a16z Podcast - Why We Should Be Optimistic About the Future

Episode Date: January 2, 2020

Many skeptics thought the internet would never reach mass adoption, but today it’s shaping global culture, is integral to our lives -- and it's just the beginning. In this conversation from our 2019 innovation summit, Kevin Kelly (Founding Executive Editor, WIRED magazine) and Marc Andreessen sit down to discuss the evolution of technology, key trends, and why they're the most optimistic people in the room.***The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments for which the issuer has not provided permission for a16z to disclose publicly as well as unannounced investments in publicly traded digital assets) is available at https://a16z.com/investments/.Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

Hi everyone. Welcome to the A6 and Z podcast. I'm Sonal. Happy New Year. Today's episode is on why we should be optimistic about the future, because it features two of the most optimistic people together in conversation. A6 and Z co-founder Mark Andreessen is interviewed by Kevin Kelly, founding executive editor of Wired Magazine and more. The conversation originally took place at our most recent annual innovation conference, the A6 and C summit, and it was also previously released on YouTube if you'd like to check it out there as well. Good afternoon. Thank you, Mark, for answering some questions.

I have a bunch of questions, which I hope that we can talk about. And these all have to do about the future, where we're going. I want to start with a question about the past. You know, a generation ago, a lot of smart people didn't think the internet was going to work. And therefore, they were unprepared for its benefits. What are we smart people today?

not prepare it for? Yeah, so you may remember actually generate, it wasn't even a lot of, it wasn't even just that a lot of people thought that the internet wasn't going to work, a lot of smart people didn't think that. In fact, the inventor. I can't resist. I can't resist on the story. Actually, the inventor of Ethernet, which is a foundational technology for the internet, spent the 90s, actually predicting the internet would crash, would collapse, and what he called it would be the gigalapse, would take down the internet by like 1936, 1997. He wrote a column at the time for a magazine called InfoWorld, and he said that if he was wrong, by, I think it was like, if the internet hadn't collapsed by 1997, he would eat his column.

And to his enormous credit in 1998, he actually went on stage at a conference. He actually ripped his column out of the magazine. He put it in a blender with water. He blended it up and he drank it on stage. So it's one of the more shining examples of intellectual honesty I've ever seen. As it turns out, he was wrong. It turns out the internet did work. So I think the big thing, I've been thinking about this a lot, you know, it feels to a lot of people

like things are getting strange. And maybe I'm the only one who feels that way, but if you read the news or just track things happen in the world, just things feel kind of weird and different over the last few years. I actually think there's an actual generational thing that's happening, and you alluded to the generational component. Like it did take 25 years to get everybody online. And like we're not quite there yet, but we're getting very close.

Like I think the most exciting thing happened in the world right now is Mukesh Shambani, who's the richest man in India, has this program called Geo, where he is literally providing internet access to the 500 million, lowest income Indians. Like from script, it's like, but like literally it's like free for six months and then it's like a dollar a month. It's like the most amazing thing. And it's like, it's working incredibly well. And so we are very, very close to every, every, at least every adult in the planet being internet connected. But it took 25 years to get there. And so, so that, so for me,

it's like, okay, so then what? One interpretation of that is, okay, we're done, we did it. The other interpretation of that is actually, okay, that's just the beginning point. Right. Right. And then it's like the beginning point of like, okay, like, what if you actually interconnected to everybody in the planet. Like what, you know, there's like the metaphor of the global, the global mind, the global brain. Like, what if you actually connected everybody together and let everybody find out what everybody else was thinking? It's one of those things that people think.

Sounds good. And then they encounter it face to face. And they're like, I don't know. Right. Right. That was like, during my time at Wired people were kind of concerned about the digital divide. And I said, the digital divide is going to cure itself. The thing you should be worried about is what happens when everybody is online.

So you think we're not prepared for what will happen when everybody. is online? No, well, I think we're not prepared, and then I think it's going to be very exciting. I mean, I think we're already seeing that in many ways. I think there's, and then I think we've kind of figured out collectively that it's going to be different, and so the initial impulse is to say things are going to get much worse, and I don't think that's right. I think things are going to get very different.

I think things will be much more positive, and we know we'll talk a lot about that today, hopefully, but things are definitely going to be different. I think one lens that I've been trying to put on it lately is kind of think about it through a cultural lens, right, sort of what happens to culture, because culture, you know, Ben, just wrote this book about culture being kind of the foundation of behavior. And I think that's really true, certainly in companies, but I think it's also true in countries and globally. And it feels like the Internet's impact on culture is just beginning, in the sense of, like, a world in which culture is based on the Internet, which is what I think is happening,

is just at the very start, right? Because it had to get universal before it could set the culture, but that's actually happening now. Okay. And at the same time, a generation ago, while there was a few people who actually did think the internet was going to work but they were also like myself expecting VR and conversational AI to happen tomorrow so what are we expecting to happen

now that it's not going to happen so I object to the question your honor so this is one of those things in our business that you deal with a lot which is because you know you find yourself you know these entrepreneurs come in and they pitch an idea And you kind of feel like you should draw a judgment on whether the idea is going to work or not. And it's something I'm really leery of doing anymore.

And the reason for that, and I think you know this from all of your reading, every successful technology that I'm aware of, you know, the things that are like all of a sudden, like the next big thing, like the iPhone in 2007, or, you know, just as an example, they all have this like incredible 25 or 40 or 50 year backstory to them. And you sometimes have to go back and excavate, right? Because you haven't heard a lot of the backstory because the previous efforts failed. Right. But if you go back and look, like there's often this, often a multi-generational run-up.

And so I'll just give you a few of my favorite examples. So iPhone, you know, hit big in 2007. I mean, I for years went around saying, well, IBM is, there was a 20-year project. IBM shipped the first smartphone in 1997 called the Simon. I thought that was true. It actually turns out it's not true. I found the other day, Radio Shack had a smartphone in 1982 with their, they literally had a phone version of their TRS 80 minicputer.

They sold about four of them. But it was a thing, right? So that had a 25-year fuse on it. Video conferencing, you know, video conferencing goes back at least to the mid-60s, to the World's Fair. The telegraph was invented in the 1870s and then sat on a shelf for 100 years before the Japanese turned it into an industry. And then another favorite is fiber optics nominally, you kind of stretch, you could say fiber optics were invented in the 1840s. Paris had a optical telegraph network under the city. you could actually do, you could actually do

telegraphy in the 1840s in Paris and it was literally there were shining flashes of light through glass tubes. So there's this like this incredibly rich backstory to all these things. And so as a consequence, it's actually less a question of like what's the new idea. It turns out the idea is probably already out there somewhere.

Right, okay. And then it's less the question of like, is it going to work? It's more the question of like, when is it going to work? And I pushed it so far, and people in our office have heard this. I pushed all the way to the point

where I just think we should assume that whatever we're being pitched is going to work. It's just a question timing. Then, of course, timing turns out to be the hard part, but it at least focuses the conversation. Right, right, right. So it's the same idea of kind of looking at the history of things. One wonders who really made all the money when electricity came along. It probably wasn't the people necessarily generating electricity. Who do you think is going to make the money

when AI comes along? Is it the AI providers? Is it the AI as service? Is it the algorithm? writers, who's going to be making money in AI? Yeah, so we think that there's two obvious business models and probably others, but the two obvious. One is to be sort of a horizontal platform provider, infrastructure provider, you know, for AI, kind of analogous to the operating system or the database or the cloud. You know, the other opportunity is kind of in, we'll say, in the verticals, and so the applications of AI, and there's certainly a lot of those.

So that's the general answer. I think that the deeper answer is there's an underlying question that I think is an even bigger question about AI that reflects directly on this, which is, is AI a few? feature or an architecture? Is AI a feature? We see this with pitches we get now, which is this like we get the pitch and it's like, here are the five things my product does, right, and bullet points one, two, three, four, five, and then oh yeah, number six is AI, right?

And so you know, it's always number six, right? Because it's the bullet that was added after they created the rest of the deck. And so it's like, okay, if AI is a feature, then that's actually correct, which is like every, basically everything is just going to kind of have AI sprinkled on it. There would be AI features kind of in every product. kind of in every product, that's possible. We are more believers in the other scenario that AI is a platform and as an architecture.

If in the same sense that like the mainframe was architecture, the mini-computers in architecture, the PC, the internet, the cloud, have been architectures. We think there's very good odds that AI is the next one of those. And if that's the case, then it means that basically, when there's an architecture shift in our business, it means basically everything above the architecture

gets rebuilt from scratch. Because the fundamental assumptions about what you're building change, right? And so you're no longer building a website, you're no longer building a mobile app, you're no longer building any of those things. You're building instead an AI engine that is just like, in the ideal case, it's just giving you the answer to whatever the question is. And if that's the case, then basically all applications will change, along with that, all infrastructure will change. Basically, the entire industry will turn over again the same way that it did with the internet and the same way it did with mobile and cloud. And so if that's the case, then it's just, it's going to be like an absolutely explosive period of growth for this entire industry.

Because it means then that all the incumbents, so suppose it incumbents rule, aren't incumbent at all. Yeah, the product's just won't be relevant anymore. I mean, I'll just give you an example. There are lots and lots of sort of, you know, business applications. Just your business apps is an example. There's lots of business apps, you know, where you basically you type data into a form and then it stores the data, and then later on you run reports against the data

and get charts. And that's been the model of business software for, you know, 50 years in different versions. You know, what if that's just not needed anymore? Like, what if in the future, what you'll do is you'll just give your AI and your business access to all, you know, email, all phone calls, all everything, all business records, all financials in the company and just let the AI give you, you know, give you the answer to whatever the question was. And you just don't go through any of the other steps. Google's a good example of this. Like they're pushing hard on this. Like the consumer version of this, right,

is search, right? So search has been the, you know, it's been the 10 blue links, you know, for 25 years now. You know, what Google's, they talk about this publicly, what they're, what they're pushing towards is, it's just like, no, it just be that answer, right? Which is what they're trying to do with their, with their voice UI's. And so that, that concept might really generalize out, right? And then everything gets rebuilt. Right. So, so, so, so, so, One of the new interfaces to AI that people are talking about is voice as the new interface. What are we likely to get wrong about voice? Yeah, so I think the thing that, if we're going to get something wrong about voice,

I think it's going to be that it would be a one-to-one replacement for existing user interaction models so that it would be like a replacement for keyboard or that it would be replacement for the mouse or for touch. Probably not, because it's a different modality, right? It's, you know, we know exactly what the keyboard, you know, after all this time, we know what's, the keyboard is for, we know it touches for, and for voice to displace those, seems like a stretch. On the other hand, to the previous question,

there has been this turning point reached, it feels like in AI applied to language, and from there to voice, to text and to speech, which is it feels to us in the technology like the natural language processing methods that people have been working on for, again, for 50 years, computer scientists have been working on getting computers to understand basically speech.

And what we're seeing now is in the technology is that that now has started to work in the same way that machine vision started to work about seven years ago. And so if that's the case, then all of a sudden the conversational UIs are about to get much better. And again, and then you couple that with,

okay, what are you actually trying to achieve when you talk to a computer? Like when you talk to a computer? Are you actually trying to like, are you trying to write a document? Are you trying to read an email? Are you trying to do all these other things that you do today?

Or are you fundamentally going to be doing something different because the machine's going to be so much smarter? And I think that's a very interesting open question. When I think about the AR mirror world, I find it very hard to imagine without it having a voice component where we can understand what you're saying besides what you're looking at. Is that an essential part of the AR world? Yeah, I think actually I'd go so far as to say it may be the case

that voice actually is the key to the AR world. Like voice may be the thing. Voice may actually be the foundation of the whole thing. You know, this is kind of a cliche at this point, but like, you know, the Apple AirPods, I think were a fundamental breakthrough. Like, it's, you know, one of these funny things where it's like, okay, wireless headphones, okay, cool, like wireless headphones where there's not even a wire connecting the two things.

Cool, okay, it seems like more of the same. But, you know, if you want, the experience you can have now is like you can wear one of these things basically all day and you can talk to it all day. And, you know, they're getting, you know, the new versions are getting better. You know, and, you know, Siri and Google Now and Cortana all these things are getting really good, really fast.

And so it may be that we have just this constant ongoing running dialogue, this kind of, you know, basically the machine talking to our ear. And then, you know, the visual overlay of AR will obviously be important invaluable, but it might be, the visual overlay might be supportive on top of the voice experience. And we could very quickly have universal language translation of speaking in the years, and I think people underestimate the change that that would bring about in the world. You'd have millions of people who are highly skilled in everything except the skill of English, now being

able to participate in a global economy. We were talking about unexpected and unexpected things. biology, which is a million times as complicated as digital. We're now talking about a biotech revolution. Are we misunderstanding what biotechnology actually is? Yeah, so that's the big bet that we've made with our bio effort that we started a few years back. We think biological science is at a turning point, at the scientific level,

and we think it's at a turning point from basically being a process of discovery of how biology works to being able to engineer biology. Up to it, including literally being able to program biology, right? Being able to actually basically be able to use electrical engineering and computer science and these mechanical engineering and these kind of fields for engineering things and be able to apply those kinds of skills to biology. If we're right about that, then the whole concept of kind of how bio and biotech work might be on the verge of really changing.

The most obvious application that would be in pharmaceuticals, you know, there's this concept of drug discovery. Right. It's always the word discovery. The discovery, it's always like, you know, discovery sounds great. It's like, it's optimistic. It's like, oh, this is, you know, like discovering things, it's fantastic. The problem is, right, discovering, like,

they literally call it that because like, they literally have to, like, run all these experiments and try to discover the drug that works. Like, try to kind of, you know, reverse engineer back from nature. And the problem is, like, sometimes they discover it, sometimes they don't, right? And so the example we always give is we talk about, with computers, right, we've been on this kind of 50-year track

of what's called Moore's Law, right? We're a chip's beginning faster and cheaper every year for a long time. In biology, in drug discovery, there's what they call e-room's law, which is more spelled backwards, E-room. And it's the cost of discovering a new drug. And it's exactly the wrong direction. It's right. It's up into the right. You know, billions of dollars now.

And so if you could actually engineer biology, right, then all of a sudden you can start to apply this, like just, you know, these decades of skills that we've built up on how to engineer things. Right. And be able to do things like engineer new pharmaceuticals from scratch. And it all runs on basically, ultimately, Moore's law. Moore's Law has been foundational to this here. It's almost hard to imagine anything we have in the modern world today without Moore's Law. Do you think Moore's Law has another 30 years run? Is it limited?

Is it finite? We'll go on forever. We'll define Moore's Law in the broadest sense of computers getting cheaper by half every couple of years. So what's your take on Moore's Law? Yeah, so the traditional definition is computer in the form of the chip. and then specifically a chip, right? So Moore's Law has always been expressed as kind of unit one of chip.

And that could be right, that could be a CPU or it could be a graphics card or it could be a graphics chip or a memory chip. And then specifically what you were doing was you were able to put more transistors on that chip for the same cost. And actually, for a long time as you did that, you were actually able to reduce the power requirement per transistor,

which was this kind of added benefit. And so chips kind of kind of got simultaneously, they got faster, they got cheaper, and they got more power efficient. And that was kind of the chronocopia effect that generated, as you said, most of what you see today in the computer industry. So the bad news is that in that form

seems to be coming to something of an end, which is we're too good at it. We've hit basically, we being the semiconductor industry broadly, the tech industry have kind of hit the limits of fundamental physics, like we're now down at the sort of deep atomic level, and it's becoming much harder to make, there's still progress, becoming much harder to make progress at the per chip level.

The good news is that the industry starting 10 or 15 years ago, the computer to broadly refocused off of what you do with a chip to what you do with a large number of chips. So kind of the old model of a chip was you make the chip more powerful because you're trying to scale up what you can do in the chip. The new model is you use thousands of chips in parallel

and you have this kind of approach to scaling out. And of course, the full form of that is what's now known as the cloud. And so we now have a 15 year head of steam going to basically be able to get good at using lots of chips to do things. And that's why you see the continued ability to accelerate you know, many, many things that you deal with are getting, you know, still much faster as if they're still on the Moore's Law.

The experiences you're having are getting faster. So we think, number one, like the rise of scale on architectures is a really big deal. Like, you know, in Modern Clouds as a developer, you don't really care about what the power of any particular chip is. You just, like, light up some more of them and they don't cost much. So there's that. The other thing is chips are now specializing. And particularly, you've got the rise of these new dedicated chips for things like neural

networks where there's another level of opportunity to optimize. And then the other kicker is the programmers. software people like me get to step up. In the old days, when computers were expensive, programmers are really good at optimizing every single step of a software program. Programmers got out of that habit probably starting 30 years ago where it didn't matter as much anymore because Morris Law was working so well. And so software today is just like massively inefficient.

There's actually, I forget the name, there's something called Worth's Law, which is it was written at the time, I don't know if it still holds, but it was somebody did benchmarks of you take Microsoft Office 2000 on a PC from 2000, and you take Microsoft Office 2007 and a PC from 2007, and every function you could do, you could now do in twice the time. Right. So, like, literally, like,

the old adage in tech in the 90s was when Andy Grove was wanting Intel and Bill Gates was running Microsoft. It was Andy Gibbeth in the form of Moore's law and then Bill taketh away. In the form of software bloke. And so, and Worth's Law literally is a mathematical proof of that.

And so like it's become prime time again for software programmers to get really good at optimization, which is like what's happening in the AI world and also in the cryptocurrency world. And so with those different approaches, it feels like we've got, you know, it feels like decades of advances ahead that aren't purely dependent on Classic Moore's law. And because if we take the long term, like thinking of a hundred year span, to have prosperity like we've seen would kind of require that computer power sort of get cheaper every year. Because if it didn't, that it's hard to imagine a world. like that. So is your confidence that we could kind of keep this going based on just sort of human ingenuity? Or do you think that there's some basic principles of science that suggest that we're just at the beginning of what we can discover? Well, so Gordon Moore, who invented Morris Law, as co-founder of Intel, he always said Morris Law was interpreted as a prophecy. And he always said it was not a prophecy. It was a goal. Right. Right. And it was a goal of basically what you could do if you focused intensely, if you focused an entire industry intensely

on a set of engineering optimizations, right, over a long period of time. So he used to say, it's just like there's nothing inevitable about it as a consequence of thousands and then tens of thousands and then millions of engineers like working to actually deliver

on these kind of semi-arbitory goals. And so I think the answer to that is we have many, many areas of improvement. As I said, the problem is we don't have the one that we have, which is this transistor doubling kind of effect. But we've got many, I mean, I mean, there's far more engineering working on all this stuff today than we're working on it in 1965 when he invented Moore's Law

or in 1995 when everybody bought a PC like we've got we we have a lot of a lot of mind power going into this we've got a lot of different technological options we've got a lot of you know incredibly impressive work happening all over the world the other thing is you can't you know you never like you know one of those things like the transistor was not obvious and then they invented that and then this integrated microchip was like not obvious and then they invented And so you don't quite know, you know, there are lots of technical proposals for how to get to the next level of Moore's Law. You know, so there's all kinds of theories around optical computing and then in the long run biological computing. Quantum computing.

Quantum computing, exactly. And so over the course of the next like 20 years, like, let's put it this way. This is one of the world's largest prizes, right? If you're the engineer who figures out how to reaccelerate Morse law or how to shift computing onto a new substrate like biology, that is the thing to do. And so that's the prize. Yeah. And that historically has been pretty motivating. Right.

kind of theme of marching forward progressively we have 4G we're talking about 5G so far 5G seems to be faster 4G with a lot of hype added to it there's a technical specification for 5G which is really awesome you know 100 gigabytes 2 millisecond latency almost impossible do you are you counting on that for the next decade that we're going to have actual what they promise with 5G yes I think there's pretty good odds we will. And the reason is because 5G has become an actual geopolitical battle. Like it's actually a very interesting twist. It's become actually a primary, like, you know, if the Cold War between the U.S. and the USSR was like defined by the space race, like at least the sort of nascent Cold War with China is actually a lot of it is around 5G, interestingly enough. I mean,

it could have been around a lot of things, but it happens that it's around 5G. And so you now have nation states that very, very badly need to win, two big nation states in particular. And so I think there's going to be a lot of, you know, so we've talked about the payoff from the space race is like all the products that got, you know, spun off from that, satellites and GPS and everything else. And the other thing on 5G, people sometimes say, you know, 5G will lead to applications

they haven't even thought of yet. And I think that's kind of true. But I look at it a little bit differently, which is a little bit like the Morse law conversation we're having, which is I look at a little bit as a math kind of question, which is there's sort of three classic rules for how networks scale and how network scaling turns into value or usefulness.

And there's sort of historically, there's what's called Sarnoff's Law, which was based on broadcast TV, which is the value of a network is equivalent to the number of nodes, right? So it scales with N, right? So a TV network with 10 million viewers is twice as valuable as a TV network with 5 million viewers.

That's kind of the obvious one. Then there was Metcalf's law, which is basically the value of network is on a number of connections between two points. And that's like how email works, right? It just emails person to person. And that correlates to n squared. So the value of the network rises exponentially

with n squared. And then there's this thing called Reed's Law, which is called the group forming law, which is the value of network is proportional to the number of groups and subgroups that conform inside the network, which turns out to be two to the nth. And if you want to have fun on your plane flights home, it's like just go and Excel and like chart, you know, n, n squared and two to the nth, right? And two to the nths just goes like straight vertical. Like you can't even put them on the same chart.

And two to the nth is like what's now happening was like social networks, right? So like Facebook groups and all these, all these other things people do with social networks and games and all these other things. And so those are like the three ways in which network growth pays off. And like all three are working now based on, you know, broadband, wired broadband. They're all working.

You know, you see it happening very much with mobile. You know, the introduction of 5G, the way I think about it is it's going to turbocharge those three networks in particular, that last one, or, you know, those last two. And so it's going to add a lot more end. There's just going to be a lot more devices on the network.

There are going to be a lot more things that those devices can do. They're going to be a lot more point-to-point connections that make sense to have. There's going to be a lot more groups that form a lot more economic activity that happens. Something that was, again, we're expecting to happen but didn't, was in the world of what's sometimes called the sharing economy.

There was a, after Airbnb and Uber, there was a stampede of companies that were going to be Uber for X, and then X was everything in the world. Very few of them have succeeded. Again, there was an expectation. We see more of them, but we haven't. So is that whole idea kind of at a dead end? is it just we're in a very slow disruption

that's going to take a while like the generational requirements that we were talking about technology earlier or is something else? So what do you think happened there and what are we looking at? Once again, I object to the question. Okay.

Throw the gavel. So I look at it a little bit differently which is this is something we try hard to do at our place. It is very tempting and we do have this conversation all the time at our place of like, okay, what about the trend what about the theme right what about the variations on the theme kind of as you said

and this is something happens when something wins big you always get this kind of you know we describe it as kind of the Hollywood model of you know it's like you know what you know what's your new movie about it's pretty woman meets the rock right or you know whatever and so in in the valley it's you know super for X or most recently superhuman for X which I'm very excited about is one of the one of the big new trends after another one of our companies so but I don't think it's really that That's not how the great ideas arrive. They don't look like that. They look like very specific.

They look at very specific theories, not general theories. So they tend to be very specific to the details of the market involved. One of the things that I think we've learned about ride sharing, why ride sharing works so well. I mean, it worked well for many reasons. But one of the reasons it works so as an idea is because as long as the driver is good, as long as they're rated at a certain level, it doesn't really matter who the driver is.

So like one of the classic examples was Uber for cleaning your house or your apartment. And it turns out you just don't want a different person over every week to clean your house. like it's a problem. And so there's a lot of these kinds of, I would say, simple, you know, sort of the simple applications of the idea that don't necessarily work. Now, by trying all those ideas, you kind of map the idea space and you start to get a better sense of like what your overall structure is. And I think what's happening now is you're starting to see another set of companies coming out the other end that have kind of fully internalized that lesson and have figured out new models at work. And so my favorite example, one of my companies

called Honor. So Honors, loosely might think of it as kind of Uber for senior care, for in-home care for seniors. It's a loose model. It's a very loose model for a couple of reasons. One is, it's really deeply not a fungible service. Like if you have an aging parent, you actually very much don't want somebody different to show up all the time.

You want the same person. And so in that case, for example, Honor actually has a full-time employment relationship, salary employment relationships with the workers, which, of course, is very different than the Uber and Lyft model. It actually turns out the matching problem is much more complicated, right?

Because when you're matching human beings in somebody's home, there's like 20 variables that you need to match on so that everybody's comfortable with the experience. As an example, you know, in some cases, you literally need people with the physical strength to be able to lift people when you're caring for them. You do want to be able to do this kind of multidimensional mapping. And so that model's really working.

And so I think we're going to see a whole set of these. Like, I think there's a big kind of vista of exploration that's going to happen from here. Okay. And I would suspect there will be dozens, if not hundreds of new models that people figure out. So speaking of new models, Do you ever think about new models for the VC industry itself

and how you would apply the principles of innovation and disruption to what you do in general? So as you look out 30 years, what kinds of innovations would you expect in the basic business that you're in? Yeah, so there's something very timeless about venture, which is there's actually a new book out called, literally called VC. It actually tells the story that has been kind of hard to get out for a long time in a really clear way, which is the modern venture model

It's actually, one of the historical precedents for it was actually how whaling expeditions got financed in the 1600s. So coming up on 500 years ago. So whaling of voyages, it was literally like, okay, you're going to have like a ship with a captain and a crew that's going to go out and try to bring back a whale.

And so it's like a problem number one is like only two thirds of the ships are going to come back, right? So high failure rate. Two is like, okay, like who, you know, what the ship is really matters, who the captain is really matters, how do you know who a good captain is? And then, you know, what's a good crew?

like, you know, are the crew, are they going to be willing to follow the captain? And then there's all these, like, strategy questions, like, do you want the captain who knows where all the whales have been caught recently? So they go there, do you want the captain that says, no, that's, area's going to be overfished. Do you want to go someplace else? And so literally all the whaling voyages, like in the colonies 500 years ago, got financed with basically angel syndicates, basically venture capital effectively. And then literally the term carry, which is sort of how VCs get paid,

It's so-called carried interest, which is like the 20 or 25% that you make, of the profits that you share. The term carry actually was the percentage of the whale that the ship carried. It was literally physical carry. It was literally that part of the whale. Like, that's where that term came from. And so there's a timelessness to the art of trying to figure out how to finance these kind of expeditions into the unknown that is, you know, that's likely to endure. The big question for me is, how will the shape of the companies, or let's say the ventures themselves, change, right? And so, you know, today there's like a well-known understood template for kind of

the prototypical Silicon Valley venture investment. And it's like a company in a certain place. It's a C corporation. It's domiciled in the U.S. It's financed a certain way. And to a certain types of employees, a certain relationship with its employees and so forth. You know, 30 years from now, you know, are we going to be financing companies here or, you know, anywhere or, you know, in two places, 50 places, 500 places. Are the company still going to have physical place or are they going to be fully virtual. Are they going to be companies or are they all going to be blockchains, right? Are they going to be, right, are they going to have actual employment relationships or are they going to have, you know, basically developers are centered through cryptocurrency? That's a

real model. And so I think the big question is like, we don't even know what the shape of companies is going to look like or ventures is going to look like in 30 years. So if I could figure that out, then I could answer what venture looks like. Without that, I think it's hard to say. Okay. So we were tempted to do a little bit of long-term thinking and long-term thinking is sort of rare and often ignored, whereas civilizations demanded as being necessary. So do you have any suggestions about how long-term thinking could be applied in Silicon Valley,

and whether you have even any suggestions to the people in this room about how they could use long-term thinking? Yeah, so the thing I've always found about long-term thinking is of course central. It's actually one of the things about the valley that I find outsiders miss the most, which is it feels like it's all moving so fast and yet like any of the important companies,

any of the important products take like a decade or more to build. And so it's like everything important basically takes a long time. And so a lot of it actually feels quite slow. And so the long-term orientation is absolutely necessary and I think we probably all agree there's not enough of it in the world. The thing about long-term thinking I've found

is like it's really easy if you know the thing is gonna work. Right? Like boy, that's completely straightforward. Like let's go on a 10-year journey to a place we know it's going to be great. The problem is, it's long-term thinking crossed with uncertainty, right? And quite possibly fatality, like the thing may just simply not work for any of a thousand reasons.

And so that's the issue. And so I think the issue is less around long-term thinking. I think the issue is more about how to deal with risk and how to deal with uncertainty and how to make really big consequential decisions in the face of, you know, literally an unknowable, you know, future landscape. And for there, I mean, this is kind of the one kind of secret weapon of venture. It's like, it's like venture is the worst of all asset classes in a lot. lot of ways in that it's like it's a liquid and it's like incredibly volatile and it's like

hit or miss in this kind of crazy way the one thing the venture really has going for it as an asset class is we have the concept of the portfolio kind of wired into the model in which you just kind of assume in top-end venture you just kind of assume fundamentally it's half the company's going to work half of them aren't right and then the classic right the class the cliche is like the ones that work then you know have to have to work enough so that they pay for the ones that don't to make to make the whole enterprise work and so if you can adapt yourself from the mentality of will this thing work, right, to will this portfolio of things basically pay off?

Well, enough things work that they'll actually pay for the portfolio. Then at that point, you can start to make risk a somewhat tractable thing to contemplate. It's still hard to divorce yourself emotionally from it, because it's just like, it's still like absolutely no, it's just terrible when any of the individual things don't work. But at least you have a conception of framework

for you're able to be able to make 10 long run bets and being able to get to the other side. Now, the response that I can get to that is, oh, that's great if you're a VC, see the problem as your portfolio, you know, you're a, you're a founder or a CEO, like, you don't, you don't get that, right? You have to, you, you have the much harder version of the problem, which is you're on a one-way journey. Like, you're the captain of the whaling ship. Yeah, there's all those other captains over there, but like, you know, they're on

their own, you're on your own. Even there, though, you know, the best-run companies tend to run experiments. They tend to run, right, multiple experiments against, against their goals. And they certainly run those experiments sequentially as they kind of, you know, try to figure out what works in a lot of cases they run experiments in parallel, as they're trying to test different things. And so I also think this kind of mentality of sort of portfolio risk also applies to how you run a company, which is you want to basically, you want to have a great deal of conviction about what you're trying to head, but you want to have a lot of flexibility inherent in how you're going to get there, right, and what the tactics are. And then you want to be

able to run a lot of experiments against that, and you can kind of diversify your risk of any one theory by doing that. And that's what governments are in some senses. They have a portfolio of different kind of prospects about the future bets, I mean, in some senses. So, I think that's an optimistic view of what governments do. Yeah. So, I mean, that's what they're, and they're adverse to risk, unfortunately. Well, the problem, the problem, governments have the risk is like an end of one, right? So there's only one government per.

Right. Right. We only get to run, you know, I mean, ex-federalism, which has been a huge advantage, I think, for the U.S., but, like, you know, the U.S. national government only gets to run one scenario. Right. And running experiments in the population is not necessarily well received. Right, because you can't tolerate failure. Yeah.

Right, right. Yeah. Failure has real consequences. So there's currently not only introspection about government, but also about capitalism. And capitalism so far has depended on growth, and growth is something that VCs pay attention to. But we're now wondering if what's the minimum amount of growth that you might need to have prosperity? Can you have prosperity with low growth? Can you have prosperity with fixed growth?

Do you have any insights about that at the civilizational scale? Yes, I think, and actually, I would even say that the issue is even more intense these days because there's now very prominent people in public life arguing that growth is bad, right? And in fact, it's, that it in fact is ruinous and destructive and that the right goal might actually be to have no growth or to actually go into negative growth, especially in the very common view in the environmental movement. So I'm a very strong proponent, a very strong believer, that growth is absolutely necessary. And I'll come back to the environmental thing in a second.

because it's a very interesting case of this. I think growth is absolutely necessary, and I think the reason growth is absolutely necessary is because you can fundamentally have two different mindset views of how the world works, right? One is positive sum, which is, you know, rising tide lifts all boats. We can all do better together,

and the other is zero sum, right? Where for me to win, somebody else must lose and vice versa. And the reason I think economic growth is so important at core is because if there is fast economic growth, then we have positive sum politics. And we start to have all these, about all these things that we can do as a society. And if we have zero-sum growth,

if we have a flat growth, or no growth, or negative growth, all of a sudden the politics becomes sharply zero-sum. And the most, you just kind of see this if you kind of track, you know, kind of the political climate. You just, basically, it's the wake of every recession, right? It's just, in the wake of every economic recession, the politics just go, like, seriously negative

on, in terms of thinking about the world's zero-sum. And then when you get zero-sum outlook in politics, That's when you get anti-immigration, that's when you get anti-trade, that's when you get anti-tech. If the world's not growing, then all that's left to do is to fight over what we already have. And so my view is like, you need to have economic growth. You need to have economic growth for all of the reasons that I would say right-wingers like economic growth, which is you want to have higher levels of material prosperity, more opportunity, more job creation, all those things. You want to have economic growth for the purpose of having, like, sane politics, like a productive political conversation.

And then I think the kicker is you also want economic growth actually for me. growth actually for many of the things that left-wing people want. One of the best books this year, new books this year, is a guy Andrew McAfee. I was written a book called, I think, more from less. And it's actually a story of a really remarkable thing that a lot of people are missing about what's happening with the environment, which is globally, carbon emissions are rising and resource utilization is rising. In the U.S., carbon emissions and resource utilization are actually falling. And so in the U.S., we have figured out to grow our economy while reducing our use of natural resources, which is a completely unexpected twist, right, to the plot

of what kind of, if you listen to environmentalists in the 60s or 70s, like nobody predicted that. And it turns out, he talks about this in the book, but it turns out basically what happens is when economies advance to a certain point, they get really, really good at doing more with less, right? They get really, really good at efficiency. And they get really good at energy efficiency. They get really used environmental resources.

They get really good at recycling in lots of different ways. And then they get really good at what's called dematerialization, which is what you is happening with digital technology, right? Which is basically taking things that used to require atoms and turning them into bits, which inherently consumes less resources. And so what you actually want, like my view on like the environmental issues is like you've got a global problem,

which is you have too many people in too many countries stuck in kind of mid the industrial revolution. They've got to grow to get to the point where they're in a fully digital economy. Like we are precisely so that they can start to have declining resource utilization. Right. I mean, the classic example is energy.

Like, you know, the big problem with, energy emissions globally, like a huge problem with emissions and with health from emissions is literally people burning wood, like in their houses, right, to be able to heat and cook. And what you want to do is you want to go to like hyper-efficient solar or ideally nuclear, right? You want to go to these like super advanced forms of technology. So actually, so you want that. And then by the way, if you want like a big social safety net, you know, and all the social programs, you want to pay for that stuff.

You also want economic growth because that generates taxes that pays for that stuff. And so like growth is the single kind of biggest form of magic that we have, right, to be able to, like, actually make progress and hold the whole thing together. And, you know, to your point about the developing countries, I think the idea of leapfrog and technology is a myth. It doesn't really work. You actually have to, if you want to have a high tech infrastructure, you actually need the intermediate roads, clean water.

You can't skip over that. And so they all need to be built out in order to have that prosperity at the end. So, you know, it seems like you don't worry about much. I don't worry about much. But one thing I do worry about is cyber conflict, cyber conflict. war, partly because I think we have no consensus about what's allowable. Does this worry you at all? So I think there's a lot of unknowns to it.

I think people are trying to figure this out, but it's a complex issue to grapple with. I will make an optimistic argument, which is going to sound a little strange. If you kind of project forward what's happening with generally cyber, with information, you know, operations of different kinds, but also with drones, you know, UAVs, and then also with unmanned, you know, unmanned fighter jets, right? Unmanned, you know, ships increasingly being built. It would be unmanned submarines at some point. If you project this stuff forward, you start to get this very interesting potential world in which, basically, the way I think about it is like all human conflict between peoples or between nation states up until now has been

basically throwing people at each other, right, throwing soldiers at each other and like letting them make the decision of who to shoot and like hoping they don't get shot, like with very serious repercussions of all those individual human decisions. You do have the prospect of basically a new world of both offense and defense. It's like completely motorized, completely mechanized, completely suffer driven and technology driven. And a lot of people, it's just immediately like, oh my God, that's horrible. You know, Terminator like, you know, SkyNet, like, you know, this is just the worst thing ever. There's a novel called Kill Decision. If you want the dystopian theory, there's a novel called Kill Decision. By Daniel Suarez. That extrapolates the drones forward

and it'll keep you up late at night. But the optimistic view would be like, boy, isn't it good that there aren't human beings involved. Isn't it good? Like, if the machines are shooting at each other, like, isn't that good? Isn't that better than if they're shooting at us? And by the way, and by the way, I would go so far as to say, like,

I don't know that I'm in favor of, like, the machines making, like, kill decisions, like decisions on who to shoot. But, like, the one thing I know is humans do that very badly. Right. Very, very, very badly. I'm the opposite of pro war. I don't want to see any of this stuff actually play out.

But if it has to play out, maybe having it be software in machines is going to be actually a better outcome. Right. I mean, it's kind of weird that we don't allow, we don't want machines to kill humans. We want other humans to kill humans. We want 18-year-olds.

We want to take 18-year-olds out of their homes, right? And we want to put a gun in their hand and send them someplace and tell them to decide who to shoot. That is going to go down in history as having been a good idea. Okay. Just strikes me as like unlikely. So we have only time for one last question, which is, I'm usually, I claim to be the most optimistic person in the room,

but with you sitting across to me, I don't think that may be true. What is your optimism based on? So my optimism, okay, so get cosmic for a second. Why not? I guess we're here. It's the last question. It's the last question. So the science fiction author, science fiction,

the science fiction authors always talk about what's called the singularity. This concept of singularity. So the singularity is basically what happens in the machines, get so smart that all of a sudden everything goes into exponential mode and all of a sudden, you know, the entire world changes. So my reading history is actually we actually were in the singularity already and that it actually started 300 years ago. and if you look at basically,

if you look at basically any chart of human welfare over time and you can look at child mortality is an obvious one but like there's, you know, there's many, many, many others and you just look at progress on that metric just look at child mortality as an example and it's just basically flat, flat, flat, flat, flat, flat for like 50,000 years, right?

And you know, this is the famous, you know, Thomas Hobbs, you know, life is, you know, nasty, brutish and short, right? It was just like the thing. Like everything was terrible everywhere, all the time, forever, the end. until 300 years ago and all of a sudden there's this knee in the curve

and then all the indicators of human welfare not uniformly across the planet but in societies that were making progress the societies that were making progress first all of a sudden all those indicators of human welfare went up into the right and it all corresponded by the way to economic growth but it was also right it was the enlightenment

it was the rise of democracy it was the rise of markets it was the rise of rationality the scientific method by the way human rights free speech free thought right and they all kind of catalyzed right around around 300 years ago and they've been making their way into the world you know in sort of increasing concentric circles kind of ever since and so we have you know I would argue like we have the answers like we actually don't need new we don't need new discoveries to have the future be much better we actually know how to do it is to apply basically those systems and and basically contra the sort of constant temptation from all kinds of people to try to you know compromise on these things or subvert these things you know basically double down on these systems. that we know work, right?

So double down on economic growth, double down on human rights, double down on markets, on capitalism, double down on the scientific method, fix science. Like we got as far as we did with science actually being pretty seriously

screwed up right now with the replication crisis. So we should fix that. And then science will all of a sudden start to work much better. Technology, right? Use of technological tools. So we literally have the systems.

Like we know how to do this. We know how to make the planet much better in every respect. And so what we just need to do is keep doing that. And then what I try to do when I read the news is notwithstanding everything's going on is basically try to look through

whatever's happened in the moment, try to look underneath and kind of say, okay, are those fundamental systems actually still working? Like is the world getting more democratic or less? Is free speech spreading or receding? Are markets expanding or falling? Are more and more people able to participate

in a modern market economy or not? And those indicators generally are all still up into the right. So let's go out and make the world better. Thank you. Yeah. Good. Thanks everybody.