For the last two years, “AI exposure” mostly meant one thing: own the infrastructure—chips, servers, hyperscalers, power, cooling, networking. That worked… until it didn’t. Now the market is starting to ask a better question: Who is actually making money from AI adoption? The key shift is this: AI is tech, but tech isn’t AI. A big chunk of what people think is “AI exposure” is really just “US tech beta” (and often the same mega-cap overlap in every portfolio). Meanwhile, when you look at curated AI universes, only ~27% can be US IT, with the rest spread across non‑US IT and non‑IT sectors like communications, industrials, consumer, and financials. Translation: if you’re buying QQQ and calling it “AI,” you’re buying concentrated infrastructure + overlap, not a clean “AI beneficiaries” basket.

Here’s the underappreciated part: adoption is still early, but it’s measurable—and it’s where the margin expansion lives. One systematic read of company communications/earnings calls shows only ~10% of businesses are using AI meaningfully in production today. But the quality of AI talk is changing fast: firms are moving from “we’re experimenting” to “here’s the ROI.” Since 2017, the count of companies explicitly discussing AI-driven ROI has risen to ~155, and those discussing AI-driven economic gains to ~675—still a minority of the investable universe, which is exactly why it matters. Even more interesting: companies that discuss AI in ways tied to outcomes have shown persistent relative outperformance (roughly +3%/yr for “AI usage,” ~+5%/yr for “economic gains/ROI” cohorts). And the market still isn’t fully pricing it—AI adoption scores have shown low correlation to valuation, which is another way of saying: this is still an “under-owned” signal compared to infrastructure.

If you want the “AI adopter” winners, you’re not looking for who buys the most GPUs. You’re looking for who can turn AI into (1) revenue lift, (2) productivity, or (3) cost-out and has the organizational ability to redeploy the gains.

Three filters that keep you honest:

These are not “AI infrastructure.” These are companies already showing evidence that AI is improving economics.

Bottom line on the winners: these are businesses where AI doesn’t need to be “AGI” to matter. A few points of conversion lift, a few hundred bps of cost-to-serve improvement, or a step-change in throughput can drive real earnings power.

Two buckets here: (A) laggards and (B) vulnerable business models.

Examples often flagged as laggards in adoption mapping:

This is the danger zone highlighted by recent software dislocations: if your product is a workflow feature, frontier models can eat it. Watch for:

I’d frame these as “prove it” shorts/underweights rather than a single ticker call—because the line between “AI winner” and “AI roadkill” can change fast once a company ships real product.

AI disruption fear is compressing terminal value, but the market is slowly separating:

The Anthropic “Claude Code Security” headline was a source of “fear selling” in security software — this is the kind of event that creates temporary mispricing (and is usually not a real fundamental displacement overnight). I continue to believe AI will be a positive for cyber security, not a negative.

SNOW reports earnings after the close on Wednesday, it is likely the most important software earnings this week.

The Supreme Court’s ruling that IEEPA doesn’t authorize tariffs could be a real near‑term catalyst for positioning (and re-positioning) across industrials, transport, retail, and anything with tariff-sensitive COGS.
What matters: this isn’t “tariffs are gone.” It’s tariff authority uncertainty + legal risk + potential re‑routing into other statutes, which keeps volatility elevated for supply-chain exposed names.

The market is starting to price the financing channel, not just the demand channel. The instant credit spreads / lenders wobble, the most levered “AI build” expressions get hit first.

The noise yesterday was the Citrini report predicting certain doom from AI. Impossible to know whether that helped contribute to yesterday’s selloff. I do think AI has been, and will continue to be disruptive. However, this creates opportunities for those who know how to spot them.

Corning is an “AI infrastructure” play hiding in plain sight: if GPUs are the brains of AI, fiber is the nervous system. As data centers scale, the hard constraint is increasingly moving data fast, reliably, and at massive bandwidth—and that drives demand for optical fiber, cable, and connectivity (not just chips). The market got a major real‑world validation of that thesis when Meta signed a multi‑year agreement worth up to $6B for Corning to supply advanced optical fiber/cable/connectivity products for AI data centers, running through 2030. Corning is also expanding manufacturing capacity in North Carolina (including its Hickory cable factory) with Meta as an anchor customer and expects to increase local employment 15%–20%—a pretty direct signal that this isn’t a “one-quarter pop,” it’s a capacity-driven demand cycle.

Implications: GLW is a credible way to express “AI capex is becoming physical,” with improved visibility when hyperscalers sign longer-dated supply agreements. The risk is that GLW is not a pure AI stock—optical can be strong while other segments (consumer/display) create volatility—and after an ~84% share surge in 2025 you want to be honest that some optimism may already be priced in.

What I’d watch next: Optical segment growth + margins, additional hyperscaler supply deals, and whether capacity adds stay tight enough to sustain pricing power.

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