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H.E.A.T.
We talk a lot here about whether AI is in a bubble and there are indicators that it is, and indicators that it isn’t. Long time readers know the way I like to play it is long the obvious winners, long the suppliers to the winners, and long the potential asymmetrical names, while at the same time having ratio spreads on QQQ and VIX, and selected short ARKK exposure for hedges. We had a lot this week, FOMC, ORCL, and AVGO and now this….
“Did Time Magazine Just Kill the AI Rally?” is the right question… but the answer isn’t as simple as “yes, bubble popped.”
The last 48 hours told you what phase of the AI trade we’re in:
Powell refilled the punch bowl (another cut + balance‑sheet expansion).
Oracle face‑planted on AI capex and funding reality.
Broadcom printed great numbers and still sold off.
OpenAI dropped GPT‑5.2 and stole back the benchmark crown.
And Time slapped “The Architects of AI” on its Person of the Year cover.
That combo usually marks the shift from “anything with AI in the deck rips” → to “show me the cash flows and the balance sheet.”
Let’s walk through it like a hedge fund would.
1. The Magazine Cover Moment
Time’s “Architects of AI” cover is classic sentiment‑peak stuff. Historically, when Time anoints an industry or corporate hero, one‑year forward returns in the related trade are negative more often than not. The theme is already common knowledge; the easy money has been made.
Think Jeff Bezos in 1999:
Amazon compounded ~17% annually from that cover to now…
…but the stock fell 80% in the first year and 92% at the trough before the long compounding started.
That’s the setup: AI can still win the decade and a lot of AI equities can get mauled over the next 12–24 months as expectations and funding realities collide.
2. What Oracle Just Told You About “AI Infrastructure”
Oracle is the first big, obvious casualty of the “If we build it, they will come” phase.
The good optics:
RPO (future contracted revenue) blew past estimates: $523B, up 438% YoY.
New AI commitments from META and NVDA, not just OpenAI.
Cloud revenue up ~33% in constant currency; total revenue +14%.
On the surface, that says “AI demand is real.”
The part the market actually traded:
Capex last quarter: $12B vs ~$8.4B expected.
FY26 capex guide: hiked from ~$35B → $50B.
Oracle has burned ~$13B of cash over the last four quarters and sits on ~$88B net debt, with the weakest credit metrics among the “AI hyperscalers.”
The majority of that gigantic RPO is still OpenAI‑linked, in a world where Google’s Gemini 3 and now GPT‑5.2 are proving how fast winners can change.
This is why the stock is ~40% below its September highs and dropped another ~11% after earnings despite the “beat” headlines. The equity is being repriced as a levered, single‑customer AI infra lender, not a neutral cloud toll road.
Key message from ORCL:
Having AI demand booked isn’t enough anymore.
You need a fortress balance sheet, diversified counterparties, and a funding plan that doesn’t rely on capital markets staying euphoric.
Oracle is now the canary in the AI debt mine.
3. Broadcom: Great Business, Price of Perfection
Broadcom’s quarter was, on the fundamentals, exactly what the AI bulls wanted:
AI semiconductor revenue +74% YoY last quarter.
Management guided to doubling AI chip revenue this quarter.
Another $11B custom‑chip order from Anthropic (on top of the prior ~$10B), plus a new fifth custom XPU customer with ~$1B of orders.
AI networking backlog alone is >$10B; total orders on hand >$73B, consolidated backlog ~$162B.
That is monstrous visibility for a chip and networking vendor.
And yet…
The stock, up ~75% YTD heading into the print, sold off 4‑5% after hours and is trading heavy.
What the market just said:
“We get it. AI demand is huge. But at this valuation, ‘great’ is the new ‘meh.’”
AVGO is still a structural winner – it’s the purest public play on custom AI silicon + networking – but the risk/reward has shifted from “face‑ripper” to “compounder you buy on drawdowns, not at any price.”
4. GPT‑5.2 vs Gemini 3: AI Is Accelerating, Not Dying
Underneath ORCL and AVGO, the technology story is doing the opposite of topping out:
Google’s Gemini 3 briefly seized the narrative, outperforming GPT‑5.1 on several benchmarks and showcasing how far custom TPUs (built with Broadcom) can go.
Yesterday, OpenAI dropped GPT‑5.2, with better performance on complex work tasks, coding, spreadsheets, and long‑context reasoning. Early takes from analysts: “OpenAI is back on top of the benchmarks.”
Translation:
The capabilities race is still accelerating. What’s changing is who gets paid and who can afford to keep racing.
5. So… Did Time Just Kill the AI Rally?
Here’s the real answer:
No, AI demand is not peaking.
Yes, the first generation of easy AI trades is probably over.
Two things are now true at the same time:
Macro is still “supportive enough.”
Powell just cut again with Core PCE/CPI still above target and restarted T‑bill purchases – stealth balance‑sheet expansion. Liquidity is not being yanked; front‑end rates are easing.The bond and credit markets are tightening the quality filter.
Long yields are sticky, term premium is elevated, and credit is finally charging real risk premia. Oracle’s CDS blowing out while its stock implodes is exactly what that looks like in practice.
Put differently:
The AI theme is alive. The AI “everything at any price” trade is dying.
6. Winners, Watchlist, Losers
Likely Winners (own the chokepoints, fund themselves)
These are the names you want to lean toward if you believe AI capex is real but capital is getting more expensive:
1. Microsoft (MSFT)
Deep OpenAI integration, diversified revenue, massive free cash flow easily covering AI infra spend.
Balance sheet means they can choose their pace of capex rather than begging markets to fund it.
2. Alphabet (GOOGL)
Gemini 3 momentum + TPU partnership with Broadcom.
Net cash, huge ad/YouTube cash machine funding AI with internal capital.
Also hedged: if custom ASICs gain share vs GPUs, GOOGL and AVGO win.
3. Broadcom (AVGO)
Custom AI silicon + AI networking with multi‑year backlog.
Not cheap, but the business quality is “toll road on AI traffic.”
Best used opportunistically: buy air pockets, not euphoria.
4. TSMC (TSM)
Quiet gatekeeper to the whole silicon arms race.
As long as AI requires cutting‑edge nodes, TSM is the foundry bottleneck.
5. “Energy & electrons” complex (for diversification)
AI doesn’t run on vibes; it runs on natural gas and power, and those markets are tightening:
TD Cowen projects that U.S. data‑center growth could add 6+ Bcf/d of gas demand by 2030, on top of LNG and industrial needs, and expects gas to average >$4/mcf long‑term and ~$5 in 2026.
Their favored E&Ps (AR, EQT, EXE, GPOR) screen at very high forward FCF yields at those price decks, with AI‑driven power burn as one of the incremental demand drivers.
If you want AI exposure that isn’t another software or chip chart, this is where you hide some capital.
Watchlist (real upside, but more path risk)
These aren’t “sell everything” names – they’re where timing and funding matter most:
Oracle (ORCL)
The business is now a leveraged bet that OpenAI & friends actually turn that $523B backlog into cash before the funding window tightens.
It can work if: (1) more META/NVDA‑style diversification shows up, (2) capex intensity peaks, and (3) they prove they can finance build‑outs without blowing the IG rating.
For now, it’s a trading sardine, not a core position.
Nvidia (NVDA)
Still the standard for training, but the narrative shifted: custom ASICs (AVGO/TPU, in‑house silicon, etc.) are clearly viable.
As long as hyperscaler capex grows, NVDA can keep printing. But the “one GPU monopoly forever” story is already being repriced into “dominant but contested supplier.”
AMD (AMD)
A credible #2 in GPUs with upside if hyperscalers push for pricing leverage against NVDA.
More cyclical and more exposed if AI capex growth slows for a couple of quarters.
AI‑native software: SNOW, MDB, DDOG, BRZE, etc.
Long‑term winners will be the ones where AI drives usage‑based revenue (more queries, more workloads) instead of being a marketing checkbox.
They’re all sensitive to any wobble in workload growth or cloud optimization cycles.
Losers (or at least, places to be very careful)
1. Levered AI infra tourists.
Public or private players whose business model is “borrow huge, build data centers, hope utilization is perfect.” Oracle’s reaction is the template for how equity and credit will treat those models when the music slows.
2. Single‑customer / single‑model exposure.
If your entire future is effectively one mega‑contract with OpenAI or one LLM vendor, congratulations: you’ve taken idiosyncratic model risk and turned it into balance‑sheet risk.
3. Late‑cycle “AI laggards” trading at AI‑leader multiples.
Names that slapped “AI” on the deck, added some GPU spend, and now trade as if they’re core to the stack. As funding gets selective, they’re the first source of cash.
7. How to Think About AI From Here
Big picture:
AI as a technology is ahead of AI as a business model. The models are racing; the monetization and unit economics are catching up.
The Fed just told you liquidity will be around, but not free forever. The bond market is forcing you to care about duration, leverage, and capex discipline again.
So if Time’s cover is a curse, it’s not “AI is dead.” It’s:
“The easy, index‑beta AI trade is over. From here, you have to own the right part of the stack – and the right side of the balance sheet.”
For me, that means:
Core AI exposure: MSFT, GOOGL, AVGO, TSM – self‑funded chokepoints.
Diversifier/hedge: High‑quality energy & power names levered to AI’s electricity draw (nat‑gas E&Ps, grid gear, data‑center power & cooling).
Tactical watchlist: NVDA, AMD, ORCL, and the better‑quality AI‑native software names – trade them around the cycle, don’t worship them.
Time didn’t kill AI. It just rang the bell that says:
“From here on, this is a stock‑picker’s market.”
News vs. Noise: What’s Moving Markets Today
A bit of a selloff in the NASDAQ after ORCL earnings, and this morning after AVGO earnings. Interestingly, precious metals continue to come back hard after topping out in October, while crypto continues to flatline at best. Long time readers know I think you should own both. I wouldn’t chase precious metals here and Bitcoin is at least holding support at the 10 day moving average and above $90K…..
I think a number of things have transformed this market over the past couple of years. First was Covid and individual investors linking up over social media. Then was individual investors taking down a hedge fund, forcing the “smart money” to pay attention to what retail is doing. There is no smarter money out their than Renaissance, so this caught my eye….
What the Renaissance story is really saying
Renaissance is the OG of “let the math handle it.” For them to even consider tweaking models because of single‑stock silliness tells you a few things:
Meme moves are now big enough to break the models.
QMMM goes from ~$5 to >$300 in a month before the SEC halts it.
BYND trades from <$2 to >$3 in days, after hitting 50 cents.
These aren’t just funny tickers; if you’re running massive factor books with lots of small‑cap shorts, these are account‑level P&L events.
Quants are re-thinking how much risk they want in illiquid, junky names.
“Adjusting models” in practice usually means:Lower position limits in micro/small caps
Higher risk charges on crowded shorts
Less leverage into “cheap but hated” factor baskets
That changes the plumbing of meme squeezes.
Less systematic shorting = less fuel for violent squeezes.
Less liquidity provision from quants = bigger air pockets when retail or a chat room piles in or exits.
Net: upside may be less repeatable, downside more brutal.
Does this mean meme stocks are dead or dying? Doubtful. Does it mean you may want to rein in risk a bit? Probably. Again, I go back to using ratio spreads to get exposure to some of these stocks (short near the money call, long multiple out of the money calls)
A Stock I’m Watching
Today’s stock is Synopsys (SNPS)…..
From a chart standpoint you have a nice potential gap fill brewing here. Fundamentally…..
Theme fit: This is pure AI picks & shovels. Every TPU/GPU/ASIC/custom accelerator arms race runs through Synopsys/ Cadence. More chips, more custom silicon, more AI at the edge = more EDA and IP demand.
The “bad” news is actually good for you: Market hates “transition year” language; that tends to give you an entry point into a structurally advantaged duopoly with multi‑year secular tailwinds.
Risk/reward: You’re buying a tollbooth on all AI silicon (Nvidia, Google TPUs, AWS Trainium/Inferentia, Meta, custom ASICs, Chinese players) at a discount to its main peer because of near‑term IP softness and a messy INTC/China narrative. That’s exactly the kind of temporary controversy you want in a compounder.
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
Thursday January 15, 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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