SPEAKER_00: Jason, you're talking about the amount of money in play here. Look how much things have changed just today. NVIDIA has been dethroned overnight by DeepSeq and is now the third most valuable company compared to the first. Crazy. That is such a change. Yeah. Wow. That's mind-blowing to me.

SPEAKER_03: The AI trade giveth the AI trade. Take us away.

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SPEAKER_06: Hey, everybody. Welcome back to This Week in Startups. It's Monday, January 27th.

SPEAKER_09: A day that will live in infamy or intrigue, or it will be a footnote in the history of AI, but we have a lot of handwringing going on. We have a story here that we touched on on Friday that seems to have captured the imagination of everybody in our industry and geopolitics. And you know it's a big story when it actually hits like the Sunday shows or it starts to hit mainstream consciousness.

SPEAKER_08: But China has, as everybody knows who listens to this program, released a model for $6 million with old iron, maybe, sort of, that could be revolutionary or it could just be a little smoke and mirror. So we're going to get into it here, and it could be both of those things, all of those things.

SPEAKER_10: And, uh, how are you, Alex?

SPEAKER_11: I'm doing really good.

SPEAKER_12: You know, I was a little bit offline yesterday, but I woke up very early this morning, hyped and stoked and ready to go. And the first tweet I saw was NASDAQ off 5% over deep, and I was like, oh man, all right, let's go.

SPEAKER_13: Well, the AI trade giveth and the AI trade taketh away. The DeepSeq project has, uh, shaken the public markets, specifically NVIDIA, uh, I think, because what this project, we'll see how true it is, uh, has done more than anything is made people question the need for CapEx spending. At the Apex, CapEx spending announcements, which happened, uh, at the end of the inauguration with, uh, Masayoshi-san, as all Apex funding announcements do, when Masa comes in.

SPEAKER_17: You know you've reached the peak, man.

SPEAKER_19: That's it. You're at the top of our efforts. That's the peak. That's the top tip. When Masa comes, he puts that little straw on the camel's back, and then, ha, bah, just snaps in half.

SPEAKER_21: Exactly.

SPEAKER_12: Like, what are we talking about? Project Stargate, the plan, uh, that was announced very recently to invest $100 billion now and up to $500 billion, Jason, over the next couple of years. Essentially, a massive data center build-out. And also, Meta last week dropped, and we talked about this, I think it was on Friday as well, a $60 to $65 billion CapEx spend for this year. A two gigawatt data center, so large it would take up a big chunk of Manhattan. And now, all of that seems ever so slightly silly because DeepSeq has shown that you can make models that are very high quality and can challenge the state of the art for what is considered to be a much lower price tag. Now, you said $6 million earlier, you're referring to the white paper that DeepSeq put out, I read the same thing. There has been discussions about what's included in that price point, should it be $6 million? I say, great, 10 exit, $60 million, still cheap compared to what we've seen out there in the market. But if you can do things more cheaply, Jason, the gist there is not really the dollar amount, but just how much compute you need to make it happen. And if you need less compute, then, well, maybe your data centers are more like financial albatrosses that will depreciate versus a way forward. So we've had a flip from, look at the money we're spending, therefore take us seriously, to look at the money we're spending on data centers, perhaps we took it ourselves too seriously.

SPEAKER_27: Yeah, and there's a number of interesting ways in which they did this in terms of building the model and how it's executed.

SPEAKER_09: And that's all in the paper.

SPEAKER_13: I think how they did it is interesting, of course.

SPEAKER_09: And I think leveling up the conversation, the question becomes, and we actually had this discussion here, which is when you have constraint, when you have limited resources, you get clever.

SPEAKER_13: So we call this sort of MacGyver it, if you remember the TV show, for those of you, there was a guy MacGyver who would, you know, basically stop a rogue nuke with gum and a paperclip and basically proved, hey, you can get a lot accomplished with a small amount of resources if you put your mind to it.

SPEAKER_09: And so this is what survival is like. This is what happens when you ban H-100s being sold into China, is they take what they have and they do the best you can. Now there is, uh, a bunch of, and I think, uh, scale AI CEO in Davos said, you know, they have a bunch of H-100s in China, all these ones that go to, um, which was it Taiwan or the Philippines?

SPEAKER_13: There's one or two markets where a lot of these get sold. Singapore, I think is another pathway. Oh, Singapore, yeah. So 20% of the H-1s, H-100s get sold in Singapore, but Singapore is not using 20% of H-100s. Wouldn't think so. So where do they wind up is always the question. So like many things that occur in China, it's a black box. There might be more to the story. Putting that aside, it's an accomplishment. It's a white paper. It's open sourced. So the experts seem to think this is, uh, largely legit. Yeah. That's the consensus is like, Hey, this is not faked in some way. And then the next card that turned over was, well, maybe they ingested other models to make this model. Mm-hmm to which I say, okay, so yeah, that sounds like they stole something, but they're all building off each other. And okay, maybe, but the output is still the same. And then I saw that the deep seek app was number one in the app store ahead of, uh, some other, which by the way, you can just pay like an outside firm five grand and do that. So it's pretty easy to game, uh, the app store. So don't read too much into that, but this is now, uh, game on because if the Chinese, uh, want to prove a point to us about AI exceptionalism, to have a hedge fund manager with, but a handful of, uh, scientists and engineers in their spare time, basically make something that races up the charts for a small amount of money. Well, what does that say about all this crazy investment we're doing?

SPEAKER_12: Well, it goes to show that right now, some very smart people with access to at least some GPUs in quantity, whatever level of quality they were, whatever can do quite a lot. And to me, the biggest point that that makes is a little bit less about China versus us, but about the fact that we are nowhere near having wrung out all the possible gains, all the possible improvements.

SPEAKER_20: improvements I read the white paper, Jason, and I I'm not going to lie when I'm reading through about how they use pure reinforcement learning and different types of cold start data and supervise fine tuning through rejection sampling. Okay. I'm not going to lie.

SPEAKER_12: That's not where I spend my time. So I'm not going to pretend to know every single word here, but when we're dealing with open source stuff, it just doesn't get held in the same kind of national grip.

SPEAKER_37: So to me, these improvements will filter into the U S market. We'll learn from them. Sure.

SPEAKER_12: And then the question becomes, is there any advantage to after we've ingested and digested these upgrades and improvements to having a lot of GPUs? Because if there is, then with deep seeks learnings, we're going to be able to accelerate very quickly. And some people are arguing that really this will make the project stargate effort and therefore XAI supercomputer and Anthropic and Amazon, et cetera, even more valuable. Gary Tan said that the news makes product stargate, I think 30 X more valuable in his view, clearly hyperbolic. But like directionally interesting. There's a new paradox.

SPEAKER_09: You know, everybody who's in intellectual, uh, circles, if you want to win the social media game, if you want to win the argument, introduce a new paradox, a new framework for thinking. A new cognitive biases and, uh, Jevon's paradox seems to be the one that Satya Nadella brought up, I guess, late at night.

SPEAKER_40: So he was up late and thinking about this, or maybe that would be, I think everyone was up late thinking about this.

David Friedberg: Yeah. You need to know this. So when you go to your next dinner party, you can mention, yes, that's just Jevon's paradox.

SPEAKER_45: Absolutely. So Jevon's paradox was originally put together to discuss energy consumption and as energy got cheaper, people used more of it.

SPEAKER_12: So essentially the idea behind Jevon's paradox is that when technological progress increases the efficiency of something, i.e. a winter resources used, people, uh, use more of it. So essentially falling cost increases demand. So if AI in this case, kind of complete the point, uh, becomes much cheaper as deep seek showed with its relatively low priced, excellent model, it doesn't mean that it's going to collapse the industry. It means that we're going to use quite a lot more of it. Does that make sense, Jason?

SPEAKER_51: Absolutely. Uh, we have a concept called induced traffic.

SPEAKER_09: I've talked about it here many times. And so people will consume more of something if it's more available. So when you had to go walk to a well and get water and pump it and bring it back to your house and you had to walk to the other side of town, people did not have lawns and lawnmowers. But when they built pipes to your house with water and it was so cheap, people decided they would put sprinklers on their front lawns and make beautiful green patches of grass that were non-native and they could look at and go, wow, look at that beautiful green grass, as opposed to just seeing it seasonally.

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SPEAKER_13: So that's a great example of induced, uh, consumption there.

SPEAKER_09: Same thing with when you make a freeway, people go, Hey, wow, there's a freeway I can get from Austin to Houston, Dallas, or, you know, or San Antonio in X number of hours, minutes.

SPEAKER_08: Maybe we'll go there for a day trip. Maybe we'll go see a concert there.

SPEAKER_09: Whereas if you had to take a dirt road, you'd be like, yeah, I'm not going there. I'll wait for the show to come here. I'll wait for the circus to come here. So that's essentially what people are now saying is, Hey, if that iron is sitting there, we will use it for something in our industry.

SPEAKER_08: I remember Dropbox and YouTube were based upon the premise that all this dark fiber and all of the storage had suddenly become really cheap and it was plummeting in cost. Therefore there was more available. Therefore the idea of hosting somebody's video for free on YouTube became viable. Whereas previously you had to pay every time somebody watched your video, you had to pay your hosting company for it. Yes. It became free when it became free.

SPEAKER_09: Then the entire concept of putting a vlog out, uh, like we're doing right now, a blog, uh, and then eventually a podcast. People said, yeah, okay, we can do this. It's a, it's free. Now, if I had to pay a dollar for every time somebody or 10 cents or even a penny, every time somebody download one of these podcasts, we'd be like, whoa, we got enough. We're going to throttle this thing.

SPEAKER_62: So what happens to this extra capacity? I guess becomes the question.

SPEAKER_45: Yeah.

SPEAKER_00: Well, so I want to bring up another point along this line before we answer that question. So talk about Jevon's paradox, increasing efficient usage of something increases the demand for it.

SPEAKER_12: So Pat Gelsinger up until, you know, 48 minutes ago, Jason, the CEO of Intel put out a very interesting tweet and he compared this moment to the gas life. So computing obeys the gas law, making it dramatically cheaper will expand the market. So I actually am a little bit torn between pessimism and optimism here because on one hand, it's lovely to see such amazing technological improvements. But on the other hand, we worry about what we're going to do with all this hardware. I wonder if we're just going to end up, instead of having a decrease in demand, we've just brought the future forward a little bit. That's correct. Yeah. Okay.

SPEAKER_13: So in that case, then none of the capacity is, is wasted because if I don't, I mean, it's kind of like if you were to build extra apartments, you know, in a market where you had massive constraint, would it cause damage or would it allow young people to get an apartment and be able to live alone?

SPEAKER_10: As opposed to five people living in an apartment like they did in, you know, the, uh, Tenderloin district of San Francisco.

SPEAKER_12: And I, I love that analogy because who are the young people in this AI moment with all this capacity built on to improving AI models? Startups. Exactly. Exactly.

SPEAKER_13: Yeah. So the, you know, this idea that startups were not going to have access to H one hundreds, you know, there might be so many H one hundreds available.

SPEAKER_09: There might be so much capacity available. If this gets easier and easier and easier to, you know, build these models that, uh, yeah, startups will use one to do something that would be considered trivial. Yep. And so w when we deploy these, we're gonna, we're always thinking, well, how do we make the money back? Right? How do we make the money back? So with that $500 billion number, it's like, well, is there $500 billion in software revenue from AI available to make the money back? Uh, okay. Well, we replace every factory worker. If we replace every, you know, truck driver, I mean, you really start to have to think about these things, but now.

SPEAKER_08: Hey, maybe you don't have to think about it as much and putting a bunch of CPU towards, you know, putting a filter on top of your photos, which is what happened with iPhones.

SPEAKER_09: You know, the, the processing power was so great. We, we came up with some uses for it and that's where, that's where we're at right now.

David Friedberg: It's like, we can do things that are seemingly, uh, unnecessary with this. Do you remember when filters were cool?

SPEAKER_20: Like, I remember when Instagram was really little, like people would like pick that, like, kind of like purplish filters, make things look a little bit more kind of exotic. That was fun.

SPEAKER_09: Yeah, it used to be done by, uh, a, a, a, a, a job known as a graphic designer. Oh. So if you had a photo of your family, you would, you could, uh, have a photographer take it as, cause you didn't have access to a camera cause they were very expensive. Yeah, yeah, yeah. Then you would have a graphic designer touch it up and they would touch it up for 200 bucks and then you'd have a Christmas card that you would go to a store and have printed. Oh. Now you just take a picture on your phone and send it to print and you're done.

SPEAKER_00: It's pretty great. That's a little bit different than, uh, the way things work now in which we upload our contact book to an online database, run that through the system we're purchasing cards from.

SPEAKER_12: And then they come free filled out for us with everyone's address. Uh, fantastic. Not at all at risk though, when it comes to, um, uh, security. Now that I think about it, giving everyone's address over to some random company. Jason, why don't we do a little demo? Why don't we show our one in a couple of different contexts here? So what I have done is downloaded, uh, deep seeks are one 7 billion parameter model, and I am hosting it locally on my MacBook pro. If you're curious, this is running a M3 pro chip, but I think 12 or 16 gigas grand, something like that. So Jason, let's sit, let's ask it, you know, who is the president of China and let's just see what it, see what it does here. What I want you to watch is how it talks to itself. So what it's going to do here is it's going to start to riff. All right. So user wants to know this looking back at our history. Let's think about this. I don't know why it's asking. It's giving me emojis. And it says the president of China is Xi Jinping. Very, very simple. Pretty easy. Took into account my, my prior context, nothing there that's going to blow. Anyone's mind. Here is the deep seek, uh, interface. And I'm running R one, as you can see right here. And I'm asking you the same question. And in testing, we discovered a relatively different set. Uh, oh, it got cut off.

SPEAKER_00: Yeah. So what we have seen here is power of open source. The hosted version of deep seek R one has clear lines around what it can and cannot answer. Clearly president of China off list. Whereas the hosted version does not. What's cool though, is you don't have to use the web one. You can just download it. It's open source and run it yourself and so it strips away all that cruft.

SPEAKER_12: And I bring all this up to say that people are saying, oh, you know, it's a censored model. I can't do anything. Look at my screenshots. Well, yeah, or you can just download it. And I think that's incredibly powerful and needs to be discussed more at the early days of these

SPEAKER_09: with the image creation, uh, a friend of mine, you know, they had these filters. You can't do celebrities. You can't do, uh, racy photos. And I had a friend who was like, Hey, look, you can do anybody. You can do anybody and you could change somebody's gender. You could do this. You could do that. You can make somebody a squid person. So the censorship in the hosted versions, I think is going to lead a lot of people to actually run this stuff locally. And then privacy. I did see people were taking the terms of service. If you think tick tock. Is a, Hey, security issue. I mean, if you're using deep seek with any information, you got your head examined. Everything you're sending is going directly to the CCP. So I would recommend not using this. It's probably got all kinds of the hosted version. Certainly everything is being sent directly to servers in China directly to the CCP and being used in using AI. So they're using AI to examine every photo you share, then correlate your IP address with your deep seek, and then build a dossier on every American, and then take public sources of data, run it against geo located data in ad networks, and they have so much information on every American. It's nuts. We need to really address all of that. But, you know, big picture, the question is going to be, will this slow down hardware consumption? And I think that that is actually possible that you might have the leaders of some companies saying, Hey, let's, um, spend the next dev cycles optimizing this, uh, and just making it run better on the existing footprint of hardware we have. And then when you're a capital allocator, you're looking at a pool of capital. Is this billion dollars better spent on more H-100s or is this billion dollars better spent on more head count? And what people might realize here is it might be better to hire more people to do more optimization on some aspect of what you're working on then by the incremental H-100s.

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SPEAKER_08: That was always going to happen. At some point, people would say, Hey, we're utilizing X percentage of this. We're out of training data.

SPEAKER_09: Let's optimize this. And then where would we deploy the capital better? How could the capital be deployed for a better return? And that's the key here. What I've always felt would happen is I think Gemini and, uh, co-pilot at Microsoft is going to become a free product. Eventually they're just going to say, you know what? We can just abstract this cost into our existing business and Apple, if they ever get their act together, which it seems like they're just going to draft off open source. Um, if they ever get their act together or they buy Claude or something like that, which I think is probably what will happen. Eventually they'll just buy one of these language model teams. So they can just start on third base with a team. They will, um, basically kill the market for paid products here. And what could you run the dev and the hosted versions down to? It might just be, you know, so de minimis at AWS, at Azure, at Google cloud, uh, you know, at Oracle's cloud that people just go, you know what, uh, there's not a business here, just like, it's really hard to have a business in storage or transport. It's maybe this like thin layer that's, uh, free for everybody, which is good for humanity.

SPEAKER_06: I think that's the other thing. If you're thinking from a human perspective, like maybe we move to the geopolitical

SPEAKER_09: ramifications of this now, um, it's going to be very hard to constrain the Chinese. Uh, I mean, I do think we win ultimately, but it's going to be hard to constrain anybody by just limiting their access to H one hundreds. Uh, they'll be able to get other chips. And when you train something, people get creative. So maybe they're making knockoff H one hundreds, um, and other kind of devices,

SPEAKER_94: other hardware platforms that will do the same thing.

SPEAKER_37: So, so you wanted this Josh Kushner to be pulled up, cause I think it does detail an

SPEAKER_00: interesting split, uh, in, in the world of venture, uh, Mark Andreessen called, um, deep seas work a gift to the world. And then, you know, uh, Josh Lux was pretty critical of that. And then here we have Joshua Kushner or Josh Kushner, really, um, saying that quote pro America technologists openly supporting a Chinese model that was trained off of leading

SPEAKER_12: us frontier models with chips to likely violate export controls. And according to their own terms of service, take us data back to China. And then he does the, the thinking emoji face.

SPEAKER_37: And Jason, I want you to explain what Josh is trying to say here. Decode the investor speak for the regulars, you know, how you can tell if an investor

SPEAKER_101: is talking their book, how they're tweeting, they're tweeting.

SPEAKER_09: So what Josh is doing here is he, you know, he is the last investor, uh, at the $150 billion chat GPT round around I said, could possibly be the peak valuation of open AI. I don't know if you remember me making that claim on all in, and I made it here as well. So, you know, I, I've been thinking about this for a long time when a company gets extremely overvalued and there's a lot of competition from extremely deep pocketed people and the technology has open source going on, you, you start to look at it and go, Hmm, I wonder if that is the highest valuation this company will ever reach because of this competition. And because so many people are going after the same price. And so there used to be a business in selling packaged browsers.

SPEAKER_101: I remember going and buying Netscape, the browser in package software, a comp USA on fifth Avenue by the public library in Manhattan, like as a kid, just like, oh, wow, look, Netscape in a box and then go get your five and a quarter inch floppy three, 3.5 inch floppy and install it. Uh, you can't, you don't have to buy a browser anymore, right?

David Friedberg: There's not a business in owning the browser.

SPEAKER_20: So I think before you go on, there's a company called Island that's making an enterprise browser with extra cybersecurity that's growing very quickly. It's on the twist 500, but for others, you're dead on back to you.

SPEAKER_72: I mean, so you're paying for security. So exactly.

SPEAKER_09: So you can build things around it and you know, obviously people did a search engine around it and you know, the people will pay for other things or pay other ways. So I think he's probably concerned, uh, about the fact that open source is just the ultimate headwind against a paid product and this open source and this kind of progress coming from small teams, I think is probably going to make people wonder, should I invest in the next round of open AI? Should I give open AI the next 10 billion, 20 billion? And so it's quite possible this might be the peak open AI valuation, and that would be pretty nerve wracking. It's also probably pretty tweaking if people are saying like, just use this, like, why would I pay for open AI? Just use this. Um, because remember that valuation gets backed into based on revenue and the revenue picture for open AI was surprisingly good. I mean, I would have 6 billion in revenue this year, uh, or last year.

Chamath Palihapitiya: So yeah, you know, it's hard to know because they keep talking about the run rate. Um, it's a little fuzzy. You know, it wouldn't be fuzzy if they would just go fricking public.

SPEAKER_126: Like, do you know how annoyed I am with data bricks and their run rate versus ARR versus historical revenue numbers that I have to then tease out chest.

SPEAKER_09: Yeah. The other complications that open AI is, you know, the lawsuit switching to a public, uh, entity from a, a nonprofit. Mm-hmm . And if it is a nonprofit, the nonprofit is entitled to the increase in value. So I think they have to buy it for 150 billion because that's what its value was, or, you know, somewhere close to that number, uh, before the flip happened. So maybe they, the investors only get from 150 billion forward.

SPEAKER_40: It's one of the most. Convoluted.

SPEAKER_00: Convoluted. Um, yeah, you ever see their Chinese, uh, F1 filing to list in the U S and then they show the, the ownership chart. Yeah. And it has like 40,000 boxes of like JVs and like dotted lines for control and like offshore, onshore. That's what open AI feels like to me. But what's interesting is that there's still some reasonable optimism out there from folks like Aaron Levy that we are seeing both high end and low end demand. So Jason, we talked about, um, open AI and their operator product. This is essentially like computer use from anthropic and it's how it can like go there and play with your browser for you very expensive right now.

SPEAKER_12: And then he, Aaron Levy points out that deep seek is cheap and also blowing up. And he says, all this points to the race is only going to continue to accelerate. So my view is I don't think we're going to have a data center over capacity problem in five years, but maybe we might have our first period of time in which if all this shakes, other people think we're not GPU or compute constraint, that would be

SPEAKER_00: interesting and not a catastrophe. I don't think, I just wonder if you were hoping that NVIDIA would double again, you

SPEAKER_37: might be a little bit sad, but I I'm not at all pessimistic about any of this. It just doesn't seem bad.

SPEAKER_09: Yeah, I mean, it's, um, we'll, we'll see what the truth is around the model, um, over time and the, like you said, early in the conversation, whatever, because this is an open source, because there's a paper, because they're sharing their innovations, it'll get incorporated into any interesting techniques or approaches will get incorporated into our research. And we have the money to deploy the hardware in America. So why not have that as a potential runway? It's like having extra capacity, but this would change.

SPEAKER_46: I think a lot of how people think about NVIDIA, the stock, and they were off double digits. Yeah. 10%?

SPEAKER_132: 15% at the, uh, at the start of the, at the start of the show. This is a chart of NVIDIA's share price for the last week.

SPEAKER_00: Uh, inclusive of where we were earlier today. And, uh, it went from roughly, I don't know, Jason, one 50 to about one 20. And if you're a multi-trillion dollar company, each 1% decline of 1 trillion is 10 billion. So every point of 3 trillion is 30. And if you have a 10 to 15% drop, that's 300 to $450 billion in deleted market cap in

SPEAKER_11: a day, which is a lot almost 17% right now.

SPEAKER_00: So that's a lot, that's a lot, a lot, a lot. So there's going to be some putting up. I think NVIDIA needs to show that demand's hot.

SPEAKER_12: Uh, I think that the closed source model companies now need to put up some big points on the board to show that they're not essentially dinosaurs that are trying to build something closed source. That's going to be open source. And you know, that's gonna be fun to do fun to watch.

SPEAKER_38: Yeah, absolutely. I think, uh, the operator product, which, you know, we've seen different versions of

SPEAKER_09: that taking over the browser versions is going to be the feature of 20, 25. I think a lot of folks are going to look at, you know, repetitive tasks.

SPEAKER_30: Uh, and this was part of the thesis with our investment in Athena, go to athenawow.com and get like a month off or something. They give a really good deal, uh, to my followers.

SPEAKER_09: And so, you know, if you're watching somebody do a repetitive task in their browser over and over again, you could basically anticipate what they're doing and build it out. So when you are building out the docket, go to thisweekinstartups.com slash docket. And you can see the docket we're working from, let's say you're talking about Nvidia and anytime you talk about Nvidia, you pop in a, uh, a chart, it might just fire off a tab and say, you know, in the early version of it might say, insert chart from white charts or wherever you use and you say, yes, or, and then next it would say inserting chart from white charts, 10, nine, eight, and you can cancel it. But if you don't cancel it, does it. And then eventually you're going to wake up in the morning and it's going to be like, here are the five most important news stories of the day that we know Jason's been talking about, Chamat's been talking about, Satya's been talking about, this account's been talking about, that account's been talking about. And here are those stories.

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SPEAKER_09: That would then basically take, I don't know, the next half of the docket and make it, you know, you're starting on second base, third base. That's really what this year is going to be about is those kind of repetitive tasks being studied and being anticipated and then being done for you because there's so much capacity out there. Why wouldn't it do it? And it might make you 20 stories for the docket or three versions of each and say, which one do you like best and you say B and you don't even say why you like it best. You just say this one, see, it feels the best, or you might say, I like this, you know, sections, it gives you seven sections per item in the document and you click on it or it watches the show, it sees what you mentioned and then it reinforcement learns it.

SPEAKER_38: So the idea of a producer would be contained in a browser agent.

SPEAKER_11: Yes.

SPEAKER_20: But to make that work, I'm either going to need to use the same browser across all my devices logged into the same model, which you give a lot of power to the OS makers,

SPEAKER_12: Android and iOS, or I'm going to have to bring my own model via an app that I then use across all my platforms. The problem is we want to have a unified AI digital assistant that knows me so we can do the reinforcement learning that you described about what I like to read and so forth. But the problem is I have such a fractured computing experience across Windows OS 10 or Mac OS, sorry, now, and then also iOS, it's a little bit, it's going to be a while to get there. I think actually.

SPEAKER_09: If you see the value in it, you would then consolidate that usage and be logged into Chrome on your mobile Chrome on your desktop. Like these are the reasons people will actually switch an operating system is for a feature this powerful. I was using Chrome OS in our organization before you got here for a couple of years and zoom became such an important product and zoom sucks inside of a browser window. I mean, you can use it. Google hangout sucks inside of a browser window that when COVID happened, I had to revert back. But in our offices, I put a Chrome box on the back of everybody's Dell monitor and it was awesome because you didn't have all this fruff that you get with iOS, Apple music, photos, messages. And we just were at work, a hundred percent focused on just work stuff. And we all loved it because it was also very fast and crisp. It didn't crash. And it was just great to just always be in the browser and have this perfect operating system. And people didn't believe me, but then they saw me doing it. And I was like, guys, you can't believe how awesome Chrome OS is. Cause I'd watched my, um, I'd watched kids using it and they had sent me a Chromebook

SPEAKER_100: and, um, one app, one killer app can make you switch. And zoom was that killer app that made us go back to iOS desktop environments.

SPEAKER_00: Yeah. Before we move on from this and talk maybe just a little bit about, uh, what people are going to do. I just want to say that right before we came on air, um, deep sea dropped a new model.

SPEAKER_12: So I, I haven't had a chance to look into this yet. Uh, it's called, uh, Janus pro seven B it makes images.

SPEAKER_126: They claim it has some cool features to it. We'll look into it. Y'all all this is to say that the, the parade, the rain on everyone's parade from deep seek is still raining. Like the, the, it's yeah.

SPEAKER_38: Great to get a kick in the ass. It's good for the industry. I think America still wins the day. I think open source still wins the day. And, uh, that's my position.

SPEAKER_101: I'll stick to it. I think it's going to be open source all the way. Oh, that's, that's interesting.

SPEAKER_165: Okay. I, I don't, I don't disagree with you.

SPEAKER_09: Um, yeah, now then how the open source is implemented. There is an opportunity for somebody like you explain this browser company that we added to the twist 500, creating secure browsers, you know, virtual browsers, all that kind of stuff. It's been around for a while for, you know, people out, you know, the NSA or CIA, they have had these type of browsers all the time, uh, that are contained and, uh, firewalled, et cetera, so you can't jump from the browser into like, you know, the CIA systems. Cause it's just a virtual machine. These are the things that are going to be the next card that drops, which is just like you loaded a version of this when we're talking about, Hey, you're trusting it. Maybe our firm has a container of, uh, when we're all working, it's watching our desktops. Everybody understands like, don't do personal stuff here. It's, you know, you're on your, you're on your corporate machine. That's like the next card that's going to drop because then it would be like, there'd be some AI manager in the sky or AI concierge in the sky. That would be like, Hey, by the way, you've been talking about this company on the podcast. There's a competitor to this company we're consider investing in.

SPEAKER_167: You should have a conversation with this person.

SPEAKER_06: That's where this all winds up is some God CEO in the sky, watching your whole

SPEAKER_09: organization run, watching other organizations run and saying, you know, at $10 million in revenue, you really need to have three more salespeople and two less customer success people, or, Hey, by the way, you could, they'd be like this, like CEO coach. Maybe you don't need to have customer success anymore. You you're really like falling behind the standard on your FAQ and your automated systems and your training, move some resources from customer support to training.

SPEAKER_08: Uh, and like, this is not far away is this guide in the cloud.

SPEAKER_12: This sure imagining like, what would this be for venture capitalists? Like this is the third time you've tried to back a personal CRM startup. Every time you do this before it's failed. Do you want to send the check?

SPEAKER_11: Click? Yes. Click.

SPEAKER_09: No, this would be what you would do with the crunch based database is say, every time this company, you know, every time you get a company pitch, it goes, here are other companies that were funded, here's why they said they failed, and then you would be talking to the founder and say, Hey, you know, I know these other three companies failed in the space. What are your thoughts on that? Which clever VCs are doing right now when they're talking to a founder, they've got chat GPT for open, or before they get on the call, they're saying, Hey, tell me about the competitive landscape. If this company were to fail, why would it fail? And that's why we're, you know, been building this database, uh, in notion and Coda of our own experience, interviewing startups and figuring out which one

SPEAKER_38: will work just because it gives you a mental model, right? Uh, as we talked about before, you know, helps your mental model on the idea

SPEAKER_132: though, about crunch space and AI, I'll just point this out that I, I saw this this morning, crunch space dot AI is coming.

SPEAKER_12: So I don't think it's impossible to see kind of where they're going to go with this. And I wonder if they can take their data and actually make something similar to what we're describing here. Cause it's going to be awesome. Final thought for me, it's going to be one hell of a year. And I'm very excited about it. Um, I do think though, it's good that people are talking more about the. Socio economic impacts of AI and earlier use doesn't be very interesting. You're like, look, if we're thinking about proximal spend, will it go to more hardware or more people? You know, we spent a lot of time talking about AI productivity, efficiency gains.

SPEAKER_174: And I wonder if the money's not going to go to more H one hundreds or more PhDs.

SPEAKER_12: I wonder if instead it's just going to go to profits shareholders. Yeah. Profits dividends.

SPEAKER_06: I don't know. That's what we, you know, we, we've been talking for a couple of years now about static team size and then we talked about consolidation.

SPEAKER_09: We don't have exactly a term for it, but, um, a CEO Twilio was just talking about, um, this sort of, uh, condensing of T condensed team size. Let's go with that for now. And condensed team size would mean just using AI to slowly take a thousand person team to 900 next year, then 800, then 700. As you do that, those people could be paid. If you're reducing costs 10% a year and you're growing revenue 10% a year, you've got this like perfect storm of earnings. The remaining people could get paid more. The dividends could be higher. And then we're going to just be sitting here with a one person company or like a

SPEAKER_08: 10 person company eventually, and the AI God in the sky running it with 10

SPEAKER_09: minions who are just there to, you know, make sure the thing doesn't go off track and dividends are going to be nuts.

SPEAKER_30: What do you think of that? I mean, let's go political, um, conspiracy theory corner.

SPEAKER_09: The timing of this is this is the timing of this. Cause somebody brought that up in the, um, in our chat, you know, we record this live, go to youtube.com search for this week in startups, hit the bell on, uh, YouTube, Niren, N-I-R-E-N says Trump back in office coincidence. So there's been a lot of saber rattling with China.

SPEAKER_180: Yeah.

SPEAKER_100: Talk of a grand deal, the tick tock, uh, divestiture, uh, and I'll just put it out there, I'm willing to run tick tock for 10% ownership and give Trump 90%.

SPEAKER_181: 9% I'll go, I'll go eight, seven, six point.

SPEAKER_17: So I can name that tune in 1% equity.

SPEAKER_08: So what do you think? Is there something to this? Is this coordinated where they're trying to rattle or shake, uh, or maybe make a point to Trump and America and the great innovation engine engine known as capitalism?

SPEAKER_11: I see.

SPEAKER_00: The thing is that that is a convenient idea because it fits into everyone's idea that the Chinese central government is so all knowing that it would have insight into every single AI company could select a winner, could then foment a new cycle to drive down

SPEAKER_12: the value of us equities. Maybe they released V3 back in December. R1 came out in January, but it's more like they just had some breakthroughs and pulled off some real innovation. Now I expect China to try to make noise about this for sure. You mentioned bots being a way to send things up the app store chain. I wouldn't be shocked if there was some chicanery going around there. But the real conspiracy theory that I think everyone should hold on to if you're going to have fun with them is that keep in mind, deep seek is the subsidiary of a hedge fund. That hedge fund subsidiary just moved the market. So they'll find interesting theories that they shorted Nvidia drop the model and makes this

SPEAKER_187: cheaper and then made a lot of noise about it.

SPEAKER_09: Hold on a second. So this whole thing could have been architected to create the greatest short of all time.

SPEAKER_12: Yeah, but that's a little bit like building a car that can fly just to drive around the block. Like, I mean, they did all, all the work to make an awesome AI model and then it happened to be so good that it changed the conversation. You, I don't know if you can see around enough corners to make that short trade a possibility,

SPEAKER_20: but they did move the market and hedge funds love to move the opposite direction because they're a hedge.

SPEAKER_09: This feels like a theory worthy of a double clicking on a bit. Uh, you know, who benefits is always the question you want to ask in those, uh, murder mysteries, Columbo. Uh, what is it? Kibono or who benefits the timing here is just so delightful, uh, and so perfect first weekend for Trump, all these great wins, this big announcement for masa.

SPEAKER_101: I mean, people knew that this announcement was coming and they might've had this done for a while and they've got to pick when to drop it.

SPEAKER_11: So interesting project. Stargate was cooking, uh, originally with Microsoft, actually, if you go back and read

SPEAKER_00: the information, it's reporting, but I will say, what does China want to do? It wants to make the U S probably a little bit less economically certain in itself and

SPEAKER_12: therefore less willing to shake up trade dynamics. So here's one way to do it. Um, okay. Let's put this down for now and talk about a couple of other things. I want to talk about startup shutdowns, Jason. We've been talking about Bench and Lever and a number of other companies that have recently hit the wall as it were. So TechCrunch has an interesting article out and they pulled some different data points from different providers of information to figure out how much did startup shutdowns accelerate last year. So Carta found a 25.6% increase in 2024 compared to 2023. Now keep in mind, Carta's data is mostly U S based companies that happen to use their cap table management software. So it's a slice of the market, but still a 25% increase is a quarter. Fair enough. Angel list. According to the same tech one story, shout out. Julie board said that there were 364 shutdowns that they could see that was up 56%. And then layoffs to FYI actually saw a decrease in shutdowns.

SPEAKER_194: So I would say two data points showing a pretty sharp increase in shutdown activity.

SPEAKER_12: One showing something a little bit flatter. I'm curious, what is the chatter on startup shutdowns this year, Jason? We've talked a lot about IPOs and MNA, but I'm curious, what's the back channel on where this is going?

SPEAKER_38: Yeah. So I think a lot of startups were holding on, trying to survive through really high

SPEAKER_09: valuations that they had raised money at.

SPEAKER_38: They had war chests and they did round after round of, uh, layoffs from peak ZERP era to now.

SPEAKER_06: And if you survived, the thesis was, Hey, maybe you could either thrive or get to the MNA finish

SPEAKER_09: line. And so I think that's, what's happening here is we're seeing that some people who tried to hold on were not able to get there. They just ran out of lift, right? They had a certain amount of altitude to get to the airport and they were, they were out of fuel. So they were gliding. And so all those gliders, you know, coming in either at the runway, laying everybody off, selling the asset, or in some cases just mismanaged and didn't, you know, land the plane properly. I'm trying to be graceful here, but you know, when you have three years of runway and you know how to land the plane gracefully, the board and the CEO and the management team did something wrong. So they probably hoped against hope that they can raise a round. And so they didn't cut their spending and they didn't pursue MNA in a really thoughtful way. So whenever you see like these things blow up spectacularly, you're like, wait a second, they raised 200 million and then they still crash into the side of the mountain. Like, how does that happen? Usually that happens. There's some conflict at the board level where one group wants to keep going for it. And they say, Hey, we'll raise around, we'll raise around. And then the round shows up and the round is incredibly punitive. It's what's called pay to play. So if you don't put money in, you lose your shares or your shares get turned into common, you get diluted, crammed down rounds, another way to say it. And so when these kinds of things happen, it's usually because the founder and the board were like, Oh, we're so close. We got this white night. We're going to close this round. And then it doesn't happen. And all of a sudden it's like, Oh, we can't make the runway boss. What are we doing? It's like, okay, we're ditching. Right. And that's what we're seeing is a bunch of people ditching planes. And then of course, what happens is the VCs just leave the board and focus on their winners because it's a power law business anyway. So you can, you got to parachute. The VCs just parachute out. They're like, we're done. Now who's left on the plane? The employees, the angel investors, uh, you know, the management team, the founders, the, sometimes the customers and yeah, boom, just big hole in the ground. So it's part of the workout process. Whenever you have a crazy party like this is everybody takes a different approach.

SPEAKER_101: You know, my approach is always, I, I, I fight till the, you know, dying breath. If the founder wants to fight, if the founder doesn't want to fight, well, then we just have to land the plane and, uh, get people their severance and, you know, a, a dignified exit if possible, you know? Yeah.

SPEAKER_09: And that's when you see all these tech crunch headlines, uh, that are like, oh, we had an amazing exit, you know, and the, the writers who are writing those stories, um, they don't have any insights. They don't usually get the actual details of it, but if there's no dollar amount announced,

David Friedberg: it's probably zero. It's or, or it's $4, but I, I want to go back to your point.

SPEAKER_09: Maybe some common shares in the other companies. So maybe, you know, uh, an acqui-hire of some, it's a saving face acquisition.

SPEAKER_20: Throw a bone, make everyone happy. The point you made about successive rounds, having tons of runway and then never getting the company's burn rate to a level that made sense for the new market conditions. It, it, I think explains my question because I was looking at the same tech crunch article and Carta data says that 32% of shutdowns that they, they saw came from

SPEAKER_12: enterprise SAS and that surprised me a little bit. Cause it feels so like, I feel like a larger percentage than I would expect from companies that would have been building durable ARR compared to say consumer startups at the same time or biotech. But if you never got your cost structure under control, then having more AR doesn't really matter. Cause if you're plus 5 million ARR and you're losing 20 million a year, no one cares.

Chamath Palihapitiya: Precisely.

SPEAKER_09: So, you know, the, the SAS model makes it easy, um, to get some number of people to get value from a business to business product, to pay for it. Sometimes people through sheer force of charisma or well can sell a great story to another enterprise to get them to pay for it. But like you said, it could be upside upside down there. They're spending $4 to make a dollar and the economics don't work out. Whereas in consumer or a biotech it's binary, either the product gets viral and takes off and people love it or it doesn't. And that goes for a medicine or a social network, either it works or it doesn't. So you can really have an easy time shutting it down. It's like, it didn't work. Experiment failed. It does not cure cancer, does not grow virally. And you just move on, you know, it's like, we did a great job. We, you know, we worked hard and we move on. This is part of, of the power of our industry is that we can have this many sharp, this many shutdowns, this much seeming chaos in the portfolios and still wind up being a viable investment category, right? We're a viable industry, even with these big swings missing and speaks volumes to our industry and how awesome it is for the world that we can just take chances

SPEAKER_100: after chances after chances and, and still be viable to get returns.

SPEAKER_09: And so now with the end of the wrath of Lena Kahn, if we can just let more, uh, M and a happen, we would see, uh, an even better outcome. So hopefully, uh, the new regime that's in power now, we'll just let it rip on M and a that's what bankers think. That's the, that's the vibes in the banking community is, you know, let the games begin, start sending out folders and packages and Hey, who wants to buy this SaaS company? Who wants to buy that SaaS company? So that SaaS company you're talking about with 5 million in revenue, maybe, you know, there's 10 people on that team who are really great. And some other SaaS company wants to buy them or Benioff or Microsoft want the team and they want to make it a feature inside of Microsoft office or a feature inside of a hubspot or Salesforce. And now people will be like, it's worth doing that because we're not going to get to the finish line and have, uh, you know, 24 months of lobbying, um, regulators.

Chamath Palihapitiya: Yeah.

SPEAKER_12: I just, I remain very, very interested in how many aqua hires we'll see this year. Just given how much you've talked about static team size, there will always be outlier performers that people want to hire and will pay insane amounts of money

SPEAKER_00: for the joke was that deep seat was spending $6 million to train a model. And then that meta had like 10 people getting paid that much per year to work on their own AI, whatever the case may be. But I just wonder if people are going to be willing to do salvage purchases to get people when they're cutting staff elsewhere. It just seems to be incongruous to me to have the static or condensed team size trend and then say, but we're going to see more M and A this year. So I think it's going to be much more focused on strategic products and then also technologies, which is different than, than doing aqua hires. Cause I think there's fewer companies that have those that might be interesting to be purchased targets.

SPEAKER_09: So you're right that they don't want to buy a thousand developers, but there's always a market for a really tight team. And the cost structure is so affordable now based on not having to bring over all these middle managers. Like those things don't exist in these new companies. So they'll just cherry pick these five people, these five people, you know? Okay.

SPEAKER_20: That makes it better for seed companies though, because they're smaller already. There's less to cut less cost structure to remove. They don't have their own. Yeah.

SPEAKER_09: And, and people will be open to it because it's like, oh, I can't get a job at the magnificent seven. So if our team can still work together, that seems like a pretty decent opportunity. And so they'll either go with their five person team and start a new company and bring this lean mean efficiency to a new product and a new cap table, or they will bring that lean mean team inside of a big enterprise, get paid big money. Uh, and they, the big enterprises don't have to be perfectly efficient. That's one of the pieces of good news. If you're going after a big prize, like say self-driving, you could be really inefficient getting there. If you're going after a big prize, like AI, do you have, or cloud computing, you don't

SPEAKER_08: have to be super, uh, efficient getting there. You can make a ton of mistakes because the, the, the prize at the end is so great.

SPEAKER_83: I'm reading, um, the NVIDIA way by take him and, uh, we were going to have

SPEAKER_12: tell you on the show, uh, but as it turns out, there's some things going on that have taken his time away from us. So we will have him on shortly, but I bring that up because NVIDIA made more mistakes

SPEAKER_00: early on than I thought. Like they, they made some pretty big mistakes early on, but one point we'll talk about

SPEAKER_12: this later, but like inclusion of sound in their graphics card, which led to a lot of compatibility issues nearly killed the company.

SPEAKER_00: Who knew, but now they are, you know, where they are, but Jason, you're talking about, uh, the amount of money in play here. Look how much things have changed just today. NVIDIA has been dethroned overnight by deep seek and is now the third most valuable company compared to the first. Crazy. That is such a change. Yeah.

SPEAKER_02: Wow. That's mind blowing to me.

Chamath Palihapitiya: The, the AI trade give us the AI trade, uh, take us away. I wonder if it's a buying opportunity. I don't know. Oh, I'm going to keep on index funding. So I'll keep on buying no matter what. Ha ha ha.

SPEAKER_224: Yeah, exactly.

SPEAKER_09: Well, I mean, this would be where stock picking, uh, goes wrong because if you thought you could pick and you just bought the magnificent seven or an index, you'd be fine today. And if you just consolidated into NVIDIA, you would have been great for three years,

SPEAKER_101: four years. And then, you know, but if you were the last person in, you would be down significantly.

SPEAKER_12: Or if you were a, an absolute genius and you decided to purchase something that came to this, uh, a two X long NVIDIA daily ETF, if you were holding that, I think you're gonna get two X down. So maybe, uh, you know, maybe you're not smarter than the market random person who's

Chamath Palihapitiya: out there. So that's using leverage to buy more NVIDIA on top of the amount the capital owns is.

David Friedberg: Yeah. Well, leverage is fun when you're making money. Be careful folks. Yeah. Be careful folks. Yeah. I mean, I think that was one of the things in Robin hood.

SPEAKER_09: I always was, you know, uh, counseling to people who use Robin hood, like the amount of cash you have and the amount of leverage you have in your account. Like I, I just go with the cash, right? You're buying power, you know, uh, is it's fine to understand those concepts if you want

SPEAKER_100: to be an aggressive trader, but I, I kinda like I'm old school buy and hold for five or 10 years. Yeah. Nice, nice way to live.

SPEAKER_00: What did Charlie Munger say the three L's that will trip up a man and this is dated. So roll with me here, but he said it was liquor ladies and leverage. Okay.

SPEAKER_236: There you go. So just keep being careful.

SPEAKER_12: Yeah. I'm going to throw in one tiny quick one before we go, Jason, which is that sail point is going public again. Uh, we missed this, I think during the election, brouhaha, cause they dropped their S1 right before inauguration. So we were all a little bit busy, uh, sail point, which people don't know does identity management, security stuff a bit like Okta. If you need an analogy when private back in 2022 for 6.9 billion sold to Tom or Bravo and

SPEAKER_00: Jason, it is still growing. They are saying that their ARR was 813 million as of October of 2024. They've only dropped numbers through Q3 of 2024.

SPEAKER_12: I presume we'll get an updated S1 filing, including Q4 data.

SPEAKER_11: So they were public.

SPEAKER_09: Yes. They were taken private for some number of years and then two cleaned up and then brought back public. Uh, which is probably what will happen with Zendesk or any number of these companies. The public markets don't believe in them. Somebody takes them private. Uh, I guess you get forced to have your shares bought cause you get dragged along in many cases.

SPEAKER_101: Uh huh. And then boom, all of a sudden the company goes public again with a cleaner cap table, cleaner books, better earnings. No, none of those things.

SPEAKER_126: No, because, because this is private equity, Jason, do you think they're going to do that?

SPEAKER_12: So what I have here is their, uh, income statement through the first nine months of 2024 compared to the first nine months of 2023. And if you look down here, you'll note that there is this insanely huge interest expense. Wow. How did they spend $140 million in three quarters on interest alone? Well, as it turns out, uh, they had to take out a $1.59 billion term loan when they were taken private to partially pay for the transaction. Got it. And they've been paying through the news for ever since.

SPEAKER_09: So they're, they got a giant loan. They bought their shares, they have interest expense that they have to pay down. Uh huh. So now the company is riddled with debt. Uh huh. It's going public. So if you were buying it now, you're buying it, uh, with a saddled with debt, which means-

SPEAKER_11: Saddled with debt. Yeah. It's, it's an insane, it's an insane thing.

SPEAKER_00: So, uh, their gross profit in that three quarter period was about 400 million and they spent 140 million of that on interest payments alone.

SPEAKER_12: That's a hobbled company because we talked a lot about private equity, making things more efficient and, you know, turning around a business and sometimes that does happen. But I really think that private equity managing to take a company private using money that it will borrow to pay them back for it, getting paid interest along the way. And then using part of the public proceeds to pay themselves back more while enjoying the upside is, it seems extractive to me. And I know this is my bad capitalist moment, but this is one time when I'm like, I don't

SPEAKER_20: think levering up companies and taking them public again in a super unprofitable fashion is the best way to do business.

SPEAKER_30: Not their problem. Right. And if the, they're selling, it's like, it's like that scene in margin, uh, call. Is it, was that the film? Yeah. Margin call.

SPEAKER_249: We're selling at the fair market. William buyer at a fair market price.

SPEAKER_30: Yeah. Yeah.

SPEAKER_09: Yeah. I mean, if people don't want to buy the company, they don't have to buy it. I wonder what that interest rate is. You know, that's where things get so interesting. It's like you think like paying six or 7% for your mortgage is hard. These, uh, corporate. Loans are much higher, right there. They're 10, 15, 20%. Uh, so they, they might have any, they might be paying a really, really big coupon to the owners of that debt.

SPEAKER_40: And so that could be quite punitive if they took a billion dollars in debt to do this transaction.

SPEAKER_00: In the next 12 months, interest payments that relate to the term loan are estimated to be approximately 176.5 million based on the interest rate of actually Jason. Guess 14%. Okay. 11.1.

Chamath Palihapitiya: Okay.

SPEAKER_101: Yeah. I mean, it would be between 10 and 15. So it's basically like a credit card.

SPEAKER_202: Um, a $1.6 billion credit card.

SPEAKER_09: Holy crap. And so I guess the question is if the company was growing 20, 30% a year and you might be able to pay down some principal depending on how much debt you had relative to the enterprise value, but you gotta take that debt out, uh, based on the enterprise value, I guess.

SPEAKER_254: So, all right, everybody, this has been another amazing episode of this week in startups. We'll see you on Wednesday. Bye-bye.