What If? So What?
What If? So What? is the podcast where we discover what’s possible with digital and figure out how to make it real in your business. Join host Jim Hertzfeld, Vice President Strategy, as he interviews industry experts and veterans to dissect the buzz, challenge the status quo, and translate grand visions into tangible actions. Because it's not just about dreaming big, it's about asking the right questions: 'What If?' 'So What?’, and most importantly, “Now What?”
What If? So What?
Wired's Kevin Kelly on Technology, AI, and the Power of Learning
In this thought-provoking episode, Jim speaks with Kevin Kelly, Senior Maverick at Wired and author of numerous influential books including "Excellent Advice for Living" and co-founder of the Long Now Foundation.
Kevin shares his wisdom on emerging technology, the danger of “Thinkism,” why adoption lags behind innovation, and how AI is reshaping work, creativity, and human connection.
From the evolution of “digital” to the future emotional bonds we’ll form with AI, Kevin offers grounded optimism and timeless advice: in a world of constant change, mastering how we learn is the best survival skill
Thank you for listening!
You can also watch the episode: https://www.youtube.com/@perficient/podcasts
Have a question for Jim? AMA - submit questions: podcast@perficient.com
FOLLOW JIM
https://www.linkedin.com/in/jimhertzfeld/
FOLLOW PERFICIENT:
https://www.linkedin.com/company/perficient/posts/?feedView=all
Kevin (00:04):
My observation about this is that the frontier is moving very, very, very fast. But the adoption is just going to take a long time because empirically, you can't just introduce this technology nakedly. You have to adjust workflow, you have organizational shape, you have to adjust the infrastructure to maximize it.
Jim (00:27):
Welcome to What If? So What? the podcast where we explore what's possible with digital and discover how to make it real in your business. I'm your host, Jim Hertzfeld, and we get done by asking digital leaders the right questions. What if, so what, and most importantly, now what? Hey, I'm very excited to have a guest. It's a title I don't think we've ever had on the show. Senior Maverick at Wired, author, many books. I think most recently the Excellent Advice for Living and one that sticks in my memory, New Rules for the New Economy, a driver of The Recommendo Newsletter. Pretty engaged with that, but Kevin Kelly, welcome to What If? So What?
Kevin (01:01):
It's great to be here. I'm looking forward to the conversation.
Jim (01:04):
Well, I really appreciate it. Anything else I left out? I know that you've done a lot in your career.
Kevin (01:10):
So, I’m Involved with the Long Now Foundation, which is trying to foster long term responsibility – we’re the people who built the 10,000-year clock ticking away in the mountain. Right.
Jim (01:18):
Right. So, Kevin, I was telling you, I feel like I've known you for a long time and even though we just met, because as I was starting out my career is when Wired came out. But actually, a little story, when I was in engineering school, I sort of treated it like a job. And I had class in the morning, and I would grab lunch on campus. And then I liked to get my homework done in the library. And in the library, there was one room devoted to newspapers and magazines, and that was kind of my favorite place to go. I just liked the tone of that place, and I was always looking for distraction. One of my distractions was the Whole Earth Review. And it really kind of touched part of me that, you know, as I was sitting there doing math problems for four hours, <laugh> and I got this sort of a different side that needed to be sort of triggered and nurtured in me. So, we go back, maybe in my own history a little farther than I thought.
Kevin (02:04):
Oh, cool.
Jim (02:04):
Great. Appreciate that. Yeah, so I try to keep kind of a diverse set of interests. You know, you've got so many great thoughts and elements of wisdom, and I think have really brought a different perspective to technology and we focus a lot on digital. I'm going to throw you some questions around that. But one of the things that this podcast is focused on for me is sort of grounding things in reality. We work with a lot of businesspeople who hear about technology and they just want to, like, how can I make money off of this as fast as possible? And so there's a lot of grounding in reality, and you've talked a lot about goofing off and experimentation, and that's really what prototyping is. And that's really what research is. You don't know what you're going to find, but I think a lot of our audience appreciates that. But they're looking for what's going to work tomorrow and what's in it for me. So, we like to explore both sides of that. But you know, you've seen a lot of technology come and go. How do you sort of spot something that you think is overhyped or is, boy, that might be bs or it might be at BBS today. Like what's your radar for judging a technology on first look,
Kevin (03:08):
I think there's a lot of Thinkism, the fallacy of Thinkism in the world, which is that Thinkism's premise is that you can figure things out by thinking about them. That you can solve problems by thinking about them. That you can evaluate things by thinking about them. And I think that's way, way overused. I like to use things. I think we discover things by using them. Yeah. So, when new things come along, I try as much as possible to use them in order to have an opinion about them, use them to understand them. Like AI, we've been thinking about it for a century. We're now at the point where we can actually use it and we're going to learn a lot more. And I learn a lot more by using them as much as I can, as a way of trying to understand and evaluate them.
So, I would say when a new technology comes along, if it at all possible to try and have the experience of it, that helps me evaluate. Otherwise, I feel kind of lost. You know, like, and there's a lot say in the medical world where it's not really that consumer facing that sense of, right, you can try the latest vaccine, although, you know, let's go. I, I'm taking all the vaccines I can is still a better way. I mean, it's one of the ways that I use to try and evaluate is to actually have a firsthand experience. And the second way is to talk to other people who are also using them in some way, because I think even the inventors of things don't really know what they've invented. There's a very famous story of Thomas Edison who invented the phonograph and shortly after he invented the phonograph, he actually didn't have any idea what it would be used for. And so, he made a list in his notebooks of the 10 things that he thought this invention the phonograph might be used for. And number one was it would record the last words of the dying. You could have them after they're gone. And the second one was that you would be able to replicate great sermons at church. And way down last was like, oh, we could record music on it. <laugh>.
Jim (05:06):
Right?
Kevin (05:07):
So even the inventors, and this is true of the current crop of LLMs, they were not trying to invent a reasoning machine. They were making language translation. Mm-hmm <affirmative>. Software. And so, in order to understand what something is, we actually have to use it for a while. So, if I can't use it for a while, I want to talk to someone else who's actually using it in some capacity because that's where we're going to learn about. Its potential, its potential for good, for harm. And it's not by thinking about it that we're going to get there.
Jim (05:38):
Yeah. I love that. I know when design thinking became very popular a few years ago, and I think it's one of those things that sort of goes in and out of style, maybe has different labels mm-hmm <affirmative>. But we kind of made the observation that, well it's kind of counterintuitive because design thinking is actually maybe building and I'm not just thinking about it, I'm just doing it, but it doesn't have the same rings. You know? So, I like that very much. I think we do way more prototypes and POCs and ironically, you know, AI, GenAI has given us a lot of opportunity to do things very quickly. Right, right. Which, and economically so. Right.
Kevin (06:12):
And you know, just generally, you didn't ask this, but it's very clear to me that the primary users of AI on a daily basis are coders. Right? I mean, again, nobody anticipated that. If you take language translation models and you keep amplifying and making them bigger and bigger, you suddenly make the best tool ever for coding. And in retrospect, well coding's kind of a language, maybe that makes sense. But that's the primary daily user of these tools right now are people who are coding. And so, in that realm, in that field, this is an absolutely disruptive technology. Right?
Jim (06:48):
Yeah. I think we're certainly applying that every day and I think there's a lot more trust in that. I would say a year ago there was a lot of hesitation around it. But again, as more people try it, it's panning out. So, people are asking, we thought people would say, if you adopt these tools in your everyday practices, can I pay you less money? What they're actually saying is, could you do more in the same amount of time? Which is mm-hmm <affirmative>. Kind of refreshing. So yeah, it's exciting.
Kevin (07:11):
Or even do better in the same amount of time.
Jim (07:13):
Well, do better. There's a pattern in AI driven software where one agent writes a code, the other one reviews it. Mm-hmm <affirmative>. As that sort of takes off. And I'm not sure how everyone else is doing it, but that from a quality perspective is definitely exceeding the human in the loop review. So, we're pretty excited about that. But I'm glad to hear that validation that sort of trying is understanding and seeing his believing. Right? Like I told my kids when they're growing up and they said they didn't like Brussels sprouts, I said, you can't have that opinion unless you try just one Brussels sprout <laugh>. So, in our house was that you at least had to try it and then you could have any opinion you wanted.
Kevin (07:46):
Exactly. Yes.
Jim (07:48):
You know, one of the things that I was curious about and love your perspective, again, sort of with your kind of long-term view, one of the things that we talk about a lot are sort of recurring themes in technology adoption. You know, so buying into hype is one of them, right? Or skipping the requirements. Like can we just skip the requirements? Can we skip the QA? Can we, do we need that QA team not listening to your end users or customers or not being in the situation that they are in. Can we just copy the other guys? Do you see patterns in terms of like the way technology is adopted and adapted? Why do you think some of these patterns recur over and over again? Do we just have short-term memories? Do we just have a new group of people? Is there some human drive that forces us to sort of misalign our expectations?
Kevin (08:34):
That's a really fair question. I would say a recent pattern that I have noticed within my own lifetime in adoption of technology is that unlike when I was say a kid in the fifties and sixties reading science fiction, I think today when people first encounter a technology, their very first reflective response is, how is this going to bite me? No, it's gonna bite me sooner or later. Right?
Jim (09:00):
It's just a matter of time. How is it gonna do it? Yeah.
Kevin (09:02):
Right? Rather than, oh, this is amazing, this is fantastic. The very first response is, I know this looks cool, but it's going to hurt somewhere. What's the catch? What's the catch <laugh>. Exactly. And I think that's not gonna go away. I don't think it's just a phase. I think that's sort of a grownup phase, but a response that we don't hear very much on that might be added to this is one of the things that I've noticed is that technologies behave very differently when they become ubiquitous. And so, this is much more about trying to forecast rather than just trying to use it. But what we're trying to think about it is that you always want to understand, like what happens when everybody has it. Because when everybody has it, there's a phase shift. Something weird happens when just a few people have it versus when everybody has it.
Mm-hmm <affirmative>. Mm-hmm <affirmative> And so, you know, it's like when the few people have cell phone versus when everybody has one. And so, I'm not McLuhan like in saying, well, things slip around and then they're inverse of it. But there is a difference. The more is different in the sense that there is a quantitative change that happens when you saturate it with ubiquity. And some of our positive and some are negative, but the species is changing. The technology actually undergoes a phase shift when you have ubiquity. And so, you kind of have to have two phases of evaluation. One when it is just a few people and then one when it's saturated.
Jim (10:21):
Yeah. We often think about there's always leaders and laggards and right fast followers and totally different kind of question here, Kevin. We've wrestled with this forever, the term digital. So, there are three words that somebody coined a phrase for the label for these words. He called them white noise words. So, I have a little bucket of white noise words, digital, strategy and innovation, which is the team I run here. So, it's compounded daily.
Kevin (10:45):
You mean you run the digital innovation strategy? Is that what you're saying? Digital?
Jim (10:48):
Yeah, yeah. The strategic innovation in digital. Right. Um, exactly. So, you know exactly what I'm saying. So alright. We just haven't found good substitutes, you know, for these terms. But one of the things that has always hung us up, I think we have great definitions of innovation that are established. Strategies are another nebulous one, you know, people can't see the difference between a strategy and a plan. So, there's been a lot written about that. But the word digital, I think you'll have a perspective on it. So, we asked the question, what is digital? We gave five definitions, and we could really only get one of them to sort of land in terms of a statistical alignment. We also threw a bunch of things out there and we said, “Is this digital or not?” So, for example, an ERP system, is that digital? No, that's not digital. Well why not? It's on a big computer, it's in the cloud. Yeah. But that's running the back office, you know. Okay. Well I scratched my head on that one and we said, what if that ERP functionality was on a mobile app? Is that digital? Oh, that's digital. Yeah, we know it when we see it. It's like, you know, certain definitions. Right? Well we don't know how to define it, but we know it when we see it. Mm-hmm <affirmative>. What's your take on that? What makes something digital in this day and age?
Kevin (11:55):
It's funny cause at least in my circle, I don't hear that term being used very much more.
Jim (12:01):
That's great. That's actually a great data point.
Kevin (12:02):
From my, from my point of view, it means things that are virtual, not analog. Mm-hmm <affirmative>. Meaning that their value is informational, communicable. And I would almost say that if it's being carried on electrons, it's digital. So like, I'm trying to think of the edge cases on, right. Most money today is digital. Mm-hmm <affirmative>. It's not just blockchain and Bitcoin. Most, yeah. The money moving around the world doesn't exist as paper. It's digital. Right. You know, after you put the ballot in the machine, you're voted digital. Yep.
Jim (12:44):
It's digitized or digitalized. Digitized.
Kevin (12:47):
That distinction hasn't really been important in kind of where I'm traveling. Sure.
Jim (12:51):
I, I bring it up because I'm actually encouraged to hear that because it'll kind of leads into my next question. Yeah. Because some of these big companies, and I think some of these large organizations do struggle with that.
Kevin (13:01):
Well, we struggle in what way?
Jim (13:02):
So, a lot of organizations find it easier to sort of manage their resources, right? It's as simple as just sort of, let's build a hierarchical structure in my organization. And I'll give you an example of maybe an insurance company. Okay. So, they have a big system, probably a mainframe that manages the claims, keeps health records, manages the core functions, what they would call the core day-to-day functions of the business. But as a member, I have my ID card in a mobile application mm-hmm <affirmative>. And so, they will look at those two services, those two functions in two different lenses with two different types of people, with two different types of leaders.
Kevin (13:36):
Oh really? Why?
Jim (13:37):
That's what we're trying to understand. And you know, where those worlds clash is when we say, well what if we wanted to provide another function where we look at the claims history?
Kevin (13:47):
Right. Right. I see.
Jim (13:48):
And I give you an alert, well wait a minute, now I have to get these people together with these people. I have to get the, you know, the legacy people together with digital people.
Kevin (13:57):
Oh.
Jim (13:58):
Actually, and I, I have a hard time managing, I think a lot of organizations struggle with that. Alright.
Kevin (14:01):
So, the image I came to me of Stuart Brand's pace layering. So, the difference is not with electronic, the difference is the radios are changing, it's the pace at which they move that's different. One is moving very fast. I think that's things like executive function moves at a much slower pace. It should, you know, you should be thinking a couple years in advance when I'm doing tomorrow. But it sounds like there's a discontinuity between the pace layering. Right. And those different ones that, that, whether they're electronic, it's just how fast they're moving. Right.
Jim (14:29):
Well I kind of, like I said, it was kind of a setup for this question. Yeah. And, and thanks for that perspective actually that pacing hadn't really factored in. You know, cause what we're trying to do is sort of like ident, like, okay, what are the attributes of something that makes it more digital or less digital? You know, aside from the...
Kevin (14:44):
Obvious digitalization, how fast this moving, how fast it's changing. Yeah. So, what's the next question?
Jim (14:48):
So, the question was, so we also find that a lot of organizations who are looking to, again modernize, let's take the example of maybe an automaker or an insurance company. And they feel that they have to make a leap. They have to get ahead. Yeah. And a lot of these companies are now they're becoming or trying to become what they call software companies. And if you go to an automaker in Detroit today, and if you go in the building, they don't look like, they don't feel like they did 10 years ago. I feel like I'm in mountain view, you know, I'm in the Midwest. Right. Right. And these companies also bring in cross-industry leaders. So, they'll say, well I'll, to get this guy from a retailer and they're going to come in and they're going to tell me how to be more digital or more agile or move a little better.
And it doesn't always work out. And I equate it to some of the things you've talked about. I may have missed this, but around sabbaticals, around goofing off. And it's kind of like I related what you've said about sabbaticals and goofing off too, well we're bringing someone in here to help us goof off faster. Right? And so, if we bring somebody in with an outside perspective, you know, then we can transform the business magically. So, I wanted your take on that. If you see that and you know, do you see companies that are very traditional, you know, maybe have, like I said, a legacy business, a manufacturing-oriented business, or an insurance company that changes very slowly. How do you see those companies adopting or changing the mindset to move very quickly or to bring on some of these innovations?
Kevin (16:14):
Yeah, I don't have a lot of experience with actually having to walk through, hold their hands, get them through that. The one thing I have learned, and by the way, most of my fans are in China and China, this has sort of been the ongoing assignment in China because China had all these state-run industrial manufacturing things that mm-hmm <affirmative>. You know, 10 times as many as in the US all being urged by the central government to digitize, to become an internet company. And they're all trying to do this. And one thing I have observed was that as AI has come in, a lot of companies think that they want to digitize so they can bring AI in, but actually there's a step after digitization that they need to do, which is they have to become a cloud company. It's not just electrifying and digitizing stuff, it is actually transformation of the entire org structure to become cloud oriented where you are.
Mm-hmm <affirmative>. And that's really the only way that the AI is going to work at a large scale in a company like that. So, I'm kind of making the challenge even worse of not just you get them to digitize, but it's like that's not even far enough. It's not to get them onto the cloud as a cloud first company to allow the AI to come in cause AI kind of needs to operate at a cloud layer. You can't just take little individualistic AI and hope to get the maximum value out of that. Right. Unless it's operating at a cloud level. Right. And so, I don't know if there's a way to connect leapfrog where we're going to go from the manufacturing tool line to the cloud directly. That might be something to consider. And maybe it's even easier to do, I don't know, it's like leapfrogging with cell phones and rural development and you just bypass the whole need to wire. Right. Right. What you're saying with copy, you're just leapfrog. So maybe there's a way to leapfrog into the cloud that would be easier to do. I don't know.
Jim (18:05):
Well, we're definitely seeing that happen. I think moving data to the cloud, moving applications to the cloud and decoupling the organization now I think these are, these are just slow adoptions. Obviously, we have a rule, we don't talk about Covid anymore because it was just too much. Mm-hmm <affirmative>. It was a topic driving too much conversation. Sure. But sure. You know, I remember immediately, this is, so we're literally five years ago, the banks that we worked with where you had to be badged in the building and on site for mm-hmm <affirmative>. Security reasons. Well somehow those security reasons all drifted away. They dissolved, you know, in in about three days. And so <laugh> and there were no problems, you know, so it's there. I think it's the will wasn't there. The means are there. By the way, I appreciate your TED talk. I noticed it was eight years ago on the second industrial revolution. And I thought with ai, and it was great to hear that. And I think you said something like, there will be 10,000 startups, AI native startups in the next Right. I wonder what that number is. Maybe we'll go back.
Kevin (18:57):
Yeah, I know exactly. Yeah.
Jim (18:59):
It might be three or four times that at this point. Yeah.
Kevin (19:01):
Especially around here. I mean it's like; I can't tell you the number. I had a friend who's at Stripe, which is, you know, the, the payment. Um Yep. They said we are not an AI company, but everyday we're losing some of our best people to AI companies. Oh yeah. They're just, and now they're all going to these AI startups everywhere in the valley. It's just incredible. Yeah.
Jim (19:20):
I'm seeing other AI native businesses that are not even tech companies, you know? Right. Where we're harnessing it. So, you know, we certainly felt that eight, 10 years ago, our machine learning and AI and cognitive computing, we were calling it cognitive computing. Mm-hmm <affirmative>. So, we've seen it there. You know, you mentioned it's been in our imaginations for a long time. I did a little trivia icebreaker in a meeting a couple weeks ago and I asked what year the term was created. I didn't double check this, but it was a paper, I think it was a professor at Dartmouth in 1956. And then I asked, well what film was the first AI themed film? And it was Brave New World from, I want to say, 1924. Mm-hmm. Don't quote me on that. Mm-hmm. But it is, it's in our imagination, it's driven a lot of things. I said, well there's probably a biblical version, you know, the Gollum or something, his AI or robots. Right. Right. So, I wouldn't want to leave this conversation without talking about it. I mean, you agree. This is beyond a hype cycle. This is, you know, we always started this conversation talking about hype cycles and what ifs. I think there's, yeah,
Kevin (20:16):
Well, there is still an incredible amount of hype and most of the hype is actually coming from the Doomers. Yeah. The Doomers are the ones who believe the hype the most <laugh>. And so, there was a TED Talk yesterday I watched live by Tristan Harris, who, I know Tristan. And it was the end of the world kind of stuff because he totally believes the hype. And so, my observation about this is that the frontier is moving very, very, very fast. But the adoption is just going to take a long time because empirically, you can't just introduce this technology nakedly, you have to adjust workflow organizational shape. Yeah. You have to adjust the infrastructure to maximize it. And it's like, it took a very long time for electricity to penetrate because in a company, it just wasn't a matter of electrifying a company. They actually had VPs of electricity back then too, by the way.
And so, you had to change the physical arrangement of how the company looked like in the offices. And you know, electricity made them taller because you could communicate up and down. And so, there was a lot of cultural implementation, which just works at a different pace than the frontier research. And so, I think it'll take us a decade to absorb the current models that we have today. There's still a lot of hype about how fast this is going to work. And I don't think it's going that fast, which means that we can regulate it in normal ways. We don't have to regulate it before it even exists, which is what people kind of want to do. So, there is still a lot of hype. I think 10 years ago there were a lot of people who really, really, really doubted that computers would ever be creative, they'd ever be able to drive a car that they would ever be able to mm-hmm <affirmative> pass SAT tests.
They really, really did not believe that. And so, we have overcome, I think for most people we have overcome that initial disbelief. And so it has made it a little easier to believe the next impossible thing. And which by the way, the thing that I'm saying that's coming next that people are not ready for is how emotional, what we're going to put in emotion to the AIs. And people have very strong bonds with them. And in the same way, if you can imagine the bonds that people have with pets who don't talk back, but these are going to talk back and they're going to have conversations. And so, people will work with them every day and become very close to them in an emotional way that we are not prepared for. And people are going to be shocked and upset that there is that kind of bonding. And I think we're not ready for that. And so, you know, there'll be a hype cycle around that as well.
Jim (22:51):
Interesting. And I think that movie's been done right with her. Right.
Kevin (22:54):
Well, that's falling in love. Yeah. But there are other kinds of bonding I'm already seeing with some young people who have the always on ai. Yeah. And they're working and it becomes this code generator. And so, I think for a lot of people being creative or productive without their AI will be for like those who don't have their glasses, and they need them to function. So, it's not like falling in love with their glasses. It's like, no, you, there is a working relationship where you're at your best, so to speak with this thing. So, you have a very strong bond.
Jim (23:29):
Well, that gets into things you've talked about with, you know, what's the best job for a robot is to drive efficiency. And I was thinking about when my kids started driving and they had to go across town, not very far. Right. And they got the Garmin out. Right. I was like, right, right. And uh, yeah, it's a form of robotics. Right. I'm gonna use this machine to tell me where to turn and, and now of course it'll do the driving for you, but
Kevin (23:52):
Sure, yeah. We had this dream that robots were gonna do our plumbing so that we can write poetry, but it turns out that the robots are gonna write poetry and we're gonna do the plumbing. Yeah.
Jim (24:02):
Because...
Kevin (24:03):
They can't do the plumbing. It's a great reversal, right.
Jim (24:05):
Kevin, sort of my last question here and I'd just love to get a final insight. Mm-hmm <affirmative>. And sort of the third verse of the podcast is the now what? So what if, so what now what you have, you have so many bits of wisdom and I encourage everyone to find it, you know, but you mentioned the Doomers, you know they are the believers, but mm-hmm <affirmative> I think there's a lot of optimism, right? And you made a comment about pain being inevitable and mm-hmm suffering being optional. So, you know, there may be change, there may be change in that's coming is a form of pain and it's inevitable. Mm-hmm <affirmative>. As you think about the changes happening in the world today, and you think about the potential of some of this technology, what is one piece of advice you would give to listeners to be prepared?
Kevin (24:44):
Hmm. Well, I think no matter what age you are, you're gonna spend the rest of your life learning new things. So, what you want to do is get really good at learning and ideally you would learn how to optimize your learning. You would figure out how you learn best in different situations, different kinds of learning. And you would actually test and improve and become better at learning and arrive at the point where you have optimized your own knowledge of how you learn best. Because you're gonna be a newbie for the rest of your life.
Jim (25:19):
That's a good self-reflection for people. Mm-hmm <affirmative>. You know, and good admission. Kevin, thank you so much for all you've done and your writings and what you've shared, especially what you've shared with us today. I really appreciate it. Thank you for joining.
Kevin (25:30):
You're very welcome. I appreciate time to be here and your great questions.
Jim (25:35):
<laugh>. Okay. Thank you.
Joe (25:37):
You've been listening to What If? So What?, a digital strategy podcast from Perficient with Jim Hertzfeld. We want to thank our Perficient colleagues, JD Norman and Rick Bauer for our music. Subscribe to the podcast and don't miss a single episode. You can find this season along with show notes at www.perficient.com. Thanks for listening.
.png)