Media platforms and AI developers face a rapidly evolving landscape where copyright protection, fair use doctrine, and open licensing frameworks intersect in unprecedented ways. Recent regulatory actions, court rulings, and industry developments have fundamentally altered the assumptions under which AI systems are trained and deployed, making compliance and ethical responsibility critical business imperatives.
The Copyright Protection Paradox
A fundamental misunderstanding persists regarding AI-generated content: fully AI-generated content cannot be copyrighted in the United States. The U.S. Copyright Office has consistently held that copyright protection requires human authorship, and mere prompting does not constitute sufficient human creative involvement. This means AI-generated outputs lack copyright protection unless a human creator makes substantial creative contributions beyond providing instructions.
However, this does not mean copyright law is irrelevant to AI systems. The critical distinction lies between the copyrightability of outputs (which AI systems cannot achieve alone) and the copyright implications of training data (which remain highly contested). Media platforms must understand that using copyrighted works in training pipelines presents distinct legal risks from the copyright status of generated content.
Fair Use Doctrine: A Fractured Landscape
The fair use defense has become the central battleground in AI copyright disputes, and recent court rulings reveal deeply conflicting precedents. The Thomson Reuters v. Ross Intelligence case found that fair use did NOT protect a competitor’s use of copyrighted materials to train AI technology, particularly where the outputs directly competed with the copyright owner’s offerings. This ruling suggested fair use doctrine was narrowing in AI contexts.
Yet a more recent Bartz v. Anthropic ruling (June 2025) delivered the opposite conclusion, finding that using lawfully acquired books to train AI models constitutes “spectacularly transformative” fair use. The critical distinction appears to be whether the copying itself serves a genuinely transformative purpose (learning statistical patterns) rather than whether outputs might eventually compete with originals.
For media platforms, the practical implication is stark: fair use cannot be relied upon as a blanket defense for AI training. Courts are increasingly evaluating whether:
- The training use is truly transformative in purpose, not merely in form
- The AI system generates outputs that compete with and substitute for original works
- The training has negative market effects on the original creator’s legitimate licensing opportunities
The U.S. Copyright Office concluded in May 2025 that when AI models trained on copyrighted works generate outputs that directly compete with originals in their existing markets, fair use protections do not apply. This marks a significant narrowing of the fair use doctrine as applied to AI.
The Emerging Licensing Imperative
Rather than waiting for uncertain court outcomes, media companies and AI platforms are proactively building a licensing infrastructure. The Copyright Clearance Center (CCC) expanded its Annual Copyright License to explicitly include internal AI use, coordinating with thousands of publishers to enable lawful AI training. This represents institutional acknowledgment that licensing—not fair use—will likely become the legal norm.
The licensing landscape, however, remains fragmented and complex. Media organizations are pursuing three concurrent strategies:
- Direct bilateral agreements with major AI providers (The Atlantic’s deal with OpenAI, Getty Images with various AI companies)
- Collective licensing arrangements through organizations like the News/Media Alliance
- Data licensing startups aggregating content for bulk licensing to AI firms
A critical challenge is the tension between AI companies’ desire for comprehensive, simple licensing and the media industry’s preference for granular control and per-work licensing. Spotify resolved this by acquiring licenses for the vast majority of recorded music; e-book platforms like Everand struggled with complex multi-publisher negotiations. The AI sector appears headed toward a similar model—large publishers will negotiate favorable blanket agreements while smaller creators may lack practical licensing mechanisms.
Open Source and Free Licenses: Complexity Beyond Code
Open source licensing for AI models introduces layers of complexity not present in traditional software. An estimated 65% of AI models on Hugging Face have no license at all, while of those that do, approximately 60% use traditional open source licenses like Apache 2.0 and MIT.
The most common AI model licenses reflect different philosophical approaches:
| License Type | Key Characteristics | AI Model Prevalence | Appropriate For |
|---|---|---|---|
| Apache 2.0 | Permissive with explicit patent grants; commercial use permitted | 97,421 models | Organizations concerned with patent protection |
| MIT | Maximally permissive; no patent grants; simplest terms | 42,831 models | Projects prioritizing ease of adoption |
| GPL3 | Copyleft; derivative works must remain open source | Wide adoption | Projects prioritizing open source propagation |
| OpenRAIL | AI-specific; includes use restrictions (e.g., preventing weaponization) | 27,919 models | Responsible AI development with guardrails |
| Llama 2 | Use restrictions preventing outputs from training other LLMs | 5,375 models | Platform-specific ecosystem protection |
The critical challenge with AI licensing is that three distinct legal relationships require definition: data licensing (rights to training data), training permission (right to use data for model development), and model deployment (distribution and use rights). Traditional open source licenses address only the final layer, leaving training data rights deeply ambiguous.
For media platforms, the implications are severe: using openly licensed content for AI training does not automatically grant unrestricted rights. Creative Commons licenses, for example, impose obligations even when content is open:
- CC-BY (Attribution): Requires attribution even for AI training outputs; providing attribution for each generated output remains technically unsolved
- CC-BY-NC (NonCommercial): Restricts commercial use of both training and deployment; violating this requires that all training phases avoid commercial contexts
- CC-BY-ND (NoDerivatives): Prohibits derivative works; using ND-licensed content as training data likely violates this restriction
Simply finding content under an open license does not create a legal free pass for AI training. Media platforms must actively verify license compatibility with their specific use case.
The EU Regulatory Framework: Mandatory Compliance
The European Union has established the world’s most prescriptive legal framework for AI training data, fundamentally shifting platform responsibilities. Beginning in August 2025, the EU AI Act Article 53 requires all general-purpose AI providers to publish summaries of training data sources. From 2026 forward, compliance becomes mandatory with stricter requirements.
The EU framework establishes three obligations:
1. Public Disclosure of Training Data
All GPAI providers must publish summaries showing:
- Data types used (text, image, video, audio)
- Sources of the data
- How copyrighted materials were handled
The European Commission released a mandatory template on July 24, 2025, requiring standardized disclosure, though it permits withholding commercially sensitive information. The template covers publicly available datasets, private datasets, scraped web content, user data, and synthetic data.
2. Copyright Opt-Out Mechanism
Under the Copyright Directive (CDSM) Article 4, creators can opt out of having their work used for text and data mining (TDM) for AI training. From 2026, AI developers must:
- Check whether data sources have copyright reservations
- Exclude opted-out content from training
- Keep evidence of compliance
Importantly, this represents a fundamental shift from the U.S. model: rather than copyright holders needing to prove infringement and fair use defenses being evaluated in court, European creators can preemptively reserve their rights.
3. The Transparency-Privacy Balance
The EU template requires aggregate disclosure while protecting legitimate business interests. It does not mandate work-by-work identification of specific copyrighted works used. Notably, the template allows providers to describe privately obtained data only in general terms when commercially sensitive. This creates a structural gap: rights holders cannot definitively determine if specific works were included in training, undermining practical licensing enforcement.
The EU Office will verify template compliance but will not adjudicate individual copyright disputes or perform work-by-work assessments. This means burden of proof for copyright infringement remains on creators in national courts.
High-Stakes Litigation Reshaping Norms
The copyright lawsuits against AI companies are establishing new precedents that extend far beyond individual cases:
The New York Times v. OpenAI/Microsoft lawsuit survived a motion to dismiss, with the federal district judge rejecting OpenAI’s fair use defense and narrowing but not eliminating claims related to training data use. This signals courts are taking copyright infringement allegations seriously despite fair use arguments.
ANI v. OpenAI (India) adds a novel dimension: claims that ChatGPT falsely attributed fabricated news stories to ANI, creating both copyright and misinformation concerns. This expands platform liability beyond mere unauthorized use to include reputational harm from misattribution.
Disney, NBCUniversal, and Warner Bros. v. Midjourney frame AI image generation tools as “bottomless pits of plagiarism,” claiming the platform was designed to induce copyright infringement through its user interface. This introduces secondary liability theory—platforms may be liable not just for their own actions but for facilitating user-generated infringement.
The GEMA v. OpenAI and Suno cases (Germany, January 2025) specifically address copyrighted song lyrics, introducing music industry licensing models to AI contexts.
These cases establish that platforms can face liability across multiple dimensions: direct infringement for training use, secondary liability for enabling user infringement, reputational harm from misattribution, and DMCA violations for bypassing protection measures.
Critical Responsibilities for Media Platforms
Media platforms must implement a multi-layered compliance framework:
1. Training Data Governance
- Maintain detailed audit trails documenting all training data sources
- Verify licenses and permissions for every dataset included
- Implement automated systems to identify and exclude opted-out content (particularly critical for EU compliance)
- Distinguish between freely available, licensed, and scraped content
- Never assume public availability equals right to use for AI training
2. Transparent Disclosure
- Prepare for mandatory disclosure obligations under EU AI Act and potentially similar U.S. requirements
- Document data sources at the category level at minimum (as per EU template)
- Establish procedures for responding to copyright opt-out requests
- Maintain evidence of copyright clearance for proprietary data
3. Content Attribution and Accountability
- Implement Content Rights Layer infrastructure to track data provenance through training
- Design outputs with source attribution where technically feasible (addressing Creative Commons BY requirements)
- Establish clear liability allocation when outputs contain copyrighted material
- Consider secondary liability exposure for user-generated outputs
4. Ethical Web Scraping
- Respect robots.txt files and website terms of service, even when not legally required
- Filter scraped data to exclude personal information, sensitive data, and protected content
- Disclose scraping purposes to website operators and data subjects
- Train scraping systems on ethical guidelines, not just technical execution
- Implement rate limiting and respectful access patterns to avoid site disruption
5. License Compliance
- Audit all openly licensed training data for use restrictions beyond copyright
- Ensure Creative Commons compliance regarding attribution, commercial use, and derivatives
- Track license requirements through model deployment, not just training
- Document how different license terms are satisfied in outputs
6. Jurisdictional Strategy
- Develop separate compliance strategies for U.S., EU, UK, and other major markets
- Account for differing fair use standards and opt-out mechanisms
- Consider whether to limit model availability in jurisdictions with stricter requirements
- Monitor evolving case law in all relevant jurisdictions
The Emerging Industry Standard
The convergence of litigation, regulation, and ethical expectations is driving an industry standard toward mandatory licensing and transparent data sourcing. The voluntary EU Code of Practice (published July 10, 2025) provides a roadmap, with major players including Amazon, Anthropic, Google, Microsoft, and OpenAI already signed on.
The most sustainable path forward involves:
- Treating content licensing as core infrastructure, not a legal edge case
- Favoring bulk licensing agreements with publishers and aggregators over attempts to rely on broad fair use defenses
- Implementing transparent data summaries even in jurisdictions where not legally required, as competitive expectation
- Building Content Rights Layer compliance into model architectures from inception, not as an afterthought
- Establishing collective licensing mechanisms modeled on music industry CMOs, particularly for smaller creators
Media platforms that proactively license content, respect copyright opt-outs, and maintain transparent data practices will emerge as trusted industry leaders. Those attempting to rely on fair use doctrine or legal ambiguity face mounting litigation risk, regulatory exposure, and reputational harm.
The era of treating copyrighted content as a free training resource is ending. The platforms that understand this transition and build compliance into their systems will define the next era of responsible AI development.