Understanding Asset Bubbles and Their Impact
Asset bubbles are a recurring feature of modern economies, but when the value of an asset inflates excessively, a boom quickly turns into a bubble. Recent examples include the dot-com bubble in the United States (1996-2000) and the housing crises around 2006 in various countries. Both ended in recessions, with the former being relatively mild and the latter catastrophically severe. The recent rapid increases in stock prices of AI-related companies have led many investors to ask: “Are we witnessing another asset price bubble?”
Contextualizing the Current AI Boom
To place the current AI boom in context, consider that Nvidia’s stock price, which manufactures many of the computing chips powering the AI industry, has tripled since early 2023. Stock prices of other AI-related companies, such as Microsoft and Alphabet (Google’s parent company), have multiplied by 2.1 and 3.2, respectively. In comparison, the S&P 500 index, which tracks the performance of the most significant U.S. companies, has only multiplied by 1.8 during the same period.
These AI-related companies are part of the S&P 500, further widening the gap between AI-related and non-AI companies. This suggests an AI bubble, but it doesn’t necessarily mean a repeat of the 2008 crisis.
How Asset Bubbles Form
The price of any stock consists of two components: its fundamental value and the inflated bubble value. If a stock’s price exceeds its fundamental value, there is a bubble in its price.
An asset’s fundamental value is the discounted sum of its expected future dividends. The key word here is “expected.” Since no one, not even ChatGPT, can predict the future, the fundamental value depends on each investor’s subjective expectations. They can be optimistic or pessimistic; some will be right, and others wrong over time.
Optimistic investors expect AI to revolutionize the world and generate (nearly) infinite profits for its owners. Not knowing which company will emerge victorious, they invest in all AI-related companies.
Pessimistic investors, on the other hand, view AI as merely a complex computer program rather than truly innovative technology and see bubbles everywhere.
A third possibility involves more sophisticated investors who recognize or know about the bubble but continue investing, hoping to ride the wave and cash out before it’s too late. This mirrors Citigroup CEO Chuck Prince’s infamous 2008 quote, “As long as the music is playing, you’ve got to get up and dance.”
As an economist, I can confidently state that it’s impossible for all AI-related companies to dominate the market. This means, without a doubt, that the value of at least some AI-related stocks has a significant bubble component.
Scarcity of Assets
Asset price bubbles can be a natural market response to the scarcity of assets. When demand for assets exceeds supply—especially in safe assets like government bonds—there’s room for newer, more novel assets to emerge.
This pattern explains the appearance of the 1990s dot-com bubble and the subsequent 2000s housing bubble. In that context, China’s growing role in financial markets increased demand for Western assets: money first flowed into dot-com companies in the 1990s and then to mortgage-backed securities when that bubble burst.
In the current context, a combination of factors has paved the way for an AI bubble: enthusiasm for new technologies, low interest rates (another sign of asset scarcity), and massive cash flows directed toward large companies.
The Bursting of the Bubble: Good, Bad, and Ugly Scenarios
At the very least, part of the rapid value increase in AI-related stocks is a bubble, and bubbles cannot be sustained indefinitely. They must eventually burst on their own or, ideally, be carefully deflated through government or central bank measures. The current AI bubble could end in one of these three scenarios: good, bad, or ugly.
The Good: Boom, Not Bubble
During the dot-com bubble, many poor companies received excessive funding; Pets.com is a classic example. However, the bubble also provided funding to companies like Google, potentially contributing to making the internet a productivity-enhancing technology.
Something similar might happen with AI. The current investment surge could, in the long term, create something positive: a technology that benefits humanity and eventually generates a return on investment. Without bubble-level cash flows, this wouldn’t be financed.
In this optimistic scenario, AI, despite potentially displacing some jobs in the short term (as most technologies do), would be positive for workers. I also assume it wouldn’t lead to human extinction, assuming governments implement appropriate and robust regulations. Adapting and providing applications for existing technologies is crucial, not inventing new ones.
The Bad: A Soft Burst
All bubbles eventually burst. We don’t know when or the extent of potential damage, but a market correction is likely when enough investors realize many companies are overvalued. This stock market decline will inevitably trigger a recession.
Hopefully, it’s short-lived, like the 2001 recession following the dot-com bubble burst. Although no recession is painless, this one was relatively mild and lasted less than a year in the U.S.
However, the AI bubble burst could be more painful because more homes are involved (either directly or indirectly through investment funds) in the stock market than two decades ago.
While it’s not central banks’ role to control asset prices, they might need to consider raising interest rates to deflate the bubble before it grows too large. A more abrupt collapse will result in a deeper and costlier recession.
The Ugly: Collapse and Fall
An AI bubble burst would be serious if it shared more characteristics with the 2000s housing bubble than we imagine. On the positive side, AI stocks aren’t homes. This is good because when housing bubbles burst, the impact on the economy is greater and more lasting than with other assets.
The housing bubble not only caused the 2008 financial crisis but also led to the collapse of the global financial system. Another reason for optimism is that banks’ role in AI financing is smaller than in housing, as much of each bank’s money is permanently tied up in mortgages.
However, a crucial warning is that we don’t know how the financial system will react if these large AI companies default on their debt. It’s alarming that this seems to be how they’re financing new investments, as a recent Bank of America analysis warned that large tech companies rely heavily on debt to build new data centers, many of which are intended for unmet demand.
