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.