Arthur Hayes has reiterated his forecast that Bitcoin could reach $1 million by 2030, linking a potential acceleration in late 2027 or early 2028 to stress in debt-funded artificial intelligence infrastructure.

Walter Bloomberg, the financial news account on X, reported that the Maelstrom chief investment officer expects Bitcoin’s strongest advance to arrive in late 2027 or early 2028. According to the reported outlook, Hayes’s $1 million Bitcoin target rests on his view that an AI investment downturn could lead governments and central banks to inject money into the financial system.

Under the reported scenario, Hayes expects financial pressure to emerge if data-center earnings cannot cover the large sums committed to construction and computing equipment. His projected Bitcoin rally depends on policymakers responding to losses among borrowers and financial institutions with measures that expand liquidity.

Bitcoin’s $1 million forecast rests on an AI credit downturn

In an Aug. 5 report, crypto.news covered Hayes’s AI credit crisis thesis, which treated much of the infrastructure spending as debt-backed property development. His argument focused on land, buildings, electricity connections and cooling systems, alongside processors that could lose value as newer equipment becomes cheaper and more efficient.

Comparing the financing risks with earlier market crashes, Hayes described the boom as a:

In his account, the danger extends beyond falling technology shares. Banks, insurers, private lenders and infrastructure investors could face losses if projects fail to earn enough to meet interest payments, leases and other obligations.

Hayes also argued that profitable technology companies could remain healthy while weaker projects and their financiers struggle. His forecast therefore centers on the debt supporting the buildout, rather than requiring every major AI company to suffer an earnings collapse.

The mismatch between hardware’s useful life and longer financing schedules helps explain his focus on 2027 and 2028. Hayes expects equipment to age while borrowers remain responsible for repayments arranged when revenue expectations were higher.

Late 2027 and 2028 feature in Hayes’s spending forecast

In the August coverage, Hayes predicted that growth in announced AI capital spending would begin slowing during the second half of 2027, with the slowdown becoming clearer in 2028. He also expected investors eventually to favor companies that reduced construction plans.

Although Hayes identified a possible period of stress, he acknowledged that he could not name the borrower that would set off a crisis or identify Bitcoin’s exact bottom. His August scenario included Bitcoin trading between $60,000 and $70,000, with possible downside toward $50,000 before an eventual advance toward $1 million.

By Sep. 22, reporting on his AI debt liquidity argument described a more specific concern: weaker demand for AI training and services could undermine the revenue assumptions behind data centers, chip purchases and related lending.

In his “Safety First” essay, Hayes argued that efforts to reduce computing costs could hurt infrastructure investments financed on expectations of heavier spending. Debt obligations would remain, he wrote, even if customers bought less computing capacity than lenders and developers had anticipated.

Apollo estimates AI financing will extend into private debt

Separate research from Apollo puts figures on the financing requirements behind the buildout. In an Aug. 14 note, chief economist Torsten Slok estimated that the AI ecosystem could support more than $2 trillion of additional investment-grade debt.

Apollo said public investment-grade markets might absorb less than $1 trillion through 2030 because of limits related to issuer concentration and credit ratings. The firm expected more than $1 trillion of financing could move into private placements, infrastructure lending, equipment financing and project-specific structures.

Using data through July, Apollo also said AI-related borrowing already represented nearly 40% of longer-duration investment-grade corporate bond supply. Its research presented private financing as a way to meet demand, with collateral and contractual protections available in some transactions.

For the potential U.S. policy response, Hayes described two paths in “Safety First.” Washington could purchase computing capacity to support the industry, becoming what he called a “compute buyer of last resort,” or provide financial assistance to insurers facing losses on AI-linked debt.

In either case, Hayes expects the response to increase the supply of money and support Bitcoin prices. The Sep. 22 report said U.S. authorities had not announced either measure in response to an AI debt crisis.

U.S. insurance regulators are tightening private credit reporting

The National Association of Insurance Commissioners has identified liquidity, pricing and transparency concerns in private credit, providing a direct U.S. connection to the lending risks discussed by Hayes.

According to its guidance, concerns about valuations, lending standards and sector exposure have contributed to withdrawal requests at some retail private credit funds. Some vehicles have used withdrawal limits, while software borrowers exposed to AI disruption have attracted closer scrutiny.

The association said those developments do not necessarily establish deterioration across private credit markets or insurers’ holdings. State regulators and NAIC staff are monitoring credit quality, valuation practices and insurer investments.

Under amendments adopted in 2025, the NAIC requires private rating rationale reports within 90 days of an annual update or rating change. The reports must contain analytical substance, according to the association’s explanation of the requirements.

For annual financial filings, the NAIC’s Statutory Accounting Principles Working Group has adopted changes effective at year-end 2026 to improve reporting of insurers’ private credit holdings.

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