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As artificial intelligence continues to reshape commerce, media, and technology, policymakers face a familiar but difficult question: how to encourage innovation without eroding the legal frameworks that made that innovation possible in the first place.
A recent submission to the White House Office of Science and Technology Policy by a coalition representing thousands of U.S. news and media publishers offers a clear position – one that has broader implications for anyone navigating copyright, licensing, and AI development.
At its core, the message is simple: strong intellectual property protections are not an obstacle to AI innovation. They are one of its driving forces.
Why Copyright Matters in the AI Conversation
Much of the public discussion around AI focuses on speed, scale, and global competition. What is often overlooked is the role that copyrighted content (journalism, books, music, images, and data) plays in building and sustaining AI systems.
Publishers argue that high-quality, original content is a critical input for effective AI models. News content, in particular, is frequently relied upon for training, testing, and "grounding" AI outputs. Without access to reliable information, AI systems are more prone to hallucinations, bias, and degraded performance over time.
From a legal standpoint, U.S. copyright law has long balanced innovation with property rights. It has adapted to each major technological shift (from photocopiers to streaming) without dismantling the incentive structure that supports creative industries.
Licensing as a Potential Path Forward
Rather than advocating blanket exceptions that would allow for the unrestricted use of copyrighted materials, publishers emphasize the role of voluntary licensing. Licensing provides legal certainty for AI developers while ensuring creators and rights holders are compensated for the value they produce.
This approach mirrors how other disruptive technologies evolved. The transition from peer-to-peer file sharing to streaming services succeeded not because IP protections were weakened, but because licensing markets matured.
From a legal perspective, licensing reduces litigation risk, supports sustainable business models, and creates predictable pathways for innovation – something that is particularly important for startups and emerging AI companies.
The Role of Transparency
Another concern raised is the lack of transparency around AI training data and web scraping practices. Many publishers have little visibility into whether their content has been used, how it has been collected, or for what purpose.
Reasonable transparency requirements, such as identifying crawling bots and disclosing general training practices, can help restore trust. Importantly, transparency need not require disclosure of trade secrets. Instead, it gives rights holders and consumers a clearer picture of how AI systems operate and what data informs their outputs.
For businesses, transparency also supports competition, allowing customers to evaluate AI products based on reliability, sourcing, and compliance.
Why Expanding Copyright Exceptions Raises Concerns
Some jurisdictions have explored broad text-and-data-mining exceptions that allow AI developers to use copyrighted materials without permission. Publishers, however, argue that these approaches have not meaningfully accelerated innovation and instead create uncertainty, shift costs to creators, and expose U.S. intellectual property to exploitation abroad.
Under U.S. law, copyright is opt-in by design. Expanding opt-out allowances places an unrealistic monitoring burden on rights holders, particularly smaller organizations that lack the resources to police constant scraping and reuse.
A Familiar Legal Principle, Applied to New Technology
The broader takeaway is one the legal system has applied repeatedly: innovation and property rights are not mutually exclusive. Courts and lawmakers have historically addressed new technologies through incremental, fact-specific analysis rather than sweeping exceptions.
As AI adoption accelerates, the same approach is likely to prevail: one that relies on licensing markets, competition enforcement, transparency, and existing IP doctrines to resolve disputes as they arise.
For businesses developing or using AI, the lesson is clear: copyright law is not going away. Understanding it – and building systems that respect it – may ultimately be a competitive advantage rather than a constraint.
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