AI Is a Supply Chain, Not a Single Stock

“AI” is a supply chain, not a single stock

The most important insight for investors is that “AI” is not one type of investment. It’s natural to think first of the model providers at the heart of the capabilities — OpenAI, Anthropic, Google, and others — but they are one piece of a much larger puzzle. Around them sits a full supply chain spanning semiconductors, data centers, the models themselves, and the software and businesses that put them to work. Each layer has its own economics, its own business models, and its own risks.

At the foundation is the semiconductor hardware, and it gets pulled on in two very different ways. The first is training — building the large language models themselves, a process that runs enormous datasets across thousands of connected servers for weeks or months. The second is inference — the everyday use of those models, where every single prompt consumes computing and memory resources to produce an answer. Together, they explain why demand and pricing for this hardware have surged, and why the companies supplying it carry the valuations they do.

Why does the distinction matter? Because a debate about any one layer — how much training compute the next model generation needs, say — is not a verdict on the whole chain. Inference demand, data center capacity, and software adoption each run on their own clock. Judging “AI” by one layer is like judging the auto industry by tire sales.

The buildout is real — and it’s in the government’s own data

Hardware has to live somewhere. Picture a data center as a warehouse packed floor-to-ceiling with servers running 24/7 — demanding security, electricity, and industrial-scale cooling. Scaling AI means building these at a pace with few precedents, and you don’t have to take Wall Street’s word for it: it shows up directly in the U.S. Census Bureau’s construction data.¹

The inflection is unmistakable. When ChatGPT launched in November 2022, the U.S. was building data centers at a $13.9 billion annual pace. As of May 2026, that figure is $59.3 billion — more than a fourfold increase in three and a half years. And in November 2025, a quiet milestone: for the first time since the Census began breaking out data centers in 2014, America began spending more on data center construction than on every other category of office construction combined.

Two honest caveats. Not all of this is strictly AI — broader cloud adoption and automation, especially since 2020, were already lifting demand for computing resources. And construction spending is an input, not a profit. But as a measure of how much real economic activity the AI theme is generating right now, it’s hard to find a cleaner one.

The central question: will hundreds of billions pay off?

Here is the debate at the heart of AI investing right now: will the enormous sums being poured into AI infrastructure — particularly by the largest technology companies — eventually generate sufficient returns? Demand for computing power has been substantial, supporting everyone who provides hardware and data center capacity. But as models improve, they also become more efficient, potentially needing less compute for a given task. That tension is a big reason AI-linked stocks have delivered their gains with stomach-churning swings.²

Since the end of 2021, an equal investment in the seven mega-cap technology leaders has returned roughly +140%, against +56% for the S&P 500 — but the path included a 48% drawdown in 2022, the tariff selloff of April 2025, and this year’s AI-efficiency scare, a roughly 19% peak-to-trough decline that bottomed on March 30, 2026. Each downswing had the same shape: optimism about the buildout, followed by doubt about whether demand would fill it.

The efficiency worry deserves a direct answer, and history offers one. It’s called the Jevons paradox: when a technology becomes more efficient and its cost falls, total consumption often rises rather than falls, because cheaper capability unlocks uses that were never economical before. Electricity didn’t stop at light bulbs. Computers didn’t stop at corporations.

The counterweight is just as important: markets have a long record of overestimating how quickly new technologies turn into profits, even when the long-term potential is entirely real. The internet enthusiasm of the late 1990s took decades to fully play out — and vaporized plenty of capital along the way for investors who confused the theme with any particular stock. The lesson isn’t to avoid the theme. It’s to hold it with a broad view of the chain and a genuinely long horizon.

Valuations reflect high expectations

As AI has captured investor attention, valuations across the technology complex have moved steadily higher. The Information Technology sector currently trades at 21.4× earnings — elevated against both its own history and the broader market — and sectors like Communication Services and Consumer Discretionary, home to several of the same mega-cap names, show a similar pattern.³ These multiples aren’t irrational: they sit on top of genuinely strong earnings growth as AI demand builds. But they are demanding, and they leave less room for disappointment.

Here’s the discipline we’d emphasize: valuations are not a timing tool. They tell you very little about what markets will do tomorrow. What they are useful for is deciding the appropriate mix of assets in a portfolio — where expectations are stretched, where they aren’t, and how much of each risk you want to own on the way to your actual financial goals. Plenty of other market segments carry attractive valuations with solid expected earnings growth of their own, which is exactly why the AI theme belongs inside a portfolio, not instead of one.

How we own the theme: the Equity Innovation SMA

Everything above is the market’s evidence. Here is our answer to it. The BRIM Equity Innovation SMA is our concentrated portfolio of what we believe are the fastest-growing companies across the AI, software, and cloud-infrastructure landscape — the supply chain in this note, expressed as an actively managed strategy. This is not a theme we discovered this quarter; it’s the strategy’s entire design, and it has a track record:

And with Q2 in the books, here is the same record scored against the complete universe of non-leveraged ETFs — every fund in America with a full track record over each period, using YCharts total-return data. Through June 30, 2026:

That cumulative +337.4% since January 2023 ranks #27 out of 3,479 non-leveraged ETFs — the top 0.75% of the universe. (All figures gross of advisory fees; 2026 is a partial year through June 30.)

One thing we want to be clear about: a concentrated growth strategy is the highest-octane way to own this theme, and it comes with the drawdowns to match — our own 2026 round trip, chronicled in our July note, is Exhibit A. That’s why it lives alongside our Growth, Core, and Low Volatility strategies inside client portfolios sized to each household’s goals and risk tolerance — the AI theme inside a portfolio, not instead of one.