Summary

Kiersten Stead, Co-Founder and Managing Partner of DCVC Bio sits down with Neil to discuss her firm’s approach to deep tech investing in biotech, her focus on platform technologies, and how to separate what’s real from what’s hype in AI.

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Tranascript

00:29
Danny Levine (co-host)
Yeah, we've got Kirsten sted on the show today. For listeners not familiar with Kirsten, who is she?

00:35
Neil Littman (host)
Kirsten is a co founder of DCVC Bio. DCVC is a venture firm that backs companies using deep tech to solve some of the biggest problems that matter in the world. DCVC Bio is really focused on computational biology. They do everything from therapeutics to AG. And so Kirsten is a scientist investor. She's focused on developing deep tech platforms in, as I mentioned, therapeutics, agricultural, food, industrial, biotech. Danny, you and I have known Kirsten for many years now, as well as her colleague John Hamer, who was another co founder of DCVC Bio. We all worked together at burl company many years ago, and so Kirsten has a wealth of experience in the space. Prior to founding DCVC Bio, as I mentioned, we worked at Burl and company together. She's a scientist.

02:01
Danny Levine (co-host)
At DCVC Bio, we're really seeing AI and computational biology changing the whole landscape. Where does DCVC Bio fit into the broader world of life sciences investing?

02:15
Neil Littman (host)
Yeah, so they were early movers in this sort of world of investing in platform based computational biology companies that, of course, includes artificial intelligence based companies that are pursuing using that type of technology for not just drug development, but also in Ag bio industrial biotech as well. And so I'm really excited to talk to Kirsten about the nuances of investing in this space. As we all know, there's a ton of hype around AI. So Kirsten is going to have a fantastic perspective on how to separate hype from reality, what they're seeing in the space. I want to dive into a couple examples from their portfolio and really just at a high level, also sort of get this notion of, okay, what does computational biology mean in today's world, because the technology is advancing so fast.

03:09
Danny Levine (co-host)
Well, if you're all set, let's do it.

03:11
Kiersten Stead
Danny.

03:14
Neil Littman (host)
Kirsten, thanks for joining us. I am incredibly excited to welcome you to the show today.

03:19
Kiersten Stead
Yeah, thanks for having me. It's nice to catch up with you again.

03:23
Neil Littman (host)
Yes, indeed. It's been a while. So today we are going to talk about deep tech AI, computational biology, and DCVC Bio's approach to investing in this rapidly evolving world. Kirsten, I'd like to first start with a high level, 30,000 foot view of DCVC as a firm. You are interested investing in technology to address large societal problems at a high level. Can you walk me through the big picture investment thesis of the.

03:51
Kiersten Stead
Sure. So, DCVC as a venture capital firm has been around for quite some time, and has always had the reputation of investing in the hard, the meaningful of which we call deep tech internally. I've heard some people call it frontier technology and that sort of thing as well. And so how we operate is we're two separate firms with our own separate and dedicated funds. There's DCVC, the original effort there that invests in all things deep tech. So that can mean everything from computational advantages to things that are really hard, like elemental or nuclear energy, rocket ships, quantum computing, and then on the life science side, autonomous robots for surgeries and things like that. That's where they've classically invested.

04:44
Kiersten Stead
And then on our side, we have a dedicated team of dedicated funds that invest in therapeutics, synthetic biology, and a little bit of agriculture as well. And so that's how we sort of divide and conquer. And then we share offices, share folks. And so it allows us the advantage of acting like quite a large firm without having to raise $3 billion funds and trying to deploy that into early opportunities. So that's our sort of advantage. And then we have folks on both sides of the coin. So we have engineers, people with physics backgrounds, and then people like ourselves who are molecular biologists, chemists, structural biologists, et cetera.

05:21
Neil Littman (host)
You're one of the co founders of DCVC Bio, which you established in 2018. Can you talk a little bit about the genesis of DCVC bio and your current focus?

05:32
Kiersten Stead
Yeah, sure. So my partner and I, John Hamer, who I think you also know, we met at a life science firm that we all worked together earlier on. And one of the limited partners there was Monsanto, along with a lot of other strategic limited partners like Bayer and others, and wanted to sort of bring venture in house. And so we left to run, to initiate and run Monsanto growth ventures. And we did that for all six years that it ran, investing in quite a large portfolio that spanned sort of 50% of capital was in therapeutic opportunities, but that explored novel platforms and dna, rna, protein. Of course, Monsanto is at its root, or was at its root, a biotechnology company.

06:19
Kiersten Stead
And then the other sort of 50% exploring this new, what were calling ag tech, but the touching of engineering or artificial intelligence technologies into what we could do for agriculture. And as a part of that strategy, we wanted to partner with a firm that specialized on that side. And so one of the most reputable firms at the time was this little firm called DCVC. And so we became limited partners in those funds, and were investors in a number of other funds, like Atlas and like. And we got along really well with them, and we started doing investments together. We had a couple of exits together. We started sort of sharing and co working in each other's spaces.

07:04
Kiersten Stead
And so when the Bayer acquisition came along and we needed know, as with all vcs, you need to have a continuous and active venture activity, they wooed us away and we established DCVC bio in about 2018. So that was the genesis.

07:22
Neil Littman (host)
Well, that's very interesting. I actually didn't realize all the backstory there, so, no. Fascinating. And of course, I do know John, so, very cool. And so, Kirsten, let's stay at the sort of high level for one more question.

07:35
Kiersten Stead
Then.

07:35
Neil Littman (host)
I want to dive into some specifics around your investment thesis. But in terms of the big picture, what are some of the boxes a potential investment needs to check off for GCVC Bio? Beyond the typical things a VC would look for in a company. So what sort of differentiators are you looking for in a company, and also in an entrepreneur?

07:54
Kiersten Stead
Yeah. So where we specialize is sort of we act as the glue between biotech investing and computational investing. So we look for companies either that are taking really big swings in biology, so we would classify them as deep know companies, like a mojo. Biopharma would be a good example of that. When we first started talking to the academic co founders of that company, no one was thinking about in vivo car t therapy, right? So this means the manufacturing of car t's and amplification within the body and eliminating a large piece of the manufacturing time. So that would be one side, right? There's no real. There's of course, computation in everything, like there's genomics in everything now. But it was sort of a big swing of biology that were really interested there.

08:47
Kiersten Stead
And then on the other side, it would be artificial intelligence driving faster, better, cheaper, or some dramatic change in the way healthcare or drug development can be practiced. And we have sort of expertise across that now on the science side. And almost everyone at DCVC is a scientist and has a technical background, including some folks that are in artificial intelligence. And so we can a, diligence those types of opportunities and b, help them grow, because we have sort of fingers in both pies, but because we're realists about what biotech needs to be. We know at the end of the day, we're going to be building an agricultural product, a drug, an article of commerce that needs to go through the regulatory system or interact with the regulatory system in some way. And so we know where the North Star there is.

09:42
Kiersten Stead
And so I think that helps entrepreneurs develop their technology in the right way, while reserving resources and company build around the idea that these have to be platforms versus pipeline type companies. And so we also are pretty good about promoting that kind of development. The other things would be pretty normal. I'm sure you've heard from other vcs, right team is really important. I think from our perspective, not only we're investing in people that are through their second times as ceos and potentially third times. So having a good, trusted relationship with trustworthy and transparent entrepreneurs is really important. But we also, our companies need to have a native expertise. So if they're an artificial intelligence company building obms or liganucleotide based medicines like crayon is, they need to have both of those expertise native within the company from the get go.

10:45
Kiersten Stead
So we don't believe that you can recycle a pharmaceutical executive team from your favorite biopharma and then expect them to hire in AI expertise later. It has to be sort of a native insight that was earned by the team that they want to develop something based on that insight.

11:03
Neil Littman (host)
There's a lot that I want to dive into that you just referenced. Let's double click on the investment thesis. And I want to start with the term computational biology and just ask how encompassing a world does that represent to you? When I think about computational biology, I think about many years ago, sort of bioinformatics, but it's much more than that today. What does that term mean to you, and how big a space does that represent?

11:31
Kiersten Stead
Yeah, I do think there still is confusion about bioinformatics versus what we sort of mean as cutting edge computational bioin. And that definition evolves like any other technology evolves. And I think the expertise keeps raising the bar. Right. So when something becomes commoditized, then you have to build on the next stack up. Right. So what we mean is that artificial intelligence as it relates to drug development has been successful. A lot of people have taken artificial intelligence, developed medicines, for example, Absellar's Covid antibodies were developed off a machine learning system. People don't realize that, but our ais for drug development and for other things are very narrow. So they are superhuman, and they allow us to do things we cannot and could not conceive of doing before, but they have to be purpose built.

12:27
Kiersten Stead
And I think computational biology for some, and bioinformatics for some, can mean what we think is table stakes for all life science companies, which is you need everything from computational biologists designing your experiments so that they fit into the clinical paradigm in which you would eventually like that drug to interact with, to setting up your limb system so that you're getting proper, clean data off of whatever data you're generating. We don't mean that. So we know that's sort of table stakes, I think, at this point of time. What we mean is a company like crayon bio that has understood the design parameters for creating rna based medicines that will never be toxic and things like that. And you need those to be purpose built. The data is generally purpose built.

13:21
Kiersten Stead
Sometimes the models or the animal models that you're using are purpose built, and you're using that to generate proprietary data that drives proprietary algorithms, where we've seen really large success in developing drugs much more reliably and with a faster timeframe, to the point that you're going into the clinic, at which point you're then overlapping with the traditional drug development process.

13:47
Neil Littman (host)
And I want to do a deeper dive on some of your investments here in a minute. But as you know, Kirsten, investors have been pretty rough on the biotech sector over the last few years. But one bright spot has been the enthusiasm for AI. So let's double click on that. I have a two part question for you. Number one, how transformational has AI been in the realm of biotech? And number two, how transformational will this technology be for biotech going forward?

14:15
Kiersten Stead
Well, I think, as with all technologies, I made a joke about genomics back in the 90s. There were genomic companies, and now it's sort of table stakes for the development of drugs and target discovery and things like that. So I think it will be a necessary part of certain aspects of drug development going forward, but I do think that it will be proprietary around certain problems that whatever group is trying to solve. So I don't think this is like the IT department in a pharmaceutical company, where there is broad applicability across many things. Certainly, there will be that on the data handling side, but for solving very specific problems, those are going to need to be purpose built to solve specific problems. I don't know if that answers your question, but I think that's where it will land.

15:10
Neil Littman (host)
It does answer my question, actually. You mentioned something really important that I want to just follow up with. And so obviously, a lot of what we can do with AI is dependent on the universe of data that can be captured and exploited. So what do you see as driving this from the data side, and are there any obstacles pertaining to sort of capturing and exploiting this massive amount of data that we're seeing?

15:34
Kiersten Stead
Well, the interesting thing, with the rise of the popularity of large language models and generative AI, which are driving so much excitement at the time that we're recording this, is that it's sort of a generalized tool, and then we're going to go forth and generate specific use cases for solving specific problems. In biology, we're sort of looking under the lamp, and we can only impute into models the data that we have and that we know and things that we've empirically tested. So we need to generate that data before it can be imputed and learned from. And that's still going to be a bottleneck for the industry, and that this isn't an engineering technology. We didn't design biology, so we have to discover what's there, and we're still in the process of discovering what's there.

16:29
Kiersten Stead
So I think we have a bit of an aversion to people who sort of hand wave over that complexity or minimize that complexity. I mean, we're pretty optimistic about if someone has a really interesting idea or concept on how to capture that type of information to generate that. Like, we'll talk about Crayon in a bit, then that's very interesting to us, but we want people to sort of be realistic about that challenge and the need for empirically derived information.

17:01
Neil Littman (host)
Yeah, that's a really good point. And this is a question that I think about a lot. And so I'd be curious how you think about this sort of concept as we're moving forward, of the AI or the technology itself almost becoming commoditized, and it's the data that is truly valuable to train those AI models. How do you think about sort of the AI and the tech versus the data set that may be proprietary to a company for a given problem set?

17:27
Kiersten Stead
Well, it's definitely, I think what you're saying can be reflected in how much does it cost to build either. Right. And the generation of novel data is going to be much more expensive and going to be the focus of a startup in the, quote, computational biology space versus the generation of algorithms to sit on top of that. So I would agree that it's the data generation that currently is more expensive in the set of companies that we're really interested in right now. Or we would argue that's the building of the platform. Right. That's their moat.

18:03
Neil Littman (host)
Yeah, I think that's how I often think about it, is sort of that data moat. So, Kirsten, as with any new technology with big promise like AI, there's a lot of hype. How do you distinguish between reality versus hype? I think this is actually a pretty big problem for investors in this space, especially those that don't have deep expertise on the technology side, that maybe have the deep expertise in the bio side of the equation.

18:31
Kiersten Stead
Yeah, we acknowledge that it's hard for firms that specialize in one side or the other. Well, the answer is we talk to them. Right. And we do diligence. So we are technical. We're a technical team on both sides. And so we'll often set up biology calls with the team, deep dives and computational deep dives with the team. So we sort of double up on the diligence on both sides. Of course, we do relent to companies that we think are hand waving over things right from the get go, where they don't seem to have an earned insight that they can prosecute on that will differentiate them from, let's say, generation one of small molecule hit discovery companies. Right. So we do see that, and if they're talented teams, we'll invest in time to guide them to where we think the value is.

19:29
Kiersten Stead
Certainly, if we like the team, we'll go through that effort. So we do see that, and we do see some of those companies getting funded by both biotech investors that want to learn in the space, and also on the other side. But I would say there's no shortcut here. Right. You have to diligence both sides.

19:47
Neil Littman (host)
Yeah. Personally, I think one of the biggest challenges, probably over the coming years or decades, will be how to know how to do the right type of due diligence to separate what's real from what's talk and what's hype. So, always interesting to sort of hear about that process. Kirsten, I want to circle back to a comment that you made just a few minutes ago about the idea of platforms. And I want to talk about platforms versus product centric companies. One of the challenges with these powerful platform technologies is how to generate value from them. Typically, you either provide services to others or develop your own products. How do you think about building out a platform centric company versus an asset centric company, and where do you see the most value being captured?

20:32
Kiersten Stead
Well, yeah, I think this alludes to one of your early questions. I don't think I got to. One of the challenges right now in this environment is that biotech investors want to see pipeline, and they're really focused on pipeline and how quickly they can be in the clinic and how quickly they can develop that, because we're in a bit of a risk off environment at the moment. For us, the companies that we have and that we're interested in do both. So they have a platform that cannot be replicated inside a larger company. And so there's an opportunity to partner with large biotech or partner with even smaller biotechs in the generation of different targets or target discovery, or using their platform for some use that company is interested in, while also simultaneously developing the platform.

21:26
Kiersten Stead
So I would say almost all of our companies have that type of arrangement which gives them, on the platform side, a bulk upfront payment that allows both the big pharma to, or big company to be able to access that technology in a way that fits what they're interested in and provides the investors in the company a source of nondilutive capital. But then also the company can continue to develop their pipeline of drugs or pipeline of products, whatever those might be. So that's the model that we pursue. We don't tend to invest in services alone. Companies, of course, we've invested in companies like unlearn AI, which you could argue is a service one way or other. We don't tend to think of it that way.

22:16
Kiersten Stead
This is a company that's generating an unlimited amount of placebo arm patients called digital twins for clinical trials, and they are commercial in Europe, et cetera. So their product there is a set of placebo patients, digital placebo patients. So we will do things like that, but we are interested in building drugs on therapeutic side. But also because you have this big proprietary platform, the company and patients will receive the most benefit if you also partner on that.

22:50
Neil Littman (host)
Yeah, makes sense. Makes sense. And actually, Kirsten, I want to just dive into that digital twin company that you mentioned, because that's fascinating. Could you talk a little bit just about what a digital twin is and how that could be used to aid drug discovery and drug development?

23:05
Kiersten Stead
Yeah. So, sadly, there are a huge number of diseases where we don't really have a standard of care that alters disease progression. And you could think of a lot of neurological diseases that fall into that category. And so in that case, we actually don't really need new, novel placebo arms. And you could also argue that they're a little bit dodgy in terms of the moral approach to these. If you know that you're putting patients into a placebo arm and their disease trajectory is known for Alzheimer's, say, or something like that, this is the case. Right? So we've run so many placebo arms in certain diseases that we know what the disease trajectory is going to be like. And these digital twins are developed off a technology called, again, a generative adversarial network.

23:59
Kiersten Stead
We can take a patient, say, for example, I'm going into an Alzheimer's clinical trial. We can create my digital twin that will progress in the placebo group, whereas, and I can go on to drug and therefore minimize the number of patients we put into placebo groups and have more of them being on drug. So that's the general idea.

24:20
Neil Littman (host)
That's very cool. I find that technology endlessly fascinating. Okay, so while we're on it, let's talk about some additional investments. So you had mentioned an earlier investment, which is a big winner for you. Accelera, could you talk a little bit about the investment thesis behind accelera, what the company does, and sort of the trajectory of the company and outcome for DCVC bio?

24:42
Kiersten Stead
Yeah, gosh. We first saw absellar, I think it was in 2018. It was one of our first investments. And I remember when were first introduced. So were introduced to the company via one of our equity partners, and we immediately liked Carl, the CEO. He's just fantastic. But the company was in early stages. Carl was still on faculty at the University of British Columbia. And so we knew immediately what we saw that was different was that he had really invested on the computational side heavily, even at early stage. So pre series a, about 50% of the staff at the company were on the computation side and developing algorithms and, of course, proprietary machinery to generate data that would go into those algorithms.

25:33
Kiersten Stead
And we saw their ability to select for b cells and sort of stratify those b cells based on everything you care about in antibody development, manufacturability, potency, specificity, et cetera, to almost any ligand you could possibly want on a timeline that we'd never seen before. And that was extremely noteworthy, and it satisfied this concept of what were talking about. Know, Carl had incredibly deep understanding of antibody development discovery, but also had this innate expertise on the computational side. And we saw from the data that they'd presented that they were able to generate antibodies that. Manufacturable antibodies, humanized antibodies in a much more rapid pace than anything we'd ever seen at the time. They were a services company, and we invested in the series a. We were the only investors in the series.

26:30
Kiersten Stead
You know, helped Carl shape the company, got him in full time in the way that the management team wanted it shaped, and we wanted it shaped. And now, as people know, they have their own internal pipeline. They're developing and gpcrs, plus they have well over 150 partnered programs. In addition to that, they were a part of this DARPA emergency response program in the case of a pandemic. And they happened to be the know, luck favors the prepared mind. Well, they happened to be the most prepared mind at the time that Covid hit, and they received the first Washington reference patient sample. And so they were able to generate a highly potent antibody off of that reference patient in a matter of weeks, and had partnered for manufacturing within a very short period of time.

27:29
Kiersten Stead
And then we know that well over a million patients have received the benefit of the antibodies they developed for Covid, including people I know. So it's been not only a very satisfying investment from a benefit to patients story, but of course, they were of the biggest ipo in 2020, and we, more than multipoly paid back our fund, and so it was a great exit for us. And we continue to have a great relationship with Carl and the team, and they're just lovely people, and the company's got a big upside. So we're really excited to see what's in the future for.

28:08
Neil Littman (host)
A great. What a great sort of case study for the space in general. And that one really highlights what you talked about previously in terms of the business model, partnering model with the platform, and developing assets. So that's been an exciting one to watch. Kirsten, let's talk about another one that you had mentioned earlier. Creon bio, which is doing some interesting work around applying AI to the world of antisense oligos. Why Creon?

28:32
Kiersten Stead
Oh, Crayon's a really interesting story. So were introduced to the founders through, actually, another CEO of ours, and this was an early start. It was one of those companies where the founders had an idea, and right away, we realized they had both the expertise to execute on that idea. They had deep experience in rna based medicines, so we can categorize these molecules sort of together, and we use them loosely. So asos, antisense grnas, any sort of rna drug you'd like. They knew and were highly aware they had been at Ionus so they knew that drug development path, but they also were physicists and mathematicians who were running the computational aspects of that. And so they wanted to address one of the challenges of developing rna based drugs, which is they are long, complex molecules, unlike small molecules, and interact. Right.

29:37
Kiersten Stead
Because of the charges involved, they can interact with our tissues. So one of the big challenges in the area, in the space is that a lot of OBM, Oligron nucleotide based medicines are toxic. And even some of the commercial ones we have interact with their neurotox for a period of time. So, one hand, it's easier to interact with the biology that you're looking for. The challenge is developing these drugs that are both safe and effective. And so they spent a significant amount of time, as we just discussed, developing survey compounds, developing a huge database, their own proprietary algorithms, their own models, et cetera, to be able to understand the design space of rna based medicines. And that's what they've done. And as a testament to that, they were able to, from initial sequence work, develop a GMP manufactured drug in five months.

30:36
Kiersten Stead
And so what the hope of the company is. So they're a platform, obviously, they're developing obms for rare disease, because in a paradigm like this, all of a sudden, rare diseases become a real possibility to develop for the speed and cost. And they're in discussions about how can they shorten sort of the toxicity, the tox part of the development pipeline, in order to get drugs to patients with ultra rare diseases. And they're also working through their own pipeline and with partners on some larger diseases as well. So, another example of this, let's do it all. We can have a pipeline, we can partner, and we can also, in this case, the sort of third leg of the stool is develop solutions for patients with ultra rare diseases.

31:30
Speaker 1
This is great.

31:31
Neil Littman (host)
I love learning about these companies and your investment thesis on these. Let's dive into one more company of your choice.

31:38
Kiersten Stead
Oh, goodness. I don't know. This is choose your own adventure. Why don't you choose?

31:44
Neil Littman (host)
There's so many to choose from. How about Empirico? You want to talk about Empirico?

31:50
Kiersten Stead
Yeah, sure. So, Empirico is a company that raised in San Diego. They don't do a lot of PR, but they are really interesting. So the CEO, Omri Gottsman, he comes out of Regeneron, but also on the computational side. And what they've done was what they realized is that a large companies were both misanutating genetic sequence information and that there was a lot more to be discovered in sort of the. If you take the UK Biobank and other resources of genomic and medical record information, that they developed a computational system that looks more like a tech company, more like a Netflix versus something you might find inside of a life science company, and have been very successful about finding novel targets. So they are a target discovery company, and they found some incredibly interesting targets, which aren't disclosed yet.

32:48
Kiersten Stead
And they are developing their own internal pipeline to those where they can use rna based medicines, and then have partnered with others, like Absellara and ionus, for modalities that they're not going to develop in house. So for antibodies and things like.

33:06
Neil Littman (host)
Cool. Very cool. So, Kirsten, let's switch gears a little bit. We've talked about some of the fantastic companies in your portfolio, some of the successes like Celera. There's often more to learn from failure than success, though. Is there a lesson learned from a past failure or challenging investment that you are willing to share?

33:30
Kiersten Stead
I think my answer, and this isn't a cop out, but I think what we always learn, and we continue to have to relearn, apparently, is that people matter the most. Early stage venture. We are in a people business, and I think we always want our companies to fail, because if they do fail, because biology is tough, and we've certainly had one of those. In one of our companies, we made a discovery sort of after were in the clinic of some novel biology that wasn't known before, and are in the process of sort of reorienting that company. So I would consider that you could consider that a failure, but that's the way we sort of want the company to fail. The team is fantastic. In another case, it's people. Right.

34:26
Kiersten Stead
In one particular case, we didn't realize that the CEO was a little bit paranoid, that didn't realize that funders were their friends, that the group of investors they had were there to help. And biology is tough, and this space is really tough, and so you need all the friends you can get. And if your predisposition is towards being a little less trustful than others, that's a real challenge for everyone around the table. So I would say that the learnings that we have are all around. You cannot get to know people well enough before you make an.

35:07
Speaker 1
That is.

35:07
Neil Littman (host)
That is for sure. It brings to mind, I think it was Andy Grover said, only the paranoid survive. I think that was his book. But of course, you can take that too far, as.

35:14
Kiersten Stead
So, yeah, you take that too far. Yeah. There's a healthy level of. I would say competitiveness is probably a better word to use than paranoid. The two things can cross over, but certainly we want people to be competitive. Ceos have done the best, but I don't know if you would interpret that as paranoid or not. Probably not.

35:41
Neil Littman (host)
Yeah, probably a fine line.

35:43
Kiersten Stead
Yeah.

35:45
Neil Littman (host)
So it is currently an extremely difficult environment for entrepreneurs looking to raise money at all stages. But I know you're focused at the earliest stages, for the most part. What advice would you offer entrepreneurs about how to best get the attention of yourself and other investors like you to maximize their chances for success?

36:09
Kiersten Stead
I think current investors want to see a team that gives them security on the development side. So I think one of the best things you can do is in past years, perhaps we've seen computational companies that have just focused on that and talk about hiring drug developers later. I don't think you can do that anymore. I think you need to have that expertise there before the round. And so that changes the accounting on how much Runway you have. So the paradigm around how you want to build the company and where to bring in people has changed between the frothy days of 2020 and now, because biotech investors want to see a firmer pipeline and a more direct path to the clinic and a successful clinical outcome. So that's the biggest thing we've seen change.

37:07
Kiersten Stead
We're telling our companies, don't pretend to be things you're not. Some investors are being more direct about that. Biotech investors, a lot of them are flocking more towards security and the pipeline paradigm. And tech investors are wanting to avoid clinical risk. But for our companies that sit in the middle, we're advising them stay the course. If the data is good, people will come. But certainly pay attention to the team build to give people comfort and make sure that your raise is large enough that you're also planning out a couple of years to weather a storm.

37:45
Neil Littman (host)
That's all. Great advice, Kirsten. We could probably talk for another couple of days about many of these topics. I just want to ask you one additional question, and I want to be cognizant of your times before we go. There was a piece in Forbes that argued that women have a unique advantage as negotiators and sought you out for advice on negotiating multi billion dollar deals. Do you think women have a negotiating advantage? And what advice would you.

38:13
Kiersten Stead
Know? I'm not an expert on this faith, but I don't think women have an advantage, that's for sure. I can only talk about, I guess, my philosophy on this. I'm pretty pragmatic about the negotiation process. I think it's a double sided educational process where we want to get entrepreneurs and companies in a place where they're fundable over the long term and get them focused on the end game, which is not raising one round of financing. It's your entire capital structure that needs you to get to develop products for patients. And so I think if you can get people aligned on that, then you're going in the right direction. But I look at the negotiation process as an alignment process, and if you can't get there, then it wasn't right to sort of begin with on both sides.

39:09
Kiersten Stead
I don't have usually pretty successful in getting people there or getting people aligned. When you can reason why you want a certain term or a certain valuation or that sort of thing. That's generally my philosophy. I know there's a difference between east coast and west coast. There's a difference between private equity firms. We certainly saw different types of terms and things come in when we had hedge funds investing in early stage biotech venture, and that was always a discussion around. If you accept this term or you put in this term, this is what will happen to the capital stack over time, and this will end up hurting you in the long run, and sort of explaining them, explaining that type of thing to them and getting people aligned, which is hopefully we're usually successful, and if we're not, we'll learn something along the way.

40:05
Neil Littman (host)
Yeah. Just out of curiosity, I would assume that the hedge funds moving into this space we're coming up with, let's say, more aggressive terms. Is that a fair assessment?

40:16
Kiersten Stead
Yeah, focusing more on the finances of the investment rather than the science. Our philosophy and what we try to communicate to them is, listen, you're going to win or lose on the science and the team here. $10 million valuation or a preference stack here or there is not going to make the difference. This isn't private equity. There's no real downside protection in biotech. Usually the science and the team works or you're pursuing another strategy. And so I think that reality of our business is new to some of the hedge funds or crossover or PE firms that tried to invest further down the development stack that we see and we're starting to see again, but we don't think it's particularly useful in our space because our space is so technically reliant, as you know.

41:14
Neil Littman (host)
Yep, makes total sense. So, Kirsten, just one final question. This episode is going to air probably just ahead of Morgan, the Morgan healthcare conference that's coming up. In January. Any plans for the conference? How do you typically spend your time.

41:26
Kiersten Stead
During Morgan trying to survive? Neil? I think Morgan is looking. It's going to be incredibly robust this year by the amounts of meetings that are being scheduled and amounts of networking. I think people are hungry to get out back and interact. That's what I'm seeing so far. So we're pretty excited about what's coming, but we spend our time meeting with startups, with business development group, at major biotech companies, with venture capitalists that we haven't met that are coming into town. So I actually really enjoy Morgan and I'm looking forward to it this year.

42:08
Neil Littman (host)
Excellent. Well, Kirsten, with that, I think we better wrap up. And I'd like to say a big thank you for joining me on the show today.

42:15
Kiersten Stead
That was great catching up.

42:20
Speaker 1
What did you think?

42:22
Neil Littman (host)
I thought that was a really fantastic conversation with Kirsten. You heard her talk a lot about computational biology, what that means, how artificial intelligence is impacting the development of novel therapeutics. I think the case studies and the investments that Kirsten talked about, I think really highlight the power of some of these technologies. You heard her talk about the Accelera story, and I think that just really illustrates a real world example about what she talked about in terms of the business model of a lot of these companies, which I think a lot of folks in the industry struggle with. But you heard Kirsten say that they are absolutely looking for platform companies, platform technologies.

43:07
Neil Littman (host)
The key is how do you thread the needle between developing and spending capital on building a platform, particularly in this environment where capital is more scarce, while also advancing the portfolio. And so Accelera, and you heard her mention others in the portfolio have been successful in pursuing this multi pronged business model where they're not just a services company, but they do have partnerships, right. Often revenue generating partnerships with larger organizations that is also a source of learning and that all helps them feed into building their own products and their own pipeline. So I think that's really unique. Obviously for startups these days with smaller teams, that can be a challenge. But again, that's sort of where the rubber meets the road and how you thread the needle.

43:52
Neil Littman (host)
So it's really interesting to hear her approach where the platform is really a huge driver of their investment thesis and they are perhaps less interested in the asset centric or asset only companies like a traditional biotech investor would be.

44:07
Danny Levine (co-host)
I did think that was interesting and also the point she made about the importance of the uniqueness of that platform in her approach. What did you think of that.

44:17
Neil Littman (host)
Yeah, I really liked that. You heard her talk quite a bit about people. I asked her about the failure question, and that was the first part of the equation she gravitated towards. But you also heard her mention that it's the team and those dynamics that's really important. So it's not just a bunch of ex big pharma folks starting a company and hiring in a bunch of technologists that doesn't create these types of companies that they're investing in. Right. So it has to be a much more compatible team where the technology folks and the engineering and all that stuff is really firmly embedded within the company. We didn't get into culture, but I imagine that's got to be a big part of it. And so I think it's really interesting.

44:59
Neil Littman (host)
And you heard her talk about Creon bio and a few others, and I don't want to overstate it and say we're in a new world, but we're seeing that from the biobird side as well, as there are these multidisciplinary teams, and part of the founding team are engineers, and the other part are maybe the biologists or the chemists. And so you really need that in the DNA of a company. And I think Kirsten really spoke to that as well.

45:24
Danny Levine (co-host)
I think the phrase she used was native insights, rather than just smacking these teams together. But as you look at other companies that are bringing in AI technology and computational biology, are you seeing companies that are just putting together a mix that's not really compatible?

45:45
Neil Littman (host)
Well, that's a really good. You know, I'm going to refer back to one thing that Kirsten said that I think is really true. I had asked a question about computational biology versus bioinformatics. Right. Bioinformatics is table stakes these days. Right. Everyone has to have it. Everyone has to do it well. So that's not even part of what would get an investor excited. My question is AI going to become the new bioinformatics over the next five to ten years? Every company is going to have it. You heard Kirsten talk about a little bit the value of the data and the data set and training the AI, right? And so those things obviously work hand in hand. So there's a lot of nuances that go into sort of building these platform companies. Part of it is having the right people and the right team.

46:29
Neil Littman (host)
Part of it is having the right data to train the models and train the AI and all of those things. So there's a lot of different components that need to sort of be in the right place at the right time with the right people driving the bus forward. And so there's a lot of companies that are trying to do it. I think there's only very few that are doing it well.

46:50
Danny Levine (co-host)
It's a point you made in the discussion, but given the ubiquity of AI today, does the differentiator become the data as opposed to the technology itself?

47:05
Neil Littman (host)
That's been part of my thesis. Right. It's the data moat. And you heard Kirsten talk about the data mode, but she mentioned, interesting point, right? You need the data to sort of build the models and to train the AI. And so they sort of go hand in hand. So it's not just about the data. If you have the data without the right people to sort of build the technology and the models, then the data is not going to be that insightful. So it's really, I think, a one two punch in the combination of both.

47:31
Danny Levine (co-host)
Well, until next time.

47:33
Neil Littman (host)
Thank you, Danny.

47:38
Speaker 1
Thanks for listening. The Bioverge podcast is a product of Bioverge, Inc. An investment platform that funds visionary entrepreneurs with the aim of transforming healthcare. Bioverge provides access and enables everyone to invest in highly vetted healthcare startups on the cutting edge of innovation, from family offices and registered investment advisors to accredited and non accredited individuals. To learn more, go to bioverge.com. This podcast is produced for Bioverge by the Levine Media Group. Music for this podcast is provided courtesy of the Jonah Levine Collective. All opinions expressed in this podcast by participants are solely their opinions do not reflect the opinion of Bioverge, Inc. Or its affiliates. The participants'opinions are based upon information they consider reliable. Neither Bioverge or its affiliates warrants its completeness or accuracy, and it should not be relied on itself.

48:38
Speaker 1
Nothing contained in accompanying this podcast shall be construed as an offer to sell, a solicitation of an offer to buy, or a recommendation to purchase any security by Bioverge, its portfolio companies, or any third party. Past performance is not indicative of future results.