Bitcoin to $1 Million by 2030: Hayes Links Rally to AI Credit Crisis
Arthur Hayes, chief investment officer at Maelstrom, has reiterated his forecast that Bitcoin could reach $1 million by 2030. He expects Bitcoin gains to accelerate in late 2027 or early 2028 if debt-funded artificial intelligence infrastructure comes under pressure.
Hayes argues that the AI boom is primarily a credit story rather than an earnings story. Data centres, power connections, buildings and computing equipment have been financed with long-term debt, while AI hardware could lose value as newer and cheaper technology emerges. If data-centre revenue fails to cover interest, leases and other obligations, banks, insurers, private lenders and infrastructure investors could face losses.
Apollo estimates that the AI ecosystem could support more than $2 trillion in additional investment-grade debt. It expects public bond markets to absorb less than $1 trillion through 2030, leaving more than $1 trillion potentially dependent on private placements, infrastructure loans, equipment financing and project-level structures.
Hayes believes a credit downturn could prompt governments and central banks to inject liquidity. Possible measures include Washington purchasing computing capacity or supporting insurers exposed to AI-linked debt. Such monetary expansion could benefit Bitcoin, although Hayes previously warned that Bitcoin could first fall to $50,000-$70,000 before a longer-term rally.
The National Association of Insurance Commissioners has also tightened reporting requirements for insurers’ private credit holdings, with changes taking effect at year-end 2026. For traders, the thesis presents a high-risk macro scenario rather than an immediate Bitcoin catalyst. AI spending data, private-credit stress, bond yields, liquidity conditions and Bitcoin support levels will be key indicators.
Neutral
The news is neutral for Bitcoin in the short term because it outlines a conditional and distant scenario rather than a confirmed market event. Hayes’s $1 million Bitcoin forecast could support bullish sentiment, especially among long-term investors, but the proposed catalyst depends on an AI credit downturn occurring in 2027 or 2028.
The thesis has two opposing effects. An AI financing crisis could initially be bearish if lenders reduce risk, liquidity tightens and investors sell volatile assets, as seen during credit-stress episodes such as the 2008 financial crisis and the 2022 crypto deleveraging cycle. Bitcoin could face pressure alongside other risk assets, with the $50,000-$70,000 range identified as a potential downside zone in Hayes’s earlier scenario.
A later policy response could become bullish. If governments or central banks provide support, expand money supply or act as buyers of last resort, traders may anticipate greater liquidity and stronger demand for scarce assets such as Bitcoin. Similar expectations of monetary easing have historically supported Bitcoin, although the response is uncertain and may take time.
In the short term, traders are more likely to react to AI capital-spending forecasts, bond-market stress, private-credit defaults, central-bank policy and Bitcoin’s technical support levels than to the forecast itself. In the long term, a genuine AI credit contraction followed by large-scale liquidity support could strengthen the Bitcoin narrative. However, the absence of a confirmed crisis and the speculative timing make a neutral classification more appropriate.