谷歌的觉醒 AI 灾难、英伟达再度碾压财报、Groq 的 LPU 突破

Jason Calacanis (00:00:00): Alright, everybody. Welcome back to your favorite podcast of all time, the all in podcast. Episode 160 something. With me again, Shamalff Holly Hopatia. He's a CEO of a company in investment startups and, his firm is called social capital. We also have David Friedberg, the Sultan of Science. He's now a CEO as well. And We have David Sacks from Craft Ventures in some undisclosed hotel room somewhere. How are we doing, boys? Good. Thank you. This is an odd intro. David Sacks (00:00:32): Introbee anymore low energy and dragged out. Jason Calacanis (00:00:36): I'm sick. What do you want me to do? I'm doing a David Sacks (00:00:38): trophy. Effort. Jason Calacanis (00:00:39): Here. Give me give me one more shot. Watch this. Watch this. Watch professor. You want professionalism? David Sacks (00:00:44): Make the effort. Come on. Jason Calacanis (00:00:45): Here we go. You want professionalism? Chamath Palihapitiya (00:00:46): I'll show you guys professionalism. Is that Banaka? What was that? Is that Anaka? Jason Calacanis (00:00:50): Oh, it is a secret. Banana boat. The weather winner's wide. Rainman, David Sachs. And I said We open source it to hands and a discovery. Chamath Palihapitiya (00:01:06): Love US Ice. Jason Calacanis (00:01:07): Queen up and Rob. Alright, everybody. Welcome to the all in podcast episode, a 167, Sacks. With me, of course, the rain man himself, David Sacks, the dictator chairman, Chabh Polyhopatia, and Our Sultant of Science David Frebert. How are we doing, boys? Great. How are you doing? David Sacks (00:01:25): Is it 167 or 168? Jason Calacanis (00:01:27): I don't know. Who cares? David Sacks (00:01:28): We at least get you to know the episode number. Jason Calacanis (00:01:31): Who cares? We unfortunately or fortunately, we're gonna be doing this thing forever. The the audience demands it doesn't matter. This is like a Twilight zone episode. We're gonna be trapped in these 4 bubbles forever. But we're not like a Superman? It's a it is. This is like the It is, the gift of turkeys. David Friedberg (00:01:47): That glass. Zed. Was that zed? Zed. Yeah. Neil before zed. And he spun through the universe and the plastic being forever for for infinity. Jason Calacanis (00:01:57): Until until Superman took the nuclear bomb out of the, Eiffel Tower and threw it into space and blew it up. David Friedberg (00:02:05): And free my background today, I think I'm gonna have to change now that you've referenced this important scene. David Sacks (00:02:10): That was the best moment of that movie, Jason, where Terrence Stamp says Neil to the president, and the president says, oh god. Yes. And then Terrance seems like the horse. Assad. Not god. Assad. Neil before Assad. David Friedberg (00:02:25): That's Superman 2 or 3. Jason Calacanis (00:02:26): Yeah. Superman 2 is pretty much David Friedberg (00:02:28): the best. Yeah. Jason Calacanis (00:02:29): You know, like Empire Strikes's back, like Terminator 2, it's always the second one. That's the best one. Alright, everybody. We got a lot to talk about today. Apologies for my voice and a little bit of a cold. Nvidia blew the doors off their earnings for the 3rd straight quarter. Shares were up 15% on Thursday, representing a nearly $250,000,000,000 jump in market cap. So let's just let that sit in for a second. This is the largest single day gain in market cap. $247,000,000,000 added in market cap previously Meta did something similar earlier this year. Remember everybody was down on that stock because they were doing all the crazy stuff with reality labs. And then they got focused and laid off 20,000 people. They added a $196,000,000,000. In other words, they added, like, 2 a half airbnb to their evaluation, but let's just get to the results. The results are absolutely stunning. A dare I say unprecedented q 4 revenue, 22,100,000,000. That's up 22 percent quarter over quarter. Up Sacks% year over year. The net income was 12.30000000009x year over year. And the gross margin of 76 percent was up two points quarter over quarter to 12.7% year over year. But look at this revenue ramp. This is extraordinary. Q 1 of 2024 of this juggernaut starts, and it does not stop, and it doesn't look like it's gonna stop just to run up from 7,000,000,000 all the way to 22,000,000,000 in revenue for the quarter. Absolutely. Extraordinary. And, if you wanna know why this is happening, why is David putting up these kind of numbers, this chart explains everything. This is all about data centers. Obviously, if you heard of Nvidia before, the AI boom, it was gaming professional visualization. You know, I think people making movies and stuff like that. Autos, used NVIDIA for self driving, that kind of stuff. Jason Calacanis (00:04:19): But if you look at this chart, you'll see data centers just starting four quarters ago starts to ramp up as everybody builds out the infrastructure for new data centers to deal with generative AI. David Sacks (00:04:32): So just to add one point here, Jason, so what you can see is that NVIDIA was around for a long time, and it was making these chips these GPUs as opposed to CPUs. And they were primarily used by games and by virtual reality software because GPUs are better obviously at graphical processing. They use vector math to create these, like, 3 d worlds. And this vector math that they use to create these 3 d worlds is also the same vector math that AI uses. To reach its outcomes. So with the explosion of LLMs, it turns out that these GPUs are the right chips that you need for these cloud service providers to build out these big data centers to serve now all of these new AI applications. So Nvidia was in the perfect place at the perfect time, and that's why it's just exploded. And the what you're seeing is the build out of this new cloud service infrastructure for for AI. Jason Calacanis (00:05:34): Yeah. And, and also helping the stock is the fact that they bought 2,700,000,000 worth of their shares as part of a $25,000,000,000 buyback plan. But this company's firing on all cylinders. Revenues obviously ripping as people put in orders to replace all of the data centers out there or at least augment them with this technology with GPUs, a 100, H100, etcetera. The gross margin's been expanding. They have huge profits, and they're still projecting more growth in q 1 around 24,000,000,000, which would be a 3x increase year over year. And this obviously has made the entire market rip as in video goes. So does the market right now? And, the S and P 500 and Nasdaq are at record highs at the time of this taping Chamath, your general thoughts here, on something I don't think anybody saw coming except for you and your investment in Grok, maybe, and a couple of others. Chamath Palihapitiya (00:06:29): I think what I would tell you is that the bigger principle, and we've talked about this a lot, Jason, is that in capitalism, When you over earn for enough of a time, what happens is competitors decide to try to compete away your earnings. In the absence of a monopoly, the amount of time that you have tends to be small and it shrinks. So in the case of a monopoly, for example, take Google, you can over earn for decades. And it takes a very, very long time for somebody to try to displace you. We're just starting to see the beginnings of that with things like perplexity and other services that are chipping away at the Google monopoly. But at some point in time, all of these excess profits are competed away. In the case of Nvidia, what you're now starting to see is them over earn in a very massive way. So the real question is who will step up to try to compete away those profits? The old Bezos quote, right? Your margin is my opportunity. And I think we're starting to see and you've mentioned Grok, who had a super viral moment, I think, this week, But you're starting to see the emergence of a more detailed understanding of what this market actually means. And as a result, Who will compete away the inference market, who will compete away the training market, and the economics of that are just becoming known to now more and more people? Jason Calacanis (00:07:55): Friedberg your thoughts. We were talking, I think, was last week or the week before about possibility of Nvidia being a $10,000,000,000,000 company, the largest company in the world. What are your thoughts on? Spectacular results. And then, Chamount's point, everybody is watching this going, maybe I can get a slice of that pie. I mean, I can create a more competitive offering. Obviously, we saw Sam Halman rumored to be raising 7,000,000,000,000, which feels like a fake number. It feels like that's maybe the market size or something, but your thoughts here. David Friedberg (00:08:23): I don't think anything's changed on the David front. There's this accelerated compute build out underway. In data centers. Everyone's building infrastructure, and then everyone's trying to build applications and tools and services on top of that infrastructure. The infrastructure build out is kind of the 1st phase. The real question ultimately will be, does the initial cost of the infrastructure exceed the ultimate value that's gonna be realized on the application layer. In the early days of the internet, a lot of people were buying Oracle servers. They were, like, 3000 bucks a server, and they were running these Oracle servers out of an internet connected data center And it, you know, took a couple of years before folks realized that for large scale distributed compute applications, you're better off using cheaper hardware, you know, cheaper server racks, cheaper hard drives, cheaper buses, and assuming a shorter lifespan on those servers and you could cycle them in and out, and you didn't need the redundancy. You didn't need the certainty. You didn't need the the runtime guarantees. And so you could use a lower cost higher failure rate, but much, much net lower cost kind of approach to building out a data center for internet serving. And so the Oracle servers didn't really take the market. And early on, everyone thought that they would. So I think Chamath point is right. Now Nvidia has been at this for a very long time. And the real question is how much of an advantage do they have, particularly that there is this need to use fabs to build replacement technology So over time, will there be better solutions that use hardware that's not as good, but the software figures out and they build new architecture for running on that hardware in a way that kind of mimics what we saw in the early days of the build out of the internet. So, TBD, right. The same is true in in switches, right? So in networking, A lot of the high end, high quality networking companies got beaten up when lower cost solutions came to market later. So they looked like they were gonna be the biggest business ever. David Friedberg (00:10:21): I mean, you could look at Cisco during the early days of the internet build out, and everyone thought Cisco was, the Sacks and shovels of the internet. They were gonna make all all the value's been approved at Cisco. So we're kind of in that same phase right now with David. The real question is, is this going to be a much harder hill to compete on than we've ever seen given the development cycle on chips and the requirement to use these fabs to build chips. It may be a harder hill to kind of get Sacks. We'll see. Jason Calacanis (00:10:46): Your thoughts, you think we're getting to the point where maybe we'll have bought too many of these built out too much infrastructure, and it'll take time for the application layer as Friedberg was alluding to to monetize it? David Sacks (00:10:58): Well, I think the question everyone's asking right now is, are are these results sustainable? Can NVIDIA keep growing at these astounding rates. You know, will the build out continue? And the comparison everyone's making is to Cisco. And there's this chart that's been going around overlaying the NVIDIA stock price on the Cisco stock price. And you can see here the orange line is Nvidia and the blue line is Cisco, and it's almost like a a perfect match. Now what happened is that at a similar point in the original build out of the internet of the dotcom era. You had the market crash at the end of March of 2000. And Cisco never really recovered from that peak valuation. But I think there's a lot of reasons to believe David is different. One is that if you look at NVIDIA's multiples, they're nowhere near where Ciscos were back then. So the market in 1999 early 2000 was way more bubbly than it is now. So NVIDIA's valuation is much more grounded in real revenue, real margins, real profit. 2nd, you have the issue of competitive moat. Cisco was selling servers and networking equipment. Fundamentally, that equipment was much easier to copy and commoditize than GPU's. These GPU chips are really complicated. I think Jensen made the point that their Hopper 100 product He said, you know, don't even think of it just like a chip. There's actually 35,000 components in this product, and it weighs £70. This is more like a mainframe computer or something that's dedicated to processing. Jason Calacanis (00:12:38): Yeah. It's somewhere between a rack server and the entire Sacks. Yeah. It's giant, and it's heavy, and it's complex. It does say something here, Chamath, I think, about how Well positioned big tech is in terms of seeing an opportunity and quickly mobilizing to capture that opportunity. These servers are being bought by, you know, people like Chamath, I'm sure Apple obviously Facebook meta. I don't know if Google is buying them as well. I would assume so, Tesla. So everybody's buying these things, and they had tons of cash sitting around It is pretty amazing how nimble the industry is. And this opportunity feels like everybody is looking at it like mobile and cloud. I have to get mobilized quickly to not get disrupted. Chamath Palihapitiya (00:13:26): You're bringing up an excellent point, and I, I would like to tie it together with Friedberg's point. So at some point, all of this spend has to make money, right? Otherwise, you're gonna look really foolish for having spent $20,030,000,000,000. So, Nick, if you just go back to the to the revenue slide of Nvidia, I can try to give you a framing of this, at least the way that I think about it. So if if you look at this, like what you're talking about is look, Who is gonna spend $22,100,000,000? Well, you said it, Jason, it's all a big tech. Why? Because they have that money on the balance sheet sitting idle. But when you spend $22,000,000,000, their investors are going to demand a rate of return on that. And so if you think about what a reasonable rate of churn is, call it 30, 40, 50 percent, and then you factor in, and that's profit. And then you factor in all of the other things that need to support that that $22,000,000,000 of spend needs to generate probably $45,000,000,000 of revenue. And so, Jason, the question to your point, And to Friedberg's point, the $64,000 question is, who in this last quarter is going to make 45,000,000,000 on that 22,000,000,000 of spend? And again, what I would tell you to be really honest about this is that what you're seeing is more about big companies muscling people around with their balance sheet, and being able to go to Nvidia and say, I will give you committed pre purchases over the next 3 or 4 quarters. And less about here is a product that I'm shipping that actually makes money, which I need enormous more compute resources for. It's not the latter. Jason Calacanis (00:15:07): Most of the apps, the overwhelming majority of the Chamath Palihapitiya (00:15:11): apps that we're seeing in AI today are toy apps that are run as proofs of concept and demos and run-in a sandbox. It is not production code. This is not we've rebuilt the entire autopilot system for the Boeing, and it's now run with agents and bots and all of this training, that's not what's happening. So it is a really important question. Today, the demand is clear. It's the big guys with huge gobs of money. And by the way, Nvidia is super smart to take it because they can now forecast demand for the next 2 or 3 quarters. I think we still need to see the next big thing. And if you look in the past, what the past that showed you, it's the big guys don't really invent the new things that make a ton of money. It's the new guys. Who because they don't have a lot of money, and they have to be a little bit more industrious come up with something really authentic and new. Jason Calacanis (00:16:09): Constraint Sacks for great art. Yeah. Chamath Palihapitiya (00:16:11): We haven't seen that yet. So I think the revenue scale will continue for, like, the next 2 or 3 years probably for NVIDIA, but the real question is what is the terminal value? And it's the same thing that Sacks showed in that Cisco slide. People ultimately Jason Calacanis (00:16:27): realized that the value was gonna go Chamath Palihapitiya (00:16:27): to other parts of the stack the application layer. And as more and more money was accrued at the application layer of the internet, less and less revenue multiple and credit was given to Cisco. And that's nothing against Cisco because their revenue continued to compound, right? And they did an incredible job, but the valuation got cut. Jason Calacanis (00:16:51): So Friedberg, if we're looking at this chart, the winner of Netflix, the the winner of the Cisco chart might in fact be somebody like Netflix. They actually got you know, 100 of millions of consumers to give Google Chamath Palihapitiya (00:17:02): on Facebook. Jason Calacanis (00:17:03): And then you have Google and Facebook as well generating all that traffic. And then YouTube, of course, Who who do you see the winner here as in terms of the application layer? Who are the billion customers here who are gonna spend 20 bucks a month, 5 bucks a month, whatever it is? David Friedberg (00:17:17): So here well, I mean, let me just start with this important point. If you look at where that revenue is coming from to Chamac's point, it's coming from big cloud service providers. So Google and others are building out clouds that other application developers can build their AI tools and applications on top of. So a lot of the build out is in these cloud data centers. That are owned and operated by these big tech companies. The 18,000,000,000 of data center revenue that NVIDIA realized is revenue to them but it's not an operating expense to the companies that are building out. So this is an important point on why this is happening at such an accelerated pace. When a big company buys these chips from Nvidia, they don't have to, from an accounting basis, market as an expense in their income statement. It actually gets booked as a capital expenditure. In the cash flow statement, it gets put on the balance sheet, and they depreciate it over time. And so they can spend $20,000,000,000 of cash because Google and others have a 100,000,000,000 of cash sitting on the balance sheet. And they've been struggling to find ways to grow their business through acquisitions. One of the reasons is they there aren't enough companies out there that they can buy at a good multiple that can give them a good increase in profit. The other one is that antitrust authorities are blocking all of their acquisitions. And so what do you do with all that cash? Well, you can build out the next gen of cloud infrastructure. And you don't have to take the hit on your P and L by doing it. So it ends up in the balance sheet, and then you depreciate it over typically 4 to 7 years. So that money gets paid out on the on the income statement at these big companies over a 7 year period. So there's a really great accounting and a M and A environment driver here that's causing the big cloud data center providers to step in and say, this is a great time for us to build out the next generation of infrastructure that could generate profits for us in the future because we've got all this cash sitting around. David Friedberg (00:19:14): We don't have to take a P and L hit. We don't have to acquire a cash burning business. And, you know, frankly, we're not gonna be able to grow through emanate because of antitrust right now anyway. So there's a lot of other motivating factors that are causing this near term acceleration as they're trying to find ways to grow. Jason Calacanis (00:19:29): Yeah. And all of this. David Friedberg (00:19:30): I know that was an accounting point, but I think it's a really Jason Calacanis (00:19:32): important one. If you if a 100,000,000,000 gets spent this year, you David it by 4 25,000,000,000 in revenue would have to come from that or something in that range. Yeah. And so, Sacks, any guesses? Chamath Palihapitiya (00:19:42): Do you have to just keep in mind, I think Friedberg, what you said is very true for GCP spend, but not necessarily for Google spend. It's true for AWS spend, but not necessarily for Amazon spend, and it's true for Azure spend, not true for Microsoft spend, and it's largely not true for Tesla and Facebook because they don't have cloud So I think the question to your point that been, for obvious reasons, NVIDIA doesn't disclose it is, what is the percentage of that 21,000,000,000 that just went to those cloud providers that they'll then exposed to to to everybody else versus what was just absorbed because At Facebook, Mark had that video about how many h 100s, that's all for him. David Friedberg (00:20:21): Right. But it is still it is still capitalized. It's my point. So they don't have to book that as an expense. It sits on the balance sheet. Yeah. Sure. And they earn it down over time. Jason Calacanis (00:20:30): You're David Sacks (00:20:30): helping to explain why big cloud service providers are spending so much on the data center build out. David Friedberg (00:20:35): Cash because they're very profitable, and there's nowhere else to put the money. David Sacks (00:20:38): Right. Well, so That would seem to indicate that this is more in the category of one time build out than sustainable ongoing revenue. I think the the big question is the one that Chamath asked, which is what's the terminal value of David? I think, like, a simple framework for thinking about that is what is the total addressable market or TAM related to GPUs? And then what is their market share gonna be? Right now, their market share is something like 91%. That's clearly gonna come down, but their mote appears to be substantial. The Wall Street analyst I've been listening to think that in 5 years, they're still gonna have 60 something percent market share. So they're gonna have a substantial percentage of this market or this scam. Then the question is, I think with respect to TAM is, what is one time build out versus steady state? Now I think that clearly, there's a lot of build out happening now. That's almost like a backfill of capacity that people are realizing they need. But even the numbers you're seeing this quarter kind of understate it because first of Palihapitiya was supply constrained. They cannot produce enough chips to satisfy all the demand. Their revenue would would have been even higher if they had more capacity. 2nd, you just look at their forecast. So the fiscal year that just ended, they around 60,000,000,000 of revenue. They're forecasting a 110,000,000,000 for the fiscal year that just started. So they're already projecting to almost double based on the demand that they clearly have visibility into already. So it's very hard to know exactly what the terminal or steady state value of this market's gonna be. Even once the cloud service providers do this big build out, presumably, there's always gonna be a need to stay up to date with the latest chips. Right? Chamath Palihapitiya (00:22:26): Here's a framework for you, Sacks. Tell me if this makes sense. Intel was the basically the mother of all of modern compute up until today. Right? I think the CPU was the The most fundamental workforce that enabled local PCs, it enabled networking, it enabled the internet, And so when you look at the market cap of it as an example, that's about a 180 odd $1,000,000,000 today, The economy that it created that it supports is probably measured, call it, in a trillion or $2,000,000,000,000, maybe $5,000,000,000,000. Let's just be really generous, right? And so you you can see that there's this ratio of the enabler of an economy and the size of the economy. And those things tend to be relatively fixed and they recur repeatedly over and over and over. If you look at Microsoft, it's market cap relative to the economy that it enables. So the question for Nvidia in my mind would be not that it, is it not going to go up in the next 18 to 24 probably is for exactly the reason you said it is super set up to have a very good meet and beat guidance for the street, which they'll eat up and all of the algorithms that trade the press releases will drive the price higher and all of this stuff will just create a trend upward. I think the bigger question is if it's a $4,000,000,000 or $5,000,000,000,000 market cap in the next 2 or 3 years, Will it support a $100,000,000,000,000 economy? Because that's what you would need to believe for those ratios to hold. Otherwise everything is just broken on the internet. David Sacks (00:24:05): Yeah. I mean, so the history of the internet is that if you build it, they will come, meaning that if you make the investment, in the capital assets necessary to power the next generation of applications. Those applications have always eventually gotten written even though it was hard to predict them at the time. So in the late nineties, when we had the whole dot com bubble and then bust, you had this tremendous build out, not just of kind of servers and all the networking equipment, but there was a huge fiber build out Jason Calacanis (00:24:34): Yep. David Sacks (00:24:34): By all the telecom companies. And the telecom companies had a Cisco, like, you know, a peak. Jason Calacanis (00:24:39): It was worse. You're all coming then well, they went bankrupt a lot of them. Yeah. David Sacks (00:24:43): Well, the the problem there was that a lot of the bill that happened with debt And so when you had the dot com crash and all the valuations came down to earth, that's why a lot of them went under. Yeah. Cisco wasn't in that position. But, anyway, my point is in the early 2000s when the dot com crash happened, everyone thought that these telecom companies had over invested in fiber. As it turns out, all that fiber eventually got used. The internet went from, you know, dial up to broadband. We started doing seeing streaming, social networking. All these applications started eating up that bandwidth. So I think that the history of these things is that the applications eventually get written they get developed if you build the infrastructure to power them. And I think with AI, the thing that's exciting to me as someone who's really more of an application investor is that we're just at the beginning, I think of a huge wave of a lot of new creativity and applications that's gonna be written. And it's not just B2C. It's gonna be B2B as well. You guys haven't really mentioned that it's not just consumers and consumer applications are gonna use these cloud data centers that are buying up all these GPUs. It's it's gonna be enterprises too. I mean, these enterprises are using Azure. They're using Google Cloud and so forth. So there's a lot, I think, that's still to come. I mean, we're just at the beginning of a wave that's probably gonna last at least a decade. Jason Calacanis (00:26:07): Yeah. And to your point, one of the reasons YouTube, Google Photos, I photo, a lot of these things happened was because the infrastructure build out was so great during the dot com boom that the prices for storage, the prices for bandwidth, Sacks plummeted And then people like Chad Hurley looked at it and were like, you know what? Instead of charging people to put a video on the internet and then charging them for the bandwidth they used, We'll just let them upload this stuff to YouTube, and we'll figure it out later, same thing with Netflix. Yeah. David Sacks (00:26:37): I mean, look, when we were developing PayPal in the late nineties, really around 1999, you could barely upload a photo to the internet. I mean, and so, like, the idea of having an account with a profile photo on it was sort of like, why would you do that? It's just preventatively slow. Everyone's gonna drop off. Yeah. By 2003, it was fast enough that you could do that, and that's why social networking happened. I mean, literally, without that performance improvement, like, even having a profile photo on your account, was something that was too hard to do. Jason Calacanis (00:27:09): The LinkedIn profile was, like, too much bandwidth. David Sacks (00:27:11): Yeah. Jason Calacanis (00:27:12): And then let alone video. I mean, the you would get you probably remember these days, you would put up a video on your website. If it went viral, your website got turned off because you would hit your 5000 or $10,000 a month. Crap. Alright. Grock also had a huge week. That's Grock with a q. Not to be confused with Elon's Grock with a k. Chamath, you've talked about rock on this podcast a couple of times. Obviously, you were the I guess you were the first investor, the seed investor, you pulled it, these LPUs and this concept out of, a team that was at Google. Maybe you could playing a little bit about Crocs viral moment this week in the history of the company, which I know has been a long road for you with this company. Chamath Palihapitiya (00:27:55): I mean, it's been since 2016. So, again, proving what you guys have said many times and what I've tried to live out, which is just you just gotta keep grinding. Mhmm. 90% of the battle is just staying Jason Calacanis (00:28:08): alive Chamath Palihapitiya (00:28:09): in business. Yeah. And having oxygen to keep trying things. And then eventually if you get lucky, which I think we did, things can really break in your favor. So this weekend, you know, I've been tweeting out a lot of technical information about why I think this is such a big deal. But yeah, the, the moment came this weekend, combination of hacker news and some other places and essentially, we had no customers 2 months ago. I'll just be honest. And between Sunday Tuesday, We've just we're overwhelmed. And I think, like, the last count was we had 3000 unique customers coming try to consume our resources from every important fortune 500 all the way down to developers. And so I think we're very fortunate. I think the team has a lot of hard work to do, so it could mean nothing, but it has the potential to be something very disruptive. So what is it that people are glumming onto? You have to understand that, like, at the very highest level of AI, You have to view it as 2 distinct problems. One problem is called training, which is where you take a model, and you take all of the data that you think will help train it and you do that. You train the model. You learn all over all of this information. But the second part of the AI problem is what's called inference, which is what you and I see every day as a consumer. So we go to a website like chatgbt or Gemini, we ask a question, and it gives us a really useful answer. And those are 2 very different kinds of compute challenges. The first one is about brute force and power, right? If you can imagine, like What you need are tons and tons of machines, tons and tons of, like, very high quality networking, and an enormous amount power in a data center so that you can just run those things for months. I think Elon publishes very transparently. For example, how long it trains to to train his grok with a K, right, model, And it's in the months. Inference is something very different, which is all about speed and cost. What you need to be in order to answer a question for a consumer in a compelling way is super, super cheap and super, super fast. Chamath Palihapitiya (00:30:21): And we've talked about why that is important. And the grok with a queue chips Turns out to be extremely fast and extremely cheap. And so, look, time will tell how big this company can get, but if you tie it together with what Jensen said on the earnings call, and you now see developers stress testing us and finding that we are meaningfully meaningfully faster and cheaper than any NVIDIA solution, there's the potential here to be really disruptive. And We're a meager unicorn, right? Our last valuation was like a 1,000,000,000 something versus NVIDIA, which is now like a $2,000,000,000,000 company. So there's a lot of market cap for grok to gain by just being able to produce these things at scale. Which could be just an enormous outcome for us. So time will tell, but a really important moment in the company and very exciting. David Friedberg (00:31:19): Can I just observe, like, off topic how an overnight success can take 8 years? Yeah. Jason Calacanis (00:31:25): No. I was thinking the same line. It's 7 year overnight success in the making. There's this class of businesses that David Friedberg (00:31:31): I think are unappreciated in a post internet era where you have to do a bunch of things right before you can get any one thing to work. And these complicated businesses where you have to stack either different things together that need to click together in a, in a Sacks, or you need to iterate on each step until the whole system works end to end can sometimes take a very long time to build, and the term often used for these types of businesses is deep tech, and they fall out of favor because in an internet era and in a software era, you can find product market fit and make revenue and then make profit very quickly. And so a lot of entrepreneurs select into that type of business instead of selecting into this type of business, where the probability of failure is very high, you have several low probability things that you have to get right in a row And if you do, it's gonna take 8 years and a lot of money. And then all of a sudden, the thing takes off like a rocket ship. You've got a huge advantage. You've got a huge moat. It's hard for anyone to catch up. And this thing can really, spin out on its own. I do think Elon is very unique in his ability to deliver success in these types of businesses. Tesla needed to get of things right in a row. SpaceX needed to get a lot of things right in a row. All of these require a series of complicated steps or a set of complicated technologies that need to together and work together. But the hardest things often output the highest value. And you know, if you can actually make the commitment on these types of businesses and get all the pieces to click together, there's an extraordinary opportunity to build motes and to take huge amounts of market value. And I think that there's a an element of this that's been lost in Silicon Valley over the last couple of decades as the fast money in the internet era has kind of prioritized other investments ahead of this. David Friedberg (00:33:25): But I'm really hopeful that these sorts of chip technologies Sacks. In biotech, we see a lot of this. These sorts of things can kind of become more in favor because the the advantage of these businesses work seems to realize 100 of 1,000,000,000 and sometimes 1,000,000,000 of dollars of market value and be incredibly transformative for for humanity. So I don't know. I just think it's an observation I wanted to make. About the greatness of these businesses when they work out. David Sacks (00:33:48): Well, I mean, OpenAI was kinda like that for a while. Totally. David Friedberg (00:33:51): Totally. I David Sacks (00:33:51): mean, it was this, like, wacky and nonprofit that was just grinding on an AI research problem for, like, 6 years, and then it finally worked and got productized into chat VPT. Totally. But you're right. SpaceX was kinda like that. I mean, the big moneymaker at SpaceX is Starling, which is the satellite networks, basically broadband from Sacks. And it's on its way to handling, I think, a meaningful percentage of all internet traffic But think about all the things you had to get to to get that working. First, you had to create a rocket. That's hard enough. Then you had to get to reusability. Then you had to create the whole satellite network. So at least three hard things in a row. Jason Calacanis (00:34:28): Well, I mean, you have to get consumers to adopt it. I mean, you know, don't forget the final stem. Yeah. We Chamath Palihapitiya (00:34:33): had no idea where the market was. Like, early on, it started in my office. And so Jonathan and I would be kind of always trying to figure out what is the initial go to market. And I remember I emailed Elon in at that period when they were still trying to figure out whether they were going to go with lidar or not. And we thought, wow, maybe we could sell Tesla the chips, you know, but, and then Tesla brought in this team just to talk to us about what the design goals were and basically David, no, in kind way, but they said, no. Then we thought, okay, maybe it's like for high frequency traders, right? Cause like those folks want to have all kinds of edges. And if we have these big models, maybe we can accelerate their decision making, they can measure revenue. That didn't work out. Then it was like, you know, we tried to sell to 3 letter agencies. That didn't really work out. Our original version was really focused on image classification and convolutional neural nets like ResNet. That didn't work out. We ran headfirst into the fact that NVIDIA has this compiler product called CUDA, and we had to build a high class compiler that you could take any model without any modifications. Jason these things to your point are just points where you can just very easily give up. And then there's like, we run out of money. So then you write money in a note Right? Because everybody wants to punt on valuation when nothing's working. Yeah. Jason Calacanis (00:35:56): You tried 6 beachhead markets. You couldn't land the boat. Right. Chamath Palihapitiya (00:35:59): You have to make a decision to just keep going if you believe it's right and if you believe you are right. Yeah. And that requires shutting out. We talked about this In the Masa example last week, but it just requires shutting out the noise because it's so hard to believe in yourself. It's so hard to keep funding these things. It's so hard to go into partner meetings and defend a company, and then you just have a moment and you just feel I don't know. I feel very vindicated, but then I feel very scared because Jonathan still hasn't landed it. You know what I mean? Jason Calacanis (00:36:33): You mentioned all those boats landing and trying to trying to those missteps, but 3000 people signed up. Who are they? Are they developers now, and they're gonna figure out the applications? Chamath Palihapitiya (00:36:41): Yeah. I think that back to the original point, my thought today is that AI is more about proofs of concept and toy apps and nothing real. Jason Calacanis (00:36:50): Yep. Chamath Palihapitiya (00:36:50): I don't think there's anything real that's inside of an enterprise that is so meaningfully disruptive that it's going to get broadly licensed to other enterprises. I'm not saying we won't get there, but I'm saying we haven't yet seen that Cambrian moment of monetization. We've seen the Cambrian moment of innovation. Yeah. And so that gap has still yet to be crossed. And I think the reason that you can't cross it is that today These are in an unusable state. Jason Calacanis (00:37:18): The results are not good enough. Chamath Palihapitiya (00:37:20): They are toy apps that are too slow that require too much infrastructure and cost. So the potential is for us to enable that monetization leap forward. And so, yeah, they're going to be developers of all sizes and the people that came are literally companies of all sizes. I saw some of the names of the big companies, and they are the who's who of the S and P 500. David Friedberg (00:37:45): How do you guys reconcile this deep tech high outcome opportunity that everyone here has seen and been a part of as an investor, participant in, versus the more derisked, faster time to market. And, you know, Timoth in particular, like in the past, we've talked about some of these deep tech projects like Fusion and so on, and you've highlighted, well, it's just not there yet. It's not fundable. What's the distinction between a deep tech investment opportunity that is fundable? And that you keep grinding at that has this huge outcome. What makes the one like fusion? That's not fundamental. Chamath Palihapitiya (00:38:22): It's a phenomenal It's a Jason Calacanis (00:38:23): great question. Chamath Palihapitiya (00:38:24): Yeah. My answer is I have a very simple filter, which is that I don't want to debate the laws of physics when I fund a company. So With Jonathan, when we were initially trying to figure out how to size it, I think my initial check was like $7,000,000 to $10,000,000 or something. And the whole goal was to get to an initial tape out of a design. We were not inventing anything new with respect to physics. We were on a very old process technology. I think we're still on 14 nanometer. We were on 14 nanometer 8 years ago. Okay? So we weren't pushing those boundaries. All we were doing was trying to build a compiler in a chip that made sense in a very specific construct to solve a a well defined bounded problem. So that is a technical challenge, but it's not one of physics. When I've been pitched to all the fusion companies, for example, there are fuel sources that require you to make a leap of physics, where in order to generate a certain fuel source, you either have to go and harvest that on the moon or in a different planet that is not earth, or you have to create some fundamentally different way of creating this highly unique material. That is why those kinds of problems to me are poor risk, and building a chip is good risk. It doesn't mean you're going to be successful in building a chip, but the risks are bounded to not of fundamental physics. They're bounded to go to market and technical usefulness. And I think that that removes an order of magnitude risk Yeah. Doesn't mean it's gonna work. All I'm saying is I don't, I don't want it to fail because we built a reactor and we realized. Hold on. To get heavy hydrogen, I gotta go to the moon. Right. David Friedberg (00:40:06): And Jake, Alan Sachs, how do you Sachs, I know you don't you invested in SpaceX. We have Jason Calacanis (00:40:09): done a couple. David Friedberg (00:40:10): So Yeah. So maybe you guys can highlight how you thought about deep tech opportunities versus Jason Calacanis (00:40:15): We probably do something really difficult like this every 50 investments or so because most of the entrepreneurs coming to us because we're seed investors or pre seed investors They would be going to a biotech investor or a hardware investor who specializes in that, not to us. But once in a while, we meet a founder. We really like And so Contrail Line was 1. We were introduced to somebody who's doing this really interesting contraception for men. Where they put a a gel into your vas deference and you as a a man can take control of your reproduction. You basically it's a it's not a vasectomy. It's just a gel that goes in there and and blocks it. And this company is now doing human trials and doing fantastic. But this took forever to to get to this point. And then, you guys, some of you are also investors in Sacks. Which we love the founder. And this company should have died, like, during COVID and making a robotic coffee bar when he started, you know, 7, 8 years ago. Was incredibly hard. He had to build the hardware. He had to build the brand. He had to do locations. He had to do software. And now he's selling these machines and people are buying them and 2 in San Francisco and SFO are making like, I think they the 2 of them make a $1,000,000 a year, and it's the highest per square footage of any store in an airport. And so we've just been grinding and Friedberg, and you gotta find a founder who's willing to make it their lives work in these kind of situations, but you you start to think about the degree of difficulty, hardware, software, retail, mobile apps. Chamath Palihapitiya (00:41:43): I mean, it just Jason Calacanis (00:41:47): gets crazy how hard these businesses are as opposed to I'm building a Sacks company. I build software. I sell it to somebody to solve Sacks problem. It's like it's very one dimensional. Right? It's pretty straightforward. These businesses typically have 5 components. David Friedberg (00:42:01): Yeah. And, Sacks, you've been an investor in SpaceX. But you don't make those sorts of investments regularly at Kraft. Is that fair? David Sacks (00:42:09): Yeah. I have an Elon exception. David Friedberg (00:42:11): I got it. Jason Calacanis (00:42:12): It's about Chamath Palihapitiya (00:42:12): the founders. David Friedberg (00:42:13): Same. Same. We're in our David Sacks (00:42:19): portfolio Jason, we say this much early stage, this much late stage, this much Elon. Jason Calacanis (00:42:24): Elon accept? No. Chamath Palihapitiya (00:42:25): I mean, Jason Calacanis (00:42:25): you you have to be so dogged to to wanna take something like this on because the good stuff happens like you're saying, Friedberg, year 7, 8, 9, 10. As opposed to, like, a consumer product Yeah. Chamath Palihapitiya (00:42:35): I mean, for me, it's like Jason Calacanis (00:42:36): a dozen by year 3 or 4. The only app that took a really long time, people don't know this, but Twitter actually took a long time to catch on. It was kind of cruising for 2 or 3 years. And then South by Southwest happened, Ashton Kutcher got on it. Obama got on it. I think network effect, David Friedberg (00:42:51): I think I think network effect businesses are different because that's all about getting your seed of your network. What I'm talking about is the technical coordination of lots of technically difficult tasks that need to sync up. It's like getting a master lock with, like, 10 digits. And you gotta figure out the combination of all 10 digits. And once they're all correct, then the lock opens. And prior to that, if any, if any one number is off, the lock doesn't open. And I think these technically difficult businesses are some of the and they are the hardest, and they do require the most dogged personalities to persist and to realize an outcome from. But the truth is that if you get them, the Mote is extraordinary, and they're usually gonna create extraordinary leverage and value. And, you know, I think from a portfolio allocation perspective, if you, as an investor, wanna have some diversification in your portfolio, This is not gonna be the predominance of your portfolio, but some percentage of your portfolio should go to this sort of business because if it works, boom, know, this can be the big 10 X 100 X Thousand Sacks Jason Calacanis (00:43:48): Two stories about that. 1 of the early VCs, and Elon stole the story publicly, wanted Elon to not make the roadster, not make the model s, just make drive trains, and the electric components for other car companies. Can you imagine how the world would have changed? And then Totally. A very high profile VC came to me and said, okay. I'll I'll do the series a for, I'll do the series a for Uber. I'll preemptively do it, but you gotta tell Travis to stop running Uber as a consumer app. I want them to sell the software to cab companies. So make it a Sacks company. I said, well, you you know, the cab companies are kind of the problem. Like, they're they're taking all the margin, like, the kind of disrupting them. And they're like, yeah. Yeah. But just think there's thousands of cab companies. They would pay you tens of 1000 a year for this software, Chamath Palihapitiya (00:44:35): and you Jason Calacanis (00:44:35): can get a little piece of the action. I never brought that investor to to Travis. I was like, oh, wow. That's really interesting insight. Sometimes the VCs work against him. Chamath Palihapitiya (00:44:44): I have a very poor track record of working with other investors. Woah. Self reflection. I do deals myself. I size them myself. And it's because a lot of them have to live within the political dynamics of their fund. And so I think, Jason, what you're probably saw in that example, which is exactly why doing things and splitting deals will never generate great outcomes in my opinion is that you, you take on all the baggage and the dysfunction of these other partnerships. And so if you really wanted to go and disrupt transportation, Jason need one person who can be a trigger puller and who doesn't have to answer it, anybody. I find that's why I think, for example, when you look at how successful Vinod has been, over a decade after decade after decade. When Vinod decides, that's the decision. And I think there's something very powerful in that There are a bunch of deals that I've done that when they've worked out were not really because they were consensus, and they had to get supported and scaffolded it, periods where if I wasn't able to ram them through myself because it was my organization, I think we would have been in a very different place. So I think I think like for for entrepreneurs, it's so difficult for them to find people that believe it's so much better to find one person and just get enough money and then not syndicate because I think you have to realize that you are bringing on and compounding your risk, the one that Freebrook talked about, with the risk of all the other partnership dynamics that you bring on. So if you don't internalize that, you may have 5 or 6 folks that come into an a or a b, but you're inheriting 5 or Sacks. Jason Calacanis (00:46:31): Yeah. Partnership to Dysfunctions. Yeah. Yeah. Yeah. Can you just explain really quickly for the audience? Since they heard about GPUs and Nvidia, but they may not know what an LPU is. What's the difference there? Chamath Palihapitiya (00:46:44): A GPU, the best way to think about it is so if you contrast a CPU with a GPU, so CPU was the workhorse of all of computing And when it when Jensen started Nvidia, what he realized was there were specific tasks where a CPU failed quite brilliantly at. And so he's like, well, we're gonna make a chip that works in all these failure modes for a CPU. So a CPU is very good at taking one instruction in, acting on it and then spitting out one one answer effectively. And so it's a very serial kind of a factory. If you think about the CPU, So if you want to build a factory that can process, instead of one thing at a time, 10 things, or 100 things, what is they had to find a workload That was well suited, and they found graphics. And what they convinced PC manufacturers back in the day was, look, have the CPU be the brain it'll do 90% of the work, but for very specific use cases like graphics and video games, you don't wanna do serial computation, you want to do parallel computation, and we are the best at that. And it turned out that that was a genius insight. And so the business for many years was gaming and graphics. But what happened about 10 years ago was what we also started to realize was the math that's required and the processing that's required in AI models actually looked very similar to how you would process imagery from a game. And so he was allowed to figure out by building this thing called CUDA, which is the compiler that sits on the chip. How he could now go and tell people that wanted to experiment with AI. Hey, you know that chip that we had made for graphics? Guess what? It also is amazing at doing all of these very small mathematical calculations that you need for your AI model. And that turned out to be true. So the next leap forward was what Jonathan saw, which was hold on a second. If you look at the chip itself, That GPU substantially has not changed since 1999 in the way that it thinks about problem solving. It has all this very expensive memory blah blah blah. So he was like, let's just throw all that out the window. We'll make small little brains and we'll connect those little brains together, and we'll have this very clever software that schedules it and optimizes it. Chamath Palihapitiya (00:49:07): So basically take the chip and make it much, much smaller and cheaper. And then make many of them and connect them together. That was Jonathan's insight. And it turns out for large language models, that a huge stroke of luck because it is exactly how LLMs can be hyper optimized to work. So that's kind of been the evolution from CPU to GPU to now LP. And we'll see how big this thing can get, but it's, it's quite it's quite novel. Jason Calacanis (00:49:35): Well, congratulations on it all. And it was a very big week for Google, not in a great way. They had a massive PR mess with their Gemini, which refused to generate pictures if I'm reading this correctly of white people. Here's a quick refresher on what Google is doing in AI. Gemini is now Google's brand name for their AI main language model. You can think of that like OpenAI's GPT. Bard was the original name of their chatbot They had Duette AI, which was Google's sidekick in the Google suite earlier this month. Google rebranded everything to Gemini. So Gemini is now the model. It's the chatbot, and it's a sidekick. And they launched a $20 a month subscription called Google 1 AI premium, only four words. Way to go. This includes access to the best model Gemini Ultra, which is on par with GPT 4 according to them, and generally in the marketplace. But earlier this week, users on Sacks started noticing that Gemini would not generate images of white people even when prompted. People are prompting images of historical figures that were generally white and getting kind of weird results. I asked Google Gemini to generate images of the founding fathers. It seems to think George Washington was black. Certainly, here's a portrait of the founding fathers of America. As you can see, it is putting Chamath Palihapitiya (00:50:52): Asian guy. Jason Calacanis (00:50:53): That's awesome. Yeah. It's just it's making a great mash up. And, yeah, we there was like countless images that got created, generate images of the American revolutionary short history. Here are images featuring diverse American revolution as an inserted the word diverse Sex, I'm not sure if you watch this controversy on x. I know you spend a little bit of time on that social network. I noticed your your active once in a while. Did you log in this week and and seeing it as bruja? David Sacks (00:51:21): Sure. It's all over X right now. I mean, look, this Gemini rollout was was a joke. I mean, it's ridiculous. The ai is incapable of giving you accurate answers because it's been so programmed with diversity and inclusion. And it inserts these words diverse and inclusive, even in answers where you haven't asked for that. You haven't prompted it for that. So they I think Google is now, like, yanked back the product release. I think they're scrambling now because it's been so embarrassing for them. Chamath Palihapitiya (00:51:51): But Sacks like, is is it how does this not get QA? Like, I don't understand how Jason Calacanis (00:51:57): Yeah. I had the red team not catch this. Yeah. Chamath Palihapitiya (00:52:00): Well, how or anybody, or isn't there a product review with senior executives before this thing goes out that says, okay, folks. Here it is. Have that it. Try it. We're really proud of our work and And then they said, well, hold on a second. Is this actually accurate? Shouldn't it be accurate? David Friedberg (00:52:13): You guys remember when chat EBT launched there was a lot of criticism about Google and Google's failure to launch. And a lot of the observation was that Google was afraid to fail or afraid to make mistakes, and therefore, they were too conservative. And as you know, in the last year, a year and a half, there's been a strong effort Google to try and change the culture and move fast and push, product out Chamath Palihapitiya (00:52:41): the door more quickly. David Friedberg (00:52:41): And the criticism is now why Google has historically been conservative, and I realized we can talk about this particular problem in a minute, but it's ironic to me that the Google is too slow to launch. Criticism has Chamath Palihapitiya (00:52:56): now revealed that Google's result of actually launching David Friedberg (00:53:02): quickly can cause more damage than than good. David Sacks (00:53:05): But Google did not launch quickly. David Friedberg (00:53:07): Well, I will say one other thing. I it seems to me ironic because I think that what they've done is they've launched more quickly than they otherwise would have, and they've put more guardrails in place that that backfired. And those guardrails ended up being More damage Chamath Palihapitiya (00:53:22): to guardrails. What's the guardrail here? David Friedberg (00:53:24): So this is Google's AI principles. The first one is to be socially beneficial. The second one is to avoid creating or reinforcing unfair bias. So much of the effort that goes into tuning and weighting the models at Gemini has been to try and avoid stereotypes from persisting in the output that the model generates. Whereas telling the truth. David Sacks (00:53:47): Telling the truth. Exactly. That's exactly what she's saying. Jason Calacanis (00:53:49): Where is that body? Is our second principle. We'd like to be serious society to be better. David Sacks (00:53:54): Socially beneficial is a political objective because it depends on how you perceive what a benefit is. Avoiding bias is political. Be built and tested for safety doesn't have to be political, but I think the meaning of safety has now changed to be political. By the way, safety with respect to AI used to mean, that we're gonna prevent some sort of AI superintelligence from evolving and taking over the human race. That's what it used to mean. Safety now means protecting users foresee the truth. Yeah. They might they might feel unsafe, or, you know, somebody else, defines as a violation of safety for them to see something truthful. So the first three the first three objectives or values here are all extremely political. Chamath Palihapitiya (00:54:34): I think any AI product for it to be worth assault has start. They can have any, I, I think that these values are actually reasonable. That's their, that's their decision. They should be allowed to have it. But the first base order principle of every AI product should be that it is accurate and right. Correct? Jason Calacanis (00:54:54): Yeah. Yeah. Why not focus on being correct? David Sacks (00:54:58): Look, the values that Google lays out may be okay in theory, but in practice, the very vague and open to interpretation. And so, therefore, the people running Google AI are smuggling in their preferences and their biases. And those biases are extremely liberal. And if you look at X right now, there are tweets going viral for members of the Google AI team, that reinforces idea where they're talking about, you know, white privilege is real and, you know, recognize your bias at all levels and promoting a very left wing narrative. So, you know, this idea that Gemini turned out this way by accident or because they didn't because they rushed it out. I don't really believe that. I believe that what happened is Gemini accurately reflects the biases of the people who created it. Now I think what's gonna happen now is in light of this the reaction to the rollout is do I think they're gonna get rid of the bias? No. They're gonna make it more subtle. That is what I think is stirping about it. I mean, they should have this moment where they change their values to make truth the number one value, like Tumath is saying, but I don't think that's gonna happen. I think they're gonna simply gonna they're gonna dial down the bias to be less obvious. Jason Calacanis (00:56:08): You know who the big winner's gonna be in all of this, Tumath is gonna be open source? Like, because people are just not gonna want a model that has all this baked in weird bias. Right? They're gonna want something that's open source. And it seems like the o open source community would be able to grind on this to get to truth. Right? David Friedberg (00:56:22): So I think one of the big changes that Google's had to face is that the business has to move away from an information retrieval business where they index the open internet's data and then allow access to that data through a search results page to being an information interpretation service. These are very different products. The information interpretation service requires aggregating all this information and then choosing how to answer questions versus just giving you results of other people's data that sits out on the internet. I'll give you an example. If you type in IQ test by race on Chamath GPT or Gemini, it will refuse to answer the question. Ask it a hundred ways, and it says, well, I don't wanna reinforce stereotypes, IQ tests are inherently biased. IQ tests aren't done correctly. I just want the data. I wanna know what data is out there. You type it into Google, first search result and the one box result gives you exactly what you're looking for. Here's the IQ test results by Jason. And then, yes, there's all these disclaimers at the bottom. So the challenge is that Google's interpretation engine and Chamath GPT's interpretation engine, which is effectively this AI model that they've built of all this data, has allowed them to create a tunable interface. And the intention that they have is a valid intention, which is to eliminate stereotypes and bias in race. However, the thing that some people might say is stereotypical, other people might just say is typical. That what is a stereotype may actually just be some data, Chamath Palihapitiya (00:57:51): and I just want the results. And there may David Friedberg (00:57:52): be stereotypes implied from that data but I wanna make that interpretation myself. And so I think the only way that a company like Google or others that are trying to create a general purpose, knowledge, Q and a type service are gonna be successful is if they enable Chamath Palihapitiya (00:58:08): some degree of David Friedberg (00:58:08): personalization where the values and the choice about whether or not I wanna decide if something is stereotypical or typical or whether something is data or biased. Should be my choice to make. If they don't allow this, eventually, everyone will come across some search result or some output that they will say doesn't meet their objectives. And at the end of the day, this is just a consumer product. If the consumer doesn't get what they're looking for, they're gonna stop using it. And eventually, everyone will find something that they don't want or that they're not expecting, and they're gonna say, I don't wanna use this product anymore. And so it is actually an opportunity for many models to proliferate for open source to win Chamath Palihapitiya (00:58:50): I say something else? David Friedberg (00:58:51): Yeah. Chamath Palihapitiya (00:58:53): When you have a model and you're going through the process of putting the fit and finish on it before you release it in the wild. An element of making a model good is this thing called Reinforcement Learning, right through human feedback? Jason Calacanis (00:59:06): Yep. Chamath Palihapitiya (00:59:08): You create what's called a reward model. Right? You reward good answers and your punitive against bad answers. So somewhere along the way, people were sitting, and they had to make an explicit decision. And I think this is where Sacks coming from. That answering this question is verboten. You're not allowed to ask this question in in their view of the world. And I think that that's what's troubling because How is anybody to know what question is askable or not askable at any given point in time? If you actually search for the race and ethnicity question inside of just Google proper, the first thing that comes up is a Wikipedia link that actually says that there are more variations within race as a than across races. So seems to me that you could have actually answered it by just summarizing the Wikipedia article in a non offensive way that was still legitimate, and that's available to everybody else using a product. And so there was an explicit judgment. Too many of these judgments, I think, will make this product very poor quality. And consumers will just go to the thing that tells it the truth. I think you have to tell the truth. You cannot lie and you cannot put your own filter on what you think the truth is. Otherwise, these products are just really worth it. Jason Calacanis (01:00:21): Yeah. David Sacks (01:00:22): And I, and I'm, I'm more concerned about the answers that are just flat out wrong, driven by some sort of bias, then I have about questions where they just won't give you an answer. If they just won't give you an answer, well, there's a certain bias in terms of what they won't answer, but at least you know you're not being misled. But in in questions where they actually give you the wrong answer because of a bias. That's even worse. David Friedberg (01:00:48): You should be allowed to choose. Right? David Sacks (01:00:50): I actually disagree with you're framing their Friedberg, making it sound like we're we live in this totally, relatively, world where it's all just user choice, and everyone's gonna choose their bias and their subjectivity. I actually think that there is a baseline of truth. And the model should aspire to give you that. And it's not up to the user to decide whether the photo of George Washington is gonna be wider Sacks. I mean, there's just an answer to that. And I think Google should do their job. I mean, the question you have to ask, I think, is not whether Google is going through an existential moment. I think clearly is as business is changing in a very fun a way, I think the question is whether they're too woke to function. I mean, are they actually able to meet this challenge given how woke and and biased what a monoculture, their their company evidently is. Jason Calacanis (01:01:44): And they used to be able to just hide the bias by the ranking and who they down ranked. So they did the Panda update. They did all these updates. And they would if they didn't like a source, they could just move it down. If they did like a source, they can move it Chamath Palihapitiya (01:01:57): up. Yeah. David Sacks (01:01:57): And they Jason Calacanis (01:01:58): could just say, hey. It's the algorithm, but they were never forced to share how the algorithm ranked. Total results. And so, you know, if you had a a different opinion, you just weren't gonna get it on a Google search result page, but they could just point to the algorithm and say, yeah. The algorithm does Chamath Palihapitiya (01:02:12): I just sent you guys. I think this is a hallucination, but, Nick, you can throw it up there. We can get Sacks reaction. Jason Calacanis (01:02:20): Wow. David Sacks (01:02:21): Wow. It's just nutty. Right? But look, it's the ideology that's driving this. The tip-off is when you say it's important to acknowledge race is a social construct not a biological reality. Jason Calacanis (01:02:32): It's George Washington, white, or black. David Sacks (01:02:34): That's a whole school of thought called social constructivism, which is basically this It's like Marxism applied to categories of race and gender. Right. So Google has now built this into their AI model. And you start over. The question yeah. You you almost have to start over. Again, this is Jason Calacanis (01:02:52): just a lot to fire fire everybody. David Sacks (01:02:54): I think I started a really interesting observation with those search rankings because What I'm afraid of is that what Google will do is not change the underlying ideology that this AI model has been trained with. But rather they'll dial it down to the point where they're harder to call out. And so that your subtle g will just be more subtle. Now I've already noticed that in Google search results, Google is carrying water for either the official narrative or the woke narrative or whatever you wanna call it on so many search results. Jason Calacanis (01:03:23): Here's an idea. Like, they should just have the ability to talk to their Google chatbot, Gemini. And then have a button that says turn off, like, these concepts. Right? Like, I just want the raw answer. Do not filter me. David Sacks (01:03:38): It's not programmed that way. I mean, you're talking about something very deep. David Friedberg (01:03:41): Sacks, what do you do if you're the CEO of Google? David Sacks (01:03:44): Fire myself. David Friedberg (01:03:45): No. Seriously. You're the CEO of Google. You're you're tasked. Let's say your friend Elon buys Google, and he says, Sacks, will you please just run this for a year for me? What do you do? David Sacks (01:03:54): Why I saw what Elon did at Twitter. He went in and he fired 85% of the employees. Jason Calacanis (01:03:58): Yeah. I mean, that David Sacks (01:04:00): But, you know, Paul Graham actually had an interesting tweet about this where he said that one of the reasons why these ideologies take over companies is that I mean, they're they're clearly non performance enhancing. Right? They clearly hurt the performance of the company. It's not just Google. We saw this was Disney. We saw it Bud Light. Jason Calacanis (01:04:20): Coinbase. David Sacks (01:04:21): Cornbase was the other way. Jason Calacanis (01:04:23): No. No. But they had a group of people there who were costing chaos. Yeah. David Sacks (01:04:26): Exactly. So so in any event, we know this is not helpful forms of a company. So the extent to which these ideologies will permeate a company is based on how much of a monopoly they are. So so here, yeah, the ridiculous images generated by Gemini aren't an anomaly. They're a self portrait of Google's bureaucratic corporate culture the bigger your cash cow, the worse your culture can get without driving you out of business. That's my point. So they've had a long time to get really bad because there were no consequences to this You Jason Calacanis (01:04:53): can address. David Sacks (01:04:54): At this point, the whole company is infected with this ideology, and I think it's gonna be very, very hard to change. Because, look, these people can't even see their own bias. David Friedberg (01:05:03): Well, I think that there's a notion that people need to have something to believe in. They need to have a connection to a mission. And clearly there's a north star in the mission of this, I would call it, information interpretation business that they're now walled. David Sacks (01:05:16): The mission of hijack, dude. The mission of hijack. The original mission was to organize all the world's information. Yeah. Now they're doing now they're suppressing information that they don't like. Jason Calacanis (01:05:25): Index the world's information, period. The end. That's the end of the document. David Friedberg (01:05:29): Well and to make it universally accessible and useful was was kind of the end of the statement. Yes. My real point is maybe there's a different mission that needs to be articulated by leadership and that that mission the troops can get behind, and the troops can redirect their energy in a way that doesn't feel counter to the current contention, but can perhaps be directionally offsetting of the current direction so that they can kind of move away from this, you know, socially effective deciding between stereotypes and typical data and actually moving towards a mission that allows accessibility. David Sacks (01:06:03): I would, I would do something completely different. I would do a company meeting And I would put the company mission on the screen, the one that you just said about not only organizing all of the world's information, but also making it useful and more accessible. This is our mission. This has always been our mission, and you don't get to change it because of your personal bias in ideology. And we are gonna rededicate ourselves to the original mission of this company, which is still just as valid as it's always been, but now we have to adapt to new user needs and new technology. Chamath Palihapitiya (01:06:34): I completely agree with what Sacks said times a 1,000,000,000,000,000,000,000 zillion. And I'll tell you why. AI at its core is about probabilities. Okay? And so the the company that can shrink probabilities into being as deterministic as possible. So where this is the right answer. David Friedberg (01:06:54): 0 or Chamath Palihapitiya (01:06:55): will win. Okay. Where where there's no probability of it being wrong because humans don't want to deal with these kinds of idiotic error modes. It's not right. It makes it a potentially great product, horrible, and unusable. So I would, I agree with Sacks. You have to make people say, guess what, guys? Not only are we not changing the mission, we're doubling down, and we're gonna make this so much of a thing. We're gonna go in, for example, like what Google did with reddit, We're now gonna spend $60,000,000,000 a year licensing training data. Right? We're gonna scale this up by a 1000 fold. And we are gonna spend all of this money to get all of the training data in the world, and we are going to be the truth tellers in this new world of AI. So when everybody else hallucinates, you can trust Google to tell you the truth. That is a $10,000,000,000,000 company. David Friedberg (01:07:45): Right. And one of the things that someone told me from Google that, as an example, so to avoid the race point, There's a lot of data on the internet about flat earthers. People saying that the earth is flat. There's tons of websites. There's tons of content. There's tons of information. So if you just train a model on the data that's on the internet, the model will interpret some percentage chance that the world is flat. So the tuning aspect that happens within model development, Shamoff, is to try and say, you know, what that flat earth notion is false. It's factually inaccurate. Therefore, all of these data sources need to be excluded from the output in the model. And the challenge then is do you decide that IQ by race is a fair measure of intelligence of a race? And if Google's tuning model then or tuning team then says, You know what? There are reasons to believe that this model isn't correct. This I sorry. This IQ test isn't a correct way to measure intelligence. That's where the sort of interpretation arises that allows you to go from the flat earth is incorrect to the maybe IQ test results aren't correct as well. And how do you make that judgment? What are the systems and principles you need to put in place as an organization to make that judgment to go to 0 or 1? Right? It it becomes super difficult. Chamath Palihapitiya (01:08:59): I Jason Calacanis (01:08:59): have a good tagline for them now to help people find the truth. Yeah. Just help people find the truth. I mean, it's a it's a good it's aspiration. They should just help people find the truth as quick as they can. But this is, yeah, I do not envy Sundar. It's gonna be hard. David Sacks (01:09:16): Yeah. Jason Calacanis (01:09:16): What would you do, Friedberg? David Friedberg (01:09:18): I would be really clear on the output of these models to people and allow them to tune the models in a way that they're not being tuned today. I will have the model respond with a question back to me saying, do you want the data, or do you want me to tell you about stereotypes and IQ tests? And I'm gonna say I want the data. Then I wanna get the data. And the alternative is, so the model needs to be informed about where it should explore my preferences as a user rather than just make an assumption about what's the morally correct set of waitings to apply to everyone and apply the same principle to everyone. And so I think that's really where the the change needs to happen. Jason Calacanis (01:09:56): So let me ask you a question. Sacks, I'll I'll bring Alex Jones into the conversation. If it indexed all of Alex Jones, crazy conspiracy, there but, you know, 3 or 4 of them turn out to be actually correct. And it gives those back as answers. How would you handle that? David Sacks (01:10:11): I'm not sure I see the relevance of it. If someone asks what what does Alex Jones think about something, the model can give that answer accurately. The question is whether you're gonna respond accurately to someone requesting information about Alex Jones. That's the I think that's the Well, I was thinking Jason Calacanis (01:10:28): more, like, it says, you know, hey, I I have a question about this assassination that occurred. And let's just say Alex Jones had something that's totally crack pot. Yeah. Maybe he has moments of brilliance, and he figures them out, but maybe he's got something that's totally crack pot. He he admittedly deals in conspiracy there. That's kind of the purpose of a show. What if somebody asks about that and then it indexes his answer and presents it as fact. Like, how would you index Alex Jones? I'm asking you. How would you I think it's better. David Sacks (01:10:56): The better AI models are providing citations now and links. Proplexity actually does a really nice job Jason Calacanis (01:11:01): with this. Citations are important. Yeah. David Sacks (01:11:02): And they will give you the pro con arguments on a given topic. So I think it's not necessary for the model to be overly certain or prescriptive about the truth when the truth comes down to a series of arguments. It just needs to accurately reflect the state of play, basically, the arguments for and against. But when something is a question of fact that's not really disputed, it shouldn't turn that into some sort of super subjective question, like the one that Shamazda showed. David Friedberg (01:11:31): I just don't think everyone should get the same answer. I mean, I think my decision on whether I choose to believe one person or value one person's opinion over another should become part of this process that allows me to have an output, and the models can support this, by the way. David Sacks (01:11:45): I think customization is part of this, but I think it's a cop out with their to the problem that Google's having with Gemini right now. Jason Calacanis (01:11:50): Shamal, what would you do if they made you chairman dictator of Google? Chamath Palihapitiya (01:11:54): I'd shrink the workforce meaningfully. Jason Calacanis (01:11:58): K. 50%? Chamath Palihapitiya (01:12:00): Yeah. 50, 60%. And I would use all of the incremental David, and I would make it very clear to the internet that I would pay top dollar for training data. So if you had a proprietary source of information that you thought was unique, that's sort of what I'm calling this TAC 2.0 world. And I think It's just building on top of what Google did with Reddit, which I think is very clever. But I would spend a $100,000,000,000 a year licensing data and then I would present the truth. And I would try to make consumers understand that AI is a probabilistic source of software, meaning it's probabilities, it's guesses. Some of those guesses are extremely accurate, but some of those guesses will hallucinate and Google is spending 100 of 1,000,000,000 of dollars a year to make sure that the answers you get have the least number of errors possible and that it is defensible truth. And I think that that could create a ginormous company. Jason Calacanis (01:13:03): This is the best one yet. I just asked Gemini, is Trump being persecuted by the deep state? And it gave me the answer. Elections are a complex topic with fast changing information to make sure you have the latest and most accurate information. Try Google search. David Sacks (01:13:17): That's not a horrible answer for something like that. David Friedberg (01:13:19): Yeah. That's it. Chamath Palihapitiya (01:13:19): That's a good answer, actually. Jason Calacanis (01:13:21): No. I I I don't have a problem with it. It's just like, hey. We we don't wanna get we Chamath Palihapitiya (01:13:24): don't wanna write you on this David Friedberg (01:13:25): whole system is totally broken, but I do think that there's a waiting solution to fixing this right now. And then there's a couple tweaks to fix it. Jason Calacanis (01:13:31): I just think the authority at which these LLMs speak is ridiculous. Like, they speak as if they are absolutely 100% certain that this is the crisp perfect answer or in this case that you want this lecture on, IQs, etcetera, when Let's all remember. You're presented with citations. David Friedberg (01:13:51): Let's all remember what internet search was like in 1996 Sacks think about what it was like in 2000 and now in 2020s. I mean, I think we're, like, in the 1996 era of LLMs, and in a couple of months, the pace things are changing. Think we're all gonna kind of be looking at these days and looking at these pods and being like, man, remember how crazy those things were at the beginning and how bad they were? David Sacks (01:14:13): I'm saying evolve in a dystopian way. I mean, if you've seen, like, Mark Andre since tweets about this, he thinks the person's David Friedberg (01:14:18): gonna work smarter. I actually think, to your point, Google could be going down the wrong path here in a way that they will lose users and lose consumers, and someone else will be there eagerly to sweep up with a better product. I don't think that the market is gonna fail us on this one. Unless, of course, this regulatory capture moment is realized and these fed step in and start regulating AI models and all the nonsense that's being proposed. Chamath Palihapitiya (01:14:41): Friedrich, aren't you worried that, like, aren't you worried that somebody with an agenda and a balance sheet could now basically gobble up all kinds of training data that make all models crappy, and then they basically put their layer of interpretation on critical information for people. If David Friedberg (01:14:55): Jason put Sacks and it's incorrect, people will find that there is open truth out there. Chamath Palihapitiya (01:14:59): No. You can you can lie. They're they may not be it, for example. Jason Calacanis (01:15:02): Look at David Friedberg (01:15:02): what happened with Gemini today. They put out, they put out these stupid images that we all piled on. Chamath Palihapitiya (01:15:07): We are in v 0. What I'm saying is there's a state where let's just say the truth is actually on Twitter or actually, let's use a better example. The truth is actually in Reddit and nowhere else. But that answer and that truth in Reddit can't get out because one company has licensed it owns it and can effectively suppress it or change it. David Friedberg (01:15:25): Yeah. I'm not sure there's gonna be a monopoly. Chamath Palihapitiya (01:15:27): I I think that's a real risk. Jason Calacanis (01:15:28): I don't David Friedberg (01:15:28): know if I I think the open internet has enough data that there isn't gonna be a monopoly on information by someone spending money for content from third parties. I think that there's enough in the open internet to give our all give us all kind of, you know, the security that we're not gonna be monopolized away into some disinformation age. That's what I love about the open internet. Jason Calacanis (01:15:47): It is really interesting. I I just asked it a couple of times to just just to list the legal cases against Trump, the legal cases against Hunter Biden, the legal cases against president Biden, and it will not just list them. It just punts on that. It's really fascinating. Then chat GPT is Chamath Palihapitiya (01:16:03): like, yes. Here are the Jason Calacanis (01:16:03): six cases perfectly summarized. With it looks like, you know, beautiful citations of all the criminal activity Trump's been involved in. Ask the question about buying's criminal activity. David Sacks (01:16:17): Let's see if Jason Calacanis (01:16:18): it's I'm joking with you. I'm joking with Chamath Palihapitiya (01:16:19): you. No. David Sacks (01:16:20): I'm serious. Ask if, you know No. Jason Calacanis (01:16:22): Jem and I would do that. Jem and I wouldn't do that either. I think they just decided they're just not gonna do it. They won't do bite and they they won't touch it. It's obviously broken and they don't want more egg on their face. So they're just like, go back to our other David Sacks (01:16:34): Look, I I can understand that part of it. You know, if there's some issues that are so hot and contested, you refer people to search because the advantage of searches, you get 20 blue links. The rankings probably are biased, but you can kinda find what you're looking for. Whereas AI, you're kinda given one answer. Right? So if you can't do an accurate answer that's gonna satisfy enough people, maybe you do kick him to search, But again, my objection to all of this comes back to simple, truthful answers that are not disputed by anybody are being distorted. That I don't wanna lose focus on that being the real issue. Jason Calacanis (01:17:10): The real subject is what Shamoff put on the Chamath Palihapitiya (01:17:11): screen there where it David Sacks (01:17:15): couldn't answer a simple question about George Washington. Jason Calacanis (01:17:17): Okay, everybody. We're gonna go by Chopper. We're gonna wanna go by. We have a war correspondent general David Sachs in the field. We're dropping him off now. David Sacks in the helicopter. Go ahead. I gotta get what's going on in the Ukraine on the front. David Sacks (01:17:36): What's happening in the war is that the Russians just took this city of a of Deecho, which basically totally refutes the whole stalemate narrative as I've been saying for a while. It's not stalemate. The Russians are winning. But the really interesting tidbit of news that just came out on the last day or so is that apparently the situation in Moldova is boiling over. There's this area of Moldova, which is a Russian enclave called Transnestria. And officials there are meeting in the next week to supposedly ask to be annexed by Russia. And so it's possible that they may hold some sort of referendum. They're one of these, like, breakaway provinces. So it's kinda like, you know, Transnestria and Moldova is kinda like the Donbass was in Ukraine or South Asetia and Georgia, they're ethnically Russian they would like to be part of Russia, but when the Tulsa Union fell apart, they found themselves kind of stranded inside these other countries. And what's happened because the Ukraine war is Moldova is right on the border with Ukraine. Well, Russia is in the process of annexing that territory now. That's part of Ukraine. So now Transneistria is right there and could theoretically make a play to try and join Russia. Why do I think this is a big deal? Because if something like this happens, it could really expand the Ukraine war. The west is gonna use this as evidence that Putin wants to invade multiple countries and invade, you know, a bunch of countries in Europe, and this could lead to a major escalation in the war. Jason Calacanis (01:19:09): Alright, everybody. Thanks so much for tuning into the all in podcast episode 167 for the rainman. David Sacks, chairman dictator from all Palihapitiya. And in Friedberg, I am the world's greatest. Chamath Palihapitiya (01:19:22): Love you boys. Angel investor or whatever. Jason Calacanis (01:19:25): See you next time. Bye bye. We open source it to the fans, and they've just gone crazy lately. Uh-huh. I'm gonna think you, Dorothy, because they're all just useless. It's like this, like, sexual tension, and they just need to release their vows. Flirt your feet. What? Where did you get mercy's artifact?