The $1 Trillion Gamble: Is the AI Boom on the Brink of a Bust?
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How Much Is the Future Worth?
To answer that question, you'd usually need to ask philosophers or economists. But if you're a tech CEO, you have an actual number: about $1 trillion. That's how much the tech industry is set to spend building out the artificial intelligence (AI) sector over the coming years. Even in Silicon Valley, where numerous companies have market capitalizations that initiate with "T," a trillion dollars is a lot of money. While you won't find more fervent evangelists for AI anywhere than in the C-suite of companies like Google and Microsoft, eventually, all that money has to be recouped. The alternative would be an economic meltdown of the sort we haven't experienced for years.
Which may just well be in the process of happening.
On Monday, the stock market continued days of heavy losses, with the S&P down 3 percent by the close of the day. The blood-letting was led by many of the same tech companies that had driven the market to record highs in recent months, with AI chip maker Nvidia falling by nearly 7 percent, and Amazon dropping 4 percent. There are a lot of different reasons, probably including some of the following: the prospect that the Federal Reserve has been too slow to cut interest rates given the weakening of the US economy; and hiring and manufacturing data from around the US in recent months has come in weaker than expected, helping to fuel this sell-off.
But there are real concerns, including that despite the hundreds of billions spent to build up the AI industry thus far, and the hundreds of billions projected to be spent in the years to come, AI companies themselves aren't yet producing much in the way of economic value. And they may not for the foreseeable future. That sound you hear could be an AI investment bubble going pop.
Big AI Dreams
No one spends a trillion dollars on something unless they really, really believe in it — and Silicon Valley really, really believes in the transformative economic potential of AI. Way back in 2018, when ChatGPT was but a gleam in OpenAI's Sam Altman's eye, Sundar Pichai—the CEO of Google—spoke with Kara Swisher about his thoughts on the state of artificial intelligence. "AI is one of the most important things humanity is working on," he said. "It's more profound than, I don't know, electricity or fire."
Electricity—now that's something. You might even think of it as humanity's first breakthrough product. But for tech leaders like Pichai, the prospect of fully general, functioning artificial intelligence was just as profound as the day a spark landed in the tinder of our paleolithic forebear who once accidentally rubbed two sticks together. And when OpenAI released ChatGPT in November 2022, showcasing to the world the wonders of truly large language models, the company which could get that genie back in the bottle suddenly had a race to win.
Investors scrambled to fund promising LLM startups, such as OpenAI (currently valued at $80 billion or more) and Anthropic (estimated at $18.4 billion). In the US alone, AI startups raised $23 billion in 2023, while over 200 of such companies worldwide have become unicorns — valued at $1 billion or above. For that sum, in part, measures the tech industry's confidence that the market for AI, eventually, will be titanically huge. Already, the consultancy PwC concluded, AI had the potential to add close to $16 trillion—not a typo—to the global economy by 2030, mostly from vastly improved labor productivity. Add that the tech giants are sitting on huge cash resources and are competing furiously against each other to be first to finish when it comes to AI. "If you believe the AI industry will be worth trillions — and that the lion's share of that value will go to the early leaders — then as Pichai said on a recent earnings call, 'the risk of underinvesting is dramatically greater than the risk of overinvesting.' But the bill is rising because generative AI is not cheap — both to build and to run.".
When the Bill Comes Due
Sam Altman himself has said that OpenAI is "the most capital intensive startup in history." That's because as models get bigger and bigger, they cost more and more to train. And that's just the cost of making the models — running them is highly expensive as well. One analysis last year estimated that it cost OpenAI $700,000 a day to run ChatGPT, chiefly in all that compute-intensive server time. The more ChatGPT and other LLMs are put into use, the higher these costs become. Silicon Valley might not have invented the saying "you have to spend money to make money," but it is living it. But the revenue such companies bring in, mainly from the subscriptions to their premium models, is just a fraction of their expenditure. In fact, The Information reported recently that OpenAI could lose as much as $5 billion this year, close to 10 times what it lost in 2022. That's not a good trajectory, and neither is ChatGPT's user numbers. More recently, tech analyst Benedict Evans tweeted that while many people and companies try out AI services like ChatGPT, only very few persist with it. (Notably, usage of ChatGPT seems to meaningfully dip during school holidays, just in case you were wondering who the power users were.)
While what LLMs can do is impressive, especially compared to what seemed possible a decade ago, promises of artificial general intelligence that could replace whole classes of workers have yet to come true. So far, it would seem that the industry suffers from a classic Silicon Valley problem: It has no product-market fit. Chatbots are not really a product as such, so their market has not yet been defined well enough. That's why everyone from Wall Street banks like Goldman Sachs to tech VCs like Sequoia Capital has been throwing up yellow caution flags around the AI industry—and why it seems that investors have begun heeding them.
To be clear, none of this suggests that AI itself doesn't still have revolutionary potential, or that the industry won't, over time, fulfill those dreams. The early 2000s' dot com crash, blamed on overinvestment and overvaluation of startups at the time, the author notes, set the stage for mega-companies of today like Google and Meta. It may one day be true that AI companies will fall but not necessarily unless their financials get better.
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