Balaji Srinivasan (2:47)

That's right. So, okay, essentially there's a long version and there's a short version which is tech and media actually share a common route in that we're both about the collection, presentation and dissemination of information. Right. So the collection of information, like they're sourcing our data. Right. Presentation, user interface or articles, dissemination, distribution, whether on social feeds or newsprint or what have you. Right. So at a very structural level, we are the Internet first digital alternative to the 20th century printing press, newspaper kind of model. We are essentially a contender, a competitor for what is upstream. You know, if you ask the question of what is upstream of, at least in the west or in the Anglosphere, and you keep asking question, what's upstream of a factory? Well, it's, you know, political this and it's capital and what's upstream of that? Well, it's. Eventually you get to money and media because media is upstream of politics and money is upstream of media, but media is also upstream of money. And that's an uroboros, where that circle eats its tail. So the venture capitalist and the journalist, right, the tech and media are actually sort of locked in a struggle for what is upstream. Right. And that's like a good way of seeing it. Like who's at the control panel, flipping the switches, hitting the buttons and so on and so forth. And with the advent of the Internet over the last 10 years, really the last three years, essentially, speech has actually been freed for the first time in our lifetimes. Because until, you know, essentially the 2010s, like, you know, there's that saying, freedom of speech belongs to those who, freedom, the press belongs to those who own one. Or never argue with a man who buys ink by the barrel. Which meant that unless you had a newspaper that you inherited, or a TV license or a radio license, all of which cost many millions of dollars, you could talk to your neighbor in the 80s or 90s, but nobody could hear you. Right. With the advent of social media and Twitter and blogs and so on and so forth, all these voices that previously had no distribution had distribution which caused a cacophony and all of this kinds of chaos in the 2000s and 2010s and early 2020s, and there was a counter reaction that tried to censor all those voices and a counter, counter reaction that uncensored them with Elon's purchase of X. And that has brought us to the present day. And one of the consequences of that was the Media was though we didn't set out to do it like, you know, Twitter set out to basically be tweeting breakfast, right. Facebook set out to like share likes and, you know, poke people or what have you. And those ended up disrupting classifieds and disrupted legacy media, disrupted print media. There's a great graph of the print media disruption. It's as a consequence, imagine like a kid who just grows to be 66250 in an elevator and squashes everybody against the wall, right? That's like what the Internet was, right? Where we just like grew and just added all of this, you know, muscle mass. And we didn't mean to do it right, but became really, really, really, really big and went from cute gadget makers and toaster makers to, in my view, the single most important force in the world. That's still underestimated, you know. You know, Orwell, the, you know, the writer, obviously. Yeah. So he, he had this saying which is, it takes an enormous effort to see what is in front of one's own face and what's in front of our own face basically every single moment of the working day.

Eric Torenberg (6:11)

The Internet.

Balaji Srinivasan (6:12)

The Internet, that's right. You know, what's upstream of AI? The Internet. What's up? Stream of drones. The Internet. What's up? Stream of your finance feed. The Internet. What's upstream of the data center spend. The Internet. It's Internet first, right, in the sense of mobile first or macro first. Internet first. Internet first. Where's your community? It's Internet first. Where's your business? How are you sharing your business? Internet first. How are you finding information? Internet first. So that is actually the organizing principle. Like, you know, fish can't see water, we swim in a sea of electrons. And that was not the case in the 80s and 90s, right. We have essentially being teleported into the matrix, right? Now, you watching this, hearing this, you are in the matrix.

Eric Torenberg (6:50)

Shout out.

Balaji Srinivasan (6:51)

We are in the matrix.

Eric Torenberg (6:52)

All the fans. All the viewers.

Balaji Srinivasan (6:53)

Yeah, all the fans, exactly. That's right. We're monitoring the situation where on the Internet, right. So the global Internet, you know, the closest precedent to it, by the way, just to digress on this for a second, is the ocean. And the reason is, you know, there's something called the law of the sea. The law of the sea governs how, you know, because you have ships that are going from Britain to Hong Kong to Brazil and so and so forth. And so you have international waters and what country controls that. And you could very legitimately have something that was thousands of miles Away, but is flagged British or Portuguese or what have you. And so for hundreds of years, there's something called the law of the sea that kind of governs that. Like when you're sending a packet from one port to another port, what law governs that? And we actually use the same words today. When you open up a computer, you have one port and it's sending packets to another port. Right? And so how can that information, how can those packets be sent? What can they do? And so, and so forth. The rule of code on the Internet is like the new rule of law. It is the new law of the sea. And you can think of the cloud as like the new oceans in this way. Right. Ideally a demilitarized zone, but of course there's also navies. Right? So with that macro context, let's bring to this moment. You know those zombie movies where like, you know, at the end of the movie, the zombie opens their eyes and like claws their way back like this, Right? Okay, so there's a guy who's a good guy named. He's a good analyst called Philip lemoine. Okay? He's PHL 43. All right, and maybe we can put this link on screen. Can we do this? Hold on, let me send you guys a link. Bang, bang. Okay, can you guys see that? Can we project that on screen?

Eric Torenberg (8:47)

Let's take a look.

Balaji Srinivasan (8:54)

Phl43 is a very smart guy. Good poster, French guy. Okay, and tell me when you got this on screen. All right, so screen sharing.

Eric Torenberg (9:05)

Can we get this?

Balaji Srinivasan (9:05)

There we go. All right, so I like both Nate and Nikita, okay? And they were basically talking about link deboosting and Philip found something important. So if you scroll down and look at these graphs, just click the graph. The first graph or the second graph will say it. All right, so essentially like a zombie movie, Basically. The New York Times, their distribution collapsed after peak woke in 2020, 2021, 2022, and after Elon took over X and he deboosted them, they basically went to complete 0, 2024, 2025. But with link deboosting turned off and the kind of repositioning of the American left to basically be less woke. And there's more I can say about that. But they are now trying to occupy some of. I shouldn't say the center. Again, that's not exactly right. But let's say facts that Republicans don't want to publish, right? For example, facts about the war or things like that. Right? Now, boom. Look, their distribution has come back, right? And so the tactics that worked five years ago. Like going direct. We do want to still use them. Don't get me wrong, it is important to speak for oneself and so on and so forth. We don't just want to go direct, we want to prove correct. Okay, that's the new. That's the next five years. Why? What happened in that intermediate era in 2022 to 2026? AI. And look, there's a lot of good about AI, but there's a lot of bad about AI. And if I love AI, I also hate AI. Why I love AI because when, you know, I've got a rule, it's called no public undisclosed AI. What that means is private use of AI for search for code. When you're not trying to bamboozle somebody, not trying to get one over on somebody. Fine, good. It's great for research, all that kind of stuff. Awesome. Public disclosed AI. When you put out a video that's obviously a cartoon or an animation or something along those lines, it's obviously AI, so nobody feels you're fooling them. But public undisclosed AI, when it is, it's not this, it's that em dash text, right? Whenever I see a slide deck that has AI content in it, it's a new lorem ipsum. It's lorem AI Ipsum, Right? Like just add an A. It just shows that either they're dumb or they think you're dumb. They're dumb because they can't tell the difference between normal text and AI text, or they think you're dumb and they can get one over on you and spend a little bit of effort to just flood you with a bunch of words. And then as soon as I see that, I ignore it and I send it to zero because it takes only a little bit of effort to send a whole, you know, just mash of a ton of words. And I don't know if any of them have been checked or read through or whatever. And so anytime I even detect there's a hint of AI in a communication, to me, I set that to zero and I set that person. It's a significant downgrade. And I think this is going to be baked into social networks where something like pangram.com, now people will tell me, and of course it's true that you'll never have a perfect AI detector. And so of course that's true. However, you can do really well. If I can recognize it with a naked eye and you can see it's not this, it's that many people are just going to, you know, how like Windows has a background where most people don't change the default. Yeah, right, yeah, just like that. Most people don't change the default settings on AI. They don't prompt it so aggressively that it's actually creative. Once it busts out of that, I actually don't have a problem with it because then it's like so good that it doesn't look AI. But if AI, right. If they haven't polished it enough, then if it's detectably slop, let's put it

Eric Torenberg (12:48)

like that, trying to find it. But then I think, yeah, there was a recent study about AI writing in newspapers becoming like incredibly increasingly common. And yeah, only 5 out of 100 AI actually said they used AI. And this was October 2025 and AI adoption has gone up quite a bit since then.

Balaji Srinivasan (13:09)

Quite a bit. And go to pangram.com for example, P A N. So I think this is pretty cool. I think something like this is going to become more and more common and basically, you know, it's alien versus Predator, right? And an enormous part. See one thing that most people don't get when they talk in the jobs and so on and so forth, right? I have a post. You can look at it if you go to biologies.com or just biologist.com and go to read it first and click AI is polytheistic, not monotheistic. Yeah, that one, yeah. So I think this is a good, I think it's a good post, right? But basically just points here. First is the entire AGI framework that is sort of implicitly monotheistic. It presumes that there's going to be a single all powerful AGI. But in practice we're seeing a polytheistic AI of many decentralized models that are good at different things. Number two, AI job. It lets you do any job because you can be a passable sound effects creator or UI designer. You're not amazing, but you can be like, you know, it's. Number three, AI is amplified intelligence, not artificial intelligence. You know, the smarter you are, the smarter the AI is. Number four, AI doesn't do it, does it? Middle to middle? Because you still have to prompt it, you still have to verify it. Then number, number five, AI doesn't take your job, it takes a job of the previous AI. Okay, what that means is the way I process all the change that comes with AI models. I have a spreadsheet which has rows for what's the best AI coding model, what's the best AI image video model, and I have a column which says January 2026, February 2026, March 2026. And when I determined that a new model has come in, that in opus's job or vice versa, then the AI takes a job of the previous AI. Okay, so AI doesn't take your job necessarily, it takes. Right. The next is AI is better for visuals than verbals. So it's better for the front end, it's better for movies and things like that because your eyes have effectively built in. Jeep immediately detect subtle things with the hands or this doesn't look completely photorealistic or whatever in a way that it takes you much more time when you're reading code. One bit off that completely changes a cryptography result that it's very hard for you to detect with your eyes. You have to do system two versus right. And Karpathi also talks about. He agreed with me. The verification gap is actually a very big deal. Right. Then another point I made again now perhaps, but killer AI, it's actually already here. It's called drones. And every country is going to pursue that. So the whole concept of oh my God, let's regulate the chat bots. And I'm not saying that one doesn't want to have some, some countermeasures on that, which I'll get to in a second, but like the idea of what's going to kill you, it's going to be the drone that's physical actuator shooting you. It's not just going to be like the super intelligence that makes things. Right. The next point, AI is probabilistic, while crypto is deterministic. AI and crypto are actually complementary. Crypto is what AI can't do because AI can solve partial differential equations, but it can't solve chaos, it can't solve turbulence. When you've got odes or PDEs that are chaotic or turbulent, effectively it cannot forecast the outcome of a hash function because, you know, like these are things which it provably cannot do.

Eric Torenberg (16:51)

You can't do what's computationally irreducible. This is a big idea. I think it's kind of underrated in the discourse.

Balaji Srinivasan (16:57)

That's exactly right. Sorry. I should say it can't easily calculate the pre image of a hash function. Right. So there are mathematical and physical bounds on an AI. Like for example, you could have a turbulent, you know, source of entropy. That would be something in that decision algorithm that AI provably could not forecast because it couldn't forecast what was going to happen to the turbulence. Right. So it's simply not omnipotent people are treating it as if it's omnipotent. Simply not right. Even like, you know, a pendulum, you know, with. With a few different weights on it. There's different ways you can quickly get into chaotic or, you know, that kind of behavior. So empirically, right now at least, and you can argue this point, whether AI is centralizing or decentralizing, but I think there's so many AI companies and there's so many AI models out there that it's pretty hard to put a lid on the thing. And then finally, if you go to the end, the optimal amount of AI is not 100%. If you scroll down a little bit further, right, 0% AI is slow, but 100% AI is slop. Just having that concept in mind means that in almost any process, you do not want to have 100% AI, but you probably, and frankly, sometimes you want 0% AI. You often want to just learn offline with pencil and paper and then speed up with AI, because AI is shortcut. And like any shortcut, it can be overused to the point that you don't know how to take the long cut. Right. Okay, it's a laugh or curve, but for AI. And then, you know, I talked about how referential is this. Whoa, we're referencing Torenberg on Torrenburg.

Eric Torenberg (18:27)

A 16Z podcast mentioned.

Balaji Srinivasan (18:28)

Yeah, a 16Z podcast, that's right. This is. This is recursive. Right? And fundamentally, my worldview on AI is it's constrained AI, it's economically constrained because every API call is expensive, and now it's ending energy constrained. Right. It's. There's so many competing models. It's mathematically constrained because it can't solve chaotic, turbulent, or cryptographic equations. It's practically constrained because you have to prompt and verify it. It does it middle to middle rather than end to end. And it's physically constrained because it requires humans to sense context and type that in via prompts rather than gathering that all for itself. And this is very different than the people who look at AI as AGI deus ex machina. That's just going to solve everything. Right. To be clear, this is as of this time of writing, maybe somebody overcomes it, but at least I'm describing what the constraints are on the current generation. Right. You could, in theory, unify the probabilistic system. One, impressionistic thinking of AI with the logical system, two, thinking that computers are good at and Claude code and so on is starting to get there. Perhaps. But I think it's still something where context breaks down and so on. And also somebody, you know that famous graph that people show that it can do longer and longer problems, the meter graph show that just big profile New

Eric Torenberg (19:37)

York Times about that graph a few days ago.

Balaji Srinivasan (19:40)

Yes. That particular graph I saw a very good, you know, once in a while out of a thousand replies on X, there's somebody who's actually, you know, GEM award. Right. How do they put it? Right.

Eric Torenberg (19:51)

Gem alarm.

Balaji Srinivasan (19:52)

Okay, Gem alarm. Yeah, exactly. Right, fine. So there was a very good counterargument that actually said that this study does not purport to show what people think it's purporting to show. And, and that the task completion, it's like, it's, it's much more questionable than it actually looks. Right. And this is important because a lot of people's paranoia and stuff is levered on this. Yeah, that's it. I think that's it against the metagraph. Right?

Eric Torenberg (20:15)

Yeah. Can we pull that up?

Balaji Srinivasan (20:15)

And pull that up? Right. So yes, this, this was like, this deserves more people looking. But fundamentally he says actually the, you know, the, the graph doesn't really look like this because A, a lot of the tasks aren't and B, like what is completion? You know, their tldr. Now I don't want to overstate this because it's certainly true at a gestalt level that if you use Claude code or CLAUDE cowork or something like that, it is possible for AI to complete long complicated tasks than it could a few years ago. That's clearly true. However, it's also true that, that you still have to supervise it a lot and check the output. It's a little bit like, I compare it to a spaceship that can go in any coordinate direction that you pointed in and move very fast, pivoting it and give it the route that it's moving on.

Moderator/Interviewer (21:14)

It's like a car.

Balaji Srinivasan (21:15)

It'll take you there very fast, but then you have to still give it a direction. And now the new thing is everybody's got a car. So now car race as opposed to you just being able to teleport somewhere. Right, okay. So, so this guy is pretty critical of it. And I think this is worth reading carefully and maybe figuring out what the true graphs are from his critique of it. Taking into account that yes, with Claude code and Claude cowork and, and so and so forth, definitely. But also taking into account that it basically like speeds up. I don't know. I, your guys view is on this, but I find that it's basically like managing, you know, where you have to write the whole thing up, context. And that's actually a lot of work, the context engineering, you know what I mean? Because you're sensing the world and you have to articulate that in clear written English and describe exactly the result. The prompting and verifying then just takes up all the work. That brings me to my next point, which is AI actually destroys arguably as many know why? Because for example, take resumes, right? AI makes it wasn't that hard to make a resume before. AI does make it easier. But you know what make makes it hard to verify a resume? AI makes it easy to write an email. It wasn't that hard to write an email before, but now it makes it really hard to filter them. Okay, so many market strangers between tribes, right? So recruiting, sales, marketing are being destroyed. A spam AI slops AI scam any, anything which is between economically disaligned tribes. There's so much that that channel is now defeated. It wasn't built for that level of adversarial behavior. It wasn't built for a channel where 99% of inbound is that it can beat the probabilistic fake detectors. Like AI spam detectors broken, right? Your normal filter is broken. So what happens is people, digital tribes, they retreat to deterministic trust and it's only the warm intro that gets through. Okay, so the sales market's been broken by AI broken by like people will sometimes show me these products and they're like, I've got this AI agent and it just spams your resume to like a thousand companies. I'm like, great. It's going to leech out what trust remains in the ecosystem where everybody claims they're a machine learning superstar, blah blah. And normally pre AI you could just read a resume and scarce to be able to write well. And now you have to carefully. I mean the people do keyword stuffing, it's true, but still, good writing was relatively scarce. Now where it's much less scarce. What's scarce is concise writing. Fine, okay. But we need a completely different paradigm for sales, for marketing. I have a whole thesis on this. I think that we're going to have to literally rebuild. Like web three actually becomes a thing where the open web, web one just gets completely corrupted and web two becomes walled gardens. But web three is the hardened, cryptographically provable signed web, the open web where things are on trails and you can actually. It's like a diamond, it's hard. AI is like a, you know, it's like a slithering thing around this diamond of cryptography that it can't fake. Right. So you that so and so signed this and AI can, you know like, like for example a letter of reference can be cryptographically signed and you can show that so and so endorsed you as opposed to that it happened. Right. That's a longer topic and actually we're building stuff related to that in every school which I can talk about. But the point is the entire concept of that's going to solve everything actually. You know, for example, if you take just that sales or marketing or recruiting market, the level of economic damage that AI did there is actually than the benefit because introduce easy fakes into a system that did not was not calibrated for that amount of easy fakery. Yeah. Generating a resume. But it radically increased the cost of verifying a resume such that less hiring happens with me so far.

Eric Torenberg (25:28)

Yeah. Just anything that requires some kind of like proof of that happens as like writing text is kind of gone now.

Balaji Srinivasan (25:37)

That's right. So what that means is AI will create lots of jobs in proctoring and. Right. You have essentially the whole KYC economy. The, the. The entire, you know, like all the stuff that people do for face ID and biometrics, all that stuff merges. But with you know, proving you're a human, you're unique, all those things merge together where becomes like this critical, critical thing. Can I log into a system? All the identity authority, all that stuff will increase 10x from where it is today. Maybe 100x. Prove that you are human, that you're unique, that you have the credential that you say you do, the endorsement that you have, you do like have multiple things. It's not just one thing, it's like all of these. Prove it cheaply for yourself. But very hard to fake. Example having I won't give out your email Eric but blank that is easy to verify, hard to fake. Right. Because you'd actually have to break into the cryptography. Like an email is something that actually carries a lot of signal with it where it's just one click for Eric to verified that he has an a16.com email but it's who didn't have DNS access and the private key is effectively associated with the async z.com domain to get the MX record and Gmail access that email username. Does that make sense? Right. So easy to verify, difficult to fake becomes a critical thing in any system that deals with strangers, which is lots of systems, an untrusted environment. I even haven't even gotten to you know, fake video, fake Photos. This gets me back to media, right? The reason. Can we go back to that? Philip Le Moyne all right, here is

Eric Torenberg (27:24)

the Pull it up.

Balaji Srinivasan (27:27)

Yes. So here is the re. Part of the reason that's happening. It's not just right. It is something where like, you know, I kind of here put this article on screen. Here we go. New one. You know, old biology. Like I'm a reality shill, right? So it's about just observable reality, what our constraints are, what the battlefield is and so on and so forth, right? So Jason, who I like, you know, is if you scroll, he is basically posting something that I was posting in 2021, which is go direct. And so and if you do from colon biology s you can see go directly. Nothing wrong with that actually. In fact, all in has done a good. All great right. However, this is necessary but not sufficient reason is as follows. If you go back one. The issue is that due to AI, right? And also due to the fact that in my view in some ways significant ways MAGA has overcorrected to the right in a huge way Now A people can't tell what's real because when you see a photo or you see a video, it literally. You literally don't know if it's real or not. Crazy bomb or something like that. Maybe it's often like some bombing from Turkey or something like that, which is

Eric Torenberg (28:49)

that a lot of these things are like obviously and then they get tens of thousands of likes anyway and X as it exists right now, doesn't really have a good mechanism for filtering that garbage out.

Balaji Srinivasan (29:00)

That's. That's right. So you have A obviously fake stuff that we know is obviously fake. B non obviously fake stuff. C stuff that's real, but it's faked in terms of it's a video since presented a video from that time and place and everything there. And that makes you very cautious about like it used to be. That video and photos were pretty hard though. For example, like the Brazilian fires like of years ago. There was a big, you know, like aerial photo of a fire and it was being used to justify and it was actually a photo that was from a journalist from like years and years earlier. And the timestamp that showed that it was. Here, let me. I'll find you this thing. Hold on. It was like a fake photo that the Atlantic thought was real, right? And. And because of that they were. There's literally a guy who was like

Eric Torenberg (29:50)

calling for invading the three most Amazon fire are fake.

Balaji Srinivasan (29:56)

Yes, here we are. So if you click this Right. Chat to you. All right, this gives you an example of. Yeah, so. So click my. Click my thing there. Okay, so just to show you why timestamps and cryptography are so important. Right click. Click the. Click those images. Right. The Emmanuel Macron used one of these misleading viral photos 40,000 times embedded in a New York Times article. The photo was taken by Lauren McIntyre, who died in 2003. In other words, that showed when that photo was taken. It was taken many years ago. Right. So these guys were saying, like, you know, the case for territorial incursion. The Amazon is far from most war. Basically, literally fake photos almost justified some crazy war on Brazil in the Atlantic. Okay, that's bad. So, and New York Times, Atlantic. Like, this is a canonical example of where decentralized cryptographic truth was able to defeat. Okay, and so we have to go to our. And I can give more examples of this. Right? So basically, you know, three examples here, let me show you this. So here's one. Okay. This now was from years ago when photos fake. Now this could be faked. Okay. But from years ago, Vitalik was able to show proof of life when people were saying that he was, you know, was. Was not real. Tell me if you got that on screen. Okay, Then click into that thread underneath. So you see, I've been thinking about this for a long time. Yeah, just click into that. Yeah. In the Brazilian photos. Scroll down a little bit further. I'll show you another example. Click this one with the graph there. The. Yeah, that one. Right. So this is something where Tesla had the vehicle law fake NYT story by a guy named John Broder that claimed that, oh, Teslas were, you know, they. They ran out of battery on the side a photo of a Tesla, you know, being hauled onto a truck. And Elon actually had the instrumental logs and was able to use digital evidence to the verbal narrative. Right. This, by the way, is one of our core strengths, numbers over letters. Okay. We can actually be quantitative. We can verify things numeric. That is actually a power that journos are generally pretty bad at. Right. Digital history. Un. Un. Unfakable cryptographic history. Go a little bit further. I'll give you a third example. So, yeah, do you see this? Defendant proved that it's content. So this is in a Chinese court, there's a patent suit and the defendant showed. Yeah, so this is like eight years ago. Defendant proved its content was really produced by showing two hash values, one on the bitcoin blockchain artifactum. It had hashed the relevant data onto relevant chains timestamp that infringement would have been impossible. Isn't this cool?

Eric Torenberg (33:05)

Cesar rule that data stored on the blockchain is admissible as evidence in trial due to the characteristics of the tech

Balaji Srinivasan (33:12)

eight years ago in China. So this stuff is actually almost a decade old at this point. You know the saying the future is already here. It's just not evenly distributed, right? So it's very important that. And this is a broader thing in tech, okay? Like, we as tech guys feel, okay, yeah, we're, you know, we're, and so and so forth. But the point is to trust us. The point is to not have to trust us, okay? The point is to have systems by math that anybody can look at. And the reason that they would trust what we're doing is they don't have to trust what we're doing. They can cryptographically verify it in the same way that you can check whether a website has SSL via the HTTPs lock symbol, whether you can, you know, check email address. Right. Whether you can check whether someone has DNS access with a. TXT record. You should be able to check via chain. Check, chain. Another way of thinking about this is, you know, if you've sent somebody something on, I don't know, a blockchain, like, you know, Bitcoin or Ethereum, have you ever sent them the either scan or record to show that you sent the money? Have you done that?

Eric Torenberg (34:27)

I don't think so, Eric.

Balaji Srinivasan (34:28)

You, you've definitely done that. Yeah. Yes. Like, hey, I sent you the money. Here's the transaction receipt, right? Yep. And for example, if, to give you a concrete example, if you went to Grok, hey, Grok, tell me you can do this live if you want, you say, hey, Grok, use onchain information, okay. To document the FTX hack that happened in late 2022. Okay. And give all reference. Grok will tell you about a hack that happened of FTX at the time all the FTX drama was happening. And in particular, it will link to that, have on chain records that show that that event for that amount. Right. There it is, right? So this is what's called on chain media. Crucially, the source of is not the New York Times and Salzburger and Salzburger employees. It is the blockchain, right, that everybody has free universal access to. It's not paywalled. It's not something where you have to pay Salzburger for him to tell you what the truth is. It is something and see what the truth is. Right? See on chain evidence and timeline, right? Initial inflows, right? So you can actually click and you can diligence it yourself. And if you ask and say, you know, give the. You know, like click. Exactly. See, Give the. So this is something which can be done for those that are on chain, right? So. Yeah, exactly. Right. So but like plotting blockchain money trail. So can you. If you go back to the thing and you say give the specific. Give an example. Specific. Either scan, link. Right? And this is the. This is the raw. So the raw. I mean, it's not the raw. So the raw data, but it's basically a URL, Right? So click that. Right? So that's something where with a little bit of technical understanding, you can literally look, see what happened. It went to the FTX account strainer and so on and so forth. Right? So you can pull all this data and analyze it yourself. You know, if you heard the term osint.

Eric Torenberg (36:52)

Yeah, we're all about that.

Balaji Srinivasan (36:54)

So this is. You're all about that. That's right. So this is like osint, but for on chain stuff. So on chain intelligence is like OSINT differences. And this is going to be important in my view right now. The verifiable information in crypto is mainly financial, where there's a major news event. And some of the key things, key things were happening on Chain with Farcaster. And, you know, so Dan Romero is the show. Eric, I think it's fair to say, right. Our friend, right?

Moderator/Interviewer (37:25)

Yeah, exactly.

Balaji Srinivasan (37:26)

Angel investor Sudan came up with a protocol called Farcaster. And it's still an amazing protocol. In fact, he basically he sold and he gave over management to the protocol to someone else. But sometimes protocol band, that's beyond any one company. And so him going and doing something else. But the protocol being around, it's still this very, very powerful protocol that I want to do a lot with essentially is like Twitter, but on Chain. Okay. And allows anybody to post, anybody to read, anybody to write, and crucially to add, verify photos, verify videos, or at least give on chain evidence. So imagine if much more was on Chain. Not simply financial data, but much of what goes into an article. You know, years ago I said something as a joke, but then it became true. I said, I gave a talk on what I called, you know, just like a sports article. Right here, I'll find, I'll find the talk. Ready? I said just like a sports article is the box score, right? Where you, you know, basically you have a box where you could automatically generate an article. A financial article is often a wrapper around a ticker symbol, right? A news article is often a wrapper around. And I'd been thinking about this for years. You can see this post here, right? And then there's like, I just sent you a link there. Right. Basically on. Let me send you the ledger of record thing. So I've been thinking about this for many years. And the, the concept is that we can separate fact from narrative. If you have a feed of on chain data and you have AI that feed, then you can auto generate stories that have no bias because the AI agent summarized the on and it can summarize it in your language with your political orientation. And you can just change the reporter by flicking the AI flags all the way down to the raw data, which when it's financial, it's on chain and eventually when it's social, it's on chain as well. Does that make sense? Right.

Eric Torenberg (39:49)

I just noticed, by the way, sorry. We have 53k live viewers on the stream right now. We have. Oh, wow, almost twice as many as Alex Jones, who has what? Million and a half.

Balaji Srinivasan (40:01)

All right, hold on. Should I, should I RT this? Should I RT the stream? I haven't actually even RT'd it from my feed or whatever.

Eric Torenberg (40:08)

Yeah, go for it.

Balaji Srinivasan (40:09)

Please do. All on. All right, send me the link.

Eric Torenberg (40:12)

Expect that.

Balaji Srinivasan (40:14)

So, okay, so the reason all this stuff is. Yeah, so, okay, sorry, I didn't mean to interrupt. So go ahead, say. We're gonna say.

Eric Torenberg (40:23)

Here is the stream link.

Balaji Srinivasan (40:26)

All right, let me put this on screen. One second. Is it on this feed?

Eric Torenberg (40:53)

It's on my feed. It's on Eric's feed also at MTS Live.

Balaji Srinivasan (40:58)

MTS Live. X MTS Live. All right, let me go there. Oh, that's ministry training strategy.

Eric Torenberg (41:07)

No, MTS Live, we're meta monitoring again.

Balaji Srinivasan (41:12)

All right, here we are. Monitor situation. All right, so it's the. All right, here we go. Bang. All right, so, so let's just qt. So on the new monitoring the situations with Eric. What's your, what's your Twitter?

Eric Torenberg (41:36)

Theo Jaffe. T H E O J A F F E E.

Balaji Srinivasan (41:42)

All right, there we go. Bang, bang. All right. Oh, all right. So coming back, what's my point? I have a talk on the ledger of record. You can, you can put that on screen. Was also a, A good post, actually. Hold on, let me show you this. There's, there's. The reason is I, you know, I got to thinking about how would you actually adjudge Kate? Truth. If you just take a look at this. This is my kind of, you know, Twitter thread on it. But I will show you a pretty good post. By a guy who he actually, you know, deleted it. But then I, I, but let me see if I can find it. The. If you look at this post is from 2020, right? You see where it says the Ledger JC on screen?

Eric Torenberg (42:48)

This one?

Balaji Srinivasan (42:49)

Yeah. Yes. The set of all crypt of onchain data, social media feeds, APIs, event streams, news, RSS. It'll take years to build. Will ultimately become the decentralized layer of facts that underpins all narrative. Closer to this, if you scroll down a little bit further, right, so click that. If you just click into that, right. So right now people were thinking about this in terms of payments, payments backend system, bank payments market data, events data. But obviously poly market, all these prediction markets, they need to have news data on chain, right? Take all these chain for trading purposes, but you need them for other purposes as well. The, the financial stuff incentivizes the information feeds, right? Like in a sense, why does. So people can in a sense bet on the Bloomberg terminal, right? So the Oracle services that actually put this information on chain contracts for various kinds of markets, right? So the news, the media actually influences the flow of money. There's a vortex that is pulling verifiable information. Market wants it there. And in a sense what the blockchain is, is like an armored car for information where you can transport that information on chain. And so if you scroll down a little bit further and again, this is like six years. I've been thinking about this for a long time. So the Weather Channel posts cryptographically. If you click into this, right. This shows all feeds that can be posted. So the Weather Channel could post signed fees of weather data, Redfin post real estate transactions. Foursquare is location data. They can make them free or they can crypto pay. And so now you have this stream of the raw data and then you have AI on top of it that turns that into legible text. I only saw GPT2 and maybe GPT3 had been out by that point. But I could. There was actually a company called Narrative Science. Okay. And which went bust. But it was a cool company. Okay. They had something which would. Let me show you what it looks like. Ready? Take this image. It's a little small, but put this on screen. Okay. Narrative Science show the possibility of something like what we. Again, you know, the thing about the future is already here. It's just not evenly distributed. Right. If you put that on screen and zoom in. Okay. Zoom, zoom, zoom. So that would take. It's a little blur. It would take your financial data and it'd say like, you know, bookings were strong this month with new pipeline, blah, blah, blah. You know, it kind of give like sports commentary on top of numbers. Does that make sense? Right, so this, it was like a domain specific AI, right? That would turn numbers into legible text.

Eric Torenberg (45:32)

Yeah.

Balaji Srinivasan (45:33)

And that was there back in the day. So I saw that and I knew about that. I'm like, you know, as AI improves, it's possible extremes of not just financial and other data and automate the process of turning box scores into sports articles, the process of turning weather feeds into reports, turning financial feeds into financial reports, turning lists of tweets into political articles. And what I didn't know is how fast it would happen. Right. Actually already, you know, I actually built some prototypes on this already. For example, here's a fun one where. So I up a year ago, actually, like as soon as ChatGPT came out, I knew the potential of this. Hold on. It's pretty funny. We. We basically did a. An nyt, but an AI nyt. We did two versions of that, right? Find this, we set this up as a replib bounty and it was so, so, so fast. In fact, hold on, where is this here? This is the. This is the original bounty. I'm pasting this into you and then I'll show you what happened to it. Ah, yes, here we go. Bingo. Okay, so I put that bounty up and then there's a kid who literally learned to code that year in replit's like summer of code. So I said, actually go back, go back for a second. Hold on, go back. So just click into my tweet first and then we'll play this video, Right? Just click my scrolling a little bit further. Yeah. Prompt with a few tweets, output an article in the style of nyt, W she, et cetera. Make the aesthetic look exactly the same. Compare the speed and cost of having AI do it versus a tradition. Oh, I think that's funny. Okay, fine.

Eric Torenberg (47:24)

Yeah.

Balaji Srinivasan (47:25)

All right. So do view quotes, right? Yeah. Then exactly right. So then there we go. Pow days. Look at his. Look at his thing. Okay. And let's zoom in on this. Let's maximize this. So basically post in a tweet, right? And you know, basically he'll hit submit, right?

Eric Torenberg (47:49)

This is back when it was called Twitter to Elon Musk on Twitter.

Balaji Srinivasan (47:53)

Yeah, this is, this is four years ago. It was right after Elon took over Twitter. Right. The GPT times, right? And so now he hit submit and literally it turned to a image and headline that looked exactly like, you know, Coca Cola as being a part of America. The same tone and everything. Okay, that was four years ago, Right. And the full thing was open source and so on and so forth. Now I actually did a follow up on this. So as much as I love Twitter and Twitter's great, I do. Where basically the main issue is Twitter's still a commercial service and so it's a closed service and so on and so forth. Right. And it's fine. It's good. It does a lot of good stuff. And you know, I think it did a lot of good for the world. But we also want open source, right. So a year later, this guy, the robojourno. Right. And here was the result of that. This was actually on Farcaster alt of that, which actually was even better. So tell me if you got that on screen. Right, the robojourner. Right. So you scroll down a little bit further. The quotes or whatever by. Let me see if I can load this

Moderator/Interviewer (49:08)

Balaji. The thing I want to ask you though is ripping. Why is their business absolutely crushed despite their credibility?

Balaji Srinivasan (49:18)

Your audio just said go. Sorry, say one time. Go ahead. Hold on.

Moderator/Interviewer (49:25)

Why is New York Times ripping despite their credibility being sort of by a large set of people? Why is their business crashing?

Eric Torenberg (49:36)

Isn't it just the games?

Balaji Srinivasan (49:37)

Well, it's too. The thing is they traded Anderson for 10,000 essentially, you know, Democrat Party members, right? So basically there's all of these party favorite. Who. You know, the equivalent is, you know, you know, the. The Chinese Communist Party has a. Has an outlet. It's called Kyusha. Do you know what that is? Right. If you go to, if you go to En Qstheory cn. Okay. And they put out a bi monthly called Kyusha. And that's actually very. It's. It's turgid part actually. Want to know what China's doing? This is in English and you can actually read it. You can actually learn a lot about, you know, just, just to know what they're doing. They put it all out there, right? So the Q subscriber is a lot like the NYT subscriber. It's like the party member in China who reads the party newsletter and says they're expected to like repeat what the them to say and to change their narrative as the party. Change their narrative. We're for Russia, we're against Russia, we're this, we're that. Right? In the same way, the NYT subscriber is whatever the Democrat Party wants, we will say. And they just kind of go back and forth in the Wind like this, as you know, they, they're for this, they're against this and they're for this again, right. They acknowledged something several years ago and vice versa, right? So once you kind of see NYT equals CCP in this sense, right? It is. They traded the, the dissidents, the people who they couldn't control, the tech guys for thousands of essentially. And so they traded power for money, right? That's what NYT did. They lost influence over the center. They lost, but they did. It's a little bit like honestly, like, like Alex Jones or something like that, right. They. They went to the, you know, they built a. An audience gone farther and farther into that audience and they never say anything. They, they have what people would call audience capture. Right? So the.

Eric Torenberg (51:43)

I think that like a lot of, A lot of the reason that the New York Times business is booming now is just because of the games like wordle, you know, the mini crossword. I think, like, I think more than stuff like that. Yeah. More than half of New York Times app screen time is. So I think without, without the games, they would be doing a lot worse financially.

Balaji Srinivasan (52:08)

That's true. That's exactly right. But basically about that is they cross subsidize it in the same way like everything gets cross subsidized where they have the fun stuff and then they, you know, have the eat your vegetable stuff. The it is. They lost the. They lost tech, right. They lost Andreessen, they lost all kinds of. Who thought that they were actually, you know, good and, and so on and so forth. Right. And. And here we go. Let me show you this. Ready? Link. Here we go. Hold on. Bang bang. Fully automated Liss affair journalism. Hold on. This was basically just finding the link here. This is, this is from a few years ago and it got done. Hold on. Here, just look at. You can put this one on screen if you want me. Just copy paste this. That's the only thing about a. Does this work? Eric, Eric, maybe you can put that on screen here. Maybe I'll type it in here.

Moderator/Interviewer (53:42)

I got another topic for you.

Balaji Srinivasan (53:43)

I just texted you.

Eric Torenberg (53:44)

You send it in the zoom Ch.

Balaji Srinivasan (53:52)

Yeah, here, I'll just put in the same chat with the.

Eric Torenberg (53:56)

Here we go.

Balaji Srinivasan (53:57)

This is just. Yeah, so this was fully automated laissez faire journalism. And there's like a video version of this which I can find. But basically you get the, the concept, right? There's actually a nicer version of this actually in a later thread. Much nicer version if I can find it. And the, the point being that this was. This was something that we could take the open source feed and we could turn it into something which was. Would look like an NYT kind of. I really want to find the actual version of this because there's like a better. There's a better version of it. Problem is I posted so much that it's kind of hard to find. See if I. It's like from two years ago. Give me a second. Ask me a question while I'm finding this. Go, go, go. Apology.

Moderator/Interviewer (54:48)

I want to hear your thoughts on clavicular.

Balaji Srinivasan (54:53)

Go ahead.

Moderator/Interviewer (54:54)

Clavicular?

Balaji Srinivasan (54:57)

What about. Oh, about clavicular.

Moderator/Interviewer (54:59)

Yeah, I mean I feel like you are on looks maxing.

Balaji Srinivasan (55:02)

Does it go Well? I. Well, I mean I'm not. I myself have certainly not looks. But what I meant to. What I do agree with what I think. Okay. What's one cheer for it? Because biotech is the. What I did say is that the. Bro science was going to become a huge, huge, huge thing. Right. And, and it did. And basically that is going to become something which is, you know, a, A major market. Right. Something which it's a little bit like, you know how Twitter was like a small thing that became a big thing and it went from, you know, just tweeting your breakfast and so on to, you know, the center of the world economy where people are quoting, you know. Right. So in the same way bro science goes from like the, the type of thing which people think is sort of a joke to the thing that is completely real. Right. And, and, and I think that's already actually midway through that with peptides and so on and so forth. And we have currently for. For weight, but I think we'll get hopefully OIC for longevity, maybe ozempic for cognition, Ozempic for many other kinds of things. We'll get large effect size drugs and, and that could be a really big deal. Right. Um, and so hold on. I want to find this thing so super bro sciencey.

Eric Torenberg (56:34)

He seems to. No, I know the line of the mainstream, but.

Balaji Srinivasan (56:38)

Yes, but it's, it's something where the. There is a aspect to what he's talking about, which is self improvement. Okay. By the way, I found this. I found this thing finally. Hold on, wait, wait, wait. That's not it on. There we go. Sometimes copy paste isn't hard to make. Mark. All right. Bingo. Click that. So this just shows a prototype. Let me explain what you're seeing on screen. If you click the first one, the first image. So what that did is that takes the open source completely free stream of on chain posts of the Foraster feed and automatically generates a front page styled to look like nyt, but that's completely free. And you can click into and you can, you know, see the articles and so on and so forth. Right. If you go to the right. Okay. You know, you can compare that to the NYT homepage. Right.

Eric Torenberg (57:51)

Is this live on.

Balaji Srinivasan (57:54)

We're gonna, we're gonna actually bring it back. Right. So, so stay tuned if you're interested in this kind of thing. I'll announce the launch on Montreal situation. Okay. We're gonna have, I'm gonna become hopefully one of the largest funders of free open source citizen journalism in the world, where anybody from anywhere can become a reporter by posting online. And then if we include that in content, that commission where we're basically, we're covering the stuff that is useful for techno capitalists. For example, what happened in India with thorium is so important, right. They've actually figured out how to get breeder reactors working. All kinds of countries should know about this. Everybody should know about thorium. There should be tutorials about, you know, exactly what they did.

Eric Torenberg (58:41)

And so there's a really good Ammonella Academy YouTube video on thorium, which everyone should watch.

Balaji Srinivasan (58:46)

Oh great.

Eric Torenberg (58:46)

He's very funny. Thorium rocks.

Balaji Srinivasan (58:49)

Oh, great. But that's nine years ago, so. But that's good. So. So the thing is this, you know, it's weird that there's so much coverage of like some. Look, obviously you know, if it bleeds, it leads is one model of what news is. And unfortunately they will make it bleed. Like they'll often cause a conflict to get, generate the reporting and the content. Like set fire to something, sell tickets to this. There's a completely different view of, you know, what the 8 billion people in the world are doing, where not in a naive Pollyanna way like, oh, why don't we report the good news of, you know, giving a cookie to somebody. That's fine, I like that too, don't get me wrong. But we report the stuff which is, let's call it investable, okay, the progress in battery, the progress in solar, the progress in nuclear, the progress also in, let's call it the resilience economy, okay. With this crazy Hormuz thing, everybody's going to be allocating capital from wants to needs. Okay? So how, you know, oil is not simply upstream of transportation, but all kinds of things from sticks. It's a, it's a hydrocarbon that's an input to many, many different things down the supply chain, right from Medicines, all types of stuff. Right, all of that. You know, actually Eric, did you know I have a. In chemical engineering from Sanford. Yeah, of course. Yeah. So I actually have a hard science background. Right. And in fact that was actually my initial thing. Right. So I want to have citizen journalism, decentralized reporting on hard science tech across the world. Right. And commission that and then pay for that and then have that basically be something where we have a certain number of those per. And they're just posting on Farcaster and so and so forth. If you're an engineer or you're, if you want to be like an editor in chief or something like that and you want to work with me on this, then reply to me or DM me with, with what you can do. Like basically non AI or rather if it's AI, disclose AI. Right. You should be like a great writer so you can verify, you know, the submissions that are coming in and. Or you should be like a great coder or something like that. Be interested in essentially taking these prototypes that I put on screen and turning them into something which is free, verifiable, open source, decentralized, international media for the world. And look again, I love X. X is great. There's a lot of great stuff about X. But as I, as I, if we go back to my original post here, you know the thing, we have an iron serious problem.

Eric Torenberg (61:29)

We have an unserious problem.

Balaji Srinivasan (61:31)

Yeah, yeah, we have a nine series problem. So the thing is we have to understand that this zombie of NYT is getting back up off the mat, that they are getting traction again. That they are, they are going to, especially with this, you know, war, in my view, it's given them a sword. They're going to be attacking all of us. They're going to be yelling at tech guys, they're going to be calling us all kinds of names whether you were for the war or not. Right. And so post this on screen this thing that I just posted so you can come back to this post. So the issue we'll need to address with cryptography and AI is some people have started linking to legacy media news sites simply because the URL has a built in form of validation in it and they can't tell what's true on a social media that's just optimized for views. Right. So that's why I said the sequel to Go Direct is Prove correct. We must prove correct, not simply Go direct. Yes. Put out your own opinion, but prove the facts. Okay, and how do we prove the facts? Cryptography, mathematics. That's a property of all human beings, not some New York media corporation, okay? People don't have to trust the tech zillionaires. They don't have to trust us because they don't have to trust us. They can just look at it on chain, right? And so we turn crypto and cryptography. Just like there's crypto information, cryptocurrency, we call this crypto information, right? Information is built in verification and validation. Okay? Now by the way, you know, tutorials are actually a pretty good place to start on this. You know why? When you go through tutorial, you actually verify it line by line in a way that you don't for most content, right? Because you're like typing that into an editor or you're trying to replicate each step. Even a recipe actually has a built in verification to it that most content doesn't. It's not like if you read some article on Egypt. It's not like you're flying out there like Indiana Jones and verifying every line in the story. You know what I mean? Right? This is gel man amnesia. You know, like, do you guys, you guys know what that is? Maybe you can put that on screen for the. Do you want to explain?

Moderator/Interviewer (63:45)

Yeah. Basically this idea that you read something in the paper, you think it's deeply and you're like, wait, I know about this topic. They're totally wrong about this. But then when you think about, when you read other things they write about that you don't have expertise in, you sort of give them benefit of doubt and assume they're. They're right about that.

Balaji Srinivasan (64:05)

Exactly right. And don't go to Wikipedia because Wikipedia is itself a terrible source for everything. Only Grokipedia, baby, we're grokopedia.

Eric Torenberg (64:12)

Only in this household is wiktionary. So I, I hope they make a groxtionary because wiktionary is actually pretty good.

Balaji Srinivasan (64:19)

Yeah, I know, but we need, we definitely need, we definitely need groxionary, right? So here we go. Hold on, Elon. If you're listening to this groxionary, honestly, by the way, Elon, it's actually absolutely insane that like Grokopedia is a world class thing and for many people, like it's literally it's better than Wikipedia for many people, they'd find it hard to remember it in the top 10 of things from rockets to cars to boring company and so on by the boring company. You know why that's actually way more important than people think? Why Iran has actually got a form of defense where they've actually put all of their bases. They built these missile cities, these subways, basically giant subways that are filled with missiles. And so they've got underground bases. And so for both offense and defense, we're going to see a lot more in the way of underground cities. So lots of cities are going to put more and more of their stuff underground because standoff missiles have kind of changed the military, you know. And actually I think New York City got its first approval. I can't believe they got an approval for this for like a 80 story underground skyscraper kind of thing. And so in a sense the underground world is like the encrypted world. All the satellite footage can't see what's happening underground because it's underground. And there's a lot of actually space under the earth and that direction for things underground and in the water and maybe under the oceans a whole. With modern engineering we might be able to do a lot more than people think. Right. So the Boring company is actually potentially a much, much, much bigger thing than people even realize. Beyond just tunnels for cars and self driving cars, it might be tunnels for cities and for homes and so on in the medium to long term. Also, you know the term air rights? Have you heard that term? No. When you're buying property, right. Property has, you know, cadesters. Yeah. So it's like boundaries, like the latitude, longitude, coordinates in XY space that define what you're actually buying. If you're buying property in cities, you also have air rights which add a z axis because you're going up in the air and you might build a skyscraper. And can you build upward in this way? Because if, you know, are you casting shadows on people nearby and taking away. So now you have, let's call it subterranean rights, which are ground rights. How low can you drill into the ground without. Obviously you can destabilize things near you and whatnot. So there's engineering considerations that's going to become a big thing anyway. Coming back in the stack. So. Elon Grockpedia. Sorry, we got Gelman amnesia. We got that on screen.

Eric Torenberg (66:54)

I hope Elon is watching this by the way, because are we the biggest live on X right now? We might be.

Balaji Srinivasan (67:00)

Yeah, we might. You can mention. Yeah, there we are. I think so. Bigger than Alex Jones.

Eric Torenberg (67:04)

So how do we check? Is there like a trending tab live?

Balaji Srinivasan (67:08)

I don't think so. Maybe so, but. Well, let's not. Let's, let's. Let's entertain the audience. So if. Yeah, so all right. So actually, can you look at the Thing I just sent you the. Not that one. The. I put it in the. The zoom Chat. PBS Twimage this out. The gym amnesia effect is as follows. You open the newspaper to an article and subset check. You know why? Well, in. In Murray Jellmount is a. And this is Michael Crichton talking. The late Michael Crichton. Late, great Michael Crichton. In Murray's case, physics. In Michael Crichton's case, show business. You read the article. See, the journalist has absolutely no understanding by the fact that, well, it's so wrong, it actually presents the story backward, reversing cause and effect. I call these the wet streets cause rains, stories, papers, fold them in any case, amusement, multiple errors and turn the page to national international affairs, right? So then, you know, like, you know, you can hit X and close out this, right? So the point is basically this is something where whatever you. If you actually model it, right, NYT was the center. And how would you know something about Japan in the 1980s or the 1990s or the early 2000s? NYT would have a reporter and they would present it to you because you'd have subscription to nyt. And how do you know about what's going on in. In Turkey or in. In the nuclear industry? They hub. And they would essentially intermediate everybody's perception of each other. Like a hall of mirrors in the center. The Japanese guy would only know through one of these centralized news agencies, right? So you think of it, they call it the media in part because it mediates your experience of reality. It's like a shimmery mirror into the shimmery hall of mirrors. And of course that power of controlling that centralized hall of mirrors where everybody perceives everybody else through this smoky looking glass. Power. This is why they were so, so, so, so, so, you know, insistent they fought this bruising and ultimately losing, try to use their centralized power to censor everybody and to stop people in particular from courting with each other. Do you remember all the freakouts, Eric, about clubhouse, like unfettered conversations. Unfettered conversations, exactly. Why? Because what it meant was all the spokesman, right, you know, celebrity and some tech person and, I don't know, some, I don't know, French guy or something like that Japanese businessman could actually all a, you know, coffee table, like what we're doing right now, right, in digital space and talk to each other without a member of the party, YT official, essentially like the Communist Party official. But I repeat myself there to monitor what we were saying, right? So no journalists, no comment, make sure everything is on message and so on. So they freaked out because they. Whether they could articulate the way I just did. They understood that the ability to set the table, set the. To portray themselves as the neutral middle ground, that that would arbit what is within and without side the boundaries of discourse. If you could do that peer to peer. If you could set your own table. Oh my God. Your unfettered conversations would break the whole thing in a sentence, by the way. And this is a funny. This is a Rorschach test. Ready? You ready for a Rorschach test of a tweet? This provoked a lot. I thought it was a good tweet. Ready? Free speech is open borders for ideology.

Eric Torenberg (70:38)

That was a good tweet.

Balaji Srinivasan (70:40)

Okay, go ahead and I'll give you my thoughts here. I'll put this one on screen.

Eric Torenberg (70:48)

I think it's roughly true. This is what we saw in what we've seen the last couple of years. It's been open borders. So there's been a rapid flourishing in all kinds of takes. But also a lot of takes pretty terrible. A lot of actual total misinformation has been spread. It's probably net good, but there are some negative consequences that could be mitigated.

Balaji Srinivasan (71:17)

You know, maybe that's right. And so actually that's my follow up there. So basically one way of thinking about it is the left cared as much about speech controls as the right cares about border controls. And so the reason they just fought so hard to control speech and the boundaries of acceptable discourse and not allow unfettered conversations is that trolling thoughts is, is sort of, I mean, it's pretty important actually what the Overton window is. It's literally in its own way like open borders, but for ideology. Right? And so if you, if you think about the negative reaction that someone on the right has to open borders, that the visceral negative reaction is someone in the centralized left has to unfettered conversations, right? And on the centralized left, let's say. And so one way of thinking about it is that what the Internet has done is it's busted all the borders, right? Why? Because post from a long time ago. You know, I use Twitter as sort of a sort of a scratch pad for ideas, you know. So here is a post time ago that I think is a good concept. The Internet increases variance, right? So here's this is it from 2019, right? So from a 30 minute sitcom to 30 second clips and 30 episode Netflix binges, okay? Basically a stable 9 to 5 job to a gig economy task or a crypto Windfall from a standard life script to living with your parents or a startup CEO at 20. Right. The Internet just increases variance. But why, I'd argue it's because what the Internet does is it removes the middleman, the mediator, the moderator. Right. Anything that. Because it allows you to. Right. So sometimes that's good. Wow. I've made a new friend in Japan. We can code an open source together. You know, Linux would not have arisen without a guy in Finland being able to reach the world for basically free with computers. Right. The bad is you have the crazy groups that can connect to each other and they can be crazy together online. All these crazy Reddit sub communities or whatever, you know, and so you just get the best, the most good and the most bad. Right. So that's a border busting kind of thing because the borders that existed each and ideology and communities got busted, at least in the Anglophone world. China's response to that is just to build giant digital borders called the great firewall around the science, linguistic and physical sphere. Right. Okay, coming back. Point is that once we realize that we now have speeches, open borders for ideology. We have, let's call it American anarchy in the digital world. Right. Everybody's yelling at each other, everybody's shouting the opposite of that. Call it Chinese control, total, top down, centralized, authoritarian, you know, you have no choice. The party determines what can be said and so on and so forth. And the thing is a lot of bill, as a prediction, not an endorsement, go to that like Carney is, you know, going to that in Canada is going to that, Europe is going to that. Because the alternative too much for them. However, I think we can fashion what I call an Internet intermediate where people opt into constraints. Okay. Like when you, when you sign a contract, that's the right libertarian way of talking about it. Or when you consent to something, that's a left libertarian way of talking about it. Right? Consent to a contract. Okay. You've given, you know, ongoing affirmative consent. You can constrain yourself or agree to something in some way. For example, when you walk into somebody's house, you know, actually, you know, the Envoy iPad things. Yeah, right. You sign like a terms into somebody's office, right. And it says you can do this, you can't do this, and so on and so forth. If you don't like those terms and conditions, you don't enter. Okay. And it's a little bit of paperwork, but something that's actually pretty standard. You know, the future is already here. It's just not evenly distributed. So I Think you're going to see something like that more and more and more on larger and larger things where you sign smart contract before entering a digital or physical territory and you agree to the terms of service of this zone that you're about to enter, which will have some constraints on both digital and physical behavior and that have examples of what is allowed and what is not allowed. And if you don't like it, then you don't have to come in.

Eric Torenberg (75:50)

We have this discord. We have rules at Discord MTS Live Join.

Balaji Srinivasan (75:57)

Exactly. Can you say what your rules are?

Eric Torenberg (76:00)

Yeah, I mean it's just very simple stuff. Let me see if I can actually pull it up. It's just like no spamming, no being like for no reason. Exactly. Just don't, don't make the server a worse place for other people.

Balaji Srinivasan (76:18)

Exactly. That's right. So that's why most practical blacks to discords have moderators and moderation policies and ban hammers. Like and by the way, so like the baseline is call an in person level of stability. Right. There's free speech and there's friend speech. Right. Like, you know, in theory speech in practice to a friend, if you use your full free speech and you start cursing and yelling at them and you're like, you can't throw me in jail for that or. Yeah, but they cannot be your friend, you know. Right. Holding out on them, whatever. Right. So friend speech is how you speak to a friend and you know, free speech is, you know, the assumption that the interactions between you and a hostile government that for you and deplatform you. And so. And that does exist. Don't get me wrong, it does exist, but it's simply not like how you interface with most people and most things at most times or shouldn't be so. And within a discord. Yeah. So like the base layer is just. You're not cursing, yelling, making an unpleasant environment, posting spam, porn, malware, you know, like this kind of stuff. Of course. But then there's a second level which is keep things on topic. Right. So if for example, it's a, it's a discord about botany. Right. I want to mainly post about plants. Okay. If you suddenly started posting about spaceships or something like that, you might even be a great poster. But actually, do you remember the Claude thing where it was all like, everything was the Golden Gate Neuron.

Eric Torenberg (77:48)

Golden Gate, Claude.

Balaji Srinivasan (77:49)

Golden Gate. Claude. Right. So Golden Gate was an example of somebody who was certainly coherent and polite, but just off topic where no matter what you asked it about, it was about the, you know, the Golden Gate Bridge. Right. And it was like this funny kind of thing or what have you, right? Where they could. They could make it just obsess about this. This is like, you know, people are, you know, and it's, you know, it's basically every post. No matter what you asked about monuments, it would bring it back to Golden Gate Bridge. It was just a monomy. Yeah, exactly. Right. And. And so no matter what you. What you asked about it say, well, oh, that's a great brain. It's. You know, it, it's. Sure. It reminds me of the arches of the Golden Gate Bridge or whatever, something along those lines.

Eric Torenberg (78:35)

Highest calorie food at McDonald's is the Golden Gate Bridge, which contains around 1.6 million calories worth of steel.

Balaji Srinivasan (78:44)

Yeah, yeah, I know. That's right. Most people don't plan exactly. It's like obsessed with it. Right. And down. What is the meaning of life? That's a better example. The one below it. Yeah. That's a very profound question. Maybe the meaning comes from our own human constructs. Golden Great Bridge, the Redwood forest, San Francisco itself. Funny. Right. Okay, so point being that within your digital community, A, the base level is the equivalent of like, you know, don't shoot, spam somebody, don't scam, whatever, don't paste porn, malware, blah, blah, blah. Then the B level is keep it on topic. Right? Like, for example, if in Japanese on a forum which is all English, that's not that polite, unless they're a Japanese monolingual and they post the translation of like that, then that might be okay. Right? And then C is don't make personal attacks on other people. You know, blah, blah, blah, blah. Point is, there's rules of the road. Those rules can be mutually incompatible. You could have a vegan forum and a carnivore forum that could both be internally polite and something that'd be on topic somewhere else. One of the most remarkable things, by the way, is how you'll see people online who are just totally crazy in some contexts. Heard the term code switching. Yeah, yeah. They'll just be a completely different person in another environment. Completely different person. They'll be light, you know, professorial even, or whatever, while they're just wilding out. Go ahead.

Moderator/Interviewer (80:17)

Now I'm just laughing at the. The concept.

Balaji Srinivasan (80:19)

It's funny, right? Yeah, yeah.

Eric Torenberg (80:21)

So we can discord.

Balaji Srinivasan (80:24)

That's right. So this concept of the Internet intermediate, which we already have online, I think the key insight is we want to print that out offline. Right. The same kind of thing that you've consented to online where you enter a slack, you enter a Reddit, you enter a discord, you consent to moderation. If you don't like it, you can leave and pick from another one of those thousand. Right. We bring that concept offline, just like with the envoy kind of thing. Except you sign a social smart contract before you enter jurisdiction. And in this fashion you opt into constraints, you start rebuilding conventions of civility. But crucially, we do it from Anglo American first principles. There's consent and there's contract. Right. You're opting into those constraints, you have free choice. It is not opt down imposed Chinese communist, you know, censorship and filtering. Does that make sense? Right. So we restore order, but through liberty. Ordered liberty. Okay, so I, that might seem very abstract, but kind of like the ledger of record stuff, I think it's going to be pretty important in the years to come. Okay, and so let me pause there. There's a lot I just said, but that's also, that gives a rationale for why the media A is mad at us because we've taken over the dissemination, presentation and collection, presentation and dissemination of information. We've disrupted them economically. There's a, you know, the print media disruption graph that I always like. Right, do we show that one? We didn't show that one yet. Right, let me show that one. This, this is. Maybe this. Probably should have shown this one first, but basically the Internet just disrupted media. That's why they're so mad at us. Right. We didn't mean to do it, but we did it. And here we go. Hold on, let me put this on screen. Let me share this with you guys. Yeah, here we go. So take this guy, put this on screen, if you wouldn't mind, and then I'll, I'll summarize and then let's do Q and A. So if you click the second graph there, there it is. So that shows. Yeah, so this is like the graph to understand tech versus media. There's many more graphs, but this one's mined, you know, the Rifleman, whatever. Right. So this, this graph is it, it captures a ton where it shows that for decades it was awesome to be in newspaper print media. Right. This thing goes from 20 billion a year in 1950 to 67 billion in the year 2000. That was like peak American empire peak, you know, media and so on and so forth. And then it's like kind of flat in the 2000s, and then it just completely, roughly in the late 2000s, especially after the financial crisis where what Happened was everybody was seeking more efficient dollars, right? Like their dollars for advertising. They didn't just want to throw them away. They needed to make them efficient. And. And at that point, Google was ready to catch the rain, right? The intern was finally ready. It was no longer just eyeballs and untargeted ads. They could do these personalized, you know, AdWords kind of links, which was still a big thing, but there's a huge innovation then. So they just started capturing all the spend. And then you see Facebook going vertical like this. And actually another big part of this is not shown is Craigslist going after classifieds. So like, suddenly, you know, the guy. Remember the thing, never argue with a man who buys ink by the barrel. Now the guy who's buying ink by the barrel is just wasting all his money because we don't have to buy ink by the barrel. We can get our information out online. So they drop from 67 billion to like 16, 19 billion. If you include, you know, digital revenue. That's like, that's a huge drop. You know, you go from 70 to $20 billion. That's like a, you know, 60, 63, 62% drop in actually a 70% drop. Okay. In. In just five years, six years, something like that on that chart. Right. So the thing about this is imagine, you know, the journalists really weren't that bad going into the early 2000s. Why? They could fly. I mean, yes, they would cancel someone from time to time. And when I say they weren't that bad, to be clear, that's all relative. Because, you know, Herbert Matthews, you know, was the one who caused Castro, and he was a New York Times reporter who reported on, who turned Castro into like a celebrity figure. John Reed helped create Lenin. Walter Durante helped create Stalin and covered up the hall. D' Moore won a Pulitzer Prize for the New York Times. David Hallerstam helped create the Vietnam War. And as Ashley Rittsberg has documented, the great Lady Wink. Yeah, there are lots of journalists where if you actually go and look. Actually, in fact, let me just give you that digression. Put this on screen. Here's four book references that will change your worldview if you aren't aware of this, right? But basically, John Reed, Walter Ranty, Edgar Snow, Herbert Matthews. It's very hard to find a communist dictator that didn't come to power due to some journalists doing PR for them, okay. Basically doing recruiting for them. They basically got them distribution, okay. And however, with that said, they were not extremely hostile in the. So you click all those books if you want Just put them on screen for a second, one by one, right? So John Reed is literally buried at the Kremlin wall because he was so important to the October Revolution, right? And he was an American who went there and wrote this book, Ten Days that Shook the World, that whitewashed the entire Communist revolution. Go to. Go back one. Click the next one. Like Walter Ranty, he won a Pulitzer Prize, Stalin's apologist for covering up the mass murder of millions of Ukrainians. And you know, by the way, is after the whole war in Ukraine, New York Times wrote all these articles on Ukraine. You know what? They never mention Walter Durante, which is their own role. The New York Times is a big part of the reason that Ukraine was ever subjugated by Soviet Russia in the first place. And sudden they reinvented themselves as a champion of Ukraine after they were the ones who like, you know, basically won a Pulcher and made money. They made money from having starving out the Ukrainians and they made money from the Ukrainian war. Just got them coming and going. This crazy, crazy thing which basically this is, you know, the kind of thing they cover up is reporting on themselves. Right. No account. You know, really, if you just liquidated NYT and took the billions of dollars and gave it to the Ukrainians for reparations, that would be justice, Right? If you go and click the next link back 1. Edgar Snow, right, so this guy. Yeah, click this guy. Edgar Park Snow. There we go. This guy. American journalism, the most important Western reporting on the communist movement in China the years before the Q Power. Right, and what was that reporting? It was like Red Star over China remained a primary source. And he's like, oh, yeah, you know, they're. They're for the people and so on and so forth. And everybody got misled by this as to. Yeah, actually, that's right. Snow depicted. See that thing on scroll up a little bit? Snow depicted Mao Zedong and his followers not as the opportunistic Red Bandits described by the nationalists, but as dedicated revolutionaries who advocated domestic reforms and were eager to resist Japanese aggression in China. They just wanted to reform. In reality, by the way, the Chinese nationalists were the ones who spent most of the blood fighting Japanese aggression. The Communists let them fight, and then they attacked them from the back. And their reforms consisted of shooting landlords in the head and, you know, all this bad stuff. Fine, okay. And you go back and the reason that they got to power is again, because of guys like Edgar Snow who did press coverage for them and basically, you know, like, formed the reputation of the Chinese Communist Party before it achieved power. Then go back one Herbert Matthews, right? This another New York Times journalist, Another communist dictator. Another journalist, another communist, but I repeat myself. So the man who invented Fidel, right, Castro, Cuba. Herbert L. Matthews in New York Times, right? And basically this, this shows that, you know, Fidel Castro was on the run. He was in hiding. And then what happened was Herbert Matthews basically wrote this whole thing which is like, Castro is alive and he's still recruiting. It'd be like saying like, Osam bin Laden is still alive and he's here and if you want to join Al Qaeda, go to this location. I didn't say that. You know, it's literally like that kind of thing, right? And so there's actually a good book by this, on all of this by Ashley Rinsberg on the Great lady winked called the Great Lady Winged, which just goes through all of these episodes, right? And shows the New York Times is never great, right? The Salsberger family, by the way, you know, just to show you a little bit more, just to show you what we're dealing with, right? Remember, you know, BLM and how like, everybody, everybody was a racist other than the New York Times, right? Well, actually, just to know here, this again, something you'll never see in the New York Times itself. The family that owns the New York Times were reportedly slaveholders. Ta da. So all white people are racist other than the white people who own the New York Times. Every. All white people are guilty of slavery. Other than the white people in New

Eric Torenberg (89:11)

York Times, who actually Yale University and Brown University, they were founded by slave owners.

Balaji Srinivasan (89:17)

Yeah. And by the way, you know, on Git. GitHub, we had to change master domain. Is Yale giving up the master's degree? I mean, no. Yeah, right. Come on, right? This is all just stuff which was basically media attacking tech, right? So the point is. And by the way, you know, who is this guy? Just to know who we're dealing with here, right? This guy is like, look, you know, the thing about tech is everybody in tech is basically new money, right? Which are not everybody. There's. There's some people, you know, who you know are like second generation VCs. But this guy by the. Everybody knows Zuckerberg, okay? Zuck is out there, he's taking the hits. You know, his face, okay, just scroll up a little bit. Just have the headline there, right? So this is. See, we know what Zuckerberg's face looks like. You can summer summon it to memory, for better or worse. And I respect Zuck. Zuck is the son of a dentist who built a gigantic platform from scratch from his computer, right? And he's out there, he's taking the hits as CEO, like it or not, you know, he's personally responsible for it and he's built, you know, like I don't agree with every single thing Meta has done, but overall, on balance I think it's given messaging and all this stuff in general. It's done a lot for, for the world and for tech. Salzburger is surrounded by a thousand reporters at all times, but you've never heard his, never seen his face for most people, never even heard his name. Am I right, Theo? You probably hadn't seen his name or heard his face until, ah, seen his, you know what I'm saying? Heard his name or seen his face till now.

Eric Torenberg (90:45)

Yeah, I had actually, because I read Yarvin, but before that.

Balaji Srinivasan (90:49)

Okay, fine, fine, fine. All right, but most people, most people have not. Yeah, most people have not. And just to give you a sense of this, right, he's inherited the New York Times company, which is a company by the way, from his father's father's father's father. This is like a fifth generation hereditary dynasty. And what does he say? He's like, oh, you tech guys aren't diverse and meritocratic enough, right? This is literally an organization where it was, you know, like, like the three competitors for the NYT throne were like three cousins, okay? So their version of the Rooney rule, you know, the Rooney rule, you're supposed to like, you know, back, you're like supposed to interview like diverse people for the top role. Their version of the Rooney Rule was like interviewing their three cousins for the top role, okay. Whereas what do we do? We interview people from all around the world, right? We have like the founder of Calendly is Nigerian and you know, Mercado Libra, Latin American and you know, Kareem is Middle Eastern and we have Indian startups and Japanese startups. Tech technology is global, right? And these guys are nepotists, okay? So in the battle of nepetus versus technologists, you know, just, just since you got me on this, right, so basically, let me just show you an amazing contrast. All right, Here we go. Look at this one. And, and most people don't have the context window to remember all of this stuff, but now with AI, we can actually do it. So if you put this one on screen, right? How punch protected the times, right? And what that's doing is it's extolling the, the values of dual class stock, okay? It's saying that because Salzburger has dual class stock, you know, then he can run the business, you know, and see a given stock. The solution is give that stock class A shares. Not enough to threaten the family's control. That's how they, you know, basically were able to. I've never see, I've never had to worry. So scroll up a little bit. Right? But I've never had to worry the times would go the way because dual class stock was so good. Okay, right. So here they're for dual class stock. But now look at this one. You can't fire Mark Zuckerberg's kids, kids tech companies using dual class. So dual class stock is good. Yeah. When it's for the New York Times company, it's for Salzberg. It's good. When it's for Zuckerberg. Right. It's bad. This is the decoder lens. You can apply to every single thing they do. You know, you may know it also as Russell conjugation. Right. Have you heard that before? Yeah. Yeah. So should I explain? Russell, conjugation is important conceptual thing here. Just a baseline. These are like, some of the basics from, like, five years ago as a media. Like, I sweat, you perspire. But she glows, right? He doxes, she leaks. But the New York Times investigates. Okay, so, like, you know how Punch protected the time with dual class stock that allowed him to serve the public interest decade after decade versus unaccountable Mark Zuckerberg's Duke Glass stock, blah, blah, blah. Right? Okay, so they just. Russell conjugate everything, and they space it out enough that, you know, they. They put the negative and pejorative connotation on it when it's tech, and they put the positive connotation on it when it's media. This is like one of their few tricks, right? They have a few other tricks, but the fact that they've lost distribution means that we can actually just punch through the armor, right? We have infrared cameras. We have basically the ability to show A and B. See, the thing is, years ago, I might have the memory to remember an article from 2012 in 2019 and put them side by side. But now we have the Internet, we have links. I can go bing, bing, like this, and just show the contrast. Another example was within a few days of each other in, like, 2019, for example, there were like, you know, free speech in Russia. Try YouTube. They seem to be for free speech. Right. This was June 2019. Okay. And then they were also. Basically, they were also. If you put that one on screen, right, Then like, literally the previous day, they were. They were against it. Right. Does YouTube radicalize? Right. And YouTube. Right. So on the one hand, see, in Russia, they were for YouTube because it helped, in their view, destabilize Russia and. Or get their content in there and so and so forth. Within America, though it was against their interest. They were against YouTube. And this showed, by the way, they messed up because they published this back to back so people's context window could capture the inverse Russell conjugation of them praising it in one story and critiquing in another. Right before they had better editors and smarter people. So it spaced this kind of inversion out over the years. Does that make sense? Right.

Eric Torenberg (95:40)

Yeah. But isn't the counter to this that this one was an op ed and they might have a range of views in the op ed? Like, I remember they got in trouble for having Tom Cotton do an op ed during BLM that was like, send in the troops.

Balaji Srinivasan (95:53)

Yeah, yeah, but that's. So actually, in fact, you brought my exact rebuttal. Obviously, the op ed page ultimately reports to publisher. And so they, of course, they have veto over it. And they demonstrated that veto power with the Tom Cotton op ed and the firing of, you know, James Bennett and, you know, the subsequent departure of Barry Weiss and the Free Press and so on and so forth. So, yes, of course, they have these various camouflage things they'll do where they're like, oh, we don't actually control the op ed page. Like, of course they control the op ed page. Right. Like, clearly they fired the guy who published this op ed. Right. And so. Duh. Right, so. Yeah, exactly. Right. So this, the tone is needlessly harsh, blah, blah, blah. And look, you know, this is from a certain place in time where everybody was, you know, losing their minds in a certain way. Right. In 2020. And it, and it reads like you're reading something from the middle of the Bolshevik Revolution or, you know, Maoist China or whatever. Right. Fin time. Okay. And you know, because later, you know, there are other. Gosh, there's a guy, Josh Barrow, who pointed out that NYT had no similar reaction to this when, when some similar kind of event was happening. Okay, I forget exactly what it was, but Josh Barrow, you know, pointed out the contrast. Right? And he's, he's actually. Yeah, it was, it was. This was like in 2022 or something like that. I forget. I forget what it was. But he's like, maybe it was China and they were shutting down an agency or causing a problem. And anyway, net. Net is. He pointed out that they had nowhere near the level of anger about issue X that they did about the Tom Cod. Not bad. Even though it was comparable. I forget what the exact matter was. The point is recursing back up a stack, you know, as, as actually as Mark said. It is not, you know, the. What do you say? He's like a. The. It is not sufficient to critique the world. The point is to change it. Right. One of the few things he got right. Okay. And so let me get the exact quote. Yeah. The philosophers have hitherto only interpreted the world in various ways. The point, however, is to change it. Absolutely true. Okay. And that is why I would never, you know, that's why conservatives always lose Conservatives, they're. The reason they call them reactionaries is they react, they always move. Second, they have no idea of what is better. They always want to go back to the old ways that have often been defeated by the time. Right. So yeah, it's like, it's like. Yeah, that's the one. The quote right there. Bingo. Right. The philosophers only interpret the world in various ways. The point error is to change it. So all of these are things which one can critique and, and, and say NYT Russell conjugates anything. That's for the NYT co. By the way, if you Google NYT co, it's a symbol, right? It's really a stock symbol. Like has one thing. No, no, no space and. Right. Like literally it's just, it's a thing and it's just go NYT CO price.

Eric Torenberg (98:59)

I think it's just.

Balaji Srinivasan (99:00)

Yeah, yeah, five, five, five. Right, yeah. So basically, I don't know why it's. Why is it being so hard to find. There we are. Right. So if you go to Max, you'll see their price crashed going into 2012. Right. Actually, this is another piece of the puzzle just for people who don't know this. So going into this, you can see that their price was low then. Right. Then they discovered that saying woke stuff basically got their traffic back up. Right. So here I'll show you this particular very important graphic. Okay. If you look at this link, so this is something which shows 1970, 2018. It's a little bit small, but if you zoom in at the top, notice how. Right. In 2013, okay, like mansplaining, toxic masculinity, these things that had never been said in the paper suddenly went absolutely vertical. Right. That is what's called an editorial decision. Okay. At the very top level of the media establishment, they made the decision to start pouring the equivalent of poison into the water supply. It's like putting sugar in all the food so that you get a short term bump in traffic at the expense of the long term health of the Republic. Okay? They cause trillions of damage to social fabric for a few million dollars worth of clicks. It's like the ethics of a copper thief who steals 100 bucks in copper but destroys like a light or a Tesla supercharger enormous damage to the commons by causing all of this conflict. But you know what? It did benefit the New York Times company, where if you go back to NYT stock that started going up around that time, right? So go back to 2013, you can see it was in the doldrums. And then suddenly they start, you know, roaring upward. Especially post Trump boom, boom, boom, boom. Like this Ant. Right. So all this kind of stuff is something that sent their stock roaring up. I mean, it's only, I mean, in our, in our, like, you know, from our standpoint, but my God, has it done more than $12 billion of damage? Holy moly. Right? It's crazy how much damage it's done. So what we need is something which actually is not simply critiquing. Like, you can go through a million examples of like, it's funny to put it this way. Like, yes. Did they cause. Did they help cause the Hull d' Amour with Walter Durante? Yes. Did they help cause the Cuban Revolution with Herbert Matthews? Unfortunately, yes. The hull cause the Vietnam War with David Halberstam's false reporting? Unfortunately, yes. And on and on. You could go through this. You could go through, you know, Jason Blair, and you can go through all the other fake stories, MIT and all the other journalists. You could go through Russell Conjugation, and you could go through the fact that they're nepotists who inherited their paper and, you know, they call meritocratic tech new money. All these names, when they. Those names are better applied to them. You can make all those points, but the point is not to critique the world. The point is to change it. So how do we change it? Decentralized cryptographic truth. Decentralized cryptographic truth, where if you go to coinmarketcap.com for a second, here's a great station. You ready? This site, coinmarketcap.com, the seemingly, you know, small site, do you know that since 2017, in 2017, it actually passed WSJ.com in traffic?

Eric Torenberg (102:33)

Wow.

Balaji Srinivasan (102:34)

Yes. Here you go. Ready? So put this on screen. So click that. This is almost 10 years ago. 9 years ago. Click that. Yep. This is Alexa. The reason is at first you might think, oh, is that comparing apples to apples? Well, Double Machete has news. But you know what else it has? It has stock prices. And in fact, in the 80s, if you did some leverage, We seem to

Moderator/Interviewer (103:13)

have lost Balaji's stream. Man, this guy's got a lot of endurance, huh? No, Balaji, we lost you for a second. Keep going.

Balaji Srinivasan (103:38)

And the equity is valued for the purpose of transaction. We're going to use the closing price in the Wall Street Journal on the day that the contract was signed. Right. So above, you know, like are the letters, but below the Wall Street Journal was bought for the numbers. It was literally bought for the feed of stock prices. Right. So looked at in that way, coin market cap is of course, it's a global wsh. Where it started with the numbers, which are the coins. The coins are the global stock market that are actually by some measures already the number four stock market coins, since someone from Japan and Brazil and Turkey can trade coins on an equal basis even if they can't get a brokerage account. So by some measures, the nyse, the NASDAQ and crypto are the three largest markets in the world. And crypto will become the number one, I think over time as NYSE and NASDAQ go crypto with tokenized stocks, tokenized equities. So it's actually not that crazy to realize actually. Yeah, for many people around the world, coin market cap is the new WSJ. And now they start to add content and analysis and so on of where coins are going and so on with me so far. Yeah, right. All right, so that is the financial information resulted in an Internet first news source that at least from a pure traffic standpoint has disrupted WSJ.com I do believe as we start putting fax on chain like via a vehicle like Farcaster, we can have something that flips nytimes.com, because every article is verifiable because it's coming from decentralized citizen journalists on the ground. This is another major point, by the way. You know, the founding fathers of America were against a standing military. You know, have you heard that? Yeah, yeah. The reason is if they had a praetorian class, right? A group of guys who were armed when everybody else was disarmed, they knew from history that that group of people would see themselves as special and so on and so forth, versus if you had a farmer soldier who was drawn from the population, then that would be represented the population, it wouldn't oppress the population and so and so forth. Right now because of the advent of industrialization and so on and so forth, it became economically infeasible. The farmer soldier couldn't just, you know, make a tank out of a, out of a shovel or whatever. They couldn't just beat plowshares into swords. So that's why you got these professionalized militaries over time. And now it's actually re decentralizing with drones and for its cyber war. And so it's over. It's a whole separate topic. But in the same way that you don't want a standing military, you don't want a standing media, which is not representative of the population. The reason being, because then that standing media, who can check them, only another journo, only wsja could check NYT could check WaPo. So there's an incentive for collusion, just like there is between any set of corporations. And what they do is they collude, and this is what they did in the 2010s and so on, where they could never be wrong about a story like Russiagate or something, because they all just basically repeated the same thing ad nauseam. And you were misinformation, disinformation, conspiracy theories, blah blah, blah, for contesting that story, which eventually later they all admitted together was false. But by admitting it together, that's another concept, the school of fish strategy. Right? Lots of these false stories we now know are false, but there's no accountability for them because you are an individual, but they are a school of fish. Okay, so that's. The school of fish strategy is basically this. Here's a great visual of it. Ready? If you put this on screen. So you are the individual, but all the NPCs just turn as a group. Okay? Right. So that is this key concept where all the journos see that this is the advantage of being an npc. If you're an npc, you're just repeating what everybody else is saying, because you're repeating what everybody else is saying. You can't be singled out. The strength in numbers. And then when the conventional wisdom shifts from oh, you know, a lab leak was a conspiracy theory to a lab leak is within the range of acceptable things. They can just shift what they're saying and they don't pay any penalty. But if you're the first to say something that's outside of the spectrum, then you can get attacked like this. Okay? Once you actually see that and you realize, oh, okay, that's, that's why there's no accountability for all the fake news, because another version is the head of the hydra, right. One reporter prints something fake, but all the other reporters repeat it. Now, they've got strength in numbers, right. This is actually something that they do very well, is they actually have a better esprit de corps and a better loyalty to each other, in a sense, than all these libertarian individualists, the sovereign individuals, whoever do. Right. They don't want to listen to each other. Right. They're like, the independence is both their strength and their weakness. Just like the NPC ness is both their weakness and their strength. Right. Okay. The other side point being, once you understand the school of fish strategy, like Russell Conjugations, just like, gives you conceptual frames to understand the strengths of the legacy media and also their weaknesses. Okay. And only by doing this, you know, the sunsuit thing. Right. If, you know, let me make sure I get it right. It's like if you know yourself and you know your rival, you will never lose a thousand battles. Yeah. If you know the enemy, know yourself, you need not fear the result of a hundred battles. Okay, and why is that? That means that you don't get in a fight unless you know you can win. Another version of it is another thing he says is successful generals win first and then go to war. Unsuccessful generals go to war first and then expect to win. Right. To be clear, we never wanted to fight the media. I never had any issues with them. It's. But they decided to fight us. Why? Because we disrupted all their economics, as I showed you earlier. Go ahead.

Moderator/Interviewer (109:03)

You were. You weren't interested in the media. The media was interested in you.

Balaji Srinivasan (109:07)

Exactly, Exactly. That's right. Like, I didn't care. I was just. I was, you know, as. As I think, you know, like, you know, Theo, we. You know, Eric and I have hung out for a long time, but just like I was a career academic, right? Literally all I did was I woke up in the morning and. And I said. And then I meditated on mathematics. Okay. That's what I did. I did computational genomics, I did math. I only gave my first public talk ever at age 33. Okay. I was a very private person. I just like, literally didn't care about any, you know, and it's only because, like, the total war that media waged on tech in the 2010s and now, to be empathetic to them, they felt it as if tech was waging a war on them. The difference is we were just building better products, and it happened to compete with their products, but their lifestyle got worse. The thing is, in the early 2000s, you could have an expense account as like a Time reporter and maybe write like, I don't know, six articles a year, fly around the world. You had a pretty high status, you had pretty high income and everybody feared and respected you because you could write a negative article on some politician and nuke them. But you didn't, you know, they weren't that unhappy, right? They weren't that mad. They were still, there was like peak America and so on and so forth. The 90s and 2000, they had these expense accounts, so they weren't like angry enough to get you. But then when that, that revenue graph collapsed from 67 billion to 16 billion, and then these nerds, the guy down the hall from them suddenly went totally vertical and became a tech zillionaire. And he doesn't know Proust or whatever, right? He doesn't know, you know, all these literary references. He just knows how to do math, right? Why is this guy doing. Well, it's one thing if your house turns into a hovel. It's another thing if the guys next door turns into a mansion. And yet it's the third thing if that happened because your house turned into a hovel, right? So we should, we should, I mean, it's funny, right? We should be empathetic to them because I, I actually never wish another man ill, to be clear, right? I always try to seek out the win win and always try to figure out, okay, how can we come to a win win relationship? How can you prosper? And we prosper because capitalism is positive sum, right? That's why I'm an internationalist and a capitalist. I try to work across borders and so and so forth. Nevertheless, some people are simply not interested in dialogue. Some people just want to watch the world burn. Some people just want to print fake news. You know, there's an unnamed journo, I'm not going to name the journo who said, let me see if I can quote it. Like, that's why we want to see these CEOs killed, right? You know, what was it on? I'm just going to quote without the said. And people wonder why we want these executives dead. Okay, so the anti tech terrorism, right, of firebombing Teslas, of, you know, shooting, you know, the Luigi Left of shooting the Brian Thompson, the shooting of the Molotov cocktail at Sam Altman's house, that kind of stuff. Those people have been radicalized in such a way that, yes, it's still important to post the true information out there, but also at the time, they're foaming at the mouth, yelling at you certainly let alone shooting at you. You can't reason with them. So that kind of person, you know, a lot of tech people really underestimate the level of anger out there. Just to talk about this for a second, I think the technologist is the capitalist of the 21st century. That might be an obvious point, but it's a non obvious point as well. Should I elaborate on that for a second? Sure, yeah. Okay. All right. So basically in the 20th century, the industrial revolution led to the rise of, you know, capitalism as we as, you know, the industrial variety of capitalism and the captains of industry and whatnot. And this did result in enormous improvements in standard of living. You had modern homes and you had running water and all this kind of stuff. But it also resulted in obviously huge wars and also a disruption of traditional means of government. All got disrupted. And as a consequence, I mean, America was actually a society that was relatively young and supple technological disruption and roll with it, right? But the older governments of Europe were totally disrupted by it. And actually, you know, that also led trick revolution, which was in its own way a new government that in a sense was adapted to the age, albeit in a malign adaptation. But the thing is that, you know, did you know Russia's stock market by some reports did better than America's stock market in the 1800s?

Eric Torenberg (113:46)

No, I believe it.

Balaji Srinivasan (113:47)

Yeah. We're not barbarians, right. And so the reason I say that is, you know, just to calibrate on what the world was. You look at this graph, right? And I'll get to my point in a second. Just click this table, right? Zoom in. Most of the world, with the exception of the U.S. canada and Australia. Yeah, you got on screen, right? So most you can actually. Russia, negative 100% China, like what does communism mean? Means you go to zero, do not pass go, do not collect $200, right? Go directly to gulag. Okay? Your pharmacies, you're shot, wife is kid is thrown into a gulag and re educated sent to a collective farm. Right. Also, by the way, a farm is a lot like a factory, you know why? Why? Because you're growing radishes or something. They have to be irrigated, they have to be aerated, they have to be fertilized At a certain time. There's a whole process that's a lot like manufacturing widgets. And a lot of that task is in the head of the farmer. And when they're shot and the farmers just sees it, doesn't just grow the radishes. By itself. It's a whole process of doing that. And where's all the equipment and so on? It's over a tech company and not knowing where any of the code and the private keys or whatever are. You can't, you can't deploy that, right? So these communists would seize the farm and they would kill the goose, the farmer. And then that's why they got famine. Because they didn't know how to operate this thing. They had to figure it all out from scratch. And that's why this whole thing was a huge disaster. Anyway, so these, basically these communist revolutions and they. It was like, why? You know, the thing is, you can say there's people who are good, smart, evil and stupid. These guys, retards. They're stupid. Here's why. Good is helping others without concern for yourself. Smart is helping others while also helping yourself. Evil is harming others while helping yourself. And stupid is harming others while also harming yourself. Right? So these communists were stupid because they harmed others while also harming themselves. They thought they were going to steal the farms, but they actually stole like a bag of donuts, right? They got nothing, okay? Because they had a famine. Fine. Point is, in this extremely negative sum activity where they envy the capitalists so much when they said capitalist, by the way, there's a term, great term from Grokipedia. It's called podkulak, okay? And I know this is getting into Russian history, but I'll explain why it's relevant. Basically, at first the communists were saying, oh, we're only going after the top hat capitalists, right? And then they eventually said, we're going after the kulaks, who are the farmers who were considered prosperous. And they had two cows, okay? You know, like a pro, like a wealthy farmer had two guys. It's like a small businessman, right? And eventually they went from the top hat billionaires to the small businessman to click this link I just sent you. Great term. Pod kalochnik, okay? Pod kalochnik encompass poor peasants, collective farm members, or even non peasants, opposing grain requisition of farm seizures, irrespective of socioeconomic status. So even if you were not a capitalist in any way, even if you were not a small businessman or kulak in any way, even if you yourself were poor, if you said, hey, taking these farms, the farmers is going to result in a famine, then you were an enemy of the people too, okay? This is how psychotic the whole thing got. Pod Kolachnik term. Where? Why is it important? Because, see this guy, I'm not sure if he was saying it sarcastically or not, but he's like, first they came for the billionaires, and I did not speak out because I was not a billionaire. Then they came for the millionaires. Well, actually, most of the violence in the 20th century was not on the basis of race. It was on the base of class. Okay, so they went after the capitalists. Point is, today the, you know, he's saying this. I couldn't tell if he was saying it sarcastically or not. But if you go back one. That's why I posted this, because that's exactly. If you go back one, just hit back. Yeah, that's exactly what happened in the Soviet Union. First they came for the millionaires, and then they came for the Pod Kalashniks, which is anybody who opposed them stealing all the property. Eric. Right. Yeah. It's a good term because it literally puts a thumb on exactly an episode in history where that did happen. And millions of people were killed in that order. Right. It was. It's nothing. It actually happened, unfortunately. Okay, now the issue is today, why do I say the technology is a capitalist of the 21st century? On one level, that's completely obvious, because we're techno capitalists. On. And it's non obvious why. Because the capitalist was. The beef with them is they were centralizing the means of production and they had these big factories and so on that nobody could afford. But we're decentralized production. Everybody has a laptop, right? And the capitalist was central. Everything was centralized and it was mass media, mass production. We're decentralizing everything. We're giving equality of opportunity to everybody in the world. Everybody in the world has basically the same, as I've said before, you have essentially the same smartphone experience as Sergey Brin, right? You have the same information at your fingertips as, you know, Elon Musk, for the most part. Right? Like, what's he on every day? He's on X. Just like you and I are. Like, in a sense, there's been an enormous global leveling. Right. With the Internet. The Internet is actually global equality. You have all these tools at your disposal. AI tools, this tool, that tool literally just hit keys on the keyboard and you can create all these things. So in a sense. Go ahead.

Moderator/Interviewer (119:21)

On that note, I'm going to have to up the wrap because MARC address is coming in two minutes.

Balaji Srinivasan (119:25)

Okay. Okay, fine. So Net Net is. We need to, as technologists, build a better form of media. Not just tell our own stories and go direct, but prove correct. Because a lot of people will be mad at us since we've been successful, since often we're ethnically different immigrants, Indians, Chinese, whatever, whatever. Right? Foreign in some sense of the term for some or you know, too class different, too ethnic different, whatever. And so we have to do is we have to have open source, verifiable, correct information where people trust us because they don't have to trust us. They can just verify the information we need. Decentralized cryptographic truth, citizen journalism, open source media. Because NYT is getting off the mat, we have to come correct by proving correct. That's it.

Moderator/Interviewer (120:12)

Amen. That's a great place to wrap Balaji. We had almost 80,000 people come in live. Thank you for being our first guest ever on MTS and Angel Investor and supporter and friend. Thank you so much Balaji.

Eric Torenberg (120:23)

Thanks so much the for coming on.

A16Z Podcast Host (120:27)

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating, or review and share it with your friends and family. For more episodes go to YouTube, Apple Podcasts and Spotify. Follow us on X16Z and subscribe to our substack@A16Z substack.com thanks again for listening listening and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold or sell any investment or financial product. This podcast has been produced by a third party and may include paid promotional advertisements, other company references, and individuals unaffiliated with A16Z. Such advertisements, companies and individuals are not endorsed by AH Capital Management, LLC, A16Z or any of its officials affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy.