Arthur Hayes has warned that the financing behind the artificial intelligence infrastructure boom could become a credit-market fault line, arguing that heavily funded data centers and power projects may resemble leveraged real estate more than conventional technology spending. In a Tuesday blog post, the BitMEX co-founder said a pullback in AI capital expenditure could expose borrowers tied to long-lived construction commitments, potentially prompting a major liquidity response that he believes would favor Bitcoin.
Hayes’s forecast rests on a highly conditional chain of events: an AI spending slowdown would need to pressure lenders and credit markets severely enough to force central banks and governments to inject large amounts of liquidity. Under that scenario, he argued Bitcoin could eventually exceed $1 million, as traders seek assets with a fixed supply during an expansion of government money and credit.
Before such a shift, Hayes expects Bitcoin to remain vulnerable to a sharper correction. He wrote that the cryptocurrency could trade between $60,000 and $70,000 and could briefly fall as low as $50,000 before a wider credit-cycle reversal supports a recovery.
Ai lease commitments add to financing concerns
Hayes’s warning comes as large technology companies accumulate unusually large future obligations for data-center capacity. Reuters reported Tuesday that Microsoft, Meta, Oracle, Amazon and Alphabet have committed roughly $1.09 trillion to leases that have not yet begun, with most of the commitments tied to data centers.
The total is almost four times the approximately $285 billion in lease liabilities already recorded by those companies, Reuters reported. The figure should not be read as direct debt in full: it represents undiscounted lease payments spread across several years, rather than the present value of borrowing that appears on a balance sheet.
Even so, the commitments show how rapidly AI infrastructure spending is moving beyond purchases of chips and software. Cloud providers are signing long-duration contracts for buildings, servers, electrical equipment and power capacity, creating fixed costs that may continue even if demand for AI services grows more slowly than expected.
That structure gives Hayes’s argument a concrete target. Data centers can be marketed as part of the technology sector, but their financing often involves the same pressures associated with property and infrastructure projects: high upfront spending, reliance on long-term occupancy or customer demand, and significant exposure to refinancing conditions.
Oracle’s contract mismatch illustrates the risk
The financial pressure is not evenly distributed among the companies expanding AI capacity. A separate Reuters analysis found that Oracle’s debt stood at about 4.3 times earnings before interest, taxes, depreciation and amortization, a commonly used measure of leverage. Alphabet, Amazon, Microsoft and Meta had debt-to-EBITDA ratios below one, according to Reuters.
Andrew Chang, an analyst at S&P Global, told Reuters that Oracle’s data-center leases create a potential mismatch because they run for 15 to 19 years while the company’s customer contracts last no more than five years. If customers reduce their commitments or fail to renew at the expected pace, Oracle could remain responsible for far longer lease obligations than its revenue agreements cover.
That does not mean AI infrastructure spending is headed for a crisis. The largest companies involved have substantial cash flows and, in several cases, relatively modest debt burdens. Their lease commitments also reflect expectations of continuing cloud-computing demand, not simply speculative construction. Yet the difference in leverage between Oracle and its larger peers shows that the AI buildout may create different levels of risk across the sector.
Hayes’s comparison with 2008 is therefore more useful as a warning about financing structures than as a direct prediction of a repeat of the global financial crisis. The 2008 collapse involved widespread exposure to mortgage-backed securities and fragile bank balance sheets. AI infrastructure expansion is concentrated among a smaller group of technology firms, data-center operators, utilities and lenders. A downturn would depend on whether spending commitments, customer demand and funding conditions weaken simultaneously.
Bitcoin and Ether remain central to Hayes’s thesis
Hayes has repeatedly linked Bitcoin’s longer-term outlook to global liquidity conditions. His latest post places the AI infrastructure boom within that framework: if debt-funded projects trigger a credit contraction, policymakers may eventually respond with measures that lower funding stress and expand liquidity.
Bitcoin’s path in such an episode would not necessarily be immediate or smooth. During sudden market stress, traders often sell liquid assets to meet margin calls or raise cash, including cryptocurrencies. Hayes’s own forecast of a possible move toward $50,000 reflects that risk before any potential liquidity-driven rebound.
Hayes also forecast Ether at $5,000 by year-end. He wrote that Maelstrom, his family office, intends to build a large Ether position while selling out-of-the-money ETH put options. Selling puts can generate option premium, but it also commits the seller to buy ETH at a predetermined price if the market falls below that level by expiry, increasing exposure during a decline.
The combination of long-term bullish targets and near-term downside expectations reflects the uncertainty surrounding the AI spending cycle. The reported scale of unstarted data-center leases gives markets another set of figures to watch: whether companies can convert massive infrastructure commitments into durable revenue before those commitments become a drag on cash flow and borrowing capacity.
Concerned about AI-driven credit risks? Deepen your outlook with our analysis: explained the impact of quantitative easing on crypto market.
Disclaimer: The content on this page is provided for general informational purposes only and does not represent the views or financial advice of Toobit. We make no guarantees regarding the accuracy or completeness of this information and shall not be held liable for any errors, omissions, or outcomes resulting from its use. Investing in digital assets involves risk; users should independently evaluate their financial situation and the risks involved. For further details, please consult our Terms of Service and Risk Disclosure.
