Looking at IP cases over the years shows that the rules keep changing as technology evolves. From musicians fighting over borrowed melodies to today’s battles with AI art generators, we’re still figuring out where to draw the line.
Who owns what when a machine creates something after learning from millions of human works? There’s no easy answer.
The decisions courts make now will affect creators everywhere. We need solutions that let new tech flourish without leaving human artists in the dust. The future of creativity depends on getting this balance right.
Music Copyright Cases: Lessons from the Pre-AI Era
The music industry has long been a battleground for intellectual property disputes, establishing critical precedents that now inform how we approach AI-related copyright challenges. These landmark cases reveal the evolving interpretation of originality, fair use, and creative ownership.
Queen & David Bowie v. Vanilla Ice (1990)
When Vanilla Ice released “Ice Ice Baby” in 1990, the distinctive bass line immediately caught listeners’ attention—including Queen and David Bowie, who recognized it as nearly identical to their 1981 collaboration “Under Pressure.” The argument failed to convince the court despite Vanilla Ice’s infamous claim that he had modified the bass line by adding a single note (demonstrating this difference in a television interview with hand gestures).
The case never reached trial, as Vanilla Ice ultimately settled out of court for an undisclosed sum. This dispute became a defining moment in sampling history, occurring at a critical juncture when hip-hop’s sampling practices were under increasing legal scrutiny. The case demonstrated that even minimal alterations to recognizable musical elements wouldn’t shield artists from copyright claims, effectively ending the early hip-hop era’s more carefree approach to sampling. Today, this case remains a cautionary tale taught in music business courses about the necessity of clearing samples before release.
Marvin Gaye Estate v. Robin Thicke & Pharrell Williams (2015)
The “Blurred Lines” case represents one of the most significant and controversial music copyright rulings in recent history. Unlike traditional infringement cases that focus on melody or specific musical phrases, this lawsuit centered on the “feel” and “vibe” of the music—the percussion, background vocals, and overall sonic landscape. When the Gaye family first heard “Blurred Lines,” they recognized similarities to Marvin’s 1977 hit “Got to Give It Up” and pursued legal action.
During the trial, Thicke admitted to being intoxicated during the song’s creation and media interviews, undermining his credibility. The jury’s verdict in favor of the Gaye estate sent shockwaves through the music industry, with many artists and producers expressing concern that the ruling effectively copyrighted a genre or style rather than specific musical expressions. This expanded view of copyright protection led many studios to preventively add songwriting credits to avoid similar lawsuits, while producers became increasingly cautious about acknowledging influences.
The case’s $5.3 million judgment and ongoing royalty requirements made it one of the largest payouts in music copyright history, fundamentally altering how the industry approaches creative inspiration.
The Verve v. The Rolling Stones (1997)
The “Bitter Sweet Symphony” saga stands as one of music’s most notorious copyright disputes. In 1997, British rock band The Verve believed they had adequately licensed a five-note sample from an orchestral version of The Rolling Stones’ “The Last Time” for their breakthrough hit. They negotiated with former Stones manager Allen Klein, who owned the rights to the orchestral arrangement through his company ABKCO, agreeing to license a small sample in exchange for 50% of the royalties.
However, after the song became a global hit, Klein claimed The Verve had used a more significant portion than agreed upon. Rather than face lengthy litigation, The Verve reluctantly surrendered 100% of the royalties and all songwriting credits to Mick Jagger and Keith Richards, despite the sample being from an orchestral version, not the original Stones recording. This meant that the Verve’s signature song—used in everything from Nike commercials to movie soundtracks—generated no income for its performers for over two decades until 2019, when Jagger and Richards voluntarily signed over all their publishing rights back to Verve frontman Richard Ashcroft in an unexpected gesture of goodwill. The case serves as a stark reminder of the nuances of sampling agreements and the potentially devastating consequences when they go wrong.
A&M Records, Inc. v. Napster, Inc. (2001)
The case centered on whether Napster, founded by 18-year-old Shawn Fanning in 1999, facilitated massive copyright infringement through its service. Unlike later peer-to-peer networks, Napster maintained central servers that indexed available files, making it easier to find specific songs and creating a central point of control and liability. The court rejected Napster’s fair use arguments, finding that downloading complete songs wasn’t transformative, harmed the commercial music market, and constituted commercial use despite being free.
Most critically, the court established that technology providers could be held liable for their users’ infringement when they had knowledge of and the ability to prevent infringing activity. Napster’s central design and awareness of widespread infringement made it impossible to claim the protection previously granted to VCR manufacturers in the Sony Betamax case. Napster closed in July 2001 and filed for bankruptcy the following year, but its legal legacy continues to shape how courts approach copyright in the digital ecosystem.
AI-Era IP Violation Cases
The rise of generative artificial intelligence has ushered in an entirely new category of intellectual property disputes, challenging existing legal frameworks and forcing courts to reconsider fundamental concepts of copyright, fair use, and creative ownership in the digital age.
Recording Industry Association of America (RIAA) v. Suno and Udio (2024–Present)
In June 2024, the music industry struck back against AI-generated music when the RIAA filed two separate lawsuits against music generation services Suno and Udio. These AI platforms allow users to generate complete songs—including vocals, instrumentation, and production—simply by entering text prompts. The lawsuits allege that these services could only function by copying and ingest “decades worth of the world’s most popular sound recordings” without permission or compensation.
RIAA Chairman Mitch Glazier stated that while the music community has embraced responsible AI development, services like Suno and Udio that claim it’s “fair” to copy artists’ work for profit undermine genuine innovation. The legal complaints describe these platforms as engaging in “willful copyright infringement on an almost unimaginable scale.” One particularly damning piece of evidence includes statements from Suno’s CEO that their model can effectively “recreate” the training data it ingested.
The outcome of these cases could establish critical precedents for how generative AI interacts with copyright-protected music, potentially requiring licensing systems similar to those used for samples or covers. With the backing of major industry organizations like the American Federation of Musicians and the Songwriters of North America, these lawsuits represent a united front from the music industry against unauthorized AI exploitation.
Authors v. OpenAI and Microsoft (2023–Present)
What began as separate lawsuits from prominent authors like Ta-Nehisi Coates, Michael Chabon, Junot Díaz, and Sarah Silverman has become a consolidated legal battle against tech giants OpenAI and Microsoft. In April 2025, twelve U.S. copyright cases were combined in New York federal court, despite most plaintiffs opposing consolidation. The authors claim their books were used without permission to train large language models like ChatGPT, citing the models’ ability to produce accurate summaries of their works as evidence.
Some authors allege OpenAI specifically used the notorious “shadow library” LibGen, which contains over 7.5 million books—a claim that gained traction when Meta CEO Mark Zuckerberg was accused of approving similar data sources. The tech companies defend their practices under the fair use doctrine, comparing AI training to non-expressive uses like Google Books’ scanning project, which was ultimately deemed legal. The consolidated case will allow a single judge to coordinate discovery and eliminate inconsistent rulings, potentially establishing industry-wide precedents.
Legal experts note that these cases raise questions about whether AI training constitutes “transformative use” under copyright law or requires new licensing frameworks. With authors and publishers staging protests outside Meta’s London offices with signs reading “Get the Zuck off our books,” the dispute highlights growing tensions between creators and AI developers.
Getty Images v. Stability AI (2023–Present)
In January 2023, Getty Images launched legal proceedings against Stability AI in London and a U.S. federal court in Delaware, claiming the AI company had copied over 12 million images from Getty’s collection without permission or compensation. This case is particularly compelling because Getty’s evidence that Stability AI’s image generator can produce distorted versions of Getty’s distinctive watermark—a smoking gun suggesting direct copying rather than incidental inclusion. Unlike individual artists who might struggle to prove specific works were in training datasets, Getty’s position as a significant commercial image provider enables it to demonstrate both unauthorized use and commercial harm.
The stock photo giant argues that Stability AI violated copyright by scraping their images and damages their business by creating a competing product that devalues professional photography. Getty Images CEO Craig Peters framed the issue not as opposition to AI advancement but as a matter of proper licensing: “We’re talking about these companies building commercial, competitive products,” he stated in interviews. The case has implications beyond Getty’s business interests, potentially establishing whether commercial image libraries require different legal treatment than publicly accessible images. With proceedings active in multiple jurisdictions, the outcome could create divergent standards for AI training data across international borders.
New York Times v. OpenAI and Microsoft (2023–Present)
In December 2023, The New York Times took unprecedented legal action against OpenAI and Microsoft, filing a lawsuit at the heart of how large language models are trained. The complaint alleged that millions of Times articles were used to train ChatGPT and other AI systems without authorization, enabling these models to generate content that directly competes with the newspaper’s reporting. What distinguishes this case from other AI copyright disputes is the Times’ claim that ChatGPT can reproduce their articles almost verbatim when prompted—effectively creating a substitute for their paid subscription service.
In March 2025, U.S. District Judge Araceli Martínez-Olguín delivered a significant ruling by denying OpenAI’s motion to dismiss the lawsuit while narrowing the focus to copyright infringement claims related to training data. The Times’ lawsuit gained additional gravity when eight Tribune Publishing newspapers filed similar claims in April 2024, suggesting a coordinated effort from the journalism industry. OpenAI has defended its practices by pointing to fair use precedents and highlighting its opt-out policies, which allow publishers to block their content from training data.
However, publishers argue that these measures came too late, after models had already been trained on their work. This case could determine whether news organizations can control how their reporting is used to train AI systems, with profound implications for journalism’s business model and the future development of large language models.
Artists v. Stability AI, Midjourney, and DeviantArt (2023–Present)
In January 2023, artists Sarah Andersen, Kelly McKernan, and Karla Ortiz initiated a landmark legal battle against leading AI image generators. Their class-action lawsuit claims these companies trained their models on billions of images scraped from the internet without artists’ consent, allowing users to generate new works that mimic specific artists’ styles.
The case gained momentum in August 2024 when U.S. District Judge William Orrick allowed copyright infringement claims to proceed, accepting two pivotal legal theories: first, that AI models constitute infringing copies by encoding artists’ works; second, that distributing these models equates to distributing copyrighted works. This case is particularly significant because it articulates the harm to artists, not just unauthorized use of their work, but the creation of unlimited derivative works that could flood the market and devalue human-created art.
The plaintiffs’ complaint cites academic research showing these models can reproduce training images with explicit prompting. Stability AI’s CEO even claims their model compresses 100,000 gigabytes of images into a form that can recreate them. With a trial scheduled for September 2026, this case could fundamentally reshape how AI art generators operate, potentially requiring licensing agreements with artists or substantial modifications to their training methods. For many creators, this case represents an existential fight for the future of human artistry in an increasingly AI-driven creative landscape.
What does This Mean for the Future?
The collision between intellectual property law and artificial intelligence has created unprecedented legal and ethical challenges that our current frameworks struggle to address. As landmark cases work through courts worldwide, several key considerations emerge for creators, technologists, and policymakers.
Traditional IP laws were designed for human creators in a world where copying required deliberate action. AI systems fundamentally challenge these assumptions, ingesting millions of works simultaneously and learning patterns rather than making direct copies. This technological reality demands thoughtful legal evolution. Courts and legislators must reconsider what constitutes fair use when training data includes copyrighted works, whether statistical patterns derived from creative works deserve protection, and how to attribute ownership when AI and humans collaborate.
Ethical AI development requires respecting the rights and contributions of human creators. Companies building generative AI systems should acknowledge that creative works have value and that their creators deserve compensation when that value is extracted. This doesn’t necessarily mean halting AI progress but rather ensuring it advances alongside—not at the expense of—human creativity. Ethical frameworks should consider power imbalances between technology companies and individual creators, ensuring that the benefits of AI advancement are shared equitably.
To mitigate legal risks, companies developing AI systems should implement several best practices:
The path forward requires collaboration between legal experts, technologists, creators, and policymakers. By addressing these challenges proactively, we can develop frameworks that foster technological innovation and human creativity, ensuring that AI systems enhance rather than undermine the cultural ecosystem that makes their existence possible.
Sreyashi Chatterjee is a SaaS content marketing consultant. When she is not writing or thinking about writing, she is watching Netflix or reading a thriller novel while sipping coffee.