Yan Chen, PhD

crypto | big tech | china tech. not financial advice. dyor.

[Chris Paxton @chris_j_paxton]

I think if you are in robotics now you absolutely need to read this

[Sawyer Merritt @SawyerMerritt]

SpaceX has announced their new Gigasat factory in Bastrop, Texas, which will start producing AI satellites by the end of 2027.

• 1,000+ acres of land owned/under contract
• Over 11 million square feet of building potential
• Solar ingots and wafer, solar cells, and AI sats will also be produced here.

[Elon Musk @elonmusk]

SpaceX AI Satellites

[Elon Musk @elonmusk]

Interview with SpaceX CFO

[Brian Armstrong @brian_armstrong]

Good take

My guess is
- demand for intelligence is near infinite
- but 80% of workloads will be running on 99% cheaper models within 12-18 months
- 20% of workloads will still run on latest gen models where IQ maxing is important (scientific breakthroughs, higher level ochestrator agents?)
- rough analogy might be what % of macbooks or gaming PCs sold have the maxed out specs for CPU/GPU, prices are falling much faster than Moore's law here though
- this leads me to think the limiting factor will be energy and compute, not better models

At Coinbase we're working hard on routing prompts to cheaper models where appropriate, and in some cases have been able to keep costs roughly flat, while token usage continues to grow exponentially.

[BoxLongs @BoxLongs]

$SNAP wild that there is literally zero pulse on wall street over specs - stock bleeding out. if this was the big moment, wouldnt there be more around it. so far $qcom deal thats it. owning $SNAP has been a cruel and unusual torture, the other day when it was ripping over $6, i said, u always get a chance to buy it down big. and here we are today. it was a private convo with @RandianCapital who gets it... 6 trading days from evans keynote....

[Sawyer Merritt @SawyerMerritt]

Cathie Wood taking her first Unsupervised @Tesla Robotaxi ride in Austin, Texas.

"The fact that I was talking to you the whole time and didn't pay any attention to the ride itself means that I think it's completely safe; I'm excited for Tesla. I'm excited for Tesla shareholders. I do think now, this slowly, slowly, slowly is moving into all at once."

Full video: https://t.co/deqpHRMJpm

[intern @intern]

Why I bet my career on robotics

- skip to 1:24 for thoughts on crypto
- skip to 5:15 for my robotics thesis

[Evan @StockMKTNewz]

SPACEX $SPCX IPO WEEK IS FINALLY HERE ... This is how SpaceX got here

2002: $27M - Musk Starts SpaceX
2010: $1B - Falcon 9's first flight
2016: $10B - Google + Fidelity invest $1B
2024: $350B - Post-election AI rally
2025: $800B - IPO plans announced
2025: $1.25T - Musk merges xAI into SpaceX
2026: $1.75T - Largest IPO in history

[Aadit Sheth @aaditsh]

Wait. Google is paying SpaceX $920 million per month for GPUs?

Google. The company that builds its own TPUs. That runs one of the largest cloud infrastructures on earth. Is renting 110,000 Nvidia GPUs from a rocket company.

I'm honestly not sure what to make of this. Either Google's AI compute needs have gotten so massive that even they can't build fast enough. Or SpaceX has built something in AI infrastructure that nobody was paying attention to. Or both.

$920M a month. $30B over the contract.

Whatever is happening behind the scenes at these companies is moving way faster than what we see publicly.

[Hunter Horsley @HHorsley]

Everyone in crypto needs to zoom out.

Unless you're a trader, as an investor, don't feel you need to focus on the week to week headlines. Don't feel you need to focus on the month to month price.

Rather, focus on (a) substance and (b) the year over year timeframe. Look at usage, onchain tech with product-market-fit, large corporates and institutions integrating/ building/ launching things in the space, the quality of teams and execution.

That's the substance. It won't be a straight line. But the substance is undeniably leaping forward from 2022 to 2026.

Recognize it's a long game. The score will take care of itself.

[Elon Musk @elonmusk]

Helpful tool for improvement. It’s just physics thinking in the limit.

[Ming @tslaming]

GOOD NEWS 🚨 Duan Yongping, the "Chinese Warren Buffett", has officially established a massive ~$1.27 Billion position in $TSLA via his firm H&H International Investment during Q1 2026 🔥

Here are the exact numbers from the latest portfolio disclosure:
📈 Shares held: 3,408,900 shares
💰 Market value: $1,267,258,575 (~$1.27 Billion)
🍰 % of total portfolio: 6.34%
🏆 Portfolio rank: 5th largest holding overall
💵 Reported price: $371.75
🛒 Activity type: New "buy" layout (representing a +6.34% shift to the overall portfolio)

💡 Why this is a major milestone

Duan Yongping is widely recognized for his highly concentrated, ultra-disciplined value investing philosophy.

What makes this $1.27 billion bet especially massive is his complete change of heart. Historically a vocal skeptic of @elonmusk, Duan shifted his stance after experiencing Tesla's FSD firsthand. He recently praised the tech as "truly remarkable", completely separating his personal opinion of Musk from the objective economic and product disruption Tesla is driving.

Having a legendary value whale deploy this much capital into Tesla provides massive institutional validation for the long-term autonomy thesis 💪

[Jukan @jukan05]

Why you should be on X:

You can see real-time supply-chain information shared directly by Korean semiconductor engineers, for free.

You get raw, unfiltered information without it going through sell-side analysts or research houses.

This is why I don’t use Reddit or other social media platforms. They don’t have this kind of information.

[Brett Adcock @adcock_brett]

3 years ago we only had 2 humanoid robots…Never thought I’d say this, but we’re now building so many robots that we need storage space for them

[Elon Musk @elonmusk]

Maybe largest in this sector of the galaxy

[Chamath Palihapitiya @chamath]

When the red line catches the blue line, if the cost of the blue products/companies don’t go down to be the same cost of the red products/companies, the red companies will win.

[Milk Road AI @MilkRoadAI]

Goldman Sachs just dropped the most precise map of where $7.6 trillion is going over the next five years and it tells you exactly which companies are standing in the middle of an unavoidable flood of capital (Save this).

The numbers are worth understanding precisely before talking about who benefits.

Goldman's baseline projects $765 billion in AI capital expenditure in 2026 alone, growing to $1.6 trillion annually by 2031.

Over the full 2026 to 2031 period, cumulative spend breaks down to $5.1 trillion in compute, $2.1 trillion in data centers, and $358 billion in power.

Nvidia is assumed to command 75% of all compute spend throughout the period, using the Rubin VR200 chip at $80,500 per GPU as the baseline.

The data center specification charts reveal how dramatically physical requirements are escalating.

A standard cloud data center runs 5–15 kW per rack while a transitional Blackwell era AI data center runs 130–200 kW per rack.

The AI factory of the future, running Rubin and Feynman silicon operates at 500+ kW per rack, at greater than 1 gigawatt scale, with liquid cooling only.

Traditional hyperscale data centers cost roughly $10 million per megawatt to build while the next generation AI data centers are being discussed at $15 to $20 million per megawatt.

Goldman identifies silicon useful life as the single biggest swing factor in the entire model.

At a 3-year replacement cycle, cumulative compute depreciation hits $3.99 trillion and at 7 years, it drops to $2.23 trillion, a $1.76 trillion difference on one assumption alone.

Power is only $358 billion of the total, but Goldman is explicit, it is the only component that can prevent the other 95% of the stack from deploying.

Amazon's Andy Jassy put it, "Our single biggest constraint is power." Connecting large data centers to the grid takes years.

Now here are the companies standing directly in the path of each layer of this capital.

Nvidia is still the most concentrated bet on the compute layer.

At 75% of $5.1 trillion in compute spend over six years, that is $3.8 trillion in cumulative revenue flowing through one company's products.

The 75% gross margin on data center GPUs is the reason every hyperscaler is trying to build custom silicon to escape it while simultaneously continuing to buy Nvidia because nothing else performs at the same level.

Vertiv is the direct infrastructure play on the data center upgrade cycle.

Every rack going from 40 kW to 500+ kW needs liquid cooling systems, power distribution, and thermal management infrastructure that simply did not exist at prior density levels.

Vertiv just deepened its liquid cooling capabilities through a strategic acquisition and was named a key partner on Hut 8's large AI-focused Texas data center campus.

The liquid cooling market is growing from $5.5 billion today to $15.75 billion by 2030, and Vertiv is the dominant provider in that market.

Vistra is the power thesis in its most direct form.

The $358 billion power segment is the critical path for the entire $7.6 trillion, and Vistra has spent the last 18 months locking up that critical path through long-term nuclear power purchase agreements.

Vistra secured a 20 year agreement with Meta for over 2,600 MW of nuclear energy, plus a separate deal with AWS from its Comanche nuclear facility.

Goldman Sachs and Jefferies both upgraded the stock after the Meta deal was announced.

The architecture of this trade is simple.

Goldman's model is not a prediction of whether AI spending happens but rather a model of the minimum physical capital required to deploy infrastructure that has already been contracted, already been announced, and is already under construction.

The compute layer requires the chips, data center layer requires cooling and power infrastructure and the power layer requires nuclear at scale on multi-decade contracts.

All three layers are being funded simultaneously, and all three have identifiable public companies sitting directly in the path of the capital.

Come join Milk Road Pro and get our full $7.6 trillion infrastructure breakdown which names across compute, data centers, and power we're currently positioned in and our full thesis on the AI trade.

Link below!

[Tesla AI @Tesla_AI]

Real-world AI is about solving for the edge case, not the average case