Worries about artificial intelligence have rattled markets in waves over the past few years, including last week's selloff. Still none of those have proven to be meaningful setbacks for tech, and the latest round appears to be much the same. That may not always be the case though.
"Hyperscalers are building capacity now and show no sign of slowing spending plans," writes 22V Research's Dauvin Peterson on Tuesday. "There is a continuing shortage of compute [capacity], which in turn is holding back the next wave of more powerful models and products that would otherwise come to market -- the constraint is supply, not appetite."
The dynamic of demand outstripping supply looks likely to persist, he notes, given that despite the breakneck pace at which the AI built-out seems to be happening, it's constrained by simple things like a lack of qualified electricians and supply chain bottlenecks.
"AI factories are relatively new and evolving and require significant learning along the way. The beneficiaries will be the top executors, from infrastructure players to bare metal powered shell and neo-cloud operators," he writes.
However the physical buildout isn't just happening in terms of data centers.
"The first stage of Physical AI is taking place in structured industrial environments, such as manufacturing, logistics, and warehousing," writes Mizuho analyst Brett Linzey.
Settings like this are a good place to start, since they're controlled environments that involve plenty of repetition, rather than unpredictable elements. Therefore industrial players are a good real world test case for the implementation of AI.
There are two ways for investors to approach this trend.
"The first is the automation layer, the incumbents that build and integrate the control systems, software, and equipment Physical AI runs on," Linzey writes. "The second is the component layer beneath them, the motors, reducers, bearings, sensors, and power content inside the machines themselves."
His favorite stocks to play these themes are Emerson Electric, Ametek, Honeywell International, Applied Industrial Technologies, Eaton Corporation, Vertiv Holdings, and Parker Hannifin.
Developments like having humanoid robots inside factories are still likely several years away at best, and it will take time for automation and AI adoption to meaningfully move the needle in terms of earnings.
"What has changed is the direction of travel and engagement in the technology," Linzey writes. "Production-scale pilots are now running in Western factories, robotic manufacturing lines are ramping, and the structural drivers behind the theme...are durable."
Yet what about companies that aren't industrial, that deal with the "open, unstructured world," as Linzey puts it, and can be far more challenging for machines?
Tech companies find it easy to introduce AI into their business models because they dovetail so well together, with the controlled environments of industrial applications not far behind.
However that's not true of most of the economy "and especially in capital-intensive, heavily regulated sectors, deep process re-engineering and data governance requirements could delay structural productivity gains well beyond what the market currently projects," writes Apollo Global Management Chief Economist Torsten Sløk.
Implementing AI in a way that benefits companies near-term is a much longer and messier process in sectors like healthcare, energy, transportation, and law, among others.
That then becomes a problem for tech too: If companies in multiple industries find that AI doesn't provide the profit and productivity gains they expected, or does so very slowly over time, they may pull back their spending on these tools. That kind of headwind isn't reflected in tech's current sky-high multiples.
"[C]ompanies will slow their AI spending if they don't see return on investment quickly, and the current focus on token optimization is an early warning that AI implementation could be a bumpier, slower road than expected," Slok concludes. "The bottom line is that a mismatch between current earnings expectations and the actual time firms need to generate ROI on AI investments could have significant implications for many AI company valuations today."
In short: The AI buildout is still going strong, and its rollout into the real world is a yearslong process that's just begun. Yet once companies have captured the low-hanging fruit, the road forward may get murkier.
Write to Teresa Rivas at teresa.rivas@barrons.com
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June 30, 2026 13:40 ET (17:40 GMT)
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