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This situation is extremely weird and it's developing and I think even when I put out this episode there will be new parts of it that I have yet to really get to. I will do my absolute best to explain in these episodes both what is happening with Deep Seek and what it means, what they've built, and what it's going to do in the future. But let's begin so as January came to a close, the entire generative AI industry found itself in a kind of chaos. In short, the recent AI bubble, and in particular the hundreds of billions of dollars being spent on it, hinged on this big idea that we need bigger models which are both trained and run on bigger and even larger GPUs, almost entirely sold by Nvidia, and in turn they're based in bigger and bigger data centers owned by companies like Microsoft, Oracle, Amazon and Google. Now there was also this expectation that this would always be the case. Hubris within this industry is kind of part of the whole deal. And generative AI was always meant to be this way, at least for the American developers. It was always meant to be energy and compute hungry. Throwing entire zoos worth of animals and boiling lakes was necessary to do this. There was never any other way to do it. And I thought, at least I've thought for a while, that this was because they just they tried to make them more efficient, but they couldn't. There was just something about transformer based architecture like the stuff that underpins ChatGPT. So the GPT model under ChatGPT either. It wasn't the case though. A Chinese artificial intelligence company that few people had really heard of, called deepsea, came along a few weeks ago with multiple models that aren't merely competitive with OpenAI's, but actually undercut them in several meaningful ways. DeepSeq's models are both open source, which means that their source code and research is public and they're significantly more efficient as well as much as 30 times cheaper to run in the case of their reasoning model R1, which is competitive with OpenAI's O1 and 50 or more times more efficient than GPT4O. It's actually kind of crazy when you think about it. And as you're going to hear, this whole thing has joke ified me all over again. And what's crazy is, is that some of them can be distilled, which I'll get to later, and run on local devices like a laptop. It's kind of crazy. And as a result, the markets have kind of panicked because the entire narrative of the AI bubble has been that these models have to be expensive because they are the future. And that's why hyperscalers had to burn $200 billion in capital expenditures for infrastructure to support this wonderful boom. And specifically the ideas of OpenAI and anthropic, the idea that there was another way to do this, that in fact we didn't need to spend all this money, and that maybe we could find a more efficient way of doing it. Well, that would require them to have another idea other than throw as much money at the problem as possible. Yeah, they just didn't consider it, it turns out. And now along has come this outsider that's upended the whole conventional understanding and perhaps even dethroned a member of America's tech royalty, Sam Altman, a man who has crafted, if not a cult of personality, some sort of public image of an unassailable visionary that will lead the vanguard in the biggest technological change since the Internet. Yeah, he's wrong. He never was doing that. I've been saying it for a while. He's never been doing this. But Deepseek isn't just an outsider. No, they're a company that's emerged as a side project from a tiny, tiny Chinese hedge fund, at least by the standards of hedge funds like $5.5 billion on the assets under management, and their founding team has nowhere near the level of fame and celebrity or even the accolades of Sam Alt Altman. It's distinctly humiliating for everyone involved that isn't Deep Seek. And on top of all of that, DeepSeek's biggest, ugliest insult is that its model, DeepSeek R1, is competitive, like I said, with OpenAI's incredibly expensive O1 reasoning model, yet significantly, and I mean, 96% cheaper to run, and it can even be run locally. Like I said, speaking to a few developers, I know one was able to run Deepseek's R1 model on their 2021 MacBook Pro with an M1 chip that is a four year old computer, not a 30,000 GPU in sight. It's kind of crazy. Worse still, Deepseek's models are made freely available to use with the source code published under the MIT Tech license, along with the research on how they were made, although not the training data, which makes some people say it's not really open source. But for the sake of argument, I'm just going to say open source. And this means, by the way, that DeepSeq's models can be adapted and used for commercial use without the need for royalties or fees. Anyone can take this and build their own. It's kind of crazy. By contrast, OpenAI is anything but open, and its last LLM to be released under the mit license was 2019's GPT2. No, no, wait, wait. Shit. Let me correct that. Deepseek's biggest, ugliest secret is actually that it's obviously taking aim at every element of OpenAI's portfolio, as the company was already dominating headlines this week, it quietly dropped its Janus Pro 7B image generation and analysis model, which the company says outperforms both stable diffusion and OpenAI's Dall E3. And those are, by the way, image generation. So you type in something, you've got Garfield with boobs, and then out comes a Garfield with juicy cans. And that's probably the first time you hear that on the podcast, but probably not the last. And as with its other code, DeepSeek has made this freely available to both commercial and personal users alike, whereas OpenAI is largely paywall dall e3. This is really it's a truly crazy situation. And it's also this cynical, vulgar version of David and Goliath. We're a tech startup backed by a shadowy Chinese hedge fund with 8 billion doll under management is somehow the plucky upstart against the lumbering, lossy, oafish $150 billion startup backed by multiple public tech companies with a market capitalization of over $3 trillion. I realize, by the way, I said earlier, $5.5 billion under management. This is why you check your notes in advance. But I'm not cutting it. This is fresh. I am inside a closet in New York. The content must flow anyway. Deepseek's V3 model, which is comparable and competitive with both OpenAI's GPT4O and Anthropic's Claude Sonnet three point models, which by the way, has some reasoning features. Like I said, it's 53 times cheaper to run the R1 when using the company's own cloud services. And as mentioned earlier, said model is effectively free for anyone to use locally or on their own cloud instances and can be taken by any commercial enterprise and turned into a product of their own should they desire to, say, compete with OpenAI, the loudest and most annoying startup of all time. In essence, deepseek and I'll get into its background and the concerns people might have about its Chinese origins released two models that perform competitively and even beat models from both OpenAI and anthropic, undercut them in price and then made them open, undermining not just the economics of the biggest generative AI companies, but laying bare exactly how they work. The magic's gone. There's no more voodoo inside Sam Altman's soul. It's all out there. And the last point is extremely important when it comes to OpenAI's reasoning model, which specifically hid its chain of thought for fear of these unsafe thoughts that might manipulate the customer. And then they added slightly under their breath that the actual reasons they did it was a competitive advantage. Now, to explain what that means. When you make a request with OpenAI's O1 model, say, give me all the states with the letter R in them, it actually shows you like the thinking. And by the way, these things don't fucking think. They're. They're computer like they don't think at all. But I'm going to use it just for this. So you see it say, okay, here are all the American states. Which ones have that letter? I'm checking all of those. It's effectively having a large language model. Check a large language model. Now the thing is, the steps they were showing you were all cleaned up. They would look nice, they would be formatted nicely. Deep Seek's chain of thought is completely laid bare, which is very interesting because it really takes the wind out of OpenAI's sails. And on top of that it allows you to see actually how these things think through things. Again, not really thinking, but still you can see things about how large language models work that these companies didn't want you to have. On top of this, OpenAI's Zero1 model has something even shittier to it, which is these chain of thought things all cost money. When you see it generate these thoughts, it's actually generating more thoughts than you see because they're hiding the chain of thought. So OpenAI is just charging you an indeterminate amount of money, an insane amount of money, as I'll get to later, but nevertheless, you don't know what you're being charged for, you don't even know what's really going on under the hood. Or you could use Deepseek, and let's be completely clear. By the way, OpenAI's literal only competitive advantage against Meta and Anthropic was its reasoning models 01 and 03. And O3, by the way, is currently in a research preview and is mostly just more of the same. Although I mentioned earlier in the show that Anthropic's Claude Sonnet 3.5 has some reasoning features, they're comparatively more rudimentary than those in 01 and 03, and I'd argue R1, which is Deep Seek's model. In an AI context, reasoning works by breaking down a prompt into a series of different steps with considerations of different approaches. Like I said earlier, effectively a large language model checking its own homework with no thinking involved. Because, like I said, they do not think or know things and OpenAI rushed to launch its O1 reasoning model last year because, and I quote, Fortune from last October, Sam Altman was eager to prove to potential investors that in the company's latest funding round that OpenAI remains at the forefront of AI development. And as I've noted in my newsletter at the time it was not particularly reliable, failing to accurately count the number of times the letter R appeared in the word strawberry, which was the codename for O1. Very funny stuff. At this point, it's Fairly obvious that OpenAI wasn't anywhere near the forefront of AI development, and now that its competitive advantage is effectively gone, there are genuine doubts about what comes next for the company. As I'll go into, there are many questionable parts of Deep Seek's story, its funding, what GPUs it has and how much it actually spent training these models. But what we definitively understand to be true is bad news for OpenAI, and I would argue every other large US tech firm that's jumped onto the generative AI bandwagon in the past few years.