AI and Intellectual Property

Training Data and Copyright

Every major AI model is trained on datasets that include copyrighted material. Large language models train on text from books, news articles, academic papers, blog posts, social media content, and code repositories. Image generation models train on billions of images scraped from the internet, including photographs, illustrations, and artworks protected by copyright. Music generation models train on recordings and compositions. In virtually all cases, the copyright holders were not asked for permission and received no compensation.

AI companies argue that training on copyrighted material constitutes fair use under U.S. copyright law. The fair use defense considers four factors: the purpose and character of the use (transformative uses are favored), the nature of the copyrighted work, the amount used, and the effect on the market for the original work. AI companies argue that training is transformative because the model does not copy or reproduce the original works but learns statistical patterns from them, similar to how a human reader learns from books without memorizing them verbatim. They argue that the model cannot substitute for the original works because it generates new content rather than reproducing existing content.

Copyright holders argue the opposite on every factor. They argue that training is commercial, not transformative, because the purpose is to build a product that competes with the original creators. They argue that models copy entire works during training (which they do, loading complete texts into memory for processing). They argue that AI-generated content directly substitutes for the market that human creators serve: AI-written articles replace freelance writers, AI-generated images replace stock photographers and illustrators, AI-generated code replaces the developers whose code was used in training. The New York Times lawsuit against OpenAI demonstrated that ChatGPT could reproduce substantial passages from Times articles nearly verbatim, undermining the argument that the model only learns patterns rather than copying content.

Multiple major lawsuits are testing these arguments. The Authors Guild, representing thousands of writers, has sued OpenAI and Meta. Getty Images sued Stability AI for training on its copyrighted image library. Individual artists, including a class action representing thousands, have sued Stability AI, Midjourney, and DeviantArt. Music publishers have sued AI music companies. These cases are proceeding through U.S. courts and will likely take years to resolve, with potential appeals reaching the Supreme Court. The outcomes will establish whether AI training on copyrighted material is a permitted use or requires licensing agreements.

Can AI-Generated Content Be Copyrighted?

U.S. copyright law protects "original works of authorship," and the Copyright Office has consistently interpreted "authorship" to require a human creator. In February 2023, the Copyright Office ruled that images generated by Midjourney in the graphic novel "Zarya of the Dawn" could not be copyrighted, while the human-authored text and the human-selected arrangement of the images could be. The office's guidance states that if AI generates content based on a human-provided prompt, the resulting content is not copyrightable because the human did not exercise sufficient creative control over the expression. The human's contribution (the prompt) is analogous to instructions given to a commissioned artist, the instructions are the human's work, but the resulting expression is the artist's (or, in this case, the machine's).

The line between human authorship with AI assistance and AI authorship with human direction is blurry and consequential. A photographer who uses AI to remove a background is clearly the author of the resulting image. A person who types "a painting of a sunset over mountains in the style of the Hudson River School" into an image generator is not clearly the author of the output. Between these extremes lies a vast grey zone: a writer who generates a draft with AI and extensively revises it, a musician who uses AI to generate a backing track and then records vocals over it, a programmer who uses AI to generate code and then integrates it into a larger system. The Copyright Office's position is that these cases must be evaluated individually, with copyrightability depending on the degree of human creative control over the final expression.

Other jurisdictions are reaching different conclusions. The UK's Copyright, Designs and Patents Act includes a provision for "computer-generated works," granting copyright to "the person by whom the arrangements necessary for the creation of the work are undertaken." This provision, originally written for much simpler automated generation, could potentially extend copyright to AI-generated content, with the human who orchestrated the generation as the copyright holder. China has granted copyright protection to AI-generated content in some cases, holding that the human who provided creative input through prompts and parameter selection exercised sufficient authorship. The divergence between jurisdictions creates practical problems for content that crosses borders.

Patents and AI Inventorship

Patent law faces a parallel question: can an AI system be listed as an inventor on a patent? Stephen Thaler filed patent applications in multiple countries listing his AI system DABUS as the inventor of two innovations (a food container with fractal geometry and a flashing light for attracting attention). The applications tested whether patent law, which grants patents to "inventors," can accommodate non-human inventors.

Every jurisdiction that has ruled on the question has concluded that current law requires human inventors. The UK Supreme Court (2023), the U.S. Federal Circuit (2022), the European Patent Office (2021), and courts in Australia (after initial allowance on appeal) have all held that an inventor must be a natural person. The reasoning varies by jurisdiction but centers on the same principle: patent rights are designed to incentivize human innovation, and granting inventorship to a machine does not serve this purpose.

The practical question is more nuanced than the DABUS cases suggest. Most AI-assisted inventions involve significant human contribution: a researcher who uses AI to screen drug candidates, identifies a promising compound, and develops it into a therapy is clearly an inventor. The AI was a tool, like a microscope or a calculator. The harder cases involve situations where the AI's contribution is the creative step: the AI identifies a non-obvious molecular structure, and the human's role is limited to setting up the AI and selecting from its outputs. Current law would still require listing the human as the inventor, even if the genuinely creative contribution came from the AI.

Economic Impact on Creative Industries

The economic disruption from AI-generated content is already measurable. A 2023 survey of freelance illustrators found that 70% reported a decline in commissions since the release of popular image generation tools. Stock photography companies have seen declining submissions from human photographers as AI-generated images flood the market. Content mills that once employed thousands of freelance writers are replacing them with AI-generated text reviewed by a smaller editorial staff. Music licensing platforms report growing catalogs of AI-generated background music priced below what human composers can compete with.

The distributional effects are uneven. Elite creators with distinctive styles, established reputations, and direct client relationships are less affected. Commodity creators, those producing generic stock images, standard blog posts, routine marketing copy, and background music, face the most direct competition. This mirrors historical patterns of technological disruption in creative industries, but the speed and breadth of displacement are unprecedented. Photography took decades to reshape illustration. AI tools reshaped freelance content markets within months of their public release.

Proposed responses range from compensation mechanisms (requiring AI companies to pay royalties to creators whose work appeared in training data) to attribution requirements (mandating that AI-generated content be labeled) to training data governance (requiring consent before copyrighted material is used for training). The EU AI Act requires general-purpose AI providers to publish summaries of training data content and comply with EU copyright law, including the text and data mining opt-out provisions of the Digital Single Market Directive. These measures are early steps in what will be a long process of adapting intellectual property frameworks to the reality of AI-generated content.