Lex Fridman Podcast #386 - #386 – Marc Andreessen: Future of the Internet, Technology, and AI Machine transcription by Qwen3-ASR 0.6B (MLX, local, Apache-2.0). May contain recognition errors. The following is a conversation with Mark Andreason, co creator of Mosaic, the first widely used web browser, co founder of Netscape, co founder of the legendary Silicon Valley venture capital firm Andreason 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." And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We got Inside Tracker for tracking your health, ExpressVPN for keeping your privacy and security on the internet, and AG1 for my daily multivitamin drink. Choose wisely, my friends. Also, if you want to work with our amazing team, we're always hiring. Go to lexfreeman dot com slash hiring. And now onto the full ad reads. 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Well, now with AI, yeah, and AI assistance being able to interact and expose the entirety of human wisdom and knowledge and information and facts and truth to us via the natural language interface, it seems like that's what search is designed to do and if AI assistance can do that better, doesn't the nature of search change? Sure, but we still have horses. Okay, when's the last time you rode a horse? It's been a while. All right, but what I mean is, will we still have Google search as the primary way that human civilization uses to interact with knowledge? I mean, search was a technology, it was a moment in time technology, which is you have in theory the world's information out on the web and you know this is this is sort of the optimal way to get to it, but yeah, like and by the way, actually Google Google has known this for a long time. I mean, they've been driving away from the ten blue links for you know for like two days. They've been trying to get away from that for a long time. What kind of links? They call the ten blue links. 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. The HTML. Guess who picked those colors. Thanks. So, thanks. I'm touching on this topic. No offense, yeah, it's good. Well, you know, look, Marshall McLuhan said that the content of each new medium is the old medium. The content of each new medium is the old. 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. Huh. Right, and so you just kind of fold the old thing into the new thing. How does that have to do with the the blue and the purple? It's just you maybe for you know maybe within AI one one of the things that AI can do for you is can generate the ten blue links. Okay, like and so like either if that's actually the useful thing to do or if you're feeling nostalgic. Um, you know, so you can generate the old InfoSeek or Alta Vista. What else was there? Yeah, yeah, in the 90s. Yeah, all these. Hey, 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 right essays and every other form of you know prior basically 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? I'm probably not. Probably we'll just have answers, but but but there will be cases where you'll want to say, okay, I want more like you know for example, site sources, right, and you wanted to do that and so so that in the different you know ten blue links site 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 that the crude kind of interface. I mean, isn't that fundamentally different? I just mean like if you're reading a scientific paper, yeah, it's got the list of sources at the end. If you want to investigate for yourself, you go read those papers. I guess that is the kind of search you talking to an AI is a kind of conversation is 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 arguing but everything I say that AI makes the counter argument. Counter argument, right? Oh, like like a Twitter like community notes, but like in real time, it will just pop up. So anytime you see my ass go to the right, you start getting nervous. Yeah, exactly. Like that's not right. Call me out of my bullshit right now. Okay, well I mean, isn't that is that exciting to use that terrifying that? I mean, search has dominated the way we interact with the internet for I don't know how long for 30 years since one with the earliest directories of website and then Google's for for 20 years and also. Um, it drove how we create content, you know, search engine optimization that entirety thing that it also drove the fact that we have web pages and what those web pages are. So I mean, that's scary to you or 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 are one of the primary sources of training data for the AI and so if there's no longer an incentive to make web pages that cuts off a significant source of future training data. So there's actually an interesting question in there. Other than that, more broadly, no, just just in the sense of like search was search was always a hack. The ten blue links was always a hack. Yeah, right. Because like if the the hypothet, you want to think about the counterfactual in the counterfactual world where the Google guys for example had had LLMs up front but 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 they bought this AI company 15 years ago. Their friend of mine is working at who's now the head of AI at Apple and they were trying to do basically knowledge semantic basically mapping and that led to what's now the Google One Box where if you ask it you know what was like his birthday it doesn't it will give you the ten blue links but it will normally just give you the answer. And so they've been walking in this direction for a long time anyway. Do you remember the semantic web? That was an idea. Yeah, how to how to convert the content of the internet into something that's interpretable by and usable by machine. Yeah, that's right. That was the thing, and the closest anybody got to that I think is a I think the company's name was MetaWeb which was where my friend John Genn Andrea was at and where they were trying to basically implement that and it was you know it was one of those things where it looked like a losing battle for a long time and then Google bought it and it was like wow this is actually really useful. Kind of a proto, sort of yeah, a little bit of a proto AI. But it turns out you don't need to rewrite the content of the internet to make it interpretable by machine. The machine can kind of just read our. Yeah, machine can can compute the compute the meaning. Now the other thing of course is you know just on search is the the LLM is just you know there there is an analogy between what's happening in the neural network in a search process like it is in some loose sense searching through the network. Yeah, right, and there's the information is the information is actually stored in the network right it's actually crystallized and stored in the network and it's kind of spread out all over the place but in a compressed representation. So you're searching, you're compressing and decompressing that thing inside, where. But the information's in there and and there is the the neural network is running a process of trying to find the appropriate piece of information in in many cases to generate to predict the next token, and so it is kind of it is doing a form of search and then 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 worded questions and the neural network will do a different kind of you know it'll search down different paths to give you different answers with different information. Yeah, and so it it sort of has a, you know, this content of the new medium is the previous medium it kind of has the search functionality kind of embedded in there to the extent that it that it's useful. So what's the motivator for creating new content on the internet? Yeah, if well, I mean actually the motivation is probably still there, but what 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 AI's, conversations with AI's. So conversations become so one on one conversation like private conversations. I mean if you if you want if obviously not the user doesn't want to but if it's a if it's a general topic, then you know so you know you know the phenomenon of the jailbreak so Dan and Sydney right this thing where there there's the the prompts that jailbreak and then you have these totally different conversations with the. It takes the the limiting bolts off the off the LMs. Yeah, for people who don't know, yeah, that's right. It makes the LMs, it removes the censorship quote unquote that's put on it by the the tech companies that create them and so this is LMs uncensored. So here's the interesting thing is among the content on the web today are a large corpus of conversations with the jailbroken LMs, both specifically Dan which was a jailbroken OpenAI GPT and then Sydney which was the jailbroken original Bing which was GPT four. And so there's 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 on on LMs and be able to in theory reach in and basically 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 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 and 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, but not necessarily majority, but it will it will certainly be a potential source. It's possible it's the majority. It's possible it's the majority. It's possible it's the majority. Also, there's another really big question. Here's another really big question: Will synthetic training data work? Right, and so if an LLM generates and you know you just sit and ask an LLM to generate all kinds of content, can you use that to train right the next version of that LLM? Specifically, is there signal in there that's additive to the content that was used to train in the first place? And one argument is by the principles of information theory, no, that's completely useless because to the extent the output is based on you know the human generated input, then all the signal that's in the synthetic output was already in the human generated input and so therefore synthetic training data is like empty calories, it doesn't help. There's another theory that says no, actually the thing that LMs are really good at is generating lots of incredible creative content, right? And so of course they can generate training data and as I'm sure you're well aware, like you know looking the world of self driving cars, right? Like we train you know 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, right, is a little weird because creating reality, visual reality seems to be still a little bit out of reach for us except in the in the autonomous vehicle space where you can really constrain things and you can really generate basically light our data right or just no so the algorithm thinks it's operating in the real world. Post post process sensor data, yeah. So if a you know you do this today, you go to LLM and you ask it for like a you know you let write me an essay on an incredibly esoteric like topic that there aren't really 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 training data for the next LLM like because right because all the signal was already in there or is it actually no that's actually new signal and and this 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 a quite a few like a handful of trillion dollar questions within this within the space that's that's one of them synthetic data. I think George Hott's pointed out to me that you could just have an LLM say okay, your patient and and another instance of it say your dog didn't have the two talk to each other or or maybe you could say a communist and a Nazi here go in that conversation you do role playing and you have. You know just like the kind of role playing you do when you have different policies RL policies when you play chess for example you do self play that kind of self play but in the space of conversation maybe that leads to this whole giant like ocean of possible conversations which would could not have been explored by looking at just human data. That's a really interesting question. And you're saying because that could tenx the power of these things. Yeah, well and then you you get into this thing also which is like you know there's the part of the LLM that just basically is doing prediction based on past data but there's also the part of the LLM where it's evolving circuitry right inside it it's evolving you know neurons functions to be able to do math and be able to you know and and you know the the some people believe that you know over time you know 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 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 books that cover different economics theories and then you have those LLMs just talk to each other like reason the way we kind of debate each other as humans on Twitter in formal debates in podcast conversations, we kind of have little kernels of wisdom here and there but if you can. Like a thousand x speed that up, can you actually arrive somewhere new? Like what's the point of conversation really? Well, you can tell when you're talking to somebody you can tell sometimes you have a conversation you're like wow this person does not have any original thoughts they are basically echoing things that other people have told them. There's other people you have a conversation with where it's like wow like 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 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 like for your life because I feel like they'll just keep converging towards each other. They'll keep convincing each other as opposed to being stubborn assholes the way humans can. So you you can experiment with this now. I do this for fun. So you can tell GPT four you know whatever debate x you know x and y communism and fascism or something and it'll it'll go for you know a couple 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 they're like these are like emotionally inflammatory topics because they're like somehow the machine is just you know figures out a way to make them agree. But it doesn't have to be like that, and you because you can add to the prompt. We, I do not want the I do not want the conversation to come to agreement. In fact, I want it to get you know more stressful, right, and argumentative, right. Um, you know as it goes, like I want I want tension to come out. I want them to become actively hostile to each other. I want them to like you know not trust each other, take anything at face value. Yeah, and it will do that. It's happy to do that. So it's going to start rendering misinformation about the other. It's going to. Well, you can steer it. 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. Um, and so like and this is how I when when I want to learn about a contentious issue, this is what I do now. Is like this is what 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 this argument is becoming too polite, you know, make it more you know make it tenser. Um, and yeah, it's thrilled to do it. So it has the capability for sure. How do you know what is true? So this is very difficult thing on the internet, but it's also a difficult thing. Maybe it's a little bit easier, but. I think it's still difficult. Maybe it's more difficult. I don't know with an LLM. To know that it just makes some shit up as I am talking to it. Um, how do we get that right? Like as as you are investigating a difficult topic. Because I find that LLMs are quite nuanced in a very refreshing way. Like it doesn't it doesn't feel biased. Like when you read news articles and tweets and just content produced by people, they usually have this. You can tell they have a very strong perspective where they're hiding. They're not stealing manning the other side. They're hiding important information or they're fabricating information in order to make their argument stronger. It's just like that feeling. Maybe it's a suspicion. Maybe it's mistrust. With LLMs, it feels like none of that is there. She's kind of like here is is what we know, but you don't know if some of those things are kind of just straight up made up. Yeah, so so several layers to the question. So one is one of the things that an LLM is good at is actually deep biasing. Um, 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, among other things, it actually knows how to do sentiment analysis and so it knows how to pull out the emotionality. Yeah, um, and so that's one of the things you can do. It's very suggestive of the the the the sense here that there is there is real potential in this issue. Um, you know I would say look the second thing is there's this there's this issue of hallucination, right? Um, and there 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 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? Um, and you know brilliant. Right, and and so when the engineers talk about it, they're like this is terrible. It's hallucinating, right? If you if artistic inclinations, you are 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 but also in the good sense of that word. There are shades of gray though. It's interesting. So we had this conversation. We're you know we're looking at my firm at AI in lots of domains and one of them is the legal domain. So we had this this conversation with this big law firm about how they're thinking about using this stuff and we we went in with the assumption that an LLM that was going to be used in the legal industry would have to be one hundred percent truthful. Verified, you know. There is this case where this lawyer apparently submitted a GPT generated brief and it had like fake you know legal case citations in it and the judge is going to he's going to get his law license stripped or something right. So so like we we just assumed it's like obviously they're going to want the super literal like you know one that never makes anything up not the creative one. But actually they said with with the law firm basically said is yeah that's true like the level of individual briefs but they said when you are actually trying to figure out like legal arguments. Right, like you you actually 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 it 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. By the way, different routes through the you know sort of history of all the 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 you know all the specific citations and so I think 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 sort of the verification thing and so you know is will will will LLMs be evolved from here to be able to do their own factual verification? Will you have sort of add on functionality like like well from alpha right where you know and other plugins where where that's the way you do the verification? You know another by the way another idea is you you might have a community of LLMs on any you know so for example you might have the creative LLM and then you might have a 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 Yan Luccoon 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 kind of corral this in a little bit. Yeah, if you were to tell me about Wikipedia before Wikipedia was created, I would have laughed at the possibility of something like that being possible. Just a handful of folks can organize, write, and self and moderate with a mostly unbiased way the entirety of human knowledge. I mean. So if there's something like the approach that Wikipedia took possible for LLMs, that's really exciting. I think that's possible. And in fact, Wikipedia today is still not today is still not deterministically correct, right? So you cannot take to the bank, right? Every single thing on every single page, but it is probabilistically correct. Right, and specifically the way I describe Wikipedia to people, it is it is more likely that Wikipedia is right than any other source you are going to find. Yeah, it's this old question right of like okay like 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 like overwhelmingly better than than than than people thought and I think I think that's where this this ends and then underneath all this is the fundamental question of where you started which is okay what you know 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 two thousand years or four thousand years of human civilization is actually getting to the truth is actually a very difficult thing to do. We're getting closer if we look at the entirety the arc of human history. Are we getting closer to the truth? I don't know. Okay, is it possible? Is it possible that we're getting very far away from the truth because of the internet because of how rapidly you can create narratives and just as the entirety of a society just move like crowds in a hysterical way along those narratives that don't have a necessary grounding in whatever the truth is. Sure, but like you know we came up with communism before the internet somehow right 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 its theoretical structure it had like real issues it had like a very deep fundamental misunderstanding of human nature and economics. Yeah, but those folks sure work very confident there was the right way. They were extremely confident and my point is they were very confident thirty nine hundred years into what we would presume to be evolution towards the truth. Yeah, and so my my my assessment is my assessment is number one there's no there's no need for there's no need for the Hegelian there's no need for the Hegelian dialectic to actually converge towards the truth. Like apparently not. Yeah, so yeah, why we're so obsessed with there being one truth? Is it possible there's just going to be multiple truth like little communities that that believe certain things and. I think it's now number one. I think it's just really difficult. Like who gets you know historically who gets to decide what the truth is? It's either the king or the priest, right? Like and so we don't live in an era anymore. If kings or priests dictating it to us and so we're kind of on our own. And so I my my my typical thing is like we just we just need a huge amount of humility and we need to be very suspicious of people who claim that they have the capital. Yeah, capitally truth and then and then we need we need to have I 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 you know we need to continue to embrace those even when they give us answers we don't like. Sure, but the internet and technology has enabled us to generate a large number of content that data that that the process the scientific process allows us sort of damages the hope laden within the scientific process because if you just have a bunch of people saying facts. On the internet and some of them are going to be LLMs. How is anything testable at all, especially that involves like human nature things like this? It's not physics. Here's a question a friend of mine just asked me on this topic. So suppose you had LLMs in equivalent of GPT four even five six seven eight. Suppose you had them in the sixteen hundreds. Yeah, and Galileo comes up for trial. Yeah, right, and you ask the LLM like is Galileo is Galileo right? Yeah, 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 you know the church at that time was you know owned the LLM they would have given it human you know human feedback to prohibit it from answering that question. Right, and so I like to take it out of our current context because that like 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 like debate that's happening about whether open source you know AI should be legal. Well, the the the actual mechanism of doing the human RL with human feedback is seems like such a fundamental and fascinating question. How do you select the humans? Exactly, how do you select the humans AI alignment right which everybody like is like oh that sounds great alignment with what human values whose human values whose human values. So and we're in this mode of like social and popular discourse. We're like you know there's you know you see this. What do you think of when you read a story in the press right now and they say you know XYZ made a baseless claim about some topic right and there's one group of people who are like aha thank 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 like. So like we're in this we're in this social context where there's the the level to which a lot of people in positions of power have become very very certain that they're in a position to determine the truth for the entire population is like. There's like there's like some bubble that has formed around that idea and at least it it flies completely in the face of everything I was ever trained about science and about reason. Um and strikes me as like you know deeply offensive and incorrect. What would you say about the state of journalism just on that topic today? Are we are we in a temporary kind of? Uh, are we experiencing a a temporary problem in terms of the incentives in terms of the the business model all that kind of stuff or is this like a decline of traditional journalism as we know it? If I always think about the counterfactual in these things which is like okay because these questions right this question heads towards it's like okay the impact of social media and the undermining of truth and all this but then you want to ask the question of like okay what if we had had the modern media environment including cable news and including social media and Twitter and everything else in 1939 or 1941. Right or 1910 or 1865 or 1850 or 1776, right? Um and like I think you just introduce like five thought experiments at once and broke my head but yes, that's there's a lot of interesting years. Well, Kennedy like Kennedy 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 in us in it with a social media context. Right, like how would LBJ have been experienced? Um, by the way, how would you know like many men FDR like the New Deal the Great Depression. I wonder where Twitter would would would think about Churchill and Hitler and Stalin. You know I mean look to this day there you know there there are lots of very interesting real questions around like how America you know got you know basically involved in World War Two and who did what when and the operations of British intelligence in American soil and did FDR this that Pearl Harbor you know. Yeah, Woodrow Wilson ran for you know his his candidacy was run on an anti war will you know this he ran on the platform of not getting involved in World War One somehow that switched you know like and I'm not even making a value judgment of these things I'm just saying like we we the way that our ancestors experienced reality was of course mediated through centralized top down right control at that point. If you if you ran those realities again with the media environment we have today, the reality would the reality would be experienced very very differently and then of course that that intermediation would cause the feedback loops to change and then reality would obviously play out. You think you think it'd be very different. Yeah, it