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.
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.
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.
There’s a new gravity well in American finance, and it’s not New York. It’s not even Silicon Valley. It’s Dallas. It’s Austin. It’s Y’all Street.
And anyone paying attention could have seen it coming. The Texas Stock Exchange (TXSE) is preparing for launch in 2026. TXSW is not some bulletin board; it’s backed by billions from institutions that have grown weary of the compliance culture and cost of New York. Goldman Sachs’s Dallas campus is now operational. BlackRock and Charles Schwab have shifted major divisions to the Lone Star State. Tesla and Samsung are expanding giga-scale manufacturing and chip fabrication plants.
A strong center of gravity for capital formation is moving south, and with it, a new cultural economy is taking shape. And AI may not save it: Scion Asset Management, “Big Short” investor Michael Burry’s hedge fund, disclosed to the SEC that it had a short bet worth $1.1 billion against Nvidia and Palantir. He’s also investing in waterthat AI burns. So not everyone is jumping off a cliff.
A New Realignment
Texas startups have raised roughly $9.8 billion in venture capital through Q3 2025, pushing the state to a consistent #4 ranking nationally. Austin remains the creative and software hub, while Dallas–Fort Worth and Houston lead in AI infrastructure, energy tech, and finance.
The TXSE will formalize what investors already know: capital markets no longer need Manhattan to function.
And that raises an uncomfortable question for the music industry:
If capital, infrastructure, and innovation no longer orbit Wall Street, why should music?
Apple Learned It the Hard Way
Despite New York’s rich musical legacy—Tin Pan Alley, Brill Building, CBGB, and the era of the major-label tower when Sony occupied that horrible AT&T building and flew sushi in from Japan for the executive dining room—the city has become an increasingly difficult place to sustain large-scale creative infrastructure. Real estate costs, over-regulation, and financial concentration have hollowed out the middle layer of production. As I’ve taught for years, the key element to building the proverbial “creative class” is cheap rent, preferably with a detached garage.
Even Apple Inc. learned long ago that creativity can’t thrive where every square foot carries a compliance surcharge. That’s why Apple’s global supply chain, data centers, and now content operations span Texas, Tennessee, and North Carolina instead of Midtown Manhattan. And then there’s the dirty power, sump pumps and subways—Electric Lady would probably never get built today.
The lesson for the music business is clear: creative capital follows economic oxygen. And right now, that oxygen is in Texas.
The Texas Music Office: A Model for How to Get It Done
If you want to understand how Texas built a durable, bipartisan music infrastructure, start with the Texas Music Office (TMO). Founded in 1990 under Governor Bill Clements, the TMO was one of the first state agencies in America to recognize the music industry not just as culture, but as economic development.
Over the decades—through governors of both parties—the TMO has become a master class in how to institutionalize support for creative enterprise without strangling it in bureaucracy. From George W. Bush’s early focus on export promotion, to Rick Perry’s integration of music into economic development, to Greg Abbott’s expansion of the Music Friendly Communities network, each administration built upon rather than dismantled what came before.
Today, the TMO supports more than 70 certified Music Friendly Communities, funds music-education grants, tracks economic data, and connects local musicians with investors and international partners. It’s a template for how a state can cultivate creative industries while maintaining fiscal discipline and accountability.
It’s also proof that cultural policy doesn’t have to be partisan—it just has to be practical.
When people ask why Texas has succeeded where others stalled, the answer is simple: the TMO stayed focused on results, not rhetoric. That’s a lesson a lot of states—and more than a few record labels—could stand to relearn.
Artist Rights Institute: Doing Our Part for Texas and Beyond
The Artist Rights Institute (ARI) has done its part to make sure that Texas and other local music and creators aren’t an afterthought in rooms that are usually dominated by platform interests and coastal trade groups.
When questions of AI training, copyright allocation, black-box royalties, and streaming transparency landed in front of the U.S. Copyright Office, Congress, and U.K. policymakers, ARI showed up with the Texas view: creators first, no speculative ticketing, no compulsory “data donation,” and no silent expropriation of recordings and songs for AI. ARI has filed comments, contributed research, and supported amicus work to make sure Texas artists, songwriters, and indie publishers are in the record — not just the usual New York, Nashville, and Los Angeles voices.
Just as important, ARI has pushed financial education for artists. Because Y’all Street doesn’t help creators if they don’t know what a discount rate is, how catalog valuations work, how to read a mechanical statement, or why AI licenses need to be expressly excluded from legacy record and publishing deals. ARI programs in Texas and Georgia have focused on: – explaining how federal policy actually hits musicians, – showing how to negotiate or at least spot AI/derivative-use clauses, – and connecting artists to local music industry infrastructure.
In other words, ARI joined other Texas and Georgia organizations to be a translator between Texas’s very real music economy and the fast-moving policy debates in Washington and the U.K. If Texas is going to be the place where music is financed, ARI wants to make sure local artists are also the ones who capture the value.
Music’s Texas Moment
Texas is no newcomer to the business of music. Its industry already generates over $13.4 billion in annual economic activity, supporting more than 91,000 jobs across its certified cities. Austin retains the crown of “Live Music Capital of the World,” but Denton, Fort Worth, and San Antonio have joined the state-certified network of “Music Friendly Communities”.
Meanwhile, universities from UT-Austin to Texas A&M study rights management, AI provenance, and royalties in the age of generative audio.
The result: a state that treats music not as nostalgia, but as an evolving economic engine. Plus we’ve got Antone’s.
Wall Street’s ‘Great Sucking Sound,’ Replayed
Ross Perot once warned of “that giant sucking sound” as jobs moved south. Thirty years later, the sound you hear isn’t manufacturing—it’s money, data, and influence flowing to Y’all Street.
If the major labels and publishers don’t track that migration, they risk becoming cultural tenants in cities they no longer own. The next catalog securitization, the next AI-royalty clearinghouse, the next Bell Labs-for-Music could just as easily be financed out of Dallas as from Midtown.
Because while New York made the hits of the last century, Texas may well finance the next one. We’ve always had the musicians, producers, authors, actors and film makers, but soon we’ll also have the money.
Y’all Ready?
The world no longer needs a Midtown address to mint creative wealth. As the TXSE prepares its debut and Texas cements its position as the nation’s innovation corridor, the music industry faces a choice:
Follow the capital—or become another cautionary tale of what happens when you mistake heritage for destiny.
Because as Apple learned long ago, even the richest history can’t compete with the freedom to build something new.
Music Tech Solutions 