Follow The Brand Podcast with Host Grant McGaugh

Beyond the Numbers Why Data-Driven Leaders Still Make Costly Mistakes

• Grant McGaugh CEO 5 STAR BDM • Season 5 • Episode 37

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A bright dashboard can feel like certainty, but certainty is often the first illusion. We sit down with Dr. Mike Orkin, a seasoned statistician who has advised casinos and Fortune 100 leaders, to explore why smart people make bad calls when the numbers look right—and how to prevent it. From margin of error to the myth of tidy causation, Mike breaks down the difference between patterns worth acting on and patterns that will quietly burn your budget.

We dig into classic misreads—like soda sales “causing” polio or alcohol “causing” lung cancer—and show how hidden variables twist decisions. Then we step onto the casino floor to see probability without the storytelling: independent trials, house edge, and why betting limits protect profits. If you’ve ever heard of positive expected value, you’ll hear why it still fails without disciplined sizing, and how the Kelly criterion turns winning odds into sustainable advantage. Along the way, we tackle lotteries, survivor bias, and the uncomfortable truth that winners often emerge because enough people played, not because someone discovered a secret.

AI enters as both accelerant and trap. Large language models thrive on correlation and can hallucinate when the evidence runs dry. Mike shares practical guardrails for using AI in places like engineering and support—pairing models with domain expertise, testing for failure modes, and resisting the lure of overconfidence. We also zoom out to a leader’s mental model: when to think deterministically, when to think probabilistically, and how to blend data with context, incentives, and human will.

If you’re steering strategy, allocating capital, or building with AI, this conversation offers a clear checklist: respect uncertainty, size your bets, interrogate correlations, and hire experts who can “think with other people’s brains.” Subscribe, share with a data-loving friend, and leave a review with one insight you’ll apply this quarter.

Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates, visit 5starbdm.com
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And don’t miss Grant McGaugh’s new book, First Light — a powerful guide to igniting your purpose and building a BRAVE brand that stands out in a changing world. - https://5starbdm.com/brave-masterclass/

See you next time on Follow The Brand!

SPEAKER_00:

Welcome everybody to the Funner Brand Podcast. I'm your host, Grant McGall, and I'm going to bring a very special guest. And I think a lot of people are going to find this super interesting. And that is because we live in a world where numbers speak louder than people, and dashboards often replace judgment. So today, my guest is Dr. Mike Orkin, a distinguished statistician, professor, and consultant whose career has taken him from casino floors to corporate boardrooms, helping leaders to understand not just the numbers, but risk, randomness, and responsibility. Dr. Orkin has advised Fortune 100 companies, worked with gaming institutions that live and die by probability, and spent decades challenging the myths that we cling to about luck, certainty, and cause and effect. So, Dr. Orkin, would you like to introduce yourself?

SPEAKER_01:

Yes. Well, it's good to uh be here, uh Grant. Um, yeah, my name is Mike Orkin, and uh I'm I'm as uh your your introduction was perfect. You covered everything. Um I've been around for a while. I uh went to college uh at uh University of California at Berkeley, where I was a math major, planning to go to work in the tech industry, but uh then in graduate school, I was uh uh excited to learn about randomness, probability. Um, a lot of the, as you point, as you said in the intro, a lot of the myths that people have about risk and about about chance and about skill versus chance, things like that.

SPEAKER_00:

Well that's what I do. You've got a book. Yes, I have a book on chance.

SPEAKER_01:

Yeah, the story of chance beyond the margin of error. And if you if you like to read things like in about public opinion polls and stuff, they always talk about something called the margin of error. The margin of error is this region in which things things happen according to the way they're supposed to happen, namely in a public opinion poll, uh the result is given within a margin of error so that you can um be pretty sure that the result is accurate. But if it's but every now and then things behind the scenes go outside the margin of error and you know it's no longer accurate. So um you have to always take into account randomness. Another area of uh where the margin of error comes in effect is when you have to make decisions in statistics like, is this the result of chance or is it the result of skill? Is this correlation due to just some random observation, or is it due to some cause and effect thing? And so statistical techniques generally allow you to make decisions like yes, yes, it is due to chance, no, it's not due to chance, within a certain margin of error, in the sense that every now and then you things don't go your way. You get a what's called a false positive. And I'm sure anyone in the in the medical profession is heard about false positives, because even when you do clinical trials, every now and then you'll get a false positive, which means that things uh are really the a result that looks like something is happening is really just due to chance.

SPEAKER_00:

Well, I tell you, and so we're gonna pack a lot of what you just talked about, because it's very, very important, especially now in this uh AI generated world, we have a lot of data, we have a lot of information. And here's my question around that, because you brought up some things randomness, all these probabilities, probably all these things lead to maybe it's the outcome you're looking for, but maybe not. And the question comes down to why do smart, well-intentioned leaders so often make bad decisions when the data looks right. How does that happen?

SPEAKER_01:

Well, um sometimes you know, even even well-intentioned leaders will make mistakes, and sometimes leaders aren't so well-intentioned and they have their own personal vested interests. But um, let me give you an example. There's um this notion of correlation, when are things related to each other? And oftentimes, people who are in positions of uh great authority mistake that for meaning causation. And uh since you we we've talked, we're sort of skirting the medical area, let me give you an example from there. So when the polio vaccine was first being developed back in the late 1950s, um a researcher discovered this correlation, a high correlation, which means there's a relationship. And the relationship was that as Coca-Cola sales go up, so does polio incidence. In other words, um some and the the conclusion that this researcher drew from this was that somehow soft drink sales are causing polio. And um that was so for it's certainly possible that that was happening, but there the person was mistaking uh correlation for causation. So when you see a relationship, it doesn't mean causation. And what really was the case was that there's a variable in the background that you couldn't see, and that was the weather. And as the weather gets warmer, two things happen. More soft drinks are sold because people drink more liquids in the warm weather, and the polio virus gets more active in the warm weather. So it had nothing to do with Coca-Cola or any soft drink causing polio. It was a background thing where it was just the weather that was causing this. Um, so that's a sort of a very common mistake. Um, there's another one in another similar mistake in um in that general area, um, in which some studies were done that showed that uh seemed to show that well, no, it did show that there's a correlation between smoking, uh, I'm sorry, between drinking alcohol and lung cancer. Right. And so there is such a correlation, just like there is a correlation between the weather, I mean between uh soft drink sales and uh polio. Um, but it turns out that that's you know, drinking lots of alcohol is not not necessarily a healthy thing to do. But the reason that it looked like drinking lots of alcoholic beverages was causing lung cancer was because when people drink alcoholic beverages, they're more likely to smoke. And it's the smoking that causes lung cancer. So people make that kind of a mistake all the time. They take um correlation in the Federal. So let's let's move to another area briefly. Um the Federal Reserve um, which controls interest rates, and which is always in the news because President Trump um wants the Federal Reserve to lower interest rates, and the the chair of the Fed, um Jerome Powell, uh doesn't want to do that. And he's been around for a while, and so is of course the President Trump. But um for the in the background here, there they're the Fed has been using from time to time, it's it's largely discredited now, this data from it's called the Phillips curve. It's a it's a graph from uh it's it's from 19th century England. And it's it's it's about inflation. It says that as employment goes up, I mean, as unemployment goes up, inflation goes down. So um they've been using that, but you can't the Fed can't just cause unemployment. They can't fire people. So what they do is um they raise interest rates, thinking that that will cause more unemployment, which it didn't. But the fact is there's uh it's just another one of these correlation things. And it's also a correlation um that's based on 19th-century data from England. What that got to do with the United States economy um is not clear at all. Um, in the last couple of years, people have sort of discredited the Phillips curve because it doesn't work for one thing, and for another thing, um, it's again, it's one of these situations where correlation, in this case, between unemployment and inflation, doesn't mean causation, depends on lots of other variables.

SPEAKER_00:

I I agree with you, Dr. Oregon. Because I've been in technology a long time and I see the data might say this or that. And you know what you don't have is I see this a lot of times. I I'm big into you know football, right? So I like uh NFL, I like watching it. They're big into statistics, right? They love statistics. They there's a statistic for everything on passing, on running, on blocking, but they don't take into account the will of that individual, the humanity of that person, because that person, maybe they were running a certain way, you know, for um a certain amount of time, maybe it's five seconds, let's say. But because their will to do it even faster, because of whatever the situation is, now it's 4.5, it changes the whole formula, everything changes. So, so the pattern becomes upset and it's not the same way. Now, here's the question why I bring that up is that because it could be dangerous if the there's an illusion in the data that creates you know some kind of uh opportunity for decision makers, and they're looking at these dashboards, and everybody got their dashboard that tells you what's in front of you, how I'm gonna get to this next level. But here's the question: why are they so easily fooled by the data that's telling them, like you just said, you know, the potential for causation, but it could be totally different?

SPEAKER_01:

Well, I'll tell you, um, so the whole the psychological factor in sports is I completely agree with you. It's very important. Um, you just watch any weekend of uh NFL games and you'll see it in action in action when some teams um you can tell they're they're they get together and they decide they're gonna win. And I mean, of course, you have to have talented players too. But um people, but I'll tell you to speak to your uh question, which is in my opinion, extremely important. People like to be locked into numbers. Um they're they're they're they take everybody, I mean myself included. Um, but you just have to be careful because um there's there's this thing of a psychological thing of being having numbers that you fix on to, and it somehow helps you feel better about what you're looking at. Like what you said, it's hard to it's much harder to pin down psychological factors and the factors of wanting to win or needing to win and this and that um in a football game than it is to look at the stats. And so it's comforting to be able to look at the stats. And now it's because of um technology and uh artificial intelligence and stuff, it's easy. You can just and to the technology of putting stuff up on the screen. So it's easy to have all kinds of numbers describing what's going on, and that's great, but it may not always be telling the story a hundred percent.

SPEAKER_00:

Now you now let's let's take that to the real world, right? And what we're talking about now, you have advised casino operators, gambling operations, right? And you've advised Fortune 100 teams. And here's the deal like, what do they understand about risk? Because R I S K there is risk in all these things that for the most part, most of us, especially small business owners, they don't really understand how that really play out in that in that type of scenario.

SPEAKER_01:

Well, let's let's just look at a casino first. Um, casino game games for the most part, some are a little bit different, but for the most part, are games in which you do the same thing over and over again that's called independent trials, and the probabilities always remain the same. So, like rolling dice, roulette, playing a roulette wheel, or you spinning uh the wheel and dropping a ball in, all these games um have certain mathematical characteristics. Namely, even though you might get lucky from bet to bet or from roll to roll of the dice or from spin to spin with the roulette wheel, they're all governed by the what's called the law of averages, which says that if you have a situation like this, not all situations are like that. Football games are not like that. But if you have a situation where doing the same thing independently over and over and the probabilities always stay the same, you don't know what's gonna happen. When I bet on seven, when I roll the dice, I don't know what's gonna happen. I have a one-sixth chance of winning the bet and a five-sixth chance of losing the bet. And if I win, I get paid four to one payoff odds. But if I make this bet over and over and over again, the law of averages is a mathematical fact that says I will actually win about one-sixth of the time and lose about five-sixths of the time. There's no getting around it. There's no training, you know. Well, of course, you can cheat, but that's not a good idea in a casino. But um casino owners, therefore, have games like this because it doesn't matter to them if somebody comes in and gets lucky, which is going to happen if they're in a play. All they care about is the total action over time. If lots of people are playing, then they know they're gonna make a certain percentage profit of every dollar bet in the long run, which is about 17% for betting on seven and craps. Now, there are certain things that can get in the way of that. One of them is let's say Elon Musk goes in and wants to bet a billion dollars on seven, which he can afford to do. Well, that could really screw up a small casino and go up make them go broke if they let them do that. Because casinos can't take if if he gets if he gets lucky, can't absorb a loss of a billion dollars like that. So casinos have betting limits, and so they won't let them do something like that. A typical casino at a at a crap's table, you can bet up to$500 or maybe a thousand dollars on a single bet. So that means everything is governed and controlled, and so the law of averages can can uh come into play. Now the same thing goes for the player, because if you're playing a game over and over again in which there's a skill factor, like blackjack, for example. Blackjack Edhorpe wrote a book in 1960 or so about some computer simulations that uh IBM programmers did. This was a long time ago, and you couldn't just have a they weren't there was no such thing as a personal computer then. And he looked at these computer simulations and found a winning strategy for blackjack. You have to do something called count cards. Yeah, and so um, and then if in that case when you play under ideal conditions, which has changed a little bit since then, um, the player has an advantage over the casino. Now the same rules apply in a sense, and that is even though you have a game with what's known as positive EV, or positive EV stands for expected value, let's say I have a 60% chance of winning every time I bet, and a 40% chance of losing, and I get the payoff odds are one to one. So that means I have um an advantage and I'll make money if I keep doing this. Well, suppose I have$10,000 and I bet it all on the first bet, because I know I have a winning game. Yeah. Well, there's a 40% chance I'll go broke, and if I go broke, I can't keep playing. So you need to have the long run in view when you're going to take risks, even if you have a positive expected value, or what happens to you on the average over time. And so a lot of people don't understand that, and they um go broke when they don't have to. They can they have a good game, they can keep playing, but they're their systems, and uh someone named uh John Kelly at the time of um J.L. Kelly, I guess were his initials, around the time of Thorpe, back in the late 1950s, was uhhematician at Bell Labs, wrote, uh did a mathematical result called the Kelly system, which says that in games like even where games where you have an advantage, you you have to control the amount you bet. And that amount is based on the fraction of your bankroll. So the more what that means is the more you have, the more you bet, the less you have, the less you bet. So this whole idea of double doubling down to cover your losses is a total disaster. Never never works. You keep doing Monday night.

SPEAKER_00:

You know, the people don't know today is Monday, and uh people are trying to hedge their bet, like I gotta make up for my betting on Sunday, you're saying.

SPEAKER_01:

Right.

SPEAKER_00:

It might not be.

SPEAKER_01:

You don't double your bet. You know, you don't double down to cover your losses. I don't care if it's in a casino or if it's uh in a political situation, they always talk about doubling down. It's just not a good idea. Um, now people can get lucky, there's no question about that. But in repeated play of a game, luck disappears, and you have only let's turn that to business now.

SPEAKER_00:

Because now you talk about the casino is a business. Well, they for businesses, they they use statistics all the time, probabilities all the time, and look at their numbers, your PL, your stocks, all these things are are are measurements of activity, and you're saying repeated activity in a controlled system will have a certain value over and over again. What does like a small business owner do do they do they play by these rules? Maybe maybe they're not aware of these rules, but you see in these big corporations, are they aware or they, in your opinion, are Or are they not?

SPEAKER_01:

I think they are. I think in the big corporations they are. But I think you'll also you'll find, well, this brings up another point that's related. And that is there are some people who will get started in their business or suddenly make a big bet and have it pay off, and they'll be successful. And it's just because they're lucky. Now, I think that in any big corporation, the people sort of understand the dynamics of risk taking to some extent. But let me give you an example that's not really in big business. Um, the Mega Millions Lottery. So that's one of the more popular lotteries, and there's a guaranteed$50 million payoff if your ticket is the winner. And there um the chance of winning the Mega Millions Lottery is about one in 290 million. And that's um kind of crazy because if you buy 50 tickets a week, you'll win the jackpot about once every 100,000 years, 112,000 years. So it's not it's it's so lot buying a lottery is a game of pure chance. It's like you're not it's not like having a business where there's skill involved and there's your smarts and your employees' smarts and uh and knowledge of how that business works can lead to success. It's just pure chance. But there's this important, this interesting thing to observe. Even with the mega millions lottery, there are winners, there are jackpot winners from time to time. So who are those people? How come there are winners of a game that's so terrible? And which your chance of winning is one in 290 million, in which mathematically, if you buy 50 tickets a week, you'll win the jackpot once every 112,000 years. How is that possible? Well, the answer is simple. It's because so many tickets are sold. And so that's one of the interesting features of randomness or chance, and that is given enough opportunity, any weird thing will happen just due to chance. So that's why they're winners. It's not because they have any particular skill, it's not because they saw the winning numbers in their oatmeal that morning, it's because they got they were part of a a much bigger crowd of people buying tickets. And so I like to say that luck is a group activity in a game of pure chance like that, or in any situation. And for every in Megaman's lottery, if you look at the big picture, for every winner, there are about 290 million losers. I don't know. So that's that's in the background. So that's true in business too. There are um some people in business are very smart, and there's some in business take big risks, but there's a back, and in this, and there's always a background of people who don't really succeed, and that's partially due to skill. So one of the things, one of the interesting questions that I've worked on as a research question is, and with um in and a lot of consulting that I've done, what's the difference between luck and skill?

SPEAKER_00:

Yes.

SPEAKER_01:

So um that's that's that's sort of a statistical question. And when you just look at the luck situation, things are very understandable, like uh the lottery, for example. But when you mix skill in with it, it becomes a different story, and it becomes sort of like what you were talking about a little while ago, namely uh professional sports. So in a professional football team, you have skill, but you there's also luck. People uh fumble, they get injuries, they don't they're not up to to play that day, or whatever. So there's a mixture of luck and skill. And that's uh one of the fascinating things about that.

SPEAKER_00:

But I love what you just said there because that's the problem. It's like a snapshot in time where the numbers you're looking at actually make sense, right? So it's like, all right, you know, if everything stays the same, your probability will probably, you know, the outcome will probably be very predictable. But we all know that in our experience, we wake up every day, there's always something happens that we didn't anticipate. There are variables, and as we get into artificial intelligence and we start to ingratiate it into our business processes, we have to understand, like you just said, what is repeatable and predictable as opposed to something that's more probabilistic? You know, it could happen or probably can happen. And the question comes down to in especially in your experience, when does that data stop being a guide and becomes more of a liability if you're dependent on that?

SPEAKER_01:

Well, that's that's a very, very good question. And it doesn't it doesn't have an easy answer, of course. But um it's it's a it's a question that people have to have to think about if they're risking money in business or whatever, um, they have to think about um not most real life situations are not going into the casino and rolling the dice over and over again. It's much, much, much more complicated than that. And it involves um doing things, as you were just saying, that change every day. So when you have this shifting landscape, that's when um the people who are just relying on getting lucky sort of fall by the wayside. And the skilled players, um whether it doesn't, whether it's football or business or whatever, if skill is allowed, then the skill eventually starts taking over. Now I have a warning. Um some people who this is about AI. Yeah. In the morning, some people have discovered recently that, well, you know, people are always complaining about AI, these large language models having what are called hallucinations, where they look at try to find things and the things aren't there, so they make stuff up. Um, and in fact, so there are cases, there are situations like um there was a court case where it turned out that the law one of the lawyers was using AI to find cases related to this case. And AI couldn't find any, so they just made up these cases completely fictitious. But now they found something even more sort of worrisome, and that is large language models are based on these using the entire internet, all that data, you have to have these big data centers, and they make it make decisions in part based on correlations. And these correlations don't mean causation, as we talked about earlier. And they start making decisions based on the notion that correlations mean causation. And in fact, the um there's a well-known uh person who identifies who talks about about this kind of thing, who talks about AI as an AI skeptic, not about the fact that it does lots of good things, but about there are these little things you have to worry about. His name is Gary Marcus, and he has a podcast newsletter. And he discovered that like the AI will you will just fixate on numbers. We were talking about that before. So it's not just people, AI fixates on numbers when it makes correlations with things and will make wrong decisions based on the fact that it's just looking at these numbers that are correlated but don't mean causation.

unknown:

Yeah.

SPEAKER_01:

And let me let me give you one more uh example of something we were talking about a few minutes ago, and that is um betting too much, then you should be betting, even if you have a winning game. So I don't know if you've heard the the the crypto king of a few years ago was this guy named Sam Bankman-Fried. SP they called him. And he um had a big one of the biggest crypto trading websites um um there was. And he was this brilliant young guy who got a degree in math and physics from MIT and started this business, and he was a billionaire before he was 30. And he and so then suddenly it turned out he was borrowing money from his investors to invest in more crypto stuff, risky crypto bets, and he wound up where he is right now as a cellmate of Diddy. Right. I don't know if he's not still in that same prison, but in prison for like 35 years. And so this guy named Zeke Foe wrote a book, good book, about you're interested in that stuff, called Number Go Up, in which he had many, many interviews with SBF before he went to prison. And SBF knew about positive EV, namely, you have positive expected value. So that means it's a good bet in the world of probability and risk taking. And so what he did was exactly what I was talking about before, and that is he had positive EV, so he wouldn't, but he didn't know about the Kelly system or about not betting too much that it was possible to bet too much money because he thought he was so smart. He would identify, and he admitted it in these interviews, these positive, but he didn't even know what he was admitting. So he would bet tons of money on good bets. But the fact that he was betting so much money caused him to eventually every now and then you lose. And when you lose, it really hurts if you're not betting a reasonable amount. And so what did he do? He borrowed from his investors, borrowed in quotes, and uh wound up down that slippery slope.

SPEAKER_00:

That was our great truths that you just talked about, and understanding the numbers really. They don't lie, they just are what they are, and if you misinterpret them, you can get yourself in a lot of trouble. And we need to understand that, I believe. And in we talk about decision making, and you have to understand no matter how advanced these tools become, you've got to you know use some wisdom and experience and some context around this, because there's real world harm when you get confused around correlation and conversation, and I'm getting that from you, especially like in in very important things like in healthcare and finance and public policy. This is very important, like you just alluded to in many, many different things that we've talked about during this podcast. So, if a leader had only one habit, I'm speaking to you, if a leader only had one habit to protect themselves from being misled by data, what should it be?

SPEAKER_01:

Well, so for I'll give you two answers. Um, the first answer is the sort of what so sort of my my basic rule for being you know in that world is to not bet too much money, even if you have a good bet, is to just take it easy and play for the long run. Because even if you don't think you're playing for the long run, you are, because that's what happens. You get you do things over time. That's how that's how life is. Um my second answer to your question is hire some experts. Yeah, I like that. Hire some statisticians. Although not all statisticians, uh not all statisticians are you know, statisticians, just like any other group of people, some of them are really smart, some of them aren't so smart.

SPEAKER_00:

Well, you gotta understand the numbers. What is the tea leaves really telling you? Right uh to your point, because even though you have a calculator, what is a calculator? You know, AI is a giant calculator, and people don't think they really understand what's happening in the background. It's a gigantic calculator. It does probabilistic, there's deterministic and probabilistic. If you don't mind, because you're a statistician, give it an artist first how from your definition, how do you determine or or define deterministic versus probabilistic?

SPEAKER_01:

Well, so let me give you a physics answer. Um, when Sir Isaac Newton was around a few hundred years ago and his contemporaries, they thought that the world was governed like a giant clock, worked, the world worked like a giant clock. That is, it everything was mechanical. And if you knew, and there may be some parts of the clock you don't understand, but that's okay. Eventually you'll understand them. And so you could understand everything, everything in the world. So, in this in the eyes of Sir Isaac Newton, who discovered amazing things about how objects move around to gravity and you know, inertia and acceleration, all these things that he and others discovered. I mean, it goes back to the ancient Greeks when Archimedes discovered uh buoyancy in water and how objects float and things like that. So that was all deterministic. You could tell if you do something, like if you push, drop an apple from the tree, you know what's going to happen. Okay, but then in the beginning, it's still with my physics uh analogy here, the beginning of the 20th century, people started discovering that that didn't always happen. That uh there's stuff called quantum physics, where um particles, subatomic particles, don't behave the way that they did in classical mechanics. And so um there were some physicists, even Einstein was forced to, who didn't want to admit that, was forced to admit that um things weren't always deterministic. And so, for example, there's something called the Heisenberg uncertainty principle. Heisenberg was a physicist around the turn of the 20th century, and he discovered that for subatomic particles, you can't, by measuring them, tell both their location and their momentum or speed. And it's impossible. You just can't tell that there's this random component. And if you nail one down, the other starts getting fuzzy. And so there's this inherent randomness to things. Um not necessarily to my bowling ball when I roll it down the the uh bowling alley, although my bowling ball is going to be more random than someone who knows how to bowl real well. But still, that's different than a subatomic particle. Well, when computers were first developed, they were based on what are called uh random number generators, which are really just called what were also called pseudomorandom number generators, because they wouldn't really do things completely at random. So this goes to what we're talking about here. Yeah. But then just recently, well, recently in the last 30 years or so, there's this notion of quantum computers, and you read about it in the news all the time. Yes, they're based on and they can do things much faster because of quantum theory. They're right, not they're not commercially available yet, but if they were, they would be able to do things much faster because of this random component when it's used properly. Now, um, not only so so recently there's an article that someone just sent me this morning about how at the University of Colorado they've actually developed um a random number generator that's really random number generator based on quantum mechanics. It's not a quantum computer yet, but it's just something that generates really rare, truly random numbers. So there is right now, in reality, there's a world where deterministic physics, for example, works. Um, it may not be 100% what's really going on, but it works. I mean, Sir Isaac Newton and Archimedes and all the famous physicists going all the way back uh to the ancient Greeks, their things work to some extent. But now there's this whole new vision of the world how things work in reality, or how things are, um, where on the subatomic level that just doesn't work anymore. So there is this difference between things happening, things being uncertain and random, and things working according to some deterministic uh rules.

SPEAKER_00:

This is so important, and I want our audience to truly understand what you're talking about. Some things are determined, one plus one equals two, some things are probabilistic, meaning one plus one could equal two, it could also equal three. What do I mean by equaling three in my mind? Because I take physics as well, is that let's say you take Dr. Morkin here, he's one. You take me, I'm Grant McGall, I am one. Now, separately, no, one, one equals two. But when we come together, a third reality takes shape. We call that this podcast and what we're talking about. It's a third reality. It can go, there's some deterministic factors to it, but there's some probabilistic. He doesn't know exactly what question I'm going to ask and that type of thing. I don't exactly know what the answers are going to be. There is going to be probably in a certain, there's a certain certainty that it's going to be in a certain realm um of uh or percentage of correctness, if that's the case, we want to use. But we need to understand this because real life actually works like this. And we need to understand this as we go deeper into this automation, I believe, and we've covered remote or robotic process automation or authentic AI, we're starting to give more control over to our machines. I want to ask you this. This is my last question I'll ask you right now. If you were advising leaders preparing for this next decade, you see where we're moving toward, what warning would you give them about overtrusting data? And what would you urge them to develop instead?

SPEAKER_01:

Well, um, I don't I I I think AI is something that's that's going to be here. It's here. And uh businesses are start are using it because it works very well in many situations, like call centers will soon be run completely by AI. There's no there's no getting around it. Um and I would just the thing that you have to be careful of is that AI. So this is this I'm not talking about the AI that's behind the development that that's in the a weapon system or something, which can get very dangerous. I'm not talking about that. I'm just saying for a typical business situation, we're using AI. Like you would use um use it for a call center or even for for people working in a company that uh programmers, for example. AI does programming really well. And in fact, um there's some there's some AI systems, one's called Claude, um that really do I have friends who use it for programming all the time. And so you have to be you have to sort of know something about programming before you start using it. And so if you're in a business and you see all these uh unbelievable things that AI is doing, that's great. I mean you're gonna use it. Um, but you have to be careful that you have someone around you who um understands the the the limitations and understands the types of mistakes it can make. Um because AI can make mistakes just like people make mistakes. And so um I I think that um the the you just need you just need to have people around you um who really understand that. And I don't think that the head of a company has really has to be an an expert in technology. I mean, maybe if you're in Silicon Valley, yeah. But you have to you have to have somebody who knows what's going on. I wouldn't just put in an AI system based on what some salesman told me. Um you have to you have to have people around and know what you're doing. A famous statistician who I knew a long time ago. I mean, he was um he was uh he was a professor, a sort of finishing up professor when I was a graduate student. Um name was John Tukey, and he was a very brilliant guy. And he he used to say um that an expert is someone who can think with other people's brains. And what he meant by that was, you know, if you're running a company, have people around you who know what they're doing and know how to use that.

SPEAKER_00:

I think you're absolutely right. And what we're getting, you know, when you're in a linear world, linear, but it's a holistic reality, meaning we only know things over sometimes through experience, which is over over time you get to know what something truly is. Like where when you're far away from something, like, oh, I think that's a hill, I think that's a tree over that hill. As you get closer and closer to it, it is. Or as you get closer and closer to it, you find it was a picture of a hill, right? It wasn't a hill of itself. We're gonna find this out as we start going down this road. And I love what you said. You need to have somebody on your team who understands the difference between what a hill is and what a picture of a hill is, right? Why that's very important because the machine, if you're driving a car, right? If it's a picture of a hill, like, well, it's not a big, but if it's an actual hill, you might need you know more machinery or whatever to get over that hump. You need to understand those things. And I've been in technology for 30 some odd plugs years, and I will bring this to my audience. The number one problem that I always found when we deploy technology is that we just never had enough, or we did not take into account the training of the individuals that are going to be utilizing the technology, you know. So they and because of that, the workflows would be different, the outcomes would be different. And and now the it they blame it on the tech, but really it wasn't the tech. It's just you didn't take into account all the variables that are necessary to get to that particular outcome. And some of these things we just don't know, right?

SPEAKER_01:

Well, that's true. With that, that's especially important today because of AI. Um, even people who are experts in AI don't really understand it. Um and uh don't understand it maybe as much as as you know as they will in 10 years. And people who are like I was I used to do a lot of programming. I've written search engines and stuff before. Um but the what the methods for doing for programming in AI using neural nets and stuff like that, uh it's not something I know how to do. So I and it would take a while to learn. And in fact, like I was just saying, um nowadays people don't even take a while to learn. They just use AI to write programs in AI. Um, so it's become a it's become a different ballgame and one in which um AI is going to certainly be important, but in which you need to have someone on your team who really knows what they're doing. I want to make a brief comment about something you said a few minutes ago about when you're uh going over a hill, you have a a picture of a tree or an actual tree. I think that's what you said.

SPEAKER_02:

Sure.

SPEAKER_01:

Um, so there was this famous um painter in the 19th century. His name was uh in the beginning of the 20th century, he's one of the surrealists, uh like around the time of Salvador Dali. His name was Magritte, M-A-G-R-I-T-D-E. French, maybe it was a Dutch guy. But anyway, he had this famous paint, he has this famous painting, it's in some museum somewhere, of a pipe. And a picture, you know, like if you smoke a pipe. Yeah. And then the caption on the painting is this is not a pipe. And the reason for that caption is it's a picture of a pipe. So it's a very famous painting.

SPEAKER_00:

Yeah, yeah, yeah. You gotta look at those things. This is so important. I really enjoyed our discussion. And I want to ask you this question because I love asking this question of my guests in the moment. You've been through a lot of podcasts, you had a lot of interviews, you're talking a lot about your book. It's your first experience on the Father Brand Podcast. How did you feel about this interview?

SPEAKER_01:

I really liked it. I thought this was a great interview. I I've had to, this is in the top, the top echelon of interviews.

SPEAKER_00:

I love that. I love that. And now you've got to tell us how to get a hold of your book because I think a lot of people are gonna want to uh peel into that.

SPEAKER_01:

Yeah, so my book is The Story of Chance Beyond the Margin of Error, and you can it by Michael Orkin, and you can get it on Amazon. They have it, they sometimes have it, sometimes don't on Barnes and Noble, so I wouldn't recommend Barnes and Noble for that for this book. So Amazon has it, or you can go to my website, drmikeorkin.com. That's dr mikeorkin.com. And there's instructions there on how to get the book.

SPEAKER_00:

Um excellent, excellent. Well, I tell you, Dr. Mike, this has been a wonderful NBA. We learn a great deal. I'm sure the audience listening in have learned a great deal as well. I encourage your entire audience to tune in to all the episodes of Follow the Brand. They can do so at my website. That's the number five. That's Star S T A R B D M. That's for branddevelopmentmasters.com. I want to thank you again and much, much success in 2026.

SPEAKER_01:

Well, thank you so much. And same to you, and I'll put up uh uh a link to your stuff uh on my uh website.

SPEAKER_00:

I'd love to hear that. You take care.

SPEAKER_01:

Okay.