← Going Big! with Kevin Gentry
Going Big! with Kevin Gentry
Going Big with Andy Kessler: Productivity, Profits, and Innovation in the Age of Artificial Intelligence
00:55:38
Loading summary
Transcript40 lines
- [00:00]
A
You don't have to know everything, every trend ahead of time. You just need to recognize them when they come along. And so my question, you know, I'm the most skeptical, believe it or not, for as optimistic as I am, I'm very skeptical. I do sniff tests on everything. Someone says something, I go, yeah, that's not right. And then I dig in, I go down rabbit holes on the web and I go, okay, is that right? And then I do what I said before is I think ahead five years, I think ahead 10 years, and I apply scale and can this be horizontal? And is it waste something until I say, okay, that's big. That's going to be the next big thing. And you have to make those analysis often quickly. Like, you can't say, ah, in five years, I'll come back and figure this out. Because in five years, everyone else has figured it out and they're the pioneers and not you. And so, you know, don't stay skeptical, but don't have static thinking, have dynamic thinking.
- [01:03]
B
Welcome to the Going Big Podcast. I'm your host, Kevin Gentry, and this is the place where we celebrate bold moves and big ideas. Each week, I sit down with inspiring leaders, entrepreneurs, and change makers who are making a significant impact in their careers and in their communities. Whether you're looking to level up your leadership, pursue your passion, or just get inspired to take your next big leap, this is where those stories come to life. Now, if you're listening on iTunes, YouTube, or anywhere else you tune into podcasts, be sure to hit that subscribe button so you'll never miss an episode. Now let's dive in to what it means to truly go big. Well, welcome back to another episode of the Going Big Podcast. I'm your host, Ken in. Gentry, as our listeners and viewers know, at this point in our history, we are going through one of the greatest technological shifts that we've ever seen. And what does that mean for our subject of going Big? Well, who better to help answer that question is our guest today, Andy Kessler. Many of our audience know him as a regular columnist for the Wall Street Journal and the author of numerous books about how innovation drives prosperity. He's got a great perspective, and I love the fact that he is such an optimist on the power of innovation. I think that's particularly appropriate for Going big. Andy, it's a real pleasure to have you as our guest today.
- [02:35]
A
Yeah, thank you so much.
- [02:36]
B
Well, I want to dive in, actually. I want to take this interview sort of in two different areas. One, at kind of a Macro level in terms of how you see the nature of innovation in technology, artificial intelligence, also what that's doing to capital markets, capital flows, and just how people see the world at kind of a macro level. You've been writing a lot about that recently as well, but also on an individual basis, and how we can learn from your own journey about how you've gone big and how you've done different things in your life. I think a lot of our listeners are always trying to understand what should I be doing. And I know you believe a lot in agency, and we can tackle that or tap into that as well. But just to kick us off in your judgment, what does it mean to go big today in 2026 with where things are with respect to artificial intelligence and everything else that's moving so quickly?
- [03:38]
A
Well, I look at it that there's three things that are a recipe for success today. The first, going big means being productive. Productive means doing more with less, being efficient and effective. You know, we could do a lot of things efficiently today. We could put the pyramids on the National Mall. But that's not effective. And so the trick is in doing more with less is finding the problems that need to be solved. The second thing is profit. I know that's, you know, that's a bad word in many circles. Profits. Oh, that's evil. It's not. It's a miracle measure of how society benefits. I mean, if people are paying for a product, unless you're a monopoly. I hate monopolies. But if someone's paying for your product, it's worth that or more to them. And the profits you generate are really societal profits. You may generate wealth for yourself, but you're generating wealth for society. So those out there that say, oh, I want to make the planet better, do something important, give back all those things. Generating profits is how you do that. It's one of the most misunderstood things out there. And then the real way to go big is to find big trends, get the wind at your back, you know, find these big waves to surf. It makes it so much easier. In Silicon Valley, that was about being smaller, cheaper, faster. You know, the whole semiconductor and chip revolution to gen make things better. But given AI, perhaps in various industries, there's a whole new set of trends that will get the wind at your back. But if you can provide, if you can combine productivity, profits, and then these big trends, that's how you go big.
- [05:23]
B
Awesome. All right, well, that's awesome too, because I want to go into both those categories about how everybody gains in this manner, through all of this drive for making things better, for creating more value for society, if we're all motivated in that direction, it has that effect of lifting all boats. But to your point about just where we are today, give us a little perspective on, just in terms of your reference to trends, where we were just five or 10 years ago, what did it mean to go big then, even relative to where we are today? I asked that just to give us the sense of the speed of change and the speed of disruption.
- [06:06]
A
Well, 10 or 15 years ago, let's say it was mobile, the mobile revolution. Smartphones and the apps that were provided over time on smartphones and at first cell phones, you make a call or you get voicemail and it was a wireless phone that you could take anywhere. Great. But when the IPH came along, it, it, it changed everything. And no one, really, not many recognized it at the time. And so I always do this thought exercise, like go back and make the presentation of today to your 2010 self, right, and try to explain ride sharing and Uber or Lyft that, you know, you can push a button and within five minutes you know, a car, an suv, whatever it is, shows up wherever you are and takes you wherever you want to go. And you're going to take any money out of your pocket. All the, you know, it's all credit card based. It's. In 2010, you would have called someone crazy if they, if they made that pitch or that you could stream TV shows to your phone or to a 98 inch TV. There were no 98 inch TVs that weren't under 50 grand or something like that, or that Gen Z gets their news from TikTok, you know, these short, strange videos. But they do. And so I look at it as the more incredulous or the more argument that you get on new technology, the more likely it's going to turn into something big. And it's the same with AI today is that the first applications of IT are to change customer service and coding and people use it to write emails and then others use it to summarize those emails that AI wrote. But it's too early to really make dire predictions about job losses and all the strange things that the fearmongers provide about AI. And so again, think back, it's only been 15 years since you had this supercomputer in your pocket and all the new things that were created from it are hard to imagine.
- [08:31]
B
Wow. Well, thank you for stressing that point. You know, my wife and I were just in Miami and We, I saw a Waymo driverless car, which, you know, always grabbed your attention. I didn't know that they'd come to Miami. I'd seen him about a year or so ago in San Francisco, and it blew my mind then for seeing it the first time there, you know, You've interviewed Tyler Cowan. I've had him on this podcast. Tyler sort of sees himself as an information billionaire, maybe aspiring information trillionaire. But I remember just a couple years ago, him telling me, hey, artificial intelligence is going to be a really big deal. And I told a. An investment friend of mine that, and he says, oh, yeah, I just don't quite get it right. And look what's happened in just those couple of years. So you're a trends guy. I know you can't. There's no crystal ball. But. But where do you see things? What will going big mean, you think, in five to 10 years, to the extent that we should either be thinking, positioning ourselves, keeping our minds open, how do you see the future?
- [09:35]
A
Well, you know, we can only take a snapshot of what is available today. But what I do is I tend to look at scale, right? The history of the semiconductor business, which a lot of new technology is built on, is it gets cheaper by 30% every year. I mean, that's just. That's Moore's law. That goes back to the, you know, the 60s and 70s. And like clockwork, 30% comes out of the cost of every bit, every gate, every function, everything that technology can do gets cheaper by 30% a year. And so the trick is to squint a little bit and not to use the AI term for making mistakes, but, you know, almost hallucinate the future, right? Think out and say, okay, such and such is available today for this cost. What it's going to be like when it's a tenth of the cost, how much bigger is that market going to be? And that's the elasticity. People call it Jevons Paradox. Forget that. It's just elasticity. It's like a super ball. You throw it down, it squishes, and then it pops up higher than you pushed it down. And that's the history of, of technology. And so, you know, the future is always hard to imagine. I'm nothing. I've hiked parts of the Appalachian Trail, and if you go up to Mount Washington in New Hampshire, once you get high enough, the fog and the, you know, the visibility goes to almost nothing. And so they built this trail with rocks, a stack of rocks and a yellow rock on the top, and you get to one and then you look and you find the next one and the next one and the next one. And. And most people can't see in the fog, but there's signs in the fog. And. And if you squint enough, you could see the future by, again, thinking of big trends and thinking of things, how much better they're going to be when they're cheaper, how much more powerful they're going to be. And if you do that, then things like Waymos and automated cars and cars that, you know, you don't even own anymore, that just they go park somewhere and charge at night and they come pick you up in the morning, take you to work and then take someone else or park somewhere, all those things are visible if you look for those sign posts in the fog. And so that's where I think the future is going to be. We're going to change every industry, much like spreadsheets changed accounting and it changed investment banking because everyone could operate a spreadsheet. It's like that today with coding as Cursor and Claude from Anthropic and others are pretty decent at writing code. You still have to test them because they do still make mistakes, but they're pretty good at writing code. And so there's a subset of our economy of people that trained and learned how to write code. Some of them may be disrupted, some of them may lose their jobs, but think about it. Everyone can write code, right? It is, is it because the tools are automated? And that's what spreadsheets did is. Is I was an analyst. And in the old days, you know, before my time, people would sit there with spreadsheets and X acto knives and Mylar paper, and all of a sudden everyone could do spreadsheets and therefore everyone could be an analyst. I. My line is Morgan Stanley and Goldman Sachs made more money from spreadsheets than Microsoft ever did. And that's just how it is of thinking about what the future can bring.
- [13:20]
B
All right, well, that's perfect, because now this is where I'd like to go. Because for those listening, you know, part of the objective here of going big is to inspire people to cast a big, bold vision. But we're all trying to think through how to find our contribution, our unique gift, how do we apply that in the world today and into the future? And your own journey is fascinating. My understanding is you started at Bell Labs as a semiconductor engineer, then you went to Wall street and were one of the leading technology analysts, then started your own hedge fund, went into the. To the Whole area of, of where capital could drive a lot of these investments in technology. And then now you're in more of a media space. How did you, if you don't mind me asking, make these calculations to shift at different times to the extent it could help us understand how we should continually think about our own continual sort of personal innovation?
- [14:22]
A
Yeah, I wish I could tell you that I had a, you know, 30 year plan and it was all mapped out and, you know, it was, it was no such thing. Is, is, you know, I like to say that I'm still trying to figure out what I want to be when I grow up. I mean, I started, I was good at math and science and in high school, and so I went to, to Cornell to study engineering, electrical engineering, so I could build computers. And, and, and no one told me when I was there that soon computers would be cheap enough to be on every desktop. And then I worked on Wall Street. No one told me these computers are gonna be able to slice and dice portfolios and turn them into mutual funds and completely democratize how Wall street operates. And so the way that you figure those things out is again, by looking for those signposts out there and figure things out. And so I liken my career to playing pinball, just bouncing off from one side to the other. And most of it I didn't do on purpose. I'd followed my nose. I looked at what was interesting and pursued it. And that's how I do with my writing too, as a columnist. I try to, you know, follow my nose as what's interesting. And hopefully, if it's interesting to me, it's, it's interesting to others. So, you know, I started in technology. I kind of, I worked at Bell Labs, which was inside, AT&T, one of the most bureaucratic companies, you know, more, more layers than the Pentagon and all these things, and got tired of it and accidentally tripped across a job following the semiconductor business, the chip business on Wall Street. I didn't know what I was doing. I didn't know what a balance sheet was or an income statement. But the gentleman that hired me into the role said, listen, I could teach you that. You can figure it out. But understanding the technology, that's where your experience will come in. And most of my competitors on Wall street were economists or MBAs, and I could understand the technology and it was quite valuable. And then an analyst on Wall street, it's called, you follow an industry, you follow companies. And one day I woke up and go, I don't want to follow these things. Anymore. I want to lead them and I want to be ahead of these trends. And so I left the analytical side of Wall street and ended up at a small firm and raised a venture fund so I could find the next wave of startups. And then with another gentleman out of JP Morgan who knew the public side, we did a fund that could do both public and private investing. We find these neat little $200 million or less. I know that's quaint. Value companies in Silicon Valley and invest in them ahead of the rest of Wall street figuring it out. And so again, followed my note, did that, and it was, it was successful enough that, you know, couple of 20 plus years ago, I said, okay, enough of this. Now I want to explain this to others. So I, I started writing. I was a contributor to the Wall Street Journal opinion page. I started writing books and then, you know, maybe it was about eight or so years ago, started doing a regular column every Monday for the, for the Wall Street Journal. So it's funny, I meet young folks and they go, well, how did you do? How did you do? How did you go from being an engineer to a writer? I said, well, part of it is I, I was accidental and, and part of it is I, I just was, was having fun and applying what I learned in, in new ways. And, and to me, that's what careers should be. I mean, anyone that sits there and plots their career when they're 22 years old doesn't recognize all the changes that are going to take place in society and technology. And so I feel very fortunate that I was allowed to follow my nose and it took me to some interesting places.
- [18:49]
B
It was pretty funny that you mentioned the bureaucracy of AT&T. My dad worked there a long time ago in the 1970s. He said, based on what he saw there in terms of the bureaucracy, he urged me to invest modestly in mci. And I'm glad I did because there you go. You know, that, that, that, that was a very disruptive time as, as well. What, what advice would you give? I mean, should we just be continual learners? Do we just need to be just like looking out into the future all the time, trying to challenge ourselves not to be wedded to what we do or wedded to the past. Any other insights or advice to those listening about how they should think about that next iteration of their professional life?
- [19:34]
A
I think all the above. I mean, you know, it helps to understand technology, but you don't have to be, you know, a full stack owner and to be able to use technology to your benefit. And you know, the thing about AI is it's a, it's a productivity powerhouse, right? And it is something that allows technology to be used by everyone, not just by engineers. And even in engineering, the things that you study in engineering, they're useful for a few years. And really what engineering teaches you is to learn how to learn. And I think there's something to that discipline and I wish other disciplines, I don't know whether it's legal or Medicine or MBAs, you know, the business world, they teach you about what's happening, but often not to learn how to learn. And that, that's the thing about AI is you don't have to be an expert on everything anymore, but what you need to know is what the problems are. I mean, you know, this whole thing about agency is, is really important because, you know, the, the technology literacy isn't as important as it was a few years ago. I mean, I liken the. I love the story. There's a, there's an app called Cal AI. You open the camera, you open the app and it opens your camera, you point out your plate of food, it tells you how many calories it is. You think, wow. I mean that's, that's terrific. It was built by an 18 year old Roslyn High school young man and he didn't, he didn't know all the technology, but he used automated, I think he used cursor or, or one of those tools to create this app. And those that have played with it and looked into it, it's about 80 or 90% accurate. It's not 100% accurate, but it's pretty close. And you know, that's, that's the cool thing about agency. And he had high agency and there was a problem put in front of him and he used tools to come up with the solution. So going back to your question about advice for, for those that are starting out is you got to figure out what the problems are. That's almost as important as studying all of the deep sciences and technologies to solve it. And you know, if you can find the problems, then you can solve it. And I don't care whether that's in education, drug discovery, healthcare. You know, there's so many problems. That's why a lot of the talk today about AI is going to, you know, destroy jobs. We're going to have 20% employment. It's nonsense because it's just, that's looking at the world in a static way. There's so many problems that are left to solve. And now we have Tools that democratize coding and all sorts of things that folks starting out can harness. But the trick is figure out what the problems are. Maybe you should go work for a bigger company and a bureaucratic company that you're not going to end your career on, but do it to find what the problems are. In oil services, there's tons of problems. How to log wells and look at geography and all those things. But to understand again what the problems are, the solving, it may have gotten easier because of AI tools.
- [23:21]
B
No, this is great. And I also love the fact not only that you're such an optimist, but that you reject victimhood, which it's so easy to slip into. So you had a column, I think, last year about, you know, AI is perfect for high agency people. And you cited the example, the guy with the app on detecting calories in food. What do you mean exactly by what is a high agency person?
- [23:50]
A
Well, that's the person that says, I'm not just going to be a cog in a larger wheel, is I'm going to figure out how to solve a specific problem. So I talked about in that column about someone that built a scheduling app for a barbershop, right? So OpenAI and Anthropic, they're not going to do scheduling apps for barbershops. They're just not. But someone sat down and says, we have a problem, is people come in and they have to wait half an hour, whatever it is, and so built an app and the same thing, there's another one, built an app to figure out the supply chain for food acquisition for a restaurant. Again, you may not get big software companies like Salesforce and others that work to do that kind of thing, but the tools are available so that you can do at least a version of it for yourself. That's what high agency means, is that you have the will to go and build something useful without asking permission, without going to a big company saying, can you do this for me? You can do it yourself and you don't have to know again, all of the hard sciences, remember what generative AI from OpenAI, Anthropic and Google, Gemini and Grokit XAI can do is they just went and scanned every, not every, but as many words that have been written in the history of mankind. And they, they pulled all that in and built a statistical model where if you prompt it correctly, it'll give you the answer to how do you build, you know, a machine that flips pancakes? I don't know, something like that. And you know a lot any of the knowledge of things that have already been invented are all out there. So you don't necessarily have to be a material science major or a biology major or a chemistry major. It helps if you, if you go deep into those things. But if you just need a cursory knowledge of those things, if you have high agency, you can build tools that help you in your job or in others jobs. And I think that's one of the biggest changes since generative AI hit a few years ago is the ability again, it's the democratization of information is allowing everyone who could figure out how these tools work to solve problems.
- [26:33]
B
All right, awesome. Okay, so let's dig in even a little bit more to artificial intelligence. I've been a little stunned when I speak before groups of people and ask how many are utilizing AI. I'm shocked by how few, particularly my age and older, raise their hand. Now of course they are because they're using a mobile app or they're using all kinds of stuff. They may not know it.
- [27:02]
A
Right.
- [27:03]
B
How do you think we should go look again, looking at trends to the future, Be thinking about it either personally, professionally, broadly, holistically, how should we be thinking about it? And I'll answer that one first and then I've got a follow up.
- [27:21]
A
Okay, so again, AI is a productivity powerhouse, right? Go back to what I said at the beginning is productivity profits and then find big trends. Is AI is that it allows you to do more with less. Some of the applications are real easy. It's here's five bullet points. Turn it into an email. Great, I got this long email. Can you read it for me and give me five bullet points? Right? So some of it is kind of reductive, is, you know, maybe we should just cut out the middleman. But you know, that's the benefit of AI. It allows you to do more with less. There's still problems with it. It's a statistical engine. It doesn't think. Apple put out a paper last year, say, look, this thing doesn't think. It imitates thinking, but it doesn't think and it hallucinates. So anyone who writes code with it or has it automatically generate code, it says, you know, build a website that does this or that. It still needs to be tested. I mean, recently software companies, you know, software as a service companies, you know, the stocks all sold off because everyone sort of woke up and said, gee, you know, anyone can replicate what Salesforce does or work day does or others. That's not true. But it tells you how, how, how quick to jumpy Wall street and stock markets are all these tools need to be tested and thoroughly tested. So the definition of what coders are has changed from someone who writes code to someone who automatically generates it. In Tesco, great customer service. If you call an 800 number asking for customer service, if it's not an AI tool now, it certainly will be because humans are expensive to answer calls. There's Indian call centers that just to lower the cost. And so these AI tools with voice, I mean it's not just you texting a question to a company, it's using your voice to ask the question in a very realistic human sounding machine will answer you. So you don't even know that it's not a human. And those are the first coding and customer service with voice, those are the first big applications. But your doctor, there's now a tool out called Open evidence. You type in the symptoms and it says, okay, here's what this might be. This is such a huge change for medicine where doctors, you know, remember we send doctors to med school and all they do is memorize, right? They memorize anatomy, they memorize this symptom, that system. And then we hope that their brains are almost AI like for the last 50 or 100 years of medicine to figure out what the symptoms are. We know that doesn't work. We know that doctors misdiagnose and you know, they don't know everything. And here's a tool, an early tool called open evidence that my friends who are doctors, they use all the time and you type it in and therefore you can think, you can take that evidence, turn it into potential symptoms, work with your patient and think with them on diagnosis as opposed to just be a robot that is try to be a robot with memorization that spits things out. Same with lawyers. You know, there's all sorts of industries that are going to change how people individually can use it. It's up to you. But as you suggested, you're using AI whether you know it or not. And there's a lot of aspects to it for someone stepping out on their career. You know, there's the AI engines that you can work on, there's the infrastructure that's being built for these data centers. There's an whole energy infrastructure that's rebuilt because there's so much electricity that's used by these hyperscalers, by these AI data centers. But the big upside for me, as we were talking about with spreadsheets, you know, Morgan Stanley made more money on spreadsheets than Microsoft did, is the idea There's a huge upside in changing industries. So people of every age who have expertise working, you know, their whole career in some industry, they're the ones that have the most likelihood of say, ooh, here's how I can use these AI tools to either lower cost or increase the output of various industries. I don't know what they all are. We could talk about them. It's drug discovery and energy exploration. There's all sorts of industries. Retailing will be, will be radically altered. That inventory will show up when it needs to show up. And if there's a forecast of rain, umbrellas show up. They do now in the streets of New York, people selling umbrellas. But Walmart or everywhere else, AI tools will be smart enough to do that things as an augmentation to the staff and the employees at these industries. It's going to be a remarkable time.
- [32:39]
B
Oh, by the way, just for fun, before we sat down and have this conversation, I asked ChatGPT, I said, tap into all of Andy's writings from the past five to seven years, but with a special focus on the last two years and tell me two or three themes that consistently emerge.
- [32:57]
A
Right.
- [32:58]
B
And I got a whole lot about, number one, the interplay of technology innovation and high agency individuals.
- [33:04]
A
Right.
- [33:04]
B
Number two was about American economic and cultural competitiveness, which I'm going to come back to in a moment.
- [33:09]
A
Yeah.
- [33:10]
B
And then the third was skepticism toward conventional wisdom and political narratives. So on that, I just. In your other recent column in the Wall Street Journal, you essentially said people need to chill out. I mean, can you calm down about all the hysteria in both directions about AI. Tell us a little bit about that, what you're, you're saying, like, you know, it's, it's, it's a, you've already said this. It's not going to be bad. It's not going to be. But, but what, what. Tell us a little bit more about how you, you tend to challenge what's out there in the media.
- [33:46]
A
Yeah, well, first of all, I should do that every once in a while with Chat gtp Ask, hey, what have I written? And you know, what dreads do? I see myself. And that's kind of fascinating what it came up with, thinking about it. That that's probably right. But I don't think I had that in my brain again, mostly because I follow my nose and, and I guess in, in when you're writing opinion pieces, it's sort of whatever, whatever you find annoying or interesting either that week or that month and that you want to write about, you know, those are the things that I dig into. And so of late there have been, you know, the fear mongering about artificial intelligence. It's, it's, you know, the first one is, you know, the biggest concern is, oh, there's going to be 20% unemployment and you know, people are going to be, everyone's going to be out of a job and the only jobs are left are, are plumbing and, you know, flipping pancakes, which maybe as I talked about before, that'll be a robot job and that's prepostics. And people said that about every piece of technology that ever came along, including the original IBM mainframes. Oh, people are going to be out of a job. It ends up creating more jobs. So again, the fear mongers, the scaremongers, they fall into two camps. Either we're entering a period of utopia, you know, hey, you're out of a job, but don't worry about it, you know, who needs to work anymore? It's, you know, we're going to enter the period of George Jetson, you know, where Rosie the Robot's going to Deliver you your 3D printed Beyond Meat meatloaf. And you know, you're going to collect universal basic income from, from the government and we're all just going to sit playing video games in our basement in our pajamas. Right? That's the utopian. It doesn't sound like Utopia to me, but you know, that's the, the utopia, you know, don't worry, just relax. We got you covered. This is a lot of the thinking of many of like Sam Altman at, at OpenAI. He's a, he ran experiments on universal basic income and you know, which basically welfare. Let's face it, it's not going to happen. The second one is worse, which is a dystopia that, you know, techno fascist oligarchs, right? That's, that's a term that gets used like Elon Musk are going to run our lives. That, you know, it's the, it's the era. There's going to be Robo Taxis, RoboCops, Terminators and Skynet that, that, you know, grades your social standing based on how many Instagram likes you get. You know, this is the stuff that people think through of the AI apocalypse and also proposal. Neither of those things are going to happen. Although my biggest fear, of course is if we ever do get utopia, it turns into dystopia quickly. Look at the Soviet Union and Cuba and I make fun of California is because those just tax, tax the living daylights out of any productive endeavor. If you don't have productivity, you don't have wealth creation for society and utopia turns into dystopia. But I don't think either of those are going to happen because of what we talked about before is the democratization of these tools is yes, there's coders that are going to lose jobs, just like there were analysts that lost jobs as these tools allowed everyone to, to become an analyst or a coder or a desktop publisher and who knows what going forward. We're all coders now and soon maybe we're all going to be doctors now or we're all going to be lawyers. I just used some AI tool to come up with a liability waiver. Took me five minutes and I didn't pay $2,000 an hour for versus a lawyer. So yes, jobs are destroyed, but millions of new jobs are going to be created. And as has happened every cycle, this time it's different. People say, no, it's never different. Maybe the speed of near term of how jobs are being destroyed, but I think it's a time of plenty coming ahead of us.
- [38:23]
B
Well, tied to that. What does this mean for America and the rest of the world? I mean, you reference California and these big tax states that have become kind of like Europe. These kind of calcified, overly regulated, risk averse places. Bureaucratic, that just stifles innovation. But it seems like America just keeps getting in on these next new trends. I mean, you mentioned technology before, but now artificial intelligence. How do you see the world and America kind of vis a vis each other? China in all of this space?
- [39:00]
A
Yeah, no, that's a great question. And Europe is the classic example. I mean if you look at the top valued companies or the top technology companies or top startups or even biotech, there's one or two in Europe and the rest are in US and then many in China, to be fair. And Europe, I think got sidelined by carbon footprints and climate change and they invested all their capital in that and destroyed their energy infrastructure. And it's been, I don't know, I got to go look it up. But it's been if you go back 15 years or even 25 years when the European Union and the US were about the same size and now the US economy is 80% larger, 60% larger, some huge number. I mean, Europe has flattened and the US has kept growing. Why? Well, one is in the us you don't need permission to innovate. You don't, you just innovate. We're in Europe. The current companies the incumbents regulate everything and control markets and become rent seeking. And yes, that happens in the US but in new things and new technology, for the most part, Washington, D.C. is 3,000 miles from Silicon Valley, thankfully and for a reason. And the other interesting trend is, and we're talking about China, it's like, but the other interesting trend was for the last decade or so is the idea, oh, you know, location and geography are dead. You can work anywhere. You can work in Iowa and you can work in North Dakota. Ms. It doesn't matter, go. And especially during the pandemic, it was like, yes, I'm going to go, I can work anywhere. And then AI came along and it's not just US based or California based, it's San Francisco based. It's where it all happens. And you know, part of it is the ecosystem and people moving from one company to another and the infrastructure and help, whether it's lawyers or whatever else, is San Francisco based. And that's a problem that the rest of the world has is AI should have happened remotely and it didn't. And I think a lot of it is not only what we talked about, but in the US we have very strong laws for property rights and patents and copyrights and things like that. And we maintain competition. Yeah, ATT had a monopoly. Oh, they failed. Right. They haven't come up with anything new since the transistor and all monopolies eventually fail. And that's one of Europe's problems and China's problems is they build barriers, right? So try to use Facebook in China. You can't Twitter, you can't Google, you know, they, they restricted it because, so no one could pull up Tiananmen Square on a Google search. And I think that's to their detriment because they don't have the constant competition. Look at AI. There's, there's, I can, I can name five companies off the top of my head in the US and yes, Alibaba and someone else in China. And Deep Sea is sort of a lower end, not at a big company. So there is some competition in Japan, but they put barriers up to foreigners and I think that's a huge mistake. Now the U.S. does that too. You know, try to buy a BYD electric car. Electric vehicle in the U.S. you can't. That's why with all the trade wars and tariffs, I said, listen, wake me up when I could buy a BYD and someone in China can use Facebook. Because until then we don't have free trade. And so the US we have universities, we have the best universities, at least for engineering and technology, to push the state of the art forward. We have the best rule of law, we have the best property rights, and we don't discourage entrepreneurs. China doesn't as much anymore. They used to. Now there's a good entrepreneurial spirit in Europe. If you've ever failed in Europe, it's hard to get a job. Like if you failed as a. Even getting a taxi driver job is hard in Europe. And that makes the US unique. And that's why I'm always on the lookout for laws and regulations that restrict innovation or restrict entrepreneurs, because that will be the little bell that rings of the death knell of American exceptionalism that it really comes because this whole freedom to innovate and that is invaluable.
- [44:12]
B
Well, here, here. Thank you very much. By the way, you and I were recently at a conference where you interviewed Michael Crow, the president of Arizona State University. And I had him also on the Going Big podcast.
- [44:24]
A
Great.
- [44:24]
B
Surely the most innovative leader in higher education in America. And it shows in terms of all the extraordinary things that they're driving. Although he very, in a very self deprecating way says when you say he's the most innovative, he says yeah, but it's a pretty low bar. But I think that's a great example of what you're saying.
- [44:43]
A
Yeah, agreed, agreed. And how we educate is going to change. I mean that hasn't changed much in K through 12 anyway since you know, I liken it to Ms. Crabtree at the Little Rascals which is, you know, almost a century old and we have a teacher in front of the classroom and they kids are falling asleep. I was one of those in the back row. And to use these tools for self paced and self testing system of education, it's going to change things if it's ever allowed to. Because K12 is still controlled by teachers unions and class size restrictions and boards of education that are very political. But I can't wait till tools come about away from K12 that parents at home can buy or get access to. At Khan Academy was one of the early examples. But I think there are so many AI tools that are going to change education and tear the power away from boards and teachers unions back to parents. Because remember during the pandemic, parents there were zoom calls because we were locked down wrongly and, and there were zoom calls and parents sat in the back and you know, behind their kids and watching and go this is what you're learning. This is how you, this is crazy. So I think there's a huge, there will be a huge push to have these same AI tools that are changing how corporations work, changing how education is done. All for the better now.
- [46:20]
B
Well, if, if technological innovation is going to disrupt and drive creative destruction in terms of especially these monopolies that you talked about, it's going to disrupt the monopoly of education, public education, so we can hope that the, the same product will occur. Andy, this has been awesome. I want to begin to draw this to a close and I do that usually with two closing questions. The first is, and I think this is especially kind of unique with you because of the different transformations that you've driven in your own professional life. But looking back on a younger version of yourself, what would you tell that younger version today to do any differently, given what you now know?
- [47:06]
A
Yeah, that's a great question because as I said, I still don't know what I want to do when I grow up. And I still think as if I was the younger version of myself, I stay loose. But the advice is stick with the trends that brought you there. I talked about Silicon Valley and smaller, cheaper, faster and, and figure those out. And then there's a bunch of sort of sub trends that I think you can grab onto. We talk about elasticity and scale, of lowering costs and markets get bigger. There's horizontal business models versus vertical. There's no IBM anymore that does everything from soup to nuts and computers. There's these little layers of microprocessors and software and memory and hard drives and software applications. So you don't need to own everything. You can do horizontal business model. The whole network effect of social media and Facebook and Twitter is that intelligence moves out to the edge of the network. That's a big trend that I'm a huge fan of. And then another is lost on many is you waste what's abundant to make up for what's scarce. So, oh, there's this new abundance movement that many on the left are saying, oh, if only government could build things again and provide abundance. And of course government can't do anything right. It's entrepreneurs and it's the private sector and it's individuals that can do things. And the trick is, I'll think of the old mills that would mill wheat or something like that. There would be water running by and they'd use that to turn a water wheel that would then grind the wheat. Is it weed into chaff? Whatever it is, that's a long lost art. But they wasted water, which is abundant to make up for what's scarce, which is which was at that time power, human power, ox power or whatever else. It's the same thing today. AI in these huge data centers is going to waste, if you will, gigawatts of power. But to make up where it's scarce, which is the ability to memorize or even draw relationships between words or between all the things that these AI tools are trained on. And so I think the younger version of myself, like I said before, no one told me that computers were going to be democratized, it'd be on every desk. No one told me that Wall street was going to democratize and slice and dice funds to be put into mutual funds and that trading would happen quickly and everything like that. I had to figure it out for myself. But you don't have to know everything, every trend ahead of time, you just need to recognize them when they come along. And so my question, you know, I'm the most skeptical, believe it or not, for as optimistic as I am, I'm very skeptical. I do sniff tests on everything. Someone says something, they go, yeah, that's not right. And then I dig in, I go down rabbit holes on the web and I go, okay, is that right? And then I do what I said before is I think ahead years or I think ahead tie 10 years and I apply scale and can this be horizontal and does it waste something until I say, okay, that's big, that's going to be the next big thing. And you have to make those analysis often quickly. Like you can't say, ah, in five years I'll come back and figure this out. Because in five years everyone else has figured it out. And, and they're the pioneers and not you. And so, you know, don't stay skeptical, but don't have static thinking, have dynamic thinking. Think of how the world will change. Much as we talked about, you know, looking 15 years ago and trying to explain Uber, well, if you thought about Uber, it was expensive and it's, you know, there are not that many cars of how it would get cheaper, how cell phones would, smartphones would be in everyone's hands, not just a few people's hands, then you could envision the future. And that's, that's the trick now.
- [51:39]
B
Excellent. Well, thank goodness you are a high agency person sometimes. Closing question. And we're gonna separate the wheat from the chaff with this for anybody listening anywhere over around the world. And we have listeners all over the world, whether they're a 22 year old doing coding and trying to figure out what the future means, or a mid career professional or Executive who's trying to think, wow, do I need to reinvent myself? What advice would you give to anyone listening about how we should think about the future in terms of going big for ourselves?
- [52:18]
A
Yeah, learn how to learn, right? Always be learning every day. Or people say, what do you read? I go, I read everything. And I think through everything. And don't go narrow. Stay wide. Learn everything. You don't have to go deep maybe on some things, but you could stay shallow. But go as wide as you can. Learn everything and learn how to learn as new problems arise. The whole thing about, about having agency is, you know, figure out what the problems are. Ask. You know, one of the tricks I used to do when I was young, I still do this today, is I'd have friends and we say, well, let's go have lunch. They go, great, where should we meet? I go, no, no, I'm coming to your office. What do you mean? I said, I'll pick you up at your office. But, you know, show me around, because I want to know what people do at, you know, accounting firms and advertising firms and media firms. This is back when I worked in New York. I would just go visit everyone. And I go, okay, what does that person do? What does that person do? And what problem are they solving? This and that. And I would learn, like by osmosis. It wasn't on purpose. I just kind of went in and my curiosity started asking questions, and I would. I build my own personal database. You know, this is what I do now. Like, I'm. Now, I'm fortunate to be able to have access to some. Anyway, CEOs and politicians. And I always go to their office. And the first thing I do, like, we'll go to cops. No, no, take me to your office. 1. I want to see the view, right? Because you can tell a lot about people by, you know, the. Do they have the best office or the highest view or the best view or not. And. But I, I then say, okay, what do you do? Like what. What's on your screen, on your computer? What. What. Who do you talk? Who reports to you? And it's amazing what you learn. And that's how you get that agency. That's how you figure out what problems there are to solve. Is. Is, you know, you know, I said before, I follow my nose, but have that curiosity. Follow your nose into places that may not have taken it in in regular, everyday life.
- [54:41]
B
Wow. Thank you very much. Love, love your optimism, love your skepticism that combines with that, but really love the intellectual curiosity that drives you, Andy Kessler, it's been a real privilege having you today on the Going Big Podcast.
- [54:55]
A
Yeah, thank you so much.
- [54:56]
B
Thanks for tuning in to the Going Big Podcast. I hope today's conversation left you feeling energized and ready to tackle your biggest goals. Don't forget to subscribe and leave us a review on iTunes, YouTube, or wherever you listen to podcasts. It really helps spread the word and it gets these inspiring stories out to more people. You can also find more content, resources and updates at our website, goingbigpodcast.com Remember, the only limits are the ones you don't challenge, the limits that you impose on yourself. Keep pushing, keep growing, and above all, keep Going Big. See you next time on the Going Big Podcast.