Sep 01 2022 73 mins 16
Seemay Chou talks about the process of building a new research organization, ticks, hiring and managing entrepreneurial scientists, non-model organisms, institutional experiments and a lot more!
Seemay is the co-founder and CEO of Arcadia Science — a research and development company focusing on underesearched areas in biology and specifically new organisms that haven't been traditionally studied in the lab. She’s also the co-founder of Trove Biolabs — a startup focused on harnessing molecules in tick saliva for skin therapies and was previously an assistant professor at UCSF.
She has thought deeply not just about scientific problems themselves, but the meta questions of how we can build better processes and institutions for discovery and invention. I hope you enjoy my conversation with Seemay Chou
Links
Seemay on Twitter (@seemaychou)
Seemay's essay about building Arcadia
Transcript
[00:02:02] Ben: So since a lot of our conversation is going to be about it how do you describe Arcadia to a smart well-read person who has never actually heard of it before?
[00:02:12] Seemay: Okay. I, I actually don't have a singular answer to this smart and educated in what realm.
[00:02:19] Ben: oh, good question. Let's assume they have taken some undergraduate science classes, but perhaps are not deeply enmeshed in, in academia. So, so like,
[00:02:31] Seemay: enmeshed in the meta science community.[00:02:35]
[00:02:35] Ben: No, no, no, no, but they've, they, they, they, they they're aware that it's a thing, but
[00:02:40] Seemay: Yeah. Okay. So for that person, I would say we're a research and development company that is interested in thinking about how we explore under researched areas in biology, new organisms that haven't been traditionally studied in the lab.
And we're thinking from first principal polls about all the different ways we can structure the organization around this to also yield outcomes around innovation and commercialization.
[00:03:07] Ben: Nice. And how would you describe it to someone who is enmeshed in the, the meta science community?
[00:03:13] Seemay: In the meta science community, I would, I would say Arcadias are meta science experiment on how we enable more science in the realm of discovery, exploration and innovation. And it's, you know, that that's where I would start. And then there's so much more that we could click into on that.
Right.
[00:03:31] Ben: And we will, we will absolutely do that. But before we get there I'm actually really [00:03:35] interested in, in Arcadia's backstory. Cuz cuz when we met, I feel like you were already , well down the, the path of spinning it up. So what's, there's, there's always a good story there. What made you wanna go do this crazy thing?
[00:03:47] Seemay: So, so the backstory of Arcadia is actually trove. Soro was my first startup that I spun out together with my co-founder of Kira post. started from a point of frustration around a set of scientific questions that I found challenging to answer in my own lab in academia. So we were very interested in my lab in thinking about all the different molecules and tick saliva that manipulate the skin barrier when a tick is feeding, but basically the, the ideal form of a team around this was, you know, like a very collaborative, highly skilled team that was, you know, strike team for like biochemical, fractionation, math spec, developing itch assays to get this done.
It was [00:04:35] not a PhD style project of like one person sort of open-endedly exploring a question. So I was struggling to figure out how to get funding for this, but that wasn't even the right question because even with the right money, like it's still very challenging to set up the right team for this in academia.
And so it was during this frustration that I started exploring with Kira about like, what is even the right way to solve this problem, because it's not gonna be through writing more grants. There's a much bigger problem here. Right? And so we started actually talking to people outside of academia. Like here's what we're trying to achieve.
And actually the outcome we're really excited about is whether it could yield information that could be acted on for an actually commercializable product, right. There's like skin diseases galore that this could potentially be helpful for. So I think that transition was really important because it went from sort of like a passive idea to, oh, wait, how do we act as agents to figure out how to set this up correctly?
[00:05:35] We started talking to angel investors, VCs people in industry. And that's how we learned that, you know, like itch is a huge area. That's an unmet need. And we had tools at our disposal to potentially explore that. So that's how tr started. And that I think was. The beginning of the end or the, the start of the beginning.
However you wanna think about it. Because what it did, was it the process of starting trove? It was so fun and it was not at all in conflict with the way I was thinking about my science, the science that was happening on the team was extremely rigorous. And I experienced like a different structure.
And that was like the light bulb in my head that not all science should be structured the same way. It really depends on what you're trying to achieve. And then I went down this rabbit hole of trying to study the history of what you might call meta science. Like what are the different structures and iterations of this that have happened over, over the history of even the United States.
And it's, hasn't always been the same. Right? And then I think [00:06:35] like, as a scientist, like once you grapple with that, that the way things are now is not how they always have been. Suddenly you have an experiment in front of you. And so that is how Arcadia became born, because I realize. Couched within this trove experiment is so many things that I've been frustrated about that I, I, I don't feel like I've been maximized as the type of scientist that I am.
And I really want to think in my career now about not how I fit into the current infrastructure, but like what other infrastructures are available to us. Right?
[00:07:08] Ben: Nice.
[00:07:09] Seemay: Yeah. So that, that was the beginning.
[00:07:11] Ben: and, and so you, you then, I, I, I'm just gonna extrapolate one more, more step. And so you sort of like looked at the, the real, the type of work that you really wanted to do and determined that, that the, the structure of Arcadia that you've built is, is like perhaps the right way to go about enabling that.
[00:07:30] Seemay: Okay. So a couple things I, I don't even know yet if Arcadia is the right way to do it. So I [00:07:35] feel like it's important for me to start this conversation there that I actually don't know. But also, yeah, it's a hypothesis and I would also say that, like, that is a beautiful summary, but it's still, it was still a little clunkier than the way you described it and the way I described it.
So there's this gap there then of like, okay, what is the optimal place for me to do my science? How do we experiment with this? And I was still acting in a pretty passive way. You know, I was around people in the bay area thinking about like new orgs. And I had heard about this from like ju and Patrick Collison and others, like people very interested in funding and experimenting with new structures.
So I thought, oh, if I could find someone else to create an organization. That I could maybe like help advise them on and be a part of, and, and so I started writing up this proposal that I was trying to actually pitch to other people like, oh, would you be interested in leading something like this? [00:08:35] Like, and the more that went on and I, I had like lots and lots and lots of conversations with other scientists in academia, trying to find who would lead this, that it took probably about six months for me to realize like, oh, in the process of doing this, I'm actually leading this.
I think and like trying to find someone to hand the keys over to when actually, like, I seem to be the most invested so far. And so I wrote up this whole proposal trying to find someone to lead it and. It came down to that like, oh, I've already done this legwork. Like maybe I should consider myself leading it.
And I've, I've definitely asked myself a bunch of times, like, was that like some weird internalized sexism on my part? Cause I was like looking for like someone, some other dude or something to like actually be in charge here. So that's actually how it started. And, and I think a couple people started suggesting to this to me, like if you feel so strongly about this, why aren't you doing this?
And I know [00:09:35] it's always an important question for a founder to ask themselves.
[00:09:38] Ben: Yeah, yeah, no, that's, that's really clutch. I appreciate you sort of going into the, the, the, the, the, the, like, not straight paths of it. Because, because I guess when we, we put these things into stories, we always like to, to make it like nice and, and linear and like, okay, then this happened and this happened, and here we are.
But in reality, it was it's, it's always that ambiguity. Can, can I actually ask two, two questions based on, on that story? One is you, you mentioned that. In academia, even if you had the money, you wouldn't be able to put together that strike team that you thought was necessary. Like why can, can you, can you unpack that a little bit?
[00:10:22] Seemay: Yeah. I mean, I think there's a lot of reasons why one of the important reasons, which is absolutely not a criticism of academia, in fact, it's maybe like my support of the [00:10:35] mission in academia is around training and education. That like part of our job as PIs and the research projects we set up is to provide an opportunity for a scientist to learn how to ask questions.
How to answer those, how to go through the whole scientific process. And that requires a level of sort of like openness and willingness to allow the person to take the reigns on that. That I think is very difficult if you're trying to hit like very concrete, aggressive milestones with a team of people, right.
Another challenge of that is, you know, the way we set up incentive structures around, you know, publishing, like we also don't set up the way we, you know, publish articles in journals to be like very collaborative or as collaborative as you would want in this scenario. Right. At the end of the day, there's a first author, there's the last author.
And that is just a reality. We all struggle with despite everyone's best intentions. And so that inherently now sets up yeah. [00:11:35] Another situation where you're trying to figure out how you, we, this collaborative effort with this reality and. Even in the best case scenario, it doesn't always feel great. Right?
Like it just like makes it harder to do the thing. And then finally, like it just, you know, for the way we fund projects in, in academia, you know, this wasn't a very hypothesis driven project. Like it's very hard to lay out specific aims for it. Beyond just the things we're gonna be trying to like, what, what, what is our process that we can lay
[00:12:08] Ben: Yeah, it's
a
[00:12:09] Seemay: I can't tell you yeah. What the outcomes are gonna be. So I did write grants on that and that was repeatedly the feedback. And then finally, there's, you know, this other thing, which is that, like, we didn't want to accidentally land on an opportunity for invi innovation. We explicitly wanted to find molecules that could be, you know, engineered for products.
Like that was [00:12:35] our hypothesis. If there is any that like. By borrowing the innovation from ticks who have evolved to feed for days to sometimes over a week that we are skipping steps to figure out the right natural product for manipulating processes in the skin that have been so challenging to, you know, solve.
So we didn't want it to be an accident. We wanted to be explicitly translational quote unquote. So that again, poses another challenge within an academic lab where you, you have a different responsibility, right?
[00:13:05] Ben: Yeah. And, and you it's there there's like that tension there between setting out to do that and then setting out to do something that is publishable, right?
[00:13:14] Seemay: Mm-hmm mm-hmm . Yeah. Yeah. And I think one of the, the hard things that I'm always trying to think about is like, what are things that have out of the things that I just listed? What are things that are appropriately different about academia and what are the things that maybe are worth a second?
[00:13:31] Ben: mm.
[00:13:32] Seemay: they might actually be holding us back even [00:13:35] within academia.
So the first thing I would say is non-negotiable that there's a training responsibility. So that is has to be true, but that's not necessarily mutually exclusive with also having the opportunity for this other kind of team. For example, we don't really have great ways in academia to properly, you know, support staff scientists at a, at a high level.
Like there's a very limited opportunity for that. And I, you know, I'm not arguing with people about like the millions of reasons why that might be. That's just a fact, you know, so that's not my problem to solve. I just, I just see that as like a challenge also like of course publishing, right? Like I think
[00:14:13] Ben: yeah,
[00:14:14] Seemay: in a best case scenario publishing should be science should be in the driver's seat and publishing should be supporting those activities.
I think we do see, you know, and I know there's a spectrum of opinions on this, but there are definitely more and more cases now where publishing seems to be in the [00:14:35] driver's seat,
[00:14:36] Ben: yeah,
[00:14:36] Seemay: dictating how the science goes on many levels. And, you know, I can only speak for myself that I, I felt that to be increasingly true as I advanced my career.
[00:14:47] Ben: yeah. And just, just to, to make it, make it really explicit that it's like the, the publishing is driving because that's how you like, make your tenure case. That's how you make any sort of credibility. Everybody's gonna be judging you based on what you're publishing as opposed to any other.
[00:15:08] Seemay: right. And more, I think the reason it felt increasingly heavy as I advanced my career was not even for those reasons, to be honest, it was because of my trainees,
[00:15:19] Ben: Hmm.
[00:15:20] Seemay: if I wanna be out. Doing my crazy thing. I have a huge responsibility now to my students, and that is something I'm not willing to like take a risk on.
And so now my hands are tied in this like other way, and their [00:15:35] careers are important to me. And if they wanna go into academia, I have to safeguard that.
[00:15:40] Ben: Yeah. I mean, it suggests. Sort of a, a distinction between sort of, regardless of academia or not academia between like training labs and maybe focused labs. And, and you could say like, yes, you, you want trainees in focus. Like you want trainees to be exposed to focused research. But like at least sort of like thinking about those differences seems really important.
[00:16:11] Seemay: Yes. Yeah. And in fact, like, you know, because I don't like to, I don't like to spend too much time, like. Criticizing people in academia, like we even grapple with this internally at Arcadia,
[00:16:25] Ben: Yeah.
[00:16:25] Seemay: like there is a fundamentally different phase of a project that we're talking about sort of like new, creating new ideas, [00:16:35] exploring de-risking and then some transition that happens where it is a sort of strike team effort of like, how do you expand on this?
How do you make sure it's executed well? And there's probably many more buckets than the, just the two I said, but it it's worthy of like a little more thought around the way we set up like approvals and budgets and management, because they're too fundamentally different things, you know?
[00:17:01] Ben: Yeah, that's actually something I, I wanted to ask about more explicitly. And this is a great segue is, is sort of like where, where do ideas come from at Arcadia? Like how, you know, it's like, there's, there's some spectrum where everybody's from, like everybody's working on, you know, their own thing to like you dictating everything.
Everything in between. So like, yeah. Can you, can you go more into like, sort of how that, that flow works almost?
[00:17:29] Seemay: So I might even reframe the question a little bit to [00:17:35] not where do ideas come from, but how do ideas evolve? Because it's
[00:17:39] Ben: please. Yeah. That's a much better reframing.
[00:17:41] Seemay: because it's rarely the case, regardless of who the idea is coming from at Arcadia, that it ends where it starts. and I think that that like fluidity is I the magic sauce.
Right. And so by and large, the ideas tend to come from the scientists themselves. Occasionally of course, like I will have a thought or Che will have a thought, but I see our roles as much more being there to like shepherd ideas in the most strategic and productive direction. And so we like, you know, I spent a lot of time thinking about like, well, what kind of resources would this take?
And, you know, Che definitely thinks about that piece as well as, you know, like what it, what would actually be the impact of this if it worked in terms of like both our innovation, as well as the knowledge base outside of Arcadia Practically speaking, something we've started doing, that's been really helpful because we've gone.
We've already gone through different iterations of this too. Like we [00:18:35] started out of like, oh, let's put out a Google survey. People can fill out where they pitch a project to us. And that like fell really flat because there's no conversation to be had there. And now they're basically writing a proposal.
Yeah. More streamlined, but it's not that qualitatively different of a process. So then we started doing these things called sandboxes, which I'm actually really enjoying right now. These are every Friday we have like an hour long session. The entire company goes and someone's up at the dry erase board.
We call it, throwing them in the sandbox and they present some idea or set of ideas or even something they're really struggling. For everybody to like, basically converse with them about it. And this has actually been a much more productive way for us to source ideas. And also for me to think collaboratively with them about like the right level of like resources, the right sort of inflection points for like, when we decide go or no, go on things.
And so that's how we're currently doing it. I mean, we're [00:19:35] like just shy of about 30 people. I, this process will probably break again. once we hit like 50 people or something, cuz it's actually just like logistically a lot of people to cram into a room and there is a level of sort of like, yeah, and then there's a level of formality that starts to happen when there's like that many people in the room.
So we'll see how it goes, but that's how it's currently working today.
[00:20:00] Ben: that's that's really cool. And, and, and so then, then like, let's, let's keep following the, the evolutionary path, right. So an idea gets sandboxed and you collectively come to some conclusion that it's like, okay, like this idea is, is like, well worth pursuing then what happens.
[00:20:16] Seemay: So then and actually we're like very much still under construction right now around this. We're trying to figure out like, how do, how do we think about budget and stuff for this type of step? But then presumably, okay, the person starts working on it. I can tell you where we're trying to go.
I, I'm not sure where there yet, where we're trying to go is turning our [00:20:35] publications into a way to like actually integrate into this process. Like, ideally I would love it as CEO, if I can be updated on what people in the order are doing through our pub site.
[00:20:49] Ben: Oh
[00:20:50] Seemay: And that, like, I'm not saying they publish every single thing they do every day.
Of course, that's crazy, crazy talk, but like that it's somewhat in line with what's happening in real time. That that is an appropriate place for me to catch up on what they're doing and think about like high level decisions and get feedback and see the feedback from the community as well, because that matters, right?
Like if, if our goal is to either generate products in the form of actual products in the world that we commercialize versus knowledge products that are useful to others and can stimulate either more thought or be used by others directly. Like I need to actually see that data in the form of like the outside world interacting with their releases.
Right. [00:21:35] So that's what we're trying to move towards, but there's a lot of challenges associated with that. Like if a, if a scientist is like needing to publish very frequently, How do we make sure we have the right resources in place to help them with that? There may be some aspects of that, that like anyone can help with like formatting or website issues or, you know, even like schematic illustrations to try and just like reduce the amount of friction around this process as much as possible.
[00:22:00] Ben: And I guess almost just like my, my concern with the like publishing everything openly very early. And this is, this is almost where, where I disagree with with some people is that there's what, what I believe Sahi Baca called like the, the like Wardy baby problem, where ideas, when you're first sort of like poking at them are just like really ugly and you like, can't even, you can't even, like, you can barely justify it to [00:22:35] anybody on your team who like, trust you let alone people who like don't have any insight into the process.
And so. Do do you, do you worry at all about like, almost just being like completely demoralized, right? Like it's just, it's so much easier to point out why something won't work early on than why it will.
[00:22:56] Seemay: Yeah, totally. Yeah.
[00:22:59] Ben: how do you
[00:22:59] Seemay: Well, I mean, yeah, no, I think that's a hard, hard challenge. I mean, and, and people, and I would say at a metal level, I get, I get a lot of that too. Like people pointing out all the ways Arcadia
[00:23:09] Ben: Yeah, I'm
[00:23:10] Seemay: or potentially going to fail. So a couple things, I mean, I think one is that just, of course I'm not asking our scientists to.
They have a random thought in the shower, like put that out into the world. right. Like there's of course some balance, like, you know, go through some amount of like thinking and like, you know, feedback with, with their most local peers on it. More, more in terms more than anything, like [00:23:35] just to like make sure by the time it goes out into the world that you're capturing precious bandwidth strategically.
Right.
[00:23:41] Ben: Yeah,
[00:23:41] Seemay: On the other hand though, like, you know, while we don't want like that totally raw thing, we are so far on the, under the spectrum right now in terms of like forgiveness of some wards. And, and it also ignores the fact that like, it's the process, right? Like ugly baby. Great. That's that's like, like the uglier the better, like put it out there because like you want that feedback.
You're not trying to be. trying to get to some ground truth here. And rigor happens through lots of like feedback throughout the entire process, especially at the beginning. And it's not even like that, that rigor doesn't happen in our current system. It's just that it doesn't make it out into the public space.
People do share their thoughts with others. They do it at the dry erase board. They share proposals with each other. There's a lot of this happening. It's just not visible. So I mean, the other thing just like culturally, what I've been trying to like emphasize at [00:24:35] Arcadia is like process, not outcomes that like, you know, talking about it directly, as well as we have like an exercise in the beginning of thinking about like, what is the correct level of like failure rate quote unquote, and like what's productive failure.
And just like, if we are actually doing like high risk, interesting science that's worth doing fundamentally, there's gotta be some inherent level of failure built in that we expect. Otherwise, we are answering questions. We already know the answer to, and then what's the fucking point. Right?
[00:25:05] Ben: Yeah,
[00:25:06] Seemay: So it almost doesn't matter what the answer to that question is.
Like people said like 20%, some people said 80%, there's a very wide range in people's heads. Cuz there's this, isn't not a precise question. Right. So there's not gonna be precise answers, but the point is like the acceptance of that fact. Right?
[00:25:24] Ben: Yeah. And also, I, I think I'm not sure if you would agree with this, but like, I, I feel like even like failure is a very fuzzy concept. In this, in this context, [00:25:35] right?
[00:25:35] Seemay: totally. I actually really hate that word. We, we are trying to rebrand it internally to pivots.
[00:25:42] Ben: Yeah. Yeah. I like that. I also, I also hate in this context, the idea of like risk, right? Like risk makes sense when it's like, you're getting like cash on cash returns, but
[00:25:54] Seemay: right.
[00:25:54] Ben: when
[00:25:55] Seemay: Yeah. Yeah. I mean, you can redefine that word in this case to say like, it's extremely risky for you to go down this safe path because you will be very likely, you know, uncovering boring things. That's a risk, right?
[00:26:13] Ben: Yeah. And then just in terms of process, I wanna go one, one step further into the, sort of like the, the like strike teams around an idea. Is it like something like where, where people just volunteer do do they get, like how, how, how do you actually like form those teams?
[00:26:30] Seemay: Yeah. So far there has not been like sort of top down forcing of people into things. I [00:26:35] mean, we are a small org at this point, but like, I think like personally, my philosophy is that like, people do their best work when they're, they feel agency and like sort of their own deep, inner inspiration to do it.
And so I try to make things more ground up because of that. Not, not just because of like some fuzzy feeling, but actually I think you'll get the best work from people, if you'd set it up that way. Having said that, you know, there are starting to be situations where we see an opportunity for a strike team project where we can, like, we need to hire someone to come.
[00:27:11] Ben: Mm-hmm
[00:27:12] Seemay: because no one existing has that skill set. So that that's a level of like flexibility that like not everybody has in other organizations, right. That you have an idea now you can hire more people onto it. So I mean, that's like obviously a huge privilege. We have to be able to do that where now we can just like transparently be like, here's the thing who wants to do it?
You know?
[00:27:32] Ben: yeah, yeah. [00:27:35] That's, that's very cool.
[00:27:36] Seemay: One more thing else. Can I just say one more thing about that
[00:27:39] Ben: of course you can see as many things as you
[00:27:40] Seemay: yeah. Actually the fact that that's possible, I feel like really liberates people at Arcadia to think more creatively because something very different happens when I ask people in the room. What other directions do you think you could go in versus what other directions do you think this project should go, could go in that we could hire someone from the outside to come do. Because now they like, oh, it doesn't have to be me. Or maybe they're maybe it's because they don't have the skillset or maybe they're attached to something else that they're working on. So making sure that in their mind, it's not framed as like an either or, but in if, and, and that they can stay in their lane with what they most wanna do.
If we decide to move forward on that, you know? Cause I, I think that's often something that like in academia, we don't get to think about things that way.
[00:28:30] Ben: Yeah, absolutely. And then the, the people that you would hire onto a [00:28:35] project, would they, like, so say, say, say the, the project then ends it, it reaches some endpoint. Do they like then sort of go back into the, the pool of people who are, are sandboxing? How do, how does that
[00:28:49] Seemay: So we, So we haven't had that challenge on a large scale yet. I would say from a human perspective, I would really like to avoid a situation where like standard biotech companies, you know, if an area gets closed out, there's a bunch of layoffs. Like it would be nice to figure out how we can like, sort of reshuffle everybody.
One of the ways this has happened, but it's not a problem yet is like we have these positions called arcade scientists, which is kind of meant for this to allow people to kind of like move around. So there's actually a couple of scientists that Arcadia that are quote unquote arcade it's meant to be like a playful term for someone who's a, a generalist in some area like biochemistry, [00:29:35] generalist computational generalist, something like that, where their job is literally to just work on like the first few months of any project.
[00:29:44] Ben: oh,
[00:29:45] Seemay: And help kind of like, de-risk like, they're really tolerant of that process. They like it. They like trying to get something brand new off the ground. And then once it becomes like more mature with like clear milestones, then we can hire someone else and then they move on to like the next thing, I think this is a skill in itself that doesn't really get highlighted in other places.
And I think it's a skillset that actually resonates with me very much personally, because if I were applying to Arcadia, that is the position that I would want.
[00:30:14] Ben: I, I think I'm in the same boat. Yeah, that, and that's, that's critical is like, there aren't a lot of organizations where you sort of like get to like come in for a stage of a project. In research, like there, it it's generally like you're, you're on this project.
[00:30:29] Seemay: And how often do you hear people complain about that in science of like, oh, so and so they're, they're [00:30:35] really great at starting things, but not finishing things. It's like, well, like how do we capitalize on that then?
[00:30:39] Ben: yeah. Make it a feature and not a bug. Yeah, no, it's like, it it's sort of like having, I I'm imagining like sort of just different positions on a, a sports team, for example. And, and I feel like I, I was thinking the other day that that analogies between like research organizations and sports teams are, are sort of underrated right.
Like you don't expect like the goal to be going and like, like scoring. Right. And you don't, you don't say like, oh, you're underperforming goalie. You didn't score any goals.
[00:31:08] Seemay: Right. That's so funny. I like literally just had a call with Sam Aman before this, where, where we were talking about this a little bit, we were talking about in a slightly different context about a role that I feel like is important in our organization of someone to help connect the dots across the different projects.
What we were sort of like conceptualizing in my call with him as like the cross pollinators, like the bees in the organization that like, know what get in the [00:31:35] mix, know what everyone's doing and help everybody connect the dots. And like, I feel like this is some sort of a supportive role. That's better understood on sports teams.
Like there's always someone that's like the glue, right? Maybe they're not the MVP, but they're the, the other guy that's like, or, you know, girl, whatever, UN gendered, but very important. Everybody understands that. And like, it's like celebrated, you know,
[00:31:58] Ben: Yeah. Yeah. And it's like, and, and the trick is, is really seeing it more like a team. Right. So that's like the, the overarching thing.
[00:32:07] Seemay: And then I'll just like, I don't know, just to highlight again though, how like these realities that you and I are talking about that I think is actually very well accepted across scientists. We all understand these different roles. Those don't come out in the very hierarchical authorship, byline of publications, which is the main currency of the system.
And so, yeah, that's been fascinating to like, sort of like relearn because when we started this publishing experiment, [00:32:35] I was primarily thinking about the main benefit being our ability to do different formats and in a very open way. But now I see that this there's this whole other thing that's probably had the most immediate impact on Arcadia science, which is the removal of the authorship byline.
[00:32:52] Ben: Mm. So, so you don't, you don't say who wrote the thing at all.
[00:32:57] Seemay: We do it's at the bottom of the article, first of all. And then it's listed in a more descriptive way of who did what, it's not this like line that's like hierarchical, whether implicitly or explicitly and for my conversations with the scientists at Arcadia, like that has been really like a, a wonderful release for them in terms of like, thinking about how do they contribute to projects and interact with each other, because it's like, it doesn't matter anymore that that currency is like off the table.
[00:33:27] Ben: Yeah. That that's very cool. And can, can I, can I change tracks a little bit and ask you about model organisms?
[00:33:34] Seemay: sure
[00:33:34] Ben: [00:33:35] so like, and this is, this is coming really from my, my naivete, but like, like what, what are model organisms? And like, why is having more of them important?
[00:33:47] Seemay: So there's, this is super, super important for me to clarify there's model organisms and there's non-model organisms, but there's actually two different ways of thinking about non-model organisms. Okay. So let me start with model organisms. A model organism is some organism that provides an extremely useful proxy for studying typically like either human biology or some conserved element of biology.
So, you know, the fact that like we have. Very similar genetic makeup to mice or flies. Like there's some shortcuts you can take in these systems that allow you to like quickly ask experimental questions that would not be easy to do in a human being. Right. Like we obviously can't do those kinds of experiments there.[00:34:35]
And so, and so, so the same is true for like ASIS, which can be a model for plants or for like biology more generally. And so these are really, really useful tools, especially if you think about historically how challenging it's been to set up new organisms, like, think about in the fifties before we could like sequence genomes as quickly or something, you know, like you really have to band together to like build some tools in a few systems that give you useful shortcuts in general, as proxies for biology
now.
[00:35:11] Ben: can I, can I, can I just double click right there? What does it mean to like set it up? Like, like what, what does it mean? Like to like, yeah.
[00:35:18] Seemay: Yeah. I mean, there's basic anything from like Turing, right? Like you have to learn how to like cultivate the organism, grow it, proliferate it. Yeah. You gotta learn how to do like basic processing of it. Like whether it's like dissections or [00:35:35] isolating cell types or something, usually some form of genetics is very useful.
So you can perturb the system in some controlled way and then ask precise questions. So those are kinda like the range of things that are typically challenging to set up and different organisms. Like, I, you can think of them as like video game characters, they have like different strengths, right?
Like different bars. Some are
[00:35:56] Ben: Yeah.
[00:35:59] Seemay: fantastic for some other reason. You know, whether it's cultivation or maybe something related to their biology. And so that's that's model organisms and. I am very much pro model organisms. Like our interest in non-model organisms is in no way in conflict with my desire to see model organisms flourish, right.
That fulfills an important purpose. And we need more, I would say, non-model organisms. Now. This is where it gets a little murky with the semantics. There's two ways you could think about it. At least one is that these are organisms that haven't quite risen to the level of this, the [00:36:35] canonical model organisms in terms of like tooling and sort of community effort around it.
And so they're on their way to becoming model, but they're just kinda like hipster, you know, model or model organisms. Maybe you could think about it like that. There's a totally different way to think about it, which is actually how Arcadia's thinking about it, is to not use them as proxy for shared biology at all.
But focus on the biology that is unique about that organism that signals some unique biological innovation that happened for that organism or plate of organisms or something. So for example, ticks releasing a bunch of like crap in their saliva, into your skin. That's not a proxy for us, like feeding on other, you know, vertebrates that is an innovation that happened because ticks have this like enormous job they've had to evolve to learn, to do well, which is to manipulate everything about your [00:37:35] circulation, your skin barrier, to make sure it's one blood meal at each of its life stages happen successfully and can happen for days to over a week.
It's extremely prolonged. It can't be detected. So that is a very cool facet about tech biology that we could now leverage to learn something different. That could be useful for human biology, but that's, it's not a proxy, right?
[00:37:58] Ben: Yeah. And so, so I was gonna ask you why ticks are cool, but I think that that's sort of self explanatory.
[00:38:05] Seemay: Oh, they're wild. Like they, like, they have this like one job to do, which is to drink your blood and not get found out.
[00:38:15] Ben: and, and I guess like, is there, so, so like with ticks, I I'm trying to, to frame this, like, is there something useful in like comparing like ticks and mosquitoes? Do they like work by the same mechanisms? Are they like completely different
[00:38:30] Seemay: yeah. There's no, there's definitely something interesting here to explore because blood [00:38:35] feeding as a behavior in some ways is a very risky behavior. Right. Any sort of parasitism like that. And actually blood
[00:38:42] Ben: That's trying to drink my blood.
[00:38:44] Seemay: Yes. That's the appropriate response. Blood feeding actually emerged multiple times over the course of evolution in different lineages and mosquitoes, leeches ticks are in very different clouds of organisms and they have like different strategies for solving the same problem that they've evolved independently.
So there's some convergence there, but there's a lot of divergence there as well. So for example, mosquitoes, and if you think about mosquitoes, leaches, and tick, this is a great spectrum because what's critically different about them is the duration of the blood
[00:39:18] Ben: Mm,
[00:39:19] Seemay: feed for a few seconds. If they're lucky, maybe in the range of minutes, leaches are like minutes to hours.
Ticks are dazed to over a week. Okay. So like temporally, like they have to deal with very different. For, for mosquitoes, they tend to focus on [00:39:35] like immediately numbing of the local area to getting it out. Right. Undetected, Lees. They they're there for a little bit longer, so they have very cool molecules around blood flow like that there's a dilation, like speeding up the amount of blood that they can intake during that period.
And then ticks have to deal with not just the like immediate response, but also longer term response, inflammation, wound healing, all these other sensations that happen. If, imagine if you stuck a needle in yourself for a week, like a lot more is going on, right?
[00:40:08] Ben: Yeah. Okay. That, that makes a lot of sense. And so, so they really are sort of unique in that temporal sense, which is actually important.
[00:40:17] Seemay: Yeah. And whether it's positive or not, it does seem to track that duration of that blood meal at least correlates with sort of the molecular complexity in terms of Sliva composition from each of these different sets of organisms. I just list. So there's way more proteins in other molecules that [00:40:35] have been detected int saliva as opposed to mosquito saliva.
[00:40:39] Ben: And, and so what you're sort of like one of your, your high level things is, is like figuring out which of those are important, what mixture of them are important and like how to replicate that for youthful purposes?
[00:40:51] Seemay: Yeah. Right, exactly. Yeah.
[00:40:54] Ben: and, and, and are there other, like, I mean, I, I guess we can imagine like farther into Arcadia's future and, and think about like, what do you have, like, almost like a, like a wishlist or roadmap of like, what other really weird organisms you want to start poking at?
[00:41:13] Seemay: So actually, so that, that is originally how we were thinking about this problem for non-model organisms like which organisms, which opportunities and that itself has evolved in the last year. Well, we realized in part, because of our, just like total paralysis around this decision, because [00:41:35] what we didn't wanna do is say, okay, now Arcadia's basically decided to double down on these other five organisms.
We've increased the Canon by five now. Great. Okay. But actually that's not what we're trying to do. Right. We're trying to highlight the like totally different way. You could think about capitalizing on interesting biology and our impact will be felt more strongly if it happens, not just in Arcadia, but beyond Arcadia for this to be a more common way.
And, and I think like Symbio is really pushing for this as a field in general. So we've gone from sort of like which organisms to thinking about. Maybe one of our most important contributions is to ask the question, how do you decide which organism, like, what is even the right set of experiments to help you understand that?
What is the right set of data? That you might wanna collect, that would help you decide, let's say for example, cuz this is an actual example. We're very interested in produce diatoms, algae, other things, which, [00:42:35] which species should you settle on? I don't know. Like there's so many, right? Like, so then we started collecting like as many we could get our hands on through publicly available databases or culture collections.
And now we are asking the meta question of like, okay, we have these, what experiments should we be doing in a high throughput way across all of these to help us decide. And that itself, that process, that engine is something that I think could be really useful for us to share with the worlds that is like hard for an individual academic lab to think about.
That is not aligned with realities of like grants and journal publications and stuff. And so, yeah. Is it like RNA seek data sets? What kind of like pheno assays might you want, you want to collect? And we now call this broadly organismal onboarding process. Like what do you need in the profile of the different organisms and like, is it, phenomics now there's structural [00:43:35] prediction pipelines that we could be running across these different genomes depending on your question, it also may be a different set of things, but wouldn't it be nice to sort of just slightly turn the ES serendipity around, like, you know, what was around you versus like, can we go in and actually systematically ask this question and get a little closer to something that is useful?
You know,
[00:43:59] Ben: Yeah.
[00:43:59] Seemay: and I think the amazing thing about this is. You know, I, and I don't wanna ignore the fact that there's been like tons of work on this front from like the field of like integrative biology and evolutionary biologists. Like there's so much cool stuff that they have found. What I wanna do is like couple their thinking in their efforts with like the latest and greatest technologies to amplify it and just like broaden the reach of the way they ask those questions.
And the thing that's awesome about biology is even if you didn't do any of this and you grabbed like a random butterfly, you would still find extremely cool stuff. So that's the
[00:44:34] Ben: [00:44:35] Right. Yeah.
[00:44:36] Seemay: like, where can we go from here now that we have all these different technologies at our disposal?
[00:44:41] Ben: Yeah. No, that's, that's extremely cool. And I wanted to ask a few questions about Arcadia's business model. And so sort of like it's, it's a public fact, unlike a lot of research organizations, Arcadia is, is a for-profit organization now, of course, that's that's a, you and I know that that's a legal designation.
And there's like, I, I almost think of there as being like some multidimensional space where it's like, on the one hand you have like, like the Chan Zuckerberg initiative, which is like, is nominally a for-profit right. In the sense of
[00:45:12] Seemay: Yeah.
[00:45:13] Ben: not a, it's not a non-profit organization. And then on the other hand, under the spectrum, you have maybe like something like a hedge fund where it's like, what is like the only purpose of this organization in the world is to turn money into more money.
Right. And so like, I, I guess I'd love to know like how you, how you think about sort of like where in that domain you
[00:45:34] Seemay: [00:45:35] Yeah. Yeah. So, okay. This
[00:45:38] Ben: and like how you sort of came to that, that
[00:45:41] Seemay: Yeah. This was not a straightforward decision because actually I originally conceived of the Arcadia as a, a non-profit entity. And I think there were a lot of assumptions and also some ignorance on my part going into that. So, okay. Lemme try and think about the succinct way to tell all this.
So I
[00:45:58] Ben: take, take, take your time.
[00:46:00] Seemay: okay. I started talking to a lot of other people at organizations. Like new science type of organizations. And I'll sort of like refrain from naming names here out of respect for people. But like they ran into a lot of issues around being a nonprofit, you know, for one, it, it impacted sort of like just sort of like operational aspects, maintaining a nonprofit, which if, if you haven't done it before, and I learned like, by reading about all this and learning about all this, like it maintaining that status is in and [00:46:35] of itself and effort, it requires legal counsel.
It requires boards, it requires oversight. It requires reporting. There's like a whole level of operations
[00:46:45] Ben: Yeah.
And you
always sort of have the government looking over your shoulder, being
[00:46:49] Seemay: Yep. And you have to go into it prepared for that. So it also introduces some friction around like how quickly you can iterate as an organization on different things.
The other thing is that like Let's say we started as a nonprofit and we realized, oh, there's a bunch of like for-profit type activities. We wanna be doing the transition of converting a nonprofit to a for-profit is actually much harder than the other way around.
[00:47:16] Ben: Mm.
[00:47:17] Seemay: And so that sort of like reversibility was also important to me given that, like, I didn't know exactly what Arcadia would ultimately look like, and I still dunno
[00:47:27] Ben: Yeah. So it's just more optionality.
[00:47:29] Seemay: Yeah. And another point is that like I do have explicit for profit interests for [00:47:35] Arcadia. This is not like, oh, I like maybe no. Like we like really want to commercialize some of our products one day. And it's, it's not because we're trying to optimize revenue it's because it's very central to our financial experiment that we're trying to think about, like new structures.
Basic scientists and basic science can be, can capture its own value in society a little bit more efficiently. And so if we believe the hypothesis that discovering new biology across a wide range of organisms could yield actionable lessons that could then be translated into real products. Then we have to make a play for figuring out how this, how to make all this work.
And I like also see an opportunity to figure out how I can make it work, such that if we do have revenue, I make sure our basic scientists get to participate in that. You know, because that is like a huge frustration for me as a basic scientist that like we haven't solved this problem. [00:48:35] Like basic science.
It's a bedrock for all downstream science. Yet
we some have to have, yeah, we have to be like siloed away from it. Like we don't get to play a part in it. And also the scientists at our Katy, I would say are not like traditional academic scientists. Like I would, I, my estimate would be like, at least a third of them have an intentional explicit interest in being part of a company one day that they helped found or spin out.
And so that's great. We have a lot of like very entrepreneurial scientists at Arcadia. And so I I'm, I'm not shying away from the fact that like, we are interested in a, for profit mission. Having said all of that, I think it's important to remember that like mission and values don't stem from tax structure, right?
Like you, there are nonprofit organizations that have like rotten values. And there are also for-profit organizations that have rotten values, like that is not the [00:49:35] dividing line for this. And so I think it puts the onus on us at Arcadia though, to continuously be rigorous with ourselves accountable to ourselves, to like define our values and mission.
But I don't think that they are like necessarily reliant on the tax structure, especially in a for-profit organization where there's only two people at the cap table and their original motivating reason to do doing this was to conduct a meta science experiment. So we have like a unique alignment with our funders on this that I think also makes us different from other for-profit orgs.
We're not a C Corp, we're an LC. And actually we're going through the process right now of exploring like B Corp status, which means that you have a, a fundamental, like mix of mission and for profit.
[00:50:21] Ben: Yeah. That was actually something that I was going to ask about just in, in terms of, I think, what sort of like implicitly. One of the reasons that people wonder about [00:50:35] the, the mixture of like research and for profit status is that like the, the, the time scales of research is just, are just long, right?
Like, like re, re research research takes a long time and is expensive. And if, if you're like, sort of answering to investors who are like really like, primarily looking for a return on their investment I feel like that, I, I mean, at least just in, in my experience and like my, my thinking about this like that, that, that's, that's my worry about it is, is that like so, so what, like having like, really like a small number of really aligned investors seems like pretty critical to being able to like, stick to your values.
[00:51:18] Seemay: Yeah, no, it's true. I mean, there were actually other people interested in funding, our Arcadian every once in a while I get reached out to still, but like me Jud and Sam and Che, like we went through the ringer together. Like we went on this journey together to get here, to [00:51:35] decide on this. And I think there is, I think built in an understanding that like, there's a chance this will fail
financially and otherwise.
Um, but, but I think the important case to consider is like that we discussed is like, what would happen if we are a scientific success, but a financial failure. What are each of you interested in doing. and that that's such an important answer. A question, right? So for both of them, the answer was we would consider the option of endowing this into a nonprofit, but only if the science is interesting.
Okay. If that is, and I'm not saying that we're gonna target that end goal, like I'm gonna fight with all my might to figure out another way, but that is a super informative answer, right? Because
[00:52:27] Ben: yeah,
[00:52:27] Seemay: delineating what the priorities are. The priority is the
science, the revenue is [00:52:35] subservient to that. And if it doesn't work fine, we will still iterate on that like top priority.
[00:52:42] Ben: Yeah, it would also be, I mean, like that would be cool. It would also be cool if, if you, I mean, it's just like, everybody thinks about like growing forever, but I think it would be incredibly cool if you all just managed to make enough revenue that you can just like, keep the cycle going
right.
[00:52:58] Seemay: Yeah. It also opens us up to a whole new pocket of investments that is difficult in like more standard sort of like LP funded situations. So, you know, given that our goal is sustainability now, like things that are like two to five X ROI are totally on the table.
[00:53:22] Ben: Yeah. Yeah, yeah.
[00:53:24] Seemay: actually that opens up a huge competitive edge for us in an area of like tools or products that like are not really that interesting to [00:53:35] LPs that are looking to achieve something else.
[00:53:38] Ben: yeah, with like a normal startup. And I think that I, I, that that's, I think really important. Like I, I think that is a big deal because there's, there's so many things that I see And, and it's like the two to five X on the amount of money that you could capture. Right. But like the, the, the amount of value that you create could be much, much larger than that.
Right. Like, and this is the whole problem. Like, I, I, I mean, it's just like the, the thing that I always run into is you look at just like the ability of people to capture the value of research. And it just is very hard to, to like capture the whole thing. And often when people try to do that, it ends up sort of like constraining it.
And so you're, you're just like, okay, with getting a reasonable return then it just lets you do so many other cool things.
[00:54:27] Seemay: yeah. I'm yeah. I think that's the vibe.
[00:54:32] Ben: that is an excellent vibe. And, and speaking [00:54:35] with the vibe and, and you mentioned this I'm, I. Interested in both, like how you like find, and then convince people to, to join Arcadia. Right. Because it's, it's like, you are, you are to some extent asking people to like play a completely different game.
Right? Like you're asking people who have been in this, this like you know, like citations and, and paper game to say like, okay, you're gonna like, stop playing that and play this other thing. And so like, yeah.
[00:55:04] Seemay: yeah. It's funny. Like I get asked this all the time, like, how do you protect the careers or whatever of people that come to Arcadia? And the solution is actually pretty simple, even though people don't think of it, which is you Don. You don't try and convince people to come. Like we are not trying to grow into an infinitely large organization.
I don't even know if we'll ever reach that number 150. Like I was just talking to Sam about like, we may break before that point. Like, that's just sort of like my cap. We may find that [00:55:35] 50 people is like the perfect number 75 is. And you know, we're actually just trying to figure out like, what is, what are the right ingredients for the thing we're trying to do?
And so therefore we don't need everybody to join. We need the right people to join and we can't absorb the risk of people who ultimately see a career path that is not well supported by Arcadia. If we absorb that, it will pull us back to the means. because we don't want anyone at Arcadia to be miserable.
We want scientists to succeed. So actually the easiest way to do that is to not try and convince people to do something they're not comfortable with and find the people for whom it feels like a natural fit. So actually think, I think I saw on Twitter, someone ask this question in your thread about what's like the, oh, an important question you asked during your interviews.
And like one of the most important questions I ask someone is where else have you applied for jobs? [00:56:35] And if they literally haven't applied anywhere outside of academia, like that's an opportunity for me to push
[00:56:43] Ben: Mm.
[00:56:44] Seemay: I'm very worried about that. Like, I, I don't want them to be quote unquote, making a sacrifice that doesn't resonate with where they're trying to go in their career.
Cuz I can't help them AF like once they come. Arcadia has to evolve like its own organism. And like, sometimes that means things that are not great for people who wanna be in academia, including like the publishing and journal bit. And so yeah, what I tell them is like, look, you have two jobs at Arcadia and both have to be equally exciting to you.
And you have to fully understand that there both your responsibility, your job is to be a scientist and a meta scientist. And that those two things have to be. You understand what that second thing is that your job is to evolve with me, provide me with feedback on like, what is working and not working [00:57:35] for you and actively participate in all the meta science experiments that we're doing around publishing translation, technology, all these things, right?
Like it can't be passive. It has to be active. If that sounds exciting to you, this is a great place for you. If you're trying to figure out how you're going to do that, have your cake and eat it too, and still have a CV that's competitive for academia in case like in a year, you know, like you go back, I, this is not the place for you.
And I, I can't as a human being, like, that's, I, I can't absorb that because like, I like, I can't help, but have some empathy for you once you're here as an individual, like, I don't want you to suffer. Right. And so we need to have those hard conversations early before they join. And there's been a few times where like, yeah, I think like I sufficiently scared someone away.
So I think it was better for them. Right? Like it's better for
[00:58:25] Ben: Yeah, totally.
[00:58:25] Seemay: if that happens. Yeah, it's harder once they're here.
[00:58:29] Ben: and, and so, so the like, The, they tend to be people who are sort of like already [00:58:35] thinking, like already have like one foot out the door of, of academia in the sense of like, they're, they're already sort of like exploring that possibility. So they've so you don't have to like get them to that point.
[00:58:48] Seemay: Right. Yes. Because like, like that's a whole journey they need to go on in, on their own, because there's so many reasons why someone might be excited to leave academia and go to another organization like this. I mean, there's push and pull. Right. So I think that's a challenge, like separating out, like, like what is just like push, because they're like upset with how things are going there versus like, do they actually understand what joining us will entail?
And are they, do they have the like optimism and the agency to like, help me do this experiment. It does require optimism. Right.
[00:59:25] Ben: absolutely.
[00:59:25] Seemay: So like sometimes like, you know, I push people, like what, where else have you applied for jobs? And they, if they can't seem to answer that very well I say, okay, let me change [00:59:35] this question.
You come to Arcadia and I die. Arcadia dissolves. It's, it's an easier way of like, it's like, I can own it. Okay. Like I died and like me and Che and Jed die. Okay. Like now what are you gonna do with your career? And like, I is a silly question, but it's kind of a serious question. Like, you know, just like, what is, how does this fit into your context of how you think about your career and is it actually going to move you towards where you're trying to go?
Because, I mean, I think like that's yeah. Another problem we're trying to solve is like scientists need to feel more agency and they won't feel agency by just jumping to another thing that they think is going to solve problems for them.
[01:00:15] Ben: Yeah, that's a really good point. And so, so this is almost a selfish question, but like where do you find these people? Right? Like you seem to, you seem to be very good at it.
[01:00:26] Seemay: Yeah. I actually don't I don't, I, I don't know the answer to that question fully because we [01:00:35] only just recently said, oh my God, we need to start collecting some data through like voluntary surveys from applicants of like, how do they know about us? You know? It seems to be a lot of like word of mouth, social media, maybe they read something that I wrote or that Che wrote or something.
And while that's been fine so far, we also like wanna think about how we like broaden that reach further. It's definitely not through their, for the most part, not through their institutes or PIs that I know.
[01:01:03] Ben: Yeah, I, but, but it is, it is like, it sounds like it does tend to be inbounds, right? Like it tends to be people like reaching out to you as opposed to the other way around.
[01:01:16] Seemay: Yeah. You know, and that's not for lack of effort. I mean, there have been definitely times where. We have like proactively gone out and tried to scout people, but it does run into that problem that I just described before of like,
[01:01:29] Ben: Yeah.
[01:01:30] Seemay: if you find them yourself, are you trying to pull them in and have they gone through their own [01:01:35] journey yet?
And so in some of those cases, while it seemed like, like we entertain like conversations for a while with a couple of candidates, we tried to scout, but ultimately that's where it ended was like, oh, they like, they need to go on their own. And like, sort of like fully explore for a bit, you know, this would be a bit risky.
But it hasn't, you know, it hasn't been all, you know a failure like that, but it, it happens a lot.
[01:01:58] Ben: Yeah, no, I mean that, that, frankly, that, that squares with my, my experience sort of like roughly, roughly trying to find people who, who fit a similar mold. So that that's, I mean, and that, that suggests a strategy, right. Is like, be like, be good at setting up some kind of lighthouse, which you, you seem to have done.
[01:02:17] Seemay: The only challenge with this, I would say, and, and we are still grappling with this is that sort of approach does make it hard to reach candidates that are sort of like historically underrepresented, because they may not see themselves as like strong candidates for such and such. And [01:02:35] so now we're, now we have this other challenge to solve of like, how do we make sure people have gone through their own process on their own, but also make sure that the opportunity is getting communicated to the right people and that they like all, everybody understands that they're a candidate, you know,
[01:02:53] Ben: Yeah. And I guess so , as long as we're recording this podcast, like what, what is that like, like if you were talking to someone who was like, what does that process even look like? Like what would I start doing? Like what would you, what would you tell someone?
[01:03:08] Seemay: Oh, to like explore a role at Arcadia.
[01:03:11] Ben: yeah. Or just like to like, go through that, like, like to, to start going through that
[01:03:16] Seemay: Yeah. Yeah, I mean, I guess like, there's probably a couple of different things. Like, I mean, one is just some deep introspection on like, what are your priorities in your life, right? Like what are you trying to achieve in your career? Beyond just like the sort of ladder thing, like what's the, what are the most important, like north stars for you?
And I think [01:03:35] like for a place like Arcadia or any of the other sort of like meta science experiments, That has to be part of it somehow. Right. Like being really interested and passionate about being part of finding a solution and being one of the risk takers for them. I think the other thing is like very pragmatic, just like literally go out there and like explore other jobs, please.
Like, I feel like, you know, like, like what is your market value? You know? Like what
[01:04:05] Ben: don't don't
[01:04:05] Seemay: Yeah. Like, and like go get that information for yourself. And then you will also feel a sense of like security, because like, even if I die and Arcadia dissolves, you will realize through that process that you have a lot of other opportunities and your skillset is highly valuable.
And so there is like solid ground underneath you, regardless of what happens here, that they need to absorb that. Right. And then also just. Like, trust me, your negotiations with me will go way better. If you come in [01:04:35] armed with information, like one of my goals with like compensation for example, is to be really accurate about making sure we're hitting the right market value for you and being equitable across the organization at Arcadia.
So like the more information you can present with me about like real market data, the better and easier that conversation will be. Right. So,
[01:04:55] Ben: no, that that's really good. I, I think it's important for people to, to think about that more. And, and I guess sort of to, to start to bring things more to a close Elon ger pointed out a really good question on, on Twitter. And so, and, and I'm sure you don't have like a, a. A really clear answer.
So like, let's, let's like reflect on it together, but like, how do we, how do we create encourage, train more of, of you, right? Like deeply technical researchers who take the initiative to step out of their comfort zones and build, or join new research institutions and like, do, do you have any sense of [01:05:35] that?
What, what would you
[01:05:36] Seemay: there's so many, uh, by younger me, I mean, I've always been sort of like, I mean, I thought I saw Ethan reply to it too, about like, so that's the founder mentality basically. And I think he is something he said in there, I was like, oh, that's totally true. Like, I'm a definite like addict of like chaos and like disruption, you know?
So, so maybe that there's certain elements of this that maybe are just naturally more comfortable to some than others. But I do think there's like an important step. We need to start taking in the general scientific ecosystem, which is to just stop gas, lighting each other. Right. Cause that's like, step number one.
Like when you realize that, like your challenges are real and potentially generalizable and worthy of solving and not just something you need to like absorb because of some, something wrong with you. Like that is, seems like the critical first step that has to happen intellectually before anything can change.
And that [01:06:35] people feel some agency to be agents of that change, because that is what happened for me. Right. Like when I started realizing like, oh, holy shit, like structures have changed before in history. Like what we're doing right now, isn't this like immutable thing. And then I started having conversations with other scientists that was key.
I probably had like a hundred zooms or something to convince myself that, oh, this is not just me complaining, like, like me struggling with this, that these are like generalizable systemic problems. We should stop gas, lighting ourselves. And then. What's the solution
[01:07:12] Ben: Yeah.
[01:07:12] Seemay: like that is like, like agency and then optimism around that.
Right. Like, I don't know if Arcadia's gonna work. The most important thing is that we try
[01:07:21] Ben: Yeah.
[01:07:22] Seemay: we need to like, get that across to scientists in like the next generation.
[01:07:27] Ben: And, and do you have, I mean, it's a very valid answer to just say, like, it's an innate trait in you that [01:07:35] comes from wherever, but like, do, do you have any sense of like what sort of instilled both that, that agency and optimism in you? Right. Like how do we, how do we encourage more agency and optimism in people?
And I, I mean, I, I have , I have no idea. But that, that seems like really cruxy, right?
[01:07:53] Seemay: Yeah. I don't know. I mean, I mean, I think one thing we cannot ignore is that there's a huge amount of privilege here. Like
[01:08:02] Ben: Oh
[01:08:02] Seemay: I have access to resources. Both like throughout my life, as well as in my relationship with Jed, that allows me to like have a broader solution space to consider. So I, I, that's very important to remember on more personal note, I think I I've thought about this a little bit.
Like just the fact that I grew up in like a very, very religious family and went through a process of sort of like leaving that religion and that culture was probably my first, you know, formative experience about [01:08:35] like questioning a system and then deciding to,
[01:08:38] Ben: Taking
[01:08:39] Seemay: you know, step away from it or, or explore around it or something.
And that, I don't know, I guess like if you're like willing to like leave God, you're like very open to leaving other things, you know, it's, that's probably something we need to like instill in people at an even younger age is just like more like. Thoughtful questioning about like our systems and, and also like providing them then immediately with like, tools to think productively about that. Not just, you know, wallowing it. And that is where the privilege does come in.
And like, I, I do wanna think more about like, yeah, how do we democratize this a little bit more through resource distribution?
[01:09:23] Ben: Yeah. A thought that actually just came to me, I'd be interested in, in your response to it is like almost it, it's it not just encouraging people to be a agent, but then like [01:09:35] rewarding that agency. Like what, what, what, what I feel like I feel like there there's right now, almost like not a strong correlation between people acting energetically.
being supported. And so like I'm imagining just some system where like, you, you have people just sort of like watching and being like, oh, like that person's like being really a agent I'm going to like, like, instead of like making them like, apply for a grant or whatever, it's just like, oh, it's like, you're being agent good job.
Keep doing that.
[01:10:07] Seemay: Yeah, I know. So I struggle with that. And I thought about that before. The reason I struggle with it is basically once you start, anytime you start putting metrics to something or rewarding a behavior, you may accidentally corrupt the ability to source like genuine behaviors in that regard. Right. And like, as someone who's like more entrepreneurial in their thinking, or like more of a disruptor, like the [01:10:35] greatest like reward you can give me is to not sit there and like obsess over this concept.
Like, like if I didn't build Arcadia, I would be like nightly, like insomniac, anxious, looping around this, you know? And that is like, what drives me to do it? Not like some other external reward. So, I don't know like what the right balance is around that, I guess,
[01:10:56] Ben: Yeah, totally.
[01:10:59] Seemay: yeah
[01:10:59] Ben: well this is, this is awesome. I, I really appreciate you, like going, going into this and, and sort of like being really like, straightforward about the, like the tensions and the thought process. And I guess something that I, I like to ask people is just like, what, what is something that you think people should be thinking about more that they're not
[01:11:23] Seemay: I think they should be thinking more about how to like, in the meta science space about how to make more of the building process like visible. Actually this relates to a question that happened on your [01:11:35] thread that I was like, oh my God, I like wanna answer that. But not for the reasons that person probably thinks they were like, basically they were like, you know, like, why do you think you'll succeed when Calco didn't.
And like, I would love to answer that question with some level of precision, but I can't, because I have no idea what Calco is doing. so like, if someone can
help
[01:11:56] Ben: can't even, we can't even benchmark against it.
[01:11:58] Seemay: to compare and I would love to avoid the pitfalls. I mean, I think there's some obvious differences between us, but it actually, the larger point is like these experiments have to happen openly.
And I'm actually in the process of trying to figure this out with like, Institute for progress. Like how do we make sure that all the different things that are happening right now, the information is available to others so that when or lose, like, I don't even think about it in that way at Arcadia. Right?
It's not about winning or losing it's about
[01:12:25] Ben: Yeah.
[01:12:27] Seemay: and we can't learn together if we can't talk about it. So would love to answer that question somehow, but I can't
[01:12:34] Ben: I [01:12:35] am so on board with that. Let's, let's figure it out.
[01:12:39] Seemay: awesome.
[01:12:41] Ben: all right. Well, see, man, thank you so much for, for being on the podcast. I'm deeply
[01:12:46] Seemay: Yeah, you're welcome.
Hi, it's Ben again. As an experiment, I'm going to try giving you a few pointers towards places that you might want to go. If you found this podcast compelling. I know, I rarely look at show notes when I'm listening to a podcast. So I'm going to verbally highlight some things you might want to look into.
So he may is active on Twitter.
She's at CMA to just her name.
We even sourced some excellent questions for this podcast there. And so it's a good place to ask questions and, and engage.
CMA wrote a piece about why she was building Arcadia that expands on some things we talked about.
On medium I'll link to that in the show notes. If you want to see some of the open publications that CMA talked about during the podcast. You can go to research dot Arcadia science.com.
And if you [01:13:35] liked this episode of the podcast in particular, You might want to listen to the ones that I've done with Arthur. And Ilan ger.
And if you liked this experiment at any of the other ones that I've done, have ideas for things to try in the podcast format or have any other feedback, just let me know.
