Sam Arbesman - Why Future Belongs to Curious People (Ep. 309)

Summary

Infinite Loops Ep. 309: Sam Arbesman — Why the Future Belongs to Curious People

Date: April 9, 2026
Host: Jim O’Shaughnessy
Guest: Sam Arbesman

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Episode Overview

This episode delves into the power of curiosity, polymathy, and the importance of reexamining both our past and present to build a better future. Jim O’Shaughnessy and Sam Arbesman discuss how curiosity, open-mindedness, and a playful approach to learning can drive innovation and help society grapple with rapid technological and social changes—especially in the age of AI. The conversation riffs through education reform, rational optimism, scientific progress, organizational models for creativity, and the value of historical knowledge.

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Key Discussion Points & Insights

1. Optimism vs. Pessimism in Thinking About the Future

• Pessimism as Sophistication
  • Sam points out that pessimism is often mistaken for intellectual sophistication, especially when forecasting the future.

    "Pessimism is often viewed as a mark of sophistication, even if you are consistently wrong... people love to hear that because it sounds like you're being very thoughtful and very serious." —Sam, [00:00]

• Rational Optimism
  • Jim identifies as a "rational optimist," believing in humanity's problem-solving abilities while acknowledging inevitable setbacks.

    "I call myself a rational optimist. I think that we are really good at solving things. I fully expect that we're going to fuck a lot of stuff up, but those have to coexist together." —Jim, [00:12]

2. Curiosity as a Key Driver

• The Value of Open-Mindedness
  • Sam argues that open-mindedness, more than intelligence, will determine who benefits from AI and new technologies.

    "If you are open minded, no matter your intelligence level, it actually does have the possibility of kind of accelerating whatever you are learning." —Sam, [04:32]

• Steel Manning and Critical Engagement
  • Jim shares his practice of steel-manning arguments he disagrees with, which he finds revealing and humbling.

    "Whenever I see something...I immediately steel man the argument for it. And it's really revealing." —Jim, [03:45]

3. Polymathy, Dabbling, and Learning Models

• Generalists Thriving in the AI Era
  • Both highlight that polymaths and generalists, because of their wide-ranging curiosity, are especially suited to leverage modern tools.

    "Generalists who are polymathic who have lots of interests, like it's catnip." —Jim, [06:10]

• The Dabbler Badge
  • Sam shares a story about a "Dabbler badge" from his mother's Girl Scouts, celebrating those who explore diverse fields, and advocates for educational systems to do the same.

    "I love that idea...we need more educational models that kind of valorize and incentivize like the Dabbler badge of Knowledge at all different ages and levels." —Sam, [12:40]

4. Rethinking Education for Modern Needs

• Iterative Reform Over Utopian Planning
  • Sam prefers "piecemeal engineering" (from Karl Popper) to radical overhauls in education—gradual, experimental change over top-down redesign.

    "I'm much more of the opinion...of piecemeal engineering. Let's experiment a little bit... reevaluate, and slowly but surely hopefully get to something better." —Sam, [10:57]

• Adult Continuing Education & Lifelong Learning
  • There’s a focus on lifelong learning, with Sam advocating for institutions that democratize continuous learning for adults.

    "...continuously learning and being curious about the world, like, that's the thing I always want to be doing. And there should be mechanisms for everyone to be involved in that." —Sam, [11:58]

5. History of Technology and Recurring Patterns

• Computing History Rhymes
  • Sam reflects on how technological debates and aspirations repeat, highlighting the 1970s People's Computer Company’s mission as still relevant today.

    "Over and over, seeing the way in which computing history and technological history just rhymes and like what we can actually learn by plumbing those depths." —Sam, [01:53]

• Mining the Past for Hidden Knowledge
  • They discuss the value of revisiting overlooked or failed historical paths and recombining forgotten ideas for modern breakthroughs.

    "I have this sense that if we just stopped publishing new research right now, we would still be able to make a huge number of advances by recombining some of the stuff that has already come before us." —Sam, [34:23]

6. AI as a Tool for Human Flourishing

• Augmentation, Not Replacement
  • Jim shares how he used AI as a tough critic to develop his fiction writing voice, stressing the importance of human-AI collaboration.

    "I just think that the ability to... It's a bit like having a tutor. ...When you use it as a tool and especially as an editor, it's pretty cool because, like, literally it'll say, yeah, that character would never say that." —Jim, [09:08]

• Potential for Overwhelm
  • Sam cautions that relentless engagement with AI can be overwhelming and not always enriching if not approached mindfully.

    "Some of the people who are most excited by AI... they're also like in practice, like totally overwhelmed where it's like they are more busy than ever... That doesn't seem like the kind of tool use that feels enriching..." —Sam, [04:32]

7. Organizational Models for Creativity and Innovation

• Protecting & Incentivizing Weirdness
  • They brainstorm research organizations designed to reward curiosity, interdisciplinary work, and long-term undirected exploration—taking inspiration from Bell Labs, Xerox PARC, and department-of-one models.

    "We've never run the experiment of what is it like to just give people almost too much freedom." —Sam, [31:03]

• Avoiding Institutional Reversion to the Mean
  • Sam notes how even innovative organizations risk becoming conventional over time and suggests metrics that favor high-variance, non-consensus ideas.

    "You want like, the high variance, where it's like, half the people think this idea is terrible and half the people think this is the only thing we should ever think about and fund." —Sam, [44:36]

8. The Power and Pitfalls of Scientific Inquiry

• Scientific Progress as Recombination
  • They discuss the nature of scientific advancement as an ongoing, sometimes messy, recombination of older ideas—referencing the Republic of Letters, Darwin, and as-yet-undigitized archives.

    "All of knowledge...it's always this kind of process of recombination, whether it's...science...standing on the shoulders of giants..." —Sam, [54:39]

• Reality as Forcing Function
  • Jim and Sam value reality-based constraints to keep researchers honest, invoking examples from Special Forces, finance, and science itself.

    "The people who are closest to reality are special forces, [emergency responders], and traders...these are people who either metaphorically face death, the trader, or actually face death." —Jim, [48:14]

9. Decentralization and Experimentation

• Decentralized Innovation
  • The Renaissance, humanism, and even new scientific institutions flourished in decentralized political systems—suggesting organizational structures today should similarly favor decentralization and experimentation.

    "...when you are confronted with a complex system and you don’t really know the best thing to do, just try a lot of different things. And so decentralization is really good for that kind of thing." —Sam, [76:59]

10. Lists, Taxonomies, and Linnaean Instinct

• List-Making as Proto-Theory
  • Sam details his practice of making lists (like sports teams named after technologies, companies named after Tolkien lore, or anatomical vacation spots!). He characterizes it as a “Linnaean instinct”—the first step in pattern recognition and future breakthroughs.

    "I just want...I want there to be more lists and I can leave the ...paradigm making to other people and I'll just be the list maker." —Sam, [97:33]

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Notable Quotes

• "Pessimism is often viewed as a mark of sophistication, even if you are consistently wrong, if you are consistently pessimistic. People love to hear that because it sounds like you're being very thoughtful and very serious." —Sam, [00:00]
• "I call myself a rational optimist. I think that we are really good at solving things. I fully expect that we're going to fuck a lot of stuff up, but those have to coexist together." —Jim, [00:12, 26:40]
• "If you are open minded, no matter your intelligence level, it actually does have the possibility of kind of accelerating whatever you are learning." —Sam, [04:32]
• "Generalists who are polymathic who have lots of interests, like it's catnip." —Jim, [06:10]
• "We need more educational models that kind of valorize and incentivize like the Dabbler badge of Knowledge at all different ages and levels." —Sam, [12:40]
• "We've never run the experiment of what is it like to just give people almost too much freedom." —Sam, [31:03]
• "You want…the high variance, where it's like, half the people think this idea is terrible and half the people think this is the only thing we should ever think about and fund. Those are the people you want…" —Sam, [44:36]
• "All of knowledge...it's always this kind of process of recombination, whether it's...science...standing on the shoulders of giants..." —Sam, [54:39]
• "Lists are my proto-theories—they’re how I recognize something’s happening before I understand it." —Sam, [93:26, paraphrased]

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Memorable Moments & Segments

• [09:08] Jim describes using AI as a “mean” editor to train his fiction-writing voice.
• [12:40] Sam’s anecdote about the "Dabbler badge" in Girl Scouts as a metaphor for polymathic education.
• [24:47–25:34] Discussion of the classic Ehrlich vs. Julian Simon bet as an example of persistent pessimism vs. rational optimism.
• [46:01] Sam outlines how to select for truly “weird,” high-variance thinkers (not just those cosplaying as weird).
• [59:17] The history lesson on the Republic of Letters and how institutionalization shifted the creative culture in science.
• [84:02] Sam’s ongoing obsession with the legacy of Maxis, SimCity, and the creative intersection of games, simulations, and learning.
• [99:23] Outlining ways to get the tech industry more engaged with its own history—via old hardware, magazines, and playful experimentation.
• [104:31] The closing “Magical Microphone” question:

  "What two thoughts would you incept in everyone’s mind?"
  Sam’s answer:

  1. Ask more questions—curiosity starts with questions.
  2. Walk more—literally.

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Timestamps for Noteworthy Sections

• 00:00–02:52 | Pessimism, Optimism, and Repeating Patterns in Technology
• 04:32–06:20 | Who Benefits from AI? Open-Mindedness and the Generalist Advantage
• 10:40–14:24 | Education Reform: Piecemeal vs. Utopian, Lifelong Learning, & the Dabbler Badge
• 29:02–34:23 | Creative Organizations: Cognitive Diversity, Protecting Weirdness, Lessons from Bell Labs/Xerox PARC
• 44:36–46:18 | Selection Criteria: High-Variance, Non-Consensus Thinkers
• 54:39–59:17 | The Republic of Letters, Royal Society, and Institutional Capture
• 76:59–80:09 | Decentralization, Experimentation, the Hollywood Writers Room Model
• 83:58–87:42 | Simulation, Games as Learning Tools, and the Enduring Power of Play
• 99:23–102:32 | Preserving the Past, Digitization, and the Importance of Historical Memory
• 104:31–106:11 | The "Magical Microphone": Incepting Curiosity and Walking

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Summary Takeaways

• Curiosity and Generalism Beat Raw Intelligence: The future’s opportunities will accrue to the most open-minded and curious, who leverage tools for augmentation, not replacement.
• Learning Never Stops: True education is less about certification and more about cultivating an insatiable desire to ask questions—lifelong, cross-disciplinary curiosity.
• History is a Goldmine, Not Dead Weight: Much innovation comes from unearthing and recombining neglected knowledge; digitization and list-making are potent first steps.
• Optimism Must Be Rational: Accept that setbacks are certain, but a disposition of iterative, rational optimism is more productive than persistent pessimism.
• Protect and Incentivize Weirdness: Creative progress comes from high-variance, non-consensus thinkers—organizations must find ways to protect and champion “dabbler” polymaths.
• Embrace Experiments and Decentralized Models: The past (Renaissance Italy, Bell Labs, the writers room, the Republic of Letters) proves that experimentation, decentralization, and cross-pollination yield breakthrough ideas.
• Actionable Final Wisdom:
  1. Be insatiably curious and ask more questions—curiosity is the engine of growth and serendipity.
  2. Walk more—great thinking happens on foot.

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Transcript

[Sam Arbisman]

Pessimism is often viewed as a mark of sophistication, even if you are consistently wrong, if you are consistently pessimistic. People love to hear that because it sounds like you're being very thoughtful and very serious.

[Kevin Kelly]

I call myself a rational optimist. I think that we are really good at solving things. I fully expect that we're going to fuck a lot of stuff up, but those have to coexist together.

[Sam Arbisman]

We need more of that. We need that kind of thing much more now than ever.

[Kevin Kelly]

My guest today is Sam Arbspin, who I look at as one of the most fascinating boundary walkers. Sam does not have any territory that confines him. He is interested in virtually anything and everything. Please enjoy my conversation with Sam Arbisman. Welcome back, Sam. And I gotta disclose, you are one of my favorite conversants because we go wild places all the time. So let's start with opening the Cabinet of Wonders.

[Sam Arbisman]

Okay.

[Kevin Kelly]

What's new? What's new in the Cabinet of Wonders?

[Sam Arbisman]

Well, so I feel like since we last spoke, we were talking about things around, like, the history of technology and technological archeology and kind of, like, spelunking. I'm definitely thinking a lot more about that. I think there's something to be said for revisiting the past of, like, the history of computing in order to kind of understand, like, paths not taken. So actually, one. And I think related to this is also just the extent to which you realize that things like time periods that feel very new are not nearly as new. So, like, for example, I don't think we talked about this last time. There was an organization in the 70s called the People's Computer Company.

[Kevin Kelly]

I know about it.

[Sam Arbisman]

Okay. Yeah. So it was this. It sounds like it's a company, but it was actually. It was a newsletter and, like, a center and kind of this weird thing. But the. I think, like, the opening newsletter in, like, 1972 had, like, their sort of, like, mission statement. And it was all. And I think it was something to the effect of, like, computers are being used against people instead of for people. They're used to control people instead of to free us. Like, we need to kind of create this people's computer company. And that feels like very much of this moment. Like, it doesn't feel like, oh, it was written decades and decades before. Like, these are things that people are still grappling with and trying to figure out, okay, how do we actually make computers, like, kind of for humans and kind of like at the human scale and all these kind of. And of course, the Interesting thing is, even though it was at the human scale, this was even before personal computers, like, they were still thinking about, like, how do we actually make them engaging for people? And I'm just struck by that kind of thing of like, over and over, seeing the way in which computing history and technological history just rhymes and like what we can actually learn by plumbing those depths.

[Kevin Kelly]

Yeah. And one of the things I always search for, what I call, er, patterns, you know, very primitive. And one of the biggest, er, patterns around innovation and technology is the way humans react to it. And there's always a subset of humans who absolutely hate that new technology. Right now, of course, we're seeing it with AI. I saw a tweet that I wanted to ask your opinion about because I want to think about it. The tweet was this. It said, I believe that AI is going to actually bifurcate society more, not less, and that smart people are going to get vastly smarter. And this is not my tweet. Whereas people who are not that smart are going to get dumber. As I read that, I thought it does have a parallel in terms of the way you think about AI. I personally think about AI as a tool that I can use. I don't want it to think for me. I want it to help me do research. I want it to help me with a bunch of that type of things. And I love going like, for example, one of my new habits. Whenever I see something that I don't really agree with, I immediately steel, man the argument for it.

[Sam Arbisman]

Okay.

[Kevin Kelly]

And it's really reveals quite a bit because I don't think I've done it yet with a single time with. Huh, that's a good point.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

What do you think about that?

[Sam Arbisman]

So that I know, I wonder if it's less true about like smart versus not as smart in terms of like what it's going to accelerate and more about like having an open mind versus not having an open mind. And I feel like many people who are smart often mischaracterize those kinds of things because I think if you have an open mind and are willing to try new things, it is going to accelerate you. If you think, oh no, this is not for me then, right? It will kind of not help at all or stultify or whatever it is. But I do think, right, if you are open minded, no matter your intelligence level, it actually does have the possibility of kind of accelerating whatever you are learning and things like that. But at the same time though, I will kind of caveat that with the fact that I have seen some of the people who are most excited by AI they are, they're excited, but they're also like in practice, like totally overwhelmed where it's like they are more busy than ever. They're spending all their time using it and they're just kind of frantic. Which I can understand. But that definitely doesn't seem like the kind of tool use that feels enriching like for, for one's humanity. So that I'm a little skeptical of. But I definitely think. Right. I think it's much more like the state of mind in terms of open mindedness of like, if you are willing to say, okay, this thing might actually be useful. Right. Then it can accelerate and it's. And it's much more. Right. Rather than kind of using it in place your own thought. It's like, okay, how can I make myself kind of the best version of myself or a better version of myself? But then there's the opposite, which is the people who say, no, this thing has nothing for me. And then therefore, then there's like nowhere to go with it. It just kind of ends and kind of cuts off questioning.

[Kevin Kelly]

Yeah, I love that distinction because obviously that was kind of the first place I went to. It isn't raw IQ or anything like that. It's really disposition, open mindedness being the top one. I personally think that. And I think you agree, but that generalists who are polymathic who have lots of interests, like it's catnip. Right. But on your point about the using it as a tool. So I'm writing a. I've written four books as you know, but I'm writing a fiction book. And so I used AI extensively to train me on developing my fiction voice. And like I watched a lot of videos on YouTube and everything and I made notes and you know, there were some really interesting things like, you know, everything's the hero's journey or whatever. But it wasn't until I created a bunch of prompts for it to be really mean to me.

[Sam Arbisman]

Okay. Yeah. And I would put it. It's the exact opposite of like, like sycophancy. Like trash everything I'm creating.

[Kevin Kelly]

Exactly. I really want you to be mean. And like one of my avatars was the meanest but smartest critic in literature. And ooh, he's wicked. Anyway, but the interaction, developing the voice. Wow. Like I, I've never used a tool that effective.

[Sam Arbisman]

That's amazing. And so. Right. And so when you're using it, are you. Right. Because I feel like a lot of like writers Certainly pre AI a lot of the time in which you kind of, like, start to develop your voice, it's oftentimes you're kind of interpolating based on other voices and, like, other authors that you. That you look up to. Has this been able to kind of, like, fast track that process of, like, helping you find your voice that much more, like, rapidly so you don't kind of have to meander for a long time until you get there?

[Kevin Kelly]

Absolutely. I love reading. Right. I love fiction. My problem with fiction is I'm an addict. Like, if I start reading fiction, I'm up until 2 because I just keep reading. But my tastes in fiction are generally literary fiction. You know, like, David Mitchell is one of my favorite Cloud Atlas and all of his. I've read everything he's written. But I also love sci fi. But my challenge was having written four nonfiction books, I had a very distinct voice in nonfiction, which I learned very quickly was a really bad voice for fiction.

[Sam Arbisman]

Interesting.

[Kevin Kelly]

So one of the parts of my learning curve was here are the authors I adore and help me develop my voice, which is very. By the way, it turns out, very different than those authors.

[Sam Arbisman]

Okay. Yeah.

[Kevin Kelly]

But the speed with which I was able to come up to writing in this new voice, I honestly don't think I could have done it without the interactive. Because I think that. That, as you know, I'm a huge fan of the senator model. Right. Human plus machine. Right. I think a lot of the criticism that you always hear about AI slop, etc. Well, that is pushing a button, saying, write me a science fiction novel. Yeah, it's gonna write. It's gonna compress it to whatever the middle kind of tier is. And it's. It. It's not being used as a tool at all. And so, like, I'll read that stuff. And I'm like, this is awful. However, when you use it as a tool and especially as an editor, it's pretty cool because, like, literally it'll say, yeah, that character would never say that. And I'll read the line that I wrote, and I'm like, God damn it. It's. Right.

[Sam Arbisman]

That's so interesting.

[Kevin Kelly]

So I just think that the ability to. It's a bit like having a tutor. Right. And we're seeing this in education right now with the various schools that are forming. What are your thoughts about that? What do you think about really changing our educational system? I have a bias here.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

I think our educational system is horrible.

[Sam Arbisman]

Okay.

[Kevin Kelly]

Especially because I think it's incredibly antiquated. It Was it was selected for training industrial workers, I kind of think to sit in a room for eight hours and take instructions, and that is not the world in which we live.

[Sam Arbisman]

So I'm definitely sympathetic to what you're saying. I. I'm also sensitive to the fact that oftentimes whenever you're kind of engaging or interrogating or trying to change any sort of large, complex system, you don't necessarily want to just throw it all out and start from scratch, because you're going to end up with a system that's equally, if not more complex and probably not as well understood. And then you have to deal with Chesterton's fence and all these other kinds of things. And so for me, I'm much more of the opinion of this kind of. And actually Karl Popper, I think in one of his books, he has this idea of kind of like utopian engineering versus piecemeal engineering. And the utopian engineering is, okay, we're going to just like create this thing with this very clear end, and oftentimes it kind of like destroys society in the path of getting to that versus piecemeal engineering and saying, okay, let's try to experiment a little bit, like here and there, change kind of things, see which works, which doesn't reevaluate, and slowly but surely hopefully get to something better. So I definitely think education has a great deal of room for improvement. I'm hesitant to say we have to just throw it all out and kind of start from scratch, but I definitely think at the margins, there's so many things we can be thinking about, and it also depends at the grade level or the scale that you're kind of thinking. So, for example, I think that one of the areas that we really haven't thought about as a society for a long time is just sort of like continuing adult education. Obviously we do think about that, but for me, just continuously learning and being curious about the world, like, that's the thing I always want to be doing. And there should be mechanisms for everyone to be involved in that. And so if we can create institutions and mechanisms for allowing that and like democratizing that kind of thing, and obviously I can probably help with that kind of thing quite a bit, then I'm all for that. So, like, there's that, then there's okay, rethinking the college level education, and I think there's lots of space for that. And then right then you can also just go all the way down to like K through 12 or even like, and infants and like toddlers, like There's I think at every stage you can probably modify it but for me it's not okay. I have a new idea. Let's throw away the old thing. It's more, let's actually build some new system or some new institution, new organization, new mechanism that's maybe more bespoke, kind of like much more adapted things like that and then allow that to compete with the other systems that are already there. And then hopefully kind of according to that like piecemeal and piecemeal engineering kind of approach, it will, there'll be some competition. The new one maybe will do really well. The old ones will actually kind of have to up their game. So yeah, I'm actually super excited but I, but I think a lot about yeah, that kind of like continuing education, like continuing adult education kind of model and you mentioned before like generalist models and polymath, that kind of things. I think that, and maybe this is also just like me justifying me being too interested in too many different things. But we need more of that. And actually so one related to the polymath stuff. My mother told me that when she was young, when she was in I guess the Girl Scouts she said so you had like all these badges you can specialize in like I don't know, knot tying or like I don't know, do arts and crafts or whatever it is. And she said there was one badge that was the Dabbler badge which was you could get a badge just for dabbling in a whole bunch of different things. And I love that idea like that. I mean that's kind of like that is my dream and we need more educational models that kind of valorize and incentivize like the Dabbler badge of Knowledge at all different ages and levels. So yeah, I think there's a lot of space there.

[Kevin Kelly]

Yeah. And just to be clear, I agree 100% with your approach. I am not in favor of top down systems being designed because you're right, I'm a huge fan of Ken Stanley's Greatness cannot be planned. Everything is iterative and like you can't decide oh I'm going to get here. And that is the only path because you've got to see all of the branching everything else so 100% agreement around the way it should happen. I think though that the idea of the it would be great and maybe it happens slowly but it would be great if the new way of education. I think maybe you're right. I think maybe start with adult continuing

[Sam Arbisman]

education because you don't have to worry about regulation or anything. You can just build a thing and

[Kevin Kelly]

you can try everything.

[Sam Arbisman]

Yeah. You can do whatever you want.

[Kevin Kelly]

We are investors in synthesis school and they've done really well with getting kids to like math, which is really interesting. And we also have some other initiatives where we are helping out with tablets that are solar powered for less advanced countries. By that I mean they don't have WI fi everywhere, but you're right. Let a thousand experiments bloom. What are your thoughts about. I know that you grew up on the science fiction comic books that you're grandfather, Right. Gave you.

[Sam Arbisman]

Yeah, he would give me all these like old magazines, like short stories and

[Kevin Kelly]

Is that not happening anymore? I don't know. I don't know. I have dreams.

[Sam Arbisman]

Science fiction or.

[Kevin Kelly]

No, no, no, no, no, no, no. Kids, like with the comic book, with that type of thing.

[Sam Arbisman]

Okay. I mean, so to be clear, like these magazines. So this was.

[Kevin Kelly]

These were not comic books.

[Sam Arbisman]

Yeah. So it was like these kind of like pulp magazines. And so it was like. So the one that my grandfather, I think most subscribed to is Analog Science Fiction and Fact. And as far as I'm aware, it's still around. I haven't like, I don't, I don't subscribe to it. But yeah, he would just give me these like shopping bags full of all the old copies, like after he. He had finished reading them. And then like I would take them, like. Like when I would go to summer camp, I would bring them all and just read them. I don't know to what extent that's out there anymore. Certainly because you can put things online, like there's just an abundance of stories out there. But in some ways maybe that just makes it a little bit more difficult to. Or at least there's another hurdle of like, just give me a whole bunch of stuff that I can kind of just like plow through. Like. Yeah. When I'm at camp or whatever. But I don't know, I certainly hope. I think there's shorts, like science fiction, short story magazines still out there. And I don't know if it's a burgeoning industry, but I really want it to be.

[Kevin Kelly]

And you were deeply influenced by the foundation series, as was I. The thing we're trying to do with Infinite Books is bring back positive science fiction because like the stuff I grew up on was like really pretty positive about the future. Obviously there were the problems. Harry Seldon forecast that you're gonna be A thousand years of Darkness. But then we kind of fell into this very dystopia Black Mirror. In fact, we published a book called White Mirror.

[Sam Arbisman]

Right? Yeah.

[Kevin Kelly]

And do you see those kinds of trends happening?

[Sam Arbisman]

I mean, so a number of years ago, Right. People had noticed that exactly, like very explicitly what you were saying. And so there's a center at Arizona State University called the center for Science and the Imagination. And they actually partnered with a number of science fiction writers. And I think the kind of like, galvanizing essay was written by Neal Stephenson. And the idea was like, can we create more positive, optimistic visions of the future? And they actually, I think they partnered writers with engineers and scientists and they would write the story and then the scientists and engineers would kind of flesh out the actual meat of whatever idea was in that and kind of show its possibility and its plausibility. I wonder if, though, some of this is as the world not only is changing kind of faster and faster, but as our ability to like, look further into the future, like that horizon gets closer and closer, that means that we just kind of, not that we're dystopian, but we kind of just cut off a little bit of that ability to kind of see further forward. And that makes it less easy to imagine these positive visions of the future. I mean, I'm kind of thinking this through as we're talking, but I would say, I mean, I think, I mean, certainly there are positive visions of the future that sometimes have in our future, but their past, a negative moment. So like, there would be. There's like some amazing future, but before that we have to kind of get through, I don't know, World War 3 or something like that. I would love to think we could get to the really positive vision without necessarily getting something really, really bad. But I just wonder if of that, if it's simply the fact that as technological change happens faster and faster, we maybe lessen our capacity to think far off in the future and that maybe somehow in our minds gets caught up with not quite dystopia, but sort of a impoverished vision of the future because everything's changing so rapidly. And so at a certain point you're like, oh, I can't really. I can't imagine what that's going to be like. And, and people have talked about this idea that like, like thinking about like, like the year 2000 was this kind of like mythical far off thing. And of course we would. As we got closer and closer to the year 2000, people still put like set stories in the year 2000, even though it was getting closer and closer and not nearly as far. And of course now it's like it is in the rearview mirror. There's actually the, the. The music duo Flight of the Conchords.

[Kevin Kelly]

I love that.

[Sam Arbisman]

So, like, they have, like, the humans are dead.

[Kevin Kelly]

Yeah.

[Sam Arbisman]

And it begins.

[Kevin Kelly]

We are the robots.

[Sam Arbisman]

It's like the distant Future, the year 2000. And I just, I love that. But I think many of us kind of are sort of anchored to that kind of past vision of the future. And I don't know, maybe we just need to start talking about. I mean, what is it like 2001 Space Odyssey? Like, the sequels were like, there was 2010 and 2061. Maybe. Yeah, start aiming for 2061. Or I guess Futurama has the year 3000, some of those kinds of things. So I feel like for every example I'm giving, you can probably think of a counterexample. But I almost wonder, right, if people are just so concerned with all the changes happening in the here and now that they just don't have the bandwidth to imagine these futures. And so it's not as if we're dystopian or utopian. We're just like very, like this moment focused. And. And that simply cashes out in the inability to imagine these positive visions.

[Kevin Kelly]

Well, I have a thesis that basically our. Our human os, our human operating system has a couple of kinks that lead to a more bleak outlook. And the first is a bias towards negativity. Right. Which if you look at evolution, and probably not the worst thing to have,

[Sam Arbisman]

if you plan for the worst, yeah, you'll probably survive.

[Kevin Kelly]

You're going to have. You're going to have a bias for negativity, but also this desire for the illusion of certainty. Right? Yeah, because it is an illusion. Unless we're talking about the sun coming up tomorrow. And have you ever noticed, like, there's nobody running around, like, pounding on tables saying, I will put better million dollars. The sun's coming up tomorrow. Right. But those two together kind of lean toward. Well, what's the worst? I mean, David Deutsch with his, you know, the, the principle that we are the greatest connectors and explainers. We're kind of at the beginning of infinity. Like, I read that, and I'm just like, I love this. I love looking at it this way. But then he also does a great job showing why so many SOC societies basically fall because of the precautionary principle. And my view has always been kind of around technology in particular. Everything is dual use, and I don't mean just AI or computers. Fire is a technology the ability to consistently relight a fire is a technology and we didn't ban fire even though fire is really dangerous. Same with electricity, can be very, very dangerous. So instead of banning things like the precautionary principle would have many of those closed minded societies do, we came up with fire engines, fire departments, fire warnings, fire extinguishers, fire exits.

[Sam Arbisman]

I will caveat that with the fact that like fire has been around for quite a long time and some of those technological advances and like the like societal advances, those are relatively recent.

[Kevin Kelly]

I know.

[Sam Arbisman]

Which is which, when you kind of think about it, it's kind of wild that it's like Benjamin Franklin was doing some of the work around like, like fire departments and things like that. And I don't know all the details but like I vaguely remember this and right. That's only several hundred years old and we've had fire for, for a long time. And so I think. So I will kind of. So I think there is some caveats there. But going back to what you're saying of like kind of pessimism and things like that, I feel like though that pessimism is often viewed as like a mark of sophistication. And I personally think like, oh, thinking about how things can go well, that's super exciting and being optimistic. These are amazing things. That being said, I feel like time and time again, even if you are consistently wrong, if you are consistently pessimistic, people love to hear that because it sounds like you're being very thoughtful and very serious. And I feel like, I mean as Paul or like recently recently passed away and. Right. And he was.

[Kevin Kelly]

And Julian Simon won the bet.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

Paul made his wife make the checkout to him because he didn't want his signature on it.

[Sam Arbisman]

I didn't even realize that.

[Kevin Kelly]

And if you do a deep dive on him, like the fact that, I mean, not to speak ill of the dead, but he was consistently wrong across his entire career. And yet they kept inviting him back. He kept having the lecture series, he kept being on tv. Meanwhile, Julian Simon, you know, the greatest resource, right? Us humans, like he won the bet. If we walked out into Union Square right now and just randomly asked people, have you ever heard of Julian Simon? How about Paul Ehrlich? It's gonna be 10 to 1 Paul.

[Sam Arbisman]

That being said though, I've seen research that showed that depending on when you had kind of started and stopped the bet, it could have been a toss up, which is. So it's a little bit more complicated. That being said, it shows that the way to think about the future is kind of this weird, complex nonlinear system. And to be so certain about it and whether it's certainly pessimistic or just certain, like oh no, this kind of. That I feel like also is not the right way to operate. And maybe it's not even just pessimism is a sign of sophistication. Maybe it's just ultimately certainty. And having that sort of righteous, simple certainty is something that people find very appealing. And going back to the human os kind of stuff, that is the kind of thing that we are drawn towards. But having that epistemic humility of like, oh, maybe there are weird non linearities and unexpected consequences and second order effects and we should kind of think through all these kinds of things that does not a sophisticated pundit make or at least an appealing pundit. But we need more of that. We need that kind of thing much more now than ever.

[Kevin Kelly]

I couldn't agree more. And I call myself a rational optimist because I fully expect that we're going to fuck a lot of stuff up. And if you don't expect that, you have not looked at history. And so the idea that, that I move forward with is I'm incredibly optimistic about humanity. I think that we are really good at solving things. We're also really good at screwing things up. But those have to coexist together. And so to be Panglossian or Pollyannish is going to be defeated. Right? Because there inevitably will come problems. And the way I look at it is there will, you know, there will always be problems, no matter how good. Whatever gets it doesn't have to be just technology. Any part of society, right? No matter how good we get it, there's always going to be problems. And sometimes we make them more apparent because as you say, like in the last, let's call it what, three or four hundred years, many of these technologies like got invented. We're missing the thousands of years where people are like, yeah, that's just the way it is. Right. And so I definitely believe that to be prepared for the problems, that's got to be part of the way. You think?

[Sam Arbisman]

Yeah, and this is like going back to like popper and like the piecemeal engineering totally just constantly saying, okay, like things are good, we can still make them better. Let's constantly try to iteratively improve and just try to shy away from sort of that utopian vision.

[Kevin Kelly]

Right?

[Sam Arbisman]

Because utopian visions often allied a lot of the complexity, but are also right, like they're utopian, they're not the real world and the real World is messy. And so let's just actually use the kinds of approaches that work with the messiness of the real world, which I think we're pretty good at it. And obviously, as the world gets more and more complex and interconnected, you have to have a certain amount of humility and. But that's kind of the whole piecemeal engineering approach, which is do things, run experiments, try to improve things bit by bit, see what works, see when things bite back and make a better world, slowly but surely.

[Kevin Kelly]

Yeah. I'm 100% in the camp of piecemeal. Right. Because you can't know. Right. It's like, I love that quote about no matter how smart somebody is, no matter how innovative, no matter how creative, you cannot ask them to make a list of things that would never occur to them. And, like, one of the things that I believe in very deeply is cognitive diversity. Right. And I think that the whole movement, you know, diversity, et cetera, they got it wrong. Right. Because they made it about skin color, about sex, about, you know, where you come from. The diversity that really works is people who think very differently. And I love the. We see it with our fellows, you know, about our fellowship program, and when we get them all together, it's just the most delightful couple of days ever.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

Because you have, like, deeply scientific people who are working on really, like, edge case projects with. With authors who.

[Sam Arbisman]

Wonderful.

[Kevin Kelly]

Don't think like that at all. But the synthesis that comes out of those is really, truly extraordinary.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

And. And so that kind of leads me into the conversation where you think it's much more important to look at biology as, say, physics, when we're dealing with these very human systems. Because, again, I'm 100% on board. The real world is messy.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

And utopian visions led to Mao, to Stalin, to Hitler. Right. They maybe weren't utopian, but they were.

[Sam Arbisman]

This is the way they were totalizing.

[Kevin Kelly]

They were totalizing. And I'm terrified of that.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

What do you think? Like, if I was going to put you on the spot and say, all right, Sam, I'm going to fund an institute that you are going to be the head of. You're going to be the executive director. Walk me through what that institute looks like.

[Sam Arbisman]

And I definitely think a lot about these kind of weird research organizations and, like, the need for more of these kinds of things. And I will caveat all this with beforehand. Like, I'm fairly agnostic as to what are the, like. Right. Kind of models. I think we just need to try more things that being Said I definitely think we were talking before like Ken Stanley and Joel Amen of like the why Greatness Cannot be Planned. I think that as much as I think a number of people recognize the importance of that kind of thing, it's very hard to incentivize omnivorous curiosity and kind of like undirected sort of approaches of just pursuing novelty or interestingness. Because oftentimes in the research world or when you're fund, even when you're funding people, not projects, there's still a certain short term nature to it and like accountability and, and so it could be like there's like blue sky research in academia, it's still subject to grant cycles and so and you can only operate over the course of like several years. And so I think I feel like there's, there's a need for just being able to say like let's make really long bets on either domains or people and say like we can't call that money back. And we also really don't want to actually know what's going on inside until far, far later than most people would be comfortable with. And, and so you can see like hints of this kind of thing when it's like Xerox parc, which of course was in the corporate industry lab setting. But my sense is part of the special secret sauce was that the administrators were very good at kind of protecting the researchers from what was happening outside. And they kind of were given a lot like a lot of freedom and a lot of time to kind of play with things. I think that there's just a lot of really long term undirected weird research or just weird researchers. We've never run the experiment of what is it like to just give people almost too much freedom. Now conversely you could say too much freedom, you need to have a little bit of constraints or something like that to actually get something good out of that. But I wonder if we've never really tried to run that experiment. And so that's something that I think is really interesting to run, just like being able to try really weird long term things. But I would also say I also now this is maybe this is an overly kind of like strawman consider like concern, which is like university settings are overly concerned with like disciplinary boundaries and we're talking like the weird kind of more polymathic stuff like that's where the interesting things are happening. But even though people want to do the kind of freeform kinds of things, they often have to like still get tenure within their department or whatever it is. And so trying to actually run some of these experiments where people can be weird misfits in between disciplinary boundaries. That would be great. That being said, I think whatever I'm proposing right now is like almost like too broad. I can see also the need for. Now I'm just like throwing out idea. Idea, fine. One of the.

[Kevin Kelly]

That's what I'm looking for.

[Sam Arbisman]

Related to actually going back to like we were, we were talking about like the history of computing earlier. I feel like whether it's history of science or history of computing or history of technology more broadly, there is, I feel like, I have this sense that if we just stopped publishing new research right now, we would still be able to make a huge number of advances by just recombining some of the stuff that has already come before us and kind of poking around inside the archives and the old things. And so we. Right. Like there's. There was. I can't remember if we, if we discussed this last time, but there's. There was this information scientist by the name of Don Swanson in the mid-1980s, and he wrote this paper about undiscovered public knowledge. And the idea behind it was like, in the vast scientific literature there, there might be a paper that says A implies B, and there might also be another paper somewhere else that says B implies C. But because no human can actually read all the literature, even though it might be true that if you combine those two papers together, A might very well imply C, no one knows this. And, and so he actually, it was very interesting. Like, he, he wasn't content leaving it as a thought experiment. He said, okay, I'm actually going to try to use computers in that case. It was like the cutting edge computational techniques of the mid-1980s, which was, I think like keyword searches in like a medical database. But he actually was able to make some advances that got published in medical journals. Even though he was just an information scientist, he didn't have medical background. And of course, since then we now have much, much more sophisticated techniques. And obviously with AI, it's kind of overclocked that ability. But whether or not it's in searching the scientific literature, whether or not it's looking in the old and forgotten kind of paths not taken of scientific advances or technological advances, there's still so much to be done in just like revisiting the past. And I think that's another thing that is really just incredibly under, underappreciated, underfunded as well. So I feel like that's. That, that's another area as well that could really benefit from something like that.

[Kevin Kelly]

Yeah, I, we're making a documentary through our films division about Bell Labs and we're working with the author of the Idea Factory. And so he's kind of doing the script and everything. And the way I think about it is academia has these kind of constraints that developed into a system that creates that hyperbolic discounting. Like, no, we need results here, we need to get this grant. And one thing feeds the other, which is I think one of the reasons why we've seen so much less kind of profound breakthroughs. And the reason I'm fascinated by the Bell Labs Xerox PARC idea is like Bell Labs gave us Claude Shannon who gave us information theory and nothing that we are doing right now we would be doing if Claude was not there. But he also spent a great deal of his time just screwing around like the trumpet that shoots fire and the chess game and like.

[Sam Arbisman]

So my sense though is that Claud Shannon was still very much an outlier, even in Bell Labs. Definitely he was, he had a very early success and was kind of given almost this like, role of like, okay, you can go off and do those kinds of things. Most of the people in Bell Labs were given a great deal of freedom, but it was, it was a certain type of freedom. And so, and I know Eric Gilliam has written about some of these kinds of things around like the idea there was the role of the systems engineer within Bell Labs, which was the people whose kind of job was to identify the really interesting problems that could then be handed out to the people who were really smart and kind of needed interesting things to think about. I'm not exactly sure exactly how they operated, but there was this very sophisticated set of mechanisms to kind of channel in an open ended way the creativity and the innovation there, but in a way that's still kind of cashed out in terms of like things that could be useful for, for Bell Labs. That being said, I mean mentioned like Claude Shannon is an outlier, one of the other ones. So Richard Hamming, who is also kind of a computer science and I guess also maybe mathematics, he, if you look at this might have been like in the 1970s, if you looked at the directory of Bell Labs, I believe so he was a. He was the chair of the computer science research department. And it turns out there were only two members in that it was him, the chair and I think his secretary. And he actually talks about how he's like, he had worked very hard to kind of construct this mechanism where he had a great deal of flexibility and freedom and you have to worry about bureaucracy now. He also was kind of an outlier, but I love that idea of like departments of one that then create this institutional space for you to do just really weird things. And. And of course he was probably the outlier in Bell Labs, but I would love that at scale of like departments of one where you can then kind of like build your little bespoke organization within a larger organization.

[Kevin Kelly]

So it seems like we're getting back to the organization I asked you to design.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

So I agree, by the way, and we're trying it in a very small way. And who we give fellowships to and grants to. We are very drawn to weird. We are very drawn to people who don't fit the mold because we think that there is a tremendous amount of information there. But you have to almost treat it like a venture capitalist. Right. Like for every nine you might get one. But we're okay, by the way, we're okay with that. But when, I mean, like I was thinking about it on the way in here today and it's like the number one question that's burning in my mind. Do you have like a metric or is it an intuitive sense for how do you establish like the good kind of weird we're looking for? Yeah, from the weird cosplay, you know the. Yeah, yeah, like the people who like, oh, weird's in now. Okay.

[Sam Arbisman]

Gonna be really weird. Yeah, I think, I mean, because this, there's a lot of like failure modes in building these kinds of organizations. And actually so related to this, one of the things I wrote this, I wrote an essay several months ago about the forces of institutional reversion to the mean, which some people have discussed. I kind of discussed it in the context of canalization of the fact that all these organizations maybe start weird or they have grand ideas, but then oftentimes they end up becoming kind of like shunted into and channeled into much more normal looking things. Which normal is great. You can do a lot of interesting things in traditional academia and corporate industry labs. But a lot of these organizations, these non traditional research organizations start with grand visions, but then kind of they might end up becoming sort of independent versions of a university department, which is not bad, but maybe not quite the original goal. Or they might start as a really strange for profit research lab, but in a certain amount of time they are basically just a startup. And some of the forces are because. And they have, they're accountable to their investors or whatever it is. And so everything and every choice is all kind of defensible but you kind of have to guard against those kinds of things. And actually going back to what you're saying of like the weirdness, there's also, there's. Right. Some people are like, oh, I'm going to kind of, yeah, cosplay, kind of like the weirdness. But one of the reasons why these organizations sometimes become like much more traditionally academic in appearance is because even the people who think that they might want to do weird things, they often have kind of in the back of their mind the concern that, oh, maybe this organization is going to fail or I'm not going to be a fit for it and where to, where would I go after that? Probably back into academia. And so I have to make myself look understandable to the world of academia and university departments. And so as a result, even if at the institutional level they're trying to do something different, the individuals might try to end up doing more traditional kinds of research. And so the one way to potentially do this is like find people who have already written off academia entirely and are like, oh, I'm just going to kind of, I have no interest in this. I'm just going to go off and do something weird or strange or non traditional and whatever term we want to use. But it's really hard. And I think partly, and going back to what I was saying before, kind of locking in the resources and the structure helps prevent some of that kind of canalization forces. But it's very tough because even if you kind of lock in the resources, if the people that you're actually populating these organizations and institutions with are not actually the right fit, they're kind of, they're, they're looking, they, they give off the appearance of weird, but they're not actually doing something that interesting then. Right. That'll also kind of force as well. And so it's, I, I feel like there's a lot of, a lot of failure modes in terms of these kinds of things. Um, I mean, one potential thing is to say rather, and in addition, like alongside thinking long term, you say, okay, this kind of thing can exist for a long term, but we'll still actually have like a lifetime where you say, okay, we're gonna, it's going to exist for 10 years, 20 years, 30 years, and then it's done. And so that kind of can help inoculate against some sort of institutional drift or mission shifting. So maybe there's kind of something in between where it's like, okay, you have to say we have to think long term, we have to incentivize certain kinds of behavior, but we also recognize that there's an inherent natural lifespan to it, and so that kind of can protect against some of those other kinds of forces as well.

[Kevin Kelly]

Again, I'm trying to get down to your selection criteria.

[Sam Arbisman]

For the people.

[Kevin Kelly]

Yeah, for the people, because it could be very useful for me with our fellowships and our grants.

[Sam Arbisman]

So I would. So I think. So there's probably. I mean, certainly you can definitely, like, there's a fine line sometimes between like, weird interesting and weird crazy. I would say potentially one. And I think people have tried to think about how to incentivize this kind of thing within even like, more traditional, like, grant funding mechanism mechanisms at like the National Science foundation or nih, which is as long as someone's proposal meets a certain, like, minimum threshold of quality, what you don't necessarily want to do is find the consensus of like, oh, these are the people that everyone thinks are great and should find. Could not agree on that you want like, the high variance, where it's like, half the people think this idea is terrible and half the people think this is the only thing we should ever think about and fund. Those are the people you want, because I think that's where you're going. And you might still have that, like, high rate of failure, but it's still going to be interesting. And then going back to kind of like this whole like, like, like finding weird stepping stones and recombining them in unexpected ways, that's probably where you're going to find the raw material of ideas that are going to be then used and productively recombined in ways you can't even imagine because they're the things that they're kind of like, they're not like, oh, that's kind of interesting, or that's an obvious thing. These are the things that sound really interesting, but half the people think, yeah, that has no relevance whatsoever or that's not even the direction we should pursue. So I feel like that non consensus kind of metric could be the way of helping identify those kinds of things.

[Kevin Kelly]

You know, I was again, for the book that I'm writing, looking up the origin of the term consensus. And the actual Latin translates to when people. People feel together, not think together.

[Sam Arbisman]

Oh, like, like sense. Okay.

[Kevin Kelly]

When they feel together. And that brings me kind of back to human os, Right, because you were mentioning scientists. Right. Like, what I grew up believing in, you know, the scientific method and, you know, Feynman, one of my heroes. No matter how elegant my theory, no matter how much I love It. If the scientific method says it's wrong, it's wrong. Right. I love that way of thinking.

[Sam Arbisman]

Still has to, like, actually engage with reality.

[Kevin Kelly]

Exactly, exactly. Right. The engagement with reality is where I really come down. Right. Because if you look at people who are really, let's say, weird, the ones that I have seen succeed, greatly succeed are those who where they're in a discipline or they're in an area of study that forces them to engage with reality. Right. And you know, Philip K. Dick had that great quote, reality is that which, once you stop believing in, it doesn't go away. And. And like, so you think of. I had a guest on who learned a lot from US Special Forces, okay. And one of the things that I absolutely loved about it was they the. The study group he was part of banned PowerPoint. Banned. They wrote everything on chalkboards or whiteboards. And they were kind of the epitome of. No, no, no. Consensus got us here. We need to think of different ways. But as part of our conversation, like, I have a friend who wrote a great short piece about Jed McKenna, the non dual philosopher, and his name is Dan Jeffries. And he was my gateway drug into Jed McKenna. But his piece was basically saying the people who are closest to reality are special forces, er, workers and traitors of all things. And the way he put it was, these are people who either metaphorically face death, the traitor, or actually face death. And he was like, that's a really great forcing function.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

And so I kind of thought, like, what kind of constraint could you put in to a process to whatever to get people to engage more with reality? Because again, back to science. Right. Again, sort of my temple. But when you look at the actual behavior of human scientists, it looks a lot like the movie Mean Girls, right? Like, Oppenheimer, David Bohm came up with hidden variables, Right? And Oppenheimer's overseers in the military had said, that guy's a communist. We don't want any stuff from him. And so Oppenheimer, and I can't remember because I did this research a bit ago, whether Oppenheimer said it or it was recorded somewhere, where Oppenheimer said to all of his other colleagues, if we cannot disprove David's theory, we must ignore it now, like, wow.

[Sam Arbisman]

So, yeah, So I would say, and that story sounds very unfortunate. That being said, I mean, I wouldn't necessarily say that, right? Like, scientists are like mean girls to the exclusion of like. And worse than.

[Kevin Kelly]

I know I'm being hyper, but I

[Sam Arbisman]

would say, I mean, it's it is science. I mean, scientists are humans and irrational, imperfect. And that being said, like this like scientific process, not necessarily just like, like there's like the scientific method, like the very like simple thing you learn in school. Like, that's, that's one thing. Like the process of science is like the rigorous means of kind of querying the world. That I think is a great way, albeit imperfect, of kind of harnessing our imperfect irrational humanity for actually better understanding of the world. Now. Right. There's many places where it doesn't always work, where there's. What is it? Like Max Planck had like the Planck's principle of kind of like, like science proceeds kind of like one funeral at a time. People have actually tested that and interrogated and said, okay, let's look at the ages of the scientists who decided to adopt and like and adhere to Darwin and like evolution by natural selection. And it turns out for what I recall, it's been a while, there is actually no correlation with age. So it actually turns out that some of the older scientists were just as willing to acknowledge this new theory as the younger scientists. And so some. So I think, I mean, sometimes that kind of does, like, that kind of thing does happen and there's the idea of like paradigm shifts and like things being overturned. You kind of have to wait for the new generation. But I do think that scientists are more willing to actually overturn kinds of things. And of course, I mean, sometimes it's one of these situations where you might talk a great game in the abstract, but when it comes to your own ideas, you might fight tooth and nail. That being said, I can't remember if I told you the story already, but. So one of my professors in graduate school, he told me this great story where he was. He went in one day and gave some lecture on some topic and the next day actually read a paper that invalidated what he had learned the day before. So he went in the following day and he said, remember what I taught you, it's wrong and if that bothers you, you need to get out of science. And so there is this sense and of course it's not all always adhere to, but. And sometimes like, yeah, more and kind of the breach or whatever, but that, that's ultimately like this idea that scientific knowledge is constantly in, in draft form and science is less about a body of facts and more about this like the process of querying the world. I think that's the ideal. Now, of course. Do we always get to that ideal? Not always, but I'd like to think kind of along the way, we're sort of like, asymptotically approaching the truth,

[Kevin Kelly]

curiously.

[Sam Arbisman]

Exactly. There's many. But it happens, and it happens unbelievably. Well, at least kind of like in the limit. Like, the fact that, like, over. If you look like over the past several centuries, we have made unbelievable advances. And that's kind of on top of the fact that scientists are people, too. And people are really irrational and kind of suck in many different ways. And yet we've still been able to make those advances. And so. So I'm willing to look at it maybe as a little bit more optimistic. But you're right. There obviously are processes that we can kind of put into place to kind of make it even better and ways of thinking about, like, what should we be optimizing for? What should we actually be incentivizing? Because I think that's another thing of, like, when we think about what are the ways, like, what are the kinds of things we incentivize in terms of what scientists do that will also affect what we get out of it. And so the way I kind of think about this is, like, you have, like, this. This whole space of, like, these are all the things that science, like, is valuable. Like, these are all the things that are valuable for science. And then you have this little subset of, like, the things that are valued by scientific academia, like, the things that get you tenure. And that's a small subset of, like, the big space. And we need to actually find ways of incentivizing all the other things, like, I don't know, building software, building tools, connecting different ideas together, doing other weird kinds of things that are just as important but far less understandable to the mechanisms of tenure or whatever it is. And so, right, maybe it's creating institutions that kind of incentivize some of those other kinds of things as well, that can just make science that much more of a successful process. But that being said, right, we've done pretty well so far.

[Kevin Kelly]

You know, you bring up Darwin, and I just finished Tom Wolf's book. Basically, he's making the claim that it was human language, not evolution, that made humans humans. But I love Tom Wolfe because he's such a bitch and he's so good at just zinging people. But the thought, as I was listening to you, about older scientists embracing, well, Darwin's grandfather and his father had advanced different ideas about evolution, like Erasmus Darwin, Right.

[Sam Arbisman]

So it was where the idea of evolution. It's interesting, the idea of evolution itself was kind of this evolutionary thing. It Kind of took time to get there. And right, like Darwin, his was like evolution by the process of natural selection. And it was a very specific, almost like algorithmic approach to evolution. And I think that was kind of, that was the key insight. And of course he spent decades like marshaling all these, all this data and information and like, it was like this very kind of like slow hunch that eventually kind of got there. But you're right, it wasn't. It didn't come out of. It didn't come out of nowhere. And I think that is, I mean, ultimately all of knowledge. Like, it's not. It's always this kind of process of recombination, whether it's like, like the idea of like, like everything is a remix as well as like the idea of like science, right. Like, and standing on the shoulders of giants and like just kind of having this slow accumulation and like weeding things out. There's this great quote by Isaac Asimov where someone wrote a letter to him saying we used to think the Earth was flat and we were wrong. And then we, then we thought the Earth was like spherical and like a perfect sphere. And we also. And like, that turned out to be. To be wrong. So like, how can we know anything? And. And Asimov wrote back saying, like, if you. Because we now it's like in a blade spheroid or whatever it is. And he and Asimov wrote back saying, if you think that thinking the Earth is flat is just as wrong as thinking the Earth is a perfect sphere, then your view is wronger than both them put together. And so it's kind of like going to the asymptotic approach. But we have the processes in place to kind of like slowly but surely kind of get there and build upon new, different things. And sometimes, right, there's going to be a lot of turmoil and change, but it's still building upon multiple different things and over time kind of getting us to these new theories and new understandings and approaches to hopefully a correct view of reality itself.

[Kevin Kelly]

Yeah. And it is inch by inch, right. And I again completely agree. You might enjoy Wolf's book because he is just absolutely the takedown on Darwin. And the man is really, really funny. And you know how he wrote, right. He invented the new journalism so he, he could get away with things that other people can't. And it's just really a fun book because making the case for language being the thing that separates us from all the other animals is just interesting in and of itself. But you know, he's like, if we apply the scientific method to Darwin's theory. And then he goes, does it meet this criteria? No. Does it meet this criteria? No. Does it meet this? And he just keeps going. And then you know, the way he has the, the text grow in the book. No.

[Sam Arbisman]

Oh, so interesting. And the truth is I, and I think like Darwin was like pretty upfront about this. Like when he was developing his theory. He like the then kind of cutting edge idea of like the way like genetics operated was sort of this like mixing. And he knew that mixing of traits would not work for selection because like you need to kind of have like a certain amount of like discrete quantitative things. And, and he like recognized like this was like a gap in his theory, but he was still like no, no this, but it's still worth describing and articulating. And of course then yeah, you had to have like, like Mendelian genetics to kind of like, like fill in kind of the missing piece. But, but I think that's okay like to have like, like we don't. And, and this is, and it's both I think important for science to kind of put forth incomplete things and totally subject them. But it's also, and almost like it's a good way to operate as a scientist. Like if you get, if you put forth the last word, you're not gonna get, you're not gonna get cited. Cause you're just, you solved it all. You wanna have something that people disagree with or argue with or expand upon. That's how you get really well cited. So I feel like there's even just like that's a good way to think about science. And I do think I actually going back to incentives and even like the way we think about publication, the fact that right now scientific papers have kind of expanded and gotten more and more complex where it's like not a single experiment or a single idea, it's often like a whole bundle of things. In some ways that slows the speed of scientific communication but also means you can't just put forth a thing and then allow it to be tested and subjected to debate and argument. And I feel like, right, we might need to incentivize somewhat different types of publication. That being said, if you think about the scientific paper, it's what is like a 400 year old technology that is also ripe for reexamination. I mean, I guess we have PDFs now instead of just print. That being said, it doesn't feel that different and people have a whole bunch of ideas of how to think of like rethink Publication as well, which could potentially incentivize different kinds of scientific output as well.

[Kevin Kelly]

And, you know, I was also reading about the Republic of Letters, which led to, of course, the creation of the Royal Society.

[Sam Arbisman]

Henry Oldenflies, he was like one of the. He was like the secretary and kind of like, like, sort of like the hub and like the node, like the central node of all the people writing together. And.

[Kevin Kelly]

And I was very taken by the deep dive on the Republic of Letters because I knew about it, but I didn't know how varied the participants were. Like, Catherine the Great was in the Republic of Letters, Frederick the Great, also Witten Voltaire, all of these people. And I was just like, it got me back to see diversity because what would happen is they would send the letters privately, they would be read, and they would only be forwarded if the person who received the letter found something interesting in that letter, but maybe had a different view or maybe add it to that view, and then that would get copied and forward, forward it. And it's kind of like, I was actually thinking about you when I was reading about this, because I'm like, that's Sam's like, you know, way of just clutching it up but having these really great things come out of it. And then of course, the, the institutional capture of the Royal Society. Right. Like, it. That was the other part I didn't really think about. It kind of killed the Republic of Letters.

[Sam Arbisman]

That's interesting. Well, and I know, and my sense is like, the Royal Society, it also had a diverse number of people, but a lot of them were just kind of the, the money men, where it was like, okay, like, we need to kind of bring them in. They're not really doing, like, quality science, but they.

[Kevin Kelly]

We need the money.

[Sam Arbisman]

But I also just like the. Right. There was both in early Royal Society as well as kind of the Republic of Letters, there was this kind of democratizing feature because, I mean, certainly scientists was not really a job at this time.

[Kevin Kelly]

Right.

[Sam Arbisman]

It was much more natural philosopher. Natural philosopher. But a lot of them, like, they were not really doing it as their main job. They were kind of doing it as other things because it didn't really exist or make sense. It was a different sort of category.

[Kevin Kelly]

Yeah.

[Sam Arbisman]

But it was also just like, there was. I don't know if it was like. I don't know if pre paradigmatic is kind of the right term for it, but it was. People just need. They, like one of the most important things. And like, if you look at like the early, like, like, like proceedings of The Royal Society or Communications. It was just like people saying, this is an interesting thing that I noticed, or I did this weird thing and I'm not really sure how it makes sense. And they kind of just throw it out there. And I love that very kind of early stage in a field where people are just collecting bits of knowledge and bits of information or like facts or saying, here's something that doesn't really fit, fit. And I want to kind of just share it with other people because maybe they'll have a different, different kind of insider approach. And I, I think about this in, certainly in like the tech world, you have like collecting bugs and glitches. Oftentimes they are actually the precondition for actually better understanding a system of like, finding that gap, like, and reducing the gap between how you think the system operates and how it actually does operate. You need the glitches and the bugs to kind of narrow that gap. And I feel like when it comes to science, that's the same thing. Like, you need to find all that kind of weird stuff that doesn't make sense. And in those early Royal Society days, that's what they were doing. They're just big, oh, I found, I heard this weird thing, or I tried this thing and I'm not really sure what it adds up to, but it's worth sharing with everyone else. And I kind of feel like we've lost a little bit of that. And yeah, there's something to be said for that. Kind of just like sharing things that don't add up and so therefore merit further consideration.

[Kevin Kelly]

You know, we have our own AI setup hardware. And I was going to ask you because I also very much agree with your idea that there is just a goldmine of information in the past. So if I were to come to you and say, okay, Sam, I'm going to try a project over here, we're going to let the AI loop loose and we're going to let our fellows loose and all of that. Like, what would you tell me about how I should populate that model and fine tune it? What I'm looking for are you should really look at the writing on computers in 1977. You should really look at the social conditions that led doctors to ignore Semmelweis's advice. Advice. What periods would you find most fertile?

[Sam Arbisman]

Most fertile? Yeah, I would say in any time where there's like begrudging after the fact, like after the fact acknowledgement of like, oh, yeah, there was something really interesting here, then you kind of want to go like several years earlier and see, okay, when there were like those really intense debates, I feel like that's, that's something there. But I also. So one other thing, and I'm. This is a little bit different than what you're asking, but I actually think, and with AI, this is even more possible now than ever is the extent to which we can think in terms of like jargon barriers. Because oftentimes not only are. Can something be useful when you kind of combine it with something else that maybe is in a different subfield. But. But oftentimes people don't even realize that they're talking about the same kinds of things. Because of jargon. Because of jargon. And so I remember seeing this in, when I was doing my postdoc and I was doing things like in network science, there was this mailing list that I was part of and it had lots of people from lots of different domains and fields. And I feel like every week or so there would be someone saying, what's a really good metric to measure the following thing? And invariably someone would email back and say, oh, this has been known for like 30 years in sociology. And it was just like, it was unbelievable to see. And I felt I actually experienced this once in my own. For myself, in my own research, where I was trying to find some sort of metric for clustering data. Couldn't figure it out. I was working with a friend of mine and we were like, we were going to just kind of create our own. We knew it wasn't going to be great. And then we said, let's just talk to the statistician down the hall. And of course he immediately told us what we need to look at. And now AI can kind of help overcome those jargon barriers. But I still feel like that you kind of really have. And maybe I'm wrong about this, but there probably still is some work to be done for productively overcoming those jargon barriers and really saying, okay, how do we actually translate one idea to another such that some mathematical model is not reinvented eight or 10 times throughout a hundred years? Which, these are the kind of things that have actually happened. And so being able to find ways to short circuit those kinds of things. And so it's almost like at the sort of like semantic or kind of conceptual level, like, what are these concepts the same? How do we make sure that even though the jargon is very different, how do we kind of recombine these kinds of things? So I would say that's another thing, like another approach that needs to be focused on because Otherwise, even if there's all this really interesting stuff in the literature, it's not even just like, how do we kind of get people to have an open mind, it's how do we get people to even be aware that this is something they should be considering.

[Kevin Kelly]

Yeah. And again to your earlier comment that like, it's pretty amazing that a lot of the advances that we enjoy today are new, are relatively new. So would you concentrate on literature from a certain period? I love the idea of something that's obvious afterwards and really contentious before. That's a. I mean, I'm not sure.

[Sam Arbisman]

And there might be like certain time periods that are better than others for. So like, for example, like earlier in the earlier days of computing, when people were thinking about, not necessarily just like, like technical abilities, but more okay, like thinking about computing in the sense of like, how do we kind of use it for, for the following use cases or like children's education, for example, or whatever it is. And it had kind of a broader sense. That being said, depending on the field, sometimes there's really something to be said for going really far back. And so my father, he's a retired dermatologist, he, this is many, many years ago, he was on the platform Innocentive, where it's like they kind of provide challenges and people kind of like try to find, often like trying to find people who are in somewhat different fields that can maybe come up with something really interesting and relevant. He saw there was some challenge for, I think someone like it was to create a non invasive biomarker for the progression of als, like the neurodegenerative disease. And my father, in looking into that, I think he found it was, it was probably over 100 years old. It was the one, I think it was like the article that first described als. It mentioned almost in passing the idea that, and I might have gotten this wrong, I'm reaching back quite a way. But it was, I think it was the idea that the patients didn't get bed sores. And it was like, oh, they kind of just mentioned it. And my father realized maybe there could actually be some sort of biomarker around skin elasticity where you could actually look at the pattern of the, like the, of like the progression of the disease by measuring skin elasticity. So it'd be non invasive and kind of leaned into like his dermatological expertise. But it was from this really old paper that was just like, here's like this weird, like this new medical disease and we kind of have to think about it. And then I kind of Mentioned this one thing in passing. And so I'm willing to almost say, like. Like, I want to keep it all. I want to keep it all. I want to search through it all. And obviously it's kind of overwhelming, but that's one of the beautiful things about these massive, large language models is you kind of don't have to choose. And I think right now, one of the things we need to really just focus on is preserving all this kind of information and digitizing. And I feel like the Internet Archive and similar kinds of organizations that are just trying to make sure that this information is preserved, digitized, except accessible, they are doing really important work because that is going to be sort of the grist and the raw material and kind of the precondition for all these kinds of advances. And right now, like, great, if storage and hard drive spaces basically infinite. Keep it all. Keep it all and find some way of actually making meaning out of it.

[Kevin Kelly]

Yeah, I agree. And I was shocked to learn that, say, for example, William James, the majority of his writings are not digitized. And we actually have a project going on where we are going up to Boston or we have people up there doing it for us. His archives, like, most of them are paper, and we are digitizing them. And the interesting thing about that is it's a massive project. And I'm talking about one thinker, William James. Right. And so I'm a huge proponent of keeping it all right, because, you know, I kind of think when the library at Alexandria burned, like, how much knowledge did we lose?

[Sam Arbisman]

Which, like, that's why, like, the, like, the Skrull Prize and like, the Vesuvius project of, like, these, like, we're making

[Kevin Kelly]

a movie about that, actually.

[Sam Arbisman]

Oh, that's amazing. Yeah. Cause, like, right, like, that kind of work. Well, first of all, the actual. The actual work of that was unbelievably exciting and, like, just amazing to use the kind of these computational advances. But I feel like more broadly, just the act of digitizing and preserving these kinds of things, it might not be the most glamorous, but it is foundational and kind of like the precondition for all these other kinds of things. And. And you think, and you look back at, like, the, like the early humanists, like, in kind of like the Renaissance era, part of what they were doing was like, I ain't thinking about what is human, but a lot of it was also just trying to rediscover and find some of the best ideas of like, a thousand years ago or a thousand years before them of, like, ancient Greek writers and ancient Roman writers because they wanted to learn and rediscover from these like previous thinkers. And I think we need to kind of do that kind of thing at scale and like find, find like the best of like the best ideas that are out there from whatever time period, wherever they are. And part of that is just simply preserving or rediscovering or finding these kind of things or making them accessible.

[Kevin Kelly]

Yeah. And humanism is interesting to me. It plays a part in the fictional novel. Novel that I'm writing. And I learned so much about how it was possible for the humanists during the Renaissance to emerge. And one of the things that I hadn't thought about was that one of the reasons why Italy in particular and certain parts of Germany were so fertile was because they had no central governing authority. There was no king with absolute power. The Germany was not united, it was a confederacy of duchies. Same with Italy with the city states. So it was very much non centralized. And I think as I was reading about it more and more, it was that lack of centralization that contributed to, to the explosion of new ideas. There wasn't a central authority to say no. Right, you can't do that. And it bleeds over into the printing press. There's a great book about the history of money. It's an Irish author and he's very funny. And so he presents Gutenberg as kind of a grifter. But how he came up with, with the idea because he grew up in a wine region. Right. Was very familiar with the wine presses. He was a jeweler. But he really got, hey, what if I use those presses for this? And the.

[Sam Arbisman]

It was like a bundle of technologies.

[Kevin Kelly]

Yes.

[Sam Arbisman]

It wasn't just a single thing.

[Kevin Kelly]

To your point, to your point. It was a bundle. And he just happened to be in the region where they were all operating. But the further part of my analysis is Humanism basically was the first, I think, of Raphael's Portrait of a Young Man. It's the single greatest piece of art that is still missing. It was looted by the Nazis. And Durer also did the first self portrait in which he created his portrait like they had reserved only for Christ prior to that. And so humanism has this kind of in your face to authority. In other words, the art of the time was the, like the Portrait of a Young man by Raphael, it's a self portrait, but he's staring at you and he's like, what. What he's implying is I don't need your permission, I don't need your Imperator. I am a human, and I can figure this out. It's one of the reasons I love humanism. Right. But the decentralized nature was something I hadn't thought a lot about. And because, for example, China had the imperial throne and they suppressed a great number of technologies that they came up with first. Right? And imagine if there wasn't this. By the way, Gutenberg got his way into the church because, again, he was an entrepreneur tour too. And he was looking and saying, you know, one of the biggest businesses for the church are letters of indulgence, which essentially was your get out of hell free card. Right. You would give money to the church, they would have the scribes do these beautiful letters of indulgence saying, no, Sam is getting into heaven. He's getting into heaven. He might have made a rather significant contribution to us.

[Sam Arbisman]

But that's just kind of by the by.

[Kevin Kelly]

That's by the by. And so what did Gutenberg do? He went to his local bishop and said, I gotta believe that the demand for letters of indulgence is way up here and that your ability to fulfill that demand is way down here. And the bishop, like, yeah. And Gutenberg goes, I have the answer for you. And. And sure enough, the Church embraced it for that reason. And then the pope of the time, I think it was innocent. And my joke about the innocent popes are they were anything but innocent. Was very, very vain. And he would. During those times, he would give readings from the Bible to congregations. And so Gutenberg published a Bible with huge text so that he wouldn't have to wear his glasses. And he was smitten, right? He was instantly smitten. But. But the point about the idea of decentralization, it got me thinking a lot differently about how a modern organization could be formed. Is that an important part of your thinking on this?

[Sam Arbisman]

I mean, I would say. I mean, I like. I'm partial to decentralization, especially kind of in the face of, like, managing and handling complexity, of, like, having, like, competition, having that kind of like that going back to, like, the piecemeal engineering kind of like that sort of, like, slowly but surely experiment, experimentational kind of approach. I think that's really important because, like, when. When you are confronted with a complex system and you don't really know the best thing to do, just try a lot of different things. And so decentralization is really good for that kind of thing. Yeah. And in some ways, actually going back to, like, organizational structures, like, if you can get, like, the department of one or two people that's decentralized and it allows you to then kind of have this like you have the kind of legible structure to do your thing, but it's distinct enough from everyone else that then you can kind of do things maybe a little bit differently. And kind of having a lot of those could be, could work really well. But I wonder if, because I've thought about, because there are a number of people who consider themselves like independent researchers. Of course as an independent researcher it's harder to get funding and things like that. And so there are going back to the theory of the firm, there are returns to scale and kind of doing everything together. But there might be ways of, in kind of a more modern way kind of having things that kind of recombine in a sort of looser way where you can have maybe a group of independent researchers that work with like some like one administrative person who kind of helps them get funding but then kind of allows them to all do their own thing. Or there's actually another, there's a, a research organization called Ink and Switch which they, they do things related to like the, like, like computational tools for thought and human computer interaction and ideas related to kind of like programming as well as many other things. And one of the models that they adhere to, that they kind of talked, they've talked about is this like Hollywood studio model where so in the same way that when you make a movie it's not all one large company. It's like, I mean sometimes there's companies, but it's people like the teams often are assembled for that project, they work on those things and then everyone goes off and works on another movie or other kinds of things.

[Kevin Kelly]

Right.

[Sam Arbisman]

And, and I feel like, and so Ink and Switch does that they have some people who are maybe a little bit more long term, but one, it's also just a great way of getting very, very high caliber content or high caliber talent in the tech world where it's like they might not be able to get them for a long term, but they're, they have three to six months free in between like a company, a corporate exit or they left something or they're thinking of doing something else and then they'll come and kind of join a little project and then they have the long term people kind of helping set the longer like more long term vision of the organization. And so I think like having that ability for researchers to kind of come together for certain kinds of things, things in this kind of distributed way is really interesting. Now that being said, it can work better in certain Kinds of research that maybe is a little bit faster. If it's like a longer term kind of thing, then maybe it's harder to kind of have that sort of swarm kind of approach. But I definitely think there's something to be said for that kind of like, decentralized approach. And I think it's really powerful.

[Kevin Kelly]

Yeah. Kind of back to the fictional book that I'm writing. We're trying the Hollywood model. I actually have a writers room with both humans who are great writers. I'm lucky that we have Infinite Books and we have a bunch of great writers and editors, but we're putting AIs in there as well, with different types of personalities, et cetera. And Jimmy Soni, our editor and chief and CEO of Infinite Books, he was skeptical about doing a writer's room because he's mostly nonfiction. And after the first one, he's like, I think I'm gonna start doing these for my nonfiction books.

[Sam Arbisman]

Interesting.

[Kevin Kelly]

Because what happens is you throw the world that you're building out there, and it's really kind of magical.

[Sam Arbisman]

Yeah.

[Kevin Kelly]

Because, like, for example, in my book, is. Is a thriller and it covers a long period in history. Right. It starts in World War I and it ends in 2027 or World War II, except, excuse me. And it ends in 2027. That's a hunt that goes on. The villains are very villainy, but the people hunting them are very talented. And anyway, in our first writer's room, Jimmy was listening, and he goes, you know what's interesting about all the characters and some of the real life people you're putting in that? And I'm like, no. And he goes, they were all orphans. And I went, I never thought of that. And so it opened this incredibly rich vein of looking into, like, orphans. What drives them? Are they bridge builders or are they wall builders? And so I went down this, and it actually affected the plot.

[Sam Arbisman]

Oh, wow.

[Kevin Kelly]

And so I'm a huge fan of the writers room approach for this particular purpose. But why they're so fun is because, again, back to the. I would have never thought of that. But also from the point of view of just kind of like AB testing with smart people, like, does this sound like something our villain would say? And there's a lot of controversy. Like, no, he would never say that because he's this way. But it really helps me as the primary author to whittle it down to. Yeah, he would say that. The other cool thing about writing fiction that I had heard from fiction writers but never experienced is when you get to know A character really, really well. They start writing themselves. And it's the weirdest I've heard people write about this kind of thing in the world. It's like I was writing the villain and I looked at it. I'm like, I would have thought of that. So you kind of get embodied.

[Sam Arbisman]

There's almost this like emergence of. Yeah. Oh, that's so interesting.

[Kevin Kelly]

So I definitely am a big fan of trying a lot of different models for old ways of doing things right. Like the single author versus the writer's room. It works very well for TV and for movies. And like I'm a huge fan of Curb youb Enthusiasm. And you know, his entire process is they just write outlines, right.

[Sam Arbisman]

And then a lot of things happen and they try many different things.

[Kevin Kelly]

So anyway, I agree that experimentation. See what works, see what you can learn from it. Now, of course not all of it's going to work and ultimately it's going to be my name on the book. So what right now are you obsessed with?

[Sam Arbisman]

What am I.

[Kevin Kelly]

Because I'm always interested in what is obsessing you.

[Sam Arbisman]

This is, I think, I believe we spoke about this last time, but it shows that I guess my obsession is still lasting, which is. So the. We talked about the company Maxis. It was like, like the studio that developed SimCity and Sims and Sim Life. And it was this weird moment in the, like, in like the early to mid-1990s when it was kind of this intersection of like gaming and simulation and complex systems and all these other interdisciplinary science approaches. And they just did all these weird things. And then the company, it. I think it took a lot of investment on and then eventually went public and then kind of was not able to sustain that and then eventually got acquired by Electronic Arts and. Yeah. And now does not exist as a studio any longer. And so it's kind of had this like kind of crash and burn kind of thing. Maybe not quite crash and burn, but an end of an era we'll say.

[Kevin Kelly]

Right.

[Sam Arbisman]

But I've been just. I'm still preoccupied with like what. What is still kind of like perennial and valuable about that. Like that kind of a mental approach to this kind of clashing of different things. And can we actually like, is it possible to like reinvigorate a Maxis 2.0 or whatever it is? And so because I like making lists, I've currently been like working and working on a list of more modern equivalent games or simulation toys or these kind of software based miniature worlds that you can kind of play with because there are a Lot of these still around, but I think people need to recognize like yeah, this is, I don't know if it's a genre, but it's a thing that should be a genre. We need more of these kinds of things. But I've also just been taken with the fact and maybe this is also just more broadly like how I think about simulation because simulation, there's high fidelity simulation of predicting the weather and that's very important and powerful and useful and it's valuable for prediction. But there's also something to be said for these small models that are toys but give you a great deal of insight into just how a complex model works. And they are so valuable like giving you a certain set of intuitive hooks or leading you to learn further about urban dynamics or whatever it is. I've actually been teaching this course at the University in Cleveland, Case Western about the art and science of decision making. And it's the seminar course and we kind of talk about mental models and complex systems and, and like non linearity and feedback and all these different things. And at one point one of the things I have my students do is I, I have them play an emulated version of SimCity 2000 that's like available on the Internet archive and not because

[Kevin Kelly]

my daughters were crazy.

[Sam Arbisman]

Yeah, that was, that was, that was my childhood. I love that game. And, and it's not because like SimCity 2000 is like, has this great degree of verisimilitude. But the thing is it's this complex systems model that's just complex enough to have things that like bounce like they bite back in unexpected ways. And, and you have to make a huge, huge amount of decisions. You have to kind of play with these and, and it's just, it's a lot of fun and like some students get it right away and play with it and other ones kind of crash and burn and that's fine too. But the whole point is like just kind of actually come to grips with a complex system. And I just want there to be more of these kinds of things because those kind of simulation tool toys are just, they're so much fun and they're, they're so wonderful and I want there to be more of them for basically every domain of like complex knowledge out there. So that's still, I mean I think we talked about it last time, but I'm just going to keep on talking about it because I, I find it so fascinating.

[Kevin Kelly]

Well, I'm also a huge fan of In Silica Sims. Right. Like you can put together huge audiences for example, and have them read your work or listen to your music or watch your movie and you get insights that you're not expecting. Right. Like, oh, I wouldn't think that that kind of like we started by building ocean profiles from the big five, but then we refine them down and so we can create huge populations depending on the media that we're trying to test. And I gotta say that we've seen some like really interesting feedback that I would have never guessed like from, you know, the Anxious processor is one of the names of one of the ones that we have in there. So I'm a huge believer in. I mean, that was the first book I wrote. Invest like the best was how you could clone your favorite money manager by taking all of the stocks in his or her portfolio, paying no attention to what they said, paying all the attention to what they did, I. E. What they bought.

[Sam Arbisman]

Right.

[Kevin Kelly]

And then you could build a rule based, factor based way of coming up with portfolios with those exact characteristics. So I definitely. And again, that's a temporal thing as well. Right. Like the only reason I was able to do that was I was lucky enough to be born in 1960. Right. If Ben Graham had all those computers, he would have done all that stuff and I would have never been able to write what works on Wall Street. I would have never been able to do that. But because I was coming of age when we were getting these extensive data sets and the computer power was fast enough, I just got lucky. Right?

[Sam Arbisman]

Yeah.

[Kevin Kelly]

So. So simulations, what. What other lists right now are really occupying your.

[Sam Arbisman]

I mean one. So I mean, I guess this list. I haven't updated this that recently, but I started creating a list of sports teams named after technologies because of course there's sports named after things from history and animals, but there needs to be more named after technologies. But it turns out there actually are a non trivial number. You have the jets and the Pistons and the spurs and the Rockets. And then of course, once you get into minor leagues, there's a whole bunch of. You have like, I think there's, there's, I don't even. This might even be a high school team. There's like something called like this. I think I found a team called the Spark Plugs. It turns out there's a ton of soccer teams named, I think Dynamo. Then there's I think one named after like some sort, some sort of bicycle that has like the big wheel and the small wheel. Like there's. And I just love this idea that at least in certain times, like technology is such a part of the culture that people actually name their sports teams after them. So I find that interesting. Another weird list is it turns out there is also a very large number of companies named after things from like Lord of the Rings and like the Tolkien's world. And of course a lot of them are kind of connected to kind of like the, I think like the extended kind of like Peter Thiel world because he's like, he and his people are very interested in kind of Tolkien. That being said. And maybe these people, maybe these companies are all connected to that. Like most of them are sort of like the good guy kind of related Tolkien stuff.

[Kevin Kelly]

Yeah.

[Sam Arbisman]

But I've increased. I think I found a company named Mordor. There was one called Sauron. And like it's a very interesting branding choice, but that that exists. But yeah. Then I've also another thing I've been thinking about. This is not quite a list, but I think also just kind of like a framing related to some of the things we're talking about. Kind of like the history of computing. And like the people's computer company is. I was in involved in this group that made this resonant computing manifesto. And the idea behind it is in many cases when you're engaging with technology and certain kind of computing things, the computing experience kind of leaves you drained or you feel really bad or like this is not something you want to really enjoy or want to be a part of. But there are, and this has been true throughout computing history, those experiences that are more resonant that actually leave you enriched. And the question becomes, how can we kind of try to incentivize or articulate kind of things that are more likely to kind of leave you enriched in kind of this more resonant computing kind of experience. And so, and certainly now with AI, there's the possibility for both like personalizing things in very bad ways, but also personalizing things in very good ways and making things that are kind of more pro social kinds of experiences. The way I kind of think about, about this is that and obviously you want kind of like human scale sort of experiences or things that kind of privilege the human even in the computing experience. But there's this great television show, Halt and Catch Fire about kind of, oh, I love that show, It's a fantastic show. And like, I think it's like in the first episode, one of the characters has this quote where they say like, the computer's not the thing, it's the thing that gets you to the thing. And I feel like oftentimes we forget that we just, we get obsessed with things that are technical or we're not doing. We're not thinking about what is the goal with compute. Like the computing. Like computing are. These are technologies that should make our lives better and more enriched and more resonant. And how do we make sure that. We make sure computers are the thing that gets us to the thing. And so that's another, that's another topic that I've been very obsessed with.

[Kevin Kelly]

Yeah, I'm interested in the new educational market models. Like what, what is drawing you there and saying, wow, this could be really cool.

[Sam Arbisman]

So you mean that space?

[Kevin Kelly]

Yeah.

[Sam Arbisman]

So oftentimes the, the thing that draws me to make a list is not necessarily a very clear thesis or idea. It's more. Something interesting is happening here and it's worthwhile like keeping track of. I kind of, I think I described this once in maybe some essay I wrote about. I kind of call it like, like the Linnaean instinct. So you have like Linnaeus, like taxon, like taxonomizing like that. Yeah, there's just this like, I'm not Googling, like very sophisticated taxonomies, but especially early on in science and we're going back to like natural philosophy stuff as well. The precondition for understanding the world, whether it's collecting glitches or things, is just finding things that all kind of seem related and worth keeping track of. And I feel like that's a kind of like the Linnaean instinct of like, whether it's weird things in computing history or. Right. Like weird like companies or sports teams with interesting names and kind of something like I just think there's something there. And I feel like with when it comes to the educational models. Right. I don't necessarily. Going back to what you're saying, what we were saying before, I don't feel the need for like throwing everything away, but I do feel like we're in this interesting moment where people are trying new things, but in a way that it's not quite one offs anymore. And even just the act of, the act of list making for me kind of gives me a sense, okay, there is a larger space. But I also would like to think that maybe sometimes those lists will also allow other people who are playing in that space to realize they are not alone, like they're doing things and maybe there can be something to be learned by connecting these different approaches. Approaches. And so I, Yeah, so I Oftentimes the list making is like the precondition for getting a sense of what's going on. It's more just like there's something happening and I want to just at least chronicle it because it's interesting. But in terms of like, do I have a larger theme? No, it's, it's really just that, that, that Linnaean instinct.

[Kevin Kelly]

But so on, on that topic, like what is a recent example of something where you've been keeping these lists of very disparate items that you're seeing emerge from the like you're having your eureka moment. Oh, that's why all these things work together.

[Sam Arbisman]

So the Overedge catalog, which is like the one about like non traditional research organizations, that one, that one maybe arose when I had a little bit more kind of theory of what was happening there. But it was still just a matter of okay, there are interesting things happening and it was more. I just wanted there to be more. And so that act was. So that act was even. It was less about eventually finding a theory and more about trying to make people realize that there needs to be more happening here. And so it's like, oh like, like if there's enough happening or maybe like not if the list is too short. Like people go out there and actually expand the list. Like make more things that can go onto that list. But I would say, I mean in terms of the educational stuff, there's glimmers there of that something at the intersection of fellowships and residencies or. And people talk about like the unschooling movement and it's like the Recurse center, which is kind of, kind of like a retreat for programmers. They talk a lot about the unschooling movement. I feel like these like creating informal or we'll say unstructured spaces to allow. I mean the thing I would say, I wouldn't say it's unstructured because they're actually, and I think they've actually talked about this. Like there is a lot of structure or structure available, but we'll say non traditional kinds of environments. There is a growth there for like people like creating like collections of people for these kinds of things. But I don't, I'm still not sure what it adds up to. Then again, maybe, maybe I'm just not good at figuring out what these things add up to. And I just have that Lenaan instinct of like, I just want, I want, I want there to be more lists and I can leave the sort of like paradigm making to other people and I'll just be the list maker.

[Kevin Kelly]

When have you just to finish on lists. What was the oddest compulsion to start a list on like, like that you Know, friends, colleagues, family are like, sam, what's going on here?

[Sam Arbisman]

I mean, definitely the sports teams named after technology.

[Kevin Kelly]

I love that one.

[Sam Arbisman]

That one's like a weird. That one. I don't even know where that came from. I was just like, I mean it was almost one of these kinds of things work in the same way that people in like the progress movement are like, we need to find ways of like valorizing innovation. I was like, this is another weird way of valorizing technological advancement. And it seems like we're not starting from scratch. There's actually a lot of technologies that have been used as names for sports teams. And it was also one of these things where in the act of creating it and then sharing it and then sharing it publicly, I then discovered that there were many, many gaps in my knowledge in like certain areas like sports that I was just unfamiliar with. And then people kind of gave me even more examples. And that was definitely a weird one. But I'm trying to think of even an even weirder one. Oh, there was this list. This was done actually. I wrote this for a. I think it's like a now defunct, like science humor magazine. But it was originally. I have no idea how this started, but it was. I found a non trivial number of like anatomical terms that sound like things you could visit on a vacation. So like the islets of Langerhans or like. And it was a very weird list. But that one, that was a lot of fun to come up with as well.

[Kevin Kelly]

I love it. So what's next for you? What are you working on now?

[Sam Arbisman]

What am I working on now? I mean, I'm still thinking a lot about like the history of computing and like what can be done with, with that. I, I still think there's a space in the tech world where it's almost like there's a need for this kind of translation where it's not necessarily like historical scholarship of computing. Because I feel like there's a lot of people doing that very, very well. But there's, there's a role for like bridge building of saying, like, how can we kind of get people who are already in the tech world more excited about actually engaging in the history of computing, which can be as simple as like, I don't know, reading old computer magazines on the Internet Archive or actually like, I don't know, playing with really old computers and hardware and software. I feel like that's something also that's worth trying to explore because, yeah, I feel like sometimes we've kind of, we too quickly narrow the space of Computing possibilities and then kind of forget all the other weird paths we can take, whether it's like, user interfaces or hardware or software, whatever it is, and it's worth revisiting. And so, yeah, there just needs to be a mechanism for. For that. And that's definitely. That I definitely. One thing that I'm kind of constantly struck by, and I have no idea what it'll end up, and it could. It could be as simple as, like, maybe there needs to be, like. Like, in the same way that, like, Buckminster Fuller would go around and give these, like, public lectures on crazy ideas, there needs to be more of that around. The history of computing in the tech world. I don't know, but those are the kind of things I like thinking about.

[Kevin Kelly]

Yeah. And as I was listening to you as one who is an early adopter of all technology. So I'm 65. Right. I, you know, on Twitter, they have the. But are you this old? And they, you know, they show, like, Windows 95. I'm like, I'm way older than that. And I got to thinking about that and the experience that I had in 1980, right. When I was trying to have the computer calculate a Black Scholes implied volatility for me, and it took five minutes. But at the time, I'm a big journal keeper. So I was rereading some of those journals back then. I was like, I can't believe that it can do it this fast.

[Sam Arbisman]

That's amazing. I love that. Right? And, like, we've, like, forgotten. Right? Like that. Like, yeah, we've. And that's. And I would say that's another interesting lesson of just, like, understanding, like, technological history, which is. Humans are really good at adapting. Yes. Which is both very powerful because it allows us to handle lots and lots of change, but leads us to kind of almost like, overwrite our memories of how things used to be. And. And I. And I feel like sometimes, yeah, things are lost when we kind of, like, forget how it was or kind of understanding. Yeah, maybe. Maybe some of. Maybe maintaining some of that friction could be useful. Sometimes it probably was not. And, like, that's fine. But. Yeah, but. But I think that, yeah, like, just revisiting some of those experiences can be at least very powerful for, like, understanding.

[Kevin Kelly]

And that's where I was delighted that I was such a journal keeper. Because we do.

[Sam Arbisman]

Because you would definitely not remember that. No, I would.

[Kevin Kelly]

Absolutely not. And we do overwrite our memories. I have nearly 50 years of proof. I started when I was a teenager with these journals, and it's Very interesting to me. Because of them, I learn. Oh, I didn't think that at all. And it's really helpful because it teaches more broadly than just that specific example. I think that we overwrite another part of human os, Right. We overwrite our memories to make them consistent with what we believe. Now that can be a challenge and, or it can be like when you go back and have the ability to go back, you can see the chain of how you came to believe what you believe now, which is really interesting. So I'm all in favor of your, your notion of let's keep everything, let's explore everything. Because, you know, there are a lot of connections that we just don't intuit. Right. Like we see in a very limited part of the electromagnetic spectrum. We like our, our senses are fabulous, but they're very, very limited. And so that's why I'm such a big fan of AI because it can in fact look into all of those and say, hey, look at how this, this and that way over there combined to make something like, really cool and interesting.

[Sam Arbisman]

I love that.

[Kevin Kelly]

Well, Sam, I could talk to you forever. I know you have another engagement here in the city that you have to get to. You do remember, I hope, where we make you emperor of the world.

[Sam Arbisman]

Yes.

[Kevin Kelly]

And. And where you can't kill anyone, you can't put anyone in a re education camp. But what you can do is we're going to give you the magical microphone which is sitting right in front of you now. And you can see, say, two things that will incept the entire population of the Earth whenever their tomorrow is. They're going to wake up with these two things and think, these are my ideas. They're going to think that they had come up with them, but they're also going to make the commitment. Unlike all the other time that I had these great unwaking or shower thoughts, I'm going to actually act on these two things. What two things are you incepting?

[Sam Arbisman]

So I think the first one is around getting people to ask more questions. I feel like, like it's one thing to say be curious. Like, I definitely want people to cultivate curiosity, but I think the key to cultivating curiosity is spurring yourself to ask more questions. Like what? Like asking the name of like, like the term for something. Because it will often lead you to realize, oh, there's this like, I don't know, entire, entire domain of architecture around these things that I didn't even know or some weird thing and woodworking or whatever it is. And so it can be as simple as, like, asking the name of something, but just more. We just ask more questions. I feel like that would be the

[Kevin Kelly]

first of that one.

[Sam Arbisman]

And the second, Walk more. I. I like walking a lot, and I think however much walking you do, you can always do more, so walk more.

[Kevin Kelly]

I love both of those. You're in the perfect city for walking.

[Sam Arbisman]

Correct.

[Kevin Kelly]

Sam, thank you so much for rejoining me. I'm already looking forward to recording number three.

[Sam Arbisman]

This is great. Thank you so much.