Challenging Deep Tech Norms: Adopting Lean Principles from Lab to Market | Mark Bjornsgaard

In this captivating episode of Lab to Market Leadership, Chris Reichhelm speaks with Mark Bjornsgaard, the visionary founder and CEO of System Two Group. With a track record of eight successful exits - notably the £200 million sale of Deep Green Technologies to Octopus Energy - Mark brings a powerful, often counterintuitive, perspective on building businesses, especially in the Deep Tech sector.

 Mark challenges the conventional wisdom of ‘build-measure-learn,’ arguing passionately for a hypothesis-first approach. He explores why the most valuable skill for an entrepreneur is the ability to ruthlessly ‘kill’ ideas that lack market validation, even if they're technologically brilliant. The conversation explores the psychological barriers to effective innovation, the crucial difference between a solution and a testable assumption, and how genuine behavioural insights drive adoption.

 Discover how System Two Group has consistently applied these disciplined methods to achieve remarkable success across diverse industries. This episode is essential listening for Deep Tech founders, corporate innovators, and anyone seeking a more effective, less wasteful path from lab to market.

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Podcast Production: Beauxhaus

Transcript

Mark Bjornsgaard: 0:00

So many people kid themselves that ideas are a good thing. Ideas are a disaster for the ideas themselves are, are a, a disastrous way to start any entrepreneurial journey. But, but it's hard to do anything else because it, it's not intellectually hard. Anyone can understand how to define a hypothesis, but, but it's awkward. It's, we're back, we're straight back to therapy. It's, it's bad on the ego. It hurts the ego to be told that you are the thing that you love. To actually acknowledge that a thing that you think is brilliant is a brilliant idea. Actually at the heart of it's got, it's, it's, there's, there's some fundamental in it. Not most people don't wanna know that.

Chris Reichhelm: 0:40

Welcome to the Lab to Market Leadership podcast. Too many advanced science and engineering companies fail to deliver their innovations from the lab to the market. We are on a mission to change that. My name is Chris Reichhelm and I'm the founder and CEO of Deep Tech leaders. Each week we speak with some of the world's leading entrepr. Entrepreneurs, investors, corporates, and policy makers about what it takes to succeed on the lab to market journey. Join US Lab to Market Leadership listeners. We are taking Lab to Market Leadership on the road. We're traveling through Europe, we're hitting the us and then we're coming back to the uk. All the while continuing our discussions with entrepreneurs, with corporate executives, with investors, with academics and policymakers trying to understand what it takes to deliver innovation and companies from the lab to the market. Today's episode is gonna be nothing, if not thought provoking. We're talking with a gentleman named Mark Bjornsgaard, who is the founder and CEO of a venture builder called System Two Group and System Two are involved in applying lean management principles and practices to the development of all kinds of businesses, including those in the deep tech universe. Mark has had eight exits all successful, including one of his most recent Deep Green Technologies, which was sold to Octopus Energy for more than 200 million pounds. Whether you can apply fully lean management thinking and practices to the lab to market universe. I'm not sure I'm sceptical, but Mark is very convincing. Let's see how he does. Let's get into it. Mark Bjornsgaard, thank you so much for joining me. No, it's great to be here. Thanks for having me. You are a big proponent and believer. In lean principles and lean thinking in, uh, for the development of businesses and, uh, you're not alone. I think the, the global software industry, the global SaaS industry has certainly grown up practicing a lot of those, uh, those disciplines. Uh, Eric Ries is held in the highest esteem, uh, as a management thinker. And if we compare the kind of 1960s, uh, seventies, eighties view of strategy, development and, and operation in the world that we live in now, or maybe now that we're more finely tuned to what reality is perhaps this, you know, this, this very lean way of thinking and acting when it comes to developing a business is the way to go. My question. Is, and I wanna frame this just at the outset of our discussion today, but my question is, how much of this can we apply to the advanced science and engineering industry, particularly to deep tech? And, and, and I ask that because for most deep tech businesses, they are developing advanced science and engineering platforms. There's a high degree of technology risk. Generally these are, uh, platforms that have been in gestation for years. They tend to be IP rich, and they are, uh, often, particularly when they're spun out of labs or universities, they're often developed with a problem vaguely in mind, but not so clearly in mind. And I think this applies in particular to life sciences. So if we're trying to create a new molecule or protein to combat disease, so we ha, you know, have a very clear understanding of what we're trying to do there. Potentially in medical devices as well. Potentially in the other deep tech industries, non-life science is non-medical. Uh, there perhaps that problem isn't framed as clearly, but nevertheless, because of the length of the technology development cycle and because of the the lab to market journey itself, it makes me wonder whether management teams building those businesses can be as nimble. As experimented, that's the word. Experimenting as lean teaches us to be. So with all of that, and we're gonna have a conversation about this for the next hour, but that's what, you know, I really questioned just how much we can go to that place. But I know you're gonna tell me and I know you're gonna have lots of examples. So before we do that, please. Let's back up. Tell us a little bit about your background, because you've got a really most unusual background, especially within these industries.

Mark Bjornsgaard: 5:31

Unusual is often a polite way of saying yes. Saying something, something polite. I so thank you for having me first of all, and yeah, I'm, I'm very much looking forward to getting into the conversation. As you say, businesses that are shuffling molecules or have high degrees of technology risk in them are themselves fascinating in a way that. Way that kind of other businesses, when you've got innovation, the business model or you've got innovation in, in, in some other aspects of the, of the model is they're different. Uh, my, my background, well, I'd studied history and politics, so that's a trouble maker's degree apparently, and I think. I'm not sure I've ever, I certainly haven't taken any sort of structured, um, clever path towards what I was, I've done, I guess I've always just looked for the adventure in life. I didn't really have that luxury, I must say, but I just couldn't bear the thought of, of doing something conventional. So my first job was working at a record label. I worked at record levels all while I was at college and just sort of stuffed jiffies, uh, out with full of CDs at the time. And then after that we set up our first startup and it was really that, that it was really that journey that I, we went on, which was, which was just go and find stuff that's interesting, that kind of gnarly pro problems to, to crack. I love gray areas. I think a lot of people in who are doing this. Love resolving. I think there's a bit of autism in there somewhere, which is, you can't just can't bear to have the thing unresolved. So I think there's a sum of that, but I think like many people doing this, it's uh, we all come from wildly different backgrounds and we also, we learn our craft and we learn our trade in different ways. I think one of the most important things. I, I think one of the most important things that I ever did, uh, was have therapy and continue to have therapy. I think some degree of self-awareness is incredibly important. I needed it by the way. I, I, um, I needed it, but that's another story. But, but I think, yeah, I, I think, I think people find themselves into this, this, this world in different strands. But if you've had a, a decent amount of success, the principle. That sort of self-awareness that, that, that, trying to model reality, trying to see what's really real and what you are, what you're being either presented with or what you, you might be kidding yourself about. That is really, that is really the unlock at a kind of meta level. And I guess that's also what lean in its own way. Does it?

Chris Reichhelm: 8:19

Were there but there no go. Sorry. Were there certain types of, so I've got a couple of questions in that you, you raised. Really interesting point. Were there certain types of problems that you were drawn to? Uh, only ones that you've gotta have commitment and passion for what you're doing. The number of people who are trying to build a star chart, and I always, I just, I can't get past that. I've always won. I've always only been able to do something that my brain has got kind of hooked into with that. Digital distribution of music or a, a, a decentralized care business, whatever it is. I've always, I've had to have something. Commitment is transformative. Uh, and, and so I think that is, I've, I've, I've, I've sort of busied myself and got involved in a whole bunch of different businesses across different sectors, and, and I think the only real, sort of, the only real thread between them is those things that I've got. Just, I've just got interested in personally. Yeah. Was it an idea or was it people or was it something else that acted as a catalyst? I think there was, uh, Michael Li, Michael Lewis on his podcast recently, he said, I just love, I, I can't remember exactly what his phrase was, but he just basically loves going up against something that's bigger, gnarly, and that's difficult. And I think I've always been quite attracted to that. I've never, I've never really sort of been that enthused about what I think of as stripe socks businesses, which is sort of traditional entrepreneurial endeavors, which are to, you know, absolutely no question with them. But I've always tried to find big trouble.'cause that's sort of where the big adventure is, I guess. Mm-hmm. But that it doesn't always end well. No, no, it doesn't. The, you, you mentioned, and, and, and thank you for sharing, as you mentioned the, the therapy piece too and the importance of emotional awareness. Um, can you talk a little bit about how, how that's, how that's helped you? I'm guessing that's helped you, you mentioned it, it, it's, it's been, I think, yeah, it's been very productive for you. But how so? I think you are you in some way, oddly, when you. When you participate in therapy, you become a much better listener because you're ultimately listening to yourself as well as the advice from a therapist. So I think, I think to some degree, it's, it's understand you, the, the understanding comes through the acknowledgement. The awareness of, of, of what is in your life. And I think that, yeah, so I think it's not so much, uh, it's not so much sort of the big insight. It's not so much removing the blockers in life because there always are, but. It's really more about, I think I wasn't, I'm still not very good at it, but I'm, but you become better at, at listening and, and creating some sort of, uh, uh, some gaps, some sort of mind space in and around whatever you are looking at, which itself is useful because you are so often we just, we just pursue or we continue because that's the, that's the, yeah, that's the trajectory. It's very difficult to ask ourselves whether something's the right thing to do all the time. Yeah, yeah. But, but as an entrepreneur, it, it's, in many ways it's critical. You've got to be asking yourself those questions, I think on a regular basis. Yes. And you've got to, uh, yeah. And you've got, and, and the most important thing of all is you've got to be able to kill your ideas. That is the single biggest, uh, that's, that I think is, I, I, I've been asked this question a bit and I think I've, I've sort of landed on that as perhaps the only thing I really can do very well is that I can be, I can be oddly sociopathic about killing an idea, which doesn't work. Mm-hmm. And I, I dunno whether that's just the character. I may, maybe there is, maybe I score high on the sociopath test, but, but I think that's also an ability. I think just acknowledging when something's wrong and being able to act on it. When you are, the number of times, the number of times, particularly in recent history where I, I dunno, you are, you are very often wrong. You are very, you are very often not wrong when you, uh, there's a, there's a saying that the first person you think, the first time you think about firing somebody, you should fire them. It's kind of the same thing with a business. Yeah. The first thing you, the first time you think about killing it, kill it. Yeah, because that, that whatever has, whatever has made it through from your subconscious is, is usually telling you something you need to hear. How old were you when you took on your first entrepreneurial journey? Or you, sorry, how old were you when you took on your first entrepreneurial role or experience? Yeah. Artist first was our first business. So 27 years ago, uh, nearly. We, uh, yeah. And that, and that was, we made all the, all the same mistakes that everyone makes in a business. We built technology. We believed we didn't test our assumptions, we raised a ton of money. Uh, and maybe you have to go through that. Maybe that rite of passage is something that you have to go through. Mm-hmm. Maybe it is, but I guess since then, I'm reflecting on the good and the bad of that business. You know, that I guess is where, where, where I sort of, I, I differed then that's where I parted ways with a more conventional approach. It was always incredibly frustrating to me that we were doing things that, that we thought were right, but we didn't have any data to suggest they were. So that was the, that was the, that was the kernel of it. Was that, did that lead to you, did that lead you to start exploring. Lean, lean principles were people talking in those days of this type of different approach? I think we knew, we were writing software, so we kind of understood the principles as sort of good software development, which, which lean kind of leans on if you like. So, yeah, I guess for me, lean always had the problem and the challenge that it started with build, it started with building something and you. Hadn't necessarily declared, declared what you're trying to learn. So that was, and I always went back to my kind of, you know, GCFE early kind of, you know, school, uh, science teacher who was, whether you define a hypothesis, you, you, you, you have an aim. You, you're trying to figure out what you're trying to learn and then you build an experiment to, to, to do that. So for me, that's where I think we started. I started to diverging my thinking. Which for the, the whole Lean Startup thing, it seemed too co to me. It seemed too focused on, on EE even. It needed to go one step back, if you like. It needs to abstract itself one step further, but the challenge with doing that is that it becomes, on the surface, life becomes very dull because if you can talk about building an MVP, then it's super exciting. It's fun, and it's, but. No one really wants to be told they have to define a hypothesis, which is, which is what I've written a book about. There's no, there's no book in the Amazon bookstore with hypothesis in the title 'cause it's unfathomably dull. You know, no one, uh, no one wants to go back to their, their, their, their school chemistry because, uh, well not many people do anyway. But I, but yeah, when, a long time ago, we figured out that actually if we got more and more disciplined about what we were trying. The test, we then realized that actually there was, that was completely wooly and that everyone is wooly about that. So when we talk about ideas, we are actually talking about these random bundles of assumption and, and actually the discipline, the approach is, is taking everything that you've got rattling around in your head, getting it out on a whiteboard and breaking it down systematically. And it doesn't have to, doesn't have to take very long. But it is so that, that's, that was our unmark. Okay. Is, does, does currently in thinking, assume that this process you are referring to right now is baked into build? They, uh, lean assumes that, that the, well, it doesn't really matter where you start, just build something and then you'll get into the learn. Yeah. Build measure, learn loop. Yeah. And that, and that is legitimate. That's legitimate. The trouble is. If you start to build something, you don't necessarily know what you're seeing, what you're trying to learn, you don't necessarily have a, a baseline say, okay, I was trying to learn that and therefore this is next one. So it, it is in itself, in my view, inherently wasteful because you don't, you don't start from position of what someone trying to learn, but the real problem is that you, you don't actually ask yourself a really awkward question right at the beginning of the process, which is. Am I really clear about the product I'm trying to sell and the customer I'm trying to acquire? Mm-hmm. So many people kid themselves that ideas are a good thing. Ideas are a disaster for the ideas themselves are, are a, a disastrous way to start any entrepreneurial journey, but, but it's hard to do anything else because it, it's not intellectually hard. Anyone can understand how to define a hypothesis, but. It's awkward. It's, we're back, we're straight back to therapy. It's, it's bad on the ego. It hurts the ego to be told that you are the thing that you love it to actually acknowledge that a thing that you think is brilliant is a brilliant idea. Actually at the heart of it's got, it's, it's, there's, there's some fundamental straw in it. Not most people don't wanna know that. And so, you know the, I think you're right. I think, and I think it's especially true for. Experienced researchers, and if we think about the deep tech industry, if we think about lab to market journeys, it's generally starting with an academic or a researcher or perhaps an experienced engineer. You know, it's, it's gonna be someone in research. Yeah. Or an experienced technologist of some kind. And the journey generally starts with, I know something, I have an appreciation for a particular area. I've been observing and operating within this area for some time. Here's my observation. This doesn't work. It needs to be solved. It represents a, a good problem and I'm gonna go and try and solve it now. And I think to your point, they, they'll ruminate on that idea. They develop that idea over time and it becomes a center point of their research, perhaps for their PhD, perhaps for. Uh, you know, another piece of research, sometimes it's given to them through a grant and they say, okay, sure. I need to work on this as part of a grant exercise, so I'm gonna go and I'm gonna, I'm gonna build my, my knowledge and skills around this particular problem. But to your point, there's not an awful lot of validation past that point. So they may develop insights. Now they may be very experienced, most generally are not, though they don't have that much experience in the space. They won't, so they're less inclined to go out and start validating, is this a problem worth solving, and how might we approach that problem? Instead, it's let's start developing something. Let's start developing an innovation and see how that goes. Yeah, yeah. That starting with technology is. Counterintuitive, but whenever you're trying to test a hypothesis, pretty much the last thing you wanna do is start with technology. So if you are a deep tech startup and you are by definition anchored in the technology, then you are gonna, it's not, it's, it's very, uh, it's very plausible that you would start with some sort of dilution. You'd start to, to, to build your business. You would think, well, I need the technology, but of course, that's the last thing you need. The absolute last thing you wanna be doing is building any technology. Hardware is hard. Uh, and, and, and the every single assumption. If, if you are, if, if you can be disciplined about what you think is the behavioral driver behind the product or service, which you are trying to offer, if you can be disciplined about that and really understand your user, then 99.9% of the time you don't need to build technology. You don't need it. You, what you need to do is. Find some other faster, quicker, cheaper way of testing the assumption. And that's generally not building any tech, but at some point, but at, at some po. So, so to validate the thinking, to validate the hypothesis. Yes, you may be right on there. Um, yeah, and I've read your presentation and so I know some of the, you know, some of the thinking that goes around this. You can work with partners to kind of help validate some of that thinking as well, but at some point. If you are developing a company that intends to be, uh, an important or play some kind of role in a value chain, which is what most deep tech companies are trying to do, they wanna become a part of a value chain. They wanna contribute to that. They wanna become proper small companies. Uh, they wanna be able to, uh, build product, scale that product and be a supply chain member in a particular domain at some point. That is we're gonna require, especially if it's deep tech technology. But so then is the point.'cause then what's the value? Well then, but then what's the value they're getting out? What value do they capture in that exercise and how do they capture the value in that exercise? The the trouble is when we start to build a business, we think we're building a business, but we're not, we're testing an assumption. So if you start a business thinking that you are building a business that's inherently wrong. So everyone starts with that assumption exactly as you've described it, but that's wrong. Because if I've got, if I'm trying to, if I, if I think I can create a new molecule to cure cancer and, and yeah, I think it's gonna take me five years, why don't I, why do, and I, and I'm trying to test the assumption that someone will buy that molecule off me. Why don't I just create a piece of paper that says I've done that. I, I've created that molecule, and then lie test whether somebody wants to buy that molecule off me. As, as reef steads all the time. There is only two, there are only two scenarios in that. One, no one will want it, in which case you haven't wasted any time building a molecule. Nobody wanted. Two, everyone bites your arm off for that molecule and and they go, oh my God. They go, Chris, I love this molecule. Please give me this molecule. And you think, brilliant, I'm gonna go, I'm gonna go and make that molecule now. Mm. But the point is there's no. You have to test the desirability assumption before you test the feasibility and the viability. There's no logic to doing anything else otherwise, otherwise you're just, you're just having a pump. Yes, yes, yes. Uh, so I think that is all. So I think that's true. I think that within the life sciences, let's stay in the life sciences, uh, for a moment, which, which has some distinctions from kind of typical deep tech. If you develop a novel molecule that is, uh, is great for, uh, I dunno, treating certain types of lung cancer, um, yeah, there is, you know, generally it's felt that there's gonna be a market for that. Um, now, it may be now just what kind of market there's gonna be for that ultimately is gonna depend, and I, my understanding, again, I don't work in these industries, but we support and advise these industries is that obviously. The, the ultimate customers are not gonna get too excited about anything until they've seen, to your point, more data. So yeah, there are lots of people out there saying, Hey, I might have this and I might have that, and so on. And if this was all easy to build, it would've been built. So they, you know, there are lots of caveats going into their communication. Well, if you were able to create something novel like this and it had efficacy within this range and so on, then yeah, we'd probably bite your arm off. Good luck developing hit. And so, so much of that challenge goes in the development of that, of that. So a hundred percent. But that's why deep tech is not deep tech business. That's why, that's why you've gotta, there's, there's always gonna be that balance, but just asking yourself who those people are, who might want your molecule and why they might want it. Yeah. Stirs you very well before you start building the molecule. Yeah. Now of course there's always a balance between, particularly in life sciences. Yeah. There's always a balance between the r and d process and the business process. Yeah. And that there's, there's always a ying and a yang and a trade off with it. But, but the trouble is a lot of people will do five years, uh, five years on something before they ask themselves, well, hang on, what's actually the behavioral driver? What's really the. The, the human hook in someone's mind that's gonna make them wanna buy this molecule. Yeah. So it's not to say, it's not to say it's for, it's all smoke and mirrors, but there's just always gonna be a balance between that and there's, and that validation, the problem is that most people actually don't want that validation. They are convinced that that molecule is, is amazing and it's become a haw crutch embedded in their soul. Yeah. And the last thing they want is somebody going, but yeah, but who's gonna buy that molecule? And for how much and where. But let's take, let me play devil's advocate. Let's take mRNA, which was a, uh, our way of combating COVID-19. And Kathleen Ko and, uh, her colleague in the us she started working on Mr. mRNA therapies, I think 30 years ago. And for the longest time, she was in the, she was, uh, she was not considered a serious researcher by prestigious academic institutions. She was not given any kind of grant funding. The, the industry, we can call it the industry, regularly looked down on the research she was doing. She knew, she believed she was onto something. And later on, her colleague in the us, and forgive me, I don't forget, I don't remember his name, but, um, but eventually they kind of carried on, uh, their research. He was operating in a field that was considered kind of acceptable. She partnered with him and together they kind of continued and, and this continued up until. We were in the throes of, of COVID-19 when all of a sudden the rest of the world got what she was on about. And they said, oh shit, you were right. And now we desperately need this. Sorry. And, and, and here we go. So I, you know, I think if I think about the mindset of a scientist, yeah, in as far as I can know, uh, one, uh, I think it's, it's, it's with that. In mind that the rest of the market may not always know No. What we know, so no, no. But that, but that, but that example, the market changed and therefore that product became of age. Right? Yeah. I took, I took a lot about, um, the, the, i, I love this idea of the Stockdale paradox, right? Where, where you have to be monk and hitman. Yes, you have to love your idea, but also be prepared to kill it. And I think, yeah, I mean, I'm being pejorative and binary to kind of try and prove a point. But the, the, the reality is it is a balance. There is a balance there. Yeah. I think too many people on the technical deep tech side don't have enough of the, of the. Of the commercial, oh, we know this, but it's not just, they don't have enough of the commercial, but the, the commercial isn't really commercial. It's really being honest enough about the mindset of themselves and their buyers. Yeah. Digging into that, constantly doing the molecule shifting, but also then digging into where you think this thing might fit and with who. Ultimately, all products and services have to be bought by a human. Us humans, we ate so rational, right? There's the reason why we call us a venture capital company, system two, because Kahneman Dki, fathers of behavioral economics, they coined the spray system one, system two, right? You have to engage your system two, and, and, and by the way, all they ever did was just sit, uh, in a room laughing all you apparently, all the stories about them was that you call it, he was just laughing that the di conman, KY was just, and all they did was just set themselves hypotheses in the middle. Yeah. And that was their way of working, which is why we called our business this, because actually we, we didn't even, of course, we didn't even invent this process. It was actually invented by a much greater mind than ourselves, which was just fall in love with trying to find what the behavioral hook is. Yeah. Before you go for a solution. I get it. I get it. It always a balance. Yeah. Can you, can you talk Mark A. Little bit about how you've seen this work well in businesses with which you've been involved? I, I couldn't, I can't get arrested teaching this process. Entrepreneurs hate it. Most entrepreneurs absolutely hate it. The visceral reaction. Most people in corporate, there are about 5% of people who can take the poison. Then come out the other side. It's a bit like whatever happened in June two when they drink the water and they nearly die, most people hate it.'cause all they, their ego just immediately fires and you are just calling them an asshole, an idiot who's got a shit idea. I mean, by the way, they, it is a shit idea. They're not necessarily calling an asshole, but they're egos can't differentiate between these two. You say your idea's shit, and you actually call you somebody. Shit. So unless you are very tentative in a way that you approach this, most people just cannot deal with this because they say, I've got this great idea and you've got, oh, brilliant. Can you just tell me the hypothesis? What's driving it? It's like, fuck, oh, I, no, I don't want to do that. So the only people who've ever made this work are, are people who've seen it work? Yeah. So we have, uh, and, and we've made it work in different corporate settings in GSK and Barclays and British Gas. Because actually a very small group of people could see it working, but the medicine is proof. I mean, it's, it's incredibly intellectually satisfying with fun and there's a lot of laughter, but it's not, it doesn't look anything like a normal innovation process. So most people, because we humans and we, we assume what we see as the norm is, right? Yeah. We, we reject it. Yeah. So it's very hard to do, weirdly. Yeah. Yeah. Yeah. Did, did you apply it to, uh, deep Green Technology or Deep Green Energy? Nah. Every business that we've done, we has come from an eight week sprint of beating the shit out of, of hypotheses. So, is that right? Wow. Yeah. Every month we've done eight and a half thousand tests, we think across our businesses. We've had eight exits. We've probably done a thousand tests for every exit we've had. And that to test that, not that, that's a hypothesis, which start with a behavioral driver, not a solution. So what we think of as a, because, Chris, because, because I want the, because I, I, I'm bugged by this. Chris will value this. So all of our businesses started like that. I mean, some are, some have come easier than others, you know, some deep green, a 10 year overnight success, whereas others have very, sort of quickly emerged. But it's not so much the ones that have, it's not so much about the ones that have been successful, it's much more about the kill rate. So we, we are really good at killing stuff. So if the, if the data from a hypothesis and an experiment doesn't look good. So many people will just go, oh no, it's fine. It's fine, it's fine. It'll be fine. Just keep going. It's like, no, no, not like a, a noise attenuation for kids' headphones is a shit idea. It's not, it's a shit idea. Because, because 'cause, um, parents don't want their kids to trash their hearing. It's a shit idea. Because if you had a kid to turn their music down and their, their mobile phone only plays their music 70%, now they're gonna buy another mobile phone. Yeah. So you don't have to be, you can be right, but the ir the market can stay irrational, right? Yes. And that's that. That's 90% of this, which is, this is a fucking great molecule. Everyone should buy this. And it's like, you are right, but they don't want it. 70% of people would rather die than exercise. Look, we've got it in our world, right? The 30% of people are morbidly obese, 30% of people are obese. Only 20% of people are healthy. So at least 70% of the population would literally rather die than be healthy. So if that's the case, we can kid ourselves about innocent A. That's interesting. So what you're basically saying is, and this is what. If I think about the boards of companies or the leadership teams of companies when they're in the early days of trying to figure out what markets they're gonna serve and how they're gonna serve those markets and the nature of their offering, they're very, in my observation, they're very rarely thinking about the behavioral drivers that are gonna lead to adoption. And they're starting with a, well, this should slot like that because they do this. And, uh, and, and it's generally based on strategy. So here's their strategy. They're going in this direction, they're deploying these technologies. This is what they're trying to do with that. We could fit like that. But those pieces, nook look nice together on the slide deck and uh, and we can see that working. But I would say that for most, that's probably the extent there might be some kind of digging around in terms of who should we be talking to in the organization, who are the ultimate sign off. Leaders of this, who do we have to influence? What other partners are gonna be around the table that we need to kind of consider and get on side? So it's almost what you're advocating is an extension of those questions. You need to keep going. You need to keep, and you know, and actually, and you may need to get away from that group, but you need to keep asking the questions, what's going to change the behavior.'cause ultimately it's gonna result in some kind of change of behavior to adopt your solution and not to keep, and not to continue doing what they're doing. Changing behavior. Correct. Even I know changing behavior is really difficult. Yeah. That's what everyone is trying to do when they're trying to launch a new products and services, they're trying to change a behavior and everything you've just listed there is what's wrong about corporate innovation. Everything is, everything is wrong with it. It's all upside down. Research is a disaster. Senior, senior people thinking they, their existing products and services give them a right to play. The idea of ideas, uh, the way that's funded, the fact that they're done all bad. Everything about a corporate innovation process is, is mostly going to, everyone should be involved. It should be a happy, fun process. It should be really quick. And, and there's a winner. I mean, all of these, all of these facets that we think we, mm-hmm. That make up good corporate innovation is why, is why one in 11 businesses succeed? Uh, because it, it, it. It Ultimately, no one, as you say, has, has asked themselves the single question, which is what behavior am I trying to change and, and, and, and have I got a product that will change that behavior? Wow. That's, um, now I know you do work with corporates and I, I've seen from your, you know, your presentation and from the stuff that I've researched that a lot of that seems to be very effective. Can you know, because most deep tech companies are startups, I. At various stages, generally the early stage, how much of this do you think can apply at the very early stage of a company's development? If you take the 30 year development of a of a molecule, why can't you start asking yourself the question right at day one? Now, that doesn't mean that you don't have to do 10 years of molecule shuffling before you've got even got something that from which you could then ethically test an assumption. But yeah. If you are in the business of business, then that's the only question you are ever gonna have to answer. Yeah. If you are in the business of, if you are, it's a bit like an artist. Uh, you know, if you are, if you are in the business of making great art, you don't care that anyone will listen to your music, knock yourself out, you know, record, record a, a chainsaw for 10 minutes and call it art. But if you wanna, if you want to sell any music, then accept the reality of, of what, of, of, of who you're trying to comm, you know, communicate to. So I think everyone can apply the principles all the time. And of course the more you do that and the more you get this balance right, the more likely you are for this bit, the sort of sales bit if you like to start to impact the r and d bit of it. Yeah. So it's like, I thought I, I'm just gonna make this molecule if, if, if you. As you start to unpack an idea and as you start to understand who's buying for what reasons, then that starts to obviously impact the, the, the, the early r and d you are doing. Yeah. So in the case of Deep Green, we were, we were building, we were building, uh, we. Right. 10 years ago, our prototypes were all with, with, with, uh, with crypto miners. We thought, well, of course if crypto's gonna be the next foundational kind of, uh, uh, uh, you know, financial, financial system, then everyone's gonna be delighted by having a crypto miner, uh, in, in their hot water tank. Turns out very bad assumption. So, you know, you can always ask yourself the most awkward question, which is. Am I doing? And, and, and because ev most businesses fail whenever an entrepreneur, any of corporate exec or any r and d deep tech company says, I've got this product. You can, statistically, you can say that they're gonna fail because they, it's, it's statistics says that they are very unlikely to be talking to one in a million who's just nailed it the first time. Yeah. It's by definition wrong. Yeah. Yeah. And I think almost every single, every. We've had a lot of conversations with investors and boards over the years, and they all say something very similar. And, and, and actually I will say that most academics say the same thing, that when it comes to commercialization, when it comes to the type of thinking that's required, uh, for a, around which a company can be developed, that has got to be, that is called commercial thinking. Too often we make the immediate association of sales, well, we gotta sell it, so let's make something and then we'll sell it and hopefully we'll get some revenue from what we've made. And you know, and isn't that what the commercialization process is all about? And actually what you are, I think what you're expanding on beautifully is this, is this notion of why, why is anyone going to change their beha? What will they. Will they be willing to change their behavior in order to accommodate what we have? Or perhaps a better way of framing that is what can we, what can we develop and round which they will be prepared to change behavior and then test that as an option and then test that assumption. Absolutely never. Focus on that and then test that assumption rather than believe you're building a product. Yeah. Yeah. Because if you start the entrepreneurship journey thinking you are an entrepreneur, you are already wrong. You are, you are only a tester of assumption. That's, to be honest, interesting. You are only, you're only ever a tester of assumption. Yeah. But that's where everyone, that's where everyone gets, that's why it's so that's understand that.'cause it's, it, it's just people think they're a race car driver when they're actually a chef, they're doing something. They, they are, they're trying to do something that is completely different from what, what's what, what, what needs to be done. Can you talk a little bit about that validation process? So let's say I decide with a small group of experimenters, all right, we're not entrepreneurs. We're not gonna fall under that brand. We're going to, you make valid points here. We're gonna become, we have certain types of capability in a particular field. Let's get outta life sciences. Let's say it's, I don't know, it's chemistry or. So let's say it's ai. Okay. And so, uh, we have certain capabilities as a group of engineers and, um, where we we're interested in, we, we could be interested in setting up a business, but we're not sure yet. Yeah. So we see ourselves as this group of experimenters. What do we do to kind of hypothesize and validate? How do we know when we're getting somewhere? How do we know when it's, when we can start kind of. Uh, to involve other parties and get capital and all of that kind of stuff. How do we know when we're, you know, making progress? Well, I get the, the really annoyingly counterintuitive thing about this all is that you must also start doing immediately. You can't spend, I. You can't spend 10 weeks scribbling on a whiteboard, hypothesizing you, you force yourself into the discipline of, or of daily and weekly experimentation. So once you've got a hypothesis, then you test it, get another hypothesis, test it. So to where does the hypothesis come from? It comes, it, it, it literally comes from talking to each other. Now you've got a hypothesis for a business. I, I could, I would, I, we do these workshops, we call them rinsing workshops. And it's, and it's, and whoever volunteers to do them is incredibly brave, but they sit on a stool and they start talking about their business. And we start to immediately say, well, what's, no, no, no. That's the solution. Oh, it's gonna be brilliant'cause it's this No, no, stop. Who's it for? Uh oh. Uh uh. For a lady, I think brilliant because it's, no, no, stop. Stop a solution. You know who it's for. Now, why do they want it? And very quickly, either people stop talking, storm out the room, or you write something on a white wall, which is because I am, I, I'm a lady. I, because I don't want to be patronizing in a car dealership, a lady will value something. So you, you have to just force yourself to never let yourself off. It never even start. Just always start behavior first, and it's crushingly awful, horrible too. So for three weeks, most teams just sit in awful existential pain, somewhere between sort misery and, and, and rage. And, and then, and then a few people go, oh, I get it. I get it. It's like. Because I, because the cup can't spill coffee. Chris will value a thermos with a top on it to go to work, and then, and then you see the penny drop and then you're off. That's all it is. It's just an intellectual. It's just, but, but you've gotta be okay with just, you've gotta die before you die. We are back to therapy. You, it's just Buddha. It's just, it's just don't have any emotional. Attachment to it. Just accept the fact that you are almost certainly wrong. And then the joy in finding something that might be useful mm-hmm. Is immense, of course. Way more joy than you can find in just pursuing a shit. I did. So I give you a really good example. We, uh, which eventually became the eBay's uh, campaign, eBay, it wasn't meant to be a campaign, it was meant be a product, but the. The, the, the hypothesis was because I find it difficult to, uh, find the specific dress I want in a secondhand marketplace where there might be 10,000 dresses on eBay, uh, red dresses. In this case, Miranda will value a service which curates her just the four, which are most suitable for her. If that's your hypothesis, you know, your, your driver is ba a secondhand marketplace is just a wash with stuff, and I, and I, I need somebody to curate it. What you don't then do is build an ai. What you do is you ring up your mate who pretends to be the AI and whoever sends you a tweet saying, I want a red dress. You very, very quickly, as quickly as you can do it with a human, send the link back and then you test how many times they open that link and click on it, and it turns out they, they, it, an unfathomable amount of times have clicked through. But the point is experiment, uh, sorry. Hypothesis experiment. Hypothesis, experiment. This is. You, the counterintuitively must working in this way, you're not allowed to, like Kahneman perky did. You're not allowed to sit there for weeks on end laughing and, and scribbling on a whiteboard. You must get into a cadence of, of scribbling on a Monday, uh mm-hmm. Uh, thinking on a Tuesday, getting it live on a Thursday and and on. And so you are forcing yourself into this discipline of experimentation rather than product development. I get it. Can you, do you have an example Mark in the enterprise space with perhaps more complex solutions, not consumers, where you can kind of do that? Because I'm a procurement director at, I don't know. Yeah. Uh, a big utility. Yeah. And I want to buy a certain types of, of solution. Let's say I'm a procurement director at a gas distribution network. This is topical right now. Yeah. I need to buy a sy I need to buy systems that are going to, uh, that are going to help my gas distribution network, uh, manage itself better, uh, that are gonna allow us to keep our costs contained, that are gonna ensure that there are no blowouts along the network.'cause those are costly and potentially very dangerous, that are going to fulfill a certain amount of regulations and so on. Are there examples like that where people can also do that kind of quick experimentation of their hypothesis and get the kind of data back that they're gonna need? Well, well firstly, what's really interesting in even what you said there in those four or five short sentences, there are, there are probably 50 ideas and there are probably 5,000 hypotheses in what you've just said. Which is, which is the problem. Is it the fact that I'm trying to detect its gas on the gas line, or is it that I'm trying to save save costs or is it that I'm trying to, so we can't even begin. Let's first of all smash that. Well, what is it? Because each of those ideas have got 20 behavioral drivers, they've got 20 users, and they've got 20 potential solutions. And each of those is an infinite monkey cube of variables that we need to wrestle with before we even then go and test. One of those thing. And therein life's the problem.'cause it's like, don fucking about Mark. Just get on with it. I want the answer. Of course you do. But that's why you're gonna make a mess of it because you, 'cause we, we really need to do some proper thinking. No research, no cling about with startups seeing nice glass fronted offices, no crapping on about things that innovation exercises proper. System two, thinking in front of a whiteboard, just really quickly, who's, what's the behavioral driver? What is it, what's more important than the, than detecting the leak or the cough thing, but where is it? I, so I'll give you a really, I'll give you a really good example in the B two Bs. I mean, well, deep Green is a really good example, right? Yeah. I mean, 10 years, we didn't build, we didn't build any data center. We just, we just, we just, we just built really shonky prototypes. Until, until we knew there was market fit. So, you know, uh, even, even the final protest, our, our, our car subscription service, we, we put that car subscription service up. We got inundated with requests for EVs. This is about seven years ago. So within 24 hours, we had to buy seven revenues, which arrived on my driveway book. We didn't buy the Reno Zoe, I've got a picture of my wife standing there like that with a transporter outside our house going, where the fuck are these cars gonna go? It's, but it's like, imagine if we bought those seven cars and then put the experiment up and thought, oh shit, no one's, no one, no one needs those cars. The, the, the most challenging B2B one. I know the fact that a defense contractor is doing stands at B2B market stands with products that don't exist. So you're taking stands at B2B at B2B conferences with products that don't exist, but you're testing the market. That's interesting. Right? That's dos really, really interesting. What you, you're a defense contractor and you've got a, you've got, you've got, um, a, a, a, I don't know, whatever. A silent propulsion mechanism. Yeah. And you think it's, well just dashboards. Why would you put a day? Why would you put a day of effort? That's so interesting. Doing. I have a couple of questions. I have a couple of questions based on this. This is really, really interesting. I mean, the first one is funding. So I, I, uh, me and my little band of experimenters wanna start experimenting. Um, yeah, but we don't have much money. We're a young group. How, but we can't say, you know, we, it's going to, we're gonna struggle to build a slide deck. Yeah, we don't know exactly where we're going yet because we haven't been through this exercise. So do you have a view on, on capitalizing exercises like this? If you are, if your intention is to ultimately build a company, we're gonna be a band of experimenters. We're gonna hopefully find what we're gonna do and then we're gonna go forward. Or is the deal that we we're supposed to somehow float ourselves? We have to kind of self-fund ourselves until we get to them. Well, I think there are two answers to the question. First of all, there aren't enough venture capital businesses who are impressed by the results of an experiment relative to the creds of the founders, but that's on us, right? Like you should, you should as, as a, as a small crack team of experimenters with not little be little money, you should get massive brownie points. Who, who needs a 20 page slide deck? I just wanna see, I just wanna see the what, what, what was the result of that experiment? Which hypothesis you were, you were testing, and what was the experiment you should get? So, so capital is wrong in that respect, but the other. But on the other end of the spectrum, you have corporate innovation. You think it's all gonna be fine in 18 months. Guess what? It's not. It's gonna take seven years. So if you are, if you are, and if your average tenure of A CMO or A CEO is only two and a half years. Be, be prepared to be planting trees under which you will never and never feel the shade, whatever. Yeah, yeah, yeah. This is a seven year journey. Yeah. Be honest with yourself about it. It's gonna take five times as long, it's gonna take five times amount, as much money. And so from a corporate point of view, just be realistic about how much and how long, and then when you haven't got much money, uh. Go, go to the venture venture capital and go to money who are impressed by, by your data, not your slide deck. That's a, that's the, that's a seriously interesting point. And I, you know, I kind of, I wonder, I wonder about the kind of people who are gonna be more likely to succeed in that type of exercise. I. So at the top of this discussion, we talked about your background. You've got an unusual background. I don't mean that pejoratively, you've got a great unusual background. I've got an unusual background. Um, and I think some of the most interesting people have unusual backgrounds, but, you know, and if I think about some of the most successful entrepreneurs in deep tech, they've got unusual backgrounds too. Elon Musk, despite what you'd, he'd have you believe. He's got an undergrad degree in economics and he is got an undergrad degree in engineering. He's not a rocket scientist. Yeah, he's not. No. He's a super learner. He's extraordinary at, uh, uh, at, at building companies. He's extraordinary at moving things very, very quickly to get done. He built Colossus XAI supercomputer in 19 days. That generally takes three to four years to build a supercomputer. He did it in 19 days. So, you know, he's got, he, he's got the ability to adapt. Uh, you know, some of the principles that we're discussing, maybe, perhaps they've emerged from him and his way of doing things and, and the way he thinks. Yeah. But, but you know, and I've known other deep tech entrepreneurs who've got backgrounds in finance and who's got, who've got backgrounds in history as well. They're not scientists, they're not engineers. The benefit they have is that when it comes to ideas, they're not wedded to them. They're not like scientists who spent a lifetime so far, 15, 20 years developing a particular topic. And you can see they have got so much writing on it. And so for them not to be right, it's gonna almost be heartbreaking because it makes 'em question, well what the hell have I been doing? And, and, and, you know, and whereas I think there, there might gotten be some superpower and being detached from it. That's almost, you know, that's what we started discussing in. I sense that's what you keep coming back to this idea of don't be so wedded to the ideas. Be wedded to the process. Yeah, yeah. Perhaps be committed to a process, be committed to that. Be committed to the process and, and, and be committed to the data. Stay true to the data. So maybe the whole lesson here is about being, is that commitment to the process as opposed commitment to a particular idea. Yeah, but a a, the paradox, there's always the para, there's always the ying and the yang in everything in life. And so you've still gotta love the idea. You've still gotta love the principle of it. Mm. You, you, but, but remain wed into the process. I think the reality is most death does, uh, most Silicon Valley massively over successful people have figured this out. This is the way they work. I and I, and, and that's probably true of most entrepreneurs throughout history. So I think what you've got now is a really interesting paradigm within, within technology where, where, where single companies with this thinking at their heart and consumed whole sectors, vectors and not, and not die. Had a, we had a natural progression of business over the, over the decades and over the such, capitalism's only 250 years old. Right. But for the last 250 years, we've had this nice cadence where pro, where businesses have come and gone. I think what we are seeing now is we've seen, we're seeing businesses with this thinking at their heart, which, and then rightly or wrongly there, that ability, that that ability to constantly course correct and constantly bring to market the right product. Actually means that they're going to, that's where we are having this concentration of wealth. So actually we, just by this, this, this one sort of little idea becomes actually the kind of focus for geopolitics, which is, which is, which is the centralization of wealth and all of the, all of the, all of the negative as well as the positive things about what we've got from technology, which is if, if you, if you work in this way. The weird thing is you can't ever be wrong because you kill things before they, they cost you too much. And that's what most Silicon Valley businesses have figured out. Yeah, that is, that is really interesting. That is really interesting. Mark, I could talk to you all day. This is, this has been absolutely fascinating. Um, uh, I think so much of what you have, uh, shared with us today makes so much sense. I think you are, uh, I think you are touching on something that is going to resonate for, uh, so many people involved in the development of these businesses, particularly with an advanced science and engineering. And, um, and, uh, and, and I thank you for taking the time to share this with us. Well, no, I, I thoroughly enjoyed the conversation. Yeah, we, we, we'll do it again. See? Yeah. I look forward to that. Mark. Thank you so much. You've been listening to the Lab to Market Leadership Podcast, brought to you by Deep Tech Leaders. This podcast has been produced by bowhouse. You can find out more about us on LinkedIn, Spotify, apple, or wherever you get your podcasts.