Arthur Hayes, co-founder of BitMEX, has outlined a scenario in which weaker demand for artificial intelligence computing power stresses debt markets linked to data centers and private credit. In a recent essay, he contended that this pressure could force U.S. authorities into responses that expand the money supply, creating conditions favorable for Bitcoin.

Hayes questioned recent statements from major U.S. AI laboratories calling for a slower pace of advanced model development. While companies including OpenAI and Anthropic have pointed to cybersecurity and safety considerations, Hayes suggested the shift may also track economic realities of high compute costs and limited profitable demand.

Scale of AI Infrastructure Financing

Demand from leading AI labs underpins more than $1 trillion in investment-grade debt and hundreds of billions of dollars in lower-rated loans and private credit. These obligations finance data centers, semiconductors, and related infrastructure through partners that include major technology firms.

Research from Apollo Global Management has projected AI infrastructure spending could reach roughly $5 trillion through 2030. Justifying that outlay would require about $2 trillion in annual AI services revenue. Consensus forecasts for five large hyperscalers show operating cash flow rising from approximately $600 billion toward $2 trillion by the end of the decade, though weaker growth could widen credit spreads and curb further capital expenditure.

Private credit funds have already restricted some investor withdrawals earlier this year, signaling stress in less liquid lending markets that overlap with AI-related obligations.

Insurance Structures and Capital Shortfalls

Hayes focused on how private equity firms have used captive insurance companies and affiliated reinsurance entities, many domiciled in Vermont, to hold policyholder premiums against AI-linked private credit. According to analysis by forensic accountant Thomas Gober, published through analyst Nick Nemeth, these structures show $1.54 trillion in affiliated reinsurance claims against only $657 billion of surplus capital.

Removing the reinsurance support would leave 29 of the top 30 U.S. insurers technically insolvent on a marked-to-market basis. Examination of three Vermont captives found they held just 3.7 cents of real assets for every dollar of obligations. One Brookfield-linked reinsurance arrangement was recorded at a $1.48 billion valuation, yet the entity reported to regulators that it carried zero actual payout liability.

Insurers have increased exposure to AI data center debt. A wave of credit downgrades, if compute demand softens, would require parent companies to post additional capital that the affiliated reinsurers cannot supply. State guaranty funds that back failed insurers typically limit coverage to $250,000 to $300,000 per policy and are themselves financed by surviving insurers that often rely on similar structures. Retirees holding annuities could face losses if the backing capital proves insufficient.

Policy Paths and Liquidity Implications

Hayes presented two primary responses open to U.S. authorities. Officials could position the government as a buyer of last resort for computing capacity, citing national security needs to sustain AI infrastructure even if commercial demand falters. Alternatively, they could provide support to insurers facing losses on private credit that threaten policyholder claims.

In either case, the actions would involve increased government borrowing, banking-system liquidity, or direct monetary measures. Hayes argued that the resulting expansion of the money supply would raise demand for scarce assets such as Bitcoin. He has previously linked similar liquidity interventions, including yen defense operations and Treasury buybacks, to upward pressure on Bitcoin prices.

The Federal Reserve raised its policy rate by 25 basis points in mid-September to a target range of 3.75 percent to 4.00 percent, citing persistent inflation. That tightening raises the cost of capital for leveraged sectors, including AI infrastructure. No official policy response to AI-related credit stress has been announced. Investment activity continues, with new financing initiatives from semiconductor firms and large bond offerings tied to AI stakes.

Hayes framed the outcome as binary for Bitcoin holders: whether authorities fund unprofitable compute capacity or backstop underwater insurance balance sheets, the common result is greater dollar liquidity. The sequence depends on whether AI-linked debt is eventually marked to market values that expose the reported capital shortfalls. Until contracts are tested by downgrades or defaults, the structures remain intact on regulatory filings.