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Wired's Kevin Kelly on Why AI Is a 50-year Overnight Success (Best of the Pod)

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Summary

At-a-Glance

  • Verdict: Skim
  • Core thesis: Kevin Kelly argues that AI's apparent sudden arrival is the payoff from decades of slow progress, and that its long-run significance lies less in replicating human intelligence than in creating many unfamiliar, specialized forms of cognition.
  • Why it matters: The interview offers useful framing for AI strategy: distinguish fast-moving software intelligence from slow physical automation, treat AI use as a learnable craft, and avoid assuming that present human cognition is the reference standard for future systems.
  • Best use: Use it as a strategic-reflection interview for forecasting discipline, AI-assisted creative/research workflows, and a contrarian lens on robotics, companions, and frontier technologies—not as an implementation guide.

Executive Summary

Kelly's central forecasting lesson is humility. Technologies can remain technically impressive yet commercially or socially slow for decades: he saw sophisticated VR in Jaron Lanier's lab in 1987, but argues that today's consumer VR is principally a million-fold cost reduction rather than a proportionately radical capability leap. AI, in contrast, is a "50-year overnight success": a long-running field that found an unexpectedly scalable path through language modeling.

He argues that people confuse recognizing intelligence with understanding it. Using the early history of electricity as an analogy, Kelly says current theories of intelligence may be as incomplete as Isaac Newton's theories of electricity. His working hypothesis is that intelligence is a compound made from multiple still-unidentified cognitive primitives, and that AI will populate a vast possibility space with minds unlike human ones rather than merely producing a single universal intelligence.

On practical AI use, Kelly is enthusiastic but not mystical. He uses OpenAI's research capabilities, outlining and organizational assistance to get past the blank page and identify gaps in his knowledge. He stresses that eliciting high-quality results is itself a substantial skill requiring long practice. His most vivid example is using AI to iteratively build an alternate-history world around da Vinci, Luther, Columbus, and Victoria—valuable primarily as participatory creation for an audience of one, not as publishable output.

The broader operating philosophy is to move between frontier exploration and structured institutions. Kelly believes governance and constraints can improve systems even while reducing early frontier freedom; he advocates preserving both frontier zones for experimentation and mature systems for people who thrive within rules. For companies, the relevant distinction is between digital AI, which can compound quickly, and embodied robotics/VR, where biological interfaces, energy efficiency, and hardware impose much slower timelines.

Key Takeaways

  • Claim: AI should be understood as a long-gestating technology whose breakthrough emerged from an unexpected route, not as an instantaneous invention. | Evidence: Kelly calls AI a "50-year overnight success" and notes that LLMs were initially advanced through language translation work, where researchers unexpectedly observed reasoning-like behavior rather than solving reasoning directly. | Implication: Ken should look for adjacent technical capabilities that may unlock a category indirectly, rather than funding or planning only around the category's stated bottleneck. | Caveat: This is a historical framing, not a claim that every current AI capability or investment will mature on the same trajectory.
  • Claim: Current AI systems may represent many possible types of intelligence rather than a linear march toward a single human-equivalent intelligence. | Evidence: Kelly invokes Marvin Minsky's "Society of Mind," mixture-of-experts architectures, and the analogy of salt as a compound: intelligence may consist of cognitive elements and higher-order combinations that have not yet been identified. | Implication: Agent and model design should prioritize fit-for-task cognitive diversity, decomposition, and ensembles over anthropomorphic benchmarks or a one-model-for-everything assumption. | Caveat: Kelly presents this as a hypothesis; neither he nor the interview establishes what the fundamental cognitive units are or how to measure them.
  • Claim: AI is especially effective for a person whose comparative advantage is editing, synthesis, and direction rather than blank-page drafting. | Evidence: Kelly says AI helps him get something onto the page, conduct research, summarize and synthesize material, organize thoughts architecturally, and expose areas where he is most ignorant. | Implication: Ken should treat AI fluency as an operating capability: build repeatable research, briefing, synthesis, and revision loops, then deliberately train people to direct and verify them. | Caveat: He explicitly says using AI well is a skill requiring extensive practice—"your 10,000 hours"—rather than merely clicking buttons.
  • Claim: A major generative-AI use case will be personal co-creation whose value is in making, not distributing, the output. | Evidence: Using OpenAI o1 Pro, Kelly generated an alternate-history project in which Leonardo da Vinci, Martin Luther, and Christopher Columbus establish a science- and religious-freedom-oriented city; he expanded it into characters, histories, ten novels, a reconciled saga, covers, and marketing materials, while saying he did not intend to show it to anyone. | Implication: Product strategy should not evaluate generative tools solely through publishable-output or enterprise-ROI metrics; creation-time engagement, self-expression, and personal narrative tools may be durable demand drivers. | Caveat: Kelly says there is no direct economic model for an "audience of one," except for the platform provider; he categorizes it as self-expression and entertainment rather than a career.
  • Claim: Embodied technologies will likely advance much more slowly than software AI because they must meet biological and energy-efficiency constraints. | Evidence: Kelly was surprised that VR took decades after his 1987 demonstration; he says the core improvement was a fall from multimillion-dollar equipment to roughly $100. He points to the human body as a benchmark of a 25-watt supercomputer paired with a quarter-horsepower engine and argues robotics remains far from that efficiency. | Implication: Separate digital-agent timelines from robotics, AR/VR, and other body-scale automation timelines; avoid transferring software-era adoption expectations directly to physical products. | Caveat: He leaves open the possibility that VR/AR could receive an unforeseen "LLM moment" through breakthroughs in lenses, focusing, projection, or an adjacent field.
  • Claim: Mature systems need governance, but progress also requires preserving new frontiers where experimentation can occur. | Evidence: Kelly compares the internet's evolution and Burning Man's shift from rule-free chaos to streets, police, bureaucracy, and safety procedures after a death; despite the loss of unconstrained freedom, he considers Burning Man better than ever. | Implication: Ken should design AI operations with both protected experimentation environments and production-grade controls, rather than framing speed and governance as mutually exclusive. | Caveat: The interview does not specify which governance mechanisms best preserve innovation or how to determine when a frontier should be formalized.
  • Claim: Career differentiation comes from becoming uniquely suited to work that others cannot or will not do, rather than competing to be conventionally best. | Evidence: Kelly's maxim is "don't aim to be the best, aim to be the only." At Wired, story ideas he repeatedly failed to assign often became projects he eventually wrote himself because he realized only he could execute them in that form. | Implication: For content, product, and investment positioning, focus on proprietary combinations of curiosity, access, workflow, and taste—not generic category leadership claims.

Detailed Brief

Forecasting discipline: why impressive technology can remain slow

  • Claims: Kelly's errors are central to his method: he was early on blockchain but underestimated Bitcoin's emergence as a store of value, expected wider non-financial blockchain adoption, and initially did not see why eBay would matter.; His interpretation of eBay is that its practical value is not primarily auction mechanics but long-tail access to obscure, out-of-print, unavailable, or unofficially sold items.; He balances engagement with technological frontiers by pairing current AI work with historical reading and hands-on workshop activity.
  • Evidence: He says VR's limiting factors included the biological realities of eye focus, device weight, and human-computer physical interaction—not simply computational hardware.; He cites Jeff Bezos's effort to build around things that do not change and connects it to his Long Now Foundation work on 10,000-year timescales.
  • Caveats: Kelly's examples are retrospective personal judgments, not a systematic forecasting framework with measurable leading indicators.; His claim that eBay succeeded mainly by moving beyond auctions is explicitly qualified as personal experience rather than verified marketplace data.
  • Implications: Forecasts should identify whether the limiting variable is software, cost, energy, human biology, regulation, or institutional adoption; these constraints move at different rates.; Counterbalance short-cycle AI narratives with historical base rates and an explicit search for what remains stable.

Creative work, editorial commissioning, and AI-enabled narrative

  • Claims: Kelly identifies as a natural editor and reluctant writer, making AI's ability to generate a working starting point particularly useful.; For commissioning exceptional work, he recommends finding young science-fiction writers and giving them journalistic assignments aligned with subjects they genuinely want to learn.; He believes strong creative results depend on matching a project to a writer's intrinsic interests rather than forcing competent writers onto unwanted topics.
  • Evidence: At Wired, Kelly assigned a story about global fiber-optic cable construction to Neal Stephenson, who produced work Kelly says he could never have written; he also cites Bruce Sterling.; His recommendation is based on the premise that emerging science-fiction writers are trained storytellers and welcome paid opportunities to research subjects that interest them.
  • Caveats: The commissioning advice is editorial judgment, not a general staffing model for technical, operational, or analytical content.; AI-generated alternate-history material demonstrates creative iteration, but not factual reliability or publication-quality prose.
  • Implications: For editorial and narrative products, recruit for distinctive story sense and curiosity, then fit assignments to the contributor rather than treating writers as interchangeable production capacity.; Use AI to expand creative option space and surface directions, while retaining human judgment over taste, coherence, factuality, and purpose.

Notable Concepts & Terms

  • 50-year overnight success: Kelly's description of AI as a field whose seemingly sudden public breakthrough rests on decades of incremental technical development.
  • Society of Mind: Marvin Minsky's idea, cited by Kelly, that intelligence emerges from many interacting processes rather than a single unified faculty.
  • Mixture of experts: An AI architecture Kelly treats as suggestive of intelligence built from specialized components or cognitive sub-functions.
  • Cognitive elements: Kelly's proposed but unidentified basic units of reasoning, learning, pattern matching, or deduction that could combine into different intelligences.
  • Audience of one: Generative output created chiefly for the user's own pleasure, reflection, or self-expression rather than for publication or monetization.
  • VR's LLM moment: A hypothetical breakthrough that could make VR/AR suddenly broadly useful, potentially arising from an unexpected adjacent technical area.
  • Aim to be the only: Kelly's career-positioning principle: seek uniquely suited work rather than competing to be the generically best.
  • Frontier and center: Kelly's model in which healthy systems preserve experimental, lightly governed frontiers while allowing mature, structured institutions to serve different people and purposes.

Operator Notes / Why Ken Should Care

  • Create separate planning horizons for software agents versus embodied automation; require any robotics, AR, or VR thesis to name the physical bottleneck—energy, ergonomics, optics, manipulation, or deployment—not just model capability.
  • Formalize an AI research workflow that moves from broad retrieval to synthesis to gap identification to human verification; assess operators on the quality of direction and validation, not merely prompt volume.
  • Test an "audience of one" product hypothesis where the user directs an evolving personal world, narrative, memory artifact, or simulation; measure sustained creative engagement rather than sharing or publishing rates alone.
  • Maintain a sandbox-to-production pathway for agent experiments: permissive environments for discovering capabilities, followed by explicit controls for access, memory, auditability, and operational reliability.
  • For differentiated media or thought-leadership projects, source emerging speculative-fiction talent or similarly strong narrative thinkers and commission work around topics they already want to investigate.

Source/Metadata

  • Title: Wired's Kevin Kelly on Why AI Is a 50-year Overnight Success (Best of the Pod)
  • Transcript words: 9298
  • Duration seconds: 3222
  • Timestamp note: No usable timestamps or chapter markers were provided. The transcript contains substantial duplicated passages near the end and a promotional outro.
Full transcript 7861 words · 41 min read
0:00

What I've learned with the future is that it's easy to make predictions and hard to make predictions that are true. Jaron Lanier showed me VR in 87. I was completely blown away, and I thought, oh my gosh, this is the future. The VR that we had back then is not that much different from the VR you get now. The difference was that it was multi-million dollars, and now it was $100. I've been very, very surprised about how slow that has been. You could say VR is still waiting for its LLM moment. AI is a 50-year overnight success.

0:50

Kevin, welcome to the show. It's a pleasure to be here. I'm glad to be seen. I am very excited to have you. In addition to being a personal hero of mine, I am a longtime Wired reader. I had a gigantic shelf of magazines growing up. So it's crazy to get to chat with you. There's a lot of things I want to talk to you about. But I want to start with, we have a mutual love of Annie Dillard. We do. She's my favorite writer. And I know she's your favorite writer as well. Tell me why you like her.

1:27

For those who aren't familiar with Annie, she burst onto the scene with a meteorite of a book called Pilgrim at Tinker Creek that, for some reason, it's an account of her spending some year musing, like a Henry David Thoreau intimate investigation of a creek in Virginia or West Virginia. I don't remember. And so it was just the writing and the style and the ideas and her blazing brilliance in being able to capture this into a few words. It was almost like poetry. There was prose. It somehow just worked on my brain. And I don't know enough about literature to describe her in relation to other writers and why it doesn't work for me as much.

2:30

But there was something about her spirit as well as her writing, which was very expansive, cosmic, enthusiastic, questioning. She talked about feeling as if, at one point in the story, that she was a bell that someone else had rung. And I felt exactly the same in response to her writing. It was like she was ringing my bell in a curious way. Yeah. Lifted and struck. There's another part in the early part of the book where she talks about the tree with the lights in it. Yeah. And that moment is so very evocative of one of her big themes, which is... You can just be walking around in ordinary life, in ordinary nature, and somehow the veil is lifted from your eyes.

3:19

And you can experience these moments of transcendence where everything feels like it's glowing from within. Yeah. So there was this, her cosmic poetic ability to take these moments. But then interstitial with that were these findings, these weird little trivia bits that you'd find in some obscure book that you would be grounded in before the next jump. It was a very distinctive way of wrapping rhapsodic ecstasy with very concrete science trivia or oddities. And that combination just somehow appealed to me. Yeah. The thing I remember is she spends a long time talking about Henley's loops, which are this little part of the kidney. Right.

4:20

And it's just like, where does that even come from? And yeah, I think she zooms out to the biggest stuff and then zooms into the smallest stuff. Right. But another thing that's really interesting about her writing, and I'm curious how you've thought about this in your own work, is she spends a lot of time talking about the beauty of the world. But she also spends a lot of time talking about the worst of the world. There's an entire chapter on insects and how gross insects are. She's also angry about it and not afraid to be angry about it. And I think a lot of writers are usually either one or the other. Either you're really angry or everything is beautiful and awesome.

5:02

And she does both pretty well. That's true. I think either she said in the book or elsewhere that she wrote the book as if there was a patient of cancer dying in her room. Oh, wow. And she's talking to them. So, yeah, it's not just saccharine. It's not just sweet kumbaya. She can be pretty harsh, too. And that is part of the attraction of the sweet-sour flavor. How has she, if at all, influenced how you write or edit? Well, in some ways, it's why I don't write at all. Because I came across that book in the weirdest of all places. It was in an American library in Kandy, Sri Lanka. Wow. And I walked in because there was air conditioning. And there was a book.

6:02

Why they had the book there, I don't know. But there was a book. It opened up. And from the first immediate paragraph, I once had a tomcat, I was caught. And I didn't put it down. And it was like, I don't know what she's doing, but I want to do that. If I could do that, I'm golden. I feel the same way. Have you read any of her other stuff? Yeah, yeah. I didn't read her fiction book. But the other books of essay, Teaching a Stone to Talk and... Holy the Firm, The Writing Life. Yeah, Holy the Firm and all those, yes. And her account of the eclipse, Total Eclipse, is never to be equaled. Anybody who writes about eclipse has to start with Annie's version.

6:42

Yeah, it's so funny because the reason I started with Annie is I love her so much. And she evokes all the same kind of feelings in me that I think she evokes for you. And gives me that same kind of energy to write stuff.

7:02

If I write stuff after I've read something that she's written, it just... It brings something out in me that I just love. And also, no one else I know likes her or even really has heard about her. I feel like a lot of her work is... It's maybe taught in school, but it's the thing that you're assigned, but people don't like as much.

7:25

Well, they probably also don't read the whole thing. They probably would have understood if they only get the excerpt. Yeah. It's probably some one passage about the lights in the trees or whatever. Yeah, it is true. Because she didn't really... I mean, she wrote a couple other books, but she didn't really go on to have a huge amount of work and a huge amount of output for whatever reason. I actually sent her a book that I did when I was riding my bicycle across the country. I did a haiku and a sketch every day. And I sent her the book, the original, because I thought I was inspired by her. I just thought that she would enjoy it.

8:11

And she actually sent some nice words in response to the book and sent it back. So that was my one... It was the one time when I wrote to a hero. That's really nice. Yeah, she did reply. I bet a reply from Annie is probably pretty rare. So that's pretty great. I think I still have it. I think it's right behind me. I should dig it up and see. She did... There was one little drawing. She said she really liked that little drawing. So that was good. Yeah. Another thing... To move on from Annie for a second. One thing that you wrote in your book, The Inevitable, that I really loved and thought was interesting

8:57

is after living online for the past three decades, first as pioneer in a rather wild, empty quarter, and then later as a builder who constructed parts of this new continent, my confidence in this inevitability is based on the depth of these technological changes. I really loved the pioneer-to-builder transition. I've been watching this show. It's an old HBO show called Deadwood. I don't know if you've seen it. I haven't seen it. But it's basically about Deadwood. I think now it's South Dakota, but it was a mining town in Indian territory before it was annexed into a state. And so it had no law.

9:31

And the whole show is about that transition from pioneer to order out of chaos. And I think there's a lot of resonance with what you wrote. And this, yeah, the transition from pioneer to builder feels like something that's deeply embedded into tech. Even with every single wave, like in this new AI wave, there's this whole movement of pioneers figuring out the whole new landscape. And then the builders move in. Tell me about what that's been like for you to participate in and watch over the last couple decades. Yeah. There are a lot, there are always feelings of loss as the freedom of no laws,

10:13

the unvarnished, the unconstrained ability to do what you want without having to ask permission. That goes away. And actually, I had another experience that was like that, which was Burning Man. Yeah. And so it had no law. And the whole show is about that transition from pioneer to order out of chaos. And I think there's a lot of resonance with what you wrote. And this transition from pioneer to builder feels like something that's deeply embedded into tech. Even with every single wave, in this new AI wave, there's this whole movement of pioneers figuring out the whole new landscape. And then the builders move in.

11:01

Tell me about what that's been like for you to participate in and watch over the last couple decades. Yeah. There are always feelings of loss as the freedom of no laws, the unvarnished, unconstrained ability to do what you want without having to ask permission, goes away. And actually, I had another experience that was like that, which was Burning Man. Yeah. And I was reminded because I did a podcast with Burning Man today. And they were reminiscing about the first, the 96 or so, the first time I was at Burning Man when there were no streets. There was no adult supervision at all. There was no sense of order. It was crazy and chaotic and wonderful.

12:13

But there was a second year, I think there was somebody who died because they got run over by a car, and they were just sleeping in a sleeping bag. And so it was like, oh, we need streets. Okay. And over years and years of Burning Man, they have more and more of the layering of law and order. And they have tons and tons of police and sheriffs and whatnot and laws and the bureaucratic stuff that I had to go through. I did artwork last year. It was unbelievable. It was like dealing with somebody in India. But at the same time, I think Burning Man is better than ever.

13:01

And so you lose something, but actually I think there's more to be gained by adding that layer of organization and structure and governance. And so what you want to have is, though, you still want to have those zones where there's a frontier. You want to keep making new territory that generates new frontiers. And some people are better on the frontier than back at the center. And I think that's a wonderful way the world would work. And so for me, I want to maintain both of constantly new frontiers where those who are suited to not having to have many rules are able to thrive. And then those who prefer to have the discipline of working within rules also can thrive.

13:30

What about in your own life? Because, as someone who's interested in new technology, that requires being on the frontier. And the interesting thing about the frontier is, if you're a little bit of a restless spirit, it's pretty cool. That's it. But being a restless spirit can be hard. It can be lonely. How have you balanced that in your own life or dealt with it in your own life? So I tend to visit the frontier. I spent a lot of my adult life in the very remote parts of Asia where there is very little infrastructure. I spent an early portion of my adulthood for many extended times in areas where there was very little modern infrastructure.

14:34

And I thoroughly, thoroughly enjoyed it, but I would have died if I needed to live there. I mean, it was a great place to visit, but it was only great because I was going to leave. I have the same kind of thing with the frontiers. It's like, yeah, Burning Man's fantastic for two weeks, but it'd be horrible to have to live there year-round. And being at the frontier of the internet or AI, my understanding is that it's a moving frontier. It's going to move. I can keep going up to the edge to see what's happening, but I don't need to stay there. And I'm going to actually come back and report anyway. So I have the liberty of being a nomad in that sense, of not occupying it.

15:19

So for me, it's a fantastic place to spend some time in, but not a place that I want to spend all my time in. And how does that work? Because I understand the frontier of going to another country and being able to come home. But with technology, you can be at the frontier in your house. And so is that structured for you? Like there are periods in your life or periods in your day where you're immersing yourself in what's new, and then periods where you're in the stability of whatever you're familiar with? Or how does it work? Yeah, right.

16:29

So one of the things that I discovered over time is all my favorite people who were best about the future were actually great historians too. So I would balance reading something about AI with trying to read something historical from the past. And I balance wrestling with the latest AI stuff with working in my workshop and using my hands. So for me, yes, that's exactly what it is. Jeff Bezos said he was trying to build a business on the things that didn't change. And so the Long Now Foundation, which I've been very central to, is trying to take a long-term view, and not just forward, but also of the past.

16:59

And so for me, I would spend time on this ephemeral frontier, but also then try to think about the next 10,000 years and the last 10,000 years. As you're digging into the current AI wave, what are the historical periods that you're thinking about or diving into? Yeah, actually, I spent a good amount of time recently reading about the discovery or invention of electricity. Because my contention right now is we have no idea what intelligence is. That we're as ignorant of it as Isaac Newton and others were of electricity when it was first encountered. And Isaac Newton, one of the smartest humans ever, was totally wrong about electricity. Right? He had weird ideas about it.

17:16

They were just wrong. What did he think about it? I have not heard anything about Isaac Newton and electricity. That's really interesting. It was one of the people who thought that there was this either phlogiston. Have you heard of phlogiston? Yeah, phlogiston. Yeah, yeah. The thing that creates fire. Yeah. It was this kind of element. There was another element. And the discovery of electricity was happening at the same time that we were understanding what elements and atoms and compounds were. So Davy and Faraday and those guys were almost discovering stuff about electricity weekly.

18:31

And the origins of the Royal Academy came out of the weekly meetings that they would have where they would sell tickets and do demos with electricity and make sparks and stuff. And one of the biggest news items in the shocks was when they, and I forget who it was, maybe Faraday, proved that electricity would happen in a vacuum. Because there goes the ether. You don't need ether. It's like, well, then what is it? Okay. And there were some of the earliest beliefs about electricity, that it was primarily a biological phenomenon. And there's all those, all those. Reflexes and frogs. Reflexes and stuff.

19:30

And so there were just endless theories about what it is, and they all reminded me of all the theories we have about what intelligence is, because we don't really know what it is. And I have been saying, I suspect that intelligence is not an element, but a compound, that it is made up of a complex of different cognitive elements. And we haven't even identified them yet, in the same way that salt's not an element. Salt is actually a compound of some elements that they had not yet identified. So you can think of the current AI as we're making some kind of salt and we don't even know what it's made from. Yeah. I think that's true.

20:07

Where it makes my mind go, and I'm curious how you would respond to this, is we actually do know what it is, but we don't know it in the same way that we know what electricity is. So we don't have an explicit, exact mathematical theory in the same way that we can talk about electricity. And we don't have a way to decompose it into parts that reduce down and then recombine into it. But we do know what it is in a different way. Like I'm talking to you and I know that you're intelligent. And that's harder to grasp. It's not the same kind of graspy, I can pin it down to the wall kind of thing.

20:33

But I think that that just may be a property of intelligence, that it is this fuzzy thing. How do you think about that? Well, I don't know. You may be confusing recognizing something with knowing it. So I think we can recognize it. But I actually don't think we know what it is. And in fact, I think our brains are incredibly opaque to introspection deliberately. I think we have these complex things that deliberately do not allow the organism to interfere and meddle with them. Could you imagine if we had access to the source code? We would be completely wrecking ourselves. But we do know what it is in a different way.

21:48

I'm talking to you, and I know that you're intelligent. And that's harder to grasp. It's not the same kind of graspy, I-can-pin-it-down-to-the-wall kind of thing. But I think that may be a property of intelligence, that it is this fuzzy thing. How do you think about that?

21:50

Well, I don't know. You may be confusing recognizing something with knowing it. So I think we can recognize it, but I actually don't think we know what it is. And in fact, I think our brains are incredibly opaque to introspection deliberately. I think we have these complex things that deliberately do not allow the organism to interfere and meddle with them. Could you imagine if we had access to the source code? We would be completely wrecking ourselves.

21:52

And what it is, again, I think we humans have a very peculiar complex of things that, if we map it out in the possibility space of all possible minds, which is a very high-dimensional space, we're going to be our compounds way at the edge. We're an edge species. We're not at the center of anything, the galaxy or the solar system or evolution. We are an edge. Our kind of intelligence will be revealed to be a very peculiar mixture that's evolved for us. And then what we're going to be doing with AIs is making hundreds of various other kinds and filling out that possibility space with many types of thinking.

21:54

And so we'll look back, and we won't even recognize maybe some of these other things as intelligence right now because we don't have a very good definition. It's like, what are the definitions, or what are the marks, for something that doesn't look like human-like intelligence? Some people say, well, there isn't anything. There's only universal intelligence, and we're just going to make more of it, that there's only one thing. And that's possible, but I suspect that's wrong. I suspect that there are many compounds and that they will be engineered to do different things. And to some degree, we won't understand even how they work, but that's because they're different.

21:57

And so I think we are very much like the early days of electricity, where we simply didn't have a clue about what it was, even though we could use it, even though we could recognize it. Where does your intuition that it's made up of compounds come from?

22:07

Well, several reasons. One is Marvin Minsky was the first who suggested it, called the Society of Mind. And then these days, the AIs have mixture of experts, where they are already doing that, where they already are taking different kinds of cognition and making them into compounds. And so I think we'll have various layers of this, like tissues, where you have cells, or molecules made from elements. And so we're going to make some very high-chain, heavy compounds of intelligence at some level, made up from lots of little bits of elemental cognition.

22:14

What we haven't done yet is done the chemistry of identifying what some of the basic cognitive units are, the cognitive elements. And we may be starting to do that. Thinking about the way neural networks work, a way to look at them is they learn many, many thousands or millions of rules for what to do in particular situations that they can partially apply and run many, many of those rules in parallel to find the right set that applies to a particular situation. So in that case, intelligence is a compound of rules that are about little micro-correlations that are applied depending on how relevant they are to a particular situation.

22:27

But it's interesting to think about how much of those rules are contingent, like they're just situational, versus they're universal. Because we do know, for example, how neural networks function at a low level. We know the atomic units. And in fact, the specific architecture, the specific set of atomic units that you use, doesn't really matter for the high-level behavior. It does to some extent, but you can get basically the same behavior no matter the simple components you use. So there's something always hard to understand in between the simple components and the outward behavior that we observe.

22:32

Danny Hill has made a computer with Tinker Toys, so you can make computers out of all kinds of elements, logic gates and stuff like that. So I don't want to confuse your neural nets. I don't think that is the basic unit. I think there is a type of reasoning or learning or something that happens with the neural nets that we haven't quite identified yet. But I would say that would be the element: what is that process of pattern matching, or if that is what it is, or deduction, or it's the logic, or what's the workflow for doing deduction? And it could be agnostic to the actual platform. It seems like it must be in some way. But yeah, that's interesting.

22:33

One of the things I love about the way that you've set up your career, and I think probably also the way that you think about creative work in general, is it's about being honest and authentic to who you are instead of what you think you should be doing. And I found that over and over again, especially at the beginning of a career, it's really easy to be like, I need to do things in this particular way. And then at some point, you're like, I don't know, at least for me, you're like, this isn't working as well as it should, and this kind of sucks, and I guess I have to do the thing that's more honest to who I am and more shaped to who I am. And it removes all of this daily friction. So honestly, hearing you talk about that in various forms over the years has been quite helpful for me.

22:39

How does that look now after many years of thinking that way? Is that still something that's on your mind, that's difficult, that you have to unstick yourself from? Or do you get used to living that way after a while?

22:51

Yeah, I mean, my own life was never very planned or deliberate in that sense. I had more directions than destinies or destinations. A phrase that I like, or advice that I like to give these days, is don't aim to be the best, aim to be the only, be the only. But that was not something that I was doing consciously when I was younger. That was something that I only realized I was doing much later. And it's part of the book about wisdom I wish I had known earlier, because I really did wish that someone had told me that earlier.

23:00

And so I think I did a bunch of things very intuitively without necessarily having a grand plan about it. And so I naturally move in that direction. And this idea of being the only was made clear to me first at Wired, where I was trying to assign stories that I had to other writers and often not getting any traction with a great idea that I couldn't sell to anybody. And after years of trying to kill it, I would wind up doing it myself and then realizing, oh, that's because only I can do it, and that's what I should be focusing on to begin with. And so that was, so I think the clarity of it, I think I have more clarity of it, but I'm still doing what I've always done, maybe being a little bit more aware of the actual process of doing it.

23:13

Yeah. I resonate very much with this, trying to get writers to do an idea. So the company that I run, it's a media company, and it's so different from any other business, because I come from the software world. And if you have a product idea and you build a little bit of it, you can have someone else build most of the product, and it can be great. But getting a writer to write your idea, it's never any good, almost never. The people who are good ghostwriters are such a small portion of writers.

23:25

Well, actually, that wasn't my experience. My experience was that the writers were even better. So I discovered, reading IEEE Spectrum, that they were laying the fiber or fiber-optic cables around the globe. And I thought, hmm, they're wiring up the new sphere. That would be a great story. So I assigned Neil Stevenson to that. He just did this masterpiece that I could never, ever have done. Bruce Sterling and the other great writers did a much better job of writing it than I ever could have. Well, I wish I had Neil Stevenson writing for me.

23:50

Okay, here's the thing. This is what I tell every aspiring writer: get the young science fiction writers of today and give them journalistic assignments. They love it because they're born storytellers, you're paying them to go learn something they want to learn, and they'll come back with something amazing. I think that's the key thing. And what I'm more talking about, rather than, I've worked with a lot of really talented writers. Everyone that I work with, I think, is super talented. And I thought, hmm, they're wiring up the new sphere. That would be a great story. So I signed Neil Stevenson to that. He just did this masterpiece that I could never, ever have done.

24:48

And Bruce Sterling and the other great writers, they did a much better job of writing it than I ever could have. Well, I wish I had Neil Stevenson writing for me. Okay, here's the thing. This is what I tell every aspiring writer: get the young science fiction writers of today and give them journalistic assignments. They love it. Because they're born storytellers, you're paying them to go learn something they want to learn, and they'll come back with something amazing. I think that's the key thing. And what I'm more talking about, rather than, I've worked with a lot of really talented writers. Everyone that I work with, I think, is super talented.

25:44

But trying to get someone to write about something they don't really want to write about, it's never going to be that good. In my experience. Well, yeah. So, you have to match it to something that they're interested in. So, yeah, that's the trick. I wasn't making them write something they weren't interested in, but I was, and sometimes that's how the conversation would start.

26:31

Okay, what's something you're really interested in that we can help make happen, that we can pay you to educate yourself in? And we could jointly come up with an idea. And again, if I was running a magazine, that's the first thing I would start doing, is finding the youngest science fiction writers and giving them assignments. I should look into that.

26:54

Mostly, I just find people who write good tweets that I find interesting. Yeah, that's fine. That's a good way, too. But find someone who can tell a good story and has a little bit of ability to fantasize, or whatever. And Neil was not Neil when we were first starting. He was still at the beginning of his career, which is, of course, why he agreed to it. Yeah. One of the things you talk about a lot is being a reluctant writer and born editor.

27:50

And just getting into AI for a second, one of the things that strikes me about AI is it puts you into editing mode much quicker. Yeah, it does. How has that worked for you? It's been great because I can get over that big hump. Just helping get something on the page and starting to work with it and illuminating the spots that I'm most ignorant of. And, yeah, for me, it's a really great way to start. Is there anything that has worked particularly well as part of your workflow to get it started? Well, I use it for research. I use some of them for research, too.

29:04

And that's another way, while you're researching, you're summarizing stuff and synthesizing. So there are these elements that begin to take place. The elements are really good at organizing things. And that's another, see, I'm not that organized normally. I kind of, I'm a gardener rather than an architect in terms of things. And so I can get a little further along in thinking about it in an architectural way. So for me, it's a great way to start on things. What are you using day-to-day AI-wise? Which models? I'm using mostly OpenAI right now. Do you use any particular one? Are you using 4.0, 0.1, 0.3? I have the 0.1 Pro. Do you like it?

30:18

Yeah, I mean, I've done some very deep research. And it's astounding. I'm just reminded again and again that this is a skill. Using it and getting the most from it is a skill that will take your 10,000 hours. And it's definitely not just pushing the buttons. It's not just clicking. And so it's very apparent to me that I need to spend a lot more time whispering and understanding how it is and how to use it. And so my needs right this moment are not such that I'm using that level of research every day. Do you have specific examples of when you've used it for research that it has blown you away?

30:54

I did something, and this is a little bit of something I've been meaning to write about, which is I had this fantasy. I had this observation that I realized Leonardo da Vinci, Martin Luther, and Christopher Columbus were all alive at the same time. And I said, this is my conversation with the AI. I said, imagine it's a snowy evening and all three are stuck in the same hotel together and they have a conversation. Give me the conversation based on your writings and their interests and their personalities. And so they did the conversation. And I said, that was amazing. But they got along so well, they decided to collaborate on a project. What would that project be?

31:43

And this was the AI's idea. The AI's idea was that they would want to start a new city in the new world that was based around science and religious freedom. And I said, okay, start writing Wikipedia articles about this city and start, we'll start filling it in. And then we had characters and peoples and histories. And then I was going on with starting to tell, actually to tell research stories based on these characters. And then I introduced other things, other contemporaries, like Queen Victoria, who was alive at the same time. She was the main rival trying to take down the city. They reached China 10 years before the Portuguese.

32:24

And then they would bring all the books from China. And so I was just making this bigger and bigger, bigger thing, world building. And I started, I got 10 different novels from it. And then I had it synthesize and iron out all the contradictions between the novels and make a big epic saga. And then I had it write the book covers and write the marketing materials. And the point of all this is that I'm not going to show it to anybody because I don't need to. The joy of creating it was better than reading it. It was the audience of one.

33:02

And so what I'm hypothesizing is that a lot of the generative stuff, the 50 million images that are generated each day with AI, 99.999% have the audience of one. They're generated for the pleasure of the co-creator. And this idea of people making feature-length movies for themselves, the pleasure will be in the generating of the movie, the co-generating of the movie that you're directing. You'll be directing the movie for yourself. Anyway, so that was a project that was using Pro because the degree of historical realism and fantasy was mind-bending. That's really cool. I love that. It seems like there's an interesting line from a thousand true fans to an audience of one.

33:39

Yeah. How have you thought about that? Well, of course, there's no economic model for the audience of one. There is for OpenAI. Right, exactly. I think this is the abundance mindset, where you have the time to do this. So I think this is not so much a business or someone's career. I think this is a different form of entertainment or self-expression. It's just like Sunday painting or keeping a journal or someone doing ice skating. It's a form of self-expression and relaxation and enjoyment, entertainment. So it's closer to entertainment than it is to actually a career. It reminds me a little bit of, are you familiar with active imagination? No.

34:31

It's part of Jungian psychotherapy. The idea being you can do active imagination where you take a dream that you've had recently and you reenter the dream world while you're waking. You explore some of the archetypes and themes, either by writing them out or just exploring them with yourself. And in doing that, it reflects back to you things that might be a little bit more latent in your psyche. So the fact that you're playing around with these characters, and these novels are going in different directions or whatever, and the decisions that you're making might say something to you about what you're processing or what you're currently thinking about.

34:48

In addition to just being really fun, it might tell you something about yourself. Mm-hmm. Yeah. So it may be something like that. And you explore some of the archetypes and themes, either by writing them out or just exploring them to, to, to, to, and with yourself. And in doing that, it reflects back to you things that might be a little bit more latent in your psyche. So the fact that you're playing around with these characters and these novels are going in different directions or whatever. And the decisions that you're making might say something to you about what you're processing or what you're currently thinking about that.

35:32

In addition to just being really fun, it might tell you something about yourself. Mm-hmm. Yeah. So it may be something like that. In fact, I think there will be very, very good AI therapists. And maybe to the extent that we may not even make that distinction, maybe some particular AI companions, AI buddies, AI partners. Will perform some of that and people will use them in that capacity, even though they're not maybe nominated as that. And again, this is something else I've been saying, is I think people are going to be shocked by the degree of emotion, emotional bonding that we will have. As we put emotions into the AIs.

36:22

And some people will be very, very close to these on an always-on basis and. Very dependent on them to do their best. Yeah. One thing that I don't know if you saw, but just came out today, a couple hours ago. No, I haven't seen the band podcast. You're in for a treat when you get off the podcast grind, but OpenAI released a new memory system for chat. And when it really thought, okay, this is definitely something I need to store. But what the new memory system does is it is able to access all the past history of all your chats that are relevant. Automatically. So you can ask things like, what do you know about me from all of our chats that I might not know about myself?

37:28

And it will just go through all these historical chats and tell you a lot of really interesting things. I've been playing around. I was playing around with it all day before we got on the show. I think you'll really, I think you'll really like it. And I think to that emotional attachment point, I want to use something that feels like it knows me. Sure. I get more, I get more, more attached to it. I personally think that's really good, but there are trade-offs, obviously. And I'll be curious to see how that changes how we relate to each other too over the next couple of years. Yeah. Yeah. That's cool.

38:27

I've noticed that you're also doing a lot of tweeting with, it looks like AI-generated images. Are you using native image gen for that? I think they're mostly Midjourney. Midjourney. Yeah. Yeah. Which for me, I just have a habit of that. It's comfortable. I know DALL-E, I mean, not DALL-E, the chat now has some, which I've tried and it's pretty good, but I just, I actually like the public aspect of the Midjourney Discord where you actually. It was a huge, it was a huge quick learning curve because you were seeing what the prompts other people were doing and how you could get there. And I liked that public aspect of it.

39:19

I think one of the interesting things about this particular one is how scared people are. And that's probably been true to some extent of previous ones. But I think in a lot of ways, with mobile, it was like people didn't even care. It just wasn't even on their minds for a while. At least in my lifetime. And I'm curious what you've learned about seeing all these different ways come and go, what you've learned about the future and how to think about the future. That's a pretty big question. I think the first thing, I think about all the places that I was wrong. Where were you wrong? Oh, so many, so many times. I was very wrong about VR.

40:13

Jaron Lanier showed me VR in '87, something like that, in his lab. And I was completely blown away. And I thought, oh my gosh, this is, this is, this is the future. This is amazing. And to be clear, the VR that we had back then, which was 30, 40 years ago, it's crazy. I can't remember. It's a lot of years ago, many decades. It's not that much different than the VR you get now. The difference was that that was multimillion dollars and it is now a hundred dollars. That's the main difference, not that it was actually that much better. Now it is better, but it's not a million times better, but it's a million times cheaper.

41:02

And so I've been very, very surprised about how resistant, how slow, how slow that has been. Because I really expected that to take off. I was wrong about eBay. This is a trivial example.

41:25

I thought, I don't get, what's, who would use this? And I just didn't have the imagination to see it. I was early to blockchain, but I didn't think Bitcoin was really going to be much. I didn't understand. I didn't foresee the way in which it became this store of value. And I expected, like everyone else, that it could be used for micropayments. But I didn't understand, or we didn't appreciate it, we didn't know how expensive it was going to be to do a processing. And so my expectations about the role of blockchain, I also thought that we would have more headway into things that were not financial.

41:56

And again, blockchain became completely overwhelmed by the amount of money in it. And it became about money and finance. And I thought that it would be used for things that had nothing to do with finance, and that really didn't happen. And so that's when I think about what I've learned with the future, is that it's easy to make predictions and hard to make predictions that are true. Well, if we go back to those specific cases, it seems like there is one case where you were like, this has definitely got a future. And two other cases where you're like, I don't think these are really that great. And what do you think you missed? Let's talk about the first one.

42:21

You're like, this definitely has a future. What do you draw from that? What do you think you missed there that made it much harder than you expected to get people to adopt it? I think it required a lot of biology and understanding, or being able to work with biological things rather than just our mind. You have focusing on your eyes. You have just the weight of the thing on your head. There's just a lot of biological things. In addition to the hardware things, you have hardware biology, and that is just going to go slower. And so one of the things I take from that is I think the arrival of robots is going to take a lot, lot, lot longer than people think.

42:40

Because we have a 25-watt supercomputer and a quarter-horsepower engine. And there's no way we can do all that kind of stuff with that kind of efficiency, anywhere close to that. And so the amount of power that we need to either compress or to eliminate by making more efficient things is a huge, huge, huge gap.

43:15

And so, again, that physicality of it, particularly around our bodies and stuff, or near the scale of our bodies, I think is going to be a lot slower. And that's what I've taken away from the thing that we've learned about VR, is that it's going to take a lot more time than just making AI. Well, that's interesting because I was going to bring up AI as an interesting parallel example where it also took a lot longer to do AI than we thought it would, but then it just all seemed to suddenly happen in 10 years. And I'm curious what you think the leading indicators are for something like, okay, so AI didn't work for 50 years.

43:37

And then suddenly in 2010 or so it started to, there were some glimmers of hope. And then ChatGPT a couple of years ago, it's like, really, wow, this is a thing. In VR, if we wanted to measure where we are in that cycle, do you have a thought for that? Yeah. And that's what I've taken away from the thing that we've learned about VR, is that it's going to take a lot more time than just making AI. Well, that's interesting because I was going to bring up AI as an interesting parallel example, where it also took a lot longer to do AI than we thought it would, but then it all seemed to suddenly happen in 10 years.

44:01

And I'm curious what you think the leading indicators are for something like, okay, so AI didn't work for 50 years. And then suddenly in 2010 or so, there started to be some glimmers of hope. And then ChatGPT a couple of years ago is like, really, wow, this is a thing. In VR, if we wanted to measure where we are in that cycle, do you have a thought for that? Yeah. I mean, you could say VR is still waiting for its LLM moment, right? Where there's some technical breakthrough in the lenses or focusing or something or projection that allows for that. So it's interesting too, that the LLMs were not working on reasoning directly.

44:40

They were doing language translation, and that's when they noticed that there was some reasoning happening in language translation, which was completely unexpected. And maybe the key technology for VR/AR will come from somewhere else, but it hasn't happened yet. It doesn't look like it's happening right now. So, yeah, AI is a 50-year overnight success. What about going back to the other two examples? So eBay and blockchain of like, hey, this isn't really that interesting. Right. Right. So eBay, I think, with the auction stuff. And I think the only reason why eBay worked is that people moved beyond the auction. I use eBay all the time, and I've never used auction.

45:37

I just have no patience for it. And so I think the idea that it was auctions, I didn't connect to, and I was never really interested in, I could be wrong. But for me, eBay has only now succeeded because most people aren't using the auctions, but I don't know if that's really true or not. Do you use auctions on eBay? I only got really into eBay when I was 10, and I was like, I want to sell everything in my dad's garage. Did you? I didn't end up doing it because selling things online as a 10-year-old was a complicated situation. Yeah. Yeah. Yeah. Yeah. But I was really into it for that reason, but I've never really bought anything on eBay. Never. I've never, I never bid.

46:39

So eBay is actually very, very useful. It's kind of like there's Amazon and then there's Alibaba and then there's eBay, which is sort of like all the things that either aren't officially for sale or are way out of stock or out of print. Or they're totally obscure. And so for me, if I can't find it on Amazon, Alibaba, then you're onto eBay. Etsy is another level, which is handmade stuff, but eBay is really good for really obscure stuff. And then they do have the option of auction, but I never use it anyway. So for me, the thing was this idea of auctioning for everything didn't seem like that was going to work.

47:08

But for a while, it did. And that was just me personally, just not being much of a bargainer type. I guess you can't win them all. I know you have a hard stop, Kevin. It was really great to get a chance to chat. Thank you so much. Would love to do this again soon. Yeah, it was a pleasure. Great things. And I'm glad there's another Andy Dillard fan. Oh my gosh, folks, you absolutely positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure unadulterated knowledge bombs about ChatGPT.

47:33

Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor, hit like, smash subscribe, and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you. Because we have a 25 watt supercomputer and a quarter horsepower engine. And, and there's no, there's no way we can do all that kind of stuff with that kind of efficiency anywhere close to that.

48:11

And so the amount of, of power that we need to either compress or to eliminate by making more efficient things is, is, is a huge, huge, huge gap. And so that, again, that physicality of it, particularly around our bodies and stuff, or near scale of our bodies, I think is, is going to be a lot slower. And that's what I've taken away from the thing that we've learned about VR is that it's going to take a lot more time than, than just making AI. Well, that's interesting because I was going to bring up AI as an interesting parallel example where it also took a lot longer to do AI than we thought it would, but then it just all seemed to suddenly happen in like 10 years.

48:55

Um, and I'm curious what you think, uh, the leading indicators are for something like, okay, so AI didn't work for 50 years. And then like suddenly in like 2010 or so it started to like, there was some like glimmers of hope. And then chat GPT a couple of years ago is like, really, wow, this is a thing. Um, in VR, like if we wanted to measure where we are in that cycle, do you have a thought for that? Yeah. I mean, you could say VR is still waiting for its LLM moment, right? Where, where, where there's some technical breakthrough, um, in the lenses or focusing or something or projection that, um, allows for that.

49:40

So it's interesting too, that the LLMs were kind of not working on reasoning directly. They were, they were doing language translation and that's the notice that there was some reasoning happening in language translation, which was completely unexpected. Um, and maybe it's, uh, the key technology for VR AR will come from somewhere else, but it hasn't happened yet. It doesn't look like it's happening, um, right now. Um, so I, yeah, um, we, you know, AI is a, yeah, it's a 50 year overnight success. Um, what about, uh, what about going back to the other two examples? Um, so eBay and blockchain of like, Hey, like, I don't, this isn't really that interesting. Right. Right.

50:32

So eBay, um, I think, uh, with the auction stuff. And I think the only reason why eBay worked is that people move beyond the auction. I use eBay all the time and I've never used auction. I just have no patience for it. And so I think the idea that it was this idea that it was auctions and that I didn't connect to, and I was never really interested in, I could be wrong. But for me, eBay is only now succeeded because most people aren't using the auctions, but I don't know if that's really true or not. Do you use auctions on eBay? I, I only, I got really into eBay when I was like 10 and I was like, I want to sell everything in my dad's garage, you know? Did you?

51:18

Uh, I didn't end up doing it cause, uh, it, it just, selling things online as a 10 year old was, it was a complicated situation. Yeah. Yeah. Yeah. Yeah. Um, but I was really into it for that reason, but I, I've never really bought anything on eBay. Never. I've never, I never bid. So, so eBay is actually very, very useful. It's kind of like there's Amazon and then there's, you know, Alibaba and then there's eBay, which is sort of like all the things that either aren't officially for sale or, you know, the way out of stock or out of print. Or they're totally obscure. And so for me, if you, if I can't find it on Amazon, Alibaba, and then you're, then you're onto eBay.

51:59

Um, Etsy is another level, which is like, you know, handmade stuff, but there's eBay is really good for like really obscure stuff. And then they do have the option of auction, but I never use it anyway. So for me, the thing was this idea of auctioning for everything. Didn't seem like that was going to work. Um, but for awhile it, it did. And that was just me personally, just not being much of a bargainer type. I guess you can't win them all. Um, um, I know you have a hard stop, uh, Kevin. Um, it was really great to get a chance to chat. Thank you so much. Um, would love to do this again soon.

52:43

Yeah, it was a pleasure. Um, great things. And, um, I'm glad there's another Andy Dillard fan.

52:57

Oh my gosh, folks, you absolutely positively have to smash that like button and subscribe to AI and I, why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure unadulterated knowledge bombs about chat GPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat craving for more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor, hit like, smash subscribe, and strap in for the ride of your life.

53:34

And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.

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