NVDA is the dominant AI/robotics infrastructure provider; current valuation appears too low given this position, though a collapse in AI spending poses a major risk.

Nvidia up 2.5%. However, so far, we haven't hit 227 on Nvidia yet. I thought we would test that level. We ended up at $222. We didn't make it to 227. I was hoping to test that breakout by Friday.

I was wrong about that. We didn't get there. We only made it to 222.

I said this morning that it was unlikely we'd make it to 227. So, I tried to follow through and said, "Okay , it looks like it's starting." " Weakens or something like that." We're still at 222.

But we were right to downgrade. Wait and see is what we were saying today.

the vast majority of that money, probably around $400 billion of it, probably goes to Nvidia. Nvidia is without a doubt the kingmaker of what's happening right now. In the entire AI ecosystem.

So, the practical number of symbols per second for a chip model would be something like 36 symbols per second on an RTX 5090 or a Blackwell 6000.

As for Vera Rubin, which is actually... I mean, this is all a reference to an astronomer. Vera Rubin was an astronomer. Vera is the CPU, and Rubin is the GPU. So, we'll focus on Rubin here.

Rubin processes about 10 times more symbols per second than an RTX 6000. This actually means that even though you have much more RAM, your symbol processing power far outweighs the difference in memory gigabytes.

Here and there. I mean, think about it. The Rubin has 288GB. To simplify the math, 288 divided by 96 equals exactly 3 times. So, this one here— I'll click here—this one has 3 times the RTX 6000's codecs.

And it has, or rather, is equipped with, video memory. It has almost 3 times, or rather 3.3 times, compared to the H100. But its performance on a practical basis, given that its bandwidth is much higher, is actually 10 times that of the A100.

On a codec-per-second basis, it's about 8 times that of the H100.

So, what this means is that these chips are actually becoming less reliant on RAM. Now, I'm not saying that high-bandwidth memory is bad. I'm just saying that as the bandwidth of these GPUs increases and they become more efficient, they technically need less space.

They need less RAM because they can handle so many tasks so quickly.

One we know for sure is going to dominate robotics. I have no doubt about it because they're already everywhere in robotics. And people don't believe it. People don't even think about it, or in my opinion, they don't see it.

It sounds simple, but it's Nvidia. Because Nvidia is literally the brain and the system that most of these robots run on. And that's kind of crazy to think about.

But let's understand for a moment what Nvidia is up to behind the scenes. Nvidia profits because these robots have to be trained. Where are they trained? On Robin chips. We just saw that same company, InScale, which I made a separate video about, sign a $3.5 billion computing deal that ends with the installation of 100,000 Vera Robin GPUs.

I guess they'll need more money, right? That's a crazy chip, and Nvidia is profiting from it because InScale wants to license those chips to companies like Figger so they can train or lease them to companies like Figger.

So they can train their robots. So, Nvidia profits in the training space.

But then you have to move on to on-device processing. Nvidia has that too; people are now training on CUDA and running robots on ROS software. 2. They train them in artificial worlds on the Omniverse platform.

The chips these robots actually run on are Thor and Oren, which are the robots' computers and brains. So, you have the Edge chip. They literally have the CPU and GPU packages for training.

They have ConnectX and BlueField DPUs for communication to and from the robot. They have the custom robot software. They have the Omniverse for artificial intelligence training.

They have the CUDA they train on and the one that runs the Edge on Oren and Thor. It's incredible.

This loop brings you back to Nvidia. That's why it seems crazy to me that Nvidia's stock is so cheap. But it's really lifting everyone up.

And so you end up funding the very Nvidia chips that make this stuff possible. It's not bad. It's pretty impressive.

The big risk is that AI spending will somehow collapse, and people will sell bear pelts before they even hunt the damn bear. And if they go into the forest and don't find any bears, there will be no funding, and therefore they won't be able to sell the pelts.

They won't be able to deliver their product. They won't be able to train their robots. Nvidia won't get paid. Computing power will run out. OpenAI and Anthropic won't get the computing power they need.

And that's where the problems start. Nvidia won't get paid.