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- [00:00]
Cindy Crawford
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Ed Zitron
Ch Ch Ch.
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Ed Zitron
World gets a little more connected, but a little further apart. But then there are moments that remind us to be more human.
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Ed Zitron
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Cindy Crawford
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Ed Zitron
At Ameca, we understand that looking out for each other isn't new or groundbreaking. It's human. Ameca Empathy is our best policy. Amazon One Medical presents Painful thoughts I could catch anything sitting in this doctor's waiting room. A kid just wiped his runny nose on my jacket, and the guy next to me sitting in a pool of perspiration insists on sharing my armrest. Next time, make an appointment with an Amazon One medical provider. There's no waiting and no sweaty guy. Amazon One Medical healthcare just got less painful. Call Zone Media hello and welcome to Better Offline. I'm your host, Ed Zitron. Better Offline 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 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.
- [13:45]
Cindy Crawford
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- [15:17]
Ed Zitron
World gets a little more connected, but a little further apart. But then there are moments that remind us to be more human.
- [15:29]
Cindy Crawford
Thank you for calling Ameca Insurance.
- [15:31]
Ed Zitron
Hey, I was just in an accident.
- [15:33]
Cindy Crawford
Don't worry, we'll get you taken care of.
- [15:35]
Ed Zitron
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- [15:45]
Cindy Crawford
Hi, I'm Cindy Crawford and I'm the founder of Meaningful Beauty. Well, I don't know about you, but like, I never liked being told, oh wow, you look so good for your age. Like, why even bother saying that? Why don't you just say you look great at any age? Every age. That's what Meaningful Beauty is all about. We create products that make you feel confident in your skin at the age you are now. Meaningful Beauty Beautiful skin at every age. Learn more@meaningfulbeauty.com.
- [16:23]
Ed Zitron
Deep Seeks models actually exist. They work at least by the standards of hallucination prone LLMs that don't, at the risk of repeating myself, know anything. They've been independently verified to be competitive in performance, and they're magnitudes cheaper in price than those from both hyperscalers, Google's Gemini, Mets, Llama, Amazon Q, and so on and so forth. And from Those released by OpenAI and anthropic DeepSeq's models don't require massive new data centers. They run on GPUs currently used to run services like ChatGPT and even work on more austere hardware. Nor do they require an endless supply of bigger, faster Nvidia 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 emissions goals, 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, they're asking a very reasonable question. Shit, did we just waste $200 billion? Anyway, let's get into the nitty gritty. What is Deepseek? First of all, if you want a super deep dive into what it is, I can't recommend venture beats. Write up enough. I'll link to it in the show notes as I us it's really good and it goes into a lot more detail than I will. But here's the too long didn't read for you. Deepseek 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 chatbots, including a chatgpt equivalent for the Chinese market. This is what we know 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 Altman's legend that we should question as well. I think the circumstances under which Sam Altman got made head of Y Combinator are extremely questionable. I'm saying you can question Deepseek 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 Deepseek did a few things differently, like open sourcing its models, although it likely built upon tech from other companies like Metaslama and the ML library Pytorch to train its models. It secured over 10,000 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, OpenAI and Anthropic have to play with. I think I'VE heard estimates of like 100,000 to 300,000 each, if not more. Now, you've likely seen or heard that Deepseek trained its latest model for $5.6 million, as opposed 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 $5.58 million number appears to be a citation of a post made by an Nvidia 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 Seq V3 comes with 671 billion parameters and was trained in around two months at the cost of $5.58 million with no additional citations of any kind, so you should take it with a pinch of salt, but it's not totally ludicrous. While there are some that have estimated the cost, Deepseek's V3 model was allegedly trained using 2048 Nvidia H800 GPUs, according to its paper, and Ben Thompson of Stratitri has made this clear that the $5.5 million 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 V3. And that's the one that's competitive with OpenAI's GPT4O model, meaning that any costs related to prior research or experiments on how to build the model were left out. Now, big, big shout out to minimaxir, the guy on Bluesky 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 deepseek's models are cheaper to train, the actual costs, especially as Deepseek doesn't share its training data, which some might argue means its models are not really open source. As I the numbers get a little harder to guess at. Thompson notes that Deepseek 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. Deepseek's models V3 and R1 are more efficient and as a result cheaper to run, and can be accessed via its API at prices that are astronomically cheaper than OpenAI's DeepSeek chat running DeepSeek's GPT4O competitive v3 model costs 0.07 cents per 1 million input tokens, as in commands given to the model and $1 $1.10 per 1 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. Let me give you some this is a dramatic price drop from the $2.50 per 1 million input tokens and $10 per 1 million output tokens that OpenAI charges for GPT4O. 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 a 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 Deepseek 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 Seq Reasoner, its reasoning model costs $0.55 per 1 million input tokens and $2.19 per 1 million output tokens. Now that sounds expensive. Maybe it is. Whatever. That's God damn nothing compared to the $15 per 1 million input tokens and $60 per 1 million output tokens of OpenAI. If I'm Sam Altman, I'm shitting myself. But there's an obvious bar here. We do not know where Deepseek 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 Deepseek other than it's connected to High Flyer, the hedge fund that I mentioned earlier that it split from in 2023. There are concerns that Deepseek could be state funded and that Deepseek'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 indulgen industrial policy and is reported to help companies in sectors where it wants to catch up with the Western world. The Made in China 2025 initiative saw a 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 Deepseek 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 Deepseek's model online mean that we'll get at least a very some sort of confirmation of whether these prices are realistic or whether they're heavily subsidised by whoever it is that backs Deepseek. It's also true that Deepseek is owned in part by a hedge fund, which likely isn't short of cash to pump into them. But as an Aside, given that OpenAI is the benefactor of billions of dollars of cloud compute credits and gets reduced pricing for Microsoft's Azure cloud services to run its actual models, it's a bit tough for them to complain about arrival being subsidised by a larger entity with the ability to absorb the cost 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 $3.2 trillion and 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 are 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 Deepseek'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.
- [25:12]
Cindy Crawford
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- [26:15]
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- [27:16]
Ed Zitron
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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. Any. Anyway, let me put it in simpler terms. Imagine living on $1,500 a month, and then imagine how you'd live on $150,000 a month and that you have to, like Brewster's Millions, spend as much of it as you can to complete a mission. A very simple 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 spend if you want to have fun. If you're going to have to eat less, potentially, or 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 $150,000 a month that you must spend, you're incentivized to 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 opt opportunity to live. OpenAI 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, the next big growth, to build bigger models and sell even bigger dreams. Like Dario Amadei of Anthropic, saying that your AI, and I quote, could surpass almost all human beings at almost everything shortly after 2027. And I just want to take a fucking second. Journalists, if you're listening to this, stop fucking quoting. Stop it. You're doing nothing. You are failing at your goddamn job every single time you quote this bullshit, this nonsense. Shortly after 2027. What the fuck does that mean? 2028, 2029, 2030? 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. Ed, calm down. Both OpenAI and Anthropic have effectively lived their existence with the infinite money Cheat from the Sims. And I know some of you might say, by the way, it's not an infinite money. Just add. You go into the console, you get my point. And both companies have been bleeding billions of dollars a year off to 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 kind of wouldn't. If they were actually worried about that happening, they would have certainly tried to do what Deepseek has done. Except they they didn't have to because both of them had the endless cache 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 $500 billion into the it's it was up to 500. I'm so tired of this shit. OpenAI and 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 vapor bullshit they spout. And it's just very frustrating. They've raised money at will with an Anthropic, by the way, is currently raising another $2 billion, valuing the company at $60 billion. And this was, I think, happening while Deep Seek was going on, which is really funny. And they've done all of this off of a narrative of we need more money than any company has 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 ever been made for 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. OpenAI tried and failed in 2023 to deliver a more efficient model to Microsoft called Arrakis. I'm sure there are teams at both Anthropic and OpenAI 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, OpenAI 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 humanely 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. Deepseek, on the other hand, had to work out a way to make its own large language models within the constraints of the hamstrung Nvidia chips that can be legally sold to China. Well, there's a whole cottaged industry of selling chips in China, using resellers and other parties to get restricted, restricted silicon into the country. The entire way in which Deepseek 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 newsletter and you can go to where's your Ed? It's at the end of the episode, but I'll also have show notes to articles like Ben Thompson's from Stratetiary because there are lots of things to read here. I know there are some really technical listeners and I'm sure you're going to flay 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 mea culpa. I 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 Deepseek has succeeded in training its models using generative data. Specifically, though, and I'm quoting Geek Wire's John Tirow, like mathematics, where correctness is unambiguous, and using, and I quote again, highly efficient reward functions that could identify 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 is in line with some of the defenses against model collapse I've heard from LLM 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, happy side effects. But I don't know if that's really going to change how good these things are. There's also a little scuttlebutt about where DeepSeq got its data. Ben Thompson at Strategiary suggest that DeepSeq's models are potentially distilling other models outputs, by which I mean having another model, say Meta's llama or OpenAI's GPT4O, which is why Deepseek identified itself as ChatGPT at one point spit out outputs specifically to train parts of deepseek. This, this obviously violates the terms of service of these tools, as OpenAI and its rivals would much rather have you not use its technology to create its next rival. And OpenAI, by the way, has recently reportedly found evidence that Deepseek used OpenAI's 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 ChatGPT to train a competing model violates its terms of service. And David Sacks, the investor in Trump administration AI and crypto czar, says it's possible that this occurred although he failed to provide evidence. I just want to say how fucking funny it is that OpenAI 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 Altman, you little worm. You fucking embarrassment to Silicon Valley. You should be ashamed of yourself for many reasons, but so much. This though, where. Oh no, you stole from me. My plagiarism 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. Sam, always worth billions of dollars. He has a five million dollar car. Cry more. You little worm. Personally, I genuinely want OpenAI to point a finger at DeepSeek 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. Waa. I'm Sam Altman. I'm a big baby. I filled my diaper because someone stole from my plagiarism machine. Kiss my ass. Kiss my ass. These companies haven't got shit. OpenAI doesn't have shit. 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, that 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 OpenAI 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 deepseek and I'll tell you how they sent the US tech market into a panic and what it actually means. The future of OpenAI anthropic and the hyperscalers 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. It's 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 Matt Osawski. You can check out more of his music and audio projects@matasowski.com m a t t o s o w s k I.com youm can email me at ezetteroffline.com or visit betteroffline.com to find more podcast links and of course, my newsletter. I also really recommend you go to chat. Where's your ed at? Visit the Discord and go to R betteroffline to check out our 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, coolzone media.com or check us.
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