Andrew Wilhelmy

Asset-Based Lender, Mgmt Consultant, Active Investor, Commercial Finance, Austrian Economics, Active Colorado dude. AZ Wildcat.

[Andrew Wilhelmy @AltFiCapital]

Land of the Free. Amazing 🇺🇸

[Hoover Institution @HooverInst]

Today, @NVIDIA is widely recognized as the most consequential technology company in the world. But when Founder and CEO Jensen Huang hatched the idea more than three decades ago, it was viewed as little more than an incredibly risky bet on a new approach to computing.

How did Huang, an immigrant from working class roots in Taiwan, build this company into what it is today? In the first installment of the Hoover Institution's Only in America series, @CondoleezzaRice sits down with Huang to explore why his rise—and that of NVIDIA—couldn't have happened anywhere else.

04:42 Jensen Huang's journey to the US
07:54 Jensen's experience at boarding school in Kentucky
14:23 How Jensen came to Silicon Valley
17:23 Founding NVIDIA
23:40 The evolution and growth of NVIDIA
27:08 Sustaining motivation amid challenges
29:01 America's exceptional tech sector
32:28 Why Huang is a "cautious optimist" on AI
35:10 Jensen Huang's Only in America story

[Andrew Wilhelmy @AltFiCapital]

@Codie_Sanchez SMB

[Deedy @deedydas]

Every single AI startup with $10B+ valuation and $100M+ revenue run rate:

Crusoe - $10B
Mercor - $10B
ElevenLabs - $11B
Baseten - $11B*
Harvey - $11B
Lovable - $12B*
OpenEvidence - $12B
Mistral - $14B
Nscale - $14.6B
Fireworks - $15B*
Sierra - $15.8B
Moonshot - $20B
Perplexity - $22.6B
Cognition - $26B
Scale - $29B**
DeepSeek - $52-59B*
Cursor - $60B*
Waymo - $126B**
xAI - $250B**
OpenAI - $852B
Anthropic - $965B

21 total companies.

*rumored / unannounced
**not fully independent

[Andrew Wilhelmy @AltFiCapital]

Amazing

[Milk Road AI @MilkRoadAI]

The hardest problem in physical AI has never been the model, it has been the data (Save this).

Language models had an extraordinary training advantage that almost no one appreciated at the time.

The entire written output of human civilization, books, articles, code, scientific papers, social media, legal documents was created by humans, from the human perspective, to be consumed by humans.

The data and the learner were perfectly aligned from day one.

Physical AI has no equivalent resource, robots experience the world from an embodied, first-person perspective sensing depth, force, position and environment from inside a physical body operating in three dimensional space.

The entire internet's video archive is almost entirely third person footage and none of it tells a robot how to orient a gripper, recalibrate on a new surface, or recover from an unexpected obstacle.

The world's largest robotics datasets combined contain roughly 5,000 hours of physical interaction data, GPT-4 trained on the equivalent of hundreds of millions of hours of text.

The data gap between language AI and physical AI is not a 10x problem but rather a 100,000x problem.

NVIDIA's answer is Cosmos 3 and it is the culmination of a three-year program specifically engineered to close that gap.

The progression Jensen described on stage mirrors exactly how reinforcement learning evolved for language models.

Teleoperation, humans physically demonstrating tasks to robots is the physical world's equivalent of RLHF.

High quality, but it scales linearly with human operators and physical robots, making it catastrophically expensive to replicate at the data volumes needed for general intelligence.

Omniverse simulation gave developers a way to generate synthetic training environments at scale, the physical world's equivalent of reinforcement learning with verifiable rewards.

But Cosmos 3 goes beyond both.

It is a unified omnimodel built on a novel Mixture of Transformers architecture, two parallel towers, one autoregressive and one diffusion trained on 20 trillion tokens of multimodal data, nearly one billion images, 400 million real and synthetic videos, spatial audio, text, and action data from both humans and robots.

The critical breakthrough is perspective invariance.

Cosmos 3 can take third-person footage that exists in abundance and reproject it into first-person perception data that robots can actually learn from.

A security camera recording of someone picking up a box, filmed from above and at an angle, can now be translated into the first-person sensory experience a robot arm needs to learn that same task.

This is the removal of the single most fundamental constraint on physical AI scaling.

Cosmos 3 opens the door to general-purpose robotic intelligence trained cheaply, updated continuously, and deployed across the physical environments, factories, warehouses, hospitals, construction sites, agriculture that represent the overwhelming majority of global GDP output.

The data center buildout is wave one, physical AI is wave two and the GPU demand that wave two will generate as hundreds of billions of agentic systems come online is an order of magnitude larger than anything wave one has produced.

Come join Milk Road Pro and get our full framework for tracking the physical AI buildout including how we think about the Cosmos 3 ecosystem, which supply chain companies we believe will compound fastest as the data bottleneck breaks, and why we believe the GPU cycle is entering its most explosive phase, not approaching its peak.

Link below!

[Andrew Wilhelmy @AltFiCapital]

@TexasAustyn 😂

[Andrew Wilhelmy @AltFiCapital]

@rossiadam 💯couldn't agree more.

[Ricardo @Ric_RTP]

Nvidia is pulling off the most sophisticated financial loop in tech history.

They invested $40 BILLION in its own customers in just 5 months.

Here's why this could blow up the entire AI economy:

Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain.

Not small checks. We're talking about billions at a time.

And almost every single one of those companies turns around and spends that money on Nvidia chips.

Follow the money:

$30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers.

$2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs.

$2 billion into Marvell for silicon photonics that connects Nvidia systems.

$2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing.

$2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure.

$3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems.

$2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure.

And the list goes on.

Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems.

Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme."

Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other."

The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way.

But here's the part that makes this genuinely complicated:

Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months.

Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone.

So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment."

And they're not wrong. Some of these bets ARE paying off like crazy.

The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months.

Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers."

They're saying it themselves: We invest in companies that buy our products.

On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means "
we're funding the companies that fund us.

The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls.

Both cases use the exact same evidence.

[Andrew Wilhelmy @AltFiCapital]

@andrewdfeldman @cerebras @FoundationCap Very cool 👍

[Andrew Wilhelmy @AltFiCapital]

@ThoughtCrimes80 Salvage yard.

[Andrew Wilhelmy @AltFiCapital]

@texasrunnerDFW There are at least 30-40 club basketball teams in Colorado we could be playing, but we are in Phoenix this weekend for our 14 yr old to play a grand total of 4 one-hour basketball games. It will cost us a little over $3000.

[Andrew Wilhelmy @AltFiCapital]

@JudicialWatch Colorado is corrupt from head to toe.

How do you verify?

[Andrew Wilhelmy @AltFiCapital]

Why is Nvidia stock going up?

[Andrew Wilhelmy @AltFiCapital]

@texasrunnerDFW Wishing you fast healing Amy 🙏

[Peter Clack @PeterDClack]

The Milankovitch cycles have been Earth's heart pacemaker for millions of years.

Orbital mechanics have dominated throughout. Cyclical shifts from icy glacial to warm interglacial periods - then back again - are not due to CO₂. They are a direct mechanical consequence of Earth’s positioning in space relative to the sun.

The three anomalies, known as the Milankovitch cycles, are 'eccentricity' (orbital shape), 'obliquity' (Earth's tilt), and 'precession' (a planetary wobble). These cycles dictate the distribution of solar insolation flux (sunlight), particularly at 65°N, which acts as the kill switch for ice sheets.

Earth is now in the Late Cenozoic Ice Age Glaciation; oddly unnoticed in a crisis about planetary warming. Global cooling is far more significant. An already cooling climate intensified 34 million years ago with the glaciation and geographical isolation of the Antarctic continent.

CO₂ is not the driver nor has it initiated the many warm interglacial cycles. Even the previous glacial period, known as the Eemian, was 2 degrees warming on average than today, yet CO₂ did not shift from a steady 295 ppm.

If the sheer tilt and wobble of the planet provided the energy to retreat miles of ice, then CO₂ was at best a faint secondary feedback in this process, nothing more.

[Andrew Wilhelmy @AltFiCapital]

@FrankBr05713205 No.

[Andrew Wilhelmy @AltFiCapital]

@Amer1can_Barbie 💯

[Andrew Wilhelmy @AltFiCapital]

@jonnajarian 💯

[Andrew Wilhelmy @AltFiCapital]

@Amer1can_Barbie @CCPISASSH0E 😂