I’m running a 10k to 1M challenge off a small account. 🚀
No spam. No guru talk. Just what I’m actually doing. Inside the group:
• Real company profits
• Washed-out names
• Mispriced memory chips
• What I do when the market gets smoked
• Short-term explosive setups + live portfoliosNo promises. Just the process.
Want in? Reply YES. Free. Lurk all you want. 👀
MU just reminded everyone: AI isn’t just a chip story.
It’s a capital-flow story. 👀
Most people look at AI and see a messy list of stocks:
$NVDA $AMD $MU $AVGO $ANET $DELL $SMCI $VST $BE
But there’s a simpler way to understand the entire cycle:
Follow the money.
Microsoft, Google, Amazon, and Meta are spending billions building AI infrastructure.
That capital doesn’t stop at GPUs.
It moves through the entire stack.
1️⃣ Compute
AI models require massive computing power.
GPUs and custom AI chips became the first bottleneck.
→ $NVDA
→ $AMD
8/
We’re not trying to chase the last wave.
We’re trying to get positioned before the next layer becomes obvious.
Compute → Infrastructure → Applications → Real-world AI
Not a recommendation.
Just the map we’re following. 👀
7/
So our positioning is split on purpose.
Power / Infrastructure:
$VST $BE
Software / AI + Biology:
$MDB $SDGR $RXRX $TWST
Small positions.
Track the data.
Watch the money.
Add only if the thesis gets confirmed.
No need to chase every green candle.
6/
$MDB — data / software infrastructure
$SDGR — computational drug discovery
$RXRX — AI + biological data + drug discovery
$TWST — DNA synthesis / synthetic biology
Different businesses.
Different risk profiles.
Same bigger theme: AI moving into the real world.
5/
Then comes the question I think gets overlooked:
After all that infrastructure is built, what does the compute actually do?
That’s where the next layer gets interesting:
AI → Software → Biology → Drug Discovery
4/
That’s why $VST and $BE are on our watchlist.
Not “guaranteed winners.”
They’re simply tied to a critical part of the infrastructure needed to keep the AI buildout running.
Power is one layer of the stack — not the whole stack.
That distinction matters.
3/
The next problem is physical.
More GPUs → more data centers.
More data centers → more electricity.
More electricity → generation, grid, transformers, cooling.
AI needs power.
And you can’t build that infrastructure overnight.
2/
Wave 1: Compute
AI chips → $NVDA $AMD
Wave 2: Memory / Storage / Data Centers
That’s where $MU and $SNDK ran.
We’re not chasing those moves.
We’re watching where the bottleneck moves next.
👮Sculpture exhibition Sculpture, addedBack at the car, I check the time—it’s already afternoon. I decide to drive to the next town, a coastal town called Brighton, where I’ve heard there’s a beautiful beach and a cozy inn to stay for the night.