Classic Economic Theory Doesn't Tell the Whole Story | Doug Howarth

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Several economists review my work and nobody has said I'm wrong. In fact, to date, nobody has challenged me in print because the math checks.
Let's go. Yeah, make it look, make it look, make it look easy.
The Ryan Hanley Show shares the original ideas, habits, and mindsets of world-class original thinkers you can use to produce extraordinary results in your life and business. This is The Way.
Hello, everyone, and welcome back to the show. Today, we have a tremendous episode for you, a conversation with Doug Howard.
Doug is a former NASA United Technologies Lockheed Raytheon executive. He has been all over the aerospace and aviation industries and in that work found patterns that didn't make sense to him.
He saw problems consistently in understanding inventory pricing that didn't line up with the results that they expected to see in those businesses, in those industries. And out of that came Doug creating his own extension to classic economics that he calls hypernomics and particularly the idea of a demand frontier and how we can apply this concept of a demand frontier to whatever business we're in, not just aerospace or aviation, to better price our products.
How do we look into our industry? How do we look into the classic demand and supply curve and add, as he states, a third dimension onto the classic 2D model that allows us to price our products to move. This episode is packed with tactical insights, deep understanding of how do you develop a new branch of economics? Where do these ideas come from? And then ultimately, how do we apply hypernomics to our business in order to properly price our product against inventory to make sure that we maximize our profit margins? This is an incredible episode.
You're absolutely going to love it. And I highly recommend that you not only spend the entire time with us today, but also dig into Doug's work.
With all that being said, as always, I love you for being on this show. I love you for listening to this show.
I love you for taking time out of your day to be part of this community. Guys, we've officially cracked the top 150 of all of Apple podcasts.
That is because of you and the fact that you do the one thing that makes the most impact on this show, which is spread the word. I appreciate the hell out of you for doing that.
Let's get on to Doug Howarth. So Doug, as you called out before we went live, I was a math major in college.
And as I described to you, I flunked my way into math because I couldn't make it through engineering school. So I'm going to try to keep up with you today.
But I'm so intrigued by the way that you're looking at this particular topic because, as you said before we went live as well, you've kind of developed a new coordinate system.

And I it just fascinates me. Individuals who are able to take something that we'll say kind of has an industry standard and is willing, not not able to, which is amazing, but also willing to step outside of that box and approach a topic.
So maybe we could just dig into this idea of hypernomics versus economics. What are the distinctions so we can level set for the audience? Sure.
Thanks. Thanks again, Ryan.
And thank you so much for having me on it. I really love your show and you do a great job with it.
So it's my big privilege to be on this. So thanks.
The primary difference between economics, as you've been taught, and hypernomics is that economics likes to talk about two-dimensional systems where they have an equilibrium between what they call supply and demand. And they draw supply and demand as two solitary lines and they intersect and where they intersect, that's where the market is happy.
And what hypernomics observes is that, no, that doesn't happen very frequently. That happens in iron ore and oil and 24 karat gold.
It doesn't happen for things that iron goes into, for example, cars and planes and things like that. So as you're probably aware, when you buy a car, say an electric car, you pay more if you get more horsepower.
And if you buy an electric car, you pay more if you get more range. Well, what that is, more of the horsepower, more of the range, you're describing two features at the same time.
That gives you what's known in math as a response surface. Specifically, it's a hyperplane.
That hyperplane, the plane that goes through the best fits through all the points that describe the range, the horsepower, and the price, that surface of best fit is a hyperplane from which you can figure out if products are overpriced, in which case maybe you want to drop the price, or underpriced, in which case you may want to raise the price. And then at the same time, so that's a 3D system there, so you've got, in cars then, you've got electric cars, you've got range, horsepower, and price.
And then on the other side of this, you've got a certain price and a number of units sold. So there's a certain number of Tesla S's that are sold, and then there's more of the Tesla Y's and then there's more of thes, and they form this series of dots that you can find an outer boundary, which we call demand frontiers, and these demand frontiers turn out to be massively important in markets, and this is something that went undiscovered until we stumbled upon it.
When I say stumbled, I basically was really grumpy. I got my degree in economics, and I never quite believed everything they were telling me, and I was suffering from a long-term condition, a kidney disease that had actually taken my IQ down.
I discovered after I got the thing fixed, I took it down by several points over the course of three decades. And then when I got a kidney transplant from my best friend, all of a sudden my brain just cleared up.
I was able to see things in new ways. And so when my wife went off to buy a washing machine, when we went off to buy a washing machine, she said that she liked the, she wanted more capacity and I thought capacity versus price, that's a 2D problem.
Then she said, we've only got one gentle cycle at home, I want more and I thought capacity cycles in price, she's got a 3D problem. And then I saw a machine I liked and it was just up one from one she liked and I said, what about this one? She says, it's too expensive.
And then I realized that when we were going to buy that one machine, it was going to send the quantity for that unit out over the course of the year by one. And we were adding to this demand frontier.
And so that was a, her problem was then capacity, cycles, price, and quantity. So she was working on a 4D problem in her head.
And it turns out, very fascinatingly, that the entire world works this way. And they work out problems in a way in which all the answers self-aggregate.
You can actually describe mathematically using statistics. So that turns out to be really important.
So diving into your washing machine example, what would be the standard explanation for how you would figure out whether something was expensive or not using kind of the 2D model? Well, in a 2D model, they would say that there's a supply curve, which they call upward sloping, which is to say when you look at quantity versus price, they would say as you add more units of that washing machine in the market, it becomes, they would say, harder for the supplier to make the washing machine. They will increase the price.
At the same time, on the demand side, they would say, well, many people can afford an expensive washing machine, then fewer and fewer people, or I should say more and more people can get a less expensive machine, and at a certain point, there would be equilibrium for that machine, and that's how they would explain it. But there's a lot of problems associated with that in that, first of all, there's more than one feature in that machine, A,, and then B, it's not true that as people make more and more of something they're going to try to charge more and more.
So for example, you and I are looking at computers and you went to school 10, 20 years ago and you remember how expensive your laptop was then that only had a little bit of the capability that you use does now and then

the price per storage keeps falling, the price per speed keeps falling and it's been falling for decades. And so what happens here is there's something called learning that's completely ignored by economics textbooks and so what happens is that learning can continue as long Because the cost stays below the price.
And so for cell phones, for example, I think you're just barely young enough to remember or old enough to remember the old brick phone that they might have had, say, in Wall Street. And that phone lasted for 30 minutes.
You had to charge it all day. And it cost the equivalent of $10 to get this big brick on your head.
But then over two years we kept improving these things and working out ways to work out the size differently. Well, the engineers and the people that are building this, they're learning the whole time and as they learn the prices start to fall.
And that's one of the missing elements in modern economics is that as we start to build more and more of a product, the prices tend to fall.

And that's one of the things that hypernomics covers that modern economics doesn't cover. Yeah, you know, it's funny.
So I remember I paid $1,600 for my very first computer in 1996, and I was like overjoyed that i had found a computer with eight megabits of ram right right right now like the baseline mac i think is like 500 megabits or 512 whatever it is right yeah and you think about that and i paid sixteen hundred dollars for i have two two max one i take on the road and one I keep set up. But the air was,

I think, 1600 bucks for 512. And that's a tough, you know, tough idea to wrap your head around.
So

if I'm a consumer, so I want to get into how we use this to build wealth. And I want to talk a

little bit about it. Also, I want to frame it also from someone who's trying to price their products.

But just keeping our framework on a consumer right now, how do I use this to my advantage

We'll see you next time. want to talk a little bit about it also.
I want to frame it also from someone who's trying to price their products, but just keeping our framework on a consumer right now, how do I use this to my advantage when I'm going out into the market and looking for a product like a computer? How do I frame my buying process so that I make sure I'm maximizing my dollars spent, understanding how hypernomics can play a role in that. Well, you kind of do this probably already.
Like when you went out and bought that computer, you were very satisfied that you got 8 megabytes. So what you're doing is you're comparing it to other computers that might have had four roughly at the same price, and maybe one that had 12 or 16 that was 2 and three times as expensive.
And then you had the processor speed, and you bought a certain processor speed. There was probably some that were better, because there's always something better than a $16, $100 computer, but there were also some that were slower.
So what you did, you were working out, along with everybody else, how you felt the value of that computer was. So when you actually made the purchase, you added a quantity of one to that and you actually validated that guy's price that he had for it.
Now, in your estimation, since you got a good deal, it might have been below this surface that existed for the speed and the memory that this computer had. We actually used the same process in buying stocks.
So we've had a self-funded fund. I have to tell everybody you can't get into this fund.
I don't want to get in trouble with the SEC. But we've got stocks out of the S&P 500 using this algorithm that we've built.
And as of last week, only buying S&P 500 stocks and only going long, we're doing 1.6 times as well as the S&P 500. And so what we're doing is we're finding the stocks that are below this surface that works out from all the inputs that we put in.
There's a surface that exists that explains all the stocks, and the ones that we find are relatively undervalued to that. Gotcha.

So what – like when you're approaching it from – now we're talking wealth building and you're thinking about how to invest in different things. Maybe so the everyday non-math major like myself or economics major like yourself.
So they can kind of start to think through because the stock market is interesting. You really find two people.
You find two types of people. The people who understand what the words mean, they understand how they relate, or maybe three.
And then you find the people who have no idea and they just throw their money in a fund and that's great. And then you got the people who know what all the words mean and actually know how to apply them, right? Sure.
So let's take the example of the person who knows what the words mean. They know how to, you know, they know the various indicators and statistics that come in associated with what they're going to see in a particular stock if they're looking on Ameritrade or whatever.
Right. What should they be looking for? And again, not investment advice, just what are some of the indicators that start to show them, hey, the standard model that people usually value a stock by, they're not looking at this metric or they're not reading that metric properly.
Sure. Well, and your listeners will know some of these things intuitively, but what's happened, I think we've seen lately in the market, people have been getting wrapped up in AI.
AI is to today as companies were back in the late 2000s, anybody that had a webpage, oh, you got a webpage, we're going to invest in you. That was a big deal, having a web page in the 90s.
Well, right now, if you've got AI in any shape or form, and a lot of these companies are doing great things with the AI, but it seems to us to be a little bit too much hype to the AI companies. So they have in stocks, if people know some of the lingo, there's a certain price that you're paying for the stock and there's a certain amount of earnings that exists for the stock.
And that ratio of the price to the earnings is called, interestingly enough, the PE ratio. When that ratio starts to get pretty high, like NVIDIA before they split with something on the order of 700, then they split 10 for 1 and it went down to 70, but it started to creep back up.
Well, if other stocks have a P-E ratio that's down below 10, you might say to yourself, it sounds to me like this stock below 10 might be undervalued, and those tend to be the stocks that we look at. Now, having said all that, what we won't do without fund the way the algorithm is set up right now, and that's not to say we couldn't come up with a new algorithm, is we probably would never buy a NVIDIA.
We wouldn't find a NVIDIA. We wouldn't find an Amazon.
We wouldn't find a Facebook as they're starting out because what we're doing is we're taking all the financials that a company has and we're kind of amalgamating them into this model that we have. And then we pick and choose, and then we say, well, who looks the best? And what oftentimes happens here is you pick a company that's been neglected by the S&P, maybe because it's been in the market for over 100 years.
A lot of the companies that are 100, 150 years old, well, people think, well, they're not doing any innovation, they're not worthwhile, and so people kind of dismiss a whole bunch of those companies. But for us, if those companies have really good financials, and this would be for your listeners too, if they have good financials and it makes sense to you and they seem undervalued, if it seems to you that they're undervalued, again, because everybody's voting the same way, if it seems to you that they're undervalued, there's a good chance that they are undervalued, in which case you want to go out and buy that stock as opposed to one that you think is overvalued.
Now, having said all that, before you were born, back in the 60s, everybody said you

can't go wrong investing in IBM. Of course, IBM made a lot of mistakes and then they fell

off the platform. Before that, people would have said in the 40s and 50s, you can't go

wrong with GM. Of course, they fell off the wagon and they made it.
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And even before that, U.S. Steel.
Well, now the new things in which to invest is Walmart. You couldn't fault anybody for investing in Walmart or Amazon or any of the other companies that have really got this massive base.
And why do people invest in Amazon? Because people invest in Amazon. So there's enough people that are going to keep their price up because they believe in what Amazon's doing.
So, you know, Amazon's still a good bet for the average person. And so would, you know, places like Facebook.
But it it seems to us that nvidia could be one of those things like facebook or or walmart but we don't know enough yet and we think the p e ratio is too high so we wouldn't go in for that right now so yeah the two and i don't profess to be a a stock picker by any uh stretch of the means but the two biggest mistakes that I see when I'm talking to friends or colleagues about their own investments often is they think, they look at the stock's price, what you have to pay for it, the value, you know, say it's 100 bucks, and they like make, they like do an equation in their mind if 100 bucks feels expensive or not, right? Like I had, um, I was talking to a buddy, um, about Tesla and I'm, I'm intrigued with the company and I have a number in my head that every time it dips below, you know, my own equations that I do in that, and that every time it dips below that number, I buy more. And he's like, Oh, you know, and, and that number, let's say it's in the high, you know, between one150 and $200.
Sure, right. And he's like, ah, $170, whatever.
That's expensive. And I go, expensive compared to what? Right, yes.
When you say expensive, do you mean are you saying the price to earnings, the P-E ratio is too high? Are you saying that they have too much debt to equity? What are you saying? Why is it too high? Is it just the number? Essentially, the number that it costs, the amount of money that it costs to purchase one share is almost meaningless, really. It has meaning, but not in the context that most people frame it.
They just look at what I pay a hundred and something dollars for a stock. Yes or no.
OK, that's the first one. The second mistake that I see people make is and this is why I love talking to individuals like yourself who have like true systems that that and I'm all about frameworks and mindsets and mental models, because as I've shared on the show in the past, I have severe ADHD, which is like actually diagnosed.
It's not just like made up or self-diagnosed. And it's caused me different issues.
One of those issues is I have the propensity to shoot from the hip. I have the potential to, you know, I'm one of those people who can be super emotional into something.
So I've had to use frameworks and systems to put guardrails up for myself. And so many investors and particularly retail small investors don't realize how emotion driven stocks are.
Oh, yeah. Like we look at the S&P today and, you know, S&P is up and to the right.
And everyone's like, you know, the economy can't be bad. Look at the S&P.
Well, if you take the top seven stocks out, the Magnificent Seven, NVIDIA, Facebook, Google, et cetera, if you take those top seven stocks out, I think the stock market is barely up now. It was actually down, I think, a few months ago.
It's now barely up, which means you're essentially flat. And so when you're looking at if you just buy the S&P 500 right now, that's great.
And maybe just buying that, an ETF or whatever is a good investment for you, especially if you're risk averse. But if you're actually trying to get gains beyond what the market can provide, it's models like what you're describing with hypernomics that allow you to do this.
Because otherwise, we're just emotion driven into a number or an idea. And as you said right now, the reason those seven stocks are so high and push back on me on any of these points if you disagree, but the reason those seven stocks are so high is exactly what you described.
It's the AI craze. Those are the seven stocks that have integrated and deployed AI capabilities the fastest and the most obvious to the market, which is why all this money is getting pumped in.
But just like every other new transformational technology that is going to become normalized. And now all of a sudden a company, you know, a boring company like Waste Management, who we would never can.
They're going to start using AI, which is going to create efficiencies and drive that stock up, but it's going to be lagging behind. And if we don't have a system to, to, to actually dissect these things, we were going to miss them because we're just going to be focused on where all the attention is.
And those are often the companies that get hit first. And like you said, you buy into, uh, uh, Nvidia, uh, at, you know, some price and then AI starts to equalize.
And that now the price per earnings ratio comes down and you're going, oh, I'm still doing okay. Well, not, not, not compared to, to what you're actually paying to what they're actually making.
And it's so, so I guess when you're, when you're describing this to someone, so you're standing on stage, you're, you're, you're doing a keynote and you get the, the line of people that come up after, which is amazing when it happens. And I'm always feel very blessed when it does.
And you start getting peppered with questions. Like when, what are the, what are the aspects of your model that people tend to miss the most? Like, what's the part where they're like, you know, I feel like I got it, but I don't all the way.
Like, what are the pieces that people tend to have a hard time grabbing onto? Well, there's this concept that we've discovered, which we call demand frontiers. And it works out that in mature markets.
So, for example, the market for business aviation is mature. It's been around for decades.
And it forms these barriers that we call frontiers. So there's this upper level.
So if you draw the dots, you've got the quantity sold for say 10 years on the horizontal axis and you've got the price on the vertical axis.. So there are Boeing has got 787s, a very large jet, and so 12 of those might have sold over a decade and then there's some smaller 737s, the BBJs, and then you go down the list and you get into the Gulf Streams.
If you take all those points together and you run a curve through them, you get a statistically significant upper demand frontier. And then if you see where they break, there's actually a point where they actually, the curve shifts and it becomes steeper.
Then there's a Pilatus PC-12, a propeller driven plane, and the Gulf Stream is in here and they form this outer barrier. So this upper barrier is price limited.
The outer barrier is saturation limited. There's just not enough money.
There's not, people have already had all the planes they're going to have. They're not going to buy anymore.
And then on the bottom of this pile of dots, again, every one of these dots is a price quantity combination, there's this lower limit that's set. And the lower limit basically says nobody in that group, because business aircraft have to go through a whole bunch of studies and they have to be certified by the FAA to a certain level, nobody below a certain price can bill anything because there's not enough margin left.
So that forms a lower limit to the market. And then the market's got an air barrier and the air barrier is basically describing how many you have to build to stay profitable because people have, in business, in aircraft building, there's something called learning and if somebody only gets to build one plane a year, they're not going to learn, they're going to forget what they did earlier.
So they have to have a certain number of planes per year that they're building. It's usually a few dozen planes would be the optimal per year, and this forms this inner barrier.
And so, one of the things that we pointed out to in my last presentation was that this company up in Reno, Nevada had this really nice plane. It was called the Ariane AS-2.
It was going to be a supersonic business jet. And they came up with a price.
We did some analysis, and they were going to charge $120 million. And by some metrics, it was worth even more than that.
And then they said, well, we're going to spend $4 and then eventually $5 billion to develop it. And that's actually got the right number of digits.
So they had the cost right and they had the price, which we call value. They had that right.

But then they said, we're going to sell 300 of these, 310 years. Now, you recall I just said that there was this curve that forms this upper demand frontier.
And there were 12 787s and there was more 737s. And then there's eventually a few hundred Gulf Streams at streams at about 60, 70 million dollars.
Well, this plane, this AS2 was beyond this frontier and if you did the math and you do a little bit of statistics on it, you'd find out that they had a chance of selling 300, but that chance was about 2% in 10 years. So in December 2020, I wrote a little LinkedIn piece that said, worth a penny, not enough pennies.
And I got a really angry response from a guy I knew who used to work back at Lockheed, where I worked, who was then a VP at this place. He said, ah, we just got a big order in.
You're all wet. We're going to make it.
And I go, good for you. Your order won't even take you to this.
They started out with 20 units. They launched with 20 units.
Remember, they wanted 300. And five years later, they still had just the 20 units.
And they want to get to 300 because they have to have so many to be able to make a profit. So I called them out and said, no, you're not going to make it.
And then six months later, they went bankrupt and they stopped writing me. So this has some real world implications for people.
And then this minimum level that I was telling you about. So another, this guy up in Nevada was a billionaire.
Another company, Eclipse Aviation, that was also funded by a billionaire. This guy was out at Microsoft and he had built a bunch of computers and he said, well, you know, I'm going to build business planes or I'm going to build personal jets, he called them.
I'm going to stamp them out like personal computers. And so he started to make this business jet, the Eclipse 500, and his price originally was, I think, $750,000 then eventually rose to $875,000.

But our analysis said it was worth $2.1 million back in the early 2000s.

And so he's selling this thing for a fraction

of what it could fetch.

And so he gets 2,600 orders in

which is way beyond what he could satisfy

and he eventually found that he couldn't make it

Thank you. 2600 orders in which is way beyond what he could satisfy and he eventually found that he couldn't make it for that price his costs were too high his price was too low and then that company went bankrupt and so these these limits these and then the response for what the thing was worth they have real meaning the meanings gathered by all the money that's being poured into the market.
So when the money's flushing into this market and people are basically voting out, they're saying, well it comes to planes, I want to go so fast, I want to have so much room, you know, I want this number of seats. That information can all be reduced to a series of equations that describe how the market's going to react to something.
And that's kind of what you want to do. You want to know that information going in before you just try to make something that won't fly, literally.
So you may have heard in Russia, Russia is building hypersonic weapons. That basically means something that's going many times the speed of sound.
China's got them. We want to have them, too.
And it would be a great thing if you made the hypersonic weapon the right size. So my own alma mater, Lockheed Martin, decides they're going to build a hypersonic weapon.
Now, they're building another one that's the right size. But the first one they came out with cost $42 million for the first one.
That's the price of a brand- new Gulfstream jet. And the Congressional Budget Office said, yeah, we love this thing.
We're going to buy 100. And they said, we're going to buy 100 at 14 million dollars.
Well if you do 20 years worth of analysis on the air to surface missiles, which this thing was, it's going to launch from a plane and land on the ground on a target. If you do the analysis, it turns out that the limit for this market was really tightly drawn, correlated as they say in math, was really tightly drawn.
And they were, again, in mathematics terms, there were 108 standard deviations, meaning they had less than one chance in one quadrillion, quadrillion, quadrillion to make that figure. And so I told them that they had no Snowbell's chance in heck doing that.
They didn't write me back. And now Lockheed, a different unit Lockheed, is doing something that's quite a bit smarter.
They're making something that's quite a bit smaller, and they have a chance to sell that. So it's got some real meaning in the market.
What often happens, and you went to engineering school, my dad was an engineer, he wanted me to be an engineer, and engineers like to make bright, shiny objects, and they think, if we build it, you know, back to, you also play baseball, well, they do the Field of Dreams thing, if we build it, they will come. Well, some people might come, but will enough people come to actually make it a profit? And that's what you need to be able to know before you go off and shove all your dough, you know, all your stack into this thing and try to make it work.
You need to know this information in advance so that you can make the best decisions. Yeah.
No, I love this. And I think I'm going to hit, I'm going to put an example out there and then you poke holes in it.
There's a lot of startup founders, a lot of entrepreneurs that listen to the show. A lot of them create, you know, SaaS products, younger guys and gals.
And I, you know, we see how many, particularly today, SAS products come into the market.

We think it's a good idea. And then a year, two, three, maybe a seed round later, the product doesn't exist or it's got to get scooped up at a discount because maybe there was some good tech, but there's no market.
And, you know, in a very kind of banal, youal, you know, kind of thought process, it's so often comes down to the fact that they completely misrepresent what this demand frontier that you're describing. I mean, as you're describing this, I'm going, this is the part that so many startup founders miss is they come in.
And I literally just had a call with call with a founder in the insurance industry a couple of days ago where he's laying out his pitch and what do you think and he uses this example for market penetration and market size that just from having been in the industry for 20 years, I was like, that's wrong. Someone who's not in the industry may look at that number and be like, oh, that sounds great.
But I'm telling you, having sold into this market for as long as I have, you know, you're what you want to charge for the value that you're delivering. There is not one.
The market is smaller than you think. And to the uptick in actual users is going to be so much longer than what you're expecting.
You're going to here and you know he had just done a perplexity search for how many independent insurance agents were there you know etc and then said well we can sell x amount in this amount of time and right it that part is is this is such a vital part especially in the early stages or when you you're ideating on how to actually deliver your product to the market.

And we just, like, skip that step. We're just like, fancy product.
Let's throw a price on it. Here we go.
And then we wonder why we run into all these. Does that make sense, what I'm saying? Does that align with what you're talking about? Sure, absolutely, Ryan.
It's part and parcel with everybody else that does. I mean, so one of the reasons I picked aircraft is that it's possible to find all the prices for the aircraft, and it's possible to find how many units have been sold.
It's possible to find all the specifications for them. But when it comes down to software as a service, SAS, I mean, we're kind of running into the same thing, too, is that we've got software that helps you pick through all that stuff, but people really don't know just yet because we're brand new.
My book came out in January and people are just starting to hear of hypernomics. People don't know that they need to know this stuff.
So when you get back to your friend trying to say he wants to put this new insurance product out there or the software for insurance. It's hard for you to know in a market that doesn't have discrete functions like insurance or, in our case, software, how many are you going to be able to sell of a given product? It's very hard to know.
We'd like to think that we're going to be an adjunct to Excel. And interestingly, I remember reading a story about there was a predecessor to Excel called Lotus 123.
You're probably not. I actually, the very first company I ever worked for used Lotus.
So I am vaguely familiar from a very past life. Yeah, I'm older than I look.
Okay. Well, for those of you who are quite a bit younger than us, Lotus 1-2-3 was one of the first widely distributed spreadsheet programs that you could buy.
And Lotus, they went down the other side. They said, well, you know, we don't know how we're going to sell this thing.
I think they anticipated selling, I'm going to give you rough numbers here, like 10,000 units. And at the end of the year, they sold something closer to 3 million units.
And the thing was that back then, nobody knew how many people really wanted to to use a spreadsheet i remember reading some history historical stuff about computers you know when computers were took up the size of a room and some very well respected scientists said well i think the world might have a need for as many as 10 computers. You've got households that have,

we've got a household here that's got 10 computers in it.

So, you know, it's to predict things that are, you know, insightful and new.

You know, when you're talking about the stock,

I just wanted to get back to that thing about things being overpriced.

You know, we're doing, we've been running neck and neck with this company, maybe some of your listeners have heard of it, called Berkshire Hathaway. And we had been for the four and a half years up until last week, doing a little bit better than they are.
Now they topped us last week. We're doing 98% of what they're doing over the course of the same period.
But their stock price for their stock A is $660,000 a share.

And then people say, oh, it can't be worth that.

Well, actually, it's exactly worth that.

It's doing better than the S&P 500.

It's doing better than us.

It's worth every nickel you could put into it.

And figuring out what something's worth is fraught with a whole bunch of things that are hard to define and things that are easier to define. So what we've kind of done is gone to the things that are easier to define, hoping that we can find a market going off in that direction.
So we've worked with some pretty large aerospace companies because aerospace is uniquely quantifiable. Automobiles would be another thing that we could do.
But when you talk to insurance products, what you're not going to see, I don't think, maybe there's a central marketplace for that, but you're not going to see a list of all the insurance companies with all their life insurance products listed, all the features of the products, the prices of the products, and the quantities sold. Am I wrong on that? Does somebody have that data? You can derive a general sense of the pricing through their state filings, but there's no single repository of what a like kind and product would cost across the marketplace.
Yeah. So that becomes, as your person that you interviewed discovers, it becomes much harder to quantify.
So in areas in which it's easy to quantify, it's pretty nice to be able to go after something. For example, one thing that hypernotics can do, it can quantify COVID.
Now, how does it do that? Well, COVID, when COVID first started out, COVID was doing very well in densely populated areas. And so you might have guessed that.
So what you can do is you can find out the infection rate per, and you can find the population per country.

And it turns out that if you were more densely populated, well, by golly, you'd have more COVID cases. Now, one thing that wasn't anticipated was that at the beginning, again, in the early 2020, that you might have thought that this had gone to some, you know, been evenly distributed among countries.
It turned out that wealthy, temperate, northern, you know, people north of the equator, countries north of the equator, were getting infected far more frequently than people in the equatorial bands and south in the southern climes. And what we figured out was, well, it was because a lot of people in the temperate zone had more money than they did in the equatorial zone, so they were more easily able to travel, and it was the travel that was spreading the germ around.
And so what you discover with this is that you have to go out and try to figure out the things that are actually driving a product or in this case COVID and you've got to be open to the fact that it may not be what you think is as you enter an arena. So back to your thing about what's helped you know who's done the best over the last four and a half years between the NASDAQ, the Dow, the S&P 500, the Wilshire 5000, us, and Berkshire Hathaway? Well, we're number two.
Berkshire Hathaway has done the best out of all that. And they are, you know, two-thirds of a million dollars to buy one share.
So it just turns out that a lot of the thinking that modern economics would have you believe is trying to rely on your intuition, and what we're telling you is that your intuition may be slightly off. Now, having said that, I have a little story that you may think is an aside, but I want to show you that it isn't.
So after I wrote my book, I went out for a run. This was about several months ago.
I got done with the run. I get off this dirt trail, and I'm standing on an asphalt parking lot near my car.
I see a little reddish black ant, which I discovered later was a rock ant. So I see this a little reddish black ant i thought to myself dr richard fineman used to study ants just for giggles he's the nobel prize winning physicist he was part of the uh manhattan project and he eventually won the nobel prize for his work on uh oh what was it it was on uh uh it'll come to me anyway but this little reddish black camp or was he string theory was Feynman string theory uh no he was quantum mechanics that's it quantum mechanics that's right i always get confused yeah which one he was yeah yeah quantum mechanics so anyway i saw this little reddish black amp single ant and i said yeah i'm gonna study this guy for a second and so i see this little reddish black ant, single ant.
And I said, yeah, I'm going to study this guy for a second. And so I see this little reddish black ant, and he starts to go in a counterclockwise fashion.
He's a little fits and starts. He goes around, and he goes around, and he eventually traces 360 degrees.
But it's not a circle because he goes out a little bit further in his circuit, you know, this trip. And then he does it again.
And again, as he stops, he's doing it fits and starts, and he comes around another 360 degrees, and he's out further still. And then I get down and look at him, and I notice that when he's stopping, he's on this asphalt.
Asphalt, as you know, is built, made a bunch of little rocks, put it into, you know, a mixture. He's standing on top of the tallest rocks in this asphalt and looking around.
And I realized in that instant that this ant was doing reconnaissance. So I raced home and I typed in ant reconnaissance and hit it into a search bar.
And sure enough, this species of ant, which has been around for at least 140 million years, does recon. And what he's doing, this little guy was looking for new places for the colony to live.
Now, how do ants go about looking for real estate? Well, they do it much like you and I do. In this case, they want to have a place that's clean.
That's very big to them. They want it to be clean.
They want to have the right

amount of headroom so they don't want it to be really open because that thing can spread a lot of wind in. They don't want it to be too close and they can't get in.
And they want to have the certain amount of room and just like you and me, so that's the, they want floor area and they, they also want to have what the equivalent, the ant equivalent to acreage. They want to be away from the neighbors.

Now this ant colony has been around for 140 million years at least, and I submit since this is done, this guy was doing what we would call hypernomic analysis. And since this ant has been doing it for 140 million years, I submit that people have been doing that since it's been people.
And that's what's really cool about this is that this is not an artificial construct. This is something that nature has done as nature evolved.
They figured out, well, we have to go to other places to be safe. And the same kind of thing happens.
It's a metaphor for trying to find the safer places in the market. What do we have to do to create our space in the world? And these ants are doing it.
And you and I, as people, we're trying to do the same thing. But the ants are just a very straightforward way of going about this.
And so it was funny, too, these researchers started to play with the ants just like Feynman would do, and they'd set up places like, hey, if I got them really good here, would they go someplace that's better? And, you know, they started to figure out that these guys knew more. And what do they have, eight or 24 neurons? They hardly have anything at all.
But collectively, collectively, they know what they're doing. And that's the important point.
There's this collective spirit that's going on with the ants. The same thing, well, a different version of this collective happens with penguins.
In the South Pole, when it gets cold, the penguins in a colony will start to tighten up and form this big huddle, and interesting, the shape of this huddle starts to mimic what you see in the stock market. If you were to plot the stocks, all the stocks in the S&P 500, the quantity here and the price here, and you put it into log-log space because it's going to be all condensed in the lower corner unless you do, you'd find basically there's a ball of stocks in the middle.
It kind of looks like a huddle. Well, penguins form these huddles at the South Pole when it starts to get cold.
And what they do is the penguins on the outside are taking all the cold air from all the guys on the inside. And then after a while, after the penguins have to be, you know, on the outside, they get cold.
They start to wander into the middle where they heat up. And so the penguins all take turns doing this and collectively work out the way to live in this really hostile environment by doing this symbiotic thing through the crowd here.
And so it turns out that there's a lot of forces that haven't been properly characterized in economics

that are much like what we see in what we might characterize as lower creatures in nature

that turn out to be very important for us to get into,

to be able to pull out these ideas so that we can use them for ourselves.

So that's kind of what we're trying to do.

Have you seen any establishment economists push back on your ideas? And if so, what are they? Well, I went to, I mean, for your listeners should know, I'm here in Southern California, just north of Los Angeles. So we have a few pretty well-known universities here.
And one of the local universities, the Extension University, offered me a speaking gig just before COVID, but then COVID hit and things fell apart. But I was allowed to get an introduction or two to the graduate school business.
And I tried to show, or I actually got into one guy's office. And he didn't tell me I was wrong, but he refused to read my book.
I think he was afraid of what it was going to tell him. And then I talked to another guy on the phone and he basically tossed me out.
People don't want to read it. They don't want to be told.
They don't want to be, they don't want to know that there's a chance that they've been wrong about what it is that they've been teaching people. And what would be the, like, I guess, what would be the core piece that they would, because that surprises me, you would think that if there's, you know, it's not like you're saying, take all of classic economics, throw it on the floor and stomp on it.
You know, what I hear you saying is, we're just building upon and adding additional layers to what we've always been taught. And correct me if I'm wrong there, but it surprises me that if there's a new, I'll use this term very broadly, algorithm for finding insights that we couldn't see before, that they wouldn't be interested in learning about that.
Well, surprisingly to date, no. I've presented this.
I mean, I just got a best paper award for a conference I gave in May and that's what allowed me to become a keynote speaker for my first time. So I'm going to join that little keynote speaker club that you have there.
But very interestingly, I've had several economists review my work and nobody has said I'm wrong. In fact, to date, nobody has challenged me in print.
Well, I take it back. I've got one or two people that have challenged me in print, but they're going after some of the minutiae about what it is that I'm talking, and they haven't assailed the crucial points, which is this multidimensional aspect of things.
Some people are trying to hold out, trying to show that there's some element of socialism or Marxism that my stuff doesn't consider. I'm just going after them.
I said, hey, ants can't afford to be socialists. There's nobody that's putting up a chunk of the ants and saying, hey, you guys stay over here while we do the rest of the work.
Ants can't afford to do that. And, neither can the penguins.
Now, that's not to say if somebody's hurt, they won't take care of them, but they wait until somebody gets to the point where they are disabled before they overtake them. So, yeah, I haven't had anybody push back on this yet, and that's been an interesting point about this, that there's no pushback.
Because the math checks. Yeah.
Math is right. I mean, we're getting correlations here.
For those of you who don't study correlations, basically you're looking at the prediction of being able to be as close to 100% or 1 as possible. These correlations, and usually correlations are, they hit a certain, what they they call p-factor or p-value, the chance that the equation came about just due to pure chance.
If that chance is less than 5%, which is the general threshold, you can basically exclude what they say, exclude the null hypothesis, which is a long-winded way of saying, I can reject the fact that your stuff doesn't work and which is a long way of saying that they have to accept the fact that I can't reject it using some of the math that you've just proven here yeah so that the math just checks out these correlations are running when it comes to aircraft and you start to add features in you're getting correlations that are up to 98% you've explained 98% of the variation of the results using the math. And that's pretty powerful stuff.
If you guys still don't understand correlation, just think redheads are crazy. Redheads are crazy.
There's a good correlation. I can say that because I have red hair in my family and in my beard when I grow it a little longer.

But so maybe that explains a lot.

But so that's really interesting.

I would I just want to poke in as much as you're willing to go on this. Oh, sure.
Yeah, around their pushback. Are they saying that hypernomics is not taking into account the fact that socialism exists or that there are socialist trends in a marketplace that must be considered that you're not considering? it's kind of the latter they they want to say that there are there are trends that you know i'm not considering here and and uh to which i reply well you can glue your stuff in you know if you want to put some stuff in that proves that there is a socialist component well sure i mean i mean in the united states we we have Social Security, that's partially socialism, we have you know we have we have the Affordable Care Act, that's another form of that.
I mean no country is pure capitalist and even the socialist company, I mean you know nominally Chinese are communist but you won't find people that have more business sense and do things in a capitalistic way than the Chinese. So the countries have a mix of this stuff.
What I'm trying to prove is that the mix that you're looking at in traditional economics has got more factors going into it than you might imagine. And so the standard arrangement is a 4D model and then again this presentation that I just gave, I actually built a 7D model that you could actually assemble piece by piece where it would show when I was talking about that Boeing business jet, the Boeing business jet was against its demand frontier and one of the engines that went into it was also against its demand frontier.
So if the engine manufacturer and the air framer, so Boeing in this case GE, wanted to both make more money with this plane, the only option is if their price was still higher than the cost, they could both drop their prices and make more money. And how do you know that unless you glue together a system that shows the two interconnected markets working at the same time? And that's what this did.
That to me is an incredibly important point here, guys, just to grab onto that for a second. What Doug is saying is essentially when we're priced at a space if we're not considering and again i'm reiterating this for myself as well as i'm learning because i love this is that when we're if we're not considering the demand frontier and we're at you know x price and as you said we need for whatever reason to create more revenue out of this business unit.
If we're not considering the demand frontier, then the thought process may be increased price or increased unit production. But what you just said is we have to take in the reality of our situation, which is where the demand frontier comes in and say, in truth, based on where you're actually set from a feature and price and supply perspective, the only way in which, you know, in this particular example, to create more top line revenue, not necessarily profit, but top line revenue would be to drop the price because then that increases based on the demand frontier, the potential to sell more units because you're actually up against what you can sell into the market at this current price, feature set, et cetera.
Guys, that idea, I know it's heady and I know it's deep, but think about the products that you are selling. Think about your own businesses and really dive into that.
And again, we're not specifically in this example talking about profitability. That's another piece to the curve that we may have to discuss.
But if we're just talking top line revenue growth, there is a point at which your current feature set and price hit this demand frontier. And there just aren't more units to be sold in the market or certainly not maybe scaled units.
You may be able to get one here or there potentially, but you're not going to be able to grow at pace. And that we have to take in the reality of this situation and not just, you know, I love the fuzzy math that happens inside Excel sheets, especially like in the early days of startups when they're like, well, you know, if that revenue is not enough, I can just change this number here in the spreadsheet and look, everything works.
That's not reality though. That's a spreadsheet.
Right. Yeah, that's really good stuff.
I want to just pivot really briefly on something that you said earlier as we close out our conversation and talk about, you know, you said that. So you had this kidney disease and a blast to have a friend give you a kidney transplant, which is absolutely amazing.
You said at that moment, your brain started to clear up. The fog started to remove.
I would love to just maybe break down how you felt and that transition to kind of coming back into yourself. What was that like? What was that moment like? Well, the odd thing about having a chronic disease in this thing, I discovered when I was 17 that my kidneys weren't working right.
And so this disease started to go downhill. So what happens when your kidneys don't work right is two things happen.
You put poison into your urine, and you're not pulling poison into your blood, so the blood poison starts to fill up. And so I discovered this in 1972.
In 1993 I went on dialysis and then in 2002 my friend Tim had given me this kidney. Now so what happens is you're coming downhill for decades and as you age you're coming.
But imagine if you're, you know, I'm 69 years old. Imagine if you're 69 years old and you wake up one morning and you're 39.
Well, that's kind of what happened when I got my friend Tim's kidney, is that the brain had been going downhill. It was filled full of poison.

And then all of a sudden, I wake up, and the thing they tell you is when you're on the

operating table, I mean, you're out, of course, but when you're on the operating table, and

as soon as they connect the kidney, I mean, your skin turns ash and gray when your kidneys

have failed.

And as soon as they hook the kidney up, your skin, as they say in the trade, your skin pinks up. You start to become pink.
And I started to pour out urine that was dark brown by the gallon. I think I put out four or five gallons, maybe six gallons of really dark urine because that's something poisoned in my body.
And I wake up with a different guy. I was just so happy.
And I realized at that moment that the brain was working differently and I wanted to do something with it. And so then a few years passed, and I worked out this way of doing things, and I said, I'm just going to quit my job.
I don't have a full idea what I'm doing here yet, but I'm going to just quit my job and pursue this for myself, the family, and all mankind will be better when hypernomics is more ubiquitous. People are going to make fewer mistakes, there will be fewer run-ups in the stock market, there will be fewer collapses, there will be, you know, the current problem, no, there's no stopping a 9-11, but you could have stopped the 2008 housing crisis.

That was manufactured because data wasn't good enough.

And this COVID problem that we had here, the United States had its inflation problem. Maybe we whipped it because the inflation is down now, but the Federal Reserve by itself, after growing the money supply by less than 5% for decades,

jumped it up 400% in two quarters. Well, I didn't tell them to do that, and maybe if they had hypernomics in their bag, they wouldn't have done that.
We wouldn't have had all the problems we had over the last half decade. That's something you need to be able to understand going forward, and we hope that hypernomutics will let people do that.
I love it. You know, it all goes back to, you know, when my friend gave me this kidney, it just opened up the ability for this little head to figure out a bunch of stuff that people hadn't gotten into, and, you know, I'm trying to get people as excited about it as I am because you end up becoming a digital explorer,

a market Marco Polo.

You're figuring out things that nobody had seen before

simply because you've got this new tool sitting in your bag

and you pull the tool out and you play with it

and all of a sudden you can see these maps form

that let you do things that you weren't able to do before. And people are going to find this very powerful going forward.
Well, for my part and the part of this audience and all the people listening, I think this has core foundational application to the startup community, to the pricing of products, especially in those early days. I mean, I'm thinking to myself of the number of companies that I've either been an advisor for, set on the board on my own company, that if I had some of these thought processes and some of these frameworks that you're describing in hypernomics in my head at those times, how many pricing conversations, how many unit creation conversations could we have either skipped altogether or had just clear answers for right off the rip and been able to get to work if we had had these things in our head.
So guys, I can't highlight enough how core these ideas are to taking a broader look at the market. I'm so excited that you shared them with the audience.
Where do people get more? We'll obviously have links to the book and everything in the show notes description, whether you're listening or watching, guys, just scroll down. You'll be able to find links to the book, but where else can they learn more about you and connect with you? Yeah, they can go to my personal website, dughowarth.com, or they can go to the company website.
We've built a company around this called Hypernomics, and it's Hypernomics.com, H-Y-P-E-R-N-O-M-I-C-S.com, and you'll find there a whole bunch of, and I'm also on LinkedIn, and what I do with LinkedIn, I put the stuff in on the company website, my website, is I have a bunch of vignettes that describe how we've used hypernomics and everything from analyzing what happens if you're a football player and want to get faster, what does that do for you, to what's the value of a spaceship, to how do I rearrange the seating down the street for this restaurant that we like to go to so that they can make more money. So there's a whole bunch of topics that it addresses and some of those are bound to rig true with some of your audience there.
They're going to bound to find something that they like because we've tried to make a smorgasbord of things that this tech can do for people. Well, I feel honored to have caught you towards the beginning of your world tour.

And I can't wait to see what comes out.

And I'll be like, I knew Doug when.

I love it.

I appreciate you so much.

I appreciate your time.

And I wish you nothing but the best.

Thanks, Ryan.

I've really had a blast on your show.

Your show is great.

And I was really lucky to be part of it.

So thank you.

Let's go.

Yeah.

Make it look. Make it look, make it look easy.

Thank you for listening to The Ryan Hanley Show.

Be sure to subscribe and leave us a comment or review wherever you listen to podcasts. Came in a time for me.

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