows, know which laws apply, and read your vendor and AI provider agreements carefully for data use and output ownership rights.
- Give worker classification renewed thought. Your lean workforce structure could draw scrutiny from regulators and investors alike.
We welcome questions on IP strategy, employment classification, or data governance for your startup.
