Could an Nvidia crash trigger another 2008-style financial crisis? What experts say

Nvidia's leadership crisis and sharp stock market decline have raised concerns about whether trouble at the AI chipmaker could spread through the global financial system. However, financial experts believe that while a severe downturn could trigger widespread losses, a repeat of the 2008 banking crisis remains unlikely.

According to CNBC, Nvidia's expanding network of investments, cloud-service commitments and financial guarantees has earned it the nickname the "central bank of AI", reflecting its growing influence over the artificial intelligence ecosystem.

The concerns intensified following co-founder Jensen Huang's resignation as CEO, with CFO Colette Kress taking over as interim chief.

Is Nvidia too big to fail?

Former Federal Deposit Insurance Corporation (FDIC) chair Sheila Bair believes Nvidia's financial structure differs fundamentally from that of a bank.

Nvidia isn't "too big to fail in a bank sense at all," Bair told CNBC.

Unlike banks, which can face sudden deposit withdrawals and liquidity crises, Nvidia manufactures products that remain commercially valuable. Its substantial equity base also provides protection against financial shocks.

Nevertheless, Bair warned that regulators must closely monitor banks' exposure to Nvidia and other AI companies.

"Nvidia going down would be a pretty seismic event," she said.

Nvidia's $400 billion financial commitments

The biggest concern is Nvidia's interconnected financing arrangements with AI infrastructure companies.

According to CNBC, Columbia Business School professor Stijn Van Nieuwerburgh estimates Nvidia's overall financial commitments at approximately $400 billion.

These include up to $36 billion in cloud-service purchasing commitments, a conditional $105 billion guarantee linked to an Ohio data-centre project involving OpenAI and SB Energy, and a potential $125 billion backstop for a larger credit programme.

Such arrangements help AI infrastructure companies secure financing and purchase Nvidia processors. However, these commitments are structured differently and would not necessarily become payable simultaneously.

Van Nieuwerburgh noted that Nvidia's approximately $3.9 trillion market capitalisation provides a substantial cushion against potential losses.

Could AI losses spread to banks?

The International Monetary Fund has warned that increasing leverage across AI companies and investors could amplify financial instability.

Jason Wu, an IMF official overseeing global markets analysis, said interconnected exposures could create reinforcing pressures between financial markets and the wider economy.

"I think that’s a real concern because both the economy and financial markets are standing on the same leg, which means a drawdown could have reinforcing effects between the two," Wu told CNBC.

Financial institutions are particularly concerned about private-credit lending, debt-funded data centres and borrowing secured against AI infrastructure.

Huang's 33-year journey

Huang, 63, co-founded Nvidia in 1993 with Chris Malachowsky and Curtis Priem, transforming the company from a gaming graphics-chip manufacturer into a global AI computing powerhouse.

Born in Taiwan, Huang studied electrical engineering at Oregon State University before earning a master's degree from Stanford University.

His leadership came under scrutiny following reports of alleged extramarital relationships involving Nvidia executive Kimberly Powell and Alice Huang, a former Megaspeed executive.

The latter reportedly attracted US intelligence attention over suspected Chinese espionage links and allegations involving restricted Nvidia chip shipments. China has rejected the espionage allegations as unsubstantiated. No wrongdoing has been established.

According to the supplied report, Nvidia's board initiated an independent investigation into whether Huang was targeted by foreign intelligence.

Nvidia shares fell 8.55% to $158.88 at Thursday's opening bell, reflecting investor concerns over the leadership transition and its potential implications for the AI industry.