Beware AI euphoria
警惕人工智能的狂热

Another week, another record high in US equity markets. Last week’s jump was triggered by the Federal Reserve’s signal that investors can look forward to more interest rate cuts this year. But deeper market bullishness is built on two things: the cash reserves of the tech giants that now dominate the markets, and belief in their ability to monetise artificial intelligence.

AI will “change the world”, we are told. It will radically increase productivity (albeit by disrupting millions of jobs). It will create a huge new wealth pie for the world to share. And, according to a breathless ARK Invest report that last week predicted a $40tn boost to global gross domestic product from AI by 2030, it will “transform every sector, impact every business, and catalyze every innovation platform”.

It’s the euphoria and sense of inevitability in this straightforward narrative that makes me nervous. Even if you believe AI will be today’s equivalent of electricity or the internet, we are at the very early stages of a highly complex multi-decade transformation that is by no means a done deal. Yet valuations are pricing in the entire sea change, and then some. A February report by Currency Research Associates pointed out that it would take 4,500 years for Nvidia’s future dividends to equal its current price. Talk about a long tail.

While Nvidia isn’t Pets.com — it has tangible revenues from selling real things — the overall AI narrative depends on many uncertain assumptions. For example, AI requires huge amounts of water and energy. There’s a push in both the US and EU to get companies to disclose their usage. Whether via carbon pricing, or a tax on resource usage, it’s quite likely that those input costs will rise significantly in the future. 

Likewise, AI developers don’t now have to own the copyright to content on which the models are trained. They don’t have to make profits on AI itself, of course; the assumption of future gains is enough to fuel the froth. Relentless techno-optimism and the illusion of inevitability is how Silicon Valley creates paper wealth. But remember, many of the proponents of “AI everywhere” were touting web3, crypto, the metaverse and the benefits of the gig economy not so long ago.

One big difference, of course, is that AI has been validated by huge, cash-rich, market-leading companies such as Microsoft, Google and Amazon. But even within those companies developers have their doubts. One senior staffer at a leading AI company recently admitted to me, when pushed, that the profit assumptions around the technology were based “more on speculation than substance”, and that it has major kinks still to be worked out.

Anyone who’s experimented with large language models can vouch for this. I wouldn’t rely on a chatbot when doing research for my own work because I don’t want to worry about the accuracy of the data I’m being fed. I also don’t want to give up my ability to curate my own informational inputs. (I’d much rather do a Google search and see sources and citations laid out.) 

I’m admittedly operating at the high end of the white-collar job spectrum. But even for more rote middle-market tasks, there are lots of questions about how to integrate AI into workflows, and whether it will really be more productive than the humans it may replace. And the humans are beginning to revolt. The Hollywood writers’ strikes were at their core about control of AI, and unions are taking on the issue of technology regulation more broadly. 

Meanwhile, the copyright backlash against AI is gaining steam. Last week, French regulators fined Google €250mn for failing to notify news publishers that it was using their articles to train its AI algorithms, and for not licensing fair deals. This follows similar suits against OpenAI and Microsoft brought by the New York Times. As AI works its way into proprietary corporate data sets, opportunities for litigation over copyright will increase, and possibly even dovetail with worker complaints over corporate surveillance.

Then there’s the monopoly problem. As Meredith Whittaker, president of the Signal Foundation and the co-founder of the AI Now Institute, wrote in 2021, modern AI advances are “primarily the product of significantly concentrated data and compute resources that reside in the hands of a few large tech corporations”. Our increasing reliance on such AI, Whittaker added, “cedes inordinate power over our lives and institutions to a handful of tech firms”. 

The so-called Magnificent Seven companies have driven AI enthusiasm and stock market gains over the past year. They have pushed the concentration of the S&P 500 to a historic extreme. But as a recent Morgan Stanley Wealth Management report notes, “index concentration has historically proved self-correcting, with some combination of regulatory, market and competitive forces, along with business cycle dynamics, undermining static leadership”. The report says “analysis suggests that equity returns have typically struggled following peaks in concentration”.

That combination of correcting factors might include the growing number of Big Tech antitrust cases and the possibility that carbon pricing and copyright fines will challenge the “free” inputs necessary to make a profit.

Whether you see AI as the next tulip bubble or the next combustion engine, it’s worth questioning how the market is pricing this story.