304: Jo Bhakdi On The Future Of Genomic Sequencing

Jo Bhakdi is the founder and CEO of Quantgene, a company deploying the world’s leading Deep Genomics solution. Their mission is to save lives through early detection, better prevention and more effective cures for all disease, starting with cancer. In this conversation, we discuss machine learning, sequencing technology, and DNA extraction procedures defines the cutting edge of genomic diagnostics, early disease detection, and precision medicine. ========================= Pomp writes a daily letter to over 50,000 investors about business, technology, and finance. He breaks down complex topics into easy to understand language, while sharing opinions on various aspects of each industry. You can subscribe at www.pompletter.com =======================

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Anthony Pompliano?

  What's up everyone, this is Anthony Pompliano. Most of you know me as Pomp. You're listening to the Pomp Podcast, simply the best podcast out there. Let's kick this thing off. Joe Backd is the founder and CEO of QuantGen, a company deploying the world's leading deep genomic solution. Their mission is to save lives through early detection, better prevention, and more effective cures for all disease, starting with cancer. In this conversation, we discuss machine learning, sequencing technology, and DNA extraction procedures, defining the cutting edge of genomic diagnostics, early disease detection, and precision medicine. I learned a ton from this conversation, and I hope you enjoy it.

• 0:41Anthony Pompliano?

  Don't forget that I write a daily letter to over 50,000 investors about business technology and finance every morning. I break down complex topics into easy to understand language while sharing opinions on various aspects of each industry. You can subscribe at pompletter.com. Again, pompletter.com, or go into the description and click the link there. All right, let's get into this episode with Joe. I hope you guys enjoy this one.

• 1:08Speaker 01

  Anthony Pompliano is a partner at Morgan Creek Digital. All opinions expressed by Pomp or his guests on this podcast are solely their opinions and do not reflect the opinions of Morgan Creek Digital or Morgan Creek Capital Management. You should not treat any opinion expressed by Pomp as a specific inducement to make a particular investment or follow a particular strategy, but only as an expression of his opinion. This podcast is for informational purposes only.

• 1:34Anthony Pompliano?

  All right, guys, bang, bang. Super excited to have this conversation with Joe here. So thanks so much for coming on and doing this. It's a pleasure, Anthony. Great to be here. Yeah, let's just get started with your background. Obviously, you guys are working on some very cutting edge medical and technology-related products, but what did you do beforehand to really get started down this path?

• 1:57Speaker 02

  Well, my background to understand how I got to this point to, you know, build Quantient together with my amazing team is that I come from a very medical and bioscience families. I was born to, you know, my parents were both bioscientists, very passionate microbiology and other life science related things, not in business, just science. And yeah, I grew up in a lab basically with my dad, learned about PCR machines when I was two, without really understanding anything, but I kind of saw it. And then when it came down to choosing my, you know, college course, what I would study, I actually wanted to get out of that.

• 2:39Speaker 02

  I thought like medicine, I've seen that enough for nineteen years of my life. I like it, it's cool, but I don't know what else to learn here. And so I basically decided, let's become an economist. I was very interested in business and also economics. And so that led me down a kind of more the business path, went into kind of strategy, consulting, then murder, acquisitions and a little finance, then quantitative finance. And I found my passion for everything quantitative, complex systems, but especially fuzzy, you know, probabilistic systems, which are important in finance and in venture capital. And then it came full circle when a family member who's also a doctor and bioscientist

• 3:19Speaker 02

  asked me for advice on a big math and statistics problem in genomics for cancer related things. And that was in 2014 and I really got sucked into that. I was at bedtime working on a kind of quantitative venture capital framework, this is slight focus also on life sciences, on the investment side. But that project kind of really triggered my interest because the deeper I dug into that question, how can you profile DNA, not fragments, but whole genomes and kind of DNAs from isolated potential tumor cells? How could you determine if something is a tumor cell if you just look at the DNA?

• 4:05Speaker 02

  Which seemed like a trivial question back then, but the more I dug into it, the more I realized this is a very complicated question.

• 4:12Speaker 02

  And I asked a bunch of friends at, you know, Berkeley and Stanford and all kinds of institutions where I knew they know a lot of things and none of them could answer the question. And then we had some good ideas and they said, well, that's definitely not something anyone has done so far.

• 4:27Speaker 02

  And we kind of solved a very important math statistics question in genomics. And that triggered basically the foundation of the creation of quantine because I realized, well, if you can solve that problem and no one has solved it, we can detect cancer at early stages in the blood. Because then we had the tools to basically identify specific locations on the genome necessary to detect cancer comprehensively.

• 4:54Anthony Pompliano?

  Got it.

• 4:56Anthony Pompliano?

  So maybe let's go deep into the science here because I think a lot of people listening, they recognize that they don't understand the science nearly as well as you do. So help us understand when you're talking about DNA, like just what is that to get started with? And then we can maybe talk about how certain cells are mutated or kind of die through disease and how disease really kind of runs through the body and what that does to the bloodstream and to those DNA cells.

• 5:25Speaker 02

  Sure. So as a first step, I think it's always important to see what cancer detection and cancer is today. It's an enormous problem. Six hundred thousand people die every single year in the United States alone of cancer. One point six million get diagnosed with cancer. Each of us has a forty percent lifetime chance of getting cancer. It's pretty severe. And the ten, over ten percent chance of dying of cancer. So it's probably one of the largest risks each of us has. And early detection of cancer is a huge game changer. If you look at survival statistics, the difference between, for example, breast cancer detected in stage one versus stage four is very massive.

• 6:07Speaker 02

  Ninety-nine percent of women have a five-year-plus survival at stage one, nine percent survival at stage four. So it's a massive difference, you know, what your chances are because if it's detected early, you just go into a hospital, get surgery, and you're cancer-free in many cases. Whereas if they said it's like a, it's terrible. It's an ordeal that goes on years and years of chemotherapy and very bad outcomes. So you wanted to detect it early. Now genomics is something that was not used or is still not really used in, in cancer detection. It is something where we do hereditary testing more and more, which means we look at your healthy genome.

• 6:48Speaker 02

  Right? So all your cells have a genome, a DNA, and that is kind of the building blocks of life. Our proteins and the proteins are encoded in the DNA. So the DNA is being read out, turned into proteins, and we have like roughly 20,000 proteins in the body, different ones. And that determines who you are. Now, that is called hereditary genetics. So all your cells have the same DNA. We can look at that DNA very easily nowadays and then tell you if you have certain predispositions. Right? If you have certain variants that other people don't have, you might be more protected or less protected against certain cancers. So that's the first step of genetics into cancer that we can tell you if you have, for example, BRC A1 variants, specifically ones in.

• 7:34Speaker 02

  As a woman, your, your chance of breast cancer is much, much higher that you get that over your lifetime. Because something in your cell metabolism is less likely to protect you.

• 7:44Anthony Pompliano?

  And how, how do we find those variances, right? So like, we know that now if you have that variance, you have a higher probability of getting breast cancer for a woman, for example. But how are those discovered?

• 7:56Speaker 02

  You mean how we scientifically discover the initial insight that there is a problem? Or how do we find it in your body?

• 8:04Anthony Pompliano?

  The initial insight that that is a, an indicator.

• 8:08Speaker 02

  Yeah, so there are multiple ways of doing that. There's a mechanistic way where you basically identify a variant and then you try to find out what that actually does. Right? So you can see, okay, if that variant is on the DNA, how does the protein change? Right? And you can say, well, if the protein changes and that protein, for example, is a tumor suppressor gene. Like TP53 and it's being changed, then it's a loss of function effect, for example. Then the protein stops working. And if that protein is designed to kill a cell if it becomes a tumor cell. Right? Like a guard that is in the cell waiting for something to go wrong. And that guard becomes disabled, then we know, okay, that will probably lead to cancer.

• 8:52Speaker 02

  There are lots of other examples, like there are tons of things that you can do. That's one way of figuring it out. Another way is population health studies, where you have basic association studies. So you look at millions or thousands of people. And you see, well, in the subgroup of people who have that variant, breast cancer is a hundred times more prevalent. Like often, it's not a hundred, it's ten times or something. And then it's more, you know, correlation versus causation. No one knows exactly what's going on, but you derive from that, well, probably bad news if you have that variant, we don't know exactly what it does. But you're in that new bucket and we see that people in that bucket have a hundred, three hundred, a thousand percent more

• 9:38Speaker 02

  a chance of developing a disease, so you should be cautious and be on the watch out.

• 9:42Anthony Pompliano?

  Got it. And really what this gets at is something I find fascinating personally, which is just, we really don't understand the human body that well, right? We understand a lot, but it is such a complex machine that we're still learning things every single day. And so we're kind of doing the best we can with the information we have, right?

• 10:02Speaker 02

  And that's exactly why we built Quantrine and Serenity, our kind of Asian facing brand. I think there's a big misconception about science and medicine. And multiple misconceptions, but one of the biggest ones is that we know a lot. Like, first of all, that's always a dumb assumption. We don't even know, like we'd know, we don't know a lot about anything. Physics or chemistry, this is all like the, all these scraping the surface of we are lucky. But in biology, it's especially extreme, I think, when you are in that field. Like every single time you go below the surface and you say, oh, this, you know, causes cancer or something. And then you dig deeper into the protein and the protein change.

• 10:46Speaker 02

  The number of proteins we actually understand what they do in cancer is probably maximum a handful, probably more of one or three, like K-RAS or, you know. B fifty three or something. I mean, even there, it's very unclear. All the details are extremely unclear. And then you dig one layer deeper and you have only questions and no answers. And, you know, every good scientist would tell you, well, we simply don't know. Like, you have to run a bunch of experiments. Once you run the experiments, you probably still don't know, and you only have some statistical probabilistic insight. And I think that's very important in medicine. It's very different from,

• 11:26Speaker 02

  for example, building rockets, right? Anything that's kind of artificially created by humans, we have a much better understanding of. If you build a computer or a rocket, you have a pretty comprehensive understanding because you build it. Everything that's naturally occurring, you have just no clue. Like, when you look at the body, if you're lucky, you know, like zero point zero one percent of what's actually going on. And it's very clear, you know, the deeper you dig, there are just vast areas of unknowns.

• 11:55Anthony Pompliano?

  Yeah. And so would it be fair to say that in a lot of the work you guys are doing, obviously, is one trying to solve some of this, but the two, just in the broader science and health industry, is that we're really focused on just trying to understand what's going on, right? Right? The solutions only come after we understand. And so that is really kind of the the base layer. And a lot of this biotechnology and things that are being seen as breakthroughs, really, it's just identifying something that's been going on in humans for, you know, thousands of years. We're just now starting to understand, given the technologies that we have.

• 12:30Speaker 02

  Absolutely. And I think there is a huge paradigm shift going on in medicine right now. Very slowly going on. So that's why we created a company to accelerate that. But the paradigm shift is the medicine of the past and the current medicine.

• 12:45Speaker 02

  The way of doing medicine is is very static, right? You go to med school, you learn a bunch of things, and you think you know something, which is a big mistake. And I think the new paradigm in medicine that news, serenity and quarantine stand for is that it's a much more dynamic system that is an equation of basically three things. Data, you need a lot of data, you need intelligence, you need to make sense of the data, and then action or systems, right? You need to then take some form of action and have some kind of infrastructure to do treatments or more diagnostics. That's not how medicine works right now. Medicine right now is static knowledge. You're like, if that, then do that. Whereas, you know, I give you one example, if you do a conventional blood test,

• 13:29Speaker 02

  PSA, for example, for prostate-specific antigen, you get one number. It says, oh, seven. And they say, oh, it's above the threshold, I might have a problem, maybe not. When we run a test on you, especially the somatic mutational test for cancer detection,

• 13:48Speaker 02

  which we didn't get into yet, that's a different thing, we're not looking at your health. We get six billion data points. So we get six billion individual nucleotide reads out of that sample. And then we have a very complicated cloud-based, you know, software and AI system that makes sense of that data. And we are building Bayesian layers on top of that to take in all additional information around you. And that system isn't perfect, but it gives you an idea, you know, what we are dealing with here. That system changes every week, like on a deeper level, because it learns from patients and studies. It gives us literally six billion times more data points than a normal sample.

• 14:33Speaker 02

  And it's operated on a different medical paradigm. And the medical paradigm is more data is always better and more intelligence. And maybe you as someone who's in tech and business, you think like, why is that like a thing like, isn't that obvious? Of course, more data. But in medicine, you know, the majority of physicians would say, no, that's not true. More data is always often bad. Because more data means I may have to make decisions that I don't want to make. Because if that test comes back positive, I have to do some things according to standard of care. Therefore, I would prefer not to do the test.

• 15:09Speaker 02

  And for non-medical professionals, it sounds absurd, especially for tech and finance, like, how is this makes any sense? But it's about standard procedures, it's about liabilities. If I don't see it, I can't do anything wrong. And that sounds ridiculous maybe to you and many listeners, but it's literally what most doctors would tell you. Like, no, it's a young patient, don't do their tests. Because if it comes back positive, I have to do all these things, then that would be wrong to do, because it's a young patient. And then you would argue, but if you know it's wrong to do, why do you do it? It's like, well, because it's standard procedure and I don't want to get into trouble. You know, that's so let's just not measure anything.

• 15:47Anthony Pompliano?

  It's absolutely wild how the healthcare system works.

• 15:50Speaker 02

  It's it is wild and it's it's especially wild because you meet so many dedicated people who are high IQ, super trained and knowledgeable that do all these stupid things and know they are stupid. And see, I know it's stupid. That's why I don't want to do it. So it's this absurd system where we have so much talent and intelligence that is kind of, yeah, in chains. They can't like, they can't do what they should and want to do. And that's why it's so important to break out and build companies that don't have to adhere to these, you know, standard procedures, but can really strongly innovate with the patient interest in mind.

• 16:27Anthony Pompliano?

  Yeah. And so let's talk a little bit about deep genomic sequencing, right? Because I think that's a term that, at least genomic sequencing, people have heard before. They, everything from high level, just like what that is, all the way to they get to things around genomic editing and kind of all the scary stuff in most people's minds. So maybe you can just start off with just like, what is deep genomic sequencing? And then we can start to talk about like, once that's done, all of the different applications of that information.

• 16:59Speaker 02

  Yes, and that's exactly where the rubber hits the road. So what I explained before, hereditary testing, that is the old school sequencing that still hasn't reached most of medicine, by the way. So old school, I mean, it's five years ago. It's still still only penetrated probably 5% of the of medicine. So we need to do that. That's very important. But what that does is it looks at your healthy genome. And to do that on a technical level, you know, you're sequencing something with your depth of 10 to 30 X. So what does it mean? It means if you have a DNA strand and you have a specific location, you want to take, you know, 10 DNAs and look at specifically that location.

• 17:43Speaker 02

  And then you build an average and say it's a T, T, T, T, G, A, T. Right? So on the same location across 10 of these. And then you say, well, an average is a T. So you have a T. Right? So it accounts for some sequencing errors. So you do it 10 times, maybe you do it 30 times. That's how you normally sequence yourselves. You basically sample a random ten to thirty DNA copies and look at the same location thirty times and tell you the average and say, "Well, Anthony, you have a T here." And that makes you regular, or it makes you different, whatever it is. Now, what deep genomics is, and Quanqing is the global leader in precision sequencing.

• 18:24Speaker 02

  So we have the most precise sequencing technology that can do the following. If you have a blood sample and you have, let's say, two thousand copies of DNA in that blood sample, of cell-free DNA, so two thousand copies that stem from two thousand different cells in your body that died and shed their DNA into the blood. Our technology can make sure with a very high level of confidence that we can investigate every single copy. So instead of picking thirty randomly out and telling you what the average is, we can tell you, "Well, we sequenced all two thousand individual copies." And for each of these copies, we can say, "This is a T, this is the T, this is the T,

• 19:08Speaker 02

  but here's a G, and here's a T," and so on. And what that allows you to do, that's a total game changer, it allows you to say, if there's any single copy in there from a cell that carried a somatic mutation. So a somatic mutation is a mutation that is not normal for your body. That's not your healthy DNA. It is something where a body cell that you have is mutated, has changed.

• 19:36Speaker 02

  Very different from a hereditary variant. So it might be that if you have blue eyes or green eyes or something, you have a G where other people have a T on that location, but all your cells have that. That's why it's you. And we ask, what we are doing, we want to identify of all the DNA, cell-free DNA in a sample, is there a single copy that stems from a single tumor cell? And that gives you a level of precision. That is completely paradigm shifting because before in cancer detection. You have protein tests. They need literally millions or billions of proteins in that sample to see any kind of filter. If you have a PSA of seven versus one, I can't tell you the exact number, but we're talking about millions to billions of

• 20:21Speaker 02

  of extra PSA proteins. You need to have in there to see that difference.

• 20:25Anthony Pompliano?

  Would it be fair to say as you're looking at the the blood of of an individual? Would it be fair to say that you're grabbing the DNA cells out of that blood? And when you're looking, if you're only using a sample, so let's say 10 or 30 or 50 or whatever the number is. You could actually miss one of the mutated cells because it doesn't end up being in your random sample. So by actually sequencing all of them, you're not only one becoming more accurate, but two, it sounds like this precision technology you have actually lets you test every single one of them, right? So it's kind of precision leads to higher degrees of accuracy.

• 21:05Speaker 02

  Exactly. So it's very simple. The math is simple. If you have a tumor in your body, early stage, right? That tumor at stage one has 100 to 200 million cells. That's a stage one tumor. That's somewhere in your body. Out of these cells, a certain number dies every day, actually a very significant number, right? So if you have 200 million, at least two to three million actually turnover per day. So they shed their DNA into your blood. If you take 20 milliliters of your blood, that's roughly, you know, 0.4% of your blood. So it's not non trivial, right? It's nearly half a percent of your entire blood if you take two big groups.

• 21:43Speaker 02

  And so you can ask the question, if you know, you have, let's say, be conservative, a million of these tumor cells die every single day and shed their DNA to the blood. Because it gets digested, so you have to divide it by 24 hours and then see how much is actually circulating it every

• 22:01Speaker 02

  minute. But you still, if you do the math, you still get to a point where it's very likely that you have 10, 20, 30 circulating tumor DNA copies in in one tube.

• 22:12Speaker 02

  So for us, but you have thousands and thousands of other cells in the tube of cell for DNA that no messes this up. So if you would randomly pick out, you know, 10 copies, that's not enough. You are very likely to miss anything in there. If it's at that concentration. So you have to make sure that you look at everything in that tube. And that is something even five years ago, that sounded like complete science fiction. We five years ago already anticipated, you know, where sequencing will be now. So we knew it's definitely possible. And so we started developing everything so we can do it now. But 10 years ago, I think 95% of scientists would have said that's complete science fiction.

• 22:57Speaker 02

  That's like a walk drive or something.

• 22:59Anthony Pompliano?

  Why did they, why did they believe that though? Right? So they, they said, hey, that's science fiction. But was it because they couldn't actually identify all of the cells in the bloodstream? Was it something around their ability to scale the testing of each cell? Like, like, why did they actually think that that was not possible?

• 23:19Speaker 02

  Because back 10 years ago, you know, you need basically three things to really make that happen. Yeah, well, four things you need some improvements or this like you, you need some quantum leap insight advances in chemistry or in strategy, how you sequence. So it was just completely not possible 10 years ago. But then people found some cool things out about how you can actually barcode DNA. So to, to reduce errors and sequencing. That's one important thing that you needed an invention that we couldn't have made like someone came up with it randomly. Without that, it's not possible. But it's also a quantitative infrastructure problem. You need sequencing technologies that are thousands of times more efficient than 10 years ago.

• 24:04Speaker 02

  And we just happen to have them now, thanks to Illumina and some other companies. You need massive computing power that 10 years ago, most bioscientists would have said, that's just not possible to ever get there. And guess what? You know, things, you know, we have massive improvement in computing power. You're reading like 200 gigabytes in the cloud, in a cloud system per sample. As opposed to one bit, right? If you, I mean, if you have a PSA test and it says seven, no, you need a byte, I think, for that. So you need one byte and we, we have 200 gigabyte files.

• 24:39Speaker 02

  So just that delta, you know, you need multiple improvements on multiple fronts that are very massive. And so, I mean, even in 2015, when we did the math on that, um, the costs for one sample, based on the sequencing technology back then, would have been roughly a million dollars, for example.

• 24:59Anthony Pompliano?

  Per sample?

• 25:00Speaker 02

  Yes. And today, you know, we are far below a thousand.

• 25:05Anthony Pompliano?

  Yeah. And so help me understand, now that you're able to test uh, every single cell that you find in the blood, uh, and do it with an increased amount of accuracy, what does that allow you to identify, uh, or kind of do that for me as a patient or an individual? Um, I either, one, couldn't get access to before from an information standpoint, or two, can you tell me, hey, you've got a higher probability, or you actually have cancer before any other test can identify it? Like, how do you put this into application now that you have the technology to do it?

• 25:40Speaker 02

  So very different from hereditary testing, it just tells you what your probability is of getting something over your lifetime. So genetic testing tells you if you currently have a tumor, which is, of course, vastly more actionable.

• 25:55Speaker 02

  So what it does is, it creates very complex, deep genomics patterns. So what a pattern means is, we look at, you know, multiple tens of thousands of locations that we predetermined before, so we know, if you look at all of them, you have very good chance of intercepting any kind of tumor variant across 15 different cancers. Um, so for each of these locations, we then get what's called a frequency, right? Because it's not yes or no, it's like, how many mutated fragments do we find? And how many non-mutated fragments? So we see at that specific location, we have 0.1% carrier cancer-associated mutation in your blood. At the next location, 0.56% carries something.

• 26:39Speaker 02

  So that gives you a two-dimensional pattern, right? A deep genomics two-dimensional pattern. And these patterns code for diseases. Of course, the question is for what diseases and how do they go for it? And that's the big statistics and I know research question. So what we did is we are running very large patient trials. We have a 10,000 patient trial going on right now, 15 different cancer types and a control cohort, so of non-diagnosed patients. And what, you know, you have to build machine learning around that. That is also hard to build because you have to do many things differently from self-driving cars. Or, you know, recommendation engines is just a totally different problem. You have to build very different things.

• 27:22Speaker 02

  And we run your pattern against the historic dataset. So we have your unique pattern and we run that against all the other patterns, like pancreatic cancer, early-stage, late-stage colorectal cancer, early-stage, late-stage, and so on. And control patients, patients that don't have cancer but are smokers, patients that are not smokers, not cancer but obese. And so we basically run that against all these different cohorts and get a match back. And that match tells us, well, Anthony looks most like X and second most like Y. And then you have a Bayesian layer above that that basically says, well, now we know the genomics match. Now we also take into consideration your age, your gender,

• 28:07Speaker 02

  other background information we have to put additional probabilistic layers, probability layers on top of it. And then we get the final call that says, well, considering all these things, the system thinks it's most likely nothing, hopefully, or it's most likely red flag for early-stage colon cancer, which doesn't mean you have colon cancer, but it means, well, there is something now to watch out and takes potentially some action. And what's very important for us is you have to not just develop technology here. You have to think about medical practice because you can't just give that to a patient and say, okay, good luck. You have to say, okay, what do we do now? And that creates all kinds of liabilities, regulatory issues, all kinds of things.

• 28:52Speaker 02

  So what we are building is a system that solves the entire equation for the patient. So we say, well, based on all these things, we think you should do a stool test. Even though normally you were not supposed to do that. Because you have enough risk here. That we say if we do a stool test, like poop in a bucket, you know, with some genomics test on top of it. We can rule out colon cancer with a high probability. Or rule in, right, whatever it is.

• 29:22Anthony Pompliano?

  Yeah, and so what I'm taking away from this is there's a couple of different steps here, right? So the first is what I'll call just collection of the raw materials. So you have to actually get the blood or whatever it is. Then there's an actual scientific process that is identifying the cells and kind of having that precision technology or science that you talked about. And now what you're talking about is this third step, which is really just data analytics, right? It's very kind of high level, it's very large data sets, but it's a something machine learning type problem where you ultimately can say, hey, we have confidence that we collected the samples from the people that we think we did.

• 30:03Anthony Pompliano?

  We've got confidence that we were able to use our precision technology to get accurate identification. Kind of the raw data that we have or feeding to the machine is accurate. And then if those machine learning algorithms and kind of computers are structured correctly, they should be smarter than the average doctor, right? And come back and say, here is exactly the path that this person is on, based on the genomic sequencing. And then you guys almost are going to build like a recommendation engine after that. That then says, you should go get this test or you should go do, you know, X, Y, or Z because of the machine learning has matched you to these other people. And that's where we know how to make that recommendation. Do I, do I have that kind of right?

• 30:45Speaker 02

  Yeah, I think you got very close. There are two more steps that make this whole thing work after that. The next step is clinical algorithms. And, you know, that is something you cannot do through a machine learning system. You have to do this. You need a lot of people involved. You need to apply what's called a Delphi method. So you have to ask a lot of experts for their opinion. And what happens under all these circumstances, right? For all these different cancers, how do we, what downstream clinical diagnostics should be applied? And here you cross the line from science into medicine. It is a very different thing. So medicine is all about, you know, who agrees? Who are other people saying the same thing?

• 31:26Speaker 02

  Because you have to get to a consensus among experts. Otherwise you're screwed. Right? You need liability out of where you need to implement that. So it is a very different field than just science. The scientists can just say, well, I think this is the best solution because here's my statistics. In clinical reality, you have to say, but who are the people who agree with that? Because it's a much more holistic problem. And then the last step, so for this clinical algorithm, said this whole thing needs to work with. Which is very complex. That's one of the main reasons why smart tech guys who go into biotech often fail. Because they lack, you know, the understanding of what goes beyond science and engineering. What goes into this weird clinical space?

• 32:08Speaker 02

  You know, this authority and doctors and experts and legal and compliance. But you have to solve that, otherwise you don't have a product. And the final step is of course the business model. Like who actually pays for that? And that again, sounds maybe trivial to most tech and finance guys like, why is this? I mean, if you save people's lives, they just paper it. Guess what? Not in medicine. So, or well, we changed that fundamentally, but it gets insanely complicated. How do you convince a insurance bureaucrat that he likes that? And if you show that you save people, like, sorry to be so frank, but they don't care. It's not their job. You have to show them that they save money.

• 32:50Speaker 02

  And you have to show them that you save money immediately or quickly over time. You know, and then, you know, things get insanely complicated. And that kills also most of the innovation. So if you don't solve four and five, all your amazing tech is not going to work because you can't finance it. And so this, this is like the, now I gave you the full picture complexity, why it's absolutely non trivial to truly understand your business model, to innovate on that front. And to be highly succinct with the medical and clinical reality of things. And then nail the tech and then you're getting somewhere.

• 33:33Anthony Pompliano?

  Yeah, it, no, it, it's absolutely nuts. And, and I guess part of that clinical side, um, when I hear the word clinical, I think of, uh, all sorts of different tests that can be run. Uh, with this genomic sequencing stuff, uh, is this only being done on humans? Was it previously done on animals? And then that's where the technology kind of was perfected. And now it's being brought to the human side. Like, like, what is the relationship there, uh, in some of that testing to, uh, non-human, uh, kind of clinical trials or whatever you could do there?

• 34:03Speaker 02

  Well, the problem is, or the venture of genomic sequencing is you don't have to do a lot of animal trials because there is no risk in just collecting the evidence. Right? So you, that was very human driven from the outset on, uh, Craig Wenta and the Human Genome Project. Uh, so you can sequence, you know, a, a lot and you don't, you know, without any kind of approval. Because if you clinical trials, you just sequence, no, no one gets hurt as long as there's no feedback. Um, so it's very different. The, the actual sequencing and developing sequencing technologies, you're free to, you know, test this on humans because you're not putting anyone at risk, especially if there's no feedback of information. Once it gets into cancer screening, of course, the game completely changes.

• 34:47Speaker 02

  And that's the problem. That's what's holding the liquid biopsy industry back. So what we discuss is called liquid biopsy, cause it's liquid and kind of a biopsy. Um, and that's where we innovated a lot to actually get out of that problem. Um, and, and get medical advancements and technologies to our members much faster than we normally would be able to do that. If you just go through Medicare and, you know, commercial insurance.

• 35:13Anthony Pompliano?

  Got it. And then I guess as you get better and better at this, right? Because part of the machine learning algorithms that are actually analyzing all of this sequencing, they should improve over time, right? It's kind of the beauty of machine learning. Help me understand, is that something where, let's say I'm testing for a variation that we believe could lead to a higher propensity for cancer, right? Can I use the advances and the accuracy that we've determined on that test in those machine learning algorithms to then apply to a different type of test? Like, is there some shared knowledge or shared analysis that can be moved from test to test?

• 35:55Anthony Pompliano?

  Or is each one of these tests, when you go to start them, they're really starting from scratch, right? So you have to rewrite the algorithms and kind of start all over again without any of that shared knowledge?

• 36:05Speaker 02

  I mean, it's one of the, I'm super excited about these things because it's probably the most challenging and rewarding data science problems I've ever seen. Well, there are some in finance that are also pretty immediately rewarding, but I mean, in a human sense.

• 36:21Speaker 02

  And you can view it as like, in a way, if you take a step back, it's like any data problem, right? If you want to determine something, if you want to figure out if a certain real estate, you know, offering is like likely to increase in value over time, you could build a crazy machine learning thing around it, like how many people look at it, where it's listed, you know, what the current price is, what the stock market does, like you could feed all that information. And how does the stock market data relate to that real estate as opposed to other real estate listings around it? Well, no one knows. You can, these are two different tests, right? You combine these test sets and you know, if you have sufficient data sets, you can make them work together.

• 37:01Speaker 02

  And that's if we test your hereditary, if you understand your hereditary variants and we understand your somatic variants, the somatic variants are also like a time-based, kind of on a timeline, time series, because we do that every year. Whereas your hereditary, you don't have to do every year because it's not going to change. So you have very different types of data sets and so you have to learn how to make them compatible. But each data set adds a significant amount of insight and dimension to the problem.

• 37:39Speaker 02

  I think there are two major challenges in medical data sets. The first one is they are often very heterogeneous. So if you have a thousand or 10,000 patients, the probability that you have the same data sets for these 10,000 is like zero, unless you do a super clean clinical trial that costs you an immense amount of money. If this is commercial patients, then I might have your hereditary genetics, but not the next one, next person's. I might have your hereditary and your somatic and good clinical records, but for the next person, I might only have hereditary and some records. So when you build machine learning systems, it's very important to account for that.

• 38:21Speaker 02

  I bet you sample the comprehensiveness of data is nearly impossible to maintain. And just cutting it down to the data dimensions that are comprehensive is kind of stupid, because you're losing so much information. So you need much more fuzzy, you know, systems that allow you to recognize much more things. And you also need to embrace more, you know, man-machine hybrids, where you have experts and analysts sitting in front of these things and use the machine learning more as a decision support system and have these mutual interactions. It's very important in medicine.

• 38:59Speaker 02

  And then the ultimate problem, of course, from a business perspective is you need to close the business model loop in order to even get that information, right? You need to figure out how do I actually make money and create a business model here? And that's very, very hard. And I'm very happy we solved it the way we solved. Because for most companies, that means you have to just do very rigid clinical trials over a decade. It costs you hundreds of millions of dollars. And that's super inflexible. And in the end, you have bad data sets because you missed out on the most important dimension. You see this with a bunch of our peers that just don't have the right data sets.

• 39:37Anthony Pompliano?

  How do you guys make money? What is the business model that you figured out that works?

• 39:42Speaker 02

  So we made a very bold step that some love and some hate. And we said, I'm an economist by trade, right? So I love to look into a system, you know, and understand why is this not working? And medical innovation innovation is simply not working if you see what's possible in theory and what's happening. There's a huge delta between what could happen based on all the tech we have and what does happen in medicine. It's ridiculous. My conviction after looking at this for like over a decade now is it all boils down not to the FDA, not to the government, not to regulations, that's smaller obstacles. It boils down to the payer problem.

• 40:26Speaker 02

  If you have socialized medicine, which we effectively have or bureaucratized medicine, which means if I protect your life as with a new technology, the value I'm generating is your value because you value your life and you know, your health and your family's health. But you're not the payer, right? Or at least not normally, we change that. So if I have to create value for people, but get paid by other people, I'm, I will stop creating value for you because you're not paying me. I start to creating value for the insurance company or Medicare, which is the same thing. So what do they want? They want to cut costs.

• 41:10Speaker 02

  They want to collect your premium and pay as little as possible, right? And they want to do so on a short time horizon. They're not interested if I save the money in 10 years because I prevented you from having stage four cancer and saved, you know, hundreds of thousands on a cancer treatment. They asked me if I give you that test now within three years, can you recoup the test? And of course I can't, it's not possible because you know, if you test a hundred people, you detect two cancers for these two, you save a lot of money, maybe. But maybe if you wouldn't detect it, maybe they would have had symptoms only four years down the road. So they are off the, you know, table for insurance companies. So.

• 41:53Speaker 02

  You know, once we address only the payer's interests, the patient is out of the picture. No one is going to innovate anything that saves your life or protect you because you're not paying and the payer is not interested in that. It sounds very brutal, but it's also very obvious for everyone who knows business. And so what we said is if you want to build a company that stands for true medical progress, you can only do that if you have a self payer model. You have to get paid by the people you protect. Because if you let other people pay for it, you're not going to protect them anymore.

• 42:28Speaker 02

  And so what we develop is like, can we develop something that is affordable for most people, but still provides margins for us that makes us a real business? And so we are like, you know, roughly $200 a month membership fee for once a year tests, it's 2,400 a year for the genetic tests, it's an $800 one-off fee for the hereditary testing, but that's once in a life thing you do. Then we have an intelligence layer. Once we do your genetics testing, that you continuously get updates on any drugs that are incompatible with your genome, that poses a risk to you. Any other insights we have on an ongoing level, that's $20 per month, ongoing.

• 43:13Speaker 02

  So it's all affordable.

• 43:17Speaker 02

  But over time, of course, we want to make this cheaper and over time, this allows us to actually build the patient base and the clinical evidence to get insurance more and more on board because we're developing that evidence over time, but not by raising a billion dollars and do it, you know, do it 200,000 patient study over 10 years. So it's a little controversial to just go for self payers, but I, we see two things. The market trend is on our side because a lot of patients understand that you have to do that if you want to get actually advanced medicine.

• 43:50Speaker 02

  And second, you know, we also see that it's the only way of doing it. Like, as I said, as an economist, I have no illusions about incentives, you know, incentives in business. If you get paid, you just want to save costs, you're not going to save people. It's very simple.

• 44:08Anthony Pompliano?

  It's funny how the same capitalistic tendencies that the finance industry has exist in medicine as well, right?

• 44:17Speaker 02

  I mean, yeah, to think.

• 44:22Speaker 02

  Yeah, to think that giant publicly traded companies, you know, pharma, insurance and so on. Don't think about the bottom line is a little delusional. And once you understand the logics of, you know, they're all, it's not that they are bad people, but you know how it works. You have to report your quarterly profits. You have to show that you grow your business. Same with pharma. If your business is to sell chemotherapy drugs and you make a hundred sixty one billion dollars a year as an industry. Are you really incentivized to cure cancer with like vitamin C? I'm not saying that this works, but I'm just saying, let's imagine vitamin C would work. That would be the worst disaster in the history of pharma that actually works.

• 45:07Speaker 02

  They would lose a hundred sixty billion dollars a year. And so the incentives are a little flawed, right? So no one is gonna, I'm not saying anyone's actively trying to not cure it. But if I pitch any big, you know, pharma company with, oh, I have this great idea. It costs only ten bucks to cure cancer if it works. I need ten million dollars to do this trial versus stage one trial. I mean, you can be guaranteed that no one is going to finance it because they say that that's an absurd proposition to us.

• 45:37Anthony Pompliano?

  Yeah, it's pretty crazy. The other thing around genome sequencing that I think a lot of people, you know, have heard of, but they probably don't understand is when you get into all of the gene editing, right? So whether it's at the individual cell level or kind of this DNA editing. Maybe talk a little bit about, it's kind of hard for me to see that world happening if we don't actually understand first. Or like we kind of have to understand the genes and the DNA and kind of what's going on before you could even think about editing. But but there's obviously technologies out there that people kind of have heard of at a high level. And how do you think about that as maybe kind of a long term path that the the medical community is likely to pursue?

• 46:20Anthony Pompliano?

  Whether it's just for, you know, curing diseases and kind of direct, you know, health applications versus like, hey, I want to change the color of my baby's eyes type stuff?

• 46:31Speaker 02

  Yeah. Well, I think the extreme depths of potential knowledge and the lack thereof in biology sounds like a daunting problem, right? So, oh, we don't understand anything, how can we even edit? But I think this immense amount of depth and complexity and the lack of knowledge opens the door also to genius, right? You can be very genius by saying like, maybe we don't have to know all these things, maybe we can hack it. Like, for example, if you know a mutation does some bad stuff, but you have no idea what, you just know in the outcome is bad, but we cannot figure out what it actually does. Maybe you don't need to figure that out, maybe you just remove that mutation through gene editing. And, you know, maybe you cut it out and replace it with a healthy piece of DNA.

• 47:15Speaker 02

  So, the whole CRISPR revolution, I think it's, you know, often things get overhyped, but CRISPR is a little bit like the internet. You know, everyone said the internet, oh god, it's gonna change our lives forever, everything's gonna change, and everyone's like, oh my god, so obnoxious, these people, they are overhyping this. But it was just true. I mean, it is changing everything. And CRISPR is very similar, because CRISPR allows you to, it's kind of crazy when you think about it, it allows you to, in a very targeted way, take any specific segment of the DNA and replace it with any desired fragment you wanna put in there, through the nucleotide. You have 3.3 billion nucleotides on your DNA, they can, through the single nucleotide,

• 47:56Speaker 02

  say, okay, at that specific location, cut it out, inject that sequence. And if you, and they can do it in living organisms, they can basically inject it into you, and it would kind of, you know, disperse it across your whole body in theory and replace it in all your cells, which is extremely freaky, because it actually works, right? So you could actually change your eye color in theory while you're like, living, and or even change all kinds of things. So, that is extremely freaky, of course, there's an, you know, enormous amount of things that can go wrong, you're probably gonna die, if you wanna change your eye color, because

• 48:40Speaker 02

  some stuff is gonna happen that no one, you know, was considering, so I wouldn't test it out at home.

• 48:47Speaker 02

  But in theory, it's certainly possible. And...

• 48:51Speaker 02

  So we know a lot of things, but compared to what we should know, it's basically zero. But that doesn't mean you can't do absolutely amazing or shocking things. And of course, I'm very, you know, we are very focused on disease and keeping people healthy and, you know, not, not let them age too fast. And there are many things you could do, but of course you need very, very deep clinical trials for that to prove safety. Especially if you start editing genomes and living. It's more regulatory issue. I mean scientifically, it's definitely extremely possible and not even that. I mean, you can do it today.

• 49:28Anthony Pompliano?

  And where do people get, so like, let's say I want to take one of those nucleotides and I want to replace it, right? So I've identified that that one is a bad one, whatever, for whatever reason. Where do I get the replacement? Is that something that I take from somewhere else in the body? Is that something that's like quote unquote lab grown? Where, where does that replacement one come from in that scenario?

• 49:49Speaker 02

  So here, you know, and these are great questions because if you go into biotech and you will learn about these things, it's absolutely stunning. You know, how hard certain things are, where you think that can't be that hard. And how easy certain things are, where you think this must be completely impossible. And then someone's like, no, it costs five bucks on the website, you get it. And, and it's like stunning. And this is one of these things, you can literally say, here's my sequence, T T A A C G A C G A or something. Right? That you, I don't know where you get this from, but whatever you do your research and say, I want this little part of the protein, change these amino acids. And so I have, you know, these 24 nucleotide sequence that I want to inject here. You can literally go on the website.

• 50:34Speaker 02

  I'm not going to promote any specific companies, but you can Google that quickly. Copy paste your T T A C things in there, take out your credit card and they send it to you two weeks later.

• 50:47Anthony Pompliano?

  No way.

• 50:48Speaker 02

  In a little too.

• 50:49Anthony Pompliano?

  No way.

• 50:51Speaker 02

  And then you have them and they are clean. They are, you have a few billion of these little sequences in there and they're all lead sequence.

• 50:58Anthony Pompliano?

  And what are people doing with those today? Are they like injecting them?

• 51:03Speaker 02

  No, there's a huge amount of stuff that where this is used. We do this every day. We order them every day. So, you know, you need them in sequencing, for example, for PCR reactions. So they're called, you know, these nucleotide sequences are hugely used all across the board there, for example, used as primers. So we can use them to basically have a little sequence that reflects another sequence on the DNA. And if you put that into a mix and do some stuff with it, it back up and down and put polymerase in it, this enzyme, then these primers will, like, they will align to be complementary, the complement on the DNA,

• 51:42Speaker 02

  and then start reading that out, like whatever comes after. So you can basically say, I want to read out whatever is on the DNA after that sequence. That's actually sequencing. So you say, okay, if you want to read out that specific part of the DNA, what you have to do is a primer that you synthesize, that sits here, and it aligns here, and then the polymerase enzyme comes here and starts complementing the rest of the DNA. And then that gets put on a sequencing machine and you read it out, and you see, oh yeah, this is my primer, I recognize it, that's what I designed. And then what comes after is the sequence that I was looking at. So I can see if you actually have a T there or a T.

• 52:21Anthony Pompliano?

  It's kind of similar to like in the movies when they, they like clap really loud so they can sync the audio in the video, right? It is kind of very similar.

• 52:29Speaker 02

  Yeah, it's the same thing, kind of, yeah.

• 52:32Anthony Pompliano?

  Yeah. Before, before we go to wrap up, you have been a Bitcoin proponent and have a pretty cool story about Chamath, who's obviously come on the podcast. Maybe tell a little bit just about like how you discovered Bitcoins. I think it was just pretty interesting that.

• 52:52Speaker 02

  Yeah, you had some very cool people on the podcast. I'm a big fan of Chamath. I think he's, of course, super smart, but he's also outspoken, and he is what a smart investor always should be, and a smart entrepreneur. He has these different perspectives. He thinks in first principles about certain things of the world and then buys them. Same with Kathy from ARK, right? Also amazing. So.

• 53:15Speaker 02

  I met Chamath in Howard Business School, there was an event, I didn't go to Howard, I was still at the conference. And I think in 2014. And back then, I had, I had heard of Bitcoin, but not really, I mean, that is six years ago. And he gave this very, you know, passionate pitch about Bitcoin and how it's gonna change everything and he, you know, he made this, I don't know, he, he just totally convinced me to get into Bitcoin, just with a little bit of money. So I bought a bunch of Bitcoins at eight hundred dollars. Because it's how I do my investing, right? I, I analyze stuff, but I'm also

• 53:54Speaker 02

  really listening to entrepreneurs and smart investors where I, on a meta level, see, okay, this guy knows something, he's smart, the way he thinks resonates with me, it's the way I think, but he thinks about different things. So I'm listening to him on these different things. And that paid off pretty well, so thanks so much.

• 54:14Anthony Pompliano?

  Yeah, that's an awesome story. Before, before we wrap up, I always ask two questions and then you get to ask me one to finish it up. But what is the most important book that you've ever read?

• 54:27Speaker 02

  Well, I actually forgot the title of the book, it was in German, I'm from Germany, so. But that book, what was actually the title? I know the cover, it was black and had some weird orange shape on it. I read this when I was ten, eleven or something. And that book was basically taking you on a journey. I forgot, you have these, you know, there are these famous guys in the US who do the similar thing. It, it takes you on the journey, on the evolutionary journey, from the Big Bang to us today. And I remember one specific thing in the book that was kind of a game changer in my mind. And that was the theory of life. Right, so the most exciting thing for me is not how monkeys turn into humans, that's like trivia, I mean, a monkey is basically a human.

• 55:13Speaker 02

  Or like even a fish turns into whatever, a rat, I know, that's like, fine, I get it. So, but the question is, how does this all start? How does something dead turn into life? And in this book, he described it in a way that was so logical, it was shocking, how dead stuff turned into life. That basically, if you have a bunch of molecules, randomly assembled proteins, right, in the, in the soup, the beginning soup thing, right, in the sea.

• 55:44Speaker 02

  And you would just put energy and and light on it. So they'd start changing. They mutate randomly, not as life, but as just dead matter as proteins like, oh, yeah, there's a little light here and it's changes like that. Um, at some point randomly in that soup, a specific protein or substance or molecule will change in a way that it will align to other molecules and replicate itself. And that's just a pure question of statistics to that point. It's like, of course, at some point, that's just a random trade of a thing. But if the second way it does that, guess what happens to the soup? These molecules would be much more, you know, prevalent than all other molecules because they're the only ones who replicate.

• 56:26Speaker 02

  And that's the first step to life. That's what viruses do, for example, without, so viruses are not alive. In case people didn't know, it's not a life form. And and so, you know, that was for me stunning because it was exactly that that magic steps like, oh, there's dead stuff, stones, and there's life. When you look at humans and stones, but when you narrow it down to this one thing, what is actually the main trait of life? To understand, well, the only thing that it does, it replicates. And you know, and that is purely, that's pure math statistics chemistry will happen inevitably because whatever replicates will be more of the other stuff. And then it's just the race who replicates more effectively, who can eat other stuff, who can develop DNA, then you're off to the races. But that was such a revelation

• 57:11Speaker 02

  because it's a purely mathematical explanation why life is inevitable.

• 57:17Anthony Pompliano?

  I I remember the first time, and I forget the name of the book as well, but I read about telomeres and they were basically talking about the replication. And and you're just like, wow, this is a whole world that I don't understand. I don't know if I want to learn more because it's scary. But at the same time, it helps you understand life for sure. Uh, you spent a lot of time thinking about uh, human life, obviously, and and kind of what humans are made of and DNA sequencing and and all of that. Uh, what about aliens? Are you a believer in aliens, not believer? What was the thoughts there?

• 57:48Speaker 02

  Um, well, the Fermi paradox is one of my favorite topics, like why, you know, if you do the math on it, it's kind of obvious. We should have millions of alien civilizations, even higher developed civilizations. And why didn't we meet them? It's one of them, you know, are they just lingering out around Earth and don't make contact because we are too primitive? Or what's the thing? And I actually have my own theory there, but I think it must be true that there are tons of civilizations. But it also is very likely that they don't go beyond a certain civilization development stage, which allows them to travel everywhere like quickly. And the question is why? So I think these are kind of first principles, how I think about it. There must be all these civilizations. They must be highly developed.

• 58:33Speaker 02

  And it's extremely likely that nearly none of them makes it beyond a certain point, which is a little scary when you think about it. And that point is, we are not at that point. It's a point where you could easily travel everywhere and see everything, which in theory shouldn't be a problem, right? So my theory is, it's a little dark. Normally, I'm very optimistic, but I'm just reasoning here. It's not my opinion. I just reason like what's most likely. And absurdly, it has actually something to do with something very trivial, you would think, and that is terrorism. I think the problem is, if you develop a civilization, right? There is a certain equation, like how much power does a civilization have?

• 59:17Speaker 02

  Have like the power to change something and to destroy things. It's always the same thing. And of course, there's a totally exponential trajectory. You know, if you go on this trajectory, you have a billion times more power in a thousand years. But that power also applies to each individual in the civilization, right? So if that power is so exponential, it's only a question of time until you reach the technology point where every single person in the civilization becomes capable of destroying the entire civilization.

• 59:50Speaker 02

  Like, it's just a question of time. And we think about atomic weapons and bioweapons, they take this time to billion, even if only 10% of the population would be able to destroy the entire civilization. You turn into that scenario where you have to rely on every single human being not to decide today who kill everyone. And I think that's an impossible equation. At some point, you know, if you have, you know, a trillion people at some point, and every single one of them at every minute could destroy all others, what are the odds that this never happens? Like, at some point, it becomes impossible. And so I think exponential take and everyone talks about AI in the end, I think that's a much more severe problem.

• 1:00:33Speaker 02

  Like at some point, your power exponential curve and the distribution, the inevitable distribution of power leads to a scenario where you can't survive.

• 1:00:44Anthony Pompliano?

  Listen, I don't think that you're very far off, right, which is kind of the scary part of a lot of this in that the technology that we have, you know, access to as individuals today, right? People always talk about you have a supercomputer in your pocket compared to, you know, the 1950s or 60s. Right? So that's only, you know, what if you kind of look at it, it's only 60 years of progress, 70 years of progress. Seems like a lifetime, right, for for one human, but on the grand scale of a human progress, it's a it's a blip. So.

• 1:01:14Speaker 02

  Yeah, no, if you just, I mean, here we are talking about why these civilizations don't exist and how they might get, you know, extinct. If you take nuclear atomic bombs or hydrogen bombs and compare them to whatever happened before, what was the biggest weapon before? Like some cannon or machine gun or something and like 1890. So then you have an atomic bomb. What's the delta? It's pretty severe. And now just go on another 200 years, if that same leap happens again on a lock scale, the next weapon category then already becomes a little shaky, the whole thing. And then another 200 years, you have another lock scale. So, you know, you have something that's like 10 million times stronger than like a big nuke. So then you can already like wipe out the planet.

• 1:01:58Speaker 02

  And then AI on top of that, so I think it's inevitable that you generate more and more of these technologies that are extremely awesome and powerful. But the margin for error becomes just slimmer and slimmer, like exponentially slimmer. If anything goes wrong, then all of us are gone. And I guarantee you in a thousand years or 2000 years, I mean, there's no way around that. Little problem. There might be a solution to the problem, but it's a very complicated solution, I see.

• 1:02:26Anthony Pompliano?

  Yeah, for sure. To wrap up, you could ask me one question. What uh what one question do you have?

• 1:02:33Speaker 02

  What do you enjoy most about your podcasts and talking to different people?

• 1:02:38Anthony Pompliano?

  Oh, this is the most selfish thing I do. I get to talk to some of the smartest people in the world and ask them all the questions that I have. Right? And yeah, I record the conversations. Everyone else can listen, but, uh, but for me, it's just, I get to learn. Um, and by far, that's the most rewarding and enjoyable part of it is, uh, you know, I learned things today. I learned things yesterday. I'll learn things tomorrow. Um, and it's pretty cool that, uh, you know, you would take an hour out of your time to sit down and kind of answer questions and, um, and teach me things. And so I'll keep doing it.

• 1:03:10Speaker 02

  How did you get into it? What was your first step? I'm kind of interested like, how did you, how did you engage on that journey? And what was the main, what were the main obstacles in the beginning that you faced?

• 1:03:20Anthony Pompliano?

  Yeah. Um, there, there was a couple of guys who, who came to me and said, Hey, you should start a podcast. And literally I was like, well, what's a podcast? Uh, cause I thought that I was listening to pre-recorded radio, uh, in the beginning. Uh, when I would listen to things like a Joe Rogan, uh, whatever. Um, and, uh, we recorded three episodes and, uh, and we launched it. And they were a huge part of helping me kind of figure out, you know, how do you launch a podcast and all that kind of stuff. And, uh, there was just enough people who paid attention to those first three episodes where I said, well, maybe I should do a fourth or a fifth. And then kind of from there, it's one of these things where, uh, you just kind of put one foot in front of the other. Right? And next thing you know, you turn around and you're like, oh, I've done over 300 of these now. Like that's kind of a lot, right?

• 1:04:01Anthony Pompliano?

  Um, and so, uh, it's been fun and I'll keep doing it until, uh, one, one day I'm going to wake up and say, all right, no more fun. I'm done. I gotta just walk away. I don't know when that's coming, but, uh, but, but I don't think anytime soon.

• 1:04:14Speaker 02

  Awesome. Yeah. I, thanks a lot for doing that. I always like, you know, I admire people who build these podcasts because I learned a lot from, you know.

• 1:04:21Anthony Pompliano?

  For sure. Where, where can we send people so that they can learn more about you and, and Quantgen?

• 1:04:27Speaker 02

  Um, choose serenity.com. Uh, that's our product site. So choose serenity.com. Uh, or directly to quantgen.com. Um, and people can also reach me at JB at quantgen.com if they want to get in touch directly.

• 1:04:43Anthony Pompliano?

  Got it. So JB at Quantgen and we'll put, uh, we'll put some links in the, uh, in the description for you as well. But listen, Joe, I really appreciate you doing this. This is super fun. I learned a ton. Um, and I hope everybody else does as well.

• 1:04:54Speaker 02

  Awesome. Thanks so much, Anthony. I really appreciate it.