He was a surgeon. Now he’s CEO of a robotics startup | David Bell, Remedy Robotics

22:03Say more for the audience that may not know that reference.

22:05David Bell:So, so Miss Frizzle, Magic School Bus, you should watch, voiced by Lily Tomlin originally. She would, uh, with her class go inside the body and inside all of these imagine, like amazing places. And I remember once I saw her go inside the bloodstream and I imagine her often on the tip of our tool and, and what would she do and what would she want, right? How would she want that tool to go if she had complete control from the tip of the tool? Right. And so that's really our approach. Really? which is like how do we perfect these procedures in ways that humans can't, right? How do we steer things?

22:41David Bell:How do we look at multiple things at once? How do we do things that a simple human cannot? That opens up a really interesting area that I'm keen to get into because part of the beauty of the product is that you make possible for expert surgeons to achieve things, as I understand, faster, with greater reliability, lower levels of sort of aftercare required because you haven't accidentally hit up against a vessel that you didn't intend to. That's the goal that we have. We have to prove that out, but the goal is for far less complications. I feel very hesitant saying that when we don't have evidence for that yet, but yes.

23:20David Bell:Okay. Well, I mean, I'll take your point. Yeah. I'll let you say it. There's a logic there that if you are moving more cleanly to the path that you want to get to, that there should be less damage along the way in the body. We await the sort of beautiful RCT that will prove that to us. But all of these things then enable them, and you sort of touched on it earlier, from the motivator of a surgeon enables them to actually also earn more money, not only do better by their patients, but potentially do more procedures and earn more money from them. But I'm also really interested in the deeper human motivators of these people that you are selling to these expert surgeons who have trained, you know, I mean, we talk a lot about what the sort of the typical surgeon presents as.

24:07They're a confident person that's worked very, very hard. They're extremely diligent, very perfectionistic. What does it look like to build something where you're saying, it's not just that I'm enabling you, I'm going to give you something you cannot do. Like how explicit is that conversation and how receptive is that audience to it?

24:24David Bell:It is relatively explicit. There's certainly a small fraction of surgeons, like from our data, roughly 10%, where we alienate them. They think they have kind of magic hands and that nothing can replicate what's in their hands from years of experience and training. And there's this kind of chutzpah that comes with thinking we can reinvent this modality. The overwhelming majority really like it. and they like it because it makes their life easier and faster. But there is something in how we message and market it because it can sound a little bit like we think we're better than you or we know what you're doing and you don't.

25:09David Bell:And that's not actually true. So there's kind of a needle to thread. Yeah. I mean, I think this is a challenge that so many companies face. Why exist unless you could do something that at least advances what humans can do without technology? and then how far can you go in that direction and building the sort of trust and confidence of the various actors. I'm actually interested, you know, we've talked a lot about patient outcomes. We haven't necessarily talked about their motivators or their feelings about this. Is it just the case of like most patients have no idea what's about to happen to them anyway, and they're in an acute situation.

25:42So their preferences are kind of moot at that point, or like how much does that play into the way you design?

25:47David Bell:Oh, unfortunately, I think that's very often true. But like, that's not the scenario around which we are designing. Yeah. I look at this through the lens of like, what would I want or what would I want for my parents in this same situation? And how do I make it a no brainer to go with our product? Right. So it should be very, very easy to explain to someone or their family member in a moment of distress why it's much better for their outcome that they go with a remote operation. rather than kind of waiting for someone to do it manually. And that's kind of a very important part of what we do. So talk to me a bit about you.

26:24You know, you're pretty far along now and you had some really exciting results. Feels early, but yeah. Well, you know, the sky is.

26:32David Bell:The limit. Yeah, exactly. But, you know, you've had a bunch of really interesting results and you've been able to go public. Like you stayed stealth for a very, very long time. Yes. But end of last year, you sort of opened yourselves up to the world with some really exciting, you know, human results. Can you walk us through what you kind of discovered through that? Of course. We kind of flopped out of stuff in a funny way, but we did an amazing human trial. Yep. It was the first time anyone had done a fully robotic endovascular procedure in a human before, which was amazing. And then we went and we did four patients fully remotely and no one had done that before either.

27:10And remind me, it was Toronto was one of the locations.

27:14David Bell:It was all in Toronto. It was all in Toronto, but not in the same space. Not in the same hospital. So another hospital around 10 miles away. And it was hugely impactful. We still need to publish the data, but all the patients did well. And the procedures were around twice as fast as what they normally would be, which was a huge outcome. And what do you put that down to? Was that the quality of the ML and the sort of diagnostic piece? It's a few different things. But normally when you're doing these procedures, you're kind of navigating under x-ray and you can't see the blood vessels. for example.

27:44David Bell:We have a feature that allows kind of registration of the preoperative blood vessels on the CT scan to the live x-ray. So all of a sudden the surgeon can see where they are. Seems obvious. It seems obvious, right? It seems obvious that you should see where you're going. And then we have a catheter where we can steer the tip to a specific point in 3D space that we want it to go. This is Lily Tomlinson's head. Correct. Exactly. Which again, seems obvious. You're moving through a blood vessel. Like, how could it not be that your tool moves exactly where you want it to go? But the nature of this surgery before is such that, like, there's a lot of guess and check and finagling and the user was not really in control of where their tip moved.

28:25David Bell:So a combination of things like that means these procedures are just kind of faster and safer. Cool. And what have you, like, have there been examples of since you've kind of really gotten the product in the hands of clinicians where you've changed the product meaningfully because of feedback? Kind of, but reluctantly. So there's a big debate about force, right? And again, I think that the high arching lesson is maybe a good one or not, but we got feedback from a lot of clinicians that we can't account for what they feel in their hands when they're doing these procedures. And it's really important that we measure force.

29:02Which is what? The strength with which they -

29:06David Bell:The strength with which this long floppy tool pushes on something two meters away while you watch an image with visual indicators of what's going on. The problem was when we measure that force, there's not really much there. Right? So what's happening is they're looking at an image and they're seeing visual indicators of them pushing against resistance or something. And they think they're feeling something in their hands. Wow. Yeah. Yeah. But from a marketing point of view, it's very difficult to be like, nah, you're wrong. You don't feel what you think you're feeling. So we actually kind of have to give them the force reading and show them.

29:42David Bell:And slowly one by one, they're like, oh yeah, maybe I'm not feeling much. I'm seeing everything. Interesting. Yeah. Yeah. Okay. So that's the sort of like rewiring as it were. Exactly. Of the kind of surgical experience. Yes. That's so interesting. we started out with Remedy going like hell for leather for the opportunity set around remote first and you talked about your own experience of you know working with patients who were coming out of parts of the pacific and other parts of remote areas that had no access to care and I can see and know why that's such a passion area for you I understand you've now made a pivot to an op well maybe in pivots the wrong way of describing it wash your mouth out of pivot yes The on-prem opportunity is big.

30:28Yes. And that is a big area of focus for the company at the moment. Talk us through that learning.

30:34David Bell:Yes, of course. So we, as a company, we started off, if I'm being honest, we wanted to build the software that allowed us to do autonomous stroke treatment remotely. And then we realized as people started to use the system that like they really enjoyed it. It was really easy for them to do, which like took a lot of mental load off. The procedures were faster and the procedures were safer. At the same time, we got a lot of regulatory feedback that like, you can't go straight to a remote procedure before you've done something locally. And so we basically thought if we can provide value locally, which we can, and the regulatory bodies who have a huge amount of power are telling us that, why not start there?

31:14David Bell:And so basically we're a company that, yes, we are passionate about access to care and yes, that's what we're pursuing. but the fastest way to get there might be with kind of a stop off with a local product that still provides value and helps patients. Brilliant. Did that feel hard as a trade-off or a learning? No, because commercially it was a much better decision. And I always looked at it to kind of, well, I'm not sure if I still look at it this way, but what's the fastest point to get to where we want to get to, which is everyone around the world has access to this perfect care. and by taking that stop off, I think we get there quicker.

31:53Brilliant. So let's talk a bit about autonomy in the product. Like, do you think it's still the desirable end state that you have a fully autonomous product?

32:02David Bell:I think full autonomy is not. Our goal and the most desired end state is supervised autonomy for a few different reasons. Number one, I don't think the patient wants a fully autonomous solution. number two we don't really provide value as a company well we provide a lot more value than by taking the clinician out of the loop a lot of fully autonomous solutions like their business model is that like we provide value by removing someone that's paid that's not really us and so the patient doesn't really want it and so why do it at all and then we've realized that like we're actually much better at controlling our own robot than the humans except for a few like tricky decisions.

32:45Oh, say more.

32:46David Bell:So we can do almost everything, but there are little bits of judgment along the way that require a human. For example, and again, it comes back to thinking about this through a patient, through the patient lens. Let's say you're treating Maria who's 80 and she has like mild dementia, but lives at home independently, but is in and out of hospital, increasingly confused. You've been up there twice to treat her stroke. You haven't quite treated it completely. There's a risk of huge complications if you go up again. Should an autonomous system be making that judgment call? I think it's very difficult for them too.

33:24David Bell:And so I think having a clinician in the loop to make those sorts of decisions while they still get paid is kind of a win-win for everyone. Yeah. So then, I mean, I'm interested in how, you know, you've come yourself from the surgical profession. Like what do your peers say about the experience of having technologies like Remedy in their kind of line of sight? Like, do they feel threatened by these kinds of technologies or by and large, are they seeing it as an opportunity for faster procedures and better patient outcomes and maybe more money? So the latter, mostly. I should say we are a different type of surgery to what people would normally think of as surgery, like taking one's appendix out.

34:06David Bell:That is very, very difficult to automate and a completely different kettle of fish. For what we're working on, as long as the surgeons are kept in the loop and get paid, they love it. And how different actually, so you're building, I mean, people may not fully appreciate this, you're building all of this from the US. You've taken on the US market predominantly first, but you're actually working in more than one market. How different have you found those different systems, the sort of different, are the systems operating with different motivators? Yes. And do you have to, how much does that shift your go-to-market approach?

34:38David Bell:Oh, extremely different. They're extremely different. And it's mostly kind of who's paying is kind of what's driving stuff. Is it just like single payer versus non-single payer systems? That's a big part of it. Really, in some way like Australia, we provide a huge amount of value for the money spent on post-stroke care, right? If you're disabled and you miss out on treatment. So is it like a quality adjusted life year calculation? Exactly. That drives the why? And it's very quantitative. In the US, it can be more difficult to tap into like who's spending money on that. And so that's not an incentive that we will really lean on as much.

35:16It's much more the like, how is the clinician feeling about it?

35:19David Bell:No, it's much more on how do you provide value acutely for the hospital that's kind of purchased the robot rather than the broader system. Okay. So then like breakdown, actually, I'm really interested in, you know, you've built this robot. You've obviously got enormous amounts of software that's always being improved upon. You've also got consumables. Yes. Talk us through the commercial model that is emerging for Remedy in this system. Yes. So the commercial model that we thought was kind of the best way to go and we still stick by is one that's motivated by how do we sell this thing and get it into hospitals as quickly as possible?

35:56David Bell:And then how do we make money as the user makes money, right? So that means that we plan to sell our robot for relatively cheap. The cost of goods of our robot is relatively low. And then make money from our consumables as the users do these procedures. And these are the catheters? These are the catheters. These are the single use things that kind of go into the body or need sterilization. Interesting. And so how does that feel when you know what the effort was to create the robot itself? You just have to bank that as a sort of fixed cost to the system. I think one doesn't want to complicate the business model too much.

36:33David Bell:But then there's like a whole other software machine learning element. And we like, how do we, do we add that into the game and charge kind of a software licensing fee? But really, I don't care, right? As long as the business model makes excellent money for us, as long as it really appeals to the incentives of the hospitals, and as long as it gives us flexibility to go into all markets around the world, which was very important for us and has gone into our strategy, I don't care how we make money. Brilliant. um so I'm then interested actually to kind of delve a bit more into the way that you think about impact and one of the things I've always loved about you Dave is that you don't hold back on what you think what you think good medical technology really looks like there's a burgeoning area of you know the intersection between AI and medical technology you've got a manifestation of that which is a sort of the software part of what Remedy does but I I do really want to impress upon our audience, like you've really chosen a particularly aligned, challenged version of having impact through medical technology.

37:40Why is this the thing? You know, you could have gone in so many different directions. In fact, you alluded to your earlier startup was, you know, using machine learning techniques to improve healthcare, but it wasn't a version that was as hard as this one. Why did you go after this?

37:55David Bell:So I think it was the obvious choice when I thought about it, the way I think about it, right? So there's a lot of healthcare technology in inverted commas out there today, but what is that working towards and what is optimal healthcare, right? So at Remedy, we would define what optimal healthcare is, right? And then there were lots of different disease states that went into kind of ensuring we have optimal healthcare and we basically ranked them by qualies. Quality adjusted life is. Or dallies rather, by dallies. And then for each of these disease states, they all have kind of a very predictable loop of care, right?

38:35David Bell:So you need to understand what the mechanism of disease is. You need to prevent it. Then you need to diagnose it. You need to treat it and you need to surveil it. And so when we put all of that in a table, cardiovascular disease and the conditions that we were focused on were all up the top. It was where we had our expertise. If we're going to work on something genuinely healthcare, that was the only way to go. Say more about genuinely healthcare. Not to be too controversial, but I think a healthcare company is one that works towards optimal health, right? And for us, we define optimal health, which I can tell you our definition.

39:12David Bell:I think it's subjective, but you can be specific. And I'd love to hear your thoughts on this. I have to remember it. But I think people like generally want to live to around 130, give or take. Do they? Maybe, maybe not. It's your definition. They want to have agency over when their life ends. Yeah. They want to be cognitively sound until the end. Mm-hmm. They want to be free of pain and disability. Yeah. Sounds good. That needs to be true for the Queen of England and also everyone around the world. Mm-hmm. And people need to be free of violence and they need to be able to make fertility and sexual decisions independently, right?

39:49David Bell:It's basically our idea of optimal healthcare. If you can do all of that, you're great. Now, there's a lot of healthcare companies that work on things that do not contribute to that at all. And I would say they are companies working around healthcare, right? So in healthcare operations or, dare I say, consumer health, whatever that is. So as long as you define your own version of optimal health and as long as you're working towards that and as long as you're kind of backed up by evidence and logic, not fluff and nonsense, you're a healthcare company. And so follow that line of reasoning. I mean, you've alluded earlier to like you stopped me saying we don't have the evidence for that yet.

40:34You obviously take very seriously the sort of proof points of remedy. How does that sort of play into, you know, you have to do some of that for regulatory purposes, but I can tell you care also about the legitimacy and integrity of those things.

40:53David Bell:I do. Remarkably, like, I find myself, I should care more and I catch myself, right? I'm biased. I'm the CEO. I think it's an amazing product. But at the end of the day, there needs to be some objective measure of how good this thing is. And that's what evidence is in healthcare. And that is how we ensure it's good for patients. It's our marketing. It's for regulatory. It's everything, right? So a little bit of that objective measure of like, does this work? Is this good? Has gone out of healthcare tech and it's a problem. What do you think would bring it back? I think when this stage of healthcare technology companies flop, it will come back.

41:36So sort of system will correct itself. Yeah.

41:39David Bell:Like there needs to be some objective measure of good, right? Like, and there's some really exciting technology, but how are you going to move the needle on my health? How are you going to extend my life? How are you going to make my life better? I don't care how exciting your technology is. The needle hasn't moved in healthcare, right? No one knows about that stuff. This is all in the venture world where like a lot of these companies are getting a lot of money, but like, will they make it to like impacting a whole bunch of patients? What would success look like for you for Remedy? Success for Remedy is one of our systems in every cath lab or hospital around the world so that we can go from that 3 % to 100 % so that 100 % of the world has access to perfect endovascular care.

42:23Setting yourself a really achievable target there.

42:26David Bell:Well, I think that's the goal. And so that, yeah, the Queen of England and, or the King of England, I should say, and Stephanie in Nigeria have access to the same care. I think that's how it should be. And then I think for us kind of doing that across multiple different conditions and pathologies and then moving across kind of the patient journey, which I alluded to before, like because of all the data we've had to collect and what we've had to do, we can provide a huge amount of value in diagnosis, in preoperative simulation, in decision making, and kind of moving across kind of the whole patient journey.

43:00Well, actually, you touched on exactly the question I was going to ask you next, which is a lot of people, it seems to me, who work in healthcare, get in this loop of like, deal with the acute thing, you deal with the acute thing, and then it's like a whack-a-mole. It's like, well, what's the pipeline of people coming to me with this acute challenge? And then I get, I work my way all the way back to sort of basically like large-scale public health programs as a sort of salve against the ultimate presentation. But the more you do that, the further away you are from the impacting of the real patient on the ground because you're impacting the potential patient and trying to change behaviors.

43:35How do you feel about the idea? You're obviously at the actual acute end, the definition of the acute end. How are you thinking about or do you care about trying to go upstream of that?

43:46David Bell:To a point in which we're expert, right? And by upstream, I mean like when the patient presents to hospital, right? Like we can help with the diagnosis, we can help with the decision-making, which kind of leads to this conflict where ideally no one would end up in hospital, right? And then where's remedy? So it was very important to me that we have a company that is super successful, even if that happens, right? Which is why we work on this multitude of pathologies and why we have value for like trauma and battlefield hemorrhage, right? And not just stuff that could potentially be cured by lifestyle measures, right?

44:25David Bell:Because we all want that. Okay. What can you imagine, like all of what you have learned, building robots, building machine learning and diagnostic tools that also allow for better visualization in the body. I know that you care about staying close to the things you know very, very well, but you mentioned earlier, I mean, there's appendices, there's all sorts of other things that we go into hospitals for and need surgeons for. Are there obvious next places that the kind of expertise you have built up through Remedy might next go? So, I mean, the next places are all within the cardiovascular system.

44:59David Bell:So there are about 30 different conditions that we plan to bite off. We're in the brain at the moment. We'll move into the heart. Which, by the way, is actually harder. Which is harder? Yeah. Like, I mean, people find brains terrifying. Yes. There are a few things about the brain that are really nice. And that is as part of the current workflow, everyone has a scan. So when you start the procedure, you know where you're going. In the heart, you're a little bit blind. You're often making a diagnosis and treating something at the same time. And that's very difficult. Is that just an information problem or is it we don't actually have the capacity to learn as much about the heart ahead of time?

45:37David Bell:No, it's that people can make relatively confident decisions about the need for treatment from a non-invasive kind of non-radiological medium like a blood test or an ECG. I see. So you're like, you need to have a procedure. Why bother with all the scanning? But in some instances, the final 5 % to 10 % is the meaningful difference between getting a good treatment and not. Well, it puts us in a difficult position, but they're not going to change healthcare for us. But you're basically then doing a procedure and making a diagnosis and doing the procedure at the same time. Interesting. So you're going to stay in the cardiovascular system, but it may involve some changes in the way that you deploy your capabilities on ML.

46:20David Bell:Correct. We will stay in the cardiovascular system, but we will start answering questions like, does this patient need treatment or not? How should we be treating them? Right. And then we may move into an ambulance. Whoa. which does allow you to deal with that like acute time horizon. Exactly, especially in trauma. And then there's still the question of there's a huge proportion of the world where we can build a robot but they're still limited by a lack of x-ray or operating room facilities. How do we provide care to that part of the world? Well, that's extremely exciting to think about the sort of exploding world of taking that because it does strike me the amount that the team has learned to get to this point is so extraordinary that there's got to be next horizons.

47:06Yes. Not to suggest that going from 3 % to 100 % all over the world itself isn't a grandiose enough goal that will keep you busy for some time. I want to then turn a little bit to building multifunctional teams. Yeah. So early on, you know, we've alluded to this, like you've done so many things simultaneously, you must have had to think quite hard about how you made difficult trade-off decisions, not only about what product you were building, what kinds of people you needed with these broad skill sets. Maybe for a founder who's also thinking about moving into a hard problem, what advice would you give them on building a team?

47:45David Bell:There's kind of a unique approach that we had, and that is that we understood the problem really well. We were problem experts, and we had a vague idea of the solution, and we really knew what the customer wanted. Because we knew all that already, we're going to go all in on technical talent, right? We're going to keep close to the customer through our clinical ties, but like our first 13, 14, 15 hires were all technical. They were kind of meticulously sourced and sought after, and we got the best that we could afford. And so that's really how we went about it. Once we built that sort of product, then we could start thinking about kind of commercialization and stuff, but we were really confident that the market wanted it.

48:28Yep. And so talk a little bit about the motivators for those people. What worked for you then to take some of these extremely talented individuals and say, hey, we know we're a startup. We're only starting. Yes, it's a big, hard problem. Like what dimension of the offer, the kind of remedy offer, particularly because you were also in stealth, right? So you didn't have the fanfare that other SF startups did. It's a regret. Is it okay? Say more about that.

48:52David Bell:So I did all the recruiting to begin with, right? And I think my response rate was okay, but getting past the spam filter or people thinking you're spam if you're still in stealth is something I should have thought about. And it was an issue. Now, once you get past there, I think people are really excited by the mission, right? But at the end of the day, money matters a lot. You've got to compensate them really well. And I think you have to compensate them. They can't feel like they're missing out. They have to feel passionate about the mission. And for a lot of the people that we were hiring, it was very important to them that we were going after something really aspirational, but tractable.

49:30David Bell:And that what they could do within it was like really specific and knowable, and they could leave their mark, which is interesting, right? So when we were hiring them, for example, we would say, you will be working on X, It will have Y impact. And that was very successful. That's amazing. And it doesn't surprise me when we talk a lot about motivators for people. And you might be aware of the sort of debate around status, wealth, and power being the kind of traditional way we think about how we motivate people. And behavioralists have now started talking about autonomy, mastery, and purpose being the ones that actually have the enduring motivation if you can tap into them.

50:11And this idea of the sort of mastery piece, like you are needed here because we need to get from here to here and you are going to be the person that will get us there.

50:20David Bell:Exactly. It's not just ego. It's actually I'm here to do a thing that I feel like I am uniquely placed to help with. And I had a lot of, I could really resonate with that because I'm in this spot because I thought about what's the best use of me. Right. And if I can tap into like, what's the best use of you? I think that really helped. And then also we had to be practical, right? For example, machine learning is a huge part of our company, right? But we're not going to be inventing new machine learning models. We are really interested in the machine learning people who are passionate about implementation.

50:55David Bell:And we need to say to them that like, we are the cutting edge of machine learning implementation. If you want that, this is us. And this is specifically what you'll be doing. And this is the best use of you. And we'll pay you a lot. Yeah. And not just a sort of razzmatazz of you get to work on exciting models. Exactly. Yeah, yeah. Where you could get someone that's like, I was excited, but I got missold. I hate vagary. And so like, I think most people hate vagary too. Yeah. Interesting. So I do want to just quickly ask you, you did the hard thing as a surgeon, becoming a surgeon, and now you're doing the hard thing as a founder.

51:28What's translatable about those two skill sets? And what's maybe something you had to unlearn from one moving to the other?

51:35David Bell:Well, I think there's an amount of ruthlessness that comes with surgical training that is useful, but doesn't always bode well in Silicon Valley and as a founder, right? Say more. I think a lot of people think Silicon Valley is very ruthless. My surgical training was very hierarchical. The boss would tell me what to do and I would do it. Now that I'm the boss, no one wants to be told what to do. They need a certain amount of flexibility, right? And so I've kind of had to unlearn that. And what else? To be quite honest, I always felt like a little bit of a square peg in a round hole in medicine.

52:14David Bell:I think medicine is this amazing profession, but I felt like it wasn't the best use of me. And I feel much more at home where I am now. So I haven't had to unlearn too much. I've had to learn a lot, but yeah. That's amazing. What's something that you sweat that you think other founders don't? Cool. I think the most important thing is what happens to the patient on the bed, right? You can raise this round, raise that round, make a lot of money. But like if a negative outcome happens to the patient on the bed, I sweat that a lot. I also sweat like talent a lot. And I presume and hope every other kind of early stage founder does too.

52:56David Bell:but like we are as good as the people we hire right and so they they have to be top-notch and happy and so I sweat that a lot. Interesting. Have you found anything that makes them happy that you think others don't understand? Similar to what we we said before right so I think I think people really like clarity especially the types of engineers we're hiring right like if you say to someone you're going to be working on x and the outcome will be y they better be working on X and the outcome better be Y. And they need very, very clear boundaries and clear instruction about what's next, how to plan.

53:33David Bell:And I basically kind of turn it over to them and ask them what makes you happy and kind of structure kind of for the really important hires. I try and kind of give them everything they want. Amazing. So my final question for you, Dave, what's something you've changed your mind about? I've changed my mind about a lot. I think someone once said something to me that like one of the biggest problems I had was that I think the whole world sees things through the same lens that I do. And every day I'm reminded of how untrue that is. So for example, I was that like irreverent CEO with no shoes on kind of problem expert, whatever.

54:11David Bell:And now we're like a series B company. And in the back of my mind, I'm like - With a multi hundred million dollar valuation. Correct. Does it matter what you wear? Does it matter that you've got all these windows open on your Zoom. To some people, it does. And so it does. And so I've had to change. Yeah, look at you. You're in a clean shirt. I know, seriously. But that's, I have a wonderful, yeah, a wonderful partner and kind of, I'm, yes. So thank you so much for joining us on Wild Hearts. Thank you for having me. It's been a real privilege.

54:48Thank you all so much for joining us for another episode of Wild Hearts. If you want to learn more from other ambitious people building, designing and creating the world that we all want to live in, then please hit the subscribe and follow button. It would mean the world to us, the founders, the operators and the investors who join us on Wild Hearts. This podcast is a labor of love from Blackbird and our production team. The show is produced by Joel Connolly of Blackbird. Our marketing genius is Laura Cofford and our editor is Andy Jones. Thank you all so much for listening and we'll see you all next week.