AI companies fund each other. What if, in the end, it were the governments who footed the bill?

Imagine you are a manufacturer of very expensive industrial machinery. Your customers would like to buy more and more of it, because they are convinced that in the future there will be a huge market for the products made using that machinery. The problem is that, for the time being, their revenue is insufficient to support these purchases. You have plenty of money, and you decide to invest part of it in the very companies that buy your machinery. These companies use the money they receive to buy more machinery from you, whilst you record new sales and can announce to your shareholders that demand continues to grow. Thanks to the investment they have received, your customers can in turn demonstrate that they have sufficient resources to expand and attract new investors.

As long as everyone is convinced that the future market will be large enough to recoup current investments, the system works perfectly well. However, there is one question that someone will have to ask sooner or later: how much money is coming in from end consumers – those who actually buy the products – and how much continues to circulate amongst companies that finance one another?

This is a particularly interesting question when applied to artificial intelligence, as it describes quite accurately some of the financial relationships that currently link some of the sector’s leading companies. The mechanism is known as circular financing, and an article published on 18 October in The Conversation by Ruipeng Liu and Sagarika Mishra, entitled ‘Who’s paying whom? The circular money machine behind the AI boom (or bubble)’, has outlined some very instructive examples of this.

Take Nvidia, for example, the manufacturer of specialised processors used to train and run large artificial intelligence systems. These processors are in high demand, are quite expensive and are an essential component of the massive data centres being built all over the world. Nvidia makes money by selling them, but it also invests in the companies that buy them.

Among these is OpenAI, the company that develops ChatGPT. Nvidia is a financial backer of OpenAI, which requires enormous computing power and therefore Nvidia processors. The manufacturer provides financial support to one of its own customers, who can use the resources received to purchase the financier’s products. Nvidia has also made financial commitments relating to the development of infrastructure for artificial intelligence, thereby helping to further sustain demand for its products.

Of course, investments and supplies are separate transactions, and their amounts must be considered in accordance with the agreements actually concluded. However, the economic relationship that is created is quite straightforward: part of the money used to finance clients’ growth comes from the very companies that profit from their expenditure.

There is also an even more curious example: Firmus, an Australian company that builds data centres. Nvidia has participated in its funding and provides it with the technology needed to build the infrastructure. Firmus has also entered into an agreement to supply computing capacity to Nvidia itself, with a potential value of up to 30 billion dollars over six years. The same company can therefore act simultaneously as an investor, supplier and customer. In turn, OpenAI has been announced as a key client of some Firmus centres in Malaysia.

Anyone looking at these companies’ results sees investments running into billions, new orders and contracts of staggering proportions. These are all genuine economic activities, but the way they are intertwined makes it difficult to determine how much of the overall growth depends on demand from outside the system – demand capable of generating sufficient revenue to sustain it over time.

The issue has also been studied by the Bank for International Settlements (BIS), an international financial institution based in Basel, founded in 1930, which promotes cooperation between central banks and studies risks to the stability of the global financial system. In a report published on 1 October, the BIS examined investments between artificial intelligence firms over the period 2021–2025. The findings are interesting: 55.2 per cent of the value of investment received by companies in the sector came from other companies in the same sector. Furthermore, when considering investments made by one artificial intelligence company in another, 46.4 per cent of their value related to transactions in which there was also a commercial supply relationship between the investor and the recipient.

These percentages do not prove that the money was used entirely to purchase the funder’s products, nor do they allow us to calculate what proportion of the companies’ revenues derives from circular financing. However, they do illustrate just how important cross-shareholdings and financial relationships have become between companies that are simultaneously each other’s customers and suppliers.

To understand why this might be a problem, let’s imagine that the forecasts regarding the future use of artificial intelligence turn out to be overly optimistic. The companies that built the data centres would be left with costly infrastructure and insufficient revenue. They would cut back on purchases of new processors, harming suppliers. The latter would see their sales decline whilst, at the same time, the value of their investments in their customers would diminish. The worsening outlook could make it more difficult to secure new funding, leading to further reductions in investment.

In a system of this kind, a company’s difficulties can therefore quickly spread to others, precisely through those links that had initially fuelled its growth. The Reserve Bank of Australia, too, in its October financial stability report, drew attention to the growing opacity of circular financing and the risks arising from expectations of particularly rapid profit growth.

There is a rather instructive historical precedent. In the 1990s, during the expansion of the Internet, some telecommunications equipment manufacturers subsidised their customers to encourage them to purchase their technologies. Expectations were sky-high, and indeed the Internet would go on to transform the world. The problem was that much of the infrastructure was built before there was sufficient demand to make it economically viable. When the telecoms bubble burst, several companies went bankrupt and many investors lost huge sums of money.

The internet naturally continued to develop, drawing in part on the infrastructure built during the speculative phase. The validity of the technology had not prevented expectations regarding the pace of its adoption from leading to excessive investment.

In the case of artificial intelligence, there is also an additional challenge. Specialised processors become technologically obsolete within a few years, due to the rapid development of new generations of chips. Those who have invested billions in purchasing them must therefore recoup the cost fairly quickly. If the expected revenues are slow to materialise, the time available to recoup the investment is reduced.

Ultimately, everything depends on how quickly artificial intelligence manages to generate a market profitable enough to sustain the immense infrastructure currently being built to serve it. The services are already used by millions of people and a great many businesses, but this alone does not allow us to determine whether future revenues will be commensurate with current investments.

The scientific and technological success of artificial intelligence can therefore coexist with the financial overvaluation of the companies that develop it. A technology can profoundly change the economy and, at the same time, cause colossal losses to those who have bet too early on its profitability.

Circular financing makes this venture particularly delicate, as it allows a portion of the demand one hopes to meet tomorrow to be anticipated today. As long as the money keeps coming in, everyone can show encouraging results. Ultimately, however, someone must pay for the services provided by the data centres, using resources earned outside the funding circuit that built them. And it is the scale of that demand – far more than the billion-euro investments announced by companies – that will determine the sector’s economic stability.

There is, however, a further factor that risks fundamentally altering the terms of the issue: the growing pressure for governments to shoulder an ever-increasing share of the demand for artificial intelligence. If the private market does not grow quickly enough to justify the investments already made, public authorities may become particularly valuable customers, as they have budgets funded by general taxation and can commit to long-term procurement programmes. Part of the risk borne by private investors would thus end up being transferred, at least indirectly, to taxpayers.

Consider, for example, the persistent push for the widespread adoption of artificial intelligence in schools. In January 2026, OpenAI launched the ‘Education for Countries’ programme, through which it proposes to governments and university systems the integration of its tools into teaching, teacher training and student activities. Among the first participants are Estonia, Greece and the Conference of Italian University Rectors. In Estonia, ChatGPT Edu has already been rolled out nationwide, reaching tens of thousands of pupils and teachers. In the United States, OpenAI has announced agreements with over a hundred educational organisations, offering free access and training to more than 300,000 educators and staff members. The initial free access encourages the widespread adoption of these tools and may create the conditions whereby, once integrated into daily activities, it becomes difficult to do without them or replace them. The result could be the creation of a stable demand for artificial intelligence services, supported in future by public budgets, regardless of whether their use is shown to actually improve learning sufficiently to justify the costs.

The same process applies to the public sector. In September 2026, OpenAI announced an agreement with the US government providing free access to licences and a 50 per cent discount on usage charges for a wide range of federal and local government bodies, with the agreement set to run until the end of 2028. Here too, the initial cost savings may encourage the adoption of tools destined to become standard components of administrative work, creating a technological dependency that could result in recurring expenditure in subsequent years. In the healthcare sector, the National Health Service announced in June 2026 that it would roll out Microsoft Copilot to over half a million staff, based on preliminary trials which reportedly demonstrated time savings in administrative tasks. These are potential benefits that warrant independent and ongoing scrutiny, particularly when the purchase is funded by public money and large-scale roll-out precedes a comprehensive assessment of the effects.

In addition to these acquisitions, there is a call for public support for the infrastructure required for artificial intelligence, through tax incentives, favourable conditions for the construction of data centres, and investment in the electricity grids needed to power them. Here too, part of the costs associated with the sector’s expansion can be passed on to the public, whilst the revenue from the infrastructure and services remains in the hands of the companies that manage them.

There is no need to imagine a coordinated plan amongst companies to understand the economic benefits of this strategy. Every company has an interest in expanding its customer base and securing favourable conditions for growth. However, when an entire sector has invested enormous sums based on forecasts of future demand that has yet to be proven, promoting public adoption also serves as a form of insurance against those forecasts failing to materialise. Presenting the use of artificial intelligence as a necessity for the modernisation of education, healthcare and public administration can help transform a technological choice – which ought to be justified by results – into an expenditure deemed inevitable.

The ultimate risk, therefore, is that, having mutually financed their own expansion and achieved colossal market valuations as a result, artificial intelligence firms will succeed in persuading governments to use public funds to guarantee a portion of the demand needed to sustain them. If the forecasts prove correct, private investors will have made extraordinary profits. If they prove to be over-optimistic, part of the losses could be absorbed through public procurement, incentives and infrastructure funded by taxpayers. This would be a particularly advantageous distribution of risk for those who took the gamble, as it would leave the private sector with the lion’s share of the benefits of success whilst placing an increasing share of the costs of any failure on the public purse. The question we should be asking governments, before any new programme involving the mandatory or widespread adoption of artificial intelligence, is therefore very simple: are we purchasing a service whose usefulness we have demonstrated, or are we helping to foot the bill for private investments that have yet to prove their sustainability?