Jeff Booth on Price deflation in healthcare and technology
What if all our everyday assumptions about economics are wrong? This week Harry speaks with author and entrepreneur Jeff Booth, who says the most powerful force for change in the future will be deflation: getting more for less. Even the healthcare industry will feel the effects, he says. Listen to find out how.
Booth is the author of The Price of Tomorrow: Why Deflation is the Key to an Abundant Future. The book argues that the most powerful force for innovation and change is not endless investment and growth, based on an inflationary idea that everything always gets more expensive, but technology-driven abundance, powered by exponential, deflationary trends in computing and storage that drive the price of everything down.
Booth says governments should stop striving to ward off deflation and recognize that economic systems built around credit, debt, and eternal inflation only reinforce radical inequality and class resentment. As the deflationary force of technology spreads to even more industries—including healthcare—it will become necessary to rewrite all the rules of business and investing, he argues. Even healthcare organizations and drug developers will be forced to adapt to exponential technology change, such as the exploding amount of data on individual patients, Booth says.
Booth co-founded Vancouver, BC-based BuildDirect, an online marketplace for building and home-improvement products, and is now a co-founder and advisor to numerous technology startups.
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Harry Glorikian: Hello, I’m Harry Glorikian. And this is Moneyball medicine. The show where we meet executives, entrepreneurs, physicians, and scientists using the power of data to reinvent healthcare from machine learning to genomics, to personalized medicine. We look at the biggest trends in patient care and healthcare management.
And we talked to people behind the trends to find out where data is making the biggest difference.
If time is flying by then technology feels like it is moving right alongside it. However, many people are seemingly unaware of this current speed. My next guest has written a book documenting this phenomenon. In his book, the price of tomorrow, he States and I quote, we live in an extraordinary time.
Technological advances are happening at a rate faster than our ability to understand them. And in a world that moves faster than we can imagine, we cannot afford to stand still. He also makes it clear that our current economic system is built upon inefficiencies and in a technological era where everything is becoming extremely efficient, we must change the way we think view and act in the new era.
On today’s show. We talk about his background interest tackle, how this applies to healthcare and explain his overall views. And most importantly, try to tackle his ultimate question. What exactly is the price of tomorrow? Please welcome Mr. Jeff Booth.
Jeff, welcome to the show.
Jeff Booth: Thanks. Thanks for having me.
Harry Glorikian: So Jeff, you wrote this book and I believe if I’m not mistaken, it came out early, early this year. If I, if I understood the timeline correctly, and you know, tell us a little bit about the book. And then I’ll ask you a little bit about yourself and then let’s go into the implications after that, of how this affects healthcare.
Because for those people that are listening, I’m, I’m sort of veering into technology and then going to come back into an economics and then coming back into healthcare in this episode.
Jeff Booth: Okay. So the high level of the book, is this, technology is deflation. and that’s not a guess. It’s a fact, technology makes things, cheaper and cheaper almost effectively to free.
Why you use Google, why you use an iPhone, why you’ve used it. Think about the apps on your phone. Think about your use of Google technology is ever bringing prices down. And that’s a giant force today in our, in our lives. and it is fighting an equally giant force of monetary easing and inflationary policy designed by governments to try to bring prices up.
And those two forces are fighting each other right now. And most of the second and third order effects in our lives with the rising inequality. what you’re saying, why house prices are rising so fast, rents are rising is a result of that fight. And the world will break if you keep on driving this fight.
So in the future, technology is more powerful force than governments. They can’t stop it ultimately, and, technology is moving so fast that it’s going to bring prices down. And if you think, if you go into that little deeper, it’s hard to, it’s hard to realize, because we’ve grown up in an inflationary world, all our lives.
And it’s hard. It’s really hard to realize, but if you go into that a little deeper, you realize why we use technology is to remove work. Okay. What, no business implements technology to make their costs go up. Right. We would have used it. You use technology to make your to make things easier. And, it’s such a great force today, but it’s, it’s moving exponentially.
And that means the depth driving that to try to stop that force is moving exponentially on the other side, to try to keep us an equilibrium. and it won’t work.
Harry Glorikian: So tell me a little bit about your background. Tell me a little about what you’ve done and sort of what got you. All jazz to write this book, or like, what was that moment when you were like, Oh, this is, this is what’s happening.
Jeff Booth: So I am a technology founder about a technology entrepreneur by background. I started a company called Build direct and grew to about half a billion dollars in market cap at one point and started many other technology companies. And, and now I’m sitting on many technology boards and everything else.
And for about 10 years, I’ve been talking about this, seeing how fast technology is moving and being at the front seat of that intersection, kind of where you are too, and seeing it in health, but seeing it on a whole bunch of different industries, how fast it’s moving. and you’d have technology technologists on one side saying how great the world is going to be.
Right. and, and the singularity folks on one side and they’re right in, in a narrow sense how fast technology is moving, but what they’re, what they’ve missed is, is what that means for our existing economic systems. And because no one was putting those two things together. Like I just, I was truthfully, I didn’t want to write a book for 10 years.
I put it off. the, the, I just realized. My kids are gonna grow up in a very different world. And it’s going to win when societies fail economics is, is, is so important to our lives and we don’t realize it. Right. It’s just about everything you’re doing is about where do you gain advantage? Where do you, can you provide for your family? Do you have enough of a share to, to, to provide everything comes down to that and you make subtle choices and, that you don’t know you’re making based on economics and those two pieces weren’t being put together. And so if technology is deflationary effectively, People believe economics is about value and it’s not economics is about scarcity and I can prove it.
I can say it. The, the, the air you breathe is arguably the most valuable thing in your life, because without it, you die in about a minute. and, and it’s free. Why is it free because it’s abundant and technology creates that abundance everywhere. It’s the same reason why your calculator on your phone is free.
Like guitar tuner is free. The, just about everything on your phone is free. Because if, because if it creates abundance and it’s hard to charge for abundance. And so, so if sort of technology creates abundance and it moves to free, it means if we design the world differently. We wouldn’t need the same amount of work.
Harry Glorikian: Well, that’s, it’s interesting. Right? So it sounds, it sounds very Star Trek ish. If you, If you’re a Trekkie, you know, everybody’s working for the better, good of what’s happening, but, but let’s talk a little bit about like, you know, I, also believe, you know, technology is a deflationary force, which, in some ways is, is very good because people get more for less. But, I also see the technology also has a way of super concentrating.
Jeff Booth: Wealth, power?
Harry Glorikian: Well, PA yeah, I think about it from a power perspective, as opposed to the wealth perspective, but that, you know, once you have more data in one place, your ability to sort of see patterns and do things with it is, is, is as, at a much greater scale as we’ve seen from, you know, what is it?
The Fang, right? Facebook, Apple, Navidea right. And Google right there. They’re the big dogs and it’s sort of difficult to see how you would ever. Get yeah, displaced them. Right. Once they’re beyond a certain point. And some of the companies I talked to about from an investing perspective, or, you know, how do we get to that Google level of escape velocity?
How do we get to that point? Where all the competitors behind us just can’t catch up from the abundance of data that we’re putting together. Right. but jumping back to two. You know where we are in this sort of, deflationary effect. Let, let’s turn that towards healthcare for a minute and say right now, everything that we do in healthcare, where technology is having an impact is done and say in a central location.
And so one of my hypothesis is just because it’s done in a central location today doesn’t mean it needs to be done in that central location tomorrow. Right. Which is one thing that I’d like to discuss. And then the other thing is, is as things get easier and done more on, you know, something like this, or, you know, something like this, as you said that the price comes down to almost nothing.
I mean, this is a medical device that’s on my arm.
Jeff Booth: And then your doctor.
Harry Glorikian: Yes, actually I was talking to my doctor yesterday. I had my video checkup and. I said, yeah, I just, I just did my blood pressure and it’s this right. And I’m tracking it on a regular basis that he can see a sort of longitudinal view on me.
Let, lets you know, if, if you don’t mind jump into that a little bit for, for everybody listening and, and sort of discuss how you see this playing it through because you know, I’ve, I’ve, I’ve read a couple of things that you’ve put together and I think you say on folding a piece of paper, 50 times. We’re on the 34th fold.
If I remember correctly from, you know, equating it to Moore’s law. maybe, maybe you want to explain that a little bit for the people listening. Cause I have an advantage cause I did a little reading before we jumped on.
Jeff Booth: Sure. So, so if you fold it by the way, concept is so important because it’s the same thing.
People miss in tech, technology. And it makes sense, but if you fold a piece of paper once on itself, twice on itself, three times on itself, and you keep folding for 50 times, that piece of paper will reach the sun. Seven. Right. And everybody will say you can only fold at seven, but imagine if you could keep folding the piece of paper, it reaches a sum.
I’ve asked that question to audiences, tens of thousands of people all over the world. and nobody gets to the sun. People that have heard it before. Some people say it to the moon, some people, but, but nobody gets to the sun. Why that’s critically and most people guess about two inches. and, and why that’s critically important.
It’s not a parlor trick. What it says about humanity is we don’t understand exponential patterns. We can’t grasp them. Right. Probably don’t, that’s what it says that because otherwise the evidence would say more people would get the answer. Right. I didn’t get the answer first. I had to look it up. Nobody gets the answer.
So humanity in general doesn’t understand the exponential patterns, but we have Moore’s law. And I would say behind the Moore’s law, other technologies that are moving in exponential patterns. Right. So, is moving so fast. And if you equate those two things, we’re unfold, 33. So that means in about a year and a half to two years, all of the, all the technology gains double we’re in this, we’re in the steep steps now.
And then in another two years at doubles again. So we look backwards, right. To look forwards and look forward and we’d draw linear relationships. And we, so we think technology is moving fast. It’s incomprehensible how fast it’s moving.
Harry Glorikian: Well, and it’s funny because I tell people like this story, because we were involved in doing the human genome and, you know, we got 1% on and everybody’s like, ah, 99% left to go.
And what they couldn’t see was the exponential driver of the data. And the next thing, you know, like five years later, four years later we were done. And I don’t think people, I’m, Idon’t fully appreciate the. Behavioral psychology that people don’t understand these, this, this jumping move that, that, that seems to happen, but they can’t wrap their heads around it, by the way.
Jeff: That’s so all the great companies today are they. So if you think about what Tesla is doing, he’s selling you a car forecasted on that exponential, right? That’s what he, that’s it. And he’s taking a deposit forecast with on the exponential that’s, what’s creating the value. Right. He’s selling you a vision that is not possible in that current world forecasted and on the exponential,
Harry Glorikian: but it’s how do I say this? It’s believable. It’s you can envision it. And I think some of the other things that we’re talking about are not necessarily easily. Graspable by the, by the [00:13:00] mind.
Jeff Booth: Yeah. I would say a lot of Elan’s stuff until he did it. A couple of times were not believable either. Right. Self landing, a rocket. And, the number of things that, that are, that were, were bad.
If you trace back they’re believable now because he’s has a history of doing it. And a lot of times he’s just forward he’s off the Mark. Right, right. And he’s, he’s a little bit late to it, but it always comes true. So that though it was so we should discuss that and we should discuss because the same thing is going to come to healthcare and we should discuss why, why we most often get it wrong.
So if you fold a piece of paper once or twice, what you do is you, because of the linear pattern, you dismiss that how fast it can move. Right. And so all of these technologies are moving at that rate. And I’ll use 3d printing as an example, I’ll bet you, most of your listeners think about 3d printing as printing this tiny little plastic piece that takes 24 hours to two, because they’re looking backwards at hype cycle.
That because, because the same thing happens on the other side, you got this hype cycle because people think that they have this grand expectation of what it’s going to be. And it comes out and it doesn’t meet that expectation and it falls apart. Right. But, but what happens is it keeps on doubling and doubling.
And if you looked at where 3d printing is right now, actually in healthcare and a whole bunch of other things, if you say where that is is staggering, how fast it’s moving and it’s going to come out and it’s going to blow people’s minds.
Harry Glorikian: Yup. And there’s a number of, I mean, I have this discussion all the time as always. It only, it’s only this good. Right. It, you know, that’s not good enough for, you still need a human being involved to make these decisions. And I’m like right today at this moment. Yes, you’re absolutely right. But at the rate that it’s changing, if I think about like, I was talking to somebody about going alpha go and like how, you know, it won the first game against a master.
And then I believe the second version, which came out a year later, Beat the first version and never had to practice it all at new was the rule.
Jeff Booth: Yeah, it took a whole bunch of PhD. I wrote about it in the book. And, at that years to be able to practice all the games on all the games and just knowing the rules that the new alpha goes, zero destroyed the previous best. And so, so that tipping point happens really fast. That. and so today, a new scientist article came out with your doctor, gets the right diagnosis about 71% of the time. And, and formerly an AI was at 72.7%. And with a new methodology right now today with a new methodology right now, that’s moved up to 77.8%.
So today it’s beating your doctor. on a whole set and tomorrow it will be 90%.
Harry Glorikian: Okay. And it’s interesting. Right. But, well, there are, you know, there are a number of papers that, that, that, you know, there’s what happens in a laboratory. And then what happens in the wild, right? Because healthcare is very messy, right.
So when you take something out of a laboratory and you put it into the wild, it doesn’t always perform at the same level. And. You know, I always see these articles that poo-pooed and so forth, but I always, you know, I equate that to what happens in technology, say what self-driving cars like the first one, not so good, right?
Every six months, every year, it just gets exponentially better. And it’s not a whole lot of years before some of these technologies that don’t perform well in the wild today will not. Exponentially improve their impact.
Jeff Booth: So let’s dig into that a little bit deeper from an AI perspective, right? Very deep and a bunch of the top researchers are friends very deep and how fast that’s my way, but let’s look at it from an AI perspective, what is first from a human perspective, what is learning? What, what is, what is intelligence.
Harry Glorikian: Well, it’s interesting learning. I always think of learning and intelligence are two different things just because you’re learning something doesn’t mean you’re intelligent
Jeff Booth: But it’s error correction, right? Correct. Mind is error correction. Right. And, and, and if you, if you’ve take his history all the way to where we are now, a giant boost in humanity was when we could record our knowledge and books. Right. And that means we could sit on top of other people’s information and error correct their information.
Right. And so humanity giant, boost off because you could record and, and correct scientific method came from that. Right. And so, what ends up happening in a human mind is we have to go really narrow. And practicing, practicing, practicing, practicing practice, you know, the whole 10,000 hours, but what a doctor does and they go to school, what a musician does, what an athlete does.
They, essentially practice something really narrowly. And by doing that they error correct. And they see patterns that other people can’t see because the energy going into it, it’s like a super highway of neurons. Right. But that they see things that other people can’t see.
That’s what, that’s, what intelligence is. It really comes down to error corrector.
Harry Glorikian: Well, and it’s funny because I’ve been debating on writing a book titled the scientific method is dead because I actually believe that computational approaches. We’ll do a much better job of giving you their first set of tests that a human may then want to do rather than start from scratch.
Jeff Booth: You get one step further, and it’s actually where you’re going at any anyways with what were you saying? The additive man, the additive ability for computers to see powder. They don’t, they’re not bound by our time and narrow patterns and they can add more data sets and the data sets that they add. They see things we don’t see.
Right. But if you say the book alone, right. The book alone, and, and some of the greatest businesses come together because they intersect two things and the world doesn’t see you put them to get together, because, because we’re all in these silos and we get really narrow and deep in a silo and we can’t come up a level good.
It takes a lot of work to learn something else and put those things together. for our, for our brains computers don’t have that same problem. They see, as you add more data sets, they can see and they can see things we couldn’t see because we didn’t got the data. Like we have to choose smaller data sets for, for our Maya to, to do our work.
Harry Glorikian: Oh, absolutely. Right. I mean, if you can only hold five concepts. I think it’s five concepts in your mind at one time. And that’s, if you’re good and keep them rolling around in there until you’re sort of see some connections, but a machine I dare say is almost unlimited depending on memory and compute power.
Jeff Booth: So what you’re seeing is, is that, so I use this example with our provincial government in BC and I, and I said the top AI, the top researchers were attracted to Canada and BC and in cancer because we had a one payer system. We had a universal health system. And that meant we have had heterogeneous data sets going back 50 years for cancer research.
And because we had more data sets across, across all different populations, sizes, ranges, times, and everything else, top researchers were attracted to our universities because they could work on giant data sets. Right. And so today, That’s moving to AI research. And the same reason that creates, see what you said, Google, Amazon, and the top AI researchers wanting to work on that because they had more data is the same thing as driving the top.
AI researchers into aggregating data sets too. That’s where that’s where your watch is going, by the way, that’s why Google bought Fitbit or the, and, and the iWatch. And for Apple, your watches, moving to aggregating data, data sets. At a staggering rate, which we’ll be able to see more patterns on your doctor.
Harry Glorikian: Yeah. I mean, we can, I, I’m already involved in a few companies where you can see data on the backend and you can see things in the data. And the first thing that always crosses mind is like, Oh my, like, I didn’t realize we could actually see that now. You really want to run a clinical trial. You want to prove that what you’re seeing is re you know, Kim repeat itself, and that it’s accurate, et cetera, right?
Because we are dealing with health and we cannot afford mistakes in healthcare. So we tend to be more cautious and a little slower, but the rate that things are moving is dramatic. And so I come to like, okay, technology’s moving in one direction. We can bring, you know, it’s a deflationary force. We can provide services to patients that we couldn’t yesterday.
I mean, I don’t want to say we should be able to take care of patients for almost nothing, but we should be able to look at a patient holistically from different data sets that are being generated on a 24, seven basis from. Wearables or, you know, your scale that you’d step on every day or whatever that should give us almost a dashboard on these patients manage them better.
Jeff Booth: And you’re right in the future, but you’re jumping because it’s so take what you just said into why they incumbent never went. Right. So Kodak invented the digital camera. Right, but, but that the digital camera competed against their film business. And so twice Steve Sasson tried to get executives to say, this is a big deal.
Now going back to abundance today, I’m sure there’s way more photos out there in the wild today. An abundance of photos. You probably take an abundance photos than you did 20 years ago. Right?. And they’re free. Everywhere, right? Yeah. And there’s lots of business models have created new value around that Facebook for one, Instagram, a whole bunch of other business models that are created, business models around or photo capture the data around photo capture. But Kodak didn’t. Right because it tried to protect its turf. And so what ends up happening, what ends up happening in any business, is, and you could say this in health today is the techno the innovation won’t reach the market right now because it’s blocked by the existing market, regulatory framework, doctors, everything else.
And they’re going to want to say, it’s too unsafe. It’s not proven. Right. And it’s going to keep moving and it’s going to keep moving and it’s going to keep moving and people are going to aggregate, aggregate, aggregate. And I’ll give you an example on your Apple watch. If Apple, if you trusted the privacy of Apple’s network and they asked for your genome and they gave you an easy way to do it and they could send to, you could provide, they could provide way better outcomes for you.
You can’t trust them totally. But you would just like you to know, would you give them your genome?
Harry Glorikian: Well, you’re talking to somebody that knows a lot about this. I’m not sure I trust anybody with my genome, but, cause I w I basically, if it was just me yes. But I think because it, moves to my kids.
I’m making a choice for them by doing what I’m doing. So I’m a little bit more cautious,
Jeff Booth: But I guess what I’m getting at is many people would make that choice.
Harry Glorikian: Yes. Agreed.
Jeff Booth: The same reason. Many people chose. To, to use digital cameras, the same reason, many people that are going on to Bitcoin today. The same reason that, many people are, used Google early on, right.
Because I provide at a point where it reaches so much value. You’ll give it more information. That’ll give you more value. And that’s what that’s, that’s what creates the flywheel of data capture. And, and here’s, here’s what I say to a lot of companies that I’m, that I’m helping. There is no second best algorithm, the best algorithm consolidates the information faster, because it’s so much better performance to everybody else.
That it captures more data at a rate. That’s that? It’s staggering.
Harry Glorikian: Yeah. Well, I mean, I think we just saw that and I, you know, the listeners might not know what GPT three is, but, the latest NLP system, but it, it compared to anything that came before, it’s, it’s unbelievable. Right. I mean, and it can do.
Everything that everything came before it does under one system.
Jeff Booth: The only difference is it took in more datasets.
Harry Glorikian: Yes. 175 billion features. I think it was compared to 135 million in the last one,
Jeff Booth: right? Yeah. The only, the only difference.
Harry Glorikian: Yeah. I it’s staggering. And that, that’s why, like, when I’m looking at different technologies in healthcare, it’s not just looking at it at what it’s doing right at this moment, but looking at how it’s going to keep incorporating data and is there this curve that it could be on to get to a point where it can either change the business model or incorporate other data sets that makes it a better predictor of what I’m trying to do next, whether it’s design a drug or diagnose a patient,
Jeff Booth: So, et cetera, let’s use a very real example, on designing a drug, right?
A 20 year process. Why it costs so much is because so many go broke on that, on that, on the way to designing a drug. and it costs, it costs billions of billions of dollars to design a drug. And then a lot of times when it’s, when it’s in the wild, it produces negative effects. Sure. So the same thing that you’re arguing that it’s great today.
It’s not right today. It’s just, it’s already know it today. Right. And, and why, why that process, why that, why does it produce negative effects? It’s all about data capture, right? It’s you trial, you trial on a small, then you get a small study and, and those people aren’t wide enough reference group.
Because your genome is different than somebody else’s genome, you eat different, you have different environmental exercise patterns and everything else, trial trial trial, and they don’t get enough people with the irrigation and it reaches the wild. And when it reaches wild with a whole bunch of different variation problems, right.
That’s the existing course. So why wouldn’t AI? Be able to solve that better?
Harry Glorikian: Well, I believe that it is, but I also believe that it’s not fully developed for all the different applications that we want it to do.
Jeff Booth: I agree. I told it like, we’re not there yet, but in concept it’s just data. The re the reason the drug shows up differently for you as it does for me is our data.
Our individual data is different. Yes.
Harry Glorikian: And, and so I you know, I would, you know, I’ve, I’ve interviewed quite a few people for the show in this area, and I’ve talked to people and I, you know, delve into this and we invest in this and there’s a lot of variables still. And to be quite honest, we haven’t graduated enough people that speak. the language of AI, as well as understand the biological side of it, right? Because the data end is critical to the analytics that are going to happen on the other end. So it’s moving, but it’s not moving say as fast as everybody would like it to, but there are some startups or smaller organizations that are doing amazing work. And my bet would be more on the little guy than it would be on the big guy.
Jeff Booth: I wish that that was true. but I suspect the by the way, I’ve Invested in a couple of areas, here as well in plant health, everything else that’s using staggering AI to do the same thing. It’s just a feedback loop.
And once you crap capture enough, what can you, what can be done when you see the, when you see the performance, the difference, That I’ll give you one example of Terra Amera company. Most of the team was, was PhDs. and, and, and researchers kind of plant, and, and plant biology as they added AI and everything else, it’s moving so much faster, right?
Because the, the scientists are actually helping inform the AI early on, and then the AI breaks loose. And it feeds back and it feeds back in a feed. It feeds back. So can some of the, some of the performance, what you see and it is incredible, but the same thing is going to be applied to health. And it’s going to be the intersection of science and AI.
And all of this is going, all of the science is going to train the AI, right. These data sets, and then you’re going to add more data sets. and it’s going to break loose. I believe though, likely because it’s such a big industry and it’s such a center that, that it’s going to be Amazon, Google, Apple, that, or they’ve been moving here for a long time and putting together some of the big pieces to be able to move here.
And so I think that they will probably consolidate some of the smaller companies. With, with before those smaller companies can aggregate enough data to be able to, compete.
Harry Glorikian: Well, it’s interesting. Right? I do believe that the, again, being from the venture world, I do believe that the buyer set has increased.
Right. Whereas if we developed a company earlier, you know, you could almost name off the. Appropriate buyers, right? Depending on what space they were in. But I think because of the computational side now, you know, I’m hearing Navidea Apple, Microsoft, I mean, hearing a lot more names that at first you would be like, what are you, why would you be interested?
And yeah, you can start to see where, especially as, as healthcare moves to from sick care to healthy care, Healthy care requires a lot more data than sick care does in a sense. All right.
Jeff Booth: So funny. So say what is actually, it’s, it’s making better predictions, that’s it. And your error correcting on each of those predictions at a, at a rate that humans can’t predict it can’t do.
That’s, that’s really what it’s doing. So you have to have a F so let’s use Google as an example. So nobody goes to page 452 on Google. And how does that happen? How do they get to the top results and everything else? Let’s not use their paid search. Let’s just use a free search. And so they have 130 trillion websites competing to get in and the first results and all of those people ever are effectively against an algorithm saying, I’m going to try this.
I’m going to try this to make my thing better. All of that competition is driving Google’s algorithm faster and faster as all of the people on the other side. Are choosing results tailor made to you. Right. And because, and they know more and more about you with each of those, each, each click, what you click on.
If you, if you click on it and then bounce, it’s a signal to Google that says that wasn’t right for you. Right. And, and when websites are, are taken down and up through that thing, and it’s, it’s, it’s all AI. So as a response mechanism, it’s a prediction machine. It’s constantly evolving. And so health is the same, right?
It’s a prediction machine. So Y why we might do intermittent fasting, why we might do this, where it’s all predicted over long periods, right? This should work over, over long periods. And, and so as, so are the drugs against certain diseases and everything else. And as you add up that and move up the stack, a lot of those things are going to.
Pull out patterns that are offering better predictions.
Harry Glorikian: And it’s interesting, right? If you think about it, right? I mean, the, we have physicians that are very specifically deep in certain areas, right. But this is not a specific, it’s not run by one thing or another it’s a system, right. So it’s incredibly interconnected and complex but at the same time we have, we still haven’t fully understood everything that causes the cascade.
Jeff Booth: Totally agree. That’s totally right, because it’s impossible for humans to do that because if you just add it up, all of the, the number, if you, you know, that, you know, because you did some of this work, how, how many in a genome, how many pieces are in your genome?
Got biome, health. Exercise environmental. There’s no way any one person or 10 people or a thousand people could even do that for one. Human is just, it’s just too, too complex. It’s too much data.
Harry Glorikian: Yeah. I still think though, from a computational perspective, I always say to people like, if you don’t have the data, if you haven’t measured it and you haven’t put it in the system, like you’re missing a critical piece of.
What it’s going to take to do this equation. Sometimes we don’t know all the data that we need to make that critical decision. I always take, you know, Joel Dudley’s paper on Alzheimer’s, where he was able to show it was an infectious disease, like herpes. That was one of the drivers of this subset of Alzheimer’s it.
You know, not something on the radar,
Jeff Booth: North Organo, but it actually, if you just said, say the thing you just talked about in GPD, three, right? when you, it’s all it is, was a way bigger dataset and it could see more.
Harry Glorikian: Right. And that’s what, that’s it almost forces conceptually, if you were to look out into the future, right?
Every hospital has its own data set. Ideally, what you’d want is all those data sets talking to each other or aggregated to get to a better answer.
Jeff Booth: I suspect what it’s going to happen. It won’t be every hospital it’ll be. So we’ll, you’ll, self-sovereign your data and you’ll choose to give it to certain people and you’ll choose to, choose. If they kind of privacy don’t share something and somebody’s going to aggregate it up at a higher level at a hospital level. You don’t actually be, there’s no way you could have enough data to be able to do this.
Harry Glorikian: Yeah, I actually, it’s funny because I believe that the shift in power of that data, because of things like the iPhone are, have gone from the providers to the patients,
Jeff Booth: So that’s that innovation that’s exactly the innovation. Why? I think it probably won’t be, it’ll be blocked by existing. Right. And, and somebody is going to, as you have an innovation that becomes 10 times better than the market. You know, this from VC, right? If something, if you don’t have a 10 time, 10 X improvement over a market condition, it’s really hard to break through the noise of a market.
Once you have 10 times, 10 times better, the market pulls you. But
Harry Glorikian: It’s interesting. Right? So if you think about, about a country like China, they don’t have the same problems. I’m not going to call them problems. They don’t have the same, existing structure. True that we have
Jeff Booth: To move it faster because they can just railroad it
Harry Glorikian: and they can say all the imaging from all these hospitals goes to 10 cent.
And we can do all the analytics on it. Right? So
Jeff Booth: Not just hospitals, how many times, even on zoom calls, does somebody, a doctor catch a disease in somebody’s eye or, or, or a lump or something like that? That person had no idea. So now if you take the imaging, just that at airports and everything else, your capture image capture everywhere.
Can be a driver of a better datasets.
Harry Glorikian: Yeah. I have to think about that. I’m not sure I want everything analyzing me all the time, but it’s, I realize it’s going to happen no matter what,
Jeff Booth: And, either just too many cameras and yes, but, but what you’re saying, if you just kind of go forward with what you’re saying is if China moves this fast, and it’s all about AI and putting those things together, then a lot of the best research for the best technology for a, for, for health will come out of China.
Harry Glorikian: Theoretically. Yes, but
Jeff Booth: Which scares me.
Harry Glorikian: Yes. But, but I have a bet. I have a bet that our creativity, it. W w we’ll be able to hold our own. Now that said, you know, I do believe that even for our fund, we should have an office in Toronto. I mean, I’m in Boston, it’s a quick hop, skip, and a jump, but there’s so much going on in Toronto from an AI perspective that it’s really hard to ignore.
Jeff Booth: Talk tons in Vancouver and Toronto. And so it’s, it’s moving so fast, but most of the top researchers in AI came out of Canada because Canada funded AI through the AI winter, right. Other governments. And so those AI researchers, Geoffrey Hinton and, NGO, those actually AI researchers, aren’t from Canada.
They moved to Canada. Take it. Is it your funding through where everybody turned it off?
Harry Glorikian: Well, we used to have people move to, we used to have people move to the U S too. Now that’s changed a little bit, but,
Jeff Booth: Well, it’s funny though, that what, what what’s happening now in the you asked is the thing that drove us prosperity.
Was that right? They, pop minds from all over the world, moving, moving there, Canada, just in one little, one little and it turned out to be a really big area was, was one of those areas, Canada as well.
Harry Glorikian: So where do you see? I don’t know. I’m trying to, you know, project out from, you know, full 33 or full 34 and say.
One or two more moves at most. I’m not even projecting out much farther than that. where do you see the next inflection points from a technology perspective and the. Impact or implications of those.
Jeff Booth: So I wrote about a bunch of these in my book. and what ends up happening is people don’t see the orthogonal impacts.
They look at the first order impact, the next thing that con so if you just take self-driving cars, right, let’s use that one example. Self-driving cars, there’s a $482 billion market insurance market that 98% of that insurance market is driver error. Right? So, so when we compare things, we say, well, self-driving cars might make mistakes and it would cause an error, probably not at that rate.
Right. That’s why the self-driving car companies like Tesla is going to self-insure. And so that entire market falls apart. think about buying a car. why do you buy a car? But you buy a car for, so you use a car 6% of the time. It as a, as a 6% utilization rate, 94% of the time it’s stored that storage means you have two parking and at home you need to park.
Now you’re doing your work. You need all of that extra road resource and everything else. And when you have self-driving cars, you might. The driving platforms are gonna look like SAS platforms, right? If you can get a car on demand whenever you want it, you’ll either buy it and rent it back to the fleet.
When you don’t use it. Or buy, or just rent it from the fleet, as long as you can have a car whenever you want,
Harry Glorikian: Except during Corona where you need disinfected
Jeff Booth: Yeah. Maybe, maybe, but those types of things. So if you add those types of things on the changes, so staggering on so many different industries, Right.
And so car companies, if you look at the forecast on, there are companies, they have production going up on a, on a production going up every year and all their forecasts, their capital, expense, allocations, and everything else. And it’s all based on 6% capacity utilization of a car. So if 6% moves to 40% or 50% they don’t need all that production. It just completely changes. So as do cities, as do insurance and everything else, that that’s just an example. but, there’s examples. So bookstores thought they were competing against Amazon early on with books. As you digitize a book. Now you can offer an audio book, you know, you can offer a Kindle,
Harry Glorikian: right.
There’s other formats
Jeff Booth: And bookstores have no advantage in those formats.
Harry Glorikian: Yeah. This is one of the things I talked to a lot of our companies about is once we have the data, what else can we do? Yeah.
Jeff Booth: And, and, and, and the existing incumbent will, will take a narrow and look at that. And what do they do?
They always, always. Oh, no, it people want, if people believe that people want to do this vote of convenience. Right. And they love, they love shopping in my bookstore. So let’s add more costs. Let’s add coffee shops to the bookstore. Right? What does blockbuster do? Blockbuster adds candy Isles to there because people love picking Hartman.
Right. Right. And they miss how fast it moves to a new format and digitizing it at that cost of that new format looks totally different. So the same thing in the existing format that gave them all their strength, kind of their acts as an impediment is a noose hanging around their neck.
Harry Glorikian: No, it’s yeah. It’s very difficult for a, you know, the 800 pound gorilla to shift.
Jeff Booth: Yeah. And it’s, it’s the same, what you just said because it happens so fast that the same thing that drove all their market power and Blockbuster’s case 9,000 stores once. And, and they’re, they’re not stupid executives. Right. They’re looking out at a linear technology. Right. And, and then, and at that time you remember that what they, internet connection was four weeks to download a movie.
And then the next year you can download a movie in 20 minutes and their businesses is dead by that time it’s too late.
Harry Glorikian: Well, that’s why I’m, I’m, I’m always a big advocate for, you have to invest in innovation so you can get up taste of what’s coming, but the other. The other problem though, is in an existing business with margins profitability shareholders, you waking up one morning and just saying we’re pivoting.
You’ve got to go down before you, you go back up again
Jeff Booth: Ohtrust me. I did you read my book yet?
Harry Glorikian: Okay. I’m about halfway through it.
Jeff Booth: Okay. So I did this correct. And we were, we were, so, so the company was almost doubling every year in sales, so it was $120 million. It doubled from 80 million to 120 million.
But before that though from 40 to 80, and I realized there’s no way to keep going at this. We have to completely change the business model, but I underestimated. I underestimated what the shareholders, because when you’re winning, when you’re winning at that rate, everybody believes in everything you do.
And, and so Warren says, yes, all in, we’re go, we’re going. And I said, it would take this long. Well, On the other side on the downdraft to get to the other side, nobody likes you so much. And so you’re very like you have a frame of reference on what this company needs to do. And it’s hard to say too, even if you believe in it, right.
That, shift, it’s hard to take a take down revenue to be able to make that make the shift or have the capital to do so.
Harry Glorikian: No, and most organizations fail along the way or blow up or. Whatever happenshappens, or they can’t do it fast enough because of infrastructure and overhead and whatever it is.
Right. and I’m watching that, like the experiments at CVS and Aetna are doing, and Walmart is doing, and all these guys are doing it. There is a massive disruption coming to the provider market. and it’s so it’s, it’s. Almost trying to wave your hands and warn these people. Right. Because it’s one thing, if a, if a company goes, if whatever, pick any company, it goes out of business.
it’s maybe it’s a, inconvenience for the most part. A hospital goes out. That’s a problem.
Jeff Booth: Yeah, totally, totally. And, and, and I think there’s a, there there’s many of them that are under lots of pressure right now.
Harry Glorikian: Yeah, I’m not sure they fully appreciate what’s coming, but
Jeff Booth: I’m on the board of a hospital foundation, in, in, in DC and, and, and moving the strategy to, to what you do, what we’ve been talking about, getting in front and using technology to innovate in the healthcare and being kind of a leader in that, bringing some of the top minds together, to do that
Harry Glorikian: Well, it’s interesting because, you know, I think right now COVID has probably pulled forward the technology side, you know, depending on what you’re talking about, somewhere between five and 10 years forward, as well as changed the regulatory environment and the reimbursement environment. So I think it’s just sped things up.
Jeff Booth: So I, this is something I, I talked to say governments about, but if you, so I, I was speaking to the house of commons in Canada and I’m, and I’m talking to them about how fast technology is taking down prices and, and, and what, what that means and where, where you have to invest in, where you don’t have to invest.
And the irony was not lost on me, that we’re talking on a zoom call to the, to, to the house of commons. Okay. No one knows on the realization that they’re using the same technology. And there isn’t one extra job in Canada from Zoom. Eric’s a friend of mine who runs Zoom, but, but, but, but we’re using technology because technology becomes borderless.
You use the best technology anywhere. So what you used to do for four to eight, when governments hit shortfalls and everything else is you used to do an infrastructure project. So let’s say build, build wider roads or bridges and everything else. And because you got a short-term boost the economy through jobs, and you’ve got a longer-term boost because it was faster to get to work and back.
Right. And so, so it produced a longer term GDP return than just spending money anywhere. the new infrastructure is all digital
Harry Glorikian: Well, but even the, even the infrastructure you’re talking about, I, you know, if you think about, you know, to the back, back in this country, the new deal, right? lots of people, lots of jobs, because everybody needed to pick and a shovel.
Now you look at when they, when they lay a road, It’s one, it’s a couple of pieces of very big machinery and their land road way faster and way easier than anything that ever happened before. So even on the technological front of robotics and that sort of technology to be able to. Lay road and, and build things it’s much faster, much cheaper and much easier than it was before.
So you don’t need the same manpower.
Jeff Booth: And what changes when you know what happens with digitization, what work or our Zoom call right now. Right. You, you can do work from anywhere. and the highway is become digital, right? So, so you’re right on, on, on roads. Yes. In a narrow sense. It takes away the way more jobs.
It’s more efficient, everything else in a broader sense. If technology, if, if the best algorithm wins and everything is about data capture and the super highways of digital and digital, then how does the government stop that?
Harry Glorikian: That’s something, yeah, that’s something that, you know, you and I have talked about, you know, I think we need to think tank around putting out policy pieces so that people are more aware of what’s coming down the pike
Jeff Booth: And invest in the areas that are, that are going to be the best. So at the, if you globally, no matter what globally, that doesn’t mean that there is going to be net new jobs globally. And that means our existing economic system cannot work globally, but each country.
if you can assume that, by the way, that’s the big geopolitical world right now. That’s the Huawei that that’s what’s happening right, right now. But the investment in technology needs to go way up to be, have any chance to be able to, have. More of the higher paying jobs on the way as it’s, as it’s deflating, but in the end it’s going to do everything is, moving down in price anyways.
Harry Glorikian: Well, I mean, there is a, an incredibly positive to that, right? That, if, if you manage it the right way in that direction, I think we’re
Jeff Booth: [00:51:40] w I believe that when you and I talked about this before, I actually can’t believe we’re fighting so far hard against abundance. Technology creates abundance and governments.
So what is inflation? What isn’t deflation inflation is only goods and services go up in relation to your currency. And inflation is the opposite goods and services go down in relation to your currency. And, and so I bet you, if anybody asks, say, would you want goods and services to go down in price? To you personally, you would say, yeah, of course I would.
Right. I get more and more for less and less. And, and, that’s how technology works. That’s how, but, but when you add it up into economy, people say, no, no, no, we want inflation. We want our currency to, but where you worth way less. And we want our prices to be more it’s insanity if you, it’s just, it’s because we’ve lived in an inflationary environment so long that we can’t see what it would look like.
Where, where we wouldn’t have to work our entire lives to be able to retire for 10 years and hope to save enough money against the threats of, in that inflationary environment so that we could protect our families. Yeah, it’s just completely inversed, but it’s so hard because we’ve grown up in a, it’s so hard to even comprehend.
Harry Glorikian: Well, this is why it would be good to have like, people thinking about this all the time and putting out thought pieces, because I’m sure that it’s moving in that direction is also, there’s a lot of implications that you and I probably haven’t even thought of yet, that, that somebody needs to go and model and think through.
Jeff Booth: So the biggest thing is the change from one system to another is ugly, but there is no way. just by the way, here’s some numbers to stop deflation from happening. The world has added $185 trillion of debt. In the last 20 years, if you didn’t have that debt, you would have already seen what would be pre where prices would be every everywhere, eventually on that path, currencies disregard destroyed and you’re adding away.
And that was before COVID. And so, so, but the transition, because we’ve made the problem so big by kicking the can down the road, the transition is going to be ugly, but that transition is going to come no matter what. Right. So that transition is going to come through either destruction of currencies and a hyperinflation eventually, or through austerity where you go through a depression, like you did it like it did in the thirties. And I wish I didn’t have to say that, but, but the truth is it’s coming no matter what.
Harry Glorikian: Well, on that happy note, you know, I hope to continue our conversations cause there’s a lot to debate and discuss in this area.
And I always try to take everything that I learned from the technology world and apply it to healthcare and all the areas that, you know, Are affected by a GPT three or, you know, a new processing system or whatever, because it has a direct implication on this world. Look forward to staying in touch and good luck with all the companies you’re working on.
And I wish you great success.
Jeff Booth: Thanks. Really look forward to keeping in touch as well.
Harry Glorikian: And that’s it for this episode. If you enjoyed Moneyball medicine, please head over to iTunes to subscribe rate. And leave a review. It is greatly appreciated. Hope you join us next time until then farewell.