Month: May 2026

The Sync Apocalypse Case Against Suno

Democracy Made Them Do It

The new Poseidon Wave Media LLC lawsuit against Suno may become another important fair use case in generative AI music because it goes straight at the weak point Judge Vince Chhabria identified in Kadrey v. Meta regarding books: market substitution and market dilution under factor four that can trump the overused “transformative” analysis.

Poseidon-v-SunoDownload



In Kadrey, Judge Chhabria ruled for Meta on fair use, but he did not give AI companies a clean bill of health. Quite the opposite. He suggested that generative AI training may often fail fair use where plaintiffs build a real record showing that the model floods or dilutes the market for the plaintiffs’ works. Like what is happening in real life with synthetic music, and in particular with synthetic music produced using Suno.

Aside from being suspicious of a grown man voluntarily calling himself “Mikey”, there’s a lot to work with in the public statements of Suno CEO Mikey Shulman. In a widely panned venture capital podcast, “Mikey” argued that traditional music creation is too difficult and time-consuming for most people, claiming that “the majority of people don’t enjoy the majority of the time they spend making music.” He framed AI music generation as a way to democratize creativity by removing the need for years of practice or technical skill.

Yes, that’s right. He’s doing it for, like, democracy, you see. Just like Daniel Ek (who is currently occupying himself after Spotify with another autonomous weapon that again violates international treaties).

Shulman also acknowledged that using copyrighted works in AI training is effectively industry standard, stating that “every AI company does” copyright infringement when building generative AI systems. His comments triggered backlash from musicians, composers and industry observers who viewed the statements as dismissive of artistic labor and revealing about AI companies’ attitudes toward copyright and human creativity. We’ll come back to this bit.

When Did Noah Build the Ark? Before the flood….

But the flooding of markets using Suno is what makes Poseidon different than other cases I’ve seen so far, kind of like the eggshell skull case lawyers study on the first day of Torts. One could argue that Poseidon’s lawsuit against Suno resembles the classic “eggshell skull” rule because AI companies may be liable for the full downstream harm caused by training on copyrighted works even if they claim they did not anticipate the scale of damage. If Suno’s infringement helped create systems that flood markets and substitute for human creators, defendants take the creative marketplace “as they find it.” You could also find it reasonably foreseeable that if one AI lab’s executives knew that “everyone was doing it” the flip side is that “everyone” can cause a good deal of market harm to everyone else.

Plaintiff Poseidon Wave Media, the entity behind the instrumental duo The American Dollar, alleges that Suno copied and ingested 236 recordings and compositions covered by 164 copyright registrations. More importantly, Poseidon alleges that its licensing revenue has fallen by nearly 80% since Suno launched.

That is not a vibes-based fair use objection. That is a market-harm theory with a ledger attached. The plaintiffs still have to prove their case, but it sounds like a pretty good starting place.

The complaint targets the precise market most vulnerable to AI substitution: sync and production music. Cinematic instrumental catalogs are valuable because they supply mood, pacing, emotional texture, and audiovisual utility. Music supervisors are concerned that fully synthetic music undermines the basic trust and clearance infrastructure on which film, television and advertising music depends. They are often pitched an apparent “artist” that turned out to be AI-generated, raising immediate clearance and provenance concerns for music supervisors, their clients and E&O insurance carriers. AI-generated tracks are fundamentally incompatible with the human authorship and rights verification required for professional sync licensing. So in addition to the human cost, there’s a broader aspect to destroying the human market—synthetic music could flood the marketplace with unverifiable works, creating legal uncertainty and making it harder for supervisors to assess ownership, permissions and creative authenticity.

Generative AI does not have to spit out an identical American Dollar track to destroy the market for American Dollar licenses. It only has to produce infinite near-substitutes at lower cost, faster speed, and no meaningful bargaining friction. That is market dilution.

That is factor four. And that is happening at a devastating rate in our business.

The Sync Apocalypse Extends the Kadrey Theory

This is also why Poseidon extends the Kadrey analysis beyond books. In the book cases, market harm may appear more abstract. In sync music, the substitution pathway is far cleaner. The buyer has a practical production need. The AI output can satisfy that need if the music supervisor looks the other way, at least for a while, particularly for commercials, “source” music, other background uses. The original license disappears. Mikey wants you to believe that’s a good thing, because democracy.

Poseidon’s allegation that licensing income collapsed after Suno launched is therefore not just damages evidence. It may be the whole fair use fight.

Suno will likely argue transformation: the model learns from recordings to generate new outputs. But Kadrey already shows why transformation is not enough if factor four turns decisively against the defendant and the plaintiff’s lawyers put on the right case. Judge Chhabria made it clear that this observation applied broadly to all fair use cases: “Generative AI has the potential to flood the market with endless amounts of images, songs, articles, books, and more.” Kadrey v. Meta Platforms, Inc., No. 23-cv-03417-VC, slip op. at 1–2 (N.D. Cal. June 25, 2025).

That makes Poseidon dangerous for Suno. The complaint does not need to prove that every Suno output is a counterfeit. It needs to show that Suno used copyrighted works to build a machine that competes directly in the licensing market with those works it ripped off.

That is the sync apocalypse theory:

First, copy the catalog.
Then, train the machine.
Then, flood the licensing market with synthetic substitutes.
Then, tell the original musicians there is no market harm because the outputs are not exact copies. Because democracy demands it.

Factor four was built for this problem, even without the democracy part. And Poseidon may be the case that forces courts to say so. And as far as the democracy part goes, I think Mikey may have taken the wrong turn on his way to Collectivism class. In our legal tradition, there’s another idea that has far greater purchase:

“The right of property… [is] that sole and despotic dominion which one man claims and exercises… in total exclusion of the right of any other individual in the universe.”
— Sir William Blackstone, Commentaries on the Laws of England, Book II, ch. 1. 

Could Suno’s Executives Be Added Personally?

One question hovering over the Poseidon complaint is whether Suno’s executives and investors could eventually be added as individual defendants. What did they know and when did they know it?

In copyright cases, corporate officers can face personal liability where they personally participated in the infringement, directed it, authorized it, or had the right and ability to supervise the infringing conduct while receiving a financial benefit from it as we saw in a couple leading cases “All persons and corporations who participate in, exercise control over or benefit from an infringement are jointly and severally liable as copyright infringers.” Gershwin Publ’g Corp. v. Columbia Artists Mgmt., Inc., 443 F.2d 1159, 1162 (2d Cir. 1971); “One who distributes a device with the object of promoting its use to infringe copyright… is liable for the resulting acts of infringement by third parties.” MGM Studios Inc. v. Grokster, Ltd., 545 U.S. 913, 936–37 (2005). See also Broad. Music, Inc. v. Hartmarx Corp., 1988 WL 128691, at *3 (N.D. Ill. Nov. 22, 1988) (“A corporate officer who directs, controls, ratifies, participates in, or is the moving force behind the infringing activity, is personally liable…”); Columbia Pictures Indus., Inc. v. Fung, 710 F.3d 1020 (9th Cir. 2013) (operator liability tied to inducement and encouragement of infringement); and then my personal favorite, Arista Records LLC v. Lime Group LLC, 784 F. Supp. 2d 398 (S.D.N.Y. 2011) (evidence of executive knowledge and encouragement relevant to secondary liability).

If Suno’s leadership approved the acquisition, copying, ingestion, or retention of copyrighted sound recordings for model training, plaintiffs may argue that the executives were not passive corporate managers. They were decision-makers in the alleged infringement pipeline.

If discovery shows that senior executives knew copyrighted commercial recordings were being copied, discussed licensing risk, chose not to license, or treated infringement exposure as a cost of doing business, the case could begin to look more like direct participation or inducement than ordinary corporate oversight. For example, Complete Music Update quotes Mikey as like “…admitting to using copyright protected music in his company’s AI training data, something that he describes as ‘stock standard’ practice that ‘every AI company does.’” He evidently said this as part of an interview he gave to leading venture capital industry podcast The Twenty Minute VC. Now I’m not saying that statement alone is enough to close a case, but it certainly is one of those whatchamacalits, an admission against interest.


Shulman’s statement is significant because it is not merely a generalized industry observation. It is an admission by a senior corporate officer that his company Suno used copyrighted works in AI training and that the practice was understood internally at Suno as normal operating procedure. In civil discovery, that seems more than enough to justify targeted subpoenas designed to identify the scope, intent and commercial exploitation of the alleged infringement. And who else participated in the policy implementation.

Courts permit broad discovery where a plaintiff can show a reasonable basis to believe relevant evidence exists. Here, the CEO publicly acknowledged both (1) use of copyrighted music in training data and (2) awareness that such conduct implicated copyright law. The statement therefore supports discovery into knowledge, willfulness, inducement and commercial benefit under cases like Grokster, Fung, and Lime Group.

The quote particularly supports subpoenas for:

  • Training datasets and provenance records identifying sound recordings, compositions, stems, embeddings, fingerprints, metadata or source libraries used in model training;
  • Internal communications discussing ingestion of copyrighted music, licensing avoidance, fair use strategy, risk assessments or litigation exposure, including with members of the Suno board of directors;
  • Board materials and investor presentations discussing training practices, copyright risk, or competitive advantages derived from unlicensed datasets;
  • Engineering documents concerning scraping pipelines, dataset assembly, deduplication, filtering and retention of copyrighted material;
  • Financial records showing revenues, subscriptions, enterprise deals or valuations tied to models trained on copyrighted works;
  • Communications with third-party dataset providers, cloud vendors or contractors involved in obtaining or processing music files;
  • Prompt/output testing records showing whether models could reproduce recognizable musical expression, styles, voices or commercially substitutive outputs;
  • Policies regarding removal requests, provenance tracking, watermarking or rights management; and
  • Executive communications, including those involving Shulman personally, concerning decisions to proceed despite known copyright objections.

The statement also strengthens arguments for discovery into willful infringement. Saying that infringement is “stock standard” and that “every AI company does” it can be framed not as innocence, but as evidence of conscious normalization of unlawful conduct. Plaintiffs could argue this reflects industry-wide deliberate disregard for licensing obligations rather than accidental or technically unavoidable copying.

Finally, the quote helps establish proportionality. Suno itself has publicly placed copyright infringement at the center of its business model and competitive narrative. Once the CEO publicly admits the conduct, defendants have a much harder time arguing that subpoenas directed at training records, executive knowledge or dataset provenance are speculative fishing expeditions.

Naming executives can sharpen the willfulness theory. It can support discovery into board materials, investor pitches, licensing discussions, data-acquisition plans, and internal risk assessments.

These claims also may open the door to the boardroom. If discovery shows that Suno’s training strategy, licensing posture, or infringement-risk tolerance was discussed at the board level, plaintiffs may seek board materials, investor communications, voting agreements, consent rights, and other governance documents. Yes, the entire odious apparatus.

That may be exceptionally relevant and productive especially if major investors had approval rights, information rights, veto rights, or board seats tied to key business decisions. In that scenario, the inquiry may not stop with management. It could reach the investors who helped authorize, finance, or control the strategy that made the alleged infringement commercially valuable.

Public reporting identifies Menlo, Lightspeed, Matrix, Founder Collective, Nat Friedman, Daniel Gross, NVentures/Nvidia, and Hallwood Media as Suno investors. I have not found a public source confirming which, if any, hold board seats or board-observer rights. Given the size and lead-investor status of Menlo and Lightspeed, board or observer rights would be plausible and even typical, but that should be confirmed through charter documents, investor rights agreements, board minutes, cap table materials, or other discovery.

Notably, many of these same issues are already surfacing in the book publisher plus Scott Turow litigation against Meta and Mark Zuckerberg, including the allegations raised in the Elsevier-related AI copyright cases and the broader author lawsuits against Meta.

Plaintiffs in those matters have increasingly focused not only on the existence of infringing training datasets, but on executive-level awareness, internal discussions concerning licensing risk, data acquisition strategy, and decisions to proceed despite known copyright concerns.

The same dynamics may emerge in the Suno litigation if discovery reveals board-level discussions, investor oversight, or strategic decisions concerning whether copyrighted music catalogs would be licensed, copied without permission, or treated as a litigation risk worth taking.

The Potential Shareholder Suit

Developing a detailed factual record against Mikey Shulman (or Mark Zuckerberg) could significantly increase the risk of a future shareholder derivative suit because it potentially transforms the case from “the company made aggressive legal bets” into “management knowingly exposed the company to massive liability while failing to fulfill fiduciary duties.”

A derivative case would likely center on fiduciary duty theories under Delaware law — particularly the duties of loyalty, oversight (Caremark), disclosure, and good faith.

The pathway looks something like this:

  1. Public admissions establish scienter groundwork

    Shulman’s statements that using copyrighted works was “stock standard” and that “every AI company does” infringement could be framed as evidence that senior management understood the conduct implicated copyright law from the outset. Plaintiffs in a derivative action would argue this was not inadvertent infringement or a technical edge case, but a conscious business strategy. Of course, it would also be interesting to see if we could find out exactly what made Mikey say such things? Any meetings he’d like to discuss? All like very democratic, I’m like so sure.
  2. Discovery in copyright litigation creates the evidentiary record

    The underlying copyright cases are what really matter. If discovery uncovers:
    • internal discussions acknowledging piracy risks,

    • deliberate avoidance of licensing,executive-level approval of infringing datasets,warnings from counsel or employees,or

    • efforts to conceal provenance,

    then plaintiffs’ firms would likely use that material to argue the board failed to exercise oversight or knowingly permitted unlawful conduct.
  3. Massive enterprise risk can trigger Caremark-style claims

    Delaware courts increasingly recognize that boards must monitor “mission critical” legal risks. For Suno, copyright compliance is not peripheral — it is existential. The entire company depends on ingesting copyrighted music. If plaintiffs could show there were inadequate controls over training data provenance, licensing, or infringement risk, they could argue the board ignored core compliance obligations.
  4. Investor disclosures become vulnerable

    Once litigation and discovery mature, shareholders may ask whether fundraising materials accurately described legal risks. If management portrayed datasets as compliant, transformative, or low-risk while internally acknowledging likely infringement, that creates exposure around disclosure duties and securities-related claims.
  5. Personal enrichment allegations amplify pressure

    Derivative plaintiffs often focus on:
    • executive compensation,liquidity events,fundraising rounds,valuation increases,and insider sales.
    The theory becomes: executives increased enterprise value through unlawful conduct while externalizing legal risk onto the corporation and shareholders.
  6. Insurance and indemnification issues emerge

    Findings of willful misconduct or bad faith can create disputes over D&O insurance coverage and indemnification rights. That dramatically increases settlement pressure and board conflict concerns.

The important strategic point is that copyright plaintiffs do not need to bring the derivative suit themselves. They only need to build the factual record. Once discovery produces emails, board materials, or executive communications suggesting knowing infringement or oversight failures, shareholder firms may step in independently.

That is why executive statements like, you know, matter so much. Public comments can later be connected to internal documents to argue that management knew exactly what it was doing, understood the legal exposure, and proceeded anyway because rapid AI scaling and market capture were prioritized over licensing compliance.

And who wants to bet that the board was leading the charge?

The Data Center Backlash Has Arrived

For years, the political conversation around AI data centers followed a familiar script that was straight out of the Chamber of Commerce. Governors competed to announce the next hyperscale campus. Counties rezoned farmland and conservation land into heavy industrial corridors. Legislatures approved enormous tax abatements with little debate. Utilities promised “economic development.” And local officials were told that if they moved too slowly, some other state would take the project instead. Kind of like because China.

Residents in Crowell, Texas are being forced to live with constant artificial daylight because of Google’s AI data center that is being built right next to them. Residents report severe 24/7 light pollution that creates artificial daylight at night (photo proof shown)

Why? Because even 10 years ago it was self-evidently true that there was no political opposition to Big Tech and nobody looked too hard at the reality of data centers in the places we had observable data like Oregon, for example. If they had, they would have known there was one thing that was absolutely true—data centers were not factories and they produced higher electric bills and fewer jobs. At least once the sugar high of construction had passed.

And speaking of jobs, in a November 2025 difference-in-differences study, economist Michael J. Hicks examined every data center opened in Texas and found zero statistically significant net employment effect — job gains in the data center sector were fully offset by losses in other industries, yielding an average treatment effect of roughly 46 workers per facility that the author concludes is “correctly interpreted as zero,” less than one-tenth the jobs generated by a single Walmart Supercenter. 

Good Jobs First has found that the three states that have measured their data center return on investment lose 52 to 91 cents on the dollar, and in Virginia alone, the sales and use tax exemption for data centers consumed 81.3% of the state’s entire economic development incentives budget in FY 2024.

But it’s not just light pollution. Even though it was patently obvious that the massive data centers that were getting built in Louisiana, Georgia, Utah and Nevada were vastly larger than the already operating data centers in Oregon and were guaranteed to chew up the environment way more, nobody bothered to put 2 and 2 together and check how deep the foundations were compared to local aquifers.

Just because she’s a socialist, doesn’t mean she’s wrong.

That script is now breaking down. I’m shocked, said no one.

As we told the UK Intellectual Property Office:

We call the IPO’s attention to the real-world example of the U.S. State of Oregon, a state that is roughly the geographical size of the UK.  Google built the first Oregon data centre in The Dalles, Oregon in 2006.  Oregon now has 125 of the very data centres that Big Tech will necessarily need to build in the UK to implement AI.  In other words, Oregon was sold much the same story that Big Tech is selling you today.

The rapid growth of Oregon data centres driven by the same tech giants like Amazon, Apple, Google, Oracle, and Meta, has significantly increased Oregon’s demand for electricity. This surge in demand has led to higher power costs, which are often passed on to local rate payers while data centre owners receive tax benefits.  This increase in price foreshadows the market effect of crowding out local rate payers in the rush for electricity to run AI—demand will only increase and increase substantially as we enter what the International Energy Agency has called “the age of electricity”.

Portland General Electric, a local power operator, has faced increasing criticism for raising rates to accommodate the encroaching electrical power needs of these data centers. Local residents argue that they unfairly bear the increased electrical costs while data centers benefit from tax incentives and other advantages granted by government. 

This is particularly galling in that the hydroelectric power in Oregon is largely produced by massive taxpayer-funded hydroelectric and other power projects built long ago. The relatively recent 125 Oregon data centres received significant tax incentives during their construction to be offset by a promise of future jobs.  While there were new temporary jobs created during the construction phase of the data centres, there are relatively few permanent jobs required to operate them long term as one would expect from digitized assets owned by AI platforms.

Of course, the UK has approximately 16 times the population of Oregon.  Given this disparity, it seems plausible that whatever problems that Oregon has with the concentration of data centers, the UK will have those same problems many times over due to the concentration of populations.

This message is getting through to elected officials around the world because citizens are freaking out.

Quietly at first, and then all at once, states and local governments across the country began pushing back. Some are freezing approvals entirely. Others are reconsidering billions in tax incentives. Some are demanding that data centers pay the real cost of the transmission infrastructure they require instead of socializing those costs onto ordinary ratepayers and anyone else who drinks water and breathes air.

This is no longer a niche zoning issue in Northern Virginia or some European bureaucratic nonsense. It is becoming a national political movement that has some real populist overtones worthy of a Brexiteer. According to the National Conference of State Legislatures (NCSL), at least 11 states have introduced statewide moratorium or ban legislation targeting data centers. Meanwhile, Good Jobs First reports more than 60 local moratorium efforts nationwidethat at least 14 states and scores of localities are failing to disclose tax abatement revenue losses they are suffering to data centers — even though they have been required to do so under Generally Accepted Accounting Principles (GAAP) since FY 2017.

The reasons vary by region as you’d suspect, but the themes are becoming remarkably consistent, many of which Artist Rights Institute raised in our comments on the US AI Action Plan and the UK IPO AI consultation:

• massive electricity demand;
• water consumption;
• transmission line expansion;
• opaque tax subsidies;
• industrialization of rural communities;
• secrecy surrounding the ultimate hyperscale users;
• and growing fear that ordinary households will subsidize AI infrastructure through higher utility bills.

What is striking is not merely the existence of resistance. It is the geographic breadth of it.

In Texas, lawmakers enacted new large-load interconnection rules while Hill County adopted a temporary construction pause and Agriculture Commissioner Sid Miller publicly called for broader scrutiny of data centers. In Virginia, long considered the unquestioned capital of the data center industry, legislators are openly debating whether to scale back tax exemptions that helped fuel “Data Center Alley.” In Illinois, Governor Pritzker proposed suspending new tax incentives entirely for two years.

Even places that aggressively courted data centers are beginning to hesitate.

In Reno, Nevada, officials adopted a pause on approving new data centers while they reevaluate land-use and infrastructure impacts. Duh. Ya think?

The Reno–Tahoe industrial corridor became a symbol of how quickly hyperscale development can transform an entire region once incentives and transmission infrastructure align. Nevada approved hundreds of millions in projected abatements over the last decade. Now local officials are asking whether the public actually understood the scale of what was being built. If you build it they will come, and they will take a huge dump in your backyard.

That same questions are emerging everywhere else: Who is the real end user? Who pays for the substations and 765-kV transmission lines? What happens if AI demand projections collapse halfway through construction? And why are local taxpayers subsidizing facilities that often employ surprisingly few permanent workers once operational? Well…not really surprisingly, but surprisingly if you believed the Chamber of Commerce hoorah.

The politics are changing because the physical footprint of AI is no longer abstract. The cloud is becoming visible. And you cannot bribe your way out of that one.

Pour some Sucre on them….

Residents now see the cooling towers. They see the transmission corridors. They hear the backup generators. In some communities they are learning about low-frequency industrial noise and infrasound issues that do not show up on ordinary decibel measurements. They see conservation land rezoned into industrial districts almost overnight. They see shell companies quietly assembling land while refusing to identify the ultimate hyperscale beneficiary.

Most importantly, they are beginning to understand that these projects are not temporary construction booms. They are permanent industrialization decisions. A 765-kV transmission corridor is not a pop-up startup. Neither is a hyperscale campus consuming as much electricity as a mid-sized city. And once the infrastructure is built, communities live with the consequences for generations.

The result is a new kind of political coalition that cuts across ideological lines. Environmental advocates, fiscal conservatives, rural landowners, grid-reliability hawks, and anti-subsidy activists are increasingly finding themselves on the same side of the debate. That does not mean the data center industry is stopping. Far from it. Billions are still flowing into AI infrastructure. Utilities continue planning enormous generation and transmission expansions. States remain eager for construction spending and property tax growth.

But the era of automatic approval is ending. The central political question is no longer whether AI infrastructure will expand. It is who bears the cost.

And there is another revealing development occurring at the federal level. What does it tell you that President Trump reportedly pulled back an executive-order framework that would have required certain AI labs to obtain government cybersecurity approval or clearance before launching advanced systems?

Whatever one thinks of the policy itself, the episode suggests intense behind-the-scenes conflict inside the administration and the AI industry over whether any meaningful federal guardrails should exist at all. Sources around Washington describe the push as a last-ditch effort by what critics derisively call the “Zombie AI Viceroy” David Sacks, the lobbyist who seemingly cannot be fired because the entire AI infrastructure race has become too politically and financially entangled. We will see whether federal safeguards reappear in another form. But at this moment, the practical reality is striking: the only governments actively imposing meaningful friction on AI infrastructure expansion are states, counties, and local municipalities.

State and Local Data Center Restriction / Tax Rollback Tracker (May 2026)

Alabama — Considering rules requiring data centers to bear infrastructure/grid costs

Arizona — Chandler pause; grid-cost proposals under consideration

California — Bills addressing ratepayer and environmental protections

Colorado — Denver moratorium; Larimer County pause; Logan County restrictions

Connecticut — Morris moratorium; Groton zoning restrictions

Florida — Enacted protections for local zoning authority and ratepayer safeguards

Georgia — HB 1059 introduced forbidding local permitting until December 2028; local pauses; estimated $2.5 billion per year in tax abatement revenue losses (highest in nation)

Illinois — Governor called for two-year pause of data center tax incentives

Indiana — Considering restructuring of tax incentive revenue sharing; fails to disclose data center costs despite ranking fifth-best in subsidy transparency nationally

Louisiana — New Orleans temporary moratorium

Maine — LD 307 moratorium on data centers over 20 MW (vetoed by Governor); local moratoria

Maryland — Proposed statewide approval restrictions (SB 931 / HB 1369)

Massachusetts — Lowell moratorium

Michigan — State moratorium proposals; Ypsilanti pause

Minnesota — Removed electricity sales tax exemption; created new annual energy-use fee; Minneapolis moratorium discussions

Nevada — Reno approval pause; growing tax-abatement controversy; Controller issues exemplary annual report of local revenue losses from state-awarded abatements

New Hampshire — HB 1265 one-year moratorium on data center construction (failed)

New Jersey — Millville ban/restrictions; prevailing wage requirement for data center construction (enacted February 2026)

New York — AB 10141 / SB 9144 statewide moratorium and Public Utility Commission rulemaking (introduced); Athens/Dryden/Mount Morris local restrictions

North Carolina — Chatham County moratorium; additional local reviews

North Dakota — Oliver County temporary moratorium activity

Ohio — Numerous local pauses; growing subsidy backlash

Oklahoma — SB 1488 moratorium until November 2029 (introduced); incentive rollback proposals

Oregon — Affordability/reliability proposals tied to large-load users

Pennsylvania — Moratorium discussions underway (HB 1370 introduced per NCSL)

South Carolina — SB 567 proposal to restrict approvals pending oversight framework (introduced)

South Dakota — SB 232 one-year statewide moratorium (introduced); local-control protections enacted

Texas — Large-load legislation; local moratoria and review fights; estimated $1 billion or more per year in tax abatement revenue losses; Hicks (2025) causal study found zero net job growth from data centers statewide

Vermont — S 205 proposed moratorium through 2030 with impact study requirement (introduced)

Virginia — HB 1515 prohibiting new approvals until interconnection requests fulfilled or July 2028 (continued); major debate over scaling back tax exemptions; estimated $1.94 billion per year in revenue losses; data center exemptions consumed 81.3% of state’s entire incentive budget in FY 2024

Washington — Restrictions tied to emissions-credit eligibility

Wisconsin — Moratorium proposal (status unverified; not listed in NCSL tracker)

The important point is not that every proposal will pass, which it may or may not. The important point is that resistance is no longer isolated. The backlash has become national. And resistance is not futile.

Federally Guaranteed Financial Preemption

The AI moratorium fight was never really about “innovation.” It was about preemption. More specifically, it was about what might be called federally guaranteed financial preemption.

That phrase matters because the walk-back campaign around the original proposal has become almost surreal. After backlash exploded over the broad federal effort to block state and local AI regulation, supporters suddenly insisted nobody was trying to force unwanted data centers, transmission lines, substations, gas plants, or hyperscale industrial infrastructure onto communities that did not want them.

Technically, that is true. Washington does not necessarily need to directly order a county commission to approve a data center. It can accomplish much the same thing by structuring the financial system around the assumption that the buildout will occur.

That is the trick.

David Sacks’ original moratorium language he stuck in the One Big Beautiful Bill Act reportedly reached not only states but “political subdivisions” as well. That means cities, counties, municipalities, and local authorities. The proposal was not merely about preventing fifty different state AI laws. It threatened to freeze local democratic responses before they could harden into enforceable policy. (And of course there was always a whiff of 5th Amendment taking about the whole doomed process.)

Then came the backlash. Suddenly the rhetoric softened into something more comforting: We just need one national framework. We are not trying to override local control. We are not trying to force data centers on anyone. But that framing ignores how infrastructure power actually works in the United States. You do not need formal federal commands if you can create overwhelming financial momentum.

Suppose the federal government provides taxpayer-backed loan guarantees for utility expansion tied to AI growth forecasts. Utilities then build new generation, transmission, substations, and grid upgrades designed around hyperscale demand projections. State utility commissions approve cost recovery. Transmission planners treat the load forecasts as inevitable. Investors price future growth into regional infrastructure decisions.

At that point, local communities are no longer arguing with a speculative proposal. They are arguing with a federally supported capital structure. That’s much harder to control.

The county commissioner is suddenly told: The transmission line is already planned. The utility already committed the generation. The state already approved portions of the recovery mechanism. The jobs are supposedly coming. The tax base is supposedly coming. The grid supposedly depends on it.

See, it’s magic. Nobody “forced” anything. Whatever were you thinking?

The machinery simply narrowed the realistic range of outcomes. That is federally guaranteed financial preemption.

And it matters because the economics of AI infrastructure are unusually fragile beneath the surface confidence. Data centers are not shopping centers. They are highly specialized industrial assets tied to assumptions about compute demand, electricity pricing, capital availability, chip supply, and continued investor faith in the AI growth curve.

Much of the current buildout depends on debt markets behaving rationally indefinitely.

That may not happen.

If AI demand softens, if monetization disappoints, if venture funding tightens, or if hyperscalers pull back from aggressive expansion schedules, communities may discover they absorbed the physical consequences of a speculative infrastructure cycle they never fully controlled in the first place.

And then comes the final insult in the “local choice” narrative.

Communities remain theoretically free to say no before the infrastructure becomes politically inevitable. They also remain theoretically free to clean up the wreckage after failure.

That means: condemnation fights, stranded industrial facilities, utility disputes, ratepayer battles, bondholder litigation, abandoned transmission corridors, water conflicts, and enormous demolition costs.

The same officials who insisted nobody forced anything can simply shrug and say: “Well, local communities always retained sovereignty.”

This is why local opposition has accelerated so dramatically across the country. Residents increasingly understand that hyperscale AI infrastructure is not an abstract software issue. It is physical industrial policy: land, water, electricity, noise, substations, transmission lines, tax incentives, utility rate structures, and debt.

The fight stopped being theoretical once people realized they were not debating apps. They were debating permanent industrial transformation of their communities.

That is also why the original AI moratorium language frightened so many people once they read it carefully. It was not merely a debate about chatbot regulation or algorithmic bias. It looked increasingly like a mechanism for suppressing state and local resistance before communities fully understood the infrastructure consequences of the AI buildout itself.

And that may explain why the rhetoric shifted so quickly after public scrutiny intensified.

Because once people understand the difference between legal preemption and financial preemption, the conversation changes entirely.

The federal government does not always need to formally eliminate local authority. Sometimes it only needs to guarantee enough money that resistance becomes structurally difficult.

That is a far more sophisticated form of power.

And a far more dangerous one,

As AI Infringement Claims move to the C-Suite and Board Room, Plaintiffs should Follow the Money to Wire Fraud, Fiduciary Duty and RICO

AI training litigation is moving from copyright pleadings to governance pleadings. The next discovery fight should follow the money all the way to the C-suite and if necessary, to the board room.

The complaint in Elsevier Inc. v. Meta Platforms, Inc., No. 1:26-cv-03689 (S.D.N.Y. filed May 5, 2026) (the “Elsevier complaint”), alleges that Meta and Mark Zuckerberg personally illegally torrented millions of copyrighted books and journal articles from notorious pirate sites, copied those works repeatedly to train Llama, and did so with knowledge that the conduct violated copyright law. 

2026-05-05-ComplaintDownload

Alternatively, the shareholder derivative complaint in SEIU Pension Plan Master Trust v. Narayen, No. 3:26-cv-03521 (N.D. Cal. filed Apr. 24, 2026) (the “SEIU complaint”), shows the same issue also moving into the boardroom: it alleges that Adobe’s officers and directors adopted and implemented an unlawful AI business strategy by using copyrighted material to develop Adobe’s AI services, exposing Adobe to litigation, reputational harm, and corporate loss. 

Both cases suggest a practical discovery vector for plaintiffs in AI-training cases: if the defendant used Anna’s Archive, LibGen, Z-Library, Sci-Hub, Books3, RedPajama, SlimPajama, or any similar pirate-derived source, plaintiffs should investigate whether the defendant merely downloaded available files or also paid for bulk access, priority access, SFTP credentials, “membership” tiers, “donations,” vendor pass-through datasets, or intermediary transfers. 

SEIU_Pension_Plan_Master_Trust-Shareholders-v.-Narayen-April-24-2026Download

The discovery question: was there a payment trail?

The current Elsevier complaint is framed principally as a copyright and DMCA case, not as a wire-fraud or shareholder derivative complaint. But its allegations make the payment question worth asking because it alleges that Meta downloaded Anna’s Archive, understood Anna’s Archive to be “essentially a bigger libgen” and “a pretty shady website,” acquired more than 81 terabytes of data through Anna’s Archive, and did not disable BitTorrent’s default distribution settings when torrenting from pirate sites. The complaint further alleges that Meta’s logs showed 134.6 TB downloaded and 40.42 TB uploaded through torrenting between April and July 2024.

It also alleges that Meta considered licensing literary works from major publishers in the training data market, discussed increasing its dataset licensing budget from $17 million to $200 million, then stopped licensing efforts after the license-versus-pirate question was escalated to Zuckerberg. Those allegations support discovery into whether Meta, its employees, contractors, affiliates, or data vendors ever communicated with, paid, negotiated with, or obtained credentials from Anna’s Archive or a similar bulk pirate-data provider. 

The public record concerning Anna’s Archive gives that discovery question additional force. As we covered before on MTP, Anna’s Archive reportedly charged tiered “membership” fees for faster download speeds, accepted cryptocurrency or gift cards, and offered AI companies “enterprise-level” high-speed SFTP transfers of a full 1.1-petabyte collection for a reported $200,000 in cryptocurrency. The complaint in Apress Media, LLC et al. v. Anna’s Archive et al., No. 1:26-cv-01850 (S.D.N.Y. filed Mar. 6, 2026) (the “Apress complaint”), similarly alleges that Anna’s Archive invited LLM developers to copy the entire collection for free or make a “donation” for faster download speeds. Publishers Weekly’s account of the Apress complaint states that Anna’s Archive publicly claimed to have provided high-speed access to its illegal collection to companies in China, Russia, and elsewhere, many of them LLMs, and that an email exchange quoted in the complaint offered premium access for $200,000 with payment suggested in cryptocurrency. It is important to note that as much as we may believe that these people are all scumbags, these allegations do not prove that any particular AI defendant paid Anna’s Archive, but they do make paid-access discovery reasonable where a defendant is already alleged to have used Anna’s Archive or comparable pirate repositories. As usual, facts matter.

Why payment alone is not wire fraud

The conditional nature of the theory also matters. Wire fraud is not “copyright infringement plus the internet,” and it is not “payment to a bad actor plus wires.” 18 U.S.C. § 1343 requires a scheme or artifice to defraud, or a scheme to obtain money or property by false or fraudulent pretenses, representations, or promises, plus interstate or foreign wire communications used for the purpose of executing that scheme.  DOJ’s formulation similarly requires voluntary and intentional participation in a scheme to defraud, intent to defraud, reasonably foreseeable use of interstate wire communications, and actual use of interstate wire communications.  DOJ also explains that the fraudulent aspect of a scheme to defraud is measured by nontechnical standards and generally involves wrongdoing in property rights by dishonest methods, trick, chicane, or overreaching. 

That is why a payment to a pirate site is not automatically wire fraud in a formal sense. A company may knowingly pay for unlawful access to copyrighted content, and that payment may be powerful evidence of willfulness, commercial purpose, knowledge, damages, or fiduciary misconduct. But if the buyer and seller both understand that the transaction is a purchase of illicit access and no materially false representation is used to obtain money, data, payment processing, procurement approval, compliance clearance, tax treatment, or concealment from a relevant victim or gatekeeper, the payment itself may not satisfy the fraud element. The discovery target should therefore likely be the deceptive aspect and proves up whether the payment was disguised as a “donation,” “membership,” “research access,” “preservation support,” “lawful dataset,” “vendor service,” “data license,” or other label that misrepresented the transaction’s purpose, legality, source, recipient, or quid pro quo.  And given who we’re dealing with, could even be designed to deceive internal accountants and co-signers depending on corporate check-writing policies.

Deceptive labeling can supply the fraud layer that ordinary infringement lacks. As we covered before on MTP, Anna’s Archive’s alleged “donation” model can be characterized as a commercial SFTP pipeline for stolen works, and the Apress complaint alleges that Anna’s Archive invited LLM developers to make a “donation” for faster download speeds. If discovery in a particular AI case shows that an AI developer, contractor, or data broker used wires, emails, SFTP credentials, cryptocurrency transactions, payment processors, invoices, procurement records, or vendor documentation to disguise a purchase of pirate training data as something lawful or altruistic, the facts may support a wire-fraud predicate theory. If discovery shows only an infringing download or torrent, the same evidence may still matter enormously to copyright liability, willfulness, damages, concealment, and fiduciary duty, but it should not be overstated as wire fraud without proof of deception and, of course, all the wire fraud elements. 

The Potential Civil RICO Angle

Civil RICO is a possible overlay, but not a shortcut and proving it up may be adjacent but separate to other claims. 18 U.S.C. § 1962(c) makes it unlawful for a person associated with an enterprise engaged in or affecting interstate commerce to conduct or participate in the conduct of the enterprise’s affairs through a pattern of racketeering activity (or collection of unlawful debt).  DOJ summarizes an 18 U.S.C. § 1962(c) violation as requiring conduct, of an enterprise, through a pattern, of racketeering activity. RICO’s definition of racketeering activity includes wire fraud under 18 U.S.C. § 1343 and criminal copyright infringement under 18 U.S.C. § 2319. Criminal copyright infringement requires a valid copyright, infringement, willfulness, and commercial advantage or private financial gain. 18 U.S.C. § 2319 provides felony penalties for certain offenses involving reproduction or distribution, including by electronic means, of at least ten copies or phonorecords of one or more copyrighted works with a total retail value of more than $2,500 during a 180-day period. Although criminal copyright infringement cases are not common, one has to ask if AI scraping of millions and millions of works is not criminal infringement, what is?

But RICO also requires relationship and continuity. A “pattern of racketeering activity” requires at least two predicate acts, but the Supreme Court has held that two predicates are not necessarily sufficient because a plaintiff or prosecutor must show that the predicates are related and amount to, or threaten, continued criminal activity. H.J. Inc. v. Northwestern Bell Telephone Co., 492 U.S. 229, 237–39 (1989). Relatedness can be shown where the acts share similar purposes, results, participants, victims, or methods of commission, or are otherwise interrelated and not isolated events.  Continuity can be closed-ended or open-ended, and the DOJ RICO guidance describes continuity as either a closed period of repeated conduct or past conduct that by its nature projects into the future with a threat of repetition.  A civil RICO plaintiff must also show injury to business or property by reason of a violation of 18 U.S.C. § 1962, and 18 U.S.C. § 1964(c) provides treble damages, costs, and attorney’s fees for such injury. 

Judge Mark C. Scarsi’s ruling in Perry v. Shein Distribution Corp. is useful because Judge Scarsi rejected the idea that large-scale copying must be treated as ordinary copyright infringement only. The court allowed independent designers’ civil RICO claims against the “fast fashion” Chinese retailer Shein to proceed, reportedly finding that the plaintiffs plausibly alleged a coordinated enterprise using copyright infringement and mail/wire fraud predicates as part of a broader scheme. The case later settled, so it is valuable as a pleading-stage roadmap.

The connection to Elsevier v. Meta is straightforward. Elsevier and other publishers allege that Meta copied millions of books and journal articles, used pirated libraries and web-scraped datasets, trained Llama on those works, and removed copyright-management information. If plaintiffs can show this was not ad hoc infringement but a coordinated corporate program approved at senior levels including Zuckerberg as they allege, using piracy, concealment, distribution, and monetization, the Shein ruling on RICO supports the argument that the conduct resembles an enterprise scheme rather than isolated copyright violations. The theory is when infringement is systematic, repeated, operationalized, concealed, and tied to enterprise monetization, RICO should not be dismissed merely because copyright is also involved in the defendant’s bad behavior.

Shareholder Derivative Action

A derivative claim does not need to prove that a payment to a pirate site was wire fraud; it can focus on whether fiduciaries knowingly caused or permitted the company to pursue AI profits through unlawful or legally reckless data acquisition. Delaware law does not charter lawbreakers because the DGCL authorizes Delaware corporations to pursue lawful business and lawful acts, 8 Del. C. §§ 101(b), 102(a)(3), and Delaware courts have stated that a fiduciary cannot be loyal to a Delaware corporation by knowingly causing it to seek profit by violating the law. In re Massey Energy Co. Derivative & Class Action Litigation, 2011 WL 2176479, at *20 (Del. Ch. May 31, 2011); see also Metro Communication Corp. BVI v. Advanced Mobilecomm Technologies Inc., 854 A.2d 121, 131, 163–64 (Del. Ch. 2004); Guttman v. Huang, 823 A.2d 492, 506 (Del. Ch. 2003).  

The same payment evidence may be even more immediately useful in shareholder derivative litigation. (Although the derivative theory is not a claim to be stapled onto a copyright-owner complaint. It is a separate governance claim for a different plaintiff and a different injury.). Under Delaware law, the board manages the corporation’s business and affairs, 8 Del. C. § 141(a), and a stockholder derivative action seeks to assert a corporate claim when demand is excused or wrongfully refused. See United Food & Commercial Workers Union & Participating Food Industry Employers Tri-State Pension Fund v. Zuckerberg, 262 A.3d 1034, 1047, 1059 (Del. 2021).

Delaware courts have also explained that directors must make a good-faith effort to implement and monitor systems that keep the board informed about legal-compliance risks, and that personal liability may follow when fiduciaries act in bad faith by utterly failing to implement such systems or consciously failing to monitor them. Stone v. Ritter, 911 A.2d 362, 370 (Del. 2006); Marchand v. Barnhill, 212 A.3d 805, 820–21 (Del. 2019). Delaware officers also owe context-specific oversight duties within their corporate remit. In re McDonald’s Corp. Stockholder Derivative Litigation, 289 A.3d 343, 359–61, 369–70 (Del. Ch. 2023). In that framework, a payment trail to a pirate repository may support allegations of knowing illegality, bad faith, internal-control failure, waste, disclosure failure, or conscious disregard of red flags. 

The SEIU complaint illustrates how quickly AI copyright allegations can become governance allegations. It alleges that Adobe is a Delaware corporation and that the defendants owed Adobe fiduciary duties of care, loyalty, good faith, diligence, fair dealing, and supervision.  The complaint alleges that Adobe used the SlimPajama dataset, which was derived from RedPajama, and that the dataset contained copyrighted works not authorized or approved by authors and copyright holders. It alleges that Adobe told the market that Firefly was commercially safe, trained on licensed content, trained on data Adobe had rights to use, and designed to respect creator rights and avoid infringing third-party intellectual property. It then alleges that those statements were false or misleading because Adobe’s AI products allegedly depended on SlimLM datasets that included pirated materials. 

The SEIU complaint also pleads the red-flag story in governance terms. It alleges that Adobe’s officers were personally involved in Adobe’s AI strategy and dataset choices, that multiple AI copyright lawsuits against competitors put all defendants on notice, and that Adobe was using datasets associated with Books3, RedPajama, the Pile, and SlimPajama. It alleges that copyright holders filed two class actions against Adobe, that Adobe’s stock dropped by more than 25% after those filings, and that Adobe faces litigation costs, potential liability, reputational harm, lost customers, and other corporate injuries. It also alleges that Adobe wasted corporate assets by paying compensation and bonuses, repurchasing shares at allegedly inflated prices, and incurring legal liability and costs associated with the copyright class actions. 

That is exactly why payment discovery matters to derivative plaintiffs. If corporate funds, procurement systems, crypto wallets, reimbursement requests, vendor invoices, or contractor payments were used to purchase pirate datasets, then the derivative theory becomes less abstract. The evidence would speak not only to whether copyrighted works were used, but to who approved the acquisition, how it was booked, what compliance review occurred, what the board or audit committee was told, whether the source was concealed, and whether public statements about “licensed,” “commercially safe,” or “rights-cleared” AI products were misleading.  If a payment was mislabeled as a donation, membership, research support, vendor service, or lawful data license, that same fact could support both a fraud-oriented discovery path and a fiduciary-duty theory focused on bad faith, oversight failure, disclosure controls, and corporate waste. 

What plaintiffs could ask for

Plaintiffs should ask for all communications with Anna’s Archive, LibGen, Z-Library, Sci-Hub, Books3 distributors, shadow-library mirrors, dataset curators, data brokers, contractors, and AI-training data vendors.  They should ask for payment records, cryptocurrency wallet addresses, exchange records, gift-card purchases, reimbursement requests, procurement records, vendor invoices, purchase orders, data-source approvals, security reviews, legal-risk memoranda, audit-committee materials, board presentations, and employee messages about “donations,” “memberships,” “enterprise access,” “SFTP,” “fast downloads,” “bulk transfer,” “shadow libraries,” “pirate datasets,” or “rights-cleared” alternatives. They should also ask for logs showing whether data was received by torrent, direct download, SFTP, cloud transfer, physical drive shipment, contractor delivery, or a repackaged dataset from an intermediary. 

In a copyright case, those materials may bear on copying, distribution, willfulness, CMI removal, concealment, damages, and market substitution. In a wire-fraud/RICO overlay, those materials may bear on whether any payment was part of a deceptive scheme executed through wires, whether the alleged predicates are related and continuous, and whether the plaintiff can show injury to business or property by reason of a RICO violation.  In a derivative suit, those materials may bear on whether officers or directors knowingly caused the company to violate law, ignored red flags, failed to maintain adequate reporting and compliance systems, approved misleading disclosures, or wasted corporate assets. See 8 Del. C. §§ 141(a), 102(b)(7); Stone v. Ritter, 911 A.2d 362, 370 (Del. 2006); Marchand v. Barnhill, 212 A.3d 805, 820–21 (Del. 2019); In re Massey Energy Co. Derivative & Class Action Litigation, 2011 WL 2176479, at *20–22 (Del. Ch. May 31, 2011); In re McDonald’s Corp. Stockholder Derivative Litigation, 289 A.3d 343, 359–61, 369–70 (Del. Ch. 2023). 

The boardroom point

AI malfeasance claims are no longer confined to the question whether a model copied books, music, code, images, or articles. The Elsevier complaint alleges a top-down AI-training strategy in which Zuckerberg, as founder, chairman, CEO, and controlling shareholder, had ultimate control over Llama development and allegedly authorized, directed, and participated in torrenting pirate collections after employees raised legal and ethical concerns. The SEIU complaint alleges that directors and officers allowed an unlawful AI business strategy, made or approved statements about commercially safe AI, ignored red flags from other AI copyright litigation, and exposed Adobe to litigation costs, stock-price decline, reputational harm, and corporate waste. Together, those pleadings suggest that plaintiffs should treat pirate-data acquisition as both an infringement issue and a governance issue. And both state and federal prosecutors should treat it as a potential RICO issue. 

The practical takeaway is simple: follow the data, but also follow the money. If a defendant merely downloaded pirate data, the case may remain principally a copyright, DMCA, and fiduciary-duty case. If discovery shows that the defendant or its agents paid for bulk pirate access, the evidence may sharpen willfulness, commercial-purpose, damages, knowledge, and governance theories. 

If discovery further shows that the payment was deceptively labeled or concealed through wires, invoices, cryptocurrency, credentials, procurement records, or vendor channels, plaintiffs may have reason to evaluate wire fraud as a potential RICO predicate, subject to all the other statutory requirements.

And even where wire fraud cannot be pleaded, the same payment trail may still be highly relevant to a shareholder derivative theory that corporate fiduciaries knowingly used corporate machinery to build AI products through unlawful data acquisition while representing to investors and customers that those AI products were trained lawfully, safely, and with licensed or rights-cleared data. 

Following the money achieves different ends to different plaintiffs. Copyright plaintiffs should use that trail to prove copying, distribution, willfulness, CMI removal, concealment, damages, and, where the evidence supports deception, continuity, enterprise participation, causation, and injury, potential wire-fraud and RICO theories. Stockholders should use the same trail differently: not to staple a derivative count onto a copyright-owner complaint, but to bring a separate governance action if the facts show that corporate fiduciaries used corporate machinery to acquire unlawful training data, ignored red flags, concealed or mislabeled the payments, or represented to investors and customers that AI products were trained lawfully, safely, and with licensed or rights-cleared data.

The point is not to turn every AI-training case into RICO or every copyright case into a derivative suit. The point is that pirate-data acquisition is no longer just a back-end engineering fact; when the data was bought, disguised, approved, ignored, or monetized at scale, it becomes a roadmap to intent, control, concealment, enterprise conduct, and boardroom accountability.

To paraphrase Deep Throat, forget the myths the media has created about Silicon Valley. The truth is these are not very bright guys and things got out of hand. Follow the money.