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See how Oracle Red Bull Racing uses AI, simulation, software engineering, and data science to improve performance in one of the world's most competitive environments. In this episode of AI Changes Everything, see how technology, race strategy, and engineering innovation help teams make faster decisions and build competitive advantage at http://www.oracle.com/aichangeseverything In this episode of AI Changes Everything, Martin Galpin, head of technology at Oracle Red Bull Racing, explores how AI, simulation, software engineering, and data science contribute to performance in Formula One. Galpin explains how Oracle Red Bull Racing develops the specialized software it cannot buy elsewhere, creating tools that help engineers, strategists, and performance teams gain an advantage in a sport where every fraction of a second matters. The discussion examines how simulation and modeling have become essential to modern car development, especially as Formula One teams operate within strict constraints on testing, wind tunnel usage, and computational resources. The conversation explores how AI and machine learning support a wide range of activities across the organization, from information retrieval and engineering productivity to sensor modeling and vehicle performance analysis. Galpin shares how generative AI is changing software engineering, why the cost of creating code is rapidly declining, and why understanding, maintaining, and governing software remains a critical responsibility for engineering teams. The episode also looks at the role of AI in race strategy, where explainability, trust, and human judgment remain essential. Galpin discusses why high-pressure decision environments require engineers and strategists to understand the recommendations AI systems provide rather than simply accepting them at face value. He explains how Oracle Red Bull Racing evaluates new technologies, balances physical and data-driven modeling approaches, and focuses on measurable outcomes when adopting AI. Beyond technology, Galpin shares his perspective on the future of software engineering, the importance of domain expertise, and how AI is reshaping the skills organizations need to succeed. The discussion concludes with practical advice on working with AI, thoughts on the future of Formula One technology, and a reminder that AI is most effective when it acts as a force multiplier for talented people rather than a replacement for them.
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Episode Transcript:
00:00:00:00 - 00:00:15:06 Unknown The views expressed in this podcast are those of the individual speakers, and do not necessarily reflect the views or policies of their organizations or iCal or its affiliates.
00:00:15:08 - 00:00:33:15 Unknown In Formula One, every decision and every second can define a championship, but when the stakes are at their highest and there's no margin for error, is technology alone enough? Or does human judgment remain the ultimate competitive advantage?
00:00:33:17 - 00:00:53:17 Unknown Hello, everyone. I'm here today with Martin Gallatin, head of technology at, Oracle Red Bull Racing. And we're gonna have a fantastic session, I'm sure. Probably one of the most exciting video series we've done. Let's get right into it. So, Martin, can you just talk about your role at Red bull? What is it that you and your team do?
00:00:53:19 - 00:01:27:11 Unknown Thanks for having me. So, yeah, I'm Martin, head of technology. Oracle Red Bull racing. We, a group of software engineers and data science, that work on, projects for, the entire campus. We work on aerodynamics, vehicle performance, race strategy, the, the simulators, it in the mix. And then we have, some machine learning data science projects that that look across all of those different functions.
00:01:27:13 - 00:01:50:19 Unknown The, the overall goal of what we do in the department is to build the software we can't buy from, from others. So software the differentiates ourselves from our competitors. So I mentioned and I realize there's a lot of things you can't go into because of, you know, company confidentiality. But I mentioned earlier what your team does is that sort of that secret sauce, that extra thing that allows you to go a little bit further.
00:01:51:10 - 00:02:15:19 Unknown Can you talk a little bit about what the role of technology is in things like, developing brake strategy or modeling the car or just getting that little extra oomph? I think formula one is a a unique environment compared to most in as far as I think, simulation modeling data has been part of how we operate for decades.
00:02:16:13 - 00:02:49:10 Unknown I think Oracle Red Bull Racing were a lot quicker to recognize the value of software and modeling on driving confidence and I think the rest of the grid is now catching up to that. I think I see driving a lot of that, but I think fundamentally in Formula One with the restriction that we have now for testing at the track for the wind tunnel, and the CFD, fundamentally, most of your car development relies on the tools that you have available to you.
00:02:50:00 - 00:03:11:02 Unknown So it is pretty fundamental in designing, a winning racecar these days. And what was it about the Oracle Red Bull Racing that kind of made you have that focus on software engineering, where perhaps some of your competition? Well, was that a design ethos from the start when the team was set up or was that all down to you and your team?
00:03:11:04 - 00:03:42:16 Unknown No, I mean, I've been, Oracle Red Bull Racing for over ten years now. And so, I have been a long time, but largely I think the, the cultural, the philosophy and the investment in and simulation modeling, was a lot earlier than that. I think the, the, I guess the easiest way to it to think about it is the, the leadership at Red bull from a, from a, from the technical side comes from a very, modeling, heavy background.
00:03:42:19 - 00:04:16:15 Unknown And I think that as a culture as approaching problems in a, in a computational way, the when you go back 15, 20 years was actually, was actually quite novel. Whereas now we've accumulated 20 years of experience in the problems where maybe others have five, ten, and the tools that we have in the foundations that we, we've laid over the years, I think, a lot of the continuity of success we've had, over the last decade and it's clearly showing in the results.
00:04:16:17 - 00:04:34:09 Unknown Now, Martin, you spoke just a few moments ago about some of the limitations when it comes to things like testing and simulation, which was a surprise to me because, you know, the impression I have is that you're pretty much unconstrained. But that's not the case, is it? Can you just give some color around what these limitations are? Yeah.
00:04:34:09 - 00:05:03:11 Unknown I mean, there was a time when we were unconstrained. The that was 20 years ago now we have a project to, to start with. So we have an overall capital, minimal, but we can spend in a given year. We have restrictions on track testing, so we are only allowed to run this year's car. At a certain number of events a year, most of that pre-season.
00:05:03:23 - 00:05:29:09 Unknown So there's no opportunity to run the race car outside of race events during the season other than some marketing events. And then we have restrictions on the wind tunnel. So how much time? We have to test the model of the car. Before we build components and, to develop their dynamics, as well as some regulation around the computational time to do safety.
00:05:29:09 - 00:06:00:01 Unknown So fluid dynamics, so that the only real areas at the moment that are not constrained is, is the simulation tools, with the exception of the wind tunnel and CFD, obviously. So it was a higher importance on those those tools being being good. Yeah, absolutely. So this is where I sort of tell it really comes in having people in the team that can just sort of be highly creative or squeeze the absolute and degree out of a piece of technology, either of any limitations on the technologies that you can use.
00:06:00:01 - 00:06:24:16 Unknown It's all within, formula one. There's not really limitations on the technology itself. There's just limitations on the constraints of our users. So as I say that the cost is is one aspect, but if we take, say, the CFD regulations, where obviously the, the fluid dynamics is more cost effective than building a model and running in a wooden tunnel.
00:06:24:18 - 00:06:50:18 Unknown But the the constraints of such the, the there's not an unlimited amount of compute capacity that we can use to do CFD in an attempt to bias the advantage away from teams. Who can you can find more, more compute capacity than others. So yeah, that's not really a constraint on the technology as such. But there is constraints that change which technologies you pick and how you approach problems.
00:06:50:18 - 00:07:13:23 Unknown And just talk a little bit more specifically about AI. Where are you using AI today? You know, can you give an insight into what sort of areas of the business where AI is playing a pivotal role? So I think when we talk about AI, obviously AI is lots of different modeling techniques. Lots of different applications of technology again, available.
00:07:13:23 - 00:07:49:11 Unknown I think we were quite early in that journey in this as far as we've had a machine learning group, in the department since, I think 2016. So almost ten years now. And obviously the landscape for AI is changed a lot in that time. But it really depends where, where in the spectrum of car development you look, there's aspects where like what organizations we can use, recent AI to drive efficiency and operational, efficiency.
00:07:50:03 - 00:08:20:16 Unknown In terms of information retrieval and building software systems that are, you know, better, integrated and smarter. But then there's also aspects closest to the car where perhaps we can use, machine learning techniques to, either that, say, model sensors on the car to reduce mass or, or improve the physical models that we, we would normally have, with, you know, surrogate model and a lot of the techniques.
00:08:20:16 - 00:08:42:10 Unknown So, yeah, it depends where in the organization you look. But I think the overall impact, I think is everywhere now. Yeah, it's become infused into what you do. And, you know, thinking about generative AI, is that something that you and your team are starting to use and might even be in back office capability as opposed to on the car itself, which is gentrified?
00:08:42:15 - 00:09:12:04 Unknown You're making use of the large language models at all? Yeah, absolutely. I think in the last few years, obviously the viability of a lens for, so for making tools better has become, genuine. And I think, again, there's two aspects is using generally as software engineers to build better tools, faster. And then there's also using a lens to help us build better tools that engineers use.
00:09:12:04 - 00:09:34:11 Unknown And I think you're starting now in the last couple of years to see that becoming a reality. In addition to the the gains, I think that everyone is experiencing with software engineering, at the moment. Yeah. And have you seen that impact? You know, because your background is software engineering, right? Where's the most profound impact been in that software engineering sort of continuum life cycle?
00:09:34:14 - 00:10:06:20 Unknown Where have you seen it? I think the the biggest thing is the pace at which has changed in the last 18 months. And I think we're in a position now in 2026 where the cost of creating code or the cost of creating software is almost fallen to zero, right? So the the the problem is not how you create software, but the cost of owning that software or the cost of building the right software and maintaining it over a long period of time is not zero.
00:10:06:22 - 00:10:30:04 Unknown So it's kind of just changed the the way you approach problems. And I think we are on that journey like I think everyone in the world is at the moment, but I don't think anyone yet has either realize the full potential of what's happening or have we reached the end game for what I think will happen in the next couple of years?
00:10:31:01 - 00:10:53:23 Unknown The the way the AI is built software, and most people have written in software over the last decade is not the way you would write software today. So it's already changed in a fairly fundamental way. Yeah, absolutely. It's been quite stark to actually watch that change over the loss. And it's been fairly recently, last 1 to 2 years.
00:10:54:01 - 00:11:19:01 Unknown Okay. So look, we've been talking about the use of AI. Are there any areas where you would prefer not to use AI or our code Red Bull Racing of taking a a decision? Not actually, no. We're not going to use AI. Perhaps that's where we need human because at the end of the day, the times when you want humans to exercise judgment control, is there any areas that you perhaps would prefer not to use AI?
00:11:19:03 - 00:11:55:19 Unknown Many, I guess to give two examples. If we take how we approach race strategy, which I know we've, we've talked about in the past and the projects we have with Oracle, over the years are pretty well documented. You would traditionally approach, race strategy projects using numerical methods, Monte Carlo simulation. And over the years for the project we've we've had with Oracle has been to to bring the, the, the barrier of compute capacity down so that we can run more simulation during the race.
00:11:55:21 - 00:12:23:03 Unknown And I think the natural evolution of that tooling is towards AI and towards rather than taking advantage of Monte Carlo methods and law of large numbers, you try and take a more data driven approach where you learn behavior, at the track instead. But I think the key, challenge with methodologies that are learned is understanding their behavior.
00:12:23:05 - 00:12:50:17 Unknown And I think the, the, the challenge that we have in adopting that technology in race scenarios where it's heated, the moment high stress environment, the stakes are very high. Is the explainability of the technology that you use. And obviously there's lots of academic research. There's lots of progress in explainable AI, over the last year. And that's it's definitely improving.
00:12:50:17 - 00:13:18:04 Unknown But I think the there's a, I guess, intellectual skepticism that you have to approach it with the means, the where we are at the moment, the human or the the strategies on the pit wall still has to take a decision and has to understand the options he was presented with. And then if we live a in another area, I think,
00:13:18:06 - 00:13:43:11 Unknown If we can model a problem physically, he would model it physically. Right. So I think where machine learning particularly maybe slightly different to AI, helps is the ability to learn models from data that they are very difficult to model physically. And I think that balance of when it's right to use a physical model, when it's right to use a data driven model, is is always a challenge in formula One.
00:13:44:06 - 00:14:11:04 Unknown Sometimes you want to use a data driven model that maybe the data is too sparse, so you can't. But the trade off between physical data driven modeling, I think is is probably the biggest challenge that that we have. Yeah. And I think anyone watching this, this interview would be quite reassured to know that there's still a human exercising decision in the pits, because otherwise potentially a device could get a little bit too boring, right?
00:14:11:04 - 00:14:37:01 Unknown If we knew that, if I were controlling it, I can I just ask a question based on sort of where you see things going. So first, but I wanted to ask, is your team in software engineering? Are you seeing a change in the profile of the software engineer and people that are being brought in and recruited into Red Bull Racing, or not, you know, has that changed over the last couple of years?
00:14:37:01 - 00:15:03:19 Unknown Do you see it changing in the future? Yeah, absolutely. At the moment, I'd say we haven't consciously changed our hiring practices as an I think it's it's premature to trade people for technology at this point in time. And obviously, this question marks about whether we should do that as a society anyway. But I think clearly the work that a software engineer does has changed, and that that is genuine today.
00:15:03:21 - 00:15:40:23 Unknown And I think that the way you will approach, entry level software engineers or graduates now on over the next few years will also change in terms of understanding the right way to use this technology to accelerate things that you build, but also creating an environment where junior engineers continue to learn. Right? Because cognitive atrophy is real, when the more you use AI to write software, particularly in your early stages of your career, the less perhaps you learn about the systems you will you're building.
00:15:40:23 - 00:16:03:12 Unknown So I think, yes, today the impact is profound. We're still on that journey of figuring out exactly what it is that we need to change. But, for sure, over the next three or 4 or 5 years, there will be, I think, a revolution in the way that you approach building software, which we're now starting to understand again.
00:16:03:12 - 00:16:41:12 Unknown That's really encouraging to hear because I think there's still this, this need to hire problem solvers. Right. And that's a very human quality. Yes. I can do a large amount of that. But the creativity in that innate problem solving, like curiosity, you know, there's no substitution for that, right? Yeah. I think I think it's interesting the it depends what you think the value in a software engineer is, is the value in the software engineer in the code that we write, or is it in the the tools that we build, understanding of how you marry requirements to, to tools or to output outcomes.
00:16:41:12 - 00:17:08:00 Unknown And I think we'll see it bias more towards that domain understanding more than it is the practice of of coding I completely agree. Yeah, but domain expertise will always jump up since we're talking about software engineering, there'll be a number of people watching this kind of thinking, you've got a dream job, Martin writes, myself included. Do you mind just giving us a little bit of insight into your own personal journey?
00:17:08:00 - 00:17:33:21 Unknown I mean, how how did you arrive at this position? What was your background? The journey? I guess the first thing to say is, yeah, obviously I'm very lucky to do what I do. I get to go to work every day and enjoy the problems that we work on and the people I work with. You know, it really is a luxury to work with people that are smarter than you, that you can learn off all day.
00:17:33:23 - 00:17:54:15 Unknown You know, whether or not it's the software engineers we have or, people in in the other engineering functions that are truly world class in aerodynamics and different topics. So that's the first thing is to recognize that, yeah, I'm very lucky in terms of how I, how I got there. I've been involved in this for a very long time.
00:17:55:02 - 00:18:19:14 Unknown I used to race cars as a kid. I, I won races and championships, but eventually I had to, get a real job. I also studied, computer science, so, naturally, computer science and racing cars. Fancy formula is the kind of nexus of those two, desires, I guess. And then I started off working at a former team.
00:18:20:09 - 00:18:38:04 Unknown I was there for five years. And then I moved to rebel, over ten years ago now. And I've really enjoyed pretty much every, every year that I've been on the team. Excellent definition, dash bottom, but you can have a little bit of fun if that's okay. And then we'll come back to a few more serious questions.
00:18:38:04 - 00:18:54:11 Unknown I always do this sort of quick fire round. So the idea behind this is you got about 30s whatever comes to your mind first, one sentence or one word answer. There is no right or wrong. Okay, here we go. One word for AI today.
00:18:54:13 - 00:19:36:19 Unknown Potential nice. One word for AI in five years. Revolutionary. Nice. AI agents hype or real shift? Real shift. Cricket. The biggest risk in AI right now. Not understanding the technology. Biggest opportunity. Real productivity gains. Very good. One thing leaders consistently get wrong misunderstanding the technology. Okay, Bell's gone, but I'm still gonna ask you one more question. If it were one AI term you wish people would stop using, what would it be?
00:19:36:21 - 00:20:00:16 Unknown People's preoccupation with Rad at the moment is something I hear a lot, and I think that comes back to to misunderstanding that the technology, and thinking that we can apply some of these techniques to every problem and and it leads to good outcomes. Yeah. That's interesting. Rock. Yep. I thought we'd got past the rank discussion, but it seems to be hanging around, isn't it?
00:20:00:16 - 00:20:27:08 Unknown Yeah, yeah, yeah. I'm hoping it's not that long. Right. So I wanted to sort of look a little bit further out. You know, two years is a long time in Formula one, so I won't ask you to plot forward more than two years, but where do you where do you see the use of technology being applied to formula one evolving over maybe the next 12 to 18 months, if you can sort of look out, please?
00:20:27:10 - 00:20:59:17 Unknown I think the role of technologies will just continue to grow in terms of its impact. I think as I say within data driven and simulation modeling led for for decades. But I think that if the trend of development or the rate of development in modeling techniques increases in a way anywhere similar set of elements for foundational models and physical modeling, I think you could start to see real profound impacts across actually the way that you design the car.
00:21:00:15 - 00:21:29:01 Unknown Obviously lens helps you with, with the building of better tooling for engineers these. But they, they are unlikely to help an engineer come up with a novel solution or for you to model a problem in a different way. And I think you're starting to see signs that similar trends are happening in, in, you know, foundational modeling for physics, for fluid dynamics.
00:21:30:09 - 00:21:55:22 Unknown And I think the, you know, the impacts of that change will potentially be greater than, than what we've seen at a lens in terms of the actual direct impact on commercial performance. Yes, absolutely. And I'm one one more question. We've the whole this whole series is called, I Changes everything. Is there one particular thing that you think I should not change?
00:21:55:22 - 00:22:17:22 Unknown Or even if we can change it, we should leave it alone. Where should not be used? I think we should. We shouldn't be approaching problems thinking that AI replaces what a person does with a. Let's say we said earlier about the strategies on the people, but I think it also applies in software engineering and, and other, other areas of the team.
00:22:18:03 - 00:22:49:23 Unknown I think formula one is fundamentally, a war of brains. And I think that you still want to have the best people. And I think the AI acts as a superpower for them in order to be more effective, but it doesn't replace them. And I think, yeah, when we approach, the adoption of AI and the introduction of it into the tools that we build or the organization, we we still have to, I think, value the experience of, of humans and engineers.
00:22:50:04 - 00:23:10:11 Unknown Absolutely. Well said. I product of the week. Martin. What are the questions I always ask is, do you have a favorite prompt or a way that you, you tend to interact with the LMS that you'd perhaps like to share? You know, I think it's quite easy to take for granted just how much information and knowledge you have available to you any moment now.
00:23:10:11 - 00:23:35:12 Unknown And one thing I find myself doing more by default is talking through technical problems, design algorithms, architecture with an alarm or a chat bot as my default for how I solve problems. And I guess more specifically in terms of prompts. When I'm doing that, I find that sometimes you actually really have to ask the alarm. Are you sure?
00:23:35:14 - 00:23:57:22 Unknown Are you sure? Can you check that before you really arrive at the right answer? Very good. That's great advice for all of us. Thank you. Well, Martin, unfortunately, we've come to the end of our discussion. I think we could go on talking for ages. And I have to say, I think you've given us a rare insight into what, you know, what's going on behind the scenes of formula One and also Red Bull Racing.
00:23:57:22 - 00:24:40:12 Unknown So thank you very, very much. I really appreciate your time. And to everyone else watching, I hope you've enjoyed this as much as I certainly have. And I'd encourage you if you want to watch more, interviews and discussions in this series, go to our Qualcomm forward slash I changes everything button. Thanks again.
