There AI financial bubble risks becoming the largest in modern economic history: 8 times higher to the subprime mortgage crisis of 2008 and even well 17 times larger than that of dot-coms of the end 90s. This is the gist of a note, with a decidedly pessimistic flavour, written by the expert Julien Garranpartner of MacroStrategy. And Garran wasn’t the only one to express some concern about AI. Economists and the most authoritative financial institutions are divided between those who speak of speculative excess and those who believe that it is a phase of physiological growth of a technology destined to profoundly change global productivity. In this in-depth analysis we will examine what those who see an imminent risk, such as the aforementioned Garran and the CEO of, say JP Morgan Jamie Dimonand what the more cautious analysts, such as those of, indicate instead Goldman Sachs.
AI bubble: the opinion of economists
Returning to the analysis company’s study MacroStrategy Partnershipwho calculated the size of the so-called “AI bubble”, let’s see some of the macroeconomic indicators he combined to arrive at saying that the AI bubble risks being the largest in history. To understand the significance of this calculation we must return to the thoughts of the Swedish economist Knut Wicksellwho lived in the 19th century, who argued that capital was allocated efficiently only when the average cost of debt for businesses exceeded nominal GDP growth by about 2 percentage points. Today, after years of low interest rates and expansionary monetary policies, this proportion has altered, generating a “Wicksellian deficit”: a measure of the proportion of capital invested inefficiently. Second Julien Garranthis “misallocated” part of GDP includes the huge flow of money to AI, including real estate, NFTs and venture capital in the calculation. This is how we arrive at the number we entered with: an AI bubble 17 times larger than the dot-com bubble.
Garran’s analysis goes further, identifying a technological limit that he says would already be visible in large-scale linguistic models or LLMlike ChatGPT. According to Garran, the costs of training these systems grow exponentially while the performance improvements decline rapidly. GPT-3for example, would have cost approx 50 million dollars; the next GPT-4it cost 10 times as much; the model GPT-5the latest one made available by OpenAI, with an estimated investment of 5 billion dollarswould not have shown progress commensurate with increasingly large investments.
This is why many people express concerns similar to those of Garran and believe that the current euphoria about AI is reminiscent of that which existed between 1998 and 2000 for the so-called dot-comswhen it was enough to add “.com” to the name of a company to make its value skyrocket on the stock market. Today the same dynamic seems to repeat itself with the term “AI”. The director of IMF Kristalina Georgieva warned of the risk of “overvaluation of technological assets” driven by excessive optimism about the potential of future productivity. Georgieva declared:
Spurred by optimism about the productivity-enhancing potential of artificial intelligence, global stock prices are rising. If a sharp correction occurs, tighter financial conditions could dampen global growth.
Even the Bank of England expressed concern about the level of market concentration: le top five companies of the index S&P 500 represent today approximately 30% of the total valuethe highest share in the last 50 years. According to data processed by the analyst Howard Silverblattthere are seven companies – Alphabet (the holding company that controls Google), Amazon, Apple, Half, Microsoft, NVIDIA And Tesla – they alone generate more than half of the earnings of the entire index. In other words, much of US economic growth depends on very few players, almost all of whom are involved in the development or use of artificial intelligence, and if something were to go wrong in the AI market, the damage to the global economy could be anything but negligible.
The CEO of JP Morgan, Jamie Dimonwarned that this concentration could result in a «significant correction» of the stock market over the next two years. Dimon does not doubt the reality and long-term value of AI, but believes that «most of the people involved will not succeed», just as happened in the Internet market at the beginning of 2000.
Could the AI bubble burst? Goldman Sachs’ position
On the other hand, Goldman Sachsanother well-known investment bank in the sector, adopts a more balanced position. While recognizing signs of overvaluation, he doesn’t think a bubble burst is around the corner.
In this regard, in fact, Peter Oppenheimermember of the board of directors of Goldman Sachs, said:
There are elements of investor behavior and market prices currently that rhyme with previous bubbles.
Despite this veiled optimism, Oppenheimer also added:
While it appears we are not yet in a bubble, high levels of market concentration and increased competition in the AI space suggest that investors should continue to focus on diversification.
AI: between innovation and speculation
What emerges, beyond estimates and future predictions, is that the artificial intelligence sector is today at crossroads between innovation and speculation. The record numbers, stock market valuations and unprecedented capital inflows signal extraordinary confidence in the sector’s potential, but also an intrinsic vulnerability: if earnings expectations are not met, the correction could be abrupt. The key could therefore be to learn to distinguish between AI as a transformative technology (which will continue to evolve and find concrete applications) and AI as a financial phenomenonwhere enthusiasm risks inflating numbers and expectations far beyond economic reality.
