Why AGI Is Noise and Data Is the Signal in Healthcare Innovation with Laurent Bride

The end of the day it's data, like having the data foundation. So we keep talking about AI and when I joined Revolution Medicine we had a lot of discussions about AI. What we need is really that strong data foundation. If you look at any innovative startup out there, people who claim they can really lower the cost or predict clinical trials outcome, companies that can predict pharmacology and whether or not a drug is have a negative effect on the organs and things like that. They are all based like the model is amazing, like the AI model they build. But it's about the data that they've been able to assemble to train the model, validate the model and that starts with that data foundation. So I think any problem that AI tackles right now, it starts with a data problem. Welcome to Signal to Noise by Riviera Partners, the podcast where leading executives share share how they cut through the noise and act on what matters most. We go beyond the headlines to explore the pivotal decisions, opportunities and inflection points that define their careers and shape the future of the companies they led. It's time to cut through the noise and get to the signal. Welcome to Signal to Noise. I'm your host Matt Skiba, Partner of Rivera Partners where I focus on executive talent for venture backed companies and Co Leader at GEMS Practice. After nearly a decade leading executive talent at Snap and serving as VP of Talent at the gaming unicorn Genies, I spent my career working alongside founders, CEOs and boards as they scale through pivotal growth moments, pre and post ipo, through transformation and across highly regulated industries. Today I'm joined by Laurent Bride, an accomplished digital and technology leader who has built and scaled enterprise platforms across data analytics and API driven ecosystems and is now applying that experience in one of the most complex and consequential industries of all healthcare. Laurent currently serves as a Chief Digital Officer at Revolution Medicines where he's helping drive innovation at the intersection of science, technology and patient outcomes. He also advises Bain Capital Ventures on the Board of Wisdom, bringing a unique perspective on governance, data trust and platform strategy. In this conversation we'll explore what it really takes to transition from traditional enterprise software into healthcare, how to innovate responsibly in a highly regulated environment and how leaders can build high performing teams while navigating compliance, data complexity and slower moving systems. Laurent, it's a pleasure to have you here today. Let's dive in. I'm good. Nice to see you again Matt. Thanks for joining us. Really excited for this one. You have worn multiple hats throughout your career and have seen multiple different journeys and Stories. So getting the chance to pick your brain on topics that we're going to get to get into here is really exciting. Well, thank you. Let's get rolling. Perfect. As the show is called Signal to Noise, I would love to hear from you directly. What's the biggest signal you're paying attention to right now and what noise are you tuning out in today's chaos that's going on in tech? Yeah, absolutely. So right now my job has changed. I joined Revolution Medicine about three months ago. So the signals have evolved over the past few months. When I think about Revolution Medicine, it's a biotech company. So what I'm really paying attention to is AI in a drug like science. So everything from drug discovery, like research and discovery to clinical development to commercialization and the impact the drug has on a patient. So how AI can play a role throughout that life cycle, I mean through specific models and the likes. So that's really the signals I'm paying attention to. So the technology itself, the companies who are innovating in that space, when it comes to the noise, it's really like I'm trying to avoid the dooms maker like, oh, AI is going to steal our jobs and it's doomsday. And companies that are promising, hey, I can do everything you do for 10 times cheaper, 50 times faster and so on. So I'm trying to remove that noise from my day to day work. Otherwise it's killing me and focusing on what matters. That's awesome. And I'm sure it's happening at just as fast a rate of speed in the biotech life sciences space as it would in health, tech and the rest of the industry as well. Absolutely. Yeah, yeah, we see that pace changing every day. There's a lot of innovation. I think what AI brought is really like lowering that entry bar and what we can do with AI models make a lot of startups very interesting and things that used to take years to build up, now you have new up and commerce like building really exciting innovating solution that we can piggyback on and use internally here. Right. I've made love it. I want to get more into that, but I'm going to wait and reserve the questions until a little bit later just so that we can learn more about you and your journey and your story and let the audience just get a, a feel for who you are and everything that you've accomplished throughout your career. So tell us about a pivotal moment in your career or leadership journey. What changed? How did you navigate this? I mean, you obviously just Admitted that you're on that path right now, but you've worn all of these different eras in the last 15, 20 years. What indicators led you to where are today as you've navigated these things? I started as a, as an engineer, so I have a math degree, computer science. And for me like the building part has been really key throughout my career. I've done that through many different companies like B2B Space, obviously the analytical world, the data integration world. What has been really pivotal was a change toward the healthcare. So maybe like me joining Komodo Health, like I knew nothing about healthcare at the time, when I was younger I wanted to be a doctor. Never happened. I have three kids, they are all in science in some ways. And for me, as part of my journey, I wanted to get there. I went From a pure B2B software to a health deck and then biotech with Revolution Medicine. But for me, that move from the traditional B2B enterprise software building products to a health tech company, Komodo Health was really something pivotal. There was a steep learning curve, but I had good partners along the way like the founders. Arif and Web have been amazing throughout that journey and I really had fun over there. And that was my ramping up into the healthcare world. Sure. Wow. What type of doctor did you want to be as you were pursuing that path early in your life? Yeah, maybe a NE doctor or maybe a surgeon. You know, I like getting my hands dirty into everything. Like I still do encoding every day or every other day. So yeah, I really like that part. But no, I choose math and computer science. I don't think it was the wrong choice in the end. And now you've got three ushering your your dreams of what you wanted to be as a young man. So that's awesome. You talked about the journey going from these sectors and industries to industries. Let's talk a little bit about kind of the talent that is in each of these different verticals because I'm sure while it's similar in nature, the personality types, there's also just different backgrounds and personalities and egos and all of these things that come into play when you think about talent and building up and identifying great teams and talent. So what separates the good and the truly great as you've navigated now the B2B space, the health tech space and now the biosciences era. When I think about those, I always stayed within technology. Like I didn't completely change career, meaning that I did. I'm not becoming a doctor at Revmed, I'm Working with many doctors for pharmacists and scientists. But the focus has kind of a shift from one industry to another. But when I think about the teams that I build or I'm building, they're a little bit different. Like in the B2B space, like I was building products, so I was building the engine. And when you think about the profiles, I, I had engineering teams, product management teams. And what was a really key was people like, close to technology who understand how to build product, how to scale and so on. As I got into the health tech and and with Komod, the journey was a little bit different. But Komodo was actually looking for somebody who had a product background because they wanted to scale their solution, go from like multiple, like vertical applications to a platform on which we can build a suite of products. So I was able to take all the things that I've learned, I mean throughout the years at business subjects, at SAP, at Talent, where I was a cto, and then bring that into that health tech industry. And when I look at the biotech, it's something similar. The profiles, if I look at Komodo and Revolution Medicine is extremely different. I engage much more with scientists, people who really understand the science, who understand the chemistry of drug, who understand biology. And the discussions are a little bit different. And for them I'm much more of an enabler versus building the core products. And going back to your question about good talent versus great talent and how they look, I think good talent is people who know the industry, who can get stuff done. But when it comes to great talent, I think it's more people who can reinvent themselves. They are going to challenge the status quo. Like it's not because we used to do things a certain way that as we move to or push for innovation, they cannot get outside of their comfort zone and try different things. So that's really how I see great talent. I think for me, great talent is also humility. People who can leave their ego aside, look for consensus, but also sometimes be a decision maker, either take risks, like from a technology standpoint, hey, we need to go after a new architecture or hey, we need to challenge the way that we've labeled data for the past two years and adopt new technology and take that kind of risk. So for me, that's really great talent. And going from one company to another, I'm always trying to take that great talent when you know it. Yeah, it's key. Last thing I want to add is you can make people good to great. Meaning that sometimes what's Important is you can take a call and turn that into a diamond by investing in the person and bringing that your philosophy on how they should be great. And I've seen a lot of that. So you can make good great as you engage with your team. No, said like a true, true great leader and coach and mentor. You can mold people into what they want to become. It's their commitment to it as well, which you can help inspire within them. You mentioned just the era of your career at SAP. I know that was spent in Europe and that you came to the States. I think around 2014, 2015 era, maybe sooner or just a little after that time period. What was that transition like for you? Because obviously SAP is a global brand, it's a global company. You interacted with US based engineers and product folks. But going from where you were in Europe to being then thrusted into the Bay Area, Silicon Valley, the upper echelon of talent on so many different levels, how was that transition? Was there a learning curve there that you had to adapt to quickly? Tell us a little bit about that time period. Yeah, actually I went the other way around. Like the first time I joined the US was in 98. So I started at Business Objects, an IoT core beard company. We were based in Paris and in the U.S. so that company, like from the get go, they say, hey, we need to be in the Bay Area for that innovation. We need to be in the US because this is where technology is happening. But they had a co engineering team in France, so started there six months. Then they offer, do you want to go to London or do you want to go to San Jose, California? That was an easy choice at the time. So I want to join and go into San Jose. So spent a few years there from the get go. Like, hey, what engineering looks like, what innovation looks like? At the same time, core engineering function was in France. And the French folks are extremely good when it comes to math and computer science. I think that's one of their challenges. Maybe more on the sales side, the marketing side. Bringing those two together has been amazing. Then we got acquired by SAP and that's how I got to SAP, spend much more time with Germany. And this is where like this, yes, indeed, different how you think and engage with the German engineers versus your Silicon Valley engineers and so on. But there's good and bad on both. So what's really key is to ensure that you build those bridges, that culturally you make sure they understand each other and you take the grade of German engineering at SAP or French engineering or whatever. In Europe or in the rest of the world? World. And you do the same with the Silicon Valley. So you just go through that journey with them and you are pushing, you're being accountable for the impact and the value that you bring along the way. I love it. Yeah, I mean it's the chameleon nature in which you're talking about having to adapt in to earn the trust of others. Especially when you've got numerous different cultures interacting with each other with different work ethics and styles and approaches and humility and egos all alike. And I think you made the right decision going to San Jose. As a Santa Clara University alum, I know those 80 degree springs that you got to experience pretty well just to take us down that path a little further here. I know we talked about the talent aspect of things, the pivotal moments of your career. If we think about the eras in which you've been a part of, you've seen the mobile era, you watched the era of cloud services come online. What are the recent trends or technologies that you feel are now overhyped? And is there one that's underappreciated even as well that may not get as much recognition because it's the air is being sucked into it by, you know, another that is being over saturated with vintrus. To your point, I'm old enough that I've been through many revolutions, no pun intended, being part of revolution medicines. But like yeah I've been through the like the three tier architecture like the mobile, the big data, the cloud SaaS and now it's the AI. So I don't think AI is overrated as a core set of technology because it's really changing our lives on a day to day basis. I was mentioning earlier how AI could be applied to every step in the way of drug discovery. I would say maybe something that's a little bit overrated right now it's AGI. So that artificial general intelligence. There's a lot of battles of egos that I see right now on the web. Like if you ask somebody what's AGI you're going to maybe or 10 people, you're going to get 10 different answers. So yeah, I would say AGI is a little bit overrated right now. I think we will continue to invest, make those core frontier and maybe science foundational models better and better as we go and bring all of that together. That's where I think the meat of the value and the impact is going to be. So AGI are a little bit overrated and if I want to Draw a parallel. I think humans are underrated. So I'm not going to talk about technology, but really humans, I grew up in Europe, we are seeing things a little bit differently. Like socially, I mentioned the noise around the doomsdays and people who are afraid that AI GI is going to take all their jobs. I think that right now, because AGI is overrated, humans will have that creativity, we have that problem solving skills that I don't see in any of those systems. We can adapt very quickly. Something that we learn in a domain, the Enterprise B2B. We can take that and move it to healthcare. It's not just about optimizing goals like a system would do, but how we can adapt to that change and constantly evolve and work as kind of a nest. So I would use those two, like AGI on one hand being overrated and humans being underrated. I totally agree with you. And from one dad to another, these are things that probably keep you up at night thinking about your children's welfare and mine are a couple years behind yours. But it is something where it's like you can't underestimate human ingenuity. Something as a species we've conquered for centuries, but in the last 150 years we witnessed it with the Industrial revolution and look at everything that happened between that era and this era and name it, rattle it off. It's every advancement in society, you know, outside of the great pyramids being built. So it's interesting you mentioned. I just want to touch on quickly the human aspect of things because I think you're spot on, especially with. And that's going to make us almost more superhuman in ways because we can be more productive and execute more. You and I were talking recently, even outside of this conversation, just around you, finding yourself energized in a new way because as you mentioned earlier, quickly just being hands on in the weeds because AI gives you that power. Talk to us a little bit about that moment for you now because I'm sure it's very. Just stimulating and exciting. Absolutely. Like as I mentioned, I was a developer back in the days, but you kind of lose touch of the. With the ground and you code less and less. What the new AI tooling, the vibe coding brought me is back into that code. I'm not just like doing a one shot prompt and do this and then fire and forget. No, Like I'm going to experiment things and I think like AI really allows me to do that is to experiment, fail fast, try new things, but get into a model where it's More like show versus tell. As example, revolution medicine. Right now, I got there about three months ago. I've prototyped three things. Two, three years ago, I would come in, I would build decks. Now I'm like putting something together. It's working code, it's not production ready. I mean, don't get me wrong, it needs a lot of work to industrialize, but at least you can show, show the value and get people on board and then you move to the next step, which is, okay, let's look at all the constraints, let's look at some of the challenges that we have to think through when it comes to the healthcare world and bring that to maybe either production or not. But that vibe, coding really help people like me a lot of ideas. Let's try something, let's show, let's bring people together. And that's how I'm using it on a daily basis. So. So it doesn't replace the critical thinking, it doesn't replace the creativity that you have as a human on how you think about problems. And if you're pretty good at prompting, and that's something that I learned the past two, three years, is how to prompt better and better and how to use LLMs to help you prompt as well. But really that's something that changed my life over the past couple of years with AI. Truly the definition of a rolling stone that gathers no moss. It's awesome. And also, I can only imagine those that are watching this as well, who are at the same level of seniority and experience that you've gotten to. Just the smile on your face, it's just you can tell you're back in the brain gymnastics that you get to do or you did earlier in your career, but now you get to apply them in a whole new way because your perspective is so much more advanced. Yeah, absolutely. Very cool. Let's transition a little bit over to leadership and kind of that journey you've been on from enterprise B2B to healthcare, you've built and led product and engineering organizations across traditional B2B and enterprise software. What surprised you most when you transitioned into healthcare? If I look at my Komodo, like from B2B to health tech and then biotech, I think that the transition from, hey, how I used to lead in B2B versus how I led in the health tech world and at Komodo was not that different. And even when I think about the biotech world, like when you think leadership, it's about making decisions, it's about being accountable for those decisions, it's about aligning on priorities, setting goals, building up your teams and so on. That is very similar from one role to another. I think the core of leadership is there. Of course there are always some nuances here and there, but that core remains. Whether you are in B2B in health tech or biotech, I think one thing that has changed, but there's an appetite to go more toward that is projects versus product or product versus project. So like the past 20 plus years of my life I spent with people that I would label as software native or a product native people. So people who think in a way where hey, I see a set of problem with one customer, I'm going to talk to many customers and build a product out of that. And I could do that in one industry or I could do that in 10 industries. So if I look at my business sub SAP talent days, we were really targeting any industry. When you think about analytics, BI solutions, when you think about data integration solutions, whether you're dealing with oil and gas, retail healthcare, finance system, data source might change, like transformation might change, the analysis might change, but the core of the technology is still there. So when you build something for one industry, you can apply it to many. So that's software product mindset. As I went into health tech, like that was one industry. So you narrow it down then you to get deeper into the domain. So really understand the domain. But here like with Komodo, we were working with payer providers, pharma, biotech like all sorts of company. But in that healthcare space, as I got into the biotech space, what I'm seeing is people are thinking more in projects like hey, I'm creating a new clinical trial or I'm going after a new compound, a new drug and so on. So they have much more of a project mindset. And I'm trying to bring more of that product mindset. Especially when you think about data products. Like if I look at how the biotech industry is transforming and innovating like data product because we have that, all the data available to us, like moving that away from a. This is a data set. Let's think data product which has a life cycle and something that could be used downstream, something that needs contract and so on and so so forth. So there's really that transformation I'm trying to bring which is thinking more like product into a world that is focusing more on the project. Got it. How does that, you mean you, you've kind of alluded to this. I'm curious because obviously the end user is different now with it Being more patient focused as opposed to a business or customer focused side of things. And so how does that then change your own mentality but then also your chances of influencing to make decisions that are a little faster or have to have a little bit more risk tolerance. Walk me through that balance because I'm sure that's also just been a learning lesson for you as well. Clearly the risks are different. When you think about biotech versus the rest of the world. There's much more consensus, there's much more science that you have to bring into decision. Yesterday I was in a meeting with a scientist. Like I love that because it's so educational for me. Like I'm just like feel like a kid learning new things like every day. But like the way that they engage, the way that they bring data to the table, the way that they think about cancer cell and the impact of a drug on cancer cells, like everything that needs to be measured and so on. It's very different from when you are in the B2B space where it's much more like technical approach to things. So I think the risk tolerance is much lower in the health tech or biotech industry because the impact on the patient and the impact doesn't mean that the patient is going to die. Like the impact could mean that if you are not thinking through that twin cycle properly then you might not get a drug in the end of a patient, the drug might not be potent enough and so on and so forth. So there's much more alignment consensus which could slow things down. But it's a necessary evil if I can use that word. When it comes to patient, when it comes to patient data, there's also a lot of risk and compliancy and regulation things that we have to think through. When you deal with marketing, click Data in the B2E world, I mean I'm not saying that hey, you can open that up and put that in the web everywhere. But no, the risk is lower than when you deal with phi or pii data. And there are a lot of things that you have to think through from how you build your data products. Who has access to your data products, what kind of governance layers that you have to put in place. Will you be able to re identify a patient? Like if I look at my Komodo days, Komodo was all about taking claims rx MX data, assembling them, creating data products, solutions, applications, AI solutions. On top of that, when customers like biotech or pharma wanted to bring their own data in and wanted to link those data sets together to get a new kind of insights. You really have to think hard about reidentification. There's some certification process, statistical modeling that you have to validate before you link data sets. So those are just examples on the where it's very different from one industry to another for sure. And that's a perfect segue into kind of just talking about medical data compliance and trust here. Especially as you talk about scaling at the same time what healthcare data comes from with an entirety of different levels of sensitivity and regulatory scrutiny. There's so much, much restrictions that are there just because it has to protect the patient, their information, be HIPAA compliant, et cetera. What are the hardest trade offs you've faced balancing this innovation with compliance? Because you're probably doing it more, a little bit more now. So because it's patient based and not customer centric on the Komodo side of that era, but maybe they are the same. So talk to me a little bit of just about that whole journey that you've had to probably learn and unlearn and then relearn again as well. I came from a world where by default I'm like, hey, all the data that we have internally as a company, outside of maybe salaries and things like that, even though in some companies it's fine, it's open. But in the world I was living in, we were trying to make the data accessible. So if you needed to search for some data assets, you could find those fairly easily to a world where it's much more about not broad data access, but much more about a least privileged kind of role based data access, meaning that by default you don't have access to anything and then it's kind of siloed in a way. I'm trying to change that game a little bit because there's data in the healthcare biotech world where you cannot move away from regulation. And when you think about JAX P validated environment, you need that to be really secured. Only a few people have access to that. You need to trace everything that you do on this data like access control, data lineage and the likes. What I'm trying to do is there might be some data like that is upstream of that drug development that I'm totally fine for many people to have access to. So when you think about research and discovery as you build like a lot of compounds on crystals that you need to validate for potential targets and so on, I'm fine with pretty much everybody in the company having access to that, that not people outside of the company because then there's secret sauce. Right out of a few thousand compounds, maybe a few will become the drug of tomorrow. So you have to protect that ip. But when it comes to internal usage, I'm really trying to democratize this access because at some point maybe an executive wants to understand, like show me everything that happened within that company with a specific compound that became a commercial drug. And right now it's challenging to do that because of how the regulation was set up. So I'm trying to evolve that. There's always the risks that you have to take into place. So you have to find the right balance between the compliance risk and the pace of innovation. And at the end of the day, I mean when you think about compliance and governance, I want to enable every users to the maximum of their capacity or access and show, I mean bring trust to how they are using the data. No, absolutely. And how has it been? I mean you're only three months in, so I'm sure it's still in the early innings of things. But as you even think about working at Comodo, the clients that you had, there are complexities obviously with this. What are the things that you've recognized that you've seen that some underestimate when it comes to these complexities because you're talking about building that's the solution that can provide this information to folks. But there are so many different things that you have to navigate as you kind of go down that path. You mentioned Komodo. If I go back to my Komodo days, when you think about claim, you might think, yeah, it's pretty standard, you know, you have claim formats. But no, like when you think about the system, like the end to end system when it comes to claim management, you're going to be connected to provider to payers and so on. And then did you have encoding, decoding systems along the way? And a lot of things could go wrong when you think about that data change. Before you can actually build a claim data product, there's a lot of complexity. When you think about doctor, you might have a doctor who prescribed you might have a doctor who put the drug in your body. Like you might have different NPIs, but when you look at the data store, it's an npi. Like that looks like a similar field but. But yeah, the complexity of how the data gets generated, the modalities. Like if I look at Komodo data, we were dealing with labs data claims data, genomic data like determinant social health type of data. When I look at the world in the biotech, there's more data, more modalities. Now you have biopsy images, you have rna, DNA omics data. So then there's the complexity of the data itself, like the different engines you need to process, analyze those data and then how do you link all those data together and manage their lineage? So this is very messy and very complex. But we have tools and when we don't, we build solutions. Sure. Would you say one of those tools is AI being a huge opportunity to kind of reduce the complexities and even kind of of help with connecting everything more efficiently? Or do you feel like. No, it's too early in the game right now because the models are still just getting ramped up in understanding what they're capable of being able to do. AI can definitively accelerate a lot of that. If I look at imaging, you can use AI system to help you look at the biopsy and at cancer cells and identify things that would take more time before. So you can use AI tools to analyze images, you can use AI tooling to maybe run the first analysis and then give a human oversight so they can definitively help in some of the verticals. And then I see also a lot of LLMs like the core LLMs that you and I are using every day in our life, like the Gemini, the OpenAI, the Claude type of more like the large language model, really helping packaging some of that together, like helping you through analysis, having those models helping you to write regulatory documents like automatically. This is where they really are helping. Got it. No, I love it. Every day is fascinating just what could be done and what's possible on so many different levels. And also probably knowing too just the impact that this is having. Not to say that where else you've been hasn't had significant impact, but you know, this is saving someone's life potentially or helping their lives, or simplifying and improving it. However you may want to look at it, which is a whole nother customer journey now for you in having to go through. Let's transition back a little bit toward the nature of the environment now and building these teams that you've been known for constructing over the course of your career. And now it's in a more regulated environment just because there's sensitivities and data, the privacy constraints that are there. How does team design change? Does it change at all when you're operating under heavy regulatory constraints? I can imagine you want someone with that knowledge and experience of doing this before, but also someone who's bright eyed and bushy tailed might be even better because you can mold and develop them in a different way. Yeah. So you need a mix of both. Absolutely. To your point, when you get into health tech or in biotech, don't think that you can just bring a team of folks who never expense anything in that domain and build novel solutions. No, you need some domain expertise. It's always great when you find someone great talent who has domain expertise and who has also tech expertise. You might be chasing unicorns. I think that as people learn, as the time goes, you will find more of those. But initially for me it was more about pairing up people that really came with a tech background. They were model in thinking products, they were model in thinking scaling. They came with best practices from an engineering standpoint, like mixing them with domain experts. So domain experts and then regulatory experts. Because you can be like, you can have really domain expertise in biotech, but if you don't touch clinical development, maybe you've not been exposed to validated JXP system. So you need a little bit of both. Creating that dynamic is key, I think clearly bringing fresh blood in those teams, people who come with different perspectives as long as they can also be flexible in their perspective. Because if you bring somebody who's going to come in and say I know better, it's my way or the highway, this is going to fail miserably. But bring somebody who's a good communicator, bring somebody who has a lot of interest in the science, then they can go well together. Now you might argue that hey, it might be very motivating for somebody who used to come from the B2B world or the startup world on the tech side and then they iterate like every week they are pushing new features and so on. They might find it too slow in the biotech industry but actually I think that if you put them in front of the science, if you take two smart people or set of people like on the tech side and then on the core science side like that can do magic. So you have to expose them to the science. I think the mission, like you mentioned that a couple of times before, of helping patient like at Revolution Medicines like we regularly see videos of patients that have been treated and you look at how well they are doing today on the cancers that were deadly. It's great to see that. Like I don't know how long it's going to be but it's, it's, it's amazing to see that and it's very motivating for people who come from the tech. And some days they're like, hey, Am I doing tech for the sake of doing tech or am I having an impact and am I having an impact on something that doesn't matter, or am I having an impact on something that does matter? That's how I think about it. That's awesome. No, it's so fascinating and I'm, I'm sure, just truly gratifying and rewarding when you do get to interact with those individuals or hear those stories just because you know that the impact is saving somebody's life. And it could collide into so many different things in so many ways on a positive way of looking at it. I love that. Looking ahead now, what capabilities will healthcare organizations need most from their digital and product leaders over the next three to five years? You're in the weeds these days. You're on Claude, you're on cursor, you're coding. So I think you've already started to show. This is the prototype that I am building in myself that people can replicate. But what would you say to that L5 engineer that has the aspirations of being in your seat in the years to come, or those directors of engineering who sit between 35 and 40 and trying to push through that glass? I think at the end of the day it's data, like having the data foundation. So we keep talking about AI and when I joined Revolution Medicine, we had a lot of discussions about AI. What we need is really that strong data foundation. If you look at any innovative startup out there, people who claim they can really lower the cost or predict clinical trials outcome, companies that can predict pharmacology and whether or not a drug is, have negative effect on the organs and things like that. They're all based like the model is amazing, the AI model they build, but it's about the data that they've been able to assemble to train the model, validate the model. And that starts with that data foundation. So I think any problem that AI tackles right now, it starts with a data problem. Do I have the data? And when I look at Revolution Medicine, like we sit on one of the most complete RAS addicted cancer database in the world and that really helps us speed things. So going back to your question, I think it starts with that, a data foundation. So over the next three to five years, how do we continue building that data foundation of something that is actionable and usable to train AI model? Like I look at those LLMs out there, they've been trained on all the public data available out there. When you start looking at specific models that solve specific problems, this is where there's a lot of fine tuning involved and that's how those models, they became better at predicting A, B and C. So the data foundation is key. And then I think that the next thing is really about building the semantic of the future. And going back to 97, when I joined Business subjects, they had that concept of a semantic layer, which is like, how do you turn a database and build a semantic layer that represents the business? And when I think about healthcare in general, can we create that semantic layer across the board? So we have the data foundation, we have the semantic layer. All of that is interconnected. So then you can give that to humans or AI models so that they can reason over those. So that's where I really want to push the industry. That's awesome. We're a champion for you to do that. And I think most everybody in society would be as well, especially if you can eliminate cancers that have plagued society for a number of years now. Which is just awesome that you guys get to play that role at Revolution in Medicine yourself, your team. It's inspiring to say the least. As we close things up here, there's a section that Riviera loves to do, especially with signals and noise and guests around rapid fire questions. And so I've got a couple here that I'd love to come at you with and get your reactions and response to it. I know we, we've talked on some of these topics, so it'll, it'll weave their way into some of these areas as well. So, so first one to come at you with is what's one leadership habit that served you well across every industry you've been in throughout your career? I would say learn something new every week or maybe every month or every day, depending on your appetite. And then just don't read about it. Like, try to apply it in a way. Learn something new and apply it. What's a misconception technologists often have about the innovation in healthcare? For a lot of people, they think like technology is the end point and technology is going to lead to better outcomes. And I don't think that's the case. The trust in the technology and the adoption of that technology to solve a real problem is so don't just come with your, hey, technology is going to solve everything. That's a misconception. Got it. And what's a skill or an additive that you think every digital leader needs to have to be effective? Or what do you think they need to develop then to be defective if they don't have it? It I think it's Mastering trade offs. I would say, like, that's the one, like making trade offs, owning those trade offs and communicating well those trade offs. So I put that into that umbrella of mastering trade offs. What's one word that you would describe or what word would you use where trust has to be at the center of being a leader? Technology leader? I would say accountability. Anything you do in life, you need to be accountable for it. Whether it's, you know, at home, whether it's at work, whether it's a decision we make. Something you did, like being accountable for it. Love it. I've got three personal questions, so they'll be quick. Mountains or ocean? Ocean. What's your favorite French restaurant in Paris? I don't have any. They're all good. Love it. We're trying to get you a free meal somewhere. What do you like to do in your downtime? I like to do triathlon. Spending a lot of time on the bike, it gives me time to reflect and I like to compete. I do a lot of racing and yeah, I love that. Well, we'll have to make sure you join us for the Rivi ride and I think for our guests, Laurent also is someone who not only competes in those races, but he helps those who are sight, I guess, you know, blind or suffer from sight issues in their efforts to complete meet triathlons and marathons. So he not only is saving lives in his day to day, but he's helping usher lives into accomplishing physical exertion on a whole new level. How do you know that? Maybe I told you before. It was our lunch in Nashville where you shared it. I was blown away. I had never met anyone who did a companion ride with somebody. That's just awesome. Just to be a motivator is great. You do it not only in the walls of revolution in medicine and at Komodo and elsewhere in your career, but you do it in people's homes, which I think is really what is a testament to who you are in your charact. So this was great, Laurent. I really appreciate you taking the time and spending it with us. Awesome. Thanks. That wraps up today's episode with Laurent Bride, Chief Digital Officer at Revolution Medicines. We explored his journey from enterprise B2B technology into healthcare, the realities of managing medical data and compliance at scale, and how leaders can drive meaningful innovation in industry where the stakes and the impacts are incredibly high. If you enjoyed this conversation, make sure to subscribe, share and leave a review. Stay tuned for more episodes of Signal to Noise, where we continue to explore the intersection of technology, leadership and innovation. Thank you. Signal to Noise is brought to you by Riviera Partners, leaders in executive search and the premier choice for tech talent. To learn more about how Riviera helps people and companies reach their full potential, visit Riviera Partners. And don't forget to search for Signal to Noise by Riviera Partners on Apple Podcasts, Spotify, or anywhere you listen to podcasts.