Federal Reserve faces rising debt load beneath AI infrastructure buildout; potential rescue scenario looms if defaults spike.

There are two quite different propositions that are frequently confused in discussions of artificial intelligence. The first is that AI is an extraordinary technology, likely to alter substantially the way human beings work, communicate, research, write software and organise businesses. The second is that the companies presently building AI infrastructure are therefore worth the enormous sums attributed to them by financial markets. The first proposition may be true without the second being true at all.

That distinction lies at the heart of a striking paper published by Myrmikan Research on 14 August 2026, entitled "AI Debt Failure Will Prompt Another Wave of Fed Bailouts." Its argument is considerably more sophisticated than the familiar claim that AI shares are in a bubble. Indeed, its central concern is not principally with shares. Myrmikan argues that beneath the spectacular valuations of AI laboratories, semiconductor manufacturers, cloud-computing companies and data centres, there has developed an increasingly elaborate structure of debt. If anticipated productivity gains from AI arrive too slowly to service that debt, the consequences may escape the technology sector altogether and enter insurance companies, pension funds and ultimately the balance sheet of the American state.

The paper is written from an unmistakably Austrian-school perspective. Its starting point is monetary history. Before fiat currencies, it argues, gold deposits and withdrawals communicated information about the appropriate price of credit. Central banking replaced this dispersed price-discovery mechanism with administrative judgement. From that moment, central bankers were required to decide whether money was too expensive or too cheap—and political and institutional incentives repeatedly encouraged them to err toward cheapness. The Great Depression, Arthur Burns's inflationary 1970s, the rescue of Continental Illinois and Alan Greenspan's response to the 1987 crash are presented as iterations of essentially the same phenomenon. Once sufficiently large financial structures become dependent on cheap credit, monetary authorities cannot permit them to liquidate without endangering the financial system.

This historical argument supplies the architecture for everything that follows. Myrmikan's contention is that the Federal Reserve has repeatedly created a ratchet. Credit expansion generates investment and rising asset prices; rising asset prices make the policy appear successful; leverage accumulates; and eventually raising rates to suppress inflation threatens the institutions that have become dependent on cheap money. The central bank then retreats. The famous "Greenspan put" was merely the most explicit version of the arrangement.

The paper's most interesting argument concerns productivity. It draws an analogy between contemporary enthusiasm for AI and Alan Greenspan's enthusiasm for computers around the turn of the millennium. Greenspan believed technological progress was increasing productive capacity sufficiently rapidly that monetary expansion could be accommodated without consumer-price inflation. Myrmikan invokes Robert Solow's famous observation that the computer age could be seen everywhere except in the productivity statistics, and argues that the eventual productivity improvements associated with computing proved slower and less spectacular than enthusiasts expected.

This distinction is important. There is an enormous difference between technological capability and measured economic productivity. A machine can perform astonishing tasks without making the economy proportionately richer. Implementation costs matter. Organisational disruption matters. Training matters. Errors matter. Regulation matters. Above all, the price of the technology matters.

Myrmikan contends that precisely this difficulty is appearing with generative AI. It points to reports of unexpectedly high corporate AI expenditure, failures of implementation and surveys suggesting that many enterprises have yet to obtain measurable returns from generative-AI investment. Agentic AI makes the problem more acute because an autonomous system undertaking a task may consume vastly greater computational resources than a system merely answering a question—while mistakes by an agent can have financial consequences rather than merely producing an incorrect paragraph.

There is a genuine insight here. Much public discussion assumes that because models are improving rapidly, productivity must improve at approximately the same rate. There is no economic law requiring this. The transition from capability to productivity involves redesigning institutions around the capability. Electricity transformed manufacturing, but factories had to be rebuilt around electric motors before the gains became overwhelming. Computers eventually transformed offices, but businesses spent decades purchasing software that was expensive, badly integrated and sometimes counterproductive. AI may ultimately prove still more important—but "ultimately" is an uncomfortable word when one has borrowed money repayable next Tuesday.

Nevertheless, this part of the Myrmikan argument should not be accepted too readily. Solow's paradox was a paradox precisely because computing eventually did generate enormous economic changes whose value conventional productivity statistics sometimes struggled to capture. AI may similarly create consumer surplus and improvements in quality that national accounting measures imperfectly. A lawyer who conducts three hours of research in twenty minutes has experienced a real productivity improvement even if billing practices, organisational inertia or GDP statistics disguise part of it.

The correct conclusion is therefore narrower than Myrmikan's rhetoric occasionally suggests. There is not yet compelling evidence that AI will not generate the productivity revolution promised for it. There is considerable reason to doubt whether it will generate that revolution quickly enough to validate every investment presently being made in anticipation of it.

That is a much more dangerous proposition for investors.

Myrmikan makes this distinction particularly effectively toward its conclusion. AI can be revolutionary and AI investments can nevertheless be disastrous. The paper compares the present boom with canals, railways and the internet. Railways remained useful after railway shareholders were ruined. Fibre-optic cable remained useful after telecommunications companies collapsed. Web browsers became indispensable precisely while becoming virtually impossible to monetise directly. The destruction of scarcity can increase social utility while destroying the economic rents upon which investors had relied.

This is perhaps the strongest conceptual point in the paper.

Technological investors frequently ask the wrong question: will people use this technology? The economically relevant question is: who will capture the surplus generated by its use?

AI unquestionably has utility. But competition between models is intense. Chinese developers and open-source models create downward pressure on inference prices. Algorithms become more efficient. Hardware improves. Knowledge diffuses between laboratories. A model capability that costs hundreds of millions of dollars to develop may become a commodity surprisingly quickly.

That creates an uncomfortable possibility. AI might become ubiquitous precisely because AI itself becomes cheap.

If so, the winners might ultimately be businesses and consumers using abundant intelligence rather than the companies that borrowed extraordinary sums to manufacture it.

The paper then turns from technology to corporate finance and becomes considerably more alarming. It portrays the contemporary AI industry not as a conventional supply chain but as a network in which suppliers, customers, financiers and investors increasingly finance one another.

AI laboratories need computing power. Hyperscalers need AI laboratories as customers. Specialist "neocloud" data-centre businesses need hyperscalers and laboratories to sign long-term purchasing commitments. Nvidia needs all of them to keep buying GPUs. Capital therefore moves around the system in circles.

The paper gives the example of Nvidia investing in AI laboratories and neoclouds while those businesses use financing to purchase Nvidia hardware. Nvidia has also undertaken commitments relating to unused computing capacity. Myrmikan's objection is not that these arrangements are fictitious—the chips and data centres certainly exist—but that apparent demand at one point in the chain may partly depend on financing supplied elsewhere in the same chain. Revenue can therefore be economically less independent than accounting statements make it appear.

That is a serious issue. There is nothing inherently improper about vendor financing or strategic investment. Aircraft manufacturers, property developers and telecommunications [*text cut off in source*]