IN THIS EPISODE: Stripe Press’s Tamara Winter talks through the broad range of scientific funding institutions with guests Professor Tyler Cowen, Arc Institute Co-Founder Professor Patrick Hsu, and Convergent Research CEO Adam Marblestone. They pay special attention to the renaissance in new, exploratory scientific funding models.
“Metascience 101” is a nine-episode set of interviews that doubles as a crash course in the debates, issues, and ideas driving the modern metascience movement. We investigate why building a genuine “science of science” matters, and how research in metascience is translating into real-world policy changes.
Episode Transcript
(Note: Episode transcripts have been lightly edited for clarity)
Caleb Watney: Welcome back! This is the Metascience 101 podcast series. In this episode, we explore the broad range of scientific funding institutions, with a special focus on exploratory new models: ARPAs, FROs, and Fast Grants, oh my! Here, Tamara Winter is in conversation with Professor Tyler Cowen, Patrick Hsu, and Adam Marblestone, all of whom are knee-deep in innovative science funding ecosystems.
Tamara Winter: There are dozens of us in the new scientific institutions community, and I feel very fortunate to have three of the people at the forefront here today. My name is Tamara Winter, and I run Stripe Press, the publishing imprint of Stripe.
Joining me is Adam Marblestone, CEO of Convergent Research. Adam is currently working to develop a strategic roadmap for future FROs. FROs, or Focused Research Organizations, tackle large scale, tightly coordinated nonprofit projects.
We also have Patrick Hsu, co-founder of the Arc Institute and — okay, this is a mouthful — Assistant Professor of Bioengineering and Deb Faculty Fellow in the College of Engineering at the University of California, Berkeley. Arc gives scientists no-strings-attached multi-year funding so they don't have to apply for external grants. It also invests in the rapid development of experimental and computational technological tools.
Finally, we have Tyler Cowen. He is the wearer of many hats. For the purposes of this conversation, he is the founder of Fast Grants, which was spun up remarkably quickly during the early days of COVID-19. Fast Grants provided $10,000 to $500,000 to scientists working on COVID-19 related projects, with decisions made in under 14 days, which is pretty remarkable.
To start this conversation, why do you all think these new scientific models are emerging now? It's interesting because you’ve all been working on this for years, so maybe it doesn’t feel sudden to you, but to me, it feels like one of those "slowly, and then all at once" moments. Why do you think the idea of new institutions for science has caught on so quickly?
Tyler Cowen: I’d say there are three factors. First, in the realm of ideas, a number of individuals — Peter Thiel, myself, Robert Gordon — kept pointing out that something is broken with science and productivity. This idea eventually gained consensus. Second, private foundations became increasingly bureaucratic. People within these systems saw how difficult they were to deal with and grew frustrated.
Finally, COVID came along. It was a true emergency, and emergencies tend to mobilize America. You had some ideas in place, with people who had lived experience saying, "Hey, things really are screwed up." Government funding agencies may not have gotten worse, but they weren't doing very impressive things to get much better. This was a perfect storm, and then you have mimetic desire, contagion, and all these fascinating experiments.
Patrick Hsu: Another way to frame this is to ask: how do scientific breakthroughs happen, and why it often seems like a relatively small cluster of labs is working on important problems at the same time? Often these are dense, overlapping, competitive periods of productivity.
I think all of the principles that Tyler just outlined apply here, along with the general pressure of institutional bureaucracy, or sclerosis, if you will, which has created a certain pressure that eventually we had to blow the top.
Maybe my hot take is that innovating on institutions, or the structures by which we work, isn’t a new idea per se. Any scientist going through training can tell you that, while there is something incredibly powerful and enabling about this system, there are also many fundamentally broken problems.
Science hasn't always been this way. If you look at incredibly productive times in the history of science, they occurred under very different organizations, priors, ways of funding things and ways of working in the labs. Now, we’re seeing a group of people who have experience running and building organizations start to apply that ambition, not just in the commercial tech startup sense, but in scientific institutions themselves.
Adam Marblestone: Yeah. I think there is a big role for the recent history of startups and for the discourse between voices in science and voices that come from the startup or VC world, the Silicon Valley ecosystem, who are thinking more about organizations. Scientists are feeling more empowered, thinking “Hey, maybe I could start an organization.”
Tyler Cowen: In the sense of startups — why can’t we do it this way? So many startups rethink a process, product, or web service from scratch. The notion that you could apply the same thinking to scientific funding has proven to be contagious.
Patrick Hsu: One of the interesting things about the scientific enterprise in academia is that a lot of professors start labs, right? There is a narrowly stereotyped process where, after finishing your postdoc and training, you're going to start an academic group. There's a huge amount of know-how and precedent on how to do this. But then we do it within the broader context of a funding, tenure, and university system that we've always taken for granted.
What’s special about this moment is that people are asking, "Does the system have to be this way? What if we started organizations with a mindset of rethinking the larger system from first principles?"
Tamara Winter: I want to get into your specific organizations, or initiatives in Tyler’s case. Adam, you've been writing about expanding what you’ve called the “base space” of scientific exploration for years. A lot of that thinking was realized in Convergent Research. So, what is a Focused Research Organization? What does Convergent do? How does it differ from the traditional small group approach to doing science?
Adam Marblestone: A Focused Research Organization is, in many ways, an incredibly simple concept. It is a critical mass of scientists, engineers, and managers all working on a single, well-defined problem — usually to build a specific tool, system, or dataset that will, in some way, benefit science or technology. It’s something that couldn’t be built in another context, like a VC-backed company, and requires a critical mass of resources and time to do that exact thing.
That is such a simple concept. You might imagine, “Well, don't we already have many such vehicles?”. The surprising thing is that many other ways of doing science or contributing to science are tied either to individual academic career paths or other structures — maybe you are starting a for-profit company or you are working in an existing National Lab project.
But there really isn't a general mechanism for identifying which things need focused teams to build — sprints, if you will. And then, how do we match those teams with all the things that they need? A startup needs leadership, it needs a technical roadmap, it needs funding. It needs to build out a handpicked team that's inherently cross-disciplinary. You are hiring the people who are not necessarily who's right around you.
There just isn't a general mechanism for doing that. Because of this, scientists don’t go around thinking, "Hey, I'm going to propose how I would have 20 engineers and project managers to work on some big lift and build something.” Instead, they're thinking about other scales and scopes of work: “What's the next grant that I should write? What should my next few students be working on?”
They are not thinking about this particular structure. As a result, philanthropists and government agencies don't know what problems researchers would tackle in that mode of a Focused Research Organization. So Convergent’s job is to be a Schelling point, bringing together all the necessary elements to coordinate those projects. That includes approaching the research community and saying, "What are the problems that you think are most important, that are biggest bottlenecks in science, where you need a new tool, or system, or dataset that requires a larger-than-usual coordination or a sprint to build?"
It means putting in place the venture incubation and creation aspects, as well as some legal and operational aspects to make sure that those projects can happen. And all of this just creates more confidence in this model being a viable path.
Tamara Winter: I mean, we're having this conversation on Stripe. We want to abstract away the complexity of starting a business. You're doing the exact same thing for the entire scientific enterprise.
Adam Marblestone: Yeah. It's a bit like the Stripe Atlas idea or the Y Combinator idea.
Tamara Winter: Exactly.
Adam Marblestone: Why aren't there more of these focused teams building particular things that are needed and not buildable otherwise?
Tamara Winter: Why aren't there more?
Tyler Cowen: I like the idea that it may be temporary: you achieve the end and then the institution dissolves. But people aren't used to that. Many institutions prolong themselves, carrying overhead, keeping friends and associates in jobs. There's something pernicious about that, but it does, in some ways, make it easier to hire basic talent. If you're going to do it differently, you have to be more innovative. You need a strong soft network to bring people in, and you need to pay them pretty well. I think that's part of the problem: people aren't used to the model of potential temporariness.
Adam Marblestone: This is where the startup inspiration goes a long way. Because we're not creating permanent institutes where the questions are "Who’s the director who can run this for 100 years? What's the biggest theme? What does this mean in 30 years?” It's just this problem.
It doesn't mean that everybody disappears afterward. Often, these problems are highly catalytic. Maybe they will spin off companies, maybe they can donate themselves to a larger nonprofit; they can create contract research organizations of longer-term nonprofits. There are lots of possibilities of what happens afterwards.
Part of the problem selection process is doing it in a way that there is a notion of an exit or a scale-up event that will happen at some point — because of the value that you are creating, or how you will plug into the ecosystem, generating demand and activity afterwards so it's not just a bye-bye at the end.
But part of that is understanding that this is the dynamic. They have to create this thing and pave their own way. The future is not completely predictable: who those partners are going to be, who those customers are going to be, where it's going to land.
That dynamism is something somewhat borrowed from how startups think. We’ve been happy to hire talented people, but they are people who are thinking differently about what they're doing.
Patrick Hsu: Arc, as we’ve built it today, is a convening center. We bring together scientists from across three partner universities (Stanford, UC Berkeley, and UCSF) in a single physical space to collaborate. The question is, if we assemble this caliber of thinkers and researchers, what science can we do when we simply unlock them to work on their very best ideas?
We’re including faculty, PhD students, postdocs, and professional research scientists beyond the training period. We’re taking elements from both academia and industry, but with the idea that long-term science requires long-term thinking and infrastructure for execution. Unlike many other fields in STEM, biology is very slow. It's messy, it's noisy. And also, fundamentally, it's always a moving target.
We have a few pretty prosaic things that we're trying to put in place at Arc. The first is — if we provide folks with long-term funding, can we remove the need for short-term optimization, like chasing rapid publications or short funding cycles? We believe in papers and scientific products, but are you truly working on the most important thing? We provide a structure both in terms of funding, but also, broadly, across the various platforms that we are going to have at Arc.
As modern biomedical research becomes increasingly dependent on really complex experimental and computational tools, you need to have a place that has the institutional know-how that holds this together. It's amazing how simply having the trains run on time effectively doesn't happen even at the very top biological labs. Simply putting that into place, building technology centers where we have larger teams of research scientists work in a larger, cross-functional, modular way, like you’d see in biotech or pharma for these more complex research workflows and processes. What kind of science can you do when you start to need to tackle bigger types of projects?
Tyler Cowen: So, let’s say it’s eight years. Let's say you knew, almost all the time, in year two whether or not you were going to renew people six years later. Would that change what you're doing? So, you have to hold onto the lemons for six years and pretend everything is fine. Does that create strain?
Patrick Hsu: There are a few different ways to think about why it needs to be renewable, right? Other places have baked-in “up-and-out” models where you come for a temporary period, maybe three, five, or seven years, and then the expectation is that you leave. What ends up happening is that folks end up hyper-optimizing for things that will allow them to show milestone-driven productivity, momentum, and trajectories.
We think that, while trying to push work out quickly isn’t necessarily a bad thing, if you fully optimize for that, it’s net bad for science. We have annual reviews and formal eight-year appointments, but we also have a range of formal and informal check-ins to make sure that folks are happy.
Ultimately, we judge our initial success by two factors: First, are we able to hire some of the very best people? And, second, when these people come to Arc, do they feel they’re working on their best ideas? Are they happy with the type of problem they’re tackling? For the folks that aren't happy, who don’t like the model, they tend to be incredibly talented individuals who can find jobs anywhere they want.
Tamara Winter: Tyler, it’s been a couple of years since you started Fast Grants. I feel like much ink has been spilled trying to understand where Fast Grants succeeded. Of course, Patrick, you also worked on Fast Grants. In what ways did the research that Fast Grants catalyzed differ from what would have happened in traditional institutions?
At some point, I’d like to talk about counterfactuals. How do we actually know that these new scientific institutions are producing new kinds of research? But let's start with Fast Grants.
Tyler Cowen: I think there are maybe three different kinds of grants we made. One was for research that likely would have happened anyway, but now could happen much more quickly. As you mentioned, we funded many people within two weeks — actually, a lot of people we could fund within two days. And when you're in a pandemic, with so many Americans dying each day, accelerating progress, even by a small amount, is worth a lot.
There’s then another class of people, which is harder for me to judge. There's then another class of people that is harder for me to judge. They might have wanted to do the work anyway, but there was a discrete switch and they needed to know within some certain timeframe if they could get the money. If we hadn’t stepped in, maybe they just wouldn’t have done it at all. That I find much harder to judge.
Then, there's a smaller number of projects where we put up grants larger than average, and I strongly suspect those projects wouldn’t have happened if we hadn’t funded them. For example, the fluvoxamine interferon trials were a tough, risky proposition. There was follow-up funding, there was some initial interest, but it wasn't clear to me that without us that could've happened at all. And that's probably turning out to be quite important.
Beyond these grants, there’s also a demonstration effect. We showed the world that science funding can be faster, and institutional responses can be faster. Government agencies and private foundations can take away lessons from this. There's not a decline in quality, in my opinion. I think the quality actually goes up. When you are forced to make a decision right away, the notion that a piece of paper sits in someone's inbox and gets passed around and it takes you three months, four months, nine months — it's not that there's some genius in the meantime pondering the whole thing and arriving at a smarter answer. You just need to prioritize getting the decision made now, and you'll do just as well. And I think we showed that that is possible.
Tamara Winter: There is something you said that I find really interesting, and I want to hear from you two. How much of the success of something like Fast Grants can we attribute to allowing people to take advantage of, or be responsive to, changes in the outside world?
There was a meaningful number of people who got Fast Grants who were working on something else and then suddenly had the permission and funding to work on the most pressing issue in the world. How much of the success of these kinds of models is about letting people be responsive to what’s happening in the world? Because, typically, if you get a grant from the NIH, it’s not always the case that you can switch the grant to work on something more pressing.
Tyler Cowen: No, we let people switch. But there were a lot of preconditions, including on the Mercatus side, where Fast Grants was housed. Mercatus had already been running Emergent Ventures, which was non-COVID related, but the philosophy there was to get people the money within a few days, less than a week. We had over a year of practice with that, and the finance team, the reporting team, my assistant — everyone knew exactly what to do. They were operating at A, A+ levels. To increase the size of the numbers and send the checks or wire transfers to different places wasn’t very hard.
We also had my board, who trusted me to run this based on previous experience, and that’s actually incredibly scarce. I think it’s a big, under-discussed problem — trust within nonprofits — so that a board will just say to the person doing the work, “Look, you just do it, we trust you.” I think that’s the hardest factor to replicate.
Patrick Hsu: On the scientific review side, which I was deeply involved in with Fast Grants — first, the infrastructure and the systems that Tyler and Mercatus developed were fundamental to the success of Fast Grants in making the awards. There’s a huge amount of plumbing that goes into place to wire the money as quickly as the universities can receive it. It was amazing that, in many cases, the money was just sitting with the universities because they didn’t know how to accept it yet.
Tyler Cowen: Or they would slow us down — the people receiving the money felt the need to slow you down.
Patrick Hsu: Yeah. It’s an interesting observation, for sure. But what we also showed was that, on the scientific review side, the process can be focused, efficient, with rapid handoffs. We got applications across an incredible diversity of immunological concepts, new types of vaccines, new clinical trial proposals, and new diagnostic concepts — non-human primate studies, for example. We had to find and corral top scientists with deep domain expertise across each of these diverse areas. We also built a software portal...
Tyler Cowen: And the Stripe talent did that. We had the best programmers in the world building a system within a few days that, from my point of view, worked perfectly. That’s something you can’t take for granted. So on the reviewing side, we had social media to get the word out, Stripe engineers building the software, Mercatus handling processes, and Patrick and I as leaders and fundraisers. Really, a lot of different pieces, each of which was essential.
Tamara Winter: It really is an incredible feat of coordination, especially given how it had to happen since you couldn't be in the same room. One thing I loved about it, especially on the Stripe side, was how much praise and status were given to the folks working on it, some of whom were in Australia — so, you were doing this across time zones as well.
Adam, I want to take it back to Convergent for a second, because the grants you make, Patrick, are renewable eight-year grants. Fast Grants didn’t necessarily have a time bound. Is the most important thing, when you're making a grant to a team, that it's a team of a certain size? Is the thing you care most about the timeline — is it five to seven years?
Adam Marblestone: Mm-hmm.
Tamara Winter: What is the most important element when evaluating a new team, project, or potential area to explore?
Adam Marblestone: Honestly, it’s heavily about the question of the counterfactual: is this something these people could organically self-organize to do? Each person in a focused research organization could, in principle, go off and write their own grants and then they could collaborate. They could do things in a more organic way to head in the same general direction.
Then there is the question: what’s the delta between what would happen if they did that versus what would happen in the FRO? That differs significantly across fields, too. In some fields, the level of technology — maybe neuroscientists need a new microchip, but neuroscientists aren’t the people who make microchips. So, to what degree do you need that industrialized push with a different structure of labor, a different structure of the staff, and a different structure of the focus and coordination inside the group, relative to what the field has available through any number of mechanisms like the NIH or philanthropies?
A big part of it is the counterfactual. Another level of that counterfactual is understanding how important is this thing that we're building. Of course, we can’t ever know for certain in advance. It might be that we have an FRO developing a new method for proteomics or measuring proteins in cells. Maybe there'll be some other way of doing proteomics that's completely better, that leapfrogs the FRO — maybe just one postdoc did that, without a team of 20 people. You can never be sure that that will be the case, but how big of an unlock do we think it will be, and how much need is there for it?
In our case, we do verify it through peer review. We have a lot of peer review of scientists saying, “If you build this, it’s not necessarily about high-risk, totally unpredictable ideas. It’s much closer to the Hubble Space Telescope or the Human Genome Project — these things are doable, but heavy lifts”. So part of the evaluation is: how significant is the unlock if we make that lift?
And the other one is the willingness and readiness of the team. It is an entrepreneurial founding team, effectively, that then goes and hires the rest of the people, and they have to be willing to do something non-traditional. They have to be willing to be completely focused on this for that period of time, they have to have both the human skills and the scientific skills on that team.
Between those factors, we get to a relatively short list at any given time, although there are many more projects than we have funding for at the moment.
Patrick Hsu: The technology centers at Arc are, in many ways, trying to tackle a similar set of challenges. We have a similar intuition for the FRO concept, which I’m a huge fan of — that you need larger teams, more diverse types of talent. You can’t rely on a single-channel type of person with core training only in molecular biology and genetics to tackle something that might require product integration, or something that’s multimodal across instrumentation, imaging, and molecular concepts. All of these different pieces require coordination and focus in a broader sense. A lot of what we do with our technology centers is bring together folks in an industrial-style research organization, embedded within the broader Arc umbrella, but highly focused on developing things like organoids, better cellular models, or better technologies for multiomic profiling of cells, or better approaches for genome and epigenome engineering at scale.
We have preselected, to some degree, five technology centers that, in many ways, work together in a coordinated fashion. It’s like that ‘90s cartoon Captain Planet, where you need earth, wind, water, and fire to get Captain Planet. These centers coordinate to run an end-to-end cycle for finding better targets for complex human diseases.
A lot of the ways we’re building them involves interdisciplinary talents. How do you actually operationalize this in a focused and efficient way to bring everyone together? There’s just a certain latent amount of time that it takes to build a lab in the first place, get a critical mass of high-quality thinkers, to get quality, physical logistics working properly. And a lot of that — we think about all of that in a centrally efficient way.
Tamara Winter: It’s so interesting. One of the things I love, that Heidi Williams always talks about, is that the conversation about new ways to do science is so focused on new ways to fund science. But so much of what all three of you are talking about are these infrastructural or scaffolding challenges that really do meaningfully impede how quickly you can do science, or the kinds of research you're able to do. This, to me, seems very interesting — I hear Heidi often talk about it, but it’s great to hear how this happens in practice.
I want to go back to the counterfactual question because it seems like a problem that people who are focused on metascience — and maybe something the Institute for Progress or Open Philanthropy can work on — don’t have rigorous ways of assessing counterfactuals. He's not in the room with us right now, but Matt Clancy touches on this a bit at New Things Under the Sun. He will identify these natural experiments and say, "Okay. There is a field and it has these properties. And this field is like it and shares similar properties. What might they learn from each other?"
But it seems like if you are at Convergent, or Arc, or Fast Grants, or even Emergent Ventures, what you want is not to be able to look at entire fields, but at the individual FRO level or experiment level, and say, "This thing wouldn’t have happened without our intervention." But we can't really do that right now. Is that a problem, or is it just me?
Tyler Cowen: I don’t agonize over counterfactuals. I think it's a bit like friends. You get a friend and, if you get some good friends, you get more good friends. Even if you're funding something where you’re not decisive about that particular project, it will bring more good projects, better deal flow, and hopefully expand the popularity of your model in a positive way. You’re never going to figure out counterfactuals in many cases. You shouldn’t do obviously foolish things, like making a grant to Google so they can expand their work in artificial intelligence — that’s clearly silly because of the counterfactual.
But within the realm of the reasonable, it's like so hard to find a truly high quality thing, person, institution to support. I say just do it.
Patrick Hsu: One of the fascinating things about, for example, the practice of science is you can talk yourself out of any damn experiment. You have a sufficiently challenging problem, you have sufficiently analytical people, there are always going to be equally compelling reasons why something will work as it won't work. Maybe many more reasons why it won't work.
So you can end up in decision paralysis or opportunity cost paralysis, and end up never actually doing anything. There’s a huge advantage in simply trying things in an operationally effective way — just doing the experiment, starting the organization, raising the funding, and giving it a go. In general, the universe trends more toward entropy and a lack of focused effort.
Tyler Cowen: The best way to protect against funding projects that would have been funded anyway is to be weird yourself — be credibly weird and signal that you’re different. You can’t control it, but you will attract projects that are not just mainstream, like Aspen Institute material or “IBM would've done this”. Nothing against those institutions, but they are very mainstream.
Adam Marblestone: I think there’s something nice, in a few ways, about having these new models, and having them be weird in that sense. On the one hand, Tom Kalil, our board chair at Convergent, likens one aspect where we see counterfactuality in the FRO process is that people wouldn’t have written these grants in the first place. It takes a long time to even spec out what you would do with $30 million or a 20-person engineering team. That's actually not something that you can just think about in your daily course of doing your thing.
Patrick Hsu: And no one is trained to think about making that size of proposal.
Adam Marblestone: They are not trained. So, the people designing your technology centers — that’s a very specialized and intricate long-term endeavor, an engineering-and-design endeavor. Not everyone can do that. But not only that. The way Tom describes it, most people don’t spend months of their lives spec-ing out a detailed plan for what they would do if they won the lottery. That would be a waste of time because there’s no way they’re ever going to win the lottery, right? They’re just wasting their time.
And in a similar way, if there's no grant, or no mechanism, that is shaped like “Now you have a 20-person engineering team building a tool that's cross-disciplinary and focused in this way,” people don't spend the time to think about it. So one counterfactual is: you get weird ideas that people haven't talked about before but may have been latent. The people who are going to come up with those ideas, almost by definition, are pretty frustrated early on. They're the people that were thinking about what they would do, despite there not being any immediate incentive or way for them to get the money to do that.
If they've already got those ideas brewing, those people are pretty weird to begin with. We see some interesting selection effects, along with the fact that there just isn’t a mechanism shaped like this. So, we know there wasn’t a foundation that would have funded this before.
Patrick Hsu: There's something really powerful about simply framing the opportunity. One of the things they talk about at ARPA-H — the ARPA-H director, Renee Wegrzyn, mentions that many people are good at coming up with million-dollar ideas, which is a standard five-year grant size. But very few people are good at coming up with $30 or $100 million ideas, as Adam has been saying multiple times.
A lot of what they’re doing in their search for a program manager to administer tens to hundreds of millions of dollars is finding people who have the experience and taste and judgment to assess things at this scale, where you have very low end, very few reps, very little experience on how to frame and organize and judge what should fit in this space.
A lot of what this general conversation is doing is simply outlining a possibility, and then building in public so that people can see it's possible, these things do get funded. Then, we can scientifically track and measure the outcomes — the things that worked, the things that didn’t work, and the wins and losses.
Adam Marblestone: Maybe over time, it will become less weird. I think it’s probably a trainable discipline to teach people to think as ARPA-like program managers for $30 or $100 million systematic engineering programs, division of labor, and these types of things. But it’s not something that many people are doing in the current system. So, these agencies are starved for this program manager phenotype that could have the vision and coordination behind a DARPA-like program. Similar for FROs. So, we do see a selection effect, where we get some pretty wild stuff.
Patrick Hsu: I just want to quickly touch on where we go in the longer term from here. When Convergent, Arc, or Mercatus spend a billion dollars, at the end of the day, this is a drop in the bucket compared to the NIH’s annual expenditure, right?
Tamara Winter: What is it? Did you say around 44 billion?
Patrick Hsu: Yeah, about $42 billion a year, increasing to maybe $50 billion in the congressional budgetary request. That’s a huge amount of money that we’re spending on basic health sciences on an annual basis. One of the things that has been so amazing to me with Fast Grants is the number of people who have said, "Fast Grants is really cool, let me just clone this model" — for longevity science, for climate change, and other areas.
It seems to be effective. People are able to do important things with the money they got at a very important and sensitive time. "Can I just clone that?" because we’ve outlined a protocol and a precedent that they can operationally implement on their own.
Tamara Winter: It's interesting because, similarly, we were talking earlier about how one of the underrated contributions of these new models is that people are building the infrastructure. And, similarly, you can replicate that, even if any one project doesn’t succeed, you’re thinking in a totally different way, almost like a portfolio approach. And if the model proves itself enough times, then people just want to try things. I don’t see how that can be a bad thing.
You all are talking about the type of person that finds themself applying for a Fast Grant, coming to Arc, or leading an FRO. I wonder if you have thoughts on which models are most advantageous for people at different stages of their life. If you're an ambitious teenager, probably you’re not going to be running an FRO, but if you’re a grad student or someone who is midway through a career looking for a change — do you have opinions on which models do you think are most appropriate or advantageous for people at different stages?
Adam Marblestone: Well, I think it’s true that the FRO model leaves a bit of a gap for people in the early stages of their career or training. It’s less about that exploration and that discovery and more about building this thing in a really professionalized, systematic way. So that does leave out some of the early development of creativity, early development of deep knowledge and deep knowledge transfer, which is where academia shines in many ways.
But for FRO founders, roughly speaking, the ARPA program manager phenotype is something that we look for. It’s not the same, necessarily, as a startup founder who wants to scale something to billions of users, but there’s some elements: there's the systematic analysis of a gap and how do you coordinate people, how do you divide labor, how do you divide disciplines to build a complex project.
We have everything from straight-out-of-PhD to “this is one of the last projects they'll do before they retire”, in terms of our FRO leaders. There's a whole spectrum in between. Some people come from academia, some people have more industry experience. We have a whole spectrum and then we try to form a founding team that has a combination of scientific, operational expertise, and different types of personalities. The common denominator is this frustration with the status quo, a concreteness of what they want to do, and a willingness to build a team.
Tyler Cowen: In virtually all institutions, we should be taking more chances on quite young people, giving them more authority, in general. My background is quite different from the rest of you at this meeting. I spent a big chunk of my career studying the financing of the creative arts, economics of the arts. That’s always my mental touchstone. When I hear about Focused Research Organizations that expire when the project is over, I think of Hollywood movies. We’ve been doing that for a long time.
You can almost always find parallels in the arts, which makes you much more optimistic about what you can do. Rapid patronage was a big thing during the Renaissance, and it worked really well. I knew when we started Fast Grants, “Oh, we can do this” because of historical examples.
And when you think of young people running things — well, who ran the Beatles? There was George Martin and Brian Epstein, but the Beatles ran the Beatles. Paul McCartney had to figure out the recording studio. We don't call that science, but that was an extremely difficult scientific project that had never been done before. And this guy, who hadn’t gone to college, at age 23 starts figuring it out and becomes a master. When you see those things happen in the arts — frequently, they happen — you become way more optimistic. “How many people can do this? How can we scale it? Can super young people contribute? Can this all work?”
You are not saying it's easy — most projects in the arts fail, too — but you think, “Yes, yes, yes, we can do this.” And you do it, or you try to do it.
Patrick Hsu: I think building an infrastructure where folks can shoot their shot is really critical. And I think a lot of what this conversation is about, is creating those opportunities for people, not simply operating within the system. It’s about where you focus your ambition. If you’re narrowly told, “Do your best science, but figure out how to do it within the system,” people hyper-optimize for that.
If you show that you can actually innovate on the system itself, that’s one of the most important things that Silicon Valley has pioneered. The seemingly impossible or irrational idea of founding a company and scaling it to billions of users — it’s not something most people normally imagine they can do. But showing that it is possible, meeting the people who have literally done this, creating an entire educational process — an entire alternative educational system — for how to found a company and how to scale one is an important cultural inspiration for what we’re doing here.
A lot of senior colleagues, professors, and university leaders ask me, “How did you come up with the idea for Arc?” One of the funny things, and it’s often hard to answer this way, is that I don’t think it’s a crazy idea. It’s maybe not even that novel, like Tyler’s saying.
Tyler Cowen: A lot of precedent in the history of the arts. Take eight years, 16 years, do your thing. Here's some money.
Patrick Hsu: Just do it. It's the Nike slogan.
Tamara Winter: Is OpenAI an FRO?
Adam Marblestone: Not exactly. I think there are elements of it that have certainly been inspirational to us. It is interesting that they started as a well-funded nonprofit that had a focus on a certain scale of infrastructure and a critical mass of team. But it was not felt that they would get the same outcome if they were a product oriented, traditionally VC backed company.
Tyler Cowen: Why isn’t that just a yes, though? Yes, they’re an FRO.
Adam Marblestone: I would say the first few years had some FRO-like characteristics. But I also think that in some ways, it's something a little bit different. They were exploring more divergent, different directions in the beginning.
If you think also about DeepMind, it has done things internally, like the AlphaGo project, to solve Go playing, or the AlphaFold project on protein folding. Those looked to me like the way that we're doing FROs: 10, 15, 20 person team, extremely well-defined outcome and finite specification of that problem, go after it. Whereas DeepMind as a whole is something that is both organic but also very well resourced. Maybe DeepMind is more like the Arc Institute. It has these shared engineering platforms and researchers with the freedom to self-organize. Sometimes they create FRO-like projects, and sometimes they don’t.
If you imagine OpenAI early on, doing many a bunch of things — some stuff in robotics, some stuff in reinforcement learning. There were a few creative people trying to do this transformer language model thing, and it ended up being the thing that took off. OpenAI was a bit like the Arc Institute, at the beginning.
It certainly has some characteristics — the mentality of it, the professional team, the bounded yet technologically intensive problem space, a non-academic but still basic science approach. A lot of the magic sauce in the first few years. Now it's like “Okay, now we're going to scale up these LLMs.”
Patrick Hsu: And maybe a key point is that OpenAI did not have, when they started, a clear end, which is a critical part of the FRO model, it sounds like.
Tyler Cowen: Wasn’t it to create AGI? And can’t the ability to evolve and be flexible be part of the FRO model? In that sense, I just want to say yes. They’re an FRO, and they’re great, and they did it.
Atdam Marblestone: I can agree with that. If you want a take home message for policy or a take home message for institutions, the finite nature of the FRO is not necessarily the most important thing. It serves certain functions: it weeds out people who want to make a giant, permanent institute with more of an academic cultural feature. It weeds out someone who doesn’t have any milestones or any clear goals that are concrete within it. So, it has a certain filtering function, but it’s a bit artificial.
In that sense — Sam Rodriques has been talking about this as well — if we’re talking about professionalized moonshot research environments, very technological, optimized around the goal, and less optimized around the historical structures of training and credit in academia, very well-funded, visionary projects — then OpenAI has all of that.
It started out as a nonprofit and now is a for-profit, but I think those things are not the essence of it.
Patrick Hsu: So FROs will grow into OpenAIs.
Adam Marblestone: Yes, a successful FRO could grow into something like OpenAI. I think with the right funding and the right people behind it, you could have FROs that have more flexibility, looking less like a single DARPA program and more like building AGI. There’s a continuum.
Tamara Winter: This is just a great reminder to reread the whitepaper that you and Sam Rodrigues wrote — was it in 2020?
Adam Marblestone: Mm-hmm.
Tamara Winter: Speaking of startups, I think there’s one area where I would like to see new scientific institutions take inspiration from startups.
In many ways, starting a startup is still risky. But if you fail, and you fail in good faith, it’s not true that your career is over or there’s nothing else you can do. Michael Nielsen talks about this, and I think he calls it the “shadows of the future” problem.
Let’s say I get a grant from you, Tyler, for two years to do something. I’m an academic, and I’m choosing to switch paths. It’s not true that I’m going to be making decisions in a vacuum — I’m going to be thinking about what happens afterward. And maybe that does end up constraining me in some important ways. So, it’s not as risk-free or as de-risked as you may hope it would be.
If I finish my FRO, Adam, and, at some point, hit one of these choke points in academia or science where you need to produce a result. If I don’t, what do I do? I’ve already defected from the regular system. Am I going to go to ARIA? Am I going to go to Arc? What do you do next?
Adam Marblestone: I think you just answered it.
Tamara Winter: You just go to Arc.
Adam Marblestone: But this is one of the reasons why it has been hard for this stuff to get going before. There is an ecosystem-level phenomenon — there is not a single institution that can solve this.
Now, with FROs, it is planned. It’s this engineering project, and you have a transition plan you’re working toward. You can spin off companies or spin off nonprofits. So, you can plan it to some extent. But some things do have risks. There’s execution risk, technical risk, to different degrees.
Certainly, with some of the things we’re talking about, where we’re giving someone eight years to work on a project, the most exciting ideas — the ones with the greatest potential — are often super unlikely to work. Some people will take on projects that are quite unlikely to succeed and won't optimize for their career in the traditional sense. Then, where do they go?
Maybe they’ll start an FRO, or become an Aria PM, or, after doing one FRO, create a technology center at Arc. Donate ourselves to Arc. There's a lot of options, but only if the ecosystem is being stimulated. Then the question, in part, is, “How sustainable is it? How much can philanthropy do? How much can the government do?”
Tyler Cowen: I think one has to liberate academics and scientists from the notion that the background level of risk should be zero. Once you start living that way, you actually accumulate risk, to some extent — the risk of becoming irrelevant becomes extremely high. It's a hard leap to make. People in the arts all know they face very high risks, and most of them fail. In many ways, it's a much healthier background for experimentation.
Patrick Hsu: And Tyler, how much can we blame tenure for this?
Tyler Cowen: Well, I view tenure as an endogenous outgrowth of the process. In schools that have gotten rid of tenure, whether you think that’s a good or bad idea, faculty behavior in terms of risk-taking isn’t all that different. Most of them stick around and do what they were doing before. So I see tenure as a pernicious side effect of a broader malaise.
Adam Marblestone: Yeah, it’s interesting with FROs, right? It really depends. If you have an academic audience, we say, "Oh, it’s only five years." But if you have someone working a software engineering job in Silicon Valley, it's more like, “Well, I’ve never stayed anywhere for more than two years. I’m always looking for the next coolest opportunity down the street.” So there is this different philosophy. Part of that is going to differ in different fields; in some fields, the skills are more or less transferrable.
Even in an FRO context, I think we do need to think about FROs also as a certain training environment. Maybe it's a training environment for team science or systems engineering as opposed to individual science. A PhD is training for individual science, but what is an FRO? A FRO is a training for these other things. I think that's important.
Patrick Hsu: Going back to the Silicon Valley inspiration, one of the really powerful cultural imprints is that if your first company fails, you are not a failure. VCs will back you for your next play under the right conditions, and with the right idea, the right team. You don’t have that scarlet letter of, your previous project didn’t work out, you burned through five to ten million dollars on the ground.
But it's actually a fundamentally optimistic take — that you've learned something about how to create the impossible, run a company, set a vision, hire people, develop a customer base. This idea — can we train people to do team science and have folks who know how to exist within that ecosystem?
People often frame this incorrectly as a basic science and industrial science divide. “In industry, we have team science, while in academia, it's more about individual science.” But I think there are significant cultural elements we can really draw from industry.
I had dinner last night with a senior colleague who spent the first couple of decades of their career at Bell Labs. They left and went to a university to become a professor after Bell Labs shut down. For them, the idea of having a guaranteed job for five, eight, ten years is something that’s unheard of — no one has that expectation. Just like artists don't have this expectation that they'll be able to be funded or work on their best idea for infinite periods of time.
Adam Marblestone: There really is that training. People might say, “If you haven’t done research, you don’t know how to run your own research group.” That may be true, but similar things happen, let's say, within FROs. We have academic scientists that come in and initially they're like, “Wow. There's too many meetings. Why am I coordinating so much with these other people on this team?”
But by the end, they really know how to coordinate effectively, plan something for a longer term basis or larger-scale basis. They're doing all sorts of things that they weren't doing in the academic setting. That's going to serve them really well in all sorts of future dimensions.
Tamara Winter: What are some cool, interesting areas of science or technology that you think are currently underinvested in? Tyler, you just gave us some.
Patrick Hsu: What’s underinvested in right now? I think most of biology. I was at an AI in biology dinner the other night where we were talking about how the model performance, these days, of LLMs, of transformers generally is incredible, even with vanishingly small amounts of data. The important thing about the data is that it needs to sample enough about the behavior of the system. The thesis of the expert biologists in the crowd is that we just measure too little of biology.
The question is, what data are we missing and how do we get it? And there’s no clear consensus on it. It often revolves around measuring more of the central dogma at the single-cell resolution — measuring more DNA and RNA and proteins and developing single cell technologies. But there’s this broader idea that we think about in my research group: biology has always been a measurement discipline. We've really been focused on things on what we can look at, whether that’s a microscope or sequencer.
But fundamentally, and this is something we appreciate a lot in microbiology, the single cell may not really be the right fundamental unit for biological function. We understand quorum sensing, and biofilms, and community behavior in the microbial context. But in the mammalian or human context, we talk a lot about measuring single cells because it's easy and cheap and you get a lot of richness of information. But we don't really have technologies that look at interoception, cell-cell communication, long range effects, things at the organ or tissue scale.
There’s a fundamental lack of technologies that allow us to peer into and measure what’s going on at the higher level of hierarchy. Maybe that's the missing data per se, but that will require fundamentally new tools.
Adam Marblestone: Just how deep the basic physics, basic measurement technology gaps are in biology, when you get to these 3-dimensional systems interacting, multi-scale — there's such a big gap. That's another reason why this is happening: you need state-of-the-art photonics and state-of-the-art biochemistry and computation to do that.
With AI right now, it is very exciting. The possibility that, in the end, the description of biology is much less a list of things or a static representation like, “This protein is located here” or “This is the sequence of this organism's genome.” It’s more like an embedding space, a machine-learned representation rather than something biologists understand. This is going to be the description of that cell or that tissue could become the new way to describe a cell or tissue. That is a possibility, but that will require this upscaled approach to data generation for sure.
Tyler Cowen: One way to approach it is to go to a typical university and see which departments are small but not totally irrelevant, and look for opportunities there. When I did my podcast with Richard Fromm, one of the most prominent ornithologists, he was telling me that the last 10 to 15 years have been an incredible revolution for ornithology. We now have data on everything, and before we didn't have data on anything. But there is a scarcity of people to do the work.
Even in areas like biomedical, you could imagine an advance in something like metabolism coming through ornithology and not just direct biomedical research. It has happened so often in the history of science, that lateral applications come from seemingly distant areas. Quantum mechanics are behind computers. Who would have thought that, right? There are so many opportunities, but talent is scarce, and money is scarce. But you can have a really big impact just by having a degree of daring in yourself — which is more scarce than IQ or even money.
Tamara Winter: About introducing the concept of an FRO, what is the ideal interaction between governments and these new scientific institutions? It's interesting to watch ARIA spring up, and SPRIN-D in Germany and of course DARPA, ARPA-E, ARPA-I, ARPA-H, and IARPA — all the ARPAs.
Tyler Cowen: Try them. I would say resist nostalgia for the past. I get a little nervous when I see people looking back at early DARPA and thinking, "Oh, that worked great. So now we're going to keep on cloning that." It just doesn't feel quite right to me. But we are seeing way more experimentation, and we need to let those models evolve as well.
I’m quite optimistic. There’s such an intense, vibrant debate about science policy with actual institutions in play — from the private sector, foundations, corporations, and governments. It's pretty phenomenal, in a small number of years.
Adam Marblestone: You don't want an exact clone, but I think the ARPA model is insanely powerful. Very, very powerful, because whatever the institutions look like at a given moment, that ARPA program manager is going to go and play the piano between those different institutions and form that central coordinator role, for these findings that are too big of a risk for individual organizations.
The ARPA model is very powerful, but exactly cloning DARPA — I don't think you want to do exactly that either. Maybe you actually want to include more FRO-like things, more OpenAI-like things. Maybe the best thing a DARPA program manager should do is nucleate an Arc Institute. It's unclear, but there should be an expanded playbook of ARPAs rather than restricting it down. But the ARPA model is super powerful, super general, and it makes sense that we have ARPA-I, ARPA-H, and so on.
Tyler Cowen: This may be my arts background coming out too much, but I see cultural self-confidence as an absolutely essential input, and it's scarce. There’s no guarantee that it’s there. Many parts of a country may not have it, or maybe none will. But when it is there, that’s when truly wonderful things happen. With institutions, you can ultimately only do so much work, but you need that magic in the air, and you need to be ready for it. That’s a far more intangible thing, but it's not impossible to steer or nudge it. You need to try that too.
Patrick Hsu: One of the really powerful effects of cloning the ARPA model is the idea that a moonshot ambition is baked into the mission of that agency. Having a governmental process for, operationally, creating more agencies with moonshot ambition as their literal reason for existence is really powerful. At the same time, I would like to see the government do more structural innovation beyond the agency level. There are lots of opportunities that could happen at a lower level of hierarchy.
But I agree with Adam’s point. For example, one thing FROs do is think systematically about the gap between what universities and corporations can do. What Fast Grants has done is to think about the gaps between large government-funded systems and individual philanthropists making individuals grants. Arc thinks about this at the intersection of universities, or basic science and industry, or biology in the technology sector.
There are these huge holes, and one of the long-term win-modes for Arc will be that people try to create more of these. That relative to the monolithic university or medical school research model, people will think that several hundred-person research institutes could be cloned, are effective models for doing breakthrough science, and should happen in multiple places.
Tamara Winter: I'm interested in — it’s still early days for all your organizations. Fast Grants has wound down, but we’re still seeing what will come of all the research that’s been done. I’m curious — what areas were you most optimistic about, or what interesting results are you starting to see? Why should I, a laywoman, be excited?
Patrick Hsu: Internally, we thought it would be a shame to bring together scientists of this caliber and simply have them work in the same building on what their labs were going to do anyway before they came to Arc. So, we think a lot, institute-wide, about how we can build better collaborative models to do bigger team science. The two major focuses for now are Alzheimer's disease and predictive biology.
One of the interesting things about biological research is that our ideas are often much bigger than what we're actually able to implement in the lab. It tends to be subscale relative to the vision. A lot of the reasons for that are remarkably prosaic — it’s because you have two postdocs doing it, or simply are only able to include an experimental component but you can't get top computational people for whatever reason.
For us, building the infrastructure so that you can tackle a problem as complex and diverse as Alzheimer's, with the cutting-edge technologies in each core area — how do you make the perturbations? How do you make human organoids with all the different cell types of the brain? And how do you read out and computationally analyze what's going on? That type of thing is the reason why senior labs can grow to 30, 40, or even 60 people — it’s essentially to own internal platforms. We’d like to centrally operationalize this.
On the virtual cell side of the house, one of the interesting things about AI is that neuroscientists have been making fun of computer scientists for decades about the concept of neural networks, having neural layers, and neurons in an ML model. But the funny thing now is that computer scientists seem to be having the last laugh. With enough scale of data and with the right kind of attention — if you can predict something from any arbitrary series of tokens and generally have very accurate predictions on what to think, say, or do next — that seems to be remarkably close to intelligence.
Even if you don't accept that this is intelligence, prediction of any set of tokens seems to pretty much mean you can do most things. For us, we simply need better model interpretability. We need to be able to make biological datasets with scale and order that have generationally been impossible, and are only possible now. This is a unique opportunity, and we’re building a team to tackle it in a best-in-class way — both across how we generate the data and how we build the models to understand it.
Adam Marblestone: I totally agree with that. That’s super exciting, and I think it's going to redefine all the different cell types or organisms. It’s all going to become part of this huge data structure.
With FROs, there’s probably two big things that we're excited about. One is that we’ve now had the first teams running in labs for a bit over a year. We’re seeing some of these theoretical questions, “the shadow of the future”: Can you hire good people? Can relatively junior people manage teams? Can they work together? These things are going, on the whole, really well. The teams have a cohesion, and they seem really channeled and streamlined toward their goals.
That’s maybe the thing we're most excited about. That is allowing us to create a better interface with the FROs to essentially say:What does the life cycle of an FRO look like? What are the things you need to be doing after month six? What are the things you need to be doing after month 10? How do you get your lab space set up? How can we help them with hiring? Some of the infrastructure is getting better.
But I think the thing I'm most optimistic about is — we're seeing a bubbling up of ideas for this. It is unlocking this creativity of scientists. We're getting proposals in areas… Again, we started in things like neuroscience. That was closer to stuff I had experience with. I was quite confident that neuroscience had multiple FRO ideas in it. I did not know that climate, measurement, and data, and agriculture, math, and epidemiology would have FRO-shaped problems. It definitely seems like they do. So we're excited about that.
Tyler Cowen: Two things I’m really excited about, on somewhat of a different plane from those last two answers. 14- to 19-year-olds: I don’t think we, as a society, have emotionally internalized how well-educated they are — the smartest ones, who are self-educated. I’m very excited about the Schmidt Futures idea to fund a lot of them. Emergent Ventures tries to do this as well.
If there’s one thing I would have everyone try to do more on, it’s targeting that age range — people who haven’t yet decided if they want to be scientists or not. Partly to get them to be scientists, or maybe entrepreneurs who contribute to science, whatever it will be — get them excited, get them into networks.
And the other is the nation of India where I now visit frequently. It seems to me India will be or already is a major talent source in the same way that Central Europe was in 1900, 1910. You just have these historical periods, Italy in the Renaissance, France in the 19th century, where things blossom. The place isn't always rich. There's ambition, there's aspiration, there's competition, there's enough English language there, internet connections are good enough.
The importance of India in scientific progress or intellectual worlds — we, here in North America, are barely beginning to figure out, and I think we should all be a lot more clued into that. That will be a third or more of the world's top talent. And that would be my number two pick.
Tamara Winter: One of the things I want to congratulate all of you on is asking more interesting sorts of questions — in your research, but also at your institutes and in the course of doing science. So, I’m curious about the next set of questions you're asking yourselves and your institutions. Where are we going next?
Tyler Cowen: For Mercatus, one big question we face is: What can we do next in India, and what should we do next with India? The answers are highly uncertain. India is quite distant. Our goal isn’t to preach anything to India, our goal is to learn from India and have good working relationships with people there.
Other parts of the world, we’re always looking at how we can attract better, more creative, and more ambitious students to our own projects. At any point in time, we support about 70 graduate students and 10 undergraduates — it’s roughly 80 people. It’s a lot of people. It’s the core of what we are, what we do. How can we make it even better?
We’ve been doing that for over 40 years, so we have a lot of experience. But experience is a trap, too. The world has changed so much over those 40-plus years, so we try to keep ourselves on our toes.
Patrick Hsu: For Arc, I would say we are a newcomer in a very long and illustrious history of American biomedical research institutes, starting with Rockefeller University in 1901, and coming out of this explosion of creating Institut Pasteur, Charité, and Berlin Hospital, to the Salk Institute and Scripps in San Diego in the '60s and '70s, the Whitehead Institute at MIT in the '90s, the Broad Institute in 2004. Each of these has been unbelievably successful places that have done incredible breakthrough science, but they were also created in a time with very specific historical and medical circumstances.
For Arc, in 2022 and 2023, we see biology changing rapidly — it's clearly accelerating even compared to 5 or 10 years ago. The types of experiments we can do now — single assays to interrogate every single gene in the human genome, when just a few years ago you could get your PhD for knocking out a single gene in a mouse and studying what it does. We're able to increase, by multiple orders of magnitude, the scale of science that we're able to look at and measure. Biology is going to dramatically change in the decades ahead, to move beyond a list of parts to understanding the embeddings.
So, we think about what types of unique technical and cultural capabilities we need to bring together to tackle unique, specific challenges today. And then, more broadly, how can we try to clone these model concepts, working as part of this exciting community.
Adam Marblestone: A lot of what we’ve been doing has been being pretty heads down, in operational mode. I have an amazing operational co-founder, Anastasia, and a lot of the things we've been trying to figure out are operational efficiencies of various kinds. For example, how do we boot up an FRO pretty quickly? Exactly who presses which button in the payroll system? Who signs the offer letters? These types of operational details are how you have multiple organizations that have some economy of scale to them, relatively autonomous but also relatively quick to set up, and amenable to people who want to mostly be focusing on science.
So, part of it is very operational. And then part of it is about finding the right balance between the internally driven nature of the FRO, very finite milestones and goals, and its external connectedness. How do we form effective scientific advisory boards for them? How do we involve industry experts that are feeding into how the FROs end up spinning things out of the project and generating impact from the project? How much does that ecosystem around the FRO, and the between them matter?
Long-term, there’s scaling questions both on the demand side and the supply side. On the supply side, there’s the question of who are all the ARPA-like program managers? Should all the people who don’t go to ARPA-H end up finding FROs? And they don’t do that, should they go to ARIA or Arc Institute? What’s the talent pipeline on the input side?
On the other side: How do we get more predictability in the process? Right now, we are a matchmaking organization that takes good ideas and teams and helps refine them and match them with individual philanthropists or combinations of philanthropists. But what’s the way to do an FRO competition? The best 10 ideas in 2025, can we just do all of them? That’s a huge scaling and funding question.
Tamara Winter: If people who are listening to this want to help you or get involved in some way, what is it that you all need? I think, Adam, you just told us what you need. But what about you all?
Tyler Cowen: Just email me, Tyler Cowen. My email is online. I respond to all emails.
Tamara Winter: Very quickly, I might add.
Tyler Cowen: Whatever advice, ideas — anything — please just write.
Patrick Hsu: Research institutes live and die by the quality of talent that we are able to bring together and our ability to vision-set and coordinate that talent to do amazing science. So, anyone who is interested in this mission or this shared set of challenges, feel free to email me as well, my email is patrick @ arcinstitute.org. I may respond less quickly than Tyler, but I’ll do my best.
Adam Marblestone: Right on. Email me too.
Tamara Winter: Excellent. Thank you all so much. This has been really fun. Cheers.
Patrick Hsu: Thank you.
Tyler Cowen: Thank you all.
Caleb Watney: Thanks for joining us for this episode of the Metascience 101 podcast series. Up next, we'll zoom in for a practical how-to on experimentation and evaluation in metascience.
Metascience 101 - EP4: "ARPAs, FROs, and Fast Grants, oh my!"