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Remembering Jim Rutt: A Life Lived at the Ragged Edge · The Vance Crowe Podcast

• 0:07
  Unknown: Welcome back to the podcast. I'm glad you're here.
• 0:10
  Today, I come with some sad news.
• 0:13
  A great man named Jim Rutz recently died. For longtime listeners of the podcast, you know that Jim has been a regular guest on my show. I've been on his show. And what you may not know is we've become great friends.
• 0:26
  Jim is a wild character. You can't describe his tone of voice,
• 0:31
  his approach to problems, his creativity, and all of the things that made this guy
• 0:37
  the larger than life character that he was.
• 0:40
  I had a chance to talk to Jim leading all the way up until his death. I know that he was enduring a type of brain cancer that was at first affecting his vision and then causing him not to be able to sleep very well and all the challenges that add on to that. I wasn't aware, however, of how close his death was.
• 1:00
  This was a bit of a sad moment, but actually I had had a chance to record Jim's legacy interview where we sat down to talk about his life, his childhood, what he thought he would do when he grew up, all of the experiences that he had, marriage, having children,
• 1:16
  working at the with the Santa Fe Institute, his game b work. It was really a marvelous
• 1:22
  way to get to know this great character in such a deep and profound way.
• 1:27
  And, while we're not gonna show that today,
• 1:30
  what I thought I would show is the public work that we've done together. So we put together a compilation of some of the fun videos of things Jim and I did together. And one of the things you'll notice is one of the clips or a couple of the clips, we're in VR.
• 1:45
  Jim was such an interesting, innovative outside guy that when I told him I had a VR headset
• 1:51
  space, and I was thinking about doing a podcast,
• 1:54
  he could not jump in fast enough. Ordered himself a VR headset, and before you know it, we were in this virtual space. And that's just the kind of guy he is.
• 2:03
  So while you maybe didn't know Jim or maybe you've known him for a long time, I hope this set of clips will give you just a small taste of what a wonderful,
• 2:13
  fun, entertaining,
• 2:14
  energetic,
• 2:15
  amazing force of energy that Jim Rutt was on this world.
• 2:20
  Thank you so much for tuning in. I really hope you enjoy this episode.
• 2:24
  For people that don't know, let's go back in your history.
• 2:27
  Who the hell are you, Jim? You're you said you were a part of a group called The Source. You are a member of The Well. You're, you know, doing this kind of avant garde back to the land plan b idea.
• 2:38
  Unknown: Where did you come from, and who are you? I was a working class kid. Grew up in the suburbs of Washington DC. My dad was a DC cop
• 2:49
  neighborhood I grew up in, about half the parents were high school dropouts, about the other half had a high school education, hardly anybody had been to college.
• 2:57
  Somehow, I lucked out and got into a very good college,
• 3:00
  MIT, went off
• 3:02
  and,
• 3:04
  you know, became a bit of a scholar, I suppose. Decided I didn't wanna go into academia. Went back into the into the business world after a couple of years of hitchhiking around the country and what have you,
• 3:14
  you know, living behind dumpsters and working at restaurants, washing dishes, and what have you. Oh, is that right? Were you on the, electric Kool Aid acid test, path of of merry pranksters and Yeah. That that was kind of a strange combination. I was we might call a redneck hippie. Right?
• 3:32
  And, yeah, I have fairly long hair, and I wasn't averse to smoking a doobie or two. But,
• 3:38
  you know, some of the more extreme stuff, nah, it wasn't for me. I, you know, I wanna have my didn't wanna have my brain cells melted. Right? And so, yeah, I had my couple of years of just,
• 3:48
  you know, enjoying the very good life. I mean, the life on the road was fun in, you know, 1975,
• 3:54
  1976,
• 3:56
  and
• 3:57
  and I enjoyed it to the fullest. And,
• 4:00
  then I came back to the real world, got involved in, got a real job working for a college textbook publisher,
• 4:06
  as a salesperson in Kentucky,
• 4:09
  in Kentucky,
• 4:10
  East Tennessee, and a corner of West, by god, Virginia.
• 4:14
  Unknown: And, that was very An MIT graduate that becomes a textbook salesman, that doesn't seem
• 4:20
  Unknown: like, was that a step down for you in your mind or a step up from the from the life of living behind a dumpster? Definitely a step up from living behind a dumpster. And even, in my later days on the road, I actually had a little efficiency apartment in Ketchum, Idaho, and it was step up from that too when I was working as the night shift manager for the local, Circle K.
• 4:42
  But no. I didn't really give a shit about success or the latter or any of that crap. Didn't give a shit at all. But one of the reasons I took the job of college textbook publishing is about the easiest job I could imagine.
• 4:52
  And I had figured out during the interviewing process that once you learn how to do it, you probably only have to work about eight weeks a year and at least work hard eight weeks a year. Had to take care of the paperwork the rest of the time. That was about right as it worked out. After about two years, I could basically work eight, maybe twelve weeks a year hard and then dick around, do a month all the rest of it by phone calls and, you know, and ordering books for professors and stuff. And so it'd leave me time working my own projects. So it was perfect. I was not interested at all in ye olde career game.
• 5:23
  And,
• 5:24
  you know, I I thought even, you know, a half assed job like that was
• 5:29
  giving the world more of its due than I really wanted. Right? If I could, you
• 5:33
  know, found some other way to make a decent living that was even more of a fuck all job, I would've I would've taken that, but I couldn't find any. And I was right. It was perfect.
• 5:43
  But but always the but. As it turned out, as I'm wandering around seeing all these college professors, 15 of them a day on average during that twelve weeks a year. Was working hard, at least after a year or two. I started seeing these early personal computers, you know, weird ones like North Star Horizons
• 6:00
  and MITTS and InSci.
• 6:02
  This is before even the TRS eighties and the Apple.
• 6:05
  And I started to talk to these people about this. I'd been a programmer when I was in college, a way to make money, basically. I'd taken a couple of programming courses, but I was not really a big computer science person. And part of it was that I didn't like culture of the big machine room, the giant IBM mainframes,
• 6:21
  and all that stuff, which was sort of the computer culture of 1972
• 6:25
  when I was in college. But this idea of having your own computer, I go, this is kinda interesting. So in 1980, I went and spent 90%
• 6:33
  of my net worth, which was a big $4,500,
• 6:37
  and bought a top of the line Apple two. I mean, top of the line, 48 k of RAM on two floppy drives.
• 6:45
  Unknown: Wow.
• 6:46
  Unknown: And a dot matrix printer.
• 6:48
  And I got back into writing programs, wrote the world's champion Othello program, beaten IBM three seventy with it, and
• 6:57
  started seeing what's what up, right, basically in the world of computing and quickly realized, ah, this is neat.
• 7:03
  And applied for some couple of jobs in that area, got some offers,
• 7:08
  and then stumbled across, as I said earlier, The Source, the world's
• 7:13
  first consumer online service. Literally, it was for, like, apples with modems in them. 300 baud modems usually, 30 characters a second, text 30 character mode only. And yet, we had most of what by 1981, we had most of what the web has today. We had
• 7:29
  email. We had chat. We had bulletin boards. We had online shopping, stock prices, newswires.
• 7:36
  We even had the world's first Catholic confession done via chat under the supervision of the archbishop of Washington.
• 7:44
  And so I was there at the creation of this crazy online world, and then I went off, started some of own companies where we applied these low cost,
• 7:53
  high volume
• 7:54
  platform
• 7:55
  technologies
• 7:56
  to high end products like Wall Street and corporate America and built a few companies, sold them.
• 8:03
  Then
• 8:04
  retired,
• 8:05
  got helped some people do another company, retired again. First time I retired, was 32, which was kinda nice. Lasted two and a half days.
• 8:13
  And I then built another couple companies for myself that did pretty well.
• 8:18
  Then got sucked back into corporate America with an offer I couldn't refuse for
• 8:23
  eventually ended up as the CTO
• 8:25
  of what's now Thomson Reuters.
• 8:28
  The in fact, I was the first CTO, executive committee member. Figured I'd, you know, work till I was 55 and retire. They had a nice fat pension plan and,
• 8:37
  you know, fairly decent stock options, whatever.
• 8:40
  And but then I got headhunted
• 8:42
  out of there, to my surprise, and became
• 8:46
  the
• 8:47
  last CEO of Network Solutions back in the days when they ran the whole domain name system.
• 8:54
  Right in the time when the domain name system was being privatized
• 8:58
  from
• 8:59
  we basically had a monopoly, a government granted monopoly. Very abusive.
• 9:03
  And the company sucked. They were the biggest clowns of misery you ever met in your life.
• 9:08
  You know, couldn't do shit,
• 9:10
  at least in the operate interestingly, they had good finance and good legal, but technologies,
• 9:16
  Unknown: you know But that's the way of any corporation. Right? You you eventually hit that sigmoidal growth where you're at the top of that that growth curve, and the only thing you can add on are more attorneys to protect your your spouse. Worse. This is what makes them so pissed me off.
• 9:30
  Unknown: It didn't really piss me off. I knew it a great opportunity. Saliva's grown like this. You know, we're in the we're the the czars of the domain business in 1999.
• 9:37
  Right? And it's straight up generating cash like you can't believe, and these clowns still couldn't execute. Couldn't pour piss out of a rubber boot if the instructions were emblazoned on the heel, as my father used my father used to say.
• 9:51
  And, so I came in with mister turnaround. It was actually the second turnaround I had done, and we got sucker turned around pretty good, and we settled all of our outstanding issues with the government, retained parts of our monopoly, the most lucrative parts,
• 10:04
  entered into the competition in the competitive parts of world. We're doing real well. Then
• 10:08
  value of this company kept going up and up and up and and up and up. Eventually, we got started getting offers to sell the company, and I said, shit. These offers are higher than I'd pay for a damn thing. And my old training at Thomson Reuters where we bought and sold 80 companies a year said, somebody offers you a lot than you think it's worth,
• 10:26
  sell.
• 10:27
  So we did. We sold
• 10:29
  Network Solutions in 2020. I stayed on a year as I agreed I would, and then I retired.
• 10:35
  Unknown: So now without further ado, we're gonna go to my man,
• 10:39
  Jim Rutt.
• 10:48
  Well,
• 10:49
  Jim Rutt, welcome
• 10:51
  back to the podcast, only this time in virtual reality.
• 10:55
  Unknown: Hey. Vance Crowe, great to be here.
• 10:58
  This is a new thing for me. I've only been in VR once before, and that was hanging out with you. And this is certainly
• 11:05
  the first time I've tried to do a VR podcast.
• 11:09
  So we're out there on the ragged edge.
• 11:12
  Unknown: Yeah. And so for listeners, people that are just listening and not watching this, we are in the
• 11:18
  Articulate Ventures underground bar. So this is the place that, we come to do our book club. And the we we've had the book club for about a year, and then we decided, well, let's try and do this in VR. And it was such a big hit. People enjoyed it so much more because of the thing that you talked about. It's kind of the the split between
• 11:39
  the loose connections that you only get from reading what other people say or seeing their, posts on on social media and the strong bonds that come from, being in person. This is somewhere in between there.
• 11:51
  Unknown: Yeah. This is quite interesting. You know, this is something I've I've been talking about for quite a while is the,
• 11:57
  weak link, strong links, and the combination of the two produces is very useful for building movements,
• 12:04
  etcetera.
• 12:05
  And then, of course, our year of Zoom has taught us that Zooms are somewhere in between also,
• 12:12
  but VR feels to me like it's,
• 12:15
  maybe closer to face to face, not the same. You know, we are primates after all. We,
• 12:21
  deal a lot with smell and, you know, details of facial expressions and and such, which at least so far aren't turning up in VR.
• 12:30
  On the other hand, as you pointed out, the audio spatial aspects are important.
• 12:35
  So it's gonna be quite fun to see how,
• 12:38
  VR fits in my taxonomy of links. I suspect somewhere between
• 12:44
  Zoom and face to face, but probably closer to Zoom than face to face.
• 12:49
  Unknown: Yeah. I do think there's something weird about it, and I am always a big proponent of, being able to see people's faces because I think, you know, the ability to see if they're smiling, if they look like they're trying to manipulate you, these are things that we've learned or evolved to learn over time
• 13:05
  really in-depth. But then I get into VR and I say, well, I don't know. There's something about the
• 13:11
  feeling like I'm in the same three-dimensional
• 13:13
  space as you that makes me feel more connected than a lot of the Zoom calls I've been on.
• 13:18
  Unknown: Yep. I agree. I think a bit more it's definitely better than Zoom,
• 13:22
  but it would be great if they could somehow capture our facial expressions and particularly our eyes.
• 13:28
  I always found eyes to be key, you know, that was was it?
• 13:32
  Somebody said the windows to the soul. And, obviously,
• 13:36
  in these very simple avatars, we don't have anything like that.
• 13:40
  Unknown: I totally agree with this, Jim. This is music to my ears. It's so rare that I found people that think like you do.
• 13:46
  Unknown: Yeah. I've had I've I've fortunately had I have a weird mind.
• 13:50
  I I never accept the conventional wisdom around me. In fact, it's probably a a fault more than a virtue is my starting stance is whatever the conventional wisdom
• 14:02
  is is wrong. Right? H. L. Mencken is my hero.
• 14:06
  And so I I I think I just come from it constitutionally that, you know, whatever the conventional wisdom is, must be ridiculous.
• 14:13
  But the amazing thing is I often find that I'm right on deeper investigation.
• 14:17
  More often right than wrong.
• 14:20
  Unknown: Yeah. I think the the one thing that I had going for me when I was at Monsanto is that I didn't really actually need the job. I mean, I could leave at any time and go do something else. And so as long as they were letting me live out my intuitions,
• 14:33
  I I was good with that. It was it was me trying to work on a problem that I thought was really, really important.
• 14:38
  And so,
• 14:39
  it it was very fun for me, but I can't imagine going back and doing it again. I mean, I I had freedom there that I don't think I will would ever get in a normal organization.
• 14:49
  Unknown: Well, you never know. You you made a very important point, and this is when I have a little presentation I used to give to second year MBA students of all the weird things called my famous career.
• 15:00
  And the the one of the big takeaways was I always felt like I didn't give a shit if it the companies fired me or not. Right?
• 15:08
  I always was arrogant and figured I could make more if I, went somewhere else and did something else. It would take me three days to get a job. Right? And, so I always approached work in corporate America that way. And amazingly, I never got fired. I probably came close once or twice. But
• 15:25
  because I was adding so much value in my contrarian
• 15:28
  ways,
• 15:30
  they always tolerated me. In fact, promoted me at a ridiculous rate.
• 15:34
  And so I'd strongly recommend that to people. And, you know, the key to that is keep your burn rate real low. I mean, there was a time when I was sort of a a pretty big wig in big corporate America
• 15:45
  where I was living on about 12% of my salary,
• 15:49
  And that didn't even count count the ridiculous bonuses that I got. Right? And so, you know, unlike some of my peers, I was one in particular who was making slightly even more ridiculous salary than myself.
• 16:02
  And this old bitch somehow figured out how to live on a 110% of that. I I don't know how he did that. So he was always in financial trouble despite making a truly ludicrous sum of money. So, you know, first rule of if if you are gonna, for whatever reason, play the the corporate game for a while, and I did for a number of years, you know, just make sure you live way below your means and maintain
• 16:24
  the aggressive and obnoxious attitude
• 16:27
  Unknown: that if the bosses fire you, they're probably doing you a favor. Like, how how do you I know you get off the Internet for six months at a time or or or at least social media.
• 16:36
  How what do you do to keep this, pace up?
• 16:40
  Unknown: It's funny. I've been reading I've been starting a 100 books a year since I was about 10,
• 16:47
  and I finished about 75.
• 16:51
  And
• 16:52
  I've done it all throughout my life, you know, whether I was a a loafing bum hitchhiking around the country, which we talked about, on your last podcast,
• 17:01
  or whether I was a hyper busy public company CEO,
• 17:05
  whose
• 17:06
  day during the workday was scheduled in fifteen minute blocks,
• 17:10
  including to go take a shit. Right?
• 17:12
  I still found time to read my 100 books a year, and I just did it late at night and early in the morning. And so I have always been a relentless reader,
• 17:23
  and of course, the Internet's made it easier to dig. So you read a reference in a book, you know, I'll often move go over to Google Scholar and pull up the reference and read that and maybe read a scientific paper or two.
• 17:35
  So I'm essentially a self directed learning machine
• 17:39
  who has read something like
• 17:41
  5,500
• 17:42
  books in my life and probably
• 17:45
  50,000 scientific papers or something like that.
• 17:49
  And so I happen to have a pretty large body of knowledge,
• 17:53
  and I would say I have a decent,
• 17:56
  though not top of the curve,
• 17:58
  ability to integrate them into a coherent body.
• 18:03
  So, you know, even if someone says something that isn't specifically
• 18:08
  about
• 18:10
  the topic we're talking about, I have a large body of other data that's analogous and by comparison that I can that I can bring to bear. And then just a little trick. I mean, it's not really a trick. It's just a thing I do about my podcast is I actually read the goddamn books. Right? A lot of podcasters don't. I actually read the books, annotate them carefully,
• 18:28
  sometimes do some side research if I wanna really be able to challenge the speaker,
• 18:33
  and I on average spend about ten hours preparing for each podcast. So combine that with this, you know, large
• 18:41
  kind of self directed education program I've been on since I was 10 years old.
• 18:46
  And then the fact that I really do read the books in detail allows me to engage the the authors, I think, in a way that
• 18:53
  is somewhat uncommon.
• 18:55
  Unknown: I used to work for Monsanto,
• 18:57
  and I actually was the director of millennial engagement.
• 19:01
  So my job was to talk with people that had come to the conclusion that Monsanto was, you know, pure evil, the the a force for for bad in the world. And so when I took this job, I was like, well, if you're gonna try and change the way that people think, you're talking about changing culture.
• 19:19
  And so I actually flew out to San Francisco and met with Stewart Brand at the the Long Now.
• 19:26
  And
• 19:27
  the biggest thing that I learned from that conversation was he said like ideas don't change by one person, they change by networks. And if you really wanna change the world,
• 19:36
  you have to find ways to
• 19:38
  ping together and stitch together people that wouldn't meet otherwise. Because that's the way that you create new ideas, that's the way complexity can emerge beyond
• 19:49
  what any one individual can put forward. So to so to be sitting here talking to somebody like you that is out there, like, actually creating the stitching on the Internet that that brings people together is phenomenal.
• 20:02
  Unknown: I I actually didn't fully realize your backstory. Yeah. I did I did a lot of that stuff. And then the complexity part, of course, after I retired, I decided, well, I don't wanna go back to work like I did the last two times I retired. Goddamn it. So let me find something that'll keep my interest for,
• 20:16
  you know, at least two years, I said. And so I surveyed all my interests, and I decided an area that I was interested in and knew a little bit about, which was called evolutionary
• 20:25
  computation.
• 20:26
  The idea of growing computers via Darwinian evolution.
• 20:30
  And so I set up a big old lab in my house with a bunch of computers and a t one Internet connection. Holy shit. 1.4
• 20:39
  Unknown: megabits.
• 20:39
  Unknown: Wow. Oh, I was using those to download music off Napster. I remember t ones. Yeah. Yeah. This was in
• 20:46
  Unknown: in 2001. It cost $1,500 a month, but what the hell? I'd sold my company for a pile of money. I could afford it.
• 20:52
  And so I started writing evolutionary neural nets that would evolve themselves to play games, and then came to the attention of the Santa Fe Institute. In fact, someone wrote a New York Times story about my doings, and
• 21:05
  and so the Santa Institute invited me to go out there. And Santa Institute, as you probably know, is the home of of complexity science,
• 21:11
  and we hit it really hit it off. And I I came out there as a researcher in residence and end up spending ten years there, And and
• 21:18
  eventually, I ended up as a chairman of the damn place. And
• 21:22
  and so I got quite a deep education
• 21:25
  in complexity science
• 21:27
  in
• 21:28
  many of its manifestations. You know, the new modern network science of people like Mark Newman,
• 21:33
  Michelle Girvin,
• 21:35
  agent based modeling with people like Rob Axtel and Josh Epstein,
• 21:41
  economophysics,
• 21:42
  guys like
• 21:43
  Doan Farmer, some of the very interesting anthropological
• 21:47
  thinking in terms of complexity, guys like George Gummerman.
• 21:51
  Man, it was, like,
• 21:53
  amazing. So
• 21:54
  Unknown: the reason that I think you're such an interesting character is that you are talking about these edge, you know, right on the the the chaotic
• 22:03
  edge
• 22:03
  where you're creating new things, you're in the Internet,
• 22:07
  and yet the article that we started this whole interview talking about, the plan b,
• 22:12
  is almost like a back to the land movement.
• 22:16
  It's it's definitely a a reverse of course in saying,
• 22:20
  get back to your community.
• 22:22
  Where did you come to this? Why why is this the conclusion you're reaching as as you, you know, age into retirement?
• 22:30
  Unknown: Yeah. This is actually a the group work of a bunch of people. This is not, my, sole invention by any means. You know, the Game Beat community started in 2013.
• 22:39
  It was about 30 people initially.
• 22:41
  Now it's probably 30,000,
• 22:44
  and there's ongoing dialogues. By way, it's game b, not plan b. People Oh, Game b. Make that, make that,
• 22:50
  mistake a lot. In fact, I believe the article you're talking to is called a journey to game b. It's on Medium,
• 22:56
  and
• 22:58
  it lays out at least my version of game b, and other people have different versions.
• 23:02
  But it is it is interesting that we are all converging to similar takes,
• 23:08
  which is despite the super linearity
• 23:10
  of things like income and patents,
• 23:13
  we've concluded that life in the big city is
• 23:17
  got more negatives than positives.
• 23:19
  Because while we may create a few more patents, we're also consuming a lot more of the earth's resources.
• 23:24
  We're living in a way that's not in touch with our basic human nature.
• 23:29
  You know, we're separated from
• 23:31
  the land and the and and
• 23:34
  the water and the plants.
• 23:36
  We're not getting our food from our own productions or productions of our friends and neighbors who we might be trading with. It's coming through this great industrial
• 23:43
  agricultural
• 23:44
  model,
• 23:45
  which has it is a miracle to figure out how save 8,000,000,000
• 23:49
  feed 8,000,000,000 people.
• 23:51
  But an awful lot of people are just eating the worst kind of, you know, industrial food.
• 23:56
  And we've concluded that we can have the best of both worlds, actually, in some sense.
• 24:00
  It's that we can
• 24:02
  create proto bees, as we call them, which are communities on the ground, anywhere from 30 people to maybe 3,000.
• 24:09
  And that these can then use network technologies to interact with each other wherever they may be. And so you have, the best of both worlds. You have a wonderful life in a beautiful, intact community,
• 24:20
  and yet you're in touch with the real world economy and other proto bees. And so you can get at least a goodly piece of that super linear scaling,
• 24:28
  and you can be smarter and and outcompete what we call the game a world, and yet not have to live in New York City or San Francisco or someplace like that. And so
• 24:40
  I would say we came to this a step at a time.
• 24:43
  You know, different people added different pieces of it. One of my key collaborators, a guy named Jordan Hall.
• 24:48
  Another one, Daniel Schmachtenberger,
• 24:51
  Brett Weinstein
• 24:53
  has been an important contributor. Nora Bateson,
• 24:57
  and many others.
• 24:59
  Bonita Roy has had a lot of very interesting ideas that have played into the evolution of this. And we, you know, we didn't know this was where we were headed, but a step at a time,
• 25:09
  as we've learned more,
• 25:11
  it just seems like this is the right step for
• 25:14
  humanity,
• 25:15
  contrary to what the experts tell us. You know, they tell us the world's gonna get more and more and more urbanized. Everybody's gonna live in a super city of 25,000,000
• 25:22
  people by the year 2100.
• 25:24
  We don't think so. We think the right answer is step back from that, build our lives at a human scale, but then use the networks for what they're good for, which is to organize
• 25:34
  capacity
• 25:35
  to do meaningful work
• 25:37
  without us all having to be in the same place at the same time.
• 25:41
  Unknown: Maybe the criticism that I'd like to hear you address is
• 25:45
  status. Right? Status is deeply important to human beings. I have a close friend named Eric Ward who says, if you have an org chart and no it doesn't matter how thin you draw the line between two people,
• 25:56
  as soon as you draw a line, somebody's gonna try and figure out who's in charge of the other one, and it's just a natural human reaction. It's think about that. You don't. Okay. It is
• 26:06
  Unknown: not.
• 26:08
  If we look back at the foragers,
• 26:10
  the hunter gatherers,
• 26:12
  which constitutes
• 26:14
  190,000
• 26:15
  of our two hundred thousand years on earth,
• 26:18
  they did not organize themselves hierarchically
• 26:21
  or in status
• 26:23
  relationships.
• 26:24
  In fact, they went to great lengths
• 26:26
  to defeat anyone who tried to play that game on them. In fact, one of the greatest books of anthropology that I know is called Hierarchy in the Forest by Chris Bohm.
• 26:36
  It actually should have been called anti hierarchy in the forest, but he called it, for whatever reason, hierarchy in the forest. He explains how
• 26:44
  forager people, despite the fact that our near relatives,
• 26:47
  the chimpanzees,
• 26:48
  very hierarchical,
• 26:49
  we managed to get out of that box.
• 26:52
  And we
• 26:53
  would not tolerate hierarchy,
• 26:55
  and in fact, anyone who tried to pull that game
• 26:59
  would be first laughed at and then ignored and then exiled. And if they kept coming back trying to say, I'm the boss, they'd be killed.
• 27:07
  And the enabling technology was unlike chimps,
• 27:10
  where the biggest baddest chimp was really pretty hard to deal with. Humans had weapons, and so even a random dude could kill the biggest baddest dude
• 27:20
  the band. And humans built this anti hierarchical way of life that
• 27:25
  lasted for 95% of our time on earth. And we believe this is a core idea of game b, that we can get back to that.
• 27:33
  As an example, you do need leadership. But how did the foragers do it? They and indigenous some indigenous peoples today, not all.
• 27:41
  They use what's called role based leadership rather than position based leadership.
• 27:46
  So the guy who was best at, you know, hunting buffalo would go out and lead the band that was hunting buffalo, but that was his only
• 27:53
  authority was to lead people on buffalo hunts. And when they were back in the camp, he wasn't the chief. Nobody gave him any extra respect.
• 28:01
  You know, Mary, was the greatest berry picker, she led people on berry picking expeditions. And, oh, by the way, if, you know, if they if someone new up and comer turned out to be better at it, they took over as the role based leader in this thing or or even, you know, the guy who's in charge of the buffalo and got tired of leading the buffalo and decides he wants to go start napping stones. He could just say, well, I'm not doing that anymore. So much, much, much more fluid and emergent.
• 28:26
  And the biggest challenge of game b is to be able to do that as well and better than than game a does it. And we believe that we will have a huge competitive advantage. Because what would you rather work in? A world where we're all collaborating
• 28:39
  together as peers in role based
• 28:41
  rotating role based leadership
• 28:43
  or one of these god awful command and control structures.
• 28:47
  We believe that over time,
• 28:49
  we will pull the talent out of game A because it will prefer
• 28:53
  the game B way of organizing work.
• 28:56
  And so our ability to parasitize
• 28:58
  game a just gets stronger and stronger over time. We pull out the talent, leave the stiffs there. They're less effective structures or more effective structures. Looks like we're gonna win. That's our that's our theory. As to the kind of projects I do for more a more serious work, my current number one project is I have had a vision for years
• 29:18
  of a different kind of neural net for AI
• 29:23
  and for a learning algorithm,
• 29:25
  different than anything else that exists.
• 29:27
  And
• 29:28
  I got it mentally specified about three years ago, and then my life took off into too many adventures,
• 29:34
  and
• 29:36
  I was never able to instantiate it as code.
• 29:39
  But this year over Christmas, I took two weeks off
• 29:42
  and spent a lot of time with my family, but I also spent three or four hours a day
• 29:47
  creating this new kind of neural net. I got it up, and I got it running, I've got it running
• 29:52
  on or I got it rudimentarily
• 29:54
  processing
• 29:57
  one of the most famous AI test problems
• 30:00
  called the MNIST
• 30:02
  handwritten digits problem. Basically, it's a set of 60,000 hand rigid written digits
• 30:07
  zero through nine with all kinds of idiosyncratic
• 30:11
  writing styles,
• 30:12
  and the AI problem is to look at the digits as a raster field, you know, a bunch of dots, and decide what digit it is. Right? And it's it's a very interesting problem because it's not
• 30:24
  real hard, but not real easy.
• 30:27
  Lots of learning algorithms will show no traction on it at all.
• 30:31
  Any decent learning algorithm will show some traction,
• 30:35
  but nobody has ever solved it at a 100% level. I think we're currently up to, like, 99.85
• 30:41
  of the 64,000
• 30:43
  digits
• 30:44
  can be,
• 30:48
  you know, labeled correctly.
• 30:50
  So I've gotten it up to that point, though I have not hooked in the learning algorithm, which is the interesting part.
• 30:56
  I'm taking a week off
• 30:58
  the week of April 5, and at least part of that week,
• 31:01
  I intend to, you know, maybe get another fifteen or twenty hours in on this entirely new
• 31:08
  radical architecture
• 31:10
  for a learning neural net with its own new learning algorithm.
• 31:14
  So that's the kind of stuff I do. The most the thing I did before that is I wrote an AI
• 31:19
  that exhibited rudimentary
• 31:21
  consciousness,
• 31:22
  at least I would so argue,
• 31:24
  Unknown: and I hooked yep. It into
• 31:27
  Unknown: Rudimentary consciousness,
• 31:29
  I instantiated
• 31:30
  it as a deer,
• 31:32
  running around in a field full of obstacles.
• 31:35
  And I,
• 31:36
  wrote the AI in one environment, and I wrote the deer in,
• 31:40
  Unity three d, which is a game programming environment so I could get some high resolution graphics. And then the the interesting and fun part is I had to write my own custom communications
• 31:50
  layer
• 31:51
  to let Unity talk to this AI engine, and I managed to create a really high speed communications pipe that was, like, how many times faster? At least a 100 times faster than the
• 32:02
  ones that the AI framework people had created, which really sucked.
• 32:06
  And being able to, you know, send the 200,000
• 32:09
  messages a second
• 32:12
  allowed me to do amazingly
• 32:13
  fine grained
• 32:14
  behaviors
• 32:15
  in the in the deer
• 32:17
  with the AI engine. So those are the kinds of things I work on.
• 32:21
  You know? Am I and, of course, one of the problems with programming is if you're not doing it every day, your fingers forget. Right? And so it always takes me two or three days to kinda ramp back up when I start a project,
• 32:34
  which is the reason I tend to do it in big chunks of time rather than saying, well, you know, don't you always have an hour here or there? Yeah. I have an hour here or there. But an hour of programming when you haven't done it in two weeks is damn close to useless.
• 32:46
  Unknown: So twice you've referenced
• 32:49
  consciousness in regards to machines,
• 32:51
  and I think for a lot of people, this seems like
• 32:55
  something
• 32:57
  akin to magic or something. Something that doesn't seem real, but that that you could talk about using words, but to really say what would it mean for a machine to be conscious.
• 33:05
  But what do you mean when you say that?
• 33:08
  Unknown: Ah, now this is this is actually the very center of my research.
• 33:12
  So I may go a little deeper here than usual.
• 33:17
  My own view
• 33:19
  is well, there's
• 33:21
  various views on this, needless to say, from those who you know, the classic Cartesian dualism,
• 33:26
  which is the root in some ways of modern thinking about consciousness,
• 33:30
  which I believe and most current researchers in consciousness believe is wrong.
• 33:35
  But, anyway, Rene Descartes
• 33:37
  believed that consciousness
• 33:39
  and the body were two separate materials
• 33:43
  and that they talked to each other through the pineal gland of all weird things,
• 33:48
  and that you could think of consciousness as being analogous to the Christian soul,
• 33:54
  and that when you die, the consciousness separated from the body, went and did its own thing, etcetera.
• 33:59
  I would say modern thinkers
• 34:01
  know mostly,
• 34:03
  at least scientific thinkers reject that view,
• 34:06
  and they believe in
• 34:08
  consciousness as being emergent from the material.
• 34:11
  So they
• 34:14
  the consciousness
• 34:15
  emerges from our brains and our bodies,
• 34:18
  and when the body dies, the consciousness dies, and it does not go on in some separate existence. It's not a separate material, so it's a uniformitarian
• 34:27
  view.
• 34:28
  Then there's a second
• 34:30
  fork,
• 34:30
  which those those who believe that consciousness
• 34:34
  believe it's called functionalism,
• 34:37
  that if you had a a computer that was similar enough to the way humans
• 34:43
  process or even process it in a different way, you could nonetheless create an identical
• 34:49
  consciousness experience to that which a human has.
• 34:54
  I would say that I'm skeptical of that, and the school that I
• 34:58
  approach this question from is the question is the school of John Searle,
• 35:03
  the Berkeley philosopher who writes some amazingly accessible books, by the way. If you wanna read a philosopher's take on
• 35:10
  the science of consciousness,
• 35:12
  look up John Searle, that's s e a r l e.
• 35:16
  His view is that our consciousness,
• 35:18
  the only one that we know of,
• 35:20
  we call it the animal consciousness,
• 35:23
  emerges from our bodies in a very complex way. It includes the brain, but it includes the gut, and it includes our skin, includes our senses, everything else. And so attempting to duplicate our exact
• 35:35
  consciousness
• 35:37
  in a machine is gonna be very, difficult and probably won't happen anytime soon.
• 35:42
  However,
• 35:42
  you know, he describes consciousness as a biological
• 35:45
  function
• 35:46
  like digestion
• 35:48
  or respiration,
• 35:49
  which has many components to it. Like, you can't put your finger on something in your brain say this is consciousness.
• 35:54
  Rather, it's a dance of lots of different things in the body and the the mind.
• 35:59
  And just
• 36:00
  like in
• 36:02
  food science
• 36:03
  and in pharmaceuticals,
• 36:05
  we have things called digesters.
• 36:07
  They use bacteria, they use fungi, they use yeast
• 36:10
  to process raw chemicals and turn them into higher value
• 36:14
  products.
• 36:15
  And we call it a digester even though that digester doesn't look anything like your colon and your stomach and your pancreas and your liver,
• 36:23
  but it does something analogous.
• 36:25
  It's kind of like digestion.
• 36:27
  So where there's a long way around. So where I come out is that things like my conscious deer, very rudimentary,
• 36:33
  but and and other machine consciousnesses
• 36:37
  that we're likely to see in the next
• 36:39
  five to twenty years are gonna be analogous
• 36:42
  to what human consciousness
• 36:45
  is, but in no means,
• 36:47
  you know, closely identical to it.
• 36:50
  I I know that's a whole series of pretty fine distinctions, but they're important when you think about this question. Yeah. I did I did a lot of that stuff. And then the complexity part, of course, after I retired, I decided, well, I don't wanna go back to work like I did the last two times I retired. Goddamn it. So let me find something that'll keep my interest for,
• 37:06
  you know, at least two years, I said. And so I surveyed all my interest, and I decided I an area that I was interested in and knew a little bit about, which was called evolutionary
• 37:15
  computation.
• 37:16
  The idea of growing computers via Darwinian evolution.
• 37:20
  And so I set up a big old lab in my house with a bunch of computers and a t one Internet connection. Holy shit. 1.4
• 37:29
  Unknown: megabits.
• 37:30
  Unknown: Wow. Oh, I was using those to download music off Napster. I remember t ones. Yeah. Yeah. This was in,
• 37:36
  Unknown: '9 in 2001. It cost $1,500 a month, but what the hell? I'd sold my company for a pile of money. I could afford it. And
• 37:43
  so I started writing evolutionary neural nets that would evolve themselves to play games, and then came to the attention of the Santa Fe Institute.
• 37:51
  In fact, someone wrote a New York Times story about my doings, and
• 37:55
  and so the Santa Institute invited me to go out there. And Santa Fe Institute, as you probably know, is the home of of complexity science,
• 38:01
  and we hit it really hit it off. And I I came out there as a researcher in residence and end up spending ten years there And and
• 38:09
  eventually, I ended up as a chairman of the damn place. And
• 38:12
  and so I got quite a deep education
• 38:15
  in complexity science
• 38:17
  in
• 38:18
  many of its manifestations. You know, the new modern network science of people like Mark Mark Newman,
• 38:23
  Michelle Girvin,
• 38:25
  agent based modeling with people like Rob Axtel and Josh Epstein,
• 38:31
  economophysics,
• 38:32
  guys like
• 38:33
  Farmer,
• 38:35
  some of the very interesting anthropological
• 38:37
  thinking in terms of complexity, guys like George Gummerman.
• 38:41
  Man, it was, like,
• 38:43
  amazing. The
• 38:44
  Unknown: funny thing about complexity is
• 38:48
  you have to be so careful when you're writing about it or you're even reading about it that you don't get into the
• 38:55
  magic land. Right? Like, complexity science is so complicated.
• 38:59
  So if you were gonna describe for people what what is the study of complexity,
• 39:03
  what does that actually mean?
• 39:05
  Unknown: It it it's the emergence
• 39:07
  of complexity
• 39:09
  from simplicity.
• 39:11
  So the the best example,
• 39:14
  is
• 39:15
  is life.
• 39:16
  Let's say us.
• 39:18
  At the bottom, we're a bunch of atoms jiggling around. Atoms are actually quite simple. You know, every hydrogen atom is identical to every other hydrogen atom. You know, take away the isotopes of deuterium and tritium. But plain old hydrogen,
• 39:31
  everyone's identical. Every electron is identical to every other electron. So this is actually a very simple domain.
• 39:37
  But then, you know, these atoms get together and produce molecules, gets more complicated. The molecules get together and produce bigger molecules, organic chemistry, which is
• 39:47
  hugely complicated,
• 39:49
  then that turns into
• 39:52
  eventually cells,
• 39:53
  which have metabolisms,
• 39:54
  which, you know, the graph of the metabolism of the simplest bacteria,
• 39:59
  I've actually seen this on the wall, a friend of mine's office,
• 40:02
  you know, cover eight feet of whiteboard in, you know, the average circle about this big. And
• 40:09
  so metabolism
• 40:10
  is an emergent. It merges from
• 40:13
  biochemistry,
• 40:14
  Then you have a single cell entity, and then magically, we don't know how, but about 500,000,000
• 40:20
  ago, the single cells started operating together as multicellular
• 40:24
  entities in the so called Cambrian explosion.
• 40:27
  And then they started evolving.
• 40:29
  And
• 40:30
  from that one event, we believe it was that one single event.
• 40:34
  Essentially, all the animal life in the world evolved relatively rapidly.
• 40:39
  And
• 40:40
  and so, basically, you have animals which start to be created from cells,
• 40:45
  which become tissues, which become organs,
• 40:47
  which become systems. Like, think of the circulatory system that Jeffrey
• 40:52
  West probably talked to you about.
• 40:55
  And then then it adds up to an organism like us.
• 40:59
  An organism lives in an ecosystem,
• 41:01
  which emerges from the interaction
• 41:04
  of all the participants, both humans and other animals as well. So that's one of the very best examples of complexity from simplicity.
• 41:13
  The other one I like to use, it cuts right to the quick,
• 41:16
  is I like to talk about the relationship between the dancer and the dance.
• 41:22
  One can think of the dancers as sort of interchangeable
• 41:25
  elements, but the dance is the emergent
• 41:28
  interaction
• 41:29
  of all the dancers
• 41:31
  under the influence of the music.
• 41:33
  So you can think of a dance as a complex
• 41:37
  emergent phenomena,
• 41:39
  that is the the dancers
• 41:42
  in motion under the influence of the field of a piece of music. The first
• 41:48
  Unknown: question that comes to mind when I think about these things, and I spoke with Ken Stanley, who's at OpenAI,
• 41:54
  is as you're building these computers and you're making them smarter and smarter, and you're adding in a learning algorithm so that they can actually teach themselves,
• 42:02
  Do you fear or is this just the fear of us plebeians or normies
• 42:06
  of a runaway AI, a system that's so smart that we can't control it or don't don't even really understand what it's doing?
• 42:15
  Unknown: Yeah. This is the so called AI risk,
• 42:18
  discussion. By the way, Ken Stanley is one of my heroes,
• 42:22
  because he's one of the people who has also done in fact, he's done more interesting work in evolutionary
• 42:27
  Unknown: neural nets than anybody else, I would say. Oh, Ken Stanley changed my life. I read his book on
• 42:34
  the
• 42:35
  why greatness can't be planned, the, you know, novelty search, and that idea led me into a whole different way of thinking. Between him and Yoshua Bach, they're heroes of mine for sure.
• 42:45
  Unknown: Yes. Yes. Most
• 42:47
  of my work originates
• 42:49
  from the University of Texas
• 42:51
  evolution
• 42:52
  neural evolution work,
• 42:54
  and Ken Stanley is one of the students of that work.
• 42:57
  And so he's in that tradition and
• 42:59
  probably the person that's pushed it the furthest, at least in terms of actually useful
• 43:04
  tools. But anyway, getting back to AI risk,
• 43:08
  it's funny. I talk about this fairly regularly. I was talking with one of my good buddies, Ben Gertzel, yesterday. He's one of he's the guy who coined the expression
• 43:16
  AGI.
• 43:17
  He's created an AGI framework. He's written 15 books on the topic,
• 43:22
  and we talk about this regularly.
• 43:24
  And
• 43:25
  we both believe that, yes, in the long run,
• 43:29
  there is risk that an AI could take off the so called technological
• 43:34
  singularity.
• 43:36
  This is a very important concept.
• 43:39
  In simplest form, let's say the following. Let's say you finally get an AI that's a teeny bit smarter than a human, say 1.1.
• 43:48
  And let's suppose you give it the job of designing its successor,
• 43:52
  and the successor it creates is 1.2
• 43:56
  as smart as a human.
• 43:58
  And then you say, oh, give give that one the job of creating its successor, and the next one comes out at 1.5,
• 44:04
  and then the one after that, 1.9,
• 44:06
  the one after that, 3.3,
• 44:08
  and then 10, and then 50, and then a thousand, and then 10,000,
• 44:13
  and then a million.
• 44:15
  So it's conceivable,
• 44:16
  and this and there is a theory that could all happen in six or eight weeks
• 44:20
  once you reach the the takeoff threshold.
• 44:23
  Now there are some of us there are people who argue on, you know, where is the takeoff threshold? Do you have to get to a million first before it's dangerous?
• 44:32
  I'm not sure. I don't think anybody knows the answer.
• 44:35
  But in terms of whether the work they're doing today like GPT three is dangerous, I'd say absolutely not because it's not you know, it's a static artifact that's been created.
• 44:44
  It can't continue to learn.
• 44:47
  It can't it doesn't have any consciousness.
• 44:49
  It doesn't have any sense of self.
• 44:51
  You know, frankly, it's, you know, no more different than an oscilloscope.
• 44:56
  Basically, you put data in, you get something out.
• 44:59
  But it's something that those of us who are working in this field need to be aware of.
• 45:05
  And at some point,
• 45:07
  you know, we'll have to become more proactive in building
• 45:10
  Unknown: AI safety into what we're doing. With people pouring money into this, you know, you said, hey. Before, if you dumped $6,000,000,000
• 45:17
  into a single shot, it wouldn't be worth it. But now there's
• 45:21
  millions,
• 45:22
  billions of dollars flooding into AI.
• 45:24
  What are they hoping
• 45:26
  comes out of this? What what do they want it for, and what will it be capable of doing in the in the utopian vision?
• 45:32
  Unknown: Well, of course, they means lots of different things. And this is really important, especially for young people thinking about their careers.
• 45:42
  You know, I really you know, I've intentionally not been involved in starting companies for twenty years, you know, more or less doing other kinds of stuff,
• 45:50
  but this stuff was so exciting when I fell into it in November
• 45:54
  2022.
• 45:56
  I said, oh, I gotta take a little test run here and see if I can build something. It turned out we could, which was kinda cool. But what it reminded me of was PCs
• 46:04
  in 1977.
• 46:07
  You know, people say, oh, it's the next Internet and they go, nope, bigger than the Internet. Oh, that's the next smartphone and I go, nope, bigger than the smartphone.
• 46:15
  Because if you take a look at a smartphone, it's just a tiny little PC, really. Took a look at the Internet, it's just a really big local area network, and we had local area networks for PCs by 1980.
• 46:26
  So I say that the root of,
• 46:30
  you know, desktop computers,
• 46:33
  smartphones,
• 46:35
  Internet,
• 46:35
  it's all with the PC in 1977.
• 46:38
  And we didn't know what we had no idea what the PCs would really be good for. It wasn't for till a year or two later somebody came up with the idea of word processing.
• 46:47
  Right?
• 46:48
  In 1977,
• 46:49
  secretary people
• 46:51
  wrote
• 46:52
  their business letters on yellow pads, gave them to their secretaries who'd type them out, give them back to them, and they'd correct it. My very first,
• 47:00
  you know, managerial job
• 47:02
  in 1980,
• 47:03
  amazingly enough, I still had a secretary who wanted me to write my crap out in
• 47:10
  on the yellow pads. I stopped pretty quickly, started using programming editors to do my writing, but but but only soon thereafter did real professional grade word processors go up. The spreadsheet,
• 47:21
  something we all live with today. I remember the first PC spreadsheet
• 47:25
  would have been about night '8 1981.
• 47:29
  I bought it in a computer store. It was with '81? Yeah. Would have been '81.
• 47:34
  It was a copy of VisiCalc,
• 47:37
  the first real spreadsheet in a ziplock bag with a hand printed label on it, and
• 47:43
  I took it home and I was like amazed. I stayed up all night long
• 47:47
  just building, woah, woah. You know, today, you can't think of anything more boring and cliched than Excel,
• 47:52
  but in 1981,
• 47:54
  the spreadsheet was,
• 47:56
  who came up with this idea? This is, like, so brilliant.
• 48:00
  And we're at that stage right now with these large language models and the other generative AI is that people are just constantly coming up with new things you can do with them. Every day somebody comes up with something
• 48:12
  cool you can do with them. Who would have thought writing movie screenplays would be a thing you could do with today's technology?
• 48:18
  But you can.
• 48:19
  And there are some of the areas where they're being
• 48:23
  relatively rapidly deployed
• 48:25
  is in customer service applications,
• 48:27
  in medical diagnostics,
• 48:29
  in
• 48:30
  repair helpers for things like
• 48:34
  auto repair, aircraft repair, industrial
• 48:37
  repair, etc.
• 48:41
  Pro programming
• 48:42
  computer programmers, when I wrote this huge program,
• 48:46
  I never could have gotten it done without
• 48:48
  using the LLMs to actually write the code. I would say I was three times as productive as I would have been without it, and I probably would have thrown it in the towel long before I got done with it if I didn't have my trusty
• 49:00
  helper. And, of course, talk to any, college professor, they'll tell you all the students are using these things for writing their essays,
• 49:08
  and so everyone is out here exploring what in the world is this thing really good for and what is it not good for? You know, it's not yet really
• 49:17
  a perfect replacement for search,
• 49:19
  but it is a pretty good
• 49:25
  Unknown: kind of thinking companion.
• 49:27
  Unknown: Give me an example. When I fir the first day I fired up chat GPT in November 2022,
• 49:32
  I said, oh, let me do a homework
• 49:36
  assignment that I had in twelfth grade honors English.
• 49:39
  And I had it compare and contrast
• 49:44
  Melville's
• 49:44
  Billy Budd with
• 49:47
  Conrad's
• 49:50
  Lord Jim. Yeah, that's what it was. And I and the thing
• 49:55
  forty five seconds later, kicked it out and said, missus Carr would have given this about a b plus, right, in honors English, twelfth grade,
• 50:03
  and that was GPT three, three point five. So that was like, woah. This stuff's pretty good. So we are, just like we did with the PCs, which at first people didn't know what the heck they would be good for, we're exploring what these things are good for. Well, of course, AI I didn't even think about that one, but yeah, AI therapy, there's lots of money being spent on that. Oh, therapy done by the AI.
• 50:25
  Unknown: Done by the AI. So now we go full cycle,
• 50:28
  Unknown: of therapy done by the AI,
• 50:32
  to, assage people's angst about dealing with AI.
• 50:36
  Unknown: Okay. So, let's let's crack open the mind of Jim Rutt and say, what are some of the darker scenarios? People talk all the time, hey, gotta be careful. This thing could get out of control. What does out of control look like?
• 50:48
  Unknown: Oh, I mean, the worst case is really, really bad. As I told you, I was gonna go the next move up from AGI.
• 50:54
  And this now that we've gotten sort of within spitting distance, or at least gunshot distance of AGI,
• 51:00
  we realize that's not the top of the stack by any means.
• 51:04
  As good as a human and almost everything a human could do,
• 51:08
  the next step is called artificial
• 51:09
  super intelligence,
• 51:11
  which is something that's
• 51:14
  a computer doing work that humans used to do that's now way better than any human has ever been at many of those tasks
• 51:23
  and
• 51:24
  we are
• 51:26
  that's where the one of the biggest risks. This is it's in the AI risk field, it's often called the paper clip maximizer risk.
• 51:35
  What a weird thing to call it, but it's where the things have gotten so smart that they can completely outfox us
• 51:41
  and do whatever they want and
• 51:43
  convince us to do what we want. If we try to put them in a security box that they will be so seductive with their use of language that they'll convince us to let them out of the box. I mean, there's all there's a whole library of scenarios about this.
• 51:56
  Unknown: Well, and the paper clip one the paper clip one is, you tell this AI, hey, do what you can to make, you know, paper clips as fast as possible, and this thing now starts taking every single resource that it possibly can to turn it into paper clips, and you're now ripping out sewer lines and smelting the metal and turning those into paper clips and You're killing all the world. People because they're because they're wasting all these this energy that could be used for making paper clips, right? It's a kind of crazy example, but it's like one of the worst case scenarios.
• 52:23
  Unknown: And then that goes into two lines.
• 52:26
  One's called FOOM. I don't know what FOOM stands for. That means super fast takeoff where once we get to some line of smart enough AI, a bit smarter than humans,
• 52:36
  suddenly you're at something a thousand or a million times smarter than a human in in two days.
• 52:42
  That's called the FOOM theory, and the other is the slow takeoff theory where,
• 52:47
  it's on a trajectory
• 52:49
  to get a thousand times smarter than a human,
• 52:52
  but,
• 52:53
  it might take ten years. And again, we don't know. I will say my own work between 2014 and 2018 in cognitive science and cognitive neuroscience
• 53:04
  has convinced me that human cognition is quite weak. We are just barely a general intelligence,
• 53:10
  just over the line. So there's a lot of room above human level of intelligence
• 53:16
  for
• 53:17
  artificial intelligence to move into, but how quickly it can find it because it will be the one that's doing the finding, not us. You know, once we get to say two x human, you give it the instruction, design your successor,
• 53:30
  and how quickly it can find its better successor will be a will be, I think, a determiner of
• 53:38
  how quickly the takeoff, how we go from smart you know, as smart as a human, a bit smarter than a human, and suddenly so smart it's not
• 53:46
  even in the same
• 53:48
  you know, so much smarter than anybody that ever lived that we're in a completely different realm.
• 53:52
  And whether that happens quickly or takes years will make a big difference on how this plays out. Oh god. This is so hard. It all comes down to the one we touched on briefly,
• 54:01
  Unknown: which is our governance capacity.
• 54:03
  If we don't fix that, then we have very little hope of addressing any of the other seven, actually eight.
• 54:10
  I know I
• 54:11
  snuck one in and didn't renumber. So I actually have nine now, not just eight. And this is a very difficult question.
• 54:18
  We've all watched politicians on TV and read about what they're up to. And you say, this list of issues I just went across, what's the chances that a US senator
• 54:28
  is gonna be able to make intelligent decisions
• 54:31
  on regulation or legislation
• 54:33
  in the future for a world like this. What do you think the chances are with today's governance structure? Probably right near zero, virtually zero. Yeah. I'd say damn close to zero. I'm with you. And so let's think about two alternatives.
• 54:48
  One,
• 54:49
  and this will be a temptation,
• 54:51
  especially as our AI gets better.
• 54:54
  What about we give the AIs themself the authority to write the laws and regulation?
• 54:59
  Unknown: What do you think about that one? Oh, man. You know, herein lies that you're handing over the very thing that you're worried about to the thing you're worried about. It seems to me like this is probably not a good solution, but maybe necessary?
• 55:12
  Unknown: Well, I sure the hell hope it's not necessary, but it might be. And this is something that we should think very long and hard before we let that happen. The second one
• 55:22
  is, I will say, it's time
• 55:25
  that we have to finally
• 55:27
  sit down and think about very serious changes in our political operating system.
• 55:33
  The US constitution was an amazing thing of genius.
• 55:37
  You know, it was written in 1787
• 55:39
  when The United States had the population of 3,000,000 people, about the same as the number of people in Kentucky.
• 55:45
  And they wrote a document which has now lived for about two hundred and thirty years and has served us reasonably well.
• 55:53
  But it was designed when the biggest company in America had a 100 employees
• 55:57
  when it took five or six weeks to get a message from New York City to London via sailing ship,
• 56:04
  you know, before the steam engine had arrived in North America,
• 56:08
  before the first bit of coal had been mined in North America.
• 56:12
  And so it is a operating system for governance,
• 56:16
  which I would argue despite the amazing things that it has brought is not up to the tasks of dealing with this list of risks and a bunch of other risks that have nothing to do with AI. So I think we have to bite the bullet and design a new political governance structure that could, one, get better people into it. Just think about this. I was having lunch with a good friend of mine just before this podcast, and we were laughing. And I told her, imagine
• 56:41
  up in heaven, John Adams, Thomas Jefferson,
• 56:44
  and James Madison
• 56:46
  sitting together around the table looking down and looking at the presidential
• 56:51
  candidates we're likely to have on offer.
• 56:53
  You know, Donald Trump,
• 56:55
  Joe Biden,
• 56:56
  and Robert f Kennedy junior.
• 56:58
  And this is in a country with 330,000,000
• 57:01
  people versus the 3,000,000 people at the time of the founding, a 100 times the population.
• 57:07
  And they come up with these three screwballs?
• 57:09
  What the hell? There's gotta be something very seriously wrong with our political operating system.
• 57:15
  Unknown: But for anybody that didn't get to check out our first one, do you wanna give an explanation
• 57:19
  of game b and kind of where you're at and where you're headed to?
• 57:23
  Unknown: Yeah. Sure. First, let me just address this idea of pushing back,
• 57:27
  which is indeed something I do on many of my podcasts. But one of the things I don't do is play gotcha journalism.
• 57:33
  You know, I'm not looking for the thing to embarrass the guy or anything like that.
• 57:37
  It's always about something substantive.
• 57:39
  Right? That okay. They say x, but I say, well, you know,
• 57:44
  you know, you haven't actually proven x. You pushed x forward as a hypothesis.
• 57:48
  The evidence here's some other evidence that points the other direction, etcetera. So, well, I push back fairly often, actually, as you know.
• 57:56
  I never do it in the gotcha sense, only on substance and only with evidence.
• 58:01
  Unknown: I think that's That's actually exactly what I mean. I think there's very few pieces I think one of the reasons people have gotten so bad at conflict is they don't actually see conflict done very often.
• 58:11
  And so I think that conflict
• 58:13
  is good. It makes everybody's ideas better. I think there's no person that I've ever heard you pushing back on that didn't actually like it because it forced them to
• 58:24
  speak their thoughts that probably they they hadn't even put some of those things that they knew into words, and I I I find it to be expansive rather than diminishing.
• 58:35
  Unknown: Yeah. And I've never gotten into an ugly conversation with anybody at all on the podcast. As you say, all of them go with the flow, and then some of them have told me afterwards, holy shit. You know, that that did take me into a whole new thinking. I'm gonna write a paper about that. So,
• 58:49
  anyway,
• 58:51
  back to game b.
• 58:52
  It's an idea that evolved
• 58:55
  from a group of our work back in 2012,
• 58:58
  which actually started to create a political party, which was kind of a fiasco,
• 59:04
  but it ended up collecting a group of
• 59:07
  eventually, grew to about 60 people, really smart, interesting people from many disciplines.
• 59:12
  And
• 59:13
  the idea of a new social operating system for society
• 59:18
  that was congruent to the challenges that we currently face,
• 59:22
  is what arose. And that's what game b is. Right? It's literally
• 59:26
  an attempt to say, alright. There's something deeply wrong with our world.
• 59:30
  And part and
• 59:32
  a lot of that has to be the fact that our institutional structures,
• 59:36
  our ways about thinking about things,
• 59:38
  truthfully haven't changed all that much much since the eighteenth and nineteenth century.
• 59:43
  You know, when I hear people argue about capitalism versus socialism, I go, why are you so in love with ancient thinkers? Right? It's twenty first century, people.
• 59:52
  You know, I don't think the answer,
• 59:54
  to what comes next with respect to how we organize our society is likely to be well described by either eighteenth century, I. E, capitalist thinking, or nineteenth century,
• 1:00:04
  I. E, socialist thinking.
• 1:00:05
  And so we're trying to do thinking at that level,
• 1:00:08
  but fresh with a blank sheet of paper with some of the smartest people around on what will it take to get humanity through the twenty first century without destroying ourselves
• 1:00:17
  Unknown: and to build a vibrant and wonderful future for the human race. When I started to first realize what was going on with complexity in the Santa Fe Institute, I think that was one of my first steps back from the edge of nihilism, right, where you think, well nothing really means anything, everything's so big, where do you fit in the billion year, you know, system of the universe.
• 1:00:38
  But then you start seeing something like complexity and you see, at least I have a sense of awe. And I think about you describing that you're building games or neural networks that can learn,
• 1:00:50
  which almost puts you in the position,
• 1:00:52
  I don't wanna overstate it, but of as a god. Right? You are able to create something that can create something else and spin off.
• 1:01:00
  Do you have this same sense of awe? Do you have this feeling of of power because you can create something that can evolve on its own?
• 1:01:09
  Unknown: As it said in that New York Times article back in, 2001,
• 1:01:12
  when I first saw it actually work, the hair on the back of my neck stood up.
• 1:01:17
  It was like, shit.
• 1:01:19
  You know? I just wrote some simple rules and told them to go do their thing, and they got smarter on their own.
• 1:01:26
  Wow.
• 1:01:27
  And, of course, now people take that to a crazy degree. You know, famously,
• 1:01:31
  DeepMinds
• 1:01:31
  at Google has created this whole family of, learning algorithms,
• 1:01:36
  called AlphaZero,
• 1:01:38
  which you can it it has no knowledge at all of a game. You just tell it the rules and it starts playing it with itself, and it becomes a world champion in, like, two days.
• 1:01:46
  AlphaZero
• 1:01:47
  is now the world's champ in
• 1:01:50
  chess, in Go, and several other games. And that's what no,
• 1:01:56
  you know, knowledge of the game other than playing itself. It's amazing.
• 1:02:01
  And, yeah, it's truly amazing. And that and and the bigger lens of complexity, of course, leads us to believe that's how everything got here, that same way, playing with itself in evolutionary competition.
• 1:02:12
  And it it takes the, you know, the statement that nothing in biology makes sense
• 1:02:17
  except through the lens of evolution. One of the famous I biologists
• 1:02:22
  said that. You can now say the history of the universe makes no sense other than through the lens, of evolution.
• 1:02:28
  And, that is a really powerful lens.
• 1:02:31
  Unknown: Well, if you enjoyed this podcast, I, hope you tune in again. This was a fun thing for us to put together in a way that allowed us to honor a guy that
• 1:02:42
  I don't know. He was just so full of life when he was here.
• 1:02:45
  I know that,
• 1:02:47
  Jim,
• 1:02:47
  was prepared for his death, and it really is a full circle to be able to do one last podcast with him.
• 1:02:55
  I'm going to sign off for now. But if you are a person that has been thinking about how can I have better conversations, different kinds of conversations, learn how to negotiate this world, you may consider coming to my class on interest based communicating?
• 1:03:10
  I'm holding a live session here in St. Louis, July sixth through eighth,
• 1:03:16
  and, I'm gonna play a little clip that talks about other people that experience this course. Alright. We'll be back whenever we are, whenever we find another interesting guest. Thanks so much for tuning in. Good negotiation is one where both parties walk away slightly disappointed.
• 1:03:32
  Unknown: That was probably my default world model BV before Vance.
• 1:03:36
  Well, Vance had a lot of these models that he created. I ingrained a lot of these models so deeply from working with Vance for a long time that I started to re communicate them to my friends. And my friends eyes had the same sort of light up of understanding of like this is a thing that could help me be more effective in life. Whenever Vance let me know that he was working on sort of putting this into more of a workshop form, I was like, yeah, man. I want that for my team like now. If my first two employees go through this so that we have this shared set of context and a shared set of language, that's gonna create value for my team going forward.
• 1:04:10
  Unknown: I wanted to get better at communication myself. I knew I had good ideas in my head that I wanted to be able to communicate to potential investors,
• 1:04:19
  and I was not super confident in being able to do that before. I thought that negotiation
• 1:04:24
  Unknown: was basically for
• 1:04:26
  people in a boardroom like CEOs,
• 1:04:29
  words like shareholder
• 1:04:31
  are involved. So I thought you had to have a lot of resources at your disposal to even care about negotiations.
• 1:04:37
  Both people had to lose or both people weren't happy in the end. I found that what you both want is achievable by understanding the needs of the other person more. A negotiation is really a way to solve a problem that you have. You can rearrange the world in such a way where all parties get something that they're happy with. So you don't need to possess
• 1:04:57
  great resources
• 1:04:59
  to be involved in a negotiation or to really benefit from knowing how to negotiate.
• 1:05:04
  Unknown: RJ and I initially were two different businesses.
• 1:05:07
  It became clear to us over the long term that we would be much more bullish on our shared future if we, like, found a way to, like, actually
• 1:05:14
  be one entity, one whole working together. That required us to have
• 1:05:19
  what to many would be very hard conversations because we're both CEOs of our own independently funded businesses. But we were able to have conversations and get to the root of what our actual interests and what our actual skill sets were. Having both people understand this pattern of communication was useful to pull out those tools when we needed them the most in, like, difficult conversations.
• 1:05:40
  Unknown: It's very helpful to have that quick language to do something more productive than or continue to, like, misunderstand or
• 1:05:46
  whatever
• 1:05:47
  Unknown: whatever else you're doing. Building relationships isn't just you communicating to the other person. It's also figuring out how to let the other person communicate to you so that they trust you. Getting into a real conversation and and diffusing tensions before you, you know, get get to business. A lot of people have a default model where they're like, I wanna raise my own money. I wanna start my own business. I wanna do my own thing. We find that they have specific interests that could help us. We become allies instead of competitors.
• 1:06:14
  Unknown: I guess the course gives you tools to know that you can say the right words to, like, extract those things out of the ether.
• 1:06:23
  Unknown: Put them in your life. Learning interest based communications from Vance might actually be the single highest leverage skill set that I ever developed before starting this company. I'm by default an introvert.
• 1:06:33
  I think that I've developed the ability to interact with people in such a way that it's energizing and it's fun and I look forward to interacting with strangers on a regular basis. You should just take it. Just do it already. Yeah. I feel like you should just do it like
• 1:06:46
  no. I don't know. I feel like it's life content. It's like definitely fun life content. I am doing exactly what I want to be doing with my life. Intra space communications did that for me more than anything else that I've learned.