There are increasingly two conversations about artificial intelligence in America, and they are beginning to collide. These are familiar opponents: Wall Street and Main Street.
Pressure from Financial Markets
The first is taking place on Wall Street.
For publicly traded companies that aggregate, distribute, and monetize enormous quantities of creative content, being seen as an AI skeptic is increasingly difficult. Investors expect an AI strategy. Analysts ask about AI on earnings calls. Companies announce AI partnerships (which can get pompous like “global strategic partnerships” and are neither), a few AI licensing arrangements, claimed AI efficiencies, AI products and AI revenue opportunities. Emphasis on the opportunities in the search for elusive ROI. Warner Music Group, for example, recently told its shareholders that it has taken an “early and aggressive approach” to AI partnerships and emphasized the variable economics of its deals with Suno and other AI companies. Universal Music Group likewise regularly highlights its growing portfolio of “responsible AI” partnerships in financial reporting.
That should hardly be surprising. AI has become deeply embedded in the capital markets themselves.
The largest technology companies are spending extraordinary sums on AI infrastructure. Chipmakers like NVIDIA finance customers who buy their chips. Reminiscent of circular “carriage deals” in the Dot Bomb era, technology companies invest in AI companies that become customers of their cloud services. Infrastructure companies borrow against anticipated demand from AI companies, while investors value many of the participants based partly upon the growth generated by the others. See how that works?
The circularity is becoming difficult to miss. NVIDIA, for example, recently agreed to provide guarantees of up to $105 billion supporting an OpenAI data-center project in Ohio while also investing in OpenAI. Broadcom reportedly is exploring tens of billions of dollars of additional financing tied to AI infrastructure. AI is no longer simply another technology sector. It increasingly influences equity valuations, credit markets, underwriting decisions, infrastructure finance and the allocation of enormous pools of investment capital.
Wall Street consequently has a powerful incentive to believe that the AI buildout will continue. Because the emperor has new clothes, but is the same old emperor.
Musicians and songwriters discover that recordings containing their performances and songs have been copied into training datasets without permission or legal basis. Songwriters discover that their compositions, especially lyrics, may have become inputs to systems capable of producing substitutes for their work. Performers discover that their names, voices and identifying characteristics may have become instructions capable of invoking their identities inside commercial products. Their property is being taken—there’s that word again—in a massive theft that should involve prison time. Because if this isn’t criminal copyright infringement, what is?
Drive a few hundred miles away from Nashville or Los Angeles and the property being taken changes, but the complaint sounds remarkably similar.
A farmer is told that a transmission corridor may cross land her family has owned for generations, backed by eminent domain that can force the family to surrender it. A rural community discovers that hundreds or thousands of acres have been assembled for a data center. Residents worry about aquifers, electricity prices, noise, gas generation and transmission lines. Governments offer tax incentives to enormously valuable technology companies while residents are told that the infrastructure is necessary because America must beat China in the AI race. State and local elected officials make zoning decisions to permit these takings, with votes that only make sense if there’s a quid pro quo under the table.
Today we began moving my moms birds! As many of you know my mom had to sell her home for transmission lines to power data centers in Coweta County. This is just a small part of this very long process. I’m hoping more people will enjoy a mix of content, going back to what made me… pic.twitter.com/LdLGjhuFFX
The common denominator between musicians and farmers isn’t artificial intelligence.
It is consent and coercion. It’s the callous taking.
But there is a deeper connection. The institutions demanding these resources did not suddenly become powerful with the invention of generative AI. Much of today’s platform economy accumulated extraordinary wealth and political influence during the preceding two decades through business models built around aggregation, scale, data collection and extraordinarily aggressive interpretations of legal safe harbors. Companies such as Meta and Google learned that once a platform becomes sufficiently large, the lives, work, attention, emails, chats, and baby pictures of its users can become inputs to be captured, scraped, optimized, aggregated and monetized.
The ongoing multidistrict litigation over social-media harms (MDL 3047) provides a sobering illustration of where that philosophy can lead. The allegations in the social media harms cases concern platforms accused of designing products to maximize engagement while exposing their own users—including children—to serious harms and exploiting those harms. Cold blooded. Whatever the ultimate disposition of those cases in MDL 3047, they illustrate a recurring tension in the platform economy: the interests of the human beings using a platform and the protections they enjoy under the law and human rights are routinely trampled by the companies operating it—for the money. And let’s not forget that when we say “companies” we actually mean the employees who went along with it and got rich doing so.
Generative AI extends that tension from users to inputs.
The same appetite for scale that drove platforms to accumulate and exploit behavioral data now creates an appetite for enormous quantities of creative work, human expression, electricity, water, land and transmission capacity. Scraping supplies one set of inputs. Political influence and infrastructure policy can supply another. And in the most extreme case, the sovereign power of eminent domain delegated to the MDL defendants can ultimately compel a property owner to surrender land for data centers serving the buildout. If not stopped, this will give Silicon Valley a political control at the federal, state and local levels never seen before.
The mechanisms are legally different. The instinct is strikingly familiar.
Acquire the input first. Argue about permission, compensation and consequences later. They’re happy to get a license when each artist and songwriter, or mom and child gets a final, non-appealable judgement if the AI companies don’t change the law as they tried and keep trying to do with federal preemption.
That is why the emerging alliance between musicians and landowners is less strange than it initially appears. Both increasingly confront institutions whose enormous financial resources can be converted into political and legal power, and whose growth depends upon obtaining resources belonging to other people.
For the musician, it may be a composition, performance, voice or identity. For the farmer, it may literally be the family farm. And increasingly, it is the perception that enormously powerful companies are building wealth and control over the economy by taking private property and humanity from people who possess considerably less economic and political power.
That is why dismissing the data-center backlash as NIMBYism—or dismissing musicians objecting to unauthorized training as Luddites—fundamentally misunderstands what is happening.
These constituencies are not necessarily anti-technology. They are objecting to a particular economic bargain.
Or, more accurately, the absence of one.
The Politics Are Arriving
This distinction matters because the data-center fight is rapidly escaping zoning commissions and utility proceedings and entering national politics. This is called “jumping the shark” in some circles.
Texas Democrats are campaigning on data centers in rural Republican territory. Candidates elsewhere are attacking electricity costs, water consumption, tax subsidies and the conversion of agricultural land.
This is an unusual political coalition because it doesn’t fit comfortably on the traditional left-right axis.
The rancher who doesn’t want a transmission line across his property may be a lifelong Republican. The songwriter who doesn’t want her catalog ingested into a generative model may be a lifelong Democrat. The homeowners who don’t want a 500-megawatt industrial complex next door may have no particular view about AI whatsoever but want to protect their family.
They nevertheless understand the same sentence:
You shouldn’t be able to take something that belongs to me merely because you say your technology needs it.
That may prove considerably more powerful politically than “AI safety.”
What If Data Centers Become Obsolete Stranded Assets?
There is another reason the industry’s present approach seems unnecessarily confrontational and coercive.
Today’s enormous data-center buildout reflects today’s technological architecture. There is no reason to assume that every aspect of that architecture will remain necessary and may become unnecessary before the data center build is completed.
Indeed, NVIDIA—the company most closely associated with the hardware powering hyperscale AI—is simultaneously pushing substantial AI computation in the opposite direction. Its DGX Spark puts powerful model inference, fine-tuning, and autonomous-agent capabilities on a desktop, while its RTX platforms increasingly allow sophisticated AI models and agents to run locally. NVIDIA expressly markets these systems as “reducing the need for cloud-based token generation resources” and, in the case of its AI workstations, as a means to “offload data center compute resources.” This does not eliminate the need for hyperscale facilities, particularly for frontier-model training, but it demonstrates that an increasing share of AI computation can migrate from centralized data centers to local devices.
It’s a trend away from depending on data centers. And if the answer was, you can’t build a gazillion data centers that inevitably will become stranded assets rather than take whatever you want, do you think that trend might accelerate? Constraints are choices.
That does not mean hyperscale data centers are disappearing. Training frontier models and serving enormous numbers of users will continue to require substantial centralized computing resources for a while.
But the direction of travel matters.
Models are becoming smaller and more efficient. Quantization reduces computational requirements. Specialized chips improve inference efficiency. More processing is moving to PCs, workstations, phones, vehicles and edge devices. Some workloads that required a data center yesterday can run locally today; workloads requiring a data center today may run locally tomorrow.
That makes the industry’s political strategy particularly shortsighted.
Why permanently alienate communities, seize land, subsidize massive infrastructure and create a nationwide political opposition movement around an architecture that technology itself is already beginning to decentralize?
True innovation would try to solve that problem if tech companies were constrained.
Take the Theft Out of AI
The same principle applies to music. The answer is not to stop artificial intelligence. The answer is to take the theft out of it.
And that requires acknowledging an uncomfortable fact about the technology as it exists today. Artificial intelligence can theoretically be useful and productive, but the major generative models did not emerge from a pristine laboratory. To one degree or another, the present generation of large models is shadowed by unresolved allegations and litigation concerning massive-scale copyright infringement, unauthorized scraping, collection of personal information and other privacy violations. Courts will ultimately decide many of those claims in their own inefficient way that Big Tech loves so much. But it is impossible to have an intellectually serious conversation about “responsible AI” while pretending that the provenance of today’s models is not itself contaminated.
That history matters because the question is not simply how AI should behave tomorrow. It is also what was taken to build the systems we have today, from whom, and without whose permission.
Just like I never believed that the law would permit “sharing” with 60 million of your closest friends in the Grokster case, I don’t believe that the AI cases will determine that the answer is because an enormously expensive technology has already been built, obtaining consent will be disregarded. (The subtext being, and if it is, we have much bigger problems.)
This isn’t that hard, people. Build models from licensed material. Ask musicians before converting their identities into commercial capabilities. Compensate creators whose work supplies valuable inputs. Give communities meaningful authority over infrastructure imposed upon them. Pay the actual cost of electricity and transmission rather than shifting it onto ratepayers. Don’t hoard power behind the meter while creating massive noise pollution and other negative externalities. Build smaller and more efficient systems.
And where existing models were built from material that should not have been taken in the first place, genuine innovators should be investing just as aggressively in provenance, licensed replacement datasets, machine unlearning and other technologies capable of removing unauthorized inputs as they invest in acquiring more compute.
That would be innovation directed at the problem rather than lobbying directed at avoiding it. Most importantly, stop treating consent as an obstacle to innovation.
The Human Artistry Campaign and Warner Music Group’s own public AI principles point toward the distinction. WMG says AI models should be licensed and that artists and songwriters should have an opt-in before their names, images, likenesses or voices are used in new AI-generated music. That is not anti-AI. It is an attempt to establish the terms under which AI can coexist with human creators. (That’s also not what happened with Suno, which is why Universal and Sony are still suing Suno.)
There is an enormous difference between saying “don’t build it” and saying “don’t build it with things you had no right to take.” Wall Street may not fully appreciate that distinction yet because markets presently reward companies for demonstrating exposure to AI growth—said another way, Wall Street rewards AI companies that take private property.
Main Street understands it instinctively.
A singer’s voice. A songwriter’s composition. A session player’s musical identity. A rancher’s land. A town’s water supply. A family’s electric bill.
They are very different things. But the political argument increasingly surrounding them is remarkably similar:
Innovation does not create an entitlement to somebody else’s property.
Today @mtgreenee stopped by to visit my childhood home that is being taken by Georgia Power under eminent domain laws to support data centers. I truely appreciate her taking the time to view our property, and understand the sick situation our county is dealing with. pic.twitter.com/vzqeenHD6N
AI can be useful. But usefulness does not cleanse provenance, technological achievement does not retroactively supply consent, and scale does not convert unauthorized taking into a legitimate business model rather than a litigious model.
Local AI may eventually make some of today’s massive infrastructure unnecessary. Properly licensed models can create new markets for artists rather than simply competing against them. Assistive AI can make human creators more productive without replacing them.
The choice therefore isn’t between AI and no AI. It is between an AI economy built by consent and one built by extraction.
The companies that recognize that distinction first may ultimately be the genuine innovators. They will stop asking how much they can take before somebody stops them and start asking how to build technology people actually want to live with.
That is how AI earns a social contract. Take the theft out of AI, and a remarkable amount of the opposition may disappear with it.
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.
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.
Music Tech Solutions 