I’ve spilled a lot of ink talking about how much capital is being poured into the AI infrastructure buildout.
But I’m not the only one who has an opinion about it.
From your friends and neighbors to the most powerful people on Wall Street, one of the biggest debates you can have today is about whether AI spending has gone too far.
That debate intensified this week after new questions around OpenAI’s growth and the near-term returns on AI investment added volatility to Big Tech earnings.
But this week’s chart suggests that the people closest to the answer think it hasn’t.
A Vote of Confidence From Inside Tech
Our chart of the week tracks insider buying across companies in the technology sector.
Image: x.com/jaykaeppel
As you can see, it has moved to its highest level in 15 years.
That’s not so unusual on its own. But what’s striking to me is when it’s happening.
Historically, insider buying tends to pick up when markets are under pressure. Executives often step in when they believe that fear has pushed prices too low.
But tech isn’t coming off some broad collapse today. Many of the companies driving this market are still trading at or near their all-time highs.
That makes this less about buying a dip and more about conviction. And I believe part of that conviction traces back to what’s happening in AI infrastructure.
Because the scale of spending here is extraordinary.
Amazon (Nasdaq: AMZN), Microsoft (Nasdaq: MSFT), Alphabet (Nasdaq: GOOG) and Meta (Nasdaq: META) are expected to spend well over $600 billion in capital expenditures this year, much of it tied directly to AI infrastructure. Some forecasts push that figure even higher over the next few years as data center expansion accelerates.
That’s the kind of capital commitment companies make when they believe they’re helping build something foundational.
We saw the same thing happen with the internet which required massive investment before its economics became obvious. So did cloud computing.
Both looked expensive before they looked inevitable. And AI seems to be following a similar path.
Yet, markets often struggle with the lag between investment and payoff. And that might be especially true here.
Many companies are still in the phase of building capacity, securing chips, expanding energy supply and developing software so they can monetize all of this compute.
And right now, the market is increasingly focused on the cost of this buildout and how quickly it will translate into returns.
But insiders appear to be looking further ahead, toward the returns.
We saw that tension play out in real time this week. Companies that leaned harder into AI spending, like Meta, were punished despite strong earnings, while others that showed clearer near-term returns were rewarded.
Here’s the thing…
Morgan Stanley Research estimates AI revenue could surpass $1 trillion by 2028. But many public valuations still largely reflect today’s businesses, not what those economics might look like just a few years out.
And that gap between present costs and future earnings is where you can often find the best opportunities.
It also might help to explain this chart.
Because if executives believe the market is too focused on what this AI infrastructure buildout costs — and too focused on how quickly it pays off — then insider buying at these levels looks completely rational.
Here’s My Take
I wouldn’t treat insider activity as a timing signal.
But I do think it’s informative that so many insiders are buying across this sector all at once. Especially when it coincides with one of the largest technology investment cycles we’ve seen in our lifetimes.
This combination is unusual.
Especially at a moment when the market is starting to question the payoff.
Then again, so is this chart. It’s telling us that the market is pricing in the AI buildout, but insiders are behaving like the payoff is still further out.
The question now is whether that payoff will start showing up in earnest over the next few quarters…
Or whether expectations have run ahead of reality.
Regards,
Ian KingChief Strategist, Banyan Hill Publishing
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