AI Litigation – The Trichordist

Your Music, AI, and the Machine Unlearning Research Hub: What Every Musician Should Know

If you’re a recording artists or musician, you have most likely heard some form of this pitch:

“Don’t worry. If you don’t like how your music is used for AI, we can always take it back out later.”

That sounds fair.

The hard part is that, today, it is not that simple. And it’s so not simple, that statement may as well be untrue.

That is why we created the Machine Unlearning Research Hub at MusicTechPolicy.com—to help musicians understand what machine unlearning is, what it can do today, and what it may be able to do tomorrow.

Think of it like baking a cake

Suppose you bake a cake with flour, eggs, butter, milk, and sugar. The eggs make the cake what it is.

Once the cake comes out of the oven, you cannot remove just the eggs. AI training works in much the same way.

When an AI developer “trains” a model (often on illegally acquired recordings as we are seeing in the litigation), it does not simply store copies of recordings in a folder. It changes billions—or even trillions—of internal values based on every recording it has been trained on and extracted from those training tracks or its “training pipeline”. Each recording and the values the AI extracts from the recording helps shape the finished model.

That’s why it knows what you mean when you ask it to play an Eric Clapton solo or a John Bonham drum fill. That is also why taking one recording back out later is so difficult.

Surely, you say, they didn’t build a dataset that cannot be corrected and only gets larger with human personal rights and copyrights? Well, they kind of did. Not that different from Google Street View. There’s a long “Hotel California” tradition with these people.

So what is machine unlearning?

Machine unlearning is a field of research that asks a simple question:

Can an AI system forget what it learned from a particular piece of data without rebuilding the entire model from scratch?

The quick answer today is not very easily, no matter what they tell you. Researchers all over the world are working on that question.

The results are a mixed bag. A cynic—who me?—might say they’re not trying very hard. But some techniques show real promise. Others work only under very limited conditions with largely toy datasets.

For today’s largest music models, there is still no widely accepted way to guarantee that every artifact of one recording—or one artist—has been completely removed.

That does not mean machine unlearning will never work. It means it is still a research problem.

How much can it fix today?

The honest answer is:

Some things. Not everything.

Researchers have developed methods that can reduce a model’s reliance on particular data or make it much less likely to produce certain results. That’s not 100% or zero depending on how you look at it. But it’s better every year and those advances matter.

But reducing influence is not the same thing as proving that a recording has been fully removed from a commercial AI model. For musicians, that difference is obviously important.

Will it get better?

Almost certainly.

Machine unlearning is receiving significant attention from universities, technology companies, and governments. Why? Well, for one thing there are countries that have the “right to be forgotten” that Google fought so hard against in Europe and especially Germany. There’s also other privacy laws that all of the AI models are essentially out of compliance with, so it’s only a matter of time until there’s a real effort to do something about it.

Five years from now, today’s methods will almost certainly look primitive. Ten years from now, the tools may be far better still. But no one can honestly promise today that future improvements will solve every problem created by yesterday’s training.

That is why decisions made now still matter. Let’s be honest, the best way to stay out of AI is to never be included in AI.

What does this mean if someone wants to license your music?

Ask yourself one simple question:

If I change my mind in three years, how will my music come back out?

Do not settle for vague promises.

Ask specific questions.

Will my recordings be kept in a separate training set?
Can they be removed without rebuilding the model?
What machine unlearning process will be used?
Has that process been independently tested?
How will you prove my music was removed?
What happens if the technology cannot fully remove it?
Who pays to remove it?

If the company cannot answer those questions today, it is worth understanding that they are asking you to rely on technology that may not yet exist in the form they hope it will. Or as Blanche Dubois said in Streetcar Named Desire, “I have always relied on the kindness of strangers.” Feel good yet?

That does not necessarily mean you should refuse every AI license.

It does mean you should know exactly what risk you are accepting.

This is why we built the Machine Unlearning Research Hub

Machine unlearning is moving quickly, and new research papers appear almost every week. Some represent genuine breakthroughs, others improve only narrow parts of the problem.

Our goal is not to tell musicians what decision to make. Our goal is to help musicians make informed decisions based on the best available evidence. If someone asks you to license your music for AI, you deserve to know one thing before you sign:

Can they realistically give it back if you ask?

Today, that question deserves a careful answer—not a marketing slogan. That is what the Machine Unlearning Research Hub is here to explore.

August 6, 2026 by Trichordist

The AI “License” That Isn’t: A Musician’s Checklist for Spotting a Covenant Not to Sue Censorship Trap in Disguise

If you saw the adverb “forever” in a contract, would it give you pause? It would me—I would wonder who allowed that to slip through. As text-to-audio generative AI models desperately try to normalize their shoot-ready-aim ingestion of likely stolen works to support their next round of financing (or in some cases perhaps a VC exit by IPO), it’s likely that we will see some efforts at “licensing” reminiscent of the Napster era. “Licenses” that actually paper over the main act—stop the lawsuit before they start. What will that look like and what should we look for buried in the not so fine print?

Generative AI companies are coming to the negotiating table with “music licensing agreements” (and probably other artists’ too). That sounds encouraging — it suggests they want to do the right thing and pay for the content they use. Trust me, they don’t. What they want is to get away with it.

Not every document titled “license” actually functions like one. Some of these deals are better described as litigation insurance dressed up in licensing language, and if you sign one without reading the small print, you may be giving away far more than you realize. Because as Tom Waits taught us in the classic Step Right Up, the large print giveth and the small print taketh away.

Here’s a practical checklist of red flags to watch for if you want to look past the hype:

☐ Check Whether the “License” Actually Includes a Perpetual “Covenant Not to Sue”

A quick definitional note: a covenant not to sue is a contractual promise in which you agree in advance never to bring a legal claim against the other party — regardless of what happens, regardless of whether you even know you have a claim, and often regardless of whether the conduct at issue would otherwise be something you could sue for or get a court to stop (like with an injunction). Unlike a license, which grants permission and can expire, a covenant not to sue can be a permanent waiver of your right to seek a remedy. It’s the difference between handing someone a key to your house and signing a contract promising you’ll never call the police no matter what they do inside.

A real license gives someone permission to use your work under defined conditions, and when the term ends, so does the permission. Watch out for agreements that include a separate clause in which you irrevocably promise — both during and after the term — never to bring any claim against the company or even its users. If the covenant not only covers copyright, publicity rights, moral rights, defamation, and lawsuits over issues that may not exist yet, you are not licensing your work. You are surrendering your right to enforce your rights, permanently. Or you know, “forever.” Make sure you understand the difference before you sign.

☐ Beware a “Non-Exclusive” License That Binds Your Co-Writers

Remember the 100% licensing debacle back in 2016? Tenants-in-common co-ownership of a copyright means any co-owner can grant a non-exclusive license without the others’ consent — but that principle can be weaponized. If an agreement requires you to represent and warrant that no third-party consent is needed, and that you will not encourage your fellow songwriters to take any position inconsistent with the deal (again with the censorship), you may be dragging your co-writers into an arrangement they never agreed to. Worse, if the covenant not to sue extends to all contributors, your co-writers’ enforcement rights could be compromised by a deal they had no say in. Before you sign, make sure the AI platform isn’t leveraging TIC principles to bind people who aren’t at the table. Here’s why this matters in practice: under TIC co-ownership, a non-exclusive license granted by one co-owner is generally binding on all co-owners, even without their consent. That means if you grant a non-exclusive license — or, more critically, a covenant not to sue — covering the full composition, your co-writer may be unable to bring an infringement claim against the same company for the same use, because the company can point to your grant as a complete defense. The co-writer’s right to sue isn’t technically extinguished, but it’s rendered practically worthless. The result is that one publisher’s signature can neutralize enforcement rights across an entire song’s ownership chain. Ask your lawyer.

☐ Look for a Sweeping Pre-Signing Release

Some agreements bury a broad release of all claims arising before the effective date — known and unknown, suspected and unsuspected. That’s not a license, it’s a preemptive settlement masquerading as a license. Worse, they may ask you to preemptively waive protections like California Civil Code Section 1542, which exists specifically to protect people in this exact situation. The statute prevents people from accidentally releasing claims they don’t yet know about. If an AI company has been training on your catalog without permission for years, a release like this could function like a contractual safe harbor and eliminate any leverage you had to seek compensation for that unauthorized use, or better yet an injunction. You know, kind of like the Music Modernization Act. Ask yourself: why does a forward-looking license need a backward-looking release? Aside from wanting a pony.

☐ Make Sure “Training Rights” Aren’t Irrevocable in Practice

A license to include your works in a training corpus sounds civilized, but look closely at the removal mechanics (and take a class in “machine unlearning“). If the company is only required to retrain its model a limited number of times per year — say, twice — then even if you pull your catalog, your works may remain embedded in the AI model for months or even “forever”. There are no guarantees that “retraining” will actually remove your works from the model and, in fact, the literature suggests it won’t. And if the covenant not to sue survives the term for anything created while the deal was active (or before the deal was active), the training that already happened is effectively locked in. You may be inadvertently granting a right for yourself and maybe your co-writers that you can never meaningfully take back.

☐ Don’t Pre-Approve a Statutory Streaming License They May Not Be Entitled To

Some AI music platforms aspire to become full on-demand streaming services — and their agreements may include language contemplating exactly that expansion, with provisions stating the company will simply obtain a blanket mechanical license from the Mechanical Licensing Collective under 17 U.S.C. § 115(d). But whether a generative AI platform that creates synthetic music qualifies for the statutory compulsory license designed for traditional on-demand streaming is a genuinely open legal question. If you agree to terms that treat this expansion as a foregone conclusion — or worse, if your covenant not to sue or conditional authorization greenlights streaming functionality by default — you may be conceding the argument before it’s ever litigated. Don’t let an agreement’s assumptions about statutory eligibility become your assumptions. That question should be tested, not waived.

There’s a deeper problem here, too. The Section 115 compulsory license has always contained an anti-piracy prerequisite: you cannot obtain a compulsory license to use a musical composition if the sound recording you’re working from was not lawfully fixed or authorized by the sound recording’s copyright owner. If an AI platform trained its model on sound recordings it ingested without authorization (as is currently being litigated), its entire statutory license theory may be built on a foundation of infringement. The compulsory license was never designed to launder unauthorized use of sound recordings into lawful use of the compositions they embody. By agreeing to terms that treat the platform’s eligibility as settled, you may be implicitly conceding that the platform’s use of those sound recordings was authorized — a concession likely worth far more than anyone is probably paying for it and that can get you sideways with the sound recording owners.

☐ Scrutinize “Guardrail Failure” Safe Harbors

Technical safeguards — input filters, output filters, vocal classifiers — are only as good as the consequences for failure. Be wary of provisions that excuse the AI company from breach liability when its guardrails fail, so long as the failures are characterized as “inadvertent” and “de minimis” which are in the eye of the beholder and just buying a lawsuit. If the agreement treats guardrail failures as non-breaches by default, you’ve effectively agreed that the company can produce unauthorized outputs of your works without meaningful accountability, as long as it promises to try to fix the problem after the fact with no stick if it fails.

☐ Watch Who Owns the AI Model — and Learned “Insights”

Ownership clauses in AI agreements often go further than you’d expect. The company may claim ownership not just of the AI model, but of all “insights” it learns which can be damn near anything. That “learning” is including those developed using your content. That language could be read to mean that everything the AI extracts, learns, or derives from your catalog belongs to the platform. Make sure you are not inadvertently ceding ownership of the creative intelligence embedded in your works.

☐ Don’t Let a Third Party’s Binding Calculation Determine Your Payment

I am no fan of market share revenue share deals, particularly when the revenue pool is a fixed number. Some revenue pool deals delegate the calculation of your market share — and therefore your payment — to a third party, and then declare that calculation to be binding on you, even if you had nothing to do with calculating either your market share (under the “license” not in general) or your share of the revenue or minimum guarantee. If you have no right to challenge the methodology or the math, or to audit that third party, you could be found to have agreed to be paid whatever someone else decides you’re owed, with no recourse if they get it wrong.

☐ Trace the Revenue Through Every Deduction Before Celebrating the Rate

A headline royalty rate means nothing if it’s applied to a revenue base that has already been carved down by layers of deductions including off the top fees, advertising costs, technical fees, inference compute costs, app-store commissions, performance royalties, and more. Each deduction may sound reasonable in isolation, but stacked together, they can reduce the revenue pool to a fraction of what you’d expect. Always model your actual payout, not just the stated percentage.

☐ Beware Conditional Authorizations That Expand the Deal Without Your Active Consent

Some agreements include provisions for future features such as API access, enterprise customer pricing, new product versions, downloads, or interactive streaming that automatically activate once a majority of other rightsholders sign on. If the trigger is what other labels or publishers do rather than what you approve, you may find that the scope of the license has expanded well beyond what you agreed to, without any additional negotiation or compensation. And remember what they say about if everyone else was running off a cliff.

☐ Censorship Clauses: Don’t Agree to Never Contradict the Deal

Representation and warranty sections sometimes include a covenant that you will not take, or encourage your artists or songwriters to take, any position at any time (whether during or after the term and whether or not truthful) that is inconsistent with the hoorah narrative about AI or the applicable license. Read that carefully. It could be interpreted to prevent you — or your writers — from ever publicly criticizing the deal, advocating for stronger protections, or supporting legislation that conflicts with the agreement’s framework, even years after it expires even if the AI platform is in breach. Aside from censoring your freedom of speech, this has nothing, and I mean nothing, to do with a license.

☐ Understand What Walled Garden Actually Means for Your Writers

A service that restricts AI-generated content to a “walled garden” with no downloads sounds contained. But if users can generate unlimited content using your works, share tracks or links to tracks across social media, and the company retains the right to expand into other product lines (including non-AI product lines like an on-demand streaming service) all within the same agreement, any “walled garden” may be pretty ephemeral.

☐ Confirm That Your Minimum Guarantee Is Actually Guaranteed

A minimum guarantee that is “fully recoupable” against future royalties is not a floor — it’s an advance. If the service underperforms, you keep the advance, but if it succeeds, the guarantee is just an interest-free loan. Make sure you understand whether the guarantee represents real minimum compensation or simply front-loaded royalties you would have earned anyway. Also, plan for both failure and success—if you’re a publisher or label, how on earth are you going to be able to account to your songwriters or artists while you’re recouping any minimum guarantee or afterwards?

☐ Demand Flow-Down Protections in the Platform’s Terms of Service

An AI platform’s Terms of Service are where your contractual protections actually meet the end user — and if the ToS doesn’t carry your rights forward, your rights may exist only on paper, if at all. Before you sign, confirm that the agreement requires the platform’s user-facing ToS to include, at a minimum:

An ownership disclaimer — users must acknowledge they acquire no copyright or ownership interest in AI outputs that embody your works.
A reverse-engineering prohibition — users must be prohibited from extracting, reconstructing, or isolating your works from the model’s outputs.
A downstream training restriction — users must be barred from using AI-generated outputs containing your works to train their own AI models.
User indemnification that flows to you — if a user misuses your works, you shouldn’t have to rely solely on the platform to make you whole.
A meaningful commercial-use definition and enforcement mechanism — if the deal says “personal, non-commercial use only,” the ToS needs to define what that means and impose real consequences for violations, not just account revocation. Remember, “non-commercial” has been used for text and data mining exceptions in various countries that are huge and unintended exceptions to copyright.
An anti-circumvention clause — users should be explicitly prohibited from attempting to bypass input/output filters and guardrails, not just prevented by technology that may fail.
Publisher review and approval rights over ToS language — if the agreement references ToS protections as part of your deal, you should have the right to review and approve the actual language implementing them.

If the platform isn’t required to flow these protections down to users, then the guardrails in your agreement are a ceiling, not a floor.

☐ Ask Whether Any Other Rightsholders Are Getting Equity — and Whether You Are, Too

If the agreement includes an MFN clause promising you’ll receive the “most favorable economic terms” offered to any other licensor, ask the obvious follow-up: are large rightsholders receiving equity stakes in the AI platform as part of their deals like the majors and Merlin did with Spotify? We’ve seen this movie before. When streaming platforms launched, labels negotiated equity positions that dramatically increased the total value of their agreements — value that was never shared with publishers or songwriters. If an AI company is offering stock, warrants, or other equity consideration to labels while offering publishers only cash royalties and a minimum guarantee, then the MFN clause is cosmetic. The “most favorable economic terms” aren’t favorable at all if they exclude the most valuable component of the deal. Before you accept the premise that you’re being treated equally, ask what the royalty rate would look like if nobody were getting equity. That’s the number that tells you whether the cash terms are fair on their own — or whether they’re subsidized by equity you’ll never see.

Now What: When an AI company presents you with a “licensing agreement,” read it like a litigator, not a dealmaker. (When presented with a contract, dealmakers look at the money, litigators look at the remedies.). The title of the document matters far less than what’s inside it. If the agreement includes a perpetual covenant not to sue, a retroactive release of claims, and an ownership clause that captures everything the AI learns from your work, what you’re being offered isn’t a license — it’s capitulation with a royalty attached.

Know what you’re signing. Read it yourself, don’t buy the hoorah.

July 22, 2026July 22, 2026 by thetrichordist

@RonanFarrow and @AndrewMarantz: Sam Altman May Control Our Future—Can He Be Trusted?

Ronan Farrow and Andrew Marantz investigate Sam Altman’s leadership of OpenAI, based on internal documents and more than 100 interviews. They center on a core tension: Altman has positioned himself as a steward of humanity’s most powerful technology, yet many colleagues and insiders question whether he can be trusted with that responsibility. Internal memos compiled by senior figures—including chief scientist Ilya Sutskever—allege a pattern of misleading statements and evasiveness, particularly around AI safety and governance. Shocking, ain’t it?

The piece traces OpenAI’s evolution from a nonprofit founded to prioritize safety over profit into a commercially driven company pursuing massive scale and valuation. Along the way, Altman is portrayed as highly ambitious, politically savvy, and willing to push boundaries—sometimes at the expense of transparency or institutional safeguards.

It also situates these concerns within the broader stakes of artificial general intelligence: if such systems emerge, the individuals controlling them could wield unprecedented global power. The article ultimately raises an unresolved question—whether the rapid centralization of technological authority in a single leader and company is compatible with the level of trust and accountability that such power demands.

Read it on the New Yorker.

April 20, 2026 by thetrichordist

Say No to Suno

Late last year, thieves disguised as construction workers broke into the Louvre during broad daylight, grabbed more than $100 million worth of crown jewels, and roared off on their motorbikes into the busy streets of Paris. While some of those thieves were later arrested, the jewelry they stole has yet to be recovered, and many fear those historic works of artistry have already been recut, reset, and resold.

Closer to home, but no less nefarious, is the brazen rip-off of artists enabled by irresponsible AI, whose profiteers are recutting, remixing, and reselling original works of artistry as something new. The hijacking of the world’s entire treasure-trove of music floods platforms with AI slop and dilutes the royalty pools of legitimate artists from whose music this slop is derived.

Meanwhile, those who are promoting this new business model are operating in broad daylight, too – minus the yellow safety vests. That is AI music company Suno, the brazen “smash and grab” platform whose “Make it Music” ad campaign suggests that the most personal and meaningful forms of music can now be fabricated by their unauthorized AI platform machinery trained on human artists’ work.

How significant is this activity? Publicly revealed data says Suno is used to generate 7 million tracks a day, a massive quantity that suggests a dominant market share of AI tracks. According to recent reports, Deezer “deems 85% of streams of fully AI-generated tracks [on its service] to be fraudulent,” and that such tracks include outputs from major generative models. As JP Morgan’s analysts said, Deezer’s data “should be indicative of the broader market.” Suno has yet to demonstrate persuasively that its platform does not, in practice, serve as a scalable input into streaming-fraud schemes — raising a serious concern that Suno has, in effect, become a fraud-fodder factory on an industrial scale.

In a February 2 LinkedIn post, Paul Sinclair, Suno’s Chief Music Officer, claims that his company’s platform is about “empowerment” that enables “billions of fans to create and play with music.” He argues that closed systems are “walled gardens” that deny people access to the full joy of music.

Ironically, Sinclair’s choice of analogy undermines his own argument. Ask yourself: just why are most gardens surrounded by fences or walls? To keep out rabbits, deer, raccoons and wild pigs seeking a free lunch. We cultivate, nurture and protect our gardens precisely because that makes them much more productive over the long run.

While Sinclair may be loath to admit it, AI is fundamentally different from past disruptive innovations in the music industry. The phonograph, cassettes, CDs, MP3s, downloads, streaming – all these technologies were about the reproduction and distribution of creative work. By contrast, irresponsible AI like Suno appropriates and plunders such creative work while undermining the commercial ecosystem for artists.

Think back to the days of Napster. What brought the music industry back from the ruinous abyss of unfettered digital piracy? It was the very “closed systems” that Sinclair derides as exclusionary. At least streaming platforms maintain access controls and content management systems that enable creator compensation, even if the economic outcomes for many creators remain inadequate. Should we be against Apple Music, Spotify, Deezer, YouTube Music, and Amazon Music? What about Netflix, Disney+ and HBO, too, while we’re at it?

At its core, Sinclair’s argument is just a tired remix of the old trope that “information wants to be free.” What that really means is: “We want your music for free.”

Artists need to understand Suno’s game. They are not putting technology in the service of artists; they are putting artists in the service of their technology. Every time artists’ creations are used by the platform, those creations have just unwittingly been contributed to the creation of endless derivatives of artists’ own work, not to mention AI slop, with limited or no remuneration back to the human creators. Suno built its business on our backs, scraping the world’s cultural output without permission, then competing against the very works exploited.

It’s also important to keep in mind that using Suno to generate audio output calls into question the copyrightability of whatever Suno creates. Most countries around the world including the US Copyright Office have been clear that generative AI outputs are largely ineligible for a copyright – meaning the economic value of the Suno creation lies solely with Suno, not with the artist using it. The only ones gaining empowerment from Suno are Suno themselves.

Many in our community are embracing responsible AI as a tool for creation, and as a means for fans to explore and interact with our artistry. That’s wonderful. But it’s not the same as creating an environment where AI-generated works sourced from our music are mass distributed to dilute our royalties or, worse yet, reward those actively seeking to commit fraud. Artists need to know the difference – all AI platforms are not the same, and Suno, which is being sued for copyright infringement, is not a platform artists should trust.

Responsible AI-generated music must evolve within a framework that respects and remunerates artists, enhances human creativity rather than supplants it, and empowers fans to engage with the music they love. At the same time, AI services must preclude mass distribution of slop and prevent fraudsters from destroying the very ecosystem that has been built to reward and sustain artists and audiences alike.

All of us, including billions of music fans, share an urgent, deep and abiding interest in protecting and rewarding human genius, even as AI continues to change our industry and the world in unimaginable ways. So in 2026, even as the Louvre continues to revamp its own approach to security, we in the arts must rise to confront those who would “smash-and-grab” our creativity for their own benefit.

Together, while embracing innovation, we must work to establish more effective safeguards – both legal and technological – that better promote and protect all creative artists, our intellectual property, and the spark of human genius.

Say no to Suno. Say yes to the beauty and bounty of the gardens that feed us all.

Signed:

Ron Gubitz, Executive Director, Music Artist Coalition

Helienne Lindvall, Songwriter and President, European Composer and Songwriter Alliance

David C. Lowery, Artist and Editor The Trichordist

Tift Merritt artist, Practitioner in Residence, Duke University and Artist Rights Alliance Board Member

Blake Morgan, artist, producer, and President of ECR Music Group.

Abby North, President, North Music Group

Chris Castle, Artist Rights Institute

February 23, 2026 by Trichordist Editor

Synthetic Emotion from The Music Department: Suno’s Unsettling Ad Campaign and the Return of Orwell’s Machine-Made Culture from 1984

In George Orwell’s 1984, the “versificator” was a machine designed to produce poetry, songs, and sentimental verse synthetically, without human thought or feeling. Its purpose was not artistic expression but industrial-scale cultural production—filling the air with endless, disposable content to occupy attention and shape perception. Nearly a century later, the comparison to modern generative music systems such as Suno is difficult to ignore. While the technologies differ dramatically, the underlying question is strikingly similar: what happens when music is produced by machines at scale rather than by human experience?

Orwell’s versificator was built for scale, not meaning (reminding you of anyone?). It generated formulaic songs for the masses, optimized for emotional familiarity rather than originality. Suno, by contrast, uses sophisticated machine learning trained on vast corpora of human-created music to generate complete recordings on demand that would be the envy of Big Brother’s Music Department. Suno can reportedly generate millions of tracks per day, a level of output impossible in any human-centered musical economy. When music becomes infinitely reproducible, the limiting factor shifts from creation to distribution and attention—precisely the dynamic Orwell imagined.

Nothing captures the versificator analogy more vividly than Suno’s own dystopian-style “first kiss” advertisingcampaign. In one widely circulated spot, the product is promoted through a stylized, synthetic emotional narrative that emphasizes instant, machine-generated musical cliche creation untethered from human musicians, vocalists, or composers. The message is not about artistic struggle, collaboration, or lived expression—it is about mediocre frictionless production. The ad unintentionally echoes Orwell’s warning: when culture can be manufactured instantly, expression becomes simulation. And on top of it, those ads are just downright creepy.

The versificator also blurred authorship. In 1984, no individual poet existed behind the machine’s output; creativity was subsumed into a system. Suno raises a comparable question. If a system trained on thousands or millions of human performances produces a new track, where does authorship reside? With the user who typed a prompt? With the engineers who built the model? With the countless musicians whose expressive choices shaped the training data? Or nowhere at all? This diffusion of authorship challenges long-standing cultural and legal assumptions about what it means to “create” music.

Another parallel lies in standardization. The versificator produced content that was emotionally predictable—pleasant, familiar, subservient and safe. Generative music systems often display a similar gravitational pull toward stylistic averages embedded in their training data that has been averaged into pablum. The result can be competent, even polished output that nevertheless lacks the unpredictability, risk, and individual voice associated with human artistry. Orwell’s concern was not that machine-generated culture would be bad, but that it would be flattened—replacing lived expression with algorithmic imitation. Substitutional, not substantial.

There is also a structural similarity in scale and economics. The versificator’s value to The Party lay in its ability to replace human labor in cultural production and to force the creation of projects that humans would find too creepy. Suno and similar systems raise analogous questions for modern musicians, particularly session players and composers whose work historically formed the backbone of recorded music. When a single system can generate instrumental tracks, arrangements, and stylistic variations instantly, the economic pressure on human contributors becomes obvious. Orwell imagined machines replacing poets; today the substitution pressure may fall first on instrumental performance, arrangement, sound designer, and production roles.

Yet the comparison has limits, and those limits matter. The versificator was a tool of centralized control in a dystopian state, designed to narrow human thought. Suno operates in a pluralistic technological environment where many artists themselves experiment with AI as a creative instrument. Unlike Orwell’s machine, generative music systems can be used collaboratively, interactively, and sometimes in ways that expand rather than suppress creative exploration. The technology is not inherently dystopian; its impact depends on how institutions, markets, and creators choose to shape it.

A deeper difference lies in intention. Orwell’s versificator was never meant to create art; it was meant to simulate it. Modern generative music systems are often framed as tools that can assist, augment, or inspire human creativity. Some artists use AI to prototype ideas, explore unfamiliar styles, or generate textures that would be difficult to produce otherwise. In these contexts, the machine functions less like a replacement and more like a new instrument—one whose cultural role is still evolving.

Still, Orwell’s versificator is highly relevant to understanding Suno’s corporate direction. When cultural production becomes industrialized, quantity can overwhelm meaning. The risk is not merely that machine-generated music exists, but that its scale reshapes attention, value, and recognition. If millions of synthetic tracks flood listening environments as is happening with some large DSPs, the signal of individual human expression may become harder to perceive—even if human creativity continues to exist beneath the surface.

The comparison between Suno and the versificator symbolizes the moment when technology challenges the boundaries of authorship, creativity, and cultural labor. Orwell warned of a world where machines produced endless culture without human voice. Today’s question is subtler: can society integrate generative systems in ways that preserve the distinctiveness of human expression rather than dissolving it into algorithmic slop?

The answer will not come from technology alone. It will depend on choices—legal, cultural, and economic—about how machine-generated music is labeled, valued, and integrated into the broader creative ecosystem. Orwell imagined a future where the machine replaced the poet. The task now is to ensure that, even in an age of generative AI, the humans remains audible.

February 13, 2026 by Trichordist

2026 Music Predictions: The Legal and Policy Fault Lines Ahead

By Chris Castle

I was grateful to Hypebot for publishing my 2026 music‑industry predictions, which focused on the legal and structural pressures already reshaping the business. For regular readers, I’m reposting those predictions here—and adding a few more that follow directly from the policy work, regulatory engagement, and royalty‑system scrutiny we’ve been immersed in over the past year with the Artist Rights Institute. These additional observations are less about trend‑spotting and more about where the underlying legal and institutional logic appears to be heading next.

1. AI Copyright Litigation Will Move From Abstract Theory to Operational Discovery

In 2026, the center of gravity in AI‑copyright cases will shift toward discovery that exposes how models are trained, weighted, filtered, and monetized. Courts will increasingly treat AI systems as commercial products rather than research experiments, and discovery fights for the good of humanity…ahem…rather than summary judgment rhetoric. The result will be pressure on platforms to settle, license, or restructure before full disclosure occurs particularly since it’s becoming increasingly likely that every frontier AI lab as ripped off the world’s culture the old fashioned way—they stole it off the Internet.

The next round of AI copyright litigation will come from fans: As more deals are done with AI like the Disney/Sora deal, fans who use Sora or other AI to create separatable rights with AI (like new characters, new story lines) or even new universes with old story lines (like maybe new versions of the Luke/Darth/Hans/Leia arc in the Old West) will start to get the idea that their IP is…well…their IP. If it’s used without compensating them or getting their permission, that whole copyright thing is going to start to get real for them.

2. Streaming Platforms Will Face Structural Payola Scrutiny, Not Just Royalty Complaints

Minimum‑payment thresholds, bundled offerings, and “greater‑of” formulas will no longer be treated as isolated business choices. Regulators and courts will begin to examine how these mechanisms function together to shift risk onto artists while preserving platform margins. Antitrust, consumer‑protection, and unfair‑competition theories will increasingly converge around the same conduct. Due to Spotify’s market dominance and intimidation factor for majors and big to medium sized independent labels, these cases will have to come from independent artists.

3. The Copyright Office Will Approve a Conditional Redesignation of the MLC

Rather than granting an unconditional redesignation of the Mechanical Licensing Collective, the Copyright Office is likely to impose conditions tied to governance, transparency, and financial stewardship. This approach allows continuity for licensees while asserting supervisory authority grounded in the statute. The message will be clear: designation is provisional, not permanent.

Digital-Licensing-Coordinator-to-USCO-2-Sept-22-2025 Download

4. The MLC’s Gundecked Investment Policy Will Be Unwound or Materially Rewritten

The practice of investing unmatched royalties as a pooled asset is becoming legally and politically indefensible. In 2026, expect the investment policy to be unwound or rewritten by new regulations to require pass‑through of gains, or strict capital‑preservation limits. Once framed as a fiduciary issue rather than a finance strategy, the current model cannot survive intact.

It’s also worth noting that the MLC’s investment portfolio has grown so large ($1.212 billion) that its investment income reported on its 2023 tax return has also grown to an amount in excess of its operating costs as measured by the administrative assessment paid by licensees.

5. An MLC Independent Royalty‑Accounting and Systems Review Will Become Inevitable

As part of a conditional redesignation, the Copyright Office may require an end‑to‑end operational review of the MLC by a top‑tier royalty‑accounting firm. Unlike a SOC report, such a review would examine whether matching, data logic, and distributions actually produce correct outcomes. Once completed, that analysis would shape litigation, policy reform, and future oversight.

6. Foreign CMOs Will Push Toward Licensee‑Pays Models

Outside the U.S., collective management organizations face rising technology costs and political scrutiny over compensation. In response, many will explore shifting more costs to licensees rather than members, reframing CMOs as infrastructure providers. Ironically, the U.S. MLC experiment may accelerate this trend abroad given the MLC’s rich salaries and vast resources for developing poorly implemented tech.

These developments are not speculative in the abstract. They follow from incentives already in motion, records already being built, and institutions increasingly unable to rely on deference alone.

7. Environmental Harms of AI Become a Core Climate Issue

We will start to see the AI labs normalize the concept of private energy generation on a massive scale to support data centers built in current green spaces. If they build or buy electric plants they do not intend to share. This whole thing about they will build small nuclear reactors and sell excess back to the local grid is crazy—there won’t be any excess and what about their behavior over the last 25 years makes you think they’ll share a thing?

So some time after Los Angeles rezones Griffith Park commercial and sells the Greek Theater to Google for a new data center and private nuclear reactor and Facebook buys the Diablo Canyon reactor, the Music Industry Climate Collective will formally integrate AI’s ecological footprint into their national and international policy agendas. After mounting evidence of data‑center water depletion, aquifer stress, and grid destabilization — particularly in drought‑prone regions — climate coalitions will conceptually reclassify AI infrastructure as a high‑impact industrial activity.

This will become acute after people realize they cannot expect the state or federal government to require new state permitting regimes because of the overwhelming political influence of Big Tech in the form of AI Viceroy-for-Life David Sacks. (He’s not going anywhere in a post-Trump era.). This will lead to environmental‑justice litigation over siting decisions and pressure to require reporting of AI‑related energy, water, and land use.

8. Criminal RICO Case Against StubHub and Affiliated Resale Networks

By late 2026, the Department of Justice brings a landmark criminal RICO indictment targeting StubHub‑linked reseller networks and individual reseller financiers for systemic ticketing fraud and money laundering. The enterprise theory alleges that major resellers, platform intermediaries, lenders, and bot‑operators coordinated to engage in wire fraud, market manipulation, speculative ticketing, and deceptive consumer practices at international scale. Prosecutors present evidence of an organized structure that used bots, fabricated scarcity, misrepresentation of seat availability, and price‑fixing algorithms to inflate profits.

This becomes the first major criminal RICO prosecution in the secondary‑ticketing economy and triggers parallel state‑level investigations and civil RICO suits. Public resellers like StubHub will face shareholder lawsuits and securities fraud allegations.

Just another bright sunshiny day.

[A version of this post first appeared on MusicTechPolicy]

January 2, 2026 by thetrichordist

Trump’s Historic Kowtow to Special Interests: Why Trump’s AI Executive Order Is a Threat to Musicians, States, and Democracy

There’s a new dance in Washington—it’s called the KowTow

Most musicians don’t spend their days thinking about executive orders. But if you care about your rights, your recordings, your royalties, or your community, or even the environment, you need to understand the Trump Administration’s new executive order on artificial intelligence. The order—presented as “Ensuring a National Policy Framework for AI”—is not a national standard at all. It is a blueprint for stripping states of their power, protecting Big Tech from accountability, and centralizing AI authority in the hands of unelected political operatives and venture capitalists. In other words, it’s business as usual for the special interests led by an unelected bureaucrat, Silicon Valley Viceroy and billionaire investor David Sacks who the New York Times recently called out as a walking conflict of interest.

You’ll Hear “National AI Standard.” That’s Fake News. IT’s Silicon valley’s wild west

Supporters of the EO claim Trump is “setting a national framework for AI.” Read it yourself. You won’t find a single policy on:
– AI systems stealing copyrights (already proven in court against Anthropic and Meta)
– AI systems inducing self-harm in children
– Whether Google can build a water‑burning data center or nuclear plant next to your neighborhood

None of that is addressed. Instead, the EO orders the federal government to sue and bully states like Florida and Texas that pass AI safety laws and threatens to cut off broadband funding unless states abandon their democratically enacted protections. They will call this “preemption” which is when federal law overrides conflicting state laws. When Congress (or sometimes a federal agency) occupies a policy area, states lose the ability to enforce different or stricter rules. There is no federal legislation (EOs don’t count), so there can be no “preemption.”

Who Really Wrote This? The Sacks–Thierer Pipeline

This EO reads like it was drafted directly from the talking points of David Sacks and Adam Thierer, the two loudest voices insisting that states must be prohibited from regulating AI. It sounds that way because it was—Trump himself gave all the credit to David Sacks in his signing ceremony.

– Adam Thierer works at Google’s R Street Institute and pushes “permissionless innovation,” meaning companies should be allowed to harm the public before regulation is allowed.
– David Sacks is a billionaire Silicon Valley investor from South Africa with hundreds of AI and crypto investments, documented by The New York Times, and stands to profit from deregulation.

Worse, the EO lards itself with references to federal agencies coordinating with the “Special Advisor for AI and Crypto,” who is—yes—David Sacks. That means DOJ, Commerce, Homeland Security, and multiple federal bodies are effectively instructed to route their AI enforcement posture through a private‑sector financier.

The Trump AI Czar—VICEROY Without Senate Confirmation

Sacks is exactly what we have been warning about for months: the unelected Trump AI Czar

He is not Senate‑confirmed.
He is not subject to conflict‑of‑interest vetting.
He is a billionaire “special government employee” with vast personal financial stakes in the outcome of AI deregulation.

Under the Constitution, you cannot assign significant executive authority to someone who never faced Senate scrutiny. Yet the EO repeatedly implies exactly that.

Even Trump’s MOST LOYAL MAGA Allies Know This Is Wrong

Trump signed the order in a closed ceremony with sycophants and tech investors—not musicians, not unions, not parents, not safety experts, not even one Red State governor.

Even political allies and activists like Mike Davis and Steve Bannon blasted the EO for gutting state powers and centralizing authority in Washington while failing to protect creators. When Bannon and Davis are warning you the order goes too far, that tells you everything you need to know. Well, almost everything.

And Then There’s Ted Cruz

On top of everything else, the one state official in the room was U.S. Senator Ted Cruz of Texas, a state that has led on AI protections for consumers. Cruz sold out Texas musicians while gutting the Constitution—knowing full well exactly what he was doing as a former Supreme Court clerk.

Why It Matters for Musicians

AI isn’t some abstract “tech issue.” It’s about who controls your work, your rights, your economic future. Right now:

– AI systems train on our recordings without consent or compensation.
– Major tech companies use federal power to avoid accountability.
– The EO protects Silicon Valley elites, not artists, fans or consumers.

This EO doesn’t protect your music, your rights, or your community. It preempts local protections and hands Big Tech a federal shield.

It’s Not a National Standard — It’s a Power Grab

What’s happening isn’t leadership. It’s *regulatory capture dressed as patriotism*. If musicians, unions, state legislators, and everyday Americans don’t push back, this EO will become a legal weapon used to silence state protections and entrench unaccountable AI power.

What David Sacks and his band of thieves is teaching the world is that he learned from Dot Bomb 1.0—the first time around, they didn’t steal enough. If you’re going to steal, steal all of it. Then the government will protect you.

December 12, 2025 by Trichordist

NYT: Silicon Valley’s Man in the White House Is Benefiting Himself and His Friends

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The New York Times published a sprawling investigation into David Sacks’s role as Trump’s A.I. and crypto czar. We’ve talked about David Sacks a few times on these pages. The Times’ piece is remarkable in scope and reporting: a venture capitalist inside the White House, steering chip policy, promoting deregulation, raising money for Trump, hosting administration events through his own podcast brand, and retaining hundreds of A.I. and crypto investments that stand to benefit from his policy work.

But for all its detail, the Times buried the lede.

The bigger story isn’t just ethics violations. or outright financial corruption. It’s that Sacks is simultaneously shaping and shielding the largest regulatory power grab in history: the A.I. moratorium and its preemption structure.

Of all the corrupt anecdotes in the New York Times must read article regarding Viceroy and leading Presidential pardon candidate David Sacks, they left out the whole AI moratorium scam, focusing instead on the more garden variety of self-dealing and outright conflicts of interest that are legion. My bet is that Mr. Sacks reeks so badly that it is hard to know what to leave out. Here’s a couple of examples:

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There is a deeper danger that the Times story never addresses: the long-term damage that will outlive David Sacks himself. Even if Sacks eventually faces investigations or prosecution for unrelated financial or securities matters — if he does — the real threat isn’t what happens to him. It’s what happens to the legal architecture he is building right now.

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If he succeeds in blocking state-law prosecutions and freezing A.I. liability for a decade, the harms won’t stop when he leaves office. They will metastasize.

Without state enforcement, A.I. companies will face no meaningful accountability for:

child suicide induced by unregulated synthetic content
mass copyright theft embedded into permanent model weights
biometric and voiceprint extraction without consent
data-center sprawl that overwhelms local water, energy, and zoning systems
surveillance architectures exported globally
algorithmic harms that cannot be litigated under preempted state laws

These harms don’t sunset when an administration ends. They calcify. It must also be said that Sacks could face state securities-law liability — including fraud, undisclosed self-dealing, and market-manipulative conflicts tied to his A.I. portfolio — because state blue-sky statutes impose duties possibly stricter than federal law. The A.I. moratorium’s preemption would vaporize these claims, shielding exactly the conduct state regulators are best positioned to police. No wonder he’s so committed to sneaking it into federal law.

The moratorium Sacks is pushing would prevent states from acting at the very moment when they are the only entities with the political will and proximity to regulate A.I. on the ground. If he succeeds, the damage will last long after Sacks has left his government role — long after his podcast fades, long after his investment portfolio exits, long after any legal consequences he might face.

The public will be living inside the system he designed.

There is one final point the public needs to understand. DavidSacksis not an anomaly. Sacks is to Trump what Eric Schmidt was to Biden: the industry’s designated emissary, embedded inside the White House to shape federal technology policy from the inside out. Swap the party labels and the personnel change, but the structural function remains the same. Remember, Schmidt bragged about writing the Biden AI executive order.

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So don’t think that if Sacks is pushed out, investigated, discredited, or even prosecuted one day — if he is — that the problem disappears. You don’t eliminate regulatory capture by removing the latest avatar of it. The next administration will simply install a different billionaire with a different portfolio and the same incentives: protect industry, weaken oversight, preempt the states, and expand the commercial reach of the companies they came in with.

The danger is not David Sacks the individual. The danger is the revolving door that lets tech titans write national A.I. policy while holding the assets that benefit from it. As much as Trump complains of the “deep state,” he’s doing his best to create the deepest of deep states.

Until that underlying structure changes, it won’t matter whether it’s Sacks, Schmidt, Thiel, Musk, Palihapitiya, or the next “technocratic savior.”

The system will keep producing them — and the public will keep paying the price. For as Sophocles taught us, it is not in our power to escape the curse.

December 1, 2025 by thetrichordist

@ArtistRights Institute Newsletter 11/17/25: Highlights from a fast-moving week in music policy, AI oversight, and artist advocacy.

@DavidSacks Isn’t a Neutral Observer—He’s an Architect of the AI Circular-Investment Maze

When White House AI Czar David Sacks tweets confidently that “there will be no federal bailout for AI” because “five major frontier model companies” will simply replace each other, he is not speaking as a neutral observer. He is speaking as a venture capitalist with overlapping financial ties to the very AI companies now engaged in the most circular investment structure Silicon Valley has engineered since the dot-com bubble—but on a scale measured not in millions or even billions, but in trillions.

Sacks is a PayPal alumnus turned political-tech kingmaker who has positioned himself at the intersection of public policy and private AI investment. His recent stint as a Special Government Employee to the federal government raised eyebrows precisely because of this dual role. Yet he now frames the AI sector as a robust ecosystem that can absorb firm-level failure without systemic consequence.

The numbers say otherwise. The diagram circulating in the X-thread exposes the real structure: mutually dependent investments tied together through cross-equity stakes, GPU pre-purchases, cloud-compute lock-ins, and stock-option-backed revenue games. So Microsoft invests in OpenAI; OpenAI pays Microsoft for cloud resources; Microsoft books the revenue and inflates its stake OpenAI. Nvidia invests in OpenAI; OpenAI buys tens of billions in Nvidia chips; Nvidia’s valuation inflates; and that valuation becomes the collateral propping up the entire sector. Oracle buys Nvidia chips; OpenAI signs a $300 billion cloud deal with Oracle; Oracle books the upside. Every player’s “growth” relies on every other player’s spending.

This is not competition. It is a closed liquidity loop. And it’s a repeat of the dot-bomb “carriage” deals that contributed to the stock market crash in 2000.

And underlying all of it is the real endgame: a frantic rush to secure taxpayer-funded backstops—through federal energy deals, subsidized data-center access, CHIPS-style grants, or Department of Energy land leases—to pay for the staggering infrastructure costs required to keep this circularity spinning. The singularity may be speculative, but the push for a public subsidy to sustain it is very real.

Call it what it is: an industry searching for a government-sized safety net while insisting it doesn’t need one.

In the meantime, the circular investing game serves another purpose: it manufactures sky-high paper valuations that can be recycled into legal war chests. Those inflated asset values are now being used to bankroll litigation and lobbying campaigns aimed at rewriting copyright, fair use, and publicity law so that AI firms can keep strip-mining culture without paying for it.

The same feedback loop that props up their stock prices is funding the effort to devalue the work of every writer, musician, actor, and visual artist on the planet—and to lock that extraction in as a permanent feature of the digital economy.

November 13, 2025November 13, 2025 by Trichordist