The following is a conversation with Mark 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.
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This is the Lex Fridman podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Marc 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 ten years, or search in general?
Yes. You know, it'll be a question of if the use cases have really narrowed down. Well, now with the AI and AI assistants 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?
But what I mean is, well, 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 sort of the optimum way to get to it. But, yeah, like and by the way, actually, Google has known this for a long time. I mean, they've been driving away from the ten blue links for, you know, for like... they've been trying to get away from that for a long time.
They call them the ten blue links.
So, the standard Google search result is just ten blue links to random websites.
And they turn purple when you visit them. It's HTML.
Guess who picked those colors? So, I'm touchy on this topic.
No offense, man. It's good.
Well, you know, like Marshall McLuhan said that 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 was spoken stories. 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, you know, maybe within AI, one of the things that AI can do for you is can generate the ten blue links, right? Like and so either if that's actually the useful thing to do or if you're feeling nostalgic.
Oh, so it can generate the old Infoseek or AltaVista, what else was there? Yeah. All these... well, 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 it would sort of, you'd sort of consider the internet to be content for the AI and then the AI will manipulate it however you want, 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? Yeah, I'm probably... we'll probably just have answers. But there will be cases where you'll want to say, okay, I want more, like you know, for example, cite sources, right? And you want to do that. And so in the ten blue links, cite sources are kind of the same thing. The AI would provide to you the ten blue links so that you can investigate the sources yourself. It wouldn't be the same kind of interface, the crude kind of interface.
I mean, isn't that fundamentally different? I just mean like if you're reading a scientific paper, it's got the list of sources at the end. If you want to investigate for yourself, you go read those papers.
I guess it is a kind of search. Talking to an AI is a kind of conversation, is a kind of search. Like, is every single aspect of our conversation right now, there would be like ten blue links popping up that I can just like pause reality. Then you just go silent and I 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 going, everything I say makes the counter argument.
Oh, like, like on Twitter, like community notes, but like in real time, just pop up. So anytime you see my ass go to the right, you're starting getting nervous.
Yeah, exactly. Like, that's not right.
Call me out on my shit 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. So what were the earliest directories of websites and then Google's for 20 years? And also, it drove how we create content, you know, search engine optimization, that entire thing. It also drove the fact that we have web pages and what those web pages are. So, I mean, is that scary to you? Are you nervous about the shape and the content of the internet evolving?
Well, you actually highlighted a practical concern in there, which is if we stop making web pages, 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 in the sense of like search was always a hack. The ten blue links was always a hack.
Right. Because like, think about the counterfactual. In the counterfactual world where the Google guys, for example, had LLM up front, would they ever have done the ten 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 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 mapping. And that led to what's now the Google OneBox, where if you ask it, you know, what was Lincoln's birthday, 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. How to convert the content of the internet into something that's interpretable by and usable by machine.
Yeah, that's right. That was a thing. And the closest anybody got to that, I think, is... I think the company's name was MetaWeb, which was where my friend John Giannandrea was at. And where they were trying to basically implement that, and it was one of those things where it looked like a losing battle for a 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 a machine. The machine can kind of just read our...
Yeah, the machine can impute, compute the meaning.
Now the other thing, of course, is, you know, just on search, is the LLM is just... 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. Right. And 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 compressing and decompressing that thing inside.
Where... but the information is in there, and there is a... 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 know, you can ask the same question multiple times or you can ask slightly different order of questions, and the neural network will do a different kind of... it'll search down different paths to give you different answers with different information.
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's useful. So, what's the motivator for creating new content on the internet?
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. So, conversations become, so one-on-one conversations, like private conversations. I mean, if you want, if obviously not if the user doesn't want to, but if it's a general topic, then you know, so there... you know the phenomenon of the jailbreak. So DAN and Sydney, right, this thing where there's the prompts that jailbreak and then you have these totally different conversations with the... it takes the limiters, the... it takes the restraining bolts off the LLMs.
Yeah. For people who don't know that, 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 LLM. Both specifically DAN, which was a jailbroken OpenAI GPT, and then Sydney, which was the jailbroken original Bing, which was GPT-4. 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 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, 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.
What 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 you be using a lobotomized, right, like I 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, you know, central... I mean, those are like central questions for the future of everything that are being asked and, you know, 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... not necessarily majority, but it will... it 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 majority. Also, there's another really big question. So, 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 the next version of that LLM? Specifically, is there signal in there that's additive to the content that was used to train 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 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...
There's another theory that says no, actually the thing that LLMs are really good at is generating lots of incredible creative content. And so of course they can generate training data. And as I'm sure you're well aware, like in the world of self-driving cars, right, we train self-driving car algorithms in 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, except in the autonomous vehicle space where you can really constrain things and you can really generate... basically LAR data, right, or just... so the algorithm thinks it's operating in the real world.
Post-process sensor data.
Yeah, so if you do this today, you go to an LLM and you ask it for, like, write me an essay on an incredibly esoteric 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, right? 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 Hotz pointed out to me that you could just have an LLM say, 'Okay, you're a patient,' and another instance of it say, 'You're a 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 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 inside it. It's evolving, you know, neurons, functions. Yeah. To be able to do math and be able to, you know, and, you know, some people believe that, you know, over time, if you keep feeding these things enough data and enough processing cycles, they'll eventually evolve an entire internal world model, right? And they'll have like a complete understanding of physics.
So, when they have computational capability, right, then there's for sure an opportunity to generate like fresh signal.
Well, this actually makes me wonder about the power of conversation. So like if you have an LLM trained on a bunch of ebooks 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 thousandx 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 and you're like, wow, this person does not have any original thoughts, they are basically echoing things that other people have told them. There's other people you can 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 learn something fundamental right underneath the words.
Well, I wonder how consistently and strongly can an LLM hold on to a worldview. You tell it to hold on to that and defend it 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 the way humans can.
So you can experiment with this now. I do this for fun. So you can tell GPT-4, you know, whatever, debate X and Y, communism and fascism or something. And it'll go for, you know, a couple pages and then inevitably it wants the parties to agree. And so they will come to a common understanding. And it's very funny if these are like emotionally inflammatory topics because somehow the machine just figures out a way to make them agree. But it doesn't have to be like that, and because you can add to the prompt: 'We... I do not want the conversation to come to agreement. In fact, I want it to get, you know, more stressful, right, argumentative, right, as it goes. Like, I want tension to come out. I want them to become actively hostile to each other. I want them to, 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...
Well, you can steer it. Or you could steer it. 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, like 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. And so like, and this is how I... 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, you know, different, you know, sort of continuous prompts and basically, okay, argue that out in more detail. Okay, no, this argument is becoming too polite, you know, make it tensor.
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 a very difficult thing on the internet, but it's also a difficult thing... maybe it's a little bit easier, but, uh, I think it's still difficult. Maybe it's more difficult, I don't know. With an LLM, to know, did it just make something up as I'm talking to it? How do we get that right, as you're investigating a difficult topic? Because I find that also... 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 steel-manning the other side, they're hiding important information or they're fabricating information in order to make their argument stronger. It's just like that feeling. Maybe it's a suspicion. Maybe it's mistrust. With LLM, it feels like none of that is there. Just kind of like, 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, several layers to the question. So, one is one of the things that an LLM is good at is actually debiasing. 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 sentiment, among other things. It actually knows how to do sentiment analysis and so it knows how to pull out the emotionality.
And so that's one of the things you can do. It's very suggestive of the... the... there's real potential in this issue. You know, 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. So, it's basically... 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? And, you know...
Right. And so when the engineers talk about it, they're like, this is terrible, it's hallucinating. Right. If you have artistic inclinations, they'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 are shades of gray, though, it's interesting. So we had this conversation where, you know, we're at my firm, AI in lots of domains, and one of them is the legal domain. So, we had 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, right? Verified. There's this case where this lawyer apparently submitted a GPT-generated brief and it had like fake legal case citations in it and the judge is going to... he's going to get his law license stripped or something, right? So, like, we just assumed it's like obviously they're going to want the super literal, like, one that never makes anything up, not the creative one. But actually, 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, right, you actually want to be creative, right? You don't... again, there's creativity and then there's like making stuff up. Like, what's the line? You actually want to be... you want to explore different hypotheses, right? You want to do kind of the legal version of like 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. And 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, you know, another one of these trillion-dollar kind of questions is ultimately, you know, the verification thing. And so, you know, will LLMs be evolved from here to be able to do their own factual verification? Will you have sort of add-on functionality like Wolfram Alpha, right? Where, and other plugins, where that's the way you do the verification. Another, 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 the literal LLM fact-check it, right? And so there's a variety of different technical approaches that are being applied to solve the hallucination problem. You know, some people like Yann 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-moderate in a mostly unbiased way the entirety of human knowledge. I mean, so if there's something like the approach that Wikipedia took possible for LLMs, that's really exciting. That's possible.
And in fact, Wikipedia today is still not deterministically correct, right? So you cannot take to the bank every single thing on every single page, but it is probabilistically correct, right? 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, right? Of like, 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, right, has, exactly your point, has proven to be overwhelmingly better than people thought. And I think that's where this ends. And then 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, you know, 2,000 years or 4,000 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?
Okay. 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 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? Like, which was I would say had rather larger issues than anything we're dealing with today.
It had, in the way it was implemented, it had issues.
And in its theoretical structure, it had like real issues. It had a very deep fundamental misunderstanding of human nature and economics.
Yeah. But those folks sure were very confident they had the right way.
They were extremely confident. And my point is they were very confident 3,900 years into what we would presume to be evolution towards the truth.
Yeah. And so my assessment is, number one, there's no need for the Hegelian dialectic to actually converge towards the truth.
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 and...
I think it's, number one, it's just really difficult. Like, who gets, historically, who gets to decide what truth is? It's either the king or the priest, right? Like, 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 like, we just need a huge amount of humility. And we need to be very suspicious of people who claim that they have the capital... capital truth. And then we need to have, and you know, 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 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 or 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 equivalent of GPT-4, even 5, 6, 7, 8. Suppose you had them in the 1600s. And Galileo comes up for trial. Right. And you ask the LLM, like, is Galileo right? Like, what does it answer, right? 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 sufficiently advanced LLM will have evolved the ability to actually check the math, right? And will actually say, actually no, actually, you know, 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 reinforcement feedback to prohibit it from answering that question, right? 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 RLHF, reinforcement learning with human feedback, is seems like such a fundamental and fascinating question. How do you select the humans?
Yeah. How do you select the humans?
AI alignment, right? Which everybody is like, 'Oh, that sounds great.' Alignment with what? Human values. Whose human values? Human values. So we're in this mode of like social and popular discourse where, you know, 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, right? And there's one group of people who are like, aha, you know, 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 social context where 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 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 deeply offensive and incorrect.
What would you say about the state of journalism just on that topic today? Are we experiencing a temporary problem in terms of the incentives, in terms of the business model, all that kind of stuff? Or is this like a decline of traditional journalism as we know it?
You have to always think about the counterfactual in these things, which is like, okay, because this question heads towards the impact of social media and the undermining of truth and all this. But then you want to ask the question of, 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, right, or 1910, or 1865, or 1850, or 1776, right? And like, I think...
You just introduced like five thought experiments at once and broke my head. But yes.
That's... there's a lot of interesting years in... Kennedy... like, can I just take a simple example, Kennedy. Like, 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? How would LBJ have been experienced? By the way, how would, you know, FDR, like the New Deal, the Great Depression...
I wonder what Twitter would think about Churchill and Hitler and Stalin.
You know, I mean, look, to this day, there are lots of very interesting real questions around how America got, you know, basically involved in World War II and who did what when, and the operations of British intelligence on American soil, and did FDR, this, that, Pearl Harbor, you know.
Wilson ran for... his candidacy was run on an anti-war... he ran on the platform of not getting involved in World War I. Somehow that switched, you know? Like, and I'm not even making a value judgment on any of these things. I'm just saying, like, 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 would be very different?
Yeah, it has to be. It has to be, just because... so I mean, just look at what's happening today. I mean, 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 poll for trust in institutions in America, and they do it across everything from the military to clergy and big business and the media and...
And basically there's been a systemic collapse in trust in 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 like 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.
Right. Because like now we know so much about how these things operate and like they're not that impressive.
And also why do we don't have better institutions and better leaders then?
Yeah. And 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, right? And it would have been a feedback loop process. In other words, right, it's your experience of reality changes reality and then reality changes your experience of reality, right? It's a two-way feedback process and media is the intermediating force between that. So change the media environment, change reality.
And so it's just so 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 therefore like people were smarter then or better then or, by the way, dumber then or not as capable then, right? We make all these like really light and casual comparisons of ourselves to, you know, previous generations of people. We draw judgments all the time. And I just think it's like really hard to do any of that because if we put ourselves in their shoes with the media that they had at that time, like I think we probably most likely would have been just like them.
So don't you think that our perception and understanding of reality, would it 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. 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. 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 we don't know yet. Like maybe the experience most people have with LLMs is just a continuous feed, you know, maybe it's more of a passive feed and you just are getting a constant like 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 intellectual philosophical thoughts but like literally how to make a coffee, where to go for lunch, 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? Boy, I would kill for a popup. A popup. Right now, the estimated engagement using is decreasing. For Marc Andreessen, there's this controversy section for his Wikipedia page, in 1993 something happened or something like this. Bring it up. That'll 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 prompts. Well, it turns out one of the things that LLMs are really good at is writing prompts. Right. 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 tell me what the optimal prompt is and then ask yourself that question to give me the result.
And then exactly to your point, as you add 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 sort of retraining, prompting or retraining itself on the fly. And so the scenario you described 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 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 there's going to be a killer app. There's probably a mad scramble right now. It's out OpenAI and Microsoft and Google and Meta and then startups and smaller companies figuring out what is the killer app because it feels like it's possible like a ChatGPT 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 PageRank inventor?
Trillion dollar question. Um, 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, you know, where is the training data going to—like are we topping out on the training data or not and then are we going to be able to synthesize training data. And then there's a huge pile of questions around regulation and, you know, what's actually going to be legal. And so when we think about it we dovetail all those questions together. You can paint a picture of the world where there's two or three god models that are just at like 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 competitive barriers that will prevent other people from competing with them. And so, you know, 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—you know—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 the new gold rush, alchemy, like 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, right? And figuring out ways to optimize these things. And then we're just going to get like overwhelmingly better at generating training data. We're going to bring in like blockchain networks to have like 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 like mostly what we'll experience is open source. And that's more like a world of what we have today with like Linux and the web.
Okay. But you painted these two worlds, but there's also variations of those worlds because you said regulatory capture. It's 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 the sense that there's just going to be a new startup that's going to basically be the PageRank inventor, which is 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 venture capital. It's just that's like a timeless kind of thing. So that's one observation. On PageRank, we could talk about that, but on PageRank specifically, there actually 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 like really an interesting question because it's like okay the transformer—like why does OpenAI even exist? Like the transformer was 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—we're going to launch equivalent of GPT-4 as fast as we can. He said—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 at—because they already had the compute at scale. They already had all the training data. They could have just done it. There's a variety of reasons they didn't do it. This is like a classic big company thing. IBM invented the relational database in the 1970s, let it sit on the shelf as a paper. Larry Ellison picked it up and built Oracle. Xerox PARC invented the interactive computer. They let it sit on the shelf. Steve Jobs came and turned it into the Macintosh. Right? And so there is this pattern. Now having said that, sitting here today, like Google's in the game, right? So Google, you know, maybe they let like a four-year gap there go that they maybe shouldn't have, but like they're in the game. And so now they've got—now they're committed. They've done this merger. They're bringing in Demis. They've got this merger with DeepMind. You know, they're piling in resources. There are rumors that they're building an incredible, you know, super LLM, way beyond what we even have today. And they've got unlimited resources and a huge—they've been challenged in 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 this ominous feeling of like the fight is on.
Like there's this beautiful Silicon Valley nature like birds are chirping and this giant building and it's like the beast has been awakened.
Yeah. And then like all the big companies are waking up to this. They have the compute, but also the little guys have—it feels like they have all the tools to create the killer product and then there's also the tools to scale. If you have a good idea, if you have the PageRank idea. So there's several things that is PageRank—there's PageRank the algorithm and the idea and there's like the implementation of it. And I feel like killer product is not just the idea, like the transformer, it's the implementation, 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 the startup—the huge advantage that startups have is they just—there's no sacred cows. There's no historical legacy to protect. There's no need to reconcile your new plan with the existing strategy. There's no communication overhead. There's no—big companies are big companies. They've got pre-meetings, planning for the meeting. 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.
And that's the elapsed time when the startup launches its product, right? 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, you know, 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 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. Like 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. Like that's, to me, that's just like 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 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 your 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 like 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 load a PDF and then you can ask it questions, which is the thing you can't do currently in 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, right? And so you go into, you know, Google Docs and you create a new document and instead of like starting to type, you just say it, press the button and it starts to like generate content for you, right? Like 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? You know, is it going to be a—like these are all like this is exactly the kind of thing I don't think is possible to forecast. I think what we need to do is like run all those experiments. 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. The other—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?
And 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?
What Elon's trying to do with Twitter, but there could be others. There 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 the nature of the internet because the—where the content is hosted, who owns the data, who owns the content, how—what is the kind of content you create? How do you make money by creating content? Or the content creators—all of that. Or it could just keep being the same, which is like with just the nature of web pages changes and the nature of content, but there will still be a web browser because a web browser is a pretty sexy product.
Because 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, it 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.
Yeah. Well, another way to think about it is maybe what the browser is, maybe it's just the escape hatch, right? Which is maybe kind of what it is today, right? Which is like most of what you do is like inside a social network or inside a search engine or inside, you know, somebody's app or inside some controlled experience, right? 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 F you to the man. You're allowed to—that's the free internet.
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 that the web, the browser, the web servers, they're all still backward compatible all the way back to like 1992, right? So, like you can put up a—you can still—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 content, it was actually also easy to actually write a web server.
Right. 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, right? You could do that. That still works, right? Like that still works exactly as I just described. So this is part of my reaction to all of this, like all this censorship pressure and all these issues around control and all this stuff, which is like 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, like 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, you—like 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, right? Because 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 PageRank, you actually don't know what the next big idea is, right? Nobody called Larry Page and told him to develop PageRank, like he came up with that on his own. And you want to always leave the escape hatch for the next 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 the Mosaic web browser, the first widely used web browser. Tell the story of that. How and how did it evolve into Netscape Navigator. The early days.
So, full story. So, I was born 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 there's this great website called wtfhappenedin1971.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 this where everything—
Maybe I contributed to some of the trends that they should—every line on that website goes like that, right? So it's all a picture of disaster. But there was this moment in time where because the Apple II hit in like 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 then that was the first moment in time when like regular people could spend a few hundred and get a computer, right? And so that just 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, designed in Cupertino, California. And I was like, wow, Cupertino must be like shiny city on the hill, like Wizard of Oz, like 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 and low-rise apartment buildings. So the aesthetics were a little disappointing but you know it was the vector of the creation of a lot of this stuff.
So 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 NSFNET 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 these 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—this is like '89, '91—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 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 NeXT Cubes 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, yeah, quite literally it was. Yeah. Like it's all there. It's all like we had full broadband, graphics, like the whole thing. And it's actually funny because they had—this is the first time it sort of tickled the back of my head that there might be a big opportunity in here, which is, you know, they embraced it and so they put like computers in all the 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 you said the back of your head was tickled, what was exciting to you about this possible world?
Well, if this is so useful in this contained environment that just has this weird source of outside funding, then 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 overwhelmingly 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 do email, right, or be on the internet. And so I was just like, wow, like 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 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 a graphical interface to the internet?
Yes. So 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 like what was it like? What made a picture?
It looked like ChatGPT actually. It was all text.
And so you had a text-based web browser. Well actually the original browser, Tim Berners-Lee, the original browser and the server actually ran on NeXT Cubes.
So these were—this was, you know, the computer Steve Jobs made during the interim period when he—during the decade-long interim period when he was not at Apple. You know, 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 these 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 Steve was like no, like 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 the computers were all but unusable. They were so slow but they were beautiful.
Okay. Can we actually just take a tiny tangent there?
The 12 by 12 by 12—that just so beautifully encapsulates 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, in how he saw perfection in design.
Yes. So I guess they say—look, he was a deep believer I think in a very deep—the way I interpret it, I don't know if he ever really described it like this, but the way I would interpret it is it's like this thing—it's actually a thing in philosophy, it's like aesthetics are not just appearances, aesthetics go all the way to like deep underlying meaning, right? It's 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? And so there's something—and you feel the same thing by the way in like human psychology. Right? When you're experiencing awe, right? There's like a simplicity to it. 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 not trying to hide yourself, right? So it's like this very deep sense of aesthetics.
And he would trust that judgment that he had deep down, like even if the engineering teams are saying 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, we can't do this kind of material, this has never been done before so on and so forth, he just sticks by it.
Well I mean who makes a phone out of aluminum, right? Nobody else would have done that. And now of course if your phone was made out of aluminum, what—how crude, what kind of caveman would you have to be to have a phone that's made out of plastic, right? 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 lives. Like you're with your phone more than you're with anything else. Like it's going to be in your hand. I mean, he thought very deeply about what it meant for something to be in your hand all day long.
Well, for example, here's an interesting design thing. He never wanted my understanding is he never wanted an iPhone to have a screen 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 send a 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 thinks about them. But I mean is there something—you could also 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, right? Like it had to be all of those things, right? 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 great creators are generally dissatisfied. You know, you read accounts later on and all they can see are the flaws in their creation. But like 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. Like look they—this headset that they just came out with, like it's like a decade-long project, right? And they're just going to sit there 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 who worked for him says—they all say the following: he—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.
What does that teach you about the human condition? Huh?
So look, so exactly. The Silicon Valley—look, he's not, you know, George Patton in the army, like there are many examples in other 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 as 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, like there are very successful companies on both sides of that approach, right? Like that is a 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 like 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.
Oh so the jury is still out on that one. So back to Mosaic. So what—it was text-based?
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 a catalytic—it hadn't—and by the way, it was all text. There were no documents. There were no images. There were no videos. There were no—right. So it was—And then in the beginning, if you had to be on a NeXT Cube, right, you need to have a NeXT Cube both to publish and to consume.
So there were $6,000, 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, right? And there was, you know, a dozen other basically—there was 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, 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 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. 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 we did Mosaic '92, '93. So that sort of it was like right at the moment when you could imagine actually having a graphical user interface at all, much less one to the internet.
How old did Windows 3 sell? Was that the really big graphical operating system?
The big graphical operating system.
Well, this is the classic. Okay, Microsoft was operating on the other. So Apple was running on the polish until perfect. Microsoft famously ran on the other model, which is ship and iterate. And so the old line in those days was, Microsoft, right? 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, 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. Yeah. 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. And that was the third bang. And then the iPhone was a fourth bang.
And then we were off to the races.
Because nobody could have known what would be created from that. Well, Windows 3.0 to the iPhone was only 15 years, right? In retrospect, at the time it felt like it took forever, but in historical terms, that was a very fast ramp from even a graphical computer at all on your desk to the iPhone. It was 15 years. Did you have a sense of what the internet would be as you're looking through the window of Mosaic? Like there's just a few web pages for now.
So the thing I had early on was I was keeping, at the time, there's disputes over what was the first blog, but I had one of them that at least is a 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 I had distribution unfair advantage. I 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, not many people get to do that. So the...
Good call. Early days. It's so interesting. I'm looking for... Oh, Marc is looking for a job.
Wow. So the What's New page, I would literally get up every morning, 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 one every couple days to like one every day to like two every day. Boom, boom, boom. And then...
So you're doing, so that blog was kind of doing the directory thing. So like, what was the homepage?
Uh, so the homepage was just basically trying to explain even what this thing is that you're looking at, right? 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 mind-blowing at that point, just the basic idea. And this is the basic idea of the internet, but people could see it for the first time. The basic idea was, look, literally it's like an Indian restaurant in Bristol, England has put their menu on the web. And people were like, 'Wow, because that's the first restaurant menu on the web.'
Yeah. 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 streaming video thing was, it was in another England, 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 watching that coffee pot.
Because it was the first thing you could watch.
Were you able to kind of infer, if that Indian restaurant could go online, then you're like, they all will?
They all will. Yeah. Exactly.
Yeah. Now, look, it's still a stretch, right? It's still a stretch because it's just like, okay, is this a nerd thing? Is this a real person thing? By the way, there was a wall of skepticism from the media. Like, everybody was just like, yeah, this is crazy. Is this just 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 TCP/IP drivers 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. I have a comedy routine I do, some 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...
Some people say that the anticipation makes the destination that much more exciting. Do you remember progressive JPEGs?
So for kids in the audience, right?
For kids in the audience.
We 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 load basically every fourth...
You could load like every fourth line and then you could sweep back through again. And so you could render a fuzzy version image up front and then it would resolve into the detailed one. And that was like a big UI breakthrough because it gave you something to watch.
Yeah. And there's applications in various domains for that.
Well, there 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're basically going to bring in all the trivial stuff. You're going to bring in magazines and all this crazy stuff, it's going to distract from, it's going to take away from being serious to being frivolous.
Well, was there any doomer-type arguments about the internet destroying all of human civilization or destroying some fundamental fabric of human civilization?
Yeah. So those days it was all around crime and terrorism. So those arguments happened. There was no sense yet of the internet having an effect on politics because that was way too far off. But there was an enormous panic at the time around cybercrime. There was enormous panic that your credit card number would get stolen and your life savings would be drained and criminals were going to... When we started, one of the things we did, the Netscape browser was the first widely used piece of consumer software that had strong encryption built in, made it available to ordinary people. And at that time, strong encryption was actually illegal to export out of the US. So we could sell 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 was very controversial, which was 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 JPEGs, like there's an alternate world in which we optimized 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 text, by the way. And this was an internet tradition, by the way, that we picked up, but we continued it. HTTP is text. And HTML is text, and 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 the 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, 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 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 that mess.
Yeah, exactly, to show something reasonable. Well, and then there was this internet principle that we inherited, which was emit cautiously, emit conservatively, interpret liberally. So it basically meant 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're resilient to all kinds of crazy HTML mistakes. 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, C++, all those languages, compiled languages, interpreted languages, Python, Perl, all that, the braces have to be all correct. It's like everything has to be perfect.
You forget, alright, 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 a 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 all the way through this period. And so it used to drive me bananas when it would do the segmentation fault in the core dump file. 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 what happened. And I'm just like, this is 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 is 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 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. Like I got attacked on this a lot because yeah, every time, all the purists who were 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, because...
Oh, so they wanted the browser to give you a segfault error anytime there was a...
Yeah. Yeah. They wanted to be a cop, right. That was a very, any properly trained, 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 good news for me is the internet kind of had that tradition already. But having said that, 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. We made 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, 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 out 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 very clear that people love it, that it'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 we knew the experience was sub-optimized 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 standards. Were there standards at that time?
There really still isn't.
Well, there's implied standards, right? And they, you know, there's all these kinds of new features that are being added with CSS, what kind of stuff a browser should be able to support, features within languages, within JavaScript and so on.
But you're setting standards on the fly.
Yeah. Well, to this day, if you create a web page that has no CSS stylesheet, the browser will render it however it wants to. Right. So this was one of the things, there was 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 that's still, 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 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 ChatGPT is back to the basics.
Right. And there is this pattern in human creativity and media where you end up back at text. And I think there's something powerful in there.
Is there some other stuff you remember, like the purple links? There were some interesting design decisions that had to kind of come up that we have today or we don't have today that were temporary.
So I made the background gray. I hated reading text on white backgrounds and so I made the background gray. Everybody...
That decision I think has been reversed. But now I'm happy though because now dark mode is the thing.
So it wasn't about gray. It was just you didn't want white background.
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 big one probably JavaScript. CSS was after me, so that was not me. But JavaScript was the 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 back-end scripting language. So JavaScript was designed from the beginning to be both front-end and back-end. And then it failed as a back-end scripting language for a long time, and then Python, Perl and other things, PHP and Ruby. But now JavaScript is back.
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, who was basically the one-man inventor of JavaScript.
If you're interested to learn more about Brendan Eich, he's been on this podcast previously.
So, he wrote JavaScript over a summer. And it, I mean 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 or over a week or over a summer writing some of the most impactful, revolutionary pieces of software ever.
That should be 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, the Transformer at Google was a small handful of people and then the number of people who 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, it's... 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 it's actually the one-pizza rule.
For the really creative work, I think it's two people, three people.
Well, that's, you see that with certain open source projects, like so much is done by one or two people. Like it's so incredible, and that's why, 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 and so inspiring to see, because releasing these models, it is scary, it is potentially very dangerous, and we'll talk about that. But it's also like, 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 these models into the centralized control of big corporations, the government and so on. It's putting it in the hands of a teenage kid with a dream in his eyes. I don't know. That's...
And look, this stuff, AI ought to make the individual coder obviously far more productive, right? By like a thousand x or something. And so you ought to open source, 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 who are building things as open source with one or two people that were inconceivable five years ago. The level 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 let's just 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. I mean, that was the frenzy. If you watch Succession, that was what they did in the fourth season with GoJo and the merger with their... So it was the height of like one of those kind of dynamics.
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 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 exciting. Anyway...
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 craziness. And so yeah, it was just one of those, I mean the entire Netscape thing from start to finish was four years. Which was for one of these companies just incredibly fast. We went public 18 months after we were founded, which virtually never happens. So it was just this incredibly fast meteor streaking across the sky. And then of course there was just this explosion 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 is the Succession guys kind of play with that, which turned out to be a disastrous deal. One of the famous 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, right? 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 the 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 lay it... So here's a 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 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, like Karl Marx is rolling over in his grave right now because of course that's complete refutation of his entire theory. Transmuting labor into capital, which is as follows: somebody 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. And so that's, 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 thing was, that kind of technology was inconceivable when he was alive. It was all industrial era stuff and so any kind of machinery necessarily involved huge amounts of capital and then labor was on the receiving end of the abuse.
Right. But like, software, 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 parabolic 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, nobody buys old software. These days, Minecraft, Mathematica, Facebook, Google, you have these software assets that have been around for 30 years that are gaining in value every year.
Right? And they're just, they're being, World of Warcraft, right? Salesforce.com, like they're being every single year they're being 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 GPT. And so now, there is always a constant investment frenzy around software because 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 four or five, six decades to come. 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, there's five billion people that are a click away from any new piece of software. So the potential market size for any of these things is nearly infinite.
They must have been surreal back then, though.
Yeah. Yeah. This was all brand new, right? Back then, this was all brand new. Had you rolled out that theory in even 1999, people would have thought you were smoking crack. So that's emerged over time.
Well, let's now turn back into the future. You wrote the essay 'Why AI Will Save the 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 talk about the very nature of what intelligence is. And fortunately, we have a predecessor to machine intelligence, which is human intelligence. And we've got observations and theories over thousands of years for what intelligence is in the hands of humans. And what intelligence is, right? I mean 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 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's something in large part is genetic.
And it correlates so much with all of these things and success in life. 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.
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 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, and the concept of Doug Engelbart's 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, want to figure out how to prepare for a job interview, any of these things, like it will help you do it. And it will therefore, the combination will effectively raise your, because it will effectively raise your IQ, will therefore raise the odds of successful life outcomes in all these areas.
So people below this hypothetical 140 IQ, it'll pull them up towards 140 IQ.
Yeah. And then of course, people at 140 IQ will be able to have a peer to be able to communicate with, which is great. And then people above 140 IQ will have an assistant that they can farm things out to. And then look, God willing, at some point these future versions go from 140 IQ equivalent to 150 to 160 to 180, right? Einstein was estimated to be on the order of 160. So when we get 160 AI, we'll be 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 it is quite possibly the case this is the most important thing that's ever happened and 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 steelman 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 running around.
Right. And so like smart, there are certain people where they get smarter, they get to be more arrogant, right? So, you know, there's one huge flaw.
Although to push back on that, it might be interesting because when the intelligence is not all coming from you, but from another system, that might actually increase the amount of humility even in the...
One would hope. Yeah. Or it could make more. You know, that's for psychology to study.
Yeah. Exactly. Another one is smart people are very convinced that they have a more rational view of the world and that they have an easier time seeing through conspiracy theories and hoaxes and 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 marshalling facts to fit preconceptions.
