We don’t need more users; we need more stewards. This line has haunted me since I first scrawled it in a notebook during the 2022 bear market, while watching the Terra collapse wipe out years of earnest community building. Today, as I read the filings in Round Hill Music Publishing’s lawsuit against Anthropic and Suno, I feel that same ache. Five hundred songs—painstakingly composed, recorded, and protected under US copyright law—were scraped into training datasets without permission, replicating the exact pattern of extraction that decentralized networks were designed to prevent.
Context: The Protocol That Wasn’t
Round Hill Music Publishing, a firm that manages a catalog of over 200,000 musical works, filed a lawsuit in the Southern District of New York alleging that Anthropic (the creator of the Claude AI) and Suno (a generative music AI platform) infringed copyright by using at least 500 of its songs as training data. The complaint invokes the US Copyright Act (17 U.S.C. § 106), asserting that the reproduction of these works into training corpora violates the exclusive rights of reproduction and derivative works. The legal basis is straightforward: you cannot copy a copyrighted song into a training set without permission, unless you can prove fair use.
But here’s where the blockchain lens sharpens the picture. The music industry has long been a poster child for centralized intermediary failure. Royalties are delayed, misattributed, and siphoned by labels and publishers. Smart contracts and NFT-based licensing have been proposed as a cure—a transparent, immutable ledger of ownership and usage. Yet the AI era has arrived faster than the industry could adopt those tools. Now, instead of a decentralized protocol enforcing rights, we have a lawsuit. And the outcome will shape not just the music industry, but the entire legal architecture of AI training data.
Core: The Data Sovereignty Gap
Based on my own experience auditing DeFi whitepapers in 2017, I’ve learned to spot the gap between rhetoric and code. The same gap exists here. These AI companies claim they are building transformative tools, but they are building them on a foundation of unlicensed extraction. The core issue is not whether AI training is transformative—it may be. The issue is that the creators have no say, no visibility, and no compensation. The legal system currently offers only two paths: litigation or licensing. But licensing is slow, and litigation is expensive.
Consider the numbers: The US Copyright Act allows statutory damages of up to $150,000 per work for willful infringement. If Round Hill can prove that all 500 songs were registered before the infringement, the potential damages exceed $75 million—plus legal fees. But here’s the hidden layer: the burden of proof lies with the plaintiff to show registration. And many songs in the catalog may not have been registered in time. This is a technicality, but it highlights a systemic failure. A blockchain-based registry, on the other hand, would provide an immutable timestamp of ownership and registration, making such proof trivial.
Trust is the only protocol that cannot be coded. Yet we can code a protocol that records who owns what, and automatically executes micropayments when an AI model queries a work. The technology exists—it’s called a decentralized content provenance layer. Projects like Audius, Musicoin, and even emerging NFT-based licensing platforms have shown the path. But adoption has been slow because the centralized system, despite its flaws, still provides a familiar comfort zone. Lawsuits like this one shatter that comfort zone.
Contrarian: The Fair Use Mirage
Many proponents of AI innovation argue that training on copyrighted works is a form of fair use, analogous to how a human musician learns from listening to thousands of songs. The famous Google Books case (Authors Guild v. Google) established that scanning books for search indexing was transformative and not a market substitute. But music generation is fundamentally different. When an AI generates a song that sounds like a specific artist’s style, it directly competes with the original creator’s market. The output is not a search result; it is a product. The fair use argument is weaker here.
Moreover, the defendants—Anthropic and Suno—are not small startups. They are well-funded entities with access to legal counsel. The lawsuit is not a David vs. Goliath. It is a battle between two giants, each wielding armies of lawyers. What gets lost in this noise is the small creators—the independent musicians who have no way to even know if their work was scraped. The centralized system only protects those who can afford to sue.
We built not for the peak, but for the valley. The valley is where the small creators live. They are the ones who need decentralized protocols that automatically enforce their rights without requiring a lawsuit. The Round Hill case is a signal that the valley is flooding. The only way to build a dam is to create a permissionless, transparent, and automated licensing layer that large AI companies voluntarily adopt because it is cheaper than litigation.
Takeaway: The Stewardship Imperative
This lawsuit will not be resolved in a few months. It will drag on through discovery, motions, and likely appeals. During that time, thousands more songs will be scraped. The window for the crypto community to build a viable alternative is closing. We don’t need more users; we need more stewards—developers, curators, and community members who will prioritize ethical data governance over speculative hype.
My advice: If you are building an AI model, do not wait for the court to tell you what is legal. Instead, implement a smart contract that pays creators automatically based on on-chain provenance. If you are a musician, register your works on a blockchain-based registry now. The future of music—and of AI—depends not on the verdict of a judge, but on the protocols we build today.
Trust is the only protocol that cannot be coded. But we can code the infrastructure that makes trust verifiable. The Round Hill case is a wake-up call. Will we answer it?