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Short answer: OpenAI did urge the U.S. government to protect AI companies’ ability to train models on copyrighted works, arguing that tighter rules could weaken U.S. competitiveness against China. But “legalize theft” is a critic’s framing, not the legal language of OpenAI’s proposal. The dispute is about whether particular uses of copyrighted works in AI development qualify as fair use—and that question is not settled for every model, dataset, or output.
What OpenAI actually asked the government to do
On March 13, 2025, OpenAI submitted recommendations to the White House Office of Science and Technology Policy as part of the process for developing a U.S. AI Action Plan. Its submission urged policymakers to preserve the ability of American developers to train AI models on copyrighted material under the fair-use doctrine. OpenAI argued that broad licensing requirements or other restrictions could make development slower and more expensive, and asked for a national approach rather than a patchwork of state rules. OpenAI’s announcement and its submission to the federal request for information set out that position.
In practical terms, OpenAI wanted the government to endorse a broad fair-use rationale for qualifying AI training. It did not formally ask officials to declare that all uses of copyrighted work are lawful, nor did it use “theft” as the legal category at issue. The proposal could have significant effects on creators’ rights and bargaining power, but it was a policy argument about copyright, not a request to legalize criminal theft.
Why OpenAI invoked China
OpenAI’s submission tied copyright policy to national competitiveness and security. Its argument was that if U.S. developers faced licensing barriers or restrictions on training data while Chinese companies could train with fewer constraints, American developers could fall behind. That is OpenAI’s warning and policy rationale—not proof that China will win the AI race, or a settled account of how Chinese companies obtain and use training data.
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The underlying questions are difficult: what data foreign companies use, what legal rules apply to them, and how effectively U.S. rules can reach conduct abroad. Whether a national-security concern justifies limiting copyright protections is a separate policy judgment. The China argument explains why OpenAI favored a permissive U.S. framework; it does not resolve the copyright analysis itself.
Why “theft” is an imprecise label
Copyright infringement and theft are not interchangeable legal terms. The copyright dispute concerns acts such as copying works into datasets, using them for training or fine-tuning, and potentially reproducing protected expression in outputs. Critics use “theft” rhetorically to stress that works may be used without the creator’s consent or payment. That criticism points to real concerns, but it should not be mistaken for the wording or legal mechanism of OpenAI’s request.
“Publicly available” also does not mean “copyright-free.” A work posted online may still be protected, and its accessibility alone does not determine whether copying it for a particular AI use is lawful.
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What fair use asks
In the United States, fair use is a fact-specific legal doctrine assessed under four statutory factors. No one factor automatically settles an AI-training dispute:
- Purpose and character: Courts consider the purpose of the use, including whether it is transformative, as well as whether it is commercial.
- Nature of the work: The analysis distinguishes, among other things, factual works from highly creative expression.
- Amount used: Courts consider how much of the work was copied, including whether the whole work was used.
- Market effect: The question includes whether the use substitutes for the original or harms existing or potential markets, including markets for licensing.
For AI, a central argument is that using works to train a model serves a different purpose from presenting those works to readers or viewers. Rights holders counter that copying entire works without permission can undermine licensing markets and that systems may sometimes produce recognizable material. The answers may differ by work, training process, model behavior, and the market evidence in a particular dispute. The U.S. Copyright Office’s AI initiative treats training, generated works, and potential infringement liability as related but distinct issues.
Training, memorization, and outputs are separate questions
It helps to separate three stages that public debate often collapses:
- Dataset copying: Was a work copied or collected for training, and was that use lawful?
- Model behavior: Does the system retain or reproduce protected expression, including through memorization?
- A particular output: Does a generated result reproduce protected expression in a way that infringes copyright?
A legal conclusion about one stage would not automatically decide the others. A training use deemed fair in a particular case would not be a blanket ruling that every output is lawful. Conversely, a problematic output would not by itself establish that every training use is unlawful.
Why creators and publishers object
Creators, publishers, and other rights holders raise concerns that extend beyond the word “theft”:
- Consent and compensation: Works may be copied without permission or payment, even when they help create commercial products.
- Market substitution: AI systems may provide material that reduces demand for originals or for licensed uses of them.
- Licensing markets: If broad fair-use protection applies, rights holders may lose the ability to build or negotiate markets for training licenses.
- Memorization and reproduction: Models can sometimes produce passages, images, code, or other recognizable material, raising questions distinct from the act of training.
- Unequal bargaining power and opacity: Large firms can bear litigation costs more easily than individual creators, while limited dataset disclosure can make it difficult to know whether a work was used.
OpenAI says its foundation models use publicly available information, licensed content, and information provided or generated by users, trainers, and researchers. It also describes filtering practices and says models do not ordinarily retain copies of training sentences. Those are the company’s descriptions of its systems, not independent findings that resolve every claim about particular works or outputs. OpenAI has also described publisher opt-out routes, including in its statements on OpenAI and journalism. An opt-out can give publishers a measure of control, but critics argue it places the burden on creators and does not necessarily address prior copying.
Policy options beyond a broad fair-use approach
There is no single obvious way to balance AI development with creators’ rights. Proposals include:
- Broad fair-use protection: This could reduce transaction costs and make development more predictable, but could weaken creators’ leverage and licensing markets.
- Mandatory licensing: Permission or payment could provide compensation and clearer commercial rules, but negotiating rights across huge datasets could be costly and favor firms with the largest budgets.
- Opt-out systems: These preserve a way for rights holders to object, but require them to identify the use and take action; implementation may be uneven.
- Opt-in or collective licensing: Affirmative permission or standardized licensing could improve consent and compensation, while creating administrative and pricing challenges.
- Transparency requirements: Documentation or disclosure about training sources could make disputes easier to assess, though the scope of disclosure and protection of confidential information would need to be addressed.
- Output-focused safeguards: Rules could target memorized or substitutive outputs rather than training as a whole, but would not answer every question about dataset copying.
These approaches can also be combined. The choice is not simply between unrestricted copying and stopping AI research; it involves decisions about who bears the costs, what uses require permission, and how to handle outputs that compete with protected works.
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On March 20, 2026, the White House issued a National Policy Framework for Artificial Intelligence. The framework stated that the administration believes training AI models on copyrighted material does not violate copyright law, while acknowledging the contrary view and recommending that courts resolve the dispute rather than Congress enact a blanket AI-specific rule. See the framework document and the White House announcement.
That was an administration position and legislative recommendation, not a Supreme Court decision, a new statute, or automatic immunity from lawsuits. It did not settle every question involving dataset copying, particular licensing agreements, memorization, or commercially substitutive outputs. Courts still have to assess disputes under the applicable law and facts; Congress would have to pass legislation to make statutory changes.
The accurate way to read the headline
OpenAI did ask the U.S. government to preserve a broad fair-use path for AI training and argued that restrictive rules could leave U.S. developers at a disadvantage to China. Critics may reasonably describe the consequences they fear as uncompensated use of creative work. But saying OpenAI asked the government to “legalize theft” overstates and mislabels the request: the policy fight is over fair use, licensing, transparency, and copyright liability, with the legal outcome still dependent on the facts and the courts.
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