What Exponential Change Really Means in Healthcare, with Azeem Azhar
As we say here on The Harry Glorikian Show, technology is changing everything about healthcare works—and the reason we keep talking about it month after month is that the changes are coming much faster than they ever did in the past. Each leap in innovation enables an even bigger leap just one step down the road. Another way of saying this is that technological change today feels exponential. And there’s nobody who can explain exponential change better than today’s guest, Azeem Azhar.
Azeem produces a widely followed newsletter about technology called Exponential View. And last year he published a book called The Exponential Age: How Accelerating Technology is Transforming Business, Politics, and Society. He has spent his whole career as an entrepreneur, investor, and writer trying to help people understand what’s driving the acceleration of technology — and how we can get better at adapting to it. Azeem argues that most of our social, business, and political institutions evolved for a period of much slower change—so we need to think about how to adapt these institutions to be more nimble. If we do that right, then maybe we can apply the enormous potential of all these new technologies, from computing to genomics, in ways that improve life for everyone.
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That's it! Thanks so much.
Harry Glorikian: Hello. I’m Harry Glorikian. Welcome to The Harry Glorikian Show, the interview podcast that explores how technology is changing everything we know about healthcare.
Artificial intelligence. Big data. Predictive analytics. In fields like these, breakthroughs are happening way faster than most people realize.
If you want to be proactive about your own health and the health of your loved ones, you’ll need to learn everything you can about how medicine is changing and how you can take advantage of all the new options.
Explaining this approaching world is the mission of my new book, The Future You. And it’s also our theme here on the show, where we bring you conversations with the innovators, caregivers, and patient advocates who are transforming the healthcare system and working to push it in positive directions.
So, when you step back and think about it, why is it that people like me write books or make podcasts about technology and healthcare?
Well, like I just said, it’s because tech is changing everything about healthcare works—and the changes are coming much faster than they ever did in the past.
In fact, the change feels like it’s accelerating.
Each leap in innovation enables an even bigger leap just one step down the road.
Another way of saying this is that technological change today feels exponential.
And there’s nobody who can explain exponential change better than today’s guest, Azeem Azhar.
Azeem produces a widely followed newsletter about technology called Exponential View.
And last year he published a book called The Exponential Age: How Accelerating Technology is Transforming Business, Politics, and Society.
He has spent his whole career as an entrepreneur, investor, and writer trying to help people understand what’s driving the acceleration of technology — and how we can get better at adapting to it.
Azeem argues that most of our social, business, and political institutions evolved for a period of much slower change. So we need to think about how to adapt these institutions to be more nimble.
If we do that right, then maybe we can apply the enormous potential of all these new technologies, from computing to genomics, in ways that improve life for everyone.
Azeem and I focus on different corners of the innovation world. But our ideas about things like the power of data are very much in sync. So this was a really fun conversation.
Here’s Azeem Azhar.
Harry Glorikian: Azeem, welcome to the show.
Azeem Azhar: Harry, what a pleasure to be here.
Harry Glorikian: I definitely want to give you a chance to sort of talk about your work and your background, so we really get a sense of who you are. But I'd first like to ask a couple of, you know, big picture questions to set the stage for everybody who's listening. You like this, your word and you use it, "exponential," in your branding and almost everything you're doing across your platform, which is what we're going to talk about. But just for people who don't, aren't maybe familiar with that word exponential. What does that word mean to you? Why do you think that that's the right word, word to explain how technology and markets are evolving today?
Azeem Azhar: Such a great question. I love the way you started with the easy questions. I'm just kidding because it's it's hard. It's hard to summarize short, but in a brief brief statement. So, you know, exponential is this idea that comes out of math. It is the idea that something grows by a fixed proportion in any given time period. An interest-bearing savings account, 3 percent growth or in the old days, we'd get 3 percent per annum, three percent compounded. And compound interest is really powerful. It's what your mom and your dad told you. Start saving early so that when you're a bit older, you'll have a huge nest egg, and it never made sense to us. And the idea behind an exponential is that these are processes which, you know, grow by that certain fixed percentage every year. And so the amount they grow grows every time. It's not like going from the age of 12 to 13 to 14 to 15 were actually proportionately—you get less older every year because when you go from 15 to 16, you get older by one fifteenth of your previous age. And when you go from 50 to fifty one, it's by one 50th, which is a smaller proportion. Someone who is growing in age exponentially would be growing by, say, 10 percent every year. So you go from 10 to 11 and that's by one year. From 20, you go to 22, two years. From 30 to 33. So that's the idea of an exponential process. It's kind of compound interest. But why I use the phrase today to describe what's going on in the economy and in the technologies that drive the economy, is that many of the key technologies that we currently rely on and will rely on as they replace old industrial processes are improving at exponential rates on a price-performance basis.
Azeem Azhar: That means that every year you get more of them for less, or every year what you got for the the same dollar you get much more. And I specifically use a threshold, and that threshold is to say essentially it's an exponential technology if it's improving by double digits, 10 percent or more every year on a compounding basis for decades. And many of the technologies that I look at increased by improve by 30, 40, 50, 60 percent or more every year, which is pretty remarkable. The reverse of that, of course, is deflation, right? These capabilities are getting much cheaper. And I think the reason that's important and the reason it describes the heartbeat of our economies is that we're at a point in development of, you know, sort of economic and technological development where these improvements can be felt. They're viscerally felt across a business cycle. Across a few years, in fact. And that isn't something that we have reliably and regularly seen in any previous point in history. The idea that this pace of change can be as fast as it as it is. And on the cover of my book The Exponential Age, which I'm holding up to you, Harry. The thing about the curve is is that it starts off really flat and a little bit boring, and you would trade that curve for a nice, straight, sharp line at 45 degrees. And then there's an inflection point when it goes suddenly goes kind of crazy and out of control. And my argument is that we are now past that inflection point and we are in that that sort of vertical moment and we're going to have to contend with it.
Harry Glorikian: Yeah, I mean, we are mentally aligned. And I try to talk to people about this. I mean, when we were doing the genome project that Applied Biosystems, you know, when we had finished, I think it was 2 percent or 4 percent of the genome, everybody's like, Oh, you have like ninety something [to go], and they couldn't see the exponential curve. And then we were done like five years later. And so it's it's this inability of the human mind. You know, it's really not designed to do that, but we're not designed to see exponential shift. We're sort of looking around that corner from an evolutionary perspective to see what's happening. But, you know? Exponential growth is not a new concept, if you think about, you know, really, I think the person that brought it to the forefront was Gordon Moore, right? With, you know, how semiconductor chips were going to keep doubling every two years and cost was going to stay flat. And you know, how do you see it playing out? Today, what is so different right now, or say, in the past two, three, four, five years. What you can see going forward that. May not have been as obvious 10 or 15 years ago.
Azeem Azhar: I mean, it is an idea that's been around with us for a long time. You know, arguably Thomas Malthus, the British scholar in the 18th century who worried about the exponential growth of the population destroying the land's carrying capacity and ability to produce crops. And of course, we have the sort of ancient Persian and Hindu stories about the vizier and the chessboard, who, you know, puts a grain of rice and doubles on each square and doubles at each time. So it's an idea that's been around for a while. The thing that I think has happened is that it's back to its back to that point, the kink, the inflection in the curve. The point at which in the story of the chess, the king gets so angry with his vizier that he chops off his head. The point with the semiconductors, where the chips get so powerful and so cheap that computing is everything, and then every way in which we live our lives is mediated through these devices. And that wasn't always the way. I mean, you and I, Harry, are men of a certain age, and we remember posting letters and receiving mail through the letterbox in the morning. And there was then, some 15 years later, there were, or 20 years later, there was a fax, right? I mean, that’s what it looked like.
Azeem Azhar: And the thing that's different now from the time of Gordon Moore is that that what he predicted and sort of saw out as his clock speed, turns out to be a process that occurs in many, many different technology fields, not just in computing. And the one that you talked about as well, genome sequencing. And in other areas like renewable energy. And so it becomes a little bit like...the clock speed of this modern economy. But the second thing that is really important is to ask that question: Where is the bend in the curve? And the math purists amongst your listeners will know that an exponential curve has no bend. It depends on where you zoom in. Whatever however you zoom, when you're really close up, you're really far away. You'll always see a band and it will always be in a different place. But the bend that we see today is the moment where we feel there is a new world now. Not an old world. There are things that generally behave differently, that what happens to these things that are connected to exponential processes are not kind of geeks and computer enthusiasts are in Silicon Valley building. They're happening all over the world. And for me, that turning point happens some point between 2011, 2012 and 2015, 2016. Because in 2009, America's largest companies were
Azeem Azhar: not in this order, Exxon, Phillips, Wal-Mart, Conoco... Sorry, Exxon Mobil, Wal-Mart, ConocoPhillips, Chevron, General Motors, General Electric, Ford, AT&T, Valero. What do all of them have in common? They are all old companies are all built on three technologies that emerged in the late 19th century. The car or the internal combustion engine, the telephone and electricity. And with the exception of Wal-Mart, every one of those big companies was founded between about 1870 and sort of 1915. And Wal-Mart is dependent on the car because you needed suburbs and you needed large cars with big trunks to haul away 40 rolls of toilet paper. So, so and that was a century long shift. And then if you look out four years after 2009, America's largest firms, in fact, the world's largest firms are all Exponential Age firms like the Tencent and the Facebooks of this world. But it's not just that at that period of time. That's the moment where solar power became for generating electricity became cheaper than generating electricity from oil or gas in in most of the world. It's the point at which the price to sequence the human genome, which you know is so much better than I do, diminished below $1000 per sequence. So all these things came together and they presented a new way of doing things, which I call the Exponential Age.
Harry Glorikian: Yeah, in my last book. I, you know, I do state that the difference between evolution and revolution is time, right? If you wait long enough, things happen evolutionarily, but at the speed that things are changing, it feels revolutionary and in how it's affecting everybody. So let's rewind and talk about your background. You've been active as a business columnist, as a journalist, a startup founder, a CEO, a leader of corporate innovation, incubators at Reuters and a venture capital partner. Lately you've built what eems like a very busy career around books and talks and podcasts and all around this theme of accelerating technologies, I'd love to hear how you how you first got interested in all these themes about technological change. You know, how society can manage this change? I know you were in Oxford. You got your master's degree in the famous PPE program. The politics, philosophy and economics. You know, was it soon after that that you went down this road? Or is Oxford where it all started?
Azeem Azhar: It started well before then in, in a weird way. So, so you know, my interest really is between sits between technology and an economic institutions and society. And I, I was born, like most of us are, to two parents, and my parents were working in in Zambia in the early 70s, and my dad was working on helping this newly independent country develop economic institutions. It didn't have them and it needed them to go through that sort of good institutions, make for healthy economies, make for social welfare and sort of civil politics. That's the argument. So he was out there doing all of that. And I was born the year after Intel released its 4004 chip, which is widely regarded as the sort of the chip that kicked off the personal computing revolution. And so, so in the backdrop of people talking about development and development economics and being curious about my own personal story, I was exposed to these ideas. I mean, you don't understand them when you're eight or 10 and you know, but you're exposed to them and you have an affiliation to them and so on. And at the same time, computers were entering into the popular consciousness.
Azeem Azhar: You know, you had C-3PO, the robot and computers in Star Trek, and I saw a computer in 1979 and I had one from 1981. And so my interest in these things, these two tracks was start set off quite early on and I really, really loved the computing. And I did, you did notice, but you don't necessarily understand that, why computers are getting more and more powerful. My first computer only had one color. Well, it had two, white and black. And my second could manage 16 at some time, probably not 16. Eight out of a palette of 16 at any given time. And they get better and better. And so alongside my life were computers getting faster. I'm learning to program them and discovering the internet and that, I think, has always sat alongside me against this kind of family curiosity. I suspect if my parents had been, I don't know, doctors, I would have been in your field in the field of bioinformatics and applying exponential technologies to health care. And if my parents had been engineers, I would have been doing something that intersected engineering and computing.
Harry Glorikian: Yeah, no, it's you know, it's interesting, I remember when we got our first chip, when I was first learning about, you know, computers like it was, you know, eight bits, right? And then 16 bits and oh my god, what can we do with them? And we were building them, and I actually have to get you a copy of my new book because I think if you read the first chapter and what you just said, you'll be like, Oh my God, we have more in common than we may think, even though you know you're where you are and I'm in the health care field to. But you were co-founder and CEO of a company, I believe that was called PeerIndex, which was a startup in the late 2000s. And even back then, you were trying to quantify people's influence on different social media platforms. And I'm trying to remember like, do I even know what the social media platform was back in 2000? It seems like so long ago, and you successfully sold it to Brandwatch in, like, 2014. What did that experience sort of teach you about, you know, the bigger issues and how technology impacts society and vice versa? Because I have to believe that you know your hands on experience and what you were seeing has to have changed the way that you thought about how fast this was going and what it was going to do.
Azeem Azhar: Oh, that is an absolutely fantastic, fantastic question. And. You know, you really get to the heart of all of the different things that you learn as a founder. When we when I started PeerIndex, the idea was really that people were going on to the internet with profiles that they maintained for themselves. So up until that point, apart from people who had been really early on the internet, like you and I who used Usenet and then early web pages for ourselves, no one really had a presence. And these social apps like MySpace and Twitter and LinkedIn and Facebook show up and they start to give people a presence. And we felt that initially there would be a clear problem around trying to discover people because at the time the internet was an open network. You could look at anyone's page on Facebook. There weren't these walled gardens. And we looked down on them. So we thought initially that there would be a an opportunity to build some kind of expertise system where I could say, "Listen, find me something that someone who knows something about, you know, sushi restaurants in Berlin." And it would help me find that person. I could connect their profile and talk to them because it was the really early, naive days before Facebook or LinkedIn had advertising on them. And we could we kind of got the technology to work, but actually the market was moving and we couldn't land that.
Azeem Azhar: And so we had to kind of pivot, as you do several times, ultimately, until we became this kind of influence analytics for marketers. But the few things that I learned. So the first one was how quickly new players in a market will go from being open to being closed. So it was 2011 when Facebook started to put the shutters down on its data and become a closed garden. And they realized that the network effect and data is what drove them forward. And the second thing was the speed with which what we did changed. So when we were getting going and doing all of this kind of analytics on Twitter and Facebook. They didn't really have data science teams. In fact, Twitter's first data scientists couldn't get a US visa and ended up helping, working with us for several months. And I think back to the fact that we used five or six different core technologies for our data stores in a seven-year period. And in that time, what we did became so much more powerful. So when we started, we had maybe like 50,000 people in this thing, it was really hard to get it to work. The entire company's resources went on it. At one point we were we had about 100 million people in the data in our dataset, or 100 million profiles in the data.
Azeem Azhar: They were all public, by the way. I should say this is all public data and it was just like a search engine in a way. And in order to update the index, we would need to run processes on thousands of computers and it would take a big, big, big servers, right? And it would take a day. Yeah. By the time we sold the company, a couple more iterations of Moore's Law, some improvements in software architecture, we were updating 400 million user profiles in real time on a couple of computers. Yep, so not only do we quadrupled the dataset, we had increased its, sort of decreased its latency. It was pretty much real time and we had reduced the amount of computers we needed by a factor of about 400. And it was a really remarkable evolution. And that gets me to the third lesson. So the second lesson is really all about that pace of change in the power of Moore's law. And then the third lesson was really that my engineers learned by doing. They figured out how to do this themselves. And whereas I was sort of roughly involved in the first design, by the time we got to the fifth iteration this was something of a process that was entirely run by some brilliant young members of the team.
Harry Glorikian: Yeah, I mean, you've got to actually cook something to understand how to do it and taste it and understand how it's going to come out. So your new book, The Exponential Age, came out this fall. You know, in the first chapter, you sort of identify two main problems, right? One is how do we perceive technology and then or the way we relate to technology and. Can you describe the two problems as you see them and maybe, maybe even hint a little? I don't want I don't want if people want to buy the book, I want them to buy it, but maybe hint that the solution?
Azeem Azhar: Yeah. Well, I mean, there are there are a couple of issues here, right, in the Exponential Age. The first is that technology creates all sorts of new potentials and we live them. We're doing this over Zoom, for example. Right. And there are. But the arrival of new potentials always means that there's an old system that is going to be partially or entirely replaced. And so I describe that process as the exponential gap. It is the gap between the potentials of the new and the way in which most of us live our lives. And the thing is, the reason I say "the way most of us live our lives" is because our lives, even in America, which doesn't like its sort of government, are governed by institutions and by regulations. You know, when you when you start to cook, you wash your hands, right? There's no law. That's just an institution, its common habit. If you have teenage kids like I do, you're battling with the fact that people are meant to talk over dinner, not stare at their phones. In the UK there is an institution that says on a red light traffic signal, you never turn. You wait. It's not like the US where you can do that. Now some of these institutions are codified like our traffic laws, and some are not.
Azeem Azhar: There are then more formal institutions of different types like, you know, the Fed or NATO or the Supreme Court. And the purpose of institutions, social, formal, legal, informal is to make life easier to live, right? Right, you don't have to remember to put our pants on. I will read a rule that says, put your pants on before you leave the house. It's like you just put them on and everybody kind of knows it. And there's no law that says you should or shouldn't, right. So they become very valuable. But the thing is that the institutions in general, by their nature, don't adapt to at the speed with which these new technologies do adapt. And even slower moving technologies like the printing press really upended institutions. I mean, Europe went into centuries of war just after the printing press emerged. So, so the central heart of the challenge is, on the one hand, we have these slightly magical technologies that do amazing things, but they somewhat break our institutions and we have to figure out how we get our institutions to adapt better. But there's a second complication to all of this, which is that which is, I think, more one that's about historical context. And that complication is that the way we have talked about technology, especially in the West in the last 40 or 50 years, has been to suggest that technology is deterministic.
Azeem Azhar: We're a bit like people in a pre-med, pre-science era who just say the child got the pox and the child died. We say the technology arrived and now we must use it. The iPhone arrived and we must use it. TheFacebook arrived, and we must use it. We've gotten into this worldview that technology is this sort of unceasing deterministic force that arrives from nowhere and that a few men and women in Silicon Valley control, can harness it. We've lost sight of the fact that technology is something that we as members of society, as business people, as innovators, as academics, as parents get to shape because it is something that we build ourselves. And that for me was a second challenge. And what I sought to do in the book, as I was describing, the Exponential Age is not only persuade people that we are in the Exponential Age, but also describe how it confuses our institutions broadly defined and also explain why our response has sometimes been a bit poor. Some a large part of which I think is connected to putting technology on a particular pedestal where we don't ask questions of it. And then hopefully at the end of this, I do give some suggestions.
Harry Glorikian: Well, it's interesting, right, I've had the pleasure of giving talks to different policy makers, and I always tell them like, you need to move faster, you need to implement policy. It's good to be a little wrong and then fix it. But don't be so far behind the curve that you, you know, some of these things need corralling otherwise, they do get a lot of, you know, get out of hand. Now in health care, we have almost the opposite. We're trying to break the silos of data so that we can improve health care, improve diagnosis, improve outcomes for patients, find new drugs.
Harry Glorikian: So I'm going to, I'm going to pivot there a little bit and sort of dive a little deeper into life sciences and health care, right, which is the focus of the show, right? And in the book, you you say that our age is defined by the emergence of several general-purpose technologies, which I'm totally aligned with, and that they are all advancing exponentially. And you actually say biology is one of them. So first, what are the most dramatic examples in your mind of exponential change in life sciences? And how do you believe they're affecting people's health?
Azeem Azhar: Well, I mean, if you got the Moderna or BioNTech vaccination, you're a lucky recipient of that technology and it's affecting people's health because it's putting a little nanobots controlled by Bill Gates in your bloodstream to get you to hand over all your bitcoin to him, is the other side of the problem. But I mean, you know, I mean, more seriously, the Moderna vaccine is an example that I give at the at the end of the book comes about so remarkably quickly by a combination of these exponential technologies. I'm just going to look up the dates. So on the 6th of January 2020, there's a release of the sequence of a coronavirus genome from from a respiratory disease in Wuhan. Yeah, and the the genome is just a string of letters, and it's put on GenBank, which is a bit like an open-source story storage for gene sequences. People started to download it, and synthetic genes were rapidly led to more than 200 different vaccines being developed. Moderna, by February the 7th, had its first vials of its vaccine. That was 31 days after the initial release of the sequence and another six days they finalized the sequence of the vaccine and 25 more days to manufacture it. And within a year of the virus sequence being made public, 24 million people had had one dose of it.
Azeem Azhar: Now that's really remarkable because in the old days, by which I mean February 2020, experts were telling us it would take at least 18 months to figure out what a vaccine might even look like, let alone tested and in place. So you see this dramatic time compression. Now what were the aspects at play? So one aspect at play was a declining cost of genome sequencing, which the machines are much cheaper. It's much cheaper to sequence these samples. That means that the entire supply chain of RNA amplifiers and so on a more widely available. This then gets shared on a website that can be run at very few dollars. It can get access to millions of people. The companies who are doing the work are using synthetic genes, which means basically writing out new bases, which is another core technology that's going through an exponential cost decline. And they're using a lot of machine learning and big data in order to explore the phenomenally complex biological space to zero in on potential candidates. So that the whole thing knits together a set of these different technologies in a very, very powerful and quite distributed combination.
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And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer. It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.
The book is now available in Kindle format. Just go to Amazon and search for The Future You by Harry Glorikian.
And now, back to the show.
Harry Glorikian: Let's step back here for just a minute. So I wonder if you have a thesis—from a fundamental technology perspective, what's really driving the exponential technological change, right? Do you think that that, is there a force maybe outside of semiconductors that are driving biology forward? What's your view? I mean, if you took the computational tools away from life sciences and drug developers, would we still see the same rapid advances in that area, and the answer could be no, because I can tell you my thoughts after you tell me yours.
Azeem Azhar: Well, we wouldn't see the same advances, but we would still see significant advances and it's hard to unpack one from another. But if you look at the I mean, you worked on the genome sequencing stuff. So you know that there's a lot of interesting aspects to do with the reagents that are used the electrochemistry, the arrays and making little ongoing improvements in those areas. There are also key improvements in the actual kind of automation of the processes between each to each step, and some of those automations are not, they're not kind of generalized robots, soft robots, they are trays that are being moved at the right time from one spot to another, stop on a kind of lab bench. So you'd still see the improvements, but you wouldn't see the same pace that we have seen from computing. And for two reasons. So one is that kind of the core ability to store lots of this data, which runs into the exabytes and then sift through it, is closely connected to storage capacity and computation capability. But also even the CAD package that the person used to redraw the designs for the new laboratory bench to handle the new vials of reagents required a computer. But yes, but you know, so what? What's your understanding as someone who is on the inside and, note to listener, that was a bit cruel because Harry is the expert on this one!
Harry Glorikian: And oh no, no, no, no. I, you know, it's interesting, right… I believe that now that information is more readily available, which again drives back to sensors, technology, computation, speed as well as storage is changing what we do. Because the information feeds our ability to generate that next idea. And most of this was really hard to get. I mean, back in the day, I mean, if you know, now I wear a medical device on my on my wrist. I mean, you know this, I look as a as a data storage device, right? Data aggregation device. And this I look at it more as a coach, right? And but the information that it's getting, you know, from me on a momentary basis is, I mean, one of the companies I helped start, I mean, we have trillions of heartbeats, trillions. Can you imagine the analytics from a machine learning and, you know, A.I. perspective that I can do on that to look for? Is there a signal of a disease? Can I see sleep apnea or one of the I could never have done that 10 years ago.
Azeem Azhar: I mean, even 10, how about I mean, five maybe, right? I mean, the thing that I find remarkable about about all of this is what it's told me. So I went from I used to check my bloods every year and so I would get a glucose reading or an insulin reading every year. I then put a CGM on continuous glucose monitor and I wore it for 16 to 18 weeks and it gave me a reading every 15 months minutes. So I literally went from once a year, which is 365 times 96, 15 minute intervals. So it's like a 40,000-fold improvement. I went to from to that every 15 minutes, and it was incredible and amazing and changed my life in so many good ways, which I'm happy to go into later. But the moment I put the 15 minute on, I kid you not, within an hour I was looking for the streaming cGMPs that give you real time feed. No 15-minute delay. And there is one that Abbott makes through a company, sells through a company called Super Sapiens. But because suddenly I was like a pilot whose altimeter doesn't just tell them you're in the air or you've hit the ground, which is what happened when I used to go once a year, I've gone to getting an altitude reading every minute, which is great, but still not brilliant for landing the plane to where I could get this every second. And this would be incredible. And I find that really amazing. I just I just and what we can then do with that across longitudinal data is just something else.
Harry Glorikian: We're totally aligned. And, you know, jumping back to the deflationary force of all this. Is. What we can do near-patient, what we can do at home, what we can do at, you know, I'll call it CVS, I think by you, it would be Boots. But what these technologies bring to us and how it helps a person manage themselves more accurately or, you know, more insightfully, I think, brings us not to chronic health, but we will be able to keep people healthier, longer and at a much, much lower cost than we did before because. As you know, every time we go to the hospital, it's usually big machines, very expensive, somebody to do the interpretation. And now if we can get that information to the patient themselves and AI and machine learning can make that information easier for them to interpret. They can actually do something actionable that that that makes a difference.
Azeem Azhar: I mean, I think it's a really remarkable opportunity with a big caveat that where we can look at look historically, so you know, we're big fans of the Hamilton musical in my household. And if you go back to that time, which is only a couple of hundred years ago and you said to them, this is the kind of magic medicine they'll have in the US by 2020. I mean, it's space tech. It's alien space tech. You know, you can go in and we measure things they didn't even know could be measured, right, like the level of antibodies in the bloodstream. And you can get that done in an hour almost anywhere, right? Yeah. And it's really quite cheap because GDP per capita in the per head in the US is like $60,000 a year. And I can go and get my blood run. A full panel run for $300 in London, one of the most expensive cities in the world. 60 grand a year. $300. Well, surely everybody's getting that done. And yet and you know this better than me. Right. You know this better than me that despite that, we don't have everyone getting their bloods done because it's just so cheap, right, there are other structural things that go on about who gets access, and I think America is a great example of this because for all the people who read, we are aware of Whoop, and have, you know, biological ages that are 10 years younger than their chronological age, you've also got like a much, much larger incidence of deaths by drug overdose and chronic obesity and sort of diseases of inflammation and so on. And that's despite having magical the magical space technology of the 2020s. So the question I think we have to have is why would we feel that next year's optoelectronic sensors from Rockly or the Series 7 or Series 8 Apple Watch will make the blindest bit of difference to health outcomes for the average American.
Harry Glorikian: Now, I totally agree with you, I mean, I think half of it is education, communication. You know, there's a lot of social and political and policy and communication issues that exist, and actually that was going to be my next, one of my next questions for you, which is: What are some of the ways that exponential change challenges our existing social and political structures? And you know, do you see any—based on all the people that you've talked to, you know, writing the book, et cetera—insights of how we're going, what those are and maybe some ideas about how we can move beyond them.
Azeem Azhar: Hmm. Well, I mean, on the health care side, I think one of the most important issues is and this is I mean, look, you've got an American audience and your health system is very different to, let's just say everyone.
Harry Glorikian: Actually, the audience is global. So everybody, I have people that all over the world that listen to this.
Azeem Azhar: Fair enough. Okay. Even better, so the rest of the world will understand this point, perhaps more, which is that, you know, in many place parts of the world, health care is treated as not, you know, it's treated differently to I take a vacation or a mutual bond that you buy, right or a car, it's not seen purely as a kind of profit vehicle. It's seen as something that serves the individual and serves a community and public health and so on matters. And I think one of the opportunities that we have is to think out for it, look out for is how do we get the benefits of aggregated health data, which is what you need. You need aggregate population wide data that connects a genotype to a phenotype. In other words, what the gene says to how it gets expressed to me physically to my biomarkers, you know, my, what's in my microbiota, what my blood pressure is on a minute by minute basis and my glucose levels and so on. And to whatever illnesses and diseases and conditions I seem to have, right, the more of that that we have, the more we can build predictive models that allow for the right kind of interventions and pre-habilitation right rather than rehabilitation. But in order to do that at the heart of that, yes, there's some technology. But at the heart of that is how do we get people's data in such a way that they are willing to provide that in a way that is not forced on them through the duress of the state or the duress of our sort of financial servitude? And so that, I think, is something that we really, really need to think about the trouble that we've had as the companies have done really well out of consumer data recently.
Azeem Azhar: And I don't just mean Google and Facebook, but even all the marketing companies before that did so through a kind of abusive use of that data where it wasn't really done for our benefit. You know, I used to get a lot of spam letters through my front door. Physical ones. I was never delighted for it, ever. And so I think that one of the things we have to think, think about is how are we going to be able to build common structures that protect our data but still create the opportunities to develop new and novel therapeutic diagnosis, early warning systems? And that's not to say there shouldn't be profit making companies on there that absolutely should be. But the trouble is, the moment that you allow the data resource to be impinged upon, then you either head down this way of kind of the sort of dominance that Facebook has, or you head down away the root of that kind of abuse of spam, junk email and so on, and junk physical mail.
Azeem Azhar: So I think there is this one idea that that emerges as an answer, which is the idea of the data commons or the data collective. Yeah. We actually have a couple of them working in health care in in the U.K., roughly. So there's one around CT scans of COVID patients. So there's lots and lots of CT scans and other kind of lung imaging of COVID patients. And that's maintained in a repository, the sort of national COVID lung imaging databank or something. And if you're if you're an approved researcher, you can get access to that and it's done on a non-commercial basis, but you could build something commercially over the top of it. Now the question is why would I give that scan over? Well, I gave give it over because I've been given a cast-iron guarantee about how it's going to be used and how my personal data will be, may or may not be used within that. I would never consider giving that kind of data to a company run by Mark Zuckerberg or, you know, anyone else. And that, I think, is the the cross-over point, which is in order to access this, the benefits of this aggregate data from all these sensors, we need to have a sort of human-centric approach to ensure that the exploitation can happen profitably, but for our benefit in the long run.
Harry Glorikian: Yeah, I mean, I'm looking at some interesting encryption technologies where nothing is ever unencrypted, but you can, you know, the algorithm can learn from the data, right? And you're not opening it up. And so there, I believe that there are some solutions that can make give the side that needs the data what they need, but protect the other side. I still think we need to policymakers and regulators to step up. That would cause that shift to happen faster. But you know, I think some of those people that are making those policies don't even understand the phone they're holding in their hands most of the time and the power that they're holding. So. You know, last set of questions is. Do you think it's possible for society to adapt to exponential change and learn how to manage it productively?
Azeem Azhar: It's a really hard question. I'm sure we will muddle through. We will muddle through because we're good at muddling through, you know? But the question is, does that muddling through look more like the depression years. Or does that muddling through look like a kind of directed Marshall Plan. Because they both get through. One comes through with sort of more productive, generative vigor? What I hoped to do in the book was to be able to express to a wider audience some underlying understanding about how the technologies work, so they can identify the right questions to to ask. And what I wanted to do for people to work in the technology field is draw some threads together because a lot of this will be familiar to them, but take those threads to their consequences. And in a way, you know, if I if I tell you, Harry, don't think of an elephant. What are you thinking about right now?
Harry Glorikian: Yeah. Yeah, of course it's not, you know, suggestive.
Azeem Azhar: And by laying out these things for these different audiences in different ways, I'm hoping that they will remember them and bear those in mind when they go out and think about how they influence the world, whether it's decisions they make from a product they might buy or not buy, or how they talk influence their elected officials or how they steer their corporate strategy or the products they choose to build. I mean, that's what you would you would hope to do. And then hopefully you create a more streamlined approach to it to the change that needs to happen. Now here's the sort of fascinating thing here, is that over the summer of 2021, the Chinese authorities across a wide range of areas went in using a number of different regulators and stamped on a whole set of Exponential Age companies, whether it was online gaming or online education. The big, multi sided social networks, a lot of fintech, a lot of crypto. And they essentially had been observing the experiment to learn, and they had figured out what things didn't align with their perceived obligations as a government to the state and to the people. Now, you know, I'm using that language because I don't want this to become a kind of polarized sort of argument.
Azeem Azhar: I'm just saying, here's a state where you may not agree with its objectives and the way it's accountable, but in its own conception, it's accountable to its people and has to look out for their benefit. And it took action on these companies in really, really abrupt ways. And. If you assume that their actions were rational and they were smart people and I've met some of them and they're super smart people, it tells you something about what one group of clever people think is needed at these times. This sort of time. And I'm not I'm not advocating for that kind of response in the US or in Western Europe, but rather than to say, you know, when your next-door neighbor, and you live in an apartment block and your next-door neighbor you don't like much runs out and says the whole building is on fire. The fact that you don't like him shouldn't mean that you should ignore the fact that there's a fire. And I think that some sometimes there is some real value in looking at how other countries are contending with this and trying to understand the rationale for it, because the Chinese were for all the strength of their state, were really struggling with the power of the exponential hedge funds in their in their domain within Europe.
Azeem Azhar: The European Union has recognized that these companies, the technologies provide a lot of benefit. But the way the companies are structured has a really challenging impact on the way in which European citizens lives operate, and they are making taking their own moves. And I'll give you a simple example, that the right to repair movement has been a very important one, and there's been a lot of legislative pressure in the in Europe that is that we should be have the right to repair our iPhones and smartphones. And having told us for years it wasn't possible suddenly, Apple in the last few days has announced all these repair kits self-repair kits. So it turns out that what is impossible means may mean what's politically expedient rather than anything else. And so my sense is that that by engaging in the conversation and being more active, we can get ultimately get better outcomes. And we don't have to go the route of China in order to achieve those, which is an incredibly sort of…
Harry Glorikian: A draconian way. Yes.
Azeem Azhar: Yeah. Very, very draconian. But equally, you can't you know where that where I hear the U.S. debate running around, which is an ultimately about Section 230 of the Communications Decency Act, and not much beyond that, I think is problematic because it's missing a lot of opportunities to sort of write the stuff and foster some amazing innovation and some amazing new businesses in this space.
Harry Glorikian: Oh yeah, that's, again, that's why, whenever I get a chance to talk to policymakers, I'm like, “You guys need to get ahead of this because you just don't understand how quickly it's moving and how much it's going to impact what's there, and what's going to happen next.” And if you think about the business model shifts by some of these... I mean, what I always tell people is like, okay, if you can now sequence a whole genome for $50 think about all the new business models and all the new opportunities that will open up versus when it was $1000. It sort of changes the paradigm, but most people don't think that we're going to see that stepwise change. Or, you know, Google was, DeepMind was doing the optical analysis, and they announced, you know, they could do one analysis and everybody was like, Oh, that's great, but it's just one. And a year later, they announced we could do 50. Right? And I'm like, you're not seeing how quickly this is changing, right? One to 50 in 12 months is, that's a huge shift, and if you consider what the next one is going to be, it changes the whole field. It could change the entire field of ophthalmology, especially when you combine it with something like telemedicine. So we could talk for hours about this. I look forward to continuing this conversation. I think that we would, you know, there's a lot of common ground, although you're I'm in health care and you're almost everywhere else.
Azeem Azhar: I mean, I have to say that the opportunity in in health care is so global as well because, you know, if you think about how long and how much it costs to train a doctor and you think about the kind of margin that live that sits on current medical devices and how fragile, they might be in certain operating environments and the thought that you could start to do more and more of this with a $40 sensor inside a $250 smartwatch is a really, really appealing and exciting, exciting one. Yeah.
Harry Glorikian: Excellent. Well, thank you so much for the time and look forward to staying in touch and I wish you great success with the book and everything else.
Azeem Azhar: Thank you so much, Harry. Appreciate it.
Harry Glorikian: That’s it for this week’s episode.
You can find past episodes of The Harry Glorikian Show and the MoneyBall Medicine show at my website, glorikian.com, under the tab Podcasts.
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