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.
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Table of Contents
H.E.A.T.
Open AI’s Code Red
Everyone’s staring at $NVDA’s multiple. Wrong screen. Late‑cycle AI risk is showing up in the balance sheets wrapped around OpenAI. New reporting suggests OpenAI’s partners/lenders are stacking ~$100B of debt that’s effectively tied to OpenAI demand—while OpenAI itself keeps the leverage light. The rough blueprint: big sponsors lever up (think $ORCL, SoftBank), data‑center SPVs borrow against long leases, and the whole chain assumes OpenAI (and OpenAI-adjacent workloads) will keep swallowing compute for years. OpenAI reportedly has massive long-dated compute commitments versus a much smaller revenue base today, and the borrowing is sitting on other people’s books. If the growth curve holds, this is genius. If adoption or pricing disappoints, the “margin call” doesn’t hit OpenAI first—it hits the lenders, the SPVs, and the public companies that issued the debt.
Now add the sentiment accelerant: Altman’s “code red” moment—an internal push to refocus on ChatGPT as competition tightens (Google’s momentum is the obvious backdrop). And zoom out: critics have been using the “circular financing / Enron” language across the AI stack—basically arguing the ecosystem is recycling capital (invest in the customer → customer buys the chips/compute → revenue prints → everyone marks higher). Whether you buy that analogy or not, the risk factor is real: concentrated demand + leveraged buildout + long-duration assets. If the demand anchor wobbles, you don’t just get multiple compression—you get credit spread widening, refinancing risk, and stranded‑asset stress in AI data centers. That’s how “tech narrative risk” turns into “system plumbing risk.”
Winners & losers (tickers)
Likely winners (if AI demand stays intact)
Picks & shovels with diversified demand: $NVDA (still the standard), networking / custom silicon leverage $AVGO, AI networking $ANET
Power + cooling / electrical spend: $VRT, $ETN, $PWR
Real asset landlords (benefit if leases stay money-good): $DLR, $EQIX
Hyperscalers that can fund capex internally (less “AI on margin”): $GOOGL, $MSFT, $AMZN, $META
Most exposed / “losers if the story cracks”
Debt-tower nodes / AI-infra financiers (spread + refinancing sensitivity): $ORCL, $OWL
Banks with growing participation in big AI project finance (cycle risk if defaults rise): $JPM, $BAC (less existential, but headline + spread risk)
Anything trading as a single-tenant “AI lease wrapper” (thin margin for error): treat these like credit, not growth (even if the equity trades like a rocket)
Concrete takeaways (what to actually watch)
Stop watching only $NVDA. Start watching credit. ORCL bond spreads, AI project-finance chatter, and private-credit risk appetite will sniff trouble before equities do.
Demand risk is the catalyst. If OpenAI/enterprise AI monetization slows, the pain shows up first in the finance layer (SPVs, lenders, sponsors), then in hardware orders.
“Code red” matters because it says competition is now real. When product focus becomes urgent, it’s usually because distribution and differentiation are tightening.
Treat “Enron” talk as a signal of fragility, not a verdict. The key question isn’t morality; it’s reflexivity: does the system require ever-cheaper capital to keep expanding?
A hedge I’m looking at is a ratio spread on QQQ puts. I’ve talked about ratio spreads a lot, but basically you are selling an around the money put and buying multiple out of the money puts. If QQQ tanks you have unlimited upside, if we get a Santa Claus rally and the QQQs close above your sold put strike price then you break even. The downside is the spread between the sold and the bought puts, you lose the most if the market is down a bit or flattish (depending on your strikes).
News vs. Noise: What’s Moving Markets Today
News: the tape is basically waiting for the Personal Income & Outlays / PCE print on Friday (Dec 5) — the first hard macro checkpoint in a market that’s been ping-ponging between “AI capex is too hot” and “the Fed is about to ease.”
The risk isn’t just the number; it’s the rates reaction. If PCE is cooler and yields break lower, you’ll likely see rate-sensitive beta (small caps, housing, long-duration tech) final-start a year-end chase. If PCE is hotter and the cut gets questioned, you’ll get the opposite: the same crowded parts of the market that resisted the pullback will start acting heavy again.
Also news (and underpriced): the global term-premium problem is back, led by Japan. U.S. 10s are sitting around ~4.10% again, and Japan’s long-end volatility is increasingly what sets the “floor” for global yields — even when U.S. narratives sound dovish.
FT’s own markets desk framed it plainly: Japanese 10-year yields are at their highest since 2007 — and that’s the kind of plumbing shock that can tighten financial conditions without the Fed doing a thing.
Takeaways
Friday isn’t about “up or down.” It’s about the reaction function. Cool PCE + yields still sticky = term premium is the story (not the Fed).
Breadth is the tell: small-caps leading on quiet days is constructive — until yields re-accelerate.
If risk is truly “back,” the laggards should lift: retail-momo + crypto being well below highs is either dry powder for catch-up or a warning that liquidity-sensitive assets haven’t healed yet.
Japan is the wildcard: if JGB volatility stays elevated, expect more cross-asset “air pockets” even if U.S. inflation cooperates.
A Stock I’m Watching
Today stock is Meta (META)….
I’m keeping an eye out for the next Mag 7 stocks to separate itself, and it could be META. The most important new signal isn’t another AI model demo — it’s capital discipline. Reports that $META is considering cutting metaverse/Reality Labs budgets by as much as ~30% (with headcount implications) tell you Zuckerberg is finally treating the metaverse as an R&D option, not a blank check, while reallocating toward the AI stack that can actually compound earnings (ads + AI agents + wearables). And that matters because Reality Labs has been a profit sink: Meta’s own segment reporting shows Reality Labs lost ~$16.1B from operations in 2023 (vs ~$13.7B in 2022). If the market is going to “reward focus” the way it just rewarded $GOOGL’s AI credibility, this is a credible path: same cash machine, fewer vanity projects, more spend that can be tied to monetization.
Where the asymmetry is: AI wearables are no longer a theory. Reuters has reported Ray-Ban Meta smart glasses have sold 2M+ units and that production capacity has been increased—meaning there’s real consumer pull, not just a developer story.
If AI glasses become a meaningful interface (notifications, camera, translation, assistants, commerce) Meta is one of the only companies with (1) distribution (FB/IG/WhatsApp), (2) an ads engine that already prints cash, and (3) incentive to push a new platform that isn’t controlled by Apple/Google. Winners if this works: $META, its wearables supply chain (glasses/optics), and the “AI efficiency” complex that improves ad ROI. Losers: anyone still pricing Meta as “ad biz + endless metaverse losses,” plus parts of the VR ecosystem depending on open-ended spend.
What I’m watching next: (1) Reality Labs loss trajectory and any explicit cap/budget language, (2) AI capex guidance versus incremental ad ROI (are they buying revenue per compute dollar?), and (3) wearables adoption signals (repeat usage, new SKUs, ecosystem). If those three line up, $META has a very plausible “break out from the pack” path — not because it’s the most exciting AI story, but because it can turn AI into margins faster than almost anyone.
How Else I Can Help You Beat Wall Street at Its Own Game
Inside H.E.A.T. is our monthly webinar series, sign up for this month’s webinar below….
Why Covered Call ETFs Suck-And What To Do Instead
Tuesday December 9, 2-3PM EST |
Covered call ETFs are everywhere — and everyone thinks they’ve found a “safe” way to collect yield in a sideways market. |
The truth? |
They cap your upside, mislead investors with “yield” that’s really your own money coming back, and often trail just owning the stock by a mile. |
Join me for a brutally honest breakdown of how these funds actually work — and what you should be doing instead. |
What You’ll Learn:
🔥 Why “high yield” covered call ETFs are often just returning your own capital |
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.
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