On September 13, 2026, BitMEX co-founder Arthur Hayes published a lengthy post on X, re-evaluating the credit risks accumulated by generative AI infrastructure over the past few years within a policy framework he referred to as "AI Safety First." Under this framework, the U.S. government would either directly assume the ultimate buyer role in the computing power market, using fiscal spending to backstop the capital expenditures of companies like Anthropic, OpenAI, and SpaceX; or allow the related bad debts to concentrate on the heads of insurance companies and other financial institutions, with the Federal Reserve providing liquidity assistance through money printing and balance sheet expansion, continuing the financial backstop model of crisis times. Hayes's key judgment is that regardless of which path is taken, at the macro level, it will converge to the same ultimate outcome—monetary expansion and liquidity release, which is one of the core variables determining the pricing of risk assets. Since he has long been seen as a barometer for macro trends in the crypto community, this analysis of the "AI Money Printing Endgame" was quickly relayed by several Chinese media outlets including PANews, Odaily, Deep Tide TechFlow, Jinse Finance, and BlockBeats that day, forming a market narrative chain of "AI capital expenditure → government or central bank backstop → liquidity spillover → crypto assets benefiting," which provided a clear medium to long-term bullish storyline for assets like Bitcoin and Ethereum: if AI safety is politicized into a promise that must be fulfilled, then the one footing the bill will be a round of new liquidity led by the U.S. dollar, and this new liquidity will ultimately need to find a pricing outlet in global risk assets.

Debt and Credit Vulnerabilities of Computing Power Under the AI Safety Narrative

Behind the storyline of “AI capital expenditure → government or central bank backstop → liquidity spillover,” there is actually a rapidly built-up balance sheet of computing power assets. Between 2020 and 2025, the advancement of generative AI technology pushed data centers, training clusters, and other infrastructures into a rhythm akin to an "arms race." Arthur Hayes clearly defines this round of AI development as a highly capital-intensive field: each round of model iteration and each upgrade of computing power entails huge upfront investments, and these expenditures are currently mainly borne by corporate financing and capital market leverage, making the combined debt and equity financing a new generation of high-leverage assets in computing power.

Under the gradually forming discourse of "AI Safety First" or "AI First" in the United States, the policy level tends to underwrite the long-term development of AI, which effectively acts as a political insurance layer for continued expansion of capital expenditure. As long as the two labels "safety" and "competitiveness" remain, companies and financiers have reasons to push computing power budgets up, elongate liabilities a bit more, and shift risks further into the future. However, Hayes’s reminder precisely points out the vulnerability: this asset-liability structure presumes a world of continuously high-speed growth in computing power demand. If the growth rate slows or even experiences a "gap," or if regulation suddenly tightens under the guise of safety, the misalignment of cash flow and debt service capacity will be exposed instantly, with bad debts spreading from corporate balance sheets to the financial system, ultimately pushing the pressure onto the government and central bank level. In his view, this layer of credit vulnerability built up by computing power debt is the real starting point that will compel fiscal and monetary authorities to initiate a new round of liquidity release.

Government Takes Over Computing Power Buying: Liquidity Effect of Implicit Fiscal Stimulus

Following this fragile chain, the first backstop approach envisioned by Hayes is to let the U.S. government stand at the bottom of the computing power market and directly become the "ultimate buyer" behind companies like Anthropic, OpenAI, and SpaceX. On the surface, these expenditures can be packaged as defense projects, critical infrastructure, or industrial policy budgets, justified by "AI Safety First." However, in essence, it is about using fiscal checks to backstop the entire chain of AI capital expenditures: as long as computing power is marked as a strategic resource, the government can take over the procurement contracts that companies originally aimed at the market, replacing highly volatile commercial demand with stable orders backed by the public sector's balance sheet.

Once this path is opened, at the macro level, what occurs is further elevation of public sector balance sheets and fiscal deficits. The government injects sustained demand for computing power and related infrastructure under the guise of budget expenditure, allowing upstream chip manufacturers, data center operators, and AI companies to gain more certain cash flows, locking in capital expenditures that would have needed to be digested by equity and debt markets over the coming years as "quasi-national policy projects" with government as the buyer. Historical experience shows that similar large-scale industry support often first manifests in the premium valuation of the related sectors and then spreads to higher beta assets: tech stocks enjoy valuation expansion under stable orders and policy endorsements, while Bitcoin and Ethereum, viewed as pure liquidity vehicles, absorb the overflow of funds and risk appetite amidst the expectation of “fiscal expansion — monetary passive adjustment.” For traders, as long as the government is willing to continue to take on computing power buying under the banner of "AI safety," this implicit fiscal stimulus will manifest as a new round of elevated risk appetite and valuation repricing in tech stocks and Bitcoin, Ethereum.

Fed Printing Money to Bail Out Asset Managers: The Migration of AI Bubble to Monetary Expansion

On another path that is more familiar yet more dangerous, Hayes shifts the perspective from the Treasury Department to the Federal Reserve. He imagines that when debts surrounding computing power, data centers, and AI infrastructure begin to concentrate into defaults, the real "garbage bin" is not tech stocks, but the insurance companies that provide the guarantees and revenue structure designs for these assets. When credit risk is concentrated and exposed within this system, regulatory bodies no longer respond with "industrial policy" language, but revert to the scripts already practiced during the 2008 financial crisis and the 2020 pandemic: the central bank intervenes, offering bailouts to financial institutions through money printing. In technical terms, this means another expansion of the Federal Reserve's balance sheet, injecting large amounts of liquidity in various forms to insurance institutions holding AI-related bad debts, redistributing losses that should have been borne by shareholders and bondholders at the monetary level.

Hayes clearly points out that the essence of this choice is not regulatory correction, but monetary expansion: through central bank endorsement, AI-related debt is "righted," lowering systemic risk, and unifying AI safety with financial stability in discourse, yet at the cost of a more accommodative dollar environment. For crypto traders, this indicates the familiar script is likely to replay—dollar liquidity first completes a "fill-in" between insurance companies and traditional financial assets, subsequently spilling into broader risk assets through low-interest rates and refinancing channels, finding Bitcoin and Ethereum as pure liquidity carriers after tech stocks. Although currently, the U.S. government and the Federal Reserve have not provided an official response to this assumption, once the chain from AI bad debts to central bank balance sheet expansion is initiated, it will become a key macro driver for a new round of valuation repricing and risk appetite elevation for Bitcoin and Ethereum.

From AI to Crypto: How Additional Liquidity Transmits to Bitcoin and Ethereum

In Hayes's deduction, the two backstop paths are essentially just selecting different "tap handles": either the fiscal side directly becomes the buyer of computing power, or the central bank provides funding support for institutions that take on AI bad debts, but regardless of which door is opened, they eventually point towards the same macro variable—marginal increase in dollar liquidity. For asset managers, this means further downtrend in returns on safe assets, nominal yields being compressed, so the portfolio must include higher beta targets to obtain excess returns. Bitcoin and Ethereum have already proven themselves to be highly elastic to dollar liquidity expansion during past easing cycles; once "AI Safety First" is interpreted by the market as an implicit money printing commitment, they will naturally be repositioned back into the leveraged positions of portfolios to express directional bets on liquidity re-expansion.

More crucially, this logic provides a new narrative structure for the existing "AI + Crypto" thematic trading. Previously, funds were more focused on valuation expansion of AI stories within tech stocks; now, Hayes explicitly strings together AI capital expenditure, government or central bank backstop, and the benefits for crypto assets into a single chain, further strengthened by concentrated reporting from multiple Chinese crypto media outlets, reinforcing the linkage imagination of "the larger the AI orders, the more potent the future money printing, the more Bitcoin and Ethereum will benefit." Although there are currently no on-chain funds or ETF subscription data that can be directly attributed to this speech, the pricing framework for traders has already quietly adjusted: AI-related policies and credit events are no longer just catalysts for tech stocks, but are viewed as important upstream variables for future liquidity and risk preferences for crypto assets, which is a new macro hypothesis that cannot be overlooked when observing the capital flows of Bitcoin and Ethereum.

Trading Clues and Uncertainties of the AI Money Printing Endgame

In Hayes's framework, whether the government directly takes over computing power buying, using fiscal measures to back AI capital expenditure, or allows AI-related bad debts to settle into insurance companies, followed by the Federal Reserve expanding its balance sheet to provide funding, both paths ultimately point to the same macro variable: monetary expansion and liquidity rise. For high beta assets like Bitcoin and Ethereum, this means that the medium to long-term trading mainline remains built around global liquidity and the dollar interest rate cycle, as long as "AI Safety First" eventually materializes into a credit backstop at the fiscal or central bank level, any signal of marginal easing may be priced in by the market in advance. However, it is important to be cautious: Hayes's statements are essentially macro policy and market analysis, not official decision documents, and no quantitative predictions on debt scale or liquidity release have been provided. Currently, trading on "AI + macro liquidity" remains at the level of logical deduction and expectation games, and short-term prices will still be subject to constraints from interest rate paths, regulatory attitudes, and genuine demand for AI infrastructure. Moving forward, traders really need to focus on the specifics of the U.S. government's fiscal and industrial policy statements under the discourse of "AI Safety First," the pace of AI infrastructure investment either slowing or accelerating, and marginal changes in the Federal Reserve's balance sheet and dollar liquidity indicators, as these variables will determine whether the "AI Money Printing Endgame" is a substantial driving force or yet another narrative bubble.

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