WEBVTT
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I've never seen healthcare move as fast as I've seen it move today.
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Two, three weeks ago, a company called Noetic GSK just said they were gonna pay him$50 million for access to their model.
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I don't think I've ever seen Pharma pay for data at that kind of size and scale.
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Reify has reimagined the way clinical trials are run.
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Today, trials get mostly run inside hospital systems.
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These guys said that's the wrong way to do it.
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We led the Series A and big owner in the company, and the last round ended up getting done at about$4 billion by Altimeter and Phenom.
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The company will do almost$200 million in revenue this year.
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The last round was done at$1.4 billion.
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We wrote a$5.5 million check.
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The founders that we invest in, we have to believe they're great storytellers, simply because one of the things we expect our founders to be able to do is to go attract the best talent.
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And there's just no way you can attract the best talent if you can't tell stories.
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We did smallest AI last year.
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He is a perfect example of why the India-US corridor works well.
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There's just something about founders when they're building in India today, they just have this.
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Hi, this is Sidhat Al Waliya.
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Welcome to the Neon Show.
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I'm your host and managing partner at Neon Fund, a fund that has invested in some of the best enterprise AI companies between the US and India corridor, like Atomic Work, CloudSec, and Spot Draft.
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Today I have with me Vignish Ravi Kumar, partner at Sierra Ventures.
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Vignish, welcome to the Neon Show.
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Thank you, Sid.
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Really appreciate it.
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So Vignish, you have been the second member of Sierra 2.0.
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So what is Sierra 1.0 and what is Sierra 2.0?
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Yeah, so I'll I'll tell you uh what Sierra 2.0 is and I'll talk about why we made the why we made the shift.
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Um so Sierra Sierra 2.0 then you know Sierra Sierra Ventures today.
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Uh we are an early stage focused fund focusing on uh enterprise AI and frontier AI.
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And enterprise AI is everything from the infrastructure layer all the way up to vertical, vertical software.
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And we invest everything from inception to series A.
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And so we're very early stage focused, usually the first or second institutional check into a company.
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Uh the firm has been around since 1982.
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And leading up to uh to 2012 when we raised Fund 10, the firm had started doing multi-stage, doing you know, different sectors doing later stage.
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And when we made the transition to the new Sierra, one of the things we we realized, or two things we realized, one, there was a big wave of B2B software that was showing up.
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And at the time in 2012, 2013, I still remember, I used to go to a lot of events and tell founders, hey, we're B2B specialist shop.
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Not many people said that we're B2B only, unlike today.
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Most firms were consumer heavy or generalist at the at the time.
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And so we found a really good niche around B2B software, early stage B2B software, number one.
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And then number two, as you're building out the resources around the firm, it's hard to scale kind of everything at once.
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And so we felt like if we could focus on early stage, um, we could provide them the right resources to help them scale.
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And, you know, there are some things we'd already built along the way.
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One of the things is our CXO advisory board.
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It's a you know group of about 75, 80, Fortune 2000 CIOs and CTOs, all tech buyers, perfect fit for our early stage company.
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So, you know, may not result in a contract the first time we make an intro, but at least they're smart enough to tell our founders like, hey, this is an interesting problem, that's not an interesting problem, and guide them.
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And so that was really the the the crux of it.
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Focus on early stage because we had the resources to help them scale, and focus on B2B because that opportunity was really starting to take off.
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And you have been for the firm with 13 years.
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Tell me how did you see the firm evolve?
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What was the fund size when you came in?
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What are the different fund size that you have seen as your journey?
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Yeah, so uh fund 10 when I first joined was a$150 million fund.
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Uh, this last fund, fund 13, was a$270 million fund.
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So we've kind of marked, we've uh we've uh marginally increased the size of the fund to keep pace with round size and uh keep pace with kind of the growth of the B2B, of the B2B world.
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And our take on it is the size that we're at, well, we want to keep kind of marginally increasing to keep pace with uh inflation and keep pace with the growth of the space.
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Um to do early stage well, you got to align your fund size with the stage of company that you're working with.
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And so it's always for us been a core principle and a core uh core focus.
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Keep the fund size aligned with this uh with the stage you're working with.
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And ultimately that gives you two things.
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One, uh, it allows you to return capital.
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Uh you can your DPI numbers uh your DPI numbers look better.
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It gives you more flexibility in terms of the types of deals you can do, how you underwrite deals.
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And then the second, second thing it does is that uh for a lot of the founders we meet with, I think one of the challenges that they have to navigate in today's world, especially, is taking on too much capital at an early stage.
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And so we try to really help them think through and be methodical about how much capital they raise from the early stage.
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That's not to say they shouldn't raise more money at the later stages, but don't get too far ahead of your skis in the early days, raise the right amount of capital, be efficient, get to the right milestones.
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And then once you get to those milestones and you're starting to see the the inflection point hit, then you go raise these big mega rounds and start scaling from there.
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And so for us, we felt by keeping the fund size in kind of the the range that I talked about, it just aligns us well with our with our founder.
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What has been the historical DPI for Sierra?
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Uh so we have been probably two two-ish to three-ish X on the earliest funds right now, and that's kind of which vintages?
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This would be 2012 and 2012 vintage, and then the other funds are probably four to six X T VPI.
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Okay.
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And um, but have some really good good companies.
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And and can you tell me about like top five or ten companies from the Sierra time that you have been at Sierra?
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Yeah, sure.
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Um, so I I would say uh there are a couple of deals that have really driven uh uh very, very large performance for us where we were early investors, and then I we have some really good marquee kind of MA deals that have that have come out over the years.
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Um maybe I'll start with the the the big big winners first.
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Uh uh one of the companies that uh we had sourced, and actually was part of my work that I that I did to build out the healthcare practice was a company called Reify Health.
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Reify.
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Reify, R-E-I-F-Y.
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Yeah.
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Um reify has basically re-imagined the way clinical trials are run.
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And so they they run what are called decentralized clinical trials.
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So today, trials get mostly run inside hospital systems.
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These guys said that's the wrong way to do it.
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If you're gonna run trials at scale, you got to take them out of the hospital and take them to where the where the where the people are.
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And so this was a company we led the Series A in in 2018.
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Uh, we re led a six million dollar round, uh, pretty reasonable valuation at the at the time.
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Um, big big owner in the company.
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And the last round ended up getting done at about$4 billion by Ultimate or Cork.
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So you still own 10% of the company?
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Uh maybe more, but I won't I won't share the exact numbers.
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But we it was it's been a really, really good outcome for us and it's been a phenomenal uh but that's at least on a 4 billion company.
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I would guess 400 to 500 million kind of an outcome.
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Like I can't share the exact numbers, but but but it's been a good, been it been a really good thing.
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And what are the some of the other companies?
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Yeah, so that was one.
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The other one uh is a company called Phenom, where Phenom, we were again series again, well known in India.
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Yeah, exactly.
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And so uh series A investor in Phenom and Phenom was an interesting.
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How big was the check, if you can yeah.
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So uh we invested in 2015.
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We wrote a five and a half million dollar check actually initially.
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Uh five and a half and five and a half million dollar initial check.
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Again, we had a pretty good ownership chunk at the time.
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I think um the at the time series A's were getting priced a lot cheaper than they are today.
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Number one, number two, uh, at the time, uh nobody wanted to do HR tech.
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And then three, nobody was getting on a plane to go to Philly to do deals.
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So it was a pretty nice opportunity for us.
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Uh the company will do almost 200 million in revenue this year, growing and scaling nicely.
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So, what's it value debt?
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Uh, the last round was done at I think 1.4 billion.
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So amazing.
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Yeah, so that that's also been a really big, big win for us as a as a fund.
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Uh, we've been uh just fortunate to find some of these really great founders and work with them.
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So that's kind of on the on the portfolio uh current portfolio side, still companies that are active uh and scaling.
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Uh, and then on the MA side, you know, some big wins.
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We had a company called Redlock that we sold to Palo Alto Networks.
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Um, that actually became the base for how big was that acquisition?
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Uh yeah, that was uh 240, 240 million dollar exit.
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Um, and the founder there uh was a guy that came out of a company called CypherCloud.
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Uh, CypherCloud had raised a lot of money.
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And back to my earlier point, he was like, Yeah, I saw the mistakes that were made when I raised too much, scale too aggressively too early on, let me build really methodically, really did a good job of figuring out who were the three, four customers that he has to go, he had to go after, got them signed, signed the expansion.
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Uh Dell Technologies came in and led the Series A after us.
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And then that company, I think from when we funded to when they sold was probably about three, four years.
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Um, so very short amount of time.
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Yeah, like got I believe if you were 15% even owner,$40 million kind of an exit.
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And this was on a on a$150 million fund.
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This was on our fund 10.
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So 0.2 fix five X DPI just from a single client.
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It was a really good outcome.
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And and I think more than anything else, the the really interesting thing about the ultimate outcome was that the core of Palo Alto's cloud security business was actually Redlocks.
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That was the very first cloud security company they bought, and it ultimately became the foundation for Palo Alto Prisma, which was which was awesome.
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Um we've had some other really good exits like Shape Security, which we sold to F5 for about a billion dollars.
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Um, and uh and a bunch of so what is the minimum ownership that you have taken recently in a company and what's the maximum ownership that you have gone to?
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Yeah, that's a good question.
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I mean, typically for us, we we like to be in the double digits in terms of ownership.
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Um the reality is the number can can vary, but the average for us is kind of right in the you know, in the teens, mid-teens, 14, 15-ish percent.
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Um, and and for us, we think that at that stage that we're getting involved with these companies, the amount of help that we're providing, that's kind of the right amount of ownership to make the risk reward, uh, risk reward make sense.
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Um, that being said, sometimes we make exceptions and maybe once or twice a fun, we'll do some later stage stuff.
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Um, but more when we see the the opportunity just for really, really big outcomes and really big companies to be built.
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And when when we have clear conviction uh in that big outcome, then we can be a little bit more flexible.
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But that tends to be very rare.
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And those are usually series Bs or series C's that we'll get involved with.
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But those okay, so you you also get an exceptional case involved in series B and Yeah, and again, it'll be like maybe one or two two deals of fund, very rare.
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Uh, we try to be very focused on our core strategy.
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And how many deals you do per fund?
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Yeah, so the the the fund strategy has been to uh focus the check size up front.
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So we try to write two to seven million dollar checks up front and take 30 to 35 shots on goal.
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So two to seven million dollar initial check, and then we'll reserve call it roughly 50% for follow-on.
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But the goal for us is two to seven million dollar checks, take 30 to 35 really high quality shots on goal.
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And that ultimately we think is the right formula to return a return a really big uh outcome on the funds that we raise.
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Obviously, as we've as our funds have increased in size, the the range has also gotten a little bit bigger.
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We're able to write slightly larger initial checks.
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Um, but we've really tried to stay disciplined around that entry point check.
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And we think that that's the right way to manage risk and manage uh and and ultimately get the most upside.
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How has US India Corridor worked for you?
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And what are like some of the Indian companies that you invested in that maybe shifted to US or didn't shift to US?
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Yeah, so uh obviously Phenom, we we talked about.
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So that one I think is probably the the most successful one we've uh we've had so far.
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Um more recently we've done smallest AI.
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Now you put six million thirty post money in that company.
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Yeah, so we did smallest AI last year.
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Um and so Darshan had uh I think we had been tracking him since he was in India, but then met him as soon as he came to the US, and then we were the we were just kind of all over that deal.
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We were like, yeah, we didn't allow anybody else to touch that exactly.
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Yeah.
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Um, and honestly, I think that like that's he has done well.
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He just raced uh, you know, next round.
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Yes, he did.
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He's he's doing really well.
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The business is doing really well.
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Um, and in our in our opinion, uh he is like the perfect example of why the India US corridor works well and why we want to do more of those deals.
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By the time he'd even come to the US, he had already done so much work from a customer perspective, right?
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He'd built a great foundation, and I think there's just something about founders when they're building in India today, they just have this like scrappiness.
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And by the time they come to the US, they they feel uh a little bit more formed than a lot of the very raw companies you even see in the US.
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And so we were big fans of it and we we want to do more of those sorts of deals.
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And how many deals do you do per year as a fund?
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The target is to do about 12, 10 to 12.
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Um, and so if you're writing 30 to 35 checks per per fund, that's like a three, three and a half year sort of cycle to deploy.
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Got it.
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And like what does your exit look like?
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Do you ultimately wait for that IPO kind of an outcome or do you sell in secondaries?
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What do you do?
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Uh well, I I guess I would say the strategy is changing for everybody right now because the markets are are a different uh at a different time than they were in the past.
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Um, we've obviously will consider secondaries, but uh I would say for the most part, one of the one of the benefits of having Sierra on the cap table is the founders get a lot of reputational uh benefits as a result.
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So we we try not to sell.
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We don't want to create any negative negative signals for our companies.
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I mean, with Reify, even Phenom as an example.
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Phenom, we invested in 2015.
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We're still on the board, still pretty actively involved with the company.
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Um, so our our goal ultimately is to help these founders build the biggest company uh possible.
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But along the way, we try to make sure that we guide them to make the right uh financial decision as well.
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Um, in some cases, like a reify, we see an IPO opportunity.
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So, you know, let's not let's not shut it down too early.
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Let's keep going and make sure that we can we can see this thing through.
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Sometimes it makes sense to go for MA because the opportunity is in our face.
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And so it just depends on the situation.
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Um, but we try to do right by the founder at the end of the day.
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And what does the process look like?
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Is it uh every partner hunts for themselves or is a consensus-based decision?
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So we have an interesting model.
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We actually have, I guess, what I call a loose consensus model.
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Um, so the bigger the check, the higher the bar for consensus.
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The smaller the check, uh, the lower the bar for consensus.
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But one of the things that my partners had actually taken away from uh, you know, during the generational transfer that that happened in 2012, um, uh just and just for context, in 2012, my partners have taken over from the last generation of partners at Sierra, one of the things they found was that if I'm doing a healthcare deal, just as an example, my partner that doesn't do healthcare shouldn't have, shouldn't be able to completely veto a deal.
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Now, if I come in and say, hey, I want to write$10 million into this like really risky thing, obviously that's a different conversation to be had.
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But um I spend time in healthcare, I talk to the people in the in the market, I know what's going on.
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Um, just because you know it's not obvious or apparent to somebody that's not done healthcare, we shouldn't, we shouldn't say no.
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And you know, my partner Ben does a lot of robotics and a lot of frontier tech stuff and things that I don't even pretend to understand some days.
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Like I shouldn't be able to veto those things either.
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And so the idea is we try to bring these founders in.
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The the partnership really tries to pattern match on the founder profile and the quality of the team and how they answer questions, how they respond to different things.
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But ultimately, the the final call comes down to the partner that's gonna lead it.
00:15:51.360 --> 00:15:57.840
So let's say for in case of Sudarshan from smallest, because he's the most recent, it'll just help our audience with Indian founders.
00:15:58.000 --> 00:16:01.120
What helped move the needle to the right uh decently sized check?
00:16:01.600 --> 00:16:04.000
I gotta be honest, that one was uh very easy.
00:16:04.159 --> 00:16:05.679
Everybody just said, yeah, you should go back.
00:16:05.759 --> 00:16:09.200
And so I don't know if that's a helpful, helpful story.
00:16:09.440 --> 00:16:16.480
Um you know, I but I but I joking aside, like the the thing about Sudarshan that really, really, really shines through are two things.
00:16:16.720 --> 00:16:19.759
One, like you can just tell he's a really sharp, smart guy.
00:16:20.159 --> 00:16:29.759
Just the way he answers questions, how methodical he was, how how well he handled and presented uh in the in the in the partnership meeting was was impressive.
00:16:29.840 --> 00:16:30.480
Number one.
00:16:30.720 --> 00:16:40.240
Number two, one of the things he did really well was he was hyper responsive, just like 24-7, just aggressive, like moved quickly.
00:16:40.399 --> 00:16:44.320
And I think those two things are the are the things that matter a lot.
00:16:44.480 --> 00:16:54.320
You know, a lot of times in these meetings, there are questions that come up that can be a little bit abstract, and our kind of thinking is like, let's just see how the founders react on their feet.
00:16:54.480 --> 00:17:02.720
And you know, we we we obviously want to help our founders and so we try to prep them a little bit, but you know, we all kind of talked about this like we don't want to coach the founders too much.
00:17:02.799 --> 00:17:06.799
We want to see them how they are, see how raw they are, see how they present, how they handle themselves.
00:17:07.039 --> 00:17:18.640
And so for us, um, I think in Sudarshan's case, he just had a really, really good presence and he was able to really articulate why he could build a big company and build a big opportunity in that space.
00:17:18.799 --> 00:17:24.799
And as we were doing our work, he was just very responsive and helpful to us as and how fast you closed from the first meeting with Sudarshan?
00:17:25.359 --> 00:17:32.240
Uh, that was a little bit of a again, a different situation because we had met him before he started fundraising formally.
00:17:32.400 --> 00:17:38.480
But I think from the time he was like quote unquote ready to raise to writing the to giving him a term sheet.
00:17:38.640 --> 00:17:41.279
I mean, it was maybe a week, a couple weeks, something like that.
00:17:41.599 --> 00:17:44.400
Like by the time he was ready, we were like we already kind of knew.
00:17:44.480 --> 00:17:49.279
And then once he came in and presented, everybody's like, Yeah, then then it was just paperwork to get get done, right?
00:17:50.559 --> 00:17:54.880
You know, offline discussion, you mentioned that you're bullish on healthcare.
00:17:55.119 --> 00:17:56.720
What are the opportunities you are bullish on?
00:17:56.799 --> 00:18:04.880
And there's some opportunities you say that if you're selling to pharma companies, then uh you might be uh sitting on the fence.
00:18:05.119 --> 00:18:05.519
Yeah.
00:18:05.839 --> 00:18:09.839
So uh I'll I'll say start by saying this.
00:18:10.559 --> 00:18:14.079
I don't think I've ever seen healthcare, and I've done healthcare for probably about eight years now.
00:18:14.160 --> 00:18:16.960
I've never seen healthcare move as fast as I've seen it move today.
00:18:17.279 --> 00:18:22.799
Like, can you give examples of like a Fortune 50 or 100 health company giving like a million-dollar check to a startup?
00:18:23.200 --> 00:18:24.400
More than a million-dollar check.
00:18:24.480 --> 00:18:27.359
I mean, you just think about somebody like an abridge, UPMC basically.
00:18:27.599 --> 00:18:28.640
But abridge is not a startup anymore.
00:18:28.720 --> 00:18:39.839
It's like but when they but when they started, the fact that UPMC, University of Pittsburgh Medical Center, decided to standardize on a bridge as their scribing system was a very meaningful thing.
00:18:40.000 --> 00:18:51.039
Um, I mean, even just uh it was two, two, three weeks ago, uh right before the JP Morgan Health Conference happened, uh, there's a company called Noetic in the in the data sp in the healthcare data space or life sciences data space.
00:18:51.119 --> 00:18:55.759
Um, GSK just said they were gonna pay him$50 million for access to their model.
00:18:55.920 --> 00:19:00.079
Like, I'm I don't think I've ever seen Pharma pay for data at that kind of size.
00:19:00.400 --> 00:19:03.200
Can you tell me examples more closer to the portfolio?
00:19:03.440 --> 00:19:04.079
Yeah, sure.
00:19:04.160 --> 00:19:13.119
Uh in in our portfolio, so Reify, um, I I won't share, share names, but they have customers paying nine figures, customers paying eight figures.
00:19:13.279 --> 00:19:16.880
Like we have very, very large deals with with pharma.
00:19:17.359 --> 00:19:21.839
And um, and and this was even this wasn't even necessarily an AI native.
00:19:21.920 --> 00:19:23.759
This was a decentralized clinical trial thing.
00:19:23.920 --> 00:19:33.599
But I think the thing to me that's interesting about this whole AI wave and why I kind of started this thread by saying I've never seen it move that fast is they all see the opportunity in AI.
00:19:33.759 --> 00:19:41.759
They know it's coming, they see the, they see the wave, they know they need to kind of catch, they they know they need to stay on top of it, and they see the value in it.
00:19:41.920 --> 00:19:51.359
So in general, we're just seeing so many companies sign 100k, a couple hundred K, even million dollar deals with very early stage companies, and that's happening pretty frequently.
00:19:51.599 --> 00:20:06.400
I think to me, the place where I'm most bullish, and I'll talk about where I'm not as bullish on the provider side or on the hospital side, I think anything that's starting to get closer to care and delivery of care is quite interesting.
00:20:06.640 --> 00:20:14.319
Um, I think the admin side, your scribing tools, your RCM tools, the market is starting to get quite crowded.
00:20:14.480 --> 00:20:18.720
And well, I think there's some interesting companies that got an early advantage.
00:20:18.799 --> 00:20:22.799
I think if you're going to play in that space right now, it's just gonna be tough.
00:20:22.960 --> 00:20:24.960
The big guys already kind of have a strategy.
00:20:25.039 --> 00:20:27.759
There's lots of well-funded startups, so that's gonna be hard.
00:20:28.079 --> 00:20:35.039
But as you talk about in the care setting, so how do you help the doctor actually help a patient navigate care?
00:20:35.279 --> 00:20:36.799
Open evidence is done really well.
00:20:36.880 --> 00:20:37.839
But that's just one company.
00:20:37.920 --> 00:20:42.319
I think there are plenty of other opportunities in like cancer and and mental health.
00:20:42.400 --> 00:20:43.440
So there are lots of things there.
00:20:43.519 --> 00:20:47.359
So that's one area I'm really bullish on is kind of on that in the setting of care.
00:20:47.599 --> 00:21:01.440
In pharma, uh, I think um they've been a little bit slower to adopt, but this Noetic deal that I just mentioned for and for those who don't know, Noetic basically built an LLM that helps uh identify patients using tumor data.
00:21:01.519 --> 00:21:05.839
So they took a bunch of tumor data, created a foundational model, and they sold that model to pharma.
00:21:06.000 --> 00:21:10.960
So now what pharma can do is you can find patients faster, you can help dose patients better.
00:21:11.039 --> 00:21:14.400
So there's a whole bunch of things you can do with you can do with that data.
00:21:14.640 --> 00:21:16.240
I think there's a ton of opportunity.
00:21:16.319 --> 00:21:19.119
And at the end of the day, pharma cares about you know a couple things.
00:21:19.279 --> 00:21:24.400
Help get a drug to market faster, help find more patients for a drug, or help keep a patient on a drug.
00:21:24.480 --> 00:21:26.480
And I think AI is hugely valuable there.
00:21:26.640 --> 00:21:34.000
And then the last thing I'll say uh this market has been behind, but the payer market, the insurance market, is very, very, very ripe for AI.
00:21:34.160 --> 00:21:44.640
The payers are just being uh are in a big financial squeeze right now because of what's happened with the government and uh cuts to Medicaid and even other parts of their PL being underwater.
00:21:44.799 --> 00:21:46.880
Um, so I think the sales cycles are long.
00:21:46.960 --> 00:21:48.319
That's just something you got to deal with.
00:21:48.400 --> 00:21:49.680
But they are in a lot of pain right now.
00:21:49.920 --> 00:21:52.720
So, what are the opportunities right now on the yeah?
00:21:52.880 --> 00:21:56.240
So on the payer side, uh, just as one example.
00:21:56.400 --> 00:22:08.319
So, as part of the big beautiful bill, Medicaid payments uh got cut and then the revenue, uh the revenue reimbursement rate has been frozen uh going through 20 starting in 20 or going through 26 and 27.
00:22:08.720 --> 00:22:12.799
Um cost of care is going up, that means their margin is getting squeezed.
00:22:12.960 --> 00:22:17.680
So anything that they can do to help take care out of the uh care out of the hospital.
00:22:17.759 --> 00:22:25.599
So push more care at home, keep patients healthier, reduce the number of visits, like all that stuff there they're trying to find ways to do.
00:22:25.839 --> 00:22:29.599
And AI actually has a pretty interesting role in all that if you if you think about it.
00:22:29.839 --> 00:22:34.640
Yes, there is the thing we can talk about, which is like claims management, claims denial, all that stuff.
00:22:34.720 --> 00:22:36.720
And I think they spend a lot of money on that.
00:22:36.880 --> 00:22:44.079
But the flip side of it is how can you help the patient schedule better, find lower cost of care, stay healthier?
00:22:44.160 --> 00:22:46.559
And AI does a really, really good job of all that.
00:22:46.720 --> 00:22:52.240
Like think about the last generation of companies you saw like Lavongo and all these companies that were doing like diabetes management.
00:22:52.480 --> 00:22:55.359
You could do diabetes management with an AI coach today.
00:22:55.519 --> 00:22:57.359
It's probably not that expensive to do.
00:22:57.519 --> 00:23:02.160
Now I think building it and actually getting the patients to use it and all that stuff is actually quite difficult.
00:23:02.240 --> 00:23:03.920
So it's not an easy thing to solve.
00:23:04.160 --> 00:23:12.319
But those things are possible today and you can build a really compelling high margin business around it, which I think changes the opportunity set for startups.
00:23:13.599 --> 00:23:18.079
And what are the things that you do at the firm other than healthcare?
00:23:18.319 --> 00:23:22.880
Yeah so I cover uh what I broadly call uh enterprise AI.
00:23:23.039 --> 00:23:31.359
And so enterprise AI is everything from HR tech like a phenom to clinical trial software like a reify to insure tech to commerce tech.
00:23:31.599 --> 00:23:48.880
So anything that's kind of at that application AI lever layer I spend time around um to me I what I find exciting, I like founders that are going after what I call under-the-surface opportunities that are not necessarily obvious to everybody, but there's a big wave kind of coming behind it.
00:23:48.960 --> 00:23:56.480
And if you can catch them early and you can get in get in at the right time the wave just kind of carries you and actually Phenom is a really good example of this.
00:23:56.559 --> 00:24:19.279
So when when we had done the deal in 2015 HR tech was not a popular thing to do in Silicon Valley at the time but you know at the things were kind of starting to turn around the labor markets were getting better uh the old systems that were in place before Phenom showed up the old ATSs were just had just been acquired by Oracle and SAP and were being sunset or not being innovated on.
00:24:19.440 --> 00:24:23.759
And so Phenom just had a very clear market and opportunity to go tackle the enterprise.
00:24:23.839 --> 00:24:32.880
And so we really like founders that have a good perspective on that sort of stuff see the wave coming have conviction in that big opportunity that big vision and we want to go back then.
00:24:33.119 --> 00:24:35.920
And are you investing in foundation models as well?
00:24:36.799 --> 00:25:51.680
We've done one we've invested in a company called Reflection but um that was at a billion valuation yeah so we did uh that one was was definitely a a higher a higher valuation higher size higher uh higher smart larger size of a deal but um we really like the founder we think that they are critical to helping enterprises really adopt AI in the long run but this is a good example of those like one off exceptions that we'll do we we think there's an opportunity in the foundation model space but it's just not something that really makes sense for our fund size so we'll do maybe one of those but um but we're not really spending as much time on the what check size would you put there like so we'd probably try to write you know somewhere in the seven to seven seven plus ish range yeah and like the other narrative that SaaS is dead right what does your firm believe well uh ask me tomorrow I'll tell you a different answer but uh um look I I think I think we all have known that the day of SAS is done as it traditionally looks if you as a company have not innovated to add ai products to your portfolio like you're probably already dead or you're dying.
00:25:51.920 --> 00:26:04.079
So to me the the the this whole narrative that SaaS is dead is actually a bad narrative because it just kind of throws everybody out the door at the same you know at the same rate and that doesn't make sense.
00:26:04.240 --> 00:26:18.240
Some companies have done a really good job of of actually leveraging AI and building AI products on top of what they have some companies have not personally I think ServiceNow is in a perfect position to go pounce and capture a ton of opportunity around all this all this AI stuff that's happening.
00:26:18.400 --> 00:26:23.359
So to me I think the the phrase SaaSpocalypse is a little bit overblown.
00:26:23.839 --> 00:26:35.759
That being said I don't think it's going to be easy for a lot of SaaS companies and obviously as early stage investors we're not investing in any SaaS companies anymore most of it is AI native building some sort of AI agents and stuff like that.
00:26:36.000 --> 00:26:42.079
But we don't we don't see it as a we don't see the SaaS pocalypse thing as a as a as an inevitability.
00:26:42.240 --> 00:27:03.920
I think some companies will actually find a way to to build something really interesting come out the other side one thing that I do think is interesting and I I I think this is important for for folks to think about one of the challenges I think actually the SaaS businesses have the last generation of SaaS businesses have is that they spent a lot of time building systems of record so that they could just ingest as much data as possible.
00:27:04.160 --> 00:27:14.559
That's actually the wrong thing to do in in the AI world in some ways it's not about volume of data it's about how good the data is and how much have you annotated the data to actually do something meaningful with it from an AI perspective.
00:27:14.799 --> 00:27:29.359
And so the companies in the SaaS world that just ingested data have no idea what data they have a bunch of garbage I think they're gonna have a really hard time getting into this AI world and making their products work in an AI native way.
00:27:29.599 --> 00:27:37.759
The companies that had a purpose behind how they captured the data, how they built all their workflows, all that stuff, I think they'll have a very interesting time in this in this AI world.
00:27:38.079 --> 00:27:46.000
Sierra published a market map of voice agents so what are the bets that you have taken this voice AI space apart from smallest?
00:27:46.240 --> 00:29:14.799
Yeah smallest I would say is the is the is the biggest bet we've taken uh we've taken so far um interestingly enough we've actually had some of our existing portfolios starting to break into voice so reify as an example because they run their call their their clinical trials business they're actually trying to implement voice into their uh implement voice into their stack uh we have a stealth company that we haven't uh announced yet but in the CRM space that's rethinking how how voices uh how voice is being done um we also have a company that called Blazel um that's leveraging voice internally to help uh they're a they're basically a marketing agency for founders um help create content and stuff like that they use voice voice agents internally to capture uh recordings and then take the recordings and help turn that into content um so those are the the bets that we've made we've obviously seen a ton of companies uh across the board though from a voice perspective so in your case how do you measure product market fit and do you usually come after it or before it yeah um I'm laughing a little bit because I think product market fit in the AI world has been even harder to tease out than in the SaaS world um I felt like in the SaaS world you kind of just had this natural just pulling motion that you could tell when when something was working I mean we saw this with with phenom for example just like quarter over quarter growth in ARR, number of customers sales productivity through the roof was all good and it was easier to spot in some ways there.
00:29:15.440 --> 00:29:19.279
But I guess let me answer the the last part of your question.
00:29:19.519 --> 00:29:25.039
We've done stuff at the inception stage so you know two founders and a PowerPoint so like way before product market fit.
00:29:25.200 --> 00:29:28.079
And then we've done stuff once they've reached product market fit as well.
00:29:28.640 --> 00:29:57.519
I think in the AI world to me the the leading indicator of product market fit is actually usage and can you actually predict number of tokens what the user is doing on the on the product are they consistently using it every day are they consistently using it every week are they using up all the credits that you're giving them I I think that is an underrated metric that we don't spend a ton of time looking at and partially because a lot of these companies are early and then the second metric I think about is gross retention.
00:29:57.680 --> 00:30:01.200
Obviously the net retention is going to be there if it works because AI is so good.
00:30:01.279 --> 00:30:07.279
I think people want to consume as much of it as possible but you really got to look at that gross retention number and the usage and metrics.
00:30:07.359 --> 00:30:10.400
And I think that's what's going to define PMF in this world.
00:30:10.720 --> 00:30:18.880
Uh we we discussed about like one of the companies where you are bullish on the founder uh but not sure about the the market risk.
00:30:19.039 --> 00:30:21.200
How do you calculate market risk as a VC?
00:30:21.440 --> 00:30:27.920
Yeah um honestly my framework was pretty easy and maybe it's not perfect but I I think it's a good way to do it.
00:30:28.000 --> 00:30:36.640
I anytime I do diligence I just try to find as many people in my network that are potential customers for a company or would have been potential customers for a company at some point.
00:30:36.880 --> 00:30:53.680
And I think if you talk to enough experts in a space people that are on the ground dealing with the pain point on a day-to-day basis that gives you enough of a basis for uh whether or not something can work and obviously you have to think about upside but not everything that has upside will have the ability to even get to that point.
00:30:53.759 --> 00:30:55.519
And so you kind of have to balance those two things.
00:30:55.599 --> 00:31:14.640
And so a lot of times the companies that I think I'm excited about for the future I can see like why this could be a big company all that stuff makes sense then you kind of come back and say hey will the customer actually buy the product today and if I don't feel that like just like I'll absolutely have to have this like feeling from the customers that's usually the the market risk that I that I find.
00:31:15.279 --> 00:31:21.279
What has kept you for 13 years at Sierra where as your peers in your industry keep on changing roles every two or three years.
00:31:21.440 --> 00:31:28.240
Yeah um look I I actually think Sierra is a little bit of a special and unique platform.
00:31:29.440 --> 00:32:10.160
Like I was mentioning to you earlier I was the second person that joined Sierra 2.0 so I've I have a lot of uh sense of pride of ownership of you know being one of the earliest folks on the team having helped build a lot of the processes having helped kind of get the firm to where it is today and so they're just uh part of me and I'm a very loyal person like I I I just think I I owe it to them who gave me a shot in the first place to make sure that the fund is successful and we um we make our LPs money we make ourselves money and uh we are successful so that's part of it um the other thing that that I like is one of the things that we we talk to LPs we often describe is this concept of majors and miners.
00:32:10.240 --> 00:33:01.039
So majors are the stuff we know well like your CRM your enterprise software kind of stuff or enterprise AI in today's world and uh and so it's like the stuff we know well so like a buck of the fund is going to go there but miners are things that we can try and be like get smart about and learn and there's no uh there's no uh uh stepping on other people's toes to go try new things right like if you find a space that you like you spend the time you do the work you bring in a deal and again it makes sense within kind of the the parameters uh there's a lot of support to go try new things and to me that's always exciting I I think in venture one of the things you can you just get stuck or trapped in is just not thinking about what's coming next and sticking to the stuff you know and so I just love the fact that we have such an open mind and have a lot of autonomy about going and trying new things.
00:33:01.119 --> 00:33:02.880
And I don't know if you get that at every shop.
00:33:03.039 --> 00:33:05.359
And so to me it's just every day is just a new day.
00:33:05.440 --> 00:33:09.039
I like I see so many cool things I'm not sure I would be able to go do that anywhere else.
00:33:09.119 --> 00:33:12.319
And so I always appreciate appreciate that that flexibility.
00:33:12.640 --> 00:33:35.839
AI is uh forcing a rethink of the enterprise stack and a lot of the stuff that the market will come today will the market will not be ready for how do you evaluate for that back to my earlier comment it's it's really just a function of trying to figure out are the most cutting edge folks in your network customers in your network willing to try this thing and do they see value?
00:33:36.079 --> 00:33:43.599
One of the challenges you often you often have and I know since you do early stage stuff you probably see this as well it's like founders have a great vision and a great pitch.
00:33:43.680 --> 00:33:55.039
They may not have the product ready but I think it there's just a certain type of founder and I think this is what makes founders great is they know how to sell the story.
00:33:55.200 --> 00:34:04.559
They know how to like really empathize with the pain point of the customer really convince them that they are the right person to go solve that problem just have a deep appreciation and understanding of the problem.
00:34:04.720 --> 00:34:16.079
And so I think you're right like in this AI world there's going to be things that seem futuristic, seem really out there but almost in the grasp of possibility or the realm of possibility.
00:34:16.400 --> 00:34:30.159
And so I just try to find these founders that can like just like articulate that really well to the experts right and then if the experts on the other side are like yeah that makes sense then it's like a very easy easy decision to go and say yeah I want to I want to invest in this thing.
00:34:30.559 --> 00:34:33.840
A little bit like Siddarshan with smallest that was part of how we did diligence.
00:34:34.239 --> 00:34:50.000
My partner Tim has built two companies in the call center space knows it super well so we're like hey let's just go have Sudarshan talk to a couple of people see what they say and uh and just like the response and the reaction that that we got is just overwhelmingly overwhelmingly positive.
00:34:50.159 --> 00:34:52.960
That's just a clear cut sign that you want to want to back this person.
00:34:53.280 --> 00:35:03.119
What are the top five anti-portfolio of yours like where you you were you know uh I don't know if I have the top could be possibly the leader.
00:35:03.280 --> 00:35:59.360
Yeah I wish I wish I had the top five I I should remember this off the top of my head I I try not to dwell on the losses too many times but uh airwalls is one that that I always uh okay you had access to airwalls we looked at airwalls very early on that one we yeah I mean and but remember when we looked at this this was probably 2014 2015 it was very very early on and at the time all they did was Australia China corridor crypto payments and a lot of it was around like mining companies and stuff like that because the resource boom was happening so like these things aren't always obvious at the at the moment but I was just thinking since since today that came up on the uh on the Robinhood portfolio that's one how many of your founders are second time versus first time in your portfolio that's a good question I I don't I don't know the data across every fund so I'll just comment on the latest fund.
00:35:59.440 --> 00:36:33.920
Yeah I'll just comment on the latest one it's probably about 5050 repeat versus versus first time founder um obviously we we really like the repeat founder I mean I think everybody does but we think we have actually a lot of the expertise to help first time founders go and and and build great companies I mean it's kind of why we just we look for just raw horsepower if like we see a founder that just has that raw horsepower that ability to go and like just get things done do things we can give them the access to the enterprise and whatever they need to be able to go and scale and build a big company but they just have to have that raw horsepower.
00:36:34.000 --> 00:36:34.960
So it's about 50-50.
00:36:36.159 --> 00:37:11.519
And let's say if you have to categorize your portfolio between strong storytellers versus not so strong storytellers versus like great executors on the tech side yeah and well I like to believe all of them are great storytellers and great operators um I it's a it's a hard question because I I think one of the things that's that's really uh a like a weird nuance to some of this stuff is strong operators ultimately become great storytellers when the number when the numbers are there.
00:37:11.760 --> 00:37:28.000
No without numbers right without without the numbers um honestly I would say like for the most part I think a lot of our founders tend to be just really great storytellers I think that's just a function of the stage we're getting even the tech people that you talk to even the tech people like they may not necessarily be flashy.
00:37:28.239 --> 00:37:46.480
But like is a great storyteller for example right but that's rare to find between a founder who's good technologically as well as a good storyteller yeah that's why like I don't think for us when when you when you say great storyteller that doesn't necessarily have to mean like very like flashy I'm not saying flashy.
00:37:46.559 --> 00:37:48.639
Yeah great storytelling is also about clarity.
00:37:48.880 --> 00:37:52.320
That's right exactly and I think for us that's the thing that we really look for.
00:37:52.400 --> 00:38:20.400
It's like more about that like clarity of where is this company going to go like one question we always like to ask and we just like to see how the founders respond is like just tell us how you build a big company like what is the path to building a big company there's no right answer to that question of course but like just to see how founders respond and sometimes you get oftentimes you get like very hand wavy answers from like the very business savvy folks and then you don't get enough of a vision from the too technical of the founders that we meet.
00:38:20.559 --> 00:38:42.079
And it's like somewhere in the middle you get these founders that have the right balance of like technical and commercial some tend to skew a little more technical some tend to skew a little more commercial but at the end of the day like the the reality is that for us the best found the founders that we invest in we have to believe they're great storytellers simply because one of the things we expect our founders to be able to do is to go attract the best talent.
00:38:42.320 --> 00:38:48.719
And there's just no way you can attract the best talent if you can't tell stories or have somebody around the table that that does that.
00:38:48.960 --> 00:39:27.519
And so for us like it's a hard question because I I think at the at the core of it at the end of the day I think they all have uh the ability to tell great stories um but I I think I think what I would say is this there are some that know what the path to a big company looks like they tend to be more technically savvy maybe not as business savvy and our job is to help kind of provide that muscle number one or number two give them the right set of a set of advisors that we can put around the company help them hire a great CRO or first account exec or whatever it is that we need to help them with and help them kind of button that side of the house.
00:39:27.679 --> 00:39:40.159
And then on the flip side the guys that are a little more commercially savvy, how do we pair them with the great technical counterpart or give them the right sort of technical counterpart on the on in the company to go help them help them build.
00:39:40.480 --> 00:39:43.199
No thank you so much Vignish love the conversation with you.
00:39:43.519 --> 00:39:49.920
Learned a lot about Sierra which I had not known earlier and looking forward to partner in some of the companies that we invest in.
00:39:50.159 --> 00:39:53.599
I appreciate it and we're all looking to do more India US corridor companies thank you so much.
00:39:53.760 --> 00:39:53.920
Thanks
00:00:01.520 --> 00:00:04.639
I've never seen healthcare move as fast as I've seen it move today.
00:00:04.799 --> 00:00:10.080
Two, three weeks ago, a company called Noetic GSK just said they were gonna pay him$50 million for access to their model.
00:00:10.320 --> 00:00:14.000
I don't think I've ever seen Pharma pay for data at that kind of size and scale.
00:00:14.240 --> 00:00:17.679
Reify has reimagined the way clinical trials are run.
00:00:17.839 --> 00:00:20.719
Today, trials get mostly run inside hospital systems.
00:00:20.960 --> 00:00:22.640
These guys said that's the wrong way to do it.
00:00:22.960 --> 00:00:29.359
We led the Series A and big owner in the company, and the last round ended up getting done at about$4 billion by Altimeter and Phenom.
00:00:29.600 --> 00:00:31.920
The company will do almost$200 million in revenue this year.
00:00:32.079 --> 00:00:34.880
The last round was done at$1.4 billion.
00:00:35.119 --> 00:00:37.920
We wrote a$5.5 million check.
00:00:38.399 --> 00:00:45.920
The founders that we invest in, we have to believe they're great storytellers, simply because one of the things we expect our founders to be able to do is to go attract the best talent.
00:00:46.159 --> 00:00:49.679
And there's just no way you can attract the best talent if you can't tell stories.
00:00:49.840 --> 00:00:51.840
We did smallest AI last year.
00:00:52.079 --> 00:00:56.159
He is a perfect example of why the India-US corridor works well.
00:00:56.320 --> 00:01:00.159
There's just something about founders when they're building in India today, they just have this.
00:01:03.600 --> 00:01:05.599
Hi, this is Sidhat Al Waliya.
00:01:05.680 --> 00:01:06.879
Welcome to the Neon Show.
00:01:07.040 --> 00:01:19.519
I'm your host and managing partner at Neon Fund, a fund that has invested in some of the best enterprise AI companies between the US and India corridor, like Atomic Work, CloudSec, and Spot Draft.
00:01:19.680 --> 00:01:23.439
Today I have with me Vignish Ravi Kumar, partner at Sierra Ventures.
00:01:23.599 --> 00:01:25.040
Vignish, welcome to the Neon Show.
00:01:25.200 --> 00:01:25.680
Thank you, Sid.
00:01:25.840 --> 00:01:26.400
Really appreciate it.
00:01:26.560 --> 00:01:29.840
So Vignish, you have been the second member of Sierra 2.0.
00:01:29.920 --> 00:01:33.439
So what is Sierra 1.0 and what is Sierra 2.0?
00:01:33.920 --> 00:01:39.519
Yeah, so I'll I'll tell you uh what Sierra 2.0 is and I'll talk about why we made the why we made the shift.
00:01:39.680 --> 00:01:43.840
Um so Sierra Sierra 2.0 then you know Sierra Sierra Ventures today.
00:01:44.000 --> 00:01:49.599
Uh we are an early stage focused fund focusing on uh enterprise AI and frontier AI.
00:01:49.760 --> 00:01:54.400
And enterprise AI is everything from the infrastructure layer all the way up to vertical, vertical software.
00:01:54.560 --> 00:01:57.040
And we invest everything from inception to series A.
00:01:57.120 --> 00:02:01.680
And so we're very early stage focused, usually the first or second institutional check into a company.
00:02:01.920 --> 00:02:04.959
Uh the firm has been around since 1982.
00:02:05.120 --> 00:02:13.840
And leading up to uh to 2012 when we raised Fund 10, the firm had started doing multi-stage, doing you know, different sectors doing later stage.
00:02:14.000 --> 00:02:23.360
And when we made the transition to the new Sierra, one of the things we we realized, or two things we realized, one, there was a big wave of B2B software that was showing up.
00:02:23.520 --> 00:02:30.560
And at the time in 2012, 2013, I still remember, I used to go to a lot of events and tell founders, hey, we're B2B specialist shop.
00:02:30.719 --> 00:02:34.000
Not many people said that we're B2B only, unlike today.
00:02:34.159 --> 00:02:37.759
Most firms were consumer heavy or generalist at the at the time.
00:02:38.000 --> 00:02:42.639
And so we found a really good niche around B2B software, early stage B2B software, number one.
00:02:42.800 --> 00:02:49.439
And then number two, as you're building out the resources around the firm, it's hard to scale kind of everything at once.
00:02:49.520 --> 00:02:55.840
And so we felt like if we could focus on early stage, um, we could provide them the right resources to help them scale.
00:02:55.919 --> 00:02:58.400
And, you know, there are some things we'd already built along the way.
00:02:58.639 --> 00:03:00.879
One of the things is our CXO advisory board.
00:03:00.960 --> 00:03:08.560
It's a you know group of about 75, 80, Fortune 2000 CIOs and CTOs, all tech buyers, perfect fit for our early stage company.
00:03:08.719 --> 00:03:18.080
So, you know, may not result in a contract the first time we make an intro, but at least they're smart enough to tell our founders like, hey, this is an interesting problem, that's not an interesting problem, and guide them.
00:03:18.159 --> 00:03:20.560
And so that was really the the the crux of it.
00:03:20.719 --> 00:03:27.680
Focus on early stage because we had the resources to help them scale, and focus on B2B because that opportunity was really starting to take off.
00:03:28.319 --> 00:03:31.039
And you have been for the firm with 13 years.
00:03:31.199 --> 00:03:33.120
Tell me how did you see the firm evolve?
00:03:33.199 --> 00:03:34.960
What was the fund size when you came in?
00:03:35.039 --> 00:03:37.599
What are the different fund size that you have seen as your journey?
00:03:37.759 --> 00:03:43.120
Yeah, so uh fund 10 when I first joined was a$150 million fund.
00:03:43.439 --> 00:03:47.199
Uh, this last fund, fund 13, was a$270 million fund.
00:03:47.360 --> 00:03:57.120
So we've kind of marked, we've uh we've uh marginally increased the size of the fund to keep pace with round size and uh keep pace with kind of the growth of the B2B, of the B2B world.
00:03:57.360 --> 00:04:07.199
And our take on it is the size that we're at, well, we want to keep kind of marginally increasing to keep pace with uh inflation and keep pace with the growth of the space.
00:04:08.080 --> 00:04:14.319
Um to do early stage well, you got to align your fund size with the stage of company that you're working with.
00:04:14.479 --> 00:04:19.199
And so it's always for us been a core principle and a core uh core focus.
00:04:19.439 --> 00:04:22.639
Keep the fund size aligned with this uh with the stage you're working with.
00:04:22.800 --> 00:04:24.560
And ultimately that gives you two things.
00:04:24.800 --> 00:04:27.279
One, uh, it allows you to return capital.
00:04:27.360 --> 00:04:31.279
Uh you can your DPI numbers uh your DPI numbers look better.
00:04:31.439 --> 00:04:35.519
It gives you more flexibility in terms of the types of deals you can do, how you underwrite deals.
00:04:35.680 --> 00:04:48.000
And then the second, second thing it does is that uh for a lot of the founders we meet with, I think one of the challenges that they have to navigate in today's world, especially, is taking on too much capital at an early stage.
00:04:48.160 --> 00:04:52.800
And so we try to really help them think through and be methodical about how much capital they raise from the early stage.
00:04:52.959 --> 00:05:03.199
That's not to say they shouldn't raise more money at the later stages, but don't get too far ahead of your skis in the early days, raise the right amount of capital, be efficient, get to the right milestones.
00:05:03.279 --> 00:05:09.600
And then once you get to those milestones and you're starting to see the the inflection point hit, then you go raise these big mega rounds and start scaling from there.
00:05:09.680 --> 00:05:17.040
And so for us, we felt by keeping the fund size in kind of the the range that I talked about, it just aligns us well with our with our founder.
00:05:17.680 --> 00:05:19.680
What has been the historical DPI for Sierra?
00:05:20.319 --> 00:05:31.199
Uh so we have been probably two two-ish to three-ish X on the earliest funds right now, and that's kind of which vintages?
00:05:31.439 --> 00:05:42.800
This would be 2012 and 2012 vintage, and then the other funds are probably four to six X T VPI.
00:05:43.199 --> 00:05:43.439
Okay.
00:05:43.920 --> 00:05:46.639
And um, but have some really good good companies.
00:05:47.279 --> 00:05:52.399
And and can you tell me about like top five or ten companies from the Sierra time that you have been at Sierra?
00:05:52.560 --> 00:05:53.199
Yeah, sure.
00:05:53.360 --> 00:06:07.040
Um, so I I would say uh there are a couple of deals that have really driven uh uh very, very large performance for us where we were early investors, and then I we have some really good marquee kind of MA deals that have that have come out over the years.
00:06:07.199 --> 00:06:10.959
Um maybe I'll start with the the the big big winners first.
00:06:11.120 --> 00:06:19.040
Uh uh one of the companies that uh we had sourced, and actually was part of my work that I that I did to build out the healthcare practice was a company called Reify Health.
00:06:19.199 --> 00:06:19.680
Reify.
00:06:19.920 --> 00:06:21.360
Reify, R-E-I-F-Y.
00:06:21.680 --> 00:06:22.000
Yeah.
00:06:22.160 --> 00:06:26.800
Um reify has basically re-imagined the way clinical trials are run.
00:06:26.959 --> 00:06:30.000
And so they they run what are called decentralized clinical trials.
00:06:30.079 --> 00:06:33.040
So today, trials get mostly run inside hospital systems.
00:06:33.279 --> 00:06:34.959
These guys said that's the wrong way to do it.
00:06:35.040 --> 00:06:41.279
If you're gonna run trials at scale, you got to take them out of the hospital and take them to where the where the where the people are.
00:06:41.519 --> 00:06:45.360
And so this was a company we led the Series A in in 2018.
00:06:45.439 --> 00:06:51.279
Uh, we re led a six million dollar round, uh, pretty reasonable valuation at the at the time.
00:06:51.439 --> 00:06:53.360
Um, big big owner in the company.
00:06:53.439 --> 00:06:57.040
And the last round ended up getting done at about$4 billion by Ultimate or Cork.
00:06:57.360 --> 00:06:58.800
So you still own 10% of the company?
00:06:58.959 --> 00:07:01.360
Uh maybe more, but I won't I won't share the exact numbers.
00:07:01.519 --> 00:07:08.480
But we it was it's been a really, really good outcome for us and it's been a phenomenal uh but that's at least on a 4 billion company.
00:07:08.639 --> 00:07:11.839
I would guess 400 to 500 million kind of an outcome.
00:07:12.000 --> 00:07:16.480
Like I can't share the exact numbers, but but but it's been a good, been it been a really good thing.
00:07:16.639 --> 00:07:18.240
And what are the some of the other companies?
00:07:18.480 --> 00:07:19.279
Yeah, so that was one.
00:07:19.439 --> 00:07:24.160
The other one uh is a company called Phenom, where Phenom, we were again series again, well known in India.
00:07:24.399 --> 00:07:24.879
Yeah, exactly.
00:07:24.959 --> 00:07:27.839
And so uh series A investor in Phenom and Phenom was an interesting.
00:07:28.079 --> 00:07:29.519
How big was the check, if you can yeah.
00:07:29.600 --> 00:07:31.600
So uh we invested in 2015.
00:07:31.680 --> 00:07:35.759
We wrote a five and a half million dollar check actually initially.
00:07:35.920 --> 00:07:39.199
Uh five and a half and five and a half million dollar initial check.
00:07:39.439 --> 00:07:42.319
Again, we had a pretty good ownership chunk at the time.
00:07:42.399 --> 00:07:47.759
I think um the at the time series A's were getting priced a lot cheaper than they are today.
00:07:47.920 --> 00:07:52.160
Number one, number two, uh, at the time, uh nobody wanted to do HR tech.
00:07:52.399 --> 00:07:55.759
And then three, nobody was getting on a plane to go to Philly to do deals.
00:07:55.920 --> 00:07:58.399
So it was a pretty nice opportunity for us.
00:07:58.480 --> 00:08:02.160
Uh the company will do almost 200 million in revenue this year, growing and scaling nicely.
00:08:02.399 --> 00:08:03.519
So, what's it value debt?
00:08:03.680 --> 00:08:06.959
Uh, the last round was done at I think 1.4 billion.
00:08:07.199 --> 00:08:08.000
So amazing.
00:08:08.160 --> 00:08:12.240
Yeah, so that that's also been a really big, big win for us as a as a fund.
00:08:12.399 --> 00:08:17.439
Uh, we've been uh just fortunate to find some of these really great founders and work with them.
00:08:17.519 --> 00:08:23.519
So that's kind of on the on the portfolio uh current portfolio side, still companies that are active uh and scaling.
00:08:23.600 --> 00:08:26.399
Uh, and then on the MA side, you know, some big wins.
00:08:26.480 --> 00:08:29.600
We had a company called Redlock that we sold to Palo Alto Networks.
00:08:29.680 --> 00:08:33.039
Um, that actually became the base for how big was that acquisition?
00:08:33.279 --> 00:08:37.039
Uh yeah, that was uh 240, 240 million dollar exit.
00:08:37.120 --> 00:08:42.159
Um, and the founder there uh was a guy that came out of a company called CypherCloud.
00:08:42.320 --> 00:08:44.720
Uh, CypherCloud had raised a lot of money.
00:08:44.879 --> 00:09:00.320
And back to my earlier point, he was like, Yeah, I saw the mistakes that were made when I raised too much, scale too aggressively too early on, let me build really methodically, really did a good job of figuring out who were the three, four customers that he has to go, he had to go after, got them signed, signed the expansion.
00:09:00.480 --> 00:09:03.600
Uh Dell Technologies came in and led the Series A after us.
00:09:03.759 --> 00:09:08.960
And then that company, I think from when we funded to when they sold was probably about three, four years.
00:09:09.039 --> 00:09:10.399
Um, so very short amount of time.
00:09:10.559 --> 00:09:15.200
Yeah, like got I believe if you were 15% even owner,$40 million kind of an exit.
00:09:15.919 --> 00:09:17.919
And this was on a on a$150 million fund.
00:09:18.000 --> 00:09:18.799
This was on our fund 10.
00:09:18.879 --> 00:09:21.600
So 0.2 fix five X DPI just from a single client.
00:09:22.159 --> 00:09:23.279
It was a really good outcome.
00:09:23.360 --> 00:09:31.840
And and I think more than anything else, the the really interesting thing about the ultimate outcome was that the core of Palo Alto's cloud security business was actually Redlocks.
00:09:32.080 --> 00:09:38.879
That was the very first cloud security company they bought, and it ultimately became the foundation for Palo Alto Prisma, which was which was awesome.
00:09:39.039 --> 00:09:44.240
Um we've had some other really good exits like Shape Security, which we sold to F5 for about a billion dollars.
00:09:44.399 --> 00:09:52.159
Um, and uh and a bunch of so what is the minimum ownership that you have taken recently in a company and what's the maximum ownership that you have gone to?
00:09:52.320 --> 00:09:53.200
Yeah, that's a good question.
00:09:53.279 --> 00:09:57.679
I mean, typically for us, we we like to be in the double digits in terms of ownership.
00:09:57.919 --> 00:10:08.240
Um the reality is the number can can vary, but the average for us is kind of right in the you know, in the teens, mid-teens, 14, 15-ish percent.
00:10:08.480 --> 00:10:20.960
Um, and and for us, we think that at that stage that we're getting involved with these companies, the amount of help that we're providing, that's kind of the right amount of ownership to make the risk reward, uh, risk reward make sense.
00:10:21.120 --> 00:10:27.279
Um, that being said, sometimes we make exceptions and maybe once or twice a fun, we'll do some later stage stuff.
00:10:27.440 --> 00:10:33.279
Um, but more when we see the the opportunity just for really, really big outcomes and really big companies to be built.
00:10:33.440 --> 00:10:39.679
And when when we have clear conviction uh in that big outcome, then we can be a little bit more flexible.
00:10:39.840 --> 00:10:41.279
But that tends to be very rare.
00:10:41.360 --> 00:10:44.639
And those are usually series Bs or series C's that we'll get involved with.
00:10:44.720 --> 00:10:52.000
But those okay, so you you also get an exceptional case involved in series B and Yeah, and again, it'll be like maybe one or two two deals of fund, very rare.
00:10:52.159 --> 00:10:54.159
Uh, we try to be very focused on our core strategy.
00:10:54.320 --> 00:10:55.919
And how many deals you do per fund?
00:10:56.159 --> 00:11:03.519
Yeah, so the the the fund strategy has been to uh focus the check size up front.
00:11:03.600 --> 00:11:08.480
So we try to write two to seven million dollar checks up front and take 30 to 35 shots on goal.
00:11:08.639 --> 00:11:13.200
So two to seven million dollar initial check, and then we'll reserve call it roughly 50% for follow-on.
00:11:13.519 --> 00:11:18.799
But the goal for us is two to seven million dollar checks, take 30 to 35 really high quality shots on goal.
00:11:19.039 --> 00:11:26.000
And that ultimately we think is the right formula to return a return a really big uh outcome on the funds that we raise.
00:11:26.159 --> 00:11:31.600
Obviously, as we've as our funds have increased in size, the the range has also gotten a little bit bigger.
00:11:31.759 --> 00:11:34.000
We're able to write slightly larger initial checks.
00:11:34.080 --> 00:11:37.600
Um, but we've really tried to stay disciplined around that entry point check.
00:11:37.679 --> 00:11:42.399
And we think that that's the right way to manage risk and manage uh and and ultimately get the most upside.
00:11:42.559 --> 00:11:44.639
How has US India Corridor worked for you?
00:11:44.720 --> 00:11:49.919
And what are like some of the Indian companies that you invested in that maybe shifted to US or didn't shift to US?
00:11:50.080 --> 00:11:52.960
Yeah, so uh obviously Phenom, we we talked about.
00:11:53.039 --> 00:11:57.679
So that one I think is probably the the most successful one we've uh we've had so far.
00:11:57.919 --> 00:12:00.159
Um more recently we've done smallest AI.
00:12:00.559 --> 00:12:02.720
Now you put six million thirty post money in that company.
00:12:02.879 --> 00:12:04.799
Yeah, so we did smallest AI last year.
00:12:04.879 --> 00:12:14.960
Um and so Darshan had uh I think we had been tracking him since he was in India, but then met him as soon as he came to the US, and then we were the we were just kind of all over that deal.
00:12:15.039 --> 00:12:18.240
We were like, yeah, we didn't allow anybody else to touch that exactly.
00:12:18.480 --> 00:12:19.120
Yeah.
00:12:20.080 --> 00:12:23.440
Um, and honestly, I think that like that's he has done well.
00:12:23.519 --> 00:12:25.120
He just raced uh, you know, next round.
00:12:25.440 --> 00:12:26.000
Yes, he did.
00:12:26.080 --> 00:12:27.279
He's he's doing really well.
00:12:27.440 --> 00:12:29.120
The business is doing really well.
00:12:29.279 --> 00:12:38.240
Um, and in our in our opinion, uh he is like the perfect example of why the India US corridor works well and why we want to do more of those deals.
00:12:38.480 --> 00:12:44.080
By the time he'd even come to the US, he had already done so much work from a customer perspective, right?
00:12:44.159 --> 00:12:50.879
He'd built a great foundation, and I think there's just something about founders when they're building in India today, they just have this like scrappiness.
00:12:50.960 --> 00:12:57.840
And by the time they come to the US, they they feel uh a little bit more formed than a lot of the very raw companies you even see in the US.
00:12:57.919 --> 00:13:01.360
And so we were big fans of it and we we want to do more of those sorts of deals.
00:13:01.600 --> 00:13:04.240
And how many deals do you do per year as a fund?
00:13:04.799 --> 00:13:07.919
The target is to do about 12, 10 to 12.
00:13:08.080 --> 00:13:15.039
Um, and so if you're writing 30 to 35 checks per per fund, that's like a three, three and a half year sort of cycle to deploy.
00:13:15.360 --> 00:13:15.919
Got it.
00:13:16.159 --> 00:13:18.080
And like what does your exit look like?
00:13:18.240 --> 00:13:23.120
Do you ultimately wait for that IPO kind of an outcome or do you sell in secondaries?
00:13:23.200 --> 00:13:23.919
What do you do?
00:13:24.240 --> 00:13:32.879
Uh well, I I guess I would say the strategy is changing for everybody right now because the markets are are a different uh at a different time than they were in the past.
00:13:33.120 --> 00:13:46.320
Um, we've obviously will consider secondaries, but uh I would say for the most part, one of the one of the benefits of having Sierra on the cap table is the founders get a lot of reputational uh benefits as a result.
00:13:46.559 --> 00:13:48.159
So we we try not to sell.
00:13:48.320 --> 00:13:51.039
We don't want to create any negative negative signals for our companies.
00:13:51.120 --> 00:13:53.440
I mean, with Reify, even Phenom as an example.
00:13:53.600 --> 00:13:55.279
Phenom, we invested in 2015.
00:13:55.360 --> 00:13:58.320
We're still on the board, still pretty actively involved with the company.
00:13:58.559 --> 00:14:03.679
Um, so our our goal ultimately is to help these founders build the biggest company uh possible.
00:14:03.919 --> 00:14:09.759
But along the way, we try to make sure that we guide them to make the right uh financial decision as well.
00:14:09.919 --> 00:14:13.440
Um, in some cases, like a reify, we see an IPO opportunity.
00:14:13.600 --> 00:14:16.639
So, you know, let's not let's not shut it down too early.
00:14:16.720 --> 00:14:20.080
Let's keep going and make sure that we can we can see this thing through.
00:14:20.159 --> 00:14:23.679
Sometimes it makes sense to go for MA because the opportunity is in our face.
00:14:23.759 --> 00:14:26.000
And so it just depends on the situation.
00:14:26.080 --> 00:14:28.240
Um, but we try to do right by the founder at the end of the day.
00:14:28.480 --> 00:14:29.600
And what does the process look like?
00:14:29.759 --> 00:14:34.000
Is it uh every partner hunts for themselves or is a consensus-based decision?
00:14:34.240 --> 00:14:35.679
So we have an interesting model.
00:14:35.759 --> 00:14:38.879
We actually have, I guess, what I call a loose consensus model.
00:14:39.039 --> 00:14:42.480
Um, so the bigger the check, the higher the bar for consensus.
00:14:42.720 --> 00:14:45.759
The smaller the check, uh, the lower the bar for consensus.
00:14:45.919 --> 00:15:10.240
But one of the things that my partners had actually taken away from uh, you know, during the generational transfer that that happened in 2012, um, uh just and just for context, in 2012, my partners have taken over from the last generation of partners at Sierra, one of the things they found was that if I'm doing a healthcare deal, just as an example, my partner that doesn't do healthcare shouldn't have, shouldn't be able to completely veto a deal.
00:15:10.399 --> 00:15:17.120
Now, if I come in and say, hey, I want to write$10 million into this like really risky thing, obviously that's a different conversation to be had.
00:15:17.279 --> 00:15:21.919
But um I spend time in healthcare, I talk to the people in the in the market, I know what's going on.
00:15:22.080 --> 00:15:27.279
Um, just because you know it's not obvious or apparent to somebody that's not done healthcare, we shouldn't, we shouldn't say no.
00:15:27.519 --> 00:15:33.759
And you know, my partner Ben does a lot of robotics and a lot of frontier tech stuff and things that I don't even pretend to understand some days.
00:15:33.840 --> 00:15:35.759
Like I shouldn't be able to veto those things either.
00:15:35.840 --> 00:15:38.639
And so the idea is we try to bring these founders in.
00:15:38.799 --> 00:15:46.639
The the partnership really tries to pattern match on the founder profile and the quality of the team and how they answer questions, how they respond to different things.
00:15:46.879 --> 00:15:51.120
But ultimately, the the final call comes down to the partner that's gonna lead it.
00:15:51.360 --> 00:15:57.840
So let's say for in case of Sudarshan from smallest, because he's the most recent, it'll just help our audience with Indian founders.
00:15:58.000 --> 00:16:01.120
What helped move the needle to the right uh decently sized check?
00:16:01.600 --> 00:16:04.000
I gotta be honest, that one was uh very easy.
00:16:04.159 --> 00:16:05.679
Everybody just said, yeah, you should go back.
00:16:05.759 --> 00:16:09.200
And so I don't know if that's a helpful, helpful story.
00:16:09.440 --> 00:16:16.480
Um you know, I but I but I joking aside, like the the thing about Sudarshan that really, really, really shines through are two things.
00:16:16.720 --> 00:16:19.759
One, like you can just tell he's a really sharp, smart guy.
00:16:20.159 --> 00:16:29.759
Just the way he answers questions, how methodical he was, how how well he handled and presented uh in the in the in the partnership meeting was was impressive.
00:16:29.840 --> 00:16:30.480
Number one.
00:16:30.720 --> 00:16:40.240
Number two, one of the things he did really well was he was hyper responsive, just like 24-7, just aggressive, like moved quickly.
00:16:40.399 --> 00:16:44.320
And I think those two things are the are the things that matter a lot.
00:16:44.480 --> 00:16:54.320
You know, a lot of times in these meetings, there are questions that come up that can be a little bit abstract, and our kind of thinking is like, let's just see how the founders react on their feet.
00:16:54.480 --> 00:17:02.720
And you know, we we we obviously want to help our founders and so we try to prep them a little bit, but you know, we all kind of talked about this like we don't want to coach the founders too much.
00:17:02.799 --> 00:17:06.799
We want to see them how they are, see how raw they are, see how they present, how they handle themselves.
00:17:07.039 --> 00:17:18.640
And so for us, um, I think in Sudarshan's case, he just had a really, really good presence and he was able to really articulate why he could build a big company and build a big opportunity in that space.
00:17:18.799 --> 00:17:24.799
And as we were doing our work, he was just very responsive and helpful to us as and how fast you closed from the first meeting with Sudarshan?
00:17:25.359 --> 00:17:32.240
Uh, that was a little bit of a again, a different situation because we had met him before he started fundraising formally.
00:17:32.400 --> 00:17:38.480
But I think from the time he was like quote unquote ready to raise to writing the to giving him a term sheet.
00:17:38.640 --> 00:17:41.279
I mean, it was maybe a week, a couple weeks, something like that.
00:17:41.599 --> 00:17:44.400
Like by the time he was ready, we were like we already kind of knew.
00:17:44.480 --> 00:17:49.279
And then once he came in and presented, everybody's like, Yeah, then then it was just paperwork to get get done, right?
00:17:50.559 --> 00:17:54.880
You know, offline discussion, you mentioned that you're bullish on healthcare.
00:17:55.119 --> 00:17:56.720
What are the opportunities you are bullish on?
00:17:56.799 --> 00:18:04.880
And there's some opportunities you say that if you're selling to pharma companies, then uh you might be uh sitting on the fence.
00:18:05.119 --> 00:18:05.519
Yeah.
00:18:05.839 --> 00:18:09.839
So uh I'll I'll say start by saying this.
00:18:10.559 --> 00:18:14.079
I don't think I've ever seen healthcare, and I've done healthcare for probably about eight years now.
00:18:14.160 --> 00:18:16.960
I've never seen healthcare move as fast as I've seen it move today.
00:18:17.279 --> 00:18:22.799
Like, can you give examples of like a Fortune 50 or 100 health company giving like a million-dollar check to a startup?
00:18:23.200 --> 00:18:24.400
More than a million-dollar check.
00:18:24.480 --> 00:18:27.359
I mean, you just think about somebody like an abridge, UPMC basically.
00:18:27.599 --> 00:18:28.640
But abridge is not a startup anymore.
00:18:28.720 --> 00:18:39.839
It's like but when they but when they started, the fact that UPMC, University of Pittsburgh Medical Center, decided to standardize on a bridge as their scribing system was a very meaningful thing.
00:18:40.000 --> 00:18:51.039
Um, I mean, even just uh it was two, two, three weeks ago, uh right before the JP Morgan Health Conference happened, uh, there's a company called Noetic in the in the data sp in the healthcare data space or life sciences data space.
00:18:51.119 --> 00:18:55.759
Um, GSK just said they were gonna pay him$50 million for access to their model.
00:18:55.920 --> 00:19:00.079
Like, I'm I don't think I've ever seen Pharma pay for data at that kind of size.
00:19:00.400 --> 00:19:03.200
Can you tell me examples more closer to the portfolio?
00:19:03.440 --> 00:19:04.079
Yeah, sure.
00:19:04.160 --> 00:19:13.119
Uh in in our portfolio, so Reify, um, I I won't share, share names, but they have customers paying nine figures, customers paying eight figures.
00:19:13.279 --> 00:19:16.880
Like we have very, very large deals with with pharma.
00:19:17.359 --> 00:19:21.839
And um, and and this was even this wasn't even necessarily an AI native.
00:19:21.920 --> 00:19:23.759
This was a decentralized clinical trial thing.
00:19:23.920 --> 00:19:33.599
But I think the thing to me that's interesting about this whole AI wave and why I kind of started this thread by saying I've never seen it move that fast is they all see the opportunity in AI.
00:19:33.759 --> 00:19:41.759
They know it's coming, they see the, they see the wave, they know they need to kind of catch, they they know they need to stay on top of it, and they see the value in it.
00:19:41.920 --> 00:19:51.359
So in general, we're just seeing so many companies sign 100k, a couple hundred K, even million dollar deals with very early stage companies, and that's happening pretty frequently.
00:19:51.599 --> 00:20:06.400
I think to me, the place where I'm most bullish, and I'll talk about where I'm not as bullish on the provider side or on the hospital side, I think anything that's starting to get closer to care and delivery of care is quite interesting.
00:20:06.640 --> 00:20:14.319
Um, I think the admin side, your scribing tools, your RCM tools, the market is starting to get quite crowded.
00:20:14.480 --> 00:20:18.720
And well, I think there's some interesting companies that got an early advantage.
00:20:18.799 --> 00:20:22.799
I think if you're going to play in that space right now, it's just gonna be tough.
00:20:22.960 --> 00:20:24.960
The big guys already kind of have a strategy.
00:20:25.039 --> 00:20:27.759
There's lots of well-funded startups, so that's gonna be hard.
00:20:28.079 --> 00:20:35.039
But as you talk about in the care setting, so how do you help the doctor actually help a patient navigate care?
00:20:35.279 --> 00:20:36.799
Open evidence is done really well.
00:20:36.880 --> 00:20:37.839
But that's just one company.
00:20:37.920 --> 00:20:42.319
I think there are plenty of other opportunities in like cancer and and mental health.
00:20:42.400 --> 00:20:43.440
So there are lots of things there.
00:20:43.519 --> 00:20:47.359
So that's one area I'm really bullish on is kind of on that in the setting of care.
00:20:47.599 --> 00:21:01.440
In pharma, uh, I think um they've been a little bit slower to adopt, but this Noetic deal that I just mentioned for and for those who don't know, Noetic basically built an LLM that helps uh identify patients using tumor data.
00:21:01.519 --> 00:21:05.839
So they took a bunch of tumor data, created a foundational model, and they sold that model to pharma.
00:21:06.000 --> 00:21:10.960
So now what pharma can do is you can find patients faster, you can help dose patients better.
00:21:11.039 --> 00:21:14.400
So there's a whole bunch of things you can do with you can do with that data.
00:21:14.640 --> 00:21:16.240
I think there's a ton of opportunity.
00:21:16.319 --> 00:21:19.119
And at the end of the day, pharma cares about you know a couple things.
00:21:19.279 --> 00:21:24.400
Help get a drug to market faster, help find more patients for a drug, or help keep a patient on a drug.
00:21:24.480 --> 00:21:26.480
And I think AI is hugely valuable there.
00:21:26.640 --> 00:21:34.000
And then the last thing I'll say uh this market has been behind, but the payer market, the insurance market, is very, very, very ripe for AI.
00:21:34.160 --> 00:21:44.640
The payers are just being uh are in a big financial squeeze right now because of what's happened with the government and uh cuts to Medicaid and even other parts of their PL being underwater.
00:21:44.799 --> 00:21:46.880
Um, so I think the sales cycles are long.
00:21:46.960 --> 00:21:48.319
That's just something you got to deal with.
00:21:48.400 --> 00:21:49.680
But they are in a lot of pain right now.
00:21:49.920 --> 00:21:52.720
So, what are the opportunities right now on the yeah?
00:21:52.880 --> 00:21:56.240
So on the payer side, uh, just as one example.
00:21:56.400 --> 00:22:08.319
So, as part of the big beautiful bill, Medicaid payments uh got cut and then the revenue, uh the revenue reimbursement rate has been frozen uh going through 20 starting in 20 or going through 26 and 27.
00:22:08.720 --> 00:22:12.799
Um cost of care is going up, that means their margin is getting squeezed.
00:22:12.960 --> 00:22:17.680
So anything that they can do to help take care out of the uh care out of the hospital.
00:22:17.759 --> 00:22:25.599
So push more care at home, keep patients healthier, reduce the number of visits, like all that stuff there they're trying to find ways to do.
00:22:25.839 --> 00:22:29.599
And AI actually has a pretty interesting role in all that if you if you think about it.
00:22:29.839 --> 00:22:34.640
Yes, there is the thing we can talk about, which is like claims management, claims denial, all that stuff.
00:22:34.720 --> 00:22:36.720
And I think they spend a lot of money on that.
00:22:36.880 --> 00:22:44.079
But the flip side of it is how can you help the patient schedule better, find lower cost of care, stay healthier?
00:22:44.160 --> 00:22:46.559
And AI does a really, really good job of all that.
00:22:46.720 --> 00:22:52.240
Like think about the last generation of companies you saw like Lavongo and all these companies that were doing like diabetes management.
00:22:52.480 --> 00:22:55.359
You could do diabetes management with an AI coach today.
00:22:55.519 --> 00:22:57.359
It's probably not that expensive to do.
00:22:57.519 --> 00:23:02.160
Now I think building it and actually getting the patients to use it and all that stuff is actually quite difficult.
00:23:02.240 --> 00:23:03.920
So it's not an easy thing to solve.
00:23:04.160 --> 00:23:12.319
But those things are possible today and you can build a really compelling high margin business around it, which I think changes the opportunity set for startups.
00:23:13.599 --> 00:23:18.079
And what are the things that you do at the firm other than healthcare?
00:23:18.319 --> 00:23:22.880
Yeah so I cover uh what I broadly call uh enterprise AI.
00:23:23.039 --> 00:23:31.359
And so enterprise AI is everything from HR tech like a phenom to clinical trial software like a reify to insure tech to commerce tech.
00:23:31.599 --> 00:23:48.880
So anything that's kind of at that application AI lever layer I spend time around um to me I what I find exciting, I like founders that are going after what I call under-the-surface opportunities that are not necessarily obvious to everybody, but there's a big wave kind of coming behind it.
00:23:48.960 --> 00:23:56.480
And if you can catch them early and you can get in get in at the right time the wave just kind of carries you and actually Phenom is a really good example of this.
00:23:56.559 --> 00:24:19.279
So when when we had done the deal in 2015 HR tech was not a popular thing to do in Silicon Valley at the time but you know at the things were kind of starting to turn around the labor markets were getting better uh the old systems that were in place before Phenom showed up the old ATSs were just had just been acquired by Oracle and SAP and were being sunset or not being innovated on.
00:24:19.440 --> 00:24:23.759
And so Phenom just had a very clear market and opportunity to go tackle the enterprise.
00:24:23.839 --> 00:24:32.880
And so we really like founders that have a good perspective on that sort of stuff see the wave coming have conviction in that big opportunity that big vision and we want to go back then.
00:24:33.119 --> 00:24:35.920
And are you investing in foundation models as well?
00:24:36.799 --> 00:25:51.680
We've done one we've invested in a company called Reflection but um that was at a billion valuation yeah so we did uh that one was was definitely a a higher a higher valuation higher size higher uh higher smart larger size of a deal but um we really like the founder we think that they are critical to helping enterprises really adopt AI in the long run but this is a good example of those like one off exceptions that we'll do we we think there's an opportunity in the foundation model space but it's just not something that really makes sense for our fund size so we'll do maybe one of those but um but we're not really spending as much time on the what check size would you put there like so we'd probably try to write you know somewhere in the seven to seven seven plus ish range yeah and like the other narrative that SaaS is dead right what does your firm believe well uh ask me tomorrow I'll tell you a different answer but uh um look I I think I think we all have known that the day of SAS is done as it traditionally looks if you as a company have not innovated to add ai products to your portfolio like you're probably already dead or you're dying.
00:25:51.920 --> 00:26:04.079
So to me the the the this whole narrative that SaaS is dead is actually a bad narrative because it just kind of throws everybody out the door at the same you know at the same rate and that doesn't make sense.
00:26:04.240 --> 00:26:18.240
Some companies have done a really good job of of actually leveraging AI and building AI products on top of what they have some companies have not personally I think ServiceNow is in a perfect position to go pounce and capture a ton of opportunity around all this all this AI stuff that's happening.
00:26:18.400 --> 00:26:23.359
So to me I think the the phrase SaaSpocalypse is a little bit overblown.
00:26:23.839 --> 00:26:35.759
That being said I don't think it's going to be easy for a lot of SaaS companies and obviously as early stage investors we're not investing in any SaaS companies anymore most of it is AI native building some sort of AI agents and stuff like that.
00:26:36.000 --> 00:26:42.079
But we don't we don't see it as a we don't see the SaaS pocalypse thing as a as a as an inevitability.
00:26:42.240 --> 00:27:03.920
I think some companies will actually find a way to to build something really interesting come out the other side one thing that I do think is interesting and I I I think this is important for for folks to think about one of the challenges I think actually the SaaS businesses have the last generation of SaaS businesses have is that they spent a lot of time building systems of record so that they could just ingest as much data as possible.
00:27:04.160 --> 00:27:14.559
That's actually the wrong thing to do in in the AI world in some ways it's not about volume of data it's about how good the data is and how much have you annotated the data to actually do something meaningful with it from an AI perspective.
00:27:14.799 --> 00:27:29.359
And so the companies in the SaaS world that just ingested data have no idea what data they have a bunch of garbage I think they're gonna have a really hard time getting into this AI world and making their products work in an AI native way.
00:27:29.599 --> 00:27:37.759
The companies that had a purpose behind how they captured the data, how they built all their workflows, all that stuff, I think they'll have a very interesting time in this in this AI world.
00:27:38.079 --> 00:27:46.000
Sierra published a market map of voice agents so what are the bets that you have taken this voice AI space apart from smallest?
00:27:46.240 --> 00:29:14.799
Yeah smallest I would say is the is the is the biggest bet we've taken uh we've taken so far um interestingly enough we've actually had some of our existing portfolios starting to break into voice so reify as an example because they run their call their their clinical trials business they're actually trying to implement voice into their uh implement voice into their stack uh we have a stealth company that we haven't uh announced yet but in the CRM space that's rethinking how how voices uh how voice is being done um we also have a company that called Blazel um that's leveraging voice internally to help uh they're a they're basically a marketing agency for founders um help create content and stuff like that they use voice voice agents internally to capture uh recordings and then take the recordings and help turn that into content um so those are the the bets that we've made we've obviously seen a ton of companies uh across the board though from a voice perspective so in your case how do you measure product market fit and do you usually come after it or before it yeah um I'm laughing a little bit because I think product market fit in the AI world has been even harder to tease out than in the SaaS world um I felt like in the SaaS world you kind of just had this natural just pulling motion that you could tell when when something was working I mean we saw this with with phenom for example just like quarter over quarter growth in ARR, number of customers sales productivity through the roof was all good and it was easier to spot in some ways there.
00:29:15.440 --> 00:29:19.279
But I guess let me answer the the last part of your question.
00:29:19.519 --> 00:29:25.039
We've done stuff at the inception stage so you know two founders and a PowerPoint so like way before product market fit.
00:29:25.200 --> 00:29:28.079
And then we've done stuff once they've reached product market fit as well.
00:29:28.640 --> 00:29:57.519
I think in the AI world to me the the leading indicator of product market fit is actually usage and can you actually predict number of tokens what the user is doing on the on the product are they consistently using it every day are they consistently using it every week are they using up all the credits that you're giving them I I think that is an underrated metric that we don't spend a ton of time looking at and partially because a lot of these companies are early and then the second metric I think about is gross retention.
00:29:57.680 --> 00:30:01.200
Obviously the net retention is going to be there if it works because AI is so good.
00:30:01.279 --> 00:30:07.279
I think people want to consume as much of it as possible but you really got to look at that gross retention number and the usage and metrics.
00:30:07.359 --> 00:30:10.400
And I think that's what's going to define PMF in this world.
00:30:10.720 --> 00:30:18.880
Uh we we discussed about like one of the companies where you are bullish on the founder uh but not sure about the the market risk.
00:30:19.039 --> 00:30:21.200
How do you calculate market risk as a VC?
00:30:21.440 --> 00:30:27.920
Yeah um honestly my framework was pretty easy and maybe it's not perfect but I I think it's a good way to do it.
00:30:28.000 --> 00:30:36.640
I anytime I do diligence I just try to find as many people in my network that are potential customers for a company or would have been potential customers for a company at some point.
00:30:36.880 --> 00:30:53.680
And I think if you talk to enough experts in a space people that are on the ground dealing with the pain point on a day-to-day basis that gives you enough of a basis for uh whether or not something can work and obviously you have to think about upside but not everything that has upside will have the ability to even get to that point.
00:30:53.759 --> 00:30:55.519
And so you kind of have to balance those two things.
00:30:55.599 --> 00:31:14.640
And so a lot of times the companies that I think I'm excited about for the future I can see like why this could be a big company all that stuff makes sense then you kind of come back and say hey will the customer actually buy the product today and if I don't feel that like just like I'll absolutely have to have this like feeling from the customers that's usually the the market risk that I that I find.
00:31:15.279 --> 00:31:21.279
What has kept you for 13 years at Sierra where as your peers in your industry keep on changing roles every two or three years.
00:31:21.440 --> 00:31:28.240
Yeah um look I I actually think Sierra is a little bit of a special and unique platform.
00:31:29.440 --> 00:32:10.160
Like I was mentioning to you earlier I was the second person that joined Sierra 2.0 so I've I have a lot of uh sense of pride of ownership of you know being one of the earliest folks on the team having helped build a lot of the processes having helped kind of get the firm to where it is today and so they're just uh part of me and I'm a very loyal person like I I I just think I I owe it to them who gave me a shot in the first place to make sure that the fund is successful and we um we make our LPs money we make ourselves money and uh we are successful so that's part of it um the other thing that that I like is one of the things that we we talk to LPs we often describe is this concept of majors and miners.
00:32:10.240 --> 00:33:01.039
So majors are the stuff we know well like your CRM your enterprise software kind of stuff or enterprise AI in today's world and uh and so it's like the stuff we know well so like a buck of the fund is going to go there but miners are things that we can try and be like get smart about and learn and there's no uh there's no uh uh stepping on other people's toes to go try new things right like if you find a space that you like you spend the time you do the work you bring in a deal and again it makes sense within kind of the the parameters uh there's a lot of support to go try new things and to me that's always exciting I I think in venture one of the things you can you just get stuck or trapped in is just not thinking about what's coming next and sticking to the stuff you know and so I just love the fact that we have such an open mind and have a lot of autonomy about going and trying new things.
00:33:01.119 --> 00:33:02.880
And I don't know if you get that at every shop.
00:33:03.039 --> 00:33:05.359
And so to me it's just every day is just a new day.
00:33:05.440 --> 00:33:09.039
I like I see so many cool things I'm not sure I would be able to go do that anywhere else.
00:33:09.119 --> 00:33:12.319
And so I always appreciate appreciate that that flexibility.
00:33:12.640 --> 00:33:35.839
AI is uh forcing a rethink of the enterprise stack and a lot of the stuff that the market will come today will the market will not be ready for how do you evaluate for that back to my earlier comment it's it's really just a function of trying to figure out are the most cutting edge folks in your network customers in your network willing to try this thing and do they see value?
00:33:36.079 --> 00:33:43.599
One of the challenges you often you often have and I know since you do early stage stuff you probably see this as well it's like founders have a great vision and a great pitch.
00:33:43.680 --> 00:33:55.039
They may not have the product ready but I think it there's just a certain type of founder and I think this is what makes founders great is they know how to sell the story.
00:33:55.200 --> 00:34:04.559
They know how to like really empathize with the pain point of the customer really convince them that they are the right person to go solve that problem just have a deep appreciation and understanding of the problem.
00:34:04.720 --> 00:34:16.079
And so I think you're right like in this AI world there's going to be things that seem futuristic, seem really out there but almost in the grasp of possibility or the realm of possibility.
00:34:16.400 --> 00:34:30.159
And so I just try to find these founders that can like just like articulate that really well to the experts right and then if the experts on the other side are like yeah that makes sense then it's like a very easy easy decision to go and say yeah I want to I want to invest in this thing.
00:34:30.559 --> 00:34:33.840
A little bit like Siddarshan with smallest that was part of how we did diligence.
00:34:34.239 --> 00:34:50.000
My partner Tim has built two companies in the call center space knows it super well so we're like hey let's just go have Sudarshan talk to a couple of people see what they say and uh and just like the response and the reaction that that we got is just overwhelmingly overwhelmingly positive.
00:34:50.159 --> 00:34:52.960
That's just a clear cut sign that you want to want to back this person.
00:34:53.280 --> 00:35:03.119
What are the top five anti-portfolio of yours like where you you were you know uh I don't know if I have the top could be possibly the leader.
00:35:03.280 --> 00:35:59.360
Yeah I wish I wish I had the top five I I should remember this off the top of my head I I try not to dwell on the losses too many times but uh airwalls is one that that I always uh okay you had access to airwalls we looked at airwalls very early on that one we yeah I mean and but remember when we looked at this this was probably 2014 2015 it was very very early on and at the time all they did was Australia China corridor crypto payments and a lot of it was around like mining companies and stuff like that because the resource boom was happening so like these things aren't always obvious at the at the moment but I was just thinking since since today that came up on the uh on the Robinhood portfolio that's one how many of your founders are second time versus first time in your portfolio that's a good question I I don't I don't know the data across every fund so I'll just comment on the latest fund.
00:35:59.440 --> 00:36:33.920
Yeah I'll just comment on the latest one it's probably about 5050 repeat versus versus first time founder um obviously we we really like the repeat founder I mean I think everybody does but we think we have actually a lot of the expertise to help first time founders go and and and build great companies I mean it's kind of why we just we look for just raw horsepower if like we see a founder that just has that raw horsepower that ability to go and like just get things done do things we can give them the access to the enterprise and whatever they need to be able to go and scale and build a big company but they just have to have that raw horsepower.
00:36:34.000 --> 00:36:34.960
So it's about 50-50.
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And let's say if you have to categorize your portfolio between strong storytellers versus not so strong storytellers versus like great executors on the tech side yeah and well I like to believe all of them are great storytellers and great operators um I it's a it's a hard question because I I think one of the things that's that's really uh a like a weird nuance to some of this stuff is strong operators ultimately become great storytellers when the number when the numbers are there.
00:37:11.760 --> 00:37:28.000
No without numbers right without without the numbers um honestly I would say like for the most part I think a lot of our founders tend to be just really great storytellers I think that's just a function of the stage we're getting even the tech people that you talk to even the tech people like they may not necessarily be flashy.
00:37:28.239 --> 00:37:46.480
But like is a great storyteller for example right but that's rare to find between a founder who's good technologically as well as a good storyteller yeah that's why like I don't think for us when when you when you say great storyteller that doesn't necessarily have to mean like very like flashy I'm not saying flashy.
00:37:46.559 --> 00:37:48.639
Yeah great storytelling is also about clarity.
00:37:48.880 --> 00:37:52.320
That's right exactly and I think for us that's the thing that we really look for.
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It's like more about that like clarity of where is this company going to go like one question we always like to ask and we just like to see how the founders respond is like just tell us how you build a big company like what is the path to building a big company there's no right answer to that question of course but like just to see how founders respond and sometimes you get oftentimes you get like very hand wavy answers from like the very business savvy folks and then you don't get enough of a vision from the too technical of the founders that we meet.
00:38:20.559 --> 00:38:42.079
And it's like somewhere in the middle you get these founders that have the right balance of like technical and commercial some tend to skew a little more technical some tend to skew a little more commercial but at the end of the day like the the reality is that for us the best found the founders that we invest in we have to believe they're great storytellers simply because one of the things we expect our founders to be able to do is to go attract the best talent.
00:38:42.320 --> 00:38:48.719
And there's just no way you can attract the best talent if you can't tell stories or have somebody around the table that that does that.
00:38:48.960 --> 00:39:27.519
And so for us like it's a hard question because I I think at the at the core of it at the end of the day I think they all have uh the ability to tell great stories um but I I think I think what I would say is this there are some that know what the path to a big company looks like they tend to be more technically savvy maybe not as business savvy and our job is to help kind of provide that muscle number one or number two give them the right set of a set of advisors that we can put around the company help them hire a great CRO or first account exec or whatever it is that we need to help them with and help them kind of button that side of the house.
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And then on the flip side the guys that are a little more commercially savvy, how do we pair them with the great technical counterpart or give them the right sort of technical counterpart on the on in the company to go help them help them build.
00:39:40.480 --> 00:39:43.199
No thank you so much Vignish love the conversation with you.
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Learned a lot about Sierra which I had not known earlier and looking forward to partner in some of the companies that we invest in.
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I appreciate it and we're all looking to do more India US corridor companies thank you so much.
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Thanks
