Reid Hoffman on AI, Consciousness, and the Future of Humanity

23:15It's like the people in this part of the distribution are correct. Normally the meme is the opposite. It's like these people are smart even though they're dumb. These people are smart even though they're smart. Everybody here, like 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 for your kid's birthday or, you know, I've 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.

23:51Yeah. It doesn't that it 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 suspect I will still get what I think is the bozo business professor answer versus the actual name of the game. But, yeah. But 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. Because five minutes, you get one and you go, oh, no, not two, not five. Oh, but three is good.

24:28And it would have taken me a day to getting to about three. Yeah. In terms of, let's go back to extrapolation. Obviously, the last few years have had incredible growth. You were involved, of course, with OpenAI since the beginning. When we look for the next few years, these brought a question as to whether scaling laws will hold, whether sort of the limitations or how far we can get with LLMs. Do we need another breakthrough of a different kind? 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, theories of super intelligence, theory of exponential getting to super intelligence soon.

25:14And what I find is usually the mistake in that is not the fact that it's extrapolating the future. That's smart and people need to do that and far too few people do. I think I remember liking your post and helping promote it if I recall. 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, it's like, no, no, it'll be an even more amazing savant than we have. But by the way, if it's only savants, there's always room for us. There's always rooms for the generalist and the cross-checker and the context awareness and all the rest of that.

25:55Now, maybe it'll cross over a threshold or not. Maybe it won't. Like, I think there's a bunch of different questions there. But that extrapolation too often goes, well, it's exponential. So in two and a half years, magic. And you're like, well, look, it is magic, but it's not all magic is the kind of way he's doing it. Now, so my own personal belief is that, 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 screws up on, you know, prime numbers and, you know, blah, blah, blah, blah, blah. How many R's in strawberries?

26:33Yes, exactly. Yeah, exactly. You know, and they go, wow, 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 three to five 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 the fusion models for various image and video tasks. Now, by the way, they wouldn't work without also having the LLMs in order to have the ontology to say, create me an Eric Tornberg as a Star Trek captain, you know, going out to, you know, explore the universe and meeting and making first contact with the Vulcans and so forth.

27:15Which, you know, now with our phone, we could do that. Right. And it would be there, courtesy OpenAI. and, you know, VO because Google's model is also very good. But it needs the LLMs for that. But the thing that people on track is it's going to be LLMs and diffusion models and I think other things with a fabric across them. Now, one of the interesting questions is, is the fabric fundamentally LLMs? Is the fabric other things? 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, you know, 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?

27:58And like one of the things that I 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. Okay, let's try to do that. Now, I don't think the whole verification of outputs, like logical, like we can't even do verification of coding, right? Like verification strikes me as very hard. Now, brilliant man. Maybe we'll figure it out. But on the other hand, hey, this is a good goal. Can we make that more programmable, reliable?

28:39I 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 foundation of the world, I mean, philosophy is the basis of everything. Actually, math comes from philosophy. It's called the Cartesian plane after Descartes. You're a philosophy major. You know this, right? So you have 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.

29:12So that's kind of the stack. So if you solve math, that's actually quite interesting because there's a professor at Rutgers, Kontorovich, who's written about this a lot. And I find this part fascinating. just as a former mathematician, because there are some very, very hard problems. 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. But, you know, the Riemann hypothesis, like, this is not like, there's no eval, right? If it's like, this is why if you look at the progression of AI, there is the AMI, the American Invitational Math Examination, where you, the answers are all just like three, it's just integers, right?

29:51It's like zero 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. And if you solve that, I mean, is that AGI? No, because the goalposts keep changing out of AGI. But math is just so interesting. AGI is the AI we haven't invented. Exactly. It's the corollary to it. It's like, you know, the worst AI you're going to try is today. Well, AGI is what you're going to have tomorrow. It's the same kind of thing. But math is a very, very interesting one as well because you have these things.

30:23It's not like solving high school math. This is like if you're able to actually logically construct a proof for something and then validate it. There's a whole programming language called Lean, which is for that. Like that stuff is also fascinating. So there's so many different vectors of attack, which is the other way of thinking about it. It's fascinating. So as you just mentioned, Alex, you're a philosophy major, but you're also very interested in deep in neuroscience. And some people say that, hey, we'll never create AI with its own consciousness because we don't even understand our own consciousness.

30:51So we don't understand how our own brain works. And then there's a broader question as, oh, will AI have its own goals or will AI have its own agency? What is sort of your view on some of these questions surrounding consciousness and the ways to AI? Well, consciousness is its own tarball, which I will say a few things about. I think agency and goals is almost certain. there is a question I think this is one of the areas where we want to have some clarity and control that was a little bit like 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 sub goals and other kinds of things and 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 paper clips and it tries to convert the entire planet into paperclips.

31:45And there's one thing that's definitely old computer, which is 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 just go try to convert everything into paperclips. It's like, it's actually, in fact, not that simple in terms of how it plays. Now, consciousness is an interesting question because you've got some very smart people, Roger Penrose, who I actually interviewed way back when on Emperor's New Mind, speaking of mathematicians, and, you know, who are like, look, actually, in fact, there's something about our form of intelligence, our form of computational intelligence that's quantum-based that has to do with how our physics work, that has to do with things like tubulars and so forth.

32:34And by the way, It's not impossible. 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 that are smart, but there's no one smarter, right, in that kind of vector. And so that's possible. I don't think you need consciousness for goal setting or reasoning. 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.

33:17And philosophers agree. Philosophers wouldn't think I was throwing them under the bus with this. They're like, yeah, this is a hard problem because it ties to agency and free will and a bunch of other things. And I think that the right thing to do is keep an open mind. Now, part of keeping an open mind, I think Mustafa Suleiman 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, 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 who said, I asked this earlier model, was it conscious?

33:54And it said, yes, so therefore it is. QED. Yes, QED. You're like, no, no, no, no. 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 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. And just like, no, this will be net super positive. And by the way, you already see elements of it.

34:29Google applied its algorithms to its own data centers, which are some of the best-tuned grid systems in the world, 40 % energy savings. I mean, just, you know, just da-da-da-da. And 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, if you want to go ask a good question that you should be trying to get good answers, that you could do something again in contributing good answers to, that's a good one.

35:12Well, the most cogent argument that I've heard against free will 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 like that makes you act a particular way. It's like an override. 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 substances? No, I don't know. So, but it's true.

35:40I mean, it's like hanger is, yeah, I'm hangry. Like that's a thing. Yes. And, you know, what is the, like do you actually want, if you're developing super intelligence, 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.

36:09One, 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, et cetera. And you get to this weird stuff in quantum, which is, well, it's a probabilistic dual supervisional 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 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 we go, no, no, idealists were disproven, they're gone.

36:49But 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, 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. And you're like, no, no, no, no.

37:24But I, so clearly I'm not an idealist. But that's why I see some resurgence of idealism happening. Well, I suspect. I suspect we'll solve for AGI before we solve for, for various definitions of AGI, before we solve for the hard problems of consciousness. Yes. I want to return to LinkedIn, how we began 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. It was the last 20 years, right? Yes. And so, and nothing's come even close. And so it's fascinating. I'm curious why people sort of underrated how hard it was.

38:02And people have this about Twitter too or other things that kind of look simple perhaps, but are actually very, very difficult to unseat and have a lot of staying power. And it's interesting, you know, OpenAI, they 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 how you think about sort of LinkedIn's durability. So, look, I obviously think LinkedIn is durable. But first and foremost, I kind of look at this as humanity, society, industry. So first and foremost is what are the things that are good for humanity?

38:32Then 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 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, you know, kind of make productive work, find productive work. And make them do them. We're going to have all this job transition 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 giving my own personal craft in my hands and pride at what we built and all the rest.

39:10Now, the thing with LinkedIn, and, you know, Alex was with me on a lot of this journey, you know, as I sought his advice on various things.

39:22A LinkedIn was one of those things where it's where the turtle eventually actually, in fact, like grows into something huge. because for many, many years, the general scuttlebutt in Silicon Valley was LinkedIn, was the dull, boring, useless thing, et cetera. 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?

39:58because 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, like one of the things that, you know, you were referencing the Seven Deadly Sins comment. And back when I started doing that in 2002, yes, I left my walker at the door. The thing that I used to say was Twitter was identity. I actually mistook it. It's wrath, right? And so it doesn't have the wrath, you know, kind of component of it. And so the thing that – and you said with LinkedIn, LinkedIn's greed, great, you know, because seven deadly sins kind of, you know, because that's a motivation that's very common across a lot of human beings.

40:41Rich and lazy. Yes, exactly. And so – or, you know, 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 I think the reason why it's been difficult to create a disruptor to LinkedIn is it's a very hard network to build. It's actually not easy. And by staying really true to it, you end up getting a lot of people going, well, this is where I am for that. And now I have a network of people with this. And we are here together collaborating and doing stuff together. And that's the thing that a new thing would have to be.

41:23And, you know, when I saw GVD4 and knew that 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 as you start with, what's the amazing thing that you can suddenly create?

42:00And part of it is like lots of these companies, like it started with, and you go, what's your business model? And you go, I don't know. 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 the 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 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 beer and it's too crowded nobody goes there anymore it's oh there are too many parents there and there's always been a new one like where 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.

42:41My kid won't let me follow him on Instagram, right? It's like he doesn't want to use Facebook. So LinkedIn has survived through all of that. But you referenced something that I think is a very interesting point, which is back in like Web 2, it was like get lots of traffic. Get amazing retention, you know, smile curve. And then you will figure out monetization. And like that isn't happening right now. It's not like get lobby. Yes, it happened with ChatGPT. It was like it's$20 a month. Yes. Right? Like the monetization was kind of built in very, very clear subscription versus like become giant. Yes.

43:11Build 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 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, like, the cogs are changing a little off. Yes. For now. 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, because you were close to us there. like we had to change to a paid model because 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.

43:48Right. Because, 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 almost It's no fun. It's like Pinterest. It's like, how are they going to make money now? 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. And that's the fundamental difference.

44:14But they have to because of the cost. They have to. Exactly. So 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 is the new counterpart. Yeah. Well, I'd be happy to work on it. Yes. It's fascinating. Some people, many people 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 what's on LinkedIn is resumes, but not necessarily references. But the same way that resumes are viral, references are like anti-viral or anti-memetic and people don't want them on the Internet.

44:44If there was a data set that people wanted on the Internet, LinkedIn would have would have done it to some degree. But, yeah, I think most people who try these attempts don't kind of appreciate sort of the subtleties of. And I've actually I mean, we do have the equivalent of book blurb references. Right. You have endorsements. Yes. Endorsements. But you don't have a negative reference. Well, 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.

45:17Now, LinkedIn is still the best way to find a negative reference. I mean, that's actually one of the things that I use LinkedIn to figure out who might know a person. And I have a standard email. You've probably gotten 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 one to 10 or, or reply, call me. It's a negative one. Yes. Yes. Right. And when you get a call me, you're like, okay. Don't even need to take the call. Yeah. It's amazing. I understand. Right. And by the way, sometimes you go, when a person writes back 10, you're like, really?

45:52Like 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, you might still call and get some information. But you're like, okay, I got a quick referential information. Whereas, by the way, more often than not, you know, when you're checking someone you really know, you get a couple call me's. And it's just that quick. Because email, one sentence thing, get back, call me. You're like, okay, I understand. it. Yeah. We have about 10 minutes left just logistics check. A couple of last things we'll get into. Is there anything you wanted to make sure?

46:26But we can do this again. This is always fun. That's great. The I'm curious, Reed, 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 used for your time? Where can you have the the biggest impact? What's your mental framework for you? 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 is possible in life and in society and work and all the rest is just amazing.

47:04And so I stay as involved with that as I possibly can. Like, it has to be something that's so important that I will stop doing that. Now, within that, you know, part of that was, you know, co-founding Manasai with Siddhartha Mukherjee, who's the CEO, author of Emporal Maladies, inventor of some T-cell therapies. 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 an instance. And so, you know, that kind of stuff. But also, 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?

47:53And so, you know, every kind of, you know, kind of well-ordered Western democracy, I've been doing this for at least 20 to 25 years. is if a minister, you know, or kind of senior person 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 because 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 U.S. and maybe China, what does that mean for how I help, you know, our people and so forth?

48:30And 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? How do I reach out? How do I talk? Sure, they've got my straw. They've got some other things. But like, how do I maximally help what I'm doing? And so putting a bunch of time into that as well. Yeah. I remember seeing your calendar and it was what seemed like seven days a week. Meetings absolutely stacked. And one of the ways in which— I've gone to six and a half days. Okay. 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 work on projects with friends, sometimes over decades.

49:05And maybe we'll close here. You've thought a lot about friendship. You've written about it. You've spoken about it. I'm curious what you've found most remarkable or most surprising about friendship or what you think more people should appreciate it, especially as we enter this AI era where people sort of are questioning the next generation, what's their going to be relationship to friendship? I actually am going to write a bunch about this specifically because 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 you just do things for me.

49:37Oh, 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, but that doesn't mean that you're friends. Friends, like, for example, like a classic way of putting it is, 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. You know, that's the kind of thing that happens because what I think fundamentally happens with friends is two people agree to help each other become the best possible versions of themselves.

50:08And by the way, sometimes that leads to friendship conversations that are tough love. They're like, yeah, you're fucking this up and I need to talk to you about it. Right. It's not, I tell you, like, you know, the whole sycophancy phase and AI thing. It's not that. It's like, how do I help you? But as part of also the thing that I gave the commencement speech at Vanderbilt a few years back and was on Friendship, and part of it was to say, look, part of Friends is not just 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.

50:47I learn things from my – it's not just that helping Alex. That joint relationship is really important. And you're going to see all kinds of nutty people saying, oh, I have your AI friend right here. And it's like, no, you don't. It's not a bi-directional relationship. It may be an awesome companion. Like just spectacular. But it's not a friend. And you need to understand like part of friend is part of when we begin to realize that life's not just about us. That it's a team sport. We go into it together. that sometimes, you know, friendship conversations are wonderful and difficult, you know, and that kind of thing.

51:18And I think that's what's really important. 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, why they should not think about AI anytime soon as friends. Well, one thing I've always appreciated about you as well is you're able to be friends with people for whom you have disagreements with or people for whom, you know, you are not close to for a few years, but you can reconnect and sort of, yeah, that ability is. Yeah, it's about us making each other the better versions of ourselves.

51:52And sometimes that, you know, 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. My pleasure. And I hope we do this again. Yeah. thanks for listening to this episode of the a16z podcast if you like this episode be sure to like comment subscribe leave us a rating or review and share it with your friends and family for more episodes go to youtube apple podcast and spotify follow us on x a16z and subscribe to our sub stack at a16z.substack.com thanks again for listening and i'll see you in the next episode as a reminder the content here is for informational purposes only, should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund.

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52:59Thank you.