#76 - Jeff Huber: Innovation, Google, GRAIL, Triatomic

25:06So even though initial treatments were positive, ultimately it kept coming back and coming back. In parallel, when I was on the Illumina board, it's a very technical place as well, shared many properties with Google. The R &D teams were sharing their latest projects with the board. And one of those projects was the seed crystal that ultimately became Grail. They were sharing data that showed when you took Illuminous sequencing technology and really turned it to its most extreme level, turned it to 11, that they could demonstrate finding traces of cancer, fragmentary DNA that shed by cancer at its earliest stages in the blood.

25:42And this is a category that's now called liquid biopsy. And by extending Illuminous technology to a further extent, there was potential to be able to detect cancer early. And it instantly resonated to me that if that technology had been available three or four or five years earlier, it could have profoundly changed the outlook for Laura. But more broadly, it could profoundly change the outlook for many, many millions of people in the future. So as Laura was going through treatment, you could see this technology coming along. Though we had access to the best doctors, best diagnostics, best treatments, unfortunately, Laura's battle was a losing battle.

26:20She passed away in the fall of 2015. She was diagnosed in 2014. And in parallel, this technology had been coming along at Illumina and the Illumina board knew I was very passionate about it and asked me to ultimately lead the cause and taking that technology out of Illumina and commercializing it where we could get the capital and the technical resources we needed and just the focus to drive that technology forward and to create what ultimately became Grail. So Laura passed away in November and Grail was launched in January of 2016, basically two months later. And back to my earlier observation of you can't choose what happens, but you can choose how you respond.

27:00That was an incredibly difficult period in time for me, for my family. But I saw this technology, this capability that had so much potential, and that's when the foundation was being built. So I dove headfirst into that with a great sense of mission and purpose. Thank you for sharing that story. I know it's difficult to go through that. What would you say were the biggest challenges you faced in building GRAIL, especially as someone coming from outside the life sciences, having to overcome this deep learning curve? There were many challenges. There was a challenge of expectation. GRAIL was launched with very high profile and very high expectations.

27:37So in some respects, we were shot out of a cannon with a 40-person science team that came over from Illumina on day one with a over$100 million Series A, which was unheard of at the time. With the additional expectation of when we mapped out the clinical study program that was needed, it was going to be over a billion dollar program. So we needed to instantly get on the road to fundraise and raise the billion dollars that we needed to do it. Though there was great initial data, it was still an unproven category that it could be done at scale. And while there was a prototype that had been created at Illumina, there was also a strong sense and a little bit of dissonance between my view and Illumina's.

28:17Illumina viewed it as, here's the proof of concept, just commercialized that. But it had a pretty profound limitation, which was it could detect cancer, the existence of cancer, but it couldn't tell you anything more about it. And I felt very strongly from the beginning that whatever we were doing needed to be actionable. So part of our clinical study structure that we created was not just using the initial prototype that had been created at Illumina, which was a great product, but looking at additional dimensions of what were the signals that we could detect in blood at the earliest stages and using additional dimensions that could inform more.

28:52And one of those is a technique that was very cutting edge at the time called methylation, which is not just looking at the DNA, but it's essentially the chemical decoration on DNA that differentiates cell behavior in your body. So you have roughly 35 trillion cells in your body. If you don't have cancer, all of your cells have the same DNA. But obviously your cells behave differently. Your brain cells behave differently than your gut cells behave differently than your heart, than your lungs, than your skin, than everything else. And methylation is essentially the software layer that runs on top of the DNA hardware that differentiates that behavior.

29:28So by using this additional dimension of sequencing where we're looking at methylation, not only could we detect aberrant cells, mutated strands of DNA, but we could also interpret the methylation to be able to predict where they came from. So it's not just saying the initial version of GRAIL would have been, you take the GRAIL test and it would say you have cancer or you don't have cancer. But if you did have cancer, good luck. Now you need to find it. What we were able to do by pressing to this next level and really focusing on the right product for patients for clinical use, we were able to have a very highly accurate prediction of where it was.

30:05So not just you have cancer, but instead you have lung cancer or ovarian cancer or pancreatic cancer so that it could be actionable. So that was another challenge. The final one was, and I underappreciated the beginning of, it really was a multidisciplinary effort where we needed the best science in the world. We needed the best clinicians in the world. We needed the best engineers and machine learning, deep learning AI engineers to be able to pull us off. And the culture differences between those groups was greater than I initially recognized. So I thought at first naively that we have the noble mission to detect cancer early when it can be cured.

30:42We've got our set of goals. Everyone should be able to align on those. But what I ultimately found is while everyone had the right intentions and positive intentions, there were a lot of cultural discontinuities or mismatches between them and the nature of how people did work. So we ended up investing a lot of time in articulating our Grail values that were the unifying elements ultimately of Grail culture so that we could bring those disciplines together in the right way to achieve what we needed to. I assume there were also many skeptics and potentially competition as well when you started. How did you navigate that landscape?

31:21I would say an absolute of startups and innovation is overcoming skepticism. Because if everyone agreed it was a good idea and everyone agreed it was possible, somebody would have done it already. So you always have skepticism. And in fact, if there isn't skepticism, your idea probably isn't unique and novel and innovative enough. So part of it is you just have to recognize that that part of the landscape that you need to overcome. And the way you overcome it or the way that we thought about it at Grail is just really putting incredible focus on integrity and rigor around what we do. I mean, if you think back to the day that was in the window where you had companies like Theranos that were out there that were making grandiose claims that had no substance behind them.

32:07And in fact, Theranos had just hit the wall right in the window where we were launching GRAIL. So one of the first questions when I would do interviews for GRAIL, actually, it was one of the last questions from journalists, and they felt like they had to ask the question and they would ask it almost sheepishly is, why is this different from Theranos? And the easy answer was it was different on every dimension, because we believed in the only thing that was common was their starting point of we take a blood sample. Beyond that, we were putting incredible scientific and technical rigor behind what we were doing.

32:38We had a board of experts that were the leading cancer doctors in the world. Our board of directors included the former head of the National Cancer Institute. On every possible dimension, we were different and differentiated. We felt that fundamentally, but also just given the skepticism from the outside world, we had to really demonstrate it at a whole nother level. Obviously, when you build a business like that, that becomes successful, you have to scale it to grow. Are there lessons from scaling Grail that you now apply when advising or investing in new startups? Yes. So as mentioned earlier, Grail was shot out of a cannon.

33:15There were incredible expectations around what we were doing. And one of the biggest challenges was that we needed to execute the largest clinical study program ever done. Ultimately, it's been over 300 ,000 subjects in the clinical study program. And our goal initially was clinical studies are done all the time. Our goal was to take best of breed and put together a system and architecture that was using best of breed of contract research organizations and existing tools. And what we found was those systems and tools had been created in a prior era that lacked the imagination of what was possible or what was coming.

33:53So for example, the state-of-the-art tool for tracking clinical study data had literally a hard limit in it of tracking 10 ,000 subjects because they couldn't imagine anyone would ever do a clinical study with more than 10 ,000 subjects. So in a lot of respects, it's like the famous Bill Gates quote from the mid-1980s when they were designing DOS for the original IBM PCs. Why would anyone ever want more than 640K of memory? So it was that equivalent in life sciences biotech. And we just found that repeated over and over again as we looked at the labs information management systems and the limitations of those.

34:26So ultimately, what we had to do to scale was really focus on first principles and in most dimensions actually build it ourself. We had the luxury of access to Google caliber engineers to be able to do that. So we ended up building our own systems ultimately for how we executed and ran the clinical studies, how we built the laboratory information management system, how we aggregated and analyzed our data. So I guess the net is really encouraging first principles thinking of what are the things that are going to differentiate you? What are the things that you have to do because it's not possible given that you want to achieve?

35:03And then what are the places where you can leverage other capabilities or state of the art so you're not doing everything? So eventually you transitioned to triatomic capital. Would you talk about the mission and origin story of triatomic capital? The transition there from Grail to Triatomic was driven by, so I went from being the founding CEO of Grail to moving to being the vice chairman. And part of that was that as I reflected a couple of years into that journey, I had gone directly from my wife's passing to immersing in Grail. And I had been working incredibly hard on that. I was getting on, it dawned on me as I was getting on a plane for I think the fifth or sixth time that month.

35:46that my children had already lost their mother. And in some respects, they had lost their father as well. And that wasn't fair and appropriate for them at all. So that was the tipping point for me of we've got the foundation built. And there are people out there who have experience in doing this at the next level that could probably do as good of a job as me on running and executing the day to day. And I could step back a little bit, focus on my family, and then still continue to be very actively involved. So I made this step to becoming vice chairman. Grail continued along. We raised the billion dollars that we needed to raise.

36:21We ran the clinical studies that were the foundational product development studies, drove an entirely AI-based machine learning, deep learning-based product development process, which I think was the first in life sciences to create the Grail product. And we were heading towards IPO with a successful launch of the product. When then a Souter came knocking, Illumina came back and expressed interest in acquiring Grail. And boards of directors always make unanimous decisions. And I was the last holdout of unanimity, but ultimately agreed and supported the acquisition because it could and should be an accelerant for Grail to get to scale and ultimately be accessible around the world.

36:57So when that deal closed, and depending on how you countered, it was somewhere between an$8 and$10 billion deal. So Grail, while it's technically, scientifically, clinically successful and impactful was also a very good investment for our early investors. When that deal closed, that then freed me up for what am I going to do next? And as I thought and reflected on that, both in the end of my time at Google, and then as I was chairman of Grail, I had started ramping up more investing, primarily angel investing, seed stage investing. And I found that process really exciting and rewarding and reinforced the thing that I about earlier, a continual learning and curiosity-driven approach.

37:37And it was just so much fun working with young entrepreneurs who were incredibly excited about what they were doing and wanted to change the world. So Tritomic, the genesis was, I ended up teaming up with two friends' colleagues who were longtime investors, one at KOTU Management, who had spent a decade there. Another who had been at Deerfield Management, spent a decade there, were really builders of those firms. And we decided to join forces in Tritomic. And our mission with Tritomic is that we want to work with great entrepreneurs to build sensory-defining businesses and technologies. Very high bar.

38:09Yes, but back to the point earlier, if you're going to do something, anything you're going to do is hard, so you might as well make sure that it's going to have an impact and make a difference. I'm sure it's very gratifying too. In some ways, you're on the other side of it, where initially you were involved in building these companies and these technologies, and now you're assisting others who have similar long-term objectives, and you can give them advice on the experiences that you've had, your learnings, how to build these businesses, how to scale them, and you can watch them grow as well. Correct.

38:39It's a different way of achieving scale. And there is a lot of benefit given my operator background and having built systems of scale, built multiple products used by a billion people at Google, and then having built Grail where we had to run the largest clinical studies ever done. So that operator mindset and perspective of having been on the front lines and been there, done that is a benefit for our founders and something that resonates with them. But it's in a more scalable way. That said, I do have with the operator in me, sometimes is a little bit impatient of I want to jump in and be very closely involved.

39:10But I try to strike the right balance of guiding, mentoring, advising, rather than hands directly on the wheel myself. Well, I know you focus on five mega trends at Triatomic. Would you describe what they are and why you've chosen these areas? Maybe one step back first on that, because it's really the foundation that all of those are built on. So our core strategy with Triatomic Capital is focused on what we call applied AI, which is a very opinionated view of where AI will ultimately have greatest impact and value. And it's a very data-first, data-centric approach. And that thesis is informed by, if you look back on the history of AI over the last certainly 10, probably more than 15 years, increasingly the output of AI systems is entirely defined by the data that you feed it.

39:57So we look for teams, companies that are generating interesting, important, valuable, proprietary data, and then using AI to unlock and accelerate the value of that data. And then as a complement to that, or another dimension of that, we also look at companies that are the fundamental enablers of the generation of data or management of that data at scale. So those are the two dimensions of applied AI. We then concentrate that in our century-defining themes. And the concept behind the century-defining themes is these are our bets on the areas that will have biggest impact over the next decades.

40:29But ultimately, the absolute test is imagine yourself a year 2100 looking back. And if you said, try to distill what defined the 21st century, what are the areas that matter the most? So those five are first we call engineered biology, which is now ability to engineer biology at a cellular level. There were some big breakthroughs on that starting at the beginning of the century, the massive breakthrough of the Human Genome Project, where now we can read DNA, we can read the code of life. Barely a decade later, we've got breakthroughs like CRISPR, where now we can edit and write the code of life.

41:03We think the intersection of those capabilities is incredibly profound. And you can already see it with the ability now to cure previously incurable diseases. We've already made huge progress on things like cystic fibrosis and sickle cell disease. And there was a recent case at UPenn of a baby being cured of a rare genetic disorder with a custom therapeutic that was created for them delivered via CRISPR. So we think that the implications of that are very significant. And at the limit, we now also control our own evolution as a species, which is both exciting and scary at the same time. But if there were one thing that stood out, we think there is potential that this century will be the one that's defined by our ability to now engineer biology at a cellular level.

41:45The other themes are important and very complementary. So the next is, we call it new materials, which is deterministic material design, computational chemistry, engineered nanomaterials. How do we build the stuff the future is made of? Third one is next-gen compute, which is what are the compute architecture and system breakthroughs that'll drive the next inflections in AI? So I started at Google 2003. My team's built the first machine learning systems of Google in 2003, 2004. The architecture of the systems being built today aren't really different than they were then. The one addition is about seven or eight years later, the introduction of the GPU to be able to handle some of the math for calculations in AI, but it's not a fundamentally different model.

42:29We think that there's opportunities. When you look at the software architecture evolution of AI, increasingly, it's inspired by biology and the architecture of the brain. So you'll hear about neural nets or transformers as instantiations of neural nets. Your brain doesn't have a CPU and a GPU and memory that's thrashing back and forth when you need to do things. It's actually a very inefficient model. Your brain has distributed memory and compute. And there are architectures now that instantiate in hardware the direction that software architecture has gone, inspired by biology and the architecture of the brain.

43:03We're very optimistic about those. Fourth theme called new energy, which is when you think in decade and century terms, energy is ultimately the limiting factor for anything that you do. And we're already seeing that with AI. So what are the next breakthroughs in energy, whether it's next-gen fission? Fusion is actually becoming increasingly real. And we'll see fusion on the grid in our lifetimes. But as a complement to that, what are storage technologies, battery technologies that can help make that transition happen? The final one is probably the most broad. We call it new economy, which is the digitization and automation of everything.

43:35So everything from robotic automation to software platforms and agentic AI systems that are changing the nature of work. So those are collectively our century defining themes. Those are the themes that we're building the firm around. Our first fund is investing in those themes because we have five themes, it doesn't mean that we do 20 % in each. It really is. We're opportunistically looking at the best opportunities within those. And back to this concept of intersections, also looking at intersections between those, because we think in those intersections, that's disruptive technology from outside that has potential to drive products and businesses that have disproportionate returns.

44:14So you have founders coming to you looking for capital and or counsel. What would you say are the most common mistakes you see them make when pitching to Triatomic? So we see a lot. We went back and did account in Q1. We do a quarterly update for our LPs and we went back with our Q1 investor letter and did account. We talked to in Q1 400 companies and through that process made two investments, but one of them had actually started earlier. So we basically made one and a half investments out of the 400. So the selectivity is incredibly high. There's one level of just is an investment the right fit for us and what we do.

44:55So we screen on, is it really an instantiation or application of applied AI with our definition? Is it one of the themes that we're focused on? So there's a set of things that no fault of the entrepreneur go away with that. The other one where I would say where they're not errors, but it's just the maturity is as an entrepreneur, you're continually balancing near-term execution with the longer-term, so what. So many times you'll get entrepreneurs that just focus on the technology and this technology is amazing without the, what are the implications? What's the business? What's the financial model?

45:29What's the distribution model? How do you get there from here? Or then you have others that are great at the marketing, but don't have the fundamental breakthrough of technology. So it really is, I think, that balance and finding the right balance of the differentiating technology, the thing that's going to make a difference and be defensible and protectable within the discipline of what's the path to get there and get to impact. What's your outlook for the pace of discovery and innovation in biotech over the next decade? And what would you say is the base case and what's the upside scenario? Biotech as an industry has had a tendency of going through swings.

46:07And I would say at the moment, the pendulum swing is out of favor. But I think that that presents a massive opportunity. Because if you look at what's going on behind the scenes, all of these trends of being able to generate increasing quantity and quality and fidelity of data, the ability to generate data is dramatically higher than it has been, and it's getting better every day. And now we have increasingly capable AI systems to integrate that data and integrate everything that we know. So that intersection, we think, is going to drive incredible breakthroughs and progress. But now you've got, at the moment, a macro market where the pendulum has swung against biotech.

46:52So the bar is incredibly high for entrepreneurs to build in the area. That said, if you look back in the history of other industries, for example, when I went to Google in 2003, 2004, that was shortly after the internet bubble meltdown. The internet was out of favor then. You could have bought Amazon for$4 a share. Google was toiling in obscurity. And it was an incredible time to build because you could get the talent that you needed, concentrate the talent that you needed, and build without the overhang of intensive competition. or crazy valuations, or things like that. So I think now is an incredible time to be building in biotech and life sciences.

47:34And I think you'll see generational companies that come out of this period, like generational companies, Amazon, Google, Facebook came out of the internet meltdown of the early 2000s. You mentioned those five century defining themes. But would you say, are there any emerging markets or technologies you believe are currently underappreciated by the broader venture community? Life sciences biotech, I think with the pendulum swing, is currently underappreciated. I think another that is significantly underappreciated is around next-gen compute. There is a tendency of people wanting to anoint winners and assume that they will always be the winner.

48:12An example of that now in AI is NVIDIA. And NVIDIA is an incredible company. They've executed well, but in a lot of respects, they were lucky to be in the right place at the right time, where they had created a product, the GPU. And a lot of people have forgotten that G stands for graphics. It was a graphics accelerator for video games and scientific computing. And they got lucky and were in the right place at the right time, where that capability was able to be applied to machine learning and machine learning training, where the vector math for graphics was able to translate into the matrix and tensor math needed for AI.

48:49And in a lot of respects, it is good and useful and was a significant step forward, but it's using a hammer for every job where now there are tools that are much better suited. So an example of that in our next-gen compute category is we made an investment in a company called Dmatrix. And in our view, Dmatrix is really the right architecture for where AI goes next. And specifically, Dmatrix was the king of the era of training, where everyone was building these large models. The GPUs were the right hammer for the job of being able to do that better than otherwise could have happened. But GPU architecture isn't particularly efficient where the market is now exploding, which is using those models, which is an area called inference or AI serving.

49:33And we think that the inference market is going to be orders of magnitude larger than the training market. And Dmatrix was a company that was built from the beginning with an architecture that's optimized for inferencing. I already alluded to it earlier of their model, their hardware model is distributed memory and compute. So a hardware architecture that's much more like your brain works and it's all computer engineering. So there's nothing for free, but by having the right architecture that matches the software architecture more efficiently, depending on the benchmark, they can be anywhere from five to 30 times the performance and price performance.

50:09And even more importantly, increasingly the power performance for AI inference serving applications. So if you think about your own personal behavior of how frequently you use ChatGPT or Gemini or Anthropic, and then imagine what that's going to look like a year or two or three from now, where not only are you doing it on your phone and on your computer, but you've got a thing whispering in your ear all the time, your useful AI assistant, we think that the market is going to be just massive. And there's going to be a big drive for much more price and power efficient solutions for doing that. I want to close with a few questions on leadership.

50:46You've obviously led transformative teams at Google, Grail, and now at Tritomic. What would you say are core leadership principles that have guided you across such different environments? I think the core ones are clarity of mission, clear objectives, and then leadership by example. on clarity of mission. Each of the companies from At Home Network to Google to Grail and now with Triatomic, those are companies or organizations that have had very clear missions. And I mentioned Grail, one of the first tasks out of the gate was to raise a billion dollars. And that was incredibly challenging and was really unprecedented for the time.

51:25But the best, most productive discussions that we had with prospective investors were, we would go in, the first slide that we would bring up was the mission. And for most of the successful meetings, we basically never got past that first slide. It was just a discussion about the mission, the importance, how we were going to do it, the culture of achieving that. And then yes, people wanted to do their diligence and they would dig into the background and the model and plans and all of that. But really people either got the mission and aligned with it or they didn't. But thankfully for us, many, many, many did.

52:00and it was really joining the cause to do that. And when I look back at Google, it was joining the cause of Google to do it. And now with Triatomic, we are very mission focused in what we want to achieve. So I think when you have the right mission, it really helps define the culture and helps build the team of people who are passionate about that. Especially if you're good at articulating that mission and explaining why it's the mission, what drives that goal, and then gaining a following, then basically everything else are just details. One of the things I used to joke at Grail was it always bothered me that the mission had one too many words.

52:37I wanted it to be a seven word mission and it was an eight word mission. It's death cancer early when it can be cured. And that was a little bit of a joke, but it is just reinforcing the more crisp and clear you can be around it. That said, if we hadn't have been acquired and if we had stayed involved, there was actually an articulation of the mission that accomplished that, which was take out the word cancer and Grail could and should have been the early detection company. So detect early when it can be cured across all diseases and conditions. So that was the next horizon to go beyond the bright spot that we're aiming towards in their term.

53:12Second is around just clarity of goals. One of the things I really took away from Google, and again, this was something that Larry Page was very passionate about, is having the right goals. So at Google, and then I've applied at every other place I've been is having a very clear goal setting process. Specifically, we call them OKRs, objectives and key results. But what's the target that you're shooting for a year from now? And then what are milestones along the way where you can see that you're making progress towards it? And then also having a mindset where those are stretch goals, where you're really pushing yourself and the organization to achieve those.

53:46And I think the power of that multiplied across the organization. And it was a fractal model. So there were company level goals, yearly goals and quarterly goals that then would reflect down to the organization level and ultimately down to the personal level where they're nested and everyone can see how those connect together was a very important one. And I'm sure it builds momentum as well, because you have these milestones and you reach it and you feel like you made a positive step in the direction towards the mission. Rather than waiting until you reach the finish line, you're making progress and that builds that following and drives incentives and continues to motivate the team.

54:22Yes. And additionally, from a management perspective, it's also creating a more frequent feedback loop. So you can see every quarter, are we on track or not? Or if we didn't achieve this goal, what happened? And what's the postmortem on why didn't we achieve that or make the progress that we wanted to make? So I think that combination of the right goals and then that built-in sense of feedback loop so that you're continually improving is a really important one. And then I think the final one is just leadership by example. I've always been a huge advocate on my teams. I would never ask anyone to do a job, but I wouldn't do myself.

54:52And I think reinforcing that and demonstrating with the team that you're there with them is really important. And they also see the standards that you execute those to. So if you're doing it right, it raises the bar for them around their personal targets. You've described multiple examples of this. You've never been afraid to jump in the deep end, even in fields you knew very little about. What advice do you have for others facing steep learning curves or are considering career pivots? So I share this frequently with my kids that we now live in an incredible time. And I mentioned it earlier where it was a little bit harder when I made the jump into life sciences biotech.

55:30after a career in tech. But we now live in a time where anything is learnable. The only limitation is the curiosity and willpower to do it. So just let curiosity drive you. And yes, now the internet exists. Everything is discoverable. Now we have incredible AI tools that can be your coach along the way to be able to learn and develop new skills. And I think a key one of those is AI itself. I think you're going to see differentiation between people who are on the outside and not engaged and people who are on the inside and using the tools and really pushing what's possible. And Jensen Huang recently had a quote that really resonated for me.

56:11It was asked, is AI going to take away people's jobs? And his take was, no, somebody using AI has the biggest potential to take away your job unless you're there and leaning in and continuing to learn. So my encouragement for everyone, for my children, my son just graduated college last year, and my encouragement to him was congratulations on graduation. You finished the first chapter. Now you've got the rest of the novel ahead, and you need to be continually learning as you go. Your education is a milestone step along the way. It's now lifetime learning that you're engaging in. I think of it as there are certain things that humans are really good at and there are certain things that computers are really good at.

56:51And if you can figure out the things a computer is better than you at and use a computer to help you do those things and then focus your time and energy and all the things a computer can really replace, that's a very powerful combination. We're on the verge of every person has potentially superhuman skills, but it's incumbent on you to figure out and learn how to adopt those. So the last question I'll ask you, Jeff, is what advice would you give to founders who want to build not just a successful company, but one that truly changed the world? I think a huge element of it, and there's some themes that we've already touched on, is really being clear on your mission of what are you trying to accomplish?

57:29And then what's the culture that you're going to build to get there? And if you look at the biggest differentiators of companies that are successful, I think it ultimately comes down to the culture, which is a combination of values and then execution of those values. So yes, you need to have the right technology, you need to have the right business model to get there. But ultimately, it's the people and the culture that you're building. And that's something that we really look deeply at. So our framework when we look at startups is team technology, timing, and then a bar of impact. And the team really is about the culture and values and focus on building that.

58:06Well, Jeff, this has been great. Highly insightful. Thank you for sharing your story, sharing all the insights that you've learned along the way and for making a big difference in this world. So truly appreciate it. Thank you very much, Alex. It's been a pleasure and an honor. Thanks for listening. We hope you enjoyed this episode. Please visit our website at insightfulinvestor.org to access past shows and learn more about our podcast. If you have questions, feel free to email us at info at insightfulinvestor.org. And if you enjoyed the discussion, please subscribe to this podcast to ensure you don't miss future episodes.

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