Arthur Hayes forecasts $1 million Bitcoin by 2030, links target to AI debt risks

AI boom and rising debt exposure

According to Hayes, a key turning point may arrive in late 2027 or early 2028, when the pace of capital investment in AI infrastructure starts to slow. He argues that as spending on data centers, advanced chips, and computing systems levels off, the heavy reliance on credit to fuel this growth could become a major concern.

Rather than focusing on the immediate profitability of AI companies, Hayes highlights the risks stemming from debt accumulated across the AI supply chain. He compared the situation to the 2008 financial crisis, suggesting that the greatest vulnerabilities may lie within lenders and infrastructure projects, rather than technology giants themselves.

Hayes maintains that if AI-focused projects are unable to generate sufficient revenue to repay their loans, resulting losses could ripple through banks, private-credit funds, and other financial institutions, prompting central banks and governments to intervene by injecting liquidity into the financial system.

This cycle, in Hayes’s view, sets the stage for scarce assets like Bitcoin to surge amid expanding money supply. He suggests the dynamics around AI borrowing could play a decisive role in shaping Bitcoin’s trajectory over the next decade.

Market data and institutional concerns

Recent figures underlined the scale of debt tied to the AI sector. Reuters reported that AI-related borrowing in the US leveraged-finance market is projected to reach $88 billion in 2026, up sharply from $20 billion in early 2025. At the same time, investors are reportedly growing more selective about funding riskier AI ventures, reflecting heightened scrutiny of potential credit exposures.

The International Monetary Fund echoed these concerns earlier this year, referencing Morgan Stanley estimates that suggest data center capital expenditures through 2028 could total $2.9 trillion. That amount far exceeds the expected cash flows of large hyperscale computing companies, underscoring the sector’s dependence on private loans, corporate debt, and securitization to support expansion.

The IMF observed that the financing gap between expected capital expenditures and operating cash flow is drawing attention to the role of non-traditional credit channels and increasing the likelihood of financial stress in the system.

Given these factors, Hayes anticipates that AI capital expenditure growth will begin decelerating in the second half of 2027. This could make it easier to identify projects based on overly optimistic assumptions about future demand, potentially exposing weaknesses in the sector’s credit structure.

For many investors, monitoring changes in AI spending and debt dynamics has become even more critical. In a market where a single Fed decision or a sudden altcoin listing can change everything in seconds, jumping between different apps for charts, news, and portfolio tracking is costing investors money. Smart traders are now utilizing privacy-first tools like CryptoAppsy to consolidate everything. Without even the hassle of creating an account, you get real-time charts, smart price s, coin-specific news, and critical macro data all on one screen.

Hayes stresses that his $1 million Bitcoin projection is tied to a chain of developments: a slowdown in AI investment, a wave of credit losses, monetary easing from policymakers, and Bitcoin’s reaction as a scarce asset. The outcome, he suggests, is highly dependent on this sequence, not just ongoing crypto adoption.

Bitcoin’s current performance

At present, Bitcoin continues to demonstrate resilience as the broader crypto market evolves. On October 1, BTC was trading at approximately $83,895, up 0.6% over the previous 24 hours.