Month: December 2025

Anna’s Archive, Spotify, and the Shadow‑Library Playbook: Why Spotify is a Crime Scene

A pirate “preservation” collective best known for shadow libraries now claims to have scraped Spotify at industrial scale, releasing massive metadata dumps and hinting at terabytes of audio to follow. That alone would be alarming. But the deeper significance lies elsewhere: this episode mirrors the same shadow-library acquisition pipeline already at the center of major AI copyright cases like Bartz v. Anthropic and Kadrey v. Meta. It raises uncomfortable questions about Spotify’s security obligations to licensors, the chilling effect of its market power on enforcement, and whether centralized streaming platforms have quietly become the most valuable—and vulnerable—training datasets in the AI economy.  Spotify sold itself as the cure for BitTorrent piracy but may have become the back door for the next generation of AI scraping.

Anna’s Archive, a pirate-adjacent “preservation” collective best known for shadow‑library aggregation is now claiming it has scraped Spotify at scale starting before July 2025—releasing a metadata torrent and promising bulk release of audio files measured in the hundreds of terabytes.  Spotify has acknowledged unauthorized access, describing scraping of public metadata and illicit tactics used to circumvent DRM to access at least some audio files.

Here’s what Anna’s Blog said:

Spotify leakDownload

The immediate story will be framed as piracy. But for Spotify, the real exposure is corporate and contractual. As a public company, Spotify has to manage security and platform-risk disclosures in a way that doesn’t mislead investors, and it has to satisfy rightsholders that its delivery architecture meets the security commitments embedded in licensing practice. If tens of millions of full-length files and user-linked playlist metadata were extractable at scale, the question isn’t only who infringed—it’s whether Spotify’s controls, incident response, and contractual assurances withstand audit, cure, and reputational scrutiny.

This is not just a piracy story or corporate disclosure embarrassment for Spotify. It is a familiar pattern for anyone tracking the book‑dataset litigation side of generative AI: shadow libraries as industrial infrastructure.  And that sounds like billion with a B.

THE SHADOW‑LIBRARY PIPELINE: FROM BOOKS TO MUSIC

In the major AI copyright cases involving books like Bartz v. Anthropic and Kadrey v. Meta, the recurring issue has not merely been whether training is fair use, but how the training corpus was acquired in the first place. Allegations in cases such as Bartz v. Anthropic and Kadrey v. Meta focus on bulk ingestion of pirated works from shadow libraries such as LibGen, Z‑Library, and Anna’s Archive as an upstream act distinct from downstream model training and fair use.  It’s this ingestion issue that has Anthropic offering a $1.5 billion with a B settlement to authors (which itself is based on the 1999 statutory damages amendment to the Copyright Act that to deter…wait for it…CD ripping).

The Spotify scrape follows the same architectural logic. Swap “books” for “tracks” and the structure seems identical:  a centralized, mirrored corpus assembled outside the licensing system, optimized for scale, and perfectly suited for machine‑learning pipelines.  Voila.

THE ENTERPRISE TURN: AI ACCESS FOR A FEE

What distinguishes this moment is that shadow libraries are no longer operating solely as donation‑funded activist projects to save humanity. Anna’s Archive has openly described an enterprise‑style access tier, offering high‑speed bulk access to AI labs and other institutional users in exchange for large “donations.”  You know, to save humanity.  Ahem…

This AI connection reframes shadow libraries as industrial suppliers in my view. The Anna’s rhetoric may be preservation for the good of mankind, but whatever you believe the motives are, the product is dataset procurement—precisely the bottleneck facing AI developers seeking massive, curated, labeled corpora.

SPOTIFY’S SECURITY OBLIGATIONS — AND WHY MARKET POWER MATTERS

Spotify is not just a random consumer app. It is a licensed distribution platform bound by dense contractual obligations to labels, distributors, and other rightsholders. It likely has made substantial commitments to labels, particularly major labels and likely publishers, too regarding its security commitments.  Large‑scale scraping raises questions that go well beyond copyright:

• What controls existed to prevent automated extraction at scale?

• What anomaly detection or rate limiting failed?

• What representations were made to licensors and licensees about safeguarding licensed content?

Here, Spotify’s monopoly (or at least dominant) market power becomes its most effective shield. Even if licensors could plausibly argue that security covenants were breached, many are economically dependent on Spotify’s distribution and algorithmic visibility. Enforcement is chilled not by law, but by leverage.

THE DOG THAT DIDN’T BARK: USER DATA

Public reporting has focused on sound recording catalog metadata and audio files. But any incident involving large‑scale access naturally raises a secondary question: what else was accessible through the same pathways? There is no public confirmation that user data has been posted. But “not posted” is not the same as “not taken.”

Listening histories, playlists, device identifiers, or internal engagement metrics would be far more valuable off‑market than in a public torrent.

SPOTIFY’S SHAREHOLDERS SAY WASSUP?

Public reporting that Spotify’s systems were scraped at scale—whether limited to metadata or extending to audio—raises issues that go beyond piracy narratives and into material risk disclosure territory. Spotify is a licensed platform whose core business depends on representations to labels, distributors, and artists about safeguarding catalog integrity and platform security.

Allegations of large-scale scraping, DRM circumvention, or control failures may implicate not only contractual obligations but also cybersecurity risk factors that a reasonable investor could consider material.

Under SEC guidance on cybersecurity disclosures, companies are expected to disclose material risks and incidents, even where investigations are ongoing, if the nature and potential scope could affect operations, relationships, or revenue. The question is not whether piracy exists—it always has—but whether centralized control failures expose Spotify to rightsholder claims, regulatory scrutiny, or renegotiation pressure that could affect future earnings.

So far (and this may change any minute so stay informed), Spotify’s public statement in media outlets is something like this:

“An investigation into unauthorized access identified that a third party scraped public metadata and used illicit tactics to circumvent DRM to access some of the platform’s audio files. We are actively investigating the incident.”

Spotify is circling the wagons and this nonstatement statement tells you everything you need to know:  “Investigating” plus “some audio files” is classic damage control.  The spin does three things at once:  Narrows scope (“some audio files”) without committing to facts; deflects responsibility (“third party,” “illicit tactics”); avoids quantification (no numbers, no dates, no systems).  

But—there is no acknowledgment of scale or contradiction.  Spotify does not address Anna’s 86 million file / 300 TB claim; or Anna’s claim that files were Spotify-native OGG Vorbis; the presence of Spotify-specific file artifacts, the inclusion of playlist/user-linked data.  They don’t even say “the claims are inaccurate.” They just… don’t…engage.  

In fact, they never mention Anna’s Archive at all.  

“Not necessarily a breach” is a lawyerly non-answer.  By emphasizing that this may not represent a breach of internal infrastructure, Spotify leaves open: credential abuse, client-side exploitation, API misuse, session hijacking, DRM circumvention at the delivery layer which seems like it must have happened.  In other words: it doesn’t reassure anyone—investors, licensors, or regulators.

Let’s not forget—Anna’s Archive wrote this up in a detailed blog post that is PUBLISHED ON THE INTERNET.   People know about it.  It’s believable but rebuttable.  So rebut it.

That may be fine for one-day PR triage—but it’s not informative enough for me and it may not be for the SEC, either.  And I can’t imagine this is what they are telling the labels or publishers.

So while I was posting this, Billboard released a new statement from Spotify:

“Spotify has identified and disabled the nefarious user accounts that engaged in unlawful scraping. We’ve implemented new safeguards for these types of anti-copyright attacks and are actively monitoring for suspicious behavior. Since day one, we have stood with the artist community against piracy, and we are actively working with our industry partners to protect creators and defend their rights.”

Oh, well “new safeguards” solves everything. In the immortal words of Ronnie Scott, and now back to sleep. This public description indicates an account-based circumvention pathway (as opposed to a traditional server intrusion) and reinforces that the incident may involve large-scale extraction through consumer-facing mechanisms. That context heightens the importance of law enforcement inquiry into the scope of data accessed and retained, the representations made by the scraping entity about the nature and purpose of its collection, and the foreseeable downstream uses of redistributed audio and user-linked data.

Spotify’s subsequent statement that it identified and disabled “nefarious user accounts” points to an account-side extraction pathway (perhaps through an account farm) rather than a traditional server breach. While server-side compromises are generally considered more severe, account-based abuse is a well-known and operationally plausible vector for large-scale data extraction when distributed across many automated accounts over time. A corpus on the order of hundreds of terabytes as Anna claims could be exfiltrated in this manner without triggering immediate public disclosure, particularly if activity was geographically distributed and designed to mimic legitimate streaming behavior. The relevant question is therefore not whether such extraction was technically possible, but when it was detected, how it was scoped internally, and whether public representations accurately reflected the magnitude and nature of the activity.

The new statement is still kind of a nondenial denial that could have been written: “Spotify identified and disabled user accounts that unlawfully scraped its platform. Spotify implemented new safeguards against these anti-copyright attacks and is actively monitoring for suspicious behavior.”

But…from an SEC perspective, this new statement makes it harder for Spotify to treat the episode as purely hypothetical risk. They’ve acknowledged an “attack,” they disabled accounts, and they changed safeguards. That’s the kind of “known event” that can support 10-Q risk factor updates (and possibly more, depending on materiality and contractual fallout).

Near silence may be defensible in the very short term (and getting shorter with each passing moment), but prolonged opacity increases the risk that disclosure comes later, under less favorable conditions.

Even if they said something like this, they’d be better off:  

Spotify identified unauthorized access in which a third party scraped public metadata and used illicit tactics to circumvent DRM to access some of the platform’s audio files. Spotify is actively investigating the incident.

At least sentient beings could tell what in the world happened.

ENTER THE SECURITIES AND EXCHANGE COMMISSION

Understand that this is just postponing the inevitable in my view.  Spotify’s response is notable less for what it says than for what it avoids. By neither confirming nor rebutting the scale and technical specifics described by Anna’s Archive, the company leaves unresolved whether tens of millions of Spotify-delivered audio files were extractable at scale—and whether existing controls matched the assurances Spotify has long made to rightsholders, users, and investors.

This disclosure probably belongs in a SEC Form 10Q, but it could go in an 8-K which will attract a bunch more attention.  Form 8-K (Item 1.05 or 8.01) is likely only required if certain thresholds are met, mostly that Spotify deems the incident is deemed material to investors now, and there is a known impact (financial, operational, or contractual), not just investigation.

Given Spotify’s current public posture (“actively investigating,” “some audio files,” “safeguards”), an 8-K would usually be triggered only if they confirm large-scale audio extraction, licensor disputes or termination notices occur, regulatory action is initiated, or remediation costs or litigation exposure become quantifiable.

Right now, Spotify appears to be deliberately staying below the 8-K threshold, and I can’t say as I blame them. There’s no telling how much of Spotify’s share price is attributed (incorrectly in my view) to Spotify selling itself as a tech play, not a music play. The financial press has yet to pick up this story as of this writing, and most of Spotify’s press is in the “how do you manage your awesomeness” BS because the financial press never has understood the music business.

At this stage, the issue fits squarely in an SEC Form 10-Q risk-factor update rather than an 8-K: the company is acknowledging elevated platform and data-extraction risk without conceding a material event that requires immediate disclosure.  That could change.

That risk factor might look something like this:

Risks Related to Unauthorized Access, Data Extraction, and Platform Security

We rely on technical controls, contractual restrictions, and digital rights management technologies to protect our platform, our content catalog, and data derived from user and licensor activity. From time to time, third parties may attempt to circumvent these controls through unauthorized access, scraping, or other illicit techniques.

We have identified instances in which third parties have engaged in unauthorized access to certain platform data, including the scraping of public metadata and the circumvention of technological measures designed to protect audio files. While we are actively investigating the scope and impact of such activity and implementing remedial measures, unauthorized access could expose us to reputational harm, regulatory scrutiny, contractual disputes with licensors, litigation, and increased costs associated with security enhancements and enforcement efforts.

In addition, even where extracted data does not include sensitive personal information, large-scale aggregation of platform data—particularly content files or behaviorally derived metrics—may be repurposed by third parties in ways that we cannot control, including for analytics, competitive modeling, or artificial intelligence training. Such downstream uses could adversely affect our relationships with licensors, artists, and other stakeholders, and may give rise to additional legal, regulatory, or commercial risks.

Any failure, or perceived failure, to prevent or respond effectively to unauthorized access or misuse of our platform could materially and adversely affect our business, financial condition, results of operations, or prospects.

THE FINAL IRONY: FROM BITTORRENT TO AI BACK DOOR

Daniel Ek built Spotify in the long shadow of peer‑to‑peer piracy. Spotify’s founding pitch to the music industry was explicit: centralized licensed access would replace BitTorrent as the dominant mode of music consumption. Spotify was sold as an anti‑piracy intervention right along with its fixed prices and shite royalty.

That argument persuaded rightsholders to centralize their catalogs inside a single platform. If you’ve ever sat in a major label marketing meeting, it’s like there are no platforms outside of Spotify.

As of today, Spotify has a huge target on its back.  It represents one of the largest curated music corpora ever assembled—the precise object desired by pirate “archivists” and AI developers alike. And the allegation on the table is not marginal leakage, but industrial‑scale scraping that somehow did not get noticed by Spotify.  Or so we are expected to believe.

At the same time, Ek has repositioned himself as a major AI and defense‑sector investor, backing companies like Helsing that frame artificial intelligence as strategic infrastructure for fully automated weapons (which are likely a violation of the Geneva Convention as I have discussed with you before). The AI sector’s most valuable input is not code, but data—large, clean, labeled datasets.

The same founder who persuaded the music industry to centralize its catalog inside Spotify as a solution to piracy now presides over a platform alleged to have left a back door open for the next extraction regime: AI scraping at scale.

Piracy did not disappear.

It centralized.

It professionalized.  

And now, it has an enterprise pricing tier.

Fasten your seatbelts.

What Would Freud Do? The Unconscious Is Not a Database — and Humans Are Not Machines

What would Freud do?

It’s a strange question to ask about AI and copyright, but a useful one. When generative-AI fans insist that training models on copyrighted works is merely “learning like a human,” they rely on a metaphor that collapses under even minimal scrutiny. Psychoanalysis—whatever one thinks of Freud’s conclusions—begins from a premise that modern AI rhetoric quietly denies: the unconscious is not a database, and humans are not machines.

As Freud wrote in The Interpretation of Dreams, “Our memory has no guarantees at all, and yet we bow more often than is objectively justified to the compulsion to believe what it says.” No AI truthiness there.

Human learning does not involve storing perfect, retrievable copies of what we read, hear, or see. Memory is reconstructive, shaped by context, emotion, repression, and time. Dreams do not replay inputs; they transform them. What persists is meaning, not a file.

AI training works in the opposite direction—obviously. Training begins with high-fidelity copying at industrial scale. It converts human expressive works into durable statistical parameters designed for reuse, recall, and synthesis for eternity. Where the human mind forgets, distorts, and misremembers as a feature of cognition, models are engineered to remember as much as possible, as efficiently as possible, and to deploy those memories at superhuman speed. Nothing like humans.

Calling these two processes “the same kind of learning” is not analogy—it is misdirection. And that misdirection matters, because copyright law was built around the limits of human expression: scarcity, imperfection, and the fact that learning does not itself create substitute works at scale.

Dream-Work Is Not a Training Pipeline

Freud’s theory of dreams turns on a simple but powerful idea: the mind does not preserve experience intact. Instead, it subjects experience to dream-work—processes like condensation (many ideas collapsed into one image), displacement (emotional significance shifted from one object to another), and symbolization (one thing representing another, allowing humans to create meaning and understanding through symbols). The result is not a copy of reality but a distorted, overdetermined construction whose origins cannot be cleanly traced.

This matters because it shows what makes human learning human. We do not internalize works as stable assets. We metabolize them. Our memories are partial, fallible, and personal. Two people can read the same book and walk away with radically different understandings—and neither “contains” the book afterward in any meaningful sense. There is no Rashamon effect for an AI.

AI training is the inverse of dream-work. It depends on perfect copying at ingestion, retention of expressive regularities across vast parameter spaces, and repeatable reuse untethered from embodiment, biography, or forgetting. If Freud’s model describes learning as transformation through loss, AI training is transformation through compression without forgetting.

One produces meaning. The other produces capacity.

The Unconscious Is Not a Database

Psychoanalysis rejects the idea that memory functions like a filing cabinet. The unconscious is not a warehouse of intact records waiting to be retrieved. Memory is reconstructed each time it is recalled, reshaped by narrative, emotion, and social context. Forgetting is not a failure of the system; it is a defining feature.

AI systems are built on the opposite premise. Training assumes that more retention is better, that fidelity is a virtue, and that expressive regularities should remain available for reuse indefinitely. What human cognition resists by design—perfect recall at scale—machine learning seeks to maximize.

This distinction alone is fatal to the “AI learns like a human” claim. Human learning is inseparable from distortion, limitation, and individuality. AI training is inseparable from durability, scalability, and reuse.

In The Divided Self, R. D. Laing rejects the idea that the mind is a kind of internal machine storing stable representations of experience. What we encounter instead is a self that exists only precariously, defined by what Laing calls “ontological security” or its absence—the sense of being real, continuous, and alive in relation to others. Experience, for Laing, is not an object that can be detached, stored, or replayed; it is lived, relational, and vulnerable to distortion. He warns repeatedly against confusing outward coherence with inner unity, emphasizing that a person may present a fluent, organized surface while remaining profoundly divided within. That distinction matters here: performance is not understanding, and intelligible output is not evidence of an interior life that has “learned” in any human sense.

Why “Unlearning” Is Not Forgetting

Once you understand this distinction, the problem with AI “unlearning” becomes obvious.

In human cognition, there is no clean undo. Memories are never stored as discrete objects that can be removed without consequence. They reappear in altered forms, entangled with other experiences. Freud’s entire thesis rests on the impossibility of clean erasure.

AI systems face the opposite dilemma. They begin with discrete, often unlawful copies, but once those works are distributed across parameters, they cannot be surgically removed with certainty. At best, developers can stop future use, delete datasets, retrain models, or apply partial mitigation techniques (none of which they are willing to even attempt). What they cannot do is prove that the expressive contribution of a particular work has been fully excised.

This is why promises (especially contractual promises) to “reverse” improper ingestion are so often overstated. The system was never designed for forgetting. It was designed for reuse.

Why This Matters for Fair Use and Market Harm

The “AI = human learning” analogy does real damage in copyright analysis because it smuggles conclusions into fair-use factor one (transformative purpose and character) and obscures factor four (market harm).

Learning has always been tolerated under copyright law because learning does not flood markets. Humans do not emerge from reading a novel with the ability to generate thousands of competing substitutes at scale. Generative models do exactly that—and only because they are trained through industrial-scale copying.

Copyright law is calibrated to human limits. When those limits disappear, the analysis must change with them. Treating AI training as merely “learning” collapses the very distinction that makes large-scale substitution legally and economically significant.

The Pensieve Fallacy

There is a world in which minds function like databases. It is a fictional one.

In Harry Potter and the Goblet of Fire, wizards can extract memories, store them in vials, and replay them perfectly using a Pensieve. Memories in that universe are discrete, stable, lossless objects. They can be removed, shared, duplicated, and inspected without distortion. As Dumbledore explained to Harry, “I use the Pensieve. One simply siphons the excess thoughts from one’s mind, pours them into the basin, and examines them at one’s leisure. It becomes easier to spot patterns and links, you understand, when they are in this form.”

That is precisely how AI advocates want us to imagine learning works.

But the Pensieve is magic because it violates everything we know about human cognition. Real memory is not extractable. It cannot be replayed faithfully. It cannot be separated from the person who experienced it. Arguably, Freud’s work exists because memory is unstable, interpretive, and shaped by conflict and context.

AI training, by contrast, operates far closer to the Pensieve than to the human mind. It depends on perfect copies, durable internal representations, and the ability to replay and recombine expressive material at will.

The irony is unavoidable: the metaphor that claims to make AI training ordinary only works by invoking fantasy.

Humans Forget. Machines Remember.

Freud would not have been persuaded by the claim that machines “learn like humans.” He would have rejected it as a category error. Human cognition is defined by imperfection, distortion, and forgetting. AI training is defined by reproduction, scale, and recall.

To believe AI learns like a human, you have to believe humans have Pensieves. They don’t. That’s why Pensieves appear in Harry Potter—not neuroscience, copyright law, or reality.

The Paradox of Huang’s Rope

If the tech industry has a signature fallacy for the 2020s aside from David Sacks, it belongs to Jensen Huang. The CEO of Nvidia has perfected a circular, self-consuming logic so brazen that it deserves a name: The Paradox of Huang’s Rope. It is the argument that China is too dangerous an AI adversary for the United States to regulate artificial intelligence at home or control export of his Nvidia chips abroad—while insisting in the very next breath that the U.S. must allow him to keep selling China the advanced Nvidia chips that make China’s advanced AI capabilities possible. The justification destroys its own premise, like handing an adversary the rope to hang you and then pointing to the length of that rope as evidence that you must keep selling more, perhaps to ensure a more “humane” hanging. I didn’t think it was possible to beat “sharing is caring” for utter fallacious bollocks.

The Paradox of Huang’s Rope works like this: First, hype China as an existential AI competitor. Second, declare that any regulatory guardrails—whether they concern training data, safety, export controls, or energy consumption—will cause America to “fall behind.” Third, invoke national security to insist that the U.S. government must not interfere with the breakneck deployment of AI systems across the economy. And finally, quietly lobby for carveouts that allow Nvidia to continue selling ever more powerful chips to the same Chinese entities supposedly creating the danger that justifies deregulation.

It is a master class in circularity: “China is dangerous because of AI → therefore we can’t regulate AI → therefore we must sell China more AI chips → therefore China is even more dangerous → therefore we must regulate even less and export even more to China.” At no point does the loop allow for the possibility that reducing the United States’ role as China’s primary AI hardware supplier might actually reduce the underlying threat. Instead, the logic insists that the only unacceptable risk is the prospect of Nvidia making slightly less money.

This is not hypothetical. While Washington debates export controls, Huang has publicly argued that restrictions on chip sales to China could “damage American technology leadership”—a claim that conflates Nvidia’s quarterly earnings with the national interest. Meanwhile, U.S. intelligence assessments warn that China is building fully autonomous weapons systems, and European analysts caution that Western-supplied chips are appearing in PLA research laboratories. Yet the policy prescription from Nvidia’s corner remains the same: no constraints on the technology, no accountability for the supply chain, and no acknowledgment that the market incentives involved have nothing to do with keeping Americans safe. And anyone who criticizes the authoritarian state run by the Chinese Communist Party is a “China Hawk” which Huang says is a “badge of shame” and “unpatriotic” because protecting America from China by cutting off chip exports “destroys the American Dream.” Say what?

The Paradox of Huang’s Rope mirrors other Cold War–style fallacies, in which companies invoke a foreign threat to justify deregulation while quietly accelerating that threat through their own commercial activity. But in the AI context, the stakes are higher. AI is not just another consumer technology; its deployment shapes military posture, labor markets, information ecosystems, and national infrastructure. A strategic environment in which U.S. corporations both enable and monetize an adversary’s technological capabilities is one that demands more regulation, not less.

Naming the fallacy matters because it exposes the intellectual sleight of hand. Once the circularity is visible, the argument collapses. The United States does not strengthen its position by feeding the very capabilities it claims to fear. And it certainly does not safeguard national security by allowing one company’s commercial ambitions to dictate the boundaries of public policy. The Paradox of Huang’s Rope should not guide American AI strategy. It should serve as a warning of how quickly national priorities can be twisted into a justification for private profit.

You Can’t Prosecute Smuggling NVIDIA chips to CCP and Authorize Sales to CCP at the Same Time

The Trump administration is attempting an impossible contradiction: selling advanced NVIDIA AI chips to China while the Department of Justice prosecutes criminal cases for smuggling the exact same chips into China.

According to the DOJ:

“Operation Gatekeeper has exposed a sophisticated smuggling network that threatens our Nation’s security by funneling cutting-edge AI technology to those who would use it against American interests,” said Ganjei. “These chips are the building blocks of AI superiority and are integral to modern military applications. The country that controls these chips will control AI technology; the country that controls AI technology will control the future. The Southern District of Texas will aggressively prosecute anyone who attempts to compromise America’s technological edge.”

That divergence from the prosecutors is not industrial policy. That is incoherence. But mostly it’s just bad advice, likely coming from White House AI Czar David Sacks, Mr. Trump’s South African AI policy advisor who may have a hard time getting a security clearance in the first place..

On one hand, DOJ is rightly bringing cases over the illegal diversion of restricted AI chips—recognizing that these processors are strategic technologies with direct national-security implications. On the other hand, the White House is signaling that access to those same chips is negotiable, subject to licensing workarounds, regulatory carve-outs, or political discretion.

You cannot treat a technology as contraband in federal court and as a commercial export in the West Wing.

Pick one.

AI Chips Are Not Consumer Electronics

The United States does not sell China F-35 fighter jets. We do not sell Patriot missile systems. We do not sell advanced avionics platforms and then act surprised when they show up embedded in military infrastructure. High-end AI accelerators are in the same category.

NVIDIA’s most advanced chips are not merely commercial products. They are general-purpose intelligence infrastructure or what China calls military-civil fusion. They train surveillance systems, military logistics platforms, cyber-offensive tools, and models capable of operating autonomous weapons and battlefield decision-making pipelines with no human in the loop.

If DOJ treats the smuggling of these chips into China as a serious federal crime—and it should—there is no coherent justification for authorizing their sale through executive discretion. Except, of course, money, or in Mr. Sacks case, more money.

Fully Autonomous Weapons—and Selling the Rope

China does not need U.S. chips to build consumer AI. It wants them for military acceleration.Advanced NVIDIA AI chips are not just about chatbots or recommendation engines. They are the backbone of fully autonomous weapons systems—autonomous targeting, swarm coordination, battlefield logistics, and decision-support models that compress the kill chain beyond meaningful human control.

There is an old warning attributed to Vladimir Lenin—that capitalists would sell the rope by which they would later be hanged. Apocryphal or not, it captures this moment with uncomfortable precision.

If NVIDIA chips are powerful enough to underpin autonomous weapons systems for allied militaries, they are powerful enough to underpin autonomous weapons systems for adversaries like China. Trump’s own National Security Strategy statement clearly says previous U.S. elites made “mistaken” assumptions about China such as the famous one that letting China into the WTO would integrate Beijing into the famous rules-based international order. Trump tells us that instead China “got rich and powerful” and used this against us, and goes on to describe the CCP’s well known predatory subsidies, unfair trade, IP theft, industrial espionage, supply-chain leverage, and fentanyl precursor exports as threats the U.S. must “end.” By selling them the most advanced AI chips?

Western governments and investors simultaneously back domestic autonomous-weapons firms—such as Europe-based Helsing, supported by Spotify CEO Daniel Ek—explicitly building AI-enabled munitions for allied defense. That makes exporting equivalent enabling infrastructure to a strategic competitor indefensible.

The AI Moratorium Makes This Worse, Not Better

This contradiction unfolds alongside a proposed federal AI moratorium executive order originating with Mr. Sacks and Adam Thierer of Google’s R Street Institute that would preempt state-level AI protections.
States are told AI is too consequential for local regulation, yet the federal government is prepared to license exports of AI’s core infrastructure abroad.

If AI is too dangerous for states to regulate, it is too dangerous to export. Preemption at home combined with permissiveness abroad is not leadership. It is capture.

This Is What Policy Capture Looks Like

The common thread is not national security. It is Silicon Valley access. David Sacks and others in the AI–VC orbit argue that AI regulation threatens U.S. competitiveness while remaining silent on where the chips go and how they are used.

When DOJ prosecutes smugglers while the White House authorizes exports, the public is entitled to ask whose interests are actually being served. Advisory roles that blur public power and private investment cannot coexist with credible national-security policymaking particularly when the advisor may not even be able to get a US national security clearance unless the President blesses it.

A Line Has to Be Drawn

If a technology is so sensitive that its unauthorized transfer justifies prosecution, its authorized transfer should be prohibited absent extraordinary national interest. AI accelerators meet that test.

Until the administration can articulate a coherent justification for exporting these capabilities to China, the answer should be no. Not licensed. Not delayed. Not cosmetically restricted.

And if that position conflicts with Silicon Valley advisers who view this as a growth opportunity, they should return to where they belong. The fact that the US is getting 25% of the deal (which i bet never finds its way into America’s general account), means nothing except confirming Lenin’s joke about selling the rope to hang ourselves, you know, kind of like TikTok.

David Sacks should go back to Silicon Valley.

This is not venture capital. This is our national security and he’s selling it like rope.

Back to Commandeering Again: David Sacks, the AI Moratorium, and the Executive Order Courts Will Hate

Why Silicon Valley’s in-network defenses can’t paper over federalism limits.

The old line attributed to music lawyer Allen Grubman is, “No conflict, no interest.” Conflicts are part of the music business. But the AI moratorium that David Sacks is pushing onto President Trump (the idea that Washington should freeze or preempt state AI protections in the absence of federal AI policy) takes that logic to a different altitude. It asks the public to accept not just conflicts of interest, but centralized control of AI governance built around the financial interests of a small advisory circle, including Mr. Sacks himself.

When the New York Times published its reporting on Sacks’s hundreds of AI investments and his role in shaping federal AI and chip policy, the reaction from Silicon Valley was immediate and predictable. What’s most notable is who didn’t show up. No broad political coalition. No bipartisan defense. Just a tight cluster of VC and AI-industry figures from he AI crypto–tech nexus, praising their friend Mr. Sacks and attacking the story.

And the pattern was unmistakable: a series of non-denial denials from people who it is fair to say are massively conflicted themselves.

No one said the Times lied.

No one refuted the documented conflicts.

Instead, Sacks’ tech bros defenders attacked tone and implied bias, and suggested the article merely arranged “negative truths” in an unflattering narrative (although the Times did not even bring up Mr. Sacks’ moratorium scheme).

And you know who has yet to defend Mr. Sacks? Donald J. Trump. Which tells you all you need to know.

The Rumored AI Executive Order and Federal Lawsuits Against States

Behind the spectacle sits the most consequential part of the story: a rumored executive order that would direct the U.S. Department of Justice to sue states whose laws “interfere with AI development.” Reuters reports that “U.S. President Donald Trump is considering an executive order that would seek to preempt state laws on artificial intelligence through lawsuits and by withholding federal funding, according to a draft of the order seen by Reuters….”

That is not standard economic policy. That is not innovation strategy. That is commandeering — the same old unconstitutional move in shiny AI packaging that we’ve discussed many times starting with the One Big Beautiful Bill Act catastrophe.

The Supreme Court has been clear on this such as in Printz v. United States (521 U.S. 898 (1997) at 925): “[O]pinions of ours have made clear that the Federal Government may not compel the States to implement,by legislation or executive action, federal regulatory programs.”

Crucially, the Printz Court teaches us what I think is the key fact. Federal policy for all the United States is to be made by the legislative process in regular order subject to a vote of the people’s representatives, or by executive branch agencies that are led by Senate-confirmed officers of the United States appointed by the President and subject to public scrutiny under the Administrative Procedures Act. Period.

The federal government then implements its own policies directly. It cannot order states to implement federal policy, including in the negative by prohibiting states from exercising their Constitutional powers in the absence of federal policy. The Supreme Court crystalized this issue in a recent Congressional commandeering case of Murphy v. NCAA (138 S. Ct. 1461 (2018)) where the court held “[t]he distinction between compelling a State to enact legislation and prohibiting a State from enacting new laws is an empty one. The basic principle—that Congress cannot issue direct orders to state legislatures—applies in either event.” Read together, Printz and Murphy extend this core principle of federalism to executive orders.

The “presumption against preemption” is a canon of statutory interpretation that the Supreme Court has repeatedly held to be a foundational principle of American federalism. It also has the benefit of common sense. The canon reflects the deep Constitutional understanding that, unless Congress clearly says otherwise—which implies Congress has spoken—states retain their traditional police powers over matters such as the health, safety, land use, consumer protection, labor, and property rights of their citizens. Courts begin with the assumption that federal law does not displace state law, especially in areas the states have regulated for generations, all of which are implicated in the AI “moratorium”.

The Supreme Court has repeatedly affirmed this principle. When Congress legislates in fields historically occupied by the states, courts require a clear and manifest purpose to preempt state authority. Ambiguous statutory language is interpreted against preemption. This is not a policy preference—it is a rule of interpretation rooted in constitutional structure and respect for state sovereignty that goes back to the Founders.

The presumption is strongest where federal action would displace general state laws rather than conflict with a specific federal command. Consumer protection statutes, zoning and land-use controls, tort law, data privacy, and child-safety laws fall squarely within this protected zone. Federal silence is not enough; nor is agency guidance or executive preference.

In practice, the presumption against preemption forces Congress to own the consequences of preemption. If lawmakers intend to strip states of enforcement authority, they must do so plainly and take political responsibility for that choice. This doctrine serves as a crucial brake on back-door federalization, preventing hidden preemption in technical provisions and preserving the ability of states to respond to emerging harms when federal action lags or stalls. Like in A.I.

Applied to an A.I. moratorium, the presumption against preemption cuts sharply against federal action. A moratorium that blocks states from legislating even where Congress has chosen not to act flips federalism on its head—turning federal inaction into total regulatory paralysis, precisely what the presumption against preemption forbids.

As the Congressional Research Service primer on preemption concludes:

The Constitution’s Supremacy Clause provides that federal law is “the supreme Law of the Land” notwithstanding any state law to the contrary. This language is the foundation for the doctrine of federal preemption, according to which federal law supersedes conflicting state laws. The Supreme Court has identified two general ways in which federal law can preempt state law. First, federal law can expressly preempt state law when a federal statute or regulation contains explicit preemptive language. Second, federal law can impliedly preempt state law when Congress’s preemptive intent is implicit in the relevant federal law’s structure and purpose.

In both express and implied preemption cases, the Supreme Court has made clear that Congress’s purpose is the “ultimate touchstone” of its statutory analysis. In analyzing congressional purpose, the Court has at times applied a canon of statutory construction known as the “presumption against preemption,” which instructs that federal law should not be read as superseding states’ historic police powers “unless that was the clear and manifest purpose of Congress.”

If there is no federal statute, no one has any idea what that purpose is, certainly no justiciabile idea. Therefore, my bet is that the Court would hold that the Executive Branch cannot unilaterally create preemption, and neither can the DOJ sue states simply because the White House dislikes their AI, privacy, or biometric laws, much less their zoning laws applied to data centers.

Why David Sacks’s Involvement Raises the Political Temperature

As Scott Fitzgerald famously wrote, the very rich are different. But here’s what’s not different—David Sacks has something he’s not used to having. A boss. And that boss has polls. And those polls are not great at the moment. It’s pretty simple, really. When you work for a politician, your job is to make sure his polls go up, not down.

David Sacks is making his boss look bad. Presidents do not relish waking up to front-page stories that suggest their “A.I. czar” holds hundreds of investments directly affected by federal A.I. strategy, that major policy proposals track industry wish lists more closely than public safeguards, or that rumored executive orders could ignite fifty-state constitutional litigation led by your supporters like Mike Davis and egged on by people like Steve Bannon.

Those stories don’t just land on the advisor; they land on the President’s desk, framed as questions of his judgment, control, and competence. And in politics, loyalty has a shelf life. The moment an advisor stops being an asset and starts becoming a daily distraction much less liability, the calculus changes fast. What matters then is not mansions, brilliance, ideology, or past service, but whether keeping that adviser costs more than cutting them loose. I give you Elon Musk.

AI Policy Cannot Be Built on Preemption-by-Advisor

At bottom, this is a bet. The question isn’t whether David Sacks is smart, well-connected, or persuasive inside the room. The real question is whether Donald Trump wants to stake his presidency on David Sacks being right—right about constitutional preemption, right about executive authority, right about federal power to block the states, and right about how courts will react.

Because if Sacks is wrong, the fallout doesn’t land on him. It lands on the President. A collapsed A.I. moratorium, fifty-state litigation, injunctions halting executive action, and judges citing basic federalism principles would all be framed as defeats for Trump, not for an advisor operating at arm’s length.

Betting the presidency on an untested legal theory pushed by a politically exposed “no conflict no interest” tech investor isn’t bold leadership. It’s unnecessary risk. When Trump’s second term is over in a few years, Trump will be in the history books for all time. No one will remember who David Sacks was.

2026 Mechanical Rate 13.1¢

The Copyright Royalty Judges have announced that the new COLA-adjusted minimum statutory rate for 2026 is 13.1¢ for physical and downloads, up from 12.7¢, effective 1/1/26. This is the last year of the Phonorecords IV rate period, so that’s an increase from the 9.1¢ frozen mechanical rate that had been in effect for 15 years.

The adjusted rate stands in stark contrast to the streaming mechanical which not only has been frozen for the entire 5 year rate period, but has actually declined substantially due to Spotify’s bundling silliness. That smooth move has set up what will no doubt be a donnybrook in Phonorecords V, i.e., the next rate proceeding which is due to start any minute now (actually more like January, which is close enough).

It must be said that the reason there’s a rate increase for physical/downloads is due to the efforts of independents who filed two rounds of comments in Phonorecords IV and also the willingness of the labels to be flexible and reasonable. I suspect that has a lot to do with the fact that at the end of the day, we are all in the same business and it’s to everyone’s advantage that songwriters thrive. Obviously, the same cannot be said of the streaming platforms like Spotify that are busy seeding AI tracks with both hands. I really don’t know what business those people think they are in, but it’s not the music business.

2025-21695Download