Arthur Hayes has tied his long-term Bitcoin bull case to a potential collapse in the financing cycle behind artificial intelligence. Speaking on Bankless, the BitMEX co-founder argued that a credit shock involving AI infrastructure could trigger an aggressive policy response and redirect capital toward crypto. His $1 million Bitcoin scenario depends on a crisis followed by large-scale money creation, not on the AI boom continuing uninterrupted.
The thesis treats AI as a capital-intensive balance-sheet expansion rather than only a technology growth story. Hayes pointed to data centers, advanced chips and related infrastructure financed through debt and long-term assumptions about asset life and future demand. The crucial risk is a mismatch between financing schedules and rapidly depreciating computing hardware, which could expose lenders if projected revenue fails to justify the investment.
Hayes Focuses on the Credit Cycle Behind AI
Hayes said the pressure could become clearer around late 2027 or 2028, when loans and capital expenditures arranged during the 2025 and 2026 buildout begin confronting the economics of older processors. In his framework, lenders may be assuming five- or six-year amortization periods for hardware that becomes commercially outdated much sooner. A forced reassessment of those assets could turn an investment slowdown into a broader credit event.
The wider financing backdrop gives the argument a measurable foundation, even if it does not prove Hayes’ predicted outcome. Reuters reported that major technology companies have increasingly used debt and equity to fund AI infrastructure, while combined spending by Alphabet, Amazon, Microsoft and Meta was expected to exceed $700 billion in 2026. Separate company disclosures compiled by Reuters showed roughly $1.09 trillion in future lease payments, mostly connected to data centers, that had not yet begun. AI expansion is creating substantial long-term obligations before demand has been fully tested.
Hayes expects policymakers to respond to a severe credit disruption with liquidity support aimed at stabilizing banks and financial markets. He argues that money creation could not reverse technological obsolescence or make older chips more competitive. Fresh liquidity would therefore have fewer reasons to return to damaged AI investments, potentially increasing demand for assets viewed as scarce and independent of the impaired credit structure.
Bitcoin Upside Relies on the Rescue, Not the Crash
Bitcoin’s role in the thesis comes after the initial disruption. Hayes believes the asset would benefit when investors seek an alternative destination for liquidity that no longer finds attractive returns in AI-linked debt or equity. The bullish catalyst is the monetary response to financial stress, rather than the destruction of technology valuations by itself.
That distinction also limits the usefulness of the $1 million figure as a conventional forecast. Hayes acknowledged that he does not know when the scenario will unfold and said he continues to revise his model. He also argued that AI currently competes with crypto for marginal investment capital, reducing the likelihood of an explosive Bitcoin rally while the technology trade remains dominant. The target is conditional on several uncertain events occurring in sequence.
The argument remains a macro thesis built around leverage, depreciation and policy behavior. It does not establish that an AI credit crisis is inevitable, that authorities would respond exactly as Hayes expects or that resulting liquidity would flow primarily into Bitcoin. The case is notable because it links Bitcoin’s strongest upside to weakness in another market’s funding structure, leaving execution and timing unresolved.