CAMBRIDGE – In recent weeks, the notion that we are witnessing an “AI
Bubble” has moved from the fringes of public debate to the mainstream.
As Financial Times commentator Katie Martin aptly put it, “Bubble-talk is breaking out everywhere.”
The debate is fueled by a surge of investment in data centers and in the
vast energy infrastructure required to train and operate the large
language models (LLMs) that drive generative AI. As with previous
speculative bubbles, rising investment volumes fuel soaring valuations,
with both reaching historic highs across public and private markets. The
so-called “Magnificent Seven” tech giants – Alphabet, Amazon, Apple,
Meta, Microsoft, Nvidia, and Tesla – dominate the S&P 500, with each boasting a market capitalization above $1 trillion, and Nvidia is now the world’s first $5 trillion company.
In the private market, OpenAI reportedly plans
to raise $30 billion at a $500 billion valuation from SoftBank, the
most exuberant investor of the post-2008 era. Notably, this fundraising
round comes even as the company’s losses totaled $5 billion in 2024 despite $3.7 billion in revenue with its cash burn expected to total $115 billion through 2029.
Much like previous speculative cycles, this one is marked by the
emergence of creative financing mechanisms. Four centuries ago, the
Dutch Tulip Mania gave rise to futures contracts on flower bulbs. The
2008 global financial crisis was fueled by exotic derivatives such as
synthetic collateralized debt obligations and credit default swaps.
Today, a similar dynamic is playing out in the circular financing loop
that links chipmakers (Nvidia, AMD), cloud providers (Microsoft,
CoreWeave, Oracle), and LLM developers like OpenAI.
While the contours of an AI bubble are hard to miss, its actual impact
will depend on whether it spills over from financial markets into the
broader economy. How – and whether – that shift will occur remains
unclear. Virtually every day brings announcements of new
multibillion-dollar AI infrastructure projects. At the same time, a
growing body of reports indicates that AI’s business applications are
delivering disappointing returns, indicating that the hype may be
running well ahead of reality.
The Ghosts of Bubbles Past
Financial bubbles can be understood in terms of their focus and locus. The first concerns what investors are betting on: Do the assets that attract speculation have the potential to boost economic productivity when deployed at scale? Second, is this activity concentrated primarily in equity or credit markets? It is debt-financed speculation that leads to economic disaster when a bubble inevitably bursts. As Moritz Schularick and Alan M. Taylor have shown, leverage-fueled bubbles have repeatedly triggered financial crises over the past century and a half. The credit bubble of 2004-07, which focused on real estate and
culminated in the global financial crisis of 2008-09, is a case in
point. It offered no promise of increased productivity, and when it
burst, the economic consequences were horrendous, prompting
unprecedented public underwriting of private losses, principally by the
US Federal Reserve.
By contrast, the focus of the tech bubble of the
late 1990s was on building the internet’s physical and logical
infrastructure on a global scale, accompanied by the first wave of
experiments in commercial applications. Speculation during this period
was mainly concentrated in public equity markets, with some spillover
into the market for tradable junk bonds, and overall leverage remained
limited. When the bubble burst, the resulting economic damage was
relatively modest and was easily contained through conventional monetary
policy.
The history of modern capitalism has been defined by a succession of such “productive bubbles.”
From railroads to electrification to the internet, waves of financial
speculation have repeatedly mobilized vast quantities of capital to fund
potentially transformational technologies whose returns could not be
known in advance.
In each of these cases, the companies that built the foundational infrastructure went bust.
Speculative funding had enabled them to build years before
trial-and-error experimentation yielded economically productive
applications. Yet no one tore up the railroad tracks, dismantled the
electricity grids, or dug up the underground fiber-optic cables. The
infrastructure remained, ready to support the creation of the imagined
“new economy,” albeit only after a painful delay and largely with new
players at the helm. The experimentation needed to discover the “killer
applications” enabled by these “General Purpose Technologies” takes
time. Those seeking instant gratification from LLMs are likely to be
disappointed.
For example, while construction of the first railroad in the United
States began in 1828, mail-order retail, the killer app in this
instance, began with the founding of Montgomery Ward in 1872. Ten years
later, Thomas Edison introduced the Age of Electricity by turning on the
Pearl Street power station, but the productivity revolution in
manufacturing caused by electrification only came in the 1930s.
Similarly, it took a generation to get from the Otto internal combustion
engine, invented in 1876, to Henry Ford’s Model T in 1908, and from
Jack Kilby’s integrated circuit (1958) to the IBM PC (1981). The first
demonstration of the proto-internet was in 1972: Amazon and Google were
founded in 1994 and 1998, respectively.
Where does the AI bubble fit on this spectrum? While much of the
investment so far has come from Big Tech’s vast cash reserves and
continued cash flow, signs of leverage are beginning to emerge. For
example, Oracle, a late entrant to the race, is compensating for its
relatively limited liquidity with a debt package of about $38 billion.
And that may be only the beginning. OpenAI has announced plans to invest at least $1 trillion
over the next five years. Given that spending of this scale will
inevitably require large-scale borrowing, LLMs have a narrow window to
prove their economic value and justify such extraordinary levels of
investment.
Early studies offered reason for optimism. Research by Stanford’s Erik Brynjolfsson and MIT’s Danielle Li and Lindsey Raymond, examining the introduction of generative AI in customer-service centers, found
that AI assistance increased worker productivity by 15%. The biggest
gains were among less experienced employees, whose productivity rose by
more than 30%.
Brynjolfsson and his co-authors also observed that employees who
followed AI recommendations became more efficient over time, and that
exposure to AI tools led to lasting skill improvements. Moreover,
customers treated AI-assisted agents more positively, showing higher
satisfaction and making fewer requests to speak with a supervisor.
The broader picture, however, appears less encouraging. A recent survey by MIT’s Project NANDA found that 95% of private-sector generative AI pilot projects are failing. Although less rigorous than Brynjolfsson’s peer-reviewed
study, the survey suggests that most corporate experiments with
generative AI have fallen short of expectations. The researchers
attributed these failures to a “learning gap” between the few firms that
obtained expert help in tailoring applications to practical business
needs – chiefly back-office administrative tasks – and those that tried
to develop in-house systems for outward-facing functions such as sales
and marketing.
The Limits of Generative AI
The main
challenge facing generative AI users stems from the nature of the
technology itself. By design, GenAI systems transform their training
data – text, images, and speech – into numerical vectors which, in turn,
are analyzed to predict the next token: syllable, pixel, or sound.
Since they are essentially probabilistic prediction engines, they
inevitably make random errors.
Earlier this year, the late Brian Cantwell Smith, former chief scientist
at Xerox’s legendary Palo Alto Research Center, succinctly described
the problem. As quoted to me by University of Edinburgh Professor Henry
Thompson, Smith observed: “It’s not good that [ChatGPT] says things that
are wrong, but what is really, irremediably bad is that it has no idea
that there is a world about which it is mistaken.”....
Some earlier visits with Mr. Janeway:
The railway mania of the 1840's is often pointed to as a productive bubble. We have on offer:
....MUCH MORE
Some earlier visits with Mr. Janeway:
The railway mania of the 1840's is often pointed to as a productive bubble. We have on offer:
Finally, as Adam Smith put it in his book on the 'sixties bull market, The Money Game: