BitMEX co-founder Arthur Hayes, who serves as chief investment officer at Maelstrom, argues in a new essay titled "Safety First" that recent vows by leading U.S. artificial intelligence laboratories to slow the race toward artificial general intelligence (AGI) over safety concerns may be less about caution and more about softening demand for AI products at prevailing prices. He points to the market's broad preference for cheaper Chinese models, suggesting a slower pace of development offers a convenient rationale for labs under commercial pressure.

If training budgets were to shrink, demand for data centers and semiconductors could contract sharply Hayes writes, placing strain on more than $1 trillion of investment-grade debt and hundreds of billions of dollars in lower-rated loans tied to AI infrastructure. At that scale, the essay's concern shifts from the technology itself to the financing stacked on top of it.

The essay contends that the major AI labs generate no profits and instead depend on profitable technology companies to provide off-balance-sheet support for debt issued to finance data center leases and chip purchases. As a result, any reduction in compute demand would pressure the valuation of that debt regardless of whether defaults occur in the near term. The pivotal question, Hayes argues, who holds this debt — and whether it was purchased with leverage.

Hayes promoted the essay in a post on X on September 22, 2026:

Check out my new essay "Safety First". Trump has a choice, print or print. "Did you hear that? The AI bros suddenly developed a conscience and are worried about humanity's survival in the face of their almost silicon-God's ascendance. It's been almost silicon-God for some time… pic.twitter.com/xMRydV3lFf

— Arthur Hayes (@CryptoHayes) September 22, 2026

Insurance Sector Flagged as Hidden Risk; Bailout Viewed as Likely

Much of the exposure, according to the essay, sits inside the U.S. insurance industry through a structure Hayes describes as captive insurance. The setup ties together two capital-markets threads: the financing of the AI buildout and private equity's turn to insurers as a pool of long-duration money. He recounts that private equity firms, confronting diminishing returns and rising capital costs, acquired insurers offering life and annuity products, whose premiums supply long-dated, patient capital. Policyholder funds were then directed into private credit and AI data center debt, while affiliated captive reinsurers — often domiciled in states such as Vermont, where disclosure requirements are limited — supplied regulatory capital buffers with minimal real backing. Because the cushion comes from affiliates rather than independent capital, its strength depends on the fortunes of the same group taking the risk, as the essay frames it.

Drawing on analysis by Nick Nameth, Hayes suggests these affiliated reinsurance arrangements could total roughly $1.54 trillion, with their true asset quality obscured by regulatory opacity.

The mechanism of failure, as he lays it out, is straightforward: if AI labs do not consume compute at anticipated levels, the cash flows supporting data center securitizations deteriorate, prompting credit downgrades. Downgrades would force parent insurers to raise capital that captive reinsurers cannot provide, exposing insolvency across the sector. Policyholders, he notes, are protected only up to roughly $250,000–$300,000 per policy in most states, with surviving insurers funding the guarantee after the fact. Because the underlying capital originates in policyholder premiums, the scenario places household money one step removed from AI infrastructure debt, with those caps as the last line of defense.

Hayes concludes that the U.S. government faces two plausible responses: acting as a "compute buyer of last resort" on national security grounds, or printing money to support insurers holding impaired AI debt. He characterizes both outcomes as dollar-liquidity expansion that would benefit Bitcoin and other crypto assets, while also forecasting a glut of cheap compute that could accelerate adoption of AI agents. The essay thus supplies its own watchlist: who ultimately holds the debt and at what leverage, whether compute demand keeps pace with the financing, and which of the two government responses materializes if stress appears.

He acknowledges the thesis is not an immediate one, describing recent crypto market choppiness as temporary while expecting continued growth in dollar supply. For crypto readers, the claimed transmission channel is therefore not the industry's own fundamentals but dollar liquidity — in Hayes's telling, the same force he expects to keep expanding.

Source: Metaverse Post