Wall Street and Big Tech companies in US are putting record debt into AI with loans, pushing immediate AI capex –capital expenditures- at risky levels before AI tools prove they can deliver real productivity gains.
This situation is placing AI as a financial asset, with growing doubts about lasting economic value. Wall Street’s rush is visible in balance sheets and bond markets, where Tech giants are issuing enormous amounts of debt to fund new data centers, while rewarding shareholders through buybacks.
The deeper question is whether this AI surge reflects real innovation or a financial cycle growing faster than technology itself.
The Debt Surge Behind Immediate AI Capex
Much of the growth story rests on immediate AI capex, as companies race to build capacity. This wave of AI capex spending has lifted GDP statistics, especially in equipment and construction sectors.
Yet a large share of hardware is imported, meaning part of the boost leaks abroad.
Data center capex comes at the center of this expansion. Warehouses filled with advanced chips are rising across the US.
Firms argue that heavy AI data center capex is essential to stay ahead of rivals. But critics warn that spending first and proving utility later carries heavy risks on economy.
In corporate filings, references to AI in capex strategies increasingly appear as justification for larger borrowing. Boards frame AI as a long-term infrastructure play. Investors, however, are asking about the ROI on AI investments, whether revenue can outpace the cost of constant upgrades.
Meanwhile, financial engineers are developing “compute-backed” loans, turning chips into collateral assets, and supporters say such tools help manage risk. But skeptics see that Wall Street is attempting an AI capex bubble forming around assets that can lose value quickly as new chips replace old ones.
Tech companies like Meta, Microsoft, and Oracle continue announcing fresh rounds of immediate AI capex, arguing that hesitation could mean falling behind competitors. The cycle feeds more borrowing, more construction and more financial exposure.
Moreover, it reduces diversification in industries because the focus is just on investing in AI.
“It has been very, very hard for investors to diversify,” Sonali Basak, chief investment strategist for iCapital, told Axios.
Capital Misallocation in AI Startups
Wall Street sees AI as a long-term driving force, and its kind of pressuring financial systems to rely on AI success. But the pressure extends beyond Big Tech, leading to a gap between investment and actual performance improvements.
Startups are absorbing record levels of venture funding, much of which is tied to AI capex spending. Valuations rise on the promise of scale, not steady income.
Some founders admit privately that demand is still experimental. They are building models that cost billions to train, hoping customers will eventually pay enough to justify the expense. In this environment, AI in capex management becomes more about raising capital than careful management.
The problem is Wall Street’s appetite for AI reinforces the cycle. Debt markets are projected to supply over $1 trillion to fund their AI ambitions this year. According to Morgan Stanley, global leader in Financial services, private credit firms are also increasing exposure, embedding AI risk deeper into the broader financial system.
The pattern signals earlier tech expansions; heavy loans, rising asset prices and optimism about future productivity. Yet economists caution that investment alone does not guarantee efficiency gains.
A new round of immediate AI capex plans is already underway. Even companies promoting AI in capex management tools face scrutiny as investors again question the ROI on AI assets.
AI in capex expansion is making short-term growth but can also hide deeper structural weakness. So, if software utility fails to match spending, there will be high debt without lasting productivity gains.
For now, markets are rewarding scale. But as AI capex spending dominates headlines, the gap between financial enthusiasm and real economic value remains wide. If revenues fail to catch up, what looks like a technological revolution could instead reveal a costly misallocation of capital.
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