The Tim Ferriss Show - #782: Legendary Inventor Danny Hillis (Plus Kevin Kelly) — Unorthodox Lessons from 400+ Patents, Solving the Impossible, Real Al vs. “AI”, Hiring Richard Feynman, Working with Steve Jobs, Creating Parallel Computing, and Much More

Episode Date: December 11, 2024

Danny Hillis is an inventor, scientist, author, and engineer. While completing his doctorate at MIT, he pioneered the parallel computers that are the basis for the processors used for AI and most high-performance computer chips. He is now a founding partner with Applied Invention, working on new ideas in cybersecurity, medicine, and agriculture.Kevin Kelly is the founding executive editor of WIRED magazine, the former editor and publisher of the Whole Earth Review, and a bestselling author of books on technology and culture, including Excellent Advice for Living. Subscribe to Kevin’s newsletter, Recomendo, at recomendo.com. Sponsors:Momentous high-quality supplements: https://livemomentous.com/tim (code TIM for 20% off)Eight Sleep’s Pod 4 Ultra sleeping solution for dynamic cooling and heating: https://eightsleep.com/tim (save between $400 and $600 on the Pod 4 Ultra)AG1 all-in-one nutritional supplement: https://DrinkAG1.com/Tim (1-year supply of Vitamin D (and 5 free AG1 travel packs) with your first subscription purchase.)*For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.For deals from sponsors of The Tim Ferriss Show, please visit tim.blog/podcast-sponsorsSign up for Tim’s email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim’s books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissPast guests on The Tim Ferriss Show include Jerry Seinfeld, Hugh Jackman, Dr. Jane Goodall, LeBron James, Kevin Hart, Doris Kearns Goodwin, Jamie Foxx, Matthew McConaughey, Esther Perel, Elizabeth Gilbert, Terry Crews, Sia, Yuval Noah Harari, Malcolm Gladwell, Madeleine Albright, Cheryl Strayed, Jim Collins, Mary Karr, Maria Popova, Sam Harris, Michael Phelps, Bob Iger, Edward Norton, Arnold Schwarzenegger, Neil Strauss, Ken Burns, Maria Sharapova, Marc Andreessen, Neil Gaiman, Neil de Grasse Tyson, Jocko Willink, Daniel Ek, Kelly Slater, Dr. Peter Attia, Seth Godin, Howard Marks, Dr. Brené Brown, Eric Schmidt, Michael Lewis, Joe Gebbia, Michael Pollan, Dr. Jordan Peterson, Vince Vaughn, Brian Koppelman, Ramit Sethi, Dax Shepard, Tony Robbins, Jim Dethmer, Dan Harris, Ray Dalio, Naval Ravikant, Vitalik Buterin, Elizabeth Lesser, Amanda Palmer, Katie Haun, Sir Richard Branson, Chuck Palahniuk, Arianna Huffington, Reid Hoffman, Bill Burr, Whitney Cummings, Rick Rubin, Dr. Vivek Murthy, Darren Aronofsky, Margaret Atwood, Mark Zuckerberg, Peter Thiel, Dr. Gabor Maté, Anne Lamott, Sarah Silverman, Dr. Andrew Huberman, and many more.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Hello boys and girls, ladies and germs. This is Tim Ferriss. Welcome to another episode of The Tim Ferriss Show, where it is my job to deconstruct world-class performers from all different fields they could be in military, entertainment, sports, or otherwise. And my guest today fits the otherwise category. I have wanted to have Danny Hillis on the show for many years now, probably four or five years. And he came up with my friend, Kevin Kelly. And I was with Kevin on two pilgrimage tours. It's a long story, but we were walking and talking for long periods of time on different continents. And I asked him in both cases, who would you suggest as a podcast guest, I must interview. Who would you suggest as a podcast guest I must interview? And we landed on Danny Hillis. So why? Why Danny Hillis?

Danny Hillis is an inventor, scientist, author, and engineer. While completing his doctorate at MIT, he pioneered the parallel computers that are the basis for the processors used for AI and most high-performance computer chips. He did that while completing his doctorate. The significance of that we'll come back to. So we will cover that. He has more than 400 issued patents covering parallel computers,

disc arrays, cancer diagnostics and treatment, various electronic optical and mechanical devices and the pinch to zoom display interface. You know, when you zoom in or zoom out on a map or something like that, yeah. Or on anything really, on an iPhone. Yeah, that thing. He is a co-founder of the Long Now Foundation and the designer of its 10,000 year mechanical clock, which sits inside a mountain in West Texas and has been funded by Jeff Bezos. We'll talk about

that a little bit. Danny has founded multiple companies, but his only regular job was as the first Disney fellow at Disney Imagineering. He has published scientific papers in Science, Nature, Modern Biology, and International Journal of Theoretical Physics. Like what does this guy not do? And written extensively on technology for Newsweek, Wired, and Scientific American. He's the author of The Pattern on the Stone, The Simple Ideas that Make Computers Work, and Connection Machine. He is now a founding partner with applied invention working on new ideas in cybersecurity, medicine and agriculture. Okay, you can find the website, there's not much of the hair because it's super top

secret, but applied invention.com. And my co host today is none other than the person who introduced me to Danny directly Kevin Kelly, Kevin Kelly, you can find him on Twitter at Kevin, the number two Kelly. He is the founding executive editor of Wired magazine, the former editor and publisher of the whole earth review and a bestselling author of books on technology and culture. And I'll just take a sidebar. He is one of the most accurate futurists I have ever met. He is repeatedly right. His books include excellent advice for living the inevitable,

what technology wants and vanishing Asia. His books include Excellent Advice for Living, The Inevitable, What Technology Wants, and Vanishing Asia. His gorgeous three-volume photo book set capturing West Central and East Asia. Thousands of photos, he did all of it himself. Kelly is the author of the popular essay, 1000 True Fans, which I've recommended a million times to anyone who has read my stuff or listened to this podcast. Subscribe to Kevin's newsletter.

It's a lot of fun. Recommendo. That's R-E-C-O-M-E-N-D-O dot com. Recommendo. Check it out. It's one of the few newsletters that I subscribe to. Every edition features six brief personal recommendations of cool stuff. And now just a few words from our sponsor. We'll jump right into it. This is a fun one. Hope you enjoy. This episode is brought to you by 8Sleep. I have been using 8Sleep pod cover for years now. Why? Well, by simply adding it to your existing mattress

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Gentlemen, Kevin, Danny, thank you for making the time for the three amigos together. I know, Danny, it's a little presumptuous for me to call us amigos just yet, but hopefully by the end of the chat. And Kevin, I must say, you know, your headline that I crafted for our podcast long ago, which was the real life most interesting man in the world. I think you might have some competition for that, that particular headline in Danny. And we'll certainly explore a lot of facets of that. But maybe we'll start with how the two of you met or connected in the first place. Do you wanna take a stab at that, Kevin?

Yeah, I was wondering, I think my recollection is that our mutual friend Stuart Brand went to MIT Mini Lab to write a book. And I think Danny was one of the people that was embedded in that circle of the mini lab and MIT. And at one point, Stuart kind of dragged them back to Sausalito, where I was editing the Hallworth review at the time. And we met. I was impressed, but that was it. And then later on, when I was running WIRE, Danny had a dream of a clock that would take 10,000 years as a sort of a way to think about the future. And he wrote a proposal which I ran

in Wired. And I thought that was really very interesting and a great way to frame the future. And that was it. But our mutual friend Stuart Brand decided that he would try to help Danny actually build the clock and made a little nonprofit called, well, we didn't have a name, it was called the Clock Library Foundation. And I was part of that original group. Then for the past almost 30 years, it seems like we've been working on together on the long now's mission to encourage long term thinking. And I've seen Danny in action in all those years. And so I think that's my recollection. Danny, does that? It comes right to me.

Okay. So I'm going to throw a bit of a wild card into things because I can't resist not doing it. So Denny, there are a million places to start with you. We could try to do something chronological. We could start with homeschooling. We could talk about AI. We could talk about dark sky weather apps. There are so many points of entry. I thought though I might be the first to begin with a mogan clamp, if that's the right term to use. So this device, this terrifying looking device and a silver briefcase full of devices. How does this fit into your story? You might need to explain it because I have no idea what this is. Okay. Well, at some point I realized that I really wasn't going to figure out what I was going

to do when I grew up. And then I always enjoyed new problems that I didn't know about. So I started a company called Applied Invention with Bran Farron, and it kind of worked on everything. And so how do you recruit people for a company like that? So one of the things that we did, we had this box of just weird stuff, like a space shuttle tile or a piece of synthetic diamond

or that weird sort of clamp cutter thing that you just mentioned. And what we'd do as part of the interview process is we'd sit people down and we'd just open up the box. And immediately you could tell, was this person a likely fit for the company? Because a lot of people would sort of wait for instructions. But most of the people that we hired would look at it and say, whoa, is that a mogan clamp? Is that a laser chairo? What is this?

And they would start picking up the pieces and talking about them and asking about them. And those were the kind of people that we wanted to hire. And it wasn't a test so much of knowledge, it was more a test of curiosity and engagement and ability to learn. Although it was amazing how many of them people recognized. That was a particularly weird one that a lot of people did not get. Because as you probably know by now, it's basically the device that is used for circumcision to make sure you don't cut off too much. It's horrifying, but also beautiful and how sterile it looks.

And I appreciated the German on it that says Rostfrei, which means rust free, which is really what you want. Yeah, rust free is, and it has a limited opening, you know, so really it's hard to overdo it. Oh, God, squirming in my ergonomic chair just thinking about this. The funniest person who ever opened that box was Robin Williams. And you can imagine where he sort of went with like space alien sex toys and things like that. He knew what everything was and gave us a elaborate description

of it. So, all right, I can't resist taking the bait. There are a lot of things I'm not going to be able to resist in this conversation. Why on earth is Robin Williams looking through this suitcase or this briefcase? Bran and I had met Robin at the Walt Disney Company. It was actually the only job I ever had. I worked for a while as something called Disney Fellow and Vice President of Imagineering. It was a job in the sense that I got a paycheck, which was actually kind of a novel experience for me

because I would usually pay the paychecks. When I saw benefits, I suddenly realized what benefits meant because always previously benefits was something I had to pay. So that was kind of a second education for me after my MIT education and completely different kinds of things, but part of it, how big companies were, part of it. So I'd like to hear, Danny, a little bit about that progression where you got your degree in, I don't know, math or computer science, and then you started a company, as you said, yourself before that.

That progression to work for Disney is not an obvious step for anybody. What were you thinking and what was your plan? I mean, you were kind of overeducated for the role in some ways. Well, I've never really had a plan, I will admit. It would be nice if people know where they're going in life. And maybe I'll figure that out someday. But opportunities present themselves. And that was a moment in my life.

When I went to MIT, I knew I wanted to work for Marvin Minsky, which is a whole other story. And I studied AI under Marvin Minsky in the early days of AI. But I realized that AI was not going to happen without big, fast parallel computers, which didn't exist at the time. So I started to build one,

which I had to build from designing the chips, the operating system, like everything from scratch. And it rapidly became too big a project for a graduate student to do at a university. Even though Darbo was giving me the money, the university didn't like a graduate student having this many employees. I did what was at the time a very unusual thing,

which is started a company as a graduate student. And in fact, MIT told me I couldn't do it. And I said, I don't see how you can say that because I'm paying you money, you're not paying me money. So they forbade me from doing it. And I just did it anyway. And in fact, I started hiring a bunch of faculty members and I hired the ex president of the university, which was Terry Wiesner, they stopped bothering me. Brought in the power lobbyists. Right.

And that was a huge success from a technical standpoint, but honestly, me and the other people that started the company had no idea about how to make a company work. I made a lot of mistakes in how I set up the business because I was really mostly wanting to just build this computer. I wasn't trying to build a company. And so we successfully built what was then the first big parallel computer.

It was something that all the experts said was impossible for various reasons. And it became the fastest computer in the world. For many years, we built the fastest computers in the world. But we never made a great business out of it. Actually, interesting enough, somebody who worked for one of our chip suppliers had a much better idea of how to make a business out of it. He took very similar chips to what we were making and he made them for video games.

And that company actually took 30 years, but it finally managed to do what we set out to do. And that was Nvidia. I've heard of it. Yeah. One of those ships is probably the power of one of your machines, right? Oh yeah.

I mean, Moore's law really worked and got to watch it play out. I mean, that was Oh, yeah. I mean, Moore's law really worked and got to watch it play out. I mean, that was 30 years ago. So think of how many times Moore's law has doubled since then. So you were describing this as how you wound up at Disney. Oh yeah. Well, when the company didn't work out, I kind of worked it out. I got all the hardware people that were working on it.

He hired his Sun Microsystems and Exchange. They're options and thinking machines for options in Sun Microsystems, which this was just before the web took off and that worked out well for them. But I decided that, you know, I'd had enough of the computer business and I just wanted to do something different. And I had twin babies and a daughter that was born on the day the company closed down. So I just kind of wanted a job for a little while.

And I had always had this kind of childhood dream of being an Imagineer. And then I was like, well, just let me be an Imagineer. And they're like, well, no, I think we have to give you more of a title than that. And so I didn't want a title anybody knew what I was supposed to do. So I asked for Disney fellow, which it turns out Salvador Dali had been the only previous Disney fellow. So I thought I was, I was on pretty safe ground there, but also they made me a vice president just so that it turns out that's very important to

a big company for some people. So they talked me into that. But it was a good thing because nobody takes you seriously unless you have some title that they understand. But it really was true that nobody knew what I was supposed to do. And actually, the guy that had approved my hiring was Frank Wells. And he unfortunately died in a helicopter crash before I showed up. So really nobody knew what I was supposed to do. But that turned out to just be a fantastic

way to get an education because I could say, you know, I want to be in the meeting where we decide what we're going to build in Florida that became Animal Kingdom or what we're going to build in Paris or what. So I would insist on being in a meeting and everybody would be a little bit worried that maybe I had some authority and nobody would say no to me. So I learned a huge amount about kind of storytelling and I would say the artistic way of looking at things rather than the engineering way of looking at things. What would be an example of that, Denny, of something you learned in terms of being able to

tell a story? Some of them shocked me as kind of bad because, you know, in some sense show business is about basically making stuff up, which is a nice way of saying lying about things. And it's not really tethered to reality. So, you know, in science, you have an argument somebody's right, but in show business, that's not really true, just somebody wins the argument, and you never really know who is right. So there's a completely different way people relate to each other.

So basically, you know, you make a movie and it either is a flop or it's a great hit, and if it's a great hit, everybody who was in the room at the time they decide to make the movie and it either is a flop or it's a great hit. And if it's a great hit, everybody who was in the room at the time they decided to make the movie gets promoted. But, you know, nobody really knows why it was a hit or who was responsible or so on. And very different from engineering where there's a sort of ground truth. Here's an example.

Like one of my first days early on, they knew they needed to get into online spaces. And they said, you know, we need to make some kind of online service or Disney online. They didn't know what it was, but they knew online was a big thing. This was kind of the the Silly Wood period when Hollywood and Silicon Valley were dancing with each other. And so they sat down and said, OK, everybody write down on a piece of paper, some sketches of what you think this thing's going to look like. And so I draw the kind of block diagram of the servers and the services and the, we got to have ways for people to log into it and a database that, you know, has a typical kind of engineering block diagram. then everybody else at the table,

we go around the table and everybody else holds up like a picture of a magic castle, or it's all like images of things that you would look at, nothing about how anything would work. And I hold up mine, everybody's like, you think it should be a bunch of boxes with lines? It was just a complete disconnect. But they knew something and they focused on different things than I did. And

after a while, I came to appreciate that things they were focusing on were extremely important. And in fact, probably the most important things to make things successful or not successful in show business. So it was a second education for me. Could you say a little bit more about the artistic way of seeing things versus the engineering way of seeing things or looking at things? And we may end up coming back at some point to Richard Feynman. I own a number of placemats that he used to use for drawing practice in his somewhat mature friendships

with one painter in particular. But I remember their debates about sort of seeing through the eyes of science versus seeing through the eyes of an artist. And I'm wondering if that artistic way of looking at the world has translated to things after Disney for you. Oh, it definitely has. And I'll focus on the part of it that influenced me the most. I mean, it was really interesting to be around people who knew how to draw. And I took drawing classes and things like, but the thing that really stuck was learning what they meant by storytelling.

So, you know, when Disney like designs a theme park, they don't think of it so much as a piece of architecture or a map. They think of it as a story. By a story, it means kind of a sequence of a narrative of going into it and experiencing it. So it sort of makes sense to the people that are going through it. And they sort of know where they are. They know what to expect.

They know when it's over, and the ride is like that, but actually the whole theme park is like that. And that way of thinking of things as a understandable emotional experience that connects with someone is very different than sort of looking at the mechanics of how the rides work, which is also very interesting, but it's very subjective and yet it can be done well and it can be done badly. And we've all been moved by watching a film or listening to a piece of music or something like that. We all know that it does affect us. It does connect with us.

And so for example, in the, the most obvious way that's influenced it was that's how I started to think about designing the 10,000-year clock. When I first thought about it, I was thinking about, well, mechanical problem. How do I keep it round? What materials do I use? But after a while, I came to realize that really the most important thing about this clock and the thing that will really determine how long it lasts is what do people think about it?

How do they experience it? How do they relate to it? What's the story? What is it about? What is the story? So for example, I'll give you a simple example. In the beginning, when I designed it, it was like an ordinary clock. It would always show you what time it was. But then I realized if it's taking away in a mountain someplace, and it doesn't care if you exist, then why should you care if it exists? So instead, I thought much more about the story of somebody going to visit the clock. And what did they see's the sequence of things where did they get confused where did they get frightened of like i'm in the wrong place. And then when they get to the clock instead of showing what time it is actually shows the time the last person was there.

Just the time and the date that the last person was there. the time and the date that the last person was there. And then when they wind the clock, it catches up to the current time. And then of course, this is sort of an idea that's obvious for anybody that's been in Disney, but if you want to take home a souvenir. So what it does is it has a place on the date where you can take a rubbing. And so you can go home with a rubbing of the date that you were there. Things like that I don't think I really would have thought about without that education at Disney.

Whereas with that education, it's obvious that those things in some sense are much more important than how you solve the technical problems. And by rubbing, do you mean almost like taking paper, putting it on a wood carving and rubbing on top of it to create an imprint? That's right. So if you did it like analog, yeah, so a piece of tracing paper, a neat paper, take a crayon or a piece of charcoal and you rub it across. And that's nice because it's something that is you because it's like your hand marks. But it's also a unique thing of the date that you were there. So it's something that kind of could only exist

because of you and because of your visit there. Stan, you run an invention company right now. And is that something that you also apply to your clients as well as when they come in? Are you trying to tell them or help them make a story out of the inventions that you are working on? Do other people really care about about as much as you do? People care about it, but I don't necessarily talk to the clients about that.

Because depending on their perspective, the thing they may care about is financial sales or the reliability of the machine or the rate of production. Or they have something that they think they care about, but very often behind it, there has to be some story for it to make sense that the people who are operating the machine or buying the product. And so I'm thinking about it that way. I'm not necessarily explaining it to the client that way. But yeah, I would say pretty much everything I do is influenced by that way of looking at things and it causes you to look at the experience of it rather than the engineering

of it. And actually, it's interesting. I mean, I was really lucky because I got to work with Steve Jobs when he was first making the Macintosh. And it was during that period he had been kind of kicked out of the campus of Apple and was in an apartment with a few pirates making the Macintosh. And at the time, this was before I had been to Disney and before I learned this, and it drove me crazy because I got calling because I knew how to make chips and Steve wanted to make a custom chip for the Macintosh initially.

And it wasn't going to happen. And I was the one that had to look at it and tell him it wasn't going to happen in the time scale he wanted, which is not a fun thing to do with Steve. And so I was like, well, you know, this is just reality. No matter how much you yell at me, it's not going to change. But you've got this simulator that Andy is making all his software work on. So why don't you just sell the simulator? And he basically blew up at me, and I was missing the point of everything. But what I realized looking back at it is Steve was really wrong about a lot of technical things, but what he was really right about was the story of how people would relate to the mission.

He had a vision about that that other people didn't have. And in some sense, it didn't matter that he was wrong about a bunch of technical things because the story was so correct. And the way that you related was so correct that all the technical things were fixable. But if you'd been wrong about the story, no amount of technical excellence would have fixed it.

And I think I only understood that in retrospect after I kind of saw people relating to the Mac. So Danny, you were doing AI for a very long time. You made the first computers that were in parallel. You called it thinking machines. Our slogan was, we want to make a machine that will be proud of us. Right. Exactly. So what is the story on AI that we're not getting right now? There's a lot of focus on all these LLMs and Neuronets, which are very old actually.

What do you think the story is? And what's the story that we're not hearing? Well, I'll tell you a story I told a long time ago about AI, which I called The Songs of Eden, which is, in some sense, it was a story about where human intelligence came from. And it was a story about a bunch of monkeys that kind of grunted and repeated each other's a bunch of monkeys that kind of grunted and repeated each other's grunts. They sung along with each other, didn't really mean anything, but they started noticing the mood of the other monkeys by the grunts they were making and their brains began to develop to keenly notice the moods of the other monkeys because they're social animals. And so they started evolving the ability

to distinguish sounds. But at the same time, there was another kind of thing that was evolving, which there was no name for it then, but we call it memes now, which is things that got repeated and that were very catchy tunes and things like that. And so there was this kind of co-evolution of these two things. One of them,

the monkeys got sort of better and better at distinguishing between the grunts. And the ideas got better and better at helping the monkeys because that's how they got repeated. And so we're sort of a symbiosis of those two things, of the monkeys and the song. So this song's in some sense evolved into human culture and human ideas. And we evolved into the monkeys that were able to hold those ideas and transfer those ideas. And so I kind of told the story that that was the way that human intelligence evolved and predicted

that that might be the way that artificial intelligence evolved. That that that might be the way that artificial intelligence evolved, that we would build machines that were sort of powerful enough, but then we kind of infect them with human culture. Now, the internet didn't exist then, but I wasn't quite sure where you were going to get the human culture or how you were going to do that. But I think that's sort of what's happened is what we've got is not so much artificial intelligence, but we've sort of got a substrate on which human intelligence can live that's not human. And the human intelligence is all of the things

that was learned from all the data that we train on in some sense. So, and this is the early stages. So you might say it's just kind of imitating right now, but it's got so many examples, it's really good at imitating. And that's always good at imitating. And that's always sort of the first part of intelligence is imitation.

I mean, child begins with imitation, then they understand more and more. So I think we're in that imitation stage right now where we've built machines that are able to do a pretty good job of imitating and they'll go beyond, they're just beginning to peek beyond the imitation stage, reason, things like that. But in the end, it's not really an artificial intelligence, it's human intelligence on an artificial substrate. That's a new phrasing and lens that I have not heard before. I will probably come back to AI, but I want to maybe ask a third of that.

And that's not the only possible form of AI. Oh, that's where we are. We will almost certainly come back to that. But I wanna zoom out for a second. You said earlier at some point, I never really had a plan, but there are people who don't have a plan and have no direction and end up, I think as Mark Andreessen put it once

as sort of a rabbit pivoting every 10 seconds going a different direction in the maze and not making any progress. Clearly you are not that rabbit. So it seems that there is some underlying sent trail or way in which you choose projects or what you will do next. How do you do that? What is your guiding sense of how you choose where to direct your attention? And you said at some point you'd want to do anything other than the computer stuff. So you shifted to the imagineering, right? And there were other

lifestyle factors, but I'm just wondering, broadly speaking, how do you choose what you're going to do next? And then once you decide on that, I'm stealing from Kevin here, but how do you proceed once you decide that you want to get into a new field? So first of all, since I sort of love the process of invention, I have to say that I think it's a misunderstood process because what the inventor does is actually a very small piece of it. What society does is it creates these preconditions for invention. And once those preconditions are in place, then it's just a matter of sort of putting together the puzzle pieces and making it work. So I always love to see those moments where all the pieces are around and somebody just needs to.

And usually they're not recognized because they're looked at by different people. They're in different disciplines and things like that. Could you give an example? A perfect example was parallel computers. It sort of now seems totally obvious, like how could anybody have not built parallel computers? But at the time, there were some pieces that weren't quite there yet until you got the ability to put multiple processors on a piece of silicon

that required a certain level of complexity of the silicon production technology. So nobody had done that. So I made the first multi-core chips. And it was also, there were proofs that computers became less and less efficient the more processors that you added to them. There was something called Amdahl's Law that was how IBM basically who pood parallel computers or people like Cray said you didn't need them.

Danny, just for people listening, could you define what parallel computing is? Yeah, parallel computing is what you do in the cloud when you have lots and lots of computers that you put onto a problem, or you do it on a single chip now that is a multi-core chip that has multiple processors on it.

It's so obvious now, it doesn't seem like an idea. But just to be clear, traditional way was you have a sequence and you would just do one thing at a time. That was the standard way. And this is, you're going to do things multiple at the same time, which is very complex because you have to do all kinds of things to coordinate, to converge. The complexity is incredibly more difficult when you're doing things in parallel. Yeah. And also, there were all these kinds of reasons why people thought it was impossible.

It's hard to believe and it took a while to understand why they were wrong. You know, it hadn't been done, but I knew it was possible because I knew the human brain worked. So, you know, the human brain has these very slow components, much slower than transistors. So I was like, well, maybe they won't be general purpose computers, but if you're going to make AI, certainly that's the way to do it. So I had some confidence that doing the thing in the unexpected way was going to work and the preconditions were there that I could design CMOS chips, make those work, build them, compiler technology was at the right place, television cameras were starting to produce digital things so that you could have eyes,

digital eyes on machines. So all the preconditions of converting audio to bits were there. So all the pieces were kind of coming together. And the only reason it wasn't being done was this sort of prejudice that it was impossible, which was sort of created for commercial reasons, I think. And so it was out there to be done and I had a reason to believe that it would work. So that's a kind of example of sort of seeing the preconditions are all there. Now that required an incredible amount of work on tens of thousands of great engineers

to get it to that point. So in some sense, all I had to do was take advantage of each of those pieces that were already there and put them together. Right now, it's very formal what we decide to work on because our partners at Applied Invention, we put three tests on things. One of them is that one of know, one of the senior partners has to be really excited about it, which is usually because it has some big impact on the world, or sometimes it's because it's just really cool technology, but, you know, usually it's because

they see it has potential for big impact. And then the partners that are not the one that's the most excited about it, and often I'm the one that's excited about it. So the other partners get to look at it and say, does this make any financial sense? And it can make financial sense because we're guaranteed not to lose too much money on it. Or it could make financial sense because there's a small chance of making a lot of money on it or, you know, you have to portfolio those things, but that sort of has to be evaluated by different people from the one that's most excited about it. Yeah, good idea.

So there's a kind of practical aspect to it that I probably didn't do in the early days. I tend to do things way too early before they made any financial sense. So now we have that bit of discipline added to it. But then the third thing that we do, which is, and this is the hardest thing to do, and we call it the non-redundancy criterion. Because by then you've got a project somebody's excited about and you know it's going to make money. And why would you say no? Well the answer is you would say no if it's going to happen anyway.

In other words, if somebody else is going to do it, why should you do it? You're wasting your time. It's like there's some reason nobody's doing it. And you know, in the case of the parallel computing thing, it was this crazy thing called Amdahl's law, which seemed to prove that it was impossible. And so you have to say, there's a unique reason why we're going to do this. We're doing something that won't get done otherwise, or won't get done for a long time, or won't get done right. So we only take projects like that. like that. And that one's a tough self-discipline to enforce, but we do do it. Just a quick thanks to one of our sponsors and we'll be right back to the show.

This episode is brought to you by AG1, the daily foundational nutritional supplement that supports whole body health. I do get asked a lot what I would take if I could only take one supplement. And the true answer is invariably AG1. It simply covers a ton of bases. I usually drink it in the mornings and frequently take their travel packs with me on the road. So what is AG1? AG1 is a science driven formulation of vitamins, probiotics and whole food source nutrients. In a single scoop, AG1 gives you support for the brain, gut and immune system. So take ownership of your health and try AG1 today. You will get a free one year supply of vitamin D

and five free AG1 travel packs with your first subscription purchase. So learn more, check it out. Go to drinkag1.com slash Tim. That's drinkag1, the number one. Drinkag1.com slash Tim. That's drink a g one. The number one drink a g one dot com slash Tim. Last time drink a g one dot com slash Tim. Check it out. Quick question on the parallel computing example. So you mentioned if I'm getting the pronunciation right Amdahl's law which indicated it was impossible. You mentioned as a perhaps counter example, obviously

in different substrates, the human brain does it. But were there other pieces of evidence that led you to believe given the constraints of the technology at the time that it was possible? No, I think that was the one that really made me have faith in it. There's something wrong with Amdahl's Law. Actually, at the time, I couldn't tell you what the flaw was in the proof of Amdahl's Law. It was pretty convincing. Now I can go back and tell you that the flaw was it assumed that you just kept doing the same size problem.

But of course, if you have a bigger, faster computer, you do a bigger problem. And so you don't just use the same problem. That's the reason cloud computing works and these giant parallel machines work is because you use gigantic problems on them. And if you tried to use the little problem that you're running on a single computer, they wouldn't be very efficient. But anyway, I didn't see that flaw at the time. I'll give you an example of something we're doing now that sort of fits that. Cybersecurity. Everybody agrees cybersecurity is a mess. Ransoms are going up. Nobody knows even how big it is because everybody hides the break-ins and so on. But everybody agrees it's getting worse rapidly. And the defense is losing against offense. And if you step back and really look at it,

the reason that it's bad is because the internet was built on a sort of flawed foundation. The basic idea of IP internet protocol was that you'd look at a packet and if it wanted to go someplace, you'd move it in that direction. And it was explicitly stated in the design principles that security was not the problem of the networks. Your security was the problem of the thing that got the packet.

And the packet can kind of claim to be from anywhere. So you get this flood of packets being delivered to you. You have no idea really where they came from. And you have to kind of guess which are the good ones and which are the bad ones. Unmarked packages from everywhere. Yeah, exactly. And so you can come up with very clever ways of guessing. But then as soon as you do that, somebody can come up with a very clever way

of getting around your heuristic of guessing. And ultimately the attackers have the advantage if the packets are anonymous. And so clearly the right thing is to have the network have a policy of what it delivers. And in some sense, we did that a little bit with firewalls of you try to see, oh, this is a bad packet, I'll cut it off or something. But again, you sort of have to guess where it came from or what it's

doing to do that. So I got together a bunch of people that had been involved in the early days of the of the internet and had built all kinds of things on top of it and had used it for very high security applications and things like that and said, how would we have designed internet protocol if we knew what we knew today, if we actually understood what cybersecurity was like, how people were really using computers, things like that. And that's a non-starter for any normal commercial company to ask that question, because obviously you're not going to replace Internet Protocol. But it was a great hypothetical that kind of captured a bunch of very smart people's imagination. And we got together and

invented something called Zero Trust Packet Routing, where every packet carries a kind of a passport and a visa that proves it has permission to go where it's going. So the network itself kind of has a policy. It doesn't try to deliver everything to everything. It delivers things that are allowed to go to where they're allowed to go. And then it turned out after we built that, that actually we looked at and said, you know, we could build this as kind of an overlay to start on the current internet. So people are starting to do that now. Now Oracle just announced a product that their cloud is going to start using this protocol.

And so I think that that's going to cause a big shift in the internet eventually, because it sort of gets at the foundational problem that sort of no sane company would have looked at as a business opportunity. And it probably isn't a business opportunity because it probably has to be open and, you know, a standard or something like that. But I think it's going to actually help the good guys and actually make the world a better place. What do you think Danny the and I'm going to keep this pretty broad, but the future of cybersecurity potentially looks like and you can choose the timeframe five years, 10

years, three years, whatever you want to paint. But I mean, there could be the dystopian sort of Cormac McCarthy version of what cybersecurity looks like. Then there's the utopian kind of island Aldous Huxley version. Then there's probably something in between. I think you will actually shift to this. And what this is, there's sort of two completely different layers of cybersecurity that have nothing to do with each other. So you'll have the kind of layer that we have right now that we depend on, which is the endpoints kind of protect themselves.

So, you know, they force you to log in and identify yourself or exchange certificates. That will all still exist, but completely independently of that will be something like, it'll be zero trust packet routing or something like that, where the network itself is kind of aware of who's sending the messages, what permissions they have, and it's actually aware of the identity and some strongly authenticated identity of it. That's a completely different system than we have now. So I think that two-layer system, actually the defender has the advantage instead of right now the attacker has the advantage. That's cool. So, Dan, I love your idea of the three criteria for deciding whether your company does things.

I assume maybe that's also your personal one too, where it's, am I excited? Is there some viable means to keep it going? And then thirdly, would it happen without me? That last one supposes a certain amount that you know something or you have some ability that other people don't have to do it. And so going back to you with the chips, like you're a young graduate student, oh, I'm just going to design a chip. I'm going to make chips. That requires either a lot of knowledge about chip making. It's not every graduate student who says,

I can make a chip. And how am I going to make, I mean, so how do you enter into this area of chip design that you don't have, but you're confident that you can make a chip? So tell me about how you get there. Maybe it just requires a lot of overconfidence. So of course it always turns out to be harder

than you think. Right. But I guess I am gravitated toward learning new things. And I've also developed the ability to kind of search out the people who really know the things and hang out with them. So find the people who really understand it, hang out with it, learn it. And so it's not that I know things other people don't, but maybe I know a different combination of things that other people do know. And I'm kind of willing to learn the things I don't know and kind of have a technique of doing it, which is hanging out with people who are

smarter than I am. Let me open that up a bit. So I feel like there are many different species of hanging out with people, right? So I could have as many group dinners with wine and banter with experts in AI as humanly possible. And who knows, maybe I'd have a hangover and a few great ideas I thought were great, at least jot it down in a notebook. Could you give a few examples of how you interact with people? Maybe because the name was invoked earlier,

you could start with Marvin Minsky and maybe your first meeting. Because maybe that'll lead us somewhere interesting. Okay, so Marvin Minsky is the person who named artificial intelligence. He and John McCarthy kind of founded the field. So when I went to MIT, I kind of knew that I wanted to do artificial intelligence and I had read about Marvin Minsky.

So I knew I wanted to work for Marvin Minsky. And I was, had to figure out how to do it. The AI lab was sort of locked up in technology square. It was hard to even physically get into it. And you couldn't get into it unless you had a key and you couldn't get a key unless you had a job there. So I decided, okay, first thing I got to get into the building.

So. Ocean's 11. Well, I did slip in a few times, but that wasn't going to work. It was pretty high security. DARPA was paying for all of the lab and I got their proposals and the proposals to NSF and I read their proposals to see what is I could possibly offer here. And I read their proposals. And you read those proposals because those were publicly available in some format because they were government funded.

Well, they were actually in the library that was in the lobby of the building, which you could get. Okay, here we go. Okay. So I'm in the lobby so I can read the proposal. Yeah. Nose and mustache, cup of coffee.

Don't mind me. Right. Yeah. Oh, yeah. All the outro marks, nose and mustache, cup of coffee. Don't mind me. Right. Yeah. So I read them and they came across, there was one thing where they said, you know, we think it's actually important that young kids program computers and we think even kids that can't read and write should program it. We don't know how to do that yet, but we think it's important.

I was like, oh, they don't know how to do it yet. So I will invent a way for kids who can't read and write to program computers. So I went off and I invented this picture way where you manipulate blocks. And then that was enough. That proposal was enough to get me an interview with Seymour Papert. It was the first one that did sort of educational computing. And he had the gates to the kingdom in terms of getting you into that. He had the gates, right.

He was inside the kingdom. So what did you say to this person? Were you like, I was cruising in the library, I came across this, it seems important to your funding that you develop X. No, no, I didn't give him all that backstory. I just said, hey, here's a really cool way I've come up with for kids that don't know how to read and write to program computers. He's like,

Oh, I've been looking for that. Okay, got it. So it wasn't like a million things that were in these proposals. He would recognize. Yeah, he immediately recognized that was something he wanted. What a coincidence. I knew who to go to. He wanted, what a coincidence. This sounds a lot like logo. It was, he invented logo. He's the guy that invented logo. And what did you invent?

I invented something called the slot machine, which is a way of programming logo with pictures. So you could arrange pictures and actually the squeak language is kind of the electronic version of what I invented by invented physical things that you put together to make a logo program. What is a logo program? It was an early computer program language that for kids. I got it. I got it. Programming language.

You would say move the square around in a circle or something. Very, very simple. Of course, people thought this was very impractical because we had to convince people someday every school will have a computer. That was considered very implausible at the time, but that was our stretch idea there that someday every school would have a computer. So I had a physical way that you could kind of program it by putting these in. And so I got hired and I got a key to the building.

Okay. So now I'm in the building. Phase one complete. I'm building it. I go up to to the building. Okay. So now I'm in the building. Phase one complete. I'm building it. I go to Marvin Minsky's office. Marvin's never there. But after a while I make friends and, you know, like where's Marvin? And it's like, oh, he comes in at night and he's working downstairs in the basement. And he's building something which is a personal computer. Whoa. And I was like, okay is a personal computer. Whoa.

Okay, that's great. But I had the key, the key. You couldn't get into the basement without the key either. But I had the key. So, sure enough, I go down there at night, and there's Marvin Minsky with his graduate students around him. You know, in those days, they were wire wrapping machines, and there were the diagrams lying around all over the place of the computer. And of course, I'm too shy to talk to Marvin and I don't really have anything useful to say to Marvin.

So I just sort of look around and I look around at the diagrams of the computers and I notice a mistake on my laptop. I go up to Marvin Minsky and I say, you know, I think there's an error here. And Marvin looks at it and says, oh yeah, yeah, that seems wrong. Fix it. And it's like, well, you mean fix it on the diagram? I mean, he's like, no, fix it on the diagram, fix it on the machine, you know, just fix it.

And so, okay. So then I look around and I find something else. I go to Marvin with him. I said, don't ask me every time, just like fix the problems. So after a while, I just started working there. And I think Marvin just sort of assumed I worked for him. And then eventually, you know, everybody, I mean, after I'm there for a few weeks and everybody else

would get tired and go home in the morning. And then Marvin, at some point he was like, where are you going? You need to ride someplace. I'm like, I need to go back by and where. He's like, yeah, why don't you just like crash in my basement? So I kind of moved into Marvin's basement. And eventually I mentioned to Marvin that I didn't actually have a job and he gave me one, but I still sort of had my job at Logo working for C-Marvin. But, you know, that was how I got into the AI lab and started working for Marvin Minsky. Hanging out with people.

Yeah. So there's one other example that you're going to give Danny outside of Marvin. Learning by hanging around people. Yeah, learning by hanging around people. They're like the Hilles method of hanging around with smart people. Well, I was going to give the Feynman example as the other. Oh, great. Yeah, let's do that.

That was a fun one too, because I had met Richard Feynman at a conference and we had really hit it off. And for people listening if they don't have any context, just a brief overview of Richard. So Richard Feynman was the Nobel Prize winning physicist that invented Feynman diagrams and quantum electrodynamics and a lot of other basic techniques that everybody uses in physics. One of the youngest people that was on the Manhattan Project. So totally brilliant, but also just a lot of fun. We really hit it off, liked him a lot and thought he was super smart. When I was starting thinking machines, I wanted him involved somehow. So I went to visit him at Caltech and he invited me to stay at his house.

He had, I explained to him, building this parallel computer and said, do you think you have any students that we could hire or hire as interns or something like that, that might be interested in working on this. And Hyman said, you know, now, he said, none of my students are crazy enough to work on something like that. I mean, that's nuts. And it's just a kooky idea. That's what he said. That's a kooky idea. And he said, well, he says, actually, maybe there's this one guy I know that would

work on it. You might hire him for a summer job, you know, but he doesn't really know much about computers, but he's a really hard worker. And I think he's pretty smart. And I was like, okay, well, that's good enough recommendation for me. What's his name? He said, Richard Feindl. So he came, he was actually showed up on the first day, was in the middle of summer and he shows up and I, of course, starting a company, you got like worrying about closing financing and things like that. And I wasn't really thinking like, what's everybody going to actually do when we get all this set up on the first day? And he shows up the first day.

He salutes, he says, Richard Feynman reporting for duty, sir. You know, what would you like me to do? And I'm like, oh, I hadn't really thought about this. So I think you're taking the nice. Oh, how would you do quantum electrodynamics on a parallel computer? And he's like, that's what you want on the first day? It's like, is that really what you need doing?

And I was like, well, actually, the truth of the matter is we don't have any like pencils or paper, nobody's gotten any supplies. He's got great, I'll be quartermaster. And so he goes out and he, he gets the supplies. That was his first job. But he kept on, you know, every summer he would come to thinking machines. And of course, we got more serious tasks. And he actually started the first quantum computing project at Thinking Machines.

So we were, again, a bit ahead of our time on that. Probably way too ahead of our time. So at the end, what I find interesting in your approach of hanging out with people is when you're going into a new field, you're not like reading the papers. You're going to talk to someone. Do you learn best by conversation and listening, or do you learn by reading some fundamental papers? I read enough papers that I have questions. You know, because you're wasting the time of a Marv Minsky or a Richard Feynman if you don't ask him something that makes him think.

And so I would say most of my learning was from the people, not the papers, but I always do homework beforehand to sort of see where the interesting questions are. And in some sense, that's easier to do when you're coming into a field from the outside, because the people inside the field have already kind of settled on a set of questions as the important question. But if you don't know much, it's sort of easier for you to see sort of the big holes that are missing. And sometimes your questions are dumb. And, you know, they explain to you why they're dumb questions, but sometimes they're like, yeah, that's actually a pretty interesting fundamental question. And, you know, if you can hit on one of those, that gets you into a conversation. But ultimately, I learned much more from people than from the papers. How did you, Danny, get into

biotechnology or just the biological sciences? Once I set up these invention companies, people would start to come to me with problems that kind of is a last resort. The engineer of last resort. You want to have a problem and nobody else can solve it. So that was the way that I got into biology is a doctor named David Agus. He was an oncologist who was really frustrated with his abilities to diagnose and treat cancer. He came to me and said, you know, we've got a problem here, you know, cancer is all these

different things and the paradigm we have for treating things just isn't working for it. And I started talking with them about it. And that led to a big collaboration. One of the things that we realized was, in some sense, cancelling isn't something you have like a disease. It's something that you do like your body does. And your body is constantly doing it. And your body is probably cancelling right now in three or four different ways, but usually it deals with it and stops it and occasionally it gets out of control.

So if you start thinking of it more like a verb and then where's the action happening? Well, the action is happening at the levels of proteins being expressed and proteins interacting. So even if I knew all your genes, I don't know what your proteins are doing. I know maybe what possible proteins are, but proteins, after they get produced by the genes,

they modify each other and they also come in by food and the bacteria or God and everything like that. So what you really want to see is the proteins, what's happening in the proteins. And nobody had a way of looking at the proteins. So we started developing a way that you could take a drop of blood or eventually a cell and look at all, just measure all the proteins in it and see how that changed with time and we started doing with mice and Studying, you know as they got cancer we could see how the proteins changed and the cascades and Then you could look at ways of interfering with this process, which is different in every form of cancer

So once you start looking at it as kind of a runtime thing rather than Something that you have. What do you mean by runtime? There's two ways of looking at what's going on in a computer. I mean, I could stare at the code for a long time, but a better way of debugging the program is to try to run the program and look at what's actually happening. And in some sense, if you look at genetics, you're looking at the program. But if you had a way of looking at all the proteins, that's the equivalent of the debugger

to see what's actually happening. And so, you know, this kind of became a different way of looking at cancer, and the National Cancer Institute got interested and gave us the money to actually, you know, make some real progress and so on. So that's how I got into that one. So going back to that, as you got into working with this doctor, is your idea, well, you probably said, I don't know that much about proteins, so I'll start to hire people who will be the expert in this. My job will be to find the people who know the most and then start to work with them.

Or are you trying to bring yourself up so that you're now an expert on proteins as well? Dr. Sinclair Well, so first of all, he was one of the world's experts. So first step was just like learn from him. But then, he knew people that were other interesting people to talk to and introduced me to them. And it was the same thing with Marvin. Marvin introduced me to other people, or the same thing with Feynman. Once Feynman introduced me to his arch enemy, Murray Gelman.

And so- Keep him close, Danny, keep him close. So in that case, it was really David Agus was the doctor that brought me into it, was my mentor. And I think with all these people, they like explaining it to somebody who doesn't understand it because they get to sort of go back to the fundamentals. And then that's a process.

If you've ever taught somebody something, you know how much you learn teaching somebody something. So, that was in some sense what I had to bring to the party was I was the blank slate that didn't know anything that was asking the dumb questions. How did the doctor find you Danny at that point? How did he end up calling you or emailing you? It was funny. He kept calling me and I didn't, you know, cause you get a lot of incoming calls. Unknown caller. You're like, no, thanks. Mostly I don't respond to them. And then finally he was resourceful enough.

One day he got, I think it was John door, Al Gore and Bill Bergman or something. He got like three different important people to call me up and say, talk to this guy. So I did. I mean, it was so far afield from things I knew about. AC- Did he explain why he hunted you down in that way? I'm just imagining within the, let's just say, I think it's fair to describe medicine sometimes as a silo, just as there are many different silos, to reach that far a field to investigate some of the questions or to try to unpack some of these issues.

At least I know a lot of doctors, MD, PhDs, researchers, not a lot of them do that necessarily. No, he's a very unusual kind of a doctor to do that, just like Dick Feynman was a very unusual kind of physicist and Marvin Minsky was a very unusual kind of computer scientist that they all, first of all, they all share a kind of playfulness and curiosity. And they all share a kind of skepticism about the experts in their field. You know, they appreciate that they know a lot of things, but they also appreciate that they're missing a lot of things. And I think that that's probably rare in a field because you're really, you know, the best strategy for becoming important in a field is kind of go with the flow, work on the accepted important questions, don't question the things that nobody's paying attention to, and don't listen to people on the outside of the field

and things like that. So yeah, these are all very unusual people to do that. And so I do have to find an unusual person that sort of is willing to put up with a dummy like me. Going back into your three criteria of you have to be excited by it, you've got to have some kind of financial basis, and no one else is doing it, I bet that there are still three or four things a month that come into you that would fit those definitions. I would think that your opportunities are even within that space, you still have to make some choices

about what you spend your limited time on. In addition to that, do you have like a fourth criteria that you're using? I'm kind of realizing, and I never articulated this before, but there's always something that you kind of want to learn about. And so, in that case, it was clear that there was a lot happening in biology that I didn't know much about. And so it was an excuse to learn about it. So how about today? I mean, how about this month? I'm sure you've got three opportunities, something interesting, maybe commit money, no one else is doing it. How did you decide what new thing to do in the last month? I should say that the make money thing isn't exactly

like you put it that way, because I've never really done things to like optimize to make the great, you know, billion dollar company or something like that. But you sort of have to have some financial model of how you're gonna pay for all of this. I mean, it has to have some sustainable way of paying for itself. It doesn't have to make you rich. I actually like Disney's formulation of it, which is we don't make movies to make money.

We make money to make movies. Yes. Yeah. I think that's a much better way of doing it. You know, you have to have something that's sustainable. Otherwise you're going around begging over the time. So your fourth one is I'm going to also learn something. This is a way for me to learn. Well, I'll tell you what, like right now I've gotten very interested in agriculture. Part of it, I got interested in it because during COVID, I moved out to a farm in New Hampshire. And I started realizing, I mean, we just grew food in our own greenhouse. And I started realizing how much better this food was than what I could get, you know, shopping at Whole Foods.

And started thinking about the whole supply chain and why was it food was so bad and expensive. And the more you looked at it, the way we do food today kind of relies on finding someplace where you can pay somebody an unfairly low wage to do something and bringing the food from there. And that's not really a sustainable future. And the land in which you can do that and just the social justice of doing that is not

going to hold up. And people want more protein, people want better food. And it's incredibly energy inefficient. You're better off in California, but here you go to a grocery store. Most of the vegetables that you find in the grocery store are many weeks old. They've been shipped across thousands of miles in refrigerator trucks, a great cost of energy. They're just about to spoil by the time they put them on the supermarket shelves.

They've had all the flavor and everything bred out of them so that they can optimize their ability to withstand shipping long distances. And the rest of the world couldn't repeat this inefficient system that we've done. And yet the rest of the world wants to eat much better food, wants to eat more protein, climate is changing. So Danny, when you're looking at a space like this, you have the seed of an interest that is prompted by this time spent in New Hampshire, where I've also spent a bunch of time. And then you start asking questions and the

peripheral vision widens to include all of these different facets that you just mentioned, right? So someone could get lost in that and just the sheer volume and complexity of all these different problems and challenges. How do you brainstorm questions and then choose which questions to pursue? I'm interested in and maybe it's because of the technique I've developed of learning things is are there ways to change the system rather than solving individual point problems within the system? You have a very systems view of the world. Yeah.

Okay. Yeah. So agriculture is like the oldest technology. So it's amazing all the solutions people have come up with, like the point problem of, you know, how do you pull out a weed or pick a tomato or any one problem has been looked at in a lot of ways and lots of inventions around it and so on. But it's surprising how few people think in terms of what's all the things that have to happen for food to get grown and, you know, it end up on the table.

And a lot of it is stuff that you don't imagine like predicting the weather, mining fertilizer, or shipping things in refrigerator trucks. And it's not things that you would think of first when you're thinking of agriculture, but that's actually what a lot of the activity is. So people have point optimized out most of the specific solutions, done a pretty good job of that, but very few, if anybody, people have kind of tried to look at it as a system and how could you rearrange the system? Now by system you don't mean, I assume, which is always dangerous habit, but you're not talking about say, some people might think of permaculture as a system, but you're extending the system to include many other aspects of food production, transport, supply chain. a natural system. And so nature does build things in terms of systems, ecologies or systems. But typically, we engineer things in terms of point solutions that get put together into

systems. Kind of cobbled together. Yeah. And that's because that's probably, you know, that's the commercial opportunities. If you make a better point solution to something, you've got a market and you can build up expertise and then competitive advantage and so on. So there's a reason why people do that. And the system things are more complicated and more likely to fail. And a lot of times I do look at it and decide this is too complicated.

I can't do anything. But sometimes you'll look at it and say, well, a lot of the easy things haven't been done. If you change this and you change that at the same time. And did you find that in agriculture? There was some low hanging fruit, pun intended. Oh yeah. In agriculture, very much like that. So clearly, for instance, things should be grown much closer to where they're eating. I mean, they don't have to be grown like in vertical farms in the city, but they could be grown a few hours away out in the suburbs and there'd be a whole lot better. But here I am in Boston, you know,

you can't hire an agricultural worker in Boston. First of all, there are very few people who know how to do it and they would demand to be paid much more than, you know, you could afford to sell the tomato for. So you have to have a better way of using labor. You have to have a better way of building greenhouses so that they work in colder climates, you have to have different breeds of plants, different fruits and vegetables that are not optimized to be shipped 2000 miles. So, you know, there are a lot

of things you have to change. But if you change all of those things up at once, there's another equilibrium point. It's a nice equilibrium where there's another sweet spot of things working together, in which many, many crops are grown much closer to where they're consumed. But you have to change a lot of things from the architecture of the greenhouses, to the jobs of the workers, to the microbiome of the soil. So you have to sort of be willing to take on all of that, which means learning a lot of new things. Are you currently in the exploratory learning phase or once you have this grab bag of different issues that need resolving to produce the outcome of having multiple foods or maybe all of your food grown or harvested and sourced near Boston, let's just say, do you rank

order those and then tackle one? Do you have teams or contractors and you try to parallel process for at the risk of using that completely incorrectly? So one thing is you need you need to find kind of a visionary source of funding. The patron, right? You need your Medici. Medici or somebody who already has this idea and is trying to make it work and hasn't figured out how to make it work, which is what happened in this case. So, and actually, the doctor that I mentioned before was already working with a company that was starting to do this, but didn't really know how to make it work. And so they came to us for help and we probably

gave them more than they ever imagined that they wanted. And together we made it into a much bigger project and I think we're really going to make a real system. And if you solve, kind of like DARPA was for ALI initially, or later other people were for ALI, I never would have been able to do the clock without Jeff Bezos kind of seeing the vision and saying, yeah, I'm willing to step forward and do this. Those are rare people. So I guess I've been lucky that I've run into a bunch of those rare visionary people that are kind of willing to take a bet on me. So you have many talents, Danny, so many talents. I'm wondering

which one you feel is your superpower? I don't know. Maybe not being afraid to learn new stuff. In some sense, maybe it's the superpower we're all born with. So maybe I've kept a superpower that kids have, right? So kids, like they're not afraid to go in and see something new and strange and start playing with it. And then after a while, there's a lot of things in the world that that gets sort of beat out of you. You learn not to do that and you get told not to do that in lots of different ways.

And I guess I was lucky enough to be around people that didn't beat it out of me. Here's what one of your kids told me. They said your superpower was a mindshifter, someone who can easily shift into different mindsets and view things from multiple perspectives. And I think I would agree with that. I think that's your lateral thinking is, to me, one of your superpowers. That may have come from my childhood because my childhood was, my father was an epidemiologist. So we lived pretty much any place that was a hepatitis epidemic, which often came with

a war and a famine too. So I lived in a lot of strange places around strange cultures. So you sort of had to mind shift into what are things like in the middle of the Congo or what are things like in Calcutta. So maybe that's how I sort of got that habit of being willing to shift my mind a bit. So maybe an angle into this, Danny, my understanding is that you homeschooled your three kids.

Why did you do it and how did you approach it and how did that turn out? First of all, I can't take personal credit for homeschooling. I mean, my wife did a lot and also we hired a bunch of tutors and we worked with a bunch of other homeschoolers and so. So you guys jointly decided. I taught them some things, but it definitely takes a village. So when I was a kid, I did bounce around all these schools and I remember. When I was a kid, I did bounce around all these schools. And I remember I had some great teachers. I had some really bad teachers too. And I remember sitting in school and thinking, I will never do this to my kids. And so I didn't. But when you had a great teacher, they would

listen to where you were and just stretch you a bit. You know, one of my favorite teachers was actually a woman named Mrs. Wilner. She was a librarian. And I was really interested in collecting rocks wherever I went. I would always go in and ask for books on rocks. And she said, okay, well, here's some books on rocks, but here's a book on electricity too. And I was like, whoa, this is, I never would have asked for a book on electricity, but she kind of led me there. And here's a science fiction book. It's like, what's science fiction?

And that was like a juvenile science fiction book called The Wonderful Trip to the Mushroom Planet. But that was like brought me into this whole other world. But so great teachers are like that. They kind of see where you are and they stretch you to someplace you can get to. And that was wonderful. And you just have a lot more opportunity to do that in homeschooling than you do in a classroom. Were there any aspects of cognitive development, curiosity or otherwise that you cultivated through the homeschooling

understanding that it wasn't just you as a lone operator doing it, but were there things that you wouldn't really emphasize or touch in traditional schooling that you guys included or emphasized? We did, but it was interesting for me. Like sometimes I would sit down to teach something that I thought was really simple. And then as I started teaching it, I realized, well, actually, this isn't so simple. There's like this other thing underneath it and another thing under. So I was actually not such a great teacher necessarily.

You're like, wait, wait, wait, I know two plus two equals four. Let's back up a minute. Yeah, exactly. You can back up. It sort of reminded me, I saw this happen in college was I had a math professor named John Colorado and he was at the blackboard once and he's writing along and he says so you can see that it's obvious that this is true and he stops And he sits there for a long time

Like it felt like ten minutes or something. We're all just like, wait a minute. It's like, yes, it's obvious. I think that's one thing that you realize about teaching is, you know, how much you don't know, like, you know, what that depends on. That was a wonderful thing. I think Dick Feynman was really inspirational in that of he really admitted when he didn't understand something. And he'd like, well, wait a minute, like, what's the one and the zero, instead of have to back up and sort of explain that, and you never really thought about it before, but digital computers

need to disambiguate. So they force everything to be the one or zero, if it's in between, you know, they force it one way or the other. That's what digital means, right? You don't put up with any in-betweens. You push it into a category of one or zero, and then you build it up from there. But you start thinking about that. Nobody ever really asked me that before when I was teaching them computers. And so, you know, Dick was always saying that he doesn't understand something unless he can derive it from first principles. So watching him do that in the field, I realized, well, you know, I don't really understand an awful lot of the things I do either. And when you teach, you sort of realize the things you don't understand.

Danny, I was just wondering, what do you try to optimize in your life these days? Well, I wish I had a lot more time ahead of me. So right now, time seems like the most precious thing to me. And you start realizing how much of a use squandered. It's like you squandered very much time. I'm not seeing that. Where were you squandered? When did you squander anything? Yeah, Danny, based on your bio. Objection, your honor. I did a lot of things that didn't work out. Okay. So let's talk about some failures. What were some of the failures?

Well, I mean, I think that Thinking Machines was my first big failure because if I had asked that financial sustainability question, really treated that as a problem of serious thought, like I was thinking about the machines, I would have done a much better job. That company didn't have to fail. It was awful when it did. It had like 500 people, almost none of whom had had another job. It was like I was hiring people straight out of MIT and we were building the fastest computer and everything was going great.

And, you know, we just did a bunch of dumb things in how we set up the business that, you know, we didn't pay enough attention to laws that were getting passed by our competitors in Congress that were making it illegal to export our products, they're making it hard for people to acquire our products. And, you know, we were just sort of blindsided by that. We did stupid stuff. You know, we were growing up, up, up. And so it didn't occur to us that we might have it, something might cause a downturn and we might not have enough cash in the bank. And now going

back, we just manage it badly. It's sad because I mean, it was a terrible moment for me. I felt like I had let down all of these people and I had let them down. So Danny, you look back on this life review and lessons learned and flash forward to today, you sit focusing on, well, you look back and realize how much time you've squandered. What rules do you have for yourself? How do you think about not squandering the time you have left?

Have you changed anything? Well, the non-redundancy is a piece of it. Right, if someone else can do it and will do it. Don't work on things that are gonna happen anyway. I still think hanging out with extraordinary people is the right thing, but there's an interesting problem with that, which I realized, which is I tended to hang out with a lot of people that were older than I was because of that, because they established themselves as extraordinary. And of course, that has been very sad

to see so many of my friends die and so on. So I'm actually very curious. I'm sure that there's a whole generation of younger, extraordinary people that I haven't met yet. And that's something I'd love to do, is meet some of those unusual people that are thinking about things differently and learn from them. So that's kind of part of my agenda these days.

Right, to find the young. So what do you think would most surprise your 20-year-old self about your life today? I guess one thing that surprised me is that it sort of has all worked out. You were not really expecting that? No, I think I was always kind of on the edge of failing in some sense. And you know, many times I did, but it still worked out. And so I think I probably worried more than I needed to, cause it always seemed like, Oh, this is pretty dangerous. Or, you know,

I'm going to be penniless or, you know, there were times when I was penniless. Yeah. I couldn't pay my mortgage. So I think I worried way too much. I worry less now. So Danny, this is going to be a fast left turn, but I've been staring at this the whole time we've been chatting and I don't know why I can use the term OCD because I have been diagnosed with it. Although I think it's actually a superpower in a bunch

of respects. I've been staring at this prompt. What is the entanglement with capital E like an hour and a half here and just waiting for the right segue, but I don't know if I'm gonna find the right segue. So what is the entanglement? One of the things that I've noticed about the world is used to be that nature and technology were very different things.

Technology was something that we designed and we understood and we controlled. Nature was this mysterious, complicated thing that we didn't understand at all and pretty much had to take and work around and kind of riff with. But those two things are becoming entangled in sort of both directions.

So things that used to be natural, like the atmosphere, or our genes, or our minds, or my knee joint are now technological artifacts, right? And the things that used to be technological and controlled and designed are actually kind of evolved and having like the internet, nobody can draw you a wiring diagram of the internet. Nobody can really tell you how chat GPT came to that conclusion. I mean, they could sort of make up a story about it, but they can't, they don't really understand it in the way that you used to understand a computer when it produced an answer because it wasn't

really designed. It was kind of a combination of designed and evolved and learned. And so what's happening is that a lot of people's use of computers is now they kind of know the magic incantations that caused this library to do that, but they don't really know like all the things that are going on underneath that, that make it work. And so it's becoming more like nature. Nature, we used to kind of know, well, here's the magic incantations we use for making beer. We don't know really why this makes good beer and this makes bad beer or this makes champagne, but we sort of know when we do this, it does that. And that's kind of becoming our relationship with computers. So I think that

what's happening is the distinction between the natural and the artificial is becoming entangled. And that idea may just kind of go away. So there is sort of almost, is no pure nature and there almost, you know, is no pure technology that we fully understand, at least not in the technology that we're using to have this conversation. For example, there is nobody who understands every piece of it. I want to put in a plug for my very first book, Out of Control, which was about that entanglement. Adam Ligato Yeah, I think you're one of the people that really got me thinking about that entanglement. And that book is probably a lot of what got me thinking about these ideas and Artists of Life got me thinking about it.

Richard Ligato And the way I would say is there's one thing with two different faces. And those, basically we had two different faces to the single thing. And we're working on that there's only one class which has two different perspectives on the same thing. You're saying natural and synthetic or nature and engineered? Yeah, there are basically different faces

of the same thing going on in the long arc of the universe where Danny might say they're being entangled, I would say that they've always been entangled, but we had kind of two separate views of them, and now we have a better view of it. Well, I think there's also something very special about this instant in time, and by this instant in time, I mean plus or minus a century. time. And by this incident time, I mean, plus or minus a century. The long now. Yeah. But I think when people look back at history, even really our lifetimes, I mean, over my lifetime, you know, the population is more than doubled.

You know, the climate has changed, the computers have come out, the everything is really very, very different in a way that that's never happened before. The population hasn't doubled in a single lifetime before, and I don't think it will again. Right. Right. So I think we are at a special moment where our sort of technological powers have gotten enough to make these things that are more complicated than we can understand. And that's kind of a qualitative change. We weren't building stuff that was more complicated than we understood before.

Or producing outputs that were completely unexpected. Danny, so question about the entanglement, and this actually relates to a name you mentioned earlier, Jeff Bezos. So he's described AI, I don't think it was specific to LLMs. I mean, it was broader than that as a discovery and not an invention, as something akin to electricity or fire. How do you think about AI? So I'm going to make a distinction between AI and what's called AI right now. Great, please. AI and what's called AI right now. Great. Please. So intelligence is a very complicated, multi-factor thing like life. It's not just one thing.

And in the beginnings of AI, we thought the things that were hard for us to do were the intelligence. Like we thought playing chess would be intelligent or solving calculus tests would be intelligence. Or translating languages. That came later, but that's, yeah. The things that were hard for us, we thought that's intelligence. And that was the stuff that early AI concentrated on. And actually, it turns out that was really the easy part. The hard part was the stuff that was, we were so good at, we didn't even notice, like recognizing a face, jumping to a conclusion, having an intuition about something. Those things were

way, way harder. So we thought producing speech would be hard. We didn't think listening would be hard, right? Because we just sort of did that without apparent effort. But listening to speech turned out to be way harder than producing speech. In the early days, there was always a box called, you know, sometimes the, what was the neural network, the pattern recognizer that was sort of was going to guess the obvious thing that was going to happen next and recognize the pattern. And we thought that was going to be the easy part, because

it was just going to be some neural networks that got trained. Now, it turns out that those neural networks had to be much, much bigger than we were guessing, way bigger than we were guessing. And you had to train them, or at least so far, we've only know how to train them with way more data than we were imagining training them with and so on. But sure enough, that box has now gotten built. That's what we call AI right now, is that little box in intelligence. And it's actually really good at kind of imitating human intelligence. And imitating is kind of a good first step. That's what my granddaughters do first. I have a granddaughter that can sit and talk to an electrician if she knows what electricity is, just by using the

right words and saying phrases that she's heard before and so on. And she can kind of fake it pretty well, but she has no idea what she's talking about. And that's mostly where AI is right at this moment. I mean, you know, it'll be at a different place a year from now and, you know, starting and people understand that and putting in different places. But it's just one little part of intelligence. It's a good start. But it's not going to do all of the things that we do that we consider intelligence until people come up with some other ideas. But people will come up with other ideas. The other big change is we've

got a lot more smart people working on it than we ever had before. So those are the people that are going to come up with all those other ideas to make it work. So I do think AI is going to happen pretty fast just because we have so many smart people working on it. Is there anything you think people are broadly speaking overestimating and underestimating with respect to the development of AI, AI not in quotation marks? I think they're over estimating the capabilities of what we have now, but underestimating what we'll be able to accomplish over the long run. And it's interesting,

I think that people get mixed up on time scales a lot. In general, I'm kind of a short-term pessimist and a long-term optimist. I think that probably applies to AI as much as anything. I'll make an observation that I've been reading the early history of the discovery of electricity, way before Tesla and Edison. I mean, like, you know, Faraday and Davies and these guys. And what was remarkable was how the smartest people at the time, like Newton and others, were just so wrong.

I mean, just so far off, the strangest ideas about what electricity was. And they really hadn't a clue. And it was just many, many years of going through. And actually, before they had the scientific journals, they had scientific demonstrations every week, where they were demoing the latest discoveries in electricity for paying tickets. And each time a week would go along,

they'd have another discovery. And they were discovering it was far more complex, far more unintuitive than what they thought. And I think that's exactly where we are with intelligence. We have no theory of intelligence. We have no idea what it is. We're just discovering some of the earliest primitives of what it might be. But I think we're as far from knowing what intelligence is as they were from

understanding what electricity was in the 1700s. I think that's fair, except maybe it's actually quite possible we'll never understand what intelligence is, because that was sort of part of the prediction of the songs of Eden papers. It may be easier to actually make intelligence than to understand intelligence. Right. We used plants for thousands and thousands of years without understanding how they work. We use a natural world without understanding how actually they're made and are governed. So we can use things that we don't understand. So what's first is us being able to

make things that we can use and don't understand. So a quick question on intelligence though, is it a useful term if we can't understand it or is it just so broad a label applied to so many things that it's kind of useless and should just be replaced by thin slicing and using more precise labels or concepts? I'm going to answer first because I think we're going to start to unbundle the concepts as we discover more things. I mean, my hypothesis is that we'll discover more about how our mind works through AI than a hundred years of neurobiology has. And we'll come to understand intelligence is not a single dimension.

I think it's a very high dimensional space in which there's lots of different primitives or elements. And part of what we're doing right now is we'll begin to discover some of those elements, and that intelligence is basically compounds. We have a compounded intelligence that's made up of lots of different kinds of cognition and stuff. And so I think we're on that path to not replace it as much as to unbundle it. And also, I would absolutely agree with what Kevin said, but I'd take it one step further, which is even if we unbundled human intelligence and did all of those things, there's still

more to intelligence than that. But there's other kinds of intelligence that we can't even imagine. And actually, those are the ones I'm most interested in because I said that I always like hanging out with people who are much smarter than I am. I would love hanging out with machines that are much smarter than people. Right. Yeah.

But smart in different ways. Or playing million color connect four with a mantis shrimp, you know? Yeah, exactly. The way I see it is that the space of all possible minds is huge and that human intelligence we're going to find out is on the edge, like we're at the edge of the galaxy. We're not at the center. We're going to be an edge species of intelligence in the map of all

possible minds. And the reason why we want AI is to arrive at these other places in the high dimensional space of thinking that we can't even imagine. That's the main thing. It's not to replace human thinking. That's boring. Nine months, we can have another human mind. But you want to have other kinds of thinking. That's the whole point.

This is related to the transition thing. But I think humans as we know them today are kind of halfway between monkeys and what we're going to become. You know, we still got a lot of monkey in this. We're not the far right in that diagram of the monkeys. No, no, no, no, definitely. We're in this transitional phase. You know, we still got a lot of monkey in us and I'm really excited by that thing that

we're going to become. So Danny, I have a question for you related to the short-term pessimists, the long-term optimist. I'm sad to report that there are lots of people, my vintage, younger, just people kind of close to my, I'm 47. So close to my age, or even quite a bit younger, mid thirties, who are on the fence with respect to having kids or have decided not to have kids because they look at climate change, they look at what they might fairly consider some of the unpredictability around AI and the fear around Skynet. And if we could go down this list of concerns they have that they cite as compelling evidence that they do not want to bring a life into this world because the future to them looks so bleak.

How do you think about the long-term future? There is a value in optimism. There's a utilitarian function to optimism, but if we're able to put that aside, maybe we can't. How do you think about 100 years from now, 200 years from now? So I understand that. But I also understand that, like, when I was a kid, we were, like, taught to hide under

our desk for when the atomic bomb was going to get dropped. And thinking even as a kid, like, this isn't going to work. I mean, I knew people who died of smallpox, you know, that disease doesn't exist anymore. When I was a kid, most other kids were hungry and were malnourished, were likely to die of childhood diseases. That's not true anymore. So yeah, when I was a kid, you know, I had friends I now understand were gay friends and only understood later and understood what they

were going through, but, you know, they couldn't say that to anybody. So there were so many things to be frightened about and yet there were so many ways in which the world just got so much better. And even in my lifetime, it did. And it's true that, you know, it also, we created a lot of problems in that process, but we've always created a lot of problems. Oh, I guess, you know, if I just look at the sweep of history, it isn't any time when you'd say, oh, I would do better going back 100 years or at least, you know, not in history. You would not want to be alive 100 years ago compared to being alive now. Especially if you're going to be born in a random place in 6.

But even, you know, I wouldn't want to be a king 100 years ago. Much better to be a peasant today than to be a king of a couple of centuries ago in terms of your health, your food that you ate, how you spend your time, so on. So your comfort, everything. So I think that there is a general trend. It's possible that some catastrophic setback that could happen. But even if that happens, I kind of believe that humans are adaptable enough or nature is adaptable enough that it'll pick up and start up again. I suppose there's a scenario where it's without humans and something else, but

I certainly optimistic, you know, the earth is going to be fine. And I actually do believe that, you know, there are people that are going to see that 10,000 year clock, decide what to do with it when it comes to the end of this 10,000 years. But you know, it won't be steady progress, it never has been. And so there's a bunch of things to worry about. I see why people are worried. But the bigger the picture you look at, the more you realize, I guess progress isn't a steady upwards thing. It's kind of two steps forward, one step back. Trey Lockerbie So let me ask you just a question about rank ordering sort of existential concerns because I am very fortunate to effectively as a job, talk to the smartest, most interesting people I can find.

And behind closed doors, generally not on the podcast, sometimes on the podcast, I have brilliant, brilliant friends, some of the smartest people I know, who are very preoccupied about climate change and basically view us as the frog in the heating pot of water that's gonna eventually reach a boil. And it's not too late, but everyone needs to act now.

And there just don't seem to be the incentives in place for that to really happen, frankly. Political will or competency as one piece of it. Then you have folks equally brilliant. In some cases, you might even argue more brilliant who say the preoccupation with climate change is completely ridiculous. It's just patently absurd that people would consider not having kids citing that as a

reason. And these are not people who are coming at it from a political perspective. They're just saying if we actually look at trying to weigh the severity of certain risks, this isn't even top five. Where do you fall on that? So I don't think that people underestimate the problem. It's like a really big problem and it's gonna cause a lot of difficulties, but people do underestimate our ability

to deal with problems. And so, yeah, it's going to be bad, and it's already starting to be bad for people. But people have dealt with a lot of bad stuff and come out of it and come out of it better and come out of it as improvement. So I don't minimize the difficulties of climate change and the challenges and it's going to be a mess. But I also know that there's a lot of super smart people that are working on all kinds of things that are going to help with it in ways that are hard to imagine. And so some of those are likely to work.

And it's kind of easier to imagine catastrophes than it is to imagine magic solutions. So it was easier for people to imagine that the population explosion was going to doom us. That was a real easy idea. But actually, Kevin was the first person to point out to me, I think, that actually our big problem in a century now is going to be the population shrink implosion. And people are starting to realize that already, which is hard for people to see. So we're kind of hardwired to pay attention to danger.

And also, there is an effect, which is that bad things happen fast and good things happen slow. Can you say more about that? So, yeah, if you read the newspaper, it's like... The bad things that happen close to you are furthest away today. It's full of bad things, you know, the plane crash, the war started and you know, there was no newspaper headline that said the majority of kids aren't hungry anymore. Because that happened over time, you know, very slowly and it's continuing to happen. And so I think that the world has actually been getting subtly better, but there was almost no headlines about the things that

did that. They weren't the attention getters. Also, some of the better things are things that didn't happen. Most of the good things are things that didn't happen. Your kid did not die. You did not get robbed on the way to work, all those things. And so there's no headline at all about the things that didn't happen. But, you know, Danny, if you had to rank your worries, what would you put at the top? Well, I don't deny that AI is an existential risk for humans as we know them. Maybe what's good about humans could go on in AIs, to be honest.

I think that's a possibility. I actually think it's more likely that AIs will help us get out of this mess. What is this mess? For example, help us deal with climate change, help us deal with the next epidemic, help us avoid the nuclear war. Or even we're just talking about population. I think it would be kind of an amazing coincidence that at the very moment where we're headed towards a population implosion, that we have robots and AIs. That's the very moment where we're headed towards a population implosion that we have robots and AIs.

That's another possibility. In some sense, it's much harder to imagine solutions to problems than it is to imagine problems. So like, this is kind of a trivial example, but when the technology for cell phones was being developed by Motorola, and I kind of knew about it, I went around and I told all my friends, you're going to have a phone in your pocket someday. It'll be just like Star Trek.

And every single one of them, without exception, said, oh, I would never want that. And they gave all kind of reasonable reasons. They solved the problems. They were like, well, if people were like on the bus, everybody would be talking on the phone in a restaurant. People would be getting phone calls. You know, people would interrupt me in the middle of the night with the wrong number. You know, they could see all the problems very vividly, but they sort of couldn't see how much it would enable them. So they all predicted that they wouldn't work.

And there was also another part of that I was involved with, we were bringing in the internet to everybody and there was the common response almost invariably, every time I talked about it, was people were worried about the haves and the have-nots. What about all the people who don't have this technology? What are you doing about that? And my response was, I'm not doing anything because the benefits of this is so good that it's going to happen anyway. The thing you want to be worried about is what happens when everybody has it.

There's going to be a lot more problems when everybody has a cell phone in their pocket, that's going to be the problem. Not because the people don't have it. So there is a sense in which there's an asymmetry where the things that break are easy to see and the things that work are hard. They're not equivalent. It takes a lot more energy to imagine something working than it is to imagine how it breaks. I'll give you a very specific example today. If you ask most people, you know,

would you like a chip inside your brain that augmented your brain and helped, pretty much everybody you talk to is gonna say no. Wouldn't want that, you know, and they'll give you lots of very good reasons why they don't want it. And a lot of them are valid, but boy, I'm pretty sure

that when that becomes possible, everybody's going to want one. Yeah. I think it'll be just like the cell phones. It'll just do so much for you that you'll put up with the problems. You'll work around with the problems, but it'll be you first. That's all I can say. Yeah. So you can all watch Danny glitching on video six months from now. Give you a beta tester. So I want to come back to something and I'm going to steal some Cliff Notes from Kevin here, but you mentioned talking in restaurants on cell phones and I'm very, very sensitive to sounds. And I see something in notes that Kevin and I were sharing. I don't know anything about this, but the name is descriptive enough that I feel like I kind of get the idea.

Babble the cone of silence. Yeah, I want it. What happened to it? What is this? It's like a Jetsons helmet that you plop on loud kids. What happened? No, it was, it's actually a cool thing that somebody should do, but it was originally the problem of open offices

and people overhearing each other's conversations. And so it turns out that the best thing to mask a conversation is somebody talking in exactly the same voice saying something different. And so this was a little machine that people could put on their desk. And we tested it, it worked, which is that it sort of listened to you talking for a while

and then it started talking kind of in your voice, but saying, it just making up sort of babble. But in your voice and kind of your intonation and so on and sort of talked over you out to the people around you. So the phenomenon was that the room got a little bit louder, but mostly people didn't notice that. Mostly there was just kind of a little buzz, but if you actually try to listen in on the conversation of the person in the desk next to you, it seemed like you could hear it, but you couldn't actually understand it.

You've got two violins playing. Yeah. Yeah. And then what happened with that was Herman Miller bought that technology for use in offices. It was actually a very sad thing. They set up a company that started. Much to my surprise, the restaurants got very interested in it, which sort of bugs me because I hate all of the noise in restaurants, but you know, things like, you know, the line at the pharmacy was interested in someone, but it was very sad

because the CEO and a heart attack and nobody had the heart to keep going. So we'll never know if the technology would have worked. Kevin, what would you like to see Danny work on? If Danny was like, I'm out of my idea bag is empty. I need, he showed up and he said, boss, Kelly reporting for duty. What do you want me to do? I mean, another way to think about that is Danny is the inventor and he has You're reporting for duty. What do you want me to do? I mean, yeah, another way to think about that is

Danny is the inventor and he has a company that invents things. What would I like Danny to invent? Yeah, exactly. If I had to make a commission, I had a billion dollars. Oh my gosh. Robot beard trimmer. Yeah, exactly.

Something that would meet all his criteria. Well, no, no, no, no, no, just for you. This doesn't would meet all his criteria. Well, no, no, no, no, no, just for you. This doesn't have to meet his criteria. No, no, no, but I mean something he'd accept. Well, he's got no ideas in this hypothetical situation. So beggars can't be choosers. How about you, Tim? What would you want? You have an idea, right? Well, it's just, it's very front of mind for me. No pun intended in this case, but I have neurodegenerative disease on both sides of my family, Alzheimer's, Parkinson's, and more. It's

quite the collection. And I've been interested and followed neuroscience. I was originally a neuroscience major way, way back in the day. And would love for you to take your blank canvas, no question is dumb, and apply it to neurodegenerative diseases. That's one that immediately leaps to mind. I know how to go about that. Somebody should do it. Which is, you know, it's the same thing with cancer, with the cancer thing. What we really need is we need a way to read out the proteins in your body dynamically, like we can read out your genes. And if we could really monitor that and read it out, you could find the processes that create disease before disease happens. So right now, when we treat disease, the cat's out of the bag,

your body does a great job of compensating for everything for a long time until it just can't handle it. But things have already gone a long way before you show any symptoms. Right, that's why so many Alzheimer's interventions fail. It's just too late stage. Yeah. So your, your body is great at masking things going wrong.

If you could look at the proteins in the body, then you could see things are starting to go wrong before you're showing any symptoms and you could see what was going wrong and you could start. Treating it before the damage starts happening right now. we start treating things after there's already lots of damage, enough damage that your body can't hide it. And so we need to head off diseases rather than treating diseases. We need to treat when you're on the way to getting a disease, not when you have a disease. And the only way to do that is to have this debugger

to understand what's going inside. And the only way to do that is to look at the proteins. And that's a tactical problem. It's a very solvable problem. We got a long way to solving it actually. And unfortunately it was kind of all screwed up by the Theranos thing, which sort of gave a bad name to all that field and made it impossible to fund.

So that was sort of the tragedy of that is that, you know, sort of one fraudulent thing kind of gave a whole field a bad name, but it will come back and it may come back soon enough to actually help you and members of your family and there are people that are doing that. So look for people that are doing that. So look for people who are doing that. I'd love to meet people who are doing that, but I think that's the path. Trey Lockerbie And procedurally, in terms of looking at the proteins, would that take the form of, and I'm grasping for straws here, but something like a grail test currently. So for cancer screening, looking at DNA fragments.

The first version of it would be a blood test. You know, it might be a finger prick that you would do regularly and just monitor it. But right now it's just, again, we have so few, because of the way the medical system is set up, we have lots of blood samples, but we don't have the blood samples very well correlated with the medical records and so on. So these are some big sort of population studies where you get a lot of

regular blood samples, that you get good proteomic inventories, which is a technology that is not quite there yet because there's no commercial opportunity for it yet, or limited commercial opportunities for EOD. But as soon as you start doing that and you start correlating what's happening with people, what was happening with their proteins before they got sick, as soon as you get that database,

then I think we'll be able to head off a lot of diseases before they happen. Systemic diseases, not infectious diseases. So Danny, I thought of two inventions I'd like to have from you. One sort of profound, the other one is sort of trivial. So the profound one was I recently had an MRI, which is an amazing piece of technology, but man, what a pain. What an unpleasant experience. I just imagine like, well, in 100 years from now, there has to be some way that they're going to have a machine that does this in a much more comfortable, easy, quick way. I've got one I'm working on now.

Okay, there you go. All right. I just had two MRIs today, so I sympathize. Oh really? Okay. My sympathies. Right, right. I mean, it has to be a better way, right?

And I've had so many of these things and every time I'm like, wow, this is a terrible experience. I am working on something. I won't do everything in MRI do, but I think it'll be more useful based on ultrasound. Okay. And here's the funny thing. MRI, the great thing about an MRI is it produces this 3D image and it can go to the doctor, the radiologist and you know, they can interpret it or an AI can interpret it because you've got an output that is some disconnected from the process of measuring

it. Ultrasound these days is not like that. Ultrasound really the person that's doing the ultrasound has a lot more information than is captured in a picture or video. They know how they're moving around. They know that they're pushing it past this muscle. They're shoving in this direction. They're using this muscle to be a lens to magnify the thing behind it. You know, they have the intent of where they're moving and why they're using this muscle to be a lens to magnify the thing behind it.

They have the intent of where they're moving at, why they're moving at. And so, they can perceive a whole lot more. And then they have to take what they perceive and write it into a report with maybe a few numbers measured or something like that. But it's not nearly as satisfying. What gets to the physician is not as useful as an X-ray or an MRI or a CAT scan or something like that. Well, there's no reason that ultrasound has to be like that. If you had either a sensor on it or a robot moving it around or you had the information of the pressures and the motion, and you had a model of tissue deformation and speed of sound through tissue and things like that. You could produce a three-dimensional image like from an MRI with ultrasound that would actually have information that you don't get from MRI. And that just hasn't been done yet.

I'd love to meet people who are doing that. If they don't do it, I might have to work on it myself. So the second trivial invention, Danny, that I'm gonna assign to you is we all love a little microwave which will instantly heat something up. I want the reverse. I want to put it in the machine and have it instantly ice cold.

There's a way of doing that is laser cooling. You do it atom by atom. Okay. It can take a while to get that half chicken. That'd be a fun one. Yeah, that would be. No, I don't know how to do that. That would be a billion dollars for sure. Don't know how to do that.

So Danny, if the sort of divine treasure of the universe just bestowed upon you $20 billion. So one of your criteria can vanish, right? In terms of the sustainability, you're covered for the foreseeable future and beyond. Let's say then it came down to only what gets you the most excited. So it could be sort of focused on that. And you were allowed to indulge for every like one or two serious projects that would have an impact. You had to do one trivial. Not trivial. I feel like, you know, that's underestimating how important something seemingly trivial could become later.

Well, I'll tell you one that's already happened, but it was like that for me. It's traveling all over the world. Chris was super interested in maps and looking at maps of where I was and so on. And I always wanted to like, take a map and like expand it and just like go into it. I had that dream since I was a kid. Got it. So like an infinite zoom. Yeah, like a pinch to zoom. Yeah, here we go. Okay, but there was no such thing, right at that time. But I really wanted that I knew I wanted pinch to zoom. So as I started building it, and I had worked with with Steve Jobs and I got a kind of a prototype of it working and I invited him over to look at it and he said, ah, you know, people won't want like fingerprint smudges all over the screen.

You wouldn't like my things very much. I kept on working on it and eventually I made actually this touch table thing and it was very expensive. It actually went into the situation room of the White House. That was during the Obama administration, Obama would show people he had this map that he could pinch to zoom, right? Then of course, Apple came out with the iPhone and other people were working on it. And then fortunately, when I did the table thing, I filed a patent. And then when the iPhone came out, of course, it did a very beautiful job of pinch to zoom and very refined version of it. And people started using it.

And then the other phone companies started doing it and Apple sued them. Apple filed a patent on pieces zoom and sued them. And actually I think one, like a billion dollars from Samsung. And so, but I had filed this patent and Samsung went back and went to the patent office and said, wait a minute, you know, this Danny's patent like predates all of this. So the patent office, oh yeah, it does. And then invalidated Apple's patent. So everybody who had androids or Samsung fund or whatever, you know, they could use pinch to zoom too. So I think that's the invention that I'm kind of proudest of because even though I never got paid a dime for it, I see like little kids who had that

same instinct that I had. Like I see them going to like a magazine and just like trying to zoom out the picture. And I know that like nobody will ever remember that as ever having been invented because it's like kids are born with it. It's become so much of a part. So, you know, when you innately want something like that and you know you want it. So Danny, how about some practical advice for people who are listening who may be inventor types about patents? Because I know you have a complicated relationship with patents. Here's the case where patents may have done some good. I know other times you're not so sure about the worth of patents.

What would you suggest to people who are inventive? For instance, at Wired, we were involved with inventing the web, and Wired invented the click-through ad banner. Brian Bebelhoff was the guy who coded that, And not one of us ever thought about patenting it. It just seemed obvious. It seemed like a really good thing. It was entirely patentable, but it just never even was in our vocabulary. And I'm not sure how much it would have been worth if we had.

But Danny, what do you think about patents and people who are inventing? What would you suggest? if we had, but Danny, what do you think about patents and people who are inventing? What would you suggest? So, first of all, I think patents might be good for inventors, but I don't think they're very good for society. So, if I had a choice, I would eliminate the patent system. Now, there might be particular things like pharmaceuticals and things like that, where you could make the trade off the other way. But I think in general around computers and I'm happy software patents are kind of getting

rejected much more and so on. So I've always felt a sense of, I guess, ambivalence about patents. So why are you patenting if you don't believe? Well, so I patent because, remember, I'm often solving problems for other people. And so, I mean, they have paid for something to happen, and so they want to own something at the end. But I think for inventors, and I know inventors that have made lots of money on patents, but you have to sort of sue people. And so they end up wasting an awful lot of their life in courtrooms. And, you know, I hate it. I occasionally get dragged into court for something that I've patented that somebody else owns. You know,

you have to get deposed and it's pretty, you know, it's a big waste of time. It's a big waste of society's resources. And the whole idea of patent system was initially to help society. It was to get inventors to disclose their patents. But I think that things that are sort of self-disclosing like pinch to zoom, when somebody sees it, you don't need to do any more disclosure about it, right? Or maybe you do about how you made it work or something, but they could take it apart and see how

you made it work. So I would say that we should ought to definitely narrow down the things that we allow to patent and I think that to inventors, I typically say, maybe file patents is trading fodder in this ridiculous game that's going to have to happen, but don't go off and sue people for violating your patent. And yeah, you might get rich that way, but it's not worth your time. It's not the way to spend your life. Are there any inventors could be past or present who really inspire you? If a intrepid inventor looked at you and they said, Danny, who are some people I should pay attention

to or study in the world of inventing, broadly speaking? Anybody stand out to you? So the ones that I admire the most and some of them have been my mentors are people like Claude Shannon, who kind of look at something really complicated and messy and get a take on it that makes it simple and understandable in a way that gives everybody else power to do something with it. Who is Claude? Claude Shannon invented the bit. Actually, another one of my mentors named it the bit, but he invented it.

He invented information theory. So he invented a way of measuring information and encoding information. He worked for Bell, Bell Telephone, and they were interested in what was the theoretical limit to amount of information you could put down a wire. Yeah, but even before that, like his master's thesis was the application of Boolean logic to switching circuits. You know, he just had this way of thinking about things that was so powerful that it gave everybody else

a way of thinking about things and everybody else a way of solving problems that we just take for granted when we measure things in megabytes and stuff like that. Somebody I lived to know, somebody that was on my thesis committee invented the bit, right? And discovered it or whatever. Those are the kind of people that I admire the most because they give everybody else the power to imagine new things and do new things. And Newton did that in physics. Feynman did that in physics with Feynman diagrams. So those are the real wows of history.

So speaking of people that you admire, I have a favorite question. What is a heresy that you have? And I define the heresy as something that you believe that the people you most admire don't believe. This is a little strange one. You're not going to like that. That's the whole point. I don't believe in cause and effect. Oh, wow. Okay. Okay. Explain what that is for people who don't know. Okay, so we look at an equation like F equals ma and we say, oh, force causes masses to accelerate when you push on it. Okay. That's seems to be what F equal ma says, just going back to Newton. But I think

that's just a story. I think that we like to tell stories in which there are agents that cause change because we're social creatures and we like to sort of personify nature. But I could rewrite F equal MA to be A equals F over M and say mass is caused by force acting on acceleration and that creates mass just as easily as I could say that the force causes the acceleration. It's just the way we tell the story. And some stories are intuitive to us and sort of make sense and fit with our intuitions, and some stories don't. And so when we can tell a story about something that's kind of explanatory and helps us guess at what's going to happen next or things like that, it's a useful story, then we believe it's true in some fundamental sense. And so, it's the way our brain works,

we're wired to look for causes and effects. And that's like why we're kind of wired to believe in God, because if you have a chain of causes and effects, then it sort of has to be a first cause at the beginning, causing all the rest of it. But I think that's just kind of the way our brain works and the way we tell stories about reality. I don't think reality actually has causes and effects. Let me poke on that a little bit. So is it that cause and effect doesn't exist or is it that we simply over apply cause and effect. And I was thinking back to the proteomics discussion and identifying changes in proteins over a sufficient data set such that you could have some predictive ability or ability to intervene earlier to hopefully mitigate or prevent disease states like Alzheimer's disease or otherwise. So does that mesh with what you

are saying or does it not? I'm not saying that thinking in terms of causing and effects isn't a useful way of thinking. Just like I'm all for storytelling. I believe in storytelling as a useful way. So I think, but we are when we tell a story about a protein pathway causing something, we're making up a story. And really look at what's happening in the physics, all those things work in the other direction too. And the story isn't really what the physics is doing. It's a sort of simplified thread of things that we can understand in what it's doing. So it is useful to kind of abstract out these threads that we can tell stories about because that gives us a handle on it and helps us manipulate

it. So I'm not saying that's not a helpful trick of thinking, but it's a trick. It's not really how the universe works, and we shouldn't fool ourselves into that, and we shouldn't get too enamored by it. And maybe when we get new kinds of AI, maybe they'll be able to think without using that trick. Right now, we can pretty much only think using that trick. Got it, yeah. And that's what digital is, okay. So computers are all about kind of playing out this fantasy of cause and effect. So by forcing everything to either be a zero or one

and nothing in between and making everything digital, we can kind of make things that almost work perfectly as if this and this cause that to happen. And so in some sense, the computer is the ultimate fantasy of putting together causes and effects and piling causes and effects and engineering them into long chains that we write with programs and control them. And this comes so close to doing exactly

what our fantasy is, that it's hard to believe it's not true. Trey Lockerbie How does, and this is way outside of my areas of expertise, so who knows if I'm painting us into a corner here, but how does quantum computing affect that presentation of computing and the forcing into one binary option or other? Alan Sussman That's exactly the right question to ask, because if you really look at true quantum computers,

it's much harder to explain it in terms of causes and effects like we do as a digital computer. You operate on it and it causes this state to turn into that state is a sort of a cause thing. But actually, the cause also involves observation of the states and just looking at it changes it. And so one of the early things will be quantum key generation, where we'll sort of have a module and say, if we do this, we get a cryptographic key that has the right properties. Now how that magic happens, very few people will have any intuition of how that happened.

And the people who do have really deep intuition will realize that it's actually not causes and effects in the way that we're used to thinking about it. So I have a half-baked amateur hunch and prediction about quantum computing, which is I think in a hundred years from now that we'll realize that quantum does not want to do computation. It's actually not going to be used a lot for computation, but there'll be something else that we'll discover that is really incredibly useful for. Other than computation, because I think computation does want to be much more cause and effect. Sometime in the 90s, I wrote a little book about how computers work.

Patterns in the stone. Patterns in the stone, that's right. It's just, you know, kind of a high school student that was interested in computers couldn't understand. It turns out mostly who likes reading the book are people who already understand everything in the book, but they like seeing it all explained. But it had a chapter on quantum computing, and this was written in the early 90s. And I got this funny call from the publisher and said, you know, this is weird thing.

Your book is the only computer book we have from the last century that's continuing to sell. And boy, did that make me feel pretty weird. Fortunately, they didn't say the last millennium, right? But they said, you know, would you like to revise it? I went back and you know, there are a lot of things that's happened since then. Understatement. But it was interesting because it was all, most of the stuff I had in the book didn't change at all. In fact, some of I would have talked about, you know, certain things more

and certain things less and so on. But one of the things I talked about was quantum computing. Really, even in quantum computing, there wasn't much I would change. And what I said about it is, you know, if you want to look for where something could be a real game changer, it's quantum computing. And it's got all this potential and all these hints that it could work. And there's good theoretical reason to believe that it would be revolutionary, but nobody's actually gotten it to be useful yet. And that's pretty much still the state that it's in. I was surprised that in the end I decided it was sort of more interesting as an historical document of how computing looked in the 90s and I didn't change it,

but most of it wouldn't have changed anyway. So let me, at the risk of this going sideways, introduce a really slippery term, but we were discussing earlier the possibility, if my memory serves me, that AI and developing AI, different types of AIs could help us get a better understanding of intelligence writ large, different types of intelligence we might, as Kevin mentioned, discover we're on the edge of the galaxy or universe. Possibility space. Exactly.

Possibility space, not in the center. Is it possible that through AI or quantum computing or other aspects of studying quantum phenomena that we will get a better grasp of what consciousness is, recognizing again that that is a term that begs definition. And I mean, there are a lot of people that take different stabs at it, but sort of what it is to be aware that we're aware perhaps would be one possible way of offering that. But also how that emerges from simpler constituent pieces that maybe at some requisite level of complexity suddenly have this emergent phenomenon, which is consciousness. Certainly that's possible. My guess, and this is really just a guess, is that consciousness is going to turn out to be way less important than we think in the sense that it's going

to be a very small piece of intelligence and it might just be a kind of a hack. For example, okay so I have a complicated idea in my mind and I turn it into a series of grunts and grunt at you and whistle and grunt and somehow you listen to those grunts and you construct an idea in your mind. And so we sort of went through this translation process. And so we have a lot of our brain is devoted to that compression process of turning the idea into grunts and turning the grunts into an idea. So given you've got all the hardware lying around, you've probably had the experience of misunderstanding somebody, but what you misunderstood is actually more interesting than what they said.

Right. Sure. Or Vikes first. Right. Because your brain took the thing that they said and expanded into a sensible idea. And maybe it was more sensible than the one that they had in the first place. Well, so you could do that within your own brain just by talking to yourself. And so, probably, given you've got all this hardware lying around for compressing and decompressing ideas, a good thing to do with the idea is to compress it, tell it to yourself, and see if you misunderstand it in an interesting way. And maybe consciousness is just some hack like that. I've often thought that one of the main benefits of language was not so much that it enabled

collaboration with other people, but that it gave us access to our own thoughts. Can you imagine trying to think without language? It's just like, it almost doesn't seem possible. So language, I think, was a dual purpose invention that mostly gave us the power of communicating with ourselves, basically. Yeah. I think consciousness may be that. I think consciousness may be our access to our own thoughts. And that may be useful, but it may not be the most critical thing in intelligence. Like maybe you could not have it and you'd still be very smart. And maybe I wouldn't even be able to tell the difference. I think in that space of possible minds, we could think like things are really, really

intelligent, they have very little consciousness, things that have a lot of consciousness that can't communicate, things that communicate, but I think consciousness is another kind of elemental primitive. Yeah. And I think you're going to have multiple entities that have access to each other's thoughts. And that might be even richer, so super consciousness that might be better. So I think this might be another case of us looking at what's apparent to us when we think

of our thinking and we're very impressed with the things that are sort of very visible to us like we were very impressed with our ability to play chess. But ultimately it might not be so important. The proverbial like drunk guy looking for his keys under the streetlight at night. And they're like, wait, I thought you left that in the barn. They go, this is where the light is. So we were coming up. I could keep going for another three hours. We're coming up on three hours now, which has gone by very, very quickly. Kevin, do you have any closing as we start to land the plane questions Questions for Danny, comments or questions,

complaints, old feuds you'd like to revive? I might want to go back to the question of what are you trying to optimize in your life? Because you were saying you were trying to optimize your time. Are there any other things that you are, the general trajectory of your life, maybe in particularly recent years,

where you feel this is what you are trying to optimize, maximize or another way of saying is like, again, when you're deciding what to do, how to spend your time, the little time that we have, what's something that you are trying to make more of? I guess I try to ask the question, will this make a difference over how much time? How long will that difference matter? Okay. If it makes a lot of difference after I'm dead, I'd rather do that.

And I think a lot of people think I want to make a difference, but I think they weight it much too much to the near term. And so for instance, I really admire Bill Gates as a philanthropist. He works really hard and he's super smart about it. But one thing that bothers me about some of the things that they do is because they try to measure everything, they try to do things that make a difference in the time that they can measure them.

And I think that that is maybe not the right metric to be optimizing because it doesn't allow for the long tail of time of impact of things. That's like, you know, when Claude Shannon like, and then it's the bet. The differences that that makes are just becoming a parent. Now after he's dad, you know, that's something that over time makes a huge difference, but if you tried to measure it during his lifetime, it would have been really hard to give it any credit. The long tail of impact. All right. I'm going to, I'll pick from my, my grab bag of favorite questions. One of them is pretty simple. It's a metaphor,

but if you could put anything on a giant billboard to get a message, an image question, anything to many, many people, hundreds of millions, billions of people. Let's just assume they understand the language. Could be a quote, could be a quote from someone else, could be a motto that you or philosophy that you live by, could be anything at all. What might you put on that billboard? It could be an ad for your company. No ads. That's the one rule. Well, in a sense, I think I've answered that because I think the most successful example of that was Stewart Brand having a picture of the

whole earth. I think he realized that when people saw that picture of the whole earth like floating in space, they would think about everything differently and they did. And so to me, there's no picture like that of the future. You can't conjure up an image. You can conjure up the image of the past, maybe the pyramids or something like that. But there is no iconic image of like the future. And if you could imagine something to put on a billboard that sort of made people see the future and believe that there was that future, that's what I'd do.

And the 10,000 year clock is the best approximation I can do to that. But I think that that's what the world needs. I think it to that. But I think that that's what the world needs. I think it needs that picture that puts the context of everything today in the context of the development of humanity over tens of thousands of years. And I think that would make it a much more optimistic picture for everybody.

And let me just add, because I don't think we have this clock is real. It exists right now. It's inside a mountain in West Texas. It's inside a vertical tunnel with a spiral staircase carved into the rock, and it's hanging almost 500 feet. It's a mammoth, mammoth monumental clock that is going to take for 10,000 years. And my impression of having visited it is that it feels like the clock

has always been inside the mountain. It feels ancient. The scale, the scope, the ambition, the tooling required, like this site identification, everything is beyond belief. And I know we haven't spent a lot of time on it. I'll link to a few things for people who are listening also. Wasn't sure how much you could say

about certain aspects of it. So we spent a lot of time on other things, but Danny, is there anything else that you would like to say about the 10,000 year clock? No, actually, I think it's good that we spent time on other things. It will speak for itself. It's a story. And actually my favorite thing about the 10,000 year clock is I run into

people regularly who've heard about it, but assume it's just a myth. Have you seen Bigfoot? No, Bigfoot in West Texas. Yeah. Well, you get all kinds of versions of this in Nevada. I ran into somebody who said it was in China, you know, but that to me is very satisfying because stories are actually what really lasts. And really, you know, your question about the billboard is like that of, you know, what's an idea you want to put in people's head that sort of stays there and an idea has a lot more sticking power

than any physical thing you could build. And so I love it that it has sort of become a story with a life of its own. And that to me is as exciting and the fact that the, you know, there's this giant thing sitting in a shaft and the mountain. Yeah. Makes me think of Indiana Jones and the last crusade. And I can imagine the tagline, see you in 10,000 years. Yeah. If anybody believed it, well, that's part of selling the story, right? And Danny, people can find applied invention at appliedinvention.com.

Is that the best website? Is there anything else? But there's nothing there. It'll just say apply. It'll have our address and zip code. All right. If for those for those fans of zip codes, you can get a flyer. But they can contact me with that way.

Perfect. Great. And I do want to meet smart people. I told you that that's like what I'm seeking for is the brilliant people with different ways of looking at the world. So Danny, have you, have you spent any time with Derek Sivers before you guys met before Derek Sivers? Do you know this name? No. Oh, all right. Well, Kevin and I both know Derek. I feel like you guys met before? Derek Sivers? Do you know this name? No. Oh, all right. Well, Kevin and I both know Derek. I feel like you guys would make for

a fun meeting. Danny, Kevin, thank you for taking the time. This has been absolutely fantastic. I have tons and tons of notes. We didn't even get to the giant robot dinosaurs another time. And for people listening, you will be able to find links to everything that we've discussed at the show notes on Tim dot blog slash podcast as per usual. And until next time, as always be just a bit kinder than is necessary, not only to others, but to yourself. And thanks for tuning in. Hey guys, this is Tim again, just a few more things before you take off. Number one, this

is five bullet Friday. Do you want to get a short email from me? Would you enjoy getting a short email from me every Friday that provides a little morsel of fun before the weekend? And Five Bullet Friday is a very short email where I share the coolest things I've found or that I've been pondering over the week. That could include favorite new albums that I've discovered, it could include gizmos and gadgets and all sorts of weird shit that I've somehow dug

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the main point, what's on the label is exactly what you're getting. And this is not true for the vast majority of companies in this industry. So this is a differentiator. Try it out for yourself and let me know what you think. Visit livemomentous.com slash Tim and use Tim at checkout for 20% off of my performance stack. One more time, that's livemomentous.com slash Tim. I'll spell it out, It's a long one. Live momentous.com slash Tim. So live momentous.com slash Tim for 20% off. This episode is brought to you by 8 Sleep.

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