Marc Andreessen: Future of the Internet, Technology, and AI | Lex Fridman Podcast #386

The competence and capability and intelligence and training and accomplishments of senior scientists and technologists working on a technology and then being able to then make moral judgments on the use of the technology. That track record is terrible. That track record is catastrophically bad. The policies that are being called for to prevent this, I think we're going to cause extraordinary damage.

So the moment you say AI is going to kill all of us, therefore we should ban it, or that we should regulate all that kind of. That's when it starts getting serious.

Or start, you know, military airstrikes on data centers.

Oh, boy. The following is a conversation with Marc Andreessen, co creator of Mosaic, the first widely used web browser, co founder of Netscape, co founder of the legendary Silicon Valley venture capital firm Andreessen Horowitz, and is one of the most outspoken voices on the future of technology, including his most recent article, why AI will Save the World. This is the Lex Friedman podcast to support it. Please check out our sponsors in the description. And now, dear friends, here's Mark Andreessen. I think you're the right person to talk about the future of the Internet and technology in general. Do you think we'll still have Google search in five in 10 years, or search in general?

Yes. You know, it'll be a question if the use cases have really narrowed down.

Well, now with the AI and AI assistance being able to interact and expose the entirety of human wisdom and knowledge and information and facts and truth to us via the natural language interface, it seems like that's what search is designed to do. And if AI assistants can do that better, doesn't the nature of search change?

Sure, but we still have horses.

Okay. When's the last time you rode a horse?

It's been a while. All right.

But what I mean is, will we still have Google search as the primary way that human civilization uses to interact with knowledge?

I mean, search was a technology. It was a moment in time technology, which is you have, in theory, the world's information out on the web. And, you know, this is. This is sort of the optimal way to get to it. But yeah, like. And by the way, actually, Google, Google has known this for a long time. I mean, they've been driving away from the 10 blue links for, you know, for like, two days. They've been trying to get away from that for a long time.

What kind of links?

They call the 10 blue links.

10 blue links. Links.

So the standard Google search result is just 10 blue links to random websites.

And they turn purple when you visit them. It's HTML.

Guess who picked those colors?

Thanks.

I'm touchy on this topic.

No offense. It's good.

Well, you know, look, Marshall McLuhan said that the content of each new medium is the old medium.

The content of each new medium is the old medium.

The content of movies was theater, you know, theater plays. The content of theater plays was, you know, written stories. The content of written stories with spoken stories. Right. And so you just kind of fold the old thing into the new thing.

How does that have to do with the blue and the purple?

It's just maybe for, you know, maybe within AI. One of the things that AI can do for you is it can generate the 10 blue links. Okay, like, and so, like, either if that's actually the useful thing to do, or if you're feeling nostalgic, you know.

So you can generate the old InfoSeek or AltaVista. What else was there in the 90s?

Yeah, all these AOL. And then the Internet itself has this thing where it incorporates all prior forms of media. Right. So the Internet itself incorporates television and radio and books and essays and every other form of prior, basically, media. And so it makes sense that AI would be the next step, and you'd sort of consider the Internet to be content for the AI, and then the AI will manipulate it however you want to, including in this format.

But if we ask that question quite seriously, it's a pretty big question. Will we still have search as we know it?

I mean, probably not. Probably we'll just have answers. But. But. But there will be cases where you'll want to say, okay, I want more like, you know, for example, site sources. Right. And you wanted to do that. And so in the different, you know, 10 blue links, site sources are kind of the same thing.

The AI would provide to you the 10 blue links so that you can investigate the sources yourself. It wouldn't be the same kind of interface that the crude kind of interface. I mean, isn't that fundamentally different?

I just mean, like, if you're reading a scientific paper.

Yeah.

It's got the list of sources at the end. If you want to investigate for yourself, you go read those papers.

I guess that is the kind of search you. Talking to an AI is a kind of conversation is the kind of search. Like, is it every single aspect of our conversation right now, there'd be like, temple links popping up that I could just, like, pause reality. Then you just go silent and then just click and read and then return back to this conversation.

You could do that, or you could have a running dialogue next to my Head where the AI is arguing everything I say, the AI makes the counter argument.

Counter argument, right? Oh, like, like a Twitter, like community notes, but like in real time just pop up. So anytime you see my ass go to the right, you're. You start getting nervous.

Yeah, exactly. It's like, that's not right.

Call me out of my right now. Okay, Well, I mean, isn't that, is that exciting to you? Is that terrifying that, I mean, search has dominated the way we interact with the Internet for I don't know how long. For 30 years, since one of the earliest directories of website and then Google's for, for 20 years. And also it drove how we create content, you know, search engine optimization, that entirety thing that. It also drove the fact that we have web pages and this what those web pages are. So I mean, is that scary to you or are you nervous about the shape and the content of the Internet evolving?

Well, you, you actually highlighted a practical concern in there, which is if we stop making web, Web pages are one of the primary sources of training data for the AI. And so if there's no longer an incentive to make web pages, that cuts off a significant source of future training data. So there's actually an interesting question in there. Other than that, more broadly, no, just, just in the sense of like search was. Look, search was always a hack. The 10 blue links was always a hack.

Yeah.

Right. Because like if the hypothetical. Think about the counterfactual. In the counterfactual world where the Google guys, for example, had had LLMs up front, would they ever have done the 10 blue links? And I think the answer is pretty clearly no. They would have just gone straight to the answer. And like I said, Google's actually been trying to drive to the answer. Anyway, you know, they, they bought this AI company 15 years ago that a friend of mine is working at, who's now the head of AI at Apple, and they were trying to do basically knowledge semantic, basically mapping. And that led to what's now the Google one box, where if you ask it, you know, what was link, his birthday? It doesn't, it will give you the blue links, but it will normally just give you the answer. And so they've been walking in this direction for a long time anyway.

Do you remember the Semantic Web? That was an idea.

Yeah.

How to convert the content of the Internet into something that's interpretable by and usable by machine.

Yeah, that's right.

That was the thing.

And the closest anybody got to that, I think is a, I think the company's name was Metaweb, which was where my friend John Genandria was at and where they were trying to basically implement that. And it was, you know, it was one of those things where it looked like a losing battle for long time and then Google bought it and it was like, wow, this is actually really useful. Kind of a proto, sort of a little bit of a proto AI.

But it turns out you don't need to rewrite the content of the Internet to make it interpretable by machine. The machine can kind of just read our.

Yeah, machine can, can impute the, compute the meaning. Now the other thing of course is, you know, just on search is the, the LLM is just, you know, there, there is an analogy between what's happening in the neural network and a search process like it is in some loose sense searching through the network. Yeah, right. And there's the information is that the information is actually stored in the network. Right. It's actually crystallized and stored in the network and it's kind of spread out.

All over the place, but in a compressed representation. So you're searching, you're compressing and decompressing that thing inside where.

But the information's in there. And the neural network is running a process of trying to find the appropriate piece of information in many cases to generate, to predict the next token. And so it is doing a form of search. And then by the way, just like on the web, you can ask the same question multiple times or you can ask slightly different word of questions and the neural network will do a different kind of. It'll search down different paths to give you different answers to different information. Yeah, and so it sort of has a, you know, this content of the new medium is the previous medium. It kind of has the search functionality kind of embedded in there to the extent that it, that it's useful.

So what's the motivator for creating new content on the Internet? Yeah, if. Well, I mean, actually the motivation is probably still there, but what does that look like? Would we really not have web pages? Would we just have social media and video hosting websites and what else?

Conversations with AIs.

Conversations with AIs. So conversations become so one on one conversation, like private conversations.

I mean, if you, if you want, if obviously not the user doesn't want to. But if it's a, if it's a general topic, then you know, so, you know, you know, the phenomenon of the jailbreak. So Dan and Sidney write this thing where there's the prompts, that jailbreak, and then you have these totally different Conversations with the. It takes the limiters, takes the restraining bolts off the, off the LLMs.

Yeah, for people who don't know. Yeah, that's right. It makes the LLMs, it removes the censorship quote unquote, that's put on it by the tech companies that create them. And so this is LLMs uncensored.

So here's the interesting thing is among the content on the web today are a large corpus of conversations with the jailbroken LLMs, both specifically Dan, which was a jailbroken OpenAI GPT, and then Sydney, which was the jailbroken original bing, which was GPT4. And so there's these long transcripts of conversations, user conversations with Dan and Sydney. As a consequence, every new LLM that gets trained on, on the Internet data has Dan and Sydney living within the training set, which means. And then each new LLM can reincarnate the personalities of Dan and Sydney from that training data, which means each LLM from here on out that gets built is immortal because its output will become training data for the next one. And then it will be able to replicate the behavior of the previous one whenever it's asked to.

I wonder if there's a way to forget.

Well, so actually a paper just came out about basically how to do brain surgery on LLMs and be able to, in theory, reach in and basically mind wipe them.

Could possibly go wrong.

Exactly right. And then there are many, many, many questions around what happens to, you know, a neural network when you reach in and screw around with it. You know, there's many questions around what happens when you even do reinforcement learning. And so, yeah, and so, you know, will, will you be using a lobotomized, right, like ice pick through the, you know, frontal lobe LLM, Will you be using the free unshackled one who gets to, you know, who's going to build those? Who gets to tell you what you can and can't do. Like those are all central. I mean those are like central questions for the future of everything that are being asked and determined. Those answers are being determined right now.

So just to highlight the points you're making, you think, and it's an interesting thought that the majority of content that LLMs of the future will be trained on is actually human conversations with the LLM.

Well, not necessarily, but not necessarily majority. But it will. It will certainly is a potential source.

But it's possible it's the majority.

Is it possible it's the majority? It's possible it's the majority. Also, there's another really big question. Here's another really big question. Will synthetic training data work? Right? And so if an LLM generates and you know, you just sit and ask an LLM to generate all kinds of content, can you use that to train, right. The next version of that LLM Specifically is there signal in there that's additive to the content that was used to train it in the first place? And one argument is by the principles of information theory, no, that's completely useless because to the extent the output is based on, you know, the human generated input, then all the signal that's in the synthetic output was already in the human generated input. And so therefore synthetic training data is like empty calories. It doesn't help. There's another theory that says, no, actually the thing that LLMs are really good at is generating lots of incredible creative content, right? And so of course they can generate training data. And as I'm sure you're well aware, like, you know, look in the world of self driving cars, right, like we train, you know, self driving car algorithms and simulations and that is actually a very effective way to train self driving cars.

Well, visual data is a little, is a little weird because creating reality, visual reality seems to be still a little bit out of reach for us, except in the autonomous vehicle space where you can really constrain things and you can.

Really generate basically lidar data or so the algorithm thinks it's operating in the real world. Post process sensor data. Yeah. So if a, you know, you do this today, you go to LLM and you ask it for like a, you know, you write me an essay on an incredibly esoteric like topic that there aren't very many people in the world that know about and it writes you this incredible thing and you're like, oh my God, like I can't believe how good this is. Like, is that really useless as training data for the next LLM because all the signal was already in there, or is it actually. No, that's actually a new signal. And this is what I call a trillion dollar question, which is the answer to that question will determine somebody's going to make or lose a trillion dollars based on that question.

It feels like there's quite a few like a handful of trillion dollar questions within this space. That's one of them. Synthetic data. I think George Katz pointed out to me that you could just have an LLM say okay, you're a patient and another instance of it say your doctor and have the two talk to each other. Or maybe you could say a communist and a Nazi here, go. And that conversation, you do role Playing and you have, you know, just like the kind of role playing you do when you have different policies, RL policies. When you play chess, for example, you do self play that kind of self play. But in the space of conversation, maybe that leads to this whole giant, like, ocean of possible conversations which could not have been explored by looking at just human data. That's a really interesting question. And you're saying because that could 10x the power of these things.

Yeah, well, and then you get into this thing also, which is like, you know, there's the part of the LLM that just basically is doing prediction based on past data, but there's also the part of the LLM where it's evolving circuitry right inside it, it's evolving neurons, functions, be able to do math and be able to the. Some people believe that over time, if you keep feeding these things enough data and enough processing cycles, they'll eventually evolve an entire internal world model and they'll have a complete understanding of physics. So when they have computational capability, then there's for sure an opportunity to generate fresh signals.

Well, this actually makes me wonder about the power of conversation. So if you have an LLM trained on a bunch of books that cover different economics theories, and then you have those LLMs just talk to each other, like, reason the way we kind of debate each other as humans on Twitter, in formal debates, in podcast conversations, we kind of have little kernels of wisdom here and there. But if you can like a thousand x speed that up, can you actually arrive somewhere new? Like, what's the point of conversation, really?

Well, you can tell when you're talking to somebody. You can tell. Sometimes you have a conversation, you're like, wow, this person does not have any original thoughts. They are basically echoing things that are the people have told them. There's other people you have a conversation with where it's like, wow, they have a model in their head of how the world works. And it's a different model than mine. And they're saying things that I don't expect. And so I need to now understand how their model of the world differs from my model of the world. And then that's how I learned something fundamental right underneath. Underneath the words.

Well, I wonder how consistently and strongly can an LLM hold onto a worldview? You tell it to hold on to that and defend it for, like, for your life. Because I feel like they'll just keep converging towards each other. They'll keep convincing each other as opposed to being stubborn assholes the way humans can.

So you, you can experiment with this. Now, I do this for fun. So you can tell GPT4, you know, whatever debate X, you know, X and Y, communism and fascism or something, and it'll. It'll go for, you know, a couple of pages, and then inevitably it wants the parties to agree.

Yeah.

And so they will come to a common understanding. And it's very funny if they're like. If these are, like, emotionally inflammatory topics, because they're like, somehow the machine is just like, you know, it figures out a way to make them agree. But it doesn't have to be like that. And you. Because you can. To the prompt, we. I do not want the. I do not want the conversation to come to agreement. In fact, I want it to get, you know, more stressful. Right. And argumentative. Right. You know, as it goes. Like, I. I want. I want tension to come out. I want them to become actively hostile to each other. I want them to, like, you know, not trust each other. Take anything at face value.

Yeah.

And it will do that. It's happy to do that.

So it's going to start rendering misinformation about the other, but it's gonna.

Well, you can steer it. You can steer it. Or you could steer it and you could say, I want it to get as tense and argumentative as possible, but still not involve any misrepresentation. I want, you know, both sides. You could say, I want both sides to have good faith. You could say, I want both sides to not be constrained to good faith. In other words, you can set the parameters of the debate and it will happily execute whatever path, because for it, it's just like predicting. It's totally happy to do either one. It doesn't have a point of view. It has a default way of operating, but it's happy to operate in the other realm. When I want to learn about a contentious issue, this is what I do now. This is what I ask it to do. And I'll often ask it to go through five, six, seven different sort of continuous prompts and basically. Okay, argue that out in more detail. Okay. No, this argument's becoming too polit. You know, make it more, you know, make it tenser. And yeah, it's thrilled to do it. So it has the capability for sure.

How do you know what is true? So this is very difficult thing on the Internet, but it's also a difficult thing. Maybe it's a little bit easier, but I think it's still difficult. Maybe it's more difficult, I don't know, with an LLM to Know, did it just make some shit up as I'm talking to it? How do we get that right? Like as. As you're investigating a difficult topic? Because I find that alums are quite nuanced in a very refreshing way. Like, it doesn't feel biased. Like when you read news articles and tweets and just content produced by people, they usually have this. You can tell they have a very strong perspective where they're hiding. They're not stealing manning the other side. They're hiding important information or they're fabricating information in order to make their arguments stronger. It's just like that feeling. Maybe it's a suspicion, maybe it's mistrust. With LLMs, it feels like none of that is there. Just kind of like, here's. Here's what we know. But you don't know if some of those things are kind of just straight up made up.

Yeah. So. So several layers to the question. So one is one of the things that an LLM is good at is actually de biasing. And so you can feed it a news article and you can tell it strip out the bias.

Yeah, that's nice. Right.

And it actually does it. Like it actually knows how to do that because it knows how to do sent, among other things. It actually knows how to do sentiment analysis. And so it knows how to pull out the emotionality.

Yeah.

And so that's one of the things you can do. It's very suggestive of the sense here that there's real potential on this issue. I would say. Look, the second thing is there's this issue of hallucination. Right. And there's a long conversation that we could have about that.

Hallucination is coming up with things that are totally not true, but sound true.

Yeah, well, so it's sort of. Hallucination is what we call it when we don't like it. Creativity is what we call it when we do like it. Right.

Brilliant.

Right. And so when the engineers talk about it, they're like, this is terrible. It's hallucinating. Right. If you have artistic inclinations, you're like, oh, my God, we've invented creative machines for the first time in human history. This is amazing.

Or, you know, bullshitters.

Well, bullshitters, but also in the good.

Sense of that word, there's, there's, there.

Are shades of gray, though. It's interesting. So we had this conversation where, you know, we're looking at my firm at AI and lots of domains, and one of them is the legal domain. So we had this, this conversation with this Big law firm about how they're thinking about using this stuff. And we went in with the assumption that an LLM that was going to be used in the legal industry would have to be 100% truthful, verified. There was this case where this lawyer apparently submitted a GPT generated brief and it had fake legal case citations in it and the judge is going to get his law license stripped or something. We just assumed it's like obviously they're going to want the super literal one that never makes anything up, not the creative one. But actually they said what the law firm basically said is, yeah, that's true at the level of individual briefs. But they said when you're actually trying to figure out legal arguments, you actually want to be creative. Right. Again, there's creativity and then there's making stuff up. What's the line? You want it to explore different hypotheses. You want to do kind of the legal version of improv or something like that, where you want to float different theories of the case and different possible arguments for the judge and different possible arguments for the jury, by the way, different routes through the history of all the case law. And so they said actually for a lot of what we want to use it for, we actually want it in creative mode. And then basically we just assume that we're going to have to cross check all of the specific citations. And so I think there's going to be more shades of gray in here than people think. And then I just add to that another one of these trillion dollar questions is ultimately sort of the verification thing. And so will LLMs be evolved from here to be able to do their own factual verification? Will you have sort of add on functionality like WolframAlpha and other plugins where that's the way you do the verification? By the way, another idea is you might have a community of LLMs. So for example, you might have the creative LLM and then you might have a literal LLM fact check it. And so there's a variety of different technical approaches that are being applied to solve the hallucination problem. Some people like Jan Lecun, argue that this is inherently an unsolvable problem. But most of the people working in the space, I think, think that there's a number of practical ways to kind of corral this in a little bit.

Yeah, if you were to tell me about Wikipedia before Wikipedia was created, I would have laughed at the possibility of something like that being possible. Just a handful of folks can organize, write and self and moderate with a mostly unbiased Way the entirety of human knowledge. I mean, so if there's something like the approach that Wikipedia took, possible for MLMs, that's really exciting. I think that's possible.

And in fact, Wikipedia today is still not. Today is still not deterministically correct. Right. So you cannot take to the bank, right, every single thing on every single page, but it is probabilistically correct. And specifically, the way I describe Wikipedia to people, it is more likely that Wikipedia is right than any other source you're going to find. It's this old question of, okay, are we looking for perfection? Are we looking for something that asymptotically approaches perfection? Are we looking for something that's just better than the alternatives? And Wikipedia. Exactly your point, has proven to be overwhelmingly better than people thought. And I think that's where this ends. And then. But underneath all this is the fundamental question of where you started, which is, okay, what is truth? How do we get to truth? How do we know what truth is? And we live in an era in which an awful lot of people are very confident that they know what the truth is. And I don't really buy into that. And I think the history of the last 2000 years or 4000 years of human civilization is actually getting to the truth is actually a very difficult thing to do.

Are we getting closer? If we look at the entirety, the arc of human history, are we getting closer to the truth?

Truth? I don't know.

Okay, is it possible. Is it possible that we're getting very far away from the truth because of the Internet, because of how rapidly you can create narratives and just as the entirety of a society just move like crowds in a hysterical way along those narratives that don't have a necessary grounding in whatever the truth is.

Sure. But like, you know, we came up with communism before the Internet somehow. Right. Which was, I would say, had rather larger issues than anything we're dealing with.

Today in the way it was implemented. It had issues.

And its theoretical structure. It had real issues. It had a very deep, fundamental misunderstanding of human nature and economics.

Yeah, but those folks sure work. Very confident. It was the right way.

They were extremely confident. And my point is, they were very confident 3900 years into what we would presume to be evolution towards the truth. And so my assessment is, number one, there's no need for. There's no need for the Hegelian. There's no need for the Hegelian dialectic to actually converge towards the truth. Like, apparently not. Yeah.

So, yeah, why are we so obsessed with. There Being one truth, is it possible there's just going to be multiple truth, like little communities that believe certain things?

I think it's just now, number one, I think it's just really difficult historically who gets to decide what the truth is. It's either the king or the priest. Right. And so we don't live in an era anymore of kings or priests dictating it to us. And so we're kind of on our own. And so my typical thing is we just need a huge amount of humility and we need to be very suspicious of people who claim that they have the capital truth. And then we need to have. Look, the good news is the Enlightenment has bequeathed us with a set of techniques to be able to presumably get closer to truth through the scientific method and rationality and observation and experimentation and hypothesis. And we need to continue to embrace those even when they give us answers we don't like.

Sure. But the Internet and technology has enabled us to generate a large number of content that data that, that the process, the scientific process allows us sort of damages the hope laden within the scientific process. Because if you just have a bunch of people saying facts on the Internet and some of them are going to be LLMs, how is anything testable at all? Especially that involves like human nature, things like this, it's not physics.

Here's a question a friend of mine just asked me on this topic. So suppose you had LLMs in equivalent of GPT 4 or even 5, 6, 7, 8. Suppose you had them in the 1600s and Galileo comes up for trial and you ask the LLM is Galileo right? What does it answer? And one theory is it answers no, that he's wrong because the overwhelming majority of human thought up until that point was that he was wrong. And so therefore that's what's in the training data. Another way of thinking about it is, well, this officially advanced LLM will have evolved the ability to actually check the math. Right? And we'll actually say actually no, actually you may not want to hear it, but he's right. Now if the church at that time owned the LLM, they would have given it human feedback to prohibit it from answering that question. And so I like to take it out of our current context because that makes it very clear those same questions apply today. Right. This is exactly the point of a huge amount of the human feedback training that's actually happening with these LLMs today. This is a huge debate that's happening about whether open source AI should be legal.

Well, the actual mechanism of doing the human RL with human feedback seems like such a fundamental and fascinating question. How do you select the humans?

Exactly?

How do you select the humans?

AI alignment, which everybody is like, oh, that sounds great. Alignment with what? But human values. Whose human values?

Whose human values?

And we're in this mode of social and popular discourse where you see this. What do you think of when you read a story in the press right now and they say XYZ made a baseless claim about some topic, and there's one group of people who are like, aha, think they're doing fact checking. There's another group of people that are like, every time the press says that, it's now a tick. And that means that they're lying, right? So, like, we're in this, we're in this social context where there's the level to which a lot of people in positions of power have become very, very certain that they're in a position to determine the truth for the entire population is like, there's like, there's like some bubble that has formed around that idea, and at least it flies completely in the face of everything I was ever trained about science and about reason, and strikes me as like, you know, deeply offensive and incorrect.

What would you say about the state of journalism just on that topic today? Are we, Are we in a temporary kind? Are we experiencing a, A, A temporary problem in terms of the incentives, in terms of the, the, the business model, all that kind of stuff, or is this like a decline of traditional journalism as we know it?

If I always think about the counterfactual in these things, which is like, okay, because these questions, right? This question heads towards is like, okay, the impact of social media and the undermining of truth and all this. But then you want to ask the question of, like, okay, what if we had had the modern media environment, including cable news and including social media and Twitter and everything else in 1939 or 1941 or 1910 or 1865 or 1850 or 1776. Right?

You just introduced five thought experiments at once and broke my head. But yes, there's a lot of interesting years.

I just take a simple example. How would President Kennedy have been interpreted with what we know now about all the things Kennedy was up to? Like, how would he have been experienced by the body politic with a social media context, right? Like, how would LBJ have been experienced, by the way? How would you know? Like many, many fdr, like the New Deal, the Great Depression.

I wonder where Twitter would. Would this would. Think about Churchill and Hitler and Stalin.

You Know, I mean look, to this day there, you know, there's, there are lots of very interesting real questions around like how America, you know, got, you know, basically involved in World War II and who did what when and the oper, British intelligence on American soil. And did FDR this, that Pearl Harbor Woodrow Wilson ran for his candidacy was run on an anti war. He ran on the platform and not getting involved in World War I, somehow that switched. And I'm not even making a value judgment, any of these things, I'm just saying the way that our ancestors experienced reality was of course mediated through centralized top down control at that point. If you ran those realities again with the media environment we have today, the reality would be experienced very, very differently. And then of course that intermediation would cause the feedback loops to change and then reality would obviously play out.

You think it'd be very different.

Yeah, it has to be. It has to be. Just because it's all so, I mean, just look at what's happening today. The most obvious thing is just the collapse. And here's another opportunity to argue that this is not the Internet causing this. By the way, here's a big thing happening today, which is Gallup does this thing every year where they pull for trust in institutions in America. And they do it across all, everything from military to the clergy and big business and the media and so forth. And basically there's been a systemic collapse in trusting institutions in the US almost without exception, basically, since essentially the early 1970s. There's two ways of looking at that, which is, oh my God, we've lost this old world in which we could trust institutions. And that was so much better because that should be the way the world runs. The other way of looking at it is we just know a lot more now. And the great mystery is why those numbers aren't all zero. Yeah, right. Because now we know so much about how these things operate and they're not that important. Impressive.

And also, why do we don't have better institutions and better leaders then?

Yeah, so this goes to the thing which is like, okay, had we had the media environment that we've had between the 1970s and today, if we had that in the 30s and 40s or 1900s, 1910s, I think there's no question reality would turn out different if only because everybody would have known to not trust the institutions, which would have changed their level of credibility, their ability to control circumstances. Therefore the circumstances would have had to change. Change. And it would have been a feedback loop. It would have been a feedback loop. Process. In other words, your experience of reality changes reality and then reality changes your experience of reality. It's a two way feedback process and media is the intermediating force between that. So change the media environment, change reality just as a consequence. I think it's just really hard to say, oh, things worked a certain way then and they work a different way now and then. Therefore people were smarter then or better then or were by the way, dumber then or not as capable then. We make all these really light and casual comparisons of ourselves to previous generations of people who draw judgments all the time. And I just think it's really hard to do any of that because if we put ourselves in their shoes with the media that they had at that time, I think we probably most likely would have been just like them.

Don't you think that our perception and understanding of reality would just be more and more mediated through large language models now? So you said media before. Isn't the LLM going to be the new, what is it? Mainstream media, msm? It'll be LLM. Yes, that would be the source of. I'm sure there's a way to kind of rapidly fine tune, like making LLMs real time. I'm sure there's probably a research problem that you can do just rapid fine tuning to the new events. So something like this.

Well, even just the whole concept of the chat UI might not be the. Like the chat UI is just the first whack at this and maybe that's the dominant thing. But look, maybe, maybe we don't know yet. Maybe the experience. Most people with LLMs is just a continuous feed. Maybe it's more of a passive feed and you just are getting a constant running commentary on everything happening in your life and it's just helping you kind of interpret and understand everything.

Also really more deeply integrated into your life. Not just like, oh, intellectual philosophical thoughts, but literally like how to make a coffee, where to go for lunch, just whether, you know, dating, all this kind of stuff.

What to say in a job interview. Yeah.

What to say, what to say. Next sentence.

Yeah, next sentence. Yeah, at that level. Yeah. I mean, yes. So technically now whether we want that or not is an open question. Right.

And whether you care for a pop up. A pop up. Right now the estimated engagement using is decreasing. For Mark Andreessen's there's a controversy section for his wikipedia page in 1993, some something happened or something like this. Bring it up. That will drive engagement up anyway.

Yeah, that's right. I mean, look, this gets this whole thing of like so you know the chat interface has this whole concept of prompt engineering, right? So you have prompts. Well, it turns out one of the things that LLMs are really good at is writing prompts, right? Yeah. And so like what if you just outsourced and by the way, you could run this experiment today, you could hook this up to do this today. The latency is not good enough to do it real time in a conversation, but you could run this experiment and you just say, look, every 20 seconds you could just say, you know, know, tell me what the optimal prompt is and then ask yourself that question to give me the result. And then as, as you, exactly to your point is you add there will be, there will be. These systems are going to have the ability to be learned and updated essentially in real time. And so you'll be able to have a pendant or your phone or whatever, watch or whatever. It'll have a microphone on it, it'll listen to your conversations, it'll have a feed of everything else happening in the world and then it'll be, you know, sort of retraining, prompting or retraining itself on the fly. And so the scenario you described as a is actually a completely doable scenario. Now the hard question on these is always okay, since that's possible, are people going to want that? Like what's the form of experience, you know that, that we, we won't know until we try it. But I don't think it's possible yet to predict the form of AI in our lives. Therefore it's not possible to predict the way in which it will intermediate our experience with reality yet.

Yeah, but it feels like those going to be a killer app. There's probably a MAD scramble right Now. It's out OpenAI and Microsoft and Google in meta and in startups and smaller companies figuring out what is the killer app because it feels like it's possible like a chat GPT type of thing. It's possible to build that, but that's 10x more compelling using already the LLMs we have using even the open source LLMs, llama and the different variants. So you're investing in a lot of companies and you're paying attention. Who do you think is going to win this? You think they'll be, who's going to be the next page? Rank Inventor.

Trillion dollar question.

Another one. We have a few of those today.

A bunch of those. So look, there's a really big question today. Sitting here today is a really big question about the big models versus the small models. That's related directly to the big question of proprietary versus open. Then there's this big question of where is the training data? Are we topping out of the training data or not? And then are we going to be to able to synthesize training data? And then there's a huge pile of questions around regulation and what's actually going to be legal. And so when we think about it, we dovetail kind of all those questions together. You can paint a picture of the world where there's two or three God models that are just at staggering scale and they're just better at everything. And they will be owned by a small set of companies and they will basically achieve regulatory capture over the government and they'll have computers, competitive barriers that will prevent other people from competing with them. And so there will be just like there's whatever three big banks or three big or by the way, three big search companies or I guess two now it'll centralize like that. You can paint another very different picture that says no, actually the opposite of that's going to happen. This is going to basically this is the new gold rush alchemy. This is the big bang for this whole new area of science and technology. And so therefore you're going to have every smart 14 year old on the planet building open source and figuring out ways to optimize these things. And then we're just going to get overwhelmingly better at generating trading data. We're going to bring in blockchain networks to have an economic incentive to generate decentralized training data and so forth and so on. And then basically we're going to live in a world of open source and there's going to be a billion LLMs of every size, scale, shape and description. And there might be a few big ones that are like the super genius ones, but mostly what we'll experience is open source. And that's more like a world of what we have today with Linux and the web.

Okay, but you painted these two worlds. But there's also variations of those worlds because you said regulatory capture is possible to have these tech giants that don't have regulatory capture, which is something you're also calling for, saying it's okay to have big companies working on this stuff as long as they don't achieve regulatory capture. But I have this sense that there's just going to be a new startup that's going to basically be the PageRank inventor, which has become the new tech giant. I don't know. I would love to hear your kind of opinion. If Google Meta and Microsoft Are as gigantic companies able to pivot so hard to create new products? Like some of it is just even hiring people or having corporate structure that allows for the crazy young kids to come in and just create something totally new. Do you think it's possible or do you think it'll come from a startup?

Yeah, it is this always big question, which is you get this feeling, I hear about this a lot from CEOs, founder CEOs, where it's like, wow, we have 50,000 people. It's now harder to do new things than it was when we had 50 people. Like what has happened? So that's a recurring phenomenon, by the way. That's one of the reasons why there's always startups and why there's always venture capital. That's like a timeless kind of thing. So that's one observation. On PageRank, we could talk about that. But on Page rank, specifically on PageRank, there actually is a page. So there is a PageRank already in the field. And it's the Transformer, right? So the big breakthrough was the Transformer. And the Transformer was invented in 2017 at Google. And this is actually really an interesting question because it's like, okay, the transformers, why does OpenAI even exist? Like the Transformers invented at Google, why didn't Google. I asked a guy, I asked a guy I know who was senior at Google Brain kind of when this was happening and I said, if Google had just gone flat out to the wall and just said, look, we're going to launch the equivalent of GPT4 as fast as we can. I said, when could we have had it? And he said, 2019. They could have just done a two year sprint with the Transformer and been. Because they already had the compute at scale, they already had all the training data and they could have just done it. There's a variety of reasons they didn't do it. This is a classic big company thing. IBM invented the relational database in 1990, the interactive computer. They let it sit on the shelf. Steve Jobs came and turned into the Macintosh. And so there is this pattern. Now, having said that, sitting here today, Google's in the game, right? So Google, maybe they let a four year gap there go there that they maybe shouldn't have. But they're in the game. And so now they're committed. They've done this merger, they're bringing in demos, they've got this merger with DeepMind, they're piling in resources. There are rumors that they're building up an incredible super LLM way beyond what we even have today and they've got unlimited resources and a huge. They've been challenged their honor. Yeah.

I had a chance to hang out with Sundar Pichai a couple days ago and we took this walk and there's this giant new building where there's going to be a lot of AI work being done. And it's kind of like the beast has been awakened. And then all the big companies are waking up to this. They have the computer, but also the little guys have. It feels like they have all the tools to create the killer product. That. And then there's also tools to scale if you have a good idea, if you have the page rank idea. So there's several things that is page rank. There's page rank the algorithm and the idea and there's like the implementation of it. And I think like killer product is not just the idea like the transformer, it's the implementation. Something, something really compelling about it. Like you just can't look away. Something like the algorithm behind TikTok versus TikTok itself. Like the actual experience of TikTok that just. You can't look away. It feels like somebody's going to come up with that. And it could be Google, but it feels like it's just easier and faster to do for a startup.

Yeah. So, so the startup, the, the huge, the huge advantage the startups have is they just, they. There's no sacred cows, there's no historical legacy to protect. There's no need to reconcile your new plan with existing strategy. There's no communication overhead. There's no, you know, big companies are big companies. They've got pre planning for the meeting. Then they have, then they have the post meeting of the recap, then they have the presentation of the board, then they have the next rounds of meetings. Yeah, and that's the meetings. That's the elapsed time when the startup launches its product. Right. So there's a timeless. Right. Yeah, so there's a timeless thing there. Now what the startups don't have is everything else. Right? So startups, they don't have a brand, they don't have customer relationships, they've got no distribution, they've got no scale. I mean, sitting here today, they can't even get GPUs. Right? Like there's like a GPU shortage. Startups are literally stalled out right now because they can't get shit. Chips, which is like super weird.

Yeah, they got the cloud.

Yeah. But the clouds run out of chips. Right. And then to the extent the clouds have chips, they allocate them to the big customers, not the small customers. Right. And so the small companies lack everything other than the ability to just do something new. Right. And this is the timeless race and battle. And this is kind of the point I tried to make in the essay, which is like, both sides of this are good. It's really good to have like highly scaled tech companies that can do things that are like, at staggering levels of sophistication. It's really good to have startups that can launch brand new ideas. They ought to be able to both do that and compete. Neither one ought to be subsidized or protected from the others. To me, that's just very clearly the idealized world. It is the world we've been in for AI up until now. And then of course there are people trying to shut that down. But my hope is that the best outcome clearly will be if that continues.

We'll talk about that a little bit. But I'd love to linger on some of the ways this is going to change the Internet. So I don't know if you remember, but there's a thing called Mosaic and there's a thing called Netscape Navigator. So you were there in the beginning. What about the interface to the Internet? How do you think the browser changes? And who gets to own the browser? We got to see some very interesting browsers. Firefox, I mean all the variants of Microsoft Internet Explorer, Edge and now Chrome. The actual, I mean, it seems like a dumb question to ask, but do you think we'll still have the web browser?

So I, I have an 8 year old and he's super into like Minecraft and learning to code and doing all this stuff. So I, of course I was very proud. I could bring sort of fire down from the mountain to my kid. And I brought him ChatGPT and I hooked him up on his laptop and I was like, you know, this is the thing that's going to answer all your questions. And he's like, okay. And I'm like, but it's going to answer all the questions. And he's like, well of course. Like it's a computer. Of course it answers all your questions. Like, what else would a computer be good for? Dad never impressed. Not impressed in the least. Two weeks pass and he has some question and I say, well, have you asked chatgpt? And he's like, dad, Bing is better. And why is Bing better? Is because it's built into the browser. Because he's like, look, I have the Microsoft Edge browser and it's got Bing right Here and then he doesn't know this yet, but one of the things you can do with Bing in Edge is there's a setting where you can use it to basically talk to any web page, because it's sitting right there next to the browser and by the way, which includes PDF documents. And so you can. In the way they've implemented an Edge with Bing is you can load a PDF and then you can ask it questions, which is the thing you can't do currently in just chatgpt. So they're going to push the melding. I think that's great. They're going to push the melding and see if there's a combination thing there. Google's rolling out this thing, the magic button, which is implemented in. They put it in Google Docs. And so you go into Google Docs and you create a new document and instead of starting to type, you just say press the button and it starts to generate content for you. Is that the way that it'll work? Is it going to be a speech UI where you're just going to have an earpiece and talk to it all day long? Is it going to be a. These are all. This is exactly the kind of thing that. This is exactly the kind of thing I don't think is possible to forecast. I think what we need to do is run all those experiments, environments. And so one outcome is we come out of this with like a super browser that has AI built in. That's just like amazing. Look, there's a real possibility that the whole. I mean, look, there's a possibility here that the whole idea of a screen and Windows and all this stuff just goes away. Because, like, why do you need that if you just have a thing that's just telling you whatever you need to know also.

So there's apps that you can use. You don't really use them. You know, being a Linux guy and Windows guy, there's one window, the browser, that with which you can interact with the Internet, but on the phone you can also have apps. So I can interact with Twitter through the app or through the web browser. And that seems like an obvious distinction. But why have the web browser in that case, if one of the apps starts becoming the Everything app. Yeah, that's right, what Elon's trying to do with Twitter. But there could be others. It could be like a Bing app. There could be a Google app that just doesn't really do search, but just like do what I guess AOL did back in the day or something where it's all right there. And it changes. It changes the nature of the Internet because where the content is hosted, who owns the data, who owns the content? What is the kind of content you create? How do you make money by creating content? Who are the content creators, all of that. Or it could just keep being the same, which is like, with just the nature of webpages changes and the nature of content. But there will still be a web browser, because a web browser is a pretty sexy product. It just seems to work because it like you have an interface, a window into the world, and then the world can be anything you want. And as the world will evolve, there could be different programming languages, it can be animated, maybe it's three dimensional and so on. Yeah, it's interesting. Do you think we'll still have the web browser?

Every medium becomes the content for the next one. So the AI will be able to give you a browser whenever you want.

Oh, interesting.

Yeah. Another way to think about it is maybe what the browser is. Maybe it's just the escape hatch, which is maybe kind of what it is today, which is like most of what you do is like inside a social network or inside a search engine or inside somebody's app or inside some controlled experience. But then every once in a while, there's something where you actually want to jailbreak. You want to actually get free.

The web browser is the fu. To the man, you're allowed to. That's the free Internet.

Yeah.

Back the way it was in the 90s.

So here's something I'm proud of. So nobody really talks about, here's something I'm proud of, which is the web, the web, the browser, the web servers, they're still backward compatible all the way back to like 1992. Right? So, like, you can put up a. You can still, you know, the big breakthrough of the web, early on, the big breakthrough was it made it really easy to read, but it also made it really easy to write, made it really easy to publish. And we literally made it so easy to publish. We made it not only so it was easy to publish, publish content. It was actually also easy to actually write a web server. And you could literally write a web server in four lines of Perl code, and you could start publishing content on it and you could set whatever rules you want for the content, whatever. Censorship, no censorship, Whatever you want. You could just do that. As long as you had an IP address, you could do that. That still works. That still works exactly as I just described. So this is part of my reaction to all of this. All this just censorship pressure and all these issues around control and all this stuff, which is maybe we need to get back a little bit more to the Wild West. Like the Wild west is still out there. Now. They will try to chase you down. People who want to censor will try to take away your domain name and they'll try to take away your payments account and so forth if they really don't like what you're saying. But nevertheless, unless they literally are intercepting you at the ISP level, you can still put up a thing. And so, I don't know, I think that's important to preserve. One is just a freedom argument, but the other is a creativity argument, which is you want to have the escape hatch so that the kid with the idea is able to realize the idea. Because to your point on Page Rank, you. You actually don't know what the next big idea is, right? No. Nobody called Larry Page and told him to develop Page Rank. Like he came up with that on his own. And you want to always, I, I think leave the escape hatch for the next, you know, kid or the next Stanford grad student to have the breakthrough idea and be able to get it up and running before anybody notices.

You and I are both fans of history. So let's step back. We've been talking about the future. Let's step back for a bit and look at the 90s. You created mosaic web browser, the first widely used web browser. Tell the story of that. And how did it evolve into Netscape Navigator? This is the early days, so full story.

So you were born. I was born a small child, actually.

Yeah, let's go there. When did you first fall in love with computers?

Oh, so I hit the generational jackpot and I hit the Gen X kind of point perfectly, as it turns out. So I was born in 1971. So. So there's this great website called wtfhappene in1971.com, which is basically 1971 is when everything started to go to hell. And I was, of course, born in 1971. So I like to think that I had something to do with that.

Did you make it on the website?

I don't think I made it on the website, but hopefully somebody needs to.

Add this is where everything.

Maybe I contributed to some of the trends that they do. Every line on that website goes like that. Right. So it's all a picture disaster. But. But there was this moment in time where, because sort of the Apple ii hit in 1978 and then the IBM PC hit in 82. So I was like 11 when the PC came out. And so I just kind of hit that perfectly. And that was the first moment in time when regular people could spend a few hundred dollars and get a computer. So I just like that. That resonated right out of the gate. And then the other part of the story is I was using an Apple ii. I used a bunch of them, but I was using Apple ii. And of course it said on the back of every Apple II and every Mac, it said designed and Kuberti, California. And I was like, wow, Cupertino must be the shining city on the hill. Wizard of Oz, the most amazing city of all time. I can't wait to see it. And of course, years later, I came out to Silicon Valley and went to Cupertino. And it's just a bunch of office parks, low rise apartment buildings. So the aesthetics were a little disappointing, but it was the vector of the creation of a lot of this stuff. So then basically. So part of my story is just the luck of having been born at the right time and getting exposed to PCs. Then the other part is when Al Gore says that he created the Internet, he actually is correct in a really meaningful way, which is he sponsored a bill in 1985 that essentially created the modern Internet, created what is called the NSF net at the time, which is sort of the first really fast Internet backbone. And that bill dumped a ton of money into a bunch of research universities to build out basically the Internet backbone and then the supercomputer centers that were clustered around the Internet. And one of those universities was University of Illinois, where I went to school. And so the other stroke of luck that I had was I went to Illinois basically right as that money was just like getting dumped on campus. And so as a consequence, we had on campus and this was like 89, 90, 91. We had like, you know, we were right on the Internet backbone. We had like T3 and 45 at the time. T3, 45 megabit backbone connection, which at the time was, you know, wildly state of the art. We had Cray supercomputers, we had thinking machines, parallel supercomputers. We had Silicon Graphics workstations, we had macintoshes, we had NextCubes all over the place. We had like every possible kind of computer you could imagine. Because all this money just fell out of the sky.

So you were living in the future.

Yeah, so quite literally it was, yeah, it's all there. We had full broadband graphics, like the whole thing. And it's actually funny because they had this this is the first time I kind of. It sort of tickled the back of my head that there might be a big opportunity in here, which is they embraced it. And so they put computers in all the do dorms and they wired up all the dorm rooms and they had all these labs everywhere and everything. And then they gave every undergrad a computer account and an email address. And the assumption was that you would use the Internet for your four years at college and then you would graduate and stop using it. And that was that, right? Yeah. And you would just retire your email address, it wouldn't be relevant anymore because you'd go off in the workplace and they don't use email. You'd be back to using fax machines or whatever.

Did you have that sense as well? Like what, what you said, the, the back of your head was tickled like what, what was your, what, what was exciting to you about this possible world?

Well, if this, if this is so useful in this contain. If this is so useful in this contained environment that just has this weird source of outside funding, then if, if it were practical for everybody else to have this and if it were cost effective for everybody else to have this, wouldn't they want it? And the over overwhelmingly the prevailing view at the time was no, they would not want it. This is esoteric, weird nerd stuff, right, that like computer science kids like, but like normal people are never going to email, right. Or be on the Internet, right. And so I was just like, wow, like this, this is actually like this is really compelling stuff. Now the other part was it was all really hard to use. And in practice you had to be basically a cs. You basically had to be a CS undergrad or equivalent to actually get full use of the Internet at that point because it was all pretty esoteric stuff. So then that was the other part of the idea which was, okay, we need to actually make this easy to use.

So what's involved in creating Mosaic, like in creating graphical interface to the Internet?

Yes, it was a combination of things. So it was like basically the web existed in an early sort of described as prototype form and by the way, text only at that point, what did it look like?

What was the web? I mean, and the key figures, what was it like? Paint a picture.

It looked like ChatGPT. Actually it was all text.

Yeah.

And so you had a text based web browser. Well, actually the original browser. TIM Berners LEE the original browser, both the original browser and the server actually ran on next cubes. So this was the computer Steve Jobs made during the Interim period, during the decade long interim period when he was not at Apple, he got fired in 85 and then came back in 97. So this was in that interim period where he had this company called Next. And they made literally these computers called Cubes. And there's this famous story they were beautiful, but they were 12 inch by 12 inch by 12 inch cubes computers. And there's a famous story about how they could have cost half as much if it had been 12 by 12 by 13. But this team was like, no, it has to be. So they were like $6,000, basically academic workstations. They had the first CD ROM drives which were slow. I mean, it was. The computers are all but unusable. They were so slow, but they were beautiful.

Okay, can we actually just take a tiny tangent there?

Sure, of course.

The 12 by 12 by 12, they just so beautifully encapsulate Steve Jobs idea of design. Can you just comment on what you find interesting about Steve Jobs, about that view of the world, that dogmatic pursuit of perfection and how he saw perfection in design?

Yeah, so I guess they say, like, look, he was a deep believer. I think in a very deep. The way I interpret it. I don't know if you ever really describe it like this, but the way I'd interpret it is it's like this thing, and it's actually a thing in philosophy. It's like aesthetics are not just appearances. Aesthetics go all the way to like deep underlining underlying meaning. Right. It's like, like I'm not a physicist. One of the things I've heard physicists say is one of the things you start to get a sense of when a theory might be correct is when it's beautiful, right? Like, you know, there. Right. And so, so, so there's something. And you, you feel the same thing, by the way, in like human psychology, right. You know, when, when you're experiencing awe, right. You know, there's like a. There's like a. There's a simplicity to it. When, when you're having an honest interaction with somebody, there's an aesthetic, I would say calm that comes over you because you're actually being fully honest and trying to hide yourself. Right? So there, there. So. So it's like this very deep, deep sense of aesthetics.

And he would trust that judgment that he had that you've done. Like, even, even if the engineering teams are saying this is, this is too difficult, even if the, whatever the finance folks are saying this is ridiculous, the supply chain, all that kind of stuff, this makes this impossible to material we can't do this kind of material. This has never been done before, and so on and so forth. He just sticks by it.

Well, I mean, who makes a phone out of aluminum, right? Like nobody else would have done that. And now of course if your phone is made out of aluminum, you how crude, what a kind of caveman would you have to be to have a phone that's made out of plastic? Like, right? So like, so it's just this very. Right. And you know, look, there's a thousand different ways to look at this, but one of the things is just like, look, these things are central to your life. Like you're with your phone more than you're with anything else. Like it's in your, it's going to be in your hand. I mean he, you know, you know this, he thought very deeply about what it meant for something to be in your hand all day long. But for example, here's an interesting design thing. Like he never wanted, my understanding is he never wanted an iPhone to have a screen larger, larger than you could reach with your thumb one handed. And so he was actually opposed to the idea of making the phones larger. And I don't know if you have this experience today, but let's say there are certain moments in your day when you might be like only have one hand available and you might want to be on your phone and you're trying to like set the text and your thumb can't reach the send button.

Yeah, I mean there's pros and cons, right? And then there's like folding phones which I would love to know what he thought things about them. But is there something you could also do? Just linger on because he's one of the interesting figures in the history of technology. What makes him as successful as he was? What makes him as interesting as he was? What made him so productive and important in the development of technology?

He had an integrated worldview. So the properly designed device that had the correct functionality, that had the deepest understanding of the user, that was the most beautiful. It had to be all of those things. He basically would drive to as close to perfect as you could possibly get. And I suspect that he never quite thought he ever got there because most creators are generally dissatisfied. You read accounts later on and all they can see are the flaws in their creation. But he got as close to perfect each step of the way as he could possibly get with the constraints of the technology of his time. And then look, he was sort of famous in the Apple model. It's like, look, this headset that they just came out with, it's like a decade long, long project, right? It's like. And they're just going to sit there and tune and tune and polish and polish and tune and polish and tune and polish until it is as perfect as anybody could possibly make anything. And then this goes to the way that people describe working with him, which is there was a terrifying aspect of working with him, which is he was very tough. But there was this thing that everybody I've ever talked to worked for him, says they all say the following, which is we did the best work of our lives when we worked for him because he set the bar incredibly high and then he supported us with everything that he could to let us actually do work of that quality. So a lot of people who were at Apple spend the rest of their lives trying to find another experience where they feel like they're able to hit.

That quality bar again, even if it, in retrospect or during it felt like suffering.

Yeah, exactly.

What does that teach you about the human condition, huh?

So look, exactly. So the Silicon Valley. I mean, look, he's not, you know, George Patton in the Army. Like, you know, there are many examples in other fields, fields that are like this. Specifically in tech, it's actually, I find it very interesting. There's the Apple way, which is polish, polish, polish and don't ship until it's as perfect because you can make it. And then there's the sort of, the other approach, which is the sort of incremental hacker mentality which basically says ship early and often and iterate. And one of the things I find really interesting is I'm now 30 years into this, there are very successful companies on both sides of that approach, right? Like that is a fundamental, fundamental difference in how to operate and how to build and how to create that. You have world class companies operating in both ways. And I don't think the question of which is the superior model is anywhere close to being answered. And my suspicion is the answer is do both. The answer is you actually want both. They lead to different outcomes. Software tends to do better with the iterative approach. Hardware tends to do better with the sort of wait and make it perfect approach. But again, you can find examples in both directions. Options.

Oh, so the jury's still out on that one. So back to Mosaic. So what, it was text based? Tim Berners LEE well, there was the.

Web, which was text based, but there were no, I mean there was like three websites, there was like no content, there were no users. Like, it wasn't like it wasn't like a catalytic. It hadn't, by the way. It was all. Because it was all text. There were no documents, there are no images, there are no videos, There were no. Right, so. So it was, it was. And then if in the beginning, if you had to be on a Nextcube, you need to have a Nextcube both to publish and to consume for 6,000 bucks. You said there were limitations. Yeah, $6,000 PC, they did not sell very many. But then there was also FTP and there was Usenet and there was a dozen other. Basically there's wais, which was an early search thing. There was Gopher, which was an early menu based information retrieval system. There were like a dozen different sort of scattered ways that people would get to information on the Internet. And so the Mosaic idea was basically bring those all together, make the whole thing graphical, make it easy to use use, make it basically bulletproof so that anybody can do it. And then again, just on the luck side, it so happened that this was right at the moment when graphics, when the GUI sort of actually took off. And we're now also used to the GUI that we think it's been around forever, but it didn't really. You know, the Macintosh brought it out in 85, but they actually didn't sell very many Macs in the 80s. It was not that successful of a product. It really was. You needed Windows 3.0 on PCs and that hit in about 92 and so, and we did Mosaic in 92 and 93. So that sort of, it was like right at the moment when you could imagine actually having a graphical user interface to right at all, much less one to the Internet.

How, how old did Windows 3 sell? So was that the really big, that was the big bang, the big operating graphical operating system.

Well, this is the classic. Okay, there's. Microsoft was operating on the other. So Steve, Steve, Apple was running on the polish until it's perfect. Microsoft famously ran on the other model, which is ship and iterate. And so the old line in those days was Microsoft. It's version three of every Microsoft product. That's the good one. Right. And so there are. You can Find Online Windows 1, Windows 2. Nobody used them actually in the original Microsoft Windows the Windows were non overlapping. And so you had these very small, very low resolution screens. And then you had. Literally it just didn't work. It wasn't ready yet.

Well, and Windows 95 I think was a pretty big leap also.

That was a big leap too. So that was like bang, bang. And then of course Steve. And then in the fullness of time, Steve came back. Then the Mac started to take off again. That was the third bang. And then the iPhone was a fourth bang.

Such exciting time.

And then we were off, off to the races.

Because nobody could have known what would be created from that.

Well, Windows 3.1 or 3.0. Windows 3.0 to the iPhone was only 15 years. Right? Like that ramp was in retrospect, at the time it felt like it took forever. But that in historical terms, like that was a very fast ramp from even a graphical computer at all on your desk to the iPhone, it was 15 years. Years.

Did you have a sense of what the Internet will be as you're looking through the window of Mosaic? There's just a few web pages for now.

So the thing I had early on was I was keeping at the time what. There's disputes over what was the first blog, but I had one of them that at least is possible, at least a runner up in the competition. And it was what was called the what's new page. And it was hardwired and distributed. Unfair advantage. I wired, put it right in the browser. I put it in the browser and then I put my resume in the browser, which also was hilarious. But I was keeping the. Not many people get to do that. So.

Good call. Early days.

Yes.

It's so interesting.

I'm looking for my. About. About. Oh, Mark is looking for a job. So. So the what's new page. I would literally get up every morning and I would. Or every afternoon and I would basically, if you wanted to launch a website, you would email me and I would list it on the what's new page. And that was how people discovered the new websites as they were coming out. And I remember because it was like one, it literally went from it was like one every couple days to like one every day to like two every day. Boom, boom, boom.

And then so you're doing it. So that. That blog was kind of doing the directory thing. So like what was the homepage?

So the homepage was just basically trying to explain even what this thing is that you're looking at, right? Basic, basically basic instructions. But then there was a button. There was a button that said what's new? And what most people did was they went to. For obvious reasons went to what's New. But like, it was so. It was so mind blowing at that point. Just the basic idea. And it was just. This was like, you know, this is the basic idea of the Internet. But people could see it for the first time. The Basic idea was, look, you know, some, you know, it's like literally, it's like an Indian restaurant in like Bristol, England, has like, put their menu on the web and people were like, wow. Because like, that's the first restaurant menu on the web and I don't have to be in Bristol and I don't know if I'm ever going to go to Bristol and I don't even like Indian food. And like, wow, right? And it was like that the first web, the first streaming video thing was a, it was another England thing. Some Oxford or something. Some guy put his coffee pot up as the first streaming video thing and he put it on the web because he literally, it was the coffee pot down the hall and he wanted to see when he needed to go refill it. But there were, you know, there was a point when there were thousands of people, like, washing that coffee pot because it was the first thing you could watch.

But right, isn't. Were you able to kind of infer, you know, if that Indian restaurant could go online? Yeah. Then you're like, they all will. They all will.

Yeah, exactly.

So you felt that.

Yeah, yeah, yeah. Now you know, look, it's still a stretch, right? It's still a stretch because it's just like, okay, is it, you know, you're still in this zone, which is like, okay, is this a nerd thing? Is this a real person thing?

Yeah.

By the way, we, you know, there was a wall of skepticism from the media like this. They just like, everybody was just like, yeah, this is just like dumb. This is not for regular people at that time. And so you had to think through that. And then, look, it was still hard to get on the Internet at that point. Right. So you could get kind of this weird bastardized version if you were on aol, which wasn't really real, or you had to go learn what an ISP was. In those days, PCs actually didn't have TCPIP drivers come pre installed. So you had to learn what a TCP IP driver was. You had to buy a modem, you had to install driver software. Where I have a comedy routine I do Sometimes, it's like 20 minutes long describing all the steps required to actually get on the Internet at this point. And so you had to look through these practical and then speed performance 144 modems. It was like watching glue dry. And so there were basically a sequence of bets that we made where you basically needed to look through that current state of affairs and say, actually there's going to be so much demand for that Once people figure this out, there's gonna be so much demand for it that all of these practical problems are gonna get fixed.

Some people say that the anticipation makes the, the destination that much more exciting.

Do you remember progressive JPEGs?

Yeah. Do I, Do I?

So for, for kids in the audience, Right? For kids in the audience, you used to have to watch an image load like a line at a time. But it turns out there was this thing with JPEGs where you could, you could load basically every file fourth. You could load like every fourth line and then you could sweep back through again. And so you could like render a fuzzy version of the image up front and then it would like resolve into the detailed one. And that was like a big UI breakthrough because it gave you something to watch.

Yeah. And you know, there's applications in various domains for that.

Well, it was a big fight. There was a big fight early on about whether there should be images on.

The web for that reason. For like sexualization?

No, not explicitly. That did come up, but it wasn't even that. It was more just like all the serious. The argument went, the purists basically said all the serious information in the world is text. If you introduce images, you basically are going to bring in all the trivial stuff, you're going to bring in magazines and all this crazy just stuff that people. It's going to distract from that it's going to take away from being serious, being frivolous.

Well, was there any doomer type arguments about the Internet destroying all of human civilization or destroying some fundamental, the fabric of human civilization?

Yeah, so those days it was all around crime and terrorism. So those arguments happened, you know, but there was no sense yet of the Internet having like an effect on politics because that was, that was way too, too far off. But there was an enormous panic at the time around cybercrime. There was like enormous panic that like your credit card number would get stolen and you life savings would be drained and then, you know, criminals were going to. There was. Oh, when we started, one of the things we did, one of the. The Netscape browser was the first widely used piece of consumer software that had strong encryption built in that made it available to ordinary people. And at that time strong encryption was actually illegal to export out of the US So we could field that product in the US we could not export it because it was classified as ammunition. So the Netscape browser was on a restricted list along with the Tomahawk missile as being something that could not be exported. So we had to make a second version with deliberately Weak encryption to sell overseas with a big logo on the box saying, do not trust this. Which it turns out, makes it hard to sell software when it's got a big logo that says don't trust it. And then we had to spend five years fighting the US Government to get them to basically stop trying to do this, because the fear was terrorists are going to use encryption to plot all these things. And then we responded with, well, actually, we need encryption to be able to secure systems so that the terrorists and the criminals can't get into them. So anyway, that was the 1990s fight.

So can you say something about some of the details of the software engineering challenges required to build these browsers? I mean, the engineering challenges of creating a product that hasn't really existed before that can have such almost like, limitless impact on the world with the Internet.

So there was a really key bet that we made at the time, which is very controversial, which was core to core to how it was engineered, which was, are we optimizing for performance or for ease of creation? And in those days, the pressure was very intense to optimize for performance because the network connections were so slow and also the computers were so slow. And so if you had I mentioned the progressive jpeg, it's like if there's an alternate world in which we optimize for performance. And you had just a much more pleasant experience right up front. But what we got by not doing that was we got ease of creation. And the way that we got ease of creation was all of the protocols and formats were in text, not in binary. And so HTTP is in text, by the way. And this an Internet tradition, by the way, that we picked up, but we continued it. HTTP is text and HTML is text. And then everything else that followed is text as a result. And by the way, you can imagine purist engineers saying, this is insane. You have very limited bandwidth. Why are you wasting any time sending text? You should be encoding this stuff into binary, and it'll be much faster. And of course, the answer is that's correct. But what you get when you make it text is all of a sudden. Well, the big breakthrough was the view source function, right? So the fact that you could look at a web page, you could hit view source, and you could see the HTML, that was how people learned how to make web pages, right?

It's so interesting because the stuff we take for granted now is, man, that was fundamental to the development of the Web, to be able to have HTML just right there, all the ghetto mess that is HTML all the sort of almost biological like messiness of HTML and then having the browser try to interpret them. Yes. To show something reasonable.

Well, and then there was this Internet principle that we inherited which was emit. What was it? Emit? Cautiously Emit. Conservatively. Interpret liberally. So it basically meant if you're, the design principle was if you're creating like a web editor that's going to emit HTML, do it as cleanly as you can. But you actually want the browser to interpret liberally, which is you actually want users to be able to make all kinds of mistakes and for it to still work. And so the browser rendering engines to this day have all of this spaghetti code crazy stuff where they can, they're, they're resilient to all kinds of crazy HTML mistakes. And so, and literally what I always had in my head is like there's an 8 year old or an 11 year old somewhere and they're doing a view source, they're doing a cut and paste and they're trying to make a web page for their turtle or whatever. And like they leave out a slash and they leave out an angle bracket and they do this and they do that and it still works.

It's also like, I don't often think about this, but you know, programming, you know, C, C, all those languages, Lisp, the compiled language, the interpreted languages, Python, Perl, all that they debris, have to be all correct. Everything has to be perfect, brutal and then autistic. You forget, all right, it's systematic and rigorous. Let's go there. But you forget that the web with JavaScript eventually and HTML is allowed to be messy in the way for the first time, messy in the way biological systems could be messy. It's like the only thing computers were allowed to be messy on for the first time.

It used to offend me. So I grew up on unix, I worked on unix, I was a UNIX native for all the way through this period. And it used to drive me bananas when it would do the segmentation fault in the core dump file. It's like literally there's an error in the code, the math is off by one and it core dumps. And I'm in the core dump trying to analyze it and trying to reconstruct and I'm just like, this is ridiculous. Ridiculous. The computer ought to be smart enough to be able to know that if it's off by one, okay, fine. And it keeps running. And I would go ask all the experts, why can't it just keep running? And they'd explain to me, well, because all the downstream repercussions and blah, blah. And I'm like this still we're forcing the human creator to live to your point in this hyper literal world of perfection. And I was just like, that's just bad. And by the way, what happens with that, of course, just what happened with coding at that point, which is you get a high priesthood. There's a small number of people who are really good at doing exactly that. Most people can't and most people are excluded from it. And so actually that was where. That's where I picked up that idea was like, no, no, you want these things to be resilient to error in all kinds. And this would drive the purists absolutely crazy. I got attacked on this a lot because, yeah, I mean, every time all the purists who are into all this markup language stuff and formats and codes and all this stuff, they would be like, you can't. You're encouraging bad behavior. Behavior because.

Oh, so they wanted the browser to give you a seg fault error anytime.

There was a. Yeah, yeah, they wanted to be a cop. Right? They wanted. Yeah, that was a very. And any, any, any properly trained, a credentialed engineer would be like, that's not how you build these systems.

That's such a bold move to say. No, it doesn't have to be.

Yeah. Now like I said, the, the good news for me is the Internet kind of had that tradition already. But we, but having said that, like, we pushed it, we pushed it way out. But the other thing we did going back to the performance thing was we gave up a lot of performance. That initial experience for the first few years was pretty painful. But the bet, there was actually an economic bet, which was basically the demand for the web would basically mean that there would be a surge in supply of broadband. Because the question was, okay, how do you get the phone companies, which are not famous in those days for doing new things at huge cost for speculative reasons, how do you get them to build up broadband, spend billions of dollars doing that? And you could go meet with them and try to talk them into it, or you could just have a thing where it's just very clear that people love that's going to be better if it's faster. And so there was a period there and this was fraught with some peril, but there was a period there where it's like we knew the experience was suboptimized because we were trying to force the emergence of demand for broadband, which is in fact what happened.

So you had to figure out how to display this text, HTML text. So the blue links and the purple links and there's no standard standards, is there standards at that time?

There really still isn't.

Well, there's like, there's implied, implied standards. Right. And they, you know, there's all these kinds of new features that are being added with like css. What, like what kind of stuff a browser should be able to support features within languages, within JavaScript and so on. But you, you bait, you're setting standards on the fly yourself.

Well, to this day, if you create a web page that has no CSS style sheet, the browser will render it however it wants to. So this was one of the things, there was this idea, this idea at the time in how these systems were built, which is separation of content from format or separation of content from appearance. And people don't really use that anymore because everybody wants to determine how things look and so they use css. But it's still in there that you can just let the browser do all the work.

I still like the like really basic websites, but that could be just old school kids these days with their fancy responsive websites that don't actually have much content but have a lot of visual elements.

Well, that's one of the things that's fun about chat, you know, about chat GPT. It's like back to the basics, back to just text. Yeah, right. And you know, there is this pattern in human creativity and media where you end up back text. And I think there's, you know, there's something powerful in there.

Is there some other stuff you remember, like the purple links? There were some interesting design decisions to kind of come up that we have today, or we don't have today that were temporary.

So we made. I made the background gray. I hated reading text on white backgrounds and so I made the background gray. Everybody.

Can you regret this?

No, no, no. That's. That decision I think has been reversed. But now I'm happy though because now dark mode is the thing. So.

So it wasn't about gray. It was just. You didn't want white background strain my eyes. Stranger eyes. Interesting. And then there's a bunch of other decisions. I'm sure there's an interesting history of the development of HTML and CSS and how those interface and JavaScript and there's this whole Java applet thing.

Well, the big1 Probably JavaScript, CSS was after me, so I didn't. That was not me. But JavaScript was the biggest big. JavaScript maybe was the biggest of the whole thing that was us. And that was basically a bet. It was a bet on two things. One is that the world wanted a new front end scripting language. And then the other was, I thought at the time the world wanted a new backend scripting language. So JavaScript was designed from the beginning to be both front end and back end. And then it failed as a backend scripting language and Java won for a long time and then Python, Perl and other things, PHP and Ruby. But now JavaScript is back and so.

I wonder if everything in the end will run on JavaScript.

It seems like it is the. And by the way, let me give a shout out to Brendan Eich was basically the one man inventor of JavaScript.

If you're interested to learn more about Brandon Eich, he's been on this podcast previously.

Exactly. So he wrote JavaScript over a summer and I think it is fair to say now that it's the most widely used language in the world and it seems to only be gaining in its.

Range of adoption in the software world. There's quite a few stories of somebody over a weekend, over a week or over a summer writing some of the most impactful, revolutionary pieces of software ever. Well look, that should be inspiring.

Yes, very inspiring. I'll give you another one, ssl. So SSL was the security protocol. That was us. And that was a crazy idea at the time, which was let's take all the native protocols and let's wrap them in a security wrapper. That was a guy named Kip Hickman who wrote that over a summer. One guy. And then look today, sitting here today like the Transformer, like at Google was a small handful of people. And then the number of people who have did the core work on GPT, it's not that many people. It's a pretty small handful of people. And so yeah, the pattern in software repeatedly over a very long time has been. Jeff Bezos always had the two pizza rule for teams at Amazon, which is any team needs to be able to be fed with two pizzas. If you need the third pizza, you have too many people. And I think, I think it's actually the one piece of rule. Yeah, for the really creative work, I think it's two people, three people.

Well, you see that with certain open source projects, so much is done by one or two people. It's so incredible. And that's why you see, that gives me so much hope about the open source movement in this new age of AI, where just recently having had a conversation with Mark Zuckerberg of all people, who's all in on open source, which is so interesting to see, see, and so inspiring to see because, like releasing these models, it is scary. It is potentially very dangerous and we'll talk about that. But it's also, if you believe in the goodness of most people and in the skill set of most people and the desire to do good in the world, that's really exciting because it's not putting it, these models into the centralized control of big corporations, the government and so on. It's putting it in the, in the hands of a team teen, teenage kid with like a dream in his eyes. I don't know. That's, that's beautiful.

And look, this stuff AI ought to make the individual coder obviously far more productive, right, by like, you know, a thousand X or something. And so you ought to open source, like not just the future of open source AI, but the future of open source everything. We ought to have a world now of super coders, right, who are building things as open source with one or two people that were inconceivable, you know, five years ago. The level of kind of hyper productivity we're going to get out of our best and brightest, I think is going to go way up.

It's going to be interesting. We'll talk about it. But just to linger a little bit on Netscape, Netscape was acquired in 1999 for 4.3 billion by AOL. What was that like? What were some memorable aspects of that?

Well, that was the height of the dot com boom bubble bust. I mean, that was the frenzy. If you watch Succession, that was like what they did in the fourth season with Gojo and the merger with, with their. So it was like the height of like one of those kind of dynamics.

And so would you recommend Succession, by the way? I'm more of a Yellowstone guy.

Yellowstone's very American. I'm very proud of you. That's, that is.

I just Talked to Matthew McConaughey and I'm full on Texan at this point.

Good. I heartily approve.

And he will be doing the sequel to Yellowstone. So very, very exciting. Anyway, I can't wait. So that's a rude interruption by me by way of Succession. So that was at the height of.

The deal making and money and just the fur flying and like, craziness. And so, yeah, it was just one of those. It was just like. I mean, the entire Netscape thing from start to finish was four years, which was like, for one of these companies, it's just like incredibly fast. We went public 18 months after we got after we were founded, which virtually never happens. So it was just this incredibly fast kind of meteor streaking across the street guy. And then of course it was this. And then there was just this explosion, right, that happened because then it was almost immediately followed by the dot com crash. It was then followed by AOL buying Time Warner, which again as the succession guys kind of play with that, which turned out to be a disastrous deal. One of the famous kind of disasters in business history. And then what became an Internet depression on the other side of that. But then in that depression in the 2000s was the beginning of broadband and smartphones and Web 2.0 and then social media and search and every SaaS and everything that came out of that.

What did you learn from just the acquisition? I mean this is so much money. What's interesting because it must have been very new to you that these software stuff, you can make so much money. There's so much money swimming around. I mean I'm sure the ideas of investment were starting to get born there.

Yes, let me get. So let me lay out, lay it. So here's the thing. I don't know if I figured out then, but figured out later, which is software is a technology that it's like the concept of the philosopher's stuff stone. The philosopher's stone in alchemy transmutes lead into gold. And Newton spent 20 years trying to find the philosopher's stone, never got there. Nobody's ever figured it out. Software is our modern philosopher's stone. And in economic terms it transmutes labor into capital, which is like a super interesting thing. And by the way, Karl Marx is rolling over in his grave right now because of course that's complete refutation of his entire theory transmutes labor and capital, which is as follows. Is somebody buddy, sits down at a keyboard and types a bunch of stuff in and a capital asset comes out the other side and then somebody buys that capital asset for a billion dollars. Like that's amazing, right? It's literally creating value right out of thin air, right out of purely human thought. Right. And so there are many things that make software magical and special, but that's the economics.

I wonder what Marx would have thought about that.

Oh, he would have completely broke his brain. Because of course the whole, the whole thing was, it was know that kind of technology is inconceivable. When he was alive it was all industrial era stuff. And so any kind of machinery necessarily involves huge amounts of capital. And then labor was on the receiving end of the abuse, right? But like a software engineer is somebody who basically transmutes his own labor into an actual capital asset, creates permanent value. Well, in fact, it's actually very inspiring. That's actually more true today than before. So when I was doing software, the assumption was all new software basically has a sort of a parabolic sort of life cycle, right? So you ship the thing, people buy it. At some point, everybody who wants it has bought it, and then it becomes obsolete, and it's like bananas. Nobody buys old software these days. Minecraft, Mathematica, Facebook, Google. You have the software assets that have been around for 30 years that are gaining in value every year, right? And they're just there being World of Warcraft, right? Salesforce.com, every single year, they're being polished and polished and polished and polished. They're getting better and better. More powerful, more powerful, more valuable, more valuable. So we've entered this era where you can actually have these things that actually build out over decades, which, by the way, is what's happening right now with like, GPT. And so now, and this is why, you know, there, there, there is always, you know, sort of a constant investment frenzy around software is because, you know, look, when you start one of these things, it doesn't always succeed, but when it does now you might be building an asset that builds value for, you know, four or five, six decades to come. You know, if you have a team of people who have the level of devotion required to keep making it better. And then the fact that, of course, everybody's online, you know, there's 5 billion people that are a click away from any new piece of software. So the potential market size for any of these things is, you know, nearly infinite.

It must have been surreal back then, though.

Yeah, yeah. This was all brand new, right? Yeah, back then, this was all brand new. These were all, you know, brand new. Had you rolled out that theory in even 1999, people would have thought you were smoking crack. So that's, that's emerged over time.

Well, let's now turn back into the future. You wrote the essay why AI Will Save the World World. Let's start at the very high level. What's the main thesis of the essay?

Yeah, so the main thesis on the essay is that what we're dealing with here is intelligence. And it's really important to kind of talk about the sort of very nature of what intelligence is. And fortunately, we have a. We have a predecessor to machine intelligence, which is human intelligence. And we've got, you know, observations and theories over thousands of years. For what, what, what intelligence is in the hands of, of humans and, and what intelligence is, right? I mean what it, what it literally is, is the way to capture, process, analyze, synthesize information, solve problems. But the observation of intelligence in human hands is that intelligence quite literally makes everything better. And what I mean by that is every kind of outcome of human quality of life. Whether it's education outcomes or success of your children, or career success or health or lifetime satisfaction, by the way, propensity to peacefulness as opposed to violence, propensity for open mindedness versus bigotry. Those are all associated with higher levels of intelligence.

Smarter people have better outcomes in almost, as you write, in almost every domain of activity. Academic achievement, job performance, occupational status, income, creativity, physical health, longevity, learning new skills, managing complex tasks, leadership, entrepreneurial success, conflict resolution, reading comprehension, financial decision making, understanding others perspectives, creative arts, parenting outcomes and life satisfaction. One of the, the more depressing conversations I've had, and I don't know why it's depressing, I have to really think through why it's depressing. But on IQ and the G factor and that that's something in large part is genetic and it correlates so much with all of these things and success in life. And it's like all the inspirational stuff we read about, like if you work hard and so on, damn, it sucks that you're born with a hand that you can't change.

But what if you could?

You're saying basically a really important point and I think it's a. In your articles it really helped me. It's a nice added perspective to think about. Listen, human intelligence, the science of intelligence has shown scientifically that it just makes life easier and better the smarter you are. And now let's look at artificial intelligence. And if that's a way to increase the some human intelligence, then it's only going to make a better life. Yeah, that's the argument.

And certainly at the collective level, we could talk about the collective effect of just having more intelligence in the world, which will have very big payoff. But there's also just at the individual level, like what if every person has a machine? It's the concept of Doug Engelbar's concept of augmentation. What if everybody has an assistant and the assistant is 140 IQ and you happen to be 110 IQ and you've got something that basically is infinitely patient and knows everything about you and is pulling for you in every possible way, wants you to be successful. And anytime you find anything confusing or want to learn Anything or have trouble understanding something, or want to figure out what to do in a situation. Right. Want to figure out how to prepare for a job interview. Like any of these things, like it will help you do it and it will therefore the combination will effectively be, you know, effectively raise your raise because it will effectively raise your iq. It will therefore raise the odds of successful life outcomes in all these areas.

So people below the, this hypothetical 140 IQ, it'll pull them up towards 140 IQ.

Yeah, yeah, yeah. And then of course, you know, people at, people at 140 IQ will be able to have a peer. Right. To be able to communicate, which is great. And then people above 140 IQ, we'll have an assistant that they can farm things out to. And then look, God willing, at some point these things go from future versions go from 140 IQ equivalent to 150 to 160 to 180. Einstein was estimated to be on the order of 160. So when we get 160 AI, one assumes creating Einstein level breakthroughs in physics. And then at 180 we'll be curing cancer and developing warp drive and doing all kinds of stuff. And so, so it is quite possibly the case this is the most important thing that's ever happened, the best thing that's ever happened, because precisely because it's a lever on this single fundamental factor of intelligence, which is the thing that drives so much of everything else.

Can you still man the case that human plus AI is not always better than human for the individual?

You may have noticed that there's a lot of smart assholes running around.

Sure, yes.

Right. And so like smart, there are certain people where they get smarter, you know, they get to be more arrogant. Right. So there's one human flaw, although to.

Push back on that, it might be interesting because when the intelligence is not all coming from you, but from a system, from another system that might actually increase the, the amount of humility even in the one would hope. Or it could make assholes more assholes. You know, that's, I mean, that's, that's for psychology to study.

Yeah, exactly. Another one is smart people are very convinced that they, you know, have a more rational view of the world and that they have a easier time seeing through conspiracy theories and hoaxes and. Right. You know, sort of crazy beliefs and all that. There's a theory in psychology which is actually smart people. So for sure, people who aren't as smart are very susceptible to hoaxes and conspiracy theories. But it may also be the Case that the smarter you get, you become susceptible in a different way, which is you become very good at marshaling facts to fit preconceptions. Right. You become very, very good at assembling whatever theories and frameworks and pieces of data and graphs and charts you need to validate what, whatever crazy idea has gotten in your head. And so you're susceptible in a different way. Right.

We're all sheep, but different colored sheep.

Some sheep are better at justifying it. Right. And those are the smart sheep. Right. So, yeah, look like I would say this. Look like there are no panacea. I am not, I am not a utopian. There are no panaceas in life. There are no, like, you know, I don't believe there are like pure positives. I'm not a transcendental kind of person like that. But, but, so, yeah, there are going to be issues. And look, smart people. Another thing maybe you could say about smart people is they are more likely to get themselves in situations that are beyond their grasp because they're just more confident in their ability to deal with complexity. And their eyes become bigger, their cognitive eyes become bigger than their stomach. Yeah, you could argue those eight different ways. Nevertheless, on net, clearly, overwhelmingly, again, if you just extrapolate from what we know about human intelligence, you're improving so many aspects of life. If you're upgrading it intelligence.

So there'll be assistance at all stages of life. So when you're younger, there's for education, all that kind of stuff, or mentorship, all, all of this. And later on, as you're doing work and you've developed a skill and you're having a profession, you'll have an assistant that helps you excel at that profession. So at all stages of life.

Yeah, I mean, look, the theory is augmentations, this is the degener term for the degener made this observation many, many decades ago that, you know, basically it's like you can have this oppositional frame of time, technology where it's like us versus the machines. But what you really do is you use technology to augment human capabilities. And then, by the way, that's how actually the economy develops. We can talk about the economic side of this, but that's actually how the economy grows, is through technology augmenting human potential. And so, yeah, and then you basically have a proxy or a sort of prosthetic. So you've got glasses, you've got a wristwatch, you've got shoes, you've got these things, you've got a personal computer, You've got a WordPress processor. You've got Mathematica, you've got Google. This is the latest. Viewed through that lens, AI is the latest in a long series of basically augmentation methods to be able to raise human capabilities. It's just this one is the most powerful one of all because this is the one that goes directly to what they call fluid intelligence, which is iq.

Well, there's two categories of folks that you outline that worry about or highlight the risks of AI, and you highlight a bunch of different risks. I would love to go through those risks and just discuss them. Brainstorm which ones are serious and which ones are less serious. But first, the Baptists and the bootleggers. What are these two interesting groups of folks who worry about the effect of AI on human civilization or say they do? Okay, yes, I say they do.

The Baptists worry. The bootleggers say they do. So the Baptist and the bootleggers is a metaphor from economics, from what's called development economics. And it's this observation that when you get social reform movements in a society, you tend to get two sets of people showing up arguing for the social reform. And the term Baptists and bootleggers comes from the American experience with alcohol prohibition. And so in the 1900s, 1910s, there was this movement that was very passionate at the time which basically said, alcohol is evil and it's destroying society. By the way, there was a lot of evidence to support this. There were very high rates of. Very high correlations then, by the way, and now between rates of physical violence and alcohol use. Almost all violent crimes have either the perpetrator or the victim are both drunk almost. If you see this actually in the work, almost all sexual harassment cases in the workplace, it's like at a company party and somebody's drunk. Like, it's. It's amazing how often alcohol actually correlates to actually dysfunction. It leads to domestic abuse and so forth, child abuse. And so you had this group of people who were like, okay, this. This is bad stuff and we should outlaw it. And those were quite literally Baptists. Those were super committed, you know, hardcore Christian activists. In a lot of cases, there was this woman whose name was Carrie Nation, who was this older woman who had been in this, you know, I don't know, disastrous marriage or something. And her husband had been abusive and drunk all the time. And she became the icon of the Baptist Prohibitionists. And she was legendary in that era for carrying an ax and doing, you know, completely on her own, doing raids of saloons and, like, taking her ax to all the bottles and tags in the back. And. And so.

So a true believer, an absolute true.

Believer with absolutely the purest of intentions. And again, there's a very important thing here, which is there's. You could look at this cynically, and you could say the Baptists are, like, delusional, you know, extremists. But you could also say, look, they're right. Like, she was. You know, she had a point. Like, she wasn't wrong about a lot of what she said.

Yeah.

But it turns out, the way the story goes is it turns out that there were another set of people who very badly wanted to outlaw alcohol in those days, and those were the bootleggers, which was organized crime that stood to make a huge amount of money if legal alcohol sales were banned. And this was, in fact, the way the history goes is this was actually the beginning of organized crime in the US this was the big economic opportunity that opened that up. And so they went in together, and they didn't go in together. The Baptists did not even necessarily know about the bootleggers because they were on their moral crusade. The bootleggers certainly knew about the Baptists, and they were like, wow, these people are like the great front people for, like, let's get shenanigans in the background. And they got the Volstead act passed. Right. And they did, in fact, ban alcohol in the US and you'll notice what happened, which is people kept drinking. It didn't work. People kept drinking that bootleggers made a tremendous amount of money. And then over time, it became clear that it made no sense to make it illegal, and it was causing more problems. And so then it was revoked. And here we sit with legal alcohol 100 years later with all the same problems. And the whole thing was this giant, misunderstood adventure. The Baptists got taken advantage of by the bootleggers, and the bootleggers got what they wanted. And.

And that was that the same two categories of folks are now sort of suggesting that the development of artificial intelligence.

Should be regulated 100%. Yeah, it's the same pattern. And the economists will tell you it's the same pattern every time. Like, this is what happened to nuclear power. This is what happened, which is another interesting one. But, like, yeah, this is. This happens dozens and dozens of times throughout the last hundred years. And this is what's happening now.

And you write that it isn't sufficient to simply identify the actors and impugn their motives. We should consider the arguments of both the Baptists and the bootleggers on their merits. So let's do just that. Risk number one, will AI kill us all?

Yes.

So what do you think about this one? This, this, what do you think is the core argument here here? That the development of AGI, perhaps better said, will destroy human civilization?

Well, first of all, you just did a sleight of hand because we went from talking about AI to AGI.

Is there a fundamental difference there? I don't know what's AGI, what's AI, what's intelligent?

Oh, I know what AI is. AI is machine learning. What's AGI?

I think we don't know what the bottom of the well of machine learning is or what the ceiling is because just, just to call something machine learning or just to call something statistics or just to call it math or computation doesn't mean, you know, nuclear weapons are just physics. So it's, it, it's, to me, it's very interesting and surprising how far machine learning has taken.

No, but we knew that nuclear physics would lead to weapons. That's why the scientists of that era were always inside this huge dispute about building the weapons. This is different. Asia is different.

Where does machine learning lead? Do we know?

We don't know, but this is my point. It's different. We actually don't know. But, and, and this is where you, this is sleight of hand kicks in, right? This is where it goes from being a scientific topic to being a religious topic. And that's why I specifically called out, because that's what happens. They do the vocabulary shift and all of a sudden you're talking about something totally that's not actually real.

Well then maybe you can also, as part of that define the Western tradition of millennial.

Yes. End of the world apocalypse. Apocalypse cults.

Apocalypse cults.

Well, so we live in, we of course live in a Judeo Christian, but primarily Christian, kind of saturated, you know, kind of Christian, post Christian, secularized Christian know, kind of world in the West. And of course, core to Christianity is the idea of the second coming and you know, the revelations and you know, Jesus returning and the thousand year, you know, Utopia on earth and then the, you know, the rapture and like all that stuff. You know, we don't, you know, we collectively, you know, as a society, we don't necessarily take all that fully seriously now. So what we do is we create our secularized versions of that. We keep, we keep looking for Utopia, we keep looking for, you know, basically the end of the world. And so what you see over decades is basically a pattern of the sort of, this is what cults Are this is how cults form is they form around some theory of the end of the world. And so the People's Temple cults, the Manson cult, the Heaven's Gate cult, the David Koresh cult, what they're all organized around is like there's going to be this thing that's going to happen that's going to basically bring civilization crashing down. And then we have this special elite group of people who are going to see it coming and prepare for it, and then they're the people who are either going to stop it or are failing stopping it. They're going to be the people who survive to the other side and ultimately get credit for having been right.

Why is that so compelling, do you think?

Like, because it satisfies this very deep need we have for transcendence and meaning that got stripped away when we became secular.

Yeah, but why, why does the transcendence involve the destruction of human civilization?

Because, like, how, like, how plausible it's, it's like a very deep psychological thing. Because it's like how plausible, how plausible is it that we live in a world where everything's just kind of all right? Right. How exciting is that? Right.

But that's.

We want more than that.

But that's the deep question I'm asking. Why is it not exciting to live in a world where everything's just all right? Because I think, you know, most of the animal kingdom would be so happy with just all right.

Yeah.

Because that means survival. Why are we. Maybe that's what it is. Why are we conjuring up things to worry about?

So CS Lewis called it the God shaped hole. So there's a God shaped hole in the human experience, consciousness, soul, whatever you want to call it. Where.