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

Transcript

[00:00:01] Laurent:  The end of the day is 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 claim they can really lower the cost or predict clinical trial’s outcome. Companies that can predict pharmacology and whether or not a drug is gonna have negative effects 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.

[00:00:54] Intro:  Welcome to Signal to Noise by Riviera Partners, the podcast where leading executives 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.

[00:01:16] Matt:  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-lead our 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, health care. 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 Wiiisdom, 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 health care, 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.

[00:02:32] Laurent:  I’m good. Nice to see you again, Matt.

[00:02:34] 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 gonna get into here is really exciting.

[00:02:46] Laurent:  Well, thank you. Let’s get rolling.

[00:02:48] Matt:  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?

[00:03:01] Laurent:  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 life cycle development. So, everything from drug discovery, like research and discovery, to clinical development, to commercialization and the impact of drug 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 the dooms maker, like, oh, AI is gonna, like, 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 then focusing on what matters.

[00:04:12] Matt:  That’s awesome. And I’m sure it’s happening at just as faster rate of speed in the biotech, life sciences space as it would in health tech and the rest of the industry as well.

[00:04:21] Laurent:  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 makes a lot of startups very interesting and things that used to take years to build up, now you have a new up in commerce, like building a really exciting, innovating solution that we can piggyback on and use internally here at RevMed.

[00:04:47] Matt:  Love it. I wanna get more into that, but I’m gonna 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 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 you are today as you’ve navigated these things?

[00:05:21] Laurent:  I started as an engineer. So, I have a math degree in 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 the B2B space, obviously, the analytical world, the data integration world. What has been really pivotal was a change towards 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 happens. I have three kids. They are all in a 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 tech 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 a good partners along the way, like the founders, R.F and Webb, have been amazing throughout that journey, and I really had fun over there. And that was my ramping up into the healthcare world.

[00:06:30] Matt:  Sure. Wow. What type of doctor did you wanna be as you were pursuing that path early in your life?

[00:06:36] Laurent:  Yeah. Maybe a neuro doctor, or maybe a surgeon. You know, I like getting my hands dirty in everything, like, I’m still doing coding every day or every other day. So, yeah, I really like that part. But, no, I chose math and computer science.

[00:06:49] Matt:  I don’t think it was the wrong choice in the end. And now you’ve got three ushering 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 was 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?

[00:07:32] Laurent:  When I think about those, I always stayed within technology. Like, I didn’t completely change career, meaning that I’m not becoming a doctor at RevMed. I’m working with many doctors, pharmacists, and scientists, but the focus has kind of shifted 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 a B2B space, like, I was building products. So, I was building the engine. And when you think about the profiles, I had engineering teams, product management teams, and what was really key was people, like, close to the technology who understand how to build product, how to scale, and so on. As I got into the health tech and with Komodo Health, 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 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 Objects, at SAP, at Alan, where I was a CTO, and then bring that into the health tech industry. And when I look at the biotech, it’s something similar. The profiles, if I look at Komodo, and like Revolution Medicine, is extremely different. Like, I engage much more with scientists. People who really understand the science, who understand the chemistry of drugs, who understand biology, and the discussions are a little bit different. And for them, I’m much more of an enabler versus, like, building the core products. And going back to your question about, like, good talent versus great talent, and how they look like, 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 gonna 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 coal 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.

[00:10:29] Matt:  No. Said like a true, great leader, and coach, and mentor. You can mold people into what they wanna 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 U.S-based engineers and product folks. But going from where you were in Europe to being then thrust 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.

[00:11:19] Laurent:  Yeah. Actually, I went the other way around. Like, the first time I joined the U.S was in ’98. So, I started at Business Objects, an IT and BI company. We were based in Paris and in the U.S. So that company, like, from the get-go, they say, we need to be in the Bay Area for that innovation. We need to be in the U.S because this is where technology is happening, but they had the core engineering team in France. So, I started there six months, then they offered, you wanna go to London or do you wanna go to San Jose, California? That was an easy choice at the time. So, I wanna 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, like core engineering function was in France, and the French folks are extremely good when it comes to math and computer science. So, 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. Spent much more time with Germany. And this is where, like, this, yes, indeed, difference 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, and you do the same with 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.

[00:12:57] Matt:  I love it. Yeah. I mean, it’s the chameleon nature in which you’re talking about having to adapt and 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, and 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 the air is being sucked into it by, you know, another that is being oversaturated with interest.

[00:13:53] Laurent:  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 AI. So, I don’t think AI is overrated, like, as a core set of technology, because it’s really changing our lives on a day-to-day basis. Like, I was mentioning earlier, like, 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, like, there’s a lot of battles of vigos that I see right now on the web. Like, if you ask somebody what’s AGI, you’re gonna, maybe or 10 people, you’re gonna 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’s gonna be. So AGI is a little bit overrated. And if I wanna draw a parallel, I think humans are underrated. So, I’m not gonna talk about technology, but really humans. I grew up in Europe. Like, we are seeing things a little bit differently, like, socially. I mentioned the noise around the doomsdays and the people who are afraid that AI, GI, is gonna 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. Like, we can adapt very quickly something that we learn in a domain, like 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 walk as, kind of, a nest. So, I would, yeah, use those two, like, AGI on one hand being overrated and humans being underrated.

[00:15:57] Matt:  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 of years behind yours, but it’s 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 wanna touch on quickly the human aspect of things because I think you’re spot on, especially with AI, and that’s gonna 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 simulating and exciting.

[00:17:01] Laurent:  Absolutely. Like, as I mentioned, I was a developer back in the days, but you kind of lose touch 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 gonna experiment with things. And I think, like, AI really allows me to do that, to experiment. Fail fast, try new things, but get into a model where it’s more like show versus tell. As an 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, the production or not. But that vibe coding really helps 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, it doesn’t replace 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.

[00:18:44] Matt:  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 a 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.

[00:19:06] Laurent:  Yeah. Absolutely.

[00:19:07] Matt:  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?

[00:19:25] Laurent:  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 of 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. Like, that is very similar. Like, from one role to another, I think, like, the core of leadership is there. Of course, there are always some juices 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 twenty-plus years of my life, I spent with people that I would label as a software native or product-native people. So, people who think in a way where, hey, I see a set of problems with one customer. I’m gonna 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 Objects, 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, health care, or finance systems, 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 our tech, like, that was one industry. So, you narrow it down, then you need 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 companies, but in that healthcare space. As I got into the biotech space, what I’ve seen is that 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. But 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 all the data available to us, like, moving that away from, hey, this is a dataset, let’s bring data product, which has a life cycle and something that could be used downstream, something that needs contract and so on and 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 a project.

[00:22:19] Matt:  Got it. How does that, I mean, 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 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.

[00:22:50] Laurent:  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 the decision. Yesterday, I was in a meeting with a scientist. Like, I love that because it’s so educational for me. Like, I just 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 cells 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’re in the B2B space, where it’s much more like a 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 gonna die. Like, the impact could mean that if you are not thinking through that end-of-cycle properly, then you might not get a drug to the end of a patient. The drug might not be put in often, and so on and so forth. So, there’s much more alignment consensus, which could slow things down, but it’s a necessary ‘evolve’, if I can use that word, when it comes to patients. When it comes to patient data, there’s also a lot of risks and compliance 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 you have to put in place? Will you be able to reidentify your 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 a biotech or pharma wanted to bring their own data in, and wanted to link those datasets together to get new kinds of insights, you really have to think hard about re-identification. There’s some certification process and statistical modeling that you have to validate before you link datasets. So, those are just examples of where it’s very different from one industry to another.

[00:25:14] Matt:  For sure. And it’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 health care data comes from with an entirety of different levels of sensitivity and regulatory scrutiny? There’s so much restrictions that are there just because it has to protect the patient, their information, be HIPAA compliant, etc. What are the hardest trade-offs you’ve faced balancing this innovation with compliance? Because you’re probably doing it 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 about that whole journey that you’ve had to probably learn and unlearn and then relearn again as well.

[00:25:59] Laurent:  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, like, 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 at least a privileged kind of a 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 are, there’s data in the healthcare biotech world where you cannot move away from a regulation. And when you think about GxP validated environment, you need that to be really secure. Only a few people have access to that. You need to trace everything that you do on those data, like access control, data lineage, and the like. 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 or 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. 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 that the risks that you have to take into a 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, like, every user to the maximum of their capacity or access and show, I mean, bring trust to how they are using the data.

[00:28:19] Matt:  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 Komodo, the clients that you had, there are complexities, obviously, with this. What are the things 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.

[00:28:49] Laurent:  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 gonna be connected to providers, 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 a doctor, you might have a doctor who prescribed, you might have a doctor who puts a 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, 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 a determinant social health type of data. When I look at the world in biotech, there’s more data, more modalities. Now you have biopsy images, you have RNA, DNA, like all mixed 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.

[00:30:17] Matt:  Sure. Would you say one of those tools is AI, being a huge opportunity to kind of reduce the complexities and even kind 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 and understanding what they’re capable of being able to do?

[00:30:38] Laurent:  AI can definitely accelerate a lot of that. If I look at, like, imaging, you can use an AI system to help you, like, look at the biopsy and, at cancer cells and identify things that would take more time before. So, you can use that 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 definitely 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 lives, like the Gemini, the OpenAI, the Claude, a 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.

[00:31:32] Matt:  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 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 wanna look at it, which is a whole another 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 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.

[00:32:32] Laurent:  Yeah. So, you need a mix of both, absolutely, to your point. When you get into health tech or biotech, don’t think that you could just bring a team of folks who never experienced anything in that domain and be all like novel solutions. No. You need some domain expertise. It’s always great when you find someone like great talent who has domain expertise and who has also tech expertise. You might be chasing unicorns. I think that as people learn, as 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 have really domain expertise in biotech, but if you don’t touch clinical development, maybe you’ve not been exposed to validated GxP systems. 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 perspectives. Because if you bring somebody who’s gonna come in and say, I know better. It’s my way or the highway. This is gonna fail miserably. But bring somebody who’s a good communicator. Bring somebody who has a lot of interests 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 a 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 gonna be, but 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.

[00:35:14] Matt:  That’s awesome. No. It’s so fascinating. And 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 in 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?

[00:36:06] Laurent:  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 Medicines, 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 claim they can really lower the cost or predict clinical trial’s outcome. Companies that can predict pharmacology and whether or not a drug has got 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. Do I have the data? And when I look at Revolution Medicines, like, we sit on one of the most complete RAS addicted cancer databases in the world, and that really helps us speed things. So, going back to your question, I think it starts with that data foundation. So, over the next 3 to 5 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 become 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 semantics of the future. And going back to ’97 when I joined Business Objects, they had that concept of a semantic layer, which is, like, how do you turn, like, a database and build a semantic layer that represents the business? And when I think about healthcare in general, like, 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 why I really wanna push the industry.

[00:38:27] Matt:  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. That Revolution Medicines, yourself, your team, it’s inspiring, to say the least. As we close things up here, there’s a section that Rivera loves to do, especially with signal-to-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’ve talked on some of these topics, so it’ll weave their way into some of these areas as well. 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?

[00:39:13] Laurent:  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.

[00:39:25] Matt:  What’s a misconception technologists often have about the innovation in healthcare?

[00:39:30] Laurent:  For a lot of people, they think, like, technology is the endpoint, and technology is gonna 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 gonna solve everything. That’s a misconception.

[00:39:47] Matt:  Got it. And what’s the 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 to be defective if they don’t have it?

[00:39:59] Laurent:  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.

[00:40:13] Matt:  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 technology leader?

[00:40:25] Laurent:  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.

[00:40:37] Matt:  Love it. I’ve got three personal questions, so they’ll be quick. Mountains or ocean? 

[00:40:42] Laurent: Ocean. 

[00:40:43] Matt: What’s your favorite French restaurant in Paris?

[00:40:46] Laurent:  I don’t have any. They’re all good.

[00:40:49] Matt:  Love it. I’m trying to get you a free meal somewhere. What do you like to do in your downtime?

[00:40:53] Laurent:  I like to do triathlon, spending a lot of time on the bike. That gives me time to reflect, and I like to compete. I do a lot of racing, and yeah, I love that. 

[00:41:05] Matt: Well, we’ll have to make sure you join us for the Riviride, and I think for our guests, Laurent also is someone who not only competes in those races, but he also helps those who are, I guess, you know, blind or suffer from sight issues in their efforts to complete 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.

[00:41:31] Laurent:  How do you know that? Maybe I told you before.

[00:41:34] Matt:  There was our lunch in Nashville, where you shared it. I was blown away. I have 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 Medicines and that 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 and your character. So, this was great, Laurent. I really appreciate you taking the time and spending it with us.

[00:41:58] Laurent:  Awesome. Thanks.

[00:42:00] Matt:  That wraps up today’s episode with Laurent Bride, Chief Digital Officer of 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.
[00:42:33] Outro:  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 rivierapartners.com. And don’t forget to search for Signal to Noise by Riviera Partners on Apple Podcasts, Spotify, or anywhere you listen to podcasts.