AI Bubble’s Hidden Debt: Will Private Credit Trigger a US Stock Market Crash?

In 2025, AI companies accounted for more than a third of all private credit deals globally. The firms that quietly funded AI's physical spine, the data centers, the GPU clusters, the power infrastructure, are not Silicon Valley VCs. They are private credit firms, alternative asset managers who moved trillions of dollars into AI while the public markets were busy watching Nvidia's stock chart. 

And now the Financial Stability Board (FSB), a global watchdog monitoring 24 central banks, has issued a formal warning that this could end in "sizeable losses." The question for anyone watching US tech stocks is not if private credit is under stress. The signs say it already is. The question is how much of the AI trade it takes down with it.

Let's break down what private credit is, who is actually funding the AI boom, why the firms doing the lending are now caught in a trap of their own making, and what the first genuine stress signals in this system look like for investors.

What Is Private Credit and How Does It Work?

Private credit is lending that happens outside the traditional banking system. After the 2008 financial crisis, banks faced stricter rules on how much they could lend, who they could lend to, and how much risk they could take. That created a funding gap for companies that needed large loans but could not easily get them from banks. Private credit firms stepped in to fill that gap. 

These are alternative asset managers such as Blackstone, Apollo, Blue Owl, KKR, and Ares. They raise money from pension funds, insurance companies, sovereign wealth funds, and wealthy investors, and then lend it directly to companies.

Think of it like a builder who needs money but banks refuse to lend. So, he goes to wealthy local lenders, the byaaj waale, who give him a private loan at a higher interest rate. That is private credit, except the borrower may be a GPU cloud company, the lenders are firms like BlackRock or Blackstone, and the loan can run into billions of dollars.

The global private credit market is now worth somewhere between $2 trillion and $3.5 trillion, depending on what you count. Almost none of it is publicly traded. Almost none of it is marked to market daily. This opacity matters a lot, and we will get to why shortly.

How Private Credit Became the Hidden Funding Engine of AI Boom

Building AI at scale is brutally expensive. A single hyperscale data center costs between $2 billion and $10 billion. GPU clusters run into hundreds of millions. Dedicated power infrastructure adds more. Hyperscalers like Meta and Microsoft cannot fund all of this without hitting their credit ratings or annoying shareholders with dilution.

Private credit offered a solution: large, custom, long-dated loans that traditional banks would not touch. The deals got very large, very fast.

Sources: Apollo press release, CoreWeave investor relations, Meta press release, BlackRock press release.

These are not speculative bets on early-stage startups. This is the physical infrastructure that AI runs on, funded by private credit.

The Double Risk in Private Credit: AI Infrastructure and SaaS Debt

Here is where this story gets genuinely different. Private credit firms are not just funding AI. They are also deeply exposed to the companies that AI is actively destroying. Software-as-a-service companies (SaaS) make up 20 to 25% of all private credit portfolios, according to 9fin. These businesses were considered safe bets for years because they had recurring, predictable subscription revenue.

That recurring revenue is now under direct attack. The same AI models that private credit firms are funding with billions of dollars are trying to replace the software these SaaS companies sell. The result is a structural problem. 

Private credit has simultaneously bet on AI succeeding as infrastructure (the data center loans) and holds massive loans to the companies that AI success is destroying (the SaaS loans).

If AI demand disappoints and the data centers sit at 30% utilization, the infrastructure loans go bad. If AI succeeds faster than expected, the software company loans go bad. There is no clean scenario here.

Private Credit Warning Signs: PIK Loans, Rising Defaults

This is not theoretical anymore. Multiple data points from early 2026 are pointing in the same direction.

Source: Bloomberg, CNBC, Morgan Stanley Research, FT, Reuters, Robert A. Stanger & Co., Prequin, 9fin

The PIK loan statistic deserves a pause. When a company cannot afford to pay interest in cash, it borrows more to cover the interest payment. Total debt grows. No cash changes hands. This is not how most struggling businesses stay alive while quietly getting worse. The share of PIK arrangements in private credit doubling between 2022 and 2025 is a sign that a meaningful slice of these borrowers were already in trouble before the AI disruption pressure even fully hit.

The stock market has noticed. Here is where private credit-related companies are trading as of June 2026.

Source: Google Finance | Data as of June 15, 2026

Simple takeaway: Private credit is not in crisis yet, but the stress is becoming visible. Investors are pulling money out, more borrowers are struggling to pay interest in cash, default risks are rising, and big banks now have large exposure to the sector. 

Is Private Credit Risk Becoming a 2008-Like Problem?

The 2008 comparison sounds extreme, but the concern is simple: hidden risk. In 2008, risky home loans were packaged into complex products and spread across banks, insurers and pension funds. The danger was not just bad loans, but that no one knew who was holding how much risk until losses hit together.

Private credit has a similar transparency issue in 2026. The FSB says “significant data challenges” make it hard to fully assess the sector’s risks. Bank exposure estimates range from $220B to $500B, which itself shows how unclear the picture is.

That is why the US Fed asked major banks in April 2026 to disclose how much private credit risk they were carrying. At the same time, some funds had so many withdrawal requests that they hit their 5% quarterly payout limit.

The big difference from 2008 is timing. Mortgage products crashed quickly because they traded in markets. Private credit loans are valued quarterly by fund managers, so losses may show up slowly over 18–24 months instead of all at once.

How Private Credit Stress Could Hit US Stocks, Tech Markets

Private credit is not a closed system. What happens inside it flows out into public markets through multiple channels. Here is each one, with the data.

• Tech exposure: Tech debt made up 16.7% of global non-financial corporate bond issuance in 2025, up from 11.6% a year earlier. SaaS companies alone account for 20–25% of private credit deals. If tech borrowers start defaulting, the impact can show up in their bonds, credit ratings, listed peers and overall tech market sentiment.
• AI funding risk: The AI boom depends on massive data centre spending, and part of that spending is funded by debt. If private credit funding slows, AI infrastructure expansion can slow too.
• Chip stock impact: The most direct public-market exposure sits in AI semiconductors and data-centre hardware. Marvell, Nvidia, AMD, Broadcom, TSMC, Micron, are among the stocks investors may track. The risk is not that all of them are equally exposed, but that their earnings expectations are increasingly tied to uninterrupted AI infrastructure spending.
• Bank exposure: JPMorgan has disclosed $50B in private credit exposure, Wells Fargo $36.2B, and Citi $22B, taking the total to over $108B across just three banks. Banks make up around 13% of the S&P 500. If banks take losses on private credit exposure, it can pressure the broader index.
• Default risk: Morgan Stanley expects private credit defaults could reach 8%, while UBS says they could hit 13% in a severe scenario. Leveraged loan defaults peaked around 10–12% during the 2008 global financial crisis. So a 13% private credit default rate would be a major credit event.
• Market chain reaction: Private credit stress can lead to tighter lending, lower AI spending, weaker tech earnings expectations, pressure on bank stocks and broader market weakness.
• S&P 500 risk: Tech is over 30% of the S&P 500. A 25% correction in tech stocks could drag the index down by roughly 7–8 percentage points, even before wider market panic is counted.

What Should Investors Track in Private Credit & AI Debt?

Whether you hold individual US stocks, a Nasdaq ETF, S&P 500 ETF via INDmoney, private credit stress is something that you need to watch out for. Here are the specific indicators worth tracking.

• PIK loans: Check how many borrowers are paying interest by taking on more debt instead of paying cash. If this crosses 15%, it means stress is getting serious.
• BDC prices: Many listed BDCs are already trading 15–20% below the value they claim for their assets. If defaults rise, those values may fall further.
• Debt due in 2028: A lot of weak borrowers need to repay or renew debt soon. S&P Global says this debt will jump from $56.6B in 2026 to $215B in 2028.
• Default risk: If these companies cannot renew their debt, they may have to default or sell assets in a hurry.

Most importantly, watch AI infrastructure utilization. The entire thesis behind the data center lending wave depends on AI compute demand growing fast enough to fill what is being built. If hyperscalers start cutting their capex guidance, or inference demand falls short of capacity being installed, the economics of trillion-dollar infrastructure loans break down quickly.

The AI trade has been spectacular. The credit structure underneath it is being stress-tested for the first time. How that test ends will matter a lot for US tech markets, and by extension, for anyone with meaningful US equity exposure in their portfolio.