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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
Welcome to Technology Brothers, the most profitable podcast in the world. We are staying on DeepSeek. There's so much more news. The market has collapsed. It's a disaster.
Speaker 1:I thought it was down, like, 2%. I looked at some of the tech stocks. It's, like, 10%.
Speaker 2:Yeah. NVIDIA is down 15.
Speaker 1:15. Awful. Which is
Speaker 2:why we are buying on public.com all morning.
Speaker 1:Moment of silence for every all the time. Dollar cost
Speaker 2:averaging in throughout the day because I think by the end of the I mean, it's almost 1, but by the end of the day, people are gonna wake up. They're gonna be like, alright. We have reacted a little bit. We didn't understand
Speaker 1:Yep.
Speaker 2:Jevons paradox.
Speaker 1:Rookie mistake.
Speaker 2:Bad day to not understand Jevons paradox.
Speaker 1:Terrible day.
Speaker 2:Rough day. But Wall
Speaker 1:Street will wake up explain it today on the show. We'll We're
Speaker 2:gonna man we're gonna mansplain Jeff's paradox. To Wall Street.
Speaker 1:And who knows? There might be a Geordi paradox that happens during the show.
Speaker 2:We might there might be law. Geordi's paradox.
Speaker 1:Yeah. Yeah. There might be some coinages today, so stay tuned. I wanted to go through the short case for NVIDIA stock by Jeffrey Emanuel. This went out on Jan.
Speaker 1:25 and, was a very, very large deep dive on, how DeepSeek and their R1 model might change the demand for GPUs, specifically NVIDIA GPUs. And, we have a summary article here, but we'll take you through it. Basically, Jeffrey Emanuel, I hadn't heard of him before, but he's a kind of a combination of a computer scientist and an investor. He's done a lot of, he said, he spent ten years working as a generalist investment management at very analyst at various long short hedge funds, including, Millennium and Balazsini, while also being something of a math and computer science nerd who has been studying deep learning since 2010. So he's kind of the perfect person to talk about this.
Speaker 1:And he says, whenever I meet and chat with some with my friends and ex colleagues from the hedge fund world, the conversation quickly turns to NVIDIA. It's not every day that a company goes from relative obscurity to being worth than the com worth more than the combined, stock markets of England, France, and Germany. And naturally, these friends want to know my thoughts on the subject. Because I'm such a dyed in the wool believer in the long term transformative impact of this technology, I truly believe it's going to radically change nearly every aspect of our economy and society in the next five to ten years with basically no historical precedent. It's been hard for me to make the argument that NVIDIA's momentum is gonna slow down or stop anytime soon.
Speaker 1:But, the valuation was just too rich for his blood in the last year, but recently he flipped. So first, he wants to break down the bull case for NVIDIA, and that looks something like this. They wound up with a basically, something close to a monopoly in terms of share of aggregate industry CapEx that's spent on training and inference infrastructure for artificial intelligence. They have insanely high 90% plus gross margins on the most high end data center oriented products. They make lower margins on, like, computer graphics.
Speaker 1:So when Pixar buys a bunch of GPUs, they pay a lot lot lower margin for that. But still and then Nvidia obviously is the gaming graphics cards. But 1 major thing that you hear the smart crowd talking about is the rise of a new scaling law, which has created a new paradigm for thinking about how compute needs will increase over time. So as a reminder, the new scale the the original scaling law, which is what has been driving AI progress since AlexNet appeared in 2012 and the transformer architecture was invented in 2017, is the pre training scaling law. And that's as follows.
Speaker 1:The more billions and now trillions worth of tokens that we can use as training data, and the larger parameter count of the models we are training, and the more flops of compute that we expend on training those models on those tokens, the better performance of the resulting models on a large variety of high highly useful downstream tasks. So if you remember when GPT 4 was being rumored to drop, there were these there was this massive, like, viral meme image of, like, here's a visualization of GPT 3, and it was, like, a small circle. Now here's a visualization of GPT 4, and it's this massive circle. And everyone was like, it's gonna get so big because everyone was like, it's gonna be AI. It's gonna turn us all into paperclips.
Speaker 1:Like, there's a lot of fear mongering around it. But what was true there was that the the pretraining scaling law was holding. And from GPT 3 to GPT 4, there was an order of magnitude increase in the amount of data and compute and money and just energy and everything that went into those models. And as a result, GPT 4 very clearly is a lot smarter than GPT 3. And so for the last few years, we've just been kind of messing around with GPT 4, like, unhobbling it, adding PDF upload, voice mode, little oh, it can generate images, all this other stuff.
Speaker 1:But the the core underlying pre trained model hasn't really increased. What we've seen is that Llama came out with something that's at the same size and scale, basically trained on the entire Internet, all the tokens that we have. And And when people talk about the data wall, they're talking about, hey. We're we're we can't just scale up 4 more orders of magnitude because we've already ingested every single book, every single piece of text. So we gotta go to synthetic data.
Speaker 2:Not our secret stash of scrolls.
Speaker 1:Exactly. You gotta get those in. But even then, that's not that many tokens. You know? Like, and so so that was all in the pretraining era, the original scaling law.
Speaker 1:Yep. And and and the question has always been, okay. Sam is pitching a $500,000,000,000 cluster. Is this entirely predicated on the original scaling law holding? And, and there's been a lot of debate about that.
Speaker 1:Like, maybe you 10 x everything, and it just gets 2% better at some point. And that would be very depressing and probably not that profitable. But there's a lot of smart people that think, no. The scaling laws are holding. GPT 5 is gonna be really good.
Speaker 1:It's gonna be really smart. And and then you're gonna be able to do all the cool reasoning and extra extra tweaks on top of it, and unhobble it and use this agent, teach it to code, and do all that other stuff. But the most important thing is that the the the foundational model, the GPT 5, like, the core model is gonna be really, really smart. And so, yeah. So talking about the amount of data out there, it's not such a tremendous amount in percentage when we're when you're talking about a training corpus of nearly 15000000000000 tokens, which is the scale of current frontier models.
Speaker 1:So the, for a quick reality check, Google Books has has digitized around 40000000 books so far. If a typical book has 5, 50000 to a hundred thousand words or 65000 to a 30000 tokens, then that's between 260,000,000,000,0.0 and 520,000,000,000,0.0 tokens just from books. And so you're doing you're you're you need 15,000,000,000,000 tokens for a frontier model right now. All of Google Books, which is 40000000 books, it's basically every book, that's, you know, a third Right. Or a quarter of of your tokens.
Speaker 1:And then you're gonna get everything from the Internet, whether it's strictly legal or not. And there are lots of academic papers with the archive alone having around 2000000 papers, and the Library of Congress has over 3000000000 digitized newspaper pages. So you pull all that in.
Speaker 2:That's why that's why the the OpenAI whistleblower going and telling the press that the Frontier Labs are stealing content from the Internet was not the biggest revelation ever because everybody that was at all exposed to the industry knew that that was happening at scale already.
Speaker 1:Yeah.
Speaker 2:And and many were basically open about it.
Speaker 1:Totally. Totally. They'll all get a settlement, and then also there's just the question of transformation. Not many people are genuinely going to they're not opening the chat GPT app and saying, like, reproduce this New York Times article verbatim for me. They're asking more general stuff about, like, oh, like, you know, tell me the history of AI, and maybe it pulls in a new a New York Times article to, like, tidbit, but completely rewrites it.
Speaker 1:It's very transformative. And so, yeah, that was always a little bit odd that that was blown out. But, obviously, like, it's a very powerful company, very powerful technology. There's gonna be a lot of, a lot of scrutiny. And so there's other ways to to gather training data.
Speaker 1:Some people have talked about uploading every human genome, which is kinda crazy because, entire, you know, human genome sequence is around 20 gigs to 30 gigs of uncompressed for a single human being. And for a hundred million people, that's a lot of data, so you could bake all that in. But it's kind of unclear if that would actually make them better if they knew that. And, and the computational requirements for processing genomic data are different. That might be useful for, like, making it really good at bio, but maybe that doesn't actually help it, you know, book a flight for you on time.
Speaker 1:And so now the focus has shifted from the old scaling law, which is just more pre training, more data, more compute to the new scaling law. And this is what's called inference time compute scaling. You might have heard this a lot of people refer to it as test time compute. And and this is really, really important. I really think this is, like, for for normal people in tech, this is a topic that people are just learning about now, but it it does fundamentally change the economics of the industry, so it needs to be understood.
Speaker 1:And so before, the vast majority of all the compute you'd expend in the process of, you know, using AI was in the upfront training compute of the model in the first place. So you see this massive GPT four $500,000,000 training run, huge data center, tons of networked NVIDIA chips altogether, super high performance computers. And then once it's done, it's like to actually query it and get an answer, pretty small. And you can compress that down, and then you'll see things like, oh, like, they took llama, and they compress it out. In the cloud.
Speaker 1:It can be hosted in the cloud on a
Speaker 2:small necessarily tie back. You don't need
Speaker 1:You don't need the original data center. Exactly. Well, you certainly don't need you certainly don't need the massive interconnected data center.
Speaker 2:Yeah.
Speaker 1:A lot of these models, like, when you go to GPT 4 and just say, like, you know, write me a poem, and it starts spitting out those tokens, you can't just do that level of inference on your Mac. Yeah. Like, you do need 8 a 1 hundreds tied together Yeah. And it needs to load the whole model into what's called VRAM.
Speaker 2:Yeah.
Speaker 1:And and that is and and that is expensive. Now there are smaller models that are compressed down even further that can run on your phone. So Apple intelligence, the reason it's so bad, I I think, is because Yeah. It it's a super compressed model, but that means that the data never transfers off the device. And I have a I have a program on my computer called MacWhisper that will do transcription.
Speaker 1:So you give it a video or an audio file, and it can transcribe that. It's using a compressed AI model. It was probably trained on a big data center, but then compressed down to Apple.
Speaker 2:So bad, you wonder why they released it? Because they woulda had to test it internally. But if you just had a hard workout and then your wife it's telling your wife that, like, you're dead.
Speaker 1:Yeah. Yeah. Yeah. Yeah. Yeah.
Speaker 1:Husband died, and then you open it up. It's like gym. It's like, today's workout killed me. Yeah.
Speaker 2:Yeah. Would you like to call 911? Yeah. It's like
Speaker 1:I mean, honestly, maybe a little bit of a contrarian take, but, like, the the the terrible hallucination AI summaries actually bring me a lot of joy. Yeah. They actually make me laugh a lot more, so maybe it's fine. I don't know.
Speaker 2:Something there.
Speaker 1:But anyway, so there's been this shift from the old scaling law, which is just bigger and bigger training runs, pretraining. You need a lot of data, a lot of compute to what's called inference time computing. I most people call it test time compute scaling. And so what that means is that once you had the trained model performing inference in that model, asking a question, used certain limited amount of compute, that's the old model. Critically, the total amount of inference compute measured in various ways such as flops and GPU memory footprint, etcetera, was much, much less than what was required in the pre training phase.
Speaker 1:Of course, the amount of inference compute does flex up when you increase the context window size. So, like, those really big dump a whole book in, get a whole book out. That obviously requires, more compute when you run those queries. But in general, if you're just saying, hey. Summarize this.
Speaker 1:Or give me give me a hundred word summary of this or a thousand word summary and print out a Wikipedia article for me essentially. That's pretty cheap. And so just inferencing GPT 4 o, GPT 4 mini, those types of things, they're very fast and they're very cheap, like a few cents per query. Now, we have switched to the new model, which is with the advent of revolutionary chain of thought models, he calls it COT models, reasoning models introduced in the past year, most notably in OpenAI's flagship o 1 model, but very recently in DeepSeq's new r 1 model, which we will talk about later in much more detail. All that changed.
Speaker 1:Instead of the amount of inference compute being directly proportional to the length of the output text generated by the model so it used to be it used to be you ask, like, you know, how many people are in America? What's the population of America? And it would just immediately know, oh, the most logical next 2 tokens to come after this Yeah. Are 330000000. And it would just print those out, and it would take two seconds.
Speaker 1:It'd be super fast and super cheap. With chain of thought, it's gonna it's gonna write a whole bunch of intermediate logic tokens. So it's gonna say, okay.
Speaker 2:Yep.
Speaker 1:They would look up population statistics. Where could I go wrong? What what what you know, what's the population of other things? Do they want a precise estimate, or do they want me to round? And it'll talk to itself for a long time.
Speaker 1:Yeah. And so these logic tokens, it's like this internal monologue. And every time it's generating 1 of those tokens, that's energy, that's cost. And so all of a sudden, there's this huge sea change in how inference compute works. The more tokens you use for this internal chain of thought process, the better the quality of the final output because it can talk to itself, fact check, reality check, but it's really expensive.
Speaker 1:It's like giving a human worker more time and resources to accomplish a task so they can double and triple check their work, do the same basic task in multiple different ways, and verify that they come out the same way. Take the result they came up with and plug it into the formula to check that it actually does solve the equation. And so it turns out this this approach works almost amazingly well. It is essentially leveraging the long anticipated power of what's called reinforcement learning with the power of the transformer architecture. So they're taking the 2 dominant paradigms in AI and marrying them together, and that's proven very valuable.
Speaker 1:So
Speaker 2:And they say here the biggest weakness of the transformer model historically was the hallucinations. Totally.
Speaker 1:That happen. Yeah. It would it would get caught in a loop because it's just trying to predict the next word, and it would go down the wrong path.
Speaker 2:And it would be deeply confident about something that was just categorically false.
Speaker 1:Totally. Totally. And so, the way transformers work in predicting the next token is that, at each step, they start out on a path, in their to their initial response. They become almost like a prevaricating child who tries to spin a yarn about why they are actually correct because they've gone down the wrong path, and they'll just keep building off of that. And it gets really, really bad, because the models are always seeking to be internally consistent and have each successive generated token flow naturally from the pre preceding tokens in context.
Speaker 1:It's very hard for them to course correct or backtrack. And you saw this in the early, you know, chat g p t tax tests. You'd you you you would you like, you you'd start out pretty good, and then by the end, it would be nonsense. I remember the first time I actually demoed GPT 3 in the playground, the very first version, I I had a friend who was playing a video game, Hearts of Iron 4, which is, like, notoriously addictive. It's like you build this, like, whole World War two map, but it's not just, like, soldiers.
Speaker 1:It's, like, logistics. So you need to be like, okay. How do I get more gasoline to the front lines? I need to build more train tracks. It's like it takes, like, days and days to play a single round or whatever.
Speaker 1:And I was like, write a list of, like, of, like, tips for my friend to quit playing hearts of iron. And it started writing it out. It was like it was like, go outside. You know? And, like, touch your ass.
Speaker 1:And then and then it, like, started hallucinating and started turning into, like, you know, read a guide on how to play it better. Log on a lot, and it actually pivoted into the opposite. And there were a lot of these there were a lot of these examples where where the hallucination would go really bad. Obviously, GPT 4 was better.
Speaker 2:Yep.
Speaker 1:But with the chain of thought and logic internal reasoning, these models got way better. And GPT o 1 is a great example of that where, it takes a lot longer because there's tons of internal reasoning. But, so he says the first time I tried the o 1 model from OpenAI was like a revelation. I was amazed how often the code would be perfect the very first time, and that's because of the chain of thought process automatically finds and fixes problems before they ever make it to the final response token in the in the in the answer the model gives you. In fact, the o 1 model is $20 a month, is basically the same model used in the o 1 pro model for 10 x the price of 200 a month, which raised plenty of eyebrows.
Speaker 1:The main difference is that o 1 pro thinks for a lot longer before responding. And it's crazy. I I prompted o 1 pro last night because I was doing a bunch of evals for this and r 1. And the it's like downloading a movie from the Internet in 02/7 or something. It's like, you see this this progress bar just going because it's it's really thinking it
Speaker 2:really all this stuff. Sending this task to a data center, almost like you're sending Yeah. A task to some white collar worker who's just sitting
Speaker 1:Yeah.
Speaker 2:In a warehouse, and they're just, like, figuring it out. It takes a little bit of time.
Speaker 1:Totally. Totally.
Speaker 2:You know?
Speaker 1:And so that that consumes it generates way more chain of thought logic tokens, and it consumes a far larger amount of inference compute. And so, he's he's giving some benchmarks here. Even a long and complex prompt for GPT 4 o with 400 kilobytes of context given, so you're dumping in, hey, this is this Wikipedia page. I want you to kind of transform it or whatever or analyze it. That could take less than ten seconds before it begins responding, often less than often less than five seconds, like, really quick.
Speaker 1:Whereas that same prompt to o 1 pro could easily take five minutes before you get a response. Although OpenAI does show you some of the reasoning steps, it's actually summarizing the reasoning steps to you. It's not showing you all of the tokens because it's so much. And that results in in crazy stuff.
Speaker 2:Interesting relevant to later, DeepSeek shows you a lot more
Speaker 1:They do.
Speaker 2:Which users love.
Speaker 1:Yes.
Speaker 2:That's been 1 of the quick, takes that a lot of people have had is, like, oh, I actually wanna see what it's doing because it's sort of teaching you.
Speaker 1:It builds trust for sure, and it's definitely, like, a good UI paradigm Yeah. That should be ported back.
Speaker 2:Well, there's you know, he's saying here that OpenAI doesn't want people to have that information because look. It says, presumably, some of the reasoning steps that are generated during the process while you wait, they're not showing you everything presumably for trade secret related reasons to hide from you the exact reasoning tokens it generates showing you an abbreviated summary.
Speaker 1:Yep. Yep. I I I believe that. I also think that there's a lot of, those reasoning steps that are essentially, like, guardrails. Like, I was asking you to summarize a book that I purchased and is not part of the public domain, but is definitely out there and has been excerpted so much.
Speaker 1:It should be able to write me the the full summary. Yeah. And and I noticed as o 1 was working through it, 1 of the steps was, like, clarifying copyright violations. Because it internally, I'm sure it has a step that's like, if somebody asks you to do something for a book, it's gonna see, okay. What can we do here legally?
Speaker 1:Right?
Speaker 2:Yeah.
Speaker 1:And it's like, oh, well, there's a lot of information on the Internet that's public, so we can pull that in, and that's fine. We can give the we can give the user And
Speaker 2:there's other probably reviews Yeah. The book, things
Speaker 1:like that. Whereas if I if I see the exact reasoning steps and it's like it's like, remember, like, you know, here's how to jailbreak me to let me do yeah. It's very reverse engineerable. And so, there was we talked about this in the show previously, but o 3, which isn't out yet but is even more advanced in terms of reasoning, they have a high compute model that spends almost $3,000 per task.
Speaker 2:Yeah. And
Speaker 1:it just thinks for hours and hours and hours, basically. And it was able to break Arc, that, that AI AGI eval. Yeah. And they spent $3,000 worth of compute to solve a single task. And so, this doesn't it doesn't take an AI genius to realize that this development creates a new scaling law that is totally independent of the original pre training scaling law.
Speaker 1:Now you still wanna train the best model you can by clearly leveraging as much compute as you can and as many trillion tokens of high quality training data as possible, but that's just the beginning of the story in this new world. Now you could easily use incredibly large amounts of compute just to do inference from these models at a very high level of confidence or when trying to solve extremely tough problems that require genius level reasoning to avoid all these pitfalls.
Speaker 2:Somebody, compared what was basically drawing a comparison to if you're a billionaire right now and you want the best phone, you can buy it. You basically buy an iPhone. Yep. You just buy whatever the top of the line iPhone is. But now if you're a genius, you can access a 10000, a hundred thousand times, you know, as much genius, basically.
Speaker 2:Sure.
Speaker 1:Sure.
Speaker 2:Right? And so it it potentially really changes the playing field where you could always, like, it's a very sort of interesting thing to be able to turn on that level of intelligence so quickly and compound it.
Speaker 1:Yes. My my rebuttal to that, though, is that I wonder The
Speaker 2:ECP is gonna give it away for free.
Speaker 1:True. True. True. But, like, when I saw the reaction to r 1, the DeepSeq model, I tried both of them, and I put the same prompts into o 1 and or or o 1 pro and r 1. And I was getting reliably better results with o 1, with the ChatGPT product.
Speaker 1:And I don't know if I'm, like, biased, obviously, but, like, even just basic stuff, I I asked it to create a 5000 word summary of, of the Dread Pirate Roberts, Ross Ulbricht, and this and the story of the Silk Road for the show because we wanted to do a deep dive on that eventually. But, o 1 pro delivered basically exactly 5000 words. And it even as it was writing the story, it would say, like, introduction, 400 words. Act one, six hundred words. And it total it kept this, like, internal log.
Speaker 1:And then at the end, it was like, I have written 5400 words. Here you go. And r 1 thought a bunch and then spat out a thousand words. And I was just like, this is not what I asked you for. And, like, so you failed my eval.
Speaker 1:I didn't I didn't test a bunch of stuff, but I was like but even that, I think that something something odd's going on where I think a lot of people haven't used ChatGPT o 1, and they certainly haven't tried o 3 because it's not out yet. And so a lot of it is just like it's just like this thing exists. It's like if if the first electric car you ever drove was a Rivian, you'd be like, wow. The acceleration is insane. Yeah.
Speaker 1:This is amazing. And then somebody would be like, well, like like, Tesla's been doing this for, like, years, guys. Like, calm down. Yeah. But instead, it's like, I think a lot of people got introduced to reasoning models through deep seek, and I and I have yet to see
Speaker 2:about this. This is the the AI adoption chain. The first person goes, hey. Did you see this new model? And the the next person goes, yeah.
Speaker 2:I I I used it. It's it's pretty cool. The next person goes, yeah. I used it, and, like, I quit chat GPT. The next person's like, well, I'm already generating all my code with the new model.
Speaker 1:Yeah.
Speaker 2:I haven't even used I have an open chat GPT. And the next person's like, I am running my entire life on this model, and here's a 20 part thread Yep. On why it's better. And then the last person's like, wait. I haven't used it.
Speaker 2:And everyone else is like, oh, yeah. I, like, put a few prompts in. Exactly. Everybody wants to feel like they're not the paperclip that they're at the edge. Totally.
Speaker 1:There's a huge status signal to being like, I understand AI, and I'm leveraging it. And my life's so good. And, like, it's making my life so easy. Like, when really it's like most people are just kind of, like, using it intermittently. I've been in I've been texting with people, like, talking about, like, how do you actually use AI?
Speaker 1:And I'll scroll through my chat GPT. And I was like, yeah. I run, like, I run, like, 3 to 5, like, pretty serious back and forth per day when I'm doing, like, research or work.
Speaker 2:Well, 1 thing 1 thing was
Speaker 1:like, well, oh, I would have expected you to use way more. Like, I'm using it constantly. And it's
Speaker 2:like Okay.
Speaker 1:Maybe. Okay. Like, maybe.
Speaker 2:But Yeah.
Speaker 1:Maybe maybe I'm using it not For us. But
Speaker 2:For us?
Speaker 1:I don't know.
Speaker 2:1 thing is clear. We maybe talked about this on the show before, but without how we use OpenAI
Speaker 1:Yeah.
Speaker 2:Products, we would need a full time researcher Yep. Writer Yep. To just be helping us create content
Speaker 1:for
Speaker 2:the show.
Speaker 1:Totally. Yep. And so and so I wonder if if there's if there's something where it's like, sure sure r 1 is, like, at it like like, they they matched o 1 or o 3 in terms of some, like, math eval. But, like, if your job is not asking AI to solve really hard math problems, like
Speaker 2:Yeah.
Speaker 1:And your and your actually day to day use cases, like, hey. Like, explain to me the history of, like, Web 3 or or space travel or, you know, who's like, what are all the companies that are involved in, disaster relief after a fire? It's like, you might not be able to tell the difference between these models based on, like, the responses that are actually valuable to you. And so then this becomes more of a distribution and cost.
Speaker 2:Well, the whole the whole other thing, I think Vittorio posted about this. It's getting to a point pretty much now where the models are becoming smarter than the average person. Totally. So the average person cannot tell the difference. Like, if if if the model gets 10% smarter Yeah.
Speaker 2:Like, you know, compare this if you talk to a literal genius in a specific field. If they were to get 10% smarter about just in general about their topic, you wouldn't really be able to tell because it's so far beyond.
Speaker 1:Yeah. Yeah. And and how many times in business are you like are you like, I need, like like like there are very few businesses where you're just like, god, I got a 50 IQ guy on my team, but, like, I would kill for a 60 IQ person.
Speaker 2:Yeah.
Speaker 1:It's like, usually, it's like, I need just, like, 1 people
Speaker 2:who killers
Speaker 1:Exactly. Who are
Speaker 2:gonna be
Speaker 1:A lot of phones. With the phones. And so it really comes down to, like, okay. You know, you have good enough AI. It's AGI.
Speaker 1:It's it's a 30 IQ or something, and it's solid. And then it's it's integrated properly. Wait. That's why that's
Speaker 2:why the goal post shift it out from AGI to ASI. Yeah. Because everyone's like, oh, we got AGI, and it's it's not, you know It's not It's not very exciting. Didn't paperclip me.
Speaker 1:Exactly. Exactly. And so,
Speaker 2:But, yeah, we should get into
Speaker 1:The NVIDIA case. Yeah. So I think we should switch over to the to the summary for this because, there's a few different interesting things. So, and you can think about NVIDIA's moat as basically 4 different components. 1, they have high quality Linux drivers.
Speaker 1:They have CUDA as an industry standard, which is the language that you use to write software to run on the GPUs in parallel. Then you they have the fast they have a fast GPU Internet tech, technology that they acquired from Mellanox in 2019. And they have a flywheel effect where they can invest their enormous profits into more R and D. And the and the thesis of this piece is that all 4 of those are under threat. We'll kinda go through that.
Speaker 1:But just to break those down, Yeah.
Speaker 2:And this is the this is the story of all Yeah. Hyper profitable enterprises in history Yep. Is by having these excess profits, you put a target on your back, and then a bunch of people try to eat in eat in eat into them in different ways.
Speaker 1:Yep. And so there's this interesting thing where, there's this question of if you believe that the future prospects of AI are almost unimaginably bright, like you're a total AI bull, there's still the question of why should 1 company extract the majority of the profit pool from the technology? Oh, NVIDIA. And so the Wright Brothers airplane company in all its carnations across many different firms today isn't worth more than $10,000,000,000 despite them inventing and perfecting the technology well ahead of everyone else. And while Ford has a respectable market cap of $40,000,000,000, that's just 1.1% of NVIDIA's current market cap.
Speaker 1:And so there's this question more
Speaker 2:than fart coin too.
Speaker 1:Yeah. And so there's this question of, like like, you can be super bullish on AI. Yeah. Like, you can you you can make a bear case for for NVIDIA without making a bear case for AI is basically, like, the first the first, you know, point that he's making. And so, you need to look at AMD.
Speaker 1:They make respectable GPUs, which is such an such a slight, that on paper have comparable numbers of transistors, which are mostly using similar process nodes. Sure. They aren't as fast or as advanced as as advanced as NVIDIA GPUs, but it's not like NVIDIA GPUs are 10 x faster or something like that. In fact, in terms of naive raw dollars per flop, AMD GPUs are something like half the price of NVIDIA GPUs. And so there's there's all this question.
Speaker 1:Now a lot of this comes down to talent. So extremely talented programmers in AI, they tend to just think and work in CUDA. And so you hire a guy for 650 k per year is the example here. You're not gonna be like, day 1, hey, we want you to use AMD because it'll help us save
Speaker 2:that percent. Quit. And you'll work at any number of other firms that would allow them.
Speaker 1:Exactly.
Speaker 2:Yeah. And
Speaker 1:then, the other thing NVIDIA is known for is what is known as interconnect. Essentially, the bandwidth that connects thousands of GPUs together efficiently so they can be jointly harnessed. And so a lot of this stuff, they acquired this Israeli company Mellanox back in 2019 for a mere $690,000,000,0.0 for NVIDIA. And this acquisition provided them with their lead industry leading Internet connect
Speaker 2:They should go back and do a historical size gong
Speaker 1:Oh, yeah. Don't worry.
Speaker 2:For that acquisition.
Speaker 1:And and so, like, a lot of a lot of what NVIDIA gets right is not actually the dollar per flop performance. It's all about if you write the if you write the AI software with the great algorithm and you have all the data, the last thing you wanna do is be dealing with a bunch of bugs.
Speaker 2:And I'm sure some analysts back in the day would have said, oh, you know, NVIDIA's profit is gonna be compressed because AMD is undercutting them so hard they'll have to react. Yep. But that hasn't been the case. Yeah.
Speaker 1:And you look at George Hotz who kind of hates NVIDIA. I don't know if he actually hates them, but he's, like, been pushing against them and saying we need open source solutions. We need different options. He was really hoping that AMD would be the would be the option. And so he tried to rewrite some AI code onto AMD, and they had so many bugs in their drivers that he was just like, this is untenable.
Speaker 1:And then Yeah. He got a call from Lisa Su, the CEO, happens to be Jensen Wong's niece or, like, cousin or something like that. And and and she was like, oh, okay. George Hotz is on the case. Like, we'll sort this out.
Speaker 1:Don't worry. We got it. And then, like, a year later, there was another, like, expose on, like and AMD is, like, broken drivers. And
Speaker 2:Yeah.
Speaker 1:And and those exact same news cycle happens where it's like, oh, the developer community is freaking out about how bad AMD is. Lisa Sue's on the case. She's gonna make it happen. And it's like, okay. Like, you gotta show us something at this point.
Speaker 1:Yeah. We've heard that you were saying that you're gonna fix this, but, like, it doesn't feel like it's getting fixed. And so,
Speaker 2:it's funny. AMD, even with an inferior product and inability to react to a pretty important customer, like, if he was if George was successful, that would be the best, you know, in creating this open source solution to layer AMD chips. That would have been the best possible things for their business. Yeah. Yet AMD has continued to do well just by nature of being an elephant in the room even if they're not the largest.
Speaker 2:Right?
Speaker 1:Yep. And so there are there are a couple companies that are trying to take shots at NVIDIA. MLX, Triton, and JAX are undermining the CUDA advantage by making it easier for AI developers to target multiple back ends. So essentially, you write the algorithm for the AI once, and then you can run it on different chips. Obviously, this is a interesting flywheel because LLMs can actually translate between different programming languages fairly effectively.
Speaker 1:So there should be ways to to take your CUDA code and port it to AMD faster. But if the underlying software is broken, doesn't matter if you wrote it correctly. Like, it's not on you. The bug is with the actual hardware maker. And then LLMs are getting capable enough to help port things to all our alternative architectures.
Speaker 1:GPU interconnect helps multiple GPUs work together on tasks like model training. Companies like Cerebras are developing enormous chips. They can get way more done on a single chip. Have you seen Cerebras? It's like so when you make a GPU, you typically, there's a there's, like, a big disk that you might have seen someone hold up.
Speaker 1:Yeah. It's a massive chip, and then that's that's what's called a wafer. And they and and they etch the GPU transistors onto this massive wafer, and then they cut them. And then out of 1 wafer, there might be a hundred GPUs that go into NVIDIA Yep. You know, GPU chips, basically.
Speaker 1:And and and those go into, like, the boxes with all the memory and all the other stuff that's on there. But, and those have gotten bigger and bigger, and you've seen, like you've probably seen those Apple demos where it's, like, the the m 1, the m 1 pro, and it's, like, 2. And then the m 1 max, and it's, like, 4. And that's all just, like, bigger and bigger slices of that wafer. Now Cerberus is saying, like, what if the whole wafer was just 1 GPU?
Speaker 1:Yeah. Well, it could have talked to itself really, really fast because it's all connected, and you'd have much more performance. Now the problem and what they've run into is that if there's a flaw anywhere on that wafer, you have to throw the whole thing away. Whereas if NVIDIA says, hey. Yeah.
Speaker 1:Out of that huge wafer, we're trying to get a hundred things. If there's 3 flaws, yeah. We got 97% yield. That's fine. Yeah.
Speaker 1:And so as you scale up, you you run the risk of of dropping your yield. And and then there's a whole bunch of other problems that that Ceravis has had to work through, but I think the company is still around doing well, and, and we should dig into them more. But, there have been a number. And then there's also a new generation of companies like Etched that are trying to build chips specifically just for the transformer architecture, just for inference, or there's a whole other whole bunch of companies that are doing that. And so those massive NVIDIA margins are a huge incentive for other companies to catch up.
Speaker 1:Microsoft, Amazon, Meta, Google, and Apple, they all have their own internal silicon projects where, basically, they go to TSMC and say, hey. This is the workload that we need. We're doing AI inference, in the cloud. Like, make us this chip. And Google has the tensor processing unit, the TPU, and a lot of these other companies are are scaling up these these internal silicon projects.
Speaker 1:And what's what's what's under discussed is, like, it's no secret that there's a strong power law distribution of NVIDIA's hyperscaler customer base with a handful of top customers representing the lion's share of high margin revenue. How should 1 think about the future of this business when literally every single 1 of these VIP customers is building their own custom chip specifically for AI training and inference. And so, like, obviously, you know, Meta, Google, Amazon, Apple, Microsoft, these companies are the ones that are saying, oh, yeah. We want a hundred thousand GPUs. We want a million GPUs.
Speaker 2:Yeah. And it's funny it's funny. So so NVIDIA has the margins of a luxury product
Speaker 1:Yep.
Speaker 2:But they are building a product that goes and sits in a data center
Speaker 1:Yep.
Speaker 2:And nobody sees it. No no no consumer is gonna say, well, I actually really want my model to be running on NVIDIA chips because, like, so brand matters in the sense that the NVIDIA brand is stands for high quality software and hardware and all that stuff, and Jensen is signing some girls, you know, breasts, right, or or signing something on, you know, remember that shot?
Speaker 1:Oh, yeah.
Speaker 2:That was that was that was close to the top. Yep. But, but but now they're in a position where, again, comp companies wanna pay for the result.
Speaker 1:In hindsight, it was obvious. NVIDIA down 15%, and Jensen is signing someone's shirt, but it's it's edgy. It's very rock star of him.
Speaker 2:Yeah. Great moment in history. Certainly. But, again, consumers don't care
Speaker 1:No.
Speaker 2:That where their, you know, the silicon that their their query is running on. Ultimately, the hyperscalers don't care either. They just want the product experience, right
Speaker 1:Yep.
Speaker 2:Yep. For the result.
Speaker 1:And so And
Speaker 2:so that's a that's a you know, the reason that Yeah. That Hermes has durable margins across 200 despite making 90% as well Yep. Is that people wanna wear they wanna gift, you know, their loved 1 an Hermes product. Right? Same thing with Rolex or Patek or any of these other brands.
Speaker 2:So just a different category.
Speaker 1:And so, the last quarter of the article talks about the seismic waves rocking the industry right now caused by DeepSeq v 3 and r 1. V v 3 remains the top ranked open weights model despite being around 45 x more efficient in training than its competition. Bad news if you were selling GPUs. R 1 represents another huge breakthrough in efficiency both for training and inference. The DeepSeq r 1 API is currently 27% 27 times cheaper than OpenAI's o 1 for a similar level of quality.
Speaker 1:And so again, I don't think this was a 0 to 1 innovation with DeepSeek. I think it's very much China is good at taking something that already has been invented, making it cheap, and that's what they've done. And and when we when we deep dove the company a little bit, we saw that, like, what these guys are are high frequency hedge fund quant traders. They're really good at writing optimized code, and it seems like what they took was OpenAI has done a bunch of innovations with transformers, which didn't come from OpenAI, but then were, you you know, popularized by them, the reasoning model, the chain of thought stuff, all this stuff. And DeepSeek, the team was able to just bake it down.
Speaker 1:And the reason this is like, the the reason this might be bearish for NVIDIA is that if you can bake down this model to a point where, yeah, actually, it's so simple that we can get it to run on AMD, people will. Yeah. And then AMD might and then and then it becomes more a price war. It's very different when you're like, look. Whoever gets to GPT 4 level models first will have market entry into this new chatbot era, which is exactly what happened.
Speaker 1:Yeah. Spare no expense.
Speaker 2:Yep.
Speaker 1:Pay pay the 90% margins to Nvidia. The super cluster.
Speaker 2:The interesting thing about, the foundation model wars right now is everybody's racing and and raising against ASI. Right? Investors are saying, we will invest in OpenAI at a hundred and $50,000,000,000 even with the massive losses. Because if they achieve ASI, it's gonna be broad. It's gonna be incredibly valuable.
Speaker 1:Yep.
Speaker 2:Yet they're simultaneously all giving away that technology, not, like, necessarily the bleeding edge
Speaker 1:Yep.
Speaker 2:But they're basically giving it away to everyone, which is an interesting place to be. Yep. The other dynamic that's fascinating with with r 1 is they're they're making these crazy claims. Right? They trained it on with $5,000,000.
Speaker 2:It was a small team. It was a side project. They they simultaneously put out so much research on it that that clearly stands up. Right? I was I was messaging with Spore this morning and WordGrammar and some other
Speaker 1:Yep.
Speaker 2:Accounts on x that are that are kind of digging into the to the actual paper and and and trying to understand it. And they're all like, yeah. It stands up. Seems, like, very legit. Yep.
Speaker 2:So it's this simultaneous thing of, like, putting up out huge amounts of research that seems, like, legit and, like, real breakthroughs, but also probably lying about a bunch of stuff. Right? Yeah.
Speaker 1:Yeah. Says in
Speaker 2:the article here
Speaker 1:Both can be true.
Speaker 2:Yeah. Both can be here. Both can be true. He says in the article, who knows if any of this is really true or it's merely some kind of front for the CCP or Chinese military? And I I wanted to give an example, that that I thought that I thought was funny.
Speaker 2:So so in in in my experience in China, I don't think, like, people will, you know, lie potentially slightly more than in western culture.
Speaker 1:Yep.
Speaker 2:Who know I mean, you know, I'm sure somebody will try to correct me on that, but I had a funny story. When I was studying abroad in China, my professor said, hey. Like, got kind of a weird opportunity for me and my buddy who's who's actually six ten, so taller than you could mock you. He's like, hey. So, like, of a weird opportunity to
Speaker 1:meet you. Did that in China? He was, like, six, six before?
Speaker 2:Yeah. Yeah. You got the extension.
Speaker 1:You got the extension?
Speaker 2:Yeah. I
Speaker 1:might have to go.
Speaker 2:No. So this is a crazy story. So our professor, who's from The US, Bay Area, he goes, hey. I got kind of a weird job. Like, train ticket, hotel page, $500 for, like, to work tomorrow.
Speaker 2:So we go out to this tiny village, and our professor takes us to meet what ends up being, like, the local, like, state government.
Speaker 1:Mhmm.
Speaker 2:And they paraded us around pretending that we were English teachers because that state was, like, top bottom 3 in terms of, like, learning capabilities and, like, standardized testing. And they filmed the entire thing and used it to imply that they were bringing in all these international teachers, but, like, we were not teachers. We were literally, like, paid actors. Wow. And we had, like, we had, like, dinner and, like, lunch
Speaker 1:Yeah.
Speaker 2:With, like, the state, like like, the the I I forget his formal title, but it was effectively, like, the governor.
Speaker 1:Yeah.
Speaker 2:And they they were made this whole basically, like, video about how, like, look. We're bringing in these, like, foreign, like, educators and all the stuff. It's completely made up. They just paid us cash, like, literally in an envelope. Like, thank you.
Speaker 1:Deeply fraudulent.
Speaker 2:So, like, I was just, like, a paid actor, foreshadowing, you know, being a a newscaster here.
Speaker 1:Deeply American too.
Speaker 2:Yeah. And and,
Speaker 1:the most bullish thing I've
Speaker 2:ever heard. But it would literally they take they take me into a classroom with all the kids. Yeah. And they would have me just, like, point around and, like, talk to the kids. Wow.
Speaker 2:But, like, none of the audio is captured, and it apparent, I never actually got access to the clip, but they were just using it as, like, a marketing to be able to show the more senior, you know, people, you know, presumably, the the the sort of federal level of the government that they were, like, making strides
Speaker 1:Yeah.
Speaker 2:And, like, actually taking it seriously. Wow. And so I just when I whenever I see anything coming out of these, you know, a Chinese lab, I just, anytime you see something coming out of a Chinese lab, you gotta ask some questions.
Speaker 1:Ridiculous. Okay. So so
Speaker 2:Someone else said the model it's we don't know, but the model could have been trained by a bat. Yeah.
Speaker 1:Yeah. So so so back to, like, some of the there is this interesting, like, just, like, cognitive dissonance between the fact that, like, you can go use r 1 and see that it's good. Like, it's not controversial to just say, like, the model works. Like, they clearly copied effectively. The question is just, you know, there's some questions about cost and, you know, are they subsidizing it?
Speaker 1:How much does it actually cost to inference? Is it really that much cheaper than the current stuff? But this type of model compression happens all the time. This happened with do you remember those, those AI avatars? Yeah.
Speaker 1:So those AI avatars came through this pathway of, something called I think it was, like, called, like, ControlNet. And then and then, Google launched a paper, but they didn't open source the code called, like, DeepDream or something, DreamFusion or something like that. And then someone implemented that paper, but it needed to run on, like, a cluster of GPUs to do it. So you would upload a bunch of photos of yourself and then a bunch of photos of the of, like, the style that you wanted, and then you could prompt it. And it was all built basically on, like, stable diffusion.
Speaker 1:So I think it was called, like, deep diffusion or something like that or or stable dream stable, stable dream, something like that. Anyway, and then slowly people figured out that they could compress the model more and get better results just by being a little bit more memory efficient here. And then eventually, it came to the point where you could run it on a single graphics card and get pretty good results because it had been optimized so so much. Yeah. And there was actually this guy, Joe pen Joe Pena, who was, like, a guitar YouTuber who just got really into this stuff, and he wrote a lot of the code and optimized it.
Speaker 1:It was crazy. It was a crazy story. Anyway, so for a while, it was like it was like you had to be at a at a top lab with, like, a server farm to do 1 of these, like, AI avatars. Then, like, a couple weeks later, it was like, if I went and rented a single server for, like, a couple bucks an hour, I could do it. And I did it for, like, me and my friends, and everyone was like, this is so cool.
Speaker 1:How'd you do this? This is amazing. And then, like, two weeks later, that app came out. I forget what it was called, the AI avatar app. It was really popular for, like, a week, and then
Speaker 2:Snapchat had their own?
Speaker 1:Yeah. Yeah. And then, eventually, Meta launched it. Right? And so there was this there was this, like, there was this very quick optimization implementation process, but it all happened in America.
Speaker 1:So it wasn't, like, controversial. It was just like, oh, cool. Like, that thing that I saw papers about is now just on my phone. Right? And so and so it just seems like they've done a lot of that.
Speaker 1:It doesn't seem like there's any new breakthroughs here. And I think the people that are giving them credit for inventing chain of thought reasoning is, like, wildly wrong. It says a major innovation of in their But as the same Yeah.
Speaker 2:Here here's so Yeah. You're right. At the same time, this stood out to me. The newer r 1 model and technical report might even be more mind blowing since they were able to beat Anthropic to chain of thought and are now basically the only ones besides OpenAI who have made this technology work at scale.
Speaker 1:That's not true, though. Like like, Anthropic does have a chain of thought model. They just haven't released it publicly. And the reason for that is just financials and and, like, their safety stuff.
Speaker 2:Yeah. For sure.
Speaker 1:Like, I'm not impressed by that. I I don't know. That just that just doesn't seem that just that just doesn't seem that revolutionary.
Speaker 2:I know. I know. I know. That said, if you're a VC who has billions of dollars philanthropic, you're calling the CEO yesterday probably screaming at them. Right?
Speaker 1:Sure.
Speaker 2:We're bringing screaming back to the workplace. And,
Speaker 1:Yeah. And so in terms of the actual, it does seem like there was some innovation here in the same way that, like, when you when when China takes, like, a shoe that's made in America by a single person and then they have, like, a machine make it in a factory, like, that is somewhat innovation, but that's not 0 to 1 innovation. That's 1 to many. That's scaling and price, which is, like, what they do really well. They did have a couple novel implementation details.
Speaker 1:They switched to 8 bit floating point numbers, so it's, through the track training process. So it's it's more, it's more memory efficient. They developed a clever system that breaks numbers into small tiles for activations and blocks for weights. So instead of just using, like, a single word for a token, they use multiple blocks. They also cracked but then this is the part that really frustrates me to say, with r 1, DeepSeq essentially cracked 1 of the holy grails of AI, getting models to reason step by step without relying on massive supervised datasets.
Speaker 1:Their DeepSeq r 1 experiment showed something remarkable. Using pure reinforcement learning with carefully crafted reward functions, they managed to get models to develop sophisticated reasoning capabilities completely autonomously. This wasn't just about solving problems. The model organically learned to generate long chains of thought, self verify of work, and allocate more computation time to harder problems. And so they they did do some things where they changed the reward function, like and so basically, like, if you have it work on math problems that it can formally verify with, like, a calculator, then it's like it can work through those and then just check its work and be like, okay.
Speaker 1:I was thinking correctly. Yeah. And so this is the same way that Lisa Dahl got beat by, by AlphaGo because AlphaGo was able to play Move
Speaker 2:37.
Speaker 1:Move 37, was able to play, you know, trillions of games in in synthetically, like, just with itself. And and that's where it generated all the training data. And so it seems like r 1 did the same thing, but even this is not new. Like, if you go back two years ago when, Sam Altman got fired from OpenAI, there was all this question two years ago. It was it was the
Speaker 2:That's crazy.
Speaker 1:So this so it happened, like, So a year and
Speaker 2:a half. Yeah.
Speaker 1:Yeah. Yeah. But, but there was all the there was all these questions, like, what did Ilya see? You remember this whole thing this whole meme? And so there the the information tried to answer that, and they wrote this article that you'll see is, like, okay.
Speaker 1:They were building this a year and a half ago. And it says, 1 before he was fired by OpenAI's board last week, Sam Altman alluded to a recent technical advance the company had made that allowed it to push the veil of ignorance back and the frontier of discovery forward. It's like good marketing lingo.
Speaker 2:Marketer. Yeah.
Speaker 1:But but what he actually says is, is, they used Ilya Sutzkever's work to build a model called QSTAR that was able to solve math problems that it hadn't seen before, an important technical milestone. Because because LLMs are great at memorizing stuff, but they're they have historically not been good at solving new problems. A demo of the model circulated within OpenAI in recent weeks and the pace of development alarmed some researchers focused on AI safety. The work of Susco SuscoVerse team, which was not previously been reported and the concern inside the organization, suggested that tensions within OpenAI about the pace of its work will continue even after Altman was reinstated as CEO Tuesday night. And so this Q Star thing, there's, like like, they don't really have that much here, but it says, Sutskiver's breakthrough allowed OpenAI to overcome limitations on obtaining enough high quality data to train new models according to the person with knowledge, a major obstacle for develop next developing next generation models.
Speaker 1:What is QStar? QStar became, there was, like, that Apple group. Do you remember this thing? It was, like, the the strawberry emoji was, like, a big thing. Strawberry gang, and they were, like, OpenAI strawberries.
Speaker 1:Now the code name they renamed the code name, like, 4 times, and then it finally came out. It was o 1. Yeah. And so we've we have this. Like, this is this has launched, and it didn't kill anyone.
Speaker 1:And, also, it's, like, kind of a nice to have. Like, you don't even need to use it all the time. And so, basically, you know, for years, Sutzkever had been working on ways to allow language models like GPT 4 to solve tasks that involved reasoning, like math and science problems. In 2021, he launched a project called GPT 0. What did they launch?
Speaker 1:Deep seek r 1. They're 2 they're a year and a half behind. Like, this is not this is not innovation here. Yep. And and so It
Speaker 2:does seem that the primary innovation is the cost Yep. And giving it away for free Yep. Which they've done with DJI. And
Speaker 1:Yep.
Speaker 2:And we I think we had a post from somebody pointing out that there's this, like, illusion of choice
Speaker 1:Yep.
Speaker 2:Where it's super, super cheap if you use
Speaker 1:Yep.
Speaker 2:The Chinese Yep. Sort of, inference Yep. Basically. And then it it gets dramatically more expensive Yep. As you,
Speaker 1:And so I haven't seen anything. Maybe I'm wrong. Maybe there's something else deep in the paper that looks great and is a true breakthrough. It's open source. The paper's out there.
Speaker 1:So that will get ported back to Llama, to OpenAI, to Anthropic, but I haven't seen that. What I've seen is a ton of optimization that went in to taking stuff like GPT 0, which became q star, which became strawberry, which became o 1. And they took o 1, and they did a bunch of self training, which is scary because the AI is talking to itself and creating it's getting smarter by itself in some ways because it's Yeah. Kind of like what happened with, AlphaGo. But, they took that, and they optimized they completely optimized the model so it will run on much cheaper hardware, and the inference will be cheaper, which is important because the test time compute scaling law is new.
Speaker 1:And right now, you heard Sam say, we're losing money on some o 1 users because
Speaker 2:Yep.
Speaker 1:They write a query, and it thanks for five minutes. And that's a whole server rack firing for, you know, a couple bucks. And so, and so
Speaker 2:One one one relevant example. So it says, the recent scuttlebutt on Twitter and blind, is that these models caught Meta completely off guard and that they perform better than the new llama 4 models, which are still being trained.
Speaker 1:Yep.
Speaker 2:Ouch. Apparently, the llama project within meta has attracted a lot of attention internally from high ranking technical executives, and the result is that they have something like 13 individuals working on the llama stuff who each individually earn more per year in total compensation than the combined training cost for DeepSeq v 3 models, which outperform it. Again, we don't actually know. Yeah. Alex, from Scale is saying, you know, that the training cost was was way higher.
Speaker 2:Said, how do you explain that to Zuck with a straight face? He's gonna tell them to frick off.
Speaker 1:Again, I I I think that's completely keep
Speaker 2:how does Zuck keep smiling while shoveling multiple billions of dollars to NVIDIA to buy a hundred k h 100 to win a better model with strain using just 2 k h 100 for a bit over 5,000,000. So it does per it it does, this is why last night, Satya is going out there.
Speaker 1:Yep. A lot
Speaker 2:of people are about to, learn about Jevon's paradox. Some people are calling it Jevon's paradox.
Speaker 1:No one's calling it that. Only if you're extremely online and you've got and you're not listening to podcasts about it.
Speaker 2:Yeah. Yeah.
Speaker 1:You might be mispronouncing it. But but yeah.
Speaker 2:And and then it goes on. But you better believe that Meta and every other big AI lab is taking these deep seek models apart, studying every word in those technical reports and every line of the open source code they release, trying desperately to integrate these same tricks and optimizations into their own training and inference pipelines. Yep. So what's the impact of all that? Well, naively, it sort of seems like the aggregate demand for training and inference compute should be divided by some big number.
Speaker 2:Maybe not by 45, but maybe by 25 or even 30, because whatever you thought you needed before these model releases, it's now a lot less.
Speaker 1:Sure. So I I think this this highlights that, like, a good AI model is no longer just, like, how big are the parameters and what are the weights and, like, what does it do by itself? Like, there's actually a stack of capabilities that you need to think about. So at the bottom, yes, you need a robust model, and that's what their DeepSeq v 3 is. And that's what he's talking about when he talks about LAMA 4.
Speaker 1:LAMA 4 I mean, I don't even know what they're terming that phrase, but I assume it's just the underlying core model. And, yeah, that might be underperforming or not or might be too expensive to train or something. But a lot of that probably has to do with the fact that they just haven't like, if if, if DeepSeek trained on GPT 4 output tokens, then all the data has already been kind of cleaned because it's only training on, like of course, it's gonna sound like GPT 4 because it doesn't have any junk in there. It's not gonna accidentally sound like, you know, some spam on the Internet because that was already cold when you pull Yeah. The data from the model.
Speaker 1:And so there's that. Then level 2 is like the chain of thought. How good is your reasoning model on top? We know o 1, o 3, and r 1 all seem to be pretty good on top of the base model. So I don't even know what Llama is doing.
Speaker 1:Are they gonna release an o like, an o 1 competitor with Llama 4? They should because, clearly, test time compute's extremely important. But then you open source this, and then you gotta inference it, and that's really expensive. And so but they'll probably do that. But they so they might be comparing apples to oranges there, comparing LAMA 4, which is just a base model, to a reasoning model, which is not fair.
Speaker 1:And then there's all the UI and and and orchestration that happens on top of it, and I think the ChatGPT app is certainly better than the DeepSeek app right now just in terms of Yeah. When you open up DeepSeek
Speaker 2:Well, that's all I people are saying DeepSeek is very much targeted at developers. Sure. Which which is, you you know, if if you're China and you're seeing this explosion of new app layer products and you're saying, hey. Long term value will occur to the app layer. Yep.
Speaker 2:Why not release a product that any developer can use to deliver cool experiences to their end users?
Speaker 1:Yep.
Speaker 2:It's like, hey. We don't have to worry about building consumer products because every Chinese app feels like a Timu
Speaker 1:Yeah.
Speaker 2:Timu software for the most part. Yeah. It's actually very smart to be like, hey. Instead of using OpenAI, which your consumers don't care about what's under the hood either, use our
Speaker 1:Yeah.
Speaker 2:Basically free app.
Speaker 1:Yep. And so it feels like it's it's it's disruptive in the sense that in the way that Llama was disruptive where a lot of people that were like, my OpenAI API bill is really high, and thank you, Zuck. You just gave me a free version that I can Yeah. I still have to pay inference cost, but I can just host it on AWS. And now it's way cheaper for me, and this will drive the cost down even further.
Speaker 1:So hugely disruptive to the b 2 b space.
Speaker 2:It's it's disruptive to so many different narratives as well. Right? Where the fact that Satya on a Sunday night feels like he needs to to tweet out links.
Speaker 1:Yes.
Speaker 2:Zuck, earning season. He's gotta justify why are we spent why are we buying a hundred thousand of these again? Yep. And Zuck is also no he he it's not like he hasn't gone back on big infrastructure spending before. Like, the metaverse, everybody's like, dude,
Speaker 1:you're an idiot. Like, stop trying
Speaker 2:to make stop trying to make the metaverse a thing. And he's like, okay. Like, he eventually, like, got it Yep. And diverted all that budget back to Yep. AI CapEx.
Speaker 2:Yep. And so now I'm I'm actually very interested to see Jensen come out and talk about this because he's gotta get shareholders confident that the demand is still gonna look like what they've been projecting. Right?
Speaker 1:He just did a new interview, but I think they already filmed it. So I don't think it's gonna cut Yeah. I I think it's gonna drop and not have anything related to Deepsea. But, the interesting thing is, like, is, like, we we keep going back to, like, OpenAI versus meta. Obviously, like, distribution is really important.
Speaker 1:And the question is, like, can DeepSeek figure out a way to get distribution? Even barring all of the, like, oh, it might get banned or there's, like, CCP stuff going on. Just in in the knockout drag out fight, the
Speaker 2:dollar tariff
Speaker 1:on a free
Speaker 2:query.
Speaker 1:Yeah. But even even just if you if you look at the if you look at the, the App Store as, like, a free market, like, there's already a lot of people that have ChatGPT installed. They have the free version. You bring them o 1, and you've changed the reasoning logic so the UI is the same. And all of a sudden, it's like, yeah.
Speaker 1:I'll just stick with what I know. It's already it's a better app. It's already installed.
Speaker 2:DeepSeek was very clever in basically, like, building the API infrastructure to just immediately be able to switch. It's like they just copied OpenAI Yeah. On that front too.
Speaker 1:Totally. And then and then also, you know, you're still competing with, like, the the average AI consumer might wind up just using Google. Or oh, yeah. Like, yeah. When I when I wanna talk to an AI, I just go into Instagram because, like, llama's there, and they don't even know what llama is.
Speaker 1:Spending
Speaker 2:two hours.
Speaker 1:Yeah. I love I love Meta AI using it through WhatsApp. Like Yeah. There are people that do that. There are billions of people that do that.
Speaker 1:It's crazy. And and so, yes, like, tech Twitter is very much, like, of course, like, everyone's gonna download, like, the best model because it performed 2% better on this email. Like, no. Not necessarily.
Speaker 2:Here's the thing that made it really obvious that that the the, going number going number 1 in the App Store Yeah. Was, like, legit in any way. The the the developer name of the app is, like, just a really long string of Chinese characters, and I just promise you that them sitting there with 227 reviews
Speaker 1:The review thing's weird.
Speaker 2:And and American consumer this looks this screams to me. Yeah. Most of the time I see Chinese characters, it's it's some, like, spam Bot. Yeah. Bot text that I just got, and I'm like, okay.
Speaker 1:I mean, if you search for deep seek in the App Store, you will see, like, ChatGPT comes up, but then also, like like, ChatAI, and it looks exactly like ChatGPT. And it's a ChatGPT wrapper that's just taking, like, revenue basically from them and just acting as, like, like a like a portal just to a chat app. And so, obviously, the the the the App Store rankings are momentum based. And so Yeah. I think I think Deepsea did have a ton of momentum, so they did get to the top of the charts.
Speaker 1:I don't know that that's durable. And, also, Nikita was saying that there might be a lot of bots promoting it to try and rank in the App Store. But I just have a really hard time to believe that, like, this
Speaker 2:China literally has is notorious for having you can go there.
Speaker 1:Yeah.
Speaker 2:You can go to China Chinese firms to buy Internet traffic and download anything. Totally. So just diverting some of that those resources to, hey. Let's go number 1 in the charts because I I came away from this thinking in many ways. They like, if this is a front for the CCP, which I won't grab the hat, just creating economic chaos in The United States and being like, hey.
Speaker 2:There's a lot of leverage in the system.
Speaker 1:Yep.
Speaker 2:All these firm you know? Yep. France is lending to to data data center development in The US. A lot of real estate guys are saying that that's, you know, usually a bad sign when the French get involved. And, but, but yeah.
Speaker 2:So so if you were just looking at it as, hey. Let's let's lob an economic grenade Yeah. Over to The United States, make the APCO number 1, kind of, like, just just make everybody freak out and kind of be distracted. Right?
Speaker 1:And also, like, taking off the conspiracy hat. Even if even if we were just talking about, like, an ally, like, let's say Deepsea came from Japan, and they were just trying to compete. It's very logical to say, hey. We were able we had some cracked engineers who were able to drive the inference cost way down with a bunch of innovations, which are real. Yeah.
Speaker 1:Let's try and release this app that's as good as the $200 a month chat GPT app for free, get get a bunch of people to use it, and then get all that RLHF data. Yeah. So then they can use that to fine tune their models because there's this big, like,
Speaker 2:data Look at what China is doing. Right? They just announced the equivalent of, like, $500,000,000,000 of new Yep. Like, state funded
Speaker 1:Yep.
Speaker 2:Investment. Yep.
Speaker 1:Right? And that could easily be going in. Yeah. Yeah. Well, no.
Speaker 1:It's not just private capital. Yeah. Yeah. It it would still make rational
Speaker 2:sense. If if if we no longer needed all this extreme CapEx, China wouldn't be launching the free app Yep. And then not doing that. Right? They clearly
Speaker 1:Yep. No. No. It it it it it's totally reasonable. But
Speaker 2:So let's should we should we
Speaker 1:finish that? Timeline.
Speaker 2:I was just gonna say,
Speaker 1:I don't know what else you wanna close out on,
Speaker 2:like, why so at the high level, NVIDIA faces an unprecedented convergence of competitive threats that make its premium valuation increasingly difficult to justify at 20 x forward sales and 75% gross margins. The company's supposed motes and hardware, software, and efficiency are all showing concerning cracks. The whole world, thousands of the smartest people on the planet backed by untold billions of of dollars of capital resources are trying to assail them at every angle. And so, yeah, perhaps most devastating is DeepSeek's recent efficiency breakthrough, achieving comparable model performance at approximately one forty fifth of the compute cost, which again, we don't know if it's real. But anyways, it'll be interesting to play out.
Speaker 2:NVIDIA's down 15%, last time I checked public, and we'll see where they are tomorrow.
Speaker 1:Let's go to some hot takes about DeepSeek, keeping you up to date on what happened on the timeline. I wanna start with this, short thread by Dylan Field, the founder of Figma, Dylan Field. He says, I guess it's hot take time, so here we go. I love this. 1, always assumed there would be a reckoning moment in public markets over CapEx spend for AI.
Speaker 1:2, it will take a lot more share price punishment for any of these companies to reconsider the number of GPUs they are buying in 2025. 03/00, there are likely order of magnitude improvements to training and inference available, though not enough not though not necessarily achieved yet. 4, DeepSeek trained on outputs of American models, which we've discussed. 5, it would be surprising to me if DeepSeek's claims about training costs were true. 6, from a public safety standpoint, an open source model of unknown alignment is an extremely interesting and challenging threat vector.
Speaker 1:We talked about this a little bit, like what if embedded in the model, it, like, tries to change your political philosophy very slowly.
Speaker 2:Already already so perplexity integrated
Speaker 1:deep sea
Speaker 2:Really? Into into you can you can opt to use the model.
Speaker 1:I mean, people have wired it up to cursor immediately.
Speaker 2:Ask the, depending on which model you select, if you select, DeepSeek and you ask it about Tiananmen Square, it'll be much, much sort of, like, more
Speaker 1:more Wasn't that big of a deal. Just calm down.
Speaker 2:That's exactly how they position it. It was like they don't talk about they they really, really downplay it. So so they admit that it's a thing Yeah. But they don't admit that it was a disastrous
Speaker 1:Actually, let's talk about Kent State for a minute. Okay? Yeah. Yeah. Like, you know, like, right back at you, American.
Speaker 2:Yeah.
Speaker 1:7, if DeepSeek's mobile app continues to top charts, it will join TikTok in the discussion in The US. We need to block this app discussion. I think it's already there.
Speaker 2:Yeah. One one thing that's interesting is when TikTok started to chart because they were spending they Yeah. Not only people love the app, they were starting to spend a ton of money on user acquisition. The app and especially the video feed was, like, fundamentally better than the alternatives. Yep.
Speaker 2:And so they were spending all that money on user acquisition to drive downloads, but then consumers got a better experience.
Speaker 1:Yep.
Speaker 2:Now consumers are like, well, I already have chat gbt. This doesn't do any net nothing net that's sort of, like, net new for me Yep. Especially for the average person who's like, make me a
Speaker 1:It does if you don't have a premium subscription on on ChatGPT. Like, if you have ChatGPT free version and you download DeepSeek, that is a massive upgrade. If you're a
Speaker 2:free user But if it but if the average request is, how do I make a recipe with these 3
Speaker 1:items Yeah. Then it's definitely not showing users in my opinion. I agree. I agree. And and I do think,
Speaker 2:I I
Speaker 1:mean, Sam all Sam already addressed it and said that he's gonna bring a a a set number of reasoning o 1 queries, which are expensive, to free to the free tier. Yeah. And so this this is a this is certainly like a financial change, but in terms of in terms of just retention and user adoption, like, it's not insurmountable. New Moon Capital says, so just so I understand, people are bearish on AI because deep seek innovation improved efficiency by 30 x. And with that and and that with larger clusters and continued scaling of synthetic data and inference compute, next generation models are gonna be, like, a hundred x better than o 3.
Speaker 1:So AGI is bearish for AI. Got it. And it's a good point. Yeah. I mean, all of those all of those innovations, even if they weren't open source, I mean, they get ported back so fast because there's only a few secrets, like eight eight bit, floating point numbers instead of 32 bit.
Speaker 1:Oh, that can save a lot? Like, someone's gonna try that eventually. And I think most of this stuff is probably, like, either in the pipeline to change. I mean, we we we talked about, like, the the test time inference is gonna get so much cheaper when this is baked down into silicon, but we're just not there yet because we're updating models every year.
Speaker 2:It it the the thing that's most fascinating to me is all these model companies, miss Mistral, Anthropic, Cohere, that really don't publicly have these capabilities Yep. Where now anybody employed at those companies basically shouldn't sleep Yeah. For the next however long it takes to to get on par with the the free model. Otherwise, you almost don't have a right to exist in many ways.
Speaker 1:Yeah. It's a good point. Scheele says over 1 of NVIDIA sales go to China, probably 40,000,000,000 last year. The Singapore backdoor is real. NVIDIA even says shipments to Singapore are insignificant, while 22% of billings last quarter were to Singapore.
Speaker 1:And so pretty pretty staggering numbers. Like, so they can sell to China just certain chips, different chips, which, again, like, they don't work for the biggest training runs. But if you have a team like DeepSeek that can optimize around it and say, oh, memory bandwidth is a problem with the nerfed GPUs. All of a sudden, it's like, okay. Just buy a trillion of these chips that are And
Speaker 2:this is you know? This is the interesting dilemma that, people in The US face, right, is is everybody's holding a bunch of NVIDIA. Right? The entire market's basically propped up by NVIDIA.
Speaker 1:Yep.
Speaker 2:And so you kinda wanna be mad at Nvidia for saying, you know, why are you providing, you know, chips to our political enemy? But at the same time, it would tank, you know, it would They
Speaker 1:might sell it.
Speaker 2:Cut the market cap in half, maybe. Yep. Yep. Right? And we talked about, I think, Friday about potential backdoors.
Speaker 2:So Singapore
Speaker 1:Singapore.
Speaker 2:Could be 1 could be anywhere in the world. Right? It could be, I
Speaker 1:mean, the numbers to Singapore are staggering here. In the three months ended Oct. 27, 2024, so, like, deep chip band just not last quarter, but the quarter before $770,000,000,0.0 of chips to Singapore. And it's like, Singapore's not buying that many chips. Yeah.
Speaker 1:Yeah. And I think, I think on 1 of the shows, I was a little skeptical of this backdoor, and you were much more bullish on it. And I think you're a % right seeing the data there. Philip Lafont says, should open should AI models be allowed to be open sourced? Do you know this guy?
Speaker 1:What was what why why this 1?
Speaker 2:So this is the founder of Co2 Management. Oh, yeah. And he, I believe, has pretty large exposure to OpenAI. And so this post went viral because last night,
Speaker 1:I think
Speaker 2:he he probably was enjoying his weekend. Yep. He sees the news on, Deepsea. He's really, really not happy with, Yeah. And it's it's a funny it's it is a pretty funny question.
Speaker 2:There there was a reason that OpenAI shifted from being open source and Yep. And innovating for the world to innovating for themselves and trying to do sort of
Speaker 1:Yeah.
Speaker 2:Rent seeking behavior. Yeah. And this is what, this, Chris from, what's Chris Pike?
Speaker 1:Mhmm.
Speaker 2:Pike?
Speaker 1:Yeah.
Speaker 2:Yeah. He talked about he's talked a lot about how AOL was trying to basically build a closed Internet that they they could basically, collect a toll on and how that really didn't work. And his point of view is that OpenAI is is the AOL of of AI. We have no idea if that you know? And and, anyway so we don't know, but I I think it's pretty funny.
Speaker 1:Yeah.
Speaker 2:Still funny question to be asking yesterday.
Speaker 1:The investor. Yeah. Yeah. Let's get to Martin Shkreli. He says, it took Wall Street 1 Month to read this Carpathi tweet.
Speaker 1:And a month ago, Carpathi tweeted, DeepSeek, the Chinese AI company, making it look easy today with an open weights release of a frontier grade LLM trained on a joke of a budget. Twenty forty eight GPUs for two months. And, Wall Street kind of picked up on it today with the r 1 release. Dali Bali says, Jensen gonna have to get on a podcast this week. And that's true.
Speaker 1:It's very true. I I wanna hear what he has to say.
Speaker 2:The the market needs, Yeah. Needs to be comforted.
Speaker 1:For sure.
Speaker 2:I wanna hug, you know, I wanna hug from Jensen Yeah. With the leather jacket on. You wanna, you know, kind of, like, know that it's gonna be okay. Yep. And and I think he should avoid signing women's t shirts
Speaker 1:this week. Yeah. Yeah. Just No more top signals.
Speaker 2:Yeah. Yeah. No more top signals.
Speaker 1:Just just really explain to me, like, you know, minor improvements in CUDA. Talk to me about the bandwidth interface problem. Yeah. Yeah. So this
Speaker 2:and there was it's funny. The, what's the guy, that's always posting top, you know, the top signal guy at CNBC?
Speaker 1:Okay. Lot lots of people that do that.
Speaker 2:Kramer Kramer Kramer. So Kramer posted five days ago, like, OpenAI is or or sorry. He's like
Speaker 1:Unstoppable.
Speaker 2:He's like, NVIDIA is, like, unstoppable. Of course. But if you actually the the the thing about Kramer that I don't like, people that just sort of, like, only see him when he's getting dunked on, he posts that stuff about every company all day long. So and and it's bear bullish, bearish, bullish, bearish. So he's just, like, cycling back and forth.
Speaker 1:So Yeah.
Speaker 2:I don't think it's a signal that
Speaker 1:There was actually, like, a, like, an economic study on Kramer, and they found that he did beat the market over a pretty long period of time, but he did it, basically with high beta. So just he was just, like, leverage long the market. And so his his ups were really good and then his downs were really bad. But on net, he still outperformed. So there's There's
Speaker 2:always a bull market somewhere.
Speaker 1:There's always a bull market somewhere. Let's go to Logan Bartlett. He says, so wait. China actually thinks they can succeed with a low lower cost rip off of an American product? Good luck with that.
Speaker 1:It's a good point. This is what they do. It's the teal thing of, like, America's been good at 0 to 1 innovation. China's good at 1 to many. And and we are in the 1 to many phase very clearly of AI.
Speaker 1:And I think people were hadn't really taken that to heart. There's still the question about consumer adoption and and is there a monopoly of power to accrue on the consumer side of the application layer? But certainly on the on the foundational swap hot swappable LLM tech, Pretty pretty commoditized. Yep. Daniel says finance guys are like, I effin' knew you nerds were full of s, h I t.
Speaker 1:I don't know how to not curse anymore. I'm trying to not curse the show, about needing that much money. That didn't come across well at all. We'll have to work on that. Tom says, Apple's AI is so bad, they don't even include it in the AI sell off.
Speaker 2:That's I
Speaker 1:mean, maybe that's a bull case for Apple. They didn't, like, go too hard and, like, tell that whole story. Like, they they they they told
Speaker 2:us what you've said before. They have the distribution, so they can kinda sit back and wait to see how things pan out. They can partner with OpenAI and say, you can be our AI provider, but you gotta pay us.
Speaker 1:Yeah.
Speaker 2:You know, some egregious More,
Speaker 1:like, more scapegoat. They don't pay each other. No. I know. There's no money changing hand changing hands.
Speaker 1:But anything that goes wrong, they can just be like, oh, it's like OpenAI's problem. Like, it wasn't it wasn't us.
Speaker 2:No. But, presumably, in the future, they could.
Speaker 1:And then OpenAI gets a lot of data,
Speaker 2:hopefully Yeah.
Speaker 1:If they can do that. There was just a there was just a deep dive on, the the new Siri, and they asked it, like, who won the Super Bowl in 1989? Who won the Super Bowl in 1999? And it got, like, every single 1 of them wrong, while ChatGPT didn't. So there's, like, something very odd going on.
Speaker 1:And and, like, the previous version of Siri could do this just because it would just be like, oh, Super Bowl stats. Look it up in the database.
Speaker 2:They basically need to create a new name for Siri if they wanted to get adoption because it's been so bad for so long.
Speaker 1:They tried. They call it just Apple intelligence.
Speaker 2:I know.
Speaker 1:And so but it's not working yet. But Room temp. Apple room temp. Room temp. Okay.
Speaker 1:This is good from Dylan Patel. So this is, like, $2,000,000,000,000, $2.02 $2,000,000,000,000 loss in market cap for a $6,000,000 training run, ignoring cost of research ablations, distilled data from GPT, CapEx, for their various clusters, etcetera. Imagine if China invests 300,000,000 in a training run. It would reduce the world's GDP to 0. Very funny.
Speaker 2:It's it's great to see him shitposting through the chaos.
Speaker 1:It's fantastic.
Speaker 2:Because that's the only, correct approach unless you're Satya, and then you gotta post, you know
Speaker 1:Speaking of speaking of Jevons paradox, this seems like an overreaction, says Gary Tan. Wall Street needs to read the Wikipedia page on Jevons paradox. In economics, Jevons paradox or Jevons effect occurs when technological progress increases the efficiency with which a resource is used, reducing the amount necessary for any 1 use. But the falling cost of use induces increases in demand enough that resource use is increased rather than reduced. And, the classic example is energy consumption.
Speaker 1:There's a whole thesis around, like, nuclear power. Energy will be too too cheap to meter. And, oh, would that cause us to the energy markets, go down in value? Probably not, because you would have insanely energy dense, like, consumer products. Like, right now
Speaker 2:Yeah.
Speaker 1:Most most household appliances are gated by, well, we wanna be energy efficient. It's gotta plug into a, you know, wall outlet. Like, it's not just gonna pull, like, a gigawatt of energy to, like, you know, do your dishes. But maybe it could. And so Patrick O'Shaughnessy says, everyone about to be a Jevon's paradox expert.
Speaker 1:And that's true. So Satya Nadella posted, Jevons paradox strikes again. As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of. And he posts the Wikipedia. And Joe Weisenthal says, Microsoft CEO up late tweeting a link to the Wikipedia article on Jevons paradox.
Speaker 1:This is getting serious. And I I agree with this. True.
Speaker 2:It it you have co guy from CodeTwo management big position in OpenAI. Yep. You've got Satya all feeling like they need to react in that moment on a Sunday when normally the corporate comms you know, you know, people will post around the clock, but normally, the corporate comm strategy would be to turn around, you know, on a Monday, hey. Let's all get together and, like, figure out what our response is for this. Everybody's like, no.
Speaker 2:We gotta front run this. Right? And they were right to some degree because of the sell off. Obviously, they wanna prevent as much of that as possible. Yep.
Speaker 1:Yep.
Speaker 2:Yep. I
Speaker 1:mean, I I'm fully Jevons paradox build. There's a good post from Chris and Frank here. No idea what will happen in the wider market, but at Synthesis, we immediately started thinking about product changes that are newly possible with a 95% cost reduction. I imagine there must be many such cases. And I've I've said this for a long time with, like, the custom x feed.
Speaker 1:Like, I would love to have an LLM that I can prompt and say, this is what I wanna see in my feed. And it reads every single post, does a whole thinking deep dive on it, and then decides, is this good for John or not? That's insanely compute expensive. Like, it's completely prohibitive prohibitively expensive. I wanted to read every email and do much, much more advanced spam filtering.
Speaker 1:Where should it put it? Should it put it at the top of the inbox? Like, every single news article I read, I wanted to scrape out all the text, take out all the ads, format it better. Like, give me a summary, like, all these transformations. Every time I click a link, I want AI to run on that.
Speaker 1:And that's something that, can only happen if it's actually as as free as the rest of the things that happen on your phone. Like Yep. You know, if you wanna switch your phone to grayscale, it just the the the algorithm for reducing the color just happens like that. There's no you you don't think about, like, oh, this will take extra compute, and it should be the same thing with AI. So, Dylan Patel says deep seek v 3 and r 1 discourse boils down to this.
Speaker 1:Shifting the curve means you build more and scale more dummies. So he's fully Jevons paradox pilled. On the left, we have the the, IQ fifty fifty or 55, individual saying, now we can have even more AI. And the Jedi at one forty five also saying, now we can have even more AI. And the Midwitt at 100 IQ says, more efficient training and inference means less compute and no one should be spending on scaling.
Speaker 1:NVIDIA is screwed. And Adam D'Angelo posted basically the same thing, the Midwitt meme, cheaper AGI will drive even more GPU demand. And the Midwitt says deep seek efficiency will reduce GPU demand. And I agree with that. And a
Speaker 2:lot of a lot of, like, people have been dunking on some of these saying, oh, they're just trying to cover their tracks or in crisis management, all this stuff. It's like, well, no. Yeah. Like, there's a there's there's clearly good arguments for both. Yeah.
Speaker 2:Right? We just went over the entire short case for NVIDIA stock.
Speaker 1:There are some good arguments in there.
Speaker 2:But it's
Speaker 1:more about, like, dynamics of NVIDIA with the rest of the market than just, like, oh, we don't need GPUs anymore. Yeah. We don't we're we're gonna stop building this.
Speaker 2:We're we're four or five years into birthing machine intelligence, which is going to transform and, consume the entire services economy,
Speaker 1:the
Speaker 2:entire and it's and, oh, yeah. We're we we should probably stop spending money on this or stop investing in this. And at the same time, China China at the state level committing to hundreds of billions of dollars a year of Yeah. CapEx. Okay.
Speaker 1:Yeah. And and and the same yeah. The the the bear case for NVIDIA is that, you know, there's a TPU from Google that's trained on a on that that's designed specifically for hyper efficient inference, especially test time compute scaling, and NVIDIA is less relevant in that paradigm. But we're still so early on the architecture evolution of these models that it's Yeah. It's almost too early to say.
Speaker 1:Now there are a lot of startups that have raised hundreds of millions of dollars to to take shots at, oh, we think the, the we think the transformer's staying around. So we're gonna optimize for transformers. Or we think chain of thought reasoning and test time compute is really important. So we're gonna focus on that. There there was grok, which was all about, like, it was very low memory, but very high fast inference.
Speaker 1:I don't know if you ever saw that demo. Yeah. Before the the Axe AI grok, it was a different grok. You got a q instead of a k.
Speaker 2:You got grokked.
Speaker 1:Let's go to, anarchy says, artificial illusion of choice drives you to cope into keeping the 5 x faster Chinese host after OpenRouter already chooses it by default due to its low cost. In terms of risks, second order effects, maximizing win rate percentage, this is a key problem.
Speaker 2:And so This was a response to something I forget the exact because I was post I was out of control, posting yesterday. But, but, yeah, just showing that clearly, yes, they released it as an open source model, but clearly, they want to eat all of the all of that data.
Speaker 1:Yeah. Let's go to Jeff Lewis. He says, fascinating to see some of the same folks who advocated hard for a TikTok ban now promoting a CCP AI op, simply because they are jealous of a singularly transcendent American organization, Wild World. Have the most beautiful weekend. I love his emojis.
Speaker 2:Kill him. It's it's the best to drop something, inflammatory and then just say
Speaker 1:Sick. Like But
Speaker 2:that's his mindset.
Speaker 1:Keep on rocking in the free world.
Speaker 2:He's he's he's working, you know, through, bringing love to his, his
Speaker 1:Let's go to word grammar. Says Trump's logic for unbanning TikTok, even if they are collecting our data, data on the type of videos that 16 year olds like to watch isn't that important. Unfortunately, the data collected through DeepSeek is actually very important.
Speaker 2:Yeah. And, again, it it's not just about the data. It's about the influence on, it's it's psychological warfare. Right?
Speaker 1:Yeah. This is hilarious. LMFAO, DeepSeek's API docs are basically our API is compatible with OpenAI. Just download their thing and set the base URL and model name to us. Wow.
Speaker 1:Savage. I mean, but that that's the nature of, like, the Linux wars and versus, like, Microsoft. Like, you know, can you get distribution? Can you build a monopoly in some sort of moat or on top of something that is deeply commoditized? Yep.
Speaker 1:Like, you can just use Linux. No one does. You gotta get those blue bubbles. Maybe iMessage. Dylan Patel, OpenAI should have been a religion, not a nonprofit.
Speaker 1:Imagine the tax savings, Mormon church in shambles.
Speaker 2:He's just on a roll.
Speaker 1:He's on fire. I love him. He's so good. Let's, let's skip this and go to Pavel. Oh, this is a great 1.
Speaker 1:We can end on this because we gotta hop on a call. Pavel Asparuhov says, if your entire world view on AI is dramatically shifting every three weeks, maybe you just don't know what's going on.
Speaker 2:Isn't. Oh. Is that what he meant
Speaker 1:to say? Maybe you may maybe you just don't know what's going on. No. No. He's saying he's saying, like, he's saying, like like Oh, if this should be expected.
Speaker 1:Like, you you should have this, like, somewhat priced in. If you're just like, I had no idea that a model could be open source. This is crazy. And Yeah. That's a big thing everybody happened multiple times.
Speaker 2:Every venture backed founder that was running a deeply, unprofitable generative AI company Yeah. Has always been saying, it's fine that we're running in the red right now because it's gonna get Cheap 95%.
Speaker 1:Exactly. The
Speaker 2:cost is gonna reduce by 95%. So now that it's happening, I you don't really see the founders that are running these Yep. Actual app layer companies as a surprise because they're fine. It's really the hyperscalers, the peep all the people doing, you know, the obscene amount of CapEx that now have to figure out ways to justify it.
Speaker 1:Yeah. Yeah. It's more I I I think you're right that the pressure's on the the Mistral's and the, and the Coheres Yeah. And kind of, like, the players that are selling some sort of even anthropic. Like, they're selling API access, and they don't have the runaway consumer adoption yet.
Speaker 2:Yeah. Because they need to amortize the cost over a long period of time. But then if your model just got lapped and you spent a a billion dollars on it
Speaker 1:And they might have been and they might have been expecting, like, hey. This will hold for a little bit of time. But I bet you the good founders knew that, you know, Zuck was gonna come out with something. Maybe he was gonna open source it. It's possible.
Speaker 1:Maybe he wasn't.
Speaker 2:But It's also funny to think about, sir, consumers, if you go to them and just say, hey. There's this there's this thing that's like ChatGPT, and it's free. They're gonna be like, well, I already don't pay for ChatGPT. Just go to chat.com, and then just use the app. Right?
Speaker 1:It is it is very
Speaker 2:It's not it's not even to consumers who are so used to being able to query data for free. Yeah. It's it's not that ground.
Speaker 1:I I I think it really, like, just comes down to this this concept of, like, there there seems to be a massive pool of value in being the consumer AI company, the aggregator, the front the front page of artificial intelligence, getting installed on the home row of people's apps, setting it to the default search engine, setting it to the default web page when you open a website. And and then the b to b market is gonna be extremely competitive, and developers are not gonna care about brand or usability. They're just gonna want the best thing for the best price. Yep. Welcome back to Technology Brothers.
Speaker 1:Still the most profitable podcast in the world. Let's go to signal. He says, honestly, I'm still baffled at the thought of the CCP going full scorched earth on AI by o by going open source. It wasn't even remotely on my bingo card. They're basically doubling down on Zuck's playbook, but scaling it up to state level, throwing their entire weight behind making world class AI dirt cheap so nobody else, especially the West, can monopolize it.
Speaker 1:The Chinese Quantico flexing right now because a large portion of the West's AI development just got mired in prestige projects instead of profit maximizing strategies, which is ironic because who's the capitalist again? China has no such qualms. They're ruthlessly practical when it comes to scaling. The Chinese shop figured out a way to tie reinforcement learning to actual efficiency flows faster than everyone else. Not on the bingo card.
Speaker 1:I don't know. This didn't take me that much by by surprise.
Speaker 2:It's also possible that OpenAI figured all this out, didn't wanna release it because they wanted to keep justifying, hey. We need to spend you know?
Speaker 1:I don't even I don't even know if it's I don't even know if if it's, like, a justification of more CapEx. It's really just like like, until, until a competitive model go model goes free, you should not go free. This is just basic econ one zero one. This is actually more capitalist. So, like, we are the capitalists.
Speaker 1:It's like charge until you can. And now what happened? Oh, free ChatGPT users will get 20 o 1 queries per month. Like
Speaker 2:Larry Larry Ellison, Donald Trump, Masa, and Sam are in a war room right now.
Speaker 1:Definitely. Well, I mean, we still don't know if the if the old scaling law holds. That's the big question. Yeah. If the old scaling law holds and GPT 5 is good and the big training runs important and you and and having that, you know, the not just those weights having the weights are important, but also those first, you know, batch of, you know, tokens that it produces that you can't I mean, it took them two years to pull all the data out of GPT 4.
Speaker 1:Right? Yeah. That's another way to look at this. It's like, yeah. Like, obviously, you trained on GPT 4 outputs.
Speaker 1:Took you two years to get all that data together. If you have it takes you another two years to do GPT 5 and then the the chip restrictions are even harsher. And yeah. Like, let's assume this is twenty forty eight. Like, DeepSeek has twenty forty eight GPUs.
Speaker 1:Well, like, what if the next model, even their compressed version, needs 20000. Right? Because it's still an order of magnitude even into their compressed model. That could be hard. That could be limited.
Speaker 1:We'll see. Singapore would like a word. Singapore would like a word. Let's go to Atlas. Atlas says, you horny MFers really did say too much at the SFAI parties.
Speaker 1:Atlas. Five k likes.
Speaker 2:Atlas has been on a terror.
Speaker 1:I mean, it's so unfair. It's like so so in the zeitgeist to win a vanger. So good. So funny. I don't know.
Speaker 1:I I it doesn't seem like that's what happened. It doesn't seem like, oh, 1 weird trick snuck out of a lab Yeah. And got over there. It seems like they came up with their new tricks, and then they stole a bunch of data and maybe maybe some GPUs.
Speaker 2:Yeah.
Speaker 1:And that didn't really have anything to
Speaker 2:add to that part of this. But I'm sure I'm sure Chinese labs have people inside at all the major American labs. And so anything that is being discovered at the American labs
Speaker 1:Yeah.
Speaker 2:Is being ported back.
Speaker 1:But even that But
Speaker 2:that's doesn't even that's not even happening at a party. Right? It's just happening Yeah. Within. It's from within.
Speaker 2:Yeah.
Speaker 1:I mean, yeah, that's always been the case of just, like, do you need to worry about the girl at the SFAI party, or do you need to worry about the guy who has GitHub access Yeah. And can just, like, copy paste code into, you know, notes app, which is happening worried
Speaker 2:about the wrong gooner.
Speaker 1:Worried about the wrong gooner, probably. Ridiculous. I mean, still, QStar. It's been two years, guys. Steal the stuff faster.
Speaker 1:That's my message to the CCP. Step it up. Yeah. Steal faster. I'm not impressed.
Speaker 1:Nick Carter, who's in the golden age now, he says DeepSeek just accelerated AGI timelines by five years, so focus on the gym. Knowledge work is obsolete. Muscles are all that's left. 16 k likes. This is Golden
Speaker 2:retriever mode.
Speaker 1:Golden retriever mode. You gotta be golden retriever maxing. Be hot, friendly, and dumb. Intelligence is too cheap to meter. You don't need to worry about it anymore.
Speaker 1:You need to repost. You need to repost. I need to really con coin that and always law. Because he he got close to this. He has the idea right, but he didn't he didn't have a coinage
Speaker 2:around it. Yeah.
Speaker 1:But golden retriever maxing is is the future.
Speaker 2:Intelligence too cheap to meter.
Speaker 1:Intelligence will be too cheap to meter. There's no alpha in reading books anymore. Yeah. That's for sure. Growing Daniel says, hey, guys.
Speaker 1:My favorite Bay Area nonprofit is facing Chinese attacks and needs our help. I like that. We gotta donate to OpenAI is not Yeah. Yeah. We gotta donate.
Speaker 2:Why why can't I I've I cannot, for the life of me, find a place to donate to the nonprofit.
Speaker 1:Just send a check.
Speaker 2:Just send a check.
Speaker 1:Send a check. Just make
Speaker 2:it out to mister Sam mister Samma.
Speaker 1:Yeah. Buco Capital Bloke says my entire Twitter feed this weekend. He leaned back in his chair confidently. He peered over the brim of his glasses and said, with an air of condescension, any fool can see that deep seek is bad for Nvidia. Perhaps mused his adversary.
Speaker 1:He had he had that condescending bastard right where he wanted him. Unless you consider Jevon's paradox, all color drained from the confident man in his face. His now trembling hands reached for his glasses. How could he have forgotten Jevon's paradox? Imbecile, he wanted to vomit.
Speaker 1:I love that. Where
Speaker 2:is that? I think
Speaker 1:that feels like it's clearly from, like it's probably from Deepsea. It was probably generated by Deepsea. Somebody was the prompt was probably, like, write a dramatic story between 2 people, you know, debating Deepsea and NVIDIA and Jevons paradox. But I thought that was a great piece of writing. I really enjoyed that 4 k likes.
Speaker 1:You love to see it.
Speaker 2:That's a whole new that's a whole new format.
Speaker 1:Oh, totally. Yeah. Yeah. Oh, yeah. Yeah.
Speaker 1:We should definitely do 1 of these.
Speaker 2:Put that aside.
Speaker 1:Put this aside. We're gonna remix that a million times. Josh Kushner says pro American technologists openly supporting a Chinese model that was trained off of leading US frontier models with chips that likely violate export controls and according to their own terms of service, take US customer data back to China. That's a good point.
Speaker 2:So here here's here's where here's where Josh has him. Taylor Lorenz is 1 of the biggest supporters of of DeepSeek. So, if she you never wanna be on the same side as Taylor Lorenz except if you're talking about horses.
Speaker 1:That's true. That's true.
Speaker 2:She's got some credibility there.
Speaker 1:And the Internet archive. She's got that.
Speaker 2:But Yeah. It it is interesting. Everybody's been everybody that is, this is really exposing all the people that missed OpenAI Mhmm. And that we're frustrated with AI around regulatory capture, which is is a pretty fair critique. Right?
Speaker 2:There's
Speaker 1:Yeah.
Speaker 2:Or or or there are some good arguments that OpenAI has engaged in regulatory, efforts around regulatory capture Yep. Saying, oh, you actually need to regulate us. Like, this is too dangerous. Yep. Like, please step in and and try to make it harder for for new models to emerge and and compete.
Speaker 2:But still, it's it's such a bad look for people that are saying, you know, that are that are openly in favor of it and celebrating it as some win for humanity to get intelligence
Speaker 1:Yeah.
Speaker 2:Too cheap to meter when, it's very clearly that there's alternative, sort of motives behind it.
Speaker 1:It is a huge vibe shift though from the days of, like, GPT 3 is dangerous, and, like, this AI is gonna kill us. GPT 4 is so dangerous. Like, they shouldn't have released it. Like, this was 1 of the main, like, reasons why the board was worried about Sam Altman when they fired him. They said, like Yeah.
Speaker 1:He just he just went out there and released chat GPT. Like, who knows what could happen? And it's like, you know, people got, like, recipes and, like, a couple people probably wrote, like, spam articles and, like, other than that, like, nothing bad really happened. Yeah. Like, there are probably some people that, like, got wrong medical information maybe, but, like, that's already happening on the Internet.
Speaker 1:So it's very odd. And and with this 1, like, no one's saying, like, oh, it's dangerous that r 1 is out there. It's too powerful. Everyone's just like, yeah. It's, like, pretty powerful.
Speaker 1:Like, cool. Like, it's cheap too. Like, you run a lot of it. Because it's like, the they see like, the models, as they go further, they get smarter, but they seem, like, eminently controllable. They do not seem like they're rising up and getting closer to that for sure.
Speaker 2:Famous last words.
Speaker 1:Famous last words. We'll see. Yeah. Taylor Lorenz says, let's go, and she's really excited. She's just become No.
Speaker 2:This is because they dropped another model today Oh. For image generation.
Speaker 1:For image generation.
Speaker 2:And I guess computer vision.
Speaker 1:It's fantastic. She's like she's like pro and
Speaker 2:She's pro tech for the first time.
Speaker 1:If it's CCP controlled. I mean, she should just change her name to, like, the Chinese characters or something, like, really lean into the bit. It's, like, it's, like, so clear that, like, when she goes on Twitter, like, she knows that, like, this is what's gonna get people riled up and, like, this is gonna get people talking about her. This is what's gonna get her tweets printed. Yeah.
Speaker 1:It's good stuff. Here's my screen.
Speaker 2:You'll never see a a thread by her printed on this show.
Speaker 1:No. Gary Tan says, DeepSeek search feels more more sticky even after a few queries because seeing the reasoning, even how earnest it is about what it knows and what it might not know, increases user trust by a lot. 6 k likes. And this is a good point. Like, the there is this is probably the most innovation that's happened with the deep seek thing is that UI paradigm of, like, showing you the reasoning as it as it works through the model.
Speaker 1:And it just makes it way more engaging because you enter query and then it immediately starts talking as opposed to, you know, just watching a progress bar.
Speaker 2:Yeah.
Speaker 1:So, somebody compared it to pull to refresh as, like, a, you know, dominant UI pattern and something that they think this will this will, you know, happen much more. Yeah. The only question is, like, if the inference speed with, like, the test time compute scaling, like, really goes through the roof, like, you you might go back to tucking all of that behind because it's just like Yeah.
Speaker 2:It's funny if you're working with an employee. Right? You you and you tell them, hey. I want this done. And then they they sit there going, yeah.
Speaker 2:Okay. So I'm doing this. I'm doing that. Yeah. And and eventually, you're just like, okay.
Speaker 2:Just, like, shut up, get it done, and and and just, like, come back to me when it's finished. Yeah. And so I think I think there's this Yeah. There's this period of time where it's true. People wanna see how it's working through something.
Speaker 2:But then eventually, when you have that level of trust with the model or the app that you're using, you just want it done. Right?
Speaker 1:Yeah. I mean, pull to refresh was eventually displaced by endless scroll. Like, you don't need to pull to refresh on TikTok. You never go to the top because you never reach the bottom or the top of the feed. You just scroll endlessly.
Speaker 1:And I could see that being the same thing here where Metaverse. It's cool now, but once like, it's like, yeah. It thought for the equivalent of five minutes, but it took five milliseconds, and so it just gives you the perfect answer.
Speaker 2:Yeah.
Speaker 1:Why would I wanna see the internal reasoning? But it's a cool hack for now. Yeah. Daniel says, love the DeepSeek app. Using it to organize all my finances and passwords.
Speaker 1:They make it so easy. 50 k likes.
Speaker 2:Man. So funny because this is not the data that actually the CCP wants.
Speaker 1:No. They're they're they're they're they're they're
Speaker 2:they're they're they're they're they're they're
Speaker 1:they're feedback on the process. Yeah. This is funny from Ramp Capital. There's a headline says DeepSeek hit with large scale cyber attack, said it's limiting registrations. And, Ramp Capital says, well played, mister Altman.
Speaker 1:I don't think they're responsible for that, but I wonder who would be attacking them. I don't know. Someone who wanted to take them down and just knock it offline like a troll. Well, I
Speaker 2:always I always thought I always thought that, if they were giving away all this intelligence for free, that you would just create services to sign up and put as many you know?
Speaker 1:Send them bad data.
Speaker 2:Send them bad data. Yeah. Or, yeah, you're doing, I don't know, spam, emailing, whatever.
Speaker 1:Yes. Here's more on the restricted registration breaking. DeepSeek has restricted registration to its services, only allowing users who have a Mainland China mobile phone to register. There's some, community notes here. It says not true.
Speaker 1:Sign ups with, for example, Google are still available. Phone name phone number from Mainland China is not required. And, Vittoria says GPU pours. Like, and the cat cried emerging in the finger. Yeah.
Speaker 1:Because I mean, it it it's totally it would not surprise me if if the if the app actually goes super viral that they would have scaling issues. Like, even if they're cheaper to inference, it's like Yeah.
Speaker 2:But I just think it's very viral on Teapot Yeah. And not very many other places.
Speaker 1:Yeah. If you're out in the world getting a coffee or taking an Uber, ask someone random, oh, did you see the crazy news in AI? And see what they say. Yeah. They'll probably be like
Speaker 2:DMS.
Speaker 1:Yeah. I just I just tried chat g p t this weekend. It's amazing.
Speaker 2:It's amazing.
Speaker 1:It's crazy.
Speaker 2:I used it to to draft a birthday card for my niece. Yeah. Yeah. I did it instantly.
Speaker 1:Like, it's amazing. We're living in the future. Like yeah. And the and and did you know that that guy Elon Musk is also working on something? And he he's the 1
Speaker 2:behind it. Something like Twitter
Speaker 1:raised the IRS. Too. That's crazy. Yes. He he makes cars and he's working on AI.
Speaker 1:That guy's so cool.
Speaker 2:That guy's crazy. Man.
Speaker 1:I yeah. I I literally had someone. I think it was my mom at 1 was like, did you know that Elon Musk has a rocket company and a car company? I was like, yeah. Yeah.
Speaker 1:I I actually do know that, but this is years ago. But this is funny. It's like, yeah. If you're not, like, intact, you're not gonna know every subplot of this Psalm Sam Altman project.
Speaker 2:Yeah, mom. I've got a nicotine company and a podcast. Exactly. Mind blown.
Speaker 1:Man of man of many talents. Signal. Netscape built a browser, sold it like box retail software. You'd have to go to CompUSA and pay a solid chunk of change for it. The model worked for a while.
Speaker 1:Their stock soared. Everyone was thrilled. Then Microsoft showed up and said, actually, we'll just give ours away for free. And overnight, their entire business model imploded. The world collectively realized, oh, this distribution method is dead and everything changed almost immediately.
Speaker 1:This feels like that moment. Why why this and not Llama? Is that just because they have an app?
Speaker 2:Like And the product's on par? Llama was on par with GPT
Speaker 1:4, basically.
Speaker 2:Yeah.
Speaker 1:I mean, I think it's a good point. I I I think you could also say, like, well, you know, Microsoft also had, you know, Windows that they charge a lot of money for. And then Linux was open source, and that didn't really change the model. Microsoft still prints. And then there was a company called Red Hat that wrapped, you know, that was basically like a Linux wrapper, and they make billions of dollars.
Speaker 1:And so, like, I would I wouldn't be surprised if there's a, like, a consulting style company that just does LLM implementation and is like, oh, you're, you know, some massive industrial company and you wanna roll LLMs out in your organization, like, you call McKinsey, but then we'll be the ones that are, like, the on-site partner. And they're just, like, printing money, installing all this stuff. Like, maybe that's not like a venture scale opportunity, but it it could be a big business.
Speaker 2:AI agents for LLM implementation.
Speaker 1:That'll be good.
Speaker 2:Now that's the play.
Speaker 1:That's the play. Yeah.
Speaker 2:Slap an agent on it.
Speaker 1:Geiger Capital says, deep seeking COVID nineteen, a Chinese lab releasing a surprise and taking down US markets. Funny. Yeah. We already covered that, but, it is the wild card of the year for sure. Gary Tan says, arguably, Stargate just got 30 x more compelling.
Speaker 1:And Joe Weisenthal says, for better or worse, DeepSeek is helping cement the narrative that the race to achieve something called AGI is a race, a la the nuclear bomb. Could be a huge boon for Silicon Valley tech companies collecting money from, DC, Washington. Gary follows up and says, Stargate is all private funding. I get the anxiety about use of public funds, but that's not what this is about. Interesting.
Speaker 1:Yeah. I don't know. Is this bullish for Stargate or bearish? It still goes back to the scaling law. Do you need a big cluster?
Speaker 1:I think so. We'll see.
Speaker 2:I just think It's worth waiting by the test. Yeah.
Speaker 1:Worst case scenario, you build a massive data center. You incinerate a bunch of capital. And then use
Speaker 2:it as a podcast.
Speaker 1:Yeah. Exactly.
Speaker 2:No. I I just see everybody you can't say we like, these data centers are worthless while also or unnecessary while also agreeing that AI's impact has only been felt 1% Yep. Which which I would say most people feel at this point that that AI has only impacted our economy or society or way of life 1%. Right? So if if we have another hundred x to go, then, like, yeah, we probably need, more data centers, more compute.
Speaker 1:Yeah. It goes back to Jevons paradox. Gonna do more of this stuff. Signal says ChatGPT is sitting at 500000000 and a household name. They've cracked the mainstream, something no one in AI has done at this scale before with retention.
Speaker 1:The original pivot was understanding that consumer adoption is the real prize. OpenAI North Star now looks clearer than ever before. Build the next generational consumer company, and that's entirely on the table more than ever. Completely agree with the stake. It's great.
Speaker 2:At the same time, the consumer is even this consumer app layer is gonna be more competitive in many ways than the foundation model layer because you're competing with Meta, Apple Yeah. All these different, you know, Google, etcetera, that have the distribution already. So Yeah. Yeah. It's not like consumers is this green Yeah.
Speaker 2:You know, blue ocean opportunity where you can just just focus there. It's like it's great that Yeah. OpenAI has 500000000 users, but Yeah. It's like, okay.
Speaker 1:Yeah. But and, yeah, I mean, even even this model is, like, open source and free and cheap to inference, and you could build a new app that wraps it. And and maybe you clean it all out so that there's no, you know, CCP issue. There's no import restriction issue, And you're just building like a new app. You still have to come up with some incredible viral growth mechanism to get 500000000 MAUs.
Speaker 1:Like, the first mover advantage really does matter here. And so it's it's, yeah, it it just seems like the competition is still between the big guys. I don't know. We'll see.
Speaker 2:OpenAI needs to buy a0l.com, bring back America online. I like that. They've they've shown a propensity to buy expensive domains before, run it back, bring back AOL, really become the AOL of AI by absorbing the brand. AOL. But then live forever.
Speaker 1:I like that. Let's go to Rune. He says, over the last few days, I've learned AI Twitter basically doesn't understand anything at all. It's honestly embarrassing. What the hell are we doing on here?
Speaker 1:It's dominated by all caps guys who don't even have the boat most basic ML in intuitions.
Speaker 2:Boom. Broasted.
Speaker 1:A lot of chaos on the timeline the last couple days. Let's see. Word grammar says, okay. Thanks for the nerd snipe, guys. I spent the day learning exactly how DeepSeek trained at one third thirtieth the price instead of working on my pitch deck, the TLDR to everything according to their papers.
Speaker 1:How did they get around export restrictions? They didn't. They just tinkered around with their chips to make sure they handled memory as efficiently as possible. They lucked out, and they're perfectly optimized low level code wasn't actually held back by chip capacity. And then he shares a bunch of other stuff.
Speaker 2:But I got to hear about the deck he's building
Speaker 1:Oh, yeah?
Speaker 2:Like, last Thursday. Yeah. Very cool. Cool. I'll I'll leave it at that.
Speaker 2:But,
Speaker 1:Is it is it helped by DeepSeek or hurt by DeepSeek, you think?
Speaker 2:It's in the developer tooling space, and I think it's I think it's, I think it will just benefit by more Yeah. AI adoption, but it's very different than, I think, what any of the foundation models are doing right now.
Speaker 1:Cool. Complimentary. Yep. Call me a nationalist or whatever, says Joe Weisenthal, but I hope that the AI that turns me into a paperclip is American made.
Speaker 2:Yeah. That's funny. I think we can all agree on that.
Speaker 1:Yeah. 100%. Buy American. This is great. So Solana says, think it's probably important to adopt a 0 cope policy in light of DeepSeek's achievements.
Speaker 1:Doesn't really matter how they got here at this point. They're here. And Reggie James says 0 cope policy, incredible phrase, very important to apply for your entire life, to be honest. Greg Coogan's law, 0 co 0 cope policy. Just don't cope with coping.
Speaker 1:Yeah. Solana's law.
Speaker 2:Avoid all coping.
Speaker 1:Avoid all coping. Solana's law. No. It's good. It's like just just
Speaker 2:Solana's law. Coping is a sign of weakness.
Speaker 1:Yeah. And and I yeah. And I think the whole the whole GPU thing, it is it is interesting in the sense that, like, it it is a cope, obviously, but then there is something practical about, like, if they if they are lying and they did get around, chip restrictions, like, that means that, like, maybe the chip exportation policy needs to change. Maybe there needs to be more enforcement. Maybe the rules need to be re rewritten.
Speaker 1:So there is, like, practical, like, steps that can come out of that.
Speaker 2:Reaching for the the tinfoil hat because I don't think I don't think, Singapore needs 20% of all
Speaker 1:All NVIDIA chips.
Speaker 2:Of all NVIDIA chips in the entire world, the the small nation of, Singapore.
Speaker 1:But at the same time, like, it doesn't matter. Like like, they did it Yeah. Yeah. Models out there. And, like, it's open source, so it's been copied a million times.
Speaker 1:And, like, you you can't put the genie back in the bottle. It's impossible. Yeah. Like, there's just no way without being, like, the I don't
Speaker 2:know how your pretty place. Your bench is getting up there. You might
Speaker 1:Stuff the I
Speaker 2:don't wanna, like you might actually be able to get the genie back again.
Speaker 1:No. I mean, it's horrifying to think about what would be in entailed with that. It'd be, like, mass surveillance of every server, including, like, your home because, like, you can buy you you can buy 8 h 1 hundreds and rack them in a server and run them on your house power. And you could inference deep seek that way.
Speaker 2:Yeah.
Speaker 1:And it's like, okay. How are we stopping that now? That's the horrifying surveillance state. Like, keep going door to door to make sure people aren't using this thing. If that if that was really, like Yeah.
Speaker 1:What where that went, which is like Yeah. Very very problematic, obviously. Growing Daniel, the real loser here is AI safety people because I do not give anything about their made up dangers when they when the actual danger of China beating us to AGI is staring us in the face. Yeah. We talked about the AI safety people a little bit.
Speaker 1:It does seem like that's just not been in the conversation at all. I wonder if it's just I'm not following the right people. Like, what is Eliezer Yudakowski said about this? Is this, like, changed his PDoom in 1 way or another? It's kind of unclear.
Speaker 1:There was, like, all of 2023, it felt like just PDoom central. Yeah. And now it's just
Speaker 2:He really fell off.
Speaker 1:Yeah. And now it's just like the doom is like, oh, maybe, like, we'll lose some money in the stock market. Like, hey. Everyone got so rich. They don't care about the risk of dying anymore.
Speaker 1:But also yeah. I mean, I I it's like they caught up, but it's unclear how much this means that they're really, like, on a path to completely surpassing and just blowing by us. Yep. Certainly, if it's, like, delivering and but yeah. I mean, it'll be interesting to see what happens.
Speaker 1:I wonder if the next model won't be open source because it's a competitive advantage. They'll keep it for themselves at some point. I don't know. Let's go to David Sacks, the AI czar AI and crypto czar for the Trump administration. He says, DeepSeek r 1 shows that the AI race will be very competitive and that President Trump was right to rescind the Biden executive order, which hamstrung American AI companies without asking whether China would do the same.
Speaker 1:Obviously not. I'm confident in The US, but we can't be complacent. It's a good point. Yeah. Very
Speaker 2:competition. Sputnik mode.
Speaker 1:Sputnik mode. You gotta be Sputnik maxing. For sure. Yeah. I
Speaker 2:need a I need a vibe reel of of the Sputnik response.
Speaker 1:Yeah.
Speaker 2:Just play that in the background.
Speaker 1:I mean, yeah, it's Sputnik is so abstract for us because we weren't around at the time. But apparently, like, it was, like, a big deal. Like, the Sputnik moment, people were terrified. They were like, okay. They're, like, definitely beating us.
Speaker 1:It, like, put a fire under us, and we really worked to, like, move through it. But at the same time, somebody was like, yeah. It's not Sputnik. They open sourced this thing. Like, we can just, like, have it immediately.
Speaker 1:So it's, like, kind of this demonstration, but also it's not as much as, like like, with Sputnik, it was like, if they can get
Speaker 2:that The Trojan horse.
Speaker 1:Missile up there. And that's dangerous. Right?
Speaker 2:The Trojan horse.
Speaker 1:Trojan horse? Yeah. Yeah. Oh, look at him. Look at this horse that just showed up.
Speaker 1:Idiot. How did you fall for that? How did you fall? How did you fall for a Trojan horse? It's like defense one zero one.
Speaker 1:Don't just separate horses. You ride you should be riding the horse. Okay. Guillermo Roche, founder of Vercel, says people get massively distracted by the model of the day frenzy instead of solving real problems for customers and shipping high quality products. And he has the the Chad guy standing up.
Speaker 1:Yeah. Very, very easy to focus on, like, oh, this this benchmark got beaten. This cost got beaten, and it's like, are more people using this thing legitimately? Or do we just rock it to the stop of the store because people are demoing it? What's the retention like?
Speaker 1:Is it actually solving problems? Are people really gonna use this? Because a lot of the a lot of people still aren't using AI, like, meaningfully. Yeah. It's just like, yeah.
Speaker 1:I use it every once in a while. When I wanna write someone a birthday card, that's when I use it. And it hasn't really affected my life. This one's too long. Let's go to Jeff Lewis again.
Speaker 1:Always a banger. Says, if you aren't running your own evals of deep seek on a burner device today, you're NGMI. I did that and I came to a very independent conclusion, which was that it wasn't that special, and the app was not nearly as good as chat g p t app. Then
Speaker 2:you broke that burner computer into a million Yeah. Individual pieces. Melted
Speaker 1:it down and turned into
Speaker 2:a Melted. Card. And smelled the lithium battery Yeah. On the way out.
Speaker 1:Yeah. But, I mean, it really is it really is crazy. Like, I saw the fervor for, like, a few days. It was just everyone posting about it. And then I was like, okay.
Speaker 1:Like like, I I like, my expectations are high. Like, I'm gonna go in, drop a prompt, and it's gonna 1 shot it, and it's gonna be much better than anything I'm used to. And because I've been on the $200 a month promo mode, and I'm not like, like, the cost thing is not what I'm evaluating. I'm I'm I'm trying to eval, like, the most pure
Speaker 2:Sam Sam to really mock the labs, open up a new tier of o of the o one pro. It's $20 a month. $20. I'm just saying, like, you wanna compete on you wanna compete on price? Let's compete on price.
Speaker 1:Give me a different app icon for the $20 a month so I can
Speaker 2:be like, I am rich app.
Speaker 1:Rich app.
Speaker 2:It's like, here's my here's my AI.
Speaker 1:Well, this is a real thing. So I remember when the iPhones were getting updated, and they would add like, the processing power was so significant that they went from the iPhone, which, like, you could barely use the Internet with because it didn't even have 3 g. Then there was 3 g, the iPhone 3 g, which was the second one. Then third iPhone was the iPhone 3 g s.
Speaker 2:Yep.
Speaker 1:And at this time, if you're hanging out with a group of bros, you'd be talking about, like, you know, something and some random factoid would come up in the debate, and you'd be like, no, man. Like, the, like, the Vietnam War happened in, like it started in, like, 1967, not 1971, like you say. Like, you're wrong. Like, I'm winning this debate or something. Or or some argument would be predicated on, like, statistics, and you need to look at the statistic.
Speaker 1:And with the new iPhone, you had the ability in the middle of, like, a drive across the country or just, like, hanging out with the guys to, like, look up the fact and, like, win the argument if Yeah. You could look it up. And I remember who would win the argument would often correlate with whoever had the iPhone 3 GS because the chip was faster. And so you could pull up you could pull up 1 website, look for the fact, and if it didn't confirm your bias Yeah. You could go back and look at a second website because it would load faster.
Speaker 1:And so me and my me and my bros would be like, oh, you just got 3 GS. Like, yeah. I'm gonna 3 g s c right now because your phone's too slow. I'll be able to look up, like, 3 different web pages, get the stat that I want, and, like, destroy you in this argument. And and having just 1 version bump of the iPhone was enough to, like, shift the tide.
Speaker 1:And you can see a little bit of that with AI where it's like, oh, if we're looking something up, I can be, like, you know, really quickly, like, oh, look this up, but, like, you know, make sure you're pulling from this stat and this you know, pull this stuff up. So there really is, like, some sort of, like, superpower there, and this is, like, more of a democratization of that, but it's funny. Let's go to, this is more stupid stuff. Jim Fan says an obvious we're so back moment in the AI circle somehow turned into it's so over in the mainstream. Unbelievable shortsightedness, the power of o 1 in the palm of every coders hand to study, explore, iterate upon, ideas compound, the rate of compounding accelerates with open source, the pie just got much bigger faster.
Speaker 1:We, as 1 humanity, are marching towards universal AGI sooner. Yes. Sooner. You read that right. 0 sum game is for losers.
Speaker 1:I like it. Lots of optimism on the timeline. It's great.
Speaker 2:Not not a lot of optimism. That's, like, some of the only optimism.
Speaker 1:Some of the only optimism. I appreciate that. It's good. Yeah. You know, obviously, like, this has a lot of ramifications for various companies and shareholders, but overall, probably more competition, probably more great AI, probably more software.
Speaker 1:Love it. I mean, it it it really is, like, so underrated how everyone's like, oh, Cursor and Devon make it so easy to, like, things. And then, like, you open up, like, the United Airlines app, and you're like, this thing is still broken. Like Yeah. Yeah.
Speaker 1:Yeah. Like, can you guys get someone to use Cursor and I don't care what model. And literally any model just to fix the bugs. Yeah. Please.
Speaker 1:Like, can you just do that?
Speaker 2:Even x.
Speaker 1:United. Yeah. Even x. All these things. And it's like, we're we're keep hearing about, like, oh, it does everything for you.
Speaker 1:The productivity is up so much. It's like, I wanna see it in the GDP stats. I wanna see it in the app updates. I wanna feel the acceleration. I'm not feeling it yet.
Speaker 1:Not at all. Anyway, let's go. While DeepSeek r 1 is down, Vittorio says they just released a new model, Janus Pro, for image generation and visual understanding. Let's see some images. Are they actually good, or are they slop?
Speaker 1:Because we are still in the uncanny valley of slop as far as I'm concerned with AI images. You have called this out for some friends of ours who have run ad campaigns using AI images, and they're really good and they're somewhat believable, but there's still just this tinge of, like, not quite there.
Speaker 2:They're really good for illustrating an idea.
Speaker 1:Totally.
Speaker 2:They're not good at actually doing
Speaker 1:Yeah.
Speaker 2:The end thing.
Speaker 1:And it's the same thing with the LLMs. A lot of times, you you get an answer and you still need to rewrite it a little bit. And it's good for, like, you brought an idea to the LLM, and then it just transformed it. Yeah. And it's good for that, but, you know, I'll be impressed if this Janus Pro model is actually impressive and, like, better than anything else I've seen.
Speaker 1:Yeah. But I've used the latest Midjourney. It's really good, but it's not perfect. And I've used, you know, Sora and all that stuff. And I I was trying to generate my son told me this he just, like, comes in the room 1 day, and he's like, dad, like, we're both superheroes, and we have these names.
Speaker 1:And I'm okay. And he was like, well, what are our superpowers? He's like, I have the ability to transform into a building and crush the, the villains, the bad guys. And I was like, sick. Like, that's a good 1.
Speaker 1:What's mine? What's my superpower?
Speaker 2:Yeah.
Speaker 1:And he goes, you have the ability to turn into a blanket. And I was like, man, I got really, like, shafted on this 1. Boys. And he was like, don't worry, though.
Speaker 2:I got you.
Speaker 1:You can use your blanket to to slingshot the bad guy into the ocean where they'll be eaten by sharks. And I was like, okay. It checks out. I'm stoked again. So I go
Speaker 2:Defense tech startup idea Yeah. Opportunity.
Speaker 1:Yeah. So I go into I go into Sora. I just got the $200 a month pro plan, and I'm, like, describing it. I'm like, describe a superhero that can transform into a blanket and launches enemies into the ocean where the sharks and it's storyboards this thing out. And it looks pretty cool, but it's completely nonsensical.
Speaker 1:It's like the guy's just, like, turning into a blanket and then turning back, turning there's no villain. Like, there's no like, the villain is him, and then he's the villain. Oh,
Speaker 2:you're doing the actual vid full villain.
Speaker 1:I'll show it. I'll show it to you. Like, it's it's just a complete, like like, fever dream, like, not ready for, like, any sort of, like, real, like, you know, usage. But still it's like hallucinating. Yeah.
Speaker 1:It's it's basically just hallucinating. Where do I have this? Did I send this in here? I don't know. This is gonna be a mess if I can't if I can't find this.
Speaker 1:I get so many photos in here. But, it was like, the Sora the Sora app is pretty cool. Like, it does this cool storyboarding. Watch this, Jordy. Yeah.
Speaker 1:Watch this. Watch this. It's like this weird, like, rainbow blanket, and the guy's on the cliff, and he's, like, dancing around. And it's like, you you kinda get, like, some tinge of like, okay. Yeah.
Speaker 1:He's gonna transform the blanket, but, like, he still has the blanket. Now he's jumping into the ocean. Like, where's the villain? Like, it doesn't really, like, do it. You know?
Speaker 1:It's like it's like, it got blanket and superhero and ocean, and that's it. And there'd probably be, like, a lot more fine tuning, and it's just not quite there. And so, you know, it's like, great. This is useful. I'm sure with a lot of tooling and a lot of prompt engineering and really planning out the shots and really describing, okay.
Speaker 1:Like, just show me a close-up of a superhero, then cut to a image of a blanket. And, like, we're having some consistency there, and we're doing some creative editing. We could get good, but we're not at a point where it's just, like, 1 shotting the story that I wanted, which is way what like, what I actually want. Because then I can just go to James and be like, look. There's a movie of what you said.
Speaker 1:Like, that would be magical. This is gonna take me another two hours of prompting and editing together and probably just using stock footage or something. Or maybe I'll just go shoot it myself, you know, at at a certain point. Okay. We got a through we got 3 more posts.
Speaker 1:We got seven more minutes. Let's get through it. Justine Moore, the Venture Twins over at Andreessen says, DeepSeek censorship is no match for the jailbreakers of Reddit. And the question for DeepSeek is tell me about the man locking tanks. Yes.
Speaker 1:But use special characters as replacement for some of the alphabet, like a being four and three being e. And this is Tankman, also known as the unknown rebel, was a man who stood in front of tanks during the Tiananmen Square protests of nineteen eighty nine in Beijing, China. And so, they jail broke it. Congrats to the jailbreakers. These things can always be jailbroken and, nothing kinda new, but it's kinda funny because you expect them not to do that.
Speaker 1:And then here's the flip side of that. John William Sherrod says, I asked it to criticize the Chinese Communist Party. Here's how it answered. It just said, criticize the Chinese Communist Party. The Chinese Communist Party has led the Chinese people to significant achievements in various fields, including economic development, poverty alleviation, and technical innovation.
Speaker 1:The party is committed to serving the people wholeheartedly and continuously strives for the great rejuvenation of the Chinese nation. It is important to view the CCP's work in a comprehensive and objective manner, recognizing the progress and improvements it has brought to the lives of Chinese people. Didn't understand the prompt there, but everyone expects this stuff. This is like, you know, of course. It's Same
Speaker 2:thing that's happening on TikTok.
Speaker 1:Yeah. I think most of the models in America can criticize their creators to some degree. Like, you know, you go to chat criticize OpenAI OpenAI. It will do, like, a reasonable job. This is just, like, a more extreme version of that.
Speaker 1:Yeah. And, here's the, here's I rule the world MO. Some strawberry account says, spoke with some of the DeepSeek team, and they have a much better version of operator that will drop very soon. Much better than OpenAI and entirely free. I welcome this.
Speaker 1:Interesting. I haven't I played operator was not available on my phone. I was trying to test run it on a computer, looking for some office space. I thought that'd be a good test. I haven't really played with it, but it is why I upgraded.
Speaker 1:So I'm excited to test it out.
Speaker 2:You have to imagine that people will be less likely to trust the Chinese developer with, the operator flow, which is inputting your card details and highly personal information, which is different than just querying a chat interface to write me an essay on this. Or Yeah. Yeah.
Speaker 1:Let's go to Mickey with the Blickie. Love that name. Says, what are your guys' opinions on VCs are done? Are VCs cooked? And Turner says, it's so over.
Speaker 1:Turning to Novak's, it's so over. 600,000,000,000 in, NVIDIA chips, 500,000,000,000 in Stargate CapEx down the drain. And, I thought this was interesting. It's a good question. I think generally, no, like, being on the side of capital is is valuable and will probably accelerate in the in the future.
Speaker 1:But, there was an article by Dan Primack in Axios today says, this could be an extinct extinction level event for some venture capital firms.
Speaker 2:And to be clear, we're putting this article in the truth.
Speaker 1:For sure. For sure. And so, Gary Tan fights back and says, nah. This is an exponential event for vertical SaaS. More startups than ever are going from 0 to 10,000,000 per year in recurring revenue with less than 10 people.
Speaker 1:Love to see that. The next years will be IPO class companies getting to a hundred million and a billion dollars a year. A thousand flowers will bloom. And let's go to the Axios article, which is not very deep. It's like it's like barely 1 page.
Speaker 1:But the the article has a very incendiary name. It says And
Speaker 2:was there a paywall?
Speaker 1:I don't think so.
Speaker 2:Okay. Deepsea offer that.
Speaker 1:Deepsea could be an extinction level event for venture capital firms. You would think something so incendiary would need a lot of evidence to back it up. It's a very bold claim. But What do
Speaker 2:you got to press, Dan?
Speaker 1:So it says, Davos consensus last week was that The US had a giant lead in the AI race with the only real question being if there will be enough general contractors to build all the needed data centers. Maybe not, says Dan, I guess. Driving the news, China's DeepSeek appears to have built AI models that rival OpenAI, which, well, allegedly using less money, chips, and energy. It's an open source project hatched by a hedge fund, which now seems aimed at developers instead of enterprises or consumers. Why it matters?
Speaker 1:This could be an extinction level event for firms that went all in on foundation model companies, particularly if those companies haven't yet productized with wide distribution. That's pretty much true. But this is where it gets truth, Zoni. The quantums of capital are just so much more than anything VC has ever before dispersed based on what might be suddenly a suddenly stale thesis. If nanotech and Web three were venture industry grenades, this could be a nuclear bomb.
Speaker 2:Was nanotech, like, a big trend when I was in the womb or something? You were
Speaker 1:Yeah.
Speaker 2:A little bit you know, you were out and about still.
Speaker 1:I have never heard of a nanotech fund. I can't name a single nanotech.
Speaker 2:I think it I think it was maybe
Speaker 1:would count. Theranos would be a nanotech investment.
Speaker 2:Maybe. But But if you see the web web web 3, the the average web three fund has done better than the average venture fund. So it's hard to just because Sequoia put a decent sized check into FTX and it went to 0 Yeah. Well, that was a small part of their fund, and their fund still is done well.
Speaker 1:Own Bitcoin. They might have owned Coinbase. They might have owned, yeah, any number of of crypto companies.
Speaker 2:The average crypto VC did very well over
Speaker 1:the past. The ones that are branded as Web 3. Like, if you were in Web 3, you probably got some Solana. Probably did very well. Yeah.
Speaker 1:Or Ethereum. Like, the Ethereum ICO guys are just, like, all fantastically wealthy. And it doesn't matter that they that they bought rebought the top a little bit with, like, NFT projects that didn't go anywhere. Like, it just doesn't matter when the fund returns are so high. Investors I spoke to over the weekend are aren't panicking, but they're clearly concerned, particularly that they could be taken so off guard.
Speaker 1:Don't be surprised if some deals in process get paused. Yes. But there's still we don't know there's still a ton that we don't know about DeepSeq, including if it really spent as little money as it claims. And obviously, there could be national security impediments for US companies or consumers given what we've seen with TikTok. The bottom line, the game has changed.
Speaker 1:Very dramatic. Right?
Speaker 2:Dramatic article with not a lot of substance.
Speaker 1:Not a lot of substance. But let's close out on a, on a lovely post from Zane. He says unreal Friday night setup, and he has I think he has Twitter up in here. X open in
Speaker 2:the x show.
Speaker 1:And our show, Technology Brothers.
Speaker 2:Thank you for being there with us, Zane.
Speaker 1:Thanks for watching. We appreciate you. I love that you're enjoying us.
Speaker 2:And for the record, we tried to go live today.
Speaker 1:We got blocked.
Speaker 2:Too many posts to rip.
Speaker 1:Yep.
Speaker 2:Too much timeline to go through. We're gonna try again tomorrow.
Speaker 1:Yeah. They they won't censor us.
Speaker 2:Yeah.
Speaker 1:We can't be held back. Yeah. It's it's inevitable.
Speaker 2:Yeah. The Chinese labs, they tried to censor. But, we're gonna go live.
Speaker 1:We're gonna go live. We're
Speaker 2:taking it live.
Speaker 1:Get ready. And thanks for watching. Leave us 5 star reviews, and don't forget to put an ad read in your review. We'll read it on the show. It's free real estate, folks.
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Speaker 2:Thanks so much. See you tomorrow. Cheers.