Launching + scaling metascience organizations, with Dr. Adam Marblestone

Speaker A: Hi, I'm Jay Goldstein. I'm, um, the CEO and founder of Founder2Leader, an executive coaching firm that specializes in equipping early stage bio and climate tech leaders for scale. This podcast, From Founder to Leader, aims to demystify what it actually looks like to build hard tech startups. Too often founders share their stories when there are newsworthy successes, the big raises and the lucrative exits. We want you to hear the real human stories that drive innovation, the crossroads decisions, the messy middle and the practical know how. We hope that these conversations will help you as you take your ideas out of the lab and build scalable solutions to improve human health and our planet. Welcome to another episode of From Founder to Leader, the human stories behind bio and climate tech startups. In this podcast episode, we are demystifying what it looks like to build hard tech startups from the ground up by sharing the real human stories and behind the headlines from People driving innovation. Our guest today is Adam Marblestone, CEO and co founder of Convergent Research. We're going to be talking with Adam about launching and scaling metascience organizations. Adam, thank you so much for joining us.

Speaker B: Thank you for having me. Thank you for being a great coach to me and many froze.

Speaker A: Truly my pleasure. Um, so tell us a little bit about you and your background and any experience you had before launching Convergent Research.

Speaker B: Um, I'm really a scientist by training. I still think the science is probably my zone of genius, uh, uh, more than many other elements. Um, I did a PhD in biophysics. Um, I got very involved in the academic side and then later in some brain computer interface companies on the neurotechnology field and various attempts to make neuroscience related technologies more scalable. Um, I was a research scientist at uh, DeepMind for a couple of years and then I took a fellowship essentially to develop the idea or refine the idea, um, behind what became Convergent research. Yeah.

Speaker A: So looking back, do you think you've always kind of been an entrepreneur?

Speaker B: Yeah, it's interesting question. I think that. Not necessarily, I wouldn't necessarily say that. Um, I remember going to college and at the time I was, my head was 100% in physics books. Basically I was just thinking about science, um, and technology. I cared about the impact. But I was very much in the science world and it wasn't until I got to college, it was the first time that I met anyone really who was going about their life thinking, oh, I'm going to go start startups or I'm going to start businesses. Um, it was just a very different philosophy. I Actually remember finding it quite unusual. Um, and foreign. Um, through my ah, scientific work I ended up having various sort of intersection points with the sort of Silicon Valley world. Um, I was a PhD student in the church lab and he was also spinning out lots of biotech startups and people were starting to think about that. Um, I was involved as a kind of semi academic co founder of a small company called biobright around 2012. Um, um, that grew out of frustration with the difficulty of sort of tracking what was going on in the lab in lab experiments which is still very much like a real problem as we're sort of thinking about AI plus the lab or lab automation, um, just sort of understanding what happened in a lab experiment. So I was involved in that um, and that was then the first time I was actually formally involved in the startup. But I was a little bit more in the academic kind of co founder role. I wasn't like a full time CEO. I was helping them get funding and refine the idea and so on and sort of along for the ride. Um, and then I was um, chief strategy officer in kernel, um, which was this um, brain uh computer interface startup that was backed by Brian Johnson. Um, and that was a different kind of experience because we were starting with a bunch of funding um, because it was essentially initially mostly self funded by Brian. And so then I saw the kind of very rapidly moving sort of deep tech startup um, and that then was very influential in terms of my thinking. Wow, this is way different in terms of how it operates compared to academic labs that I've worked in and has different affordances that I then wanted to apply back to the uh, focus research organizations that Convergent has been developing.

Speaker A: Yeah, so tell us about Convergent research. What is it? Can you explain it to everyone who love.

Speaker B: So we are an incubator and a parent organization for a category of project that we call focused research organizations. Um, I think we're the first fro uh incubator in the space and have been very much involved in sort of promulgating the concept also to other organizations. And these are nonprofit startup inspired uh, science and technology kind of research organizations. Um, they often build scientific infrastructure. So I think about things like the Hubble Space Telescope. Um, this was a piece of scientific infrastructure that was built for the astronomy community that was totally transformative to the field of astronomy. But it wasn't itself an astronomy discovery nor was it a traditional commercial type startup. It was an industrialized approach to building scientific infrastructure. So we've developed um, 10 going on 12 or 13 of those, um, uh, in different fields. Um, yeah. Biology and otherwise. Yeah.

Speaker A: Convergent research considers itself a metascience org. Is that correct?

Speaker B: Yeah, um, I think that we can definitely be placed into that category. Um, there are lots of interesting questions about sort of what is the emerging set of types of metascience orgs. Um, but we could get into that or not. Yeah.

Speaker A: Why is there a need for these types of organizations right now?

Speaker B: Yeah, so one way that we frame the observation, um, if you sort of go back to the first half of the 20th century, um, there was a bunch of science happening, but there wasn't really a sort of standardized format for how the government invests in science at a super large scale until sort of like the post World War II period, um, and with Vannevar Bush and sort of basically the creation of the sort of post war research infrastructure with the National Institutes of Health and the National Science foundation, um, DOE national labs and so on. And that initially started out at this sort of relatively small system, but since then it has proliferated massively. But in the process, um, arguably our scientific research sort of systems, the way that we fund research and the way that we organize research so the types of teams and performers and organizations that get funded, um, has prematurely standardized unless certain certain type of format. So the vast majority of federal funding that goes out, for example, um, is academic, uh, grants. So relatively small grants that support a few years of work on a well defined project by a team which is almost always a university professor with an existing lab, with maybe a couple of postdocs and graduate students working on that project to produce preliminary data for the next round of peer review which occurs by a certain type of process, um, and certain way of scoring and so on. And so actually an enormous amount of resources, research is all fit into this template. Um, that's the sort of standard academic template. There's also the sort of standard startup template where VCs are looking to return, you know, 100x returns or so on for you know, to their LPs for some subset of companies within um, a short number of years. Um, and so between those two formats is kind of a huge amount of what research looks like. And even if you're in an institute or you're in some other type of setting, um, unless you have kind of an enormous amount of core funding and a really, a very, kind of very distinct thesis in some institutes, which is rare, um, you're still working in that mode even if you're not in a traditional university. Um, and so basically uh, the question of there are various ways to think about metascience. There's a sort of analytical part of metascience which is kind of how do things work within that system? What are the dynamics of science and publishing? But in this more sort of applied metascience, um, it's well, what are different institutional forms could look like? How do we experiment on the process of science itself and the institutional forms? Should we actually have a much more diverse um, range of different uh, formats for doing the research? Yeah.

Speaker A: What were the early days like for convergent research and how did you transition it from being just an idea to something that had funding and legs and could scale?

Speaker B: Well, the very earliest phase, um, back when I was just a fellow, um, my colleague Sam Rodriquez and I had proposed the idea of these focus research organizations. We were originally proposing it actually as part of uh. Well originally, originally we're proposing it for specific neuroscience ah, projects that we failed to get some other institutes and so on to do. Um, but recognizing the pattern, um, we were going and talking to uh, myself and Tom Kalil especially were going and talking to lots of scientists across different fields and saying is there a need for this? Let's uh, say deep tech startup inspired research lab that builds scientific infrastructure across fields. Could that be transformative in different fields? So we were going and talking to many people and we sort of synthesized this into more like policy white papers because it was actually about to be a uh, new presidential administration, this was back in 2020 and we were thinking okay, maybe the government could use a ah, new form for doing moonshots, um, that we could sort of promulgate as a suggested policy. Um, and uh, we ended up though in the process partly um, through the depth of these interactions with scientists, we ended up having some shovel ready ideas and we were lucky um, that um, we were able to get some philanthropists to say okay, what if we took a bet on a couple of these? Um and so we took the most sort of shovel ready uh, teams uh, we had at 11 and cultivarium, um, which are now both focused research organizations that are doing quite well. Um, and uh, we were looking to get them going. Um, and we needed to build an institutional home for that that was compatible with uh, funders needs in terms of being able to grant to a nonprofit that was compatible with the scientists needs in terms of having sort of the freedom to operate quickly. Um, that was going to give us enough ability to sort of oversee and support um, and govern these projects and make changes if we needed to. Um, and that hopefully would then be a home for a bunch of others. Um, and so we had to relatively quickly build both the projects and the home or support structure for the projects, um, at the same time. Um, so we started out, you know, we didn't have a tech transfer policy, we didn't have uh, an operating agreement for the fro LLCs. And um, there was um, a period where we were sort of incubating the org, um, and then we spun it out as this own nonprofit um, and have since done several iterations of sort of restructuring how it works.

Speaker A: You make it sound so easy, like we just found some philanthropists and let's go. But I mean was it that easy? And how do you actually find these philanthropists?

Speaker B: Yeah, um, the philanthropy world is very different I think from the VC fundraising world. I think if you, if you have a really good sort of VC backable business idea, yes, there's definitely effects in terms of sort of networks and strategies for going after these, these firms. But when push comes to shove there are potentially dozens of firms that could want to co invest and want to come in and get as much share as they can, um, and not be left out of a VC deal at a certain point. Philanthropy can be kind of the opposite as people almost, even though their job is to spend money, they may be almost avoiding um, over committing or so on. And there's a lot of questions about each individual, uh, high net worth, individual foundation, family, office, et cetera has their own individual interest because it's not about just number go up in terms of money. They have their own idiosyncratic interest. So you have to go and really align um, and try to understand what problem each philanthropic entity is trying to solve. Um, yeah, we were lucky that um, at the time, uh, Eric and Wendy Schmidt, uh, Schmidt Futures had been supporting my innovation fellowship in the first place to even pursue this question about metascience ideas. And that was partly because of our now board chair Tom uh, Kalil, um, who had been uh, chief innovation officer at Schmidt Futures and he had previously worked in the White House on science policy. So there was an interest in this type of question, this metascience question as well as in specific uh, sort of platform technologies, tools, data sets, um, sometimes um, depending on philanthropist background. But people who come from the tech world are sort of highly familiar with this idea of platform technologies can be extremely impactful on fields. Your traditional philanthropist who may be interested in curing a specific disease or something like that may not actually be familiar with that concept. But we were lucky, um, that A few initial anchor philanthropists were able to really grok this idea of platform technologies. It's important how you organize companies as well. Um, have kind of very well defined milestones. And so I think this focused research organization idea was appealing to some initial cadre. But then since then, now we have 35 or 36 different um, philanthropic groups or contributors, um, some of which are interested in very specific technical topics or cause areas, some of which are interested in the metascience question. And some of that is being done directly to convergent, and a lot of that is on building a consortium of funders for each specific project, um, which is a lot of work. But we are lucky that we've been able to get momentum so that um, if we talk about the fro model, people are starting to know what that means, have interest in how that could help solve their problems. Um, and we have a certain amount of momentum, um, and ability to go and have these conversations and narrow down to what a particular donor might want to do.

Speaker A: How much money have you brought in for convergent research since the beginning?

Speaker B: I, um, don't have the exact number off the top of my head. It's depending on how you count money in the bank versus sort of commitments and so on. But it's over 300 million.

Speaker A: You feel like, wow, that's a lot of money that you've been able to contribute to the ecosystem.

Speaker B: Something I sometimes say about this category is that it's a lot of money and it's also m something that's kind of wonderful about it is in principle it's a somewhat finitely solvable problem. So I like to say if you imagine that there are, let's say 20 fields of science, um, or 2000s, 20 sort of discrete areas where there might be some equivalent of a mini Hubble Space Telescope, right. Um, on that order and there's on the order of five things that are as important as a mini Hubble Space Telescope would be in a given area. Um, we've got about 10 going total, but if you sort of multiply that five times that 20, that's about on the order of 100 things. Um, so if each of those things cost 30 to $50 million, um, that's still a lot, but it's kind of sing digit billions of dollars is in some sense enough to crack this area of metascience and experiment and say how, how can this genre of meta science, uh, you know, shift in how we organize things, benefit all areas of science? That's like a several billion dollar problem. So okay, so we're getting to be um, maybe a little less than 10% of the way there. Um, but that's a lot better than being you know one, you know, you know one, one millionth of the way. There's um. Yeah.

Speaker A: When you first started it was you and your co founder Anastasia and now you have a pretty big team. How big is that team?

Speaker B: Yes, um, well Anastasia and I started it. It was just us. She's been incredibly crucial in building the organization um, in so many aspects. Um, we started out it was essentially us and the froze and an HR person. Um now um, we have expanded that so we have um, on the team we have a general counsel, we have um, accounting hr, um programs and kind of launch support communications, um, sort of science roadmaping work going on. So we have um, I think about 25 people in the core now.

Speaker A: Looking back, what are you most proud of of your work with Convergent research to date?

Speaker B: Gosh, I haven't thought much about that one. Um, yeah, gosh, I mean I think that. Let me think about what it is. I mean one of the things I'm really proud of is sort of our ability to take what then was fairly speculative bets both on science and on people um, and see that pan out. Um, and I guess just our um. I mean there's many things, many of them due to Anastasia or other people or stuff the frozen have done in some level. Not necessarily me but I think that m. Initially there was a certain amount of pushback. You're never going to be able to get great scientists to work on this because it doesn't offer tenure, it doesn't offer equity. Uh, in a startup. Um, people aren't going to fund this. Um, you know the milestones are going to be too loose or too tight or too weak or too strong. Um, and all sorts of concerns that people had. And I think that um, we were able to sort of stick through it and in an honest way, not necessarily over promising and dealing with problems when they did sometimes happen. But um, we now have people who we had no idea how this projects were going to go and we've seen the leaders really up level and actually you've been helpful for that in several instances. But we've seen the leaders really up level in terms of running um, these fro companies. Um and we've started to see

Speaker A: the

Speaker B: uh, science happening and to see that the science looks different than what would otherwise happen. Yeah.

Speaker A: What has been some of the greatest challenges for you running convergent research at

Speaker B: the beginning of something Everything is very hectic, and there is not a lot of infrastructure in place to sort of solve the problems that you need. So I remember, um, you know, we got this going, um, and all of a sudden there's just enormous interest from outside scientists. So I was talking to lots and lots of outside scientists, and all of a sudden we had started the clock, so we had to do more fundraising. So doing all sorts of fundraising. Um, and we also have to figure out how to internally manage the projects and what are the internal systems and everything. And of course, Anastasia kind of built an org that does a lot of those functions. Um, but I remember a moment early there where I didn't have an executive assistant, and I felt like I would need an executive assistant in order to take the time to get an executive assistant. I just booked solid all day with this funder, this scientist, this internal problem, just complete chaos. I didn't have time to say, okay, well, the next two weeks we need to spend recruiting executive assistant and interviewing them, and then they can fix my schedule. And so you have these pile up of stuff where there's so much stuff that you can't fix any of the stuff. Um, and so that was. I think probably many startup people have experienced something like this that was. That was certainly chaotic. I think it was also something hard, that this was something that required a lot of trust on a lot of stakeholders. Um, you know, again, the founders were showing up and we were saying, you know, we're a nonprofit, but, yeah, we haven't. We haven't like, finalized our tech transfer policy. So I can't actually tell you 100% what's going to happen with IP, you know, when you got started. So that takes a lot of trust on the part of a scientist, takes a lot of trust on the part of a funder. Um, and things do go wrong. Um, uh, founding teams split up internally. Um, roles change. Um, uh, IP hasn't been a blocker. Um, but, um, you have to maintain all this trust. Um, and, uh, yeah, doing that, sometimes you don't have as much as you would like to offer. Like, I would like to offer you a great answer to this, this tech transfer question or something like that, or some other question about how this is going to work in the future, or I would like to have an answer that we're definitely going to be able to fund the last portion of your project or whatever that we're fundraising for. Um, get this for launched, if you're committing to roadmapping it with us. And it's been a Lot of chicken and egg problems that have to be kind of co developed with each other. And so we've had to build a lot of trust and I think it has mostly been paying off. But um, uh, sometimes we haven't. Yeah, sometimes we haven't had a full answer to uh, something and we've had to eat, eat risks or ask people to eat risks, various points that they were, you know, more or less comfortable doing.

Speaker A: So how have you evolved as a leader over the last four years as you've scaled from a two person team to a 25 person team with a lot, a lot of money and a lot of people working on important problems?

Speaker B: I mostly just gained an appreciation of leadership. I'm starting to evolve, but I've gained an appreciation of uh, how early I am on some elements of that curve. Um, yeah, I mean I think what the mitigating factor for the, you know, you need an executive assistant in order to have an executive assistant is that does start to compound. So the team starts to create structure around you, um, and the team starts to do a lot more. So you know, now I show up to an all hands meeting and you know, Anastasia's chief of staff has helped create a template for that which we've, you know, scheduled time to, you know, fill that out, you know, beforehand. So I mean you and other people actually Jamie, have sort of helped me to see the importance of sort of templates, structures, processes that can sort of mitigate this kind of onslaught of just all these different irons in the fire in such a way that you then have time to do the actual mental processing. So at this point I feel much more able to sort of say, okay, what are the major strategic questions? What are the major issues we face on funding or people or science? And sort of like I have time to do each of those things. But that is basically we had to cross through a phase of kind of bootstrapping all of that. Um, but we have been able to bootstrap it to a point where um, it's not that I have to constantly be smarter and faster and whatever. It's more that there's a, you cross a threshold where there is more of a support layer in your own organization for the leadership to function well.

Speaker A: So yeah, my last question is always asking you to give a piece of advice to our listeners. If there was one thing that you wish you'd known earlier in your entrepreneurial journey, what might you share with our listeners?

Speaker B: Um, I mean, I think that I. So on the one hand, um, you know, don't over kind of fit to rubrics or formats or expectations. I mean, the reality is that a focus research organization is a totally different thing in many ways than a startup. We've had the problem that by saying, oh, this is what startups do, or this is what DARPA does, sometimes we use those terminology and then someone will be like, oh, it must be exactly like startups, or exactly like darpa. And in fact it's a new and subtle and different thing. Um, so the meta level is just like, in some sense, like over communicates like things that might be obvious to you, like that. It is, it is like has aspects that are inspired by startups or by darpa. Um, but it's actually something different. So that might be obvious to you but not obvious to your audience. So one lesson is just like over communicate even things that you think are obvious to you, um, and don't over templatize because everything is very bespoke. Um, but I've also come to just enormously see the value of reusing what other people have developed. So, um, you have your playbooks and we have a board meeting templates, we have fro board meeting templates. And this to me encapsulates, um, some of my learnings, um, is that by giving them the right fro board meeting template, they then give us the information that we need back from them and they really understand, basically fill out this worksheet, um, this sort of mental model of over communicate and understand that people may not be on the same wavelength as you, but a way to get them on the same wavelength is to make them fill out a more structured template or workflow. I think teachers know a lot of this, you know, with worksheets in classrooms and things like that. But, um, the extent to which you can, you can sort of reuse existing templates and there are things that are shared with startups and so on. So basically, yeah, I guess maybe it's like figure out, figure out what are the things that you can reuse and then figure out what are the things where you people may be assuming a certain template, but that template is actually wrong. And if you can delineate those two things, um, there's a lot of just kind of reuse of what people already know, uh, that you can use.

Speaker A: Adam, thank you so much for joining today. It's been really awesome to hear about your experience and I, I know there are lots of folks out there right now who are feeling like current funding models are leaving an enormous gaping hole in moving science forward. And so thank you for trailblazing with new models for all of us out there.

Speaker B: Thanks for having me.

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