Who owns the code? In AI, SaaS, and custom software, misunderstandings over intellectual property rights can derail entire businesses. This article explores the legal frameworks that define code ownership, highlights the risks of unclear agreements, and outlines strategies to protect your company’s most valuable assets.
Introduction
The question of who owns the code underpinning AI models, SaaS platforms, and custom software is not academic—it is a critical business issue that directly affects valuation, operational control, and long-term scalability. Too often, companies assume they own what they have paid for, only to discover later that absent explicit agreements, the legal default is far less favorable. For mid-sized and large non-public companies, including those engaged in AI, biotech, and enterprise SaaS, misunderstanding these boundaries can expose the organization to substantial legal and financial risk.
This article examines the legal frameworks governing code ownership in the United States, with global considerations, and provides a roadmap for securing clear, enforceable rights over intellectual property assets.
The Legal Foundations: Copyright Law and the Work-for-Hire Doctrine
Under U.S. copyright law (17 U.S.C. § 101), the default rule is that the author of a work owns the copyright. In the case of software, this means the developer—the person who writes the code—owns it by default. The “work-for-hire” doctrine applies narrowly and typically only to work created by employees within the scope of their employment or specific categories of commissioned works, such as contributions to collective works, if a written agreement explicitly designates the work as such.
In the context of AI systems, SaaS platforms, and bespoke software development, most code is authored by contractors, vendors, or third parties. Unless a formal, signed agreement assigns the intellectual property rights to the company, ownership remains with the developer. Without such an assignment, the hiring party may at best hold an implied, limited license to use the code, a position that can become a point of contention in future licensing, sales, or funding events.
AI Systems and Layered IP: Code, Models, and Data
The complexities of AI introduce multiple layers of intellectual property that must be considered. A typical AI system includes the source code, the training data, the trained model (including weights and architecture), and the outputs generated by the model. Each of these components can be subject to distinct IP regimes and licensing obligations.
For example, a company developing an AI diagnostic tool may rely on third-party libraries (e.g., TensorFlow, PyTorch) governed by permissive licenses, while integrating proprietary datasets for model training. The trained model, its weights and parameters, may itself be considered a derivative work, potentially implicating the terms of the upstream licenses. Absent a clear, comprehensive agreement that addresses these dependencies, companies risk inadvertently incorporating restrictive licensing terms, such as those found in copyleft licenses like the GPL, into their proprietary systems.
Furthermore, ownership of training data can present additional complications. Data sources may be subject to contractual, regulatory, or privacy constraints (e.g., GDPR, HIPAA). Failing to address these issues upfront can result in significant downstream risk, including loss of exclusivity, compliance violations, and potential liability.
SaaS Platforms: Code, Configurations, and Customer Deliverables
In SaaS, the distinction between the core platform code, custom integrations, and customer-specific configurations is critical. Generally, the platform code remains the intellectual property of the SaaS provider, licensed to customers under the terms of a subscription agreement. However, enterprise clients often request or expect custom modifications, integrations, or deliverables. Without carefully drafted contract language, disputes may arise over whether the client has any ownership rights in those deliverables.
The key is to define, with precision, the scope of the license granted to the customer, the ownership of any custom code or configurations, and the limitations on the customer’s ability to reverse-engineer, reproduce, or sublicense the underlying technology. A well-structured SaaS agreement should also address confidentiality, trade secret protection, and usage restrictions in a manner that anticipates future scaling and third-party audits.
Strategic Framework for Securing Code Ownership
To protect intellectual property and mitigate risk, companies should implement a robust legal framework that includes the following elements:
* Clear, signed intellectual property assignment agreements for all developers, including employees, contractors, and vendors.
* Explicit terms in all service agreements addressing ownership of code, models, datasets, and deliverables.
* Careful management of open-source and third-party dependencies, including compliance with licensing obligations.
* Data usage agreements that account for privacy, regulatory, and contractual constraints.
* Defined customer licensing terms that limit rights to use the service while preserving the company’s ownership of core technology.
* Periodic intellectual property audits to ensure that contractual terms, codebases, and business practices remain aligned.
Failure to implement these protections can result in contested ownership claims, restrictions on product commercialization, and exposure to legal actions that threaten the core value of the business.
Real-World Implications: A Case Study
Consider the example of a mid-sized healthcare AI company that outsourced development of a proprietary diagnostic tool. Believing that payment alone secured ownership, they neglected to formalize IP assignment. When the contractor later asserted copyright over the code, the company’s ability to license its software to enterprise clients was jeopardized. In the context of a pending acquisition, this unresolved IP issue resulted in significant valuation reductions, delayed closing, and costly legal settlements.
This scenario is not uncommon. The failure to address ownership at the outset is a recurring risk across AI, SaaS, and custom software projects. It underscores the need for a proactive, legally sound approach to intellectual property management.
Conclusion: Ownership is the Foundation of Enterprise Value
Intellectual property, particularly in the form of proprietary code, AI models, and associated data—is the foundation upon which the enterprise value of many companies rests. In high-growth sectors such as AI, biotech, and SaaS, ownership is not assumed; it must be secured through deliberate legal structures.
Rock Law assists companies worldwide in developing, negotiating, and enforcing robust intellectual property frameworks. We help clients avoid the pitfalls of unclear agreements, protect their innovations, and safeguard their future.
For a consultation on securing your code and protecting your business, visit: [https://rock.cliogrow.com/book](https://rock.cliogrow.com/book)