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The California Gold Rush left an outsized imprint on America. Some 300,000 people flocked there from 1848 to 1955, from as far away as the Ottoman Empire. Prospectors massacred Indigenous people to take the gold from their lands in the Sierra Nevada mountains. And they boosted the economies of nearby states and faraway countries from whence they bought their supplies.
Gold provided the motivation for California – a former Mexican territory then controlled by the US military – to become a state with laws of its own. And yet, few “49ers” as prospectors were known, struck it rich. It was the merchants selling prospectors food and shovels who made the money. One, a Bavarian immigrant named Levi Strauss who sold denim overalls to the gold bugs passing through San Francisco, may be the most remembered figure of his day.
California is going through another investment rush these days. This time it’s centered in Silicon Valley. The pot of gold is more elusive but potentially much bigger: Artificial Intelligence. What this rush leaves in its wake will shape the long-term future of civilization – or maybe not?
The question everyone seems to be asking is: is AI a bubble? Lots of people seem to think so, including Open AI’s Sam Altman and the Bank of England. How else to explain Nvidia’s stock price, which more than doubled from April to November, based entirely on the expectation, nay hope, that AI will produce a super-intelligence that can do everything humans do but better.
Nvidia – like Levi Strauss back in the day – is at least selling something: computer chips. The valuations of many of the other AI plays – like Open AI or Anthropic – are based largely on the dream.
The big analytical challenge, however, is to figure out what kind of bubble this is. Is it the kind that will ravage the economy when it bursts? What will it leave of value once it pops?
Bubbles all share one characteristic – besotted investors in pursuit of a dream. But they come in many flavors. Not 20 years ago, we suffered the housing bubble, when home prices rose to stratospheric heights and almost brought down the financial system as they crashed back to earth. Less than a decade earlier, it was the dot-com bubble that burst, when investors realized that Webvan, Pets.com and the like were not worth billions just because they used the Internet.
A few years before that we witnessed the rise and collapse of the East Asian bubble – with ancillary bubblettes in Russia and Brazil – when money rushed into these emerging markets, freaked and rushed out. There was the Tequila Crisis, which pummeled the Mexican peso and its economy. And the Japanese bubble, when the value of the Nikkei 225 stock index tripled over four years before it fell by 60% over the next two and a half.
Bubbles have plagued the world’s finances at least since the 17th century, when Dutch investors fell in and out of love with tulips. In the 18th century, French, Dutch and British investors produced what came to be known as the South Sea bubble by giving in to euphoria over the value of potential of new trade routes across the Atlantic.
That bubble ended with an Act of the British Parliament “to Restrain the Extravagant and Unwarrantable Practice of Raising Money by Voluntary Subscription For Carrying on Projects Dangerous to the Trade and Subjects of the United Kingdom.” It came to be known as the Bubble Act.
Virtually every new frontier opened up to investment has led to a speculative bubble. Investors have scrambled to tap into its promise only to overdo it and stampede in retreat. Economists Carmen Reinhart and Kenneth Rogoff found that of the world’s 66 major economies, including developed nations and big developing countries, only Portugal, Austria, Belgium and the Netherlands had avoided a banking crisis between 1945 and 2007. By the end of 2008 none of them were unscathed.
So the most important question as one evaluates the frenzied AI investment landscape is not really whether it will pop or not, but what sort of legacy it will leave behind. Would the fallout include a hobbled financial system and an intractable, prolonged recession, as the bursting of the housing bubble left in its wake? Or is it more likely to look like the dot-com bubble, whose bursting produced a comparatively shallow economic downturn and ultimately gave the world the modern internet?
As I pointed out in my last column about AI, Gita Gopinath, former chief economist of the International Monetary Fund, calculated that a stock market crash equivalent to that which ended the dot-com boom would erase some $20tn in American household wealth and another $15tn abroad, enough to strangle consumer spending and induce a recession.
But the economic pain would depend to a large extent on how the AI investment surge is being financed. One problem is that we don’t really know.
The housing bubble was built from a boom in mortgage finance, as yield-seeking banks stuffed themselves with bonds built of bundles of mortgages to increasingly uncreditworthy borrowers. When the borrowers couldn’t pay, the boom left a forest of damaged balance sheets in its wake, from over-indebted households with no access to credit, to a banking system hobbled by worthless bonds. Financing froze. It took years for America’s credit-driven economy to recover.
AI could produce a similar landscape. A critical determinant is how much debt is at stake. It wouldn’t be such a problem if the bubble were financed largely from the cash pile of Alphabet and Amazon, Microsoft and Facebook. They might lose their shirt, but who cares. The worrying bit is that it seems they are increasingly relying on borrowing, which means the prospect of a bursting bubble would again put the financial system at risk.
Big Tech has raised nearly $250bn in debt so far this year, according to Bloomberg, a record. Analysts at Morgan Stanley suggest that debt will be needed to fill a $1.5tn funding gap to ramp up spending on data centers and hardware. Problematically, it is getting hard to follow the money, as Nvidia, Open AI and others in the ecosystem buy into each other, clouding who, in the end, will be left holding the bag.
The other question is to what extent the AI that the Silicon Valley faithful are building will endure. Railways survived the 19th century railway bust. The Internet survived the dot-com implosion. Is there anything of sufficient value to justify the current moment of euphoria, even if it heads south for a time?
Until a few weeks ago, I would have said sure: there must be something in Chat GPT or Claude that will raise business productivity. But to justify the vast quantities of money they are going to have to build something really impressive – as in superhuman general intelligence impressive. Over the last several weeks, a thought has bubbled up through the ecosystem that they won’t.
It’s a thought built on the thoughts of techier minds than mine. Yann LeCun, until recently Meta’s chief scientist and a winner of the Turing Award, has been saying that the massive spend on Large Language Models that today define the AI space is misguided. Artificial General Intelligence – aka the Superhuman – can only come about by dropping LLMs – which are essentially massive correlation engines – and switching to something else called a world model architecture, where machines develop a “mental” model of the outside world.
If he’s right, that would be one big oops for much of today’s AI spend. Nvidia and the rest of us may be about to learn, once again, that just because you sold a load of jeans and shovels, it doesn’t mean there is gold in them thar hills.
