AI 자체를 사지 말고, AI를 활용하는 기업을 사라

THE DAILY --- H.E.A.T. Torching Wall Street’s Obsolete Playbook. Delivered straight to your inbox 5 days a week. Subscribe Home Posts Stop Buying “AI”... Start Buying the Companies Using It Stop Buying “AI”... Start Buying the Companies Using It Feb 24, 2026 I’ve been a trader and investor for 44 years. I left Wall Street long ago—-once I understood that their obsolete advice is designed to profit them, not you. Today, my firm manages around $4 billion in ETFs, and I don’t answer to anybody. I tell the truth because trying to fool investors doesn’t help them, or me. In Daily --- H.E.A.T. , I show you how to Hedge against disaster, find your Edge, exploit Asymmetric opportunities, and ride major Themes before Wall Street catches on. Table of Contents --- H.E.A.T. The playbook: how to spot real AI adopters (vs. AI … Winners: specific “AI adopters” with tangible ROI … Consumer / Retail: AI turns scale into margin Financials: AI is coming for back office cost and … Industrials / Defense / Complex systems: AI moneti … Healthcare: AI shows up as throughput, accuracy, a … Data / Logistics: AI turns information into pricin … Software that enables adoption (not “featureware”) Losers: who gets squeezed if they don’t adopt (or … A) “AI laggards” (low adoption intensity / underin … B) “Feature risk” businesses (AI turns the product … How I’d position the “AI adopters” theme (practica … --- News vs. Noise: What’s Moving Markets Today Software is still being repriced for “AI disinterm … “Anti‑AI” is becoming an actual positioning framew … Tariff regime “whiplash” just became a live macro … AI capex is still accelerating — but the market is … Trump’s State of the Union is tonight, could we ge … --- A Stock I’m Watching Corning (GLW) --- In Case You Missed It $4B Fund Manager Matt Tuttle: The 'HEAT' Strategy … Talking stocks with Kirk Chisholm --- H.E.A.T. 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. The playbook: how to spot real AI adopters (vs. AI tourists) 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: Quantified outcomes (best signal): management gives numbers—conversion lift, hours saved, cost per ticket, shrink reduction, faster cycle times, etc. Data + distribution (moat): companies with proprietary data and embedded distribution can compound AI improvements (banks, retailers, insurers, logistics, defense/industrial platforms). Operating leverage capture (who keeps the win?): the business must be able to retain savings via pricing power, switching costs, regulation, or scale—otherwise AI benefits get competed away. Winners: specific “AI adopters” with tangible ROI signals These are not “AI infrastructure.” These are companies already showing evidence that AI is improving economics. Consumer / Retail: AI turns scale into margin WMT (Walmart) – A classic “data + distribution” story: demand forecasting, inventory, logistics, and automation. If adoption is the new edge, Walmart is built for it. TGT (Target) – Operational AI/robotics and inventory automation; real productivity is what wins retail in a weak consumer tape. EL (Estée Lauder) – Marketing ROI is one of the first places AI shows up; campaign optimization and personalization can be a quiet gross margin tailwind. Financials: AI is coming for back office cost and front office targeting JPM (JPMorgan) – Scale buyer + internal platform builder; AI is a productivity weapon across compliance, fraud, servicing, and sales coverage. BAC (Bank of America) – Automation + servicing leverage; financials are packed with repetitive processes where AI can compress cost-to-serve. ING (ING) – Similar “process + compliance + personalization” runway. Industrials / Defense / Complex systems: AI monetizes complexity CAT (Caterpillar) – Predictive maintenance, fleet optimization, and customer uptime are where AI becomes “sticky” and monetizable. RTX (RTX) – Proprietary digital/AI tools matter in high-spec manufacturing and sustainment—faster cycle times and higher output is real ROI. Healthcare: AI shows up as throughput, accuracy, and admin cost-out UNH (UnitedHealth) – Admin/claims, care navigation, and provider workflow are huge targets for AI productivity. RHHBY (Roche) / ROG.SW – Data-rich, regulated, long-cycle businesses can retain AI gains longer than hypercompetitive consumer apps. PFE (Pfizer) – R&D and operational acceleration: even small cycle-time improvements are valuable at scale. Data / Logistics: AI turns information into pricing power SPGI (S&P Global) – “Data tollbooth” model: embedding AI into datasets/workflows can expand product value without linear headcount. CHRW (C.H. Robinson) – Logistics is pure optimization; AI can lift margins by improving routing, pricing, and utilization. Software that enables adoption (not “featureware”) PLTR (Palantir) – This one sits in the “adopter enabler” lane: it’s not just selling tools, it’s often operationalizing AI inside large orgs. 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. Losers: who gets squeezed if they don’t adopt (or if AI makes their product a feature) Two buckets here: (A) laggards and (B) vulnerable business models. A) “AI laggards” (low adoption intensity / underinvestment signals) Examples often flagged as laggards in adoption mapping: DG (Dollar General) – Retail is a knife fight; if competitors automate faster, laggards feel it in shrink, labor, and inventory. UI (Ubiquiti) – Hardware businesses without clear software/data flywheels can struggle to translate AI into durable economics. GOLD (Barrick Gold) – Commodities businesses can use AI, but the market often treats them as price-takers; adoption doesn’t automatically equal re-rating. FCNCA (First Citizens Bankshares) – Regional banks have huge process upside but often lack scale/tech investment pace. MONC.MI (Moncler) / MONRY (ADR) – Luxury can benefit from AI in marketing and supply chain, but “laggard” signals suggest slower execution. B) “Feature risk” businesses (AI turns the product into a checkbox) This is the danger zone highlighted by recent software dislocations: if your product is a workflow feature, frontier models can eat it. Watch for: Tools with thin moats (easy switching, undifferentiated data, low integration depth) Pricing power that was based on UX, not proprietary outcomes Heavy reliance on seat-based pricing without demonstrable ROI lift 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. How I’d position the “AI adopters” theme (practically) Stop confusing “AI exposure” with “tech exposure.” You already own a lot of infrastructure if you own the index. Rotate some AI risk from builders → adopters where ROI can show up in margins, not capex. Screen for quantified benefits (conversion lift, hours saved, cost-out) and build a basket that spans sectors—because adoption is an economy-wide story. --- News vs. Noise: What’s Moving Markets Today Software is still being repriced for “AI disintermediation,” but the market is starting to discriminate AI disruption fear is compressing terminal value, but the market is slowly separating: infra/observability/data = more insulated / can be AI beneficiaries apps/back-office/martech = still under pressure 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. “Anti‑AI” is becoming an actual positioning framework own companies where the core revenue is tied to physical systems + regulated workflows + consumables AI may help them, but it can’t replace them Tariff regime “whiplash” just became a live macro factor again 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. AI capex is still accelerating — but the market is now policing “who pays for it” 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. Trump’s State of the Union is tonight, could we get an UFO mention? I doubt it, but you never know….. 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. --- A Stock I’m Watching Corning (GLW) 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. --- In Case You Missed It $4B Fund Manager Matt Tuttle: The 'HEAT' Strategy for 2026 Talking stocks with Kirk Chisholm The --- H.E.A.T. (Hedge, Edge, Asymmetry and Theme) Formula is designed to empower investors to spot opportunities, think independently, make smarter (often contrarian) moves, and build real wealth. The views and opinions expressed herein are those of the Chief Executive Officer and Portfolio Manager for Tuttle Capital Management (TCM) and are subject to change without notice. The data and information provided is derived from sources deemed to be reliable but we cannot guarantee its accuracy. Investing in securities is subject to risk including the possible loss of principal. Trade notifications are for informational purposes only. 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