Latest / StartUp Health NOW Podcast / Investing in AI-Native Healthcare with Menlo Ventures' Greg Yap and Unity Stoakes

Transcript

Investing in AI-Native Healthcare with Menlo Ventures' Greg Yap and Unity Stoakes · StartUp Health NOW Podcast

• 0:00
  [chime] Yeah, I've never been the inventor of the technology, but it's always been trying to commercialize, trying to find the right applications, and then we kind of bring those technologies to market.
• 0:13
  And sometimes it's been, you know, just the right moment. You know, sometimes we've been too early.
• 0:18
  You know, it's the, you're either on the cutting edge or you're on the bleeding edge, and sometimes you're doing the cutting and sometimes you're doing the bleeding, and I've definitely done both.
• 0:24
  But it's always been kind of novel technology, high throughput technology, AI machine learning, and then some breakthrough application that we've been chasing. And that kinda led me to investing, right?
• 0:36
  [upbeat music] Welcome, everyone.
• 0:55
  I'm Unity Stoakes, co-founder of Startup Health, and I am excited to welcome Greg Yap, partner at Menlo Ventures, to today's fireside chat.
• 1:06
  Menlo, as many of you probably already know, is one of the world's leading VCs, and Greg, uh, really brings, uh, I think, an extraordinary and valuable perspective to the Startup Health community.
• 1:20
  So excited, uh, about today's conversation because before becoming an investor, Greg spent two decades as a founder and operator himself across therapeutics, diagnostics, digital health, and healthcare technology.
• 1:37
  Today, Greg invests across life sciences and healthcare at Menlo, and backing a lot of companies really across the intersection of biology, software, AI, and care delivery.
• 1:49
  So we're gonna be exploring where Greg sees the biggest opportunities in health innovation, what Menlo is looking for, uh, and really what early-stage founders need to know to build and get funded in this rapidly changing market.
• 2:08
  As always, this is meant to be an interactive conversation, so please post your questions in the chat, and when we open it up for questions, uh, in a few minutes, we'll do our best to call on you.
• 2:20
  Ask your questions early, as we often run out of time. We'll call on you, and if you could please introduce yourself before asking your question. So Greg, welcome. Oh, thank you, Unity. It's great to be here.
• 2:32
  It's nice to see you all. To kick things off, I thought we'd start by getting to know you, as you spent two decades as a, as a founder, as an operator, and you've been at big institutions.
• 2:44
  But before becoming an investor, maybe just a bit about your background to give us context and how that experience shapes what you look for as an investor today.
• 2:57
  Yeah, so I, I've spent my whole career basically at the intersection of new technology and healthcare. You know, I, I have been involved in a variety of different kind of perspectives on that market, but it's been...
• 3:09
  I've been a, a founder, you know, a couple of times. I was-- I start, helped start and run a, a, a therapeutics company and a digital health company.
• 3:18
  You know, I worked at Roche for, for several years and, uh, ran their cancer diagnostics life cycle.
• 3:24
  So I've been through the FDA, uh, six different times, Class two, Class three, run clinical trials in most of the different regions of the world.
• 3:32
  You know, and then have spent, you know, a number of years at Affymetrix in the early days of genomics.
• 3:36
  You know, I started and ran two businesses for them, the genetics business, you know, and then I, I started the genetics business and then ra- and then ran the diagnostics business. Again, kind of early days of genomics.
• 3:48
  I've kinda seen the, the industry from different angles. You know, they've bought smaller companies from the smaller, uh, company side. I've helped to sell smaller companies.
• 3:57
  You know, and then it's also been, you know, a, a common through line has always been kinda novel technology, you know, being developed, typically very high throughput technology.
• 4:06
  You know, and then, uh, you know, often, you know, almost always kinda AI machine learning to analyze that flood of data, you know, and then some novel application in life sciences and healthcare.
• 4:17
  You know, and, and while I've worked in, as you mentioned, you know, therapeutics and genomics and diagnostics, you know, and digital health, you know, it's always been kind of that through line of trying to help find...
• 4:28
  You know, I've never been the inventor of the technology, but it's always been trying to commercialize, trying to find the right applications, and then we kinda bring those technologies to market.
• 4:37
  And sometimes it's been, you know, just the right moment. You know, sometimes we've been too early.
• 4:42
  You know, it's the, you're either on the cutting edge or you're on the bleeding edge, and sometimes you're doing the cutting and sometimes you're doing the bleeding, and I've definitely done both.
• 4:49
  But it's always been kind of novel technology, high throughput technology, AI machine learning, and then some breakthrough application that we've been chasing. And that kinda led me to investing, right?
• 5:00
  It's-- I've now been in the privileged place the last eight or nine years or so at Menlo, you know, where, you know, now I basically work across all the areas that, you know, kind of the additives that as an operator, you know, I now get to invest across, and that's been a tremendous, you know, pr- tremendous privilege and, and responsibility.
• 5:19
  Can you tell us a little bit of, of context about Menlo? What drew you to Menlo? And I'm sure it's, it's, it's been around for decades at this point- Yeah...
• 5:27
  but it's probably also transformed a bit since you've been there over the last- Yeah... eight or nine years. I mean, I think, I think Menlo is a new story and an old story at the same time, right?
• 5:36
  I think we see ourselves today as, you know, we, we aspire to be the leading AI investor, you know, across all sectors. I, I have helped to lead our life science and healthcare practices, you know, for the last years.
• 5:51
  But, you know, we invest across the spectrum, for everything from the models like Anthropic, you know, AI infrastructure, you know, AI applications, uh, consumer healthcare.
• 6:01
  You know, we invest across the spectrum, and our goal is to be the leading AI investor across all of those areas. You know, but we are also, at, at the same time, we're a 50-year-old venture fund.
• 6:12
  In fact, we're celebrating our 50th anniversary this year. Thank you. We're one of the oldest, one of the original firms in Silicon Valley. You know, we started in 1976, you know, when, when Menlo...
• 6:24
  when nobody knew where Menlo Park was. I think today our firm has really evolved through all of the epochs of technology, you know, and now we're hitting this AI wave.
• 6:34
  And I think really with the team that we've built and the investments that we've made and the thesis work that we've done, we're really excited about what comes next.
• 6:43
  And, and if I'm not mistaken, you can tell us a little bit about this, you, you've just raised, I think, one of your largest of capital in the firm's 50-year history.
• 6:53
  As you, specifically in healthcare and the firm more broadly, looks across healthcare and life sciences and, and other kind of crossover sectors, where are you most excited to invest?
• 7:06
  Is there always now an AI lens or, or how it... what's the thesis today that would be relevant to startups thinking about how they might fit with- within Men- Yeah... within Menlo? Yeah.
• 7:19
  So I do think we're, we're investors across the spectrum. But I do think everything today, you know, if you're not using AI and you're not AI native, you're trying to figure out how to involve AI.
• 7:30
  You know, it's, it is unusual for us to see a pitch today that doesn't have AI in some form.
• 7:36
  You know, but I do, I think AI is moving so fast that a lot of the best ideas that we get most excited about are ones that have AI at their core.
• 7:46
  That's not to say every single thing that we'll do will be a same approach to AI. It is definitely, we talk internally and externally, we are all in on AI, right?
• 7:55
  And so I think that's how we want to, to stand up for our entrepreneurs. And it, it seems like you also do, like let's stay within healthcare, bio, the other related areas, but it'll cross over sometimes to consumer.
• 8:11
  For example, I know you're an investor in function health. Yep. And there's these overs, I, I think maybe you called them pods at one point- Mm-hmm...
• 8:19
  where within the firm you're doing stuff across sectors and merging those. Yeah. I think the way we think about our industry focuses is in, uh, what we call pods, right, which are essentially sectors.
• 8:31
  I mentioned the, uh, digital health pod, you know, and the life sciences pod. We also have pods for all of the other, uh, sectors that I mentioned.
• 8:39
  But one of the things that we really like is the investments at these kind of intersections, right?
• 8:44
  In, within our digital health pod as, as a specific example, you know, we'll look at things at the intersection of consumer and digital health, and so the folks from the consumer team and the digital health team can collaborate to help evaluate that investment and then also to help support that company.
• 9:00
  You know, and that's the same thing is true on, you know, there could be intersections between healthcare and cyber or healthcare and fintech or healthcare and kind of AI infrastructure.
• 9:10
  But we do a lot of work across kind of n- you know, kind of novel foundation models. You know, those can cross... those can have multiple applications.
• 9:19
  And so we, we love the things that, that allow us to bring, uh, different groups within Menlo together. We always have two partners working on every deal. You know, many of our investments, you know, they're...
• 9:31
  it, it just depends on where they are in their life cycle, what the specific need is.
• 9:35
  But I think one of the things that we feel like makes us a little distinctive, you know, is the degree to which we collaborate, you know, across the firm, you know, in service of our portfolio companies.
• 9:46
  And then you do even seed all the way up this, this, the stack as a company grows? Yeah. Our, our firm, we have two major investment vehicles. We have a, our venture fund, which does mostly seed and A.
• 10:01
  You know, and then our inflection fund, which we think of as kind of an early growth position that does kind of series B through D.
• 10:08
  And so we can invest across the spectrum of, of private, uh, investments, you know, and then do everything.
• 10:14
  We, we, we do everything from company creation, you know, although that's a, that's a small focused effort, you know, at Menlo, all the way up to kind of large, these large growth rounds.
• 10:26
  I've, I've seen you do both, but are you looking for something very... Like, do you already have something that you're looking for or is stuff coming in net new that- Yeah...
• 10:38
  maybe you're, you're willing to look and, and how do you kind of think of the thesis over the past two, three years even, even the past year? What, what are you looking for now moving forward? Yeah.
• 10:49
  I would, I would say it's always a mix, right? I think we are always... We do a lot of thesis work as a firm. Like, we've also become known for our market research recently in the area of AI.
• 10:59
  So we do an annual report on AI adoption in the enterprise, AI adoption in consumer, AI adoption in healthcare. If anyone's interested in those reports, they're available on our website. We, all of those were 2025.
• 11:12
  We released new editions in 2025, and we are releasing new editions this year in 2026. And so that helps inform our thesis work, you know, where we are trying to figure out, hey, where is the field going?
• 11:24
  You know, what sectors do we wanna invest in?
• 11:27
  As one example in healthcare specifically, you know, last year in our healthcare report, we identified patient engagement as the fastest growing sector within healthcare AI adoption.
• 11:38
  You know, surprises healthcare people and non-healthcare people is that healthcare is the number one sector in AI adoption. We found that for two years in a row, growing the fastest.
• 11:47
  And the biggest area of growth, not the biggest area as of last year, but the biggest area of growth was patient engagement.
• 11:54
  And so after a great amount of thesis work, we recently invested in Assort Health, you know, as kind of one of the leaders in, in patient engagement.
• 12:02
  So that's kind of an example of market research, thesis work, and investing kind of all, all coupled into one.
• 12:10
  And so that's kind of one, you know, a, a major focus of our firm is that we do do a lot of fundamental thesis work. You know, and I'm compressing a lot of complexity into that- Yeah, sure...
• 12:20
  statement, but obviously there's a lot more that goes into that. Yeah, and it, it's just, it's so exciting. You see companies like OpenEvidence emerge, you know- Yeah... in the past couple of years. Yeah.
• 12:29
  You see even how patients or everyday people are going to the large models and- Yeah... hundreds of millions of, of people a week asking health- Yeah... related questions, putting data in and- Yep...
• 12:42
  really going there first is quite extraordinary. I mean, there's been Google, everybody going to Google to ask questions, and now they're going to, to AI. It's, it's- Yeah... quite a, a big shift very rapidly.
• 12:55
  And OpenEvidence is another example. That's also a portfolio of company of ours, but it's one where we invested, you know, relatively late for Menlo, but at the growth stage.
• 13:03
  You know, it's something that we've been following and excited about, but, but made that investment. And, you know, that was more, you know, finding the right opportunity.
• 13:11
  And so it's that combination of thesis work, you know, and then the right opportunity, right? Sometimes the opportunity comes in to us, and sometimes we go out and help try to create the opportunity.
• 13:22
  As I mentioned, help start companies. If we can't find the right company to invest in, we will help to, to start a company. That's like an incubation, an early... Like, you'll incubate a company, get it going.
• 13:34
  So for an early stage founder then, what's gotta be true before you believe the company's found kind of a wedge or something that is venture scalable as a business from your perspective? Yeah. Really- Would that be true?
• 13:49
  Yeah, yeah. It really depends on stage. You know, a- as I mentioned, you know, we, we do everything from, from incubation and seed all the way to growth.
• 13:57
  We are trying to expand the amount of seed investing that we do and trying to expand the amount of later stage investing that we do, and so those are two focuses for us.
• 14:07
  You know, on the seed side, I think especially the team becomes even more important, right?
• 14:14
  I mean, the team was always a critical part of our investment thesis, but what I think is interesting in this era of AI is that product differentiation becomes a more fragile moat, right?
• 14:26
  In, in some cases, it just doesn't become a moat at all, right?
• 14:29
  Because the ability to build product instead of being limited by the number of, of software engineers or developers or, you know, that you could find, you know, the ability of somebody who really understands a workflow and a customer, right?
• 14:46
  The ability to translate that into early stage product, you know, more quickly than it used to, right? Yeah.
• 14:52
  And so therefore that's great because it allows people to get in the market quicker, but it also reduces the barriers to competition. You know, and so
• 15:01
  if the differentiation of product in a relative sense decreases, it's really the, an assessment of the differentiation of team, right? Can this team...
• 15:10
  If this team has better product today, you know, do we, you know, can we believe that the team will continue to build better and better product, stay ahead of the, of the group, out-execute the competitors that are being, being more easily enabled by, by AI coding tools as an example.
• 15:28
  Right. You know. And so- Yeah, so when-... that makes a, a difference... as, as, you know, someone's... They've got a strong team, maybe, maybe they're unknown to you, um, because they, it's their first time around.
• 15:39
  You know, what, what gets a founder in the door before you've said yes or no the door, and I guess digging a little deeper into that, what makes that first meeting, you and the team that have a second meeting, what, what have, what have they demonstrated?
• 15:56
  Yeah. And, and so you're talking about once they're in the door, what gets them from first meeting to second meeting, or you're talking about- Well, both. Like, first, how do they even... What's the process- Yeah...
• 16:04
  of getting in the door? Um- Yeah... and, you know, that would be one. Like, what's, what, what are some tips of, hey, how do we get on Greg's radar or the firm's radar? What, what's the right avenue in?
• 16:15
  'Cause I'm sure there's a lot of questions. "Hey, can I get- Yeah... an intro to Greg?" You know, but what's the right pathway in? Yeah.
• 16:22
  And then once you've gotten in the door, then what do they gotta show up with- Yeah... to be really impressive? Yeah.
• 16:30
  And again, this is different at different stages, or, or at least not, not wildly different, but subtly different. If we were talking about... So maybe first, very early stage company, you know, we can...
• 16:39
  If I wanna start with the seed stage company, you know, the presumption there is that the company is a small number of people. You know, you know, mostly, you know, it could be all founders or mostly founders.
• 16:51
  You know, and it, and relatively little customer feedback, right? You've got maybe a, maybe some design partners, but not that much, right?
• 16:59
  So if that is the ch- if that is the case, you know, then, you know, it really is about the people, you know, and about a demo, but the understanding of the market. You know, kind of that, that founder market fit, right?
• 17:12
  Because you really don't yet have customer feedback. You really don't have product feedback.
• 17:17
  You know, and so then it's really just, you, if it's people, you know, I, I do think it is partly getting the warm introduction obviously helps a ton, right? Yeah. We, we, we all will take some cold meetings, right?
• 17:30
  Depending on how busy we each are in that moment and, you know, if, if somebody writes something in the email that catches our attention.
• 17:39
  But you know, honestly, as having been an entrepreneur myself, you know, it is a little random, right? If you're trying to go cold, right, there's a randomness to it. And it, and- E- especially when AI- So-...
• 17:50
  is writing all the emails today. Yeah. And, and, and none of us can tell what's AI slop, you know, really. Like, you can't- Yeah... it's hard to tell what's AI slop and what's just, you know, not, you know,
• 18:02
  what's and what's not. Sure. And so getting, uh, getting that warm introduction, you know, im- uh, having impressed someone else, you know, who, who I can, who I respect, right?
• 18:14
  That's, that i- that, it's 100, that's 100%, right? Yeah. If you can impress someone that I respect, I will give that person time. Right. And so everything else can work, right, but the most...
• 18:27
  That's the, the most reliable way to get, I think, almost any VC's attention. Yeah. As you get to slightly later stages, I think there are...
• 18:37
  The, obviously the data, you know, the, the traction, the customers, right, those are, are ways to get, to, to get, uh, a VC's attention.
• 18:46
  And then obviously as we talked about, you, if you do your homework on that VC, right, if, if there's a thesis area that I'm interested in, eventually I'm gonna publish something, you know, about that, about that work, right?
• 18:59
  I'm not gonna keep that work entirely secret. You may not... There may be points at which it's impossible to know, and you, you sort of have to, you g- get lucky.
• 19:07
  But the alignment of a VC's thesis area with your business is obviously the other thing that's 100%, right? That will work all the time in terms of getting- Right... that person's attention.
• 19:18
  You know, sometimes that's easy to, to discern from the outside. Sometimes, unfortunately, it isn't. So I'm gonna ask one, one more question, then I'm gonna open it up to the floor here. Great questions are coming in.
• 19:31
  We're gonna organize those. But if, if anyone has questions, please start putting them in the chat. We'll do our best to get to them. But Greg, how... The market's very dynamic.
• 19:42
  It's been transforming so rapidly, certainly over the, the past two years. It seems like every week it's evolving. How should founders think about fundraising in the current market?
• 19:54
  If, I guess let's break it into two buckets.
• 19:55
  Just getting started very super early, raising your first seed, and then if you're already starting to get traction and scaling, how should founders be thinking about capital raising, say, over the next six to 12 months, or as far out as you can kind [laughs] of predict?
• 20:16
  Um, [sighs] I, I mean, it's hard to, it's hard to predict the future.
• 20:21
  I, I, I do think I subscribe to the, you know, try to raise, try to raise more money than you think you actually need, because something always goes wrong. I, I think that's, that's my, my school of thought, right?
• 20:34
  The, the... It's, it's important to, you know, you... The, the dilution is painful, you know, and, you know, nobody wants to take more money than they need.
• 20:45
  You know, but the, the probability that you need a little more money than you think you did, you know, is, is relatively high. I think that can apply across stages. This has just been an extraordinary, uh, conversation.
• 20:56
  We're coming up just at the end here, so Greg, I just wanna thank you so much for sharing your wisdom, uh, with the group. Just to kind of wrap up, leave us with some positive energy.
• 21:08
  You've been a builder and operator yourself. You know, what's, what's your biggest wisdom, lessons learned, insights for this group that are all extraordinary builders, they're working really hard.
• 21:19
  Just with some optimism for the future at this very exciting, dynamic time. Yeah. I, I mean, I do think this is the best time in, in all of history to be a founder, right? I really do, right?
• 21:31
  The ability to, to build, you know, has historically been really constrained to a small number of people, right?
• 21:39
  And, and the, the barriers to building, you know, getting, you know, software engineers, right, has b- has really limited, you know, the ability of our whole industry to innovate new things.
• 21:51
  You know, I think the, the ability for AI to, to enhance every person in an organization, right?
• 21:59
  Not in all the ways that we m- we might want it to initially, but in, but in terms of being able to build product more quickly, to get it to customers faster, to get feedback from customers, you know, I think AI is transforming the way that we, that we build things.
• 22:15
  And so the ability to go fast, you know, to, to innovate, to get things into customers' hands, to experiment, you know, I, I think is, is, is unprecedented today. You know, and so it will come with its own,
• 22:31
  you know, barriers and hurdles and fears and, and whether these companies need to evaluate success in the same ways, you know, I think is, is an important question, right?
• 22:41
  Of maybe there are many smaller companies, you know, which actually is not great for a venture investor like myself. [laughs] But could be actually quite good for the industry, right?
• 22:51
  The ability for small teams to build things that are enterprise grade, you know, on the one hand, and consumer grade on the other hand, right? Those are, those are both high bar.
• 23:01
  That, to me, those are not one is higher or lower, but they're just different bars. The ability for small teams to do that is greater now than at any point in our history, right?
• 23:10
  And so for you guys at the front edge of taking advantage of that, you know, I'm, I'm excited to see what you guys build. Thank you so much, Greg, and thanks to all the Health Transformers for your amazing questions.
• 23:21
  This was a great session. Thanks. Uh, there's another, uh, Fireside Chat next Tuesday at 12 noon Eastern Standard Time. Everyone have a wonderful day. Hey, Greg. Someone heard your voice when he walked by.
• 23:32
  Ah, look at that. [laughs] Hey there. Good to see you. Nice seeing you. How are you guys? Thanks for doing this. So good for our community. Yeah, absolutely. Thank you, Phyllis. Great job. Nice to see you. All right.
• 23:40
  All right. Everyone, bye now. Thanks. Bye. So take care everyone. [outro music] And finally, an invitation to all of the extraordinary Health Transformers.
• 23:54
  Whether you are a startup, someone building the future of health, or whether you are an investor, funder, or buyer of the solutions out there in the marketplace, this is an invitation to you to join the Startup Health member community.
• 24:09
  Learn more at startuphealth.com. Look forward to hopefully seeing you soon. [outro music]