Category: AI Meltdown

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.

The AI Subsidy Is Over. Or Maybe It’s Just Beginning.


The current narrative says the “AI subsidy era” is ending. Prices are rising. Rate limits are tightening. Ads are creeping in. Enterprise tiers are replacing all-you-can-eat plans. In short: users will finally start paying what AI actually costs.

Haydon Field writing in The Verge tells us:

Earlier this month, millions of OpenClaw users woke up to a sweeping mandate: The viral AI agent tool, which this year took the worldwide tech industry by storm, had been severely restricted by Anthropic.

Anthropic, like other leading AI labs, was under immense pressure to lessen the strain on its systems and start turning a profit. So if the users wanted its Claude AI to power their popular agents, they’d have to start paying handsomely for the privilege.

“Our subscriptions weren’t built for the usage patterns of these third-party tools,” wrote Boris Cherny, head of Claude Code, on X. “We want to be intentional in managing our growth to continue to serve our customers sustainably long-term. This change is a step toward that.”

The announcement was a sign of the times. Investors have poured hundreds of billions of dollars into companies like OpenAI and Anthropic to help them scale and build out their compute. Now, they’re expecting returns. After years of offering cheap or totally free access to advanced AI systems, the bill is starting to come due — and downstream, users are beginning to feel the pinch.

That’s true but it’s leaving out a lot.

Yes, the consumer subsidy—venture-backed underpricing of inference—may be winding down. But the broader subsidy system that made AI possible isn’t going away. It’s expanding. Just ask President Trump.

To understand why, you have to go back to the last great digital disruption.

From P2P to Streaming to AI

Start with Napster.

P2P didn’t just enable infringement. It rewired expectations. It taught users that all music should be available, instantly, for free. Why? Because there was gold in them long tails. Forget about supply and demand, we had infinite supply so demand would take care of itself.

It’s for sale

Every artist, songwriter, label and publisher in the history of recorded music were not compensated for this shift. They were its involuntary financiers. Their catalogs created the demand, the network effects, and the user adoption that built the early internet music economy.

Streaming—think Spotify—didn’t reverse that logic. It formalized it. (Remember, streaming saved us from piracy and we should all be so grateful.) It actually transferred that involuntary financing from the p2p balance sheet to Spotify’s, and took it public.


Streaming platforms accepted a new baseline: the entire world’s repertoire must be available at all times, regardless of demand. That is a costly and structurally inefficient mandate, but it became the price of competing in a market shaped by P2P expectations. Licensing systems like the Mechanical Licensing Collective (MLC) were built to support that scale, but the underlying premise remained: total availability first, compensation second.

AI changes the game again.

AI Doesn’t Just Distribute Works. It Consumes Them.

P2P distributed music. Streaming licensed it. AI models ingest it.

That’s the critical difference.

Generative AI systems are trained on massive corpora that include copyrighted works, performances, and what we might call personhood signals—voice, style, tone, phrasing, and creative identity. These inputs are not just indexed or streamed. They are transmogrified (see what I did there) into model weights that can generate new outputs that compete with, mimic, or substitute for the originals.

So the role of the artist evolves:
    •    In P2P: unpaid distributor subsidy
    •    In streaming: underpaid inventory supplier
    •    In AI: uncompensated production input
That is not a marginal shift. It is a structural one.

The Real Subsidy Stack

When people say the “AI subsidy era is over,” they are usually talking about one thing: cheap access to compute.
But AI has always depended on a multi-layered subsidy stack:

    Creators – supply training data, cultural value, and identity signals without compensation or consent
    Users – supply prompts, feedback, and behavioral data that improve the models
    Communities – absorb land use, water consumption, and environmental costs
    Ratepayers – fund grid upgrades, transmission, and reliability for data center demand
    Venture capital – underwrites early losses to drive adoption and scale

The shift we are seeing now is not the end of subsidies. It’s a reallocation. Or as a cynic might say, it’s rearranging the deck chairs to hide the lifeboats.

Users may start paying more. But creators still aren’t being paid for training. Communities are still being asked to host infrastructure. And the physical footprint of AI is accelerating. Just ask President Trump.

The World Turned Upside Down

What makes this moment different is the scale of the buildout.
We are not just talking about apps anymore. We are talking about an industrial transformation:
    •    New data centers the size of small cities
    •    High-voltage transmission lines
    •    Water-intensive cooling systems
    •    Semiconductor supply chains
    •    And even discussions of new nuclear capacity to support compute demand

This is infrastructure on the scale of a national project, or more like national mobilization. But it is being built on top of a premise that has not been resolved: the uncompensated use of human creative work as training input.

That is the inversion: We are building power plants for systems that depend on not paying the people whose work makes those systems possible.

A Better Frame

The cleanest way to understand this is as a continuum:

P2P turned infringement into consumer expectation.
Streaming turned that expectation into platform infrastructure.
AI turns uncompensated authorship into industrial feedstock.

Or more bluntly:
The AI free ride is not ending. It is being re-invoiced. Users may now see higher prices. But the deeper subsidies—creative, environmental, and civic—remain off the books.

What Comes Next

If the industry is serious about “pricing AI correctly,” it cannot stop at compute.

It has to address:
    •    Compensation frameworks for training data
    •    Attribution and provenance standards
    •    Licensing models for style and voice
    •    Infrastructure cost allocation (who pays for the grid?)
    •    Governance of large-scale compute deployment

Otherwise, we are not exiting the subsidy era. We are doing what Big Tech lives for.

We are scaling it.

And this time, instead of a few server racks in a dorm room, we are building an global energy system around it.

The SXSW–PwC Report Is Polished—But It’s Still a Conference Echo Chamber of an Echo Chamber

The 2026 SXSW–PwC Insights Report is well-written, highly readable, and professionally assembled with lots of graphics. It succeeds at what it sets out to do: synthesize themes from a sprawling, interdisciplinary conference into something digestible for executives and strategists.

But it is important to be clear about what this document actually is—and what it is not.

It is not a study.
It is not an empirical analysis.
And it is certainly not the product of anything resembling peer review.

It is a reflection of conference discourse. And lunches. But at least they don’t mention “because China.”

The missing story: creators and labor

Perhaps the most notable gap—particularly given SXSW’s cultural footprint as a music festival that never paid a musician it couldn’t stiff—is the absence of a meaningful discussion of creators and labor.

Adding insult to injury, the report’s most conspicuous nod to the music business that spawned SXSW is in the report section titled “Replay vs. Breakout Hit,” a cute music metaphor for what is essentially a self-grading exercise. Why are we not surprised. For a conference rooted in the labor and culture of musicians, the report has remarkably little to say about musicians as workers or rights-holders. Or at all. It reads a bit like those tech offices that name their conference rooms after artists while inside those rooms people figure out how to disintermediate, devalue, or extract from the artists themselves. Not mentioning names but their initials are Google.

Technology throughout the report is framed as expanding capability, but not as transferring wealth.

There is little engagement with:
– whether creators are compensated or displaced
– how value flows through AI systems
– the asymmetry between platforms and individuals

This is not a minor omission. It goes to the core of whether the trends being described are sustainable—or extractive.

The “Replay vs. Breakout Hit” page is less a retrospective than a self-grading exercise. It does not test last year’s insights against events or outcomes. It simply shows that if you keep attending the same conference circuit, you can usually hear enough of the same themes to call your prior buzzwords validated.

SXSW sits at the intersection of music, film, and technology. If a report emerging from that environment cannot meaningfully account for creators, it is not just incomplete—it is asking the wrong question.

Start with the source: SXSW is not a neutral environment

The report is based on PwC’s attendance at more than 100 SXSW sessions and conversations with “thought leaders.” That sounds comprehensive, but it also tells you everything you need to know about the limits of the exercise. And that’s a whole lot of lunches.

SXSW—like TED and similar marquee events—is not designed to test ideas. It is designed to showcase them.

Panels are curated. Speakers are selected. Topics are framed in advance. And most importantly, participants are there for a reason: to promote something. A company. A framework. A product. A worldview. And oh, yes. A band.

That doesn’t make the content worthless. But it does mean the incentives are not aligned with truth-seeking.

They are aligned with visibility.

When panels become pitch environments

In practice, this structure often produces what anyone who has spent time in these rooms recognizes immediately: panels that function less as discussions and more as coordinated signaling exercises.

Especially in the tech space, you frequently see:
– Panelists advancing aligned narratives about “inevitable” technological change
– Framing that assumes adoption rather than interrogates the wisdom of adoption
– Soft, non-adversarial questioning that avoids meaningful challenge

And yes, there have long been instances where the “moderator” is not a neutral facilitator at all, but an industry advocate or policy lobbyist shaping the conversation, sometimes with only a token dissenting voice on stage who wasn’t in on the joke and looked confused.

The result is not debate. It is choreography.

Narrative momentum is not economic reality

SXSW is a narrative marketplace. It is very good at surfacing what people are excited about. But more precisely, SXSW is very good at surfacing what people with funding are excited about—which is usually themselves. And also their products and the narratives that make both more valuable. It is also a place where the ability to show up is itself a form of signaling—funding is not just the topic, it is the price of admission. Did I say “themselves”?

That framing matters because it explains why the output looks the way it does. The report is not simply identifying trends—it is reflecting a filtered environment in which access, funding, investment capital, and narrative are tightly intertwined.

The report expands and echoes those incentives like a meta-leave behind pitch sheet.

The SXSW–PwC report does not correct for this dynamic—it amplifies it.

By design, the report takes curated panels featuring self-selected speakers operating in a self-promotional environment
and distills them into “insights” for business leaders.

That is a closed loop.

What emerges is not independent analysis, but a refined version of the same narratives that were already being performed on stage—particularly around AI, innovation, and organizational transformation. Like every other tech-influenced conference.

The AI story: all acceleration, limited friction

Unsurprisingly, AI dominates the report.

The framing is familiar:
– AI as a creative amplifier
– AI as a competitive necessity
– AI as an organizational transformation layer

What is much less developed are the counterweights:
– Substitution effects (especially in creative labor markets)
– Market dilution and “flooding” dynamics
– Legal and regulatory constraints (copyright, privacy, liability)
– The question of who actually captures the value created

Instead, AI is largely treated as a capability problem: How quickly can organizations adopt and deploy? Thinking that leads to statements like this:

Complex stories underperform, while reactive, emotionally charged content thrives—and bad actors reverse-engineer those dynamics to move from the margins to the mainstream. Compounding the problem, under-resourced newsrooms are losing experienced journalists needed to maintain editorial standards, leaving the information vacuum to be filled by algorithmically optimized noise.

Yes, experienced journalists are just up and leaving, wowza. What’s the world coming to? Any interest in connecting some dots there, PwC lunchers?

Not only does the report not even dig an inch deep into any issue involving labor, or question the bargaining leverage that AI confers on employers much less ask who benefits, who loses, and under what terms?

“Act now or fall behind” is not analysis. Like many consulting-adjacent outputs, the report leans heavily on urgency. But these claims are not tied to measurable benchmarks or falsifiable outcomes.

One More Thing

The real issue with reports like this is not that they are wrong.

It is that they are produced within an environment where skepticism is disincentivized and narratives are shaped before the conversation even begins.

The SXSW–PwC report captures that environment faithfully. But it does not escape it.

And in that sense, it perfectly illustrates why you don’t turn to a firm like PwC to analyze creators—they’re looking through the wrong lens from the start. The report shows little evidence that anyone with direct experience representing creators was meaningfully involved in reviewing it.

To be clear, this is not inherently a flaw. SXSW has hosted genuinely thoughtful and introspective panels, alongside plenty of circular admiration society panels as well. But no one has ever suggested that polling those panels would produce anything resembling decision-maker work product. And, to be fair, bravo to the PwC employees who managed to get their trip expensed to talk their book. That’s the true spirit of SXSW.