Author: thetrichordist

There Is No ‘Right to Train’: How AI Labs Are Trying to Manufacture a Safe Harbor for Theft

Every few months, an AI company wins a procedural round in court or secures a sympathetic sound bite about “transformative fair use.” Within hours, the headlines declare a new doctrine of spin: the right to train AI on copyrighted works. But let’s be clear — no such right exists and probably never will.  That doesn’t mean they won’t keep trying.

A “right to train” is not found anywhere in the Copyright Act or any other law.  It’s also not found in court cases on fair-use that the AI lobby leans on. It’s a slogan and it’s spin, not a statute. What we’re watching is a coordinated effort by the major AI labs to manufacture a safe harbor through litigation — using every favorable fair-use ruling to carve out what looks like a precedent for blanket immunity.  Then they’ll get one of their shills in Congress or a state legislature to introduce legislation as though a “right to train” was there all along.

How the “Right to Train” Narrative Took Shape

The phrase first appeared in tech-industry briefs and policy papers describing model training as a kind of “machine learning fair use.” The logic goes like this: since humans can read a book and learn from it, a machine should be able to “learn” from the same book without permission.

That analogy collapses under scrutiny. First of all, humans typically bought the book they read or checked it out from a library.  Humans don’t make bit-for-bit copies of everything they read, and they don’t reproduce or monetize those copies at global scale. AI training does exactly that — storing expressive works inside model weights, then re-deploying them to generate derivative material.

But the repetitive chant of the term “right to train” serves a purpose: to normalize the idea that AI companies are entitled to scrape, store, and replicate human creativity without consent. Each time a court finds a narrow fair-use defense in a context that doesn’t involve piracy or derivative outputs (because they lose on training on stolen goods like in the Anthropic and Meta cases), the labs and their shills trumpet it as proof that training itself is categorically protected. It isn’t and no court has ever ruled that it is and likely never will.

Fair Use Is Not a Safe Harbor

Fair use is a case-by-case defense to copyright infringement, not a standing permission slip. It weighs purpose, amount, transformation, and market effect — all of which vary depending on the facts. But AI companies are trying to convert that flexible doctrine into a brand new safe harbor: a default assumption that all training is fair use unless proven otherwise.  They love a safe harbor in Silicon Valley and routinely abuse them like Section 230, the DMCA and Title I of the Music Modernization Act.

That’s exactly backward. The Copyright Office’s own report makes clear that the legality of training depends on how the data was acquired and what the model does with it.  A developer who trains on pirated or paywalled material like Anthropic, Meta and probably all of them to one degree or another, can’t launder infringement through the word “training.”

Even if courts were to recognize limited fair use for truly lawful training, that protection would never extend to datasets built from pirate websites, torrent mirrors, or unlicensed repositories like Sci-Hub, Z-Library, or Common Crawl’s scraped paywalls—more on the scummy Common Crawl another time. The DMCA’s safe harbors don’t protect platforms that knowingly host stolen goods — and neither would any hypothetical “right to train.”

Yet a safe harbor is precisely what the labs are seeking: a doctrine that would retroactively bless mass infringement like Spotify got in the Music Modernization Act and preempt accountability for the sources they used.  

And not only do they want a safe harbor — they want it for free.  No licenses, no royalties, no dataset audits, no compensation. What do they want?  FREE STUFF.  When do they want it?  NOW!  Just blanket immunity, subsidized by every artist, author, and journalist whose work they ingested without consent or payment.

The Real Motive Behind the Push

The reason AI companies need a “right to train” is simple: without it, they have no reliable legal basis for the data that powers their models and they are too cheap to pay and to careless to take the time to license. Most of their “training corpora” were built years before any licenses were contemplated — scraped from the open web, archives, and pirate libraries under the assumption that no one would notice.

This is particularly important for books.  Training on books is vital for AI models because books provide structured, high-quality language, complex reasoning, and deep cultural context. They teach models coherence, logic, and creativity that short-form internet text lacks. Without books, AI systems lose depth, nuance, and the ability to understand sustained argument, narrative, and style. 

Without books, AI labs have no business.  That’s why they steal books.  Very simple, really.

Now that creators are suing, the labs are trying to reverse-engineer legitimacy. They want to turn each court ruling that nudges fair use in their direction into a brick in the wall of a judicially-manufactured safe harbor — one that Congress never passed and rights-holders never agreed to and would never agree to.

But safe harbors are meant to protect good-faith intermediaries who act responsibly once notified of infringement. AI labs are not intermediaries; they are direct beneficiaries. Their entire business model depends on retaining the stolen data permanently in model weights that cannot be erased.  The “right to train” is not a right — it’s a rhetorical weapon to make theft sound inevitable and a demand from the richest corporations in commercial history for yet another government-sponsored subsidy of infringement by bad actors.

The Myth of the Inevitable Machine

AI’s defenders claim that training on copyrighted works is as natural as human learning. But there’s nothing natural about hoarding other people’s labor at planetary scale and calling it innovation. The truth is simpler: the “right to train” is a marketing term invented to launder unlawful data practices into respectability.

If courts and lawmakers don’t call it what it is — a manufactured, safe harbor for piracy to benefit some of the biggest free riders who ever snarfed down corporate welfare — then history will repeat itself. What Grokster tried to do with distribution, AI is trying to do with cognition: privatize the world’s creative output and claim immunity for the theft.

by thetrichordist

Artist Rights Are Innovation, Too! White House Opens AI Policy RFI and Artists Should Be Heard

The White House has opened a major Request for Information (RFI) on the future of artificial intelligence regulation — and anyone can submit a comment. That means you. This is not just another government exercise. It’s a real opportunity for creators, musicians, songwriters, and artists to make their voices heard in shaping the laws that will govern AI and its impact on culture for decades to come.

Too often, artists find out about these processes after the decisions are already made. This time, we don’t have to be left out. The comment period is open now, and you don’t need to be a lawyer or a lobbyist to participate — you just need to care about the future of your work and your rights. Remember—property rights are innovation, too, just ask Hernando de Soto (Mystery of Capital) or any honest economist.

Here are four key issues in the RFI that matter deeply to artists — and why your voice is critical on each:

1. Transparency and Provenance: Artists Deserve to Know When Their Work Is Used

One of the most important questions in the RFI asks how AI companies should document and disclose the creative works used to train their models. Right now, most platforms hide behind trade secrets and refuse to reveal what they ingested. For artists, that means you might never know if your songs, photographs, or writing were taken without permission — even if they now power billion-dollar AI products.

This RFI is a chance to demand real provenance requirements: records of what was used, when, and how. Without this transparency, artists cannot protect their rights or seek compensation. A strong public record of support for provenance could shape future rules and force platforms into accountability.

2. Derivative Works and AI Memory: Creativity Shouldn’t Be Stolen Twice

The RFI also raises a subtle but crucial issue: even if companies delete unauthorized copies of works from their training sets, the models still retain and exploit those works in their weights and “memory.” This internal use is itself a derivative work — and it should be treated as one under the law.

Artists should urge regulators to clarify that training outputs and model weights built from copyrighted material are not immune from copyright. This is essential to closing a dangerous loophole: without it, platforms can claim to “delete” your work while continuing to profit from its presence inside their AI systems.

3. Meaningful Opt-Out: Creators Must Control How Their Work Is Used

Another critical question is whether creators should have a clear, meaningful opt-out mechanism that prevents their work from being used in AI training or generation without permission. As Artist Rights Institute and many others have demonstrated, “Robots.txt” disclaimers buried in obscure places are not enough. Artists need a legally enforceable system—not another worthless DMCA-style notice and notice and notice and notice and notice and maybe takedown system that platforms must respect and that regulators can audit.

A robust opt-out system would restore agency to creators, giving them the ability to decide if, when, and how their work enters AI pipelines. It would also create pressure on companies to build legitimate licensing systems rather than relying on theft.

4. Anti-Piracy Rule: National Security Is Not a License to Steal

Finally, the RFI invites comment on how national priorities should shape AI development and it’s vital that artists speak clearly here. There must be a bright-line rule that training AI models on pirated content is never excused by national security or “public interest” arguments. This is a real thing—pirate libraries are clearly front and center in AI litigation which have largely turned into piracy cases because the AI lab “national champions” steal books and everything else.

If a private soldier stole a carton of milk from a chow hall, he’d likely lose his security clearance. Yet some AI companies have built entire models on stolen creative works and now argue that government contracts justify their conduct. That logic is backwards. A nation that excuses intellectual property theft in the name of “security” corrodes the rule of law and undermines the very innovation it claims to protect. On top of it, the truth of the case is that the man Zuckerberg is a thief, yet he is invited to dinner at the White House.

A clear anti-piracy rule would ensure that public-private partnerships in AI development follow the same legal and ethical standards we expect of every citizen — and that creators are not forced to subsidize government technology programs with uncompensated labor. Any “AI champion” who steals should lose or be denied a security clearance.

Your Voice Matters — Submit a Comment

The White House needs to hear directly from creators — not just from tech companies and trade associations. Comments from artists, songwriters, and creative professionals will help shape how regulators understand the stakes and set the boundaries.

You don’t need legal training to submit a comment. Speak from your own experience: how unauthorized use affects your work, why transparency matters, what a meaningful opt-out would look like, and why piracy can never be justified by national security.

👉 Submit your comment here before the October 27 deadline.

2025-18737Download
by thetrichordist

@DanMilmo: Top UK artists urge Starmer to protect their work on eve of Trump visit

UK artists including Paul McCartney, Kate Bush and Elton John urged Prime Minister Keir Starmer to protect creators before a UK-US tech pact tied to President Donald Trump’s visit. In a letter, they accuse Labour of blocking transparency rules that would force AI firms to disclose training data and warn proposals enabling training on copyrighted works without permission could let an artist’s life’s work be stolen. Citing human rights documents like the International Covenant on Economic, Social and Cultural Rights, the Berne convention and the European Convention on Human Rights, they frame the issue as a human-rights breach. Peer Beeban Kidron criticised US-heavy working groups. Government says no decision yet and promises a report by March. 

Read the post on The Guardian

by thetrichordist

Senator Josh @HawleyMO Throws Down on Big Tech’s Copyright Theft

 I believe Americans should have the ability to defend their human data, and their rights to that data, against the largest copyright theft in the history of the world. 

Millions of Americans have spent the past two decades speaking and engaging online. Many of you here today have online profiles and writings and creative productions that you care deeply about. And rightly so. It’s your work. It’s you.

What if I told you that AI models have already been trained on enough copyrighted works to fill the Library of Congress 22 times over? For me, that makes it very simple: We need a legal mechanism that allows Americans to freely defend those creations. I say let’s empower human beings by protecting the very human data they create. Assign property rights to specific forms of data, create legal liability for the companies who use that data and, finally, fully repeal Section 230. Open the courtroom doors. Let the people sue those who take their rights, including those who do it using AI.

Third, we must add sensible guardrails to the emergent AI economy and hold concentrated economic power to account. These giant companies have made no secret of their ambitions to radically reshape our economic life. So, we ought to require transparency and reporting each time they replace a working man with a machine.

And the government should inspect all of these frontier AI systems, so we can better understand what the tech titans plan to build and deploy. 

Ultimately, when it comes to guardrails, protecting our children should be our lodestar. You may have seen recently how Meta green-lit its own chatbots to have sensual conversations with children—yes, you heard me right. Meta’s own internal documents permitted lurid conversations that no parent would ever contemplate. And most tragically, ChatGPT recently encouraged a troubled teenager to commit suicide—even providing detailed instructions on how to do it.

We absolutely must require and enforce rigorous technical standards to bar inappropriate or harmful interactions with minors. And we should think seriously about age verification for chatbots and agents. We don’t let kids drive or drink or do a thousand other harmful things. The same standards should apply to AI.

Fourth and finally, while Congress gets its act together to do all of this, we can’t kneecap our state governments from moving first. Some of you may have seen that there was a major effort in Congress to ban states from regulating AI for 10 years—and a whole decade is an eternity when it comes to AI development and deployment. This terrible policy was nearly adopted in the reconciliation bill this summer, and it could have thrown out strong anti-porn and child online safety laws, to name a few. Think about that: conservatives out to destroy the very concept of federalism that they cherish … all in the name of Big Tech. Well, we killed it on the Senate floor. And we ought to make sure that bad idea stays dead.

We’ve faced technological disruption before—and we’ve acted to make technology serve us, the people. Powered flight changed travel forever, but you can’t land a plane on your driveway. Splitting the atom fundamentally changed our view of physics, but nobody expects to run a personal reactor in their basement. The internet completely recast communication and media, but YouTube will still take down your video if you violate a copyright. By the same token, we can—and we should—demand that AI empower Americans, not destroy their rights . . . or their jobs . . . or their lives.

by thetrichordist

Why Artists Are Striking Spotify Over Daniel Ek’s AI-Offensive Weapons Bet—and Why It Matters for AI Deals

Over the summer, a growing group of artists began pulling their catalogs from Spotify—not over miserable and Dickensian-level royalties alone, but over Spotify CEO Daniel Ek’s vast investment in Helsing, a European weapons company.  Helsing builds AI-enabled offensive weapons systems that skirt international human rights law, specifically Article 36 of the Geneva Conventions. Deerhoof helped kick off the current wave; other artists (including Xiu Xiu, King Gizzard & the Lizard Wizard, Hotline TNT, The Mynabirds, WU LYF, Kadhja Bonet, and Young Widows) have followed or announced plans to do so.

What is Helsing—and what does it build?

Helsing is a Munich-based defense-tech firm founded in 2021. It began with AI software for perception, decision-support, and electronic warfare, and has expanded into hardware. The company markets the HX‑2 “AI strike drone,” described as a software‑defined loitering munition intended to engage artillery and armored targets at significant range—and kill people. It emphasizes resilience to electronic warfare, swarm/networked tactics via its Altra recon‑strike platform, and a human in/on the loop for critical decisions, and that limited role for humans in killing other humans is where it runs into Geneva Convention issues.   Trust me, they know this.

The X-2 Strike Drone

Beyond drones, Helsing provides AI electronic‑warfare upgrades for Germany’s Eurofighter EK (with Saab), and has been contracted to supply AI software for Europe’s Future Combat Air System (FCAS). Public briefings and reporting indicate an active role supporting Ukraine since 2022, and a growing UK footprint linked to defense modernization initiatives. In 2025, Ek’s investment firm led a major funding round that valued Helsing in the multibillion‑euro range alongside contracts in the UK, Germany, and Sweden.

So let’s be clear—Helsing is not making some super tourniquet or AI medical device that has a dual use in civilian and military applications.  This is Masters of War stuff.  Which, for Mr. Ek’s benefit, is a song.

Why artists care

For these artists, the issue isn’t abstract: they see a direct line between Spotify‑generated wealth and AI‑enabled lethality, especially as Helsing moves from software into weaponized autonomy at scale. That ethical conflict is why exit statements explicitly connect Dickensian streaming economics and streamshare thresholds to military investment choices.  In fact, it remains to be seen whether Spotify itself is using its AI products and the tech and data behind them for Helsing’s weapons applications.

How many artists have left?

There’s no official tally. Reporting describes a wave of departures and names specific acts. The list continues to evolve as more artists reassess their positions.

The financial impact—on Spotify vs. on artists

For Spotify, a handful of indie exits barely moves the needle. The reason is the pro‑rata or “streamshare” payout model: each rightsholder’s share is proportional to total streams, not a fixed per‑stream rate except if you’re “lucky” enough to get a “greater of” formula. Remove a small catalog and its share simply reallocates to others. For artists, leaving can be meaningful—some replace streams with direct sales (Bandcamp, vinyl, fan campaigns) and often report higher revenue per fan. But at platform scale, the macro‑economics barely budge.  

Of course because of Spotify’s tying relationships with talent buyers for venues (explicit or implicit) not being on Spotify can be the kiss of death for a new artist competing for a Wednesday night at a local venue when the venue checks your Spotify stats.

Why this is a cautionary tale for AI labs

Two practices make artist exits feel symbolically loud but structurally quiet—and they’re exactly what frontier AI should avoid:

1) Revenue‑share pools with opaque rules. Pro‑rata “streamshare” pushes smaller players toward zero; any exit just enriches whoever remains. AI platforms contemplating rev‑share training or retrieval deals should learn from this: user‑centric or usage‑metered deals with transparent accounting are more legible than giant, shifting pools.

2) NDA‑sealed terms. The streaming era normalized NDAs that bury rates and conditions. If AI deals copy that playbook—confidential blacklists, secret style‑prompt fees, unpublished audit rights—contributors will see protest as the only lever. Transparency beats backlash.

3) Weapons Related Use Cases for AI.  We all know that the frontier labs like Google, Amazon, Microsoft and others are all also competing like trained seals for contracts from the Department of War.  They use the same technology trained on culture ripped off from artists to kill people for money.

A clearer picture of Helsing’s products and customers

• HX‑2 AI Strike Drone: beyond‑line‑of‑sight strike profile, on‑board target re‑identification, EW‑resilient, swarm‑capable via Altra; multiple payload options; human in/on the loop.
• Eurofighter EK (Germany): with Saab, AI‑enabled electronic‑warfare upgrade for Luftwaffe Eurofighters oriented to SEAD/DEAD roles.
• FCAS AI Backbone (Europe): software/AI layer for the next‑generation air combat system under European procurement.
• UK footprint: framework contracting in the UK defense ecosystem, tied to strike/targeting modernization efforts.
• Ukraine: public reporting indicates delivery of strike drones; company statements reference activity supporting Ukraine since 2022.

The bigger cultural point

Whether you applaud or oppose war tech, the ethical through‑line in these protests is consistent: creators don’t want their work—or the wealth it generates—financing AI (especially autonomous) weaponry. Because the platform’s pro‑rata economics make individual exits financially quiet, the conflict migrates into public signaling and brand pressure.

What would a better model look like for AI?

• Opt‑in, auditable deals for creative inputs to AI models (training and RAG) with clear unit economics and published baseline terms.
• User‑centric or usage‑metered payouts (by contributor, by model, by retrieval) instead of a single, shifting revenue pool.
• Public registries and audit logs so participants can verify where money comes from and where it goes.
• No gag clauses on baseline rates or audit rights.

The strike against Spotify is about values as much as value. Ek’s bet on Helsing—drones, electronic warfare, autonomous weapons—makes those values impossible for some artists to ignore. Thanks to the pro‑rata royalty machine, the exits won’t dent Spotify’s bottom line—but they should warn AI platforms against repeating the same opaque rev‑shares and NDAs that leave creators feeling voiceless in streaming.

by thetrichordist

If you got one of these emails from Spotify, you might be interested

Spotify failed to consult any of the people who drive fans to the data abattoir: the musicians, artists, podcasters and authors.

Spotify has quietly tightened the screws on AI this summer—while simultaneously clarifying how it uses your data to power its own machine‑learning features. For artists, rightsholders, developers, and policy folks, the combination matters: Spotify is making it harder for outsiders to train models on Spotify data, even as it codifies its own first‑party uses like AI DJ and personalized playlists.

Spotify is drawing a bright line: no training models on Spotify; yes to Spotify training its own. If you’re an artist or developer, that means stronger contractual leverage against third‑party scrapers—but also a need to sharpen your own data‑governance and licensing posture. Expect other platforms in music and podcasting to follow suit—and for regulators to ask tougher questions about how platform ML features are audited, licensed, and accounted for.

Below is a plain‑English (hopefully) breakdown of what changed, what’s new or newly explicit, and the practical implications for different stakeholders.

Explicit ban on using Spotify to train AI models (third parties). 

Spotify’s User Guidelines now flatly prohibit “crawling” or “scraping” the service and, crucially, “using any part of the Services or Content to train a machine learning or AI model.” That’s a categorical no for bots and bulk data slurps. The Developer Policy mirrors this: apps using the Web API may not “use the Spotify Platform or any Spotify Content to train a machine learning or AI model.” In short: if your product ingests Spotify data, you’re in violation of the rules and risk enforcement and access revocation.

Spotify’s own AI/ML uses are clearer—and broad. 

The Privacy Policy (effective August 27, 2025) spells out that Spotify uses personal data to “develop and train” algorithmic and machine‑learning models to improve recommendations, build AI features (like AI DJ and AI playlists), and enforce rules. That legal basis is framed largely as Spotify’s “legitimate interests.” Translation: your usage, voice, and other data can feed Spotify’s own models.

The user content license is very broad. 

If you post “User Content” (messages, playlist titles, descriptions, images, comments, etc.), you grant Spotify a worldwide, sublicensable, transferable, royalty‑free, irrevocable license to reproduce, modify, create derivative works from, distribute, perform, and display that content in any medium. That’s standard platform drafting these days, but the scope—including derivative works—has AI‑era consequences for anything you upload to or create within Spotify’s ecosystem (e.g., playlist titles, cover images, comments).

Anti‑manipulation and anti‑automation rules are baked in. 

The User Guidelines and Developer Policy double down on bans against bots, artificial streaming, and traffic manipulation. If you’re building tools that touch the Spotify graph, treat “no automated collection, no metric‑gaming, no derived profiling” as table stakes—or risk enforcement, up to termination of access.

Data‑sharing signals to rightsholders continue. 

Spotify says it can provide pseudonymized listening data to rightsholders under existing deals. That’s not new, but in the ML context it underscores why parallel data flows to third parties are tightly controlled: Spotify wants to be the gateway for data, not the faucet you can plumb yourself.

What this means by role:

• Artists & labels: The AI‑training ban gives you a clear contractual hook against services that scrape Spotify to build recommenders, clones, or vocal/style models. Document violations (timestamps, IPs, payloads) and send notices citing the User Guidelines and Developer Policy. Meanwhile, assume your own usage and voice interactions can be used to improve Spotify’s models—something to consider for privacy reviews and internal policies.

• Publishers and collecting societies: The combination of “no third‑party training” + “first‑party ML training” is a policy trend to watch across platforms. It raises familiar questions about derivative data, model outputs, and whether platform machine learning features create new accounting categories—or require new audit rights—in future licenses.

• Policymakers: Read this as another brick in the “closed data/open model risk” wall. Platforms restrict external extraction while expanding internal model claims. That asymmetry will shape future debates over data‑access mandates, competition remedies, and model‑audit rights—especially where platform ML features may substitute for third‑party discovery tools.

Practical to‑dos

1) For rights owners: Add explicit “no platform‑sourced training” language in your vendor, distributor, or analytics contracts. Track and log known scrapers and third‑party tools that might be training off Spotify. Consider notice letters that cite the specific clauses.

2) For privacy and legal teams: Update DPIAs and data maps. Spotify’s Privacy Policy identifies “User Data,” “Usage Data,” “Voice Data,” “Message Data,” and more as inputs for ML features under legitimate interest. If you rely on Spotify data for compliance reports, make sure you’re only using permitted, properly aggregated outputs—not raw exports.

3) For users: I will be posting a guideline to how to clawback your data. I may not hit everything so always open to suggestions about whatever else that others spot.

Spotify’s terms give it very broad rights to collect, combine, and use your data (listening history, device/ads data, voice features, third-party signals) for personalization, ads, and product R&D. They also take a broad license to user content you upload (e.g., playlist art). 

Key cites

• User Guidelines: prohibition on scraping and on “using any part of the Services or Content to train a machine learning or AI model.”

• Developer Policy (effective May 15, 2025): “Do not use the Spotify Platform or any Spotify Content to train a machine learning or AI model…” Also bans analyzing Spotify content to create new/derived listenership metrics or user profiles for ad targeting.

• Privacy Policy (effective Aug. 27, 2025): Spotify uses personal data to “develop and train” ML models for recommendations, AI DJ/AI playlists, and rule‑enforcement, primarily under “legitimate interests.”

• Terms & Conditions of Use: very broad license to Spotify for any “User Content” you post, including the right to “create derivative works” and to use content by any means and media worldwide, irrevocably.

[A version of this post first appeared on MusicTechPolicy]

by thetrichordist

Artist Rights Institute and Abby North Press Copyright Office for Accountability in MLC Redesignation

On August 22, 2025, the Artist Rights Institute, together with music publisher Abby North, filed joint comments with the U.S. Copyright Office as part of the agency’s ongoing five-year redesignation review of the Mechanical Licensing Collective (MLC). The comments memorialize an ex parte meeting with senior Copyright Office attorneys and stress that this redesignation process must not become a perfunctory exercise. Instead, it should serve as a meaningful opportunity to hold the MLC accountable for its statutory obligations.

The filing underscores that Congress deliberately gave the Copyright Office broad regulatory oversight because the MLC was established as an experiment under the Music Modernization Act. After five years, the evidence points to serious deficiencies: continuing metadata errors, lack of access to bulk matching tools for rightsholders, opaque governance decisions, and unresolved questions about audits and litigation. Most strikingly, the MLC’s unilateral investment of unmatched royalties in the securities markets—totaling more than $1.2 billion on the MLC’s latest tax return—raises concerns about statutory authority and fiduciary duties.

The joint comments argue that interest on unmatched royalties was intended by Congress as a penalty on licensees for failure to timely match and pay, not as a windfall for the MLC itself. By adopting an unauthorized investment policy, the MLC risks stepping outside its mandate, exposing its officers and directors to fiduciary liability.

To restore transparency and trust, the Institute and Ms. North propose clear regulatory reforms: conditional redesignation tied to performance benchmarks, publication of vendor match rates, real-time disclosure of governance actions, clarified metadata responsibilities, and monthly reporting of investment holdings. These reforms would align the U.S. system with international best practices and protect songwriters whose livelihoods depend on fair and accurate royalty distributions.

Read the full joint comments below.

Artist-Rights-Institute-to-USCO-1-Aug-22-2025Download
by thetrichordist

United for Artists’ Rights: Amicus Briefs Filed in Vetter v. Resnik Support Global Copyright Termination for Songwriters and Authors: The Authors Guild, Inc., Dramatists Legal Defense Fund, Inc., Novelists, Inc., Romance Writers Of America, Inc., Society Of Composers & Lyricists, Inc. and Songwriters Guild Of America, Inc.

In Vetter v. Resnik, songwriter Cyril Vetter won his trial case in Baton Rouge allowing him to recover worldwide rights in his song “Double Shot of My Baby’s Love” after serving his 35 year termination notice on his former publisher, Resnik Music Group. The publisher appealed. The Fifth Circuit Court of Appeals will hear the case and currently is weighing whether U.S. copyright termination rights include “foreign” territories—a question that strikes at the heart of artists’ ability to reclaim their work worldwide (whatever “foreign” means).

Cyril’s attorney Tim Kappel explains the case if you need an explainer:

An astonishing number of friend of the court briefs were filed by many songwriter groups. We’re going to post them all and today’s brief is by The Authors Guild, Inc., Dramatists Legal Defense Fund, Inc., Novelists, Inc., Romance Writers Of America, Inc., Society Of Composers & Lyricists, Inc. and Songwriters Guild Of America, Inc.

We believe the answer must be yes. Congress gave creators and their heirs the right to regain control of their work after decades, and that promise means little if global rights are excluded. The outcome of this case could either reaffirm that promise—or open the door for multinational publishers to sidestep it entirely.

That’s why we’re sharing friend of the court briefs from across the creative communities. Each one brings a different perspective—but all defend the principle that artists deserve a real, global right to take back what’s theirs, because as Chris said Congress did not give authors a second bite at half the apple.

Read the latest amicus brief below.

Authors Guild et al Amicus BriefDownload
by thetrichordist