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Bloomberg Audio Studios, podcasts, radio, news. Hello, and welcome to another episode of the Odd Lots podcast. I'm Joe Wiesenthal. And I'm Tracy Alaway. Tracy, the deep-seag selloff.
That's right. It's pretty deep. Has anyone made that joke yet? We're in deep-seek. I don't think anyone has made that joke yet. I will say, you know it's bad in markets when all the headlines are about standard deviations. And then you know it's really bad when you see people start to say, it's not a crash. It's a healthy correction. That's the real cope.
But just for real seeing and setting, we've done some very timely interviews about tech concentration in the market lately and how so much of the market is this big concentrated bet on AI, et cetera. Anyway, on Monday, I think people will be listening to this on Tuesday. Markets got clobbered in video. One of the big winners, as of the time I'm talking about this, 3.30 PM on Monday down to 17%. So we're talking major losses really across the tech complex.
Basically, it seems to be catalyzed by the introduction of this high-performance, open-source Chinese AI model called DeepSeek. I was born from what we know out of a hedge fund. Apparently, it was very cheap to train, very cheap to build the tech constraints. At this point, it didn't seem to be much of a problem. They may be a problem going forward. But yes, here is something the entire market betting on a lot of companies making AI and are now concerns about, of course, a cheap Chinese competitor.
I just realized, Joe, this is actually your fault, isn't it? This last week you wrote that you were a deep-seek AI, bro, and look what you've done. You've wiped $560 billion off of NVIDIA's market. That's you. Anyway, one of the interesting questions, though, is that this was sort of announced in a white paper in December. Why did it take until January 27th for really to freak people out?
Big questions. Anyway, let's jump right into it. We really do have the perfect guest. Someone who was here for our election eve special, a guy who knows all about numbers and AI and quant stuff. And he writes a sub stack that has become, for me, a daily absolute must read where he writes an extraordinary amount. I don't even know how he writes so much on a given day. We're going to be speaking with his Vee Moshovitz. He is the author of the Don't Worry About the Vaz blog or sub stack.
Zvi, you're also a deep-seak AI brilliant. You've switched to using that. So I use a wide variety of different AI. So I will use Claude from Anthropic. I will use O1 from chat GPT from OpenAI. I'll use Gemini sometimes and I'll use perplexity for web searches. But yeah, I'll use R1, the new deep-seat model for certain types of queries where I want to see how it thinks and see the logic laid out. And then I can judge, like, did that make sense? Do I agree with that?
So one of the things that seems to be freaking people out as well as the market is that, purportedly, this was trained on a very low cost, something like $5.5 million for DeepSeek V3. Although I've seen people erroneously say that the $5.5 million was for all of its R1 models, and that's not what it says.
in the technical paper, it was just for V3. But anyway, oh, I should mention, it also seems like a big chunk of it was built on llama, so they're sort of piggybacking off of others' investment. But anyway, $5.5 million to train. Is that a realistic, and then b, do we have any sense of how they were able to do that?
So we have a very good sense of exactly what they did because they're unusually open and they gave us technical papers that tell us what they did. They still hid some parts of the process, especially with getting from V3, which was trained for the 5.5 million to R1, which is the reasoning model for additional millions of dollars, where they tried to make it a little bit harder for us to duplicate it by not sharing their reinforcement learning techniques. But we shouldn't get over anchored or carried away with the 5.5 million dollar number. It's not that it's not real, it's very real.
But in order to get that ability to spend $5.5 million and get the model to pop out, they had to acquire the data, they had to hire the engineers, they had to build their own cluster, they had to over-optimize to the bone their cluster because they're having problems of chip access thanks to their export controls and they're trading on 800s.
And the way that they did this was they did all these sorts of mini-optimizations, including just exactly integrating the hardware, the software, everything they were doing in order to train as cheaply as possible on 15 trillion tokens and get the same level of performance or close to the same level of performance as other companies have gotten with much, much more compute.
But it doesn't mean that you can get your own model for $5.5 million, even though they told you a lot of the information. In total, they're spending hundreds of millions of dollars to get this result. Wait, explain that further. Why does it still take hundreds of millions? And does this mean if it takes hundreds of millions of dollars that the gap between what they're able to do versus the, say, American labs is perhaps not as wide as maybe people think? Well, what?
DeepSeek is doing is they have less access to chips. They can't just buy NVIDIA chips the same way that, you know, open AI or Microsoft or anthropic can buy NVIDIA chips. So instead, they had to make good use, very, very efficient, killer use of the chips that they did have.
So they focused on all of these optimizations and all of these ways that they could save on compute. But in order to get there, they had to spend a lot of money to figure out how to do that and to build the infrastructure to do that. And once they knew what to do, it cost them $5.5 million. And they shared a lot of that information. And this has dramatically reduced the cost of somebody who wants to follow in their footsteps and train a new model because they've shown the way.
of many of their optimizations that people didn't realize they could do or didn't realize how to do them that can now very easily be copied. But it does not mean that you are 5.5 million dollars away from your own V3. So the other thing that is freaking people out is the fact that this is open source, right? We all remember the days when open AI was more open and now it's moved to closed source. Why do you think they did that? And how big a deal is that?
So this is one of those things where they have a story, and you can believe their story or not believe their story. But their story is that they are essentially ideologically in favor of the idea that everyone should have access to the same AI, that AI should be shared with the world, especially that China should help pump out its own ecosystem, and that they should help grow all of the AI for the betterment of humanity. And they're going to get artificial general intelligence, and they are going to open source that as well.
And this is the main point of DeepSeq. This is why DeepSeq exists. They're disclaiming even having a business model, really. And they're an outgrowth of a hedge fund. And the hedge fund makes money.
And maybe they can just do this if they choose to do that. Or maybe they will end up with a different business model. But it was obviously very concerning from a lot of angles if you open source increasingly capable models because artificial general intelligence means something that's as smart and capable as you and I as a human and perhaps more so. And if you just hand that over in open form to anybody in the world who wants to do anything with it,
then we don't know how dangerous that is, but it's existentially risky at some limit to unleash things that are smarter, more capable, more competitive than us that are then going to be free and loose to engage in whatever any human directs them to do.
I have a really dumb question, but I hear people say artificial general intelligence all the time, AGI. What does that actually mean? There is a lot of dispute over exactly what that means. The words are not used consistently, but it stands for artificial general intelligence. Generally, it is understood to mean you can do any task that can be done on a computer, that can be done cognitively only, as well as a human.
I mean, most of these things do things much better than me. I don't know how to code. But I get that there are still some things. Maybe they wouldn't be as good as some proving some of the are you a human test. Everyone's talking about Jevin's paradox. And so we see in video and Broadcom shares these chip companies, they're getting.
crumbled today. And one of the theories like, oh, no, with all these optimizations and so forth, researchers will just use those and they'll still have max demand for compute. And so it won't actually change the ultimate end for compute. How are you thinking about this question?
So I'm definitely a Jevons Paradox, bro, right now, from the perspective of this space. So you don't think it'll have a negative impact and just the amount of compute demanded? The tweet I sent this morning was NVIDIA down 11% pre-market on news that his chips are highly useful. And I believe that what we've shown is that yes, you can get a lot more, in some sense, out of each NVIDIA chip than you expected. You can get more AI. And if there was a limited amount of stuff to do with AI,
And once you did that stuff, you were done. Then that would be a different story. But that's very much not the case. As we get further along towards AGI, as these AIs get more capable, we're going to want to use them for more and more things more and more often. And most importantly, the entire revolution of R1 and also OpenAI's O1 is inference time compute. What that means is every time you ask a question,
It's going to use more compute, more cycles of GPUs to think for longer, to basically use more tokens or words to figure out what the best possible answer is. And this scales, not necessarily without limit, but it scales very, very far. So, OpenAI's new 03 is capable of thinking for, you know, many minutes. It's capable of potentially spending, you know, hundreds or even in theory, thousands of dollars or more on individual query. And
If you knock that down by an order of magnitude, that almost certainly gets you to use it more for a given result, not use it less, because that is in fact starting to get prohibitive. And over time, if you have the ability to spend remarkably little money and then get things like virtual employees and abilities to answer any question out of the sun,
Yeah, there's basically unlimited demand to do that or to scale up the quality of the answers as the price drops. So I basically expect that as fast as the video can manufacture chips.
and we can put them into data centers and give them electrical power, people will be happy to pie those chips. At the risk of angering the Jevons paradox bros, just to push on the Nvidia point a little bit more. So my understanding of DeepSeek is that one of the reasons it's special is because it doesn't rely on specialized components, custom operators. And so it can work on a variety of GPUs.
Is there a scenario where AI becomes so free and plentiful, which could in theory be good for NVIDIA, but at the same time, because it's easy to run on a bunch of other GPUs, people start using more like ASIC chips, like customized chips for a specific purpose?
I mean, in the long run, we will almost certainly see specialized inference chips, whether they're from the video or they're from someone else. And we will almost certainly see various different advancements. The today's chips are going to be obsolete in a few years. That's how AI works, right? There's all these rapid advancements, but
I think NVIDIA is in a very good position to take advantage of all of this. I certainly don't think that you'll just use your laptop to run the best AGIs and therefore we don't have to worry about buying GPUs is a poor position. It's certainly possible that rivals will come up with superior chips. That's always possible. NVIDIA does not have a monopoly. But NVIDIA certainly seems to be in a dominant position right now.
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There are two kinds of people in the world, people who think about climate change and people who are doing something about it. On the Zero Podcast, we talk to both kinds of people, people you've heard of like Bill Gates. I'm looking at what the world has to do to get to zero, not using climate as a moral crusade. And Justin Trudeau.
There are still people who are hell-bent on reversing our approach on fighting climate change. And the creative minds you haven't heard of yet really don't need to have a tomato in December. It's gonna taste like nothing anyway. Just don't do it.
What we've made here is inspired by Shotskin. It is much more simplified than actual Shotskin. Drilling industry has come up with some of the most creative job titles. Yeah. Tell me more. You can imagine. Tool pusher. No. Driller. Motorman. Mudlogger. It is serious stuff, but never doom and gloom. I am Akshad Ratty. Listen to Zero Every Thursday from Bloomberg Podcasts on Apple, Spotify, or anywhere else you get your podcast.
It seems to me, I mean, I know there's others, but it seems to be in the US, there's like three main AI producers of models that people know about. There's open AI, there's Claude, and then there's Meta with Llama. And it's worth knowing that Meta is green today, that the stock is actually up as of the time I'm talking about this 1.1%. Just go through each one real quickly how the sort of deep-seak shock affects them and their viability and where they stand today.
I think the most amazing thing about your question is that you forgot about Google. Oh, yeah, right. Yeah, that's very telling, isn't it? Oh, I never used Gemini. Yeah, that's surprising. Yeah, that's a very... Gemini Flash thinking their version of 01 and R1 got updated a few days ago. And there are many reports that it's actually very good now and potentially competitive.
And effectively, it's free to use for a lot of people on AI studio. But nobody I know has taken the time to check and find out how good it is because we've all been too obsessed with being deep-seapros. And Google's had its rhetorical lunch eaten over and over and over again. December, like OpenAI would come out with advanced after advanced after advanced. Then Google would have advanced after advanced after advanced. And Google's would be seemingly actually, if anything, more impressive. And yet everyone would always just talk about OpenAI. So this is not even new. Something is going on there. So in terms of OpenAI, OpenAI
should be very nervous in some sense, of course, because they have the reasoning models. And now the reasoning model has been copied much more effectively than previously. And the competition is a hell of a lot cheaper than when OpenAI is charging. So it's a direct threat to the business model, for obvious reasons. And it looks like their lead in reasoning models is smaller and faster to undo than you would expect. Because if DeepSee can do it, of course, anthropic and Google can do it, and everyone else can do it as well.
which produces Claude has not yet produced their own reasoning model. They clearly are operating under a shortage of compute in some sense. So it's entirely possible that they have chosen not to launch a reasoning model, even though they could or not focused on training one as quickly as possible until they've addressed this problem. They're continuously taking investment. We should expect them to solve their problems over time, but
They seem like they should be duress directly concerned because they're less of a directly competitive product, in some sense. But also they tend to market to effectively much more aware people, so their people will also know about deep-seak, and they will have a choice to make. If I was meta, I would be far more worried.
especially if I was on their gen AI team and wanting to keep my job because meta's lunch has been eaten massively here, right? Meta with llama had the best open models and all the best open models were effectively fine tunes of llama and now Deepseat comes out and this is absolutely not in any way a fine tune of llama. This is their own product and
And V3 was already blowing everything that met ahead out of the water. R1, there are reports that it's better than their new version that they're training now. It's better than lava 4, which I would expect to be true. And so there's no
point in releasing an inferior open model of everyone on the open model community, just be like, why don't I just use deep-seek? Tracy, it's interesting that as V said, the people who should be nervous are the employees of Meta, not Meta itself, because Meta is up. And so you gotta wonder, it's like, well, maybe they don't, I don't know, maybe they don't need to invest as much in their own open source. AI, if there's a better one out there, now the stock is up. Anyway, keep your- The market has been very strange from my perspective on how it reacts to different things that Meta does.
For a while, Meta would announce, we're spending more in AI. We're investing in all these data centers. We're training all of these models, and the market would go, what are you doing? This is another metaverse or something, and we're gonna hammer your stock and we're gonna drag you down. And then with the most recent $65 billion announced spend, then Meta was up. Presumably they're gonna use it mostly for inference, effectively, in a lot of scenarios, because they had these massive inference costs to wanna put AI over Facebook and Instagram.
So, you know, if anything like, you know, I think the market might be speculating that this means that they will know how to train better llamas that are cheaper to operate and their costs will go down and then they'll be in a better position. And that theory isn't crazy.
Since we all just collectively remembered Google, I have a question that's sort of been in the back of my mind. I think Joe has brought this up before as well. But when Google debuted, it took years and years and years for people to sort of catch up to the search function. And actually, no one ever really caught up.
So Google has like dominated for years. Why is it when it comes to these chatbots? There aren't like higher, wider boats around these businesses. So one reason is that.
everyone's training on roughly the same data, meeting the entire internet and all of human knowledge. So it's very hard to get that much of a permanent data edge there unless you're creating synthetic data off of your own models, which is what OpenAI is plausibly doing now. Another reason is because everybody is scaling as fast as possible and adding zeros to everything on a periodic basis. In calendar time, it doesn't take that long before your rival
is going to have access to more compute than you had. And they're copying your techniques more aggressively. There's just a lot less secret sauce. There's only so many algorithms. Fundamentally, everyone is relying on the scaling laws. It's called the bitter lesson. It's the idea that you just scale more. You just use more compute. You just use more data. You just use more parameters. And deep seek is saying maybe you can do more optimizations. You can get around this problem and still get a superior model. But mostly,
There's been a lot of just, I can catch up to you by copying what you did, also because I can see the outputs, right? I can query your model and I can use your model's outputs to actively train my model. And you see this in things like most models that get trained, you ask them who trains you and they will often say, oh, I'm from OpenAI.
The internet's gotten so weird. I just, the internet is so weird. This is me, Moshovitch. Thank you so much for running over to the odd lots and helping us record this emergency pod on the deep-seek sell-off that was fantastic. All right, thank you.
Tracy, I love talking to this V. We got to just sort of make him our AI or our AI guy. I mean, to be honest, we could probably have him back on again this week because there's going to be stuff happening. Maybe we will. And obviously it's we could go a lot longer. This is a really exciting story. This is a really exciting story. And things are just getting really weird these days.
It is kind of crazy how fast all of this is. Yeah. And then the other thing I would say is just the bitter lesson. Great name for a band. Oh, totally, totally great. Maybe when we do our AI themed prog rock band. Yes. That could be our name. Yes. Let's do that. Okay. Shall we leave it there? Let's leave it there. This has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.
And I'm Joe Wiesenthal. You can follow me at the stalwart. Follow our guests, Vee Moshovitz. He's at this Vee. Also, definitely check out his free sub stack. It's a must-read for me. Don't worry about the vase. Really great stuff every single day. Follow our producers, Carmen Rodriguez, at Carmen Armin, Dashal Bennett, at Dashbot, in Kilbrooks, at Kilbrooks.
For more OddLots content, go to bloomberg.com slash OddLots. We have transcripts a blog and a newsletter, and you can chat about all of these topics 24-7 in our Discord Discord.gg slash OddLots. Maybe we'll get Zv to do a Q&A on there with me. Oh yeah, that'd be great. And if you enjoy OddLots, if you like it when we roll out these emergency episodes, then please leave us a positive review on your favorite platform. Thanks for listening.
There are two kinds of people in the world, people who think about climate change and people who are doing something about it. On the Zero Podcast, we talk to both kinds of people, people you've heard of like Bill Gates. I'm looking at what the world has to do to get to zero, not using climate as a moral crusade. And Justin Trudeau.
There are still people who are hell-bent on reversing our approach on fighting climate change. And the creative minds you haven't heard of yet really don't need to have a tomato in December. It's gonna taste like nothing anyway. Just don't do it.
What we've made here is inspired by Shock Skin. It is much more simplified than actual Shock Skin. Drilling industry has come up with some of the most creative job titles. Yeah. Tell me more. You can imagine. Tool pusher. No. Driller. Motorman. Mudlogger. It is serious stuff, but never doom and gloom. I am Akshad Ratty. Listen to Zero Every Thursday from Bloomberg Podcasts on Apple, Spotify, or anywhere else you get your podcast.