A version of this post first appeared on TKer.co
If AI is all it’s cracked up to be, the winners in the stock market should extend far beyond the large-cap tech hyperscalers currently building the AI infrastructure.
As BofA’s Savita Subramanian wrote back in June 2023: “The larger benefit may be had by old-economy, inefficient companies that can increase earnings power more permanently from efficiency and productivity gains.”
It’s too early to say conclusively, but the market may be in the process of getting in front of this phase of the AI narrative, as small-cap stocks have recently been outperforming large-cap stocks.
As Wells Fargo’s Ohsung Kwon argues, small-cap stocks (as tracked by the Russell 2000, or RTY) could see a bigger tailwind from AI than large-cap stocks (as tracked by the S&P 500 or SPY).
“We see signs that small caps have been slower to adopt AI than large caps,” Kwon wrote on Monday. “We believe the next leg of AI adoption is in small caps — the longer-term bull case for RTY. We estimate every 1% labor cost saving translates to a ~2% EPS boost for SPX, but >6% for RTY.”
This is what makes the promise of AI truly exciting for investors: The beneficiaries aren’t limited to those developing the technology. Companies across industries have been exploring AI applications, and many are already implementing them in their operations.
To be clear, we’re still in the early innings of the AI era, and it’ll take time before we better understand the actual impact on productivity across sectors.
But if costs indeed come down materially and productivity improves beyond just tech companies, it would be consistent with past technological revolutions.
In a recent research note, Bridgewater’s Greg Jensen drew parallels between AI today and electrification in the 1920s and the internet boom of the late 1990s.
“At least in the near term, AI seems likely to follow the classic J-curve productivity path of prior general-purpose technologies like electricity or the internet—requiring a lot of upfront investment that doesn’t immediately improve productivity, but eventually proving transformative,” he wrote.
As you can see in the charts, it took years for the economy to realize the productivity booms promised by electricity and the internet.
“We believe AI capex is set to significantly support U.S. growth in the coming years and that many of the second-order consequences of this investment are not priced in,” he added.
Read Jensen’s whole note here.
Carlyle Group’s Jason Thomas took a closer look at those historical experiences. And he wasn’t shy about addressing head-on the risk that we may be in a bubble like we were in 1929 and 2000. In fact, the title of his note is: “Bubbles as a Feature, Not a Bug.”
“Financial markets tend to be very good at spotting technological revolutions but have rarely proven able to anticipate their second-and-third-order effects,” Thomas wrote. “Bubbles deflate not because the technology disappoints, but because so much of its economics flow downstream to the companies that employ it and the new industries it spawns.”
He discussed the decades-long process of harnessing and deploying electricity from the 1880s to the 1920s. “The earnings and valuations of electric holding companies and equipment manufacturers both tripled; between 1925 and 1929, industry-wide annualized returns exceeded 50%.”
Those companies weren’t able to sidestep the crash of 1929. But what followed is notable.
“Electrified factories drove down manufacturers’ production costs and electrified homes stimulated demand for their products, like refrigerators, vacuums, and radios,” Thomas observed. “This is where far more of electrification’s economic value ultimately accrued, with 11% annualized returns on the stocks of durable goods manufacturers over a decade stretching through the depths of the Great Depression.”
Thomas noted that after the dotcom bubble burst in 2000, businesses leveraging the internet similarly outperformed in the wake of the market crash.
“These same phenomena repeated themselves in the internet boom of the late 1990s,” he wrote. “Real capex grew at a 24% annualized rate as investors focused on bandwidth, router architecture, and processing power bottlenecks. But the economic value of that IT and telecom hardware manifested downstream. Networked computing allowed manufacturers and retailers to exploit new sales channels, more precisely calibrate production and inventories to sales, and streamline logistics networks and supply chains.
“A technology doesn’t need to fizzle for its bubble to deflate,” Thomas continued. “Likewise, those inclined to focus on ‘irrationality’ or ‘market euphoria’ miss how bubbles seem a natural outgrowth of epochal shifts. If the market didn’t misjudge the importance of hardware bottlenecks and first-mover advantages, who’d be willing to fund the research and infrastructure that ultimately makes the technological revolution, and value downstream from it, possible?”
Read Thomas’ whole note here.
As we’ve discussed before, history is riddled with examples of game-changing technologies leading to overinvestment followed by market corrections.
Unfortunately, it’s not easy to predict when those corrections will happen and what they will look like.
But the bottom line is that investors should be mindful of the risk that the hot stocks most directly exposed to the AI capex boom are at risk of falling behind.
In fact, this may already be happening.
On Wednesday, Deutsche Bank’s Jim Reid shared this chart of where the various AI-exposed stocks were trading relative to their all-time highs — as the S&P 500 itself was climbing to new heights.
“What’s striking about the [S&P 500’s] move back to the peak, though, is how much leadership has shifted over the past quarter,” Reid wrote.
By the way, this dynamic is a reminder that it’s possible for the overall market to move higher even as its leaders fall behind.