The AI Industry’s Mounting “Credit Card Debt” Problem

A Bubble in the Making?

Not long ago, Sam Altmanthe CEO of OpenAI and arguably one of the most influential voices in artificial intelligencemade a comment that startled even people who have been following the industry closely. He admitted, quite plainly, that we might be in a stage where investors are “overexcited about AI.” That’s an unusual level of candor for someone at the helm of a company riding the AI wave. Usually, executives downplay talk of bubbles. Altman, though, leaned into it, even repeating the word “bubble” during his appearance.

The effect was immediate. Investors, already jittery, reacted by pulling back from tech stocks, and the selloff added fuel to the uneasy suspicion that maybe this AI gold rush isn’t built on rocksolid ground.

Failure Rates Nobody Wants to Talk About

A big reason for that uneasiness comes from research, particularly an MIT study that found about 95 percent of generative AI business experiments are either failing outright or stalling before they can become useful. Imagine that: almost every company you hear boasting about “AI integration” might be quietly discovering that it doesn’t actually save them money or make operations smoother.

It’s easy to understand the hypechatbots that can write code, generate images, or simulate a customer service rep sound irresistible. But the reality inside boardrooms seems to be messier. For many firms, the tools are clunky, expensive, and, in some cases, unnecessary. In other words, the “AI revolution” is proving much harder to monetize than glossy demos suggest.

The Debt Problem: Buying Servers on the Corporate Credit Card

And here’s where things start to look downright precarious. Building the infrastructure to power these massive AI systemsespecially large language modelsrequires staggering amounts of money. We’re not talking about a few racks of servers; these are energydevouring data centers filled with specialized chips that companies like NVIDIA can barely produce fast enough.

The bill for all this hardware and electricity? It’s largely being shoved onto the corporate equivalent of a credit card. Bloomberg recently compared it to the dotcom era, when telecom giants built out broadband networks faster than they could figure out how to make money from them. Back then, the industry overbuilt and overborrowed, leading to some infamous bankruptcies and massive asset writedowns.

Daniel Sorid from Citigroup put it bluntly: credit investors can’t help but think back to that crash when looking at AI’s debtfueled expansion.

The Rise of Private Credit

What’s especially concerning is the type of borrowing taking place. Instead of relying mostly on traditional corporate debtsecured by cash flowAI companies are increasingly tapping into private credit markets. UBS reports that private credit funding for AI has hit around $50 billion per quarter over the past three quarters. For context, that’s already double or triple what public markets are providing, and that doesn’t even count the megadeals from giants like Meta.

Private credit isn’t inherently bad, but it tends to be riskier, less transparent, and more vulnerable if the market mood shifts. Investors chasing yield may be pouring money into these loans with stars in their eyes, without fully reckoning with whether AI can deliver the returns to pay it all back.

A LongTerm Bet on a ShortTerm Mystery

There’s also a fundamental mismatch between the financing structure and the technology itself. Data centers are financed on 20 to 30year terms. But can anyone honestly say they know what AI infrastructure will even look like in five years, let alone thirty? Ruth Yang from S&P Global Ratings highlighted that uncomfortable truth: these longterm deals are being built around a technology still in its adolescence.

It’s a bit like mortgaging your house for three decades based on the assumption that today’s gaming console will be the dominant entertainment platform in 2050.

The Fear of Missing Out (AI FOMO)

Still, despite these concerns, there’s no sign of spending slowing down. Executives across industries are under relentless pressure not to be left behind. No one wants to be the cautious CEO remembered as the one who “missed the AI revolution.” Business Insider called it “AI FOMO,” and it’s a good term for the feverish climate.

Even Altman’s own company, OpenAI, seems worried about the frenzy. They recently issued a blog post warning investors about scamsfirms pretending to have access to OpenAI equity, offering deals that, as the company put it, “carry no economic value.” That kind of warning usually happens when hype is so high that opportunists start circling.

Is the Bubble Inevitable?

So, are we headed for another dotcomstyle implosion? That’s the milliondollar question. Some argue that even if AI companies are bleeding cash now, the longterm potential is so transformative that it justifies the investment. They point to earlier periods of overbuildinglike the early days of the internetwhere initial excess eventually laid the groundwork for genuine breakthroughs. After all, if telecom companies hadn’t overbuilt in the 2000s, would streaming services and cloud computing exist in the way they do today?

But others see more danger than opportunity. If most AI integrations are already failing, and if debt loads keep climbing, the risk isn’t just wasted moneyit’s systemic fragility. Private credit markets are less tested than public markets, and a cascade of defaults could rattle more than just the AI sector.

A Fragile Balancing Act

The truth probably lies somewhere in between. AI is almost certainly not a fadit’s going to stick around and evolve. But the pace and scale of current investment may be unsustainable, at least in the short run. When you pour billions into something with no clear revenue model, you’re gambling. Sometimes that gamble pays off spectacularly, but just as often it ends in sobering losses.

In the meantime, the industry is caught in a strange contradiction: companies must keep spending to maintain the illusion of momentum, yet every new loan deepens the sense of déjà vu for investors who lived through past bubbles. Whether this ends in a soft correction or a hard crash will depend on how quickly AI finds real, durable business modelsand whether lenders have the patience to wait.

Open Your Mind !!!

Source: Futurism