A media Hello and welcome to Better Offline. I'm your host ed Zitron. What a lot of you have been getting in touch? Yes, you're getting your Deep Seek episode. In fact, this is the first of a two parter. This will come out on Friday, which is when you're listening to this, and then it'll follow up on Monday. I apologize. I spent a lot of Monday writing this and also learning about a lot of this stuff in an attempt to distill it as best I could. 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, what it means, what they've built, and what it's going to do in the future. But let's again, so, as general came to a close, the entire generative AI industry found itself in a kind of chaos. In sure, 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 hungary. Throwing entire Zoo's 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 Chat GPT, so the GPT model under Chat GPT either. It wasn't the case, though. A Chinese artificial intelligence company that few people had really heard of, called deep Seak came along a few weeks ago with multiple models that aren't merely competitive with open aiyes, but actually undercut them in several meaningful ways. Deep Seak'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 thirty times cheaper to run. In the case of their reasoning model are one which is competitive with open Aizo one and fifteen or more times more efficient than GPT four. Oh, it's actually kind of crazy when you think about it, and as you're going to hear, this whole thing has jokeified me all over again. And what's crazy is that some of them can be distilled, which I'll get too 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 two hundred billion dollars in capital expenditures for infrastructure to support this wonderful boom, and specifically the ideas of open AI 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 rather than throw as much money at the problem as possible. Yeah, they just didn't consider it, it turns out. And now as long as 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 Deep Seek isn't just an outsider now. They are a company that's emerged as a side project from a tiny, tiny Chinese hedge fund, at least by the stands of hedge funds, like five point five billion dollars on the assets under management, and their founding team has nowhere near the level of fame and celebrity or even the accolades of Sam Moltman. It's distinctly humiliating for everyone involved that is in Deep Seek. And on top of all of that, Deep Seek's biggest ugliest insult is that its model, deep seek are one, is competitive, like I said, with open AI's incredibly expensive oh one reasoning model, yet significantly and I mean ninety six percent 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 deep Seek's R one model and their twenty twenty one MacBook Pro with an M one chip that is a four year old computer, not a thirty thousand GPU inside. It's kind of crazy. Worse still, Deep seeks models are made freely available to use, with the source code published under the MIT tech line, 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 deep seeks 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, open ai is anything but open and its last LM to be released under the MIT license was twenty nineteen. Is GBT two No no wait wait, ship let me correct that deep Seek's biggest ugliest secret is actually that it's obviously taking aim at every element of open aiy's portfolio. As the company was already dominating headlines, this week it quietly dropped its Janus Pro seven B image generation and analysis model, which the company says outperforms both stable diffusion and open AI's Daly three. And those are by the way image generation thinks. So you type in something you like Garfield with boobs, and then outcomes of 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, deep Seek has made this freely available to both commercial and personal users alike, whereas open ai is largely paywall darly three. This is really, it's a truly crazy situation. And it's also this cynical, vulgar version of David and Goliath, where a tech startup back by a shadowy Chinese hedge fund with eight billion dollars under management is somehow the plucky upstart against the lumbering loss eo fish one hundred and fifty billion dollars startup back by multiple public tech companies with a market capitalization of other three trillion dollars I realized, by the way I said earlier, five point five billion dollars 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. Deep Seek's V three model, which is comparable and competitive with both open AI's GPT four roh and anthropics Claude Sonnet three point five models, which by the way, has some reasoning features. As I said, it's fifty three times cheaper to run the R one 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 could be taken by any commercial enterprise and turned into a product of their own should they desire to say, compete with open Ai, 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 open Air 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 Samultman's soul. It's all out there. And the last point is extremely important when it comes to open EI's reasoning model, which specifically hid its chain of thought for fear of these unsafe thoughts that might manipulate the customer. And then they add 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 open Aiy's oh one model, say give me all the states with the letter are in them, it actually shows you like the thinking. And by the way, these things don't fucking think. They're they're computer bullshit, 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 Seak's chain of thought is completely laid bare, which is very interesting because it really takes the wind out of open Aiy's sales. 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, open aiy one 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 open ai is just charging you an indeterminate amount of money, an insane amount of money, as I'll get too 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 deep seek and let's be completely clear, by the way, open AI's literal only competitive advantage against Meta and Anthropic was its reasoning models OH one and O three and O three, 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 anthropics. Claudes Sonnet three point five has some reasoning features. They're comparatively more rudimentary than those in O one and O three, and i'd argue are one, 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 no things an open Ai rushed to launch its O one reasoning model last year because, and I quote fortune from last October, Sam Mormon was eager to prove to potential investors that in the company's latest funding around, the open ai 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 code name for one very funny stuff. At this point, it's fairly obvious that open ai 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 Deepseek's story. It's funding, what GPUs it has, and how much it actually spent training these models. But what we definitively understand to be true is badly for open Ai, and I would argue every other large US tech firm that's jumped onto the generative AI bandwagon in the past few years. Deep seeks models actually exist. They work, at least by the standards of hallucination PRONELLA lams that don't, at the risk of repeating myself know anything. They've been independently verified to be competitive in performance, and their magnitudes cheaper in price than those from both hyperscalers, Google's Gemini, Mets, Lama, Amazon Que and so on and so forth, and from those released by open Ai and Anthropic. Deep seeks models don't require massive new data centers. They run on GPUs currently used to run services like chat, GPT, and even work on more austere hardware, Nor do they require an endless supply of bigger, faster Nvidio GPUs every single year to progress. The entire AI bubble was inflated based on the premise that these models were simply impossible to build without burning massive amounts of cash, straining the power grid, and blowing past emission skulls, and that these costs were both necessary and really good because they'd lead to creating powerful AI, something that's yet to happen. And it's kind of obvious at this point that that wasn't true. Now the markets are sitting around there asking a very reasonable question, Shit, did we just waste two hundred billion dollars? Anyway, let's get into the nitty grit. What is deep Seek? First of all, if you want to super deep dive into what it is, I can't recommend venture beats right up enough. I'll link to it in the show notes as they usually do. It's really good and it goes into a lot more detail than I woar. But here's the too long didn't read for you. Deep Seek is a spin off from a Chinese hedge fund called high Flyer Quant. It's a relatively small and young company, and from its inception it went big on algorithmic and AI driven trading. Later it started building its own standalone chat bots, including a chat GPT equivalent for the Chinese market. This is what we need, right Now, I'm sure some of you will say, oh, well, who knows if that's really true. Sure, I think that that's fair. I also think that there are parts of Sam Mortman's legend that we should question as well. I think the circumstances under which Sam Mortman got made head of y Combinator are extremely questionable. I'm saying you can question deep Seek, and indeed you should. We should be more critical of these powerful companies, but don't do it halfway. If we're going to be worried, let's be worried about everyone. Now. Deepseak did a few things differently, like open sourcing its models, although it likely built upon take from other companies like Metaslama and the mL library PyTorch to train its models. It's secured over ten thousand Nvidia GPUs right before the US imposed export restrictions, which sounds like a lot, but it's a fraction of what the big AI labs like Google, Open Air, and Anthropic have to play with. I think I've heard estimates of like one hundred thousand to three hundred thousand each, if not more. Now you've likely seen or heard that deep Seak trained its latest model for five point six million dollars a poster to the insane amounts that I'll get to later, and I want to be clear that any and all mentions of this number are estimates. In fact, the provenance of the five point five to eight million dollar number appears to be a citation of a post made by an nvidio engineer in an article from the South China Morning Post, which links to another article from the South China Morning Post which simply states that deep Seat V three comes with six hundred and seventy one billion parameters and was trained in around two months at the cost of five point five eight million dollars with no additional citations of any kind. So you should take it with a pinch of salt. But it's not totally ludicrous. Well, there are some that have estimated the cost. Deep Seeks V three models allegedly trained using two thousand and forty eight n video h eight hundred GPUs according to its paper, and Ben Thompson of Strategory has made this clear that the five point five million dollar number only covers the literal training cost of the official training run, and this is made fairly clear in the paper by the way of V three, and that's the one that's competitive with Opening Eyes GPT four O model, meaning that any costs related to prior research or experiments on how to build the mooddle were left out. Now big big shower to Minimaxer the guy on Blue Sky and Twitter, he's great. He is wonderful, and also added that this is fairly standard for the industry. Again, you choose how you feel about this, but I want to give you the information. And while it's safe to say that deep Seak's models are cheaper to train, the actual costs, especially as deep Seak doesn't share its training data, which some might argue means its models are not really open source. As I said, the numbers get a little harder to guess at. Thompson notes that Deep Seek had to craft a bunch of elegant workarounds to make the model perform, including writing code that ultimately changed how GPUs actually communicated with each other. This functionality isn't otherwise possible using Nvidia's developer tools. They really had to get in there. It's kind of cool. Deep seaks models V three and R one are more efficient and as a result, cheaper to run, and can be accessed via its API at prices that are astronomically cheaper than open eyes, Deep seat Chat running deep six GPT four oh competitive V three model cost zero points zero seven cents per one million input tokens as in commands given to the model, and one dollar one ten per one million output tokens as in the resulting output from the model. I know that these numbers kind of like just sound like numbers like you, Maybe you don't have context, so let me give you some. This is a dramatic price drop from the two dollars fifty cents per one million input tokens and ten dollars per one million output tokens the open Ai charges for GPT four. Oh, this isn't just undercutting, this is this is a bunker buster. If. Now, there is a side that I'll kind of get into a little bit later, in that you are using models hosted in the country that you don't know, probably China. There are data concerns. But again, you can put this on your own server. You could put this in Google Cloud. Both Microsoft and Google are apparently thinking about it now. The Information reported that Google had added it to Google Cloud. No they did not. They didn't do that. They allowed you to connect hugging face. This is a whole bunch of technical stuff that if you understand, you'll be like, yeah, Ed, I know. Long story short, the hyperscalers are already bringing deep Seek out, and I'll get to why that's bad later in detail. But it's also very funny. Now here's something else that's funny. Deep seek reasoner. It's reasoning model costs that fifty five cents per one million input tokens and two dollars and nineteen cents per one million output tokens. Now that sounds expensive. Maybe it is. Whatever, that's goddamn nothing compared to the fifteen dollars per one million input tokens and sixty dollars per one million output tokens of open ai WOF. If I'm Sam Orman, I'm shitting myself. But there's an obvious bar here. We do not know where deep seek is hosting its models, who has access to that data, or where that data is coming from or going to. We don't know who funds deep Seek other than it's connected to High Flyer, the hedge fund that I mentioned earlier that it's split from. In twenty twenty three, there are concerns that deep seak could be stayed funded, and that deep Seek's low prices are a kind of geopolitical weapon breaking the back of the generative AI industry in America. I'm not really sure whether that's the case or not. It's certainly true that China has long treated AI as a strategic part of its national industrial policy and is reported to help companies and sectors where it wants to catch up with the Western world. The Made in China twenty twenty five initiatives SAW are reported hundreds of billions of dollars provided to Chinese firms working in industries like chip making, aviation, and yeah AI. The extent of that support isn't exactly transparent, surprise, surprise, and so it's not entirely out of the realm of possibility that deep Seek is also the recipient of state aid. The good news is that we're going to find out fairly quickly. American AI infrastructure company Grok is already bringing deep Seek's model online, meaning that we'll get at least a very some sort of confirmation of whether these prices are realistic or whether they're heavily subsidized by whoever it is that backs deep Seek. It's also true that deep seek is owned in part by a hedge fund, which likely isn't short of cash to pump into them. But as in the side, given the open AI is the benefactor of billions of dollars of cloud compute credits and gets reduced pricing for Microsoft's zero cloud services to run its actual models, it's a bit tough for them to complain about Arrival being subsidized by a larger entity with the ability to absorb the costs of doing business should that be the case. Same goes for anthropic by the way, and yes, I know Microsoft isn't a state, but with a market cap of three point two trillion dollars in quarterly revenues, larger than the combined GDPs of some EU and NATO nations, it's kind of the next best thing. But I digress. Whatever concerns there may be about malign Chinese influence of bordering on irrelevant outside of the low prices, of course, offered by deepseek itself, and even that is speculative at this point. Once these models are hosted elsewhere, and once deep Seek's methods, which I'll get to in a little bit, are recreated, and by the way, that's not really going to take very long. I believe we're going to see that these prices are indicative of how cheap these models are to run. So you might be wondering, how the hell is this so much cheaper? And that's a bloody good question. And because I'm me, I have a hypothesis. I do not believe that the companies making these foundation models, such as Open Air and Anthropic, have actually been incentivized to do more with less. And because they're chummy little relationships with hyperscalers like Amazon, Google and Microsoft were focused almost entirely on making the biggest, most hugest models possible, using the biggest, even hugerris chips. And because the absence of profitability didn't stop them from raising more money. Well, they've never had to be fucking efficient, have they. They've never had to try. Maybe they should buy less avocado fucking toast. Anyway, let me put it in simpler terms. Imagine living on fifteen hundred dollars a month, and then imagine how you'd live on one hundred and fifty thousand dollars a month, and that you have to, like Brewster's millions, spend as much much of it as you can to complete a mission, a very simple mission. Live. In the former example, you concern survival, you have a limited amount of money and must make it go as far as possible, with real sacrifices to be made with every dollar you spent. If you want to have fun, you're going to have to eat less. Potentially all the food you eat will have to be cheaper. You have to live on a budget. You have to make decisions, and indeed you might learn to cook at home. You might walk more, you might do things that will help you not spend all your money. In the latter example, where you have one hundred and fifty thousand dollars a month that you must spend, your incentivize the splurge to lean into excess to pursue this vague idea of living your life, your actions are dictated not by any existential threats, or indeed any kind of future planning, but by whatever you perceive to be an opportunity to live. Open AI and anthropic are emblematic of what happens when survival takes a back seat to living. They have been incentivized by frothy venture capital and public markets desperate for the next big thing thing, the next big growth to build bigger models and sell even bigger dreams. Like Dario Amaday of Anthropics saying that your AI and I quote could surpass almost all human beings at almost everything shortly after twenty twenty seven, I just want to take a fucking second. Journalist, if you're listening to this, stop fucking quoting this bullshit. Stop it. You're doing nothing. You are failing at your goddamn job every single time you quote this bullshit, this nonsense. Shortly after twenty twenty seven. What the fuck does that mean? Twenty twenty eight, twenty twenty nine, twenty thirty, what does surpassing humans and almost everything even mean? This shit doesn't work. This shit is not good. Oh my god. Anyway, back to the podcast, the Calm Damn. Both Open AI and Anthropic have effectively lived their existence with the infinite money cheap from the SIMS. And I know some of you might say, by the way, it's not an infant money, you just add you go into the console. You get my point. And both companies have been bleeding billions of dollars a year after revenue, and that's, by the way, making billions of dollars and then still losing billions is insane, and they still operated as if money would never run out because it and it wouldn't. If they were actually worried about that happening, they would have certainly tried to do what Deep seek has done, except they didn't have to because both of them had the endless cash and access to GPUs from either Microsoft, Amazon or Google. And the stargate thing is just I will mention it later, just long story short. They're not going to put five hundred billion dollars into the it was up to five hundred bill I'm so tired of this shit. Open iron anthropic have never been made to sweat, unlike me in this closet where I'm recording this. And they've received endless amount of free marketing from a tech and business media happy to print whatever vapid bullshit they spout, and it's just very frustrating. They've raised money at will with ananthropic, by the way, is currently raising another two billion dollars, valuing the company at sixty billion dollars. And this was I think happening while deep Zeek was going on, which is really funny. And they've done all of this off of a narrative of them. We need more money than any company is ever needed ever because the things we're doing have to cost this much. There is no other way. You must give us more money. My name is Sam Altman. I need more money than has ever been made from my huge, beautiful company that sucks and needs money to train it. Help me, please, My big, beautiful sick company is dying, but the best and most important company of all time. It's also normal. Now. Do I think that they were aware that there were methods to make their models more efficient? Sure, open AI tried and failed in twenty twenty three to deliver a more efficient model to Microsoft called Arakis. I'm sure there are teams that both Anthropic and OPENII that are specifically dedicated to making things kind of more efficient. But they didn't have to do it, and so they didn't. And as I've written before in my newsletter and argued on this very podcast, open AI simply burns money and have been allowed to burn money, and up until recently likely would have been allowed to burn even more money because everybody, all of the American model developers, appeared to agree that the only way to develop large language models was to make them as big as humanly possible and work out troublesome stuff like making them profitable or turning them into a useful thing later, which is I presume when AGI happens, a thing that they're still in the process of defining, let alone doing. Deep Seek, on the other hand, had to work out a way to make its own large language models within the constraints of the hamstrung end video chips that can be legally sold to China. While there's a whole cottaged industry of selling chips in Chines using resellers and other parties to get restricted silicon into the country, the entire way in which deep Seek went about developing its models suggests that it was working around very specific memory bandwidth constraints, meaning that the amount of data that could be fed into it and out of it and into the chips. In essence, doing more with less wasn't something it shows, but it's something they had to do. I've touched already on the technical how of these models in greater depth, and you can really read in that in my news there and you can go to whez your hed not at it's at the end of the episode. But I'll also have show notes to what cales like Ben Thompson's some strategory because there are lots of things to read here. I know there are some really technical listeners, and I'm sure you're gonna flame me in my emails. Please go and read it. I'm not wrong. I've checked with a lot of people too, and by the way, all of this austerity stuff seems to have worked. There's also the training data situation and another mayor culper. I've previously discussed the concept of model collapse and how feeding synthetic data, which is training data created by a generative model, into another model, could end up teaching it bad habits, which in turn would destroy the model. But it seems that deep Seekers succeeded in training its models using generative data specifically though, and I'm quoting geekwise John Turou like mathematics where correctness is unambiguous, and using and i quote again, highly efficient reward functions that could identify with which new training examples would actually improve the model, avoiding wasted compute on redundant data, and it seems to have worked. Though model collapse may still be a possibility. This approach extremely precise use of synthetic data is in line with some of the defenses against model collapse I've heard from LLLM developers i've talked to. This is also a situation where we don't know the exact training data, and it doesn't negate any of the previous points I've made about model collapse. Now we'll see what happens there. But synthetic data might work where the output is something that you could figure out using a calculator. But when you get into anything a bit more fuzzy, like written text or anything with an element of analysis, you'll likely encounter some unhappy side effects. But I don't know if that's really going to change how good these things are. There's also a little scuttle about about where deep seak got its data. Ben Thompson, that's trategory suggests that deep seek's models are potentially distilling other model's outputs, by which I mean having another model, say metas LAMA or open ais GPT four H, which is why deep seak identified itself as chet GPT at one point spit out outputs specifically to train parts of deep Seek. This obviously violates the terms of service of these tools, as open AI and its rivals with much rather, have you not use its technology to create its next rival. And open Aye, by the way, has recently reportedly found evidence that deep seek used open AIS models to train its rivals. And this is from the Financial Times, although it failed to make any formal allegations, but it did say that using chat gpt to train a competing model violates its terms of service, and David Sachs, the investor in Trump administration AI and cryptos are, says it's possible that this occurred, although he failed to provide evidence. I just want to say, how fucking funny it is that open air is going where where you're stealing my stuff? Don't steal my things? Where fucking coward, pansy bastard bitches. Fucking hell, what a what a bunch of whiny babies. Oh no, my plagiarism machine got plagiarized. Where kiss my entire asshole, sam Orman, you little worm, you fucking embarrassment to Silicon Valley. You should be ashamed of yourself for many reasons, but so much this though. Where Yeah, oh no, you stole from use my plagier is the machine that requires me to steal from literally every artist and author on the Internet. The thing where we went on YouTube and transcribed everything and fed it into the machine. That's that's not stealing, that's good. But you using our model to generate answers. That's just not fair. What a bunch of babies, you guys say. I'm almos worth billions of dollars. He has a five million dollar car. Cry more, you little worm. Personally, I genuinely want open ai to point a finger at Deep Seek and accuse it of IP theft, mostly for the yucks, but also for the hypocrisy factor. This is a company that, as I've just very cleanly said, exists purely from the wholesale industrial larceny of content produced by literally a fucking everyone, And now they're crying way. I'm sam Olman. I'm a big baby. I've filled my diaper because someone stole from my plagiarism machine. Kiss my ass, Kiss my ass. These companies haven't got shit. Open ai doesn't have shit. They they don't have anything, They don't have a next product without reasoning, they haven't got anything. And now they don't have that disgusting justification that overspending the fat, ugly American startup culture of spending as much as you can to build America's next top monopoly. They should be fucking ashamed of themselves. They shouldn't be billionaires, they should be poverty stricken. They should have to pay everyone they stole for And it's just it sickens me seeing the reaction from some people on this, seeing the sinophobia, but seeing this level of defensiveness of a company like open AI or Anthropic, And as I'll get into next episode, we are really running out of time here, and I think Deep Seek is really I think it could be really the end of days for these companies. I don't know how much they've got left time wise, or even money wise, and I'm not sure how they even raise money. But in the next episode, I'm going to deep dive into Deep Seek and I'll tell you how they sent the US tech market into a panic and what it actually means the future of open Aianthropic and the hyperscale is backing them. This has been a crazy few days. I hope this has helped, and on Monday you'll find out more. Thank you so much for listening. The support I've got for the show has been incredible, and the emails I've got about Deep Seek. I've been trying Okay, I've really been trying so the fastest I could do it. But I'm so happy to do this show, and I'm so grateful for all of you. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matasowski. You can check out more of his music and audio projects at Matasowski dot com, M A T T O. S O w Ski dot com. You can email me at easy at Better Offline dot com or visit Better Offline dot com to find more podcast links and of course, my newsletter. I also really recommend you go to chat dot where's youreaed dot at to visit the disc and go to our slash Better Offline to check out I'll Reddit. Thank you so much for listening. Better Offline is a production of cool Zone Media. For more from cool Zone Media, visit our website Coolzonemedia dot com, or check us out on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

A media Hello and welcome to Better Offline. I'm your host ed Zitron. What a lot of you have been getting in touch? Yes, you're getting your Deep Seek episode. In fact, this is the first of a two parter. This will come out on Friday, which is when you're listening to this, and then it'll follow up on Monday. I apologize. I spent a lot of Monday writing this and also learning about a lot of this stuff in an attempt to distill it as best I could. 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, what it means, what they've built, and what it's going to do in the future. But let's again, so, as general came to a close, the entire generative AI industry found itself in a kind of chaos. In sure, 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 hungary. Throwing entire Zoo's 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 Chat GPT, so the GPT model under Chat GPT either. It wasn't the case, though. A Chinese artificial intelligence company that few people had really heard of, called deep Seak came along a few weeks ago with multiple models that aren't merely competitive with open aiyes, but actually undercut them in several meaningful ways. Deep Seak'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 thirty times cheaper to run. In the case of their reasoning model are one which is competitive with open Aizo one and fifteen or more times more efficient than GPT four. Oh, it's actually kind of crazy when you think about it, and as you're going to hear, this whole thing has jokeified me all over again. And what's crazy is that some of them can be distilled, which I'll get too 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 two hundred billion dollars in capital expenditures for infrastructure to support this wonderful boom, and specifically the ideas of open AI 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 rather than throw as much money at the problem as possible. Yeah, they just didn't consider it, it turns out. And now as long as 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 Deep Seek isn't just an outsider now. They are a company that's emerged as a side project from a tiny, tiny Chinese hedge fund, at least by the stands of hedge funds, like five point five billion dollars on the assets under management, and their founding team has nowhere near the level of fame and celebrity or even the accolades of Sam Moltman. It's distinctly humiliating for everyone involved that is in Deep Seek. And on top of all of that, Deep Seek's biggest ugliest insult is that its model, deep seek are one, is competitive, like I said, with open AI's incredibly expensive oh one reasoning model, yet significantly and I mean ninety six percent 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 deep Seek's R one model and their twenty twenty one MacBook Pro with an M one chip that is a four year old computer, not a thirty thousand GPU inside. It's kind of crazy. Worse still, Deep seeks models are made freely available to use, with the source code published under the MIT tech line, 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 deep seeks 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, open ai is anything but open and its last LM to be released under the MIT license was twenty nineteen. Is GBT two No no wait wait, ship let me correct that deep Seek's biggest ugliest secret is actually that it's obviously taking aim at every element of open aiy's portfolio. As the company was already dominating headlines, this week it quietly dropped its Janus Pro seven B image generation and analysis model, which the company says outperforms both stable diffusion and open AI's Daly three. And those are by the way image generation thinks. So you type in something you like Garfield with boobs, and then outcomes of 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, deep Seek has made this freely available to both commercial and personal users alike, whereas open ai is largely paywall darly three. This is really, it's a truly crazy situation. And it's also this cynical, vulgar version of David and Goliath, where a tech startup back by a shadowy Chinese hedge fund with eight billion dollars under management is somehow the plucky upstart against the lumbering loss eo fish one hundred and fifty billion dollars startup back by multiple public tech companies with a market capitalization of other three trillion dollars I realized, by the way I said earlier, five point five billion dollars 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. Deep Seek's V three model, which is comparable and competitive with both open AI's GPT four roh and anthropics Claude Sonnet three point five models, which by the way, has some reasoning features. As I said, it's fifty three times cheaper to run the R one 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 could be taken by any commercial enterprise and turned into a product of their own should they desire to say, compete with open Ai, 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 open Air 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 Samultman's soul. It's all out there. And the last point is extremely important when it comes to open EI's reasoning model, which specifically hid its chain of thought for fear of these unsafe thoughts that might manipulate the customer. And then they add 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 open Aiy's oh one model, say give me all the states with the letter are in them, it actually shows you like the thinking. And by the way, these things don't fucking think. They're they're computer bullshit, 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 Seak's chain of thought is completely laid bare, which is very interesting because it really takes the wind out of open Aiy's sales. 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, open aiy one 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 open ai is just charging you an indeterminate amount of money, an insane amount of money, as I'll get too 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 deep seek and let's be completely clear, by the way, open AI's literal only competitive advantage against Meta and Anthropic was its reasoning models OH one and O three and O three, 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 anthropics. Claudes Sonnet three point five has some reasoning features. They're comparatively more rudimentary than those in O one and O three, and i'd argue are one, 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 no things an open Ai rushed to launch its O one reasoning model last year because, and I quote fortune from last October, Sam Mormon was eager to prove to potential investors that in the company's latest funding around, the open ai 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 code name for one very funny stuff. At this point, it's fairly obvious that open ai 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 Deepseek's story. It's funding, what GPUs it has, and how much it actually spent training these models. But what we definitively understand to be true is badly for open Ai, and I would argue every other large US tech firm that's jumped onto the generative AI bandwagon in the past few years. Deep seeks models actually exist. They work, at least by the standards of hallucination PRONELLA lams that don't, at the risk of repeating myself know anything. They've been independently verified to be competitive in performance, and their magnitudes cheaper in price than those from both hyperscalers, Google's Gemini, Mets, Lama, Amazon Que and so on and so forth, and from those released by open Ai and Anthropic. Deep seeks models don't require massive new data centers. They run on GPUs currently used to run services like chat, GPT, and even work on more austere hardware, Nor do they require an endless supply of bigger, faster Nvidio GPUs every single year to progress. The entire AI bubble was inflated based on the premise that these models were simply impossible to build without burning massive amounts of cash, straining the power grid, and blowing past emission skulls, and that these costs were both necessary and really good because they'd lead to creating powerful AI, something that's yet to happen. And it's kind of obvious at this point that that wasn't true. Now the markets are sitting around there asking a very reasonable question, Shit, did we just waste two hundred billion dollars? Anyway, let's get into the nitty grit. What is deep Seek? First of all, if you want to super deep dive into what it is, I can't recommend venture beats right up enough. I'll link to it in the show notes as they usually do. It's really good and it goes into a lot more detail than I woar. But here's the too long didn't read for you. Deep Seek is a spin off from a Chinese hedge fund called high Flyer Quant. It's a relatively small and young company, and from its inception it went big on algorithmic and AI driven trading. Later it started building its own standalone chat bots, including a chat GPT equivalent for the Chinese market. This is what we need, right Now, I'm sure some of you will say, oh, well, who knows if that's really true. Sure, I think that that's fair. I also think that there are parts of Sam Mortman's legend that we should question as well. I think the circumstances under which Sam Mortman got made head of y Combinator are extremely questionable. I'm saying you can question deep Seek, and indeed you should. We should be more critical of these powerful companies, but don't do it halfway. If we're going to be worried, let's be worried about everyone. Now. Deepseak did a few things differently, like open sourcing its models, although it likely built upon take from other companies like Metaslama and the mL library PyTorch to train its models. It's secured over ten thousand Nvidia GPUs right before the US imposed export restrictions, which sounds like a lot, but it's a fraction of what the big AI labs like Google, Open Air, and Anthropic have to play with. I think I've heard estimates of like one hundred thousand to three hundred thousand each, if not more. Now you've likely seen or heard that deep Seak trained its latest model for five point six million dollars a poster to the insane amounts that I'll get to later, and I want to be clear that any and all mentions of this number are estimates. In fact, the provenance of the five point five to eight million dollar number appears to be a citation of a post made by an nvidio engineer in an article from the South China Morning Post, which links to another article from the South China Morning Post which simply states that deep Seat V three comes with six hundred and seventy one billion parameters and was trained in around two months at the cost of five point five eight million dollars with no additional citations of any kind. So you should take it with a pinch of salt. But it's not totally ludicrous. Well, there are some that have estimated the cost. Deep Seeks V three models allegedly trained using two thousand and forty eight n video h eight hundred GPUs according to its paper, and Ben Thompson of Strategory has made this clear that the five point five million dollar number only covers the literal training cost of the official training run, and this is made fairly clear in the paper by the way of V three, and that's the one that's competitive with Opening Eyes GPT four O model, meaning that any costs related to prior research or experiments on how to build the mooddle were left out. Now big big shower to Minimaxer the guy on Blue Sky and Twitter, he's great. He is wonderful, and also added that this is fairly standard for the industry. Again, you choose how you feel about this, but I want to give you the information. And while it's safe to say that deep Seak's models are cheaper to train, the actual costs, especially as deep Seak doesn't share its training data, which some might argue means its models are not really open source. As I said, the numbers get a little harder to guess at. Thompson notes that Deep Seek had to craft a bunch of elegant workarounds to make the model perform, including writing code that ultimately changed how GPUs actually communicated with each other. This functionality isn't otherwise possible using Nvidia's developer tools. They really had to get in there. It's kind of cool. Deep seaks models V three and R one are more efficient and as a result, cheaper to run, and can be accessed via its API at prices that are astronomically cheaper than open eyes, Deep seat Chat running deep six GPT four oh competitive V three model cost zero points zero seven cents per one million input tokens as in commands given to the model, and one dollar one ten per one million output tokens as in the resulting output from the model. I know that these numbers kind of like just sound like numbers like you, Maybe you don't have context, so let me give you some. This is a dramatic price drop from the two dollars fifty cents per one million input tokens and ten dollars per one million output tokens the open Ai charges for GPT four. Oh, this isn't just undercutting, this is this is a bunker buster. If. Now, there is a side that I'll kind of get into a little bit later, in that you are using models hosted in the country that you don't know, probably China. There are data concerns. But again, you can put this on your own server. You could put this in Google Cloud. Both Microsoft and Google are apparently thinking about it now. The Information reported that Google had added it to Google Cloud. No they did not. They didn't do that. They allowed you to connect hugging face. This is a whole bunch of technical stuff that if you understand, you'll be like, yeah, Ed, I know. Long story short, the hyperscalers are already bringing deep Seek out, and I'll get to why that's bad later in detail. But it's also very funny. Now here's something else that's funny. Deep seek reasoner. It's reasoning model costs that fifty five cents per one million input tokens and two dollars and nineteen cents per one million output tokens. Now that sounds expensive. Maybe it is. Whatever, that's goddamn nothing compared to the fifteen dollars per one million input tokens and sixty dollars per one million output tokens of open ai WOF. If I'm Sam Orman, I'm shitting myself. But there's an obvious bar here. We do not know where deep seek is hosting its models, who has access to that data, or where that data is coming from or going to. We don't know who funds deep Seek other than it's connected to High Flyer, the hedge fund that I mentioned earlier that it's split from. In twenty twenty three, there are concerns that deep seak could be stayed funded, and that deep Seek's low prices are a kind of geopolitical weapon breaking the back of the generative AI industry in America. I'm not really sure whether that's the case or not. It's certainly true that China has long treated AI as a strategic part of its national industrial policy and is reported to help companies and sectors where it wants to catch up with the Western world. The Made in China twenty twenty five initiatives SAW are reported hundreds of billions of dollars provided to Chinese firms working in industries like chip making, aviation, and yeah AI. The extent of that support isn't exactly transparent, surprise, surprise, and so it's not entirely out of the realm of possibility that deep Seek is also the recipient of state aid. The good news is that we're going to find out fairly quickly. American AI infrastructure company Grok is already bringing deep Seek's model online, meaning that we'll get at least a very some sort of confirmation of whether these prices are realistic or whether they're heavily subsidized by whoever it is that backs deep Seek. It's also true that deep seek is owned in part by a hedge fund, which likely isn't short of cash to pump into them. But as in the side, given the open AI is the benefactor of billions of dollars of cloud compute credits and gets reduced pricing for Microsoft's zero cloud services to run its actual models, it's a bit tough for them to complain about Arrival being subsidized by a larger entity with the ability to absorb the costs of doing business should that be the case. Same goes for anthropic by the way, and yes, I know Microsoft isn't a state, but with a market cap of three point two trillion dollars in quarterly revenues, larger than the combined GDPs of some EU and NATO nations, it's kind of the next best thing. But I digress. Whatever concerns there may be about malign Chinese influence of bordering on irrelevant outside of the low prices, of course, offered by deepseek itself, and even that is speculative at this point. Once these models are hosted elsewhere, and once deep Seek's methods, which I'll get to in a little bit, are recreated, and by the way, that's not really going to take very long. I believe we're going to see that these prices are indicative of how cheap these models are to run. So you might be wondering, how the hell is this so much cheaper? And that's a bloody good question. And because I'm me, I have a hypothesis. I do not believe that the companies making these foundation models, such as Open Air and Anthropic, have actually been incentivized to do more with less. And because they're chummy little relationships with hyperscalers like Amazon, Google and Microsoft were focused almost entirely on making the biggest, most hugest models possible, using the biggest, even hugerris chips. And because the absence of profitability didn't stop them from raising more money. Well, they've never had to be fucking efficient, have they. They've never had to try. Maybe they should buy less avocado fucking toast. Anyway, let me put it in simpler terms. Imagine living on fifteen hundred dollars a month, and then imagine how you'd live on one hundred and fifty thousand dollars a month, and that you have to, like Brewster's millions, spend as much much of it as you can to complete a mission, a very simple mission. Live. In the former example, you concern survival, you have a limited amount of money and must make it go as far as possible, with real sacrifices to be made with every dollar you spent. If you want to have fun, you're going to have to eat less. Potentially all the food you eat will have to be cheaper. You have to live on a budget. You have to make decisions, and indeed you might learn to cook at home. You might walk more, you might do things that will help you not spend all your money. In the latter example, where you have one hundred and fifty thousand dollars a month that you must spend, your incentivize the splurge to lean into excess to pursue this vague idea of living your life, your actions are dictated not by any existential threats, or indeed any kind of future planning, but by whatever you perceive to be an opportunity to live. Open AI and anthropic are emblematic of what happens when survival takes a back seat to living. They have been incentivized by frothy venture capital and public markets desperate for the next big thing thing, the next big growth to build bigger models and sell even bigger dreams. Like Dario Amaday of Anthropics saying that your AI and I quote could surpass almost all human beings at almost everything shortly after twenty twenty seven, I just want to take a fucking second. Journalist, if you're listening to this, stop fucking quoting this bullshit. Stop it. You're doing nothing. You are failing at your goddamn job every single time you quote this bullshit, this nonsense. Shortly after twenty twenty seven. What the fuck does that mean? Twenty twenty eight, twenty twenty nine, twenty thirty, what does surpassing humans and almost everything even mean? This shit doesn't work. This shit is not good. Oh my god. Anyway, back to the podcast, the Calm Damn. Both Open AI and Anthropic have effectively lived their existence with the infinite money cheap from the SIMS. And I know some of you might say, by the way, it's not an infant money, you just add you go into the console. You get my point. And both companies have been bleeding billions of dollars a year after revenue, and that's, by the way, making billions of dollars and then still losing billions is insane, and they still operated as if money would never run out because it and it wouldn't. If they were actually worried about that happening, they would have certainly tried to do what Deep seek has done, except they didn't have to because both of them had the endless cash and access to GPUs from either Microsoft, Amazon or Google. And the stargate thing is just I will mention it later, just long story short. They're not going to put five hundred billion dollars into the it was up to five hundred bill I'm so tired of this shit. Open iron anthropic have never been made to sweat, unlike me in this closet where I'm recording this. And they've received endless amount of free marketing from a tech and business media happy to print whatever vapid bullshit they spout, and it's just very frustrating. They've raised money at will with ananthropic, by the way, is currently raising another two billion dollars, valuing the company at sixty billion dollars. And this was I think happening while deep Zeek was going on, which is really funny. And they've done all of this off of a narrative of them. We need more money than any company is ever needed ever because the things we're doing have to cost this much. There is no other way. You must give us more money. My name is Sam Altman. I need more money than has ever been made from my huge, beautiful company that sucks and needs money to train it. Help me, please, My big, beautiful sick company is dying, but the best and most important company of all time. It's also normal. Now. Do I think that they were aware that there were methods to make their models more efficient? Sure, open AI tried and failed in twenty twenty three to deliver a more efficient model to Microsoft called Arakis. I'm sure there are teams that both Anthropic and OPENII that are specifically dedicated to making things kind of more efficient. But they didn't have to do it, and so they didn't. And as I've written before in my newsletter and argued on this very podcast, open AI simply burns money and have been allowed to burn money, and up until recently likely would have been allowed to burn even more money because everybody, all of the American model developers, appeared to agree that the only way to develop large language models was to make them as big as humanly possible and work out troublesome stuff like making them profitable or turning them into a useful thing later, which is I presume when AGI happens, a thing that they're still in the process of defining, let alone doing. Deep Seek, on the other hand, had to work out a way to make its own large language models within the constraints of the hamstrung end video chips that can be legally sold to China. While there's a whole cottaged industry of selling chips in Chines using resellers and other parties to get restricted silicon into the country, the entire way in which deep Seek went about developing its models suggests that it was working around very specific memory bandwidth constraints, meaning that the amount of data that could be fed into it and out of it and into the chips. In essence, doing more with less wasn't something it shows, but it's something they had to do. I've touched already on the technical how of these models in greater depth, and you can really read in that in my news there and you can go to whez your hed not at it's at the end of the episode. But I'll also have show notes to what cales like Ben Thompson's some strategory because there are lots of things to read here. I know there are some really technical listeners, and I'm sure you're gonna flame me in my emails. Please go and read it. I'm not wrong. I've checked with a lot of people too, and by the way, all of this austerity stuff seems to have worked. There's also the training data situation and another mayor culper. I've previously discussed the concept of model collapse and how feeding synthetic data, which is training data created by a generative model, into another model, could end up teaching it bad habits, which in turn would destroy the model. But it seems that deep Seekers succeeded in training its models using generative data specifically though, and I'm quoting geekwise John Turou like mathematics where correctness is unambiguous, and using and i quote again, highly efficient reward functions that could identify with which new training examples would actually improve the model, avoiding wasted compute on redundant data, and it seems to have worked. Though model collapse may still be a possibility. This approach extremely precise use of synthetic data is in line with some of the defenses against model collapse I've heard from LLLM developers i've talked to. This is also a situation where we don't know the exact training data, and it doesn't negate any of the previous points I've made about model collapse. Now we'll see what happens there. But synthetic data might work where the output is something that you could figure out using a calculator. But when you get into anything a bit more fuzzy, like written text or anything with an element of analysis, you'll likely encounter some unhappy side effects. But I don't know if that's really going to change how good these things are. There's also a little scuttle about about where deep seak got its data. Ben Thompson, that's trategory suggests that deep seek's models are potentially distilling other model's outputs, by which I mean having another model, say metas LAMA or open ais GPT four H, which is why deep seak identified itself as chet GPT at one point spit out outputs specifically to train parts of deep Seek. This obviously violates the terms of service of these tools, as open AI and its rivals with much rather, have you not use its technology to create its next rival. And open Aye, by the way, has recently reportedly found evidence that deep seek used open AIS models to train its rivals. And this is from the Financial Times, although it failed to make any formal allegations, but it did say that using chat gpt to train a competing model violates its terms of service, and David Sachs, the investor in Trump administration AI and cryptos are, says it's possible that this occurred, although he failed to provide evidence. I just want to say, how fucking funny it is that open air is going where where you're stealing my stuff? Don't steal my things? Where fucking coward, pansy bastard bitches. Fucking hell, what a what a bunch of whiny babies. Oh no, my plagiarism machine got plagiarized. Where kiss my entire asshole, sam Orman, you little worm, you fucking embarrassment to Silicon Valley. You should be ashamed of yourself for many reasons, but so much this though. Where Yeah, oh no, you stole from use my plagier is the machine that requires me to steal from literally every artist and author on the Internet. The thing where we went on YouTube and transcribed everything and fed it into the machine. That's that's not stealing, that's good. But you using our model to generate answers. That's just not fair. What a bunch of babies, you guys say. I'm almos worth billions of dollars. He has a five million dollar car. Cry more, you little worm. Personally, I genuinely want open ai to point a finger at Deep Seek and accuse it of IP theft, mostly for the yucks, but also for the hypocrisy factor. This is a company that, as I've just very cleanly said, exists purely from the wholesale industrial larceny of content produced by literally a fucking everyone, And now they're crying way. I'm sam Olman. I'm a big baby. I've filled my diaper because someone stole from my plagiarism machine. Kiss my ass, Kiss my ass. These companies haven't got shit. Open ai doesn't have shit. They they don't have anything, They don't have a next product without reasoning, they haven't got anything. And now they don't have that disgusting justification that overspending the fat, ugly American startup culture of spending as much as you can to build America's next top monopoly. They should be fucking ashamed of themselves. They shouldn't be billionaires, they should be poverty stricken. They should have to pay everyone they stole for And it's just it sickens me seeing the reaction from some people on this, seeing the sinophobia, but seeing this level of defensiveness of a company like open AI or Anthropic, And as I'll get into next episode, we are really running out of time here, and I think Deep Seek is really I think it could be really the end of days for these companies. I don't know how much they've got left time wise, or even money wise, and I'm not sure how they even raise money. But in the next episode, I'm going to deep dive into Deep Seek and I'll tell you how they sent the US tech market into a panic and what it actually means the future of open Aianthropic and the hyperscale is backing them. This has been a crazy few days. I hope this has helped, and on Monday you'll find out more. Thank you so much for listening. The support I've got for the show has been incredible, and the emails I've got about Deep Seek. I've been trying Okay, I've really been trying so the fastest I could do it. But I'm so happy to do this show, and I'm so grateful for all of you. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matasowski. You can check out more of his music and audio projects at Matasowski dot com, M A T T O. S O w Ski dot com. You can email me at easy at Better Offline dot com or visit Better Offline dot com to find more podcast links and of course, my newsletter. I also really recommend you go to chat dot where's youreaed dot at to visit the disc and go to our slash Better Offline to check out I'll Reddit. Thank you so much for listening. Better Offline is a production of cool Zone Media. For more from cool Zone Media, visit our website Coolzonemedia dot com, or check us out on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.