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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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Surprisingly so, but then I look at the life and the limb and the health of Sir Winston Churchill.
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To support it, please check out our sponsors in the description.
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And now, dear friends, here s Mark Andreessen.
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I think you re the right person to talk about the future of the internet and technology in general.
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Do you think we ll still have Google search in five, in 10 years or search in general?
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Yes.
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You know, it d be a question if the use cases have really narrowed down.
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Well, now with 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.
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And if AI assistants can do that better, doesn t the nature of search change?
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Sure, but we still have horses.
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Okay.
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When s the last time you rode a horse?
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It s been a while.
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All right.
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But what I mean is, will we still have Google search as the primary way that human civilization uses to interact with knowledge?
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I mean, search was a technology.
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It was a moment in time technology, which is you have, in theory, the world s information out on the web.
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you know, this is sort of the optimal way to get to it.
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And by the way, actually Google has known this for a long time.
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I mean, they ve been driving away from the 10 blue links for like two decades.
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They ve been trying to get away from that for a long time.
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What kind of links?
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They call the 10 blue links.
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10 blue links.
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So the standard Google search result is just 10 blue links to random websites.
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And they turn purple when you visit them.
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That s HTML.
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Guess who picked those colors?
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Thanks.
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I m touchy on this topic.
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No offense.
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Well, you know, like Marshall McLuhan said that the content of each new medium is the old medium.
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The content of each new medium is the old medium.
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The content of movies was theater, you know, theater plays.
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The content of theater plays was, you know, written stories.
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The content of written stories was spoken stories.
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Huh.
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Right.
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And so you just kind of fold the old thing into the new thing.
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How does that have to do with the blue and the purple?
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You just, you know, maybe for, you know, maybe within AI, one of the things that AI can do for you is it can generate the 10 blue links.
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Okay.
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Like, and so like if either, if that s actually the useful thing to do, or if you re feeling nostalgic.
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So it can generate the old InfoSeek or AltaVista.
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What else was there in the 90s?
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Yeah, all these.
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And then the internet itself has this thing where it incorporates all prior forms of media, right?
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So the internet itself incorporates television and radio and books and essays and every other form of prior basically media.
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And so it makes sense that AI would be the next step and you d sort of consider the internet to be content for
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the AI and then the AI will manipulate it however you want, including in this format.
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But if we ask that question quite seriously, it s a pretty big question.
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Will we still have search as we know it?
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Probably not.
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Probably we ll just have answers.
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But there will be cases where you ll want to say, okay, I want more like, you know, for example, site sources.
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and you want it to do that.
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And so the 10 blue links site sources are kind of the same thing.
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The AI would provide to you the 10 blue links so that you can investigate the sources yourself.
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It wouldn t be the same kind of interface, the crude kind of interface.
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Isn t that fundamentally different?
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I just mean like if you re reading a scientific paper, it s got list of sources at the end.
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If you want to investigate for yourself, you go read those papers.
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I guess that is the kind of search you talking to an AI is a kind of conversation is the kind of search.
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Like you said, every single aspect of our conversation right now, there d be like 10 blue links popping up that I could just like pause reality.
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Then you just go silent and then just click and read and then return back to this conversation.
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You could do that.
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Or you could have a running dialogue next to my head where the AI is arguing with everything I say.
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The AI makes the counter argument.
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kind of argument.
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Right.
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Oh, like, like a Twitter, like community notes, but like in real time, it ll just pop up.
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So anytime you see my ass go to the right, you start getting nervous.
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Yeah, exactly.
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It s like, oh, that s not right.
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Call me out on my bullshit right now.
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Okay.
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Well, I mean, isn t that, is that exciting to you?
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Is that terrifying that
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I mean, search has dominated the way we interact with the internet for, I don t know how long, for 30 years?
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So it s one of the earliest directories of website, and then Google was for 20 years, and also...
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It drove how we create content, you know, search engine optimization, that entirety thing.
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It also drove the fact that we have webpages and what those webpages are.
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So, I mean, is that scary to you or are you nervous about the shape and the content of the internet evolving?
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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.
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And so if there s no longer an incentive to make web pages, that cuts off a significant source of future training data.
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So there s actually an interesting question in there.
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Other than that, more broadly, no.
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Just in the sense of, like, search was always a hack.
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The 10 blue links was always a hack.
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Yeah, right.
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Because like, if the, the hypothesis, when I think about the counterfactual and the counterfactual world where the Google guys, for example, had had LLMs up front, would they ever have done the 10 blue links?
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And I think the answer is pretty clearly no, they would have just gone straight to the answer.
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And like I said, Google s actually been trying to drive to the answer anyway.
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You know, they, they bought this AI company 15 years ago that a friend of mine is working on, who s now the head of AI at Apple.
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And they were trying to do basically knowledge semantic, basically mapping.
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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 10 blue links, but it will normally just give you the answer.
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And so they ve been walking in this direction for a long time anyway.
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Do you remember the semantic web?
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That was an idea.
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How to convert the content of the internet into something that s interpretable by and usable by machine.
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That was the thing.
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And the closest anybody got to that, 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.
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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.
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kind of a proto, sort of, yeah, a little bit of a proto AI.
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But it turns out you don t need to rewrite the content of the internet to make it interpretable by machine.
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The machine can kind of just read R. Yeah, the machine can compute the meaning.
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Now, the other thing, of course, is, you know, just on search is the LLM is just, you know, 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, right?
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And there s the information is that, and the information is actually stored in the network, right?
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It s actually crystallized and stored in the network, and it s kind of spread out all over the place.
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But in a compressed representation.
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So you re searching,
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you re compressing and decompressing that thing inside.
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But the information s in there, and the neural network is running a process of trying to find the appropriate piece of information, in many cases, to generate, to predict the next token.
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And so it is kind of, it is doing a form of search.
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And then, by the way, just like on the web, you can ask the same question multiple times, or you can ask slightly different word of questions, and the neural network will do a different kind of, it ll search down different paths to give you different answers to different information.
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yeah and so it it sort of has a you know this content of the new medium is that his previous medium it kind of has the search functionality kind of embedded in there to the extent that it that it s useful
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So what s the motivator for creating new content on the internet?
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Well, I mean, actually, the motivation s probably still there, but what does that look like?
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Would we really not have webpages?
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Would we just have social media and video hosting websites?
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And what else?
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Conversations with AIs.
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Conversations with AIs.
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So conversations become...
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so one-on-one conversation like private conversations i mean if you if you want if obviously now the user doesn t want to but if it s a if it s a general topic um then you know so you know you know the the phenomenon of the jailbreak so dan and sydney right this thing where there there s the the the prompts that jailbreak and then you have these totally different conversations with the
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It takes the limiters, takes the restraining bolts off the LLMs.
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Yeah, for people who don t know, that s right.
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It makes the LLMs, it removes the censorship, quote unquote, that s put on it by the tech companies that create them.
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And so this is,
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llms uncensored so here s the interesting thing is among the content on the web today are a large corpus of conversations with the jailbroken llms both specifically dan which was a jailbroken open ai gpt and then sydney which was the jailbroken original bing which was gpt4
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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 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
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Well, so actually a paper just came out about basically how to do brain surgery on alums and be able to, in theory, reach in and basically mind-wipe them.
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What could possibly go wrong?
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Exactly, right?
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And then there are many, many, many questions around what happens to a neural network when you reach in and screw around with it.
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There s many questions around what happens when you even do reinforcement learning.
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Um, and so, um, yeah.
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And so, you know, we ll, we ll, you ll be using a lobotomized, right?
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Like I speak through the frontal lobe LLM, will you be using the free on shackled one who gets to, you know, who s going to build those, um, who gets to tell you what you can and can t do.
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Like, those are all central.
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I mean, those are like central questions for the future of everything that are being asked and, and, and, and, you know, determine those answers have been determined right now.
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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.
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Well, not necessarily, but not necessarily majority, but it will certainly is a potential source.
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But it s possible it s the majority.
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It s possible it s the majority, it s possible it s the majority.
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Also, there s another really big question.
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Here s another really big question.
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Will synthetic training data work?
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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
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there s another theory that says, no, actually, the thing that LLMs are really good at is generating lots of incredible creative content, right?
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And so, of course, they can generate training data.
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And as I m sure you re well aware, like, you know, look in the world of self-driving cars, right?
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Like, we train, you know, self-driving car algorithms and simulations, and that is actually a very effective way to train self-driving cars.
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Well, visual data is a little weird, because creating reality, visual reality, seems to be still a little bit out of reach for us, except in the autonomous vehicle space, where you can really constrain things and you can really— Basically, LIDAR data, right?
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So the algorithm thinks it s operating in the real world, post-process sensor data.
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Yeah.
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So if a, you know, you do this today, you go to LLM and you ask it for like a, you know, write me an essay on an incredibly esoteric like topic that there aren t very many people in the world that know about.
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And it writes you this incredible thing.
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And you re like, Oh my God, like, I can t believe how good this is.
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Like, is that really useless as training data for the next LLM?
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Like, because, right.
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Cause all the signal was already in there or is it actually, no, that s actually a new signal.
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And I, and this, this is what I call a trillion dollar question, which is the answer to that question will determine somebody who s going to make or lose a trillion dollars based on that question.
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It feels like there s quite a few, like a handful of trillion dollar questions within this space.
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That s one of them, synthetic data.
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I think George Haas pointed out to me that you could just have an NLM say, okay, you re a patient, and in another instance of it, say your doctor and have the two talk to each other.
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Or maybe you could say, a communist and a Nazi.
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Here, go.
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And that conversation, you do role playing, and you have,
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You know, just like the kind of role playing you do when you have different policies.
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RL policies when you play chess, for example, and 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.
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That s a really interesting question.
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And you re saying, because that could 10x the power of these things.
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Well, and then you get into this thing also, which is like, you know, there s the part of the LLM that just basically is doing prediction based on past data, but there s also the part of the LLM where it s evolving circuitry, right?
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Inside it, it s evolving, you know, neurons, functions, be able to do math and be able to, you know, and, you know, 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?
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And they ll have like a complete understanding of physics.
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So when they have computational capability, right, then there s for sure an opportunity to generate like fresh signal.
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Well, this actually makes me wonder about the power of conversation.
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So if you have an LLM trained on a bunch of books that cover different economics theories, and then you have those LLMs just talk to each other, like reason, the way we kind of debate each other as humans on Twitter, in formal debates, in podcast conversations, we kind of have little kernels of wisdom here and there, but if you can like a thousand X speed that up,
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Can you actually arrive somewhere new?
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Like, what s the point of conversation, really?
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Well, you can tell when you re talking to somebody, you can tell.
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Sometimes you have a conversation.
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You re like, wow, this person does not have any original thoughts.
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They are basically echoing things that other people have told them.
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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.
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And it s a different model than mine.
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And they re saying things that I don t expect.
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And so I need to now understand how their model of the world differs from my model of the world.
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And then that s how I learned something fundamental.
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right underneath the words.
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I wonder how consistently and strongly can an LLM hold on to a worldview.
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You tell it to hold on to that and defend it for
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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.
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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 they re like, if these are like emotionally inflammatory topics, because they re like, somehow the machine is just, you know, it figures out a way to make them agree.
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But it doesn t have to be like that.
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And because you can add to the prompt, I do not want the conversation to come to agreement.
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In fact, I want it to get more stressful and argumentative as it goes.
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I want tension to come out.
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I want them to become actively hostile to each other.
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I want them to not trust each other, take anything at face value.
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And it will do that.
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It s happy to do that.
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So it s going to start rendering misinformation, uh, about the other, but it s good.
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You can steer it, you can steer it, or you could steer it and you could say, I want it to get as tense and argumentative as possible, but still not involve any misrepresentation.
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I want, you know, both sides to, you could say, I want both sides to have good faith.
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You could say, I want both sides to not be constrained to good faith.
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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.
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It doesn t have a point of view.
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it has a default way of operating, but it s happy to operate in the other realm.
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Um, and so like, and this is how I, when I want to learn about a contentious issue, this is what I do now is like, this is what I, this is what I ask it to do.
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SPEAKER_00
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.
1
SPEAKER_00
Okay.
1
SPEAKER_00
No, this, this argument is becoming too polite, you know, make it more, you know, make it tenser.
1
SPEAKER_00
Um, and yeah, it s thrilled to do it.
1
SPEAKER_00
So it has the capability for sure.
1
SPEAKER_01
how do you know what is true?
1
SPEAKER_01
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 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 m talking to it.
1
SPEAKER_01
How do we get that right?
1
SPEAKER_01
Like as you re investigating a difficult topic,
1
SPEAKER_01
because I find that alums are quite nuanced in a very refreshing way.
1
SPEAKER_01
It doesn t feel biased.
1
SPEAKER_01
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.
1
SPEAKER_01
They re not stealing and manning the other side.
1
SPEAKER_01
They re hiding important information or they re fabricating information in order to make their arguments stronger.
1
SPEAKER_01
It s just like that feeling.
1
SPEAKER_01
Maybe it s a suspicion, maybe it s mistrust.
1
SPEAKER_01
With LLMs it feels like none of that is there.
1
SPEAKER_01
It s just kind of like, here s what we know.
1
SPEAKER_01
But you don t know if some of those things are kind of just straight up made up.
1
SPEAKER_00
So there s several layers to the question.
1
SPEAKER_00
So one is one of the things that an LLM is good at is actually debiasing.
1
SPEAKER_00
Um, and so you can feed it a news article and you can tell it strip out the bias.
1
SPEAKER_00
Yeah, that s nice.
1
SPEAKER_00
Right.
1
SPEAKER_00
And it actually does it like it actually knows how to do that because it knows how to do among other things.
1
SPEAKER_00
It actually knows how to do sentiment analysis.
1
SPEAKER_00
And so it knows how to pull out the emotionality.
1
SPEAKER_00
Yeah.
1
SPEAKER_00
um and so uh that s one of things you can do it s very suggestive of the of the the the sense here that there s there s 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 uh coming up with things that are totally not true but sound true
1
SPEAKER_00
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?
1
SPEAKER_00
And, you know.
1
SPEAKER_00
Brilliant.
1
SPEAKER_00
Right, and so when the engineers talk about it, they re like, this is terrible, it s hallucinating, right?
1
SPEAKER_00
If you have artistic inclinations, you re like, oh my God, we ve invented creative machines for the first time in human history, this is amazing.
1
SPEAKER_01
Or, you know, bullshitters.
1
SPEAKER_00
Well, bullshitter, but also.
1
SPEAKER_01
In the good sense of that word.
1
SPEAKER_00
There are shades of gray, though.
1
SPEAKER_00
It s interesting.
1
SPEAKER_00
So we had this conversation.
1
SPEAKER_00
We re looking at my firm at AI and lots of domains, and one of them is the legal domain.
1
SPEAKER_00
So we had this conversation with this big law firm about how they re thinking about using this stuff.
1
SPEAKER_00
And we went in with the assumption that an LLM that was going to be used in the legal industry would have to be 100% truthful, verified.
1
SPEAKER_00
there s this case where this lawyer apparently submitted a gpt uh generated brief and it had like fake you know legal case citations in it and the judge is gonna he s gonna get his law license stripped or something right so so like we we just assumed it s like obviously they re gonna want the super literal like you know one that never makes anything up not the creative one
1
SPEAKER_00
But actually they said, what the law firm basically said is, yeah, that s true at the level of individual briefs, but they said when you re actually trying to figure out legal arguments, you actually want to be creative.
1
SPEAKER_00
Again, there s creativity and then there s making stuff up.
1
SPEAKER_00
what s the line?
1
SPEAKER_00
You actually want it to explore different hypotheses.
1
SPEAKER_00
You want to do the legal version of improv or something like that, where you want to float different theories of the case and different possible arguments for the judge and different possible arguments for the jury.
1
SPEAKER_00
By the way, different routes through the history of all the case law.
1
SPEAKER_00
And so they said, actually, for a lot of what we want to use it for, we actually want it in creative mode.
1
SPEAKER_00
And then basically, we just assume that we re going to have to cross-check all of the specific citations.
1
SPEAKER_00
And so I think there s going to be more shades of gray in here than people think.
1
SPEAKER_00
And then I just add to that, another one of these trillion dollar kind of questions is ultimately sort of the verification thing.
1
SPEAKER_00
And so will LLMs be evolved from here to be able to do their own factual verification?
1
SPEAKER_00
Will you have sort of add-on functionality like Wolfram Alpha and other plugins where that s the way you do the verification?
1
SPEAKER_00
you know.
1
SPEAKER_00
Another, by the way, another idea is 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 the literal LLM fact check it, right?
1
SPEAKER_00
And so there s a variety of different technical approaches that are being applied to solve the hallucination problem.
1
SPEAKER_00
You know, some people like Jan LeCun argue that this is inherently an unsolvable problem, but most of the people working in the space, I think, think that there s a number of practical ways to kind of kind of corral this in a little bit.
1
SPEAKER_01
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.
1
SPEAKER_01
Just a handful of folks can organize, write, and moderate with a mostly unbiased way the entirety of human knowledge.
1
SPEAKER_01
So if there s something like the approach that Wikipedia took possible for MLMs, that s really exciting.
1
SPEAKER_01
Do you think that s possible?
1
SPEAKER_00
And in fact, Wikipedia today is still not deterministically correct.
1
SPEAKER_00
So you cannot take to the bank every single thing on every single page.
1
SPEAKER_00
But it is probabilistically correct.
1
SPEAKER_00
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.
1
SPEAKER_00
yeah it s this old question right of like okay like are we looking for perfection um are we looking for something that asymptotically approaches uh 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 uh than people thought and i i think i i think that s where this this ends and then underneath all this is the fundamental question of uh where you started which is okay what you know what is truth
1
SPEAKER_00
How do we get to truth?
1
SPEAKER_00
How do we know what truth is?
1
SPEAKER_00
And we live in an era in which an awful lot of people are very confident that they know what the truth is.
1
SPEAKER_00
And I don t really buy into that.
1
SPEAKER_00
And I think the history of the last, you know, 2000 years or 4000 years of human civilization is actually getting to the truth is actually a very difficult thing to do.
1
SPEAKER_01
Are we getting closer?
1
SPEAKER_01
If we look at the entirety, the arc of human history, are we getting closer to the truth?
1
SPEAKER_01
I don t know.
1
SPEAKER_01
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...
1
SPEAKER_01
move like crowds in a hysterical way along those narratives that don t have a necessary grounding in whatever the truth is.
1
SPEAKER_00
Sure, but like, you know, we came up with communism before the internet somehow, right?
1
SPEAKER_00
Like, which was, I would say had rather larger issues than anything we re dealing with today.
1
SPEAKER_01
It had, in the way it was implemented, it had issues.
1
SPEAKER_00
And its theoretical structure, it had, like, real issues.
1
SPEAKER_00
It had, like, a very deep fundamental misunderstanding of human nature and economics.
1
SPEAKER_01
Yeah, but those folks sure were very confident it was the right way.
1
SPEAKER_00
They were extremely confident.
1
SPEAKER_00
And my point is, they were very confident 3,900 years into what you would presume to be evolution towards the truth.
1
SPEAKER_00
Yeah.
1
SPEAKER_00
And so my assessment is,
1
SPEAKER_00
My assessment is, number one, there s no need for the Hegelian dialectic to actually converge towards the truth.
1
SPEAKER_00
Like, apparently not.
1
SPEAKER_01
Yeah, so yeah, why are we so obsessed with there being one truth?
1
SPEAKER_01
Is it possible there s just going to be multiple truths, like little communities that believe certain things?
1
SPEAKER_00
I think it s now number one.
1
SPEAKER_00
I think it s just really difficult.
1
SPEAKER_00
Like who gets, you know, historically who gets to decide what the truth is.
1
SPEAKER_00
It s either the king or the priest, right?
1
SPEAKER_00
Like, and so we don t live in an era anymore of kings or priests dictating it to us.
1
SPEAKER_00
And so we re kind of on our own.
1
SPEAKER_00
And so I, I, my, my, my, my typical thing is like, we just, we, we just need a huge amount of humility.
1
SPEAKER_00
Um, and we need to be very suspicious of people who claim that they have the capital capital truth.
1
SPEAKER_00
And then, and then we need, we need to have, I know, look, the good news is the enlightenment
1
SPEAKER_00
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.
1
SPEAKER_00
And, you know, we need to continue to embrace those even when they give us answers we don t like.
1
SPEAKER_01
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.
1
SPEAKER_01
Because if you just have a bunch of people saying facts on the internet, and some of them are going to be LLMs,
1
SPEAKER_00
you 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 gpt4 even five six seven eight suppose you had them in the 1600s 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
1
SPEAKER_00
right?
1
SPEAKER_00
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.
1
SPEAKER_00
And so therefore, that s what s in the training data.
1
SPEAKER_00
Another way of thinking about it is, well, a sufficiently advanced LLM will have evolved the ability to actually check the math, right?
1
SPEAKER_00
And will actually say, actually, no, actually, you may not want to hear it, but he s right.
1
SPEAKER_00
Now, if the church at that time owned the LLM, they would have given it human
1
SPEAKER_00
with feedback to prohibit it from answering that question.
1
SPEAKER_00
And so I like to take it out of our current context, because that makes it very clear.
1
SPEAKER_00
Those same questions apply today.
1
SPEAKER_00
This is exactly the point of a huge amount of the human feedback training that s actually happening with these LLMs today.
1
SPEAKER_00
This is a huge debate that s happening about whether open source AI should be legal.
1
SPEAKER_01
Well, the actual mechanism of doing the human RL with human feedback
1
SPEAKER_01
is seems like such a fundamental and fascinating question.
1
SPEAKER_01
How do you select the humans?
1
SPEAKER_01
Exactly.
1
SPEAKER_01
How do you select the humans?
1
SPEAKER_00
ai alignment right which everybody like is like oh that sounds great alignment with what human values whose human values whose human values so we re 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 think you know they re doing fact checking
1
SPEAKER_00
there s another group of people that are like every time the press says that it s now a tech 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
1
SPEAKER_00
there s like, there s like some bubble that has formed around that idea.
1
SPEAKER_00
And at least it flies completely in the face of everything I was ever trained about science and about reason.
1
SPEAKER_00
Um, and it strikes me as like, you know, deeply offensive and incorrect.
1
SPEAKER_01
What would you say about the state of journalism just on that topic today?
1
SPEAKER_01
Are we, are we in a temporary kind of a
1
SPEAKER_01
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?
1
SPEAKER_00
You have to 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
1
SPEAKER_00
including cable news and including social media and Twitter and everything else in 1939 or 1941, right?
1
SPEAKER_00
Or 1910 or 1865 or 1850 or 1776.
1
SPEAKER_00
Right.
1
SPEAKER_01
Um, 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.
1
SPEAKER_00
I was just taking a simple example.
1
SPEAKER_00
Like how would president Kennedy have been interpreted?
1
SPEAKER_00
It was what we know now about all the things Kennedy was up to.
1
SPEAKER_00
Like how would he have been experienced by the body politic in a with a social media context?
1
SPEAKER_00
Right, like how would LBJ have been experienced?
1
SPEAKER_00
But by the way, how would you know like many men FDR?
1
SPEAKER_00
Like the New Deal the Great Depression.
1
SPEAKER_01
I wonder where Twitter would would just would think about Churchill and Hitler and Stalin
1
SPEAKER_00
you know i mean look to this day there you know there s there are lots of very interesting real questions around like how america you know got you know basically involved in world war ii and who did what when and the operations of british intelligence in american soil and did fdr this that for harvard you know yeah wardrow wilson ran for you know his his candidacy was run on an anti-war will you know this he ran on the platform not getting involved world war one somehow that switched you know like
1
SPEAKER_00
i m not even making a value judgment of these things i m just saying like 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 do you think you think you think it d be very different
1
SPEAKER_00
Yeah, it has to be.
1
SPEAKER_00
It has to be just because it s also, I mean, just look at what s happening today.
1
SPEAKER_00
I mean, just, I mean, the most obvious thing is just the, the collapse.
1
SPEAKER_00
And here s another opportunity to argue that this is not the internet causing this, by the way.
1
SPEAKER_00
Um, here s a big thing happening today, which is, uh, Gallup does this thing every year where they do, they pull for trust in institutions in America and they do it across all the different, everything from military to clergy and big business and the media and so forth.
1
SPEAKER_00
Right.
1
SPEAKER_00
um and basically there s been a systemic collapse um interest in institutions in the u.s almost without exception basically since essentially the early 1970s um 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 yeah right because like now we know so much about how these things operate and like they re not that impressive
1
SPEAKER_01
And also why we don t have better institutions and better leaders then.
1
SPEAKER_00
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
2
SPEAKER_01
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.
2
SPEAKER_01
Therefore, the circumstances would have had to change.
2
SPEAKER_01
And it would have been a feedback loop process.
2
SPEAKER_01
In other words, your experience of reality changes reality, and then reality changes your experience of reality.
2
SPEAKER_01
It s a two-way feedback process, and media is the intermediating force.
2
SPEAKER_01
that.
2
SPEAKER_01
And so change the media environment, change reality.
2
SPEAKER_00
Yeah.
2
SPEAKER_01
And so it s just so just as a 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.
2
SPEAKER_01
And then therefore, like people were smarter than or better than or, you know, by the way, dumber than or not as capable then, right?
2
SPEAKER_01
We we make all these like really light and casual like comparisons of ourselves to, you know, previous generations of people, you know, we draw judgments all the time and I just think it s like really hard to do any of that because if we
2
SPEAKER_01
If we put ourselves in their shoes with the media that they had at that time, I think we probably most likely would have been just like them.
2
SPEAKER_00
So don t you think that our perception and understanding of reality would be more and more mediated through large language models now?
2
SPEAKER_00
Yeah.
2
SPEAKER_00
So you said media before.
2
SPEAKER_00
Isn t the LLM going to be the new, what is it, mainstream media, MSM?
2
SPEAKER_00
It ll be LLM.
2
SPEAKER_00
That would be the source of, I m sure there s a way to kind of rapidly fine tune, like making LLMs real time.
2
SPEAKER_00
I m sure there s probably a research problem that you can do just rapid fine tuning to the new events, something like this.
2
SPEAKER_01
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.
2
SPEAKER_01
Like maybe the experience most people have with LLMs is just a continuous feed.
2
SPEAKER_01
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.
2
SPEAKER_00
Also really more deeply integrated into your life, not just like, oh, like intellectual philosophical thoughts, but like literally like how to make a coffee, where to go for lunch, just whether, you know, dating, all this kind of stuff.
2
SPEAKER_01
What to say in a job interview, yeah.
2
SPEAKER_00
What to say.
2
SPEAKER_00
Yeah, exactly.
2
SPEAKER_00
Next sentence.
2
SPEAKER_01
Yeah, next sentence.
2
SPEAKER_01
Yeah, at that level.
2
SPEAKER_01
Yeah, I mean, yes.
2
SPEAKER_01
So technically, now, whether we want that or not is an open question, right?
2
SPEAKER_01
And whether people use that.
2
SPEAKER_00
Boy, I would kill for a pop-up.
2
SPEAKER_00
A pop-up right now.
2
SPEAKER_00
The estimated engagement using is decreasing.
2
SPEAKER_00
For Mark Andreessen s, there s this controversy section for his Wikipedia page.
2
SPEAKER_00
In 1993, something happened or something like this.
2
SPEAKER_00
Bring it up.
2
SPEAKER_00
That will drive engagement up.
2
SPEAKER_00
Anyway.
2
SPEAKER_00
Yeah, that s right.
2
SPEAKER_01
I mean, look.
2
SPEAKER_01
This gets this whole thing of like, so, you know, the chat interface has this whole concept of prompt engineering, right?
2
SPEAKER_01
Yes.
2
SPEAKER_01
Well, it turns out one of the things that LLMs are really good at is writing prompts.
2
SPEAKER_01
Right?
2
SPEAKER_00
Yeah.
2
SPEAKER_01
And so, like, what if you just outsourced?
2
SPEAKER_01
And by the way, you could run this experiment today.
2
SPEAKER_01
You could hook this up to do this today.
2
SPEAKER_01
The latency is not good enough to do it real time in a conversation, but you could run this experiment and you just say, look, every 20 seconds you could just say, you know,
2
SPEAKER_01
you know, tell me what the optimal prompt is, and then ask yourself that question to give me the result.
2
SPEAKER_01
And then as you, as you, exactly to your point, as you add, there will be, there will be, these systems are going to have the ability to be learned and updated essentially in real time.
2
SPEAKER_01
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.
2
SPEAKER_01
It ll have a feed of everything else happening in the world.
2
SPEAKER_01
And then it ll be, you know, sort of retraining, prompting or retraining itself on the fly.
2
SPEAKER_01
And so the scenario you described is actually a completely doable scenario.
2
SPEAKER_01
Now,
2
SPEAKER_01
The hard question on this is always, OK, since that s possible, are people going to want that?
2
SPEAKER_01
Like, what s the form of experience?
2
SPEAKER_01
You know, that we won t know until we try it.
2
SPEAKER_01
But I don t think it s possible yet to predict the form of AI in our lives.
2
SPEAKER_01
Therefore, it s not possible to predict the way in which it will intermediate our experience with reality yet.
2
SPEAKER_00
Yeah, but it feels like there s going to be a killer app.
2
SPEAKER_00
there s probably a mad scramble right now inside OpenAI and Microsoft and Google and Meta and in startups and smaller companies figuring out what is the killer app, because it feels like it s possible, like a chat GPT type of thing, it s possible to build that, but that s 10x more compelling using already the LLMs we have, using even the open source LLMs, Lama and the different variants.
2
SPEAKER_00
So you re investing in a lot of companies and you re paying attention.
2
SPEAKER_00
Who do you think is gonna win this?
2
SPEAKER_00
Do you think there ll be?
2
SPEAKER_00
Who s gonna be the next page rank inventor?
2
SPEAKER_01
Trillion dollar question.
2
SPEAKER_00
Another one, we have a few of those today.
2
SPEAKER_01
There s a bunch of those.
2
SPEAKER_01
So look, there s a really big question today, sitting here today, there s a really big question about the big models versus the small models.
2
SPEAKER_01
That s related directly to the big question of proprietary versus open.
2
SPEAKER_01
Then there s this big question of where is the training data, are we topping out of the training data or not?
2
SPEAKER_01
And then are we gonna be able to synthesize training data?
2
SPEAKER_01
And then there s a huge pile of questions around regulation and, you know, what s actually going to be legal.
2
SPEAKER_01
And so I would when we think about it, we dovetail kind of all those all those questions together.
2
SPEAKER_01
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.
2
SPEAKER_01
Um, 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, you know, competing with them.
2
SPEAKER_01
And so, you know, there will be, you know, just like there s like, you know, whatever, three big banks or three big, you know, or by the way, three big search companies, or I guess two now, you know, it ll, it ll centralize like that.
2
SPEAKER_01
Um, you can paint another very different picture that says, no, I m actually the opposite of that s going to happen.
2
SPEAKER_01
This is going to basically that this is the new goal.
2
SPEAKER_01
You know, this is the new gold rush.
2
SPEAKER_01
alchemy like you know this is the this is the big bang for this whole new area of of science and technology and so therefore you re going to have every smart 14 year old on the planet building open source right you know and figure out a way to optimize these things um and then you know we re just going to get like overwhelmingly better at generating trading data we re going to you know 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
2
SPEAKER_01
right, of every size, scale, shape, and description, and there might be a few big ones that are like the super genius ones, but mostly what we ll experience is open source, and that s more like a world of what we have today with Linux and the web.
2
SPEAKER_01
So.
2
SPEAKER_00
Okay, but you painted these two worlds, but there s also variations of those worlds, because you said regulatory capture is possible to have these tech giants that don t have regulatory capture, which is something you re also calling for, saying it s okay to have big companies working on this stuff.
2
SPEAKER_00
as long as they don t achieve regulatory capture.
2
SPEAKER_00
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 has become the new tech giant.
2
SPEAKER_00
I don t know, I would love to hear your kind of opinion if Google, Meta, and Microsoft 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.
2
SPEAKER_00
Do you think it s possible or do you think it ll come from a startup?
2
SPEAKER_01
Yeah.
2
SPEAKER_01
It is this always big question, which is you get this feeling.
2
SPEAKER_01
I hear about this a lot from CEOs, founder CEOs, where it s like, wow, we have 50,000 people.
2
SPEAKER_01
It s now harder to do new things than it was when we had 50 people.
2
SPEAKER_00
Yeah.
2
SPEAKER_01
Like what has happened?
2
SPEAKER_01
So that s a recurring phenomenon.
2
SPEAKER_01
Um, by the way, that s one of the reasons why there s always startups and why there s venture capital.
2
SPEAKER_01
Um, it s just, that s, that s like a timeless,
2
SPEAKER_01
kind of thing.
2
SPEAKER_01
So that s one observation.
2
SPEAKER_01
On PageRank, we can talk about that, but on PageRank, specifically on PageRank, there actually is a page.
2
SPEAKER_01
So there is a PageRank already in the field, and it s the transformer, right?
2
SPEAKER_01
So the big breakthrough was the transformer.
2
SPEAKER_01
And the transformer was invented in 2017 at Google.
2
SPEAKER_01
And this is actually, like, really an interesting question, because it s like, okay, the Transformers, like, why does OpenAI even exist?
2
SPEAKER_01
Like, the Transformers invented at Google, why didn t Google?
2
SPEAKER_01
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 the equivalent of GPT-4 as fast as we can, he said, I said, when could we have had it?
2
SPEAKER_01
And he said, 2019.
2
SPEAKER_01
Yeah.
2
SPEAKER_01
They could have just done a two-year sprint with the Transformer and been, because they already had the compute at scale, they already had all the training data, they could have just done it.
2
SPEAKER_01
There s a variety of reasons they didn t do it.
2
SPEAKER_01
This is like a classic big company thing.
2
SPEAKER_01
IBM invented the relational database in the 1970s, let it sit on the shelf as a paper.
2
SPEAKER_01
Larry Ellison picked it up and built Oracle.
2
SPEAKER_01
Xerox PARC invented the interactive computer.
2
SPEAKER_01
They let it sit on the shelf.
2
SPEAKER_01
Steve Jobs came and turned it into the Macintosh.
2
SPEAKER_01
Right.
2
SPEAKER_01
And so there is this pattern now, having said that sitting here today, like Google s in the game, right?
2
SPEAKER_01
So Google, you know, maybe, maybe they, maybe they let like a four year gap there, go there that they maybe shouldn t have, but like they re in the game.
2
SPEAKER_01
And so now they ve got, you know, now they re committed, they ve done this merger, they re bringing in demos, they ve got this merger with deep mind, you know, they re piling in resources.
2
SPEAKER_01
There are rumors that they re, you know, building up an incredible, you know, super LLM, um, you know, way beyond what we even have today.
2
SPEAKER_01
And they ve got, you know, unlimited resources and a huge, you know, they ve been challenged with their honor.
2
SPEAKER_00
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
2
SPEAKER_00
like the fight is on.
2
SPEAKER_00
There s this beautiful Silicon Valley nature, like birds are chirping in this giant building.
2
SPEAKER_00
And it s like the beast has been awakened.
2
SPEAKER_00
And then like all the big companies are waking up to this.
2
SPEAKER_00
They have the compute, but also the little guys have,
2
SPEAKER_00
It feels like they have all the tools to create the killer product.
2
SPEAKER_00
And then there s also tools to scale.
2
SPEAKER_00
If you have a good idea, if you have the page rank idea.
2
SPEAKER_00
So there s several things that is page rank.
2
SPEAKER_00
There s page rank, the algorithm.
2
SPEAKER_00
and the idea, and there s like the implementation of it.
2
SPEAKER_00
And I feel like killer product is not just the idea, like the transform, it s the implementation.
2
SPEAKER_00
Something really compelling about it, like you just can t look away.
2
SPEAKER_00
Something like the algorithm behind TikTok versus TikTok itself, like the actual experience of TikTok that just you can t look away.
2
SPEAKER_00
It feels like somebody s gonna come up with that.
2
SPEAKER_00
And it could be Google, but it feels like it s just easier and faster to do for a startup.
2
SPEAKER_01
Yeah.
2
SPEAKER_01
So, so the startup, the, the huge, the huge advantage of startups have is they just, there, there s no sacred cows.
2
SPEAKER_01
There s no historical legacy to protect.
2
SPEAKER_01
There s no need to reconcile your new plan with existing strategy.
2
SPEAKER_01
There s no communication overhead.
2
SPEAKER_01
There s no, you know, big companies are big companies.
2
SPEAKER_01
They ve got pre-meetings planning for the meeting.
2
SPEAKER_01
Then they have, then they have the post meeting of the recap.
2
SPEAKER_01
Then they have the presentation of the board.
2
SPEAKER_01
Then they have the next rounds of meetings.
2
SPEAKER_00
Yeah.
2
SPEAKER_00
And that s the meeting.
2
SPEAKER_01
That s the elapsed time when the startup launches its product.
2
SPEAKER_01
Right.
2
SPEAKER_01
So, so, so, so there s a timeless, right?
2
SPEAKER_00
Yeah.
2
SPEAKER_01
So there s a timeless thing there now.
2
SPEAKER_01
Yeah.
2
SPEAKER_01
What the startups don t have is everything else, right?
2
SPEAKER_01
So startups, they don t have a brand, they don t have customer relationships, they ve got no distribution, they ve got no scale.
2
SPEAKER_01
I mean, sitting here today, they can t even get GPUs, right?
2
SPEAKER_01
Like there s like a GPU shortage.
2
SPEAKER_01
Startups are literally stalled out right now because they can t get chips, which is like super weird.
2
SPEAKER_00
Yeah, they got the cloud.
2
SPEAKER_01
Yeah, but the clouds run out of chips, right?
2
SPEAKER_01
And then to the extent the clouds have chips, they allocate them to the big customers, not the small customers, right?
2
SPEAKER_01
And so the small companies lack everything other than the ability to just do something new.
2
SPEAKER_00
Yeah.
2
SPEAKER_01
Right.
2
SPEAKER_01
And this is the timeless race and battle.
2
SPEAKER_01
And this is kind of the point I tried to make in the essay, which is like both sides of this are good.
2
SPEAKER_01
Like, it s really good to have like highly scaled tech companies that can do things that are like at staggering levels of sophistication.
2
SPEAKER_01
It s really good to have startups that can launch brand new ideas.
2
SPEAKER_01
They ought to be able to both do that and compete.
2
SPEAKER_01
Neither one ought to be subsidized or protected from the others.
2
SPEAKER_01
Like that s that s to me, that s just like very clearly the idealized world.
2
SPEAKER_01
It is the world we ve been in for up until now.
2
SPEAKER_01
And then, of course, there are people trying to shut that down.
2
SPEAKER_01
But my hope is that, you know, the best outcome clearly will be if that continues.
2
SPEAKER_00
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.
2
SPEAKER_00
So I don t know if you remember, but there s a thing called Mosaic and there s a thing called Netscape Navigator.
2
SPEAKER_00
So you were there in the beginning.
2
SPEAKER_00
What about the interface to the internet?
2
SPEAKER_00
How do you think the browser changes?
2
SPEAKER_00
and who gets to own the browser.
2
SPEAKER_00
We got to see some very interesting browsers.
2
SPEAKER_00
Firefox, I mean, all the variants of Microsoft Internet Explorer, Edge, and now Chrome.
2
SPEAKER_00
The actual... I mean, it seems like a dumb question to ask, but do you think we ll still have the web browser?
2
SPEAKER_01
So I, uh, I have an eight year old and he s super into, it s like Minecraft and learning to code and doing all this stuff.
2
SPEAKER_01
So I, I, of course I was very proud.
2
SPEAKER_01
I couldn t bring sort of fire down from the mountain to my kid.
2
SPEAKER_01
And I brought him chat GPT and I hooked him up on his, on his, on his, on his laptop.
2
SPEAKER_01
And I was like, you know, this is the thing that s going to answer all your questions.
2
SPEAKER_01
And he s like, okay.
2
SPEAKER_01
And I m like, but it s going to answer all your questions.
2
SPEAKER_01
And he s like, well, of course, like it s a computer.
2
SPEAKER_01
Of course, it answers all your questions.
2
SPEAKER_01
Like what else would a computer be good for dad?
2
SPEAKER_01
Um, never impressed.
2
SPEAKER_01
Not impressed in the least two weeks pass.
2
SPEAKER_01
Um, and he has some question.
2
SPEAKER_01
Um, and I say, well, have you asked Chad JPT?
2
SPEAKER_01
And he s like, dad, being is better.
2
SPEAKER_01
And why is Bing better is because it s built into the browser.
2
SPEAKER_01
Because he s like, look, I have the Microsoft Edge browser and like it s got Bing right here.
2
SPEAKER_01
And then he doesn t know this yet, but one of the things you can do with Bing and Edge is there s a setting where you can use it to basically talk to any web page.
2
SPEAKER_01
because it s sitting right there next to the browser.
2
SPEAKER_01
And by the way, which includes PDF documents.
2
SPEAKER_01
And so the way they ve implemented it in Edge with Bing is you can load a PDF and then you can ask it questions, which is the thing you can t do currently in just chat GPT.
2
SPEAKER_01
So they re going to push the melding.
2
SPEAKER_01
I think that s great.
2
SPEAKER_01
They re going to push the melding and see if there s a combination thing there.
2
SPEAKER_01
Google s rolling out this thing the magic button, which is implemented in they put in Google Docs, right?
2
SPEAKER_01
And so you go to, you know, Google Docs and you create a new document and you, you know, you instead of like, you know, starting to type, you just, you know, say it press the button and it starts to like generate content for you.
2
SPEAKER_01
Right?
2
SPEAKER_01
Like, is that the way that it ll work?
2
SPEAKER_01
Is it going to be a speech UI where you re just going to have an earpiece and talk to it all day long?
2
SPEAKER_01
You know, is it going to be a like, these are all like, this is exactly the kind of thing that I don t
2
SPEAKER_01
This is exactly the kind of thing I don t think is possible to forecast.
2
SPEAKER_01
I think what we need to do is run all those experiments.
2
SPEAKER_01
And so one outcome is we come out of this with a super browser that has AI built in that s just amazing.
2
SPEAKER_01
Look, there s a real possibility.
2
SPEAKER_01
I mean, look, there s a possibility here that the whole idea of a screen
2
SPEAKER_00
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 um 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
2
SPEAKER_00
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?
2
SPEAKER_00
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,
2
SPEAKER_00
It changes the nature of the internet because where the content is hosted, who owns the data, who owns the content, what is the kind of content you create, how do you make money by creating content, who are the content creators, all of that.
2
SPEAKER_00
Or it could just keep being the same, which is the nature of web page changes and the nature of content, but there would still be a web browser.
2
SPEAKER_00
Because a web browser s a pretty sexy product.
2
SPEAKER_00
It just seems to work.
2
SPEAKER_00
Because you have an interface, a window into the world, and then the world can be anything you want.
2
SPEAKER_00
And as the world will evolve, there could be different programming languages, it can be animated, maybe it s three-dimensional and so on.
2
SPEAKER_00
Yeah, it s interesting.
2
SPEAKER_00
Do you think we ll still have the web browser?
2
SPEAKER_01
every medium becomes the content for the next one.
2
SPEAKER_01
So the AI will be able to give you a browser whenever you want.
2
SPEAKER_01
Oh, interesting.
2
SPEAKER_01
Yeah.
2
SPEAKER_01
Another way to think about it is maybe what the browser is, maybe it s just the escape hatch, right?
2
SPEAKER_01
Which is maybe kind of what it is today.
2
SPEAKER_01
Right.
2
SPEAKER_01
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.
2
SPEAKER_01
Right.
2
SPEAKER_01
But then every once in a while, there s something where you actually want to jailbreak.
2
SPEAKER_01
You want to actually get free.
2
SPEAKER_00
The web browser is the F you to the man you re allowed to.
2
SPEAKER_00
That s the free Internet.
2
SPEAKER_00
Yeah.
2
SPEAKER_00
Back the way it was in the 90s.
2
SPEAKER_01
So here s something I m proud of.
2
SPEAKER_01
So nobody really talks about it.
2
SPEAKER_01
Here s something I m proud of, which is that the web, the web, the browser, the web servers, they re all, they re still backward compatible all the way back to like 1992.
2
SPEAKER_01
Right.
2
SPEAKER_01
So like you can put up a, you can still, you know, the big breakthrough of the web early on, the big breakthrough was it made it really easy to read, but it also made it really easy to write, made it really easy to publish.
2
SPEAKER_01
And we literally made it so easy to publish.
2
SPEAKER_01
We made it not only easy to publish content, it was actually also easy to actually write a web server.
2
SPEAKER_01
Yeah, right.
2
SPEAKER_01
And you can literally write a web server in four lines of braille code, and you could start publishing content on it.
2
SPEAKER_01
And you could set whatever rules you want for the content, whatever censorship, no censorship, whatever you want, you could just do that.
2
SPEAKER_01
And as long
3
SPEAKER_01
you had an IP address, right, you could do that.
3
SPEAKER_01
That still works.
3
SPEAKER_01
Like that still works exactly as I just described.
3
SPEAKER_01
So this is part of my reaction to all of this, like, you know, all this just censorship pressure and all this, you know, 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.
3
SPEAKER_01
Like the Wild West is still out there.
3
SPEAKER_01
Now they will, they will try to chase you down.
3
SPEAKER_01
Like they ll try to, you know, people who want to censor will try to take away your, your, um, you know, your domain name and they ll try to take away your payments account and so forth if they really don t like what you, what you re saying.
3
SPEAKER_01
But, but nevertheless, you like, unless they literally are intercepting you at the ISP level, like you can still put up a thing.
3
SPEAKER_01
Um, and so I, I don t know, I think that s important to preserve, right?
3
SPEAKER_01
Like, because, because, because, I mean, one is just a freedom argument, but the other is a creativity argument.
3
SPEAKER_01
Which is you want to have the escape hatch so that the kid with the idea is able to realize the idea.
3
SPEAKER_01
Because to your point on PageRank, you actually don t know what the next big idea is.
3
SPEAKER_01
Nobody called Larry Page and told him to develop PageRank.
3
SPEAKER_01
He came up with that on his own.
3
SPEAKER_01
And you want to always, I think, 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.
3
SPEAKER_00
You and I are both fans of history, so let s step back.
3
SPEAKER_00
We ve been talking about the future.
3
SPEAKER_00
Let s step back for a bit and look at the 90s.
3
SPEAKER_00
You created Mosaic Web Browser, the first widely used web browser.
3
SPEAKER_00
Tell the story of that, and how did it evolve into Netscape Navigator?
3
SPEAKER_00
This is the early days.
3
SPEAKER_01
Full story.
3
SPEAKER_01
You were born.
3
SPEAKER_00
I was born, a small child.
3
SPEAKER_00
Actually, yeah, let s go there.
3
SPEAKER_00
When did you first fall in love with computers?
3
SPEAKER_01
Oh, so I hit the generational jackpot and I hit the Gen X kind of point perfectly, as it turns out.
3
SPEAKER_01
So I was born in 1971.
3
SPEAKER_01
So there s this great website called WTF happened in 1971 dot com, which is basically 1970 when everything started to go to hell.
3
SPEAKER_01
And I was, of course, born in 1971.
3
SPEAKER_01
So I like to think that I had something to do with that.
3
SPEAKER_00
Did you make it on the website?
3
SPEAKER_01
I have.
3
SPEAKER_01
I don t think I made it on the website, but, you know, somebody needs to add.
3
SPEAKER_00
This is this is where everything maybe I contributed to some of the trends.
3
SPEAKER_01
that they do.
3
SPEAKER_01
Every line on that website goes like that, right?
3
SPEAKER_01
So it s all a picture disaster.
3
SPEAKER_01
But there was this moment in time where, because sort of the Apple 2 hit in like 1978, and then the IBM PC hit in 82.
3
SPEAKER_01
So I was like 11 when the PC came out.
3
SPEAKER_01
And so I just kind of hit that perfectly.
3
SPEAKER_01
And then that was the first moment in time when regular people could spend a few hundred dollars and get a computer, right?
3
SPEAKER_01
And so that resonated right out of the gate.
3
SPEAKER_01
And then the other part of the story is, you know, I was using an Apple II.
3
SPEAKER_01
I used a bunch of them, but I was using Apple II.
3
SPEAKER_01
And of course, it said on the back of every Apple II and every Mac, it said, you know, designed in Cupertino, California.
3
SPEAKER_01
And I was like, wow, Cupertino must be the shining city on the hill, like the most amazing city of all time.
3
SPEAKER_01
I can t wait to see it.
3
SPEAKER_01
And of course, years later, I came out to Silicon Valley and went to Cupertino, and it s just a bunch of office parks.
3
SPEAKER_01
low-rise apartment buildings.
3
SPEAKER_01
So the aesthetics were a little disappointing, but it was the vector of the creation of a lot of this stuff.
3
SPEAKER_01
Part of my story is just the luck of having been born at the right time and getting exposed to PCs.
3
SPEAKER_01
Then the other part is
3
SPEAKER_01
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.
3
SPEAKER_01
And, you know, that bill dumped a ton of money into a bunch of research universities to build out basically the internet backbone and then the supercomputer centers that were clustered around the internet.
3
SPEAKER_01
And one of those universities was the University of Illinois.
3
SPEAKER_01
where I went to school.
3
SPEAKER_01
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.
3
SPEAKER_01
And so as a consequence, we had on campus, and this is like, you know, 89, 90, 91,
3
SPEAKER_01
We had like, you know, we were right on the internet backbone.
3
SPEAKER_01
We had like T3 and 45, at the time T3, 45 megabit backbone connection, which at the time was, you know, wildly state of the art.
3
SPEAKER_01
We had Cray supercomputers.
3
SPEAKER_01
We had thinking machines, parallel supercomputers.
3
SPEAKER_01
We had Silicon Graphics workstations.
3
SPEAKER_01
We had Macintoshes.
3
SPEAKER_01
We had NextCubes all over the place.
3
SPEAKER_01
We had like every possible kind of computer you could imagine because all this money just fell out of the sky.
3
SPEAKER_00
Um, you were living in the future.
3
SPEAKER_01
Yeah.
3
SPEAKER_01
So quite literally it was, yeah, like it s all, it s all there.
3
SPEAKER_01
It s all like we had full broadband graphics, like the whole thing.
3
SPEAKER_01
And, and it s actually funny cause they had this, this is the first time I kind of, it sort of tickled the back of my head that there might be a big opportunity in here, which is, you know, they, they embraced it.
3
SPEAKER_01
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.
3
SPEAKER_01
And then they, they gave every undergrad a computer account and an email address.
3
SPEAKER_01
Um,
3
SPEAKER_01
And the assumption was that you would use the internet for your four years of college, and then you would graduate and stop using it.
3
SPEAKER_01
And that was that, right?
3
SPEAKER_01
And you would just retire your email address.
3
SPEAKER_01
It wouldn t be relevant anymore, because you d go off in the workplace and they don t use email.
3
SPEAKER_01
You d be back to using fax machines or whatever.
3
SPEAKER_00
Did you have that sense as well?
3
SPEAKER_00
You said the back of your head was tickled.
3
SPEAKER_00
What was exciting to you about this possible world?
3
SPEAKER_01
if this if this is so useful in this container if this is so useful in this contained environment that just has this weird source of outside funding then if if it were practical for everybody else to have this and if it were cost effective for everybody else to have this wouldn t they want it and the overwhelmingly the prevailing view at the time was no they would not want it this is esoteric weird nerd stuff right that like computer science kids like but like normal people are never going to do email right or be on the internet right um and so i was just like wow like this this is actually like this is really compelling stuff
3
SPEAKER_01
Now, the other part was it was all really hard to use.
3
SPEAKER_01
And in practice, you had to be basically a CS.
3
SPEAKER_01
You basically had to be a CS undergrad or equivalent to actually get full use of the Internet at that point, because it was all pretty esoteric stuff.
3
SPEAKER_01
So then that was the other part of the idea, which was, OK, we need to actually make this easy to use.
3
SPEAKER_00
So what s involved in creating Mosaic, like in creating a graphical interface to the Internet?
3
SPEAKER_01
Yes, it was a combination of things.
3
SPEAKER_01
So it was like basically the web existed in an early sort of described as prototype form.
3
SPEAKER_01
And by the way, text only at that point.
3
SPEAKER_00
What did it look like?
3
SPEAKER_00
What was the web?
3
SPEAKER_00
I mean, and the key figures?
3
SPEAKER_00
Like, what was it?
3
SPEAKER_00
What was it like?
3
SPEAKER_01
Paint a picture.
3
SPEAKER_01
It looked like JGPT, actually.
3
SPEAKER_01
It was all text.
3
SPEAKER_00
Yeah.
3
SPEAKER_01
And so you had a text-based web browser.
3
SPEAKER_01
Well, actually, the original browser, Tim Berners-Lee, the original browser, both the original browser and the server actually ran on NextCubes.
3
SPEAKER_01
So this was the computer Steve Jobs made during the decade-long interim period when he was not at Apple.
3
SPEAKER_01
You know, he got fired in 85 and then came back in 97.
3
SPEAKER_01
So this was in that interim period where he had this company called next and they made these literally these computers called cubes.
3
SPEAKER_01
And there s this famous story.
3
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They were beautiful, but they were 12 inch by 12 inch by 12 inch cubes computers.
3
SPEAKER_01
And there s a famous story about how they could have cost half as much if it had been 12 by 12 by 13.
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Steve was like, no, it has to be.
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So they were like $6,000, basically, academic workstations.
3
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They had the first CD-ROM drives, which were slow.
3
SPEAKER_01
I mean, the computers are all but unusable.
3
SPEAKER_01
They were so slow, but they were beautiful.
3
SPEAKER_01
Can we actually just take a tiny tangent there?
3
SPEAKER_01
Sure, of course.
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SPEAKER_00
The 12 by 12 by 12 that just so beautifully encapsulates Steve Jobs  idea of design.
3
SPEAKER_00
Can you just comment on what you find interesting about Steve Jobs, about that view of the world, that dogmatic pursuit of perfection and how he saw perfection in design?
3
SPEAKER_01
Yes, I guess I say like, look, he was a deep believer, I think, in a very deep way.
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SPEAKER_01
I interpret it.
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I don t know if you ever really described it like this, but the way I interpret it is it s like it s like this thing, and it s actually a thing in philosophy.
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It s like aesthetics are not just appearances.
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SPEAKER_01
Aesthetics go all the way to like deep underlying underlying meaning, right?
3
SPEAKER_01
It s like
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SPEAKER_01
I m not a physicist.
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SPEAKER_01
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?
3
SPEAKER_01
Like, you know, right?
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SPEAKER_01
And so, so, so there s something and you feel the same thing, by the way, in like human psychology, right?
3
SPEAKER_01
You know, when you re experiencing awe, right?
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SPEAKER_01
You know, there s like a, there s like a, there s a simplicity to it.
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SPEAKER_01
When, when you re having an honest interaction with somebody, there s an aesthetic, I would say calm comes over you because you re actually being fully honest and trying to hide yourself, right?
3
SPEAKER_01
So there, so, so it s like this very deep sense of aesthetics.
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SPEAKER_00
And he would trust that judgment that he had deep down.
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SPEAKER_00
Even if the engineering teams are saying this is too difficult, even if 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, and so on and so forth, he just sticks by it.
3
SPEAKER_01
Well, I mean, who makes a phone out of aluminum, right?
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Like nobody else would have done that.
3
SPEAKER_01
Uh, and now of course, if your phone was made out of aluminum, you know, how crude, what kind of caveman would you have to be to have a phone that s made out of plastic?
3
SPEAKER_01
Like, right.
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SPEAKER_01
So like, so it s just this very right.
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SPEAKER_01
And you know, look, it s, it s, there s a thousand different ways to look at this, but one of the things is just like, look, these things are central to your life.
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Like you re with your phone more than you re with anything else.
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Like it s in your, it s going to be in your hand.
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SPEAKER_01
I mean, he, you know, you know, this, he thought very deeply about what it meant for something to be in your hand all day long.
3
SPEAKER_01
Yeah.
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SPEAKER_01
Well, for example, here s an interesting design thing.
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My understanding is he never wanted an iPhone to have a screen larger than you could reach with your thumb one handed.
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And so he was actually opposed to the idea of making the phones larger.
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SPEAKER_01
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 only have one hand available and you might want to be on your phone.
3
SPEAKER_00
Yeah.
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SPEAKER_01
And you re trying to like...
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send a text and your thumb can t reach the send button.
3
SPEAKER_00
Yeah, I mean, there s pros and cons, right?
3
SPEAKER_00
And then there s like folding phones, which I would love to know what he thinks about them.
3
SPEAKER_00
But is there something you could also just linger on?
3
SPEAKER_00
Because he s one of the interesting figures in the history of technology.
3
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What makes him as successful as he was?
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What makes him as interesting as he was?
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SPEAKER_00
What made him so productive and important in the development of technology?
3
SPEAKER_01
he had an integrated worldview.
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SPEAKER_01
So the properly designed device that had the correct functionality, that had the deepest understanding of the user, that was the most beautiful, right?
3
SPEAKER_01
It had to be all of those things, right?
3
SPEAKER_01
He basically would drive to as close to perfect as you could possibly get, right?
3
SPEAKER_01
And I suspect that he never quite thought he ever got there because most great creators are generally dissatisfied.
3
SPEAKER_01
You read
3
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accounts later on and all they can see are the flaws in their creation.
3
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But he got as close to perfect each step of the way as he could possibly get with the constraints of the technology of his time.
3
SPEAKER_01
And then, look, he was sort of famous in the Apple model.
3
SPEAKER_01
This headset that they just came out with, it s like a decade-long
3
SPEAKER_01
project right it s like you and they re just gonna sit there and tune and tune and polish and polish and tune and polish and tune and polish until it is as perfect as anybody could possibly make anything yeah and then this goes to the the way that people describe working with him was which is you know there was a terrifying aspect of working with him which is you know he was you know he was very tough
3
SPEAKER_01
But there was this thing that everybody I ve ever talked to worked for him says that they all say the following, which is he we did the best work of our lives when we worked for him because he set the bar incredibly high.
3
SPEAKER_01
And then he supported us with everything that he could to let us actually do work of that quality.
3
SPEAKER_01
And 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 doing it felt like suffering.
3
SPEAKER_00
Yeah, exactly.
3
SPEAKER_00
What does that teach you about the human condition, huh?
3
SPEAKER_01
Exactly.
3
SPEAKER_01
So the Silicon Valley, I mean, look, he s not, you know, George Patton in the army.
3
SPEAKER_01
Like, you know, there are many examples in other fields, you know, that are like this.
3
SPEAKER_01
Specifically in tech, it s actually, I find it very interesting.
3
SPEAKER_01
There s the Apple way, which is polish, polish, polish, and don t ship until it s as perfect as you can make it.
3
SPEAKER_01
And then there s the sort of the other approach, which is the sort of incremental hacker mentality.
3
SPEAKER_01
which basically says ship early and often and iterate.
3
SPEAKER_01
And one of the things I find really interesting is, I m now 30 years into this, there are very successful companies on both sides of that approach.
3
SPEAKER_01
That is a fundamental
3
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difference, right, in how to operate and how to build and how to create that you have world-class companies operating in both ways.
3
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And I don t think the question of like, which is the superior model is anywhere close to being answered.
3
SPEAKER_01
Like, and my suspicion is the answer is do both.
3
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The answer is you actually want both.
3
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They lead to different outcomes.
3
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Software tends to do better with the iterative approach.
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Hardware tends to do better with the
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you know, sort of wait and make it perfect approach.
3
SPEAKER_01
But again, you can find examples in both directions.
3
SPEAKER_00
So the jury s still out on that one.
3
SPEAKER_00
So back to Mosaic.
3
SPEAKER_00
So what, it was text-based.
3
SPEAKER_00
Tim Berners-Lee
3
SPEAKER_01
Well, there was the web, which was text based, but there were no, I mean, there was like three websites.
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There was like no content.
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There were no users.
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Like it wasn t like a, it wasn t like a catalytic.
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It hadn t been there by the way.
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SPEAKER_01
It was all because it was all texts.
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SPEAKER_01
There were no documents, there are no images, there are no videos.
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SPEAKER_01
There were no, right.
3
SPEAKER_01
So, so it was, it was, and then if, if in the beginning, if you had to be on a next cube, you need to have a next cube both to publish and to consume.
3
SPEAKER_01
So,
3
SPEAKER_00
So there were 6,000 bucks, you said?
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There were limitations.
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Yeah, $6,000 PC.
3
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They did not sell very many.
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But then there was also FTP, and there was Usenet, and there was a dozen other, basically.
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There s Waste, which was an early search thing.
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There was Gopher, which was an early menu-based information retrieval system.
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There were like a dozen different sort of scattered ways that people would get to information on the internet.
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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.
3
SPEAKER_01
And then again, just on the luck side, it so happened that this was right at the moment when graphics, when the GUI sort of actually took off.
3
SPEAKER_01
And we re now also used to the GUI that we think it s been around forever, but it didn t really, you know, the Macintosh brought it out in 85, but they actually didn t sell very many Macs in the 80s.
3
SPEAKER_01
It was not that successful of a product.
3
SPEAKER_01
It really was, you needed Windows 3.0 on PCs and that hit in about 92.
3
SPEAKER_01
And so, and we did Mosaic in 92, 93.
3
SPEAKER_01
So that sort of, it was like right at the moment when you could imagine actually having a graphical user interface, right, at all, much less one to the internet.
3
SPEAKER_00
How old did Windows 3 sell?
3
SPEAKER_00
So was that the really big- That was the big bang.
3
SPEAKER_00
The big operating, graphical operating system?
3
SPEAKER_01
Well, this is the classic, okay, this Microsoft was operating on the other.
3
SPEAKER_01
So Steve, Steve, Apple was running on the polish it until it s perfect.
3
SPEAKER_01
Microsoft famously ran on the other model, which is ship and iterate.
3
SPEAKER_01
And so in the old line in those days was Microsoft, right?
3
SPEAKER_01
It s version three of every Microsoft product.
3
SPEAKER_01
That s the, that s the good one, right?
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SPEAKER_01
And so there, there are, you can, you can find online Windows one, Windows two, nobody used them.
3
SPEAKER_01
Actually, the original windows, the, in the original Microsoft windows, the windows were non-overlapping.
3
SPEAKER_01
And so you had these very small, very low resolution screens, and then you had literally, it just didn t work.
3
SPEAKER_00
It wasn t ready yet.
3
SPEAKER_00
And Windows 95 I think was a pretty big leap also.
3
SPEAKER_00
That was a big leap too.
3
SPEAKER_01
So that was like bang, bang, and then of course Steve, and then in the fullness of time, Steve came back, then the Mac started to take off again, that was the third bang, and then the iPhone was the fourth bang.
3
SPEAKER_00
Such exciting time.
3
SPEAKER_00
And then we were off to the races because nobody could have known what would be created from that.
3
SPEAKER_01
Well, Windows 3.1 or 3.0, Windows 3.0 to the iPhone was only 15 years.
3
SPEAKER_01
Right?
3
SPEAKER_01
Like that ramp was, in retrospect, at the time it felt like it took forever, but in historical terms, like that was a very fast ramp from even a graphical computer at all on your desk to the iPhone.
3
SPEAKER_01
It was 15 years.
3
SPEAKER_00
Did you have a sense of what the internet will be as you re looking through the window of Mosaic?
3
SPEAKER_00
Like, there s just a few web pages for now.
3
SPEAKER_01
So the thing I had early on was I was keeping at the time what there s disputes over what was the first blog, but I had one of them that at least is a is a is a possible at least a runner up in the competition.
3
SPEAKER_01
And it was what was called the what s new page.
3
SPEAKER_01
And it was it was it was a hardwired and distribution unfair advantage.
3
SPEAKER_01
I ve wired put it right in the browser.
3
SPEAKER_01
I put it in the browser and then I put my resume in the browser.
3
SPEAKER_01
Yeah, so it was hilarious.
3
SPEAKER_01
But I was keeping the not many people get to get to do that.
3
SPEAKER_00
So no, the good good call and early days.
3
SPEAKER_00
Yes.
3
SPEAKER_00
It s so interesting.
3
SPEAKER_01
I m looking for my about about.
3
SPEAKER_01
Oh, Mark is looking for a job.
3
SPEAKER_00
Yeah.
3
SPEAKER_01
So so the what s new page, I would literally get up every morning and I would every afternoon.
3
SPEAKER_01
Um, and I would basically, if you wanted to launch a website, you would email me, um, and I would list it on the most new page.
3
SPEAKER_01
And that was how people discovered the new websites as they were coming out.
3
SPEAKER_01
And I remember cause it was like one, it literally went from, it was like one every couple of days to like one every day to like two every day.
3
SPEAKER_00
So that blog was kind of doing the directory thing, so like, what was the homepage?
3
SPEAKER_01
So the homepage was just basically trying to explain even what this thing is that you re looking at, right, the basic, basically basic instructions.
3
SPEAKER_01
But then 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.
3
SPEAKER_01
But like, it was so, it was so mind-blowing at that point, just the basic idea.
3
SPEAKER_01
And it was just, this was like, you know, this was basically the internet, but people could see it for the first time.
3
SPEAKER_01
The basic idea was, look, you know, some, you know, it s like literally, it s like an Indian restaurant in like Bristol, England has like put their menu on the web.
3
SPEAKER_01
And people were like, wow.
3
SPEAKER_01
Whoa.
3
SPEAKER_01
Because like, that s the first restaurant menu on the web.
3
SPEAKER_01
Yeah.
3
SPEAKER_01
And I don t have to be in Bristol.
3
SPEAKER_01
And I don t know if I m ever going to go to Bristol and I don t even like Indian food.
3
SPEAKER_01
And like, wow.
3
SPEAKER_01
Right and it was like that the first web the first streaming video thing was a it was another England some Oxford or something
4
SPEAKER_00
thing.
4
SPEAKER_00
Um, some guy, uh, put, uh, his coffee pot up as the first, uh, streaming, uh, video thing.
4
SPEAKER_00
And he put it on the web cause he literally, it was the coffee pot down the hall and he wanted to see when he needed to go refill it.
4
SPEAKER_00
Um, but there were, you know, there was a point when there were thousands of people like watching that coffee pot because it was the first thing you could watch.
4
SPEAKER_02
Right.
4
SPEAKER_02
Isn t, uh, were you able to kind of infer
4
SPEAKER_00
you know if that indian restaurant could go online yeah then you re like they all will they all will yeah exactly so you felt that yeah yeah yeah now you know look it s still a stretch right it s still a stretch because it s just like okay is that you know you re still in this zone which is like okay is this a nerd thing is this a real person thing yeah um by the way we you know there was a wall of skepticism from the media like they just like everybody was just like yeah this is the crazy this is just like um this is not you know this is not for regular people at that time
4
SPEAKER_00
um and so you you had to think through that and then look it was still 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 like learn what an isp was um you know in those days pcs actually didn t have tcp ip drivers come pre-installed so you had to learn what a tcp ip driver was you had to buy a modem you had to install driver software
4
SPEAKER_00
Um, I have a comedy routine.
4
SPEAKER_00
I do something like 20 minutes long describing all the steps required to actually get on the internet.
4
SPEAKER_00
Um, and so you, you had to, you had to look through these practical, well, and then, and then, uh, and then speed performance, 14, four modems, right?
4
SPEAKER_00
Like it was like watching, you know, glue dry.
4
SPEAKER_00
Um, like, and so you, you had to, you had to, there were basically a sequence of bets that we made where you basically needed to look through that current state of affairs and say, actually, there s going to be so much demand for that.
4
SPEAKER_00
Once people figure this out, there s gonna be so much demand for it, that all of these practical problems are going to get fixed.
4
SPEAKER_02
Yes.
4
SPEAKER_02
Some people say that the anticipation makes the destination that much more exciting.
4
SPEAKER_02
Do you remember progressive JPEGs?
4
SPEAKER_02
Yeah, do I?
4
SPEAKER_02
Do I?
4
SPEAKER_00
So for kids in the audience, right?
4
SPEAKER_00
You used to have to watch an image load.
4
SPEAKER_00
like a line at the time but it turns out there was this thing with jpegs where you could load basically every fourth you could load like every fourth uh line and then and then you could sweep back through again and so you could like render a fuzzy version image up front and then it would like resolve into the detailed one and that was like a big ui breakthrough because it gave you something to watch
4
SPEAKER_00
yeah and uh you know there s applications in various domains for that uh well it s a big fight there s a big fight early on about whether there should be images on the web um and for that reason for like sexualization no not not explicitly that 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
4
SPEAKER_00
if you introduce images, you re basically going to bring in all the trivial stuff.
4
SPEAKER_00
You re going to bring in magazines and, you know, all this crazy stuff that, you know, people, you know, it s going to distract from that.
4
SPEAKER_00
It s going to go take away from being serious, being frivolous.
4
SPEAKER_02
Well, was there any Doomer type arguments about the internet destroying all of human civilization or destroying some fundamental fabric of human civilization?
4
SPEAKER_00
Yeah, so it was those days, it was all around crime and terrorism.
4
SPEAKER_00
So those arguments happened, you know, but there was no sense yet of the internet having like an effect on politics or because that was that was way too far off.
4
SPEAKER_00
But there was an enormous panic at the time around cybercrime.
4
SPEAKER_00
There was like enormous panic that like your credit card number would get stolen and you d use life savings to be drained.
4
SPEAKER_00
And then, you know, criminals were gonna there was, oh, when we started, one of the things we did, one of the Netscape browser was the first widely used piece of consumer software that had strong encryption built in, made it available to ordinary people.
4
SPEAKER_00
And at that time, strong encryption was actually illegal to export out of the US.
4
SPEAKER_00
So we could feel that product in the US, we could not export it because it was classified as ammunition.
4
SPEAKER_00
So the Netscape browser was on a restricted list along with the Tomahawk missile as being something that could not be exported.
4
SPEAKER_00
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.
4
SPEAKER_00
And then we had to spend five years fighting the US government to get them to basically stop trying to do this.
4
SPEAKER_00
um but because the fear the fear was terrorists are going to use encryption right to like plot you know all these all these all these things um and then you know we we responded with well actually we need encryption to be able to secure systems so the terrorists and the criminals can t get into them so that was anyway that was the that was the 1990s fight
4
SPEAKER_02
So can you say something about some of the details of the software engineering challenges required to build these browsers?
4
SPEAKER_02
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.
4
SPEAKER_00
So there was a really key bet that we made at the time, which is very controversial, which was core to core to how it was engineered, which was, are we optimizing for performance or for ease of creation?
4
SPEAKER_00
And in those days, the pressure was very intense to optimize for performance because the network connections were so slow.
4
SPEAKER_00
And also the computers were so slow.
4
SPEAKER_00
Um, and so if you had mentioned the progressive JPEGs, like if, if, if there s an alternate world in which we optimize for performance and it just, you had just a much more pleasant experience right up front.
4
SPEAKER_00
But what we got by not doing that was we got ease of creation.
4
SPEAKER_00
And the way that we got ease of creation was all of the protocols and formats were in text, not in binary.
4
SPEAKER_00
And so HTTP is in text.
4
SPEAKER_00
By the way, and this was an internet tradition, by the way, that we picked up, but we continued it.
4
SPEAKER_00
HTTP is text and HTML is text.
4
SPEAKER_00
And then everything else that followed is text.
4
SPEAKER_00
As a result, and by the way, you can imagine purist engineers saying this is insane.
4
SPEAKER_00
You have very limited bandwidth.
4
SPEAKER_00
Why are you wasting any time sending text?
4
SPEAKER_00
You should be encoding this stuff into binary and it ll be much faster.
4
SPEAKER_00
Of course, the answer is that s correct.
4
SPEAKER_00
But what you get when you make it text is all of a sudden, well, the big breakthrough was the view source function, right?
4
SPEAKER_00
So the fact that you could look at a web page, you could hit view source and you could see the HTML.
4
SPEAKER_00
That was how people learn how to make web pages, right?
4
SPEAKER_02
It s so interesting because the stuff we take for granted now,
4
SPEAKER_02
is, man, that was fundamental to the development of the web, to be able to have HTML just right there.
4
SPEAKER_02
All the ghetto mess that is HTML, all the sort of almost biological messiness of HTML, and then having the browser try to interpret that mess to show something reasonable.
4
SPEAKER_00
Well, and then there was this internet principle that we inherited, which was emit, what was it emit cautiously emit conservatively interpret liberally.
4
SPEAKER_00
So it basically meant if you re in the design principle was if you re if you re creating like a web editor is going to emit HTML, like do it as cleanly as you can.
4
SPEAKER_00
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.
4
SPEAKER_00
Yeah.
4
SPEAKER_00
and so the browser rendering engines to this day have all of this spaghetti code crazy stuff where they can they re resilient to all kinds of crazy html mistakes and so and literally what i always had in my head is like there s an eight-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
4
SPEAKER_02
It s also, I don t often think about this, but programming, C++, C, C++, all those languages, Lisp, the compiled languages, the interpreted languages, Python, Perl, all of that, the brace has to be all correct.
4
SPEAKER_02
Everything has to be perfect.
4
SPEAKER_02
And then you forget, all right.
4
SPEAKER_02
It s systematic and rigorous, let s go there.
4
SPEAKER_02
But you forget that the,
4
SPEAKER_02
the web with JavaScript eventually, and HTML is allowed to be messy in the way, for the first time, messy in the way biological systems could be messy.
4
SPEAKER_02
It s like the only thing computers were allowed to be messy on for the first time.
4
SPEAKER_00
It used to offend me.
4
SPEAKER_00
So I grew up on Unix.
4
SPEAKER_00
I worked on Unix.
4
SPEAKER_00
I was a Unix native for all the way through this period.
4
SPEAKER_00
And so, and it used to drive me bananas when it would do the segmentation fault in the core dump file.
4
SPEAKER_00
It s like literally there s like an error in the code.
4
SPEAKER_00
The math is off by one and it core dumps.
4
SPEAKER_00
And I m in the core dump trying to analyze it and trying to reconstruct.
4
SPEAKER_00
And I m just like, this is ridiculous.
4
SPEAKER_00
Like the computer ought to be smart enough to be able to know that if it s off by one, okay, fine.
4
SPEAKER_00
And it keeps running.
4
SPEAKER_00
And I would go ask all the experts, like, why can t it just keep running?
4
SPEAKER_00
And they d explain to me, well, because all the downstream repercussions and blah, blah.
4
SPEAKER_00
And I m like, this still like, you know, we re forcing the human creator to live, to your point, in this hyper literal, literal world of perfection.
4
SPEAKER_00
And I was just like, that s just, that s just bad.
4
SPEAKER_00
And by the way, you know, what happens with that, of course, is what happened with coding at that point, which is you get a high priesthood.
4
SPEAKER_00
You know, there s a small number of people who are really good at doing exactly that.
4
SPEAKER_00
Most people can t and most people are excluded from it.
4
SPEAKER_00
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.
4
SPEAKER_00
And this would drive the purists absolutely crazy.
4
SPEAKER_00
Like, I got attacked on this, like, a lot.
4
SPEAKER_00
Because, yeah, I mean, like, every time, you know, all the purists who are, like, into all this, like, markup language stuff and formats and codes and all this stuff, they would be like, you know, you can t, you re encouraging bad behavior because
4
SPEAKER_02
Oh, so they wanted the browser to give you a segfault error any time there was a... Yeah, yeah, they wanted it to be a copy, right?
4
SPEAKER_00
They wanted that.
4
SPEAKER_00
Yeah, that was a very... And any properly trained and credentialed engineer would be like, that s not how you build these systems.
4
SPEAKER_02
That s such a bold move to say, no, it doesn t have to be.
4
SPEAKER_00
Now, like I said, the good news for me is the internet kind of had that tradition already.
4
SPEAKER_00
But having said that, we pushed it.
4
SPEAKER_00
We pushed it way out.
4
SPEAKER_00
But the other thing we did, going back to the performance thing, was we gave up a lot of performance.
4
SPEAKER_00
That initial experience for the first few years was pretty painful.
4
SPEAKER_00
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.
4
SPEAKER_00
Because the question was, OK, how do you get the phone companies, which are not famous in those days for doing new things,
4
SPEAKER_00
at huge cost for like speculative reasons, like how do you get them to build up broadband, you know, spend billions of dollars doing that.
4
SPEAKER_00
And, you know, you could go meet with them and try to talk them into it.
4
SPEAKER_00
Or you could just have a thing where it s just very clear that it s going to be the people love that s going to be better if it s faster.
4
SPEAKER_00
And so that there was a period there and this was this was fraught with some peril, but there was a period there where it s like 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.
4
SPEAKER_02
So you have to figure out how to display this text, HTML text.
4
SPEAKER_02
So the blue links and the purple links, and there s no standards.
4
SPEAKER_02
Is there standards at that time?
4
SPEAKER_02
There really still isn t.
4
SPEAKER_02
Well, there s implied standards, right?
4
SPEAKER_02
And there s all these kinds of new features that are being added, like CSS, what kind of stuff a browser should be able to support, features within languages, within JavaScript, and so on.
4
SPEAKER_02
But you re setting standards on the fly yourself.
4
SPEAKER_00
Well, to this day, if you if you create a web page that has no CSS style sheet, the browser will render it however it wants to.
4
SPEAKER_00
Right.
4
SPEAKER_00
So this was one of the things there was this idea, this idea at the time and how these systems were built, which is separation of content from format or separation of content from appearance.
4
SPEAKER_00
And that s still people don t really use that anymore because everybody wants to determine how things look.
4
SPEAKER_00
And so they use CSS.
4
SPEAKER_00
But it s still in there that you can just let the browser do all the work.
4
SPEAKER_02
I still like the, like a 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.
4
SPEAKER_00
Well, that s one of the things that s fun about chat, you know, about chat GPT.
4
SPEAKER_02
It s like back to the basics, back to just text.
4
SPEAKER_00
Yeah.
4
SPEAKER_00
Right.
4
SPEAKER_00
And you know, there is this pattern in human creativity and media where you end up back at text.
4
SPEAKER_00
And I think there s, you know, there s something powerful in there.
4
SPEAKER_02
Is there some other stuff you remember, like the purple links?
4
SPEAKER_02
There were some interesting design decisions to kind of come up that we have today or we don t have today, that were temporary.
4
SPEAKER_00
So I made the background gray.
4
SPEAKER_00
I hated reading text on white backgrounds, and so I made the background gray.
4
SPEAKER_02
Do you regret this?
4
SPEAKER_00
No, no, no.
4
SPEAKER_00
That decision, I think, has been reversed.
4
SPEAKER_00
But now I m happy, though, because now dark mode is the thing.
4
SPEAKER_02
So it wasn t about gray, it was just you didn t want a white background.
4
SPEAKER_00
Strained my eyes.
4
SPEAKER_00
Strained your eyes.
4
SPEAKER_02
Interesting.
4
SPEAKER_02
And then there s a bunch of other decisions.
4
SPEAKER_02
I m sure there s an interesting history of the development of HTML and CSS and all those.
4
SPEAKER_02
interface in JavaScript, and there s this whole Java applet thing.
4
SPEAKER_00
Well, the big one, probably JavaScript.
4
SPEAKER_00
CSS was after me, so I didn t know it was not me, but JavaScript was the big, JavaScript maybe was the biggest of the whole thing.
4
SPEAKER_00
That was us.
4
SPEAKER_00
And that was basically a bet.
4
SPEAKER_00
It was a bet on two things.
4
SPEAKER_00
One is that the world wanted a new front-end scripting language.
4
SPEAKER_00
And then the other was we thought at the time the world wanted a new back-end scripting language.
4
SPEAKER_00
So JavaScript was designed from the beginning to be both front-end and back-end.
4
SPEAKER_00
And then it failed as a back-end scripting language, and Java won for a long time, and then Python, Perl, and other things, PHP, and Ruby.
4
SPEAKER_00
But now JavaScript is back, and so... I wonder if everything in the end will run on JavaScript.
4
SPEAKER_00
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.
4
SPEAKER_02
If you re interested to learn more about Brendan Eich, he s been on this podcast previously.
4
SPEAKER_00
So he wrote JavaScript over a summer, and I think it is fair to say now that it s the most widely used language in the world, and it seems to only be gaining in its range of adoption.
4
SPEAKER_02
You know, in the software world, there s quite a few stories of somebody over a weekend or over a week or over a summer
4
SPEAKER_02
writing some of the most impactful revolutionary pieces of software ever.
4
SPEAKER_02
That should be inspiring, yes?
4
SPEAKER_00
Very inspiring.
4
SPEAKER_00
I ll give you another one.
4
SPEAKER_00
SSL.
4
SPEAKER_00
So SSL was the security protocol.
4
SPEAKER_00
That was us.
4
SPEAKER_00
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.
4
SPEAKER_00
That was a guy named Kip Hickman who wrote that over a summer.
4
SPEAKER_00
One guy.
4
SPEAKER_00
Um, and then look today, sitting here today, like the transformer, like at Google was a small handful of people.
4
SPEAKER_00
And then, you know, the number of people who have did like the core work on GPT, it s not that many people.
4
SPEAKER_00
It s a pretty small handful of people.
4
SPEAKER_00
Um, and so yeah, the, the pattern in software repeatedly over a very long time has been, it s, it s a,
4
SPEAKER_00
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.
4
SPEAKER_00
If you need the third pizza, you have too many people.
4
SPEAKER_00
And I think it s actually the one pizza rule.
4
SPEAKER_00
For the really creative work, I think it s two people, three people.
4
SPEAKER_02
Well, you see that with certain open source projects, so much is done by one or two people.
4
SPEAKER_02
It s so incredible.
4
SPEAKER_02
And that s why you see, that gives me so much hope about the open source movement in this new age of AI.
4
SPEAKER_02
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.
4
SPEAKER_02
It is potentially very dangerous, and we ll talk about that, but it s also,
4
SPEAKER_02
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.
4
SPEAKER_02
Because it s not putting these models into the centralized control of big corporations, the government, and so on.
4
SPEAKER_02
It s putting it in the hands of a teenage kid with a dream in his eyes.
4
SPEAKER_02
I don t know.
4
SPEAKER_02
That s beautiful.
4
SPEAKER_00
And look, this stuff, AI ought to make the individual coder obviously far more productive, right, by like, you know, a thousand X or something.
4
SPEAKER_00
And so you ought to open source, like not just the future of open source AI, but the future of open source everything.
4
SPEAKER_00
We ought to have a world now of super coders, right, who are building things as open source with one or two people that were inconceivable, you know, five years ago.
4
SPEAKER_00
the level of kind of hyper productivity we re gonna get out of our best and brightest, I think is gonna go way up.
4
SPEAKER_02
It s gonna be interesting.
4
SPEAKER_02
We ll talk about it, but let s just linger a little bit on Netscape.
4
SPEAKER_02
Netscape was acquired in 1999 for 4.3 billion by AOL.
4
SPEAKER_02
What was that like?
4
SPEAKER_02
What were some memorable aspects of that?
4
SPEAKER_00
Well, that was the height of the dot-com boom-bubble-bust.
4
SPEAKER_00
I mean, that was the frenzy.
4
SPEAKER_00
If you watch Succession, that was like what they did in the fourth season with Gojo and the merger.
4
SPEAKER_00
So it was like the height of one of those kind of dynamics.
4
SPEAKER_02
Would you recommend Succession, by the way?
4
SPEAKER_02
I m more of a Yellowstone guy.
4
SPEAKER_00
very American.
4
SPEAKER_00
I m very proud of you.
4
SPEAKER_02
I just talked to Matthew McConaughey and I m full-on Texan at this point.
4
SPEAKER_02
Good.
4
SPEAKER_00
I heartily approve.
4
SPEAKER_02
And he will be doing the sequel to Yellowstone.
4
SPEAKER_02
Very exciting.
4
SPEAKER_02
Anyway, so that s a rude interruption by me by way of succession.
4
SPEAKER_00
Uh, so that was at the height of the deal making and money and just the for flying and like craziness.
4
SPEAKER_00
And so, yeah, it was just one of those.
4
SPEAKER_00
It was just like, I mean, there s the entire Netscape thing from start to finish was four years, um, which was like for, for one of these companies, it s just like incredibly fast.
4
SPEAKER_00
You know, we went public 18 months after we were founded, which virtually never happens.
4
SPEAKER_00
So it was just this incredibly fast kind of meteor streaking across the sky.
4
SPEAKER_00
And then, of course, it was this.
4
SPEAKER_00
And then there was just this explosion, right, that happened.
4
SPEAKER_00
Because then it was almost immediately followed by the dot-com crash.
4
SPEAKER_00
It was then followed by AOL buying Time Warner, which, again, the Succession guys kind of play with that, which turned out to be a disastrous deal.
4
SPEAKER_00
One of the famous, you know, kind of disastrous and
5
SPEAKER_00
business history.
5
SPEAKER_00
Um, and then, um, and then, you know, what became an internet depression on the other side of that, but then in that depression in the two thousands was the beginning of broadband and smartphones and web 2.0, right.
5
SPEAKER_00
And then social media and search and every SAS and everything that came out of that.
5
SPEAKER_01
So what did you learn from just the acquisition?
5
SPEAKER_01
I mean, this is so much money.
5
SPEAKER_01
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.
5
SPEAKER_01
I mean, I m sure the ideas of investment were starting to get born there.
5
SPEAKER_00
Yes, so let me lay it out.
5
SPEAKER_00
So here s the thing, I don t know if I figured it out then, but I figured it out later, which is software is a technology that, it s like the concept of the philosopher s stone.
5
SPEAKER_00
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.
5
SPEAKER_00
Software is our modern Philosopher s Stone, and in economic terms, it transmutes labor into capital.
5
SPEAKER_00
which is like a super interesting thing.
5
SPEAKER_00
And by the way, like Karl Marx is rolling over in his grave right now, because of course that s a complete refutation of his entire theory.
5
SPEAKER_00
Transfused labor into capital, which is as follows, is 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.
5
SPEAKER_00
That s amazing.
5
SPEAKER_00
It s literally creating value out of thin air, out of purely human thought.
5
SPEAKER_00
There are many things that make software magical and special, but that s the economics of it.
5
SPEAKER_00
I wonder what Marx would have thought about that.
5
SPEAKER_00
Oh, he would have completely broke his brain because, of course, the whole thing was—he
5
SPEAKER_00
That kind of technology is inconceivable when he was alive.
5
SPEAKER_00
It was all industrial era stuff.
5
SPEAKER_00
And so any kind of machinery necessarily involved huge amounts of capital, and then labor was on the receiving end of the abuse.
5
SPEAKER_00
But a software engineer is somebody who basically transmutes his own labor into an actual capital asset, creates permanent value.
5
SPEAKER_00
Well, in fact, it s actually very inspiring.
5
SPEAKER_00
That s actually more true today than before.
5
SPEAKER_00
So when I was doing software, the assumption was all new software basically has a sort of a parabolic sort of lifecycle, right?
5
SPEAKER_00
So you ship the thing, people buy it.
5
SPEAKER_00
At some point, everybody who wants it has bought it, and then it becomes obsolete, and it s like bananas.
5
SPEAKER_00
Nobody buys old software.
5
SPEAKER_00
Um, these days, um, Minecraft, um, Mathematica, you know, Facebook, Google, um, you have the software assets that are, you know, have been around for 30 years that are gaining in value every year, right?
5
SPEAKER_00
And they re just there being the World of Warcraft, right?
5
SPEAKER_00
Salesforce dot com, like they re being every single year, they re being polished and polished and polished and polished.
5
SPEAKER_00
They re getting better and better, more powerful, more powerful, more valuable, more valuable.
5
SPEAKER_00
So we ve entered this era where you can actually have these things that actually build out over decades, which, by the way, is what s happening right now with like GPT.
5
SPEAKER_00
Um, and so, um, now, and this is why, you know, there, there, there is always, you know, sort of a constant investment frenzy around software is because, you know, look, when you start one of these things, it doesn t always succeed, but when it does now, you might be building an asset that builds value for, you know, four or five, six decades to come.
5
SPEAKER_00
Um, you know, if you have a team of people who are, have the level of devotion required to keep making it better.
5
SPEAKER_00
And then the fact that, of course, everybody s online, you know, there s five billion people that are a click away from any new piece of software.
5
SPEAKER_00
So the potential market size for any of these things is, you know, nearly infinite.
5
SPEAKER_01
It must have been surreal back then, though.
5
SPEAKER_00
Yeah, yeah.
5
SPEAKER_00
This was all brand new, right?
5
SPEAKER_00
Yeah.
5
SPEAKER_00
Back then, this was all brand new.
5
SPEAKER_00
These were all, you know, brand new.
5
SPEAKER_00
Had you rolled out that theory in even 1999, people would have thought you were smoking crack.
5
SPEAKER_00
So that s emerged over time.
5
SPEAKER_01
Well, let s now turn back into the future.
5
SPEAKER_01
You wrote the essay, Why AI Will Save the World.
5
SPEAKER_01
Let s start at the very high level.
5
SPEAKER_01
What s the main thesis of the essay?
5
SPEAKER_00
Yeah, so the main thesis on the essay is that what we re dealing with here is intelligence.
5
SPEAKER_00
And it s really important to kind of talk about the sort of very nature of what intelligence is.
5
SPEAKER_00
And fortunately, we have a predecessor to machine intelligence, which is human intelligence.
5
SPEAKER_00
And we ve got observations and theories over thousands of years for what intelligence is in the hands of humans.
5
SPEAKER_00
And what intelligence is, right, I mean, what it literally is, is the way to capture, process, analyze, synthesize information, solve problems.
5
SPEAKER_00
Um, but the observation of, of, of intelligence in human hands is that intelligence quite literally makes everything better.
5
SPEAKER_00
Um, and what I mean by that is every kind of outcome of like human quality of life, whether it s education outcomes or success of your Children.
5
SPEAKER_00
career success or health or lifetime satisfaction.
5
SPEAKER_00
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.
5
SPEAKER_01
Smarter people have better outcomes in almost, as you write, in almost every domain of activity.
5
SPEAKER_01
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.
5
SPEAKER_01
more depressing conversations I ve had.
5
SPEAKER_01
And I don t know why it s depressing.
5
SPEAKER_01
I have to really think through why it s depressing.
5
SPEAKER_01
But on IQ and the G factor, and that that s something in large part is genetic.
5
SPEAKER_01
And it correlates so much with all of these things and success in life.
5
SPEAKER_01
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.
5
SPEAKER_00
But what if you could?
5
SPEAKER_01
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.
5
SPEAKER_01
And now, let s look at artificial intelligence.
5
SPEAKER_01
And,
5
SPEAKER_01
if that s a way to increase some human intelligence, then it s only going to make a better life.
5
SPEAKER_01
That s the argument.
5
SPEAKER_00
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.
5
SPEAKER_00
But there s also just at the individual level, like what if every person has a machine, you know, and it s the concept of augment, Doug Engelbart s concept of augmentation.
5
SPEAKER_00
Um, you know, what if everybody has a, an assistant and the assistant is, you know, 140 IQ, um, and you happen to be 110 IQ, um, and you ve got, you know, 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.
5
SPEAKER_00
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.
5
SPEAKER_00
right?
5
SPEAKER_00
When I figure out how to prepare for a job interview, like any of these things, like it will help you do it.
5
SPEAKER_00
And it will therefore, the combination will effectively be, you know, effectively raise your race because it will effectively raise your IQ will therefore raise the odds of successful life outcomes in all these areas.
5
SPEAKER_01
So people below the hypothetical 140 IQ, it ll pull them off towards 140 IQ.
5
SPEAKER_01
Yeah.
5
SPEAKER_00
Yeah.
5
SPEAKER_00
And then, of course, you know, people at 140 IQ will be able to have a peer, right, to be able to communicate, which is great.
5
SPEAKER_00
And then people above 140 IQ will have an assistant that they can farm things out to.
5
SPEAKER_00
And then, look, God willing, you know, at some point these things go from, future versions go from 140 IQ equivalent to 150 to 160 to 180, right?
5
SPEAKER_00
Like Einstein was estimated to be on the order of 160.
5
SPEAKER_00
You know, so when we get, you know, 160 AI, like we ll be, you know, one assumes creating Einstein level breakthroughs in physics and
5
SPEAKER_00
And then at 180, we ll be, you know, curing cancer and developing warp drive and doing all kinds of stuff.
5
SPEAKER_00
And so it is quite possibly the case, this is the most important thing that s ever happened, the best thing that s ever happened.
5
SPEAKER_00
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.
5
SPEAKER_01
Can you still imagine the case that human plus AI is not always better than human for the individual?
5
SPEAKER_00
You may have noticed that there s a lot of smart assholes running around.
5
SPEAKER_00
Sure, yes.
5
SPEAKER_00
Right?
5
SPEAKER_00
And so, like, smart, there are certain people where they get smarter, you know, they get to be more arrogant, right?
5
SPEAKER_00
So, you know, there s one huge flaw.
5
SPEAKER_01
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 assholes.
5
SPEAKER_01
One would hope.
5
SPEAKER_01
Or it could make assholes more assholes.
5
SPEAKER_01
I mean, that s for psychology to study.
5
SPEAKER_00
Yeah, exactly.
5
SPEAKER_00
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 sort of crazy beliefs and all that.
5
SPEAKER_00
There s a theory in psychology, which is actually smart people.
5
SPEAKER_00
So for sure, people who aren t as smart are very susceptible to hoaxes and conspiracy theories.
5
SPEAKER_00
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, right?
5
SPEAKER_00
You become very, very good at assembling whatever theories and frameworks and pieces of data and graphs and charts you need to validate whatever crazy ideas got in your head.
5
SPEAKER_00
And so you re susceptible in a different way.
5
SPEAKER_01
We re all sheep, but different colored sheep.
5
SPEAKER_00
Some sheep are better at justifying it, right?
5
SPEAKER_00
And those are the, you know, those are the smart sheep, right?
5
SPEAKER_00
So yeah, look, like, I would say this, look, like, there are no panacea, I am not, I am not a utopian, there are no panaceas in life.
5
SPEAKER_00
There are no, like, you know, I don t believe there are, like, pure positives.
5
SPEAKER_00
I m not a transcendental kind of person like that.
5
SPEAKER_00
you know, so yeah, there are going to be issues.
5
SPEAKER_00
Um, uh, and, um, and, you know, look, smart people, maybe you could say about smart people is they are more likely to get themselves in situations that are, you know, beyond their grasp, you know, because they re just more confident in their ability to deal with complexity and their, their eyes become bigger.
5
SPEAKER_00
Their, their cognitive eyes become bigger than their stomach, you know?
5
SPEAKER_00
So yeah, you could argue those eight different ways.
5
SPEAKER_00
Nevertheless, on net, right, clearly, overwhelmingly, again, if you just extrapolate from what we know about human intelligence, you re, you re improving so many aspects of life.
5
SPEAKER_00
If you re upgrading intelligence,
5
SPEAKER_01
So there ll be assistance at all stages of life.
5
SPEAKER_01
So when you re younger, there s for education, all that kind of stuff, or mentorship, all of this.
5
SPEAKER_01
And later on as you re doing work and you ve developed a skill and you re having a profession, you ll have an assistant that helps you excel at that profession.
5
SPEAKER_01
So at all stages of life.
5
SPEAKER_00
Yeah.
5
SPEAKER_00
I mean, look, the theory is augmentations.
5
SPEAKER_00
This is the Deg-Engelbart s term.
5
SPEAKER_00
Deg-Engelbart made this observation many, many decades ago that, you know, basically it s like you can have this oppositional frame of technology where it s like us versus the machines.
5
SPEAKER_00
But what you really do is you use technology to augment human capabilities.
5
SPEAKER_00
And by the way, that s how actually the economy develops.
5
SPEAKER_00
We can talk about the economic side of this, but that s actually how the economy grows, is through technology augmenting human potential.
5
SPEAKER_00
And so yeah, and then you basically have a proxy or a sort of prosthetic.
5
SPEAKER_00
So like you ve got glasses, you ve got a wristwatch, you ve got shoes, you ve got these things, you ve got a personal computer, you ve got a word processor, you ve got Mathematica, you ve got Google.
5
SPEAKER_00
Viewed through that lens, AI is the latest in a long series of basically augmentation methods to be able to raise human capabilities.
5
SPEAKER_00
It s just this one is the most powerful one of all because this is the one that goes directly to what they call fluid intelligence.
5
SPEAKER_00
which is like you.
5
SPEAKER_01
Well, there s two categories of folks that you outline that worry about or highlight the risks of AI, and you highlight a bunch of different risks.
5
SPEAKER_01
I would love to go through those risks and just discuss them, brainstorm which ones are serious and which ones are less serious.
5
SPEAKER_01
But first, the Baptists and the bootleggers.
5
SPEAKER_01
What are these two interesting groups of folks who worry about the effect of AI on human civilization?
5
SPEAKER_00
So the Baptist and the bootleggers is a metaphor from economics from what s called development economics And it s this observation that when you get social reform movements in a society you tend to get two sets of people showing up arguing for the social reform and
5
SPEAKER_00
Um, and the, the, the term baptism bootleggers comes from the American experience with alcohol prohibition.
5
SPEAKER_00
Um, and so in the 1900s, 1910s, um, there was this movement that was very passionate at the time, which basically said alcohol is evil, uh, and it s destroying society.
5
SPEAKER_00
Um, by the way, there was a lot of evidence to support this.
5
SPEAKER_00
Um, there was very high rates of, uh, very high correlations then, by the way, and now, uh, between rates of physical violence and alcohol use, um, almost all violent crimes have either the perpetrator or the victim are both drunk.
5
SPEAKER_00
almost all if you see this actually in the work almost all sexual harassment cases in the workplace it s like at a company party and somebody s drunk like it s it s amazing how often alcohol actually correlates to actually just dysfunction it leads to domestic abuse um and so forth child abuse and so you had this group of people who were like okay this this is bad stuff and we should outlaw it and and those were quite literally baptists those were super committed you know hardcore christian activists in a lot of cases
5
SPEAKER_00
There was this woman whose name was Carrie Nation, who was this older woman who had been in this, you know, I don t know, disastrous marriage or something.
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SPEAKER_00
And her husband had been abusive and drunk all the time.
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SPEAKER_00
And she became the icon of the Baptist prohibitionists.
5
SPEAKER_00
And she was legendary in that era for carrying an axe and doing, you know, completely on her own, doing raids of saloons and like taking her axe to all the bottles and tags in the back.
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SPEAKER_00
And so.
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SPEAKER_01
So a true believer.
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SPEAKER_00
An absolute true believer with absolutely the purest of intentions.
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SPEAKER_00
And again,
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SPEAKER_00
There s a very important thing here, which is you could look at this cynically, and you could say the Baptists are delusional extremists, but you could also say, look, they re right.
5
SPEAKER_00
She had a point.
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SPEAKER_00
She wasn t wrong about a lot of what she said.
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SPEAKER_00
But it turns out, the way the story goes, is it turns out that there were another set of people who very badly wanted to outlaw alcohol in those days, and those were the bootleggers, which was organized crime that stood to make a huge amount of money if legal alcohol sales were banned.
5
SPEAKER_00
And this was, in fact, the way the history goes is this was actually the beginning of organized crime in the U.S.
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SPEAKER_00
This was the big economic opportunity that opened that up.
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SPEAKER_00
And so they went in together and they didn t go in together like the Baptist did not even necessarily know about the bootleggers because they were on their moral crusade.
5
SPEAKER_00
The bootleggers certainly knew about the Baptist.
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SPEAKER_00
And they were like, wow, this is these people are like the great front people for like, you know, it s good PR shenanigans in the background.
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SPEAKER_00
Yeah.
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SPEAKER_00
And they got the Volstead Act passed.
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SPEAKER_00
right?
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SPEAKER_00
And they did, in fact, ban alcohol in the U. S. And you ll notice what happened, which is people kept drinking.
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SPEAKER_00
It didn t work.
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SPEAKER_00
People kept drinking.
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SPEAKER_00
Um, that bootleggers made a tremendous amount of money.
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SPEAKER_00
Um, and then over time it became clear that it made no sense to make it illegal and it was causing more problems.
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SPEAKER_00
And so then it was revoked.
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SPEAKER_00
And here we sit with legal alcohol 100 years later with all the same problems.
5
SPEAKER_00
Um, and you know, the whole thing was this like giant misadventure.
5
SPEAKER_00
Uh, the Baptist got taken advantage of by the bootleggers and the bootleggers got what they wanted and, and that was that.
5
SPEAKER_01
The same two categories of folks are now sort of suggesting that the development of artificial intelligence should be regulated.
5
100%.
5
SPEAKER_00
Yeah, it s the same pattern.
5
SPEAKER_00
The economists will tell you it s the same pattern every time.
5
SPEAKER_00
Like, this is what happened with nuclear power.
5
SPEAKER_00
This is what happened, which is another interesting one.
5
SPEAKER_00
But like, yeah, this happens dozens and dozens of times.
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SPEAKER_00
throughout the last hundred years.
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SPEAKER_00
And this is what s happening now.
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SPEAKER_01
And you write that it isn t sufficient to simply identify the actors and impugn their motives.
5
SPEAKER_01
We should consider the arguments of both the Baptists and the bootleggers on their merits.
5
SPEAKER_01
So let s do just that.
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SPEAKER_01
Risk number one.
5
SPEAKER_01
Will AI kill us all?
5
SPEAKER_01
Yes.
5
SPEAKER_01
So
5
SPEAKER_01
What do you think about this one?
5
SPEAKER_01
What do you think is the core argument here?
5
SPEAKER_01
That the development of AGI, perhaps better said, will destroy human civilization.
5
SPEAKER_00
Well, first of all, you just did a sleight of hand because we went from talking about AI to AGI.
5
SPEAKER_01
Is there a fundamental difference there?
5
SPEAKER_01
I don t know.
5
SPEAKER_01
What s AGI?
5
SPEAKER_01
What s AI?
5
SPEAKER_01
What s intelligence?
5
SPEAKER_00
Well, I know what AI is.
5
SPEAKER_00
AI is machine learning.
5
SPEAKER_00
What s AGI?
5
SPEAKER_01
I think we don t know what the bottom of the well of machine learning is or what the ceiling is.
5
SPEAKER_01
Because just to call something machine learning or just to call something statistics or just to call it math or computation doesn t mean, you know, nuclear weapons are just physics.
5
SPEAKER_01
To me, it s very interesting and surprising how far machine learning has taken us.
5
SPEAKER_00
No, but we knew that nuclear physics would lead to weapons.
5
SPEAKER_00
That s why the scientists of that era were always in this huge dispute about building the weapons.
5
SPEAKER_00
This is different.
5
SPEAKER_01
Where does machine learning lead?
5
SPEAKER_01
Do we know?
5
SPEAKER_00
We don t know.
5
SPEAKER_00
But this is my point.
5
SPEAKER_00
It s different.
5
SPEAKER_00
We actually don t know.
5
SPEAKER_00
And this is where the sleight of hand kicks in.
5
SPEAKER_00
This is where it goes from being a scientific topic to being a religious topic.
5
SPEAKER_00
And that s why I specifically called out that, because that s what happens.
5
SPEAKER_00
They do the vocabulary shift.
5
SPEAKER_00
All of a sudden, you re talking about something totally that s not actually real.
5
SPEAKER_01
Well, then maybe you could also, as part of that, define the Western tradition of millenarianism.
5
SPEAKER_01
Yes.
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SPEAKER_00
End of the world.
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SPEAKER_00
Apocalypse.
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SPEAKER_00
What is it?
5
SPEAKER_00
Apocalypse cults.
5
SPEAKER_01
Apocalypse cults.
5
SPEAKER_00
Well, so we live in, we of course live in a Judeo-Christian, but primarily Christian, kind of saturated, you know, kind of Christian, post-Christian, secularized Christian, you know, kind of world in the West.
5
SPEAKER_00
And of course, core to Christianity is the idea of the second coming and
6
SPEAKER_01
the revelations, and Jesus returning, and the thousand-year utopia on earth, and then the rapture, and all that stuff.
6
SPEAKER_01
We collectively, as a society, we don t necessarily take all that fully seriously now.
6
SPEAKER_01
So what we do is we create our secularized versions of that.
6
SPEAKER_01
We keep looking for utopia.
6
SPEAKER_01
We keep looking for basically the end of the world.
6
SPEAKER_01
And so what you see over decades is basically a pattern of these sort of, this is what cults are, this is how cults form as they form around some theory of the end of the world.
6
SPEAKER_01
And so the People s Temple cult, the Manson cult, the Heaven s Gate cult, the David Koresh cult, you know, what they re all organized around is like there s going to be this thing that s going to happen that s going to basically bring civilization crashing down.
6
SPEAKER_01
And then we have this special elite group of people who are going to see it coming and prepare for it.
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SPEAKER_01
And then they re the people who are either going to stop it or are failing stopping it.
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SPEAKER_01
They re going to be the people who survive to the other side and ultimately get credit for having been right.
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SPEAKER_03
Why is that so compelling, do you think?
6
SPEAKER_01
Because it satisfies this very deep need we have for transcendence and meaning that got stripped away when we became secular.
6
SPEAKER_02
Yeah, but why does transcendence involve the destruction of human civilization?
6
SPEAKER_01
It s like a very deep psychological thing because it s like, how plausible is it that we live in a world where everything s just kind of all right?
6
SPEAKER_01
How exciting is that?
6
SPEAKER_03
We want more than that.
6
SPEAKER_03
But that s the deep question I m asking.
6
SPEAKER_03
Why is it not exciting to live in a world where everything s just all right?
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SPEAKER_03
I think most of the animal kingdom would be so happy with just all right.
6
SPEAKER_03
Because that means survival.
6
SPEAKER_03
Maybe that s what it is.
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SPEAKER_03
Why are we conjuring up things to worry about?
6
SPEAKER_01
So C.S.
6
SPEAKER_01
Lewis called it the god-shaped hole.
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SPEAKER_01
So there s a god-shaped hole in the human experience, consciousness, soul, whatever you want to call it, where there s got to be something that s bigger than all this.
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SPEAKER_01
There s got to be something transcendent.
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SPEAKER_01
There s got to be something that is bigger, right?
6
SPEAKER_01
Bigger.
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SPEAKER_01
Bigger purpose, a bigger meaning.
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SPEAKER_01
And so we have run the experiment of, you know, we re just going to use science and rationality and kind of, you know, everything s just going to kind of be as it appears.
6
SPEAKER_01
And a large number of people have found that very deeply wanting and have constructed narratives.
6
SPEAKER_01
And this is the story of the 20th century, right?
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SPEAKER_01
Communism, right, was one of those.
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SPEAKER_01
Communism was a form of this.
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SPEAKER_01
Nazism was a form of this.
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SPEAKER_01
You know, some people, you know, you can see movements like this playing out all over the world right now.
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SPEAKER_03
So you construct a kind of devil, a kind of source of evil, and we re going to transcend beyond it.
6
SPEAKER_01
Yeah.
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SPEAKER_01
And the millenarian, the millenarians kind of, when you see a millenarian cult, they put a really specific point on it, which is end of the world, right?
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SPEAKER_01
There is some change coming.
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SPEAKER_01
And that change that s coming is so profound and so important that it s either going to lead to utopia or hell on earth.
6
SPEAKER_01
Right.
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SPEAKER_01
And it is going to.
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SPEAKER_01
And then, you know, it s like, what if you actually knew that that was going to happen?
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SPEAKER_01
Right.
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SPEAKER_01
What would you what what would you do?
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SPEAKER_01
Right.
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SPEAKER_01
How would you prepare yourself for it?
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SPEAKER_01
How would you come together with a group of like minded people?
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SPEAKER_01
Right.
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SPEAKER_01
How would you what would you do?
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SPEAKER_01
Would you plan like caches of weapons in the woods?
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SPEAKER_01
Would you like, you know, I don t know, create underground underground bunkers.
6
SPEAKER_01
Would you, you know, spend your life trying to figure out a way to avoid having it happen?
6
SPEAKER_03
Yeah, that s a really compelling, exciting idea to have a club over, to have a little bit of travel, like you get together on a Saturday night and drink some beers and talk about the end of the world and how you are the only ones who have figured it out.
6
SPEAKER_01
And then and then once you lock in on that, like, how can you do anything else with your life?
6
SPEAKER_01
Like, this is obviously the thing that you have to do.
6
SPEAKER_01
And then and then there s a psychological effect you alluded to.
6
SPEAKER_01
There s a psychological effect.
6
SPEAKER_01
If you take a set of true believers and you leave them to themselves, they get more radical because they self radicalize each other.
6
SPEAKER_03
That said, yes, it doesn t mean they re not sometimes right.
6
SPEAKER_01
Yeah, the end of the world might be.
6
SPEAKER_01
Yes, correct.
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SPEAKER_01
Like they might be right.
6
SPEAKER_03
Yeah.
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SPEAKER_03
But like we have some pamphlets for you.
6
SPEAKER_03
I mean, we ll talk about nuclear weapons, because you have a really interesting little moment that I learned about in your essay, but you know, sometimes it could be right.
6
SPEAKER_03
Because we re still, you re developing more and more powerful technologies in this case, and we don t know what the impact it will have on human civilization.
6
SPEAKER_03
Well, we can highlight all the different predictions about how it will be positive, but the risks are there, and you discussed some of them.
6
SPEAKER_01
Well, the steel man, the steel man is the steel man.
6
SPEAKER_01
Well, actually the steel man and his reputation are the same, which is you can t predict what s going to happen.
6
SPEAKER_01
Right.
6
SPEAKER_01
You, right.
6
SPEAKER_01
You can t rule out that this will not end everything.
6
SPEAKER_01
Right.
6
SPEAKER_01
But the response to that is you have just made a completely non-scientific claim.
6
SPEAKER_01
You ve made a religious claim, not a scientific claim.
6
SPEAKER_03
How does it get disproven?
6
SPEAKER_01
There is.
6
SPEAKER_01
And there s no, by definition with these kinds of claims, there s no way to disprove them.
6
SPEAKER_01
Yeah.
6
SPEAKER_01
Right?
6
SPEAKER_01
And so there s no, you just go right on the list.
6
SPEAKER_01
There s no hypothesis.
6
SPEAKER_01
There s no testability of the hypothesis.
6
SPEAKER_01
There s no way to falsify the hypothesis.
6
SPEAKER_01
There s no way to measure progress along the arc.
6
SPEAKER_01
Like, it s just all completely missing.
6
SPEAKER_01
And so it s not scientific.
6
SPEAKER_03
Well, I don t think it s completely missing.
6
SPEAKER_03
It s somewhat missing.
6
SPEAKER_03
So for example, the people that say AI is going to kill all of us,
6
SPEAKER_03
I mean, they usually have ideas about how to do that, whether it s the paperclip maximizer or, you know, it escapes.
6
SPEAKER_03
There s a mechanism by which you can imagine it killing all humans.
6
SPEAKER_03
And you can disprove it by saying there is a limit to the speed at which intelligence increases.
6
SPEAKER_03
maybe show that the sort of rigorously really described model, like how it could happen and say, no, here s a physics limitation.
6
SPEAKER_03
There s a physical limitation to how these systems would actually do damage to human civilization.
6
SPEAKER_03
And it is possible they will kill 10 to 20% of the population, but it seems impossible for them to kill 99%.
6
SPEAKER_01
It was practical counter-arguments, right?
6
SPEAKER_01
So you mentioned basically what I described as the thermodynamic counter-argument.
6
SPEAKER_01
Sitting here today, it s like, where would the evil AGI get the GPUs?
6
SPEAKER_01
Because they don t exist.
6
SPEAKER_01
So you re going to have a very frustrated baby evil AGI who s going to be trying to buy Nvidia stock or something to get them to finally make some chips.
6
SPEAKER_01
So the serious form of that is the thermodynamic argument, which is like, OK, where s the energy going to come from?
6
SPEAKER_01
Where s the processor going to be running?
6
SPEAKER_01
Where s the data center going to be happening?
6
SPEAKER_01
How is this going to be happening in secret such that you know it s not?
6
SPEAKER_01
So that s a practical counter-argument to the runaway AGI thing.
6
SPEAKER_01
And we can argue that, discuss that.
6
SPEAKER_01
I have a deeper objection to it, which is this is all forecasting, it s all modeling, it s all future prediction, it s all future hypothesizing.
6
SPEAKER_01
It s not science.
6
SPEAKER_01
It is not.
6
SPEAKER_01
It is the opposite of science.
6
SPEAKER_01
So the pull-up Carl Sagan, extraordinary claims require extraordinary proof, right?
6
SPEAKER_01
These are extraordinary claims.
6
SPEAKER_01
The policies that are being called for, right, to prevent this are of extraordinary magnitude, and I think we re going to cause extraordinary damage.
6
SPEAKER_01
And this is all being done on the basis of something that is literally not scientific.
6
SPEAKER_01
It s not a testable hypothesis.
6
SPEAKER_03
So the moment you say AI is going to kill all of us, therefore we should ban it or that we should regulate all that kind of stuff, that s when it starts getting serious.
6
SPEAKER_01
Or start, you know, military airstrikes on data centers.
6
SPEAKER_03
Oh boy.
6
SPEAKER_01
Right?
6
SPEAKER_01
And like... Yeah, that s when it starts getting real weird.
6
SPEAKER_01
So here s the problem with millenarian cults.
6
SPEAKER_01
They have a hard time staying away from violence.
6
SPEAKER_02
Yeah, but violence is so fun.
6
SPEAKER_01
If you re on the right end of it, they have a hard time avoiding violence.
6
SPEAKER_01
The reason they have a hard time avoiding violence is if you actually believe the claim, right, then what would you do to stop the end of the world?
6
SPEAKER_01
Well, you would do anything, right?
6
SPEAKER_01
And so and this is where you get if you just look at the history of millenarian cults This is where you get the people s temple and everybody killing themselves in the jungle And this is where you get Charles Manson and you know sending in me to kill kill the pigs Like this is the problem with these they have a very hard time to run the line at actual violence And I think I think in this case there s there I mean they re already calling for it like today and you know where this goes from here as they get more worked up like I think it s like really concerning and
6
SPEAKER_03
Okay, but that s kind of the extremes.
6
SPEAKER_03
The extremes of anything are always concerning.
6
SPEAKER_03
It s also possible to kind of believe that AI has a very high likelihood of killing all of us, but there s
6
SPEAKER_03
and therefore we should maybe consider slowing development or regulating.
6
SPEAKER_03
So not violence or any of these kinds of things, but saying like, all right, let s take a pause here.
6
SPEAKER_03
You know, biological weapons, nuclear weapons, like whoa, whoa, whoa, whoa, whoa, whoa, this is like serious stuff.
6
SPEAKER_03
We should be careful.
6
SPEAKER_03
So it is possible to kind of have a more rational response, right, if you believe this risk is real.
6
SPEAKER_03
Yes, so is it possible to have a scientific approach to the prediction of the future?
6
SPEAKER_01
I mean, we just went through this with COVID.
6
SPEAKER_01
What do we know about modeling?
6
SPEAKER_00
What do we learn about modeling with COVID?
6
SPEAKER_00
There s a lot of lessons.
6
SPEAKER_00
They didn t work at all.
6
SPEAKER_00
They worked poorly.
6
SPEAKER_00
The models were terrible.
6
SPEAKER_00
The models were useless.
6
SPEAKER_03
I don t know if the models were useless or the people interpreting the models and then the centralized institutions that were creating policy rapidly based on the models.
6
SPEAKER_03
and leveraging the models in order to support their narratives versus actually interpreting the error bars and the models and all that kind of stuff.
6
SPEAKER_01
What you had with COVID, my view, you had with COVID is you had these experts showing up.
6
SPEAKER_01
They claimed to be scientists, and they had no testable hypotheses whatsoever.
6
SPEAKER_01
They had a bunch of models.
6
SPEAKER_01
They had a bunch of forecasts, and they had a bunch of theories, and they laid these out in front of policymakers, and policymakers freaked out and panicked, right?
6
SPEAKER_01
and implemented a whole bunch of really terrible decisions that we re still living with the consequences of.
6
SPEAKER_01
And there was never any empirical foundation to any of the models.
6
SPEAKER_01
None of them ever came true.
6
SPEAKER_03
Yeah, to push back, there were certainly Baptists and bootleggers in the context of this pandemic, but there s still a usefulness to models, no?
6
SPEAKER_01
I mean, not if they re reliably wrong, right?
6
SPEAKER_01
Then they re actually anti-useful, right?
6
SPEAKER_01
They re actually damaging.
6
SPEAKER_03
But what do you do with a pandemic?
6
SPEAKER_03
What do you do with any kind of threat?
6
SPEAKER_03
Don t you want to kind of have several models to play with as part of the discussion of like, what the hell do we do here?
6
SPEAKER_03
I mean, do they work?
6
SPEAKER_01
Because they re an expectation that they actually like work, that they have actual predictive value.
6
SPEAKER_01
I mean, as far as I can tell with COVID, we just sigh out the policymakers just sigh up themselves into believing that there was.
6
SPEAKER_01
I mean, look, the scientists, the scientists were at fault.
6
SPEAKER_01
The quote unquote scientists showed up.
6
SPEAKER_01
So I had some insight into this.
6
SPEAKER_01
So there was I remember the Imperial College models out of London were the ones that were like, these are the gold standard models.
6
SPEAKER_03
Yeah.
6
SPEAKER_01
So a friend of mine runs a big software company, and he was like, wow, this is like COVID s really scary.
6
SPEAKER_01
And he s like, you know, he contacted this research and he s like, you know, do you need some help?
6
SPEAKER_01
You ve been just building this model on your own for 20 years.
6
SPEAKER_01
Do you need some like our coders to basically restructure it so it can be fully adapted for COVID?
6
SPEAKER_01
the guy said yes and sent over the code.
6
SPEAKER_01
And my friend said it was like the worst spaghetti code he s ever seen.
6
SPEAKER_03
That doesn t mean it s not possible to construct a good model of pandemic with the correct error bars with a high number of parameters that are continuously many times a day updated as we get more data about a pandemic.
6
SPEAKER_03
I would like to believe.
6
SPEAKER_03
When a pandemic hits the world, the best computer scientists in the world, the best software engineers respond aggressively and as input take the data that we know about the virus and it s an output, say here s what s happening.
6
SPEAKER_03
in terms of how quickly it s spreading, in terms of hospitalization and deaths and all that kind of stuff.
6
SPEAKER_03
Here s how likely, how contagious it likely is.
6
SPEAKER_03
Here s how deadly it likely is based on different conditions, based on different ages and demographics and all that kind of stuff.
6
SPEAKER_03
So here s the best kinds of policy.
6
SPEAKER_03
It feels like,
6
SPEAKER_03
you could have models, machine learning, that kind of, they don t perfectly predict the future, but they help you do something, because there s pandemics that are like.
6
SPEAKER_03
Meh, they don t really do much harm.
6
SPEAKER_03
And there s pandemics, you can imagine them, they could do a huge amount of harm.
6
SPEAKER_03
Like they can kill a lot of people.
6
SPEAKER_03
So you should probably have some kind of data-driven models that keep updating, that allow you to make decisions that are based like where, how bad is this thing?
6
SPEAKER_03
Now you can criticize.
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SPEAKER_03
how horrible all that went with the response to this pandemic.
6
SPEAKER_03
But I just feel like there might be some value to models.
6
SPEAKER_01
So to be useful at some point, it has to be predictive, right?
6
SPEAKER_01
So the easy thing for me to do is to say, obviously, you re right.
6
SPEAKER_01
Obviously, I want to see that just as much as you do, because anything that makes it easier to navigate through society through a wrenching risk like that, that sounds great.
6
SPEAKER_01
You know, the harder objection to it is just simply you are trying to model a complex dynamic system with 8 billion moving parts, like, not possible.
6
SPEAKER_01
It s very tough.
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SPEAKER_01
Can t be done.
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SPEAKER_01
Complex systems can t be done.
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SPEAKER_03
Machine learning says hold my beer, but is it possible?
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SPEAKER_03
No?
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SPEAKER_01
I don t know.
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SPEAKER_01
I would like to believe that it is.
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SPEAKER_01
I ll put it this way.
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SPEAKER_01
I think where you and I would agree is I think we would like that to be the case.
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SPEAKER_01
We are strongly in favor of it.
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SPEAKER_01
I think we would also agree that no such thing, with respect to COVID or pandemics, no such thing, at least neither you nor I think are aware, I m not aware of anything like that today.
6
SPEAKER_03
My main worry with the response to the pandemic is that, same as with aliens, is that even if such a thing existed, and it s possible it existed,
6
SPEAKER_03
the policymakers were not paying attention.
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SPEAKER_03
There was no mechanism that allowed those kinds of models to percolate out.
6
SPEAKER_01
Oh, I think we had the opposite problem during COVID.
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SPEAKER_01
I think the policymakers, I think these people with basically fake science had too much access to the policymakers.
6
SPEAKER_03
Right.
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SPEAKER_03
But the policymakers also wanted, they had a narrative in mind, and they also wanted to use whatever model that fit that narrative to help them out.
6
SPEAKER_03
So it felt like there was a lot of politics and not enough science.
6
SPEAKER_01
Although a big part of what was happening, a big reason we got lockdowns for as long as we did was because these scientists came in with these doomsday scenarios that were just completely off the hook.
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SPEAKER_03
Scientists in quotes.
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SPEAKER_03
Let s not quote-unquote scientists.
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SPEAKER_03
Let s give love to science.
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SPEAKER_03
That is the way out.
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SPEAKER_01
Science is a process of testing hypotheses.
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SPEAKER_01
Modeling does not involve testable hypotheses.
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SPEAKER_01
I don t even know that modeling actually qualifies as science.
6
SPEAKER_01
Maybe that s a side conversation we could have sometime over a beer.
6
SPEAKER_00
It s a really interesting, but what do we do about the future?
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SPEAKER_00
I mean, what, what?
6
SPEAKER_01
So number one is when we start with number one, humility goes back to this thing of how do we determine the truth?
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SPEAKER_01
Number two is we don t believe, you know, it s the old, I ve got a hammer.
6
SPEAKER_01
Everything looks like a nail, right?
6
SPEAKER_01
Um, uh, I ve got, uh, this is one of the reasons I gave you, I gave Alexa book, um, uh, which is the topic of the book is what happens when scientists basically stray off the path of technical knowledge and start to weigh in on politics and societal issues.
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SPEAKER_03
Um, in this case, philosophers,
6
SPEAKER_01
Well, in this case philosophers, but he actually talks in this book about like Einstein.
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SPEAKER_01
He talks about the nuclear age and Einstein.
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SPEAKER_01
He talks about the physicists actually doing very similar things at the time.
6
SPEAKER_03
The book is One Reason Goes on Holiday, Philosophers and Politics by Nevin
6
SPEAKER_01
And it s just a story.
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SPEAKER_01
There are other books on this topic, but this is a new one that s really good.
6
SPEAKER_01
It s just a story of what happens when experts in a certain domain decide to weigh in and become basically social engineers and basically political advisors.
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SPEAKER_01
And it s just a story of just unending catastrophe.
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SPEAKER_01
And I think that s what happened with COVID again.
6
SPEAKER_03
Yeah, I found this book a highly entertaining and eye-opening read filled with amazing anecdotes of irrationality and craziness by famous recent philosophers.
6
SPEAKER_01
After you read this book, you will not look at Einstein the same.
6
SPEAKER_03
Oh, boy.
6
SPEAKER_03
Yeah.
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SPEAKER_03
Don t destroy my heroes.
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SPEAKER_01
You will not be a hero of yours anymore.
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SPEAKER_01
I m sorry.
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SPEAKER_01
You probably shouldn t read the book.
6
SPEAKER_01
All right.
6
SPEAKER_01
But here s the thing.
6
SPEAKER_01
The AI risk people, they don t even have the COVID model, at least not that I m aware of.
6
SPEAKER_01
No.
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SPEAKER_01
There s not even the equivalent of the COVID model.
6
SPEAKER_01
They don t even have the spaghetti code.
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SPEAKER_01
they ve got a theory and a warning and a this and a that.
6
SPEAKER_01
And like, if you ask, like, okay, well, here s, here s, I mean, the ultimate example is, okay, how do we know, right?
6
SPEAKER_01
How do we know that an AI is running away?
6
SPEAKER_01
Like, how do we know that the fume takeoff thing is actually happening?
6
SPEAKER_01
And the only answer that any of these guys have given that I ve ever seen is, oh, it s when the loss rate, the loss function in the training drops, right?
6
SPEAKER_01
That s when you need to like, shut down the data center, right?
6
SPEAKER_01
And it s like, well, that s also what happens when you re successfully training a model.
6
SPEAKER_01
Like,
6
SPEAKER_01
Like, what even is, this is not science.
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SPEAKER_01
This is not, it s not anything.
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SPEAKER_01
It s not a model.
6
SPEAKER_01
It s not anything.
6
SPEAKER_01
There s nothing to, arguing with it is like, you know, punching jello.
6
SPEAKER_01
Like there s, what do you even respond to?
6
SPEAKER_03
So just push back on that.
6
SPEAKER_03
I don t think they have good metrics of, yeah, when the fume is happening, but I think it s possible to have that.
6
SPEAKER_03
Like, I just, just as you speak now, I mean, it s possible to imagine there could be measures.
6
SPEAKER_03
It s been 20 years.
6
SPEAKER_03
No, for sure, but it s been only weeks since we had a big enough breakthrough in language models.
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SPEAKER_03
We can start to actually have, the thing is, the AI Doomer stuff didn t have any actual systems to really work with.
6
SPEAKER_03
And now there s real systems you can start to analyze, like how does this stuff go wrong?
6
SPEAKER_03
And I think you kind of agree that there is a lot of risks that we can analyze.
6
SPEAKER_03
The benefits outweigh the risks in many cases.
6
SPEAKER_01
Well, the risks are not existential.
6
SPEAKER_01
Yes, well not in the not not in the foot not in the phone paperclip.
6
SPEAKER_01
Let me okay There s another slight of hand that you just alluded to there s another slight of hand that happens, which is very I m very good at the sleight of hand thing Scientific so the book superintelligence, right, which is like the Nick Bostrom s book, which is like the origin of a lot of this stuff Which is written, you know, whatever ten years ago or something So he does this really fascinating thing in the book, which is he basically says There are many possible routes to machine intelligence
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SPEAKER_01
to artificial intelligence, and he describes all the different routes to artificial intelligence, all the different possible—everything from biological augmentation through to, you know, all these different things.
7
SPEAKER_01
One of the ones that he does not describe is large language models because, of course, the book was written before they were invented, and so they didn t exist.
7
SPEAKER_01
In the book, he describes them all, and then he proceeds to treat them all as if they re exactly the same thing.
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SPEAKER_01
He presents them all as sort of an equivalent risk to be dealt with in an equivalent way to be thought about the same way.
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SPEAKER_01
And then the quote-unquote risk that s actually emerged is actually a completely different technology than he was even imagining.
7
SPEAKER_01
And yet all of his theories and beliefs are being transplanted by this movement straight onto this new technology.
7
SPEAKER_01
And so again, there s no other area of science or technology where you do that.
7
SPEAKER_01
When you re dealing with organic chemistry versus inorganic chemistry, you don t just say, oh, with respect to either one, basically, maybe growing up and eating the world or something, they re just gonna operate the same way.
7
SPEAKER_01
You don t.
7
SPEAKER_00
But you can start talking about, as we get more and more actual systems that start to get more and more intelligent, you can start to actually have more scientific arguments here.
7
SPEAKER_00
High level, you can talk about the threat of autonomous weapon systems back before we had any automation in the military.
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SPEAKER_00
And that would be like very fuzzy kind of logic.
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SPEAKER_00
But the more and more you have drones, they re becoming more and more autonomous, you can start imagining, okay, what does that actually look like?
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SPEAKER_00
And what s the actual threat of autonomous weapon systems?
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SPEAKER_00
How does it go wrong?
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SPEAKER_00
And still, it s very vague.
7
SPEAKER_00
But you start to get a sense of like, all right, it should probably be illegal or wrong or not allowed to do like,
7
SPEAKER_00
mass deployment of fully autonomous drones that are doing aerial strikes on large areas.
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SPEAKER_00
I think it should be required.
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SPEAKER_00
Right.
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SPEAKER_01
No, no, no.
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SPEAKER_01
I think it should be required that only aerial vehicles are automated.
7
SPEAKER_00
Okay, so you want to go the other way.
7
SPEAKER_01
I want to go the other way.
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SPEAKER_01
I think it s obvious that the machine is going to make a better decision than the human pilot.
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SPEAKER_01
I think it s obvious that it s in the best interest of both the attacker and the defender and humanity at large if machines are making more of these decisions and not people.
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SPEAKER_01
I think people make terrible decisions in times of war.
7
SPEAKER_00
But like there s ways this can go wrong too, right?
7
SPEAKER_01
Well, wars go terribly wrong now.
7
SPEAKER_01
This goes back to that whole thing about does the self-driving car need to be perfect versus does it need to be better than the human driver?
7
SPEAKER_00
Yeah.
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SPEAKER_01
Does the automated drone need to be perfect or does it need to be better than a human pilot at making decisions under enormous amounts of stress and uncertainty?
7
SPEAKER_00
Yeah, well, on average, the worry that AI folks have is the runaway.
7
SPEAKER_00
They re going to come alive, right?
7
SPEAKER_01
Then again, that s the sleight of hand, right?
7
SPEAKER_00
Or not come alive.
7
SPEAKER_00
Well, no, hold on a second.
7
SPEAKER_00
You lose control.
7
SPEAKER_01
But then they re going to develop goals of their own.
7
SPEAKER_01
They re going to develop a mind of their own.
7
SPEAKER_01
They re going to develop their own
7
SPEAKER_00
No, more like a Chernobyl-style meltdown, like just bugs in the code accidentally, you know, force you—results in the bombing of, like, large civilian areas to a degree that s not possible in the current military strategies controlled by humans.
7
SPEAKER_01
Actually, we ve been doing a lot of mass bombings of cities for a very long time.
7
SPEAKER_00
And a lot of civilians died.
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SPEAKER_01
And a lot of civilians died.
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SPEAKER_01
And if you watch the documentary, The Fog of War, McNamara, it spends a big part of it talking about the firebombing of the Japanese cities, burning them straight to the ground.
7
SPEAKER_01
The devastation in Japan, American military firebombing the cities in Japan was a considerably bigger devastation than the use of nukes.
7
SPEAKER_01
So we ve been doing that for a long time.
7
SPEAKER_01
We also did that to Germany, by the way.
7
SPEAKER_01
Germany did that to us.
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SPEAKER_01
That s an old tradition.
7
SPEAKER_01
The minute we got airplanes, we started doing indiscriminate bombing.
7
SPEAKER_00
So one of the things we re still doing it, the modern US military can do with technology with automation, but technology more broadly, is higher and higher precision strikes.
7
SPEAKER_01
Yeah.
7
SPEAKER_01
So precision is obviously precision.
7
SPEAKER_01
And this is the JDAM.
7
SPEAKER_01
Right.
7
SPEAKER_01
So there was this big advance, this big advance called the JDAM, which basically was trapping a GPS transceiver to a to a to an unguided bomb and turning it into a guided guided bomb.
7
SPEAKER_01
And yeah, that s great.
7
SPEAKER_01
Like, look, that s been a big advance.
7
SPEAKER_01
And that s like a baby version of this question, which is, okay, do you want the human pilot guessing where the bomb s gonna land, or do you want the machine guiding the bomb to its destination?
7
SPEAKER_01
That s the baby version of the question.
7
SPEAKER_01
The next version of the question is, do you want the human or the machine deciding whether to drop the bomb?
7
SPEAKER_01
Everybody just assumes the human s gonna do a better job for what I think are fundamentally suspicious reasons.
7
SPEAKER_01
Emotional, psychological reasons.
7
SPEAKER_01
Yeah, I think it s very clear that the machine s gonna do a better job making that decision, because the humans making that decision are god-awful, just terrible.
7
SPEAKER_01
yeah right and so so yeah so this is the this is the thing and then let s get to the there was can i one more sleight of hand yes okay please i m a magician you could say one more sleight of hand these things are going to be so smart right that they re going to be able to destroy the world and wreak havoc and like do all this stuff and plan and do all this stuff and evade us and have all their secret things and their secret factories and all this stuff but they re so stupid
7
SPEAKER_01
that they re going to get like tangled up in their code.
7
SPEAKER_01
And that s the thing.
7
SPEAKER_01
They re not going to come alive, but there s going to be some bug that s going to cause them to like turn us all into paper, like that.
7
SPEAKER_01
They re not going to, they re going to be genius in every way other than the actual bad goal.
7
SPEAKER_01
And that s just like a ridiculous discrepancy.
7
SPEAKER_01
And you can prove this today.
7
SPEAKER_01
You can actually address this today for the first time with LLMs, which is you can actually ask LLMs to resolve moral dilemmas.
7
SPEAKER_01
So you can create the scenario, dot, dot, dot, this, that, this, that, this, that.
7
SPEAKER_01
What would you as the AI do in this circumstance?
7
SPEAKER_01
And they don t just say, destroy all humans, destroy all humans.
7
SPEAKER_01
They will give you actually very nuanced, moral, practical, trade-off-oriented answers.
7
SPEAKER_01
And so we actually already have the kind of AI that can actually think this through and can actually reason about goals.
7
SPEAKER_00
Well, the hope is that AGI or various superintelligent systems have some of the nuance that LLMs have.
7
SPEAKER_00
And the intuition is they most likely will because even these LLMs have the nuance.
7
SPEAKER_01
LLMs are really, this is actually worth spending a moment on, LLMs are really interesting to have moral conversations with.
7
SPEAKER_01
And that, I didn t expect I d be having a moral conversation with a machine in my lifetime.
7
SPEAKER_00
And let s remember, we re not really having a conversation with a machine, we re having a conversation with the entirety of the collective intelligence of the human species.
7
SPEAKER_01
Exactly.
7
SPEAKER_00
Yes, correct.
7
SPEAKER_00
But it s possible to imagine autonomous weapon systems that are not using LLMs.
7
SPEAKER_01
But if they re smart enough to be scary, why are they not smart enough to be wise?
7
SPEAKER_01
Like that s the part where it s like, I don t know how you get the one without the other.
7
SPEAKER_00
Is it possible to be super intelligent without being super wise?
7
SPEAKER_01
Well, you re, again, you re back to that.
7
SPEAKER_01
I mean, then you re back to a classic autistic computer, right?
7
SPEAKER_01
Like you re back to just like a blind rule follower.
7
SPEAKER_01
I ve got this like core is the paperclip thing.
7
SPEAKER_01
I ve got this core rule and I m just going to follow it to the end of the earth.
7
SPEAKER_01
And it s like, well, but everything you re going to be doing to execute that rule is going to be super genius level that humans aren t going to be able to counter.
7
SPEAKER_01
It s just a, it s a, it s a mismatch in the definition of what the system is capable of.
7
SPEAKER_00
Unlikely, but not impossible, I think.
7
SPEAKER_00
But again, here you get to like, okay, like... No, I m not saying when it s unlikely, but not impossible.
7
SPEAKER_00
If it s unlikely, that means the fear should be correctly calibrated.
7
SPEAKER_01
Extraordinary claims require extraordinary proof.
7
SPEAKER_00
Well, okay.
7
SPEAKER_00
So one interesting sort of tangent I would love to take on this, because you mentioned this in the essay about nuclear, which was also
7
SPEAKER_00
I mean, you don t shy away from a little bit of a spicy take.
7
SPEAKER_00
So Robert Oppenheimer famously said, now I am become death, the destroyer of worlds, as he witnessed the first detonation of a nuclear weapon on July 16th, 1945.
7
SPEAKER_00
And you write an interesting historical perspective
7
SPEAKER_00
Quote, recall that John von Neumann responded to Robert Oppenheimer s famous hand-wringing about the role of creating nuclear weapons, which, you note, helped end World War II and prevent World War III, with some people confessed guilt to claim credit for the sin.
7
SPEAKER_00
And you also mentioned that Truman was harsher after meeting Oppenheimer.
7
SPEAKER_00
He said that, don t let that crybaby in here again.
7
SPEAKER_01
Real quote, real quote, by the way, from Dean Atchison.
7
SPEAKER_01
Oh boy.
7
SPEAKER_01
Because Oppenheimer didn t just say the famous line.
7
SPEAKER_00
Yeah.
7
SPEAKER_01
He then spent years going around basically moaning, you know, going on TV and going into the White House and basically like, just like doing this hair shirt, you know, thing self, you know, this sort of self critical, like, Oh my God, I can t believe how awful I am.
7
SPEAKER_00
So he s the, he s widely considered perhaps because of the hang ringing is the father of the atomic bomb.
7
SPEAKER_01
This is von Neumann s criticism of him, is he tried to have his cake and eat it too.
7
SPEAKER_01
Von Neumann, of course, is a very different kind of personality, and he s just like, yeah, this is an incredibly useful thing.
7
SPEAKER_01
I m glad we did it.
7
SPEAKER_00
Yeah, well, Von Neumann is widely credited as being one of the smartest humans of the 20th century.
7
SPEAKER_00
Certain people, everybody says, like, this is the smartest person I ve ever met when they ve met him.
7
SPEAKER_00
Anyway, that doesn t mean smart doesn t mean wise.
7
SPEAKER_00
I would love to sort of,
7
SPEAKER_00
Can you make the case both for and against the critique of Oppenheimer here?
7
SPEAKER_00
Because we re talking about nuclear weapons.
7
SPEAKER_00
Boy, do they seem dangerous.
7
SPEAKER_01
Well, so the critique goes deeper.
7
SPEAKER_01
And I left this out.
7
SPEAKER_01
Here s the real substance.
7
SPEAKER_01
I left it out because I didn t want to dwell on nukes in my paper.
7
SPEAKER_01
But here s the deeper thing that happened.
7
SPEAKER_01
And I m really curious.
7
SPEAKER_01
This movie coming out this summer, I m really curious to see how far he pushes this, because this is the real drama in the story, which is it wasn t just a question of our nukes, good or bad.
7
SPEAKER_01
It was a question of should Russia also have them?
7
SPEAKER_01
Um, and what, what actually happened, um, was Russia got the, America invented the bomb, Russia got the bomb, they got the bomb through espionage, they got American and, you know, they got American scientists and foreign scientists working on the American project, some combination of the two, uh, basically gave the Russians the designs for the bomb.
7
SPEAKER_01
And that s how the Russians got the bomb.
7
SPEAKER_01
There s this dispute to this day of Oppenheimer s role in that.
7
SPEAKER_01
If you read all the histories, the kind of composite picture, and by the way, we now know a lot actually about Soviet espionage in that era because there s been all this declassified material in the last 20 years that actually shows a lot of very interesting things.
7
SPEAKER_01
But if you kind of read all the histories what you kind of get is Oppenheimer himself probably was not a He probably did not hand over the nuclear secrets himself.
7
SPEAKER_01
However, he was close to many people who did Including family members and there were other members of the Manhattan Project who were Russian Soviet SS and did hand over the bomb and so the view that Oppenheimer and people like him had that this thing is awful and terrible and oh my god, and you know all this stuff and
7
SPEAKER_01
you could argue, fed into this ethos at the time that resulted in Baptists thinking that the only principle thing to do is to give the Russians the bomb.
7
SPEAKER_01
And so the moral beliefs on this thing and the public discussion and the role that the inventors of this technology play, this is the point of this book, when they kind of take on this sort of public intellectual moral kind of thing, it can have real consequences, right?
7
SPEAKER_01
Because we live in a very different world today because Russia got the bomb than we would have lived in had they not gotten the bomb.
7
SPEAKER_01
Right, the entire 20th century, second half of the 20th century would have played out very different had those people not given Russia the bomb.
7
SPEAKER_01
And so the stakes were very high then.
7
SPEAKER_01
The good news today is nobody sitting here today, I don t think, worrying about like an analogous situation with respect to like, I m not really worried that Sam Altman is going to decide to give, you know, the Chinese the design for
7
SPEAKER_01
AI, although he did just speak at a Chinese conference, which is interesting.
7
SPEAKER_01
But however, I don t think that s what s at play here.
7
SPEAKER_01
But what s at play here are all these other fundamental issues around, what do we believe about this?
7
SPEAKER_01
And then what laws and regulations and restrictions are we going to put on it?
7
SPEAKER_01
And that s where I draw a direct straight line.
7
SPEAKER_01
Anyway, and my reading of the history on nukes is the people who were doing the full hair shirt public, this is awful, this is terrible, actually had catastrophically bad results from taking those views.
7
SPEAKER_01
And that s what I m worried is going to happen again.
7
SPEAKER_00
But is there a case to be made that you really need to wake the public up to the dangers of nuclear weapons when they were first dropped?
7
SPEAKER_00
Like, really, like, educate them on, like, this is an extremely dangerous and destructive weapon.
7
SPEAKER_01
I think the education kind of happened quick and early.
7
SPEAKER_01
Like, it was pretty obvious.
7
SPEAKER_01
We dropped one bomb and destroyed an entire city.
7
SPEAKER_00
Yeah, so 80,000 people dead.
7
SPEAKER_00
But the reporting of that, you can report that in all kinds of ways.
7
SPEAKER_00
You can do all kinds of slants, like war is horrible, war is terrible.
7
SPEAKER_00
You can make it seem like the use of nuclear weapons is just a part of war and all that kind of stuff.
7
SPEAKER_00
something about the reporting and the discussion of nuclear weapons resulted in us being terrified in awe of the power of nuclear weapons.
7
SPEAKER_00
And that potentially fed in a positive way towards the game theory of mutually shared destruction.
7
SPEAKER_01
Well, so this gets to what actually happened.
7
SPEAKER_01
Let s get to what actually happened.
7
SPEAKER_00
Some of this is me playing devil s advocate here.
7
SPEAKER_01
Yeah, yeah, sure, of course.
7
SPEAKER_01
Let s get to what actually happened and then kind of back into that.
7
SPEAKER_01
So what actually happened, I believe, and again, I think this is a reasonable reading of history, is what actually happened was Nukes then prevented World War III.
7
SPEAKER_01
And they prevented World War III through the game theory of mutually assured destruction.
7
SPEAKER_01
Had nukes not existed, there would have been no reason why the Cold War did not go hot.
7
SPEAKER_01
And the military planners at the time, on both sides, thought that there was going to be World War III on the plains of Europe, and they thought there was going to be like 100 million people dead.
7
SPEAKER_01
It was like the most obvious thing in the world to happen.
7
SPEAKER_01
And it s the dog that didn t bark.
7
SPEAKER_01
It may be the best single net thing that happened in the entire 20th century is that that didn t happen.
7
SPEAKER_00
Yeah, actually, just on that point, you say a lot of really brilliant things.
7
SPEAKER_00
It hit me just as you were saying it.
7
SPEAKER_00
I don t know why it hit me for the first time, but we got two wars in a span of 20 years.
7
SPEAKER_00
Like, we could have kept getting more and more world wars, and more and more ruthless.
7
SPEAKER_00
And actually, you could have had a US versus Russia war.
7
SPEAKER_01
You could have.
7
SPEAKER_01
By the way, there s another hypothetical scenario.
7
SPEAKER_01
The other hypothetical scenario is the Americans got the bomb, the Russians didn t.
7
SPEAKER_01
Right.
7
SPEAKER_01
And then America is the big dog.
7
SPEAKER_01
And then maybe America would have had the capability to actually roll back the Iron Curtain.
7
SPEAKER_01
I don t know whether that would have happened, but like it s entirely possible.
7
SPEAKER_01
Right.
7
SPEAKER_01
And the act of these people who had these moral positions about because they could forecast, they could model, they could forecast the future of how this technology would get used, made a horrific mistake because they basically ensured that the Iron Curtain would continue for 50 years longer than it would have otherwise.
7
SPEAKER_01
And again, like these are counterfactuals.
7
SPEAKER_01
I don t know that that s what would have happened.
7
SPEAKER_01
But like the decision to hand the bomb over was a big decision.
7
SPEAKER_01
made by people who were very full of themselves.
7
SPEAKER_00
But so me as an American, me as a person that loves America, I also wonder if U.S.
7
SPEAKER_00
was the only ones with nuclear weapons.
7
SPEAKER_01
That was the argument for handing, that was the guys who, the guys who handed over the bomb, that was actually their moral argument.
7
SPEAKER_00
Yeah, I would probably not hand it over to, I would be careful about the regimes you hand it over to.
7
SPEAKER_00
Maybe give it to like the British or something?
7
SPEAKER_00
Or like,
7
SPEAKER_00
Uh, like a democratically elected government.
7
SPEAKER_01
Well, look, there are people to this day who think that those by the Soviet spies did the right thing because they created a balance of terror as opposed to the U.S.
7
SPEAKER_01
having just and by the way, let me let me balance of terror.
7
SPEAKER_01
Let s tell the full version.
7
SPEAKER_00
Such a sexy ring to it.
7
SPEAKER_01
OK, so the full version of the story is John von Neumann is a hero of both yours and mine.
7
SPEAKER_01
The full version of the story is he advocated for a first strike.
7
SPEAKER_01
So when the U.S.
7
SPEAKER_01
had the bomb and Russia did not, he advocated for, he said, we need to strike them right now.
7
SPEAKER_00
Strike Russia.
7
SPEAKER_01
Yeah.
7
SPEAKER_00
Yes.
7
SPEAKER_01
Yes, because he said World War Three is inevitable.
7
SPEAKER_01
He was very hardcore.
7
SPEAKER_01
His theory was World War Three is inevitable.
7
SPEAKER_01
We re definitely going to have World War Three.
7
SPEAKER_01
The only way to stop World War Three is we have to take them out right now, and we have to take them out right now before they get the bomb, because this is our last chance.
7
SPEAKER_00
Now again, like- Is this an example of philosophers in politics?
7
SPEAKER_01
I don t know if that s in there or not, but this is in the Standard Von Neumann.
7
SPEAKER_00
No, but is it- Yeah.
7
SPEAKER_01
Meaning is that- Yeah, this is on the other side.
7
SPEAKER_01
So most of the case studies, most of the case studies in books like this are the crazy people on the left.
7
SPEAKER_01
Yeah.
7
SPEAKER_01
Von Neumann is a story arguably of the crazy people on the right.
7
SPEAKER_00
Yeah, stick to computing, John.
7
SPEAKER_01
Well, this is the thing, and this is the general principle that goes back to our core thing, which is like, I don t know whether any of these people should be making any of these calls.
7
SPEAKER_01
Yeah.
7
SPEAKER_01
Because there s nothing in either von Neumann s background or Oppenheimer s background or any of these people s background that qualifies them as moral authorities.
7
SPEAKER_00
Well, this actually brings up the point of, in AI, who are the good people to reason about the morality, the ethics?
7
SPEAKER_00
Outside of these risks, outside of, like, the more complicated stuff that you agree on is, you know, this will go into the hands of bad guys, and all the kinds of ways they ll do is interesting and dangerous, is dangerous in interesting, unpredictable ways, and who is the right person, who are the right kinds of people to make decisions how to respond to it?
7
SPEAKER_00
Is it tech people?
7
SPEAKER_01
So the history of these fields, this is what he talks about in the book, the history of these fields is that the competence and capability and intelligence and training and accomplishments of senior scientists and technologists working on a technology and then being able to then make moral judgments on the use of their technology, that track record is terrible.
7
SPEAKER_01
That track record is like catastrophically bad.
7
SPEAKER_00
The people that develop that technology are usually not going to be the right people.
7
SPEAKER_01
So the claim is, of course, they re the knowledgeable ones, but the problem is they ve spent their entire life in a lab, right?
7
SPEAKER_01
They re not theologians.
8
SPEAKER_00
thing.
8
SPEAKER_00
But so what you find, what you find when you read, when you read this, when you look at these histories, what you find is they generally are very thinly informed on history, sociology, on, on, on theology, on morality, ethics.
8
SPEAKER_00
They tend to manufacture their own worldviews from scratch.
8
SPEAKER_00
They tend to be very sort of thin.
8
SPEAKER_00
Um, they re not remotely the arguments that you would be having if you got like a group of highly qualified theologians or philosophers or, you know, um,
8
SPEAKER_01
Well, let me, as the devil s advocate takes a sip of whiskey, say that I agree with that, but also it seems like the people who are doing the ethics departments in these tech companies go sometimes the other way.
8
SPEAKER_01
Yes.
8
SPEAKER_01
They re not nuanced on history or theology or this kind of stuff.
8
SPEAKER_01
It almost becomes a kind of outraged activism towards directions that don t seem to be grounded in history and humility and nuance.
8
SPEAKER_01
It s again, drenched with arrogance.
8
SPEAKER_01
So I m not sure which is worse.
8
SPEAKER_00
Oh no, they re both bad.
8
SPEAKER_00
So definitely not them either.
8
SPEAKER_01
But I guess,
8
SPEAKER_00
but look this is a hard yeah it s a hard problem this is our problem and this goes back to where we started which is okay who has the truth and it s like well um you know like how do societies arrive at like truth and how do we figure these things out and like our elected leaders play some role in it you know we all play some role in it um there have to be some set of public intellectuals at some point that bring you know rationality and judgment humility to it yeah those people are few and far between we should probably prize them very highly
8
SPEAKER_01
Yes, celebrate humility in our public leaders.
8
SPEAKER_01
So getting to risk number two, will AI ruin our society?
8
SPEAKER_01
Short version, as you write, if the murder robots don t get us, the hate speech and misinformation will.
8
SPEAKER_01
And the action you recommend, in short, don t let the thought police suppress AI.
8
SPEAKER_01
Well, what is this risk of the effect of misinformation in society that s going to be catalyzed by AI?
8
SPEAKER_00
Yeah, so this is the social media, this is what you just alluded to, it s the activism kind of thing that s popped up in these companies and in the industry.
8
SPEAKER_00
And it s basically, from my perspective, it s basically part two of the war that played out over social media over the last 10 years.
8
SPEAKER_00
um because you probably remember social media 10 years ago was basically who even wants this who wants who wants a photo of what your cat had for breakfast like this stuff is like silly and trivial and why can t these nerds like figure out how to invent something like useful and powerful and then you know certain things happened in the political system and then it sort of the polarity on that discussion switched all the way to social media is like the worst most corrosive most terrible most awful technology ever invented and then it leads to you know terrible the wrong you know politicians and policies and
8
SPEAKER_00
politics and all this stuff.
8
SPEAKER_00
And that all got catalyzed into this very big kind of angry movement, both inside and outside the companies, to kind of bring social media to heal.
8
SPEAKER_00
And that got focused in particular on two topics, so-called hate speech and so-called misinformation.
8
SPEAKER_00
And that s been the saga playing out for the last decade.
8
SPEAKER_00
And I don t even really want to even argue the pros and cons of the sides, just to observe that that s been like a huge fight and has had big consequences to how these companies operate.
8
SPEAKER_00
Um, basically that same, those same sets of theories, that same activist approach, that same energy is being transplanted straight to AI.
8
SPEAKER_00
And you see that already happening.
8
SPEAKER_00
It s why, you know, chat GPT will answer, let s say certain questions and not others.
8
SPEAKER_00
Uh, it s why it gives you the canned speech about, you know, whenever it starts with as a large language model, I cannot, you know, basically means that somebody has reached in there and told it, it can t talk about certain topics.
8
SPEAKER_01
Um, do you think some of that is good?
8
SPEAKER_00
So it s an interesting question.
8
SPEAKER_00
So a couple of observations.
8
SPEAKER_00
So one is the people who find this the most frustrating are the people who are worried about the murder robots.
8
SPEAKER_00
And in fact, the so-called ex-risk people, they started with the term AI safety.
8
SPEAKER_00
The term became AI alignment.
8
SPEAKER_00
When the term became AI alignment is when this switch happened from we re worried it s going to kill us all to we re worried about hazbish and misinformation.
8
SPEAKER_00
The AIX risk people have now renamed their thing AI not kill everyone ism, which I have to admit is a catchy term.
8
SPEAKER_00
And they are very frustrated by the fact that the sort of activist driven hate speech misinformation kind of thing is taking over, which is what s happened, it s taken over.
8
SPEAKER_00
The AI ethics field has been taken over by the hate speech misinformation people.
8
SPEAKER_00
um you know look would i like to live in a world in which like everybody was nice to each other all the time and nobody ever said anything mean and nobody ever used a bad word and everything was always accurate and honest like that sounds great do i want to live in a world where there s like a centralized thought police working through the tech companies to enforce the view of a small set of elites that they re going to determine what the rest of us think and feel like absolutely not there could be a middle ground somewhere like wikipedia type of moderation there s moderation wikipedia
8
SPEAKER_01
that it s somehow crowdsourced, where you don t have centralized elites, but it s also not completely just a free-for-all, because if you have the entirety of human knowledge at your fingertips, you can do a lot of harm.
8
SPEAKER_01
If you have a good assistant that s completely uncensored, they can help you build a bomb, they can help you mess people up,
8
SPEAKER_01
with people s physical well-being, right?
8
SPEAKER_01
Because that information is out there on the internet.
8
SPEAKER_01
Presumably, it would be, you could see the positives in censoring some aspects of an AI model when it s helping you commit literal violence.
8
SPEAKER_00
And there s a section, later section of the essay where I talk about bad people doing bad things.
8
SPEAKER_00
Yes.
8
SPEAKER_00
Right.
8
SPEAKER_00
Which, and there s a set of things that we should discuss there.
8
SPEAKER_00
Yeah.
8
SPEAKER_00
What happens in practice is these lines, as you alluded to this already, these lines are not easy to draw.
8
SPEAKER_00
And what I ve observed in the social media version of this is, the way I describe it, is the slippery slope is not a fallacy, it s an inevitability.
8
SPEAKER_00
The minute you have this kind of activist personality that gets in a position to make these decisions,
8
SPEAKER_00
they take it straight to infinity.
8
SPEAKER_00
It goes into the crazy zone almost immediately and never comes back because people become drunk with power.
8
SPEAKER_00
Look, if you re in the position to determine what the entire world thinks and feels and reads and says, you re going to take it.
8
SPEAKER_00
Elon has ventilated this with the Twitter files over the last three months, and it s just crystal clear how bad it got there.
8
SPEAKER_00
Now, the reason for optimism is what Elon is doing with community notes.
8
SPEAKER_00
So community notes is actually a very interesting thing.
8
SPEAKER_00
So what Elon is trying to do with community notes is he s trying to have it where there s only a community note when people who have previously disagreed on many topics agree on this one.
8
SPEAKER_01
Yes, that s what I m trying to get at is like there could be Wikipedia-like models or community notes type of models where allows you to essentially either provide context or censor in a way that does not resist the slippery slope nature.
8
SPEAKER_00
Now, there s another hour.
8
SPEAKER_00
There s an entirely different approach here, which is basically we have a eyes that are producing content.
8
SPEAKER_00
We could also have a eyes that are consuming content, right?
8
SPEAKER_00
And so one of the things that your assistant could do for you is help you consume all the content, right?
8
SPEAKER_00
And basically tell you when you re getting played.
8
SPEAKER_00
So for example, I m going to want the AI that my kid uses, right, to be very, you know, child safe, and I m going to want it to filter for him all kinds of inappropriate stuff that he shouldn t be saying just because he s a kid.
8
SPEAKER_00
Yeah, right.
8
SPEAKER_00
And you see what I m saying is you can implement that.
8
SPEAKER_00
You could use it architecturally.
8
SPEAKER_00
You could say you can solve this on the client side, right?
8
SPEAKER_00
Solving on the server side gives you an opportunity to dictate for the entire world, which I think is where you take the slippery slope to hell.
8
SPEAKER_00
There s another architectural approach, which is to solve this on the client side, which is certainly what I would endorse.
8
SPEAKER_01
It s AI risk number five, will AI lead to bad people doing bad things?
8
SPEAKER_01
I can just imagine language models used to do so many bad things, but the hope is there that you can have large language models used to then defend against it by more people, by smarter people, by more effective people, skilled people, all that kind of stuff.
8
SPEAKER_00
Three-part argument on bad people doing bad things.
8
SPEAKER_00
So number one, right, you can use the technology defensively, and we should be using AI to build broad-spectrum vaccines and antibiotics for bioweapons, and we should be using AI to hunt terrorists and catch criminals, and we should be doing all kinds of stuff like that.
8
SPEAKER_00
And in fact, we should be doing those things even just to basically go eliminate risk from regular pathogens that aren t constructed by an AI.
8
SPEAKER_00
So there s the whole defensive set of things.
8
SPEAKER_00
Second is, we have many laws on the books about the actual bad things.
8
SPEAKER_00
So it is actually illegal to commit crimes, to commit terrorist acts, to build pathogens with the intent to deploy them to kill people.
8
SPEAKER_00
And so we actually don t need new laws for the vast majority of scenarios.
8
SPEAKER_00
We actually already have the laws in the book.
8
SPEAKER_00
on the books the third argument is the minute and this is sort of the foundational one that gets really tough but the minute you get into this thing which which you were kind of getting into which is like okay but like don t you need censorship sometimes right and don t you need restriction sometimes it s like okay what is the cost of that um and in particular in the world of open source right um and so is open source ai going to be allowed or not
8
SPEAKER_00
Um, if open source AI is not allowed, um, then what is the regime that s going to be necessary legally and technically to prevent it from developing?
8
SPEAKER_00
Right.
8
SPEAKER_00
And here again is where you get into, and people have proposed that these kinds of things you get into, I would say, pretty extreme territory pretty fast.
8
SPEAKER_00
Do we have a monitor agent on every CPU and GPU that reports back to the government what we re doing with our computers?
8
SPEAKER_00
Are we seizing GPU clusters to get beyond a certain size?
8
SPEAKER_00
And then, by the way, how are we doing all that globally?
8
SPEAKER_00
And if China is developing an LLM beyond the scale that we think is allowable, are we going to invade?
8
SPEAKER_00
Right.
8
SPEAKER_00
And you have figures on the AIX risk side who are advocating, you know, potentially up to nuclear strikes to prevent, you know, this kind of thing.
8
SPEAKER_00
And so here you get into this thing.
8
SPEAKER_00
And again, you know, you could maybe say this is, you know, you could even say this is good, bad or indifferent or whatever.
8
SPEAKER_00
But like, here s the comparison of nukes.
8
SPEAKER_00
The comparison of nukes is very dangerous because one is just nukes for just a bomb, although we can come back to nuclear power.
8
SPEAKER_00
But the other thing was like with nukes, you could control plutonium, right?
8
SPEAKER_00
You could track plutonium and it was like hard to come by.
8
SPEAKER_00
AI is just math and code, right?
8
SPEAKER_00
And it s in like math textbooks and it s like there are YouTube videos that teach you how to build it and like there s open so it s already open source, you know, there s a 40 billion parameter model running around already called Falcon online that anybody can download.
8
SPEAKER_00
Um, and so, okay, you, you walk down the logic path that says we need to have guardrails on this and you find yourself in a authoritarian totalitarian regime of thought control and machine control that would be so brutal.
8
SPEAKER_00
that you would have destroyed the society that you re trying to protect.
8
SPEAKER_00
And so I just don t see how that actually works.
8
SPEAKER_01
So you have to understand my brain is going full steam ahead here, because I agree with basically everything you re saying when I m trying to play devil s advocate here.
8
SPEAKER_01
Because, okay, you highlight the fact that there is a slippery slope to human nature.
8
SPEAKER_01
The moment you censor something, you start to censor everything.
8
SPEAKER_01
The alignment starts out sounding nice, but then you start to align to the beliefs of some select group of people, and then it s just your beliefs.
8
SPEAKER_01
The number of people you re aligning to is smaller and smaller as that group becomes more and more powerful.
8
SPEAKER_01
Okay, but that just speaks to the people that censor are usually the assholes, and the assholes get richer.
8
SPEAKER_01
I wonder if it s possible to do without that for AI.
8
SPEAKER_01
One way to ask this question is do you think the base models, the baseline foundation models should be open sourced?
8
SPEAKER_01
Like what Mark Zuckerberg is saying they want to do.
8
SPEAKER_00
So look, I think it s totally appropriate that companies that are in the business of producing a product or service should be able to have a wide range of policies that they put.
8
SPEAKER_00
And I ll just say again, I want a heavily censored model for my eight-year-old.
8
SPEAKER_00
I actually want that.
8
SPEAKER_00
I would pay more money for the one that s more heavily censored than the one that s not.
8
SPEAKER_00
And so there are certainly scenarios where companies will make that decision.
8
SPEAKER_00
Look, an interesting thing you brought up, is this really a speech issue?
8
SPEAKER_00
One of the things that the big tech companies are dealing with is that content generated from an LLM is not covered under Section 230, which is the law that protects internet platform companies from being sued for user-generated content.
8
SPEAKER_00
Um, and so it s actually, yes.
8
SPEAKER_00
And so there s actually, there s actually a question.
8
SPEAKER_00
I think there s still a question, which is can big, can big American companies actually feel generative AI at all?
8
SPEAKER_00
Or is the liability actually going to just ultimately convince them that they can t do it because the minute the thing says something bad and it doesn t even need to be hate speech, it could just be like an inaccurate, it could hallucinate a product, you know, detail on a vacuum cleaner, you know, and all of a sudden the vacuum cleaner company sues.
8
SPEAKER_00
for misrepresentation, and there s an asymmetry there, right?
8
SPEAKER_00
Because the LLM is going to be producing billions of answers to questions, and it only needs to get a few wrong to help.
8
SPEAKER_01
So loss has to get updated really quick here.
8
SPEAKER_00
Yeah, and nobody knows what to do with that, right?
8
SPEAKER_00
So anyway, there are big questions around how companies operate.
8
SPEAKER_00
at all.
8
SPEAKER_00
So we talk about those.
8
SPEAKER_00
But then there s this other question of like, OK, the open source.
8
SPEAKER_00
So what about open source?
8
SPEAKER_00
And my answer to your question is kind of like, obviously, yes, the models have there has to be full open source here because to live in a world in which that open source is not allowed is a world of draconian speech control, human control, machine control.
8
SPEAKER_00
I mean, you know, black helicopters with jackbooted thugs coming out, rappelling down and seizing your GPU like territory.
8
SPEAKER_00
Well, no, no, I m 100 percent serious.
8
SPEAKER_01
That s you re saying slippery slope always leads there.
8
SPEAKER_00
No, no, no, no, no, no.
8
SPEAKER_00
That s what s required to enforce it.
8
SPEAKER_00
Like, how will you enforce a ban on open source?
8
SPEAKER_01
No, you could add friction to it, like hard to get the models because people will always be able to get the models, but it ll be more in the shadows, right?
8
SPEAKER_00
The leading open source model right now is from the UAE.
8
SPEAKER_00
Like, the next time they do that, what do we do?
8
SPEAKER_00
Yeah.
8
SPEAKER_01
Like, oh, I see.
8
SPEAKER_01
You re like,
8
SPEAKER_00
The 14-year-old in Indonesia comes out with a breakthrough model.
8
SPEAKER_00
You know, we talked about most great software comes from a small number of people.
8
SPEAKER_00
Some kid comes out with some big new breakthrough in quantization or something and has some huge breakthrough.
8
SPEAKER_00
And like, what are we going to like invade Indonesia and arrest him?
8
SPEAKER_01
It seems like in terms of size of models and effectiveness of models, the big tech companies will probably lead the way for quite a few years.
8
SPEAKER_01
And the question is of what policies they should use.
8
SPEAKER_01
The kid in Indonesia should not be regulated, but should Google, Meta, Microsoft, OpenAI be regulated?
8
SPEAKER_00
Well, so this goes, OK, so when does it become dangerous?
8
SPEAKER_00
Yeah.
8
SPEAKER_00
Right.
8
SPEAKER_00
Is the danger that it s, quote, as powerful as the current leading commercial model, or is it that it is it is just at some other arbitrary threshold?
8
SPEAKER_00
Yeah.
8
SPEAKER_00
And then, by the way, like, look, how do we know?
8
SPEAKER_00
Like what we know today is that you need like a lot of money to, like, train these things.
8
SPEAKER_00
But there are advances being made every week on training efficiency and, you know, data, all kinds of synthetic data.
8
SPEAKER_00
Look, I don t even like the synthetic data thing we re talking about.
8
SPEAKER_00
Maybe some kid figures out a way to auto generate synthetic.
8
SPEAKER_01
That s going to change everything.
8
SPEAKER_00
Yeah, exactly.
8
SPEAKER_00
And so, like, sitting here today, like, the breakthrough just happened, right?
8
SPEAKER_00
You made this point.
8
SPEAKER_00
Like, the breakthrough just happened.
8
SPEAKER_00
So we don t know what the shape of this technology is going to be.
8
SPEAKER_00
I mean, the big shock, the big shock here is that, you know, whatever number of billions of parameters basically represents at least a very big percentage of human thought.
8
SPEAKER_00
Like, who would have imagined that?
8
SPEAKER_00
And then there s already work underway.
8
SPEAKER_00
There was just this paper that just came out that basically takes a GPT-3 scale model and compresses it down to run on a single 32-core CPU.
8
SPEAKER_00
Like, who would have predicted that?
8
SPEAKER_00
Yeah.
8
SPEAKER_00
You know, some of these models now you can run on Raspberry Pis.
8
SPEAKER_00
Like, today they re very slow, but like, you know, maybe they ll be able to, you know, give real performance.
8
SPEAKER_00
You know, like,
8
SPEAKER_00
It s math and here we re back and here we re back.
8
SPEAKER_00
It s math and code.
8
SPEAKER_00
It s math and code.
8
SPEAKER_00
It s math, code and data.
8
SPEAKER_01
It s bits.
8
SPEAKER_01
Mark s just like walked away at this point.
8
SPEAKER_01
Screw it.
8
SPEAKER_01
I don t know what to do with this.
8
SPEAKER_01
You guys created this whole internet thing.
8
SPEAKER_01
Yeah.
8
SPEAKER_01
Yeah.
8
SPEAKER_01
I m a huge believer in open source here.
8
SPEAKER_00
So my argument is we re going to have to see here s my argument is my argument, my full arguments is AI is going to be like air, it s going to be everywhere.
8
SPEAKER_00
Like it s just this is just going to be in text.
8
SPEAKER_00
It already is, it s going to be in textbooks, and kids are going to grow up knowing how to do this.
8
SPEAKER_00
And it s just going to be a thing, it s going to be in the air.
8
SPEAKER_00
And you can t like pull this back anymore, you can pull back air.
8
SPEAKER_00
And so you just have to figure out how to live in this world.
8
SPEAKER_00
Right.
8
SPEAKER_00
And then that and then that s where I think like all this hand wringing about AI risk is basically a complete waste of time, because the effort should go into okay, what are what is the defensive approach?
8
SPEAKER_00
And so if you re worried about AI-generated pathogens, the right thing to do is to have a permanent Project Warp Speed, funded lavishly.
8
SPEAKER_00
Let s do a Manhattan Project for biological defense, and let s build AIs, and let s have broad-spectrum vaccines where we re insulated from every pathogen.
8
SPEAKER_01
And the interesting thing is, because it s software,
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a kid in his basement, teenager, could build a system that defends against the worst.
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SPEAKER_01
And to me, defense is super exciting.
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SPEAKER_01
If you believe in the good of human nature, that most people want to do good, to be the savior of humanity is really exciting.
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SPEAKER_01
Not, okay, that s a dramatic statement, but to help people, to help people.
8
SPEAKER_01
Yeah, okay, what about, just to jump around, what about the risk of, will AI lead to crippling inequality?
8
SPEAKER_01
You know, because we re kind of saying everybody s life will become better.
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SPEAKER_01
Is it possible that the rich get richer here?
8
SPEAKER_00
Yeah, so this goes, this actually ironically goes back to Marxism.
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SPEAKER_00
So, because this was the, so the core claim of Marxism, right, basically was that the owner, the owners of capital would be
9
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own the means of production, and then over time they would basically accumulate all the wealth.
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SPEAKER_01
The workers would be paying in, you know, and getting nothing in return, because they wouldn t be needed anymore, right?
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SPEAKER_01
Marx was very worried about what he called mechanization, or what later became known as automation, and that, you know, the workers would be immiserated and the capitalists would end up with all.
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And so this was one of the core principles of Marxism.
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SPEAKER_01
Of course, it turned out to be wrong about every previous wave of technology.
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SPEAKER_01
um the reason it turned out to be wrong about every previous wave of technology is that the way that the self-interested owner of the machines makes the most money is by providing the production capability in the form of products and services to the most people the most customers as possible right the largest this is one of those funny things where every ceo knows this intuitively and yet it s like hard to explain from the outside
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SPEAKER_01
The way you make the most money in any business is by selling to the largest market you can possibly get to.
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The largest market you can possibly get to is everybody on the planet.
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SPEAKER_01
And so every large company does everything that it can to drive down prices, to be able to get volumes up, to be able to get to everybody on the planet.
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SPEAKER_01
And that happened with everything from electricity, it happened with telephones, it happened with radio, it happened with automobiles, it happened with smartphones, it happened with
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uh, PCs.
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SPEAKER_01
Um, it happened with the internet.
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SPEAKER_01
Um, it happened with mobile broadband.
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SPEAKER_01
Um, it s happened by the way with Coca-Cola and it s happened with like every, you know, basically every industrially produced, you know, go to service people want, you want to drive it to the largest possible market.
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SPEAKER_01
And then as proof of that, it s already happened.
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SPEAKER_01
Right.
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SPEAKER_01
Which is the early adopters of like chat, GPT and bang are not like, you know, Exxon and Boeing there, you know, your uncle and your nephew,
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SPEAKER_01
Right.
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It s just like free.
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SPEAKER_01
It s either freely available online or it s available for 20 bucks a month or something.
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SPEAKER_01
But you know, these things went, this, this, this technology went mass market immediately.
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SPEAKER_01
Um, and so look, the, the, the owners of the means of production that whoever does this doesn t mention these trillion dollar questions.
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SPEAKER_01
There are people who are going to get really rich doing this, producing these things, but they re going to get really rich by taking this technology to the broadest possible market.
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SPEAKER_00
So yes, they ll get rich, but they ll get rich having a huge positive impact on
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making that making the technology available to everybody.
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SPEAKER_01
Yeah, right.
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SPEAKER_01
And again, smartphone, same thing.
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SPEAKER_01
So there s this amazing kind of twist in in business history, which is you cannot spend $10,000 on a smartphone.
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right?
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SPEAKER_01
You can t spend $100,000.
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SPEAKER_01
You can t spend like I would buy the $1,000,000 smartphone like I m signed up for it.
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SPEAKER_01
Like if it s like suppose $1,000,000 smartphone was like much better than the $1,000 smartphone like I m there to buy it.
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SPEAKER_01
It doesn t exist.
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SPEAKER_01
Why doesn t it exist?
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SPEAKER_01
Apple makes so much more money driving the price further down from $1,000 than they would trying to harvest, right?
9
SPEAKER_01
And so it s just this repeating pattern you see over and over again.
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SPEAKER_01
Um, where the, and, and, and what s, what s great about it, what s great about it is you, you do not need to rely on anybody s enlightened, right?
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SPEAKER_01
Generosity to do this.
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SPEAKER_01
You just need to rely on capitalist self-interest.
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SPEAKER_00
Uh, what about AI taking our jobs?
9
SPEAKER_01
Yeah.
9
SPEAKER_01
So very, very similar thing here.
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SPEAKER_01
Um, there s sort of a, there s a core fallacy, which again was, was very common in Marxism, which is what s called the lump of labor fallacy.
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SPEAKER_01
And this is sort of the fallacy that there is a, only a fixed amount of work to be done in the world.
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And if the, and it s all being done today by people, and then if machines do it, there s no other work to be done by people.
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SPEAKER_01
And that s just a completely backwards view on how the economy develops and grows, because what happens is not in fact that.
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What happens is the introduction of technology into production process causes prices to fall.
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SPEAKER_01
As prices fall, consumers have more spending power.
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SPEAKER_01
As consumers have more spending power, they create new demand.
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SPEAKER_01
That new demand then causes capital and labor to form into new enterprises to satisfy new wants and needs, and the result is more jobs and higher wages.
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SPEAKER_00
New wants and needs.
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SPEAKER_00
The worry is that the creation of new wants and needs at a rapid rate
9
SPEAKER_00
Well, I mean, there s a lot of turnover in jobs, so people will lose jobs.
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SPEAKER_00
Just the actual experience of losing a job and having to learn new things and new skills is painful for the individual.
9
SPEAKER_01
Well, two things.
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SPEAKER_01
One is the new jobs are often much better.
9
SPEAKER_01
Um, so this actually came up that there was this panic about a decade ago and all the truck drivers are going to lose their jobs, right?
9
SPEAKER_01
And number one, that didn t happen because we haven t figured out a way to actually
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SPEAKER_01
finish that yet but but the other thing was like like truck driver like i grew up in a town that was basically consisted of a truck stop right and i like knew a lot of truck drivers and like truck drivers live a decade shorter than everybody else like they it s a it s a it s actually like a very dangerous like they get like literally they have like higher rates of skin cancer and on the left side of their on the left side of their body from from being in the sun all the time the vibration of being in the truck is actually very damaging to your to your physiology
9
SPEAKER_00
And there s actually, perhaps partially because of that reason, there s a shortage of people who want to be truck drivers.
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SPEAKER_01
Yeah.
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SPEAKER_01
The question always you want to ask somebody like that is, do you want your kid to be doing this job?
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SPEAKER_01
And most of them will tell you no.
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SPEAKER_01
Like, I want my kid to be sitting in a cubicle somewhere where they don t die ten years earlier.
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SPEAKER_01
And so, so the new jobs, number one, the new jobs are often better, but you don t get the new jobs until you go through the change.
9
SPEAKER_01
And then to your point, the, the training thing, you know, it s always the issue is can, can people adapt?
9
SPEAKER_01
And again, here you need to imagine living in a world in which everybody has the AI assistant capability, right.
9
SPEAKER_01
To be able to pick up new skills much more quickly and be able to have some, you know, be able to have a machine to work with to augment their skills.
9
SPEAKER_00
It s still gonna be painful, but that s the process of life.
9
SPEAKER_01
It s painful for some people.
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SPEAKER_01
I mean, there s no question it s painful for some people.
9
SPEAKER_01
Yes, it s not, again, I m not a utopian on this, and it s not like it s positive for everybody in the moment, but it has been overwhelmingly positive for 300 years.
9
SPEAKER_01
I mean, look, the concern here, this concern has played out for literally centuries, and this is the story of the Luddites.
9
SPEAKER_01
Um, you may remember there was a panic in the 2000s around outsourcing was going to take all the jobs.
9
SPEAKER_01
There was a panic in the 2010s that robots were going to take all the jobs.
9
SPEAKER_01
Um, in 2019 before COVID, we had more jobs at higher wages, both in the country and in the world than at any point in human history.
9
SPEAKER_01
And so the overwhelming evidence is that the net gain here is like, just like wildly positive.
9
SPEAKER_01
Uh, and most, most people like overwhelmingly come out the other side being huge beneficiaries of this.
9
SPEAKER_00
So you write that the single greatest risk, this is the risk you re most convinced by, the single greatest risk of AI is that China wins global AI dominance and we, the United States and the West, do not.
9
SPEAKER_00
Can you elaborate?
9
SPEAKER_01
Yeah, so this is the other thing, which is a lot of the sort of AI risk debates today sort of assume that we re the only game in town, right?
9
SPEAKER_01
And so we have the ability to kind of sit in the United States and criticize ourselves and have our government beat up on our companies and figure out a way to restrict what our companies can do.
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SPEAKER_01
know, we re going to, you know, we re going to ban this and ban that restrict this and do that.
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SPEAKER_01
And then there s this like other like force out there that like doesn t believe we have any power over them whatsoever.
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SPEAKER_01
Uh, and they have no desire to sign up for whatever rules we decide to put in place.
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SPEAKER_01
Um, and they re going to do whatever it is they re going to do.
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SPEAKER_01
And we have no control over it at all.
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SPEAKER_01
And it s China, and specifically the Chinese Communist Party.
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SPEAKER_01
And they have a completely publicized, open, you know, plan for what they re going to do with AI.
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SPEAKER_01
And it is not what we have in mind.
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SPEAKER_01
And not only do they have that as a vision and a plan for their society, but they also have it as a vision and plan for the rest of the world.
9
SPEAKER_00
So their plan is what, surveillance?
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SPEAKER_01
Yeah, authoritarian control.
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SPEAKER_01
So authoritarian population control.
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SPEAKER_01
You know, good old-fashioned communist authoritarian control and surveillance and enforcement and social credit scores and all the rest of it.
9
SPEAKER_01
And you are gonna be monitored and metered within an inch of everything all the time.
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SPEAKER_01
And it s gonna, you know, it s basically the end of human freedom and that s their goal and, you know, they justify it on the basis of that s what leads to peace.
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SPEAKER_00
And you re worried that the,
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SPEAKER_00
regulating in the United States will halt progress enough to where, uh, the Chinese government would win that race.
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SPEAKER_01
So their plan.
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SPEAKER_01
Yeah.
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SPEAKER_01
Yes.
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Yes.
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SPEAKER_01
And the reason for that is they, and again, they re very public on this.
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SPEAKER_01
They, they have, their plan is to proliferate their approach around the world.
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SPEAKER_01
Um, and they have this program called the digital Silk Road.
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SPEAKER_01
right, which is building on their Silk Road investment program.
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SPEAKER_01
And they ve been laying networking infrastructure all over the world with their 5G, right, work with their company Huawei.
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SPEAKER_01
And so they ve been laying all this fabric, financial and technological fabric, all over the world.
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SPEAKER_01
And their plan is to roll out their vision of AI on top of that and to have every other country be running their version.
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SPEAKER_01
And then if you re a country prone to authoritarianism, you re going to find this to be an incredible way to become more authoritarian.
9
SPEAKER_01
If you re a country, by the way, not prone to authoritarianism, you re going to have the Chinese Communist Party running your infrastructure and having backdoors into it.
9
SPEAKER_01
Right.
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SPEAKER_01
Which is also not good.
9
SPEAKER_00
What s your sense of where they stand in terms of the race towards superintelligence as compared to the United States?
9
SPEAKER_01
Yeah, so good news is they re behind.
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SPEAKER_01
But bad news is they, you know, they, let s just say they get access to everything we do.
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SPEAKER_01
So they re probably a year behind at each point in time, but they get, you know, downloads, I think, of basically all of our work on a regular basis, through a variety of means.
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SPEAKER_01
And they are, you know, at least we ll see, they re at least putting out reports of very competitive, just put out a report last week of a GPT 3.5 analog.
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SPEAKER_01
They put out this report.
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SPEAKER_01
I forget what it s called, but they put out this report of this LLM.
9
SPEAKER_01
When OpenAI puts out, one of the ways they test GPT is they run it through standardized exams like the SAT, right?
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SPEAKER_01
Just how you can kind of gauge how smart it is.
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SPEAKER_01
And so the Chinese report, they ran their LLM through the Chinese equivalent of the SAT.
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SPEAKER_01
And it includes a section on Marxism and a section on Mao Zedong thought.
9
SPEAKER_01
And it turns out their AI does very well on both of those topics, right?
9
SPEAKER_01
This alignment thing.
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SPEAKER_01
Communist AI, right?
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SPEAKER_01
Like literal communist AI, right?
9
SPEAKER_01
And so their vision is like that s the, you know, so, you know, you can just imagine like you re a school, you know, you re a kid 10 years from now in Argentina or in Germany or in
9
SPEAKER_01
who knows where, uh, Indonesia, and you ask the AI to explain to you, like, how the economy works, and it gives you the most cheery, upbeat explanation of Chinese-style communism you ve ever heard, right?
9
SPEAKER_01
So, like, the stakes here are, like, really big.
9
SPEAKER_00
Well, my, as we ve been talking about, my hope is not just for the United States, but with just, uh, the kit in his basement, the open-source LLM.
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SPEAKER_00
because I don t know if I trust large centralized institutions with super powerful AI, no matter what their ideology, because power corrupts.
9
SPEAKER_00
You ve been investing in tech companies for about, let s say 20 years, and about 15 of which was with Andreessen Horowitz.
9
SPEAKER_00
What interesting trends in tech have you seen over that time?
9
SPEAKER_00
Let s just talk about companies and just the evolution of the tech industry.
9
SPEAKER_01
I mean, the big shift over 20 years has been that tech used to be a tools industry for basically from like 1940 through to about 2010, almost all the big successful companies were picks and shovels companies.
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SPEAKER_01
So PC, database, smartphone, some tool that somebody else would pick up and use.
9
SPEAKER_01
Since 2010, most of the big wins have been in applications.
9
SPEAKER_01
So a company that starts in an existing industry and goes directly to the customer in that industry.
9
SPEAKER_01
And the early examples there were like Uber and Lyft and Airbnb.
9
SPEAKER_01
And then that model is kind of elaborating out.
9
SPEAKER_01
The AI thing is actually a reversion on that for now, because most of the AI business right now is actually in cloud provision of AI APIs for other people to build on.
9
SPEAKER_00
But the big thing will probably be an app.
9
SPEAKER_01
Yeah, I think I think most of the money, I think probably will be in whatever.
9
SPEAKER_01
Yeah, you re a financial advisor or your AI doctor or your AI lawyer, or, you know, take your pick of whatever the domain is.
9
SPEAKER_01
And there and what s interesting is, you know, we, the valley kind of does everything we are entrepreneurs kind of elaborate every possible idea.
9
SPEAKER_01
And so there will be a set of companies that like make AI, something that can be purchased and used by large law firms.
9
SPEAKER_01
And then there will be other companies that just go direct to market as a as an AI lawyer.
9
SPEAKER_00
What advice could you give for a startup founder?
9
SPEAKER_00
Just having seen so many successful companies, so many companies that fail also, what advice could you give to a startup founder, someone who wants to build the next super successful startup in the tech space?
9
SPEAKER_00
The Googles, the Apples, the Twitters.
9
SPEAKER_01
Yeah, so the great thing about the really great founders is they don t take any advice.
9
SPEAKER_01
So if you find yourself listening to advice, maybe you shouldn t do it.
9
SPEAKER_00
That s actually just to elaborate on that.
9
SPEAKER_00
If you could also speak to great founders too, like what makes a great founder?
9
SPEAKER_01
So it makes a great founder is super smart, um, coupled with super energetic, coupled with super courageous.
9
SPEAKER_00
I think it s some of those, those three and intelligence, passion and courage.
9
SPEAKER_01
The first two are traits and the third one is a choice.
9
SPEAKER_01
I think courage is a choice.
9
SPEAKER_01
Well, cause courage is a question of pain tolerance, right?
9
SPEAKER_01
Um, so, um, how, how many times are you willing to get punched in the face before you quit?
9
SPEAKER_01
Um, and
9
SPEAKER_01
Here s maybe the biggest thing people don t understand about what it s like to be a startup founder is it gets, it gets very romanticized, right?
9
SPEAKER_01
Um, and even when, even when they fail, it still gets romanticized about like what a great adventure it was.
9
SPEAKER_01
But like the reality of it is most of what happens is people telling you no.
9
SPEAKER_01
And then they usually follow that with you re stupid, right?
9
SPEAKER_01
No, I will not come to work for you.
9
SPEAKER_01
Um, and I will not leave my cushy job at Google to come work for you.
9
SPEAKER_01
No, I m not going to buy your product.
9
SPEAKER_01
You know, no, I m not going to run a story about your company.
9
SPEAKER_01
No, I m not this, that, the other thing.
9
SPEAKER_01
And so a huge amount of what people have to do is just get used to just getting punched.
9
SPEAKER_01
And the reason people don t understand this is because when you re a founder, you cannot let on that this is happening because it will cause people to think that you re weak and they ll lose faith in you.
9
SPEAKER_01
So you have to pretend that you re having a great time when you re dying inside, right?
9
SPEAKER_01
It s just a misery.
9
SPEAKER_00
But why did they do it?
9
SPEAKER_01
What do they do?
9
SPEAKER_01
Yeah, that s the thing.
9
SPEAKER_01
It s like it is a level.
9
SPEAKER_01
This is actually one of the conclusions, I think.
9
SPEAKER_01
For most of these people on a risk-adjusted basis, it s probably an irrational act.
9
SPEAKER_01
They could probably be more financially successful on average if they just got a real job at a big company.
9
SPEAKER_01
But some people just have an irrational need to do something new and build something for themselves.
9
SPEAKER_01
Some people just can t tolerate having bosses.
9
SPEAKER_01
Here s a fun thing.
9
SPEAKER_01
How do you reference check founders?
9
SPEAKER_01
Right, so you call it, you know, normally you reference check your time hiring somebody as you call the bosses, you know, and you find out if they were good employees.
9
SPEAKER_01
And now you re trying to reference check Steve Jobs, right?
9
SPEAKER_01
And it s like, oh God, he was terrible.
9
SPEAKER_01
You know, he was a terrible employee.
9
SPEAKER_01
He never did what we told him to do.
9
SPEAKER_00
Yeah.
9
SPEAKER_00
So what s a good reference?
9
SPEAKER_00
Do you want the previous boss to actually say that they never did what you told them to do?
9
SPEAKER_00
That might be a good thing.
9
SPEAKER_01
Well, ideally, ideally what you want is, I would like to go to work for that person.
9
SPEAKER_01
He worked for me here and now I d like to work for him.
9
SPEAKER_01
Now, unfortunately, most people can t their egos can t can t handle that.
9
SPEAKER_01
So they won t say that.
9
SPEAKER_01
But that s the ideal.
9
SPEAKER_00
What advice would you give to those folks in the space of intelligence, passion and courage?
9
SPEAKER_01
So I think the other big thing is you see people sometimes who say, I want to start a company and then they kind of work through the process of coming up with an idea.
9
SPEAKER_01
And generally those don t work as well as the case where somebody has the idea first and then they kind of realize that there s an opportunity to build a company and then they just turn out to be the right kind of person to do that.
9
SPEAKER_00
When you say idea, do you mean long-term big vision, or do you mean specifics of product?
9
SPEAKER_01
I would say specific.
9
SPEAKER_01
Specifically, yes, specifics.
9
SPEAKER_01
Because for the first five years, you don t get to have vision.
9
SPEAKER_01
You ve just got to build something people want, and you ve got to figure out a way to sell it to them.
9
SPEAKER_01
It s very practical.
9
SPEAKER_00
Or you never get to big vision.
9
SPEAKER_00
So the first the first part, you have an idea of a set of products or the first product that can actually make some money.
9
SPEAKER_01
Yeah, like it s got a first product s got to work, by which I mean like it has to technically work, but then it has to actually fit into the category in the customer s mind of something that they want.
9
SPEAKER_01
And then and then, by the way, the other part is they have to want to pay for it.
9
SPEAKER_01
Like somebody s got to pay the bills.
9
SPEAKER_01
And so you ve got to figure out how to price it and whether you can actually extract the money.
9
SPEAKER_01
Yeah.
9
SPEAKER_01
So usually it is much more predictable.
9
SPEAKER_01
Success is never predictable, but it s more predictable if you start with a great idea and then back into starting the company.
9
SPEAKER_01
Um, so this is what we did, you know, we had mosaic before we had an escape.
9
SPEAKER_01
The Google guys had the Google search engine working at Stanford.
9
SPEAKER_01
Um, right.
9
SPEAKER_01
Um, the, um, uh, you know, yeah, actually there s tons of examples where they, you know, uh, Pierre Omidyar had eBay working before he left his previous job.
9
SPEAKER_00
So I really love that idea of just having a thing, a prototype that actually works before you even begin to remotely scale.
9
SPEAKER_01
Yeah.
9
SPEAKER_01
By the way, it s also far easier to raise money, right?
9
SPEAKER_01
Like the ideal pitch that we receive is here s the thing that works.
9
SPEAKER_01
Would you like to invest in our company or not?
9
SPEAKER_01
Like that s so much easier than here s 30 slides with a dream, right?
9
SPEAKER_01
Um,
9
SPEAKER_01
And then we have this concept called the Idea Maze, which Balaji Srinivasan came up with when he was with us.
9
SPEAKER_01
So then there s this thing, this goes to mythology, which is, you know, there s a mythology that kind of, you know, these ideas, you know, kind of arrive like magic or people kind of stumble into them.
9
SPEAKER_01
It s like eBay with the pest dispensers or something.
9
SPEAKER_01
The reality usually with the big successes is that the founder has been chewing on the problem for five or 10 years before they start the company.
9
SPEAKER_01
And they often worked on it in school, or they even experimented on it when they were a kid.
9
SPEAKER_01
And they ve been kind of training up over that period of time to be able to do the thing.
9
SPEAKER_01
So they re like a true domain expert.
9
SPEAKER_01
And it sort of sounds like mom and apple pie, which is, yeah, you want to be a domain expert in what you re doing.
9
SPEAKER_01
But you would, you know, the mythology is so strong of like, oh, I just like had this idea in the shower and now I m doing it like it s generally not that.
9
SPEAKER_00
No, because maybe in the shower you had the exact product implementation details.
9
SPEAKER_00
But yeah, usually you re going to be for like years, if not decades, thinking about like everything around that.
9
SPEAKER_01
we call it the idea maze because the idea maze basically is like there s all these permutations like for any idea there s like all these different permutations who should the customer be what shape forms the product has
10
SPEAKER_00
and how should we take it to market and all these things.
10
SPEAKER_00
Um, and so, um, the really smart founders have thought through all these scenarios by the time they go out to raise money.
10
SPEAKER_00
Um, and they have like detailed answers, um, on every one of those fronts because they put so much thought into it.
10
SPEAKER_00
Um, the sort of the, the, the sort of more, uh, haphazard founders haven t thought about any of any of that.
10
SPEAKER_00
And it s the detailed ones who tend to do much better.
10
SPEAKER_01
So how do you know what to take a leap if you have a cushy job or happy life?
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I mean, the best reason is just because you can t tolerate not doing it, right?
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This is the kind of thing where if you have to be advised into doing it, you probably shouldn t do it.
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And so it s probably the opposite, which is you just have such a burning sense of this has to be done.
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I have to do this.
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I have no choice.
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What if it s going to lead to a lot of pain?
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It s going to lead to a lot of pain.
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I think that s what if it means losing sort of social relationships and damaging your relationship with loved ones and all that kind of stuff.
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Yeah, look, so like it s going to put you in a social tunnel for sure, right?
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So you re going to like, you know, there s this game you can play on Twitter, which is you can do any whiff of the idea that there s basically any such thing as work-life balance and that people should actually work hard and everybody gets mad.
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But like the truth is like all the successful founders are working 80 hour weeks and they re working, you know, they form various
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very strong social bonds with the people they work with.
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They tend to lose a lot of friends on the outside or put those friendships on ice.
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Like, that s just the nature of the of the thing.
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You know, for most people, that s worth the tradeoff.
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You know, the advantage, you know, maybe younger founders have is maybe they have less, you know, maybe they re not, you know, for example, if they re not married yet or don t have kids yet, that s an easier thing to bite off.
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Can you be an older founder?
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Yeah, you definitely can.
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Yeah.
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Yeah.
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Many of the most successful founders are second, third, fourth time founders.
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They re in their 30s, 40s, 50s.
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The good news of being an older founder is you know more.
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And you know a lot more about what to do, which is very helpful.
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The problem is, okay, now you ve got like a spouse and a family and kids and like you ve got to go to the baseball game and like you can t go to the base, you know, and so it s good.
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Life is full of difficult choices, Mark Hendryson.
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You ve written a blog post on what you ve been up to.
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You wrote this in October, 2022.
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Quote, mostly I try to learn a lot.
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For example, the political events of 2014 to 2016 made clear to me that I didn t understand politics at all.
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Referencing maybe some of this.
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this book here.
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So I deliberately withdrew from political engagement and fundraising and instead read my way back into history and as far to the political left and political right as I could.
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So just high-level question, what s your approach to learning?
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Yeah, so it s basically, I would say it s autodidact, so it s sort of going down the rabbit holes.
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Um, so it s a combination of say, I kind of alluded to it in that, in that quote, it s a combination of breadth and depth.
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Um, and so I tend to, yeah, I tend to, I go broad by the nature of what I do.
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I go broad, but then I tend to go deep in a rabbit hole for awhile, read everything I can and then come out of it.
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And I might not, I might not revisit that rabbit hole for another decade.
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And in that blog post that I recommend people go check out, you actually list a bunch of different books that you recommend on different topics on the American left and the American right.
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It s just a lot of really good stuff.
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The best explanation for the current structure of our society and politics, you give two recommendations.
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Four books on the Spanish Civil War.
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Six books on deep history of the American right.
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Comprehensive biographies of Adolf Hitler, one of which I read and can recommend.
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Six books on the deep history of the American left.
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So American right and American left, looking at the history to give you the context.
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Biography of Vladimir Lenin, two of them.
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on the French Revolution.
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Actually, I have never read a biography on Lenin.
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Maybe that will be useful.
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Everything s been so Marx-focused.
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The Sebastian biography of Lenin is extraordinary.
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Victor Sebastian.
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It ll blow your mind.
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Yeah.
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So it s still useful to read.
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It s incredible.
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Yeah, it s incredible.
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I actually think it s the single best book on the Soviet Union.
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So that, the perspective of Lenin might be the best way to look at the Soviet Union versus Stalin, versus Marx, versus, very interesting.
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So two books on fascism and anti-fascism by the same author, Paul Gottfried.
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Brilliant book on the nature of mass movements and collective psychology.
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The definitive work on intellectual life under totalitarianism, The Captive Mind.
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The definitive work on the practical life under totalitarianism,
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There s a bunch, there s a bunch.
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And the single best book, first of all, the list here is just incredible, but you say the single best book I have found on who we are and how we got here is The Ancient City by Numa Dennis Vostell D. Koulakis.
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I like it.
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What did you learn about who we are as a human civilization from that book?
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Yeah, so this is a fascinating book.
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This one s free.
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It s free, by the way.
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It s a book from the 1860s.
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You can download it or you can buy printouts of it.
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But it was this guy who was a professor at the Sorbonne in the 1860s, and he was apparently a savant on antiquity, on Greek and Roman antiquity.
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And the reason I say that is because his sources are 100% original Greek and Roman sources.
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So he wrote basically a history of Western civilization from on the order of 4,000 years ago to basically the present times, entirely working on original Greek and Roman sources.
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And what he was specifically trying to do was he was trying to reconstruct from the stories of the Greeks and the Romans, he was trying to reconstruct what life in the West was like before the Greeks and the Romans, which was in the civilization known as the Indo-Europeans.
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Um, and the short answer is, and this is sort of circa 4,000, you know, 2000 BC to, you know, sort of 500 BC, kind of that 1500 year stretch where civilization developed.
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Uh, and his conclusion was basically cults.
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Um, they were basically cults and civilization was organized into cults and the intensity of the cults was like a million fold beyond anything that we would recognize today.
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Like, it was a level of all-encompassing belief and action around religion that was at a level of extremeness that we wouldn t even recognize it.
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And so, specifically, he tells the story of, basically, there were three levels of cults.
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There was the family cult, the tribal cult, and then the city cult, as society scaled up.
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And then each cult was a joint cult of family gods, which were ancestor gods, and then nature gods.
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Um, and then you were bonding into a family, a tribe or a city was based on your adherence to that religion.
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Um, people, uh, who were not of your family tribe city worship different gods, which gave you not just the right, but the responsibility to kill them on site.
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So they were serious about their cults.
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Hardcore.
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By the way, shocking development, I did not realize there s zero concept of individual rights.
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Like, even up through the Greeks, and even in the Romans, they didn t have the concept of individual rights.
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Like, the idea that as an individual you have some right, it s just like, nope.
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Right.
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And you look back and you re just like, wow, that s just like crazily like fascist in a degree that we wouldn t recognize today.
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But it s like, well, they were living under extreme pressure for survival.
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And you know, the theory goes, you could not have people running around making claims to individual rights when you re just trying to get like your tribe through the winter.
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Right.
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Like you need like hardcore command and control.
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And so and actually, through a modern political lens, those cults were basically both fascist and communist.
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They were fascist in terms of social control, and then they were communist in terms of economics.
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But you think that s fundamentally that like pull towards cults is within us.
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So my conclusion from this book, so the way we naturally think about the world we live in today is like, we basically have such an improved version of everything that came before us, right?
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Like we have basically, we ve figured out all these things around morality and ethics and democracy and all these things.
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And like, they were basically stupid and retrograde and we re like smart and sophisticated.
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And we ve improved all this.
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After reading that book, I now believe in many ways the opposite, which is no, actually, we are still running in that original model.
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We re just running in an incredibly diluted version of it.
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So we re still running basically in cults.
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It s just our cults are at like a thousandth or a millionth the level of intensity.
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And so just to take religions, the modern experience of a Christian in our time, even somebody who considers them a devout Christian, is just a shadow of the level of intensity of somebody who belonged to a religion back in that period.
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And then by the way, it goes back to our AI discussion, we then sort of endlessly create new cults.
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We re trying to fill the void.
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And the void is a void of bonding.
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Living in their era, like everybody living today, transparent in that era would view it as just completely intolerable in terms of the loss of freedom and the level of basically fascist control.
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However, every single person in that era, and he really stresses this, they knew exactly where they stood.
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They knew exactly where they belonged.
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They knew exactly what their purpose was.
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They knew exactly what they needed to do every day.
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They knew exactly why they were doing it.
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They had total certainty about their place in the universe.
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So the question of meaning, the question of purpose was very distinctly, clearly defined for them.
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Absolutely, overwhelmingly, indisputably, undeniably.
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As we turn the volume down on the cultism, we start to, the search for meaning starts getting harder and harder.
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Yes, because we don t have that.
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We are ungrounded, we are uncentered, and we all feel it, right?
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And that s why we reach for, you know, it s why we still reach for religion, it s why we reach for, you know, people start to take on, you know, let s say, you know, a faith in science, maybe beyond where they should put it.
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Uh, you know, and by the way, like sports teams are like, um, you know, they re like a tiny little version of a cult and, you know, the, you know, apple keynotes are a tiny little version of a cult, right?
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You know, political, you know, and there s called, you know, there s full blown cults on both sides of the political spectrum right now.
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Right.
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Um, you know, operating in plain sight, but still not full blown compared as to what it was compared to what it used.
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I mean, we would today consider a full blown, but like, yes, they re, they re at like, I don t know, a hundred thousandth or something of the intensity of, of, of what people had back then.
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So we live in a world today that in many ways is more advanced and moral and so forth.
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And it s certainly a lot nicer, much nicer world to live in.
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But we live in a world that s like very washed out.
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It s like everything has become very colorless and gray as compared to how people used to experience things, which is, I think, why we re so prone to reach for drama.
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There s something in us deeply evolved where we want that back.
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And I wonder where it s all headed as we turn the volume down more and more.
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What advice would you give to young folks today in high school and college, how to be successful in their career, how to be successful in their life?
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Yeah.
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So the tools that are available today, I mean, are just like, I sometimes, you know, bore, I sometimes bore, you know, kids by describing like what it was like to go look up a book, you know, to try to like discover a fact and, you know, in the, in the old days, the 1970s, 1980s, go to the library and the card catalog and the whole thing.
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You go through all that work, and then the book is checked out, and you have to wait two weeks.
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To be in a world not only where you can get the answer to any question, but also the world now, the AI world, where you ve got the assistant that will help you do anything, help you teach, learn anything, your ability both to learn and also to produce is just, I don t know, a million-fold beyond what it used to be.
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I have a blog post I ve been wanting to write, which I call, Where Are the Hyperproductive People?
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That s a good question.
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With these tools, there should be authors that are writing hundreds of thousands of outstanding books.
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Well, with the authors, there s a consumption question, too.
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But yeah, well, maybe not.
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Maybe not.
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You re right.
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So the tools are much more powerful.
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They re getting much more powerful every day.
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Artists, musicians.
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Why aren t musicians producing a thousand times the number of songs?
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The tools are spectacular.
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So what s the explanation?
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And by way of advice, is motivation starting to be turned down a little bit or what?
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I think it might be distraction.
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Distraction.
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It s so easy to just sit and consume that I think people get distracted from production.
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But if you wanted to, as a young person, if you wanted to really stand out, you could get on a hyper productivity curve very early on.
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There s a great story in Roman history of Pliny the Elder, who was this legendary statesman that died in the Vesuvius eruption trying to rescue his friends.
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But he was famous both for basically being a polymath, but also being an author.
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And he wrote, apparently, hundreds of books, most of which have been lost.
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But he wrote all these encyclopedias.
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And he literally would be reading and writing all day long, no matter what else was going on.
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And so he would travel with four slaves.
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And two of them were responsible for reading to him, and two of them were responsible for taking dictation.
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And so he d be going cross country and literally he would be writing books all the time.
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And apparently they were spectacular.
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There s only a few that have survived, but apparently they were amazing.
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So there s a lot of value to being somebody who finds focus in this life.
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Yeah.
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And there are examples.
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There s this guy, Judge, what s his name, Posner, who wrote like 40 books and was also a great federal judge.
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Our friend Balje, I think is like this.
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He s one of these where his output is just prodigious.
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And so it s like, yeah, I mean, with these tools, why not?
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And I kind of think we re at this interesting kind of freeze frame moment where, like, these tools are now in everybody s hands and everybody s just kind of staring at them trying to figure out what to do.
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Yeah.
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The new tools.
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We have discovered fire.
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Yeah.
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And trying to figure out how to use it to cook.
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Yeah.
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Right.
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You told Tim Ferriss that the perfect day is caffeine for 10 hours and alcohol for four hours.
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You didn t think I d be mentioning this, did you?
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It balances everything out perfectly, as you said.
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So let me ask, what s the secret to balance and maybe to happiness in life?
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I don t believe in balance, so I m the wrong person to ask.
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Can you elaborate why you don t believe in balance?
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I mean, maybe it s just, and I, look, I think people, I think people are wired differently.
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So I think it s hard to generalize this kind of thing, but I am much happier and more satisfied when I m fully committed to something.
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So I m very much in favor of imbalance.
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Yeah.
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Imbalance.
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And that applies to work, to life, to everything.
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Now I happen to have whatever twist of personality traits lead that in non-destructive dimensions, including the fact that I now no longer do the 10-4 plan.
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I stop drinking.
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I do the caffeine, but not the alcohol.
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So there s something in my personality where whatever maladaption I have is inclining me towards productive things, not unproductive things.
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So you re one of the wealthiest people in the world.
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What s the relationship between wealth and happiness?
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Money and happiness.
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So I think happiness
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I don t think happiness is the thing to strive for.
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I think satisfaction is the thing.
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That just sounds like happiness, but turned down a bit.
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No, deeper.
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So happiness is, you know, a walk in the woods at sunset, an ice cream cone, a kiss.
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The first ice cream cone is great.
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The thousandth ice cream cone, not so much.
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At some point, the walks in the woods get boring.
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What s the distinction between happiness and satisfaction?
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SPEAKER_00
I think satisfaction is a deeper thing, which is like having found a purpose and fulfilling it, being useful.
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So just something that permeates all your days, just this general contentment of being useful.
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That I m fully satisfying my faculties, that I m fully delivering on the gifts that I ve been given, that I m net making the world better, that I m contributing to the people around me, and that I can look back and say, wow, that was hard, but it was worth it.
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I think generally seems to lead people in a better state than pursuit of pleasure, pursuit of quote-unquote happiness.
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Does money have anything to do with that?
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I think the founders, the founding fathers in the U.S.
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threw this off kilter when they used the phrase pursuit of happiness.
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I think they should have said... Pursuit of satisfaction.
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If they said pursuit of satisfaction, we might live in a better world today.
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Well, you know, they could have elaborated on a lot of things.
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They could have tweaked the second amendment a little bit.
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I think they were smarter than I realized.
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They said, you know what, we re going to make it ambiguous and let these humans figure out the rest.
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These tribal cult-like humans figure out the rest.
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But money empowers that.
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So I think, and I think they re, I mean, look, I think Elon is, I don t think I m even a great example, but I think Elon would be the great example of this, which is like, you know, look, he s a guy who from every, every day of his life from the day he started making money at all, he just plows into the, into the next thing.
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Um, and so I think, I think money is definitely an enabler for satisfaction.
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I was supposed to say money applied to happiness leads people down very dark paths, very destructive avenues.
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SPEAKER_00
Uh, money applied to satisfaction, I think could be, it gives a real tool.
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I always, by the way, I was like, you know, Elon is the case study for behavior.
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SPEAKER_00
But the other thing that has always really made me think is Larry Page was asked one time what his approach to philanthropy was, and he said, oh, I m just, my philanthropic plan is just give all the money to Elon.
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Right?
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Well, let me actually ask you about Elon.
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What are your,
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You ve interacted with quite a lot of successful engineers and business people.
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What do you think is special about Elon?
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We talked about Steve Jobs.
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What do you think is special about him as a leader, as an innovator?
10
SPEAKER_00
Yeah, so the core of it is he s back to the future.
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SPEAKER_00
So he is doing the most leading edge things in the world, but with a really deeply old school approach.
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And so to find comparisons to Elon, you need to go to like Henry Ford and Thomas Watson and Howard Hughes and Andrew Carnegie.
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right?
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Um, Leland Stanford, um, John D. Rockefeller, right?
10
SPEAKER_00
You need to go to the, what we re called the bourgeois capitalists, like the hardcore business owner operators who basically built, you know, basically built industrialized society, um, vendor built.
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SPEAKER_00
Um, and it s a level of hands-on commitment, um, and, uh, depth, um, in the business, um, coupled with an absolute
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SPEAKER_02
absolute priority, uh, towards truth, um, and towards, um, how to put it, science and technology, uh, town to first principles.
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That is just like absolute, just like, it was just like unbelievably absolute.
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He really is ideal that he s only ever talking to engineers.
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Like he does not tolerate bullshit.
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He has the bullshit dollars to anybody I ve ever met.
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Um, he wants ground truth on every single topic.
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Um, and he runs his businesses directly day to day devoted to getting to ground truth and every single topic.
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So, uh,
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You think it was a good decision for him to buy Twitter?
11
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I have developed a view in life to not second guess Elon Musk.
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I know this is going to sound crazy and unfounded, but... Well, I mean, he s got quite a track record.
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I mean, look, the car was a crazy, I mean, the car was, I mean, look.
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He s done a lot of things that seem crazy.
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Starting a new car company in the United States of America, the last time somebody really tried to do that was the 1950s.
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And it was called Tucker Automotive.
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And it was such a disaster.
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They made a movie about what a disaster it was.
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Um, and then Rockets, like who does that?
11
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Like that s, there s obviously no way to start a new rocket company.
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Like those days are over and then to do those at the same time.
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So after he pulled those two off, like, okay, fine.
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Like,
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Like, this is one of my areas of like, whatever opinions I had about that is just like, okay, clearly are not relevant.
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Like this is, you just, at some point you just like bet on the person.
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And in general, I wish more people would lean on celebrating and supporting versus deriding and destroying.
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Oh, yeah.
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I mean, look, he drives resentment like it s like he is a magnet for resentment.
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Like his critics are the most miserable, resentful people in the world.
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Like it s almost a perfect match of like the most idealized, you know, technologist, you know, of the century, coupled with like just his critics are just bitter as can be.
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I mean, it s sort of very darkly comic to watch.
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Well, he fuels the fire of that by being an asshole on Twitter at times, which is fascinating to watch the drama of human civilization given our cult roots just fully on fire.
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He s running a cult.
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You could say that.
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Very successfully.
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So now, now that our cults have gone and we search for meaning, what do you think is the meaning of this whole thing?
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What s the meaning of life, Mark Andreessen?
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I don t know the answer to that.
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Um, I think the meaning of, uh, of, uh, the closest I get to it is what I said about satisfaction.
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So it s basically like, okay, we were given what we have.
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Like we should basically do our best.
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What s the role of love in that mix?
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I mean, like what s the point of life if you re, yeah, without love, like, yeah.
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So love is a big part of that satisfaction.
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And look, like taking care of people is like a wonderful thing.
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Like, you know, mentality, you know, there are pathological forms of taking care of people, but there s also a very fundamental, you know, kind of aspect of taking care of people.
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Like, for example, I happen to be somebody who believes that capitalism and taking care of people are actually, they re actually the same thing.
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Um, somebody once said, capitalism is how you take care of people you don t know.
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Right.
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Um, right.
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And so like, yeah, I think it s like deeply woven into the whole thing.
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Um, you know, there s a long conversation to be had about that, but yeah.
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Yeah, creating products that are used by millions of people and bring them joy in smaller, big ways.
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And then capitalism kind of enables that, encourages that.
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David Friedman says there s only three ways to get somebody to do something for somebody else.
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Love, money, and force.
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Love and money are better, of course.
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That s a good ordering, I think.
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We should bet on those.
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Try love first.
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If that doesn t work, then money.
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And then force.
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Well, don t even try that one.
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Mark, you re an incredible person.
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I ve been a huge fan.
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I m glad I finally got a chance to talk.
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I m a fan of everything you do, everything you do, including on Twitter.
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It s a huge honor to meet you, to talk with you.
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Thanks again for doing this.
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Awesome.
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Thank you, Lex.
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Thanks for listening to this conversation with Marc Andreessen.
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To support this podcast, please check out our sponsors in the description.
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And now let me leave you with some words from Marc Andreessen himself.
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The world is a very malleable place.
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If you know what you want and you go for it with maximum energy and drive and passion, the world will often reconfigure itself around you much more quickly and easily than you would think.
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Thank you for listening and hope to see you next time.