If Your App Uses Generative AI, Who Owns the Output?

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Generative AI is no longer just a research buzzword, it’s core infrastructure for apps that produce text, images, video, design, code, or other creative outputs. While many organizations embrace these capabilities, one of the messiest strategic questions remains: who actually owns the content that your app generates? The answer matters for IP risk, product positioning, contractual terms, compliance, training data strategy, and platform liability.

In 2026, current IP law in the United States and many other jurisdictions still treats copyright as something only humans can hold, which leaves purely machine-generated content in a legal grey area absent meaningful human input. This uncertainty means product teams must be proactive rather than passive when integrating generative AI into their core value streams.

Under U.S. copyright law, only humans can be authors and thus own a copyright. Courts and the U.S. Copyright Office have repeatedly confirmed that works produced wholly by algorithms without sufficient human creative control are not eligible for copyright protection. This forces a fundamental rethinking of how rights attach to machine-generated output.

Put another way:

• • If an AI system autonomously produces content with little human intervention, that content is likely ineligible for copyright protection.
  • If a human exercises sufficient creative control (by making decisions about structure, expression, or content selection) those human-directed elements may be protectable. This distinction is a practical building block for IP strategy.

Not just in the U.S., other jurisdictions grapple with the tension between AI creation and IP ownership. For instance, under French law, IP rights may depend on the level of human involvement and originality, challenging teams to carefully document creative contributions when integrating GenAI output.

Since statutory copyright protections are ambiguous or unavailable for raw AI outputs, contracts become your most powerful tool for assigning and clarifying rights.

Apps using generative AI should explicitly define:

• • who owns the raw output
  • whether the platform retains a license to use, distribute, or modify it
  • any rights the platform has to train or improve models using user inputs or outputs

Legacy legal frameworks are not reliable on their own, you have to contract around them. Many leading AI platforms and SaaS providers already embed specifics in their terms so that users and developers have clarity about rights and licenses.

Your agreements should clearly distinguish whether:

• • you are assigning ownership of output to another party (e.g., the end user), or
  • you are granting a license for defined uses of the output.

Assignment gives the other party broader rights, while a license lets you retain core rights for reuse or licensing to third parties, an important distinction for commercial platforms. Keep in mind that oftentimes the end license is a mix of both. Open source software for example cannot be owned by you, but the custom code, the processes, and the final product can be.

Different stakeholders may have valid claims, depending on how your app uses GenAI.

If your app treats the person entering the prompts as the creative driver, you might assign ownership rights to them in your terms. However, this doesn’t automatically entitle them to copyright under current law unless their input rises to the level of substantial creative contribution.

In some commercial contexts, especially when the AI outputs play a role in your core SaaS offering, you might retain ownership or wide-ranging licenses to reuse generative content for training, analysis, and improvement. This should be clearly documented to avoid disputes.

In jurisdictions or for content types where no clear human ownership exists, the output may effectively sit in the public domain. This rarely aligns with business interests, which is why product and legal teams often use contracts to create ownership or license rights where statute does not.

When drafting or updating contracts for apps with generative AI, product and legal teams should:

✅ Use Explicit IP Assignment or Licensing Clauses

Make it clear who gets what rights in AI outputs, including downstream uses and derivative works.

✅ Address Derivative Inputs

Ensure that users warrant they have rights in any input they provide (e.g. uploaded images or text), and that using these inputs to generate output doesn’t create liability or conflicting claims.

✅ Retain Rights for Model Training

If your business model includes improving AI capabilities through data, include licenses to use user inputs and generated outputs for training and quality improvements.

✅ Document Human Contribution

If part of your strategy involves claiming human authorship (for copyright protection), clearly log the decision points and human edits that distinguish your output.

Generative AI can also produce designs, algorithms, or inventions. Patent law similarly requires a human inventor, and courts are hesitant to grant patents for inventions conceived entirely by machines. Trademark rights, on the other hand, apply when outputs function as source identifiers and meet traditional standards, but using AI-generated logos without distinctiveness can invite disputes.

Legislation like the Generative AI Copyright Disclosure Act, requiring transparency about copyrighted works used in training, may introduce new compliance requirements for AI platforms in the near future.

Ongoing lawsuits, including cases against generative AI companies for training data issues, are shaping practical expectations around IP risk and may ultimately influence product strategy and contractual norms.

Generative AI doesn’t just change how products work, it changes how value, risk, and ownership are defined. With the law still catching up, there is no automatic or default answer to who owns AI-generated output. Instead, ownership is shaped by a combination of human involvement, product design choices, and—most critically—how those choices are documented in contracts. Teams that ignore this reality risk ambiguity, disputes, and downstream compliance headaches. Teams that address it early can turn uncertainty into a strategic advantage.

If your app uses generative AI and you’re unsure how ownership, licensing, or risk should be structured, you don’t have to navigate it alone. Sourcetoad works closely with leading legal experts in the generative AI space to help product teams design systems, contracts, and workflows that stand up to real-world scrutiny. If you have questions about how GenAI impacts your product or business, get in touch and let’s talk through it.