AI models are generating content at scale and pulling data from copyrighted sources to do it. That’s triggered lawsuits across publishing, photography, code, and music, most of which remain unresolved. This article unpacks the legal fault lines: how the law views training data, who owns AI outputs, and how companies can limit their copyright exposure.
Businesses building with generative AI are discovering that intellectual property law isn’t settled. Founders, legal teams, and investors face a growing list of questions: Is AI training legal if the input data includes protected works? Can a business claim ownership over content that no human authored? What happens if AI-generated outputs include language, visuals, or code pulled from someone else’s IP?
These are not theoretical problems. They define commercial risk for startups deploying models and legal risk for companies using AI-generated materials in their products. Courts are being asked to draw new lines around decades-old law, and until they do, businesses need to navigate ambiguity with structure, not assumption.
The Two Main Legal Challenges: Training Data and AI Output
Most copyright lawsuits against AI companies center on training large language or image models using copyrighted material scraped from the internet.
AI developers typically build models by feeding them vast datasets, many of which include books, articles, software code, images, and other content that’s under copyright protection. This data is used without permission, licensing, or compensation to the original creators. The legal question is whether that use qualifies as transformative or whether it constitutes reproduction or derivative use in violation of the Copyright Act.
The second wave focuses on the outputs: whether the AI’s content violates copyright, and if so, who’s liable.
When generative AI tools produce text, visuals, or code that resemble protected work, the question becomes whether that output infringes. Plaintiffs argue that some models regenerate verbatim or near-verbatim content from their training data. AI companies argue the output is original, the result of probability, not duplication, and should be treated like any other tool used by humans.
These questions strike at the foundation of AI development and how the law applies to non-human creators.
If training data is ruled to infringe, the legality of every large-scale model built on web-scraped content is at risk. If outputs are considered derivative or uncopyrightable, then businesses relying on AI to create content articles, logos, marketing copy, and software face ownership gaps. Until the law is settled, companies need to build defensible practices into every layer of their AI strategy.
Key Copyright Questions in Recent Lawsuits
Is Training on Copyrighted Data Considered “Fair Use”?
Defendants argue that using web-scraped content to train AI is transformative, thus falling under fair use.
They claim that models do not store or display the original works, but instead use the data to identify patterns and statistical relationships that enable the generation of new content. This, they argue, is a transformative process: the AI is learning, not copying.
Plaintiffs say it’s unauthorized copying at scale, used to compete with the original works.
Rights holders argue that scraping and processing copyrighted works without a license violates the reproduction right under Section 106 of the Copyright Act. They also argue that the resulting AI products compete in the same market as the originals, undercutting their licensing value.
Courts have not reached a consistent answer, and outcomes may vary by circuit or fact pattern.
Some cases have been allowed to proceed; others partially dismissed. The lack of federal legislation and appellate clarity leaves businesses without a clear safe harbor. Until higher courts weigh in or Congress creates new rules, companies must assume that this issue will remain live in litigation.
Who Is the Legal Author of AI-Generated Content?
U.S. Copyright Office has stated that purely AI-generated content is not copyrightable.
In recent policy updates and rejection letters, the Copyright Office has made it clear: works generated entirely by machines, without meaningful human authorship, are not eligible for copyright protection in the U.S. This rule applies regardless of how complex or creative the output appears.
If humans contribute meaningful, creative input, the work may be eligible, but the line is unclear.
The Office has acknowledged that some AI-assisted works may qualify for protection if the human role is substantial enough. But there is no bright-line rule. Writing a prompt is not enough. Reviewing, selecting, and modifying the output may be, but only if the human contribution rises to the level of original authorship.
Businesses using generative AI face ownership uncertainty and enforcement limitations unless they structure authorship intentionally.
If your team is using AI to generate content that supports sales, marketing, or product development, you need a clear authorship strategy. That means documenting human contribution, assigning rights through contracts, and avoiding reliance on unprotectable outputs. Otherwise, your business may lack both ownership and recourse.
How Businesses Can Mitigate AI Copyright Risks
The legal questions surrounding AI and copyright aren’t settled. That doesn’t mean companies should wait. Risk exposure is real, and enforcement is already underway. Businesses building, deploying, or integrating AI tools need to move now by structuring their models, workflows, and contracts around enforceability.
Vet training data sources if you develop AI models or contractually require vendors to do so.
If your business is training proprietary models, know what’s in the dataset. Don’t assume scraped content is free to use. If you’re sourcing third-party models, your agreements must require the vendor to guarantee data provenance. That’s not a wishlist item. It’s a liability shield.
Clarify ownership and license terms in user-generated AI content platforms.
If your platform allows users to create content with AI, define who owns the output. Make the license scope clear who can use the content, for what purpose, and with what restrictions. Ambiguity here exposes the business to claims on both ends: from rights holders upstream and users downstream.
Avoid using AI to replicate or remix protected works unless you have a license or legal opinion.
If the model generates outputs that imitate or stylize known works, whether music, visual art, or branded copy, you need to treat that as derivative use. Without a license or a legal opinion backing the use, the business is exposed.
Consult counsel to structure commercial use of AI-generated content and manage infringement claims proactively.
AI tools move fast. The law moves slowly. In between is legal exposure unless you’ve built an enforcement strategy, authorship structure, and licensing logic into your business model. This is where legal counsel matters most.
Control Copyright Risk Before Your AI Strategy Becomes a Liability
AI may change how content is created, but the law still controls how it’s used. The companies that win won’t be the fastest; they’ll be the ones that structured early and scaled with legal clarity.
Traverse Legal advises businesses deploying or commercializing AI on how to manage copyright exposure from day one. That includes reviewing training data, structuring ownership, navigating user-generated output, and building license frameworks that hold up when tested.
If your model learns from it or outputs it, you need legal control. Anything less is exposure waiting to surface.
The post AI and Copyright Infringement: What the Law Is Still Deciding first appeared on Traverse Legal.