Intro - 00:00:08: Startups Podcast by Excedr. Join us as we speak with first-time founders, serial entrepreneurs, and experienced investors about the challenges and triumphs of running a biotech startup from pre-seed to IPO with your host, Jon Chee. Welcome back to the Biotech Startups Podcast. Today we bring you part two of our four-part conversation with Scott Chetham, co-founder and CEO of Faro Health. In part one, Scott took us through his upbringing on Australia's Gold Coast, his training across physics and biomedical engineering, and the breakthrough wave of cardiac electrophysiology and catheter mapping. In this part, Scott traces his pivotal decision to leave academia for industry, his tenure leading technology and commercialization at Impedimed from early lymphedema detection in cancer survivors to body composition monitoring at the management realities of scaling high-growth teams. He and Jon also dig into founder psychology, balancing broad strategic vision with detailed execution, and the enduring leadership philosophies that shaped his approach to building healthcare enterprises. Let's dive into part two of our conversation with Scott Chetham.
Scott Chetham - 00:01:35: By the time I got to the end of my Ph.D. I knew I didn't want to be in academia. I was done from that perspective. As someone says, the arguments are so large because the stakes are so small. I'd rather have bigger stakes. And I also wanted to see my stuff have an impact directly on people. I was never going to be a career academic, although I do freely admit when I retire I would love to lecture. But I feel like I need experience to have a useful viewpoint because that's one thing my university does and continues to do very well. It has its core academics that teach certain things and the core fundamentals.
The rest is people towards the end of their career who would come in and teach one thing really well in the last couple of years of the degree and it was the applied version of the subject matter and it made it much more interesting because it made it real. I didn't always like the application. I had to sit through like three months of measuring the stress and strain in bone on remodeling because that was the application of like how you would do this thing. And then you go into the mechanics of like molecular drug development others to like influence that. But I never thought I'd spend so much time looking at slides of like bone material and even the labs were like literally cutting up pieces of bone, slicing them out, putting stress and strain material and looking at measuring electrical response and then seeing how does osmolarity change? Can you deliver a drug differently? It was fascinating because it was so applied. But I just don't know that much about bone.
Jon Chee - 00:03:02: Yeah, yeah, yeah, yeah.
Scott Chetham - 00:03:03: So you didn't get to pick sometimes the application of it, but it was always applied theory. I like that. I think that opposing University of the University of Queensland where I started it was purely academic, so it was interesting to have the two counterpoints. I actually enjoyed the other one a lot more. But I think if you wanted to teach, you'd probably be better off at the other University of Queensland. Whereas like very applied.
And that was good for a PhD because for me it meant that's how I ended up even running a clinical trial. My clinical trial, yeah, as part of my PhDs. It was all applied physics, I should say. I was in the physics department, which sounds more impressive like than it is a PhD in physics because internally we're considered the weaker arm of physics. So split it was theoretical physics and that's some people like quantum like all of that stuff, general and special relativity. I was the medical side, so we had the radiologists and like medical side. But I was in that part. Everything we did was grounded in healthcare and so I was in that part. It meant thesis defense and everything was interesting because you had to satisfy both sides with anything you did.
But it was an interesting time because I liked the fact that I got to do actual stuff with people and you know, it was a specific program, it was industry sponsored so I had to do a small part of a bigger program. And so that was kind of fun because I got to see the direct output of it and that kind of slotted into a much bigger program the university was running at the time in cardiac imaging. As I said, it matched in with what I was doing clinically. So in an interesting way it all fit together. And it also gave me a launch point into industry and they liked to hire from our university because you basically came through industry sponsored programs and you knew how to research and how to answer a question. I got told this at the beginning of my PhD and I was like what? Because I go what should you be able to do at the end of a PhD? And they said the whole aim is you should be able to ask a scientific question. That's it. I actually now agree with it. You got to ask the right question and then it might take you a lifetime to answer it, but you do have to ask the right question. And that's kind of how they orientated it.
Jon Chee - 00:05:03: And just to like underline that just in our experience too, especially with everything again just for AI, it's like everything is shifting from like having the answer to asking the right questions. And I don't know, like, I come from the lab and I share that same philosophy, so it comes like natural. To me, that's just like how we think in the lab. But that's like not a normal feeling for people who aren't familiar with this because, like, I'm having a blast. I'm like, oh, great. Like I can like ask the right questions, get it to go in different directions and like uncover things that I want uncovered. But like, that's like a learn that's not in like everyday curriculum.
Scott Chetham - 00:05:39: It's not. Because I think, yeah, I mean, the other way I put what you said is that knowledge is now a commodity and it's your ability to ask the right question and the right next question that is really where the value is. And that's the same in drug discovery. It's like, what is the next question? Particularly clinical development, which is the art of how do you get a molecule from prove it safe and effective and get a label or get a registration trial successful. And that's asking the right questions like, what am I measuring in these people to prove that's effective and when and in what people? So it's like, am I measuring the right thing in the right people at the right time? Then how do I ensure the safety of people? And those are simple questions. But to do that quite well involves massive amounts of research. That's what some of this does really well. But it has to be grounded research and it has to be very specific and accurate. So this is one of the things we've learned is a lot of when you query an LLM, you get its retained knowledge or it's like a person, if you ask them something and they'll give you what they remember, that's very dangerous. What you really want them to do is go off and research all this material in a way that is traceable. That's a lot of what we do is like things along those lines.
And then it's the same in workflow automation is that it's repeating what good people do. That's the hard part. Because automating clinical trials from end to end, which is where I hope we are five years from now, doesn't mean there's no humans involved. It's just it means that we compress the massive amount of human research and human work time down by about 50%, which will have a huge impact on everything. But to do that well involves vast amounts of what I would say, change management. People have to change the way they work. They have to work within these systems and it involves training and I think that's for me has been the biggest learning curve for this is when we work with, you know, six of the top 10. Some of these have 30, 40, 50 years of accumulated process now and how do you work with those teams to get effective change and who've been in jobs for 30 years doing it in the same way? That's hard. Some of that is the real work that goes behind this.
And then just as life sciences, everyone wants everything yesterday. So there's just expectations management as well, because people think, oh well I can just automate this thing and it'll all be instant and done today. And there's a lot of expectation management. Yes, things are a lot faster than they used to. Yes, we can develop a lot faster, but we can't train and get you to change as fast as you think you can. It's an interesting time. I think we're learning a lot of new skills on how to be a good service provider to these people and help them achieve their goals. And I think that's just the fundamental learning curve for us because we're having to do it increasing scale and that's just a whole other series of things.
Jon Chee - 00:08:20: Yeah. And even like for us as we've been going through the transformation of like basically revamping the way we work and we're not even that large of a company. Like there's like a dozen of us and even changing like in a company as our size is hard, like and you can imagine you add a couple zeros to that. This is an aircraft carrier. Like you're trying to turn this aircraft carrier without just like blowing the thing up. And like, you know, say what you will about like Mark Zuckerberg and like Meta, but he's like trying to turn this like aircraft carrier quick. I commend him for that because like he kind of sees where on the execution, you know, okay, maybe there's like things that we could have done better but like the change management's really hard stuff and it's painful stuff. It's not comfortable.
Scott Chetham - 00:09:08: It's not. But you know, we're a fraction of the, not even a rounding error on Meta. And I think even now I forget like we do 360 feedbacks every so often. And I had one a few years ago in the early days where it's like one of the feedbacks for me is people feel like they get whiplash in the early days because you pivot. It's not so much now, but you pivot a lot. Do you forget I can change the CEO? Like, I can change direction and do something and pivot now, but you've got this huge tower behind you that increasingly gets longer and longer. And you know, the effect, as I said down that end is actually can be wildly catastrophic. And I can see for Mark, like, it's not surprising that it's very hard to understand some of the secondary knock-on impacts. So I think what we're getting better at internally now is like previewing things before we do anything and getting a lot of more feedback. But even then, changes to start super hard.
Jon Chee - 00:09:58: And at least what I've seen help with this is, this is the methodology in which we do. It's like a lot of show and tell previewing it to like check out what I've built like, or check out like what we're working on right now and kind of like getting everyone to feel like they're like along for the ride and not just getting something. Like I get the feedback or I would just like airdrop, like, all right, this is the new thing we're doing, like, and I just like live and breathe in this chaos. But like, not everyone thrives in that level of chaos.
Scott Chetham - 00:10:23: Yeah, I think that is the difference between. I think if you're particularly a CEO of a founder, you're much more comfortable in being uncertain and I think other people less so. And you kind of forget that. I also think it's interesting, the one thing I observed too is that a lot of people who tend to like be resistant to this also struggle to focus. I don't mean that as like a negative. I think it's just that I think some of what can be a good superpower if you're CEO is yes, you have to pivot and listen to a lot of information, but you can also focus and execute and check a lot of stuff out. Not everyone can do that. And I think I am just getting better and learning how to like, work with all the different personality types that are required to be successful. Because we need everybody. I need people who are happy doing the same thing again and again. Because I can't do that.
Jon Chee - 00:11:10: Yeah, I can't.
Scott Chetham - 00:11:11: Yeah. And so, you know, central part of everything that we do, as you said, it's like, how do you bring everybody along? And just don't forget that, you know, behind me there's a huge tail company. It's funny when you get up and given all hands, I turn around, there's like over 60 people there and seem to be like 75.
Jon Chee - 00:11:28: Yeah.
Scott Chetham - 00:11:28: I remember when it started with myself and my dog.
Jon Chee - 00:11:31: Yeah, yeah, that's it.
Scott Chetham - 00:11:32: That was the company five years ago. And it was like, okay, this is different.
Jon Chee - 00:11:37: Yeah, this is different.
Scott Chetham - 00:11:39: Yeah. And so it's not the same thing.
Jon Chee - 00:11:41: By the way, I had the same experience too. Like, it was literally me like at a kitchen table and my pup like, and just like in a computer. So that's like, pretty much it. I have to remind myself that too. I love your analogy. It's just like as a company grows, you get that real whiplash. There's just like, it goes all the way and ripples through and can be like, catastrophic. Like, as you said, like, on the kind of like, ground floor. Work in progress.
Scott Chetham - 00:12:04: Yeah. And I think I've gotten better. Like, one of the things I learned, like, I read this whole article, it was about a CEO of a company and her name was Tanya. It was in Business Insider. And I honestly can't remember the name of the company. But the thing I took away from the long article, because they have really good, like, metrics for retention and other bits and pieces. And the thing she did is she ran this thing called Tea with Tanya. And so she would pick random, like, four to five people every single week without fail. And this was like a 10,000 people company. It's one of the big ones. And she would go and have tea with all of them.
Jon Chee - 00:12:35: Awesome.
Scott Chetham - 00:12:36: Somewhere in the Midwest, in the United States. I just wish I could remember the company. And that's how she kept a track of what was going on and what was propagating down and like, what was everyone hearing as they grow? And she was saying it was one of her most effective tools for having a good feel of like, how is like, the temperature in the company what messaging was propagating? Everyone focused on the right things or the goal. Does everyone know all the goals and their roles and responsibilities within it? And, you know, it wasn't a test for them, but it was her way to know what was going on. I thought, that's a great thing.
Jon Chee - 00:13:06: That's awesome. Because I think, like, it kind of reminds me of how Home Depot, they would like, always just like, stop by, like a given store and just actually see what's happening on the floor. Like, the founders would just like, go in and roll in and just like, kind of just see. And I was like, you can't forget. Like, that's like the face of it. Like, these are where the people are interacting and you can't lose sight of that because the second you start like managing it via spreadsheet, you're getting in trouble.
Scott Chetham - 00:13:30: Yeah, no, I think that's right. I mean Home Depot is frankly an amazing employer. Always remember my wife works for child protection services. She's worked her way up into kind of management now, but runs the court system. But before that she was obviously would take children to court, like away from families and things like that. And because of this we've had a concealed address, like conceal our license plates and things like that. But whenever we're walking around Home Depot, I know my wife has seen a former client because all of a sudden her head turns straight and she won't acknowledge them because she's not allowed unless they come up to her because obviously it's not for a good reason. Like if she knows them, it's not for good reasons. This seems to always happen in Home Depot. And we're walking along, my wife's head will snap and then someone comes over, one of the staff members and go, Reneila, blah blah, blah. And my wife would go, hi, how you going? How it's going? And blah, blah, blah. And they'd leave. And my wife got it like the last one was like, you would never know. She was a crack addict and we had to take away the kids from her and her husband.
Jon Chee - 00:14:30: Oh wow.
Scott Chetham - 00:14:30: I was like, you can. She goes, yeah. No, but they went through the program, they did the work, they both got full time jobs, they went through rehab, they got their kids back. She said, look at them now. So I mean it's a tough, horrible job, but there's a lot of wins there. But Home Depot is one of the only people I know that like regularly seems to hire these people and from all accounts they look like great employees because they're just so happy because they've had to pick their lives back up together.
Jon Chee - 00:14:54: Yeah, it's pretty awesome. It's like pretty inspiring. When I read the book on Home Depot, I was just like, how can a company of this scale continue to like, like they're massive. Like they're like a freaking massive operation. But you still have people who are on the floor. Exactly what you said. They're just like happy.
Scott Chetham - 00:15:13: Yeah, they make the company. It's like Trader Joe's. They make the company.
Jon Chee - 00:15:16: Yeah, yeah. Which is so freaking cool.
Scott Chetham - 00:15:18: Yeah. So I mean they're a great employer but I think, yeah, we're digressing a bit. So from finish a PhD, I went to a company called Impedimed. Terrible name. I wish they'd change it. It was an interesting company because it does a few things. Think of it as more of the diagnostic or theranostic space. And they did a couple of things and one of them is really interesting. And one of the two things that I was the most interested because they sell quite a few things is one is lymphedema detection. So lymphedema, the most common one is the sequelae of breast cancer. So you run. I can't tell you the stats anymore because it's changed a lot with some of the new immunotherapies. But in the early days with radiotherapy for treating breast cancer, you damage the lymph nodes on the affected side and you'd get elephantitis of that limb. And the incidence was quite large. And you can see it. You can always usually spot breast cancer survivors on planes. And the reason what you can see is they'll have a gauntlet like a glove that doesn't cover their fingers but covers the palm of their hand and goes up their wrists. And if you see ladies wearing it, it's generally on one side. Generally probably there's a reasonable chance they're a breast cancer survivor because you need that gauntlet on there to stop like if you have an immune rate, if you bump your arm, if the lymph nodes are damaged, altitude, get bitten by a bee or it just takes time, you'll get lymphedema. So like elephantitis. And so what Impedimed's technology for that case can do is it can detect it within a few months of post you coming out of breast cancer intervention, surgery and radiotherapy and tell you if you're ever likely to develop it. It's a very strong screen out mechanism. But then if you are at risk you do have to follow it. But the nice thing is with regular screening you will never have to go on to develop lymphedema, which is very disfiguring and it's actually quite painful. You get the same thing, by the way, for any gynecological testicular cancer on both legs in males and females. The impact is not small, but you don't tend to see it because people like to cover it up because it is disappearing. And so they had an interesting technology there.
The other application was body composition monitoring. And so I spent a bit of time on that former athlete boy who's still interested, still do a lot of this. We did a lot of the early research into body composition and also its changes. So for the early generation of fat loss drugs. So before GLP1s. So for a bit of time we supported like there's a big lab, Dimfly Gallagher's in NYU. So a lot of the early results for obesity monitoring for GLP1s and everything is all done out of these labs for changing in body composition and other bits and pieces. And so this was some of the earlier technology there. It's still used for that, by the way. It's just that the tough thing there is like the market's not as big as you think, because who really needs to know their body fat that accurately? Unless you're in drug research or you're looking for fluid compartment changes like lymphedema. It's one of these great tools looking for a... it's like a hammer looking for the right application. But it was a fun company because one I got to kind of learn about and experience like all these different applications. And we supported some of the clinical research for these early programs. I remember doing work of all people, Novo Nordisk early GLP1s. We were the ones providing all that equipment. This is how farther back that program goes. Like a decade ago I spent a lot of time in Copenhagen flying in. So that was a chunk of that in the clinical research because we were the provider of some of that technology for measuring those changes back in the early days of that program. This is when Novo bought that in, I think. Yeah, NYU, I think was where Lily was. It's a long time ago. So I'm struggling to remember who did what. But so I got a lot of exposure through them to into the big pharma companies, Novartis and a few others. We work with Novartis and Holper actually. That's what Intersection Medicals, we work with Novartis on congestive heart failure monitoring for some of their programs in Cerealax. And that's how I stepped through these things.
And the intersection was by what I call like Versant Ventures, who is a... so Versant funds a lot of the, or found a lot of the biotech companies. And so if you think of a progressive step always in that direction, it's always been sort of clinical research, sometimes providing tools into those programs, sometimes being part of those programs and through Versant kind of supporting those companies as well through those programs. So I've always been in kind of some one direction in support of a lot of clinical research either for the company ourselves because one of the things, if you're measuring body composition, you actually have to prove you can do it. So you have your own clinical research programs that I ran there, and intersection for them. And then we partnered with drug companies as well to support their own like endpoints, like can you measure these things? And so that's the natural, natural progression of why I'm now in a career and a company that spends most of its time helping design better to get the drug. Are you asking the right question in the right people at the right time to prove this drug does what you want to claim it does? And so in many ways everything I've been doing is just the same progressive thing to this point, just building up to it through different aspects of it.
And the leap into what I call AI and software is because of Google. Until this point, until like Intersection, I'd spent in person, I'd spent all my time on the molecule side or the device side in clinical research and not so much on as a big software. So the big thing for me, and particularly how I got involved in AI is because Google X reached out and said, well, we've got all this stuff that we need to prove is safe and effective. And that also included Deep Brain at the time, not DeepMind, but the Google Brain team because they had these big diabetic retinopathy programs using not LLMs but adjacent technology. But this is the group who came up with the transform model. And I know that group quite well. We supported them on my team, supported them in their healthcare ambitions. So Google X reached out, I joined. I actually was technically, I think more correctly, I was a Google X employee. I need to fix my LinkedIn. It's wrong in a few places, the stuff that's not quite here. And then we spun out Verily. But my purview is like I had to support the Google X brain and Verily in their development operations. So how do you get this thing from beginning to end? And part of that is we had a number of programs with all the large pharmaceutical companies which was building technology to help them in clinical development. And that's how I more got into software, because I got to see at scale, like how does software get developed.
At that point I could see the direction of where I was heading. I got the timing a bit wrong. I thought it would be a bit faster, to be honest. I took a guess that we will be able to automate what is clinical development, which is like 12 years to prove a molecule as safe and effective it is today. We should be able to get that down by 50% because that 50% is within our controls. It's internal stuff. It's the work. It's a lot of frankly horrible amount of data assembly, processing and things. People still need to make decisions, but it's the time between decisions that's so long because it's so much work and it's very, sometimes very repetitive. And I've built those teams and run them and it was like, okay, the thesis is we can get this down by 50%. I'm going to go off. I parted ways with Verily next and on very good terms, still friends with everybody. And I was like, I want to go and do this thing. I thought I was going to do this thing here, but we end up doing this other thing. And I'm glad for you, but I want to do my thing. And so I spent a year planning that out. Well, I took a sabbatical Google, thankfully I was unemployed. So I helped them hire my replacement. We actually split my job into three. Helped Google hire someone, Verily hired somebody, X hired their own person. I helped them on that and then just made myself available as part of the terms while I worked out what I was going to do next.
And so that's how Faro came about is like, well, I know this is going to happen, but we were built on earlier technology and we were doing okay. We were working our way through it. But when LLMs came along now our pace of being able to kind of deliver much more impact at speed just dramatically accelerated. And the stuff we can do now is amazing compared to even like six to eight months ago.
Jon Chee - 00:23:09: That's the exact same feeling. It's just like actual like massive inflection point. Like my co founder and I, the stuff that we were doing that would take like months of like, just like laborious, just like chopping of wood, just like absolutely condensed. And like it's kind of this crazy thing now because, like, I don't think it's an understatement. It's just like now you're just kind of like limited by like your creativity. Now it's more of a problem like how do I prioritize like all of these things? Like, I'm struggling now because I just have like these like walls of ideas and like initiatives I want to kick off. But I know for sure they're not all equal in terms of like impact because like I can easily just go down just like these like rabbit holes and it's like hard.
Scott Chetham - 00:23:53: Yeah. And I think, well, that's the challenge we've got now is all prioritization. And our customers all want everything yesterday. And it's like, of the hundred things you want, I'm not joking. We'll do like six or seven of them in the next six or seven months. It's like, help us choose. So, like, now we're putting together customer roundtables and other things to get around to a line. And it's not that they're hard to do. Yes, they require creativity, but we've got enough, like, tech stack and everything to know how to do it. It's more it's the prioritization issue that can actually be very hard to communicate because it's like, well, hang on, why can't you do this now? It's like, well, if you've asked me literally for a hundred things and I'm being very, I'm actually not exaggerating. That is not possible for anybody. Even like the behemoth in my space Viva could not possibly do this. But let's choose together the highest value things. I think we've gone to, like, purely a prioritization problem right now is like, our growth is more limited by what we can practically do. But I also think because coding has gotten a little bit cheaper and time efficient, the burden now sits in design and product. And I think we're hiring more product managers all the time. But we also need product managers that can use these tools and are now prepared to roll up their hands and, like, actually build a prototype. But it also means combining them with subject matter expertise. It's an interesting intersection of, like, the skills required now don't sit in one person. But it's not coding anymore. So the bottleneck just moved and we have a new bottleneck. And I'm trying to unpack that right now with hiring and other bits and pieces. But I also think the slow part is getting business alignment on the this is the problem we're solving, and we're solving for this reason.
Jon Chee - 00:25:30: It's so crazy as you described this, because it's exactly what we're feeling too. Like, we're like, rapidly hiring because, like, I just have found these new bottlenecks that didn't exist. Like, my team half joking. They're like, Jon, you're just like, idea volcano. Like, we don't want this to stop, but we need some sort of, like, way to like, make sure that we're not just, like, doing the lower kind of priority stuff. Like, we got to focus on, like, the stuff that, like, has, like, immediate urgency.
Scott Chetham - 00:25:57: Yeah, I mean, I think that's the balancing act because now we're a much more mature company. We've still got legacy products that we have to support and keep growing the value propositional. But there's really cool stuff that a startup could do that we can't like a true startup, because I'm not sure where, I think it's more a tween now. We're not quite a startup anymore, but we're not like anything. So we're kind of the worst place. We're a tween and so we'll get through this. But it's again, it's prioritization and it's like, okay, are we solving the right problem for the right people? Is this the right value proposition? Because we just can't do it all, but no one could. I just think there's, we just live in an interesting time where I think people think because of AI everything should be done tomorrow. And it's like, no, it's not because we can't do it. It's because there's literally just a lot to... as you say, there's a lot that can be done now that couldn't be done before. And I also think because we serve enterprise customers, they have such huge pressures on them to transform. And there's this belief that if they just adopt a generative AI, it'll solve it, but that's not it. And there's a lot of harnessing to get this, particularly for regulated spaces. The product's not an LLM that's commodity. It's the wrappers around it and the business knowledge and coding in the wrapping. And I think people underestimate how much work that is. And we said QA. It's a, the QA work is enormous.
Jon Chee - 00:27:14: Yeah, it's actually brutal.
Scott Chetham - 00:27:17: Yeah, I know that's it now. And I think we've got a little smarter now. We now front load QA. So the data sets and everything now have to be come up with an examples at creation. And that's actually helping because it means we've got test sets from day one. That was a brutal learning curve. Now we've inverted it because otherwise you get a cool product and then you spend six months validating it. Because a lot of what we do is incredibly like complicated. Like this is requires humans.
So one example that we had a top five company. So we have one workflow that does the equivalent of what about five people each working for three weeks does highly specialized. That's probably more accurate how we got run the account. They benchmarked this against their internal teams. So they had like five studies that already put through this and they already knew all the answers through their own work. And they had all the time and the money spent on it. And the test for us is they gave us all five. And in 45 minutes, it's about 45 minutes to run. And that's a massive parallel processing of processes. And it was more accurate. But this stuff is complex. Like the amount of data and processing and context builds and like complicated workflows and verification is enormous. That sat behind this.
That's why I said that thing to build was maybe eight weeks, it was six weeks of brutal verification. And it was kind of probably the first of its type in our field. That thing is a really beautiful piece of engineering because it compresses a huge amount of what I call regulatory and clinical knowledge review into what today is very opinionated and also subject. You get tired. The one thing is they don't get tired and they're just regimented. And so you can force these things to do work really well and do this very repetitive work better than humans are probably positioned to. And that's kind of it. As I said, just to get this thing because of all the types of designs you could ever do in a protocol and all the types of things you could study and all the types of things to make that work on everything is just, and then verify. It's huge.
Jon Chee - 00:29:14: Oh, it's massive.
Scott Chetham - 00:29:15: And so that's the exciting upside. You can only do so many of these at a time.
Jon Chee - 00:29:19: Yeah, for sure. And it's funny because like as you're describing this, obviously what we're doing is not in a clinical setting. But one of my colleagues spent a long time doing QA on like this process and like his agentic process that he had created, like start to finish. He just finished all his QA like at the end of last week. And then he like let it rip into production and like without a hitch. It was like it worked. And he went from what he spends 75% of his day on, now he spends less than 5% a week on it. And it just like exactly what you said is just like, it will just like chew through it. And I just had my one-on-one with him and he was just like, my whole day has changed. Like this took up 75% of my day and now I can work on different problems. Which is so cool because like it was so liberating because now he feels like a kid in like a candy toy shop. He's like, okay, well like what can I do now? What's the next problem to solve? Like this one we've basically solved. And, like, we've, like, codified it in this agentic process. And, like, now he's having a blast because he's like, all right, cool. These are all the things that I've been, like, waiting to like. It's all the idea shelf stuff that has been languishing on the wine rack for such a long time. He's like, sweet. I get to dust this one off and crack it and, like, start something that I haven't been able to do ever. Or he's like, I didn't think I was going to do this for years because of this other process.
Scott Chetham - 00:30:38: Yeah. And that's, I think the power of what's coming to, like, clinical development is that over the next five years, the massive amount of time and staff it takes to get one of these things through is going to dramatically reduce. I mean, one of the things, I'll point out the reason everyone likes to beat up on pharma companies because of drug pricing, but it's actually, people should be surprised. It's not more. And the reason is, is that if it takes you 12 years and a bit over $2 billion to get a drug through clinical development, that's one. But 90% fail. So every successful one has to pay for the other nine that didn't. So now you've got, like, it's not $2 million, it's about $22 billion. And it's only getting worse every year that you have to pay back that you spent 10 years. Imagine that interest on that over the 10 years that it took. Obviously, like, it ramps up. Now you have to pay that back or no one would be prepared to invest because of the risk in drug discovery. So you're starting with a $222 billion hole. You have to dig out before you can even, like, sell something. And the only way you'll get drug pricing down, it's not through aggressive pricing or negotiating with companies or blah, blah, blah, because they cost what they cost to get to market. You have to shrink this clinical development timeline. We've got to get the cost and the amount of labor down. It's the only way we'll see an impact. Because this is where the money is being spent and it's not wasteful spending. It works. Obviously we get drugs to market, but now we have newer technology that we can shrink it. So this kind of has to happen. And it's the painful steps of working through it bit by bit, because no one's got anything end to end yet. I think we are the furthest ahead than anybody in doing this. And it's still early days, but the winds, you know, there's some significant wins already and I think they said it'll just build up and build up and then I think it'll be a different challenge. Like we'll have new challenges to solve, but at least it won't be this.
Jon Chee - 00:32:27: So you're talking about like the time savings being reduced, like things in our control. We can like squeeze it down 50%. Is that one to one commensurate with price as well? Like clinical trials will be 50% cheaper.
Outro - 00:32:40: That's it for part two of our series with Scott Chetham. Next time, Scott examines the structural friction across clinical trials, the economics of pharma development, machines versus emerging biotechs, and why protocol design complexity has become the defining bottleneck in bringing life-saving drugs to patients. Join us next Monday for part three of the Biotech Startups Podcast. The Biotech Startups Podcast is produced by Excedr. Don't want to miss an episode? Search for the Biotech Startups Podcast wherever you get your podcasts and click subscribe. Excedr provides research labs with equipment leases on founder-friendly terms to support paths to exceptional outcomes. To learn more, visit our website www.excedr.com. On behalf of the team here at Excedr, thanks for listening. The Biotech Startups Podcast provides general insights into the life science sector through the experiences of its guests. The use of information on this podcast or materials linked from the podcast is at the user's own risk. The views expressed by the participants are their own and are not the views of Excedr or sponsors. No reference to any product, service or company in the podcast is an endorsement by Excedr or its guests
