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How to Use AI in Everyday Life: Productivity, Parenting, and the Future of Work

Episode 36 · 11 August 2025 · 1 hr 5 min

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Show notes

AI Doom or Abundance? What AI Is, What It Isn't, and What It Means for Your Job

Will AI destroy us or bring abundance? Randy Lorensen and Dr. John Oberg talk about AI with intellectual honesty: how machines that teach themselves got here, what the doomers warn, and why they both still lean optimistic.

They start with Richard Sutton's essay "The Bitter Lesson", the idea that the biggest progress comes from methods that let machines learn for themselves, and trace it from IBM's Deep Blue beating Garry Kasparov to AlphaZero teaching itself chess. They also talk about Grok 4, whether first movers really win, regulation and the race with China, and Tesla's self-driving data.

On jobs, they agree your job is more likely to go to someone using AI than to AI itself. They talk about how AI helped turn a rough first draft into a best-selling book with a human in the loop, what AI means for education, and why they don't think you can put the genie back in the bottle.

After the break, they read the warnings from Elon Musk, Sam Altman, Dario Amodei and others, discuss the Claude Opus blackmail test, and weigh the optimists' view. They close with advice for kids and for anyone who loses a job to AI: learn how to learn, and don't overlook the skilled trades.

The Bitter Lesson, AlphaZero, and AI and Jobs

What you'll discover:

  • The Bitter Lesson: why machines that teach themselves win
  • From Deep Blue to AlphaZero, and what chess tells us about AI
  • Why we judge self-driving cars by a tougher standard than human drivers
  • AI and jobs: why your job may go to someone using AI
  • What Musk, Altman and Amodei warn about, and the Claude blackmail test
  • How to prepare: learning how to learn, and the skilled trades

Timestamps

  • [00:00] Welcome: what AI is, what it isn't, and whether it will destroy us or bring abundance
  • [00:35] A different tone: talking about AI with intellectual honesty
  • [01:30] "The Bitter Lesson": machines that teach themselves
  • [03:34] From IBM's Deep Blue beating Kasparov to Stockfish
  • [06:44] AlphaZero teaches itself chess in hours
  • [08:26] Go, poker bots, and computers that learn every scenario
  • [09:20] Grok 4 and the leapfrogging AI models
  • [11:32] Is there a first-mover advantage? Apple, Facebook and tech diffusion
  • [13:11] Regulation, energy, and the race with China
  • [14:33] Terminator and Skynet fears
  • [15:16] Tesla self-driving: brute force and billions of miles
  • [17:21] Why we hold computers to a higher standard than human drivers
  • [18:21] Why humans decide emotionally and justify with data
  • [21:06] Scientists who change their minds, and giving people room to grow
  • [23:01] Micro, mezzo and macro: how to give people hope
  • [24:00] AI agents on the org chart: your job will go to someone using AI
  • [24:56] Hallucinations, and why LLMs can't yet teach themselves
  • [25:55] How AI makes life more abundant right now
  • [29:35] Writing a book with AI, with a human in the loop
  • [30:51] AI as a calculator, and what it means for education
  • [33:41] Retraining workers, and whether the job-loss fear is overblown
  • [35:06] A future where AI and robots can do almost everything
  • [36:00] "Everything that can be invented has been invented"
  • [37:24] Empathy for people mid-career, and why you can't put the genie back in the bottle
  • [39:28] Sponsor break: Premium Cabinets
  • [40:20] The doom and gloom: Elon Musk's warnings
  • [41:29] The Manhattan Project's odds, and a family story about the first bomb
  • [42:31] Big bold predictions, and how to read Musk's 5-10%
  • [43:20] Sam Altman, and the move to a for-profit model
  • [44:15] Dario Amodei on entry-level jobs, and this summer's internships
  • [46:06] The Claude Opus blackmail test
  • [48:11] Marketing, case studies, and healthy skepticism
  • [49:05] Inevitably smarter than us: abundance, job loss or extinction?
  • [50:06] Personalized medicine and mental health care
  • [52:01] What can I do about it? Find ways to make a positive impact
  • [53:00] Becoming a multiplanetary species
  • [53:56] Using AI to solve isolation and rebuild community
  • [54:55] The real fear: a Ready Player One echo chamber
  • [56:51] What AI can't do: just be there
  • [58:45] The optimists: Sundar Pichai, Demis Hassabis and Jensen Huang
  • [1:00:50] Some pain in the transition, and honoring the wisdom of elders
  • [1:02:36] Advice for kids and for people who lose their jobs to AI
  • [1:03:35] The skilled trades
  • [1:04:33] Wrap-up

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About the hosts

This is a hosts-only episode with Randy Lorensen and Dr. John Oberg (Anthony Marino isn't on this one). Host bios are on the host pages linked below.

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Meet the hosts: Randy Lorensen · Dr. John Oberg · Anthony Marino

Transcript

A full transcript is below. It was generated automatically from the episode audio, so expect some spelling errors in names and terms.

[00:02] today is AI. We hear it everywhere. Well, we're going to talk about what is it, what it isn't. Will it destroy us? Will it bring unreal abundance to the world and much more. So, sit back and enjoy. And I think you're going to learn some things about AI and how to approach Abundance. Why settle for less? As always, I am here with my co-host, Dr. John Oberg. John, how you doing today? — Good, man. Tales of AI. That's where we're headed today. I'm excited about this. I think it's going to take a little bit of a turn for our listeners. Not in a bad way, but I think in a different way, different tone, different conversation. And when you talk about abundance, you've got to talk about things with intellectual honesty. And I think that's where we're headed today is like how do we get into things that that are on people's minds that maybe aren't talked about, you know, everywhere. And we're going to dive into that. — Yeah. We we talk you and I talk about and with our group of people we talk about AI a lot, right? And and I think both of us are well I don't we're not technical but we're probably both in the very top percent of how effectively and much we use it. I think we've both put a lot of time and effort into getting good at it.

[01:12] And we're not going to talk about that today as much but more just where it's going, what it is, and — what it could be, what it couldn't be. And I think some of the, you know, and maybe some things that are going to surprise the listener about like regulation on AI. And I think that some of our positions, at least some of my positions may surprise a few people that know me. I don't know. We'll find out. — All right. Well, I'm now I'm curious. So, I want I think a good place to start I was listening to the All-In podcast recently and they referenced a blog and this blog's from 2019 which seems might as well be BC, right? When it comes to AI research, but it was from a professor at the University of Texas. I apologize, I don't have his first name, but Sutton is the last name. And the blog, if anybody wants to look at, it's a fairly short read. It's called The Bitter Lesson, and we'll put it in the show notes. uh it's probably a 2 3 4 minute read but what he argues to really summarize it is that the best and highest level of progress comes not from human design but from methods that allow machines to learn or teach themselves.

[02:16] — Yeah, I think most people today would actually argue with that in the sense that machines aren't teaching themselves in the way that the common person thinks about that. But the article rightfully points out that when humans get in to try to be important in the process instead of letting the data and the science drive the process, we only slow things down and get in the way. So I when I read the article, I really thought, yep, I totally agree with the premise that, you know, when when we we try to be important where we're less important, that's a problem. But that that's different than the idea of like machines teaching themselves, which LLMs do not do effectively today. Like all the research is showing that when you leave one particular model to its own devices, it makes itself more dumb over time due to hallucinations. But but again, conceptually, the idea that humans get in the way, true story. — Well, we might we might debate a little bit of this, but but let's let's go back to some early I don't know if it's early AI, but but pretty early for, you know, for most people. — Well, to to IBM's big blue, right, the chess machine. And I know you through Amber have some insight into this cuz Amber was very connected with, you know, — Amber got to see some of the cool stuff and I got to hear some stuff. Amber's my wife who was at one point uh ran marketing for IoT and AI at IBM, but I I don't I don't know that I have like some special like — inside juice necessarily.

[03:34] — Well, if we go back to Big Blue, Big Blue beat Gary Kasparov, who at the time was considered he was the world champion, considered the greatest chess player in history. — Yeah. And — so I have inside interest like like I was super interested because of where my wife worked, but but I don't Yeah, I don't want to claim that I know anything more than anybody else who knew in fact there's a lot of people who know more about it than I do just from public information. But having someone beat having a computer beat a chess champion — holy cow. — That was that was a big step, right? And I thought I put the date. I clearly didn't. It was okay. I don't know the date here. — I'm find I'll find out. — But this was completely this is the human part, right? This was all done. It was more of an expert system in a lot of ways than it was AI. But — 1997 they programmed human beings took chess knowledge and taught the computer to be better. By the way, first attempt, it should be noted, failed. They learned from that, improved on it, and then beat Casper off the second time around. — I will say in the 1970s, you had uh the Wamper actually learned from itself playing tic-tac-toe in the famous movie War Games.

[04:43] — I mean I I only joke about that because the Jetson are out here, right? I mean, everyone joked about the Jetsons and like you can't really have like a self-moving vehicle and think like, you know, aircraft that fly themselves except Oh, wait, we do. — Right. And so I I think so tongue and cheek like the Jetsons aren't going to happen, but they're here, right? — You know, the wumper isn't but it's here, right? Right. Like I think I think we are — Yeah. and and maybe you explain to the listener what an expert system versus like a large large language model is or I can give a simple example of — go ahead. — Yeah. I mean so an expert system is a whole series of rules right where you program in all the rules where a large language model uses a vector database to go find information in one example of this where it goes out and says okay what's the closest information and then it predicts what the next right word is as it produces this generative answer. — Right. Well so from big blue the next iteration was 2008. Right. So 9 years later and it was something called — Oh, so Big Blue I think was 1997. So now we're talking what is a little little further than that. — 11 years. Yeah. Okay. Sorry. Yeah. 11 years later.

[05:47] — Well, what they did with Stockfish was in essence the same thing. They just gave it way more data. And because it had more data, it learned more. It became smarter or you know — more moves ahead too, right? Like if you said, "Hey, why don't you think ahead five moves or think ahead 800 moves or what?" you know, I don't know what the real numbers were, but I mean, the further the further you think ahead and and they use something called Monte Carlo anal or a version of Monte Carlo analysis, right? And I'm not sure if you did that in poker or not, but in finance, you do Monte Carlo analysis, which is you take all the variables and you look at all the different probabilities of which variable to make the best possible decision given that moment. And the more processing power you have, the the more uh information you can look at, the the better you can predict what's going to happen. Yeah, they just gave it they just fed it all these human games, right? — And they gave it more and they gave it more to look at. So they allowed it to go further out so they could look at more moves, right? — They they just gave it a lot more data. And so with more data, it became much more powerful, much more effective. And now it became even more dominant. Well, in 2017, so 9 years after that, — somebody created something called Alpha Zero, and it was a computer only version. And now, if I'm not mistaken, they didn't even teach it the rules. It just played the game and it had to figure out the rules itself. And what were, you know, it it wouldn't allow it to make moves. Well, the computer just played itself. And in 4 hours, it played millions of games with no human input. And after just 4 hours of training, it played the ver the previous Stockfish, which had had years and years of development and giving it data. out of a hundred games it beat it 28 to zero with 72 draws after four hours of training itself.

[07:30] — Yeah. I think that goes back to like you know a couple things right. So you Moore's law which is like technology doubles in speed every x number 18 months I think is Moore's law. — And then you have this Monte Carlo analysis and it's like nine years later right? So you've got 18 months, 36 months, 72 months, and then what's the eight you're 16 or 32 times more processing power, which means you have that much more ability to get data into your system. And what this thing was doing, Alpha Zero, according to, you know, 10 seconds of research that I did was it just created a more robust marcology tree. — It it tried things that humans wouldn't try, right? It — like going out 800 moves. — Yeah. But well, but it it might do a move that a human would never consider or a human considers too risky or the human ego gets in the way and it — or fear or Yeah. Sure. — And it wasn't worried, right? Because there's no consequences. It's just learning and it just learned every possible scenario. Yeah. — Right. And then this was applied to Go and a whole bunch of different games with this, you know, Go is I don't I've played Go once or twice, but it's — I've never played I want to, but it's so complic I'm still a beginner at chess.

[08:37] — Yeah. Far more complex, right? And I know some of this has been applied to poker and I'm not really in the poker world anymore, but I know I know there is a problem now with bots playing online and making it very very difficult even for pros to beat the game. — I had never even thought about that. So like if you were really smart about it, you'd hook up a bot to your online account and it becomes like trading. — Yeah. I mean this this has been happening, right? But this is this is one of the problems with online poker now is that you you don't know if you're playing against a human. — So interesting. — And pretty soon that'll be robots. you won't know if you're playing against a robot or not — or or with a neural implant or I mean — right so um well let's talk about — Elon Musk and I think you know just we try to keep these evergreen but here not long ago Grock 4.0 came out — and they're saying all of the metrics and the standards this is now the most powerful model have you played with Grock 4.0 you know, just a little bit before we came in. And I will say with my experience, there's there's model jumps ahead, right? So, it's like chat GPT happens, then Gemini does something, then Grock does something, then Llama does something, and then it cycles through again in a different order. So much so that when we're developing AI inside my companies, we create a platform that we can plug and unplug models as they make those leaps forward.

[09:56] And then, you know, sometimes you'll have people that use one model in one company. So chat GPT and you become really proficient with prompting that particular model, they update another model and you have to learn how to prompt that new model or if you move from GPT to Gemini, Gemini to Gro, Grock to like you have to learn how to work within that model most successfully. So I'm not surprised at all that Grock has jumped forward. I I I do um have a perception that I don't have a lot of data around that that Elon seems willing to kind of do things more scientifically without people's feelings around it. Maybe so more so than some other people. Although that's only because I've read more press on him than I've read on Altman or anybody else. Well, what I what what stands out to me with the Grock being so far ahead is if you think of the head start that OpenAI and Claude and these and Google — they had a big head start — in a lot of way but you hold on but but Elon also was an early investor in chat GPT and OpenAI and he he was super involved in Yeah. Now I don't know how in the weeds he was but I think maybe the head start wasn't as clear as started 10 years later.

[11:05] — Well, fair enough. And also Tesla is really an AI company more than they are a car company in a lot of ways. Uh but you know and if you really think think about Apple — I mean Apple's been working on Siri for — 20 years. — Still sucks. I mean it's it's pretty terrible, right? Um, and Facebook has thrown unlimited resources at this and — yeah, but I I'm not going to count Apple out yet because So, okay, so my master's degree was in tech diffusion. We studied a lot of like commercialization of technology and I was shocked because going into that degree, what I thought was that um the the first mover advantage was really significant. But you know when I finished my masters in the in the middle of the as the the data at that point was very very clear that the first mover beat the second mover only ever so slightly and the resources spent by the second mover were materially less. So the return on investment for the second mover was generally speaking I'm speaking in broad generalizations you know much much much much better. So I don't count Apple out because Apple has said they don't always need to be the first mover. in many many cases they are welcome they are totally willing to be the second mover in a closed system to maximize how far they can move the user experience. So I think we're a little early to count them out.

[12:25] Well, the thing in when I was researching this and looking into it that stood out to me about what Elon did with with Grock is he got way more computing power and just went bigger and bigger and bigger and even now to run the computing power he's he's having energy issues which yeah is a it's a whole another topic. We could do a whole podcast about the future and abundance and energy and tied into it. But he is importing because the regulation, he can't build it himself. It's going to take so long to get through the regulatory hurdles. He's actually an whole energy plant from overseas to build it here to somehow get ahead of the the regulations, right? And it's mostly to train the AI and to have the comput. I mean, he's just taken the approach. I mean, it opens up so many questions. It opens up questions about nuclear power. It opens up questions about regulating AI and what we're going to try to do. And I think — I mean, the pragmatic part of me thinks like if we're not careful and we try to regulate AI too much, like we could put the US or, you know, the Western world at a huge competitive disadvantage with, you know, countries like China. Like just think about that for a minute. If we say, "Hey, we must slow down development of AI." And and China says, or Russia says, "We're going to go whole hog like, you know, you know, just totally untethered, that could put us in a really bad position from a defense perspective."

[13:51] And I mean, we're getting ahead of ourselves, but we're here, so let's let's go down that rabbit hole. There's a lot of people that think we should do this, right? There's whole groups that think as we get to some of the doom and gloom or later that AI could kill us and take all the jobs and everything else we'll talk about. But and they're like, "Yeah, we need to stop all production." And that's I just think it's unrealistic because if we stop, China's not — Russia's not stopping. I mean, — not a chance. All it just makes us more vulnerable. Right. — Right. And and so I think there's it's interesting some of the people that we'll talk about that are warning that this could kill us are also moving they're the people pushing it forward the fastest which again we're getting ahead of ourselves. Well, and you know, there's the whole like Terminator Skynet scenario is one people, one set of fears. And you know, while I could poo poo that, and I think I even did poo poo that in a previous episode, I I also want to say I get it. Like, you know, saying that Skynet's not going to happen is like saying the Jetson isn't going to happen. Like I I who knows it is it possible? Sure. Is it probable? I don't think so. But the people who make that argument, like I could see that. Now, the China thing though, like that's like that's for — right? So, you know, the other thing that Elon's done with self-driving is he's taken about it different than everybody else. And his whole strategy is we're just going to self-drive and we're just going to there's a little bit of the brute force now, but it's the computer teaching itself. And I've noticed while I've had my Tesla, it wanted way more human feedback. Doesn't seem to care quite as much about my feedback anymore. It's learn it. Again, it's brute force and millions or billions of miles driven and the system just keeps getting better and better and they've got such a head start.

[15:36] — I wish I I remembered the data so I'm going to give you some some numbers that aren't right but it was something like you know the accidents per driven mile you know if it was like you know I can't remember the number like one fatality for every 80 million miles driven for a human and like one fatality for every 137 miles driven 37 million miles driven for Tesla. So it was it was reduced by like whatever percentage that is. Um and and so and that was years ago, right? And and so now you have to ask yourself like if the data I mean I I'm a scientist so I want to look at the actual data and see what the real data says, but I've got to imagine computers are going to be better drivers than — Yeah, that I I I tried to pull it quick. I tried to pull this up quick and I'm getting a lot of different stuff here, but I roughly remember the numbers of the average car gets in an accident every two to 300,000 miles. The average Tesla human driven is more like every 8 to 900,000 miles. So, it was like 3x.

[16:41] on self-driving it was something like 8 or 8 and a half million miles. And I will just tell you like Austin, we have Teslas all over. Drive around and see how many of them have body damage. Yeah, — it's really really rare. Right. — Well, and to be fair, that body damage may come from somebody hitting a Tesla, not a Tesla hitting something else, — right? So, you know, and it it's it's interesting for me to watch this and people when I tell them I get in the car, I push a button and it just drives itself, they're like, "You you trust it?" I'm like, "Man, if you just get in there, you realize it drives better than humans." And well, it's funny. People self-report. Most humans would self-report they're better than average. And just by definition, you can't have more than half the population better than average. — Yeah. And here's the thing. I'm not saying it won't ever get in an accident, but we have this weird standard as If if we get, let's just say humans, we get in 10 accidents, right? But if if somebody's in self-driving and in the same number of miles gets in one accident, people are like, "See, that's the computer. If if that was a human that went even though humans are you know 10 or I think the numbers we just looked at probably closer to 30 times less likely to get it in an accident.

[17:49] It's a great it's a great way of just pointing like when emotion drives us, right? It's like we we look at, you know, if if someone I love has a really rare disease, it becomes a really important topic for me, right? But it's, you know, but the question is then how many people die in high-speed accidents or falling asleep at the wheel accidents or, you know, I not don't need to look it up, but like how many people die because of falling asleep at the wheel versus drunk driving today because of all the work that's been done. And — yeah, we we I w I wish I wish we were just a little more data driven versus finding the data to make our point. — But what let's talk about that. I mean, we're humans. What I mean, what is the why aren't we? — Well, because we the way the human brain works, we make a decision emotionally, then we justify it logically. So, we we've made the decision in an instant and then we go find the data to support our position. And that's just that's the way the brain works. And so — I think it's I think it's limited us in a lot of ways, right? That we we come in, we have this these preconceived notions. And I think it's the same reason companies are usually they're usually beaten by somebody from the outside, right? Disrupted that inside they they they go, "This is how it's done." And somebody from a completely different industry comes in and sees things that they just don't see.

[19:04] — Right. And I I think this has happened in academia, right? If somebody's written a textbook and they have a belief system and somebody comes in and challenges that it's we want to win. We want to be we're all part of teams. Yeah. I mean that's not I mean the way the scientific method was designed was that so we would be hopefully permanently curious and try to move the frontier of knowledge forward. But the reality is someone's getting paid to do research. They've made a career out of it. They've got tenure. They whatever the case may be. So there's still that emotion that you know little bit of protectionism. We can all hope that we're not that way. I mean, I I could hope that I am and you can hope that you are and Sure. — No, it's hard. I'm not I'm not I'm not saying it even critically. I mean I mean maybe a little critically, but it's not it's just it's humans like we're flawed. — Yeah. But but once we've put our effort and time, I mean I I feel a little sorry for people, right? If somebody has done this research and they've spent their entire life coming to this conclusion and somebody else comes along and proves it wrong, like that's got to be heartening as hell. I just before this podcast I'm at a discussion with Dr. Dr. Dustin Williams, who's my partner in the medical practice, who has been on the podcast, and we were talking about another doctor. And one of the things I respect about this doctor who's an influential uh physician, was that he will go out, find research, and point take a position and really take a position, and then you might hear him come back six or 18 months later and he'll take the opposite position with new data. And and I really appreciate that about a scientist or any professional who can, you know, um have a strong conviction that's loosely held like that. That's such an important part of what we talked about is, you know, strong convictions loosely held lead to really healthy discourse and and the and the movement of kind of the the collective intelligence. And I I appreciate that. I think that's where we are with AI is we, you know, if we can have strong convictions, AI is almost forcing us to keep those strong convictions loosely held because the pace now is so fast and so in our face that but as you pointed out earlier, it's uncomfortable, which means some people want to slow it down for their own comfort versus the the reality of where the information is headed.

[21:06] — Well, I love and applaud that doctor that, you know, they're willing to do that, right? — Mark Hyman, by the way. He lives here in Austin. Have never met the guy. I hope I can meet him sometime, but I I I just have a tremendous amount of respect for the work he's doing, but even more so for the professional integrity he has to do that. I just I have a huge amount of respect for that. No, — I think I think that's amazing. Right. And but most people aren't willing to do that. And some of that is I think it's society, it's the media, it's look at politicians, right? If a politician has has a belief system or as a campaign — and 20 years later they change it, they get lambasted. Now, sometimes it's because they're a hypocrite or they flip-flopped or they sold out. I get it, right? I'm the last person that's going to back up the politician here, right? People that know me. But if they had a belief system that they truly believed 20 years ago and now they have new I like you said, Dr. Markman, I want them to change. And I tell people, you know, all the time in, in our consulting practice, it's like, hey, look, you know, you got to give people the room to grow and change. You got to give them the space to do it. And if you hold them to what you knew about them previously and they're trying to move forward, that may not be fair for that relationship. So, — yeah. I mean, I think of it an airplane pilot, right? If an airplane pilot gets in and we have a flight plan and all of a sudden there's a storm that moves into the way of the flight plan.

[22:27] — Please change direction. — Yes. Can we change the flight plan, please? I'd like to live. Right. — That's it. That's exactly right. Right. — but but there are some pilots with so much hubris that that leads to accidents like like like we see that this aviation is a great example of that. — Definitely. So, what else? I mean, how do we overcome this as humans? I mean, I think this is AI learns better without us in a lot of way, but our ego, our hubris, our — pride, I mean, whatever else gets gets in the way. I was a guest on a podcast this morning and uh we talked about like you know I think the question at the end was like how do you give somebody hope you know and and my perspective on this is similar to my perspective on a lot of things I'm a social worker by training and so I tend to think about things in the micromeo and macro level meaning that the micro level is the individual the mezzo level is the community and the and the macro level is then the policy and we may be living in a world with imperfect policies and imperfect communities but That doesn't have to mean that we give up all of our agencies as individuals. Like we can still find ways to move forward in the in the face of policies that may not be perfect or communities that may be imperfect, which I think all policies are imperfect and all people are imperfect and all community and so then what what do we do? And so for AI, I think that means embrace it for what it could be. My my position is that AI is going to change the way we work. Um, I think that every organization in the future is going to have an organizational chart that includes both people and AI on the organizational chart. In the same way that some org charts have people and processes on their org chart today, those processes are just going to be listed as AI agents or AI processes that are getting run. And and the worker of the future is going to be the person that is not necessarily the job being replaced by AI. Someone said it great the other day, it's like not that your job's going to be replaced by AI. your job is going to be replaced by someone who does their job with AI better, — And so, how do you now become a manager of that set of processes and we're going to have to uplevel in the same way that people who made buggy whips are out of an industry, right, 100 years, 120 years ago, we're we're moving — Yeah. Well, and I think one of the things that, you know, you and I have talked about is, you know, AI is wrong, too. AI makes makes mistakes all the time or and — you know my feeling is that a lot of times when it makes a mistake it's because I gave it the wrong information.

[24:56] I didn't I didn't ask the right question — but sometimes it hallucinates makes things up tries to find the right answer etc. a hallucination's real and when you and this is this okay and so this is where AI has a problem right when AI learns from itself it learns and we're talking about LLM specifically now — um the LLM goes and learns from its own hallucinations and that just makes the problem perpetually worse right and so that's why there's probably less risk in like like where we are today with AI than some people are worried about not not to poo poo the whole thing but like that I'm just not too worried about AI becoming the next human or the next evolution of humanity. Um I I get where people are coming from like, hey, we used to be amiebas. I got it. I hear Not too worried about that. — So, going back to abundance, and we're going to end on abundance with this. We'll end on a happy note later on. But going back to abundance, like how how do you see people making their life more abundant with AI right now? — Well, I just think about like what I see happening today. There are people that have I mean the internet gave information access to people in a way they've never had that information before but but there's still the processing of that information that was overwhelming and so today I think now we have a way to get in and curate that information in a way that wasn't possible for the lay person before and then you get people that can use that fulcrum across so many different ways at their job. So if you're a computer coder, you can I talked to a guy who's like, I can code 50 times better now and with far less difficulty. And and and 50 times was like a literal thing for him, not a figurative thing. You know, I talked to someone who's like, I can do one unit of work, it was 40 hours two years ago, and now they can produce 4,000 units of work in two hours. And so those are some lever points where I think you can find people doing things more effectively. I was able to craft um an AI that took in a hundred job applications with a company and just sort through them and tell people what to look at on the resumes. It didn't actually do the assessment for the human manager. What it did is it pointed out where to look to find the things that were important to them so that the process went much smoother. And so I think I think those are the I mean I think there's so many things we can do to make the work that people don't like to do easier to do today. think that's an immediate win.

[27:21] — Yeah, it's definitely made my life easier. My productivity has gone up — Um, is it making us dumber? — I I think I think there are people that are going to lean on it and that it's not going to force them to go deeper into their domain and there are other people where it's going to force them deeper into their domain. And so I think the answer is definitively it depends. — I think that's right. I I think there's a lot of people that are becoming reliant. I think you know before the podcast we were talking about I had a rental car in Indianapolis last week and I've become so dumb down by the Tesla driving me that driving seemed like seemed like a chore when I parked the car because I didn't put it in park it kept moving I was like oh I'm in a different car now right so little things like that but I think people are doing that with their work and their thinking and you know they're just there's not as much it it takes away a lot of the brain power and a out of the thought. — Yeah. I you know, Randy, how are your horseback skills right now? — They're not that good. — Okay. And so 100 years, the transition to cars, that might have been a really scary thing. Like driving a car, like, oh my gosh, I'm I'm losing my horseback skills. But I mean, do you really feel like your horseback skills and your lack thereof today are really holding you — No, but there's there's always a trade-off, right? And I agree with you by the way that some of the things I have been able to take on some projects with AI that because of just I mean there's one thing in particular that in a 10 or 12 hour process I I think I'm saving hundreds of hours if not years — of research, right? So and it allows me to go deeper. That being said, if you want to use the horseback analogy, riding a horse takes a certain amount of muscle energy. You're outside. It's probably better for you and healthier in some ways than I'm not, by the way, I'm not going to go trade in my car and and next time you see me, I'm not going to be, you know, having to put a saddle on.

[29:14] — I was going to ask cuz we could put a hitching post outside. — There's a there's an upside. There's a trade-off as well. — I think that's right. And I think and and then again, I think you've got, you know, whole industries that are built around health and wellness and gyms and all those things that weren't a thing, you know, 150, 200 years ago. We didn't have the need for the gymnasiums in the same way that we have them today. And I recognize that, you know, for some people they're going to make a judgment that that's a bad thing or a good thing. I think I just recognize like there are things that I want to do in my life that AI has made infinitely easier. You know, I I I did my first draft of a book in 2017 and it was — I mean, objectively horrific. It was terrible, you know. And then uh you know, earlier this year released a book. It was a bestseller. you know, and and now I've got a best-selling book and largely in part to the, you know, human in the loop, but I used AI to shortcut parts of that process to make it what I wanted to be with my voice, my content, my stuff. Um, but legitimately would not have had that done.

[30:16] — You know, — same experience. I I hired a ghostriter by the way and I paid a ghost writer 2x what I did paid to do it on my own and it took me less time less energy than the ghost like I mean over that's unbelievable. So I mean yeah so I think I think it's just again it depends where you where you apply it. You know graphing calculators I think were some version of this when I was in college like you had to learn how to code to use them at their kind of at their peak and if you had a business calculator you had to understand the formulas and and so I think AI is is the next you know gener I'm I'm overly simplifying but — no I think that's a really good way I look at that a lot. I've I've used that analogy for people. Think of it like a calculator, right? — Um education, how's this going to impact — Look, I teach it that you know graduate school level and I tell students like you're going to write these papers and do this work and I require you to site all of your work. So, if you're going to use artificial intelligence, I really encourage you to do that and then just site exactly where you used it and how you did it. And uh and then I'm, you know, and I'm okay with that. Um and if you choose not to use AI and go the other way, then that's just up to you.

[31:26] But I don't understand in this world why you would do math without a calculator or why would you do, you know, profit and loss statements without a spreadsheet or why you like I just don't like if if you want to do it because you think you're learning it that way fundamentally, there's the right classes for that. Mine is not one of them. Where does this go? That's today, 5, 10, 20 years in the future. What's it look — I mean, we're building AI right now for patients that is teaching patients how to be better at being a patient. I have to imagine that we're going to have more case studies, more educational simulation, more experiential learning. It's going to go back to being the schoolhouse where you had like a school teacher for every 12 kids, but now you're going to have, you know, probably, you know, a much higher ratio with much better education. And frankly, to me, it's about meeting people where they are. And think about an AI's ability to meet a child where the child is to make sure they have the maximum learning opportunity for that child. I think we're going to get to a point very quickly if we're open to it where we can really let people uh lean into their strengths and and I have some really big questions. I sit on a a board that is uh you know regional or national in scope depending how you look at it around education and innovation in education and I'm really pushing hard for like how do we how do we really think about education differently and do we want to go talk about the way that things are done in Europe where someone's put on their career path when they're in their early teens. And I I don't understand enough of that yet to know if that's the right or the wrong answer. What I do know is that you can get surgeons, you know, four years out out of high school that are competent doing surgery instead of 12 years after high school. Are the surgeons there the same, better, worse than I I don't have the data to know that answer, but it certainly is notable to me that if you meet people where they are, help them specialize in focus, get people focused on trades, like I I don't know. I just I think we're going to see some really interesting inflection points over the next few years.

[33:23] — Yeah. I think I think one of the things going back to the threat of humans being the the choke point, there's a lot of people that don't want to disrupt the education system because a lot of people are employed there. And and I think that inevitably when that happens, there's going to be a point where AI is doing better than humans and we're going to have to retrain a whole bunch of people in the workforce. And a lot of those are people that have been giving their entire life, right? they've had meaning and they've — I look I I I have a lot of empathy for it and I believe it's a specious argument. Can I tell you why? — Go ahead. — There there were times where businesses large companies had huge rooms full of people writing on paper spreadsheets going through all of this data crunching, data collection. And this impacted the commercial real estate environment because you have smaller offices now. It impacted the number of jobs of people who were doing that by putting computers in their hands. That's not a job anymore. — I get it. That's that history is littered with this, right? I mean, there there's there's literally thousands if not millions of examples where the new technology, yes, it disrupts these jobs, but it it tends to create more jobs and more abundance, which back more abundance. The people that are, you know, in some cases on the poverty line today are living better than most emperors over the history of time. like and and and that is in no way me trying to say we shouldn't work on the problems of poverty or minimizing somebody else's problems at all. So not doing that but also in full recognition that we you know the tide has been rising for a long period of time. So let's you and I I think disagree on the time frame of when this is going to happen, but I think we both agree that at some point AI combined with robotics and whatever else is going to be so damn smart that it can do almost everything, right? I mean I I envision a future at some point where there's robots, AI creating food. It's brought to us the the the cost of production is almost nothing. Um and everything is moving towards zero. Now, there's a big big choke point with energy and we better get our together in this country with energy production and allowing energy if we want to keep up and have that future — and how you deal with the byproducts of that energy production and there's a lot of big problems to solve. Yeah.

[35:42] — Yeah. So, but I think I think we both humans, if we're thinking about meaning, purpose, why they're here, there's a lot of people that without a job, men in particular, without a job, that meaning and that purpose goes away. What do you Where does that go? — I mean, man, just think just take me back to what you understand about technology like 20,000 years ago, 10,000 years ago. — I don't know. 20,000 years ago, we don't know anything. Let's go five. — Yeah. So, I mean, I I look at that and it's like I think that, you know, there's almost none of the jobs that were available then that are available — you know, and I think that there's a little bit like the guy at the, you know, the was it the patent and trademark office in the late 1800s was like, "Hey, we can go ahead and shut the US PTO down because everything that ever could be invented has already been invented." Like, think about the ridiculousness of that statement. like like air travel had not been like like airplanes hadn't been invented and we went from like Kittyhawk to the moon in you know — less than 100 years.

[36:45] — Look, there's the there's the Star Wars future, right? I mean, or star not Star Wars um Star Trek future where they're going through the galaxy finding new things, etc., etc., etc. — There's a book called Iminent, which I'm going to be a little little weird here. There's a book called imminent by a guy who worked for like the part of the US government and he's a very you know credible sounding author who goes through like extraterrestrial life and he's talking about like forms of propulsion that defy physics as we know it which leads to a whole another like there could be entire fields of and this is your argument to me all the time we just don't understand so many things — like we can't even fathom what those jobs are going to be so do I worry about that no because humans are going to work on those problems while computers deal with the problems we have today in the same way cars took care of horses. Like I just so I think that's happening. — I agree and I tend to look I I tend to be on your side on this. I'm playing devil's advocate a little bit because I think there's a lot of people that are on the other side, but I also — and that's reasonable, right? It's it's — I I have a little more empathy, maybe a lot more empathy in this case than I do with a lot of technological advances. I think this is going to happen so fast, so quickly and we start, you know, we talked about teachers. I mean, it could be driving jobs, it's white collar jobs, it's it's just all these jobs start going away.

[38:10] There's going to be some blood in the streets. I mean, I I there's there's going to be people that have a really hard time with this trans with this — 100%. I I think I think society is going to thrive and people there are going to be some people that really have a challenge with it. Particularly people that are mid to late career that are going to have a harder time retooling their career like absolutely and should we should we have really humane ways of handling those problems? Absolutely. We should talk about that as as a thing to do. I just don't believe that stop trying to put the genie back in the bottle is the right answer. And I think the more time we spend arguing about that, the less time we're spending solving the problem in a way that's actually going to work and be helpful for those people. — Yeah. I I think putting the genie back in the bottle. It would be like if one country now said we're going to get rid of our nuclear weapons, — but or you know, well, I don't know how many count let's say 10 countries have nuclear weapons and nine of them get rid of them and one doesn't, right? I mean, that that's that's ultimately the — Well, let's let's go to break. When we come back, we're going to go into kind of the two extremes, the doom and gloom, like what are some of the predictions that some really smart people are saying. And we've we've been hitting around the edges of that a lot. And then we're going to talk about, I think, where you and I both think things are going that, you know, it it can really lead us and will lead us toward a more abundant future.

[39:27] — Love it. — All right. So, listeners, we'll be back in uh right after the break. — Hey, it's Randy from Tales of Abundance. Is starting a business part of your journey? Do you have ambition but no clear path? Maybe you feel trapped in your current career. Do you want a real business that can produce $250,000 or more income for you and your family? We have an opportunity for a profitable business that can be part of your path to abundance. I want to personally invite you to see the proven system we've built. It's designed to give you that freedom and control you've been searching for. Visit premiumc to learn more. That's premiumc Listeners, welcome back to Tales of Abundance. Why settle for more? We've been talking about AI today and — uh why settle for less? I love it. That's, you know, — AI written by AI. All the new scripting. Darn it. — There we go. — All right. So, we said we come back, we're going to talk about the doom and gloom because I again — Oh, that's why. Why settle for more? You had the doom and gloom hat on. — All right. So, all right. So, here are some some quotes that I looked up using AI nonetheless. And I just asked Elon Musk predictions, right? One of the people intimately involved in in AI.

[40:41] With AI, we are summoning the demon. All right, that was one quote. Um, another quote from Elon was AI is our biggest ex why can't I talk today? Existential threat. Um, here's another one from a few years ago on the Lex Freedman podcast. his estimated risk of it basically ending civilization, killing us all, mind you, is 5 to 10%. Possibly higher. That one jumps out to me because, you know, when when we were developing the nuclear bomb, right, the Manhattan project, I believe it was — I think the there was a real debate if we should be doing this and there were people that said we shouldn't. And there they put the chances. It was somewhere way under 1%. And there was a there was a discussion. — Yeah. But they thought, I mean, I think my recollection of this was they thought there was a chance that the chain reaction would just take us out on day one. Funny story about that. My grandfather was a flight surgeon in the US Army Airore in New Mexico, Alamagordo, and he was flying in the air the day the first bomb was detonated toward Albuquerque. And they came on and they told him to land the plane or they'd be shot down. And he put it together decades later that the reason they told him he'd be shot down, they weren't sure that flying through Albuquerque, he wouldn't be knocked out of the sky when they'd hit the bomb.

[42:00] — Yeah. Excuse me. — So, so it's not the first time. And it seems, look, on its surface, that seems really irresponsible to me. And we've talked about earlier in the episode, not everybody's going to stop, right? Like it's there are people that are going to keep developing AI. So, — and wasn't Oppenheimer Oppenheimer doing physics for somebody else at one point? I don't remember the details. — I mean, a lot of those people were ger a lot of them were Nazis, frankly. I mean, a lot of NASA were Nazis. Yeah, I think — Warner von Braonn or whatever I think was a Nazi. And I hear, you know, Elon Musk and some of the comments he's made like like most visionaries, it's like big bold predict like the number of times he said we're going to have full self-driving cars this year, number of years in a row, right? And so when I hear him say 5 to 10%, what I hear is really smart guy, really worried about it, doesn't get the timing right every time, and also is the best marketer can. He's like, I don't spend any money on marketing yet he is the market. So So I I take it with a grain of salt like super smart. He's concerned. I hear it. Um, but to say that like AI is a bigger threat to society than isolation, I just I have a hard time as a, you know, as a social scientist and as a health scientist, uh, buying into that.

[43:15] — I get it. Um, here a couple more quotes from Sam Alman, who is the head of Open AI. Um, warns that AI could pose an ex why can I not say — existential is the word you're looking — I'm really struggling today. threat on par with nuclear war believes that mitigating extinction risk from AI should be a global priority, but again keeps moving forward as fast as — That's also the guy that took it out of the open open-source model into a for-profit model. Yeah. — Right. There's there's definitely some hypocrisy there, right? — Well, it's I don't know if it's hypocrisy or just economic self-interest, right? Like we should definitely think about doing that as long as it makes me rich in the process. — Yeah. Here's another here's another quote from him. He admits AI will probably most likely lead to the end of the world. Jokingly underscoring the seriousness while building powerful AI companies, right? — Will probably most likely I love the certainty of that. That's great. Um — there's definitely a possibility that it — Yeah. And from Anthropics founder Daario, I can't say his last name. We'll just call him Dario. uh warns AI could eliminate up to 50% of entry-level white collar roles in the next one to five years potentially causing 10 to 20% — Now that now that one see I think so the number of internships in the country this summer for college students and this is 100% anecdotal but I I've got a sample size that's you know it's notable. Um I don't think there's nearly the number of internships for white collar positions as there have been in the past. No, there's I I think I've mentioned on here before my or certainly in you and I in conversation. Um my son's best friend graduated from with a computer science degree and can't find a job in computer science and none of the other people he graduated from from a major college. Yeah. And none of them they're just almost none of them are finding jobs right now.

[45:06] — It's hard. — Right. So earlier when you were saying you know the quote and I've used it before that that you know it won't be AI that takes your job. will be somebody using it, but they might take 50 jobs, — right? Or 100 jobs or a thousand jobs. — Yeah. And look, I think, you know, someone was talking today about, you know, you can get a job as a prompt engineer today. So, as a prompt engineer more effective than someone who's a computer programmer and I don't know the answer to that, but I mean the reality is that things are changing and they're changing faster now. And so, where they change a little slower before and a little harder to see, it's far more stark today. — Yeah. So Jeffrey Hinton warns AI could destroy humanity with up to a 10 to 20% chance in the next 30 years and calls for basic income in response to job — He's at Google, right? And notably in California where universal basic income is a real thing in terms of — Yeah. Um you know the other thing that I think and I got a bunch more there there there's a whole bunch of quotes here but you know the other thing that I think is concerning in this is you're hearing reports like I Have you heard the the news one where they they told the AI they were going to shut it down, but they also gave it information that it's the the guy running it was having an affair on his wife, which he wasn't, but they fed it the information as a test.

[46:24] Have you Have you heard that story? — I did not know. — So, they had an AI. They gave it this information. I just said that he was cheating on his wife, having an affair, and then they basically told that they were going to shut it down and it started blackmailing him in trying to stay alive. And there have been other things where when they've been shutting — Where is that? I want to read that. That sounds fascinating. Which AI was it? — I don't know. I I look I mean, we can try and Google it and find it while we're looking right here. Let me put in the Google. — I just morbidly curious about this one. I don't know why I'm not I Why am I using Google instead of chat? But — I don't know. Um, that there's uh, you know, there's just there have been copy or or where AI has copied itself when it said it was going to be shut down, right? — I mean, interesting. I mean, at what point does it is it smart enough that it — becomes sensient? — Yeah, we're a long way from that today. I mean, that point may be coming and it may be coming faster, but we're a long way from that today. — Here it is. It was at anthropic claude for opus model was given access to emails suggesting it was going to be replaced among among the emails there was a mention of an engineer involved in in having in the replacement having an extra marital affair.

[47:40] — And it started blackmailing him. — So listeners can read into that more. I mean we're not I won't bore them with — Did it open a bank account? I'm going to go read into that. Again more curiosity. Not saying it's but just like I mean what does Yeah. I'm going to buy I question some of these things too. Like there was a media story a year ago and I don't remember the exact but I think it was a New York Times, New York Post, something like that — and the person was talking about something with an online dating and it — but I got to say there's so much work right now being done to understand search engine optimization and you know artificial intelligence engine optimization for marketing purposes that you know there there's a reasonable chance that some people believe any marketing is good marketing that they've created these case studies so that they can put it out there to get you know, domain authority and expert. Like I I'm a little bit skeptical in in some of this stuff in in just the media more broadly. So — yeah, I think I see it happening faster than you do, but I do think it's inevitable that it's smarter than us.

[48:42] And if it's not already, it's really really close. — Smarter is interesting, right? I mean, in terms of ability to proc, I think I'm a pretty smart guy, but my ability to process as much information as an AI, like there's no comparison. Like, I I can't catch up and never will. — But then how I can harness and act on that information is a different thing. — Well, but go back to go back to where we started early with chess, right? And the the loop that once AI started teaching itself how much better it is in just four hours. It can take mult at some point it will be able to take all these different disciplines and put them together. I think it's I think it's inevitable it's smarter than us. Now again I think this will lead toward abundance but it could mean taking jobs. I mean on the other end of the spectrum is it kills all of us, right? It we become a slave. — Yeah. But you're forgetting the part about Yeah. Okay. Maybe it kills all of us. Maybe it cures cancer, — Maybe it figures out this new type of plasma hyperdrive that gets us through, you know, black holes to nirvana. Like I — well it also just might ignore us, right? I mean I mean it also just, you know, could ignore us. I mean, I I think of it, you know, if somebody's walking along and we see ants, you know, we don't, you know, we don't really see them as a threat. We just kind of keep going. Yeah. So, it could be. So, let's go let's go to the other side, right?

[49:58] What is the — what is the abundance? — Yeah. I mean I think about just the way that you know the way that AI can model information and the way like I think about what it could do for pharmarmacology and for personalized medicine and what it's already doing for personalized medicine with our practice and what it could do for the reduction of you know the cost of mental health care delivery where we have not enough you know mental healthare workers and uh it's you know too costly in many circumstances and what it can do for optimizing you know any one of a hundred health care problems or mental health problem like I just so Yeah, if you take it and you apply it to kind of some of the hardest problems or you know what if it does get applied to this whole thing about like you know um you know ve like um unidentified you know aerial vehicles that people don't know how to explain their propulsion systems or make the next big breakthroughs in string theory or like I I I think those things have to be considered at least as likely. Oh, I think I think that's the most likely, right? But there is the poker player in me, right? That I look at there's the perfect scenario, right? The abundance.

[51:04] We have the Star Trek future where we are flying through the air. Everybody's living off of credits and the whatever. I haven't seen Star Trek in years, but they had the replicators where it just made stuff and — try the triquarters to do all the this ideal world, right? There's there's a certain percentage chance of that and there's also a certain percentage of — it kills us all. I don't think that's the most likely outcome. But — but but yeah, yeah, I I weigh that against there's a certain likelihood that the sun doesn't come up tomorrow and there's a certain likelihood that you know there there's all these whatifs and I go back to like Steven Cvy's stuff that that I read in seven habits of highly effective people like you have a circle of influence and a circle of concern and I try to grow my circle of influence. I try to shrink my circle of concern. I don't read a whole lot of news for that reason. And so it's like sometimes the percentages are the percentages and I get all that and and you know better than anyone that sometimes it's a low percentage chance and it happens anyway. B to your favor and your detriment. You talk about three poker hands that changed your life. Not in a great way maybe but also maybe in the best way. Who knows? So I mean so for me it's I go back to the you know macro, messo and micro. What can I do about that? What I can do is I can go find AI. I can go find ways to really impact the world positively, create that fulcrum, make a big lever, and then do the best damn thing I can today. And that means I have to train myself, be better at prompting, train myself. And I think that's the right answer for people is what impact is it going to have on your life? Well, what impact do you want it to have on your life? And how do you want to engage with your life and move yourself forward?

[52:38] — I think that's great advice. You know, one of the things I thought about in this is, you know, I'm always trying to figure out when something bad happens, what what's the benefit? what's the you know what what good or what what what should I be grateful for that probably isn't immediately obvious but — let's just say in the worst case let's say the worst case happened and it took over but it also simultaneously it helped us become a multilanetary species right it helps Elon Musk take us to Mars or somebody else to the moon or whatever — and an asteroid hit the earth and that's what saves humanity simultaneously right is it allows us to to get off this rock onto a different one I mean — did you watch Armageddon recently long time ago. — I thought you might have channeling your last night's movie choices. — Long time ago, John. Um, so look, I I desperately want it to be and I think I I think the most likely outcome is we have true abundance of food and resources and you combine it with robots and 3D printing and flying cars and that everything becomes cheaper. every I mean the natural state of the world we have inflation and this is a whole another topic you know I'm passionate about with the Fed and printing money but the national the natural state of the world is deflationary — and everything becomes easier — yeah I think that's right but I think you know what what if what if it allowed us to enter put all of our time and energy into the world's biggest current problem my point of view isolation and relationships and people like redevelop skills into relationship redevelop skills into kind of non-romantic love redevelop skills into the highest possible performance of community read like like if that were the thing and and GDP wasn't the measure right the real measure was GHP like we've talked about in other you know gross happiness product or or what if we found an entirely different way of measuring what success for a species looked like I mean certainly what I can tell you is the way humans um measure success today by and large as individuals is not the way that a species operates to kind of keep itself growing, thriving, whole. And there's something to be learned from that. And so if this lightens our load in such a way that we can now focus our time and energy, time, attention, energy, and passion towards the things that are most important, at least that we say are.

[54:55] And and so I my fear about AI is it becomes so hyperpersonalized, it becomes everyone's individualized echo chamber like Ready Player One. Like that to me would be the biggest yikes moment. Not the Terminator and Skynet, but Ready Player One in that where everyone is so like living in in the secondary Second Life type of environment. Like that to me would be the biggest tragedy if that — The Matrix, right? I mean, we've we've we've seen a couple of those movies. — Uh, you know, Zuckerberg, — what did Zuckerberg say a couple months ago about, — you know, in the future he thinks people though their four or five best friends might be AIS? — Yeah, but I think — that's that's scary to me. — Yeah. I just Yeah. and and and misguided and and I I think that if AI helps us to make better friends and by letting us have the time, energy, attention, like that's how we use it today at work, I think it's right on. I think to the extent that to the extent that you get people that just make you feel good, you self soo like that to me is kind of akin to opium like or heroin like like you're just you're just trying to feel good for the sake of feeling good, not for actually being healthy. And so you're giving a painkiller, you're not giving the antibiotic, you're doing that that to me is malpractice. And so that's the place where I think it's like hey develop all the science as best you can the same way you've looked at basic science for decades centuries basic science basic science frontier of knowledge learn learn then the discipline comes in the application toward a healthy planet a healthy society a healthy person a healthy community like that's where we have to have the discipline — yeah one of the one of the podcasts somebody said I don't remember where I I'd like to give credit I'm terrible at that but they said that the real breakthrough is when AI guy learns the scientific method. And I thought, boy, that now now we're really going somewhere. But I I'd take it as a step.

[56:37] — The scientific method is great for for basic science, maybe a little bit less so for applied science, although I'll get a whole bunch of people that comment and argue with me on that one. I mean, the the real genius of this is like giving us the time, energy, and attention to say like, point it at this problem, point it at that problem, or just to allow us the time to go have lunch together and just be. That's the one thing AI doesn't do. It can't just be right there. It it can emulate that, but that's not the same as doing it. — Well, what I was going to say is I think I think that's wise that if it can figure out the scientific method, but a step beyond that, going back to the chess, right, where it starts teaching itself, what if it improves on the scientific method? What if it or what if it comes up with a better we just assume that's the best because that's been the best for — centuries, right? But all of a sudden, — I hope it I hope it does, but you're still kind of in the cerebral cortex of processing. you're not in the lyic system or the amydala where you're processing emotion like you're you're just not doing that. And so and right now today we don't have a computer system that it can emulate some of those types of decisions and decision-m matrices like using what we talked about but it can't actually feel. And maybe it develops that, maybe it doesn't. Maybe we develop that for it, maybe we don't.

[57:46] I don't I don't have any — the only push back I might have is that I kind of think about our brain and our central nervous system and all that as a computer system in a lot of ways. No, but it has different processors, right? And so if you look at the lyic system, it processes information differently than superics. — I got it. But it's still a lot of electrical pulses and different things that are — it can be emulated by a computer for sure. But I think that's where I don't know. I just I think that that humanto human relationship is something special. And I don't, you know, it just made me think in this very moment. I don't know enough about how bees relate to one another, but they seem to have something special, too. And I'm going have to go do an AI search on that and see see if there's some kind of analog here that I'm missing. — All right. the the B the B rabbit hole. — There it is. — There we go. And I by the way, there is something there. I've heard somebody talk about this. I just don't remember. I don't remember the specific. — I'm gonna use AI to find a whole frontier of knowledge about it and see if it can kind of get it all together for me and see if there's a good analog. — All right. I see your next book coming right there. All right. Um well, just by the way, just real quickly, when I when I also asked AI about the positive things, more the rainbows and unicorns version of AI, it had far fewer, right?

[58:50] And I think that's why neither one of us like the news. If it bleeds, it leads. They don't like to talk about the good news and the good quotes don't get much — um Sundar Pakai I'm probably butchering his last name from Google Alphabet um he said the future of AI is not about replacing humans it's about augumenting human capabilities which is similar to what you said — and Dennis Habibus who I don't know who that is takes a more optimistic stance calling some fears exaggerating citing new job creation product you know productivity And Jensen Hong from Nvidia also just has optimism that human creativity and productivity will balance the disruption. So — I I think one thing not said there Randy that's worth just mentioning is that you know in the US we have this ability for people to kind of move and and change their station in life economically. That's a really unusual thing in other parts of the world. So when you travel throughout Europe or Asia, it's not what I would say normal in a society that you can change your economic station in life. I think AI does have the potential to open the door for people to be able to change their station in life should they choose to do so. Not suggesting that everyone should want to do it because that's a very kind of um American culture and it may not be the right thing for somebody family culture.

[1:00:10] So, I'm not suggesting that like we're better than somebody else, but I think it does open the door for opportunity to make it a more democratized education, more democratized information, more democratized opportunity. I do believe that's true. And I think you're going to see much more opportunity in places that didn't have the infrastructure for it before. AI can bring that infrastructure there more easily, and that's going to mean a lot more opportunity for a lot of people. Whether that's good or bad for society, I don't know. But I I tend to view that as a good thing because I am American. So let's just I think time will tell. But I I think that's one of the things I don't hear being talked about enough is just how we're going to democratize opportunity through information exchange. It's much more easy to grab hold of for people. — I look I I think we both are aligned that we think it's going to be a good thing that's going to it's going to drive abundance. I just think there's going to be a little pain along the way and uh and there's going to be you know the transition is going to be hard for some people and but humans were adaptable. That's that's one of the things I don't think we give ourselves enough I think we earlier we were talking about how we give ourselves too much credit for what we know and what we think but I don't think we give ourselves enough credit for being adaptable and resilient as a species.

[1:01:19] — Well, and here's what I'll say because you've you've pointed out that maybe I'm a little too polyiana in the sense I don't think it's going to impact people as badly as you might think it's going to. So, let me just fully acknowledge there are some people that are going to get um from a career perspective at least left behind and they're it's going to really hurt some individuals and some families. I get that. I think maybe the challenge for us and for our listeners is like, you know, we have in this country not done as good a job of honoring the um the generation before us in their wisdom and drawing that back into the collective intelligence. And so this might be a great time for those people to have a new role of giving us uh some of the experience from before I was born, before you were born, and making sure that we don't lose that experience as we move the generations forward. I think there's a really important voice there. And so maybe we use some of our newfound time energy to make space for those voices and find a way to give that uh that group of people that are most hardly hit a real spot at the table. I don't know how that plays out, but I think that might be a great challenge for all of us to think about.

[1:02:23] — I I think that's really wise. And I think I think a final question we can end on, John, is you and I both have kids who are very likely going to be greatly impacted by this, probably more than you and I, right? Um, and whether it's kids or it could be the 50-year-old guy or gal that just lost their job because of AI at whatever company, what what advice or what should they be thinking about and how do they best prepare for this world that's clearly — Yeah, I'm talking to my kids a lot about um understanding themselves and where their strengths are really early and then developing professional skills uh quickly so they can learn how to learn. So learning how to learn, I think, is a really important skill set for someone entering the workforce right now. and then really understanding professional and personal relationships so they can navigate those things. That's a skill that's deteriorating across society. And so having those two fundamental skills I think is going to put people a step — I think that's great. And I would I would also just add learn AI, use it, play with it like like become an expert in it and and take advantage of it. And if somebody's really worried about the job, there's a lot of trade jobs. Like there is a huge huge shortage right now of plumbers, electricians. Yes.

[1:03:35] — Just people that can swing a hammer. I mean that is a there's hundreds and hundred I mean millions of jobs — in all levels of skill too. Like highly highly trade skilled trades and then just skilled trades and then less skilled trades. Absolutely. — And those jobs they pay good money and they're paying more because of the shortage. They're paying more and more and more all the time. I think that's — I think that's I think that's another avenue. I'm not saying that's what everybody wants to do, but — Well, you'd say robots are take those jobs at some point, too. — Yeah. But I think they're going to be a little slower. They are. I I do think that, right? I absolutely do. I I think that's part of the world of abundance we come in. Yeah. — And is that robots, you know, combined with AI are going to take those, but for the short term, at least it's it'll help them transition and then, you know, I would say take those profits, put in Bitcoin, and you'll be fine. But — so my question, — I had to get it in there. My question is how do you get keep the generations with all the experience engaged so we don't lose those really hard-fought lessons and then how do you take all that money and put it in Bitcoin once you've won the lesson — there. There we go. — That's it. — Let's end it on that. Um John, thank you. This has been an interesting interesting discussion. Yeah.

[1:04:38] — And uh I'm sure we'll come back. This will this will be a recurring because things happen so fast in AI that it seems like every week there's endless information. Uh listeners, thank you as always for listening. Subscribe, download, tell your friends, tell your neighbors, and everybody have a fantastic day. — Only tell people you like. — There you go. — Talk soon. — Listen and subscribe to the podcast wherever you get your podcast. Click follow for more content like this or go to talesofabundance.com to join our community and get free tools.