E167: Google's Woke AI disaster, Nvidia smashes earnings (again), Groq's LPU breakthrough & more

Jason Calacanis 0:00 All right, everybody. Welcome back to your favorite podcast of all time, the All In Podcast, episode 160 something. With me again, Chamath Palihapitiya. He's a CEO of a company and he invests in startups, and, uh, his firm is called Social Capital. We also have David Freiberg, 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 we doing, boys?

Chamath Palihapitiya 0:29 Good. Thank you. This is an odd intro.

David Sacks 0:31 Ah, could your intro be any more low energy and dragged out?

Jason Calacanis 0:36 (laughs) I'm sick. What do you want me to do? You want me to drink a-

David Sacks 0:38 Geez, try and fake the effort.

Jason Calacanis 0:39 ... throat lozenge? All right here, give, give me one more shot. Watch this. (clears throat)

David Sacks 0:42 (laughs)

Jason Calacanis 0:42 Watch this. Watch profession- You want professionalism? Here we go.

David Sacks 0:44 Fake the effort, come on.

Jason Calacanis 0:45 Here we go. You want professionalism? I'll show you guys professionalism.

Chamath Palihapitiya 0:48 Is that Binaca? What was that?

Jason Calacanis 0:50 This?

Chamath Palihapitiya 0:50 Is that Binaca?

Jason Calacanis 0:50 Oh, it is a secret.

Chamath Palihapitiya 0:52 (laughs)

Jason Calacanis 0:52 (laughs)

Chamath Palihapitiya 0:52 (laughs)

Jason Calacanis 0:52 Banana boat.

Narrator 0:55 We're going all in. Let your winners ride. Rain Man David Sacks. We're going all in. And I said we open sourced it to the fans and they've just gone crazy with it. Love you guys. Queen of Quinoa. I'm going all in.

Jason Calacanis 1:11 All right, everybody. Welcome to the All In Podcast, episode 167, 168.

Chamath Palihapitiya 1:15 (laughs)

Jason Calacanis 1:15 With me, of course, the Rain Man himself, David Sacks, the Dictator Chairman, Chamath Palihapitiya, and our Sultan of Science, David Freiberg. How we doing, boys?

Chamath Palihapitiya 1:23 Great.

David Sacks 1:24 Oh, great.

Chamath Palihapitiya 1:24 How are we doing?

Jason Calacanis 1:24 Is that high energy enough for you?

Chamath Palihapitiya 1:25 Yeah.

David Sacks 1:25 Is it 167 or 168?

Jason Calacanis 1:27 I don't know. Who cares?

David Sacks 1:28 Can we at least get you to know the episode number?

Jason Calacanis 1:31 Who cares? We... Unfortunately or fortunately, we're going to be doing this thing forever. The, the audience demands it.

Chamath Palihapitiya 1:37 (laughs)

Jason Calacanis 1:37 It doesn't matter. This is like a Twilight Zone episode. We're going to be trapped in these four bubbles forever. You know, like Superman?

Chamath Palihapitiya 1:43 Black Mirror.

Jason Calacanis 1:44 It's up... It is. It's, this is like the... It is, uh, The Gift and the Curse.

Chamath Palihapitiya 1:47 Oh, when they were trapped in that glass, uh-

Jason Calacanis 1:49 Yeah, yeah.

Chamath Palihapitiya 1:50 ... Zed? Was that his name?

Jason Calacanis 1:50 Zod.

Chamath Palihapitiya 1:50 Zed? Zod, yeah.

Jason Calacanis 1:51 Zod. Kneel Before Zod.

Chamath Palihapitiya 1:53 And he spun through the universe in the plastic-

Jason Calacanis 1:55 Yeah.

Chamath Palihapitiya 1:55 ... thing forever for, for infinity?

Jason Calacanis 1:57 Yeah, until, until Superman took the nuclear bomb out of the, uh, Eiffel Tower and threw it into space and blew it up and freed them.

Chamath Palihapitiya 2:05 You know, my background today-

Jason Calacanis 2:06 And he fought with him.

Chamath Palihapitiya 2:07 ... I think I'm gonna have to change now that you've referenced this important scene.

David Sacks 2:10 That was the best moment of that movie, J-Cal. Where Terence Stamp says, "Kneel," to the president and the president says, "Oh God."

Chamath Palihapitiya 2:17 Yes, yes. (laughs) Yes, yes.

David Sacks 2:17 And then Terence Stamp's like, "Uh, Zod." (laughs)

Chamath Palihapitiya 2:21 Kneel before Zod. Zod.

David Sacks 2:21 Not God, Zod. (laughs)

Chamath Palihapitiya 2:24 Zod. Kneel before Zod.

David Sacks 2:25 (laughs)

Chamath Palihapitiya 2:25 That was Superman II or III? Two.

Jason Calacanis 2:26 Yeah, Superman II is pretty much the best.

Chamath Palihapitiya 2:28 Two. Yeah.

Jason Calacanis 2:29 You know, like Empire Strikes Back, like Terminator 2, it's always the second one-

Chamath Palihapitiya 2:32 Yes.

Jason Calacanis 2:32 ... that's the best one.

Jason Calacanis 2:34 All right, everybody. We got a lot to talk about today. Apologies for my voice, getting a little bit of a cold. NVIDIA blew the doors off their earnings for the third straight quarter. Shares were up 15% on Thursday, representing a nearly $250 billion 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 billion 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 $196 billion. In other words, they added, like, two and a half Airbnbs to their valuation. But let's just get to the results. The results are absolutely stunning and dare I say unprecedented. Q4 revenue, 22.1 billion. That's up 22% quarter over quarter, up 265% year over year. The net income was 12.3 billion, 9X year over year. And the gross margin of 76% was up two points quarter over quarter, 12.7% year over year. But look at this revenue ramp. This is extraordinary. Q1 of 2024, this juggernaut starts, and it does not stop and it doesn't look like it's gonna stop, just a run up from seven billion go all the way to 22 billion in revenue for the quarter. Absolutely extraordinary. And, uh, if you wanna know why this is happening, why is NVIDIA 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 visualizations, you know, I think people making movies and stuff like that. Autos, uh, used NVIDIA for self-driving, that kind of stuff. 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 4:32 So, just to add one point here, Jason.

Jason Calacanis 4:35 Yeah.

David Sacks 4:35 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, 3D worlds. And this vector math that they use to create these 3D worlds is also the same vector math that AI uses to reach its outcome. 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 buil- 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 this has exploded and the, what you're seeing is the buildout of this new cloud service infrastructure for, for AI.

Jason Calacanis 5:34 Yeah, and, um, i- and also helping the stock is the fact that they bought back 2.7 billion worth of their shares as part of a $25 billion buyback plan. But this company's firing on all cylinders. Revenue's 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, A100s, H100s, et cetera. The gross margin's been expanding. They have huge profits.And they're still projecting more growth in Q1, around 24 billion, which would be a 3X increase year-over-year. And this obviously has made the entire market rip. As NVIDIA goes, so does the market right now. And, uh, the S&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.

David Friedberg 6:29 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 s- 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 7:55 Freeberg, your thoughts. We were talking, I think, it was last week or the week before, about the possibility of NVIDIA being a $10 trillion company, the largest company in the world. What are your thoughts on these spectacular results? And then Chamath's point, everybody is watching this going, "Hmm, maybe I can get a slice of that pie and maybe I can create a more competitive offering." Obviously, we saw Sam Altman rumored to be raising seven trillion, which feels like a fake number. It feels like that's maybe the market size or something, but your thoughts here?

Chamath Palihapitiya 8:23 I don't think anything's changed on the NVIDIA front. There's this accelerated compute buildout 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 buildout is kind of the first 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 3,000 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's 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 buildout of the internet? So, um, TBD, right? The same is true in, 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, and so they looked like they were gonna be the biggest business ever. I mean, you could look at Cisco during the early days of the internet buildout, and everyone thought Cisco was, uh, the picks and shovels of the internet, and they were gonna make all the, all the value's gonna accrue to Cisco. So, we're kind of in that same phase right now with NVIDIA. The real question is, is this gonna 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 up. So, we'll see.

Jason Calacanis 10:46 Sachs, your thoughts? Do you think, um, we're getting to the point where maybe we'll have bought too many of these, uh, built out too much infrastructure, and it'll take time for the application layer, as Freeberg was alluding to, to monetize it?

David Sacks 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 buildout 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 buildout of the internet, of the dot-com era, you had the market crash at the end of March of, uh, 2000. And Cisco never really recovered from that peak valuation. Um, but I think there's a lot of reasons to believe NVIDIA is different. One is that if you look at NVIDIA's multiples, they're nowhere near where Cisco's were back then. So, the market in 1999 and 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.Second, 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 GPUs. 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 pounds."

Jason Calacanis 12:35 Yeah.

David Sacks 12:35 This is more like a mainframe computer or something that's dedicated to processing.

Jason Calacanis 12:38 Yeah, it's somewhere between a rack server and the entire rack. (laughs) Yeah.

David Sacks 12:43 Right.

Jason Calacanis 12:43 It's giant and it's heavy and it's complex.

David Sacks 12:45 Right.

Jason Calacanis 12:45 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 o- opportunity. These servers are being bought by, you know, people like Amazon, 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, uh, and they have 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."

David Friedberg 13:27 You're bringing up an excellent point, and I w- 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, you're gonna look really foolish for having spent 20 and 30 and $40 billion. 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 $22.1 billion? Well, you said it, Jason, it's all big tech. Why? Because they have that money on the balance sheet sitting idle. But when you spend $22 billion, their investors are going to demand a rate of return on that.

Jason Calacanis 14:08 Mm-hmm.

David Friedberg 14:09 And so if you think about what a reasonable rate of return is, call it 30, 40, 50%, 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 billion of spend needs to generate probably $45 billion of revenue.

Jason Calacanis 14:26 Mm-hmm.

David Friedberg 14:27 And so Jason, the question, to your point, and to Friedberg's point, the $64,000 question is, who in this last quarter is gonna make 45 billion on that 22 billion 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 three or four 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. Most of the apps, the overwhelming majority of the 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 two or three quarters. I think we still need to see the next big thing. And if you look in the past, what the past has 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 16:09 Yeah, constraint makes for great art.

David Sacks 16:11 Yeah.

David Friedberg 16:11 We haven't seen that yet.

Jason Calacanis 16:12 Yeah.

David Friedberg 16:12 So, I think the revenue scale will continue for like the next two or three years probably, for Nvidia. But the real question is, what is the terminal value? And it's the same thing that Sax showed in that Cisco slide. People ultimately realized that the value was gonna go 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 16:51 So, Freidberg, if we're looking at this chart, the winner of Netflix... No, the winner of the Cisco chart might in fact be somebody like Netflix. They actually got, you know, hundreds of millions of consumers to give them cash.

David Friedberg 17:02 Google and Facebook.

Jason Calacanis 17:03 And then you have Google and Facebook as well generating all that traffic, and then YouTube, of course. 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, five bucks a month, whatever it is?

Chamath Palihapitiya 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 Chamath'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 billion 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, at such an accelerated pace. When a big company buys these chips from Nvidia, they don't have to, from an accounting basis, mark it 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 billion of cash, 'cause Google and others have $100 billion 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.

Jason Calacanis 18:43 Yeah.

Chamath Palihapitiya 18:43 And you don't have to take the hit on your P&L by doing it. So it ends up on the balance sheet, and then you depreciate it over typically four to seven years. So that money gets paid out on the- on the income statement at these big companies over a seven-year period. So there's a really great accounting and M&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. We don't have to take a P&L hit. We don't have to acquire a cash-burning business. And, you know, frankly, we're not going to be able to grow through M&A 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 19:29 Yeah. And all of this-

Chamath Palihapitiya 19:30 I know- I know that was an accounting point, but I think it's a really important motivator here.

Jason Calacanis 19:32 I think it's a valid one. If you- if 100 billion gets spent this year, you divide it by four, 25 billion in revenue would have to come from that or something in that range, yeah. And so, Sax, any guesses?

David Friedberg 19:42 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- and Facebook, 'cause they don't have clouds. 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 billion that just went to those cloud providers that they'll then expose to-

Chamath Palihapitiya 20:14 Capitalize.

David Friedberg 20:14 ... to- to everybody else, versus what was just absorbed? Because at Facebook, Mark had that video about how many H100s. That's all for him.

Chamath Palihapitiya 20:21 Right, but it is still- it is still capitalized, is my point. So they don't have to book that as an expense. It sits on the balance sheet.

David Friedberg 20:28 Yeah, sure, of course.

Chamath Palihapitiya 20:29 And they earn- and they earn it down over time.

David Sacks 20:30 You're helping to explain why these big cloud source providers are spending so much on the data center build-out.

Chamath Palihapitiya 20:34 Because they have so much cash, 'cause they're very profitable, and there's nowhere else to put the money.

David Sacks 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 NVIDIA? I think a- a- 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 moat appears to be substantial. Uh, the Wall Street analysts I've been listening to think that in five years, they're still gonna have 60-something percent market share. So they're gonna have a substantial percentage of this market or this TAM. 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 all, NVIDIA 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. Second, you just look at their forecast. So the fiscal year that just ended, they did around 60 billion of revenue. They're forecasting 110 billion 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 going to be a need to stay up to date with the latest chips, right?

David Friedberg 22:26 Here's a framework for you, Sax. 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 workhorse 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, it's about 180 odd billion dollars today. The economy that it created, that it supports, is probably measured, call it in- $1 trillion or $2 trillion, maybe $5 trillion. 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, its 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 months? It 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 or $5 trillion market cap in the next two or three years, will it support $100 trillion economy?

David Sacks 24:00 Right.

David Friedberg 24:00 Because that's what you would need to believe for those ratios to hold. Otherwise-

David Sacks 24:04 Well, you-

David Friedberg 24:04 ... everything is just broken on the internet.

David Sacks 24:06 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 '90s, when we had the whole dot-com bubble and then bust, you had this tremendous buildout, not just of kind of servers and all the networking equipment, but there was a huge fiber buildout-

Jason Calacanis 24:34 Yep.

David Sacks 24:34 ... by all the telecom companies. And the telecom companies had a Cisco-like, you know, uh, peak-

Jason Calacanis 24:39 It was worse.

David Sacks 24:40 ... market cap.

Jason Calacanis 24:40 You know, WorldCom and them, w- they went bankrupt, a lot of them.

David Sacks 24:42 Yeah.

Jason Calacanis 24:43 Yeah.

David Sacks 24:43 Well, the, the problem there was that a lot of the buildout 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.

Jason Calacanis 24:53 Yeah.

David Sacks 24:53 Cisco wasn't in that position. But in any event, my point is, in the early 2000s when the dot-com crash happened, everyone thought that these telecom companies had overinvested 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 th- 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.

Jason Calacanis 25:55 Yeah.

David Sacks 25:55 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 26:08 Yeah, and to your point, y- one of the reasons YouTube, Google Photos, iPhoto, a lot of these things happened was because the infrastructure buildout 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-

David Sacks 26:27 Right.

Jason Calacanis 26:27 ... 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.

David Sacks 26:37 Yeah, I mean, look, when we were (laughs) developing PayPal in the late '90s, really around 1999, uh, you could barely upload a photo to the internet.

Jason Calacanis 26:46 Yeah.

David Sacks 26:47 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 prohibitively slow. Everyone's gonna drop off."

Jason Calacanis 26:54 Yeah.

David Sacks 26:55 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-

Jason Calacanis 27:08 Yeah.

David Sacks 27:08 ... something that was too hard to do.

Jason Calacanis 27:09 Your LinkedIn (laughs) profile was, like, too much bandwidth.

David Sacks 27:11 Yeah.

Jason Calacanis 27:12 And then let alone video. I mean, the, you would get, yeah, you probably remember these days. You would put up a video on your website. If it went viral, your website got turned off because y- you would hit your $5,000 or $10,000-a-month cap.

Jason Calacanis 27:26 All right, Groq also had a huge week. That's Groq with a Q, not to be confused with Elon's Grok with a K. Chamath, you've talked about Groq on this podcast a couple of times. Obviously, you were the, I guess you were the first investor, the seed investor. You pulled these LPUs and this concept out of, uh, a team that was at Google. Maybe you could explain a little bit about Groq's viral moment this week and the history of the company, which I know has been a long road for you with this company.

David Friedberg 27:55 I mean, it's been since 2016, so it, 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.

Jason Calacanis 28:05 Mm-hmm.

David Friedberg 28:06 90% of the battle is just staying-

Jason Calacanis 28:08 Alive?

David Friedberg 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 two months ago. (laughs) I'll just be honest. And between Sunday and Tuesday, we've just, we're overwhelmed.

Jason Calacanis 28:41 Mm-hmm.

David Friedberg 28:41 And I think, like, the last count was we had 3,000 unique customers come and 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 glomming onto? You have to understand that, like, at the very highest level of AI, you have to view it as two 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 ChatGPT or Gemini, we ask a question, and it gives us a really useful answer, and those are two 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 of 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 Groq 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 and we've talked about why that is important. And the Groq with a Q 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 a potential here to be really disruptive. And we're a meager unicorn, right?

Jason Calacanis 30:58 Mm.

David Friedberg 30:59 Our last valuation was like a billion something versus NVIDIA which is now like a two trillion dollar company. So there's a lot of market cap for Groq to gain by just being able to produce these things at scale, which could be just a, an enormous outcome for us. So time will tell, but a really important moment in the company and very exciting.

Chamath Palihapitiya 31:19 Can I just observe like off-topic how an overnight success can take eight years? (laughs)

Jason Calacanis 31:25 Yeah. No, I was thinking the same line. It's a seven-year overnight success in the making.

Chamath Palihapitiya 31:29 There's this class of businesses that 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 stack 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 that's 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 eight 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, um, 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 a lot 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 click 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 moats-

Jason Calacanis 33:10 Mmm.

Chamath Palihapitiya 33:10 ... 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. But I'm really hopeful that these sorts of chip technologies, SpaceX, in biotech we see a lot of this, these sorts of things can kind of become more in favor because the, the advantage as these businesses work seems to realize hundreds of billions and sometimes trillions of dollars of market value and be incredibly transformative 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 33:48 Well, I mean, OpenAI was kind of like that for a while.

Chamath Palihapitiya 33:51 Totally. Totally.

David Sacks 33:51 I mean, it was this like wacky nonprofit that was just grinding on an AI research problem for like six years and then it finally worked and got productized into ChatGPT.

Chamath Palihapitiya 34:01 Totally.

David Sacks 34:02 But you're right, SpaceX was kind of like that. I mean, the big money maker at SpaceX is Starlink, which is the satellite network. It's basically broadband from space 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.

Chamath Palihapitiya 34:29 Well, and then you have to get consumers-

David Sacks 34:30 And Tesla was kinda the same way.

Chamath Palihapitiya 34:30 ... to adopt it. I mean, you know.

David Sacks 34:32 Right.

David Friedberg 34:32 Yeah.

David Sacks 34:32 Don't forget the final step. Yeah.

David Friedberg 34:33 We 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 gonna 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 said no, in kind way but they said no. Then we thought, "Okay, maybe it's like for high frequency traders," right? Because like those folks wanna 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 three-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. All these things, to your point, are just points where you can just very easily give up, and then there's like, "We ran out of money." So then you write money in a note, right? Because everybody wants to punt on valuation when nothing's working. (laughs)

David Sacks 35:56 Yeah. Yeah. You tried six

David Friedberg 35:57 Oh my gosh.

David Sacks 35:57 ... beachhead market, you couldn't land the boat. Right.

David Friedberg 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.

Chamath Palihapitiya 36:06 Yeah.

David Friedberg 36:06 And that requires shutting out, we talked about this in the Masa example last week, but it just requires shutting out the noise 'cause 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 36:33 You mentioned all those boats landing and trying to, trying to... those missteps, but 3,000 people signed up. Who are they? Are they, are they developers now and they're gonna figure out the applications?

David Friedberg 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 36:50 Yep.

David Friedberg 36:50 I don't think there's anything real that's inside of an enterprise that is so meaningfully disruptive that it's gonna 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.

Jason Calacanis 37:09 Yeah.

David Friedberg 37:09 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 37:18 The results are not good enough.

David Friedberg 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.

Jason Calacanis 37:33 Yeah.

David Friedberg 37:33 And so yeah, there are 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&P 500.

Chamath Palihapitiya 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 de-risked, faster time to market? And, you know, Chamath 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, uh, what makes the one like Fusion not fundable?

David Friedberg 38:22 That's a, it's a, it's a phenomenal ?

Jason Calacanis 38:23 That's a great question, yeah.

David Friedberg 38:24 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 to $10 million 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 eight years ago, okay? So we weren't pushing those boundaries. All we were doing was trying to build a compiler and 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 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 gonna 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 in the outcome. So, you know if-

Chamath Palihapitiya 39:53 Yeah, I mean there's, there's still like a bunch of things that have to be right in a row to make it work, but, yeah.

David Friedberg 39:57 Yeah, it doesn't mean it's gonna work.

Chamath Palihapitiya 39:58 Yeah.

David Friedberg 39:59 All I'm saying is I don't, I don't want it to fail because we built a reactor and we realize, hold on, to get heavy hydrogen, I gotta go to the moon.

Chamath Palihapitiya 40:05 Right. And J. Cal and Sax, how do... Sax, I know you don't... You invested in SpaceX-

Jason Calacanis 40:09 No, no. We, we have done a couple, so...

Chamath Palihapitiya 40:10 But like, yeah, so maybe you guys can highlight how you've thought about deep tech opportunities versus what you focus on.

Jason Calacanis 40:14 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 Contraline was one. Uh, we were introduced to somebody who's doing this really interesting contraception for men where they put, uh, a gel into your vas deferens 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, uh, you guys, some of you are also investors in CafeX, which we love the founder, and this company should have died like during COVID, and (laughs) making a robotic coffee bar when he started, you know, seven, eight years ago was incredibly hard. He had to build the hardware, he had to build a brand, he had to do locations, he had to do software, and now he's selling these machines and people are buying them, and the two in San Francisco at SFO are making like, uh... I think they, the two of them make a million dollars a year, and it's the highest per square footage of any store in an airport. And so we've just been grinding and grinding, and you gotta find a founder who's willing to make it their life's work in these kind of situations, but y- you start to think about the degree of difficulty, hardware, software, retail (laughs) , mobile apps. I mean, it just gets crazy how hard these businesses are as opposed to, "I'm building a SaaS company." I build software, I sell it to somebody to solve their SaaS problem. It's like, it's very one-dimensional, right? Uh, it's pretty straightforward. These businesses typically have five components.

Chamath Palihapitiya 42:01 Yeah, and Sax, you've been an investor in SpaceX, but you don't make those sorts of investments regularly that craft. Is that fair?

David Sacks 42:09 Yeah, I'm an Elon exception.

Chamath Palihapitiya 42:11 Okay. (laughs)

Jason Calacanis 42:12 It's about the founder, right?

David Friedberg 42:13 Same. Same. (laughs)

David Sacks 42:18 (laughs)

Chamath Palihapitiya 42:18 Same.

Jason Calacanis 42:18 We're all good.

David Sacks 42:18 No, in our portfolio allocation we say, "This much early stage, this much late stage, this much Elon."

David Friedberg 42:24 Elon exception. Yeah. I mean, y- you have to be so dogged to, to...

Jason Calacanis 42:28 ... wanna take something like this on, 'cause the good stuff happens, like you're saying, Friedberg, year seven, eight, nine, 10. As opposed to, like, a consumer product-

Chamath Palihapitiya 42:35 Yeah, I mean for me it's like-

Jason Calacanis 42:35 ... either works or it doesn't by year three or four. 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, it was kinda cruising for two or three years, and then South By Southwest happened, Ashton Kutcher got on it, Obama got on it, and then it took off.

Chamath Palihapitiya 42:51 I think the network effect, I think, I think network effect businesses are different, because that's all about getting your seat at your network.

Jason Calacanis 42:56 Yeah.

Chamath Palihapitiya 42:56 What I'm talking about is the co- technical coordination of lots of technically difficult tasks that-

Jason Calacanis 43:01 Yeah.

Chamath Palihapitiya 43:01 ... 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 per- persist and to realize an outcome from. But the truth is that if you get them, the moat 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, you know, this can be the big 10x, 100x, 1000x.

Jason Calacanis 43:47 Two stories about that. (laughs) One of the V- early VCs, and Elon's told this story publicly, wanted Elon to s- 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've changed? And then ...

Chamath Palihapitiya 44:04 Totally.

Jason Calacanis 44:04 A, a very high-profile VC came to me and said, "Okay, I'll, I'll do the Series A for, um, 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 him to sell the software to cab companies. So make it a SaaS company." And 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 thousands of dollars a year for this software, and you can get a little piece of the action." I never brought that investor to (laughs) to Travis. I was like, "Oh wow, that's really interesting insight." Sometimes the VCs work against him.

David Friedberg 44:44 I have a very poor track record of working with other investors.

Jason Calacanis 44:49 Whoa, self-reflection.

David Friedberg 44:50 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 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, y- you need one person who can be a trigger puller and who doesn't have to answer to anybody, I find. That's why I think, for example, when you look at how successful Vinod has been over 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 d- 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 at periods where if I wasn't able to ram them through myself, because it was my organization, I think we would've 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.

Jason Calacanis 46:12 Hm.

David Friedberg 46:12 Because I think you have to realize that you are bringing on and compounding your risk, the one that Friedberg 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 five or six folks that come into an A or a B, but you're inheriting five or six ...

Jason Calacanis 46:31 Yeah.

David Friedberg 46:32 ... partnership dynamics.

Jason Calacanis 46:33 The dysfunctions, yeah, yeah.

David Friedberg 46:34 Yeah.

Jason Calacanis 46:35 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? Wh- what's the difference there, Jamal?

David Friedberg 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 wanna 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. 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's 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 LPU. And we'll see how big this thing can get, but it's, um-

Jason Calacanis 49:33 Yeah.

David Friedberg 49:33 ... it's quite, it's quite novel.

Jason Calacanis 49:35 Well, congratulations on it all. And

Jason Calacanis 49:37 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 (laughs) , of white people. Here's a, a quick refresher on what Google's 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 Duet AI, which was Google 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 One AI Premium. Uh, only four words. (laughs) Way to go. This includes access to the best model, Gemini Ultra, which is on par with GPT-4 according to them, uh, and generally in the marketplace. But earlier this week, users on X started noticing that Gemini (laughs) would not generate images of white people even when prompted. People were prompting it for images of historical figures that were, uh, 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-

David Friedberg 50:51 (laughs) There's an Asian guy. That's awesome.

Jason Calacanis 50:54 Yeah, it's just, it's making a great mashup.

David Friedberg 50:58 Mm-hmm.

Jason Calacanis 50:58 And, uh, yeah, we, there was, like, countless images that got created. "Generate images of the American Revolutionary..." Sure is, "Here are images featuring diverse American Revolutionaries," and inserted the word diverse. Sax, I'm not sure if you watched this controversy on X. I know you spend a little bit of time on that social network. I noticed you're, you're active once in a while. Did you log in this week and, and see any of this brouhaha?

David Sacks 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 isn't capable 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 has now, like, yanked back the product release. I think they're scrambling now because it's been so embarrassing for them.

David Friedberg 51:52 But Sax, like, is, is it... How does this not get QA'd? Like, I don't-

David Sacks 51:56 (laughs)

David Friedberg 51:56 ... understand how-

Jason Calacanis 51:58 Yeah, how did the red team not catch this? Yeah.

David Friedberg 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 at it, try it. We're really proud of our work." And, and then they say, "Well, hold on a second, is this actually accurate? Shouldn't it be accurate?"

Chamath Palihapitiya 52:14 You guys remember when ChatGPT launched and 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 to year and a half, there's been a strong effort at Google to try and change the culture and move fast and push a product out the door more quickly. And the criticism is now why Google (laughs) has historically been conservative. And I realize 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 now revealed that Google's result of actually launching quickly can cause more damage than (laughs) , than good.

David Sacks 53:05 But Google did not launch quickly.

Chamath Palihapitiya 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 damaging.

David Friedberg 53:21 But what are the guardrails? What's the guardrail here?

Chamath Palihapitiya 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.

David Sacks 53:45 Whereas-

Jason Calacanis 53:47 Telling the truth.

David Sacks 53:47 ... telling the tru- Exactly. That's exactly what I was gonna say.

Jason Calacanis 53:50 Yeah. Changing society-

David Sacks 53:50 Where is that value?

Jason Calacanis 53:50 ... is our second principle. (laughs) We'd like to steer a society to be better.

David Sacks 53:53 No, I think 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 from seeing the truth.

Chamath Palihapitiya 54:22 I feel unsafe (laughs) .

Jason Calacanis 54:22 You feel unsafe?

David Sacks 54:23 Yeah, because they might, they might feel unsafe, or, you know, somebody else d- uh, defines it as a violation of safety for them to see something truthful. So the first three, their first three objectives or values here are all extremely political.

David Friedberg 54:35 I think any AI product, for it to be worth its salt, has to 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.

Jason Calacanis 54:53 Correct? (laughs)

Chamath Palihapitiya 54:54 Yeah.

David Friedberg 54:55 Yeah.

Jason Calacanis 54:56 Why not? Focus on being correct.

Chamath Palihapitiya 54:58 Totally agree with that. Look, the values that Google lays out may be okay in theory, but in practice, they're 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 from members of the Google AI team that reinforce this 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- uh, 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.

Jason Calacanis 55:53 Hmm.

Chamath Palihapitiya 55:54 That is what I think is disturbing about it. I mean, they should have this moment where they change their values to make truth the number one value, like Chamath is saying, but I don't think that's gonna happen. I think they're gonna simply going to- they're gonna dial down the bias to be less obvious.

Jason Calacanis 56:08 You know who the big winner is gonna be in all of this, Chamath, is gonna be open source. Like, 'cause 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- an- and it seems like the o- open source community will be able to grind on this to- to get to truth, right?

Chamath Palihapitiya 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 ChatGPT or Gemini, it will refuse to answer the question. Ask it 100 ways and it says, "Well, I don't want to 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 se- search result and the one box result gives you exactly what you're looking for. Here's the IQ test results by race, and then, yes, there's all these disclaimers at the bottom. So the challenge is that Google's interpretation engine and ChatGPT'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 and I just want the results, and there may 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&A type service are gonna be successful is if they enable some degree of 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.

David Friedberg 58:50 Can I say something else?

Jason Calacanis 58:51 Yeah.

David Friedberg 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 59:06 Yep.

David Friedberg 59:08 You create what's called a reward model, right? You reward good answers and you're 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 Sax is 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 races acr- 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 worthless.

Chamath Palihapitiya 1:00:21 Yes. Yeah, and I- and I'm- I'm more concerned about the answers that are just flat out wrong, driven by some sort of bias, than I am 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.

David Sacks 1:00:41 ... but in, a, in questions where they actually give you the wrong answer because of a bias, that's even worse. And-

Chamath Palihapitiya 1:00:49 You should be allowed to choose, right?

David Sacks 1:00:50 I actually disagree with your framing there, Friedberg. You're making it sound like we're, we live in this totally relativized world where it's all just user choice and everyone's gonna choose their bias and their subjectivity. I actually think that th- 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 white or black. I mean, there's just an answer to that. And I think Google should just 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 it clearly is. Its business is changing in a very fundamental way. I think the question is whether they're too woke to function. I mean, are they actually gonna be able to meet this challenge given how woke and, and biased, what a monoculture their, their company evidently is?

Jason Calacanis 1:01:44 Well, 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 could move it up.

David Sacks 1:01:57 Yeah.

Jason Calacanis 1:01:58 And they could just say, "Hey, it's the algorithm," but they were never forced to share how the algorithm ranked-

David Sacks 1:02:02 Totally.

Jason Calacanis 1:02:02 ... the results. And so, you know, i- if you had 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 it."

Chamath Palihapitiya 1:02:12 I just sent you guys, I think this is a hallucination, but Nick, you can throw it up there if we can get Sax's reaction.

Jason Calacanis 1:02:18 (laughs)

Chamath Palihapitiya 1:02:20 Wow.

David Sacks 1:02:21 (laughs) Wow, this is 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 1:02:32 Is George Washington (laughs) white or black?

David Sacks 1:02:34 That's a whole school of thought called social constructivism, which is basically this, um, it's like Marxism applied to-

Jason Calacanis 1:02:41 Oh, Lord.

David Sacks 1:02:41 ... categories of, of race and gender.

Chamath Palihapitiya 1:02:43 Right.

David Sacks 1:02:44 So, Google has now built this into their AI model.

Jason Calacanis 1:02:48 Oof.

David Sacks 1:02:48 And e- again-

Jason Calacanis 1:02:49 Start over.

David Sacks 1:02:49 ... the question... Yeah, y- you almost have to start over again.

Chamath Palihapitiya 1:02:52 Rip it all out.

David Sacks 1:02:52 It's just too up to function.

Chamath Palihapitiya 1:02:53 Fire ou-... fire everybody.

David Sacks 1:02:54 Now, now, JK, I think you made-

Chamath Palihapitiya 1:02:55 Start over.

David Sacks 1:02:55 ... 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's been trained with, but rather, they'll dial it down to the point where they're harder to call out. And so the ideology-

Jason Calacanis 1:03:10 More subtle.

David Sacks 1:03:10 ... will just be more subtle. Now, I've already noticed that in Google search results, e- Google is carrying water for either the official narrative or the woke narrative, whatever you wanna call it, on so many search results.

Jason Calacanis 1:03:23 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 1:03:38 It's not programmed that way. I mean, you're talking about something very deep.

Chamath Palihapitiya 1:03:41 Sax, what do you do if you're the CEO of Google?

David Sacks 1:03:44 Uh, fire myself? (laughs)

Chamath Palihapitiya 1:03:46 No, seriously.

David Sacks 1:03:46 (laughs)

Chamath Palihapitiya 1:03:46 You're the CEO of Google. You're, you're tasked. Let's say your friend Elon buys Google, and he says, "Sax, will you please just run this for a year for me?" What do you do?

David Sacks 1:03:54 Well, I saw what Elon did at Twitter. He went in and he fired 85% of the employees.

Jason Calacanis 1:03:58 Yeah. I mean, that-

David Sacks 1:04:00 But, you know, P- 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 with Disney, we saw it with Bud Light.

Jason Calacanis 1:04:20 Coinbase.

David Sacks 1:04:22 Coinbase was the other way.

Jason Calacanis 1:04:23 No, no, but they had a group of people there who were causing chaos and then-

David Sacks 1:04:26 It was, it... Yeah, exactly.

Jason Calacanis 1:04:27 ... then you get-

David Sacks 1:04:27 So, so in any event, we know this does not help performance 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.

Chamath Palihapitiya 1:04:53 You can drift.

David Sacks 1:04:54 So this place, 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.

Chamath Palihapitiya 1:05:03 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 waltzing into.

David Sacks 1:05:16 The mission got hijacked, dude. The mission got hijacked.

Chamath Palihapitiya 1:05:18 Right, that's what I'm saying.

David Sacks 1:05:19 The original mission was-

Jason Calacanis 1:05:20 And I think the question is-

David Sacks 1:05:20 ... to organize all the world's information.

Chamath Palihapitiya 1:05:22 The end.

Jason Calacanis 1:05:22 Yeah.

David Sacks 1:05:22 Now they're doing... Now they're suppressing information that they don't like.

Jason Calacanis 1:05:25 Yeah, stop. Index the world's information, period. The end. That's the end of the document.

Chamath Palihapitiya 1:05:29 Well, and to make it universally-

Jason Calacanis 1:05:31 We, we don't need to do anything else.

Chamath Palihapitiya 1:05:31 ... accessible and useful was, was kind of the end-

David Sacks 1:05:33 Yes.

Chamath Palihapitiya 1:05:33 ... of the statement.

David Sacks 1:05:34 Yes.

Chamath Palihapitiya 1:05:35 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 intention, 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 1:06:04 And you know what? 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-

Chamath Palihapitiya 1:06:15 Accessible and-

David Sacks 1:06:15 ... and retriev- accessible and retrieval.

Chamath Palihapitiya 1:06:16 Accessible and useful. Yeah.

David Sacks 1:06:17 And saying, "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 1:06:34 I completely agree with what Sax said times a billion trillion zillion, and I'll tell you why. AI, at its core, is about probabilities.

David Friedberg 1:06:44 Okay? And so the, th- th- the company that can shrink probabilities into being as deterministic as possible, so where this is the right answer-

Chamath Palihapitiya 1:06:54 Zero or one.

David Friedberg 1:06:55 ... will win. Okay?

Chamath Palihapitiya 1:06:57 Yeah.

David Friedberg 1:06:57 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 Sax. 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 and..." For example, like what Google did with Reddit, "We're now gonna spend $60 billion a year licensing training data," right? "We're gonna scale this up by a thousandfold 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 trillion company.

Chamath Palihapitiya 1:07:45 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.

Jason Calacanis 1:08:00 Kyrie Irving.

Chamath Palihapitiya 1:08:02 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, Chamath, 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," uh, 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 isn't correct 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 zero or one? Right? It, it becomes super difficult.

Jason Calacanis 1:08:59 I have a good tagline for them now, "To help people find the truth."

Chamath Palihapitiya 1:09:03 Yeah.

Jason Calacanis 1:09:04 "Just help people find the truth." I mean, it's a, it's a good, it's aspirational. They should just help people find the truth as quick as they can. Uh, but this is, yeah. Yeah. Oof. I do not envy Sundar. This is gonna be hard.

Chamath Palihapitiya 1:09:16 Yeah.

Jason Calacanis 1:09:17 What would you do, Freeberg?

Chamath Palihapitiya 1:09:18 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-"

Jason Calacanis 1:09:38 Hmm.

Chamath Palihapitiya 1:09:38 "... and then I wanna get the data." And the alternative is-

Jason Calacanis 1:09:40 Got it.

Chamath Palihapitiya 1:09:40 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 weightings 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 1:09:56 So let me ask you a question, Sax. Uh, I'll, I'll bring Alex Jones into the conversation. If it indexed all of Alex Jones' crazy conspiracy theories, but, you know, three or four of them turn out to be actually correct and it ha- gives those back as answers, how would you handle that?

David Sacks 1:10:12 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, uh, I think that's the, uh, analogous situation.

Jason Calacanis 1:10:27 Well, I was thinking more, like, it says, you know, "Hey, I, I have a question about this assassination that occurred," and let's just say Alex Jones said something that's totally crackpot. He, uh, maybe he has moments of brilliance and he figures something out, but maybe he's got something that's totally crackpot and he, he admittedly deals in conspiracy theory. That's kind of the purpose of his show. What if somebody asks about that and then it indexes his answer and presents it as fact?

David Sacks 1:10:52 Well-

Jason Calacanis 1:10:52 Like, how would you index Alex Jones, I'm asking you. How, how would you index his information?

David Sacks 1:10:55 I think, I think the better, the better AI models are providing citations now and links.

Jason Calacanis 1:10:59 Hmm.

David Sacks 1:10:59 Perplexity actually does a really nice job with this.

Jason Calacanis 1:11:01 Citations are important, yeah.

David Sacks 1:11:02 And they will give you the pro and 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 Chamath just showed.

Chamath Palihapitiya 1:11:32 So-

Jason Calacanis 1:11:32 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 because-

David Sacks 1:11:45 I think customization is part of this, but I think it's a cop-out with respect to the problem that Google's having with Gemini right now.

Jason Calacanis 1:11:50 Chamath, what would you do if they made you chairman dictator of Google?

David Friedberg 1:11:54 I'd shrink the workforce meaningfully.

Jason Calacanis 1:11:58 Okay. 50%?

David Friedberg 1:12:00 Yeah, 50, 60%. And I would use all of the incremental savings 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 tech 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 $100 billion 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 its probabilities, its guesses, some of those guesses are extremely accurate-But some of those guesses will hallucinate, and Google is spending hundreds of billions 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 1:13:03 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." (laughs)

David Sacks 1:13:17 That's not a horrible answer for something like that, right?

David Friedberg 1:13:19 Yeah, that's a- that's a good answer, actually.

Jason Calacanis 1:13:21 Yeah. No. I- I- I don't have a problem with it. It's just like, hey, we- we don't wanna ge- we don't wanna write you an answer.

Chamath Palihapitiya 1:13:25 I don't feel like this 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 over time.

Jason Calacanis 1:13:32 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, um, IQs, et cetera, when-

Chamath Palihapitiya 1:13:49 Let's all remember-

Jason Calacanis 1:13:50 ... it should present it with citations.

Chamath Palihapitiya 1:13:51 Let's all remember what internet search was like in 1996 and 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. I 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? (laughs)

David Sacks 1:14:13 What if they evolve in a dystopian way? I mean, have you seen, like, Marc Andreessen's tweets about this? He thinks the prompts only get worse-

Chamath Palihapitiya 1:14:19 I think it's a competitive market, Sacks.

David Sacks 1:14:19 ... not better.

Chamath Palihapitiya 1:14:19 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 feds step in and start regulating AI models and all the nonsense that's being proposed.

David Friedberg 1:14:41 Friedberg, 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?

Chamath Palihapitiya 1:14:56 If the output sucks and it's incorrect, people will find that. There is open truth out there.

David Friedberg 1:14:59 But people will not know. You can- you can lie. There- they may not be... For example, let's say the tr-

Chamath Palihapitiya 1:15:02 Look at what happened with Gemini today. Like, they put out- they put out these stupid images, and we all piled on.

David Friedberg 1:15:07 We are in V0. 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.

Chamath Palihapitiya 1:15:26 Yeah, I'm not sure there's gonna be a monopoly. I- I-

David Friedberg 1:15:28 I think that's a real risk.

Chamath Palihapitiya 1:15:28 I don't 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 1:15:48 It is really interesting. I- I just asked it a couple of times to just- 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. Really fascinating. Then ChatGPT is like, "Yes, here are the six cases perfectly summarized with, it looks like, you know, beautiful citations of all the criminal activity Trump's been involved in."

David Sacks 1:16:15 Ask the question about Biden's criminal activity. Let's see if it's equally-

David Friedberg 1:16:19 I'm joking with you. (laughs) I'm joking with you.

David Sacks 1:16:19 No. I'm serious. Ask if, you know-

Jason Calacanis 1:16:21 No. Gemini wouldn't do Biden.

David Sacks 1:16:22 That's where you start to see.

Jason Calacanis 1:16:24 Gemini wouldn't do-

David Sacks 1:16:24 Yeah.

Jason Calacanis 1:16:24 ... Biden either.

David Sacks 1:16:25 Okay.

Jason Calacanis 1:16:25 I think they just decided they're just not gonna do it. They won't do Biden. 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 product."

David Sacks 1:16:34 Look, I- I can understand that part of it. You know?

Chamath Palihapitiya 1:16:37 Yeah.

David Sacks 1:16:37 If there are some issues that are so hot and contested, you refer people to search. Because the advantage of search is you get 20 blue links. The rankings probably are biased, but you can kind of 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 ob- objection to all this comes back to simple, truthful answers that are not disputed by anybody are being distorted.

Jason Calacanis 1:17:07 Yeah.

David Sacks 1:17:07 That... I don't wanna lose focus on that being the real issue.

Jason Calacanis 1:17:11 Hmm.

David Sacks 1:17:12 The real subject is what Chamath put on the screen there, where it couldn't answer a simple question about

David Sacks 1:17:17 George Washington.

Jason Calacanis 1:17:17 Okay, everybody. We're gonna go by chopper.

Chamath Palihapitiya 1:17:20 W- war cor- wait, war corner. We need a war corner. (laughs)

Jason Calacanis 1:17:22 We're gonna go by chopper to the... We have our war correspondent, General David Sacks in the field. Uh, we're dropping him off now.

Chamath Palihapitiya 1:17:31 (laughs)

Jason Calacanis 1:17:31 David Sacks in the helicopter.

Chamath Palihapitiya 1:17:33 (laughs)

David Sacks 1:17:33 (laughs)

Jason Calacanis 1:17:33 Go and tell