The market is crashing, panic is spreading.
At this very moment, Trump’s team suddenly announced: they may acquire U.S. shares of artificial intelligence companies!
This is not a coincidence, but a signal.
History has repeatedly proven:
Whenever you follow President Trump in buying stocks, it often marks the beginning of great wealth.
I’ll say this once. These 8 stocks are going to make generational wealth for many by year end…
1. Nvidia~ $NVDA
2. Microsoft ~ $MSFT
3. Alphabet ~ $GOOG
4. Amazon ~ $AMZN
5. Marvell Technology ~ $MRVL
6. UMeta Platforms ~ $META
7. IonQ ~ $IONQ
8. Palantir ~ $PLTR
Many people ask me: why don’t you charge?
The answer is simple: I’ve already made enough.
Sharing is my passion — that’s why I insist on publishing for free.
Longtime TSLA holder since 2013, never sold a share. Requested 3700 SpaceX IPO shares via Fidelity. Strategy: keep 20% and flip the rest. Expecting IPO rocket but a sell-off after several months, might dip below $1.75T valuation. What’s y’all’s SpaceX strategy
"If all you ever did was buy high-quality stocks on the 200-week moving average, you would beat the S&P 500 by a large margin over time. The problem is, few human beings have that kind of discipline."
- Charlie Munger
$MA
GOLD Is About to Repeat 1979. Last Time This Happened, It Pumped Hard.
1979: Iran war → oil price 2x → crisis → Dump → Pump
2026: Iran war → oil price 2x → crisis → Dump →
30,000 hours of footage, equivalent to 3 years and 7 months, were filmed to capture the blooming of 77 types of flowers, and the result is spectacular.
The biggest takeaway isn’t that AI demand is at an all-time high.
It’s that we’re discovering compute isn’t the scarce asset.
Power is.
When the richest companies in the world are waiting on grid connections, you’re no longer in a technology race.
You’re in an infrastructure race.
South Korea's $KOSPI is VERY over-extended
Pullback to the 10-week ema = ~20% decline
Pullback to the 40-week ema = ~42% decline
Pullback in semis + memory stocks will be painful
Charlie Munger, one of the greatest investors of all time, said:
"Buy high quality assets on the 200 week moving average and you'll beat the S&P 500 by a large margin."
The best buy signal in Bitcoin isn't a pattern or an indicator.
It's the 200 week moving average.
Every time $BTC has touched it, it marked a generational bottom.
2015. 2019. 2020. 2022.
We just touched it again at $61,800.
SPACEX COULD BECOME THE BIGGEST EXIT LIQUIDITY EVENT IN MARKET HISTORY.
And the data backs it.
Most of the biggest IPOs of the last 15 years eventually crashed hard after listing.
• Robinhood: -90%
• Rivian: -88%
• Lyft: -79%
• Uber: -68%
• Palantir: -53%
• Coinbase: -57%
• Snap: -56%
• Twitter: -58%
• Facebook: -54%
The median max drawdown across major IPOs was around -54% within 1 year.
And these were not small companies.
Most of them were industry leaders, heavily hyped by media, backed by top institutions, and considered “must own” stocks before listing.
Now look at SpaceX.
The company is reportedly targeting a valuation of around $1.75-$2 TRILLION.
That would instantly make it one of the largest companies in the world.
At the same time:
• Retail demand is extremely high
• The IPO float may stay very small
• And early investors are sitting on massive gains from private rounds.
This is the exact setup where IPO prices can disconnect from fundamentals very quickly.
History shows retail investors usually enter during peak excitement while early investors slowly get liquidity after listing.
Facebook became a great business long term. Still, its stock max drawdown within a year was 54%.
Coinbase became one of the largest crypto companies in the world. Still, its stock max drawdown within a year was 57%.
A successful company does not always mean it'll go up only after IPO.
That is the part retail investors usually ignore during highly hyped listings and become the exit liquidity.
🦔UC Berkeley's computer science department just posted its worst failure rates in years. 35.3% of CS 10 students got F's in spring 2026, up from under 10% in prior semesters. Professor Dan Garcia says the primary driver is a "vast increase in academic dishonesty" through LLMs. Students use AI to complete assignments, never learn the material, then fail exams. His office hours, once full, are now empty.
My Take
Companies are firing experienced engineers while the pipeline that produces new ones is being gutted by the same technology. Students use AI to bypass the hard part of learning, show up to exams without the understanding, and fail. One professor discovered a student's linear algebra class had an "open AI" policy for homework and exams. That student then couldn't do basic linear algebra in the next course.
Both ends of the workforce are eroding at the same time. Senior engineers are getting cut to fund AI spending. Junior engineers are graduating without the skills because AI did their coursework. And the companies spending trillions on these tools haven't connected those two facts yet.
Hedgie🤗
TIER 1 — THE IRREPLACEABLE (Core Bottlenecks)
#1 $NVDA — The entire AI compute stack runs through NVDA. Every hyperscaler, every neocloud. NVDA remains the clearest monetization point for AI infrastructure spending — the GPU, networking, and software ecosystem still pull demand toward one platform. Nothing replaces it.
#2 $MU — The tightest supply constraint in the stack right now. Micron is the only U.S. producer of high-bandwidth memory and the fastest-growing of the three global suppliers. Its 2026 HBM output was already sold out by December 2025. Every AI accelerator built by Nvidia, AMD, or the hyperscalers needs HBM. HBM3E → HBM4 transition is a massive tailwind.
#3 $NOW — The AI software layer that runs on top of all this hardware. Enterprise AI orchestration, agentic workflows, and automation at scale. Premium valuation is justified by best-in-class platform stickiness and expanding AI product suite.
#4 $AVGO (Broadcom) — Custom AI silicon (XPUs) for Google, Meta, and others. Broadcom captures custom AI silicon and networking spend and converts AI infrastructure demand into large, durable cash-flow streams.
#5 $TSM (TSMC) — Manufactures every chip that matters. NVDA, AMD, and the hyperscalers all run through TSMC — it covers the chip manufacturing layer of the full AI stack.