00:12:46 · Mark Jacobstein
[00:12:46] S1: Engineering problems, and information processing problems, and early bioinformatics problems. So maybe let's jump ahead then to Guardant and put the company together in 2012. One of the things that I think is really interesting, I was lucky enough to join Guardant when in the early days when really the the only thing that Guardant was selling was an assay for therapy selection to get tumors characterized genomically and get people onto the right therapies, which was the business for a long time and still is. But the North Star was always kind of early detection. Was that true when you and Murali started the firm in 2012? Early detection was the thing?
00:13:23 · Helmy Eltoukhy
[00:13:23] S0: Yeah, it was always the vision. Like, our first pitches were essentially the ultimate goal is a simple blood test, annual physical for early detection of cancer, but we have this, I think, concept of intellectual humility and understanding that, look, people have been thinking about early detection of cancer for at least dozens, hundreds, you know, a hundred years and so on, and so it's probably going to be more difficult than we expect, so let's do it in a way that's iterative. Let's start with solving easier problems first and then scaling essentially the technology that allows us to solve that problem, or the commercial technology that allows us to run that as our essentially compass for developing the next technology and the next one on our march towards early detection. It's similar to like, you think about Tesla starting with the Model S or the Roadster and then moving to the X and then the Model three, there's a technology scaled, you can expand it to wider and wider sort of use cases. So, we really had that sort of I I think that's the benefit of coming from the semiconductor field is that we were ingrained in Moore's Law and we understood that when you have scaling laws and you improve things, you can essentially widen the aperture in terms of how those technologies are used and the use cases that they're applied to. And so, we always said, okay, the first technology will have a sensitivity of X. It can be used to detect stage three, stage four cancer in existing cancer patients, mashing them with better therapy. We improve it another 10X. We can help fifteen million cancer survivors with residual disease detection, we improve it another 10x, then we can do stage one disease and do early detection of cancer. And so that was always part of the business plan. It's pretty
00:15:14 · Mark Jacobstein
[00:15:14] S1: cool that it's actually happened and sort of remarkable that it essentially happened, I think, on the timelines that you guys laid out in 2012. Some other things didn't actually happen. We just... We were just talking about in there, I'd I'd never heard this story before that the first version of this was gonna be with Amplicons, and you decided that that's that's not good. Talk about some of the early choices you made that were the wrong choices and pivots that you had to make along the way to sort of rescue the business. Because, I mean, there's always some really important things that happen, I think, in the first few years that you look back on them and things could have gone the other way.
00:15:45 · Helmy Eltoukhy
[00:15:45] S0: Yeah, mean, as you said, the first version of the technology we developed was one that we realized, I think, a little bit too late that it wasn't going to scale to where we wanted it to go, and so we made the difficult decision of scrapping it, starting over, and you have investors, you have dwindling cash, you have all these things, and I think it was a hard decision, but one that, in the end, ultimately proved to be the right decision. So that was one for sure that was not easy. There's one that where things were going right, and this is probably something that a lot of companies deal with, that where you have product market fit, you see things scaling. So this is 2015. We had launched just in 2014 the 360, the first test, and things were scaling really rapidly. The volumes were amazing. We were essentially, I think, taking our largest competitor at the time was Foundation Medicine. They were on the tissue side. We were on the blood side. We heard an earnings call that, they missed because of competitive noise, so we were very flattered that we were considered competitive noise in the space, and unfortunately, we didn't have the strength in our reimbursement team that we did now. So we were missing our numbers in terms of the sort of reimbursement amounts that we're getting. So we're doing too much volume, we weren't getting paid enough, and so our burn was sort of increasing, and so we made the difficult decision to sort of have a rift in our sales team, so we said, Let's pull back. This is growing too fast, and I would say that that decision probably lost us at least eighteen months of market share in terms of where we could have been if we hadn't stopped. It's very hard to sort of turn things off and turn them back on, and you end up losing a lot more than I think. And it basically gave competitors time to come in, the market froze a little bit. So in retrospect, it was probably the wrong decision.
00:18:00 · Mark Jacobstein
[00:18:00] S1: Although, in some ways, actually, maybe I actually think of that as an entrepreneur. It's actually, you made a really hard but important and right decision, which was like not to burn the cap. I mean, you guys had demonstrated the ability to raise a lot of money, but not an infinite amount of money. Points to something else I wanna talk about in Guardant's business. For better or for worse, it's an incredibly complicated business. I love businesses like a WhatsApp where you have to do like a single thing right. In healthcare, as we were talking about in the other room, you need the right science, but you also need to execute on the product side and on the lab ops side and on the marketing side and on the sales side and on the reimbursement side. And I'm curious how you none of that is related to building a business like Guardant. So I'm curious, like, how'd you learn that? Regrets or anything along the way, lessons along the way for people in this room? Most of the people in this room are like frontier builders. There's a lot of PhD, a lot of scientists in the room, a lot of frontier builders, many trying to build their first businesses. And I'm sort of curious, how'd you learn how to run all these other parts of the business? Any important lessons along the way?
00:19:07 · Helmy Eltoukhy
[00:19:07] S0: Yeah, so the healthcare, I mean, diagnostics is a tough business. It's in most other areas that are not regulated, the consumer side, tech side, build it and you ship it. It's kind of pretty simple. Here, I mean, it's not saying it's simple, obviously there's complicated stuff that goes into that, but here you have to build it, you have to validate it, you have to there's turnaround time like concerns, there's regulatory approvals, reimbursement, there's physician education, then there's selling it, and it's a weird sort of space too in the sense that the person deciding to use your device doesn't pay for it. So like health economics doesn't work the same way in terms of like demand supply or like price elasticity, and so a lot of people come into this market and they say, Oh, I'm gonna price it 10x lower than everyone else. Every one of those companies that has done that doesn't exist anymore because they basically have just imploded their gross margins, have no money for R and D, and can't compete with the other companies. So you have to kind of understand the space you're playing in. So I think a lot of it comes from just not sort of taking cookie cutter principles from one field and applying them to another. It's really understanding from first principles what drives the specific areas, like what drives consumer adoption, what drives payer adoption, all those pieces. And then it's hiring the best people in each of these areas. Don't know half as much as my teammates and colleagues at the company in each one of those domains, but look, it's common sense. Everyone's a smart person here. You can tell who knows something and who doesn't. And so it's building that really strong team and understanding that you do need that expertise in five or six different areas. That's why these are expensive companies because you need that expertise in six or seven different areas to be around the table to be able to understand things. And maybe the last thing I'll say is that I don't think enough credit is given to the importance of sort of business model innovation in healthcare. I would say it is maybe more important than sort of technological innovation.