The AI Bubble Is Much Worse Than They Think: Remembering the Last War

The trouble with calling speculative bubbles a feature of capitalism is that everyone remembers the survivors. Nobody remembers how bad the last war really was.

I’m a fan of Ed Dowd and read him pretty regularly. He makes an interesting argument in his recent Substack essay, “Investment Bubbles Are a Feature of Capitalism, Not a Bug“. His proposition is familiar: speculative booms finance infrastructure that survives the inevitable financial collapse. Railroads, electrical grids, and dot-bomb era fiber-optic networks were all overbuilt during speculative manias. Investors lost fortunes, companies went bankrupt, but society eventually benefited from the infrastructure left behind.

There is considerable historical truth to this argument. Unfortunately, I think it substantially understates the problem with the artificial intelligence bubble. Not because Dowd is wrong about speculative finance, but because this time the consequences may extend far beyond the investors who made the bets.

I say that as a veteran of the dot-bomb collapse. I was there for the last war. And one thing I’ve learned is that, with the passage of time, everyone remembers the victories, the heroic survivors, and the technological progress. Very few remember how bad the war actually was. And who went to prison or should have.

It’s easy to look back at the dot-com bubble and see Amazon, Google, and the great expansion of the Internet (not to mention Salesforce, PayPal and Nvidia itself). It’s harder to remember the rabid fear of missing out, the many, many businesses that disappeared, the employees laid off, the careers interrupted, the savings wiped out, the layoffs, the bankruptcies, and the communities that experienced the collapse as something considerably less abstract than creative destruction.

The historical narrative makes the winners inevitable and the losers incidental. They weren’t incidental at the time and the only thing that seemed inevitable was the financial wipeout.

The Survivors Write the History

The comforting story about the dot-com crash is that companies built too much fiber-optic infrastructure, the telecommunications companies went broke, Socks.com was a joke, and the next generation acquired the assets at bargain prices. Everyone eventually got cheaper bandwidth, and the Internet flourished. There is truth in that story, too. But it compresses years of financial destruction into a happy ending. The market may sort itself out in the long run, but it’s also true that in the long run we’re all dead.

The NASDAQ Composite fell approximately 78% from its March 2000 peak to its October 2002 trough. Companies evaporated. Investment capital disappeared. Workers who had been told they were participating in an inevitable technological revolution discovered that inevitability did not extend to their employment or stock options. Some investors could afford to wait for the next cycle. Others could not.

Dowd doesn’t deny that destruction. In fact, he describes it. What I question is his conclusion that the surviving infrastructure demonstrates that the process was ultimately a net positive. That conclusion requires accounting for the losses as well as the gains.

The important question is not whether the Internet ultimately succeeded which I guess it did if you weren’t in the music business. The question is whether that eventual success justified every speculative investment made in its name, or excused the damage caused along the way.

I suggest that it did not. And that brings us to AI. Let’s leave aside the sector-specific existential harm. The internet came for the music business—AI is coming for musicians. But there’s a lot more to worry about this time.

This Time We’re Not Just Rolling Up Dark Fiber

Dowd’s historical analogy becomes much less reassuring when we look at the physical characteristics of the assets being created. Fiber-optic cable is relatively durable infrastructure that powers the Internet. Railroad rights-of-way can have value for generations. Electric transmission systems can support a wide variety of economic activities. But artificial intelligence data centers are something else.

Data centers depend on extraordinarily expensive computing equipment that depreciates rapidly and may become economically obsolete long before the buildings housing it deteriorate. Their cooling systems, electrical configurations, and operating requirements can be highly specialized. Their profitability depends not merely on the existence of demand for computing, but on whether that demand will support the continuing expense of providing it.

Buying a bankrupt railroad at twenty cents on the dollar is one proposition. Railroad tracks can be upgraded. Buying a bankrupt AI data center filled with yesterday’s chips, whose replacement costs may exceed the market value of the enterprise, is quite another. Some of the associated infrastructure will certainly retain value. Transmission lines, substations, and buildings may find alternative uses. But we cannot simply assume that every billion dollars invested in AI infrastructure becomes a permanent billion-dollar contribution to society.

The distinction is between infrastructure that becomes cheaper after a financial collapse and infrastructure that becomes obsolete. Those are not the same thing. I don’t see a conga line of buyers for obsolete chips.

Railroads Didn’t Destroy Aquifers

There is another problem with Dowd’s historical analogy that deserves special attention. Railroads, electrical grids, and fiber-optic networks certainly imposed environmental costs. Railroads transformed landscapes, disrupted ecosystems, and facilitated resource extraction. Electric generation produced enormous pollution. These were hardly environmentally benign enterprises.

But the infrastructure itself did not ordinarily depend on consuming vast quantities of local groundwater to remain operational. Neither did they build a private electrical generator system or behind-the-meter nuclear power. A bankrupt railroad could leave behind tracks that another operator might use. A bankrupt telecommunications company could leave behind fiber-optic cable that continued carrying traffic for years.

An AI data center, especially in a water-stressed region, presents a different proposition altogether. Its operation may depend on substantial, continuing water consumption, whether directly through cooling or indirectly through electricity generation. If that demand contributes to depletion of an aquifer, the environmental loss may persist long after the data center itself becomes obsolete and the investors have evaporated.

In many rural regions, groundwater depletion can contribute to land subsidence, soil degradation, and cascading and broader ecological damage. Where combined with drought, vegetation loss, and unsustainable land use, it may also contribute to desertification.

Not every data center consumes enormous amounts of local groundwater. Cooling technology, water sourcing, climate, and local hydrology matter. But the possibility of irreversible damage ought to be accounted for before construction, not merely acknowledged after the financing collapses. God told Noah to build the Ark before the rain.

You cannot buy a depleted aquifer out of bankruptcy at twenty cents on the dollar and assume the water will return.

And that is the fundamental accounting error in the economically rational story about infrastructure bubbles. Dowd counts the value of the railroad tracks, the generating stations, and the fiber-optic cables that survived the speculative crash. We also need to account for the natural resources consumed, the landscapes transformed, and the public liabilities left behind? The prospect of further public support if private capital retreats is not hypothetical policy territory; it is a risk worth examining before commitments become irreversible.

The comparison becomes even more troubling when the useful life of the computing equipment may be measured in years while the consequences of groundwater depletion may persist for generations. The railroad tracks survived the railroad bubble. Will the aquifers survive the AI bubble? That is a considerably more serious question than whether investors will eventually find bargains among the wreckage.

The Land, the Water, and the Electricity Don’t Go Bankrupt

The aquifer problem illustrates a larger weakness in Dowd’s analysis. Financial capital is not the only resource being consumed. Across the country, communities are being asked to accommodate immense data-center campuses, new generating capacity, transmission corridors, substations, water infrastructure, and associated industrial development. And indirectly, for now, bear the cost of these negative externalities.

Some projects require extraordinary amounts of electricity. Others threaten to transform agricultural land, rural landscapes, and communities whose residents never imagined they would become industrial centers serving the AI economy. Much less find their family homes taken by eminent domain to build power plants, transmission lines, and substations.

The AI investors can measure their exposure in dollars and securities. The communities may measure theirs in acres, gallons, utility bills, and lost opportunities to use their land and resources for other purposes. These are different kinds of risk.

If an AI developer goes bankrupt, the investors may write down the value of the enterprise. The local aquifer does not receive a corresponding write-up. The farmland does not automatically return to agricultural production or the original family owners. The transmission corridor does not disappear. And the surrounding community does not recover the years spent fighting over its future.

Financial losses can be discharged or restructured in bankruptcy. Environmental damage and community disruption often cannot. The problem with using the history of infrastructure bubbles to rationalize today’s AI buildout is that it assumes the surviving infrastructure represents a residual benefit without fully examining what was consumed to create it.

A financial bubble can destroy financial capital. An infrastructure bubble can also destroy natural capital. One is fungible and can be replaced. One is not fungible and cannot be replaced. And unlike a distressed asset, depleted natural capital may not be available for purchase, refinancing, or reuse. Nature may not give us a second act.

The Bailout Has Already Begun

There is another feature of the AI bubble that makes the comparison to earlier infrastructure booms particularly questionable. The public is already helping finance it whether they realize it or not.

Across the country, state and local governments are granting data-center developers sales-tax exemptions, property-tax abatements, infrastructure incentives, and other subsidies. According to the National Conference of State Legislatures, 38 states offer dedicated data-center tax incentives. Many beneficiaries are among the wealthiest and best-capitalized corporations in history. These are not struggling entrepreneurs who cannot obtain financing for a promising new invention. They are companies spending extraordinary sums on infrastructure they insist is indispensable to their future profitability. And their biggest allies include the President of the United States and the World Economic Forum. These are not plucky little startups.

If the investments are so compelling, why must taxpayers subsidize them?

The answer is not self-evident. Yet the public is already surrendering tax revenue to support a speculative investment cycle whose ultimate economics remain uncertain. Call these incentives economic development if you like. From the taxpayer’s perspective, they are a subsidy today, not a hypothetical bailout tomorrow.

And that brings us to the increasingly circular character of AI financing.

Major technology companies invest in AI developers that purchase their computing services. Chipmakers like Nvidia provide financing and guarantees for projects that buy their chips. Cloud providers commit capital to customers that commit to buying cloud capacity. Nvidia’s own securities filings disclose substantial guarantees supporting AI infrastructure leases, illustrating how suppliers, customers, and financiers can become financially interdependent. Each transaction may have a legitimate commercial explanation. Collectively, however, they raise an uncomfortable question: How much of the AI industry’s apparent demand is being financed by the same companies that benefit from supplying it? Where is the ROI?

The distinction matters because customer financing is not the same thing as independent customer demand. A company can sustain enormous spending commitments while investors remain willing to supply fresh capital. But eventually the enterprise must generate enough revenue from customers outside the financing circle to justify the investment.

What happens when the new money slows down? Not that it’s a ponzi scheme or anything.

The developers may still owe billions under computing contracts. The lenders may still hold debt secured by rapidly depreciating equipment. The utilities may already have built transmission and generating infrastructure. And the communities may already have surrendered tax revenue, groundwater, farmland, and control over their surroundings.

At that point, the “Because China” argument for further public assistance practically writes itself: these facilities are essential to national competitiveness, the electrical infrastructure has already been built, the jobs must be protected, and allowing the industry to collapse would threaten the broader economy.

That pressure from Washington DC does not conclusively establish that another TARP-style bailout is inevitable. It establishes that the circumstances in which one might be demanded are foreseeable and are already on the horizon.

The taxpayer is already subsidizing the boom. The danger is that the taxpayer will also be asked to finance the bust. And unlike the venture capitalists, the taxpayers never agreed to make the investment. And most of them wouldn’t want more AI if anyone asked.

Who Gets the Bill?

Those existing subsidies are only the first layer of public exposure. The second is the risk that utilities and governments will be left supporting infrastructure built for demand that never fully materializes. The AI buildout increasingly depends on institutions that extend well beyond the private investors making speculative bets.

Consider an electric utility that expands its transmission and generation resources to accommodate anticipated data-center demand. If the demand materializes and the customer pays its full share, the investment may be justified. But what happens if the developer disappears, the project is canceled, the promised electrical load never materializes, or technological improvements make the facility uneconomic?

Who pays for the infrastructure? The answer depends on utility contracts, regulatory treatment, and financial guarantees. But absent adequate protections, some costs may be shifted to ordinary ratepayers. People who never invested a dime in artificial intelligence may find themselves paying for an unsuccessful artificial intelligence investment through their monthly electric bills or a taxpayer bailout.

That is not creative destruction. That is the socialization of private speculative risk. Similar questions arise with tax abatements, public infrastructure improvements, water-system expansions, and other incentives offered to attract developments promising extraordinary economic benefits. And there is a particularly troubling possibility: communities may end up paying for infrastructure that no longer has the economic purpose used to justify its construction.

A railroad might be reorganized and continue carrying freight. A transmission line built primarily to serve an anticipated AI load could become an expensive stranded asset if the demand never arrives. The investors may be gone. The ratepayers will still be there. I give you Amtrak.

The New Gold Rush Is Taking Place in Someone Else’s Backyard

One striking characteristic of the data-center expansion is the increasingly organized resistance from ordinary people. In Texas, Georgia, Virginia, and elsewhere, communities are confronting proposals that could permanently change the character of the places where they live. Their objections are not necessarily objections to technological progress. They are often objections to who gets to make the decisions, who receives the benefits, and who bears the costs.

A developer can describe a 900-megawatt data center as an investment in the future. The people living nearby may see an enormous industrial load competing for electricity and water, accompanied by transmission lines, altered landscapes, uncertain employment benefits, and possible increases in their utility bills. Both descriptions may refer to the same project. But only one necessarily appears in the investor presentation.

There is a fundamental difference between financing speculative infrastructure with voluntary investment capital and requiring a community to absorb the environmental and economic consequences of that speculation. The residents are not venture capitalists. They did not buy shares in an AI company. They were not promised equity in exchange for the use of their groundwater, their electrical infrastructure, or their community’s future.

And they should not be treated as involuntary guarantors of the industry’s business model.

The Economics of Obsolescence

Dowd correctly identifies the threat that lower-cost AI models, increasing competition, and changes in the economics of computing pose to current investment assumptions. But this observation also undermines the conclusion he draws from the historical infrastructure cycle. As AI models become dramatically more efficient, today’s infrastructure may well prove unnecessary or uneconomic very quickly.

Certainly, cheaper computing could stimulate additional demand. That possibility should not be dismissed. But neither should the opposite possibility: technological progress may undermine the value of facilities whose investors assumed that computing requirements would continue growing along a particular trajectory.

A railroad track laid in 1873 might remain useful in 1923. A data center designed around a particular generation of GPUs may become economically obsolete in a fraction of that time. But the data center industry tells communities that these projects are long-term investments. The industry’s own research anticipates major infrastructure renovations within the first decade and continual replacement of computing equipment. The question is not simply whether a data center becomes obsolete. It is who pays to keep it from becoming obsolete—and who bears the “stranded asset” costs if the economics stop working.

Research from Microsoft published in 2026 acknowledges that successive generations of AI hardware demand greater electrical power and more demanding cooling systems, potentially making existing data center infrastructure obsolete. Modern high-density GPU racks can produce four to eight times the heat of conventional CPU systems. (Rearchitecting the Datacenter Lifecycle for AI: A TCO-Driven Framework (https://arxiv.org/abs/2509.26534), Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri, and Ricardo Bianchini, Microsoft Research, ISCA 2026, June 2026.). This is not merely a matter of replacing servers. It can require substantial reconstruction of electrical distribution, cooling systems, and physical facilities.

Industry reporting (https://www.datacenterknowledge.com/data-center-construction/ai-demands-stretch-the-limits-of-data-center-retrofits) confirms that existing data centers are already encountering these limits. Facilities designed for conventional computing may be unable to accommodate the power density, liquid cooling, and structural loads of newer AI systems without extensive renovation. Some can be economically retrofitted. Others may be unsuitable for the next generation of hardware.

And what happens when an operating data center finally closes? An August 2026 analysis from Columbia Law School’s Sabin Center for Climate Change Law warns that abandoned hyperscale facilities may be exceptionally difficult to repurpose.(https://blogs.law.columbia.edu/climatechange/2026/08/06/decommissioning-data-centers-avoiding-stranded-assets/) Their enormous size, specialized infrastructure, electrical equipment, and environmental cleanup requirements distinguish them from ordinary commercial buildings. The analysis recommends mandatory decommissioning plans and financial assurance mechanisms, including bonds, to prevent communities from inheriting the cleanup bill. Some Pennsylvania jurisdictions have already adopted such requirements, including provisions for decommissioning and financial security.

There is no reliable national count of data centers already rendered economically obsolete by AI. That absence of data should not be confused with an absence of risk. The evidence instead reveals an industry facing rapid technological change, recurring capital requirements, uncertain future demand, and substantial public infrastructure exposure. Before granting tax abatements, expanding transmission capacity, or committing scarce water resources, communities should demand answers to four questions: How long will this particular facility remain economically competitive? Who pays for its upgrades? Who pays if the developer leaves? And who guarantees that the land and infrastructure will be restored if the project fails?

A server can be replaced. An aquifer cannot necessarily be restored. And a community should not be expected to underwrite the consequences of technological obsolescence while the developer keeps the upside. More importantly, the environmental consequences of building and operating that facility may substantially outlast the useful life of its equipment. This is the uncomfortable asymmetry at the center of the AI investment bubble.

The technology may become obsolete quickly. The environmental consequences may not. There is no reason to assume that the next generation of AI companies will need precisely the gigantic facilities being built today, or that the residual value of those facilities will justify the resources consumed and liabilities incurred in their construction. That would be the first time that evolving technologies got bigger rather than smaller. The fact that some entrepreneurs may eventually purchase distressed AI infrastructure at bargain prices does not establish that society received such a bargain.

The Cost to Human Creativity

Unlike the earlier infrastructure booms, generative AI is being promoted not simply as a means of increasing human productivity but also as a substitute for human work. In the music business, the distinction is especially apparent. Companies developing generative AI systems have used enormous quantities of copyrighted works in circumstances that have generated substantial disputes over authorization, compensation, and the rights of the people whose creative labor supplied the underlying material.

The industry’s investment proposition squarely depends on capturing the value of human creativity while reducing the cost of obtaining it. That introduces another kind of potentially irreversible consequence. An artist whose livelihood is displaced by a speculative technology cannot necessarily recover the lost opportunities when the company deploying it goes bankrupt. A songwriter whose works have been used without authorization does not automatically receive compensation because an AI developer’s assets are purchased by another company. And a creative profession weakened by years of uncompensated technological substitution may not spontaneously regenerate after the market corrects.

The infrastructure bubble may leave behind computing facilities. But what happens to the human infrastructure—the trained musicians, writers, performers, and other creators whose work helped make the technology possible? That question is missing from the traditional story about technological progress emerging from financial wreckage.

Everyone Forgets How Bad the Last War Really Was

Perhaps the most troubling feature of Dowd’s argument is not his economic historical premise, but the reassurance that some may draw from it. Yes, capitalism has repeatedly financed useful infrastructure through speculative excess. Yes, investors have repeatedly lost enormous amounts of money. And yes, subsequent generations have sometimes benefited from the wreckage.

But history is not an accounting exercise in which eventual benefits automatically erase earlier losses. As they say, in the long run we are all dead. The dot-com collapse was not an amusing intermission between the invention of the Internet and the arrival of AI. It was a devastating economic event for many people who had invested their money, careers, and confidence in what they were told was the future. You know, because China. I remember.

And unlike the dot-com boom, the AI infrastructure boom is reaching deeply into the physical environment, the electric grid, water resources, local government finances, and the livelihoods of people who never volunteered to participate. When the speculative capital disappears, those obligations and consequences may remain. That makes the stakes considerably higher than an investor’s return on capital. I’m going out on a limb here, but my bet is that the Sam Altman’s of this world could care less who loses money or has their lives destroyed. He clearly could care less about musicians and authors, I have no doubt about that part.

Dowd may be right that speculative bubbles are a recurring feature of capitalism. But that observation should lead us to a very different conclusion. If bubbles are a predictable feature of capitalism, then protecting the public from their predictable consequences ought to be an equally predictable feature of public policy. Which it most definitely is not. We should not wait for the crash to discover whether adequate safeguards were in place.

We should insist now on enforceable protections against stranded asset utility costs, meaningful financial assurances, responsible water use, environmental restoration obligations, and independent scrutiny of the economic claims used to justify these enormous projects. And we should stop assuming that the eventual survival of a technology necessarily vindicates the financial and political decisions made in its name.

The next generation may inherit valuable infrastructure from today’s artificial intelligence boom. It may also inherit obsolete equipment, depleted aquifers, stranded electrical assets, disrupted labor markets, and communities left to repair damage that never appeared on the investors’ balance sheets. Both outcomes are possible. Both belong in the analysis.

Those of us who lived through the dot-bomb years know that the history of a speculative boom looks very different from the other side of the crash. Everyone remembers who won the last war. Too few remember what the war cost.

The railroad tracks survived the railroad bubble. Will the aquifers survive the AI bubble? Will the musicians?

The AI Capex Party May Be Nearing Last Call

For the past two years, Wall Street has treated artificial intelligence as a one-way trade. Hyperscalers, semiconductor companies, utilities, private-credit funds, and data-center developers have committed hundreds of billions of dollars to what may ultimately become nearly $1 trillion in AI-related infrastructure investment over roughly two years.

The underlying assumption has been remarkably consistent: demand for increasingly powerful AI models will continue growing fast enough to justify unprecedented spending on chips, data centers, transmission lines, substations, and electric generation.

But investment booms rarely end because one assumption proves wrong. They end when several assumptions begin to weaken at the same time.

That appears to be happening.

Ed Dowd’s recent Substack analysis argues that the economics supporting today’s AI buildout are becoming increasingly fragile. Financing is tightening. Enterprise customers are demanding clearer returns on investment. Open-weight models continue improving while driving prices lower. And perhaps most importantly, the physical infrastructure required to support AI is becoming a political issue.

Gary Marcus recently challenged David Sacks’ argument that regulation is the principal threat to American AI leadership (Sacks really needs some new sheet music). Marcus instead argued that the industry faces a far more fundamental economic problem:

“The real issue is that LLMs are commodities; lots of people know how to make them, and everybody is doing more or less the same thing, training on more or less the same data. That means nobody has a technical moat. Which means you get price wars and low margins and more and more competitors over time.”

If Marcus is right, Wall Street may eventually discover that AI resembles cloud computing more than pharmaceuticals. There may be tremendous demand—but not necessarily extraordinary profits. That observation dovetails with Goldman Sachs’ increasingly cautious assessment of the AI investment cycle. Goldman has repeatedly warned investors that the buildout depends on continued access to capital, sustained enterprise demand, adequate electric power, and enough economically valuable use cases to justify unprecedented capital expenditures.

AI does not exist in ‘the cloud.’ It exists on electric grids. Every new model depends on substations, transmission lines, transformers, cooling systems, water supplies, and local political consent.

For months, we’ve tracked what has become a genuine data center backlash. Communities across Texas, Georgia, Louisiana, Virginia, Oklahoma, Utah, Alabama, and elsewhere are increasingly questioning the costs of hosting massive AI infrastructure.

Politicians, meanwhile, are discovering that AI infrastructure is much easier to announce than it is to build. Many governors and local officials have promoted data centers by assuring taxpayers that the projects will ‘pay their own way.’ But that message begins to unravel the moment the infrastructure breaks ground or annexes farmland.

A homeowner facing a 765-kV transmission line across family property is unlikely to be persuaded that the project is privately financed. Likewise, a rancher confronting eminent domain does not care whether the transmission costs appear on a utility bill, a corporate balance sheet, or a tax-abatement agreement. The injury is the same: the family home, ranch, or farm is permanently altered to support infrastructure serving distant customers—who are often anonymous.

In the Texas Hill Country, landowners have mobilized against new transmission corridors intended to serve future electric demand, including AI-related growth. In Coweta County, Georgia, residents organized after learning that transmission infrastructure associated with large-scale data-center development could cut through long-held family properties. The debate quickly ceased being about economics and became about land, community, and the limits of eminent domain.

Petition update! pic.twitter.com/YQO8RVPbKF
— Ansley Brown (@Ansleysgarden) July 22, 2026

This is where many elected officials have found themselves trying to have it both ways. They assure taxpayers that private investment will shoulder the costs while simultaneously offering substantial tax abatements, infrastructure incentives, expedited permitting, and other forms of public support. Then, when opposition emerges, they discover that the political issue is no longer who pays for the infrastructure—it’s who lives with it.

For families whose property lies in the path of a transmission corridor, ‘the data centers will pay for themselves’ is not an answer. Their concern is not the financing model. Their concern is keeping the home that has been in the family for generations.

None of this means AI is a passing fad. Transformative technologies often survive speculative bubbles. The internet certainly did. But many companies that financed the dot-com boom did not survive intact, and many investors paid dearly for assuming that technological transformation automatically translated into sustainable profits.

Today’s AI investment cycle rests on multiple pillars: inexpensive capital, robust enterprise demand, premium pricing, abundant electricity, and political support for rapid infrastructure expansion. Gary Marcus questions the durability of the competitive moat. Goldman Sachs questions whether the economics can support the investment. Communities across America are questioning whether they should bear the physical burdens.

Those three conversations are converging. The story is no longer simply about faster models or larger training runs. It is about economics, infrastructure, and public acceptance. The market has spent the last two years pricing AI as though all three will remain aligned indefinitely. History suggests that is a very demanding assumption.

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-Suno Download

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:

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.
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.
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.
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.
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.
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?

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,

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.

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.

AI, Soft Power, and the New Thucydides Trap

The White House’s latest AI framework reads like a familiar story dressed in new clothes: we must move fast, avoid “overregulation,” and ensure that the United States “wins” the AI race—because China.

That framing is not new. It is, in fact, a modern version of the Thucydides Trap: the idea that when a rising power threatens to displace an established one, conflict—economic, political, or otherwise—becomes more likely. But what is striking here is not the invocation of competition. It’s how narrowly that competition is defined.

The framework implicitly treats AI dominance as a function of compute, capital, and model scale. Build bigger models faster, feed them more data, and ensure that domestic firms face as few constraints as possible. In that telling, creators, rights, and consent become secondary considerations—at best friction, at worst obstacles.

But that is a profound misread of where U.S. advantage actually lies.

American leadership has never been just about scale. It has been about legitimacy—the ability to build systems that other countries, companies, and individuals trust enough to adopt. That is the essence of soft power. And soft power is not generated by extraction; it is generated by rules that are perceived as fair.

When U.S. policy signals that training on creative works without meaningful consent is acceptable—or even necessary to “win”—it risks trading long-term legitimacy for short-term acceleration. That is a dangerous bargain. It tells the world that American AI leadership is built not on innovation alone, but on the uncompensated appropriation of global cultural and informational resources.

Other jurisdictions are already responding. The EU is experimenting with transparency mandates. Rights holders globally are pushing for enforceable consent regimes. Even countries that want to encourage AI development are increasingly wary of frameworks that look like data extraction at scale without accountability.

This is where the Thucydides analogy breaks down—or at least becomes more complicated. The real risk is not simply that China catches up technologically. It is that the United States, in trying to outrun that possibility, undermines the normative foundations of its own leadership.

Soft power erosion is not dramatic. It doesn’t announce itself with a headline. It accumulates quietly: in trade negotiations, in regulatory divergence, in the willingness of other countries to align—or not align—with U.S. standards. Over time, that erosion can matter more than any benchmark score or model release.

There is another path. The United States could lead by insisting that AI development is compatible with consent, compensation, and provenance. It could treat creators not as inputs to be harvested, but as stakeholders in a system that depends on their work. It could build infrastructure—technical and legal—that makes those principles operational, not aspirational.

That approach may look slower in the short term. It may impose costs that competitors are willing to ignore. But it is also how durable leadership is built.

Because in the long run, the question is not just who builds the most powerful models. It is who builds systems that the rest of the world is willing to trust.

And that is a competition the United States cannot afford to lose.

Update: Trump Floats “Ratepayer Protection” Pledges as Grassroots Revolt Over Data Centers Spreads

For the better part of a year, local opposition to AI hyperscaler data centers has been dismissed as NIMBYism—yet it is a movement that has gained real traction. Rural counties worried about water draw. Suburban communities objecting to diesel backup generators. Landowners frustrated over transmission corridors cutting through farmland and massive data centers removing large swaths of productive land in essentially irreversible dedication to AI.

Local politics around data-center construction often turn on land use, water, and power. Officials welcome tax base and jobs, but residents worry about noise, transmission lines, diesel backup generators, and groundwater consumption. Zoning boards and county commissioners become battlegrounds where developers promise infrastructure upgrades and community benefits while opponents push for setbacks, environmental review, and limits on incentives. Utilities and grid operators weigh reliability and cost shifting, especially where hyperscale demand requires new substations or high-voltage lines. Rural areas face pressure from land aggregation and fast-track permitting, while cities debate transparency, property-tax abatements, and whether long-term public costs outweigh near-term economic gains.

But the politics just escalated.

According to multiple reports, President Trump is preparing to highlight “ratepayer protection pledges” from major tech companies during his State of the Union address tonight — urging AI and cloud companies to publicly commit that residential electricity customers will not bear the cost of new data-center load.

That confirms concerns from Trump advisor Peter Navarro over the last couple months and is not a small signal.

For months, grassroots organizers have warned that hyperscale AI buildout could increase local electricity rates, force costly new transmission lines, accelerate natural gas plant approvals, and strain already fragile regional grids. And then there’s the nuclear issues as hyperscalers openly promote new nuclear plants. Until now, much of the policy conversation has centered on growth and competitiveness, you know, because China. The Trump pivot reframes the issue around consumer protection — closely tracking the concerns raised by grassroots opponents.

What the White House Is Signaling

The reported approach stops short of imposing a formal price cap on electricity or shifting costs to taxpayers. Instead, policymakers are signaling that large technology firms — particularly hyperscale operators — should voluntarily shoulder the marginal power costs created by their own demand growth.

In practice, this means encouraging companies such as Microsoft, Alphabet, Amazon, and OpenAI to fund grid upgrades, transmission extensions, standby generation, and other infrastructure required to serve new data-center loads, rather than socializing those costs across ordinary ratepayers. The political logic is straightforward: if hyperscale demand is driving billions in new utility investment, the beneficiaries should internalize the expense. The strategy relies on negotiated commitments, public-utility leverage, and reputational pressure rather than mandates, aiming to avoid rate shocks while still enabling continued digital-infrastructure expansion.

We’ll see.

In parallel, the administration has backed efforts to expand electricity supply in regions experiencing sharp data-center load growth, pairing political support with regulatory acceleration. In practice, this has meant encouraging grid operators to run emergency or supplemental capacity auctions—for example, in markets like PJM or ERCOT—to secure short-lead-time generation such as gas peaker plants, temporary turbines, and large-scale battery storage. Policymakers have also supported fast-track permitting and uprates at existing nuclear and natural-gas facilities, along with expedited approvals for new combined-cycle plants where reliability risks are rising. In some areas, utilities are advancing transmission expansions and demand-response programs to bridge near-term gaps. The goal is to bring firm capacity online quickly enough to keep pace with AI-driven electricity demand without triggering reliability shortfalls or price spikes.

Supposedly, Trump’s message is if data centers drive the demand spike, data centers should fund the solution. That makes sense, but count me as a skeptic as to whether this will actually happen, or whether hyperscalers will come to the taxpayer. You know, because China. But let’s sell China Nvidia chips.

Why This Matters for the Grassroots Fight

Grassroots opposition to large-scale data centers has crystallized around three increasingly defined pillars — each with its own constituency and political leverage.

1. Land Use and Community Character.
Residents object to the scale and industrial footprint of hyperscale campuses: multi-building complexes, 24/7 lighting, diesel backup generators, high-security fencing, and new high-voltage transmission corridors. In rural counties, projects can involve the quiet aggregation of farmland followed by rezoning from agricultural to industrial use. In suburban areas, neighbors focus on setbacks, noise from cooling systems, and visual impact. Planning and zoning hearings have become flashpoints where local control collides with state-level economic development priorities.

2. Environmental and Water Stress.
Data centers are energy- and water-intensive facilities. In water-constrained regions, evaporative cooling systems raise concerns about aquifer drawdown and drought resilience. Environmental advocates question lifecycle emissions from new gas-fired generation built to serve AI load, as well as the cumulative impact of substations, transmission lines, and backup generators. Even where companies pledge renewable procurement, critics argue that incremental demand can still drive fossil fuel buildout in constrained grids.

3. Electricity Costs and Grid Strain.
The most politically volatile pillar is ratepayer impact. Local activists argue that if hyperscale demand requires billions in new generation, transmission, and distribution investment, those costs could be socialized through higher retail rates. Concerns also extend to reliability — whether rapid load growth risks price spikes, capacity shortfalls, or emergency measures during extreme weather.

And then there’s the jobs myth. The “data center jobs” pitch often overstates long-term employment. Construction phases can generate hundreds of temporary union and trade jobs—electricians, concrete crews, steel, and site work—sometimes for 12–24 months. But once operational, hyperscale facilities are highly automated and run by surprisingly small permanent staffs relative to their footprint and power load. A multi-building campus consuming hundreds of megawatts may employ only a few dozen to low hundreds of full-time workers, focused on security, facilities management, and network operations. For rural counties weighing tax abatements and infrastructure upgrades, the gap between short-term construction labor and modest permanent payroll becomes a central economic-development question.

By elevating electricity price protection to a presidential talking point, the administration effectively validates this third pillar. What began as local testimony at zoning meetings is now part of national energy policy framing: the principle that ordinary households should not subsidize AI infrastructure through their power bills. That rhetorical shift transforms a local grievance into a broader political issue with statewide and federal implications.

This is no longer just a zoning fight. It is now a kitchen-table affordability issue. Which may be a good start.

The Uncomfortable Math

AI data centers run 24/7, require enormous continuous baseload power, often demand dedicated substations, and can trigger multi-billion-dollar transmission upgrades. In regulated utility regions, those upgrades may be socialized across ratepayers unless cost allocation rules are enforced.

That is the central fear: even if tech companies pay for direct interconnection, broader grid reinforcement costs may still reach residential customers. If “ratepayer protection” pledges gain traction, this would mark a major federal acknowledgement that the risk is politically real.

Why This Is Bigger Than Trump

Governors in data-center-heavy states have also expressed concern. Utilities want load growth but fear rate shock. Grid operators face pressure to accelerate capacity procurement without triggering bill spikes. Grassroots activists have argued the AI buildout is outpacing responsible grid planning — and that argument has now moved from local meetings to national politics.

Whether any president—including Trump—can truly compel hyperscale tech firms to absorb rising power and infrastructure costs remains uncertain. Without formal regulation, enforcement tools are limited to negotiation, procurement leverage, and public pressure, all of which depend on the companies’ strategic interests.

Voluntary pledges can signal cooperation but lack binding force especially if market conditions shift. The Trump announcement also raises a political question: does the “pledge” represent a balancing act inside the administration between economic populists and China hawks like Peter Navarro, often associated with industrial-policy cost discipline, and pro-AI growth lobbyists such as Silicon Valley’s AI Viceroy David Sacks? If so, the commitment may reflect an internal compromise as much as an external policy toward accelerationist hyperscalers.

Data-center growth is turning electricity affordability into a geopolitical issue, not just a local zoning fight. When hyperscalers drop a 100–500 MW load into a market, they can tighten reserve margins, push up wholesale prices, and force expensive transmission and distribution upgrades—costs that governments then have to allocate between the new entrant and everyone else. That same demand can crowd out electrification priorities (heat pumps, EVs, industrial decarbonization) or trigger emergency procurement of “firm” power—often gas—because reliability deadlines don’t wait for ideal renewable buildouts.

We are way past McDonald’s on the Champs-Élysées

This is where “net zero” starts to look like it’s in the rear-view mirror. Many jurisdictions still talk about decarbonization, but the near-term political imperative is keeping the lights on and bills stable. If the choice is between fast AI load growth and strict emissions trajectories, the operational reality in many grids is that fossil backup and accelerated thermal approvals re-enter the picture—sometimes explicitly, sometimes quietly. Meanwhile, countries with abundant cheap power (hydro, nuclear, subsidized gas) gain leverage as preferred data-center destinations, while constrained grids face moratoria, queue rationing, and public backlash.

Data-center expansion is rapidly turning electricity policy into a global political and economic tradeoff. When hyperscale facilities add hundreds of megawatts of demand, they can tighten capacity margins, raise wholesale prices, and force costly grid upgrades—decisions governments must make about who ultimately pays. In many markets, this new load competes directly with electrification goals such as EV adoption, heat pumps, and industrial decarbonization. Reliability timelines often drive utilities toward fast, firm capacity—frequently gas—because intermittent renewables and storage cannot always be deployed quickly enough.

In that sense, Trump’s choices increasingly resemble a classic “guns and butter” dilemma. Policymakers must balance the strategic push for AI infrastructure and digital competitiveness against long-term climate commitments. While net-zero targets remain official policy in many jurisdictions, near-term choices often prioritize keeping power reliable and affordable, even if that means slowing emissions progress. The tension does not necessarily mean decarbonization disappears, but it underscores the difficulty of advancing both rapid AI build-out and strict net-zero trajectories simultaneously under real-world grid constraints.

Rate Payers Get the Immediate Proof: Utility bills

If the White House advances voluntary ratepayer-protection pledges, several trajectories could unfold. Technology companies may publicly commit to absorbing incremental grid and infrastructure costs, framing the move as responsible corporate citizenship. Personally, I don’t think Trump actually believes it, and I fully expect that the teleprompter will say one thing, and then in a classic Trump aside, he will undercut the speech writers.

Utilities, facing rising capital requirements, could press for clearer cost-allocation rules to ensure large-load customers bear system expansion expenses. State public-utility commissions might reopen tariffs and special-contract pricing for hyperscale users, testing how far voluntary commitments translate into enforceable rate structures.

Meanwhile, grassroots groups are likely to demand transparent accounting to verify that ordinary customers are insulated from price impacts. Yet the full economic value of any pledge will emerge only over years of build-out and rate cases—long after the current administration, and Trump himself, are no longer in office.

For the moment, the debate has shifted. Grassroots opposition is no longer just about land or water. It is about who pays when AI reshapes the grid — and now the president is talking about it.

Let’s say I’m wrong and Trump is serious about reigning in AI. If Trump were able to make such a policy stick, it could mark a broader shift in how governments confront the external costs of rapid AI expansion. Requiring hyperscalers to internalize infrastructure and power burdens could slow the breakneck build-out that fuels large-scale model training and synthetic media proliferation.

For artists and performers, that deceleration could matter. The fight over voice, likeness, and identity—already highlighted by figures such as Brad Pitt and Tom Cruise ripped off by China’s Seedance 2.0 —centers on protecting human personhood from industrial-scale replication. A structural slowdown in AI growth would not end that conflict, but it could rebalance leverage, giving creators, unions, and policymakers more time to establish enforceable guardrails.

Infrastructure, Not Aspiration: Why Permissioned AI Begins With a Hard Reset

Paul Sinclair’s framing of generative music AI as a choice between “open studios” and permissioned systems makes a basic category mistake. Consent is not a creative philosophy or a branding position. It is a systems constraint. You cannot “prefer” consent into existence. A permissioned system either enforces authorization at the level where machine learning actually occurs—or it does not exist at all.

That distinction matters not only for artists, but for the long-term viability of AI companies themselves. Platforms built on unresolved legal exposure may scale quickly, but they do so on borrowed time. Systems built on enforceable consent may grow more slowly at first, but they compound durability, defensibility, and investor confidence over time. Legality is not friction. It is infrastructure. It’s a real “eat your vegetables” moment.

The Great Reset

Before any discussion of opt-in, licensing, or future governance, one prerequisite must be stated plainly: a true permissioned system requires a hard reset of the model itself. A model trained on unlicensed material cannot be transformed into a consent-based system through policy changes, interface controls, or aspirational language. Once unauthorized material is ingested and used for training, it becomes inseparable from the trained model. There is no technical “undo” button.

The debate is often framed as openness versus restriction, innovation versus control. That framing misses the point. The real divide is whether a system is built to respect authorization where machine learning actually happens. A permissioned system cannot be layered on top of models trained without permission, nor can it be achieved by declaring legacy models “deprecated.” Machine learning systems do not forget unless they are reset. The purpose of a trained model is remembering—preserving statistical patterns learned from its data—not forgetting. Models persist, shape downstream outputs, and retain economic value long after they are removed from public view. Administrative terminology is not remediation.

Recent industry language about future “licensed models” implicitly concedes this reality. If a platform intends to operate on a consent basis, the logical consequence is unavoidable: permissioned AI begins with scrapping the contaminated model and rebuilding from zero using authorized data only.

Why “Untraining” Does Not Solve the Problem

Some argue that problematic material can simply be removed from an existing model through “untraining.” In practice, this is not a reliable solution. Modern machine-learning systems do not store discrete copies of works; they encode diffuse statistical relationships across millions or billions of parameters. Once learned, those relationships cannot be surgically excised with confidence. It’s not Harry Potter’s Pensieve.

Even where partial removal techniques exist, they are typically approximate, difficult to verify, and dependent on assumptions about how information is represented internally. A model may appear compliant while still reflecting patterns derived from unauthorized data. For systems claiming to operate on affirmative permission, approximation is not enough. If consent is foundational, the only defensible approach is reconstruction from a clean, authorized corpus.

The Structural Requirements of Consent

Once a genuine reset occurs, the technical requirements of a permissioned system become unavoidable.

Authorized training corpus. Every recording, composition, and performance used for training must be included through affirmative permission. If unauthorized works remain, the model remains non-consensual.

Provenance at the work level. Each training input must be traceable to specific authorized recordings and compositions with auditable metadata identifying the scope of permission.

Enforceable consent, including withdrawal. Authorization must allow meaningful limits and revocation, with systems capable of responding in ways that materially affect training and outputs.

Segregation of licensed and unlicensed data. Permissioned systems require strict internal separation to prevent contamination through shared embeddings or cross-trained models.

Transparency and auditability. Permission claims must be supported by documentation capable of independent verification. Transparency here is engineering documentation, not marketing copy.

These are not policy preferences. They are practical consequences of a consent-based architecture.

The Economic Reality—and Upside—of Reset

Rebuilding models from scratch is expensive. Curating authorized data, retraining systems, implementing provenance, and maintaining compliance infrastructure all require significant investment. Not every actor will be able—or willing—to bear that cost. But that burden is not an argument against permission. It is the price of admission.

Crucially, that cost is also largely non-recurring. A platform that undertakes a true reset creates something scarce in the current AI market: a verifiably permissioned model with reduced litigation risk, clearer regulatory posture, and greater long-term defensibility. Over time, such systems are more likely to attract durable partnerships, survive scrutiny, and justify sustained valuation.

Throughout technological history, companies that rebuilt to comply with emerging legal standards ultimately outperformed those that tried to outrun them. Permissioned AI follows the same pattern. What looks expensive in the short term often proves cheaper than compounding legal uncertainty.

Architecture, Not Branding

This is why distinctions between “walled garden,” “opt-in,” or other permission-based labels tend to collapse under technical scrutiny. Whatever the terminology, a system grounded in authorization must satisfy the same engineering conditions—and must begin with the same reset. Branding may vary; infrastructure does not.

Permissioned AI is possible. But it is reconstructive, not incremental. It requires acknowledging that past models are incompatible with future claims of consent. It requires making the difficult choice to start over.

The irony is that legality is not the enemy of scale—it is the only path to scale that survives. Permission is not aspiration. It is architecture.

Grassroots Revolt Against Data Centers Goes National: Water Use Now the Flashpoint

Over the last two weeks, grassroots opposition to data centers has moved from sporadic local skirmishes to a recognizable national pattern. While earlier fights centered on land use, noise, and tax incentives, the current phase is more focused and more dangerous for developers: water.

Across multiple states, residents are demanding to see the “water math” behind proposed data centers—how much water will be consumed (not just withdrawn), where it will come from, whether utilities can actually supply it during drought conditions, and what enforceable reporting and mitigation requirements will apply. In arid regions, water scarcity is an obvious constraint. But what’s new is that even in traditionally water-secure states, opponents are now framing data centers as industrial-scale consumptive users whose needs collide directly with residential growth, agriculture, and climate volatility.

The result: moratoria, rezoning denials, delayed hearings, task forces, and early-stage organizing efforts aimed at blocking projects before entitlements are locked in.

Below is a snapshot of how that opposition has played out state by state over the last two weeks.

State-by-State Breakdown

Virginia

Virginia remains ground zero for organized pushback.

Botetourt County: Residents confronted the Western Virginia Water Authority over a proposed Google data center, pressing officials about long-term water supply impacts and groundwater sustainability.

Hanover County (Richmond region): The Planning Commission voted against recommending rezoning for a large multi-building data center project.

State Legislature: Lawmakers are advancing reform proposals that would require water-use modeling and disclosure.

Georgia

Metro Atlanta / Middle Georgia: Local governments’ recruitment of hyperscale facilities is colliding with resident concerns.

DeKalb County: An extended moratorium reflects a pause-and-rewrite-the-rules strategy.

Monroe County / Forsyth area: Data centers have become a local political issue.

Arizona

The state has moved to curb groundwater use in rural basins via new regulatory designations requiring tracking and reporting.

Local organizing frames AI data centers as unsuitable for arid regions.

Maryland

Prince George’s County (Landover Mall site): Organized opposition centered on environmental justice and utility burdens.

Authorities have responded with a pause/moratorium and a task force.

Indiana

Indianapolis (Martindale-Brightwood): Packed rezoning hearings forced extended timelines.

Greensburg: Overflow crowds framed the fight around water-user rankings.

Oklahoma

Luther (OKC metro): Organized opposition before formal filings.

Michigan

Broad local opposition with water and utility impacts cited.

State-level skirmishes over incentives intersect with water-capacity debates.

North Carolina

Apex (Wake County area): Residents object to strain on electricity and water.

Wisconsin & Pennsylvania

Corporate messaging shifts in response to opposition; Microsoft acknowledged infrastructure and water burdens.

The Through-Line: “Show Us the Water Math”

Across these states, the grassroots playbook has converged:

Pack the hearing.

Demand water-use modeling and disclosure.

Attack rezoning and tax incentives.

Force moratoria until enforceable rules exist.

Residents are demanding hard numbers: consumptive losses, aquifer drawdown rates, utility-system capacity, drought contingencies, and legally binding mitigation.

Why This Matters for AI Policy

This revolt exposes the physical contradiction at the heart of the AI infrastructure build-out: compute is abstract in policy rhetoric but experienced locally as land, water, power, and noise.

Communities are rejecting a development model that externalizes its physical costs onto local water systems and ratepayers.

Water is now the primary political weapon communities are using to block, delay, and reshape AI infrastructure projects.

Read the local news:

America’s AI Boom Is Running Into An Unplanned Water Problem (Ken Silverstein/Forbes)

Residents raise water concerns over proposed Google data center (Allyssa Beatty/WDBJ7 News)

How data centers are rattling a Georgia Senate special election (Greg Bluesetein/Atlanta Journal Constitution)

A perfect, wild storm’: widely loathed datacenters see little US political opposition (Tom Perkins/The Guardian)

Hanover Planning Commission votes to deny rezoning request for data center development (Joi Fultz/WTVR)

Microsoft rolls out initiative to limit data-center power costs, water use impact (Reuters)

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