The rapid integration of artificial intelligence into commercial products has created a legal blind spot that neither tech vendors nor their customers are fully prepared for. Traditional software contracts were never designed to handle questions like who owns AI-generated content, what happens when a model is trained on copyrighted material, or who bears liability when an AI system produces harmful output. As businesses rush to deploy AI tools, legal experts are warning that outdated contract language could leave companies exposed to significant financial and legal risk.

Why Standard Software Contracts Don't Work for AI?

The problem starts with a fundamental legal reality: under current U.S. law, copyright protection requires human authorship. The U.S. Copyright Office has made this clear in guidance issued as recently as January 2025, stating that material generated solely by AI without sufficient human creative contribution cannot be copyrighted. Even prompts alone do not qualify as human authorship.

Patent law has the same requirement. The U.S. Patent and Trademark Office (USPTO) has consistently ruled that AI systems cannot be listed as inventors on patents. A landmark 2022 federal court decision, Thaler v. Vidal, reinforced this principle, making clear that while AI-assisted inventions may be patentable, the human must contribute significantly to the conception of each claim. Simply prompting an AI system or giving it a general goal does not establish inventorship.

These legal constraints create a puzzle for businesses: if AI-generated content cannot be copyrighted, who actually owns it? And if ownership is unclear, how can vendors and customers protect themselves contractually? Traditional software licensing agreements simply do not address these questions, leaving both parties vulnerable.

What Are the Key Risks Companies Face?

The ownership gap is just the beginning. Companies integrating AI tools face several distinct categories of risk that standard contracts fail to cover:

• Training Data Liability: AI models trained on copyrighted materials raise unresolved questions about whether such use constitutes fair use or requires licensing. Courts are still adjudicating cases like Andersen v. Stability AI Ltd., where copyright infringement claims have been allowed to proceed to discovery, leaving the legal landscape uncertain.
• Model Improvement Rights: When customer data is used to fine-tune or improve a vendor's model, contracts must clarify whether the vendor can use those improvements for other customers or whether the customer retains proprietary rights over their data.
• Output Liability and Indemnification: Traditional software-as-a-service (SaaS) contracts often disclaim liability for output accuracy or third-party IP infringement. But AI systems can produce harmful, inaccurate, or legally problematic outputs, and existing contract language may leave customers without recourse.
• Data Privacy Compliance: AI systems intersect with data privacy laws like the California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA), which broadly define personal information to include AI inputs, outputs, profiles, and inferences. Contracts must address how customer data will be used and protected.

How Should Tech Vendors Structure AI Agreements?

Technology providers deploying AI need to rethink their standard contract templates. Legal experts recommend several key provisions that differ substantially from traditional software licensing.

First, vendors should clearly retain ownership of their underlying models, algorithms, and training methodologies while granting customers appropriate usage licenses. Critically, any output ownership granted to customers should not extend to the model itself or improvements made to it. This protects the vendor's core intellectual property while still offering customers value.

Second, vendors should implement clear usage restrictions and acceptable use policies. Given regulatory uncertainty around AI, providers should prohibit uses like discriminatory decision-making or generating illegal content, and reserve the right to update these policies as laws evolve. This protects vendors from liability for customer misuse.

Third, vendors should carefully limit their representations and warranties. Many AI vendors disclaim warranties regarding output accuracy, non-infringement of third-party intellectual property, or fitness for particular purposes. However, larger vendors increasingly offer limited intellectual property indemnities subject to specific exclusions, such as customer modifications or combination with other products. Where representations are made, they should be qualified by knowledge or materiality limitations.

Finally, indemnification obligations require careful scoping. Many vendors decline to indemnify for customer use of outputs, arguing that customers control deployment. Where indemnification is provided, it should be subject to reasonable caps and exclusions for misuse or modification of outputs.

What Should Customers Demand in AI Contracts?

Customers integrating AI tools face distinct risks and should negotiate protective provisions that go beyond standard software agreements. Legal experts recommend a different approach than vendors typically offer.

Customers should seek clear restrictions on the vendor's use of customer data for training purposes. While an outright prohibition on using customer data to train models for other customers is ideal, vendors may negotiate alternatives such as permitting use of aggregated or de-identified data. Given privacy regulations, this contractual clarity is essential.

Customers should also demand robust indemnification against third-party claims that the AI's training data or outputs infringe intellectual property rights. Contracts should expressly extend indemnification language to cover model outputs and predictions, not merely traditional software. This is where significant exposure lies, and traditional SaaS language may leave AI-specific risks uncovered.

Additionally, customers deploying AI in high-risk contexts, particularly those subject to emerging state AI laws, should negotiate audit rights to verify compliance with bias mitigation, accuracy standards, and regulatory requirements. This transparency is critical for managing liability.

Finally, customers should seek higher liability caps or carve-outs from liability limitations for intellectual property indemnification, data security breaches, confidentiality violations, gross negligence, and willful misconduct. Given AI's potential for significant harm, standard liability caps may be insufficient.

Steps to Modernize Your AI Contracts Today

• Audit Existing Agreements: Review all current software and service agreements to identify gaps in AI-specific language. Check whether contracts address output ownership, training data provenance, model improvements, and indemnification for AI-generated content.
• Define Data Ownership Clearly: Add explicit provisions specifying which party owns customer data, how it may be used, whether it can be used for model training, and what happens to data after the contract ends. Include restrictions aligned with CCPA, CPRA, and other privacy regulations.
• Establish Output Ownership Terms: Specify who owns AI-generated outputs, whether customers can use outputs commercially, whether vendors can use outputs for other purposes, and how derivative works are handled. Make clear that contractual ownership does not create copyright protection if the output lacks human authorship.
• Add AI-Specific Indemnification: Include indemnification language that explicitly covers model outputs, predictions, and AI-generated content, not just traditional software. Define what the vendor will and will not indemnify, and establish reasonable caps and exclusions.
• Include Audit and Compliance Rights: Add provisions allowing customers to audit vendor compliance with bias mitigation, accuracy standards, and regulatory requirements. Specify what documentation vendors must provide and how frequently audits can occur.

The legal landscape governing AI ownership and liability remains unsettled, with courts still working through foundational questions about copyright, patent rights, and fair use. However, businesses cannot wait for perfect legal clarity. By updating contracts now to address AI-specific risks, both vendors and customers can protect their interests and reduce exposure to costly disputes down the road.