Are we already in an AI bubble?
Here’s the uncomfortable truth about bubbles: you never really know you’re in one until it bursts. Put simply, an economic bubble is an overvaluation of assets, which triggers inflated investment and speculation. With gen AI, there is a risk that future demand has been overestimated.
The gen AI revolution didn’t emerge overnight, but its democratization in late 2022 and 2023 triggered an AI-investment frenzy. What started as excitement about large language models (LLMs) quickly transformed into a major infrastructure buildout spanning the entire technology value chain. The global tech giants – known as “hyperscalers” – poured hundreds of billions of dollars into AI capabilities. This investment wave spanned everything from construction firms building huge data centers to manufacturers supplying servers and specialized chips, and logistics companies moving it all around the globe.
By early 2025, concern peaked among risk analysts as valuations climbed to astronomical levels, and since then we’ve witnessed various episodes of price corrections and recoveries. Market sentiment remains still volatile and concerns about AI investment and associated cashflows still remain.
Four critical risk factors
While gen AI is clearly supporting global growth today, several alarm bells threaten business models across the AI boom.
1. Debt-fueled growth
Many AI investments are funded with a mix of capital and debt, with future cash flow based on anticipated demand earmarked to repay current debt. While tech giants and “hyper-scalers” can self-fund much of their AI infrastructure from operating cash flows, smaller players are at risk if projections fall short.
Moreover, a typical data center business plan is built around a 25–30 year timeframe. Since the financing behind the infrastructure is often on a much shorter scale of about 15 years, companies will have to refinance about halfway through their business plan – and this debt will become more expensive if demand lags, creating even greater risk of insolvency.
2. Demand uncertainty
The current demand for AI is undeniable. However, whether it’s sustainable over decades is an open question. Due to the long timelines of investments, there’s a risk this demand will drop by the time companies need to refinance their debt. The electric vehicle (EV) industry serves as a cautionary example for unpredictable demand: the strength of the technology is clear, but actual adoption rates have been much slower to pick up than first predicted, leaving automakers with stranded investments.
3. Technology risk
AI evolves so rapidly that today’s innovations may be obsolete tomorrow. This has happened in other sectors before – a company’s valuation outstrips manufacturing capacity or consumer demand, or its technology can’t keep up with the competition, and so it risks default.
In the case of AI specifically, open-source models have dented the value of competitors that provide proprietary models, and put pressure on their margins.
4. Supply chain risk
As well as robust electricity grids, proximity to customers and stable operating environments, data centers require a number of specialized parts such as chips. A company that builds data centers but faces shortages of key materials or parts (which is not inconceivable when demand is high) could see the return on their investment come under peril.
However, not all AI investments carry equal risk. At Allianz Trade, we assess bubble risk across market dynamics, macroeconomics, political environment, company-specific business models and other financial indicators to understand what makes companies resilient.
Mapping risk across the AI value chain
We currently see overheating in some specific areas, especially in data centers and model providers. The danger zone is what industry insiders call “neo clouds”: data centers built specifically for gen AI, with expensive, specialized chipsets, heavy debt financing and total dependence on sustained premium pricing. When they face price wars or lack of demand, these operators struggle to meet obligations.
IT distributors, manufacturers and construction companies tied to data center building are also exposed, although they’re less at risk if they’re able to pivot their business.
The safest positions belong to tech giants with multiple revenue streams that can absorb a $50 billion loss and continue operating. For a smaller, specialized player, a $5 billion loss may spell the end.
Nevertheless, the domino effect across the economy at large of a bubble burst would see a stock market value drop of up to 30%, comparable to the COVID-19 crash, according to our estimates. The cascading insolvency effects are harder to quantify but could be extensive.
How to navigate uncertain waters
AI is reshaping how we work, communicate and solve problems. The question isn’t whether AI will transform the economy – it clearly will – but whether the current investment matches realistic demand trajectories.
Allianz Trade’s 1,000+ analysts monitor AI-related risk by working closely with companies across the value chain, providing trade credit insurance, specialty credit and surety solutions based on thorough assessments. And while the market seems overheated in certain areas, we haven’t reached the critical point where a bubble burst becomes inevitable. But the warning signs are mounting, and companies should prepare for the consequences.
Transparency and adaptability are key to remaining afloat should the bubble burst. This means maintaining open communication with buyers and financial partners and choosing customers carefully, leveraging market insights to avoid those with excessive concentration on AI-related revenues and excessive debt. The winners of the AI boom won’t be determined by technology alone, but by how wisely companies manage risk.
