Q Bio CEO and Founder, Jeff Kaditz joined podcast host and Rebel One Ventures Founding Partner, Sergio Marrero, on the RBL1 Live podcast in July 2021, in which he discusses the inspiration behind Q Bio — a “Star Trek” executive physical that provides everyone a clinical, whole-body Digital Twin — and how it can help meet the increasing need to scale a doctor’s time, as well as advice to other entrepreneurs, and more.
Sergio Marrero: Hello, everyone, this is Sergio Marrero. We’re here at another episode of Rebel One Live with the CEO and founder of Q Bio. Jeffrey Kaditz. Thanks for joining us, Jeffrey.
Jeff Kaditz: Great to be here. Thanks for having me.
Sergio Marrero: Yeah. So I’ve been anticipating this interview for those that don’t know. Jeffrey is a serial entrepreneur. Q Bio has raised over $80 million from firms such as Khosla Ventures, Andreessen Horowitz, Founders Fund, among others. He was also formerly CTO and Founder of Affirm, Chief Data Scientist at DeNA, and a graduate at Carnegie Mellon, majoring in physics and computer science. So excited and humbled to hear a little bit about your story and your founder journey from not only founding a finance company, but now, a company changing healthcare and innovation. So I’ll dive in and just open it up for you. Can you share with the audience? A little bit about Q Bio?
Jeff: So that’s a lot to live up to, I’ll try not to disappoint. …Q Bio, I actually started thinking about it as an undergrad. …I was in high energy physics and designing high energy physics experiments, and really, the question that I had was, is it possible to measure everything about the human body cheap enough, fast enough, non-invasively enough so that you could kind of give everybody, an entire population, a Star Trek physical every year? Like, can you measure everything that’s changing in the human body? Can you make it fast enough and cheap enough to do that? And when would that be possible, even in theory. So I started thinking about that, actually, almost 20 years ago, and then, about four or five years into Affirm, when Affirm was kind of taking off, I felt that it would be fine. …I thought that it was time to kind of work on something that was a little bit more of a passion project… I think that was a good, big opportunity.
Sergio: That’s amazing and if we were to take a step back, you know, even for your your first, even looking at your studies, …physics, computer science, and then you shifted to be, you know, a founder at Affirm, how did you how did you make that first shift into why did you pick FinTech company to start?
Jeff: Actually, Affirm was the fourth company I started. So I always knew in high school when I stumbled across a book called The New New Thing, written by Michael Lewis, which is really about Jim Clark and Marc Andreessen and the rise of Netscape, among other things. And after I read that book, when I was about 17, I knew that someday I was gonna move to California and start companies. So I think I didn’t know how I was going to do that. And so when I went to college, I kind of, I think I just studied, I didn’t know what I should study. …I originally was like, Well, I’m going to study physics, because it was interesting. But also, I felt like it was just a good general, very broad scale skillset to have with you, regardless of what you want to do. And in the process of doing that, I ended up writing a lot of software, which led me to getting a computer science degree as well, because I was at a school where it was very good computer science program, which ended up I think, being very complimentary, because I ended up I feel like coming out of college with not only a degree and kind of a general understanding of how the physical world works, but I think of computer science, which isn’t really about programming more about information theory, I think, and also got to kind of get an understanding for how the virtual world works or how, you know, information moves, and what are the laws that govern how information can move. And so, I felt, and it turns out that those two things are really useful if you want to be an entrepreneur and build something.
Sergio: And what, since you’ve been thinking about this for, you know, 20 years on how to make the technology come alive, on, you know, doing these fast scans and learning, you know about someone’s health very quickly, what what were some of the challenges that that made that journey so long?
Jeff: Well, I think I think the biggest thing that I was really waiting for was for computers to be fast enough to deal with the amount of information that would be coming off of a scanner like this. But also, you know, our proprietary technology is something that can scan the anatomy of somebody’s anatomy. But the other part of it was seeing what I what I think of is, we’re starting in the early 2000s, which is I think is the trend was the kind of digitization of biology right, like, rather than biology being a lot more lexicology in the sense that it was an observed observational science. Now with things like genetics, epigenetics, transcriptomics, proteomics, metabolomics, it’s really becoming much more of a quantitative science, where you can measure the state of biological systems. And if you look back, historically, in you know, just in human civilization, things go from being an art to a science, or like a meta science to a science, when we develop the tools to measure changes in the system in a commodity way, right, like, the invention of the telescope, led to our understanding of planetary motion, like thermometers, help us understand the weather, you know, like, weather, even something as simple as a weather vane allows you to measure the direction of the wind. And but until we can measure it in a quantitative way, that’s kind of the backbone of the scientific method is, I can measure something about a system, which means I can then measure how it’s changing, and then I can try and predict the future state of a system. And if my model agrees with the actual next measurement I take, I say, okay, I understand the laws of the system. Now, the next step, usually, in determining whether or not your theory is correct to say, can I actually perturb the system and see if I can correctly influence the next measurement? And that’s fundamentally the scientific method, right, is to try and understand how systems change and predict the next change. That’s what meteorology is about. That’s what astrophysics, well, that’s what it all boils down to. So, you know, the idea …was, well, if we want to make medicine really a science, what we really need is the ability to measure changes in our bodies as a system. Because until we can do that we don’t really have the information we need to make forecasts, or build models that predict future changes in it, which ultimately, can lead to say, is this person going to get sick? Or is this person sick? Right, at the end of the day, a diagnostic is a predictive model.
Sergio: And one of the aspects that in reading up about Q Bio, the whole conversation they call it a digital twin, you know, scanning someone’s information and having in there, can you share a little bit with the audience that may not be familiar with it, what the concept of a digital twin is?
Jeff: In manufacturing mostly – there’s a lot of really good examples, especially like in airplanes, or especially like formula race cars, where digital twins of physical objects are used, so that you can, let’s say, simulate the aerodynamics of a system and improve it or simulate the efficiency of an engine, jet engine, or a like a car engine. And so you can actually iterate computationally before you go to production, right. And so this concept in manufacturing has been around for a while. But, you know, the question was, could you kind of apply the same idea, but a little bit of a different way to the human body and say, well, if I can now sequence somebody’s genome cheaply, I can measure inflammation from their blood, urine, saliva very cheaply, and that’s getting cheaper. You know, the missing piece from our perspective was, there’s no real cheap way to measure changes in our anatomy. And our feeling was if you can make that a commodity along with commoditized genetics, and blood where urine works lab work, then you can actually start to think about building a digital twin for a person. And so that would kind of change the paradigm of what a physical is to being, you know, once in a while, you go to a doctor and ask them some questions to something that’s much more quantitative, and much more about measuring what’s changed in your body since your last medical exam, and then fitting that to a model that can potentially forecast what your next visit might look like and what changes you could make to improve that.
Sergio: Awesome, like, I start to think about it, so let me take a step back. So do you envision this technology being more at the doctor replacing a physical or also at home in people’s homes? …I start thinking about is people, almost notifying people before they are pre-diabetic or, you know, like almost stopping people from becoming sick, you know, put that power in their hands if you’re able to, is that kind of where, where you guys are going?
Jeff: No I think it’s a little bit different, I actually think that the best analogy I can give is, so if you step back and look at the economic problem, the fundamental economic problem in healthcare, especially if we want to move to any kind of proactive or preventative model, is simply that there’s a scarcity of doctors’ time, like there simply isn’t enough doctors on the planet to see every person every year. And it’s not even necessarily the best use of the doctors time for them to spend their time measuring information about you. And so our feeling is trying to automate decision making, like any kind of AI, or whatever you want to call it, diagnostics, is really putting the cart before the horse. Like the first order problem to solve is: can you automate the collection of data to a degree that you can actually determine who needs to see a doctor?
Because the doctor can only see 1,000 people a year, who’s to say that they couldn’t care for 10,000 people and you could automatically determine from that, the 1,000 that they actually need to see in a given year. So our goal is actually, you know, initially not is, like most people think is to be a diagnostic. It’s actually more of to be a triage platform, actually, I would say, very similar to the way COVID was triage in the population, right, like, think about how that worked, you had these drive thru sites, where in 30 minutes, you get like a nasal swab, you go home, and if you get a text message from your doctor, you had to televisit, if you don’t hear anything, you just go back if need to get another test, I think the future of primary care, the UI is the entry point looks similar, where you have almost like these very high throughput sites where in 30 minutes, you can go get everything non-invasively measured about your body. And if you don’t hear from your doctor, you just go back next year. If the doctor wants to talk to you, you get a text message and they schedule a televisit. And that really, really scales well. And what the real goal of that platform is, is not to necessarily diagnose, but it’s to stratify risk in a population and determine who needs to use a doctor’s time in any given year. Because if you look at this, in the United States, 70% of all doctor’s visits are unnecessary. So there’s, there’s a huge, that’s a huge problem, because that means that 70% of the time that a doctor spends is basically wasted time, and that’s the limiting resource in healthcare. So our question is, can we make it so that 70% of doctors’ time is spent on people who actually need attention versus 30% of the time?
Sergio: Awesome. And that makes a lot of sense in terms of making, especially with the cost of healthcare in the US, if you can make things more efficient, there’s also impact there to be saved. What, asking a more personal question, what inspired this for you like, well, where did the idea first come from?
Jeff: You know, it was a combination of things. But I think, you know, I’ve had, I’ve lost people, my family, you know, to things that I think, could have been caught earlier. But I don’t think I’m unique at all in that. I mean, that’s kind of a universal truth in our healthcare system. A lot of people have terrible stories. I have had, you know, personal health incidents where I was frustrated with the answers I got from doctors, and then really surprised coming from the background in science, how little information, or at least objective information was being used by doctors to determine what could be wrong. And it was, honestly, just very unscientific. …And it gave me a lot of time to think about, well, what are the tools you would need to make – you know, so we stop calling it the art of medicine and called it the science of medicine. And I think the fundamental capability that we lack, that is true of any scientific discipline, is first we need the tools to be able to measure what’s changing in our bodies. Like that’s just the fundamental first step. And until we have that, honestly, I feel like everything we do is just going to be incrementally better in a broken system, but we’re fundamentally missing the capability, which is, you know, a commodity way to understand what’s changing in our bodies.
Sergio: Interesting. On the journey of building this you’ve, you know that you’ve raised over $80 million, I think the last round was $60 million Series B, which is amazing. Can you share a little bit about that, for the aspiring founders that are seeing your track record of serial entrepreneurship? How did you go about that? And why, I guess, why 60 million in terms of an industrial product such as this?
Jeff: Well, I mean, so, it’s either a lot or a little depending on how you look at it. I mean, to build the technology involved with building, you know, scanners that have a lot of really complex parts is just putting atoms together is just fundamentally, harder and more expensive.
Sergio: Hardware is hard.
Jeff: Just moving atoms is more expensive than moving bits. So, I think, you know, certainly trying to solve a problem this difficult, is, it helps a lot to have, to be frank, have made a lot of people a lot of money in the past. You know, so you know, that, you know, because net net, it’s like, they can make this bet, and they’re still up on bets on me. So, so that helps, certainly, but I think the other aspect of it is kind of figuring out, it’s a bit of a negotiation with investors, especially if you’re going to come, you know, and make what sound like crazy claims. I didn’t certainly didn’t go to school for biology, I have no background in biology or medical devices. Yet to come to say, I think I can put together a team that can do this better, is, is a stretch, I think. And it’s certainly difficult to find investors who I think can do the due diligence required, or even understand the math required to justify this. I think I would break most companies down into either execution risk or technical risk, and I think historically, in Silicon Valley, investors took technical risk. I think now, investors mostly take execution risk or business model risk. There’s very few investors these days, and I think it’s kind of on the rebound, who actually take technical risk, and who kind of say, okay, here’s a problem that if it can be solved, you know, we’ll worry about the business model later, because it’s potentially a such a valuable problem to solve. Most companies that are started today, you know, it’s much more about, well, is there a market for this? But the product itself isn’t that hard to build, you know, it’s an app or a website, or, you know, can they get traction kind of thing, like, what is the cost of customer acquisition? And those are, if you’re solving very hard technical problems, those are second order problems too. Is material science, up to the challenge, you know, is there a breakthrough in material science needed or can this be done now, are computers fast enough to solve this reasonably or can this be done now? And that’s technical risk, and I think it’s kind of about convincing investors, depending on you know, what kind of company you want to start why, you know, why your approach kind of mitigates those risks, whether it’s a business model risk or execution risk or technical risk.
Sergio: I like the way you frame that is, how can we, you know, in terms of getting investors to make more bets, focused on maybe the technical risk of, of problems that need to be solved, there seems to be a movement in, in the climate space, partly because there’s a lot of externalities happening around the globe. What are, in your opinion, some things that we can do to move more investors to back really tough problems like this one, that would benefit folks and needs to be solved?
Jeff: That’s a tough question, because at the end of the day –
Sergio: That’s why I asked you, I was like, Jeff’s a smart guy, he’s got to have a good answer.
Jeff: I have been on both sides, you know, both making investments and then being a partner in a lot of ventures, a limited partner in a lot of venture capital firms. You know, the way I look at companies that I want to start versus companies I want to invest in is pretty different. And there are I mean, there are times that I will invest in companies where, where I think this is a problem that has to be solved, and some but at the end of the day, if you’re an investor like If you’re a professional fund manager, and you have a billion dollars to allocate, you know, it’s not hard necessarily to allocate a billion dollars, but the problem is, is that if I have 100 companies, what I really want to do is maximize the return. And there are very hard problems, that would be very good for humanity to solve, where the return might be like the you know, the expected value of the return simply isn’t as good because it’s much riskier, or because the market size is smaller. Whereas, you know, there are, there are tons of very simple technical problems that can be solved. I mean, you look at something like Uber, or Lyft, right, like, the barrier to entry is very, very low, but it’s the kind of thing that everybody on the planet can use very quickly. And if you have billions of people using something, and you can make a few dollars a year from them, you have a very big business. And within a couple years versus very hard technical problems might require 10 years of investment, and with unclear, you know, margins returning on my business model. So I think that’s a really hard problem. Like, it’s always funny too, you see companies that I don’t think are solving a problem that’s really important for humanity, raising a billion dollars, and it probably cost a billion dollars to go to the moon. And when you kind of step back and say, Well, what is better for humanity in terms of moving civilization forward? Is it funding a, you know, the modern debit card company or is it going to the moon? And that’s not really the right question, investor’s thinking was, what is the likelihood of me returning money to my LPs? So I think it’s a tough problem. And honestly, this is why government funding and research is so important and I think we’re kind of living in an age, unfortunately, where that process is breaking down a little bit, and we’re seeing where private individuals- but the reality that we’re seeing what we’re seeing, though, is evidence that this is broken in both private markets and public markets, is that it takes people that have insane wealth, like the three, let’s say, the richest people in the world, are now doing things that governments can’t do. And private investors wouldn’t fund like, everybody wants to invest in SpaceX now, but like, Elon risked a lot of his own money first, everybody was like you’re throwing away money, like onto cars and rockets. So it’s not like there was a lot of individual risk that’s involved that those guys are taking before the investors can start blocking. And I think that’s still a problem. And I don’t know that I have a good solution for it, honestly.
Sergio: Awesome. And any suggestions on the government side? You mentioned you know, some of it might be breaking down. But even in terms of suggestions of improvement, for supporting the early stage innovation?
Jeff: Yeah, I mean, it’s complicated. I mean, I think one of the government’s obviously, really important – the infrastructure we have, the roads that we have, interstates, all of those things are funded by taxpayer money and maintained by taxpayer money. I think there was a question that you have to ask yourself as a citizen is, you know, is the dollars that I spend that go to the government, what am I getting in return for it? And I think right now, you know, there’s …an argument that the government isn’t allocating those dollars, as wisely as potentially some private individuals can. So does that mean you lower taxes? Or does that mean, you need smarter government allocation of funds? So it’s both really. You know, in theory, it won’t have the same ultimate goal, which is how do you allocate capital in a way that benefits society best, right? And I think the debate between lower and higher taxes really is who’s in a better place to do it? I think the tricky part is that the government can often make bets that might not have short term ROI the way a private investor might want to and so they can do things that seem less rational to a private investor. So I think it’s a complicated thing. I will say that people in general are pretty predictable in they will do what you incentivize them to do in general. So I do think that making policies that incentivize the behaviors and wants would go a long way. Because a lot of times, the policies that get created don’t necessarily incentivize the behaviors that you really want to see in the marketplace.
Sergio: That’s a great insight. I’m going to pause there for the people watching on the livestream. So thanks again. Everyone watching this is Jeffrey Kaditz, CEO and founder of Q Bio. Revolutionizing healthcare diagnostics and beyond so excited for your next steps. And again, tune in next week for more amazing founders. So thanks again, Jeff, for joining us.
