Artificial Intelligence How to spot an AI bubble

If Super Bowl ads promoting artificial intelligence sparked a feeling of déjà vu, you’re not alone. Dot-com start-ups in 2000 and cryptocurrency companies in 2022 dominated TV commercials during the big game, shortly before both suffered epic meltdowns.

This year’s ads aired amid a surge of investments, increasingly fueled by debt. Alphabet raised over $30 billion in USD-equivalent debt, including a 100-year bond, a rare structure that hasn’t happened since Motorola’s 1997 issuance. With so much money pouring into AI, it’s natural to wonder if today’s investments are signs of a bubble. Such moments are hard to call in real time, and even when bubbles burst, the underlying technologies can eventually change the world.

That’s why our team is tracking signs of volatility as well as compelling long-term AI investment opportunities. Will this time be different? Only time will tell, but here are six areas we’re watching:

1. Frothy IPOs

High-profile IPOs from a who’s who of generative AI companies are expected this year. Among those reportedly exploring their options: Anthropic, ChatGPT-maker OpenAI and SpaceX, which recently merged with xAI.

“It’s a logical step that has so far been missing from the AI boom,” says Chris Buchbinder, portfolio manager for CGVV — Capital Group U.S. Large Value ETF. With the anticipation are concerns about valuations, circular financing and whether these companies can match investor expectations.

“One of the elements that inflated and sustained the 1990s tech bubble was accelerating revenue growth with the promise of profitability later,” Buchbinder explains. “These pre-IPO companies are the modern-day equivalent. When they eventually go public and investors get a more detailed glimpse into their financials, high growth rates are likely to be rewarded.”

Investors are especially eager to track OpenAI’s earnings growth. The company has committed $1.3 trillion in purchases by 2031 to suppliers including Oracle, CoreWeave, Microsoft, Amazon, NVIDIA and others. That concentration has turned OpenAI into a bellwether for AI’s growth story.

“As long as growth is sustained, funding is likely to remain available to OpenAI and other AI leaders,” Buchbinder adds. “At some point things will slow down and the environment will become more difficult, but we aren’t there yet as we are still early in adoption and monetization curves.”

2. Debt-fueled growth

Alphabet’s 100-year bonds highlight the growing trend of hyperscalers leaning more heavily on debt, even as the commercial payoff remains uncertain. AI-related debt issuance jumped 112% in 2025, compared to the prior year, and 2026 is shaping up to be even bigger.

Although the sheer size of today’s issuances is making headlines, “these are high-quality companies that represent a sliver of the investment-grade (BBB/Baa and above) debt market, which is a stark contrast to their dominance in the S&P 500 Index,” says Damien McCann, portfolio manager for CGMS — Capital Group U.S. Multi-Sector Income ETF.

Most of these companies currently have low leverage and are issuing debt to fund AI-related capital expenditure (capex), which helps optimize their capital structure. “Given their sizable cash reserves and healthy cash flows, they can likely fund these projects on their own, even when considering the increased capex,” McCann explains. “In my view, this reduces the systemic risks considerably.”

McCann argues this is a sharp contrast to companies in the dot-com era. “Many tech companies in the late 1990s were operating with limited or even negative cash flow, relying heavily on equity issuances and more speculative venture capital,” he says. “Companies like WorldCom piled on debt and increased leverage considerably for their fiber network build-out while Pets.com raised large sums despite unproven demand.”

Importantly, several of today’s investment-grade debt deals have been issued at the parent company level, which offers many advantages, McCann says. Chief among them is that their value is tied to the collective cash flows and worth of the company. For example, Alphabet is the parent company of Google, YouTube, Waymo, DeepMind and other subsidiaries. “It’s an important distinction because you’re not lending to a structure that only exists to fund investments in AI.”

Still, investors have demanded additional yield to own AI-related bonds compared to similarly rated peers, a premium reflecting the large quantum of bonds issued, in addition to modestly higher issuer leverage and uncertainty about whether demand for AI will continue at the current pace.

3. Creative financing

Another concern is so-called vendor financing. Money is looping between the same companies, with start-ups and hyperscalers buying from one another and helping each other grow revenue. Case in point: Amazon and Google each invested billions in Anthropic, an AI systems start-up. In return, Anthropic agreed to use Amazon Web Services and Google’s services and products.

The ‘90s saw similar circular arrangements with Lucent Technologies lending excessively to cash-strapped start-ups so they could buy Lucent’s equipment. Those customers eventually were unable to pay, forcing Lucent to restate revenue and take huge write-offs.

McCann believes a near-term house of cards scenario is highly unlikely for hyperscalers. “Unlike Lucent, they’re lending only a small fraction of their cash flows,” he says. “Their financial strength generally gives them flexibility to pursue alternative ways of funding their expansion plans, which may include off-balance sheet arrangements or project-finance deals.” Meta, for instance, has a joint venture with Blue Owl Capital called Beignet Investor to build a supersized data center in Louisiana dubbed Hyperion. Microsoft, meanwhile, has signed short-term deals with data center providers called neoclouds that are viewed as operating expenses rather than long-term capital investments.

Because the AI build-out is still considered early, these non-traditional deals will likely increase in the coming year, particularly with private credit. “They may be attractive in certain instances but do require additional scrutiny from potential lenders because they’re structured to limit the financial risks to the parent company,” McCann explains. “While I believe in the transformative power of this technology, I’m not in any rush to invest in these deals. It’s going to come down to the individual structure and what the contracts look like, including an evaluation of the financial support provided by the hyperscalers.”

4. Overbuilding

If you build it, the thinking goes, growth will follow. In the early 2000s, telecom companies poured billions into fiber optic cable networks, believing demand for transmitting internet data was unlimited. What happened instead was an oversupply that led to massive asset write-downs and investor losses.

According to U.S. economist Jared Franz, “It is important to remember that overinvesting is a feature, not a bug, of any major advance in technology.” At some point the companies will shift their focus to investing more efficiently.

Today, hyperscalers believe building more data centers is essential to expanding AI inference, or the ability to run generative AI models for everyday use. As Franz puts it, the infrastructure is required to take on more AI workloads tied to training and inference, with the latter requiring reliable, always-on computing to serve users in real time.

Thus, investors pay close attention to the performance of new AI models and their updates, he adds. If gains begin to plateau, that could signal AI demand may not keep pace with spending. Nevertheless, Franz believes that the current capacity hyperscalers being fabricated over the next two years could be repurposed into other businesses. “For some companies, there will be demand for that compute even if AI demand slows.”

5. Resource constraints

The availability of electricity has emerged as an urgent issue for AI’s growth potential, according to fixed income analyst Julian James. That’s because data centers require memory, power, chips, copper and water. Obstacles in these areas could hamper infrastructure development, slowing capex spending by hyperscalers and pressure development timelines.

“When I met with utility sector CEOs in late 2025, many said that the availability of electricity and the extended timeline for data centers to be connected to the electric grid are the biggest constraints for expansion, which are required for the growth of AI,” he says. “The key bottleneck is a shortage of skilled workers capable of building new power plants and the transmission lines needed to connect new data centers to the grid.”

For now, the U.S. appears to have sufficient excess electricity to meet growing demand through 2028 or 2029. After that, without a significant increase in electric power generation, the pace of data center growth could slow, James explains.

Utility companies have begun to invest more in their electric grids in the face of aging infrastructure and higher demand. “Overall capex for utility companies may jump to around $200 billion from about $130bn to $135bn in 2020, with as much as 20% of that tied to AI,” James adds. Because utilities want to maintain their investment-grade ratings, he expects many will turn to equity or equity-linked financing to fund capex.

6. Slowing economic growth

It’s impossible to talk about a potential AI bubble without understanding where we are in the U.S. economic cycle, says Franz. Hallmarks of late-cycle periods typically include sustained increases in inflation, interest rates, wages and other conditions that prompt the Federal Reserve to tighten policy.

Franz believes the U.S. economy is currently in the middle stage of the cycle, a phase that is generally resilient. He sees long-term U.S. productivity growth rising to 3% to 4%, which could keep wage gains elevated and support broader economic growth.

“Of course, there is always the potential for other external shocks to cause AI-driven stocks to fall dramatically,” notes Franz, citing breakthroughs that substantially reduce training costs or investors pulling back on data center financing. “I believe we are in the early stage of the AI technology build-out, with demand continuing to outstrip supply.”

Over time, the contest to dominate AI will produce winners and losers, but today’s tech hyperscalers generate substantial earnings and free cash flow so a big bubble burst comparable to the dot-com era is less likely. “Every cycle will have some level of excess, and there will likely be moments of volatility, but this one appears to have more room to run,” he concludes.

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