DeepSeek R1 & The Short Case For Nvidia Stock | Jeffrey Emanuel

I basically was

trying to help my organic search ranking of my little YouTube uh tool. And then it's like in the process, I may have inadvertently contributed to $2 trillion getting wiped off global equity markets. Because you know, the fact is, all of the news headlines came out saying

the stock market crash because Deepseek. I'd like to point out that the Deep Seek V3 technical paper, it came out

December 27th.

Yeah.

Okay ago. That's a month ago. Even the newer model, the R1 model that does the chain of thought, that paper came out a week ago. And people were all over that. So why suddenly on Monday did everything crash? And I'd like to think it's because I'm pretty sure it is that I wrote this article in a way that sort of speaks to

hedge fund managers

so they can understand it. And I published it uh like in the middle, you know, of the night on Friday,

and then it started taking off.

And then it got shared by Chamath, who has, you know, whatever, 1.8 million. Right. And Chamath, and it it's been viewed over two million times. Naval Rov account has two and a half million. And then like the uh Y Combinator, Gary Tan, and the Y Combinator account. Between them, they have millions of followers. And not only did they share it, but they were like very effusive in their praise about this is really smart. And that

went crazy.

Everyone is talking about this new Deep Seek AI model from China that is reportedly 45 times more cost efficient than US-based iModels and charges 95% less money to use than ChatGPT. As a result, Nvidia is down 20%, wiping out $600 billion in market value. And both OpenAI and Meta's AI labs are scrambling to discover how a relatively unheard of Chinese AI lab was able to outperform their very expensive models with a Chinese-grown model that just cost $6 million to train. The guest on the show today is Jeffrey Emmanuel, who actually thinks that this part of the story, the Deep Seek AI model part, is over-indexed on. And it's actually a confluence of other factors that is contributing to the unbundling of Nvidia's market share. And it's not the release of DeepSeek that triggered the 20% drawdown, but instead a 12,000-word article that he wrote on his blog that quickly went from just a few handful of readers to over 2 million readers over the weekend that actually coincided with a 20% drop in NVIDIA price when the market opened on Monday. In this episode, Jeffrey and I go through his article and reasoning behind why NVIDIA is under threat of getting unbundled by other chip suppliers, in addition to Deep Seek's impact upon the entire resource supply chain of training and inference around LLM models. Let's go ahead and get right into this episode with Jeffrey. But first, a moment to talk about some of these fantastic sponsors that make the show possible. Bankless Nation, very excited to introduce Jeffrey Emmanuel. He is both an investor and a technologist. He, however, is a very specific flavor of both of those things. On the tech side, he is deeply informed about the research advances that come out of major AI labs like OpenAI, Meta, Google. And on the investing side, he plays in the markets as a value investor, one who dares to go short at times. Jeffrey released an article on his blog called The Short Case for Nvidia Stock, which has been echoing across the tech industry as this new Deep Seek model has fired a shot all the way from China across the bow of the US AI industry and has left the US based AI companies scrambling, reeling both TradFi and crypto markets as everyone learns to digest Deep Seek's impact upon the world. Jeffrey, welcome to Bankless.

Thanks for having me.

Uh, Jeffrey, I really enjoyed your article. I want to kind of start with the punchline. I want to read one of the last paragraphs in your article that I really felt uh felt summed up the entire digestion of everyone's analysis on how uh the new Deep Seek model has impacted the market. So, this is actually actually the second to last uh paragraph in your article. You wrote, perhaps the most devastating to NVIDIA's mote is DeepSeek's recent efficiency breakthrough, achieving comparable model performance at approximately 145th the compute cost. This suggests the entire industry has been massively overprovisioning compute resources. Combined with the emergence of more efficient inference architectures through chain of thought models, the aggregate demand for compute could be significantly lower than current projections assume. The economics here are compelling. When DeepSeek can match GPT-4 level performance while charging 95% less for API calls, it suggests either Nvidia's customers are burning cash unnecessarily or margins must come down dramatically. To me, Jeffrey, that was the punchline for I think what everyone felt on the markets Monday when Nvidia stock fell 17%. To me, I'm I'm summing this up as there is a tug of war between hardware and software. And with the emergence of Deep Seek, uh the software side of this tug of war got a very large W. That's my interpretation, that's my analysis. Check me on that. How do you feel about that kind of conclusion?

You know, it's it's funny because

That the deep seek is the part that everybody's the the most focused on. But I actually think the whole shirt thesis still works pretty well without that.

Um for all the other reasons that we can discuss. And

you know, the the one issue with the deep seek is that it's funny, there's this thing, Jevons paradox, or uh, you know, which is like

Nobody was talking about this until suddenly now everybody's saying Jevins, every other word. And you know, it's it's something that comes from energy uh economics, which is like you think you make things more energy efficient, great, we're gonna use less energy. But then what ends up happening is that the price of energy goes down and everybody wants to use more energy, and so it actually increases demand for energy. And so everyone's saying now that oh, this deep sea thing is wrong because of the of Jevons. And um,

you know, I'm I am sympathetic to that um to a degree, but it's not

always so clear

and

It's not like the Jevon stuff happens immediately. Like there's there's often,

you know, um, sort of what causes booms and busts is these sort of temporary dislocations between anticipated demand and realized demand.

And really, you know, what what I think people miss is that

the big decisions about CapEx come down to a couple people, like,

you know, Mark Zuckerberg, and and there a lot of it is sort of gut feel, like Masayoshi's son. Like, is this the good time to just

P push on the accelerator. And I think someone like Zach has to s take a step back and say, listen, I know my guys are really smart, but

maybe,

you know, the answer is not necessarily to spend another, you know, three billion dollars on NVIDIA chips that are very expensive. Uh and, you know, where

I mean, literally like they're paying forty grand for

a GPU that's costing NVIDIA maybe what, 3,500 bucks to make. So it's

They're putting a lot of money in uh NVIDIA's pocket.

And maybe they can um, you know, pump the brakes just a little bit and then see if they can sort of still, you know, because they projected that they needed a certain amount

of chips

for their forecasted demand.

So if they can,

you know, and and the Deep Seek stuff is all public. So they can look at the technical report, they can start making these changes themselves internally, theoretically,

uh, for the at least for the next generation of models they're training.

And as a result, maybe they can kind of, you know, because it's

I think there is still some skepticism on, you know, Wall Street that like, are

are they gonna see a return on this money?

Because it's not like anyone's paying for to use all this, you know, meta AI stuff yet. And so I think it's um it's it's a little uh

it's it

I'm not convinced that the Jevin oh yeah, well, Jevons. It's like, okay, well, let's see if that's actually the case. But then, you know, really separately from that, like I was saying, even if you remove Deep Seek entirely.

I believe that NVIDIA

particular and

I want to clarify, I'm such a bull on AI. I'm about as bullish, like 99th percentile on

AI as anyone you will ever meet. I like live in the AI future all day, every day. I have three Claude accounts. I'm like, you know, using I I I'm using this stuff nonstop all day, every day. So I I'm a huge believer like

but.

NVIDIA as a company,

they, you know, this is just goes back to my sort of training in investing, is that

you see you see this over and over again that with the one exception of a regulatory, like enforced monopoly,

um

you do not have companies just get to print infinite profits without, you know, with with you know triple digit revenue growth with 90% gross margins. You don't get to see and without having everyone and their brother trying to figure out a way to beat them. And and that's what's happening. And so you look at

you know, these companies, Cerebrus and Grok with the Q, like

these these companies ha already have extremely compelling hardware that, you know, largely does get around the Nvidia um mode, for at least for inference. And uh, you know, in the case of Cerebrus for I think for training too.

And you, you know, there's all these other sort of and I mean

the other thing is like

You know, normal companies of the scale of NVIDIA tend to have extremely diversified revenue sources. Whereas NVIDIA,