The recent surge in Oracle’s stock¹ is reminiscent of how it and other related companies behaved in the dot-com bubble – the more revenues were driven by the mania of the day (the internet then and AI now), the higher the firm’s stock rose.

Indeed, the sharp rise (initially 35%) in Oracle’s stock was curious because some elements of its earnings release were weaker than expected.² It is increasingly clear that there is a circular element in the investment spending of some of the very large AI firms and the revenues of others. Other AI-driven firms, notably Palantir, trade at stratospheric valuation levels — in this case a price-to-sales of over 125 times;³ three times is considered expensive for ‘normal’ companies.

So, while earnings growth for the large tech firms is impressively high in places, it looks increasingly precarious in terms of the growth momentum required to sustain current stock valuations and raises the risk that investors are overly optimistic and that we are in a bubble.

Manias, Panics and Crashes

In that context, I have spent some time re-reading the work of Charles Kindleberger, an important economist whose career intersected monetary systems and stock market bubbles, two issues that are top-of-mind for investors and economists today.

Kindleberger had an interesting career. His PhD adviser at Columbia was Henry Parker Willis (a key architect of the Federal Reserve Act in 1913 and the first Secretary of the Federal Reserve Board that became what we now know as the ‘Fed’). Then, in one of his first jobs at the US Treasury, Kindleberger worked for Harry Dexter White, the interlocutor and ‘rival’ of J. M. Keynes at the Bretton Woods conference.

To that end, Kindleberger had a very strong sense of the creation of the monetary infrastructure that has built today’s economic world. He had the opportunity to play a role in this as one of the architects of the Marshall Plan, which did so much to spur growth in post-war Europe and to cement the view of the US as the benevolent world power. Benn Steil’s book ‘The Marshall Plan: The Dawn of the Cold War’ is worth a read.

Beyond his policy work, Kindleberger is best known for ‘Manias, Panics and Crashes’, the best outline of how asset bubbles form and are followed by crashes.

How bubbles start

It is highly pertinent today because a variety of stock market valuation indicators (the long-term ‘Shiller price-earnings ratio’,⁴ the ‘Buffett Indicator’⁵ as well as measures of market concentration – the largest ten stocks of S&P 500 account for 40% of the entire market)⁶ point to the kind of market behaviour seen only in market bubbles like in 2001. Consistent with this, various investors as well as entrepreneurs like OpenAI’s Sam Altman are warning of a ‘bubble’.⁷

One test of the bubble thesis is to follow Kindleberger’s theory that asset price bubbles follow a common speculative cycle. According to Kindleberger, bubbles often start with an innovation – in a technology (i.e., railways) or a financial policy or market structure, or even a growth ‘miracle’ (note all the Tiger economies from Hong Kong to Ireland have seen boom/bust cycles) towards which investors channel capital, and then even more as asset prices rise and a narrative around the ‘mania’ begins to build.

This effect helps to loosen the strings of the overall economy and financial sector, but around this point asset prices are reaching incredible levels as investor euphoria intensifies, drawing in further speculation until prices then turn down and the house of cards collapses in a crash. The collapse is always greater when households, institutions and individuals have borrowed on the back of high asset prices, and logically there is greater contagion across the economy.

AI as the spark

One lesson from the Kindleberger book is that ‘new’ things – inventions or economic policy liberalisations – often provide the spark for a new speculative bubble that can grow and destabilise a financial system if enough speculative capital is driven towards it.

AI is a case in point. For context, Morgan Stanley estimates that over $3 trillion will be invested in AI-related infrastructure (data centres, energy), with about half of that coming from the cash flow of the large technology companies and the rest from private credit.⁸ It is clear that the large technology firms (from Microsoft to Nvidia) are the driving force behind this boom, especially so in the context of the fiscal weakness of most Western governments.

The recent results season was instructive in this respect. Often in a bubble, the dot-com one being a good example, the earnings associated with the bubble are only ‘prospective’ or somehow inflated. This is not the case with the large technology firms so far — by and large they report very strong earnings,⁹ which helps to soften the bubble argument.

The catch is the circularity of the capital expenditure by the large technology firms — Meta, for example, is spending aggressively on data centres and chips and running down its cash levels aggressively (as it did when it arguably overinvested in the metaverse). To that extent, the large tech firms are making a bold bet to get ahead in the AI game, but it is a concentrated and possibly existential bet.

Atypical context for bubbles

If the strong cash flow position of the firms at the centre of this bubble is unusual in the context of the Kindleberger framework, two other factors also stand out as atypical.

The first is that short and long-term interest rates in the major economies are close to ‘neutral’, neither too hot nor too cold.¹⁰ Bubbles are often characterised or preceded by ‘easy money’, though it has to be recognised that the last decade has been one of near continual stimulus (from QE to fiscal spending).

The other unusual factor is that the stock bubble is occurring against a backdrop of intense geopolitical and economic policy uncertainty — from great power competition to an unravelling trade order to a reconfiguring of America’s role in the world. The one way in which these dislocations fit the bubble narrative is that AI is a strategic asset, a ‘must-have’, that leads to an environment where, for example, the US government aims to take a strategic stake in Intel.¹¹

I think we are now ‘in’ an AI-centric market bubble, though not at the end of it in the sense of there being a ‘mania’ proper. In the short term, we might well see a wobble in AI stocks before they pick up again.

This time the risk is not that valuations are not supported by earnings, but rather the circularity of those earnings and the attendant risk that a recession, or policy impediment to AI (it is perceived to ‘kill’ jobs), leads to negative earnings momentum, which then leaves valuations highly vulnerable to a correction.

Focus on credit markets

Another related factor I am watching is the credit market. Credit spreads are at their lows for the past forty years, but as the cash piles of corporations like Meta and even Apple dwindle, rising credit spreads may signal that markets are worried about the deployment of so much capital into the AI ecosystem. This in turn may prove a wake-up call to equity markets that stock valuations are simply too high to be sustained.

From this point onwards, valuations for tech companies and the broader US equity market suggest that for asset allocators, future returns will be lower if history is a good guide. For other investors, the best hope they can have is that we are in the middle rather than final years of a bubble.