This is actually one of the things I think people don't realize about Silicon Valley. You start with what's the amazing thing that you can suddenly create. Lots of these companies, and you go, "What's your business model?" They go, "I don't know." You're like, "Yeah, we're going to try to work it out. But, I can create something amazing here." And that's actually one of the fundamental, call it the religion of Silicon Valley and the knowledge of Silicon Valley that I so much love and admire and embody. Reed, welcome to This Week in Startups podcast. It's great to be here. So, Reed, you're one of the most successful Web 2 investors of of that era, you know, Facebook, LinkedIn obviously which you co-created, Airbnb, many, many others. And you had several frameworks that helped you do that, one of which was the seven deadly sins which we talk about often and love. As you're thinking about AI investing, what what what's your framework or or worldview that you take to your AI investing? So, obviously we're all looking through a glass darkly, looking through a fog with strobe lights that don't really, you know, are hard to understand what's going on. So, we're all navigating this new this new universe. So, I don't don't know if I have a crisper framework. The seven deadly sins still work because that's a question of what is infrastruct psychological infrastructure across all 8 billion plus human beings. And But, I'd say there's a couple things. So, first is um there is going to be a set of things that are the kind of the obvious line of sight. Obvious line of sight, bunch of stuff with chatbots, bunch of stuff productivity, coding assistants, you know, da da da da da. And so, and by the way, that's still worth investing in, but obviously obvious line of sight means it's obvious to everybody. Line of sight. And so, so, you know, doing a differential investment is harder. The second area is well, what does this mean cuz too often people say in an area of disruption that everything changes as opposed to significant things change. So, like you were mentioning Web 2.0 and LinkedIn and and obviously, you know, part of this with a platform change, you go, "Okay, well, are there now new LinkedIns that are possible because of AI or something like that?" And obviously, given my own heritage, I would love LinkedIn to be that. But, you know, it's whatever. I'm always pro innovation entrepreneurship, best possible thing for humanity. Um but, like what are the kind of more traditional like the kind of things that haven't changed? Network effects, you know, enterprise integration, you know, other kinds of things that that the new platform um upsets the apple cart, but you're still going to be putting that apple cart kind of back together in some way, and what is that? And then the third, um which is probably where I've been putting most of my time, has been what I think of as Silicon Valley blind spots. Because what we tend to be like Silicon Valley is is one of the most amazing places in the world. There's a network of intense co-opetition, learning, you know, invention, you know, kind of building new things, etc., which is just great. But, we also have our canons. We have our kind of blind spots. And a classic one for us tends to be well, everything should be done in CS, everything should be done in software, everything should be done in bits. And that's the most relevant thing because by the way, it's a great area to invest. Um but, it was like, "Okay, what are the areas where the AI revolution will be magical, but won't be within the Silicon Valley blind spots?" And that's probably where I've been putting the majority of my co-founding time, invention time, um you know, kind of investment time, etc. Because like I think usually a blind spot on something that's very, very big, Yeah. right, is precisely the kinds of things that you go, "Okay, you have a a long runway to create something that could be like another one of the iconic companies." Yeah. Let's go deeper on that cuz we're also talking just just before this about how people focus so much on the productivity and the workflow sides, but they're missing other other elements. So, say more about other other things that you find more interesting now. Well, so um So, one of the things I, you know, kind of told my partners back at Greylock in 2015, so it's like 10 years ago, um was I said, "Look, there's going to be a bunch of things on productivity around AI. I'll help, right? Like, you know, I'll you know, you have companies you want me to to work with that you're doing, great, that's awesome. You know, enterprise productivity, etc., you know, things that Greylock tends to specialize on. But, I said, "Actually, in fact, what I think that's here, getting the blind spots, is um is also going to be some things like, you know, what, you know, as you guys both know, Madison AI, um which is how do we create a drug discovery factory that works at the speed of software." Yeah. Right. Now, obviously, there's regulatory, obviously there's biological bits, obviously da da da. And so, does it the won't be purely speed of software, but how do we do this? And they said, "Oh, well, what do you know about biology?" And the answer is "Zero." Well, it may be not quite zero, you know, been on the board of Biohub for 10 years, I'm on the board of Arc, etc. Like, I've been thinking about the intersection of the world of atoms and the world of bits, and you have biological bits which are kind of halfway between atoms and bits in various ways. I've been thinking about this a lot and kind of what the things are. Not so much with a specific company focus as much as a "What are things that elevate human life?" you know, kind of focus. Part of the reason why Biohub, part of the reason why Arc. But then I was like, "Well, wait a minute. Actually, now with AI, and you have the acceleration." Cuz like for example, actually I'll This detour will be fun. So, roughly also around 10 years ago, I was asked to give a a talk to the Stanford Long-Term Planning Commission. And um what I told them uh was that they should basically divert and and put all of their energy into AI tools for every single discipline. Mhm. And this is well before ChatGPT and all the rest. And the metaphor I used was a search metaphor because think if you had a custom search productivity tool in every single discipline. Now, back then, I could imagine it I could build one for every discipline other than theoretical math Today, you might even be able to do theoretical math and theoretical physics. Exactly. And so, do that. Like, transform knowledge generation, knowledge communication, knowledge analysis. Well, that kind of same thing now thinking well, well, the biological system is still too complex to simulate. We've got all these amazing things with LLMs, but like the classic Silicon Valley blind spot is, "Oh, we'll just put it all in simulation and drugs will fall out." Right. That simulation is difficult. Now, part of the insight that you begin to see from like the work with Alpha, you know, Go and AlphaZero is cuz like people used to think, "Ah, physical material is going to take quantum computing." Now, quantum computing could do really amazing things, but actually simply doing prediction and getting that prediction right, and by the way, it doesn't have to be right 100% of the time, has to be right like 1% of the time because you can validate the other 99% were were it right, and then finding that one thing. And so, literally, it's like it's not a needle in a haystack, it's like a needle in a solar system. Right. And it's like, "But, you could possibly do that." And that's part of what led to like, "Okay, Silicon Valley will classically go, we'll put it all in simulation, and that will solve it." Nope, that's not going to work. Or, "Oh no, we're going to have a super intelligent drug researcher, and that will be 2 years down the thing." I actually, look, maybe someday, not soon. Right. So, anyway, that was the kind of thing that was the the the in other different areas. Now, part of it's also um you know, kind of what a lot of people don't realize. Actually, if I'm not going too long, I'll do I'll go I'll go to the other example that that I gave cuz that you'll love this. Um this will echo some of our conversations from 10, 15 years ago. Um So, um I am prepping for a debate about on Sunday this week on whether or not AIs will replace all doctors in a small number of years. Now, the pro case is very easy, which is we have massively increasing capabilities. If you look at ChatGPT today, um you'd go like for example, advice to everyone who's listening to this, if you're not using ChatGPT or equivalent as a second opinion, you're out of your mind, you're ignorant. You get a serious result, check it as a second opinion. And by the way, if it diverges, then go get a third. Um and so, the diagnostic capabilities, these are much better knowledge stores than any human being on the planet. So, you go, "Well, if a doctor is just a knowledge store, yeah, that's going away." However, the question is actually I think things that really do mean doctor, and it's not like, "Oh, someone will hold your hand and says, 'Oh, it's okay.'" etc. Um you know, I actually think there will be a position for a doctor 10 years from now, 20 years from now. It won't be as the knowledge store, it will be as a user of the as an expert user of the knowledge store, but it's not going to be "Oh, because I went to med school for 10 years, and I memorized things intensely, that's why I'm a doctor." That's all going away. Great. That part But, that But, there's a lot of other parts to being a doctor. Now, so I went to ChatGPT Pro, you know, using deep research, I went to Claude, you know, 4.5 deep research, I went to Gemini Ultra, I went to Copilot deep research, and I in all of these things I was doing everything I knew about prompting to try to give me the best possible arguments for my position. Cuz I thought, well, I'm about to debate on AI, of course I should be using AI to debate. The answers were B- minus or B, despite absolute top thing. And I'm not like maybe there's probably better prompters in the world, but I've been doing this since I got access to GPT-4 6 months before the public did. Right, so I've I've got some experience in the whole prompting thing. It's not like I'm an amateur prompter. And so I looked at this and I went, oh, this is very interesting and a telling of where current LLMs are limited in their reasoning capabilities because um what it did is it basically did, you know, 10 to 15 minutes of like 32 GPU compute clusters doing inference. Bringing off all in amazing work relative to a work that an analyst would have produced in 3 days was produced in 10 minutes. And of course I set it up all in parallel, you know, with different browser tabs all all going into the different systems and then ran the comparisons across them everything. But its flaw was that it was giving me a consensus opinion about how articles in good magazines, good things are arguing for that position today. And all of that was weak cuz it was kind of like, oh, you need to have humans cross-check the diagnosis. Right? Like it was a common theme across this. And I'm like, well, by the way, very clearly we know as technologists that human cross-checking the diagnosis, we're going to have AIs cross-checking the diagnosis. We're going to have AIs cross-checking the AIs that cross-check the diagnosis. And sure, there'll be humans around here somewhere, but like that's not going to be the central place to say in 20 years doctors are going to be cross-checking the diagnosis cuz by the way, what doctors should be learning very quickly is if you have believe something different than the consensus opinion that an AI gives you, you'd better have a very good reason and you're going to go do some investigation. Doesn't mean the AI is always right. That's actually part of what you're like what we're going to need in all of our professions is is more sideways thinking, more lateral thinking. The okay, this is good consensus opinion. Now, what if it's not consensus opinion? That's what doctors need to be doing. That's what lawyers will need to be doing. That's what coders will need to be doing. You know, that's what it is. And LLMs are still pretty structurally limited there. What's funny is my my favorite saying is by Richard Feynman, science is the belief in the ignorance of experts. Yes. And there are so many professions where the credentialism is the expertness. Right? It's like it's it's if this then that. And it's like I have MD, therefore I know. I have JD, therefore I know. And that's that's why coding is actually a little bit ahead of it because it's like I don't care where you got your degree. This is a it's kind of ahead of the rest of society. Now, um it's funny yeah, Milton Friedman one time got asked um because he was, you know, famous libertarian, uh don't you think that brain surgeons should be credentialed? And it's like, yeah, the market will figure that out. It seems kind of crazy, right? But that's how we we now do coding when you're in the world of bits. Um but it feels like a lot of the reasons why you have this, you know, very not not very advanced thinking is because so much of it is built upon layers of credentialism. And that's that's a very good heuristic. And historically it has been. If you have a doctor that graduated at the top of their class from Harvard Medical School, it's like probably a good doctor. And by the way, you politically wanted that >> Yes. 3 years ago. Right. Right? It's like, no, no, I need someone who has the knowledge base. Do you have it? Great. Right. But now we have a knowledge base. Yeah. I totally agree. That was the reason I was saying you would love this cuz it echoes of our expert discussion. >> thought you were going to get into um you know, bits versus atoms where it's kind of interesting right now where it's like all this high value work like Goldman Sachs sell-side analyst, that's deep research. Right? Whereas fold my laundry, that's $100,000 of capex. So it doesn't work as well as somebody that you could pay $10 an hour to. And it's like the atom stuff is so hard to actually disrupt. Um and we're going to get there eventually, but that's where Silicon Valley certainly has a blind spot, but it's like a capex versus opex or like, you know, bits versus atoms. The atoms is another part, but that's also reason why bio cuz bios are the are the are the are the bitty atoms. Yes, yes, yes. And what's the what's the best explanation for why it's so hard to figure out folding laundry, but so easy to figure out um Well, it's it's actually not that hard to figure out or why it's taken us much longer, what much more expensive cuz we couldn't it would've been hard to foresee that in advance. Well, I remember I talked to Ilya about this a few years ago. And it's like, why is it that if you read an Asimov novel where it talked about like how, you know, people will cook for you and fold your laundry. Like why have none of these things happened? Um and it's like, well, you just never had a brain that was smart enough. This is part of the problem is that you could I mean yes, you have things like, you know, how do you actually pick up this water bottle? And it turns out your hands are very very well like why are humans more advanced than every other species? So there are two reasons. Number one is we have opposable thumbs. And then number two is we've come up with a language system that we could pass down from generation to generation, which is writing. Dolphins are very smart. Like there there was actually a whole theory which is it wasn't just brain size, it was brain to body size. So humans were the highest. Nope, not true. And now that we've actually measured every single animal, there are a lot of animals that have more brain over body size. Um like that that that ratio is intuitive. An elephant or of a dolphin or I forgot the numbers, but there are a bunch that are actually more advanced than humans, but they don't have opposable thumbs. And because of that they never developed writing, so they can't actually iterate from generation to generation. And humans did. And then of course like the human condition was like it was this and then the industrial revolution then it went like that and now it's continued like this. >> By the way, this is the reason why in the last 4 or 5 years one of the things I realized is, you know, um cuz the classic uh classification of human beings is Homo sapiens. I actually think we're Homo technae. Because it's that iteration through technology. Yes. Yes, exactly. Whatever version, right? Typing, you know, but it's we iterate through technology. That's the actual thing goes to future generations, builds on science, you know, all the rest of it. And that's what I think is really key. Yeah. A couple of other explanations could be that we have more training data on white-collar work than sort of, you know, pick picking things up. Or as some people make this evolutionary argument that we've been using our disposable thumbs for way longer than we've been, say, you know, reading or writing. >> yeah, it's the the lizard brain. Like most of your brain is not the neocortex. And like that's the like draw and paint and everything else, which is actually very very hard. You can't find a dolphin that can draw or paint and that's probably cuz they don't have opposable thumbs, but it's also like maybe that part of the brain hasn't developed, but you have like you have billions of years of evolution for these somewhat autonomous responses like fight or flight, that's been around for a long long time well before drawing painting. But I think the main issue is just like you have battery chemistry problems. Like I can't like it turns out like a lithium-ion battery is pretty cool, but the energy density of that is terrible relative to ATP with cells. Right? Like you have all of these reasons why robotics don't work, but first and foremost is the brain was never very good. So you had robotics like Fanuc, which makes assembly line robots. Those work really well, but it's like very deterministic or highly deterministic. But once you go into like, you know, multiple degrees of freedom, you have to get so many things to work and the capex it's like I need $100,000 to have a robot fold my laundry. And we have so many extra people that will do that work, the economics never made sense. But this is why Japan is a leader in robotics. Because they can't hire anybody. So therefore I might as well build True story, I went bowling in Japan and they had a robot to give like a vending machine robot that would give you your bowling shoes. And then it would clean the bowling shoes. Right? And it's like you would never build that here because you'd hire some guy from the local high school and he'd go do that. Yeah, and much cheaper and actually more effective. But it's this capex like the capex line and the opex line when they cross, then it's like, ooh, I should build robots. So that's the other thing you probably need, but if the cost goes down, then of course it goes in in favor of capex versus opex. I think there's a lot of couple things to go in deeper on the robot side. So one is the density the the the the bits to value. Right? So like in language when we encapsulated all these things even into like romance novels, there's a high bits to value. Whereas when you're going to in the whole world, there's a lot of like how do you we abstract from all those bits and how do you abstract them? There's another part of it which is kind of common sense awareness. Like this is one of the things that like when I look at, you know, GPT-2, 3, 4, 5, it's a progression of savants. Right? And the savants are amazing. It doesn't mean the savant it but but like when it makes mistakes, like as a classic thing, so Microsoft has had running for years now agents talking to each other long for like just like let's go for a year and do that and see what happens. And so often they get into like, oh, thank you. No, thank you. No, thank you. One month later, thank you. No, thank you. Which human beings are like, stop. Right? Like just like it's and that's like a that's a simple way of putting the context awareness thing of saying, no, no, no, no, let's let's stay very context aware. And even as magical as the progression has been, like much much better bit data, much much better reasoning, much much better uh personalization, etc. etc., context awareness only is a proxy of that. Yeah. Yeah. I want to go deeper on um your question about doctors reading cuz I we just released one of your talks around, you know, software eating labor. And I'm curious where you how you what sort of frameworks you have for thinking about what spaces are going to have more of this copilot model versus what spaces are it's going to be what sort of replacing the work entirely. Uh I have I wish I could I'm going to use an LLM to go predict the future, but I'm going to get a B- minus apparently. So maybe I'll answer when I get a B plus. Um I think a lot of it is like the natural like there there's this cue morphic version which is okay, well, I I trust the doctor. Everybody trusts the doctor. The heuristic is where did you go to medical school? Apparently two-thirds of doctors now use open evidence. Mhm. Um which is like Chat GPT, but it ingested the New England Journal of Medicine and have like a license to that. So Yeah, Daniel Nadler. Good guy. Um can sure, right? So yeah, so so that seems like there's no reason not to do that. Like my my seven deadly sins version uh I'll simplify it which is like everybody wants to be lazier and richer. Mhm. So if this is a way that I can like get more patients and do less work, of course people are going to use this. There's no reason not to. Um but does it replace that particular thing? And actually most of like the the software eats labor thing, it doesn't actually eat labor. Right now, the thing that's working the best is not like, "Hey, I have a product where everybody's going to lose their job." Nobody's going to buy that product. It's very very hard to get that distributed as opposed to, "I will give you this magic product that allows you to be lazier." Obviously, it's not framed this way like lazy and rich and sounds kind of uh you know, not not great. But I'm going to let you work fewer hours and make more money. And that's that's a very killer combo. And if you have a product like that, um and it's delivered by somebody that already has that heuristic of expertise, these are just going to go one after another and get adopted adopted adopted. And then eventually, you're going to have cases like the one that you mentioned where if you don't use Chat GPT when you get a medical diagnosis, you're insane. Yeah. But that is not fully diffused across the population. Well, it's barely diffused. >> No, I I know. Yeah, no no no no, but you were saying not fully. I mean, part of the reason everyone start doing it. >> Yes. Oh 100%. >> What's that? It's the fastest growing product of all time again. It's barely, you know. >> that's why I'm convinced that AI is massively underhyped because in in Silicon Valley, you might not make that claim. Maybe it's overhyped, maybe evaluation, whatever. But I think once I meet somebody in the real world and I show them this stuff, they have no idea. And part of it is like they see the IBM Watson commercials and like, "Oh, that's AI." No, that's not AI, right? Or they see the fake AI. They've seen Chat GPT 2 years ago. It didn't solve a problem and uh it's funny I I I made this blog post. You know, that that when when you were my investor at TrialPay, I called it never judge people in the present and this is a mistake. It's it's a category error that a lot of big company people make. But I mean that almost metaphorically. And the way that I wrote this blog post was I found a video of Tiger Woods. He was 2 and 1/2 years old. He hit a perfectly straight drive. Mhm. And uh he was on, you know, not the I think the Tonight Show or something. And there were two ways of watching that video. You could say, "Well, I'm 44. I can hit a drive much further than that kid." Which is correct. Or you could say, "Wow, if that 2 and 1/2 year old kid keeps that up, he could be really really good." Yes. And most people judge things on the present. Yes. And that's why it's underhyped because it's like they tried at some point in time. Yes. Um there's a distribution of when they tried it. Like probabilistically, it's in the past. And like, "Oh, that didn't work for my use case. It doesn't work." >> Yes. And that's that's that's bad. But so I think it's going to diffuse largely around this like lazy rich Yeah. like concept and that's where a lot of these things have taken off. And I see it less at the very very big companies because you have a principal agent problem at the very big companies. Like, "Okay, my company made money or saved money. I'm a director of XYZ. Like all I know is that I want to leave earlier and get promoted." Yeah. And how does that actually help me? It helps the ethereal being of the corporation. Whereas at a smaller business or a sole proprietor or an individual doctor where I run a dermatology clinic and somehow I can have five times as many patients or I'm a plaintiff's attorney, I can have five times as many settlements. It's like, of course I'm going to use that because I get to be lazier and richer. Yeah. Yep, 100%. That seems like a good model. By the way, the other one you're reminding me, Ethan Mollick has a quote here that I use often that He's great. Yes. The worst AI you're ever going to use is the AI you're using today cuz it's to remind you use it tomorrow. Yep. Yeah. Yeah. And a lot of the skeptics is exactly this. It's like, "Well, I tried it 2 months ago and it didn't solve this problem. Therefore, it's bad." It's like you're you're judging it on the present. Like you have to extrapolate. Um and you don't want to get like too extrapolatory on like, you know, "Oh, LLMs have this." Like you actually have I feel like the two types of people that are underhyping AI are people that know nothing and people that know everything. Yes. It's really interesting. It's like the meme where it's like idiot meme where it's like the people that's in Yeah, it's like the the people in the the this part of the distribution are correct. Normally, the meme is the opposite. It's like these part these people are smart even though they're dumb. These people are smart even though they're smart. Everybody here like this is this part of the curve is actually correct because they're the ones that are using it to get richer and be lazier. The other thing I also tell people is if you haven't found a use of AI that helps you on something serious today, not just write a sonnet sonnet for your kids' birthday or you know, I got these ingredients in my fridge, what should I make? Do those, too. But if you haven't for something like work, for like something is serious about what you're doing, you're not trying hard enough. Yeah. Yeah. It's isn't that it work does everything. Like for example, I still think if I put in like, "How should Reid Hoffman make money investing in AI?" And I'll go try that again. I'm like I suspect I will still get what I think is the bozo business professor answer versus the actual game name of the game. But um everyone should be trying. And I, you know, like for example, we put when we get decks, we put them in and say, "Give me a due diligence plan." Right? If not everybody here doing that, that's a mistake. Yeah. Uh cuz you 5 minutes, you get one and you go, "Oh, no, not two, not five. Oh, but three is good and it would have taken me a day to getting to about three." Yeah. Yeah. Um in terms of let's go back to extrapolation. Obviously, the last few years have had incredible um growth. You you were involved of course with Open AI since the beginning. When we look for the next few years, um it's brought a question as to whether scaling laws will hold, whether sort of the limitations um or how far we can get with with LLMs. Um do we need another breakthrough of a different kind? What what is your view on some of these questions? So one of the things we, you know, we all swim in this universe of extrapolating the future. One of the things that's great about Silicon Valley and so you get such things as, you know, theories of singularity, think theories of superintelligence, theory of exponential getting to superintelligence soon. And what I find is usually the mistake in that is not the fact of extrapolating the future. That's smart and people need to do that and far too few people people do. I think I remember liking your post and helping promote it if I recall. Um but it's the notion of well, what curve is that? Like if it's a savant curve, that's different than oh my gosh, it's an apotheosis and now it's God. You know, you know, it's like, "No, no, no. It'll be a an even more amazing savant than we have." But by the way, if it's only savants, there's always a room for us. There's always rooms for the generalist and the cross-checker and the context awareness and all the rest of it. Now, maybe maybe it'll cross over a threshold or not, maybe it won't, you know. Like I think there's a bunch of different questions here, but that extrapolation too often goes, "Well, it's exponential. So in 2 and 1/2 years, magic." And you're like, "Well, look, it is magic, but it's not all magic." Is the is the kind of way he's doing it. Now, so my own personal belief is that um look, so the critics of LLMs make a mistake in that and you know, we can go through all the different critics. Oh, not knowledge representation. It it screws up on, you know, prime numbers and you know, blah blah blah blah blah. We've all heard >> How many Rs in strawberry? Yes, exactly. Yeah. You know, and they're like, "Oh, see, it's broken." And you're like, "You're missing the magic." Right? Like yes, maybe there's some structural things that over time even in 3 to 5 years will continue to be a difficult problem for LLMs. But AI is not just the one LLM to rule them all. It's a combination of models. We already have combination of models. We use diffusion models for various image and video tasks. Now, by the way, they wouldn't work without also having the LLMs in order to to have the ontology to say, "Create me an Eric Thornberg as a Star Trek captain, you know, going out to, you know, explore the universe and meeting making first contact with the Vulcans and so forth." Which, you know, now with our phone, we could do that. Right? And it would be there uh courtesy Open AI. Uh and you know, VO cuz Google's model is also very good. But it needs LLMs for that. But the thing that people aren't tracking is it's going to be LLMs and diffusion models and I think other things with a with a fabric across them. Now, one of the interesting questions is is the fabric fundamentally LLMs? Is the fabric other things? Like I think that's a TBD on this. And the degree to which it gets to intelligence is an interesting question. Now, one of the things I think is a um you know, like I I talk to all the critics intensely not because I necessarily agree with the criticism, but I'm trying to get to the what's the kernel of insight. >> Yeah. And like one of the things that I uh loved about, you know, kind of a set of recent conversations with Stuart Russell was saying, "Hey, if we could actually get the fabric of these models to be more predictable, that would greatly allay the fears of what happens if something goes amok." Well, okay, let's try to do that. Now, I don't think the whole verification of outputs like like logical like we can't even do verification of coding. Right? Like verification of strikes me as very hard. Now, brilliant man, maybe Google figure it out. But the um but but on the other hand, the hey, this is a good goal. Can we make that more programmable, reliable? I think that is a good goal that people that very smart people should be working on. And by the way, smart AIs. Well, that's some of the math side. It's like if you think about the the foundation of the world, I mean, uh philosophy is the basis of everything. Actually, math comes from philosophy. It's called the Cartesian plane after Descartes. You know, you're philosophy based. You know this, right? So you have you have uh philosophy, math, physics. Like why did Newton build calculus to understand the real world? So math, physics. Physics gets you chemistry. Chemistry gets you biology and then biology gets you psychology. So, that's kind of the stack. So, if you solve math, that's actually quite interesting because um there's a professor at Rutgers, uh Kontorovich, who's written about this a lot. Um and I find this part fascinating just as a former mathematician because there are some very, very hard problems. Um there there's a rumor that the Navier-Stokes equation is going to be solved by DeepMind, which would be huge. That's one of the Clay Math problems. >> Yeah. But, you know, the Riemann hypothesis, like this is not like there's no eval. Yes. >> Right? If it's like uh this is why if you look at the progression of AI, there's the AIME, the American Invitational Math Examination, where you the answers are all just like three the it's just integers. Right? It's like 0 to 999 is the answer, and then of course you can keep trying different things, and then you either get the right answer or you don't, and it's very, very easy to do that. Whereas, once you get to proofs, very, very hard. Yes. Um and if you solve that, I mean, is that AGI? No, because the the goalpost keep changing at AGI. >> Yes. But, math is just so interesting. >> AGI is the AI we haven't invented. >> Exactly. That's exactly it. It's it's the corollary to it's like, you know, if the worst AI you're going to try is today, well, AGI is what you're going to have tomorrow, right? It's it's the same same kind of thing. But, math is a very, very interesting one as well because again, you have these things. It's not like solving high school math. Right? This is like if you're able to actually logically construct a proof for something and then validate it. Um there's a whole programming language called Lean, which is for that. Like that that stuff is also fascinating. So, there's so many different vectors of attack, which is the other the other way of thinking about it. It's fascinating. So, uh as you just mentioned, Alex, you're a philosophy major, but you're also very interested and deep in neuroscience. And some people say that hey, we'll never create AI with its own sort of consciousness because we don't even understand our own consciousness. We don't understand how our our own brain works. Um and then there's a broad question is, oh, will AI have its own goals? Or will AI have its own agency? Uh what is what is sort of your view on on some of these questions around in consciousness and what is AI? Well, consciousness is its own hairball, which I will say a few things about. I think agency and goals is almost certain. Um there is a question. I think this is one of the areas where we want to ex um have some clarity and control. That was a little bit like the the kind of question, what kind of compute fabric holds it together? Because you can't get complex problem-solving without it being able to set its own minimum subgoals and other kinds of things. And so, so goal setting and behavior and inference from it. And that's where you get the classic kind of like, well, you tell it to maximize you tell it to maximize paperclips and it tries to convert the entire planet into paperclips. And there's one thing that's definitely old com computer where that there was just no context awareness, something I even worry about modern AI systems. But, on the other hand, it's like, look, if you're actually creating an intelligence, they don't go, oh, let me let me like let me just go try to convert everything into paperclips. It's like it's it's actually in fact not that simple in terms of how it plays. Now, um now consciousness is an interesting question because you got some very smart people, Roger Penrose, um who I actually interviewed way back when on Emperor's New Mind, speaking of mathematicians. Um and um you know, who are like, look, actually in fact, there's something thing about our form of intelligence, our form of of of computational intelligence that's quantum-based that has to do with how our physics work that has to do with things like true tubulars and so forth. And by the way, it's not impossible. Like that's that's that's a it's a coherent theory from a very smart mathematician. Like one of the world's smartest, right? Like it's kind of in the category of there's other people as smart, but there's no one smarter. Right? In in in in that kind of vector. And so, so that's possible. Um I don't think you need consciousness for um goal setting uh or reasoning. Um I'm not even sure you need consciousness for certain forms of self-awareness. There may be some forms of self-awareness that consciousness is necessary for. It's a tricky thing. Philosophers have been trying to address this not very well for as long as we've got records of philosophy. Right? And And philosophers agree. I'm not philosophers wouldn't think I was throwing them under the bus with this. They're like, yeah, this is a hard problem. Cuz it ties to agency and free will and a bunch of other things. And and I think that the right thing to do is keep an open mind. Now, part of keeping an open mind, I think um Mustafa Suleyman wrote a very good piece in the last month or two on like semi-consciousness, which is we make too many mistakes all of the Turing test, a piece of brilliance, which is um well, it talks to us, so therefore it's fully intelligent and all the rest. And so, similarly, you had that kind of you know, kind of nutty event from that Google engineer that I asked this earlier model, was it conscious? And it said, yes, so therefore it is. >> QED. Yes, QED. You're like, no, no, no, no. It's like you have to be not misled by that kind of thing. And like, for example, you know, the kind of thing that you know, what what I actually think most people obsess about the wrong things when it comes to AI. They obsess about the climate change stuff because actually in fact, if you apply intelligence at the scale and availability of electricity, you're going to help climate change. You're going to solve grids and appliances and a bunch of other stuff. It's just like, no, this will be net super positive. And by the way, you already see elements of it. Um Google applied its algorithms to its own data centers, which are um some of the best-tuned grid systems in the world, 40% energy savings. I mean, just you know, just that da da da. I'm just applying it. So, that's the mistake. But, one of the areas I think is this question around like what is the way that we want children growing up with AIs? What is their epistemology? What is their learning curves? You know, what are the things that kind of play to this because that kind of question is something that we want to be very intentional about in terms of how we're doing it. And I think that's like like if you want to go ask a good question that we should be trying to get good answers that you could do something in in contributing good answers to, that's a good one. Yeah. Well, the the most cogent argument that I've heard against free will uh is just that we are biochemical machines. So, if you want to test somebody's free will, get them very hungry, very angry, like all of these things where it's just there's a hormone. It's like norepinephrine. It's just like that makes you act a particular way. It's like an override. Yes. So, you have this like free will thing, but then you just insert a certain chemical, and then like boom, it changes. Are you saying you're not a Cartesian? You don't have a little pineal gland that connects the two hemispheres? No, I I don't I don't know. So, but it's true. I mean, it's like how like hunger is yeah, I'm hungry. Like that's a thing. Yes. And you know, what what is the like do do you actually want, if you're developing superintelligence, do you want to have this like kind of silly override? I mean, the reason why people go to jail sometimes that are perfectly normal is they get very angry. They do things that are kind of like out of character. But, it's actually not out of character if you think about this free will override of just like chemicals going through your bloodstream, which is kind of crazy to think about. >> Look, since we're on a geeky, nerdy podcast, I'm going to say two geeky, nerdy things. Are one, the classic one is people say, yes, we are biochemical machines, but let's not be overly simplistic on what a biochemical machine is. That's like the Penrose, quantum computing, etc. And you get to this weird stuff in quantum, which is, well, it's it it's a probabilistic dual super positional form until it's measured. Why is there magic in measurement? And is that magic in measurement something that's conscious? You know, blah blah blah. So, there's a bunch of stuff there. The other um thing that I think is interesting that we're seeing as a resurgence in philosophy a little bit is idealism. Like we would have thought as physical materialists that that we go, no, no, idealists were disproven, they're gone. But, actually beginning to say, no, actually in fact, what exists is thinking. And that all of the physical things around us come from that thinking. And obviously, we see versions of this because, you know, uh I find myself entertained frequently here in Silicon Valley by people saying we're living in a simulation. I know it, you know it. And you're like, well, your simulation theory is very much like Christian intelligent design theory. It's the I have things that I can't explain, so therefore creator. No, therefore simulation. No, therefore creator of simulation. You're like, no, no, no, but I you know, so clearly I'm not an idealist. But, that's why I see some resurgence of idealism happening. I suspect geeky. I suspect we'll solve for AGI before we solve for for various definitions of AGI before we solve for the hard problems of of consciousness. >> Yes. Um I want to return to uh LinkedIn. How we began the the conversation because we were lucky to or I was lucky to work many years with you. We would get pitches every week about a LinkedIn disruptor. Yes. Last 20 years, right? >> Yes. And so, and nothing's come even close. >> Nope. And so, it's fascinating. I'm curious why people sort of underrated how hard it was. And people have this about Twitter, too, other things that kind of look simple, perhaps, but are actually very, very difficult to unseat and have a lot of staying power. And And it's interesting, you know, OpenAI said they're coming out with a job service to quote use AI to help find the perfect matches between what companies need and what workers can offer. I'm curious to see how you think about sort of LinkedIn's durability. So, look, I obviously think LinkedIn is durable, but first and foremost, I I kind of look at this as humanity, society, industry. So, first and foremost is, what are the things that are good for humanity? Then what's good for society? Then what's good for industry? And by the way, we do industry to be good for society and humanity. It's not an it's not oppositional. It's just a, you know, how you're making these decisions and what you're thinking about. So, I would be delighted if there were new, amazing things that helped people um you know, kind of um make productive work, find productive work, and make them do them. We're having going to have all this job transition uh coming from technological disruption with AI. Like it would be awesome. I of course would be extra awesome if it was LinkedIn bringing it, just given my own personal craft of my hands and pride at what we built and all the rest. Now, the thing with LinkedIn and, you know, Alex was with me on a lot of this journey. Uh you know, as I sought his advice on various things. Um the the uh LinkedIn was one of those things where it's where the turtle eventually actually in fact like grows into something huge. Cuz for many, many years, the general scuttlebutt in Silicon Valley was LinkedIn was the was the the bought dull, boring, useless thing, etc. And it was going to be Friendster. Probably most people listening to this don't know what Friendster is. Then MySpace. Maybe a few people have heard of that. Right? You know, and then of course we got, you know, Facebook and Meta and, you know, TikTok and all the rest. And part of the thing for LinkedIn is it's built a network that's hard to build. Right? Because it doesn't have the same sizzle and pizzazz that photo sharing has. It doesn't have the same sizzle and pizzazz that, you know, um you know, like one of the things that uh you know, you were referencing the seven deadly sins comment. Um and back when I started doing that, 2002, yes, I left my walker at the door. Um uh the the thing that I used to say was uh Twitter was identity. I actually mistook it, it's wrath. Right? And it so doesn't have the wrath, you know, kind of component of it. And so, um and so the uh you know, the thing that and you said with LinkedIn, LinkedIn greed, great, you know, cuz seven deadly sins kind of um you know, cuz cuz that's, you know, a motivation that's very common across a lot of human beings. Rich and lazy. Yes, exactly. And so, or, you know, you're you're putting it in the punchy way, but simply being productive. >> Yeah. More value creation and accruing some of that value to yourself. And so, um and so I think the reason why it's been difficult to create a uh a disruptor to LinkedIn is it's a very hard network to build. It's actually not easy. And um and by staying really true to it, you end up getting a lot of people going, "Well, this is this is where I am for that." And now I have a network of people who this are and we are here together collaborating and doing stuff together. And that's the thing that a new thing would have to be. Um and you know, I uh you know, I uh when I saw GVD4, um and uh knew that uh Microsoft had access to this, I called the LinkedIn people and said, "You guys have got to get in the room to see this." Right? Because you need to start thinking about what are the ways we help people more with that. Because you start with This is actually one of the things that I think people don't realize about Silicon Valley because, you know, the general discussion is, "Oh, you're trying to make all this money through equity and all this revenue." Of course, you know, business people are trying to do that. But they don't realize is you start with what's the amazing thing that you can suddenly create. And part of it is like lots of these companies, like it started with and you go, "What's your business model?" You go, "I don't know." You're like, "Yeah, we're going to try to work it out. But I can create something amazing here." And that's actually one of the fundamental like places of what they, you know, call it the religion of Silicon Valley and the knowledge of Silicon Valley that I so much, you know, love and admire and embody. That's that's actually a a question that I have. So, I'll say one thing, it's a huge compliment to LinkedIn, it's anti-fragile. Yes. And that like Facebook, oh nobody goes there anymore. It's like the Yogi Berra and it's too crowded, nobody goes there anymore. It's like, "Oh, there are too many parents there." And there's always been a new one. Like what did Snap? Like how did Snap start? Like all these other networks started because people didn't want to hang out with their boomer parents. Um my my kid won't let me follow him on Instagram, right? So, he doesn't want to use Facebook. So, LinkedIn has has survived through all of that. But you referenced something that I think is a very interesting uh point, which is back in like Web 2, it was like get lots of traffic, Yes. get amazing retention, you know, smile curve, and then you will figure out monetization. >> Yes. And like that isn't happening right now. >> Yeah. It's not like get lot of Yes, it happened with ChatGPT, but it's like it's $20 a month. Yes. >> Right? Like the monetization was kind of built in very, very clear subscription versus like become giant, build a giant like Do you think there will be new ones of those with AI? >> Yes. And there will be new kind of freemium. It's it's part of our tool chest. Now, part of the reason why it's more tricky, especially when you're doing open AI, is because the the um like the cogs are changing a little often. Yes, right? Yes. No, no, but like and so you just can't This is one of the reasons why at PayPal we had to change to like we, as you know, cuz you were close to us there. Like we had to change to a paid model cuz we're like, "Oh, look, we have exponentiating volume, which means exponentiating cost curve, which means despite having raised hundreds of millions of dollars, we could literally count the we could point to the hour that we'd go out of business." Right? Cuz you know, no, you can't have an exponentiating cost curve. So, I think that's one of the reasons why some of it has been different in AI because you like you can't have an exponentiating cost curve without at least a following revenue curve. >> Right. But it's it's almost no fun. It's like Pinterest. It's like, "How are they going to make money?" Now, it's a big public company. It's like there were a lot of these during that era and now it's like they're burning lots of money, they're raising lots of money, but the subscription revenue is baked in from day zero. >> Yeah. And that's that's the fundamental >> have to cuz of the cost curve. >> They have to, exactly. >> Yeah. Um so, I'm I'm waiting for like one of these like, you know, net new companies that appeals to probably one of the seven deadly sins that's the the the the the new counterpart. >> Yeah. Well, I'd be happy to work on one. Yes. Yes. But yeah, it is fascinating some people may have tried sort of different angles on LinkedIn. And one that I was curious about a few years ago was sort of this idea of could you get um with some LinkedIn as resumes but not necessarily references. But it's the way that resumes are viral, references are like anti-viral or anti-emetic and people don't want them on the internet. If there was a data set that people wanted on the internet, LinkedIn would would have would have done it to some degree. But uh yeah, I think most people who who try these attempts don't kind of appreciate um sort of the subtleties of uh And I've actually I mean, we do have the equivalent of book blurb references. >> Right, endorsements. Yes, endorsements. >> have negative references. Well, but but by the way, part of the reason why negative references is you have complexity in social relationships, that's the negative virality point that you were just making. And then you also have complexity on like you know, kind of not just legal liability, but social relationships and a bunch of other stuff. Now, LinkedIn is still the best way to find a negative reference. I mean, that's actually one of the things that that I use LinkedIn to figure out who might know a person. >> Yeah. And I have a standard email. You probably got a bunch of these from me where I've where I've I email people saying, um "Could you rate this person for me from 1 to 10 or or reply call me." The negative what? Yes. Yes. And when you get a call me, you're like, "Okay." Don't even need to take the call. Yeah, I understand. Right. And by the way, sometimes you go when a person writes back 10, you're like, "Really? Like best person you know?" Right? But what you're looking for is like a set of eight nines. And if you get a set of eight nines, like you might still call and get some get some information, but you're like, "Okay, I got to I got a quick reference information." Whereas by the way, more often than not, you know, when you're checking somebody, you're like, "Oh, you you get a couple call mes." Cuz cuz my And it's just that quick cuz it's email, one sentence thing, get back, call me. You're like, "Okay, I understand." Yeah. Um We have about 10 minutes left, just a logistics check. Um a couple last things we'll get into. Um is there anything you wanted to make sure But we can do this again. This is always fun. Yeah, and it's great. The um I'm curious, Reid, as you've sort of continued to up-level in your career and have more opportunities and they seem to compound, especially, you know, post-selling LinkedIn, how have you decided where is the highest leverage use for for your time? Or where can you have the the the the biggest impact? What's your mental framework like? So, I mean, one of the things that I'm sure I speak for all three of us is an amazing time to be alive. I mean, this AI and the transformation of what it means for evolving homo technae and what what is possible in life and in society and work and all the rest is just amazing. And so, I stay as uh involved with that as I possibly can. Like it has to be something that's so important that I will stop doing that. Yeah. Right. Now, within that, you know, part of that was, you know, co-founding uh Monase AI with Siddhartha Mukherjee, who's CEO, Emperor of all uh author of Emperor of all maladies, um inventor of um some T-cell therapies. So, it was uh like like for example, getting an instruction from him on the FDA process, you know, that's the kind of thing that makes us all run screaming for the hills. Right? As a as an instance. Um and so, uh you know, that kind of stuff. But also, um you know, like one of the things I think is really important is as technology drives more and more of everything that's going on in society, how do we make government more intelligent on technology? And so, you know, every kind of um you know, kind of well-ordered Western democracy, um I've done been doing this for at least 20 to 25 years. If if a minister, you know, or kind of senior person from a from a democracy comes and asks for advice, I give it to them. So, you know, just last week I was in France talking with Macron cuz he's trying to figure out like how do I help French industry, French society, French people? What are the things I need to be doing? You know, if all the frontier models are going to be built in the US and maybe China, what does that mean for how I help, you know, our people and so forth? And and he's doing the exact right thing, which is I understand that I have a potential challenge, what do I do to help my people? Yeah. How do I reach out? How do I talk? Sure, they've got Mistral, they've got some other things, but like how do I maximally help when I'm doing? And so, I'm putting a bunch of time into that as well. Yeah. I remember seeing your your your calendar and it was would seem like seven days a week, meetings absolutely stacked. And one of the ways in which >> I've gone to six and a half days. So, I'm glad you've calmed down. One of the ways in which you're able to do that, one, it's important problems, but two, you you work on projects with friends. Yeah, sometimes over decades. And you you maybe we'll close here. You've thought a lot about friendship. You've You've You've written about it. You've spoken about it. I'm I'm curious what you've found um most remarkable or most surprising um about friendship or what you think more people should appreciate, especially as we enter this AI era where people Yeah, sort of are questioning, you know, what the next generation what's their going to be relationship to AI. >> I actually am going to write a bunch about this specifically cuz AI is now bringing some very important things that people need to understand, which is friendship is a joint relationship. It's not a oh, you're just loyal to me or oh, you just do things for me. Oh, this person does things for me. Well, there's a lot of people who do things for you. Your bus driver does things for you. You know, like like but that doesn't mean that you're friends. Friends, like for example, like a classic way of putting it is like, "Oh, I had a really bad day and I show up to my friend Alex and I want to talk to him." And then Alex is like, "Oh, my god, here's my day." I'm like, "Oh, your day is much worse. We're going to talk about your day versus my day." Really, you know, that's the kind of thing that happens cuz what I think fundamentally happens with friends is two people agree to help each other become the best possible versions of themselves. >> Yeah. And by the way, sometimes that leads to friendship conversations that are tough love. They're like, "Yeah, you're this up and I need to talk to you about it." Right? It's not I tell you like, you know, the the whole sycophancy phase and AI thing and I was It's not that. It's like the how do how do I help you, but it's part of also the thing that I I gave the commencement speech at Vanderbilt a few years back and it was on friendship and part of it was to say, "Look, part of friends is not just does does Alex help me, but Alex allows me to help him." Right? And as part of that, that's part of how I become a deeper friend. I learn things from it. It's not just help that helping Alex. That joint relationship's really important. And you're going to see all kinds of nutty people saying, "Oh, I have your AI friend right here." It's like, "No, you don't. It's not a bidirectional relationship." Maybe a awesome companion. Like just spectacular, but it's not a friend. You need to understand like part of friend is part of when we begin to realize that life's not just about us. Yeah. That we that it's a team sport. We go into it together. Um that sometimes, you know, friendship conversations are wonderful and difficult. You know, and that kind of thing. And I think that's what's really important. And And now that, you know, we've got this blurriness that AI has created, it's like, "Shoot. I have to go write some of this very soon." So that people understand how to navigate it and why they should not think about AI anytime soon as friends. >> did it when when they have always appreciated about you as well as you're able to be friends with people who for whom you have disagreements with or people for whom, you know, you were not close to for a few years, but you can reconnect and sort of Yeah, that ability is um Yeah, you know, it's about us making each other the better versions of ourselves. And And sometimes that, you know, those sometimes those go through rough patches. Yeah. I think it's a great place to close. Reid, thanks so much for coming on the podcast. >> And I hope we do this again. Excellent.