FED Minutes & Portfolio Reviews (11/19/2025)
Timeline
0:00 – QQQ 5% Rule bounce, weak-bounce failure, buy/sell programs at 600/606
3:00 – Explanation of Phil’s 5% Rule, Fibonacci, and how programs create support/resistance
8:50 – Big-picture market view: post-summer giveback and only ~8% up year-over-year
10:30 – Why Phil is cautiously bullish despite AI hype and prior sell-off
11:45 – Q3 earnings season: Bodie/AGI research process and collaboration
13:30 – S&P earnings growth concentrated in tech and index distortion
15:55 – Comparing tech vs. traditional companies (Walmart, Target, Levi’s, P&G, JNJ, Pfizer)
18:00 – Nvidia’s profit explosion, valuation expansion, and personal Nvidia story
20:20 – How Nvidia’s chips enabled AI; AI boom mechanics
23:50 – Scale of tech wealth vs. historic fortunes (Howard Hughes, modern billionaires)
27:45 – Low-margin retail vs. high-margin tech; where profits really are
30:00 – AI adoption math: costs, project failure rates, and risk of mass layoffs
35:00 – Macro risk: unemployment, weak safety net, and lack of preparation for AI disruption
36:25 – “Hairpin turns” analogy for markets navigating AI and earnings
38:15 – Where AI money comes from (cash hoards of big tech; limited global profit pool)
41:30 – Historical tech waves: electricity, water, internet vs. today’s AI buildout
42:45 – Companies must adopt AI to survive; skeptics are wrong about it “going away”
43:55 – Fed meeting setup: earnings season says “don’t short everything yet”
45:00 – Structural limits: chips, power, water, and fragmented AI infrastructure buildout
46:10 – HBO/WBD example: mature subscription economics vs. AI revenue dreams
49:00 – Core fear: not enough real-world money to fund AI at current expectations
50:00 – Fed minutes and repo/liquidity stress; QT nearing limits
56:00 – Large language models and “million monkeys” explanation; scaling vs. slowdown
1:12:00 – Fed has quietly pivoted: QT/Repo issues, stealth easing, and inflation above target
1:20:00 – Internal Fed split; odds of no December cut and market disappointment
1:25:30 – Portfolio review: overview of positions and recent performance
1:26:00 – Big winners/losers: Nvidia, mega-cap tech, Eli Lilly, Merck, ISRG
1:31:00 – ISRG long-term thesis (AI + robotic surgery opportunity, but valuation rich)
1:31:45 – Tesla: extreme valuation, skepticism, and short/hedge positioning
1:36:00 – Short-Term and “Money Talk” portfolios: income-focused spreads and hedges
1:39:00 – Oracle: turning a “bad” short-term trade into a long-term income spread
1:43:00 – New hedge via TZA ahead of Nvidia earnings
1:44:30 – Target deep value case and AI/ChatGPT shopping experiment
1:46:00 – Why consumer-facing AI apps struggle with adoption (Hitchhiker’s Guide example)
1:49:00 – People want quick answers vs. detailed AI output; Google-style behavior
1:51:00 – Final portfolio/hedging reminders
1:54:00 – Closing: Nvidia earnings as key test, importance of hedging vs. exiting market entirely
Summary
Phil walks through the market’s failure at the QQQ weak-bounce line (606) and explains how his 5% Rule, buy/sell programs, and psychological support levels interact around 600. From there, he zooms out to the broader market: despite all the post-AI rally excitement, the S&P is only up about 8% year-over-year, which he sees as fundamentally reasonable given Q3 earnings.
Most of the earnings strength is concentrated in big tech—especially Nvidia—which Phil uses to contrast high-margin tech giants with low-margin retailers like Walmart and Target. He illustrates how Nvidia’s profit explosion is unprecedented, how AI workloads emerged from GPU architecture, and how the entire AI boom is built on a relatively small pool of global profits. His core fear: the amount of money required to sustain AI ambitions far exceeds what most sectors can realistically generate, and AI is nowhere near delivering the productivity needed to justify those costs. This raises two opposite macro risks—an economic downturn from widespread AI-driven layoffs, or an AI investment bubble that collapses under its own financial weight.
Phil then shifts to the Fed: the latest minutes show growing repo stress, limits on quantitative tightening, and a possible pause in December despite inflation still above target. Liquidity concerns—not economic strength—appear to be driving policy. That creates short-term bullishness but long-term structural risk.
In the portfolio section, Phil reviews major holdings and adjustments. He highlights strength in companies like Nvidia, Google, Eli Lilly, Merck, and ISRG, while reiterating his bearish stance on Tesla’s valuation. He demonstrates how he fixes slow-moving positions (like Oracle) by converting them into long-term income spreads, adds TZA hedges ahead of Nvidia earnings, and reinforces the importance of hedging instead of selling everything when nervous.
He ends by noting that Nvidia’s upcoming report is the key catalyst. Strong results would force sidelined investors back into the market, while weak results could trigger a broader decline—underscoring why proper hedging matters more than market timing.
