Bankless - 206 - Decentralized Science | Brian Armstrong & Patrick Joyce

Episode Date: January 22, 2024

✨ DEBRIEF | Ryan & David unpacking the episode: https://www.bankless.com/debrief-research-hub  ------ Science is a consensus technology. So is crypto. How can we use crypto to fix science?   We have Brian Armstrong on today to talk about this. Yes, the Brian Amstrong from Coinbase. He’s joined by his co-founder for Research Hub and scientist Patrick Joyce. In this episode, we explore the history of science, the pitfalls of TradFi, and how we can use crypto technology to build better incentive mechanisms for a more enlightened future. ---- 🏹 USE PODCAST24 FOR 10% OFF https://bankless.cc/Citizen2024  ------ BANKLESS SPONSOR TOOLS: 🐙KRAKEN | MOST-TRUSTED CRYPTO EXCHANGE ⁠https://k.xyz/bankless-pod-q2   ⁠  🛞MANTLE | MODULAR LAYER 2 NETWORK https://bankless.cc/Mantle  🔗CELO | CEL2 COMING SOON https://bankless.cc/Celo   🗣️TOKU | CRYPTO EMPLOYMENT SOLUTION https://bankless.cc/Toku  —— Timestamps 0:00 Intro 4:00 We’re Talking About Science 12:00 Consensus Technology 23:00 The Mission of Science 28:00 Metascience 33:00 The Problem with Science Today 43:00 Bad Incentive Structures 47:30 How Crypto Can Help 50:00 Research Hub 48:30 Peer to Peer Science 1:04:00 DeSci vs TradSci 1:11:000 The SciFi Future 1:16:00 Life at Research Hub ------ RESOURCES Get Involved https://www.researchhub.com/post/979/researchhub-raises-5m-to-help-scientists-monetize-their-research  Zuzalu DeSci Episode https://www.youtube.com/watch?v=pjmWpZK95dM  Research Hub on Twitter https://twitter.com/ResearchHub 

Welcome to bankless, where we explore the frontier of internet money and internet finance. That's what I usually say when I start these episodes. But David, we're actually exploring the frontier of science today. Decentralized science. And I still think it applies. The new frontier of science. Yeah, this is a new frontier. And this is another front running of the opportunity.

So as usual, guys, I am Ryan, John Adams. I'm here with David Hoffman. And we're here to help you become more bankless. So the episode today, science is really a consensus technology at the, the end of the day. Just like crypto, the question on our minds is, can we use crypto to fix science? And you'll never guess who we have on the episode. Brian Armstrong on the episode to talk to us about this. Yes, Brian Armstrong from Coinbase, he's joined by his co-founder and scientist Patrick Joyce. That's not the co-founder of Coinbase. That's the co-founder of Research Hub, which is another

company that Brian founded, which is going to be subject matter for today. We also talk about, Number one, why science matters. Number two, why science is broken today. Number three, how crypto can fix it. And number four, what they are building over there at Research Hub. David, I know you've done some D-Sai episodes in the past and put them out on bankless. Why was this episode significant to you? D-SI, short for decentralized science, is an emerging part of the cryptosphere that's pretty

far adjacent to probably what we typically talk about on these episodes and what bankless listeners are used to. but it is nonetheless a crypto subject matter with a ton of values and ethos and principles that every listener of this podcast will be incredibly familiar with, but applied in a new sphere, the sphere of science rather than this sphere of finance. As it turns out, the problems that we see in the traditional financial system, the closed walls, the rent extraction, the middlemen, is not actually only a problem of the financial world. it is also a problem of maybe the whole entire world.

And now we are talking about it in the context of scientific institutions, the universities, the who's giving the grants, who's winning the IP rights. And there is a lot of middlemen in this world of science, as we learned in this episode today. And so I think bankless listeners who have been with bankless for a long time will be able to apply patterns and principles that we have been teaching about here on bankless, but now in a new context. So take the principles of crypto, take them up from the lens of finance and turn it over to the lens of science. And we'll be able to have an interesting episode where we learn a lot with the same kind of foundational knowledge that we already have. Yeah, bankless listeners, see where you can spot the MEV in the scientific institutions and the corruption kind of see thing in. I mean, man, it's similar rules apply.

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of Ethereum. Experience Web3 development the way it was always meant to be. Secure, fast, cheap, and friction-free. Visit arbitram.io and get your journey started in one of the largest Ethereum communities. Bankless Station, I'm excited to introduce you to Patrick Joyce, a researcher, a scientist, if you will, and the co-founder of Research Hub, Patrick. Welcome to Bankless. It's good to be here, David. Thanks for having us. And also on the episode today, we have Brian Armstrong. You probably know him as the co-founder and CEO of Coinbase. but today he joins us as a co-founder of Research Hub as well. I bet you didn't know that.

And the powerful idea of rebuilding our modern system of doing science. Brian, when we usually have you on the episode, we're usually talking about crypto or Coinbase or, you know, something in the related field that we usually talk about on bank lists. But today we're talking about science. What gives? What's the deal? Why do we need to talk about science?

Yeah, well, you know, many years ago, of course, crypto took off as a trading use case. and I started to think more and more about how can crypto be used for a variety of things in our economy, how can it be used to bring communities together? And so I felt like the best way to learn was to go try doing something. And I was fortunate enough to meet Patrick Joyce and a few other early team members over at Research Hub. And I figured, let's see what can happen here if you apply crypto to a community and see if you can bring something unique together. So science is something I've always been passionate about, you know, in my family, they're scientists. I have friends who are scientists. And it was funny. I'd always hear them

nights and weekends kind of complain about the inefficiencies of publishing, getting in journals, you know, the university research model, and 50% of the overhead went to the university for some reason, and it took years to get in these journals. And I always had this thought like, wow, in open source software, we have such a huge advantage in terms of how people collaborate all over the world. You know, you put work in progress out. Sometimes some random person on the other side of the world submits a poll request. They have the other piece of the puzzle. I've had that experience as a developer. And, you know, I always had this passion for science and felt like it could be more efficient. So when I was fortunate enough to meet the research hub team, I was like, all right, let's try doing

something. And we can talk about it today what they've been able to accomplish. I think it's really impressive. Brian, would you consider yourself a scientist? You said you had scientists in your family, but are you scientists, right? So, okay, computer science. Computer scientist. Does that count? Well, you know, there's kind of this joke that if you have to put science in the name, it's not actually a science. There's no such thing as physics science and everything. I remember my professors in college would say, you know, computer science is a real science now. There's enough body of research and all this kind of stuff. So, I mean, I did get a master's degree in computer science, but I was never one of these people kind of who was really good at writing math proofs and publishing papers.

I was always just like, man, I just want to build products. I just want to hack on stuff. So I was like more of a software engineer and like a hacker than I was a true computer. scientist who I think of as somebody who writes, you know, math proofs and papers. And they're not like sitting there trying to get the first, you know, 100,000 users in their product. We have some of those in crypto, though, don't we? Some of these mathy, like cryptography researchers, we'd call them scientists. Vitolic is like that. Obviously, Satoshi was like that. So I knew enough computer science and economics to read the Bitcoin white paper. And I was like,

wow, this is really hard to understand. I had to read it like five times. But, you know, that's an example of a research paper that found. founded an entire industry and like obviously coinbase was entirely built on that research paper. It's also very interesting that that was published anonymously because we're going to, we'll come back to that and maybe a little bit later in the episode, how it's not really safe like to talk about every idea in science today. That's kind of an interesting topic. And people follow these like trends and if you offend the wrong person, it's like political, all these kind of things which shouldn't be that way because science is supposed to be about seeking truth,

even if it's like, you know, a very difficult truth to look at. But anyway, I don't consider myself a scientist. Hopefully someday I'll be like an honorary scientist, but I'm more of a software engineer, hacker, hopefully product builder, you know, but Patrick Joyce, he's got true scientist credentials. So, you know, he brings that side of the equation. As we progress in this conversation, I think this conversation will follow a pretty similar pattern that most crypto episodes will follow. We'll start with like laying down some foundations about what is science, what does science do for humanity, how has the system evolved? Why has it become broken? And how can crypto help rebuild it? There's going to be, I think, a lot of patterns that will match,

even though we're talking about something pretty far away from crypto, I think there's going to be very similar sentiments as we progress in this conversation. But Patrick, I want to actually learn a little bit more about you as we get into this conversation. Tell us a little bit about your background and maybe tease us with the illumination that you had about how crypto can start to innovate with science. Yeah, absolutely. So I'm like a huge science nerd. When I was in college, I was really lucky and got to do some undergraduate research at Johns Hopkins studying tumor biology, specifically like the epigenetic signature of cancer stem cells. The lab at Hopkins was this H.H.M. Howard Hughes Medical Institute funded lab that had like 30

of the brightest people I've ever worked with in an environment where they were allowed to just kind of like on their own volition explore what they were intellectually curious about. I was like, wow, this is amazing. You're basically just like exploring the world through a test tube. and I fell in love with benchwork. So I decided to start a PhD in Weckar Biology. Quickly found that not all labs are quite as lovely as a HHMI-funded Hopkins Lab. There's a lot of issues with the way that capital is distributed in science that creates a lot of pressure on the participants within the ecosystem.

That kind of erodes, I think, the value of the results that are produced. So I started a PhD in Wecker Biology, quickly saw a lot of these issues that resulted in a low quality of life for the average researcher. along with a low quality of accuracy of the average scientific result. I was frustrated by this, decided to leave, started medical school, very interested in psychiatry and mental health. And after spending some time in the clinic, kind of found that, hey, like the quality of our knowledge around mental health is also not that good. We have a lot of treatments out there, but they really don't work that well and actually barely better than a placebo across all of the patients that they're given to for conditions like anxiety, depression, PTSD. some of the most common mental health disorders that exist in the world.

And so I was kind of frustrated with this as well and was like, hey, what's going on here? Like, why don't we have better medications kind of at the end of the pipeline of academic knowledge creation? And in my mind, I thought it was kind of this broken way we allocate capital within basic science. So I kind of like stupidly at the time was like, hey, you know what? I'll like try my hand at making a startup to try and fix these issues. So I took a year off from medical school, built like the V1 of a product. You know, it was very lucky. He came across Brian's blog posts where he described a lot of like the similar issues in science that I was working on myself.

And, you know, gotten contact with him. We've been able to build research up now for about three and a half years. So it's been a great journey. I feel very lucky to be working on this problem. It's a really cool story. And to just build on to what David was saying. I think there's some similarities with the typical bankless podcast. And in fact, where I want to continue this conversation, maybe our first stop on the journey here is back to like basics.

You know, one thing that we realized when we started learning about crypto is like, well, in order to understand crypto or Bitcoin or Ethereum or anything, you actually have to understand money in general. And wow, this is a much deeper topic than it first appears. Money is just not something that is created out of thin air. There's a bunch of history here. There's kind of institutions. There's social coordination. There's sociology. There's other sciences involved in the process of creating money.

So take us to the basement level of science. So when you think about science, okay? this is like, I guess when did science emerge historically? I mean, can we go back to like Greek philosophers? Was that the beginning of science? Or do we go to like Isaac Newton? Is that really the beginning of science? And like, what is it? Is it a way of thinking about the world? Is it a philosophy? I've often kind of wondered going to this conversation, well, maybe science is some sort of consensus technology, similar to a blockchain in some ways, where we're all trying to figure out what truth actually is and the reality of the world. And so there's consensus. Maybe

it's all of these things. Tell us, Patrick, from a scientist's perspective, an actual molecular biologist who've not had on the Begless podcast, what is science? What is this thing? Yeah, so I kind of think a science as like this way to think about the world that is repeatable and testable that helps humans kind of uncover what is true and helps us manipulate our environment in the future. I think probably the easiest way to describe this structure is the scientific method. You can have an observation about the world. The sky is blue. Ask a question, why is the sky blue? And then create a testable hypothesis that you can use experimental data in order to confirm or reject your hypothesis. So yeah, I think it's a way for humans to come together on what is

actually true about the world. Okay. And Brian, what would you add to that? Like, are there any ties with what we understand about crypto? Is there like, is there some sort of different philosophy underlying science, like it appears to be a major reason that humanity has progressed through the last 400 or 500 years. Is it like a social consensus technology as well? What would you add to this? Yeah, I mean, if you go back and look at the history of this, there's kind of, I think it goes back to enlightenment values, right? And you can kind of read about some of these topics in Wikipedia or wherever about, you know, empiricism and rationalism and skepticism. And I guess, you know, I'm not a historian on this, but my high level understanding is that, you know, there was a shift

in thinking at that time, which was a breakthrough, that previously people, they were looking for these explanations of natural phenomenon and why did they occur in the world. And it was largely being taught from, you know, like a religious or a spiritual mindset. You know, if there's thunder and lightning in the sky, like, you know, maybe God is angry at us. And people would come up with these kind of philosophical or, you know, religious explanations. And, you know, people were just guessing. But whoever kind of had the best story, it was like memetic and it would spread throughout society. And so this idea of empiricism or rationalism or skepticism was was a breakthrough way of saying, okay, well, let's come up with a testable, a falsifiable hypothesis. How do we even know what's true?

Our brains are sort of naturally evolved to deceive us, right? We might come up with something just because of how we feel, we feel scared or we feel angry. And like, you know, there's all kinds of like fallacies that your brain can fall into. And most people, if you're not actively taught, this separate way of thinking, it's very easy to end up believing something because it feels right or because, you know, you were exposed to it in a certain way or whatever. So I think this different approach is kind of the foundation of science and enlightenment values. And, you know, sometimes you see people say things like, well, trust the science, right? I mean, maybe on the surface, that's a good idea, but part of science is actually questioning things

from first principles and how do we even know that that's true, right? And so it's sort of a, it's an anachronism to sort of say, like, just blindly trust the science. That's not what science is about. Science is about questioning each other. Which science should we trust? Yeah, exactly. And I mean, I think science is at its best when, you know, there can be an accepted theory that has a lot of evidence. And somebody comes up with a new theory and it's testable, it's falsifiable. And a very counterintuitive result is shown. And suddenly everybody has to question it again. Okay, maybe we had the wrong model of how the actual world works. But you see that human biases creep into this all the time. And people, they don't want to believe that new thing, even in the face of new evidence. And of course, there's famous examples of this, like, you know, I think Copernicus kind of came up with this heliocentric version of the world that the earth revolves around the sun and not the other way around. And, you know, people like Galileo were actually kind of convicted, I guess, of heresy or whatever, you know, they called it back then. And I think some people might have, even been put to death for, like, proposing these heliocentric versions of the universe, right?

So, you know, we see, like, much more moderate or minor versions of that in modern society where there's a belief that somebody has about the way the world works and someone proposes an alternative. And there's not actually a safe forum to go and propose that in science because you might offend, it's like, you know, you might offend somebody or, like, you might harm your career because, like, these very famous scientists have been proposing an alternative for their entire career and to go against them, you know, puts your own career at risk. So anyway, that's a bit about the history of science. I do think crypto has a unique role to play here. And it's actually become its own thing kind of like defy. Now this is called DeCi, right? Decentralized Science. And this is

still in its infancy. Research Hub is a number of really cool companies that are playing in this space. But, you know, incentives matter. And crypto is in a way for to align people's incentives to bring them together to try to accomplish better outcomes in science. And Again, make it a little bit more like open source software. There's different roles people can play. Some people are doing breakthrough science itself. Other people are going to be more like trying to replicate that science. Other people might be more like editors to that science.

Other people might be sort of like science educators, you know, trying to translate it to a lay audience. If you have some breakthrough in science, how do you go commercialize that and make it so that products can come from that that actually benefit people, right? So I think crypto, if you zoom out to the 20,000-foot view, it's just a tool to align incentives. and the incentives in science are kind of broken, right? There's not a true economy where if you come up with some breakthrough like CRISPR, you know, the people who did that, Jennifer Dodd and others, they should probably be billionaires from that.

You know, it's created so much value for the world and it will in the future. But that's not how it works currently. Science often exists in sort of a false economy of based on citations and tenure. And so you get all kinds of inefficient outcomes where people are publishing something which is really not a breakthrough. They're dressing it up in all kinds of fancy. language to try to get their PhD or approve, you know, like 10 other people in their field will read it at some journal, but it actually does nothing for the world. And of the two to three

million papers that are published every year, most of them are never read. They don't replicate. They don't have any kind of real breakthrough. They're just someone trying to get their PhD with a bunch of fancy language. And so I think if we can align science back to real incentives, maybe crypto can help and that we can get more breakthrough. Like, we can accelerate science. That's actually the mission of research hub and have that eventually translated to real products for real people. Yeah, fascinating. And we're circling in on kind of some of the problems and the solutions here in a roundabout way. And Patrick, I'm wondering, what do you think of Brian's definition of, your science and why it's important? Yeah, I think it makes a lot of sense. It is kind of ironic

that sort of the catchphrase of the last few years has been trust the science when the motto of the first scientific journal was actually nullison verba, which means take no one's word for it. So basically, like this inherent skepticism where there are theories. about the world that we only believe if they've been tested, you know, multiple, multiple times. And the strongest ones are the theories that are able to be resilient to these tests. For instance, like the acceleration of gravity, 9.81 meters per second squared, but you can drop a rock 100,000 times, and you're going to get the same measure for acceleration of gravity.

So, yeah, I think what we want to build with Research Hub is kind of a new system that helps to make it easy to test hypotheses using objective data and then allows the world to have a better understanding of what's going on around us, rather than just inherently trusting the science or trusting the experts who are kind of the people behind the dissemination of scientific knowledge at the moment. Can we just reinforce again the value of science here for our audience?

Because I don't think people think about kind of the basement level principles very much. So we had kind of this enlightenment where we discovered the effectiveness of the scientificness of the scientific method that was in the 1600s. And most listeners learn the scientific method in grade school, right? Which is you have a hypothesis, you test it and see if you can kind of like know the hypothesis and repeat the experiment, that sort of thing. It's quite amazing that such a very simple primitive. And it does sound very simple. It's just like a series of steps, right? With it kind of a measurable outcome at the end. It's sort of deceptively simple that such a

protocol could like absolutely transform humanity. And indeed, that's kind of what it did, right? It brought us out of kind of the dark ages. When we think about technology, most listeners to this podcast will be probably tech accelerationists. They're in crypto, right? They're technophiles. They love technology. You think about how does technology actually get created? Well, it's on the basis of this public good, of this recipe, of this protocol called

science, the scientific method. And that's absolutely wild. And like, we can't have technology. We can't have accelerationism. We can't have the civilization, all the good stuff that we want without science. that's the kind of the bottom layer of all of these things. And the process from turning science into technology is something interesting to talk about. But I just want to reinforce the value proposition here. Like, look around you. Like this conversation, everything you see and appreciate

about humanity and all of the fantastic things that we've done in human civilization are because science. It's like that's the one thing that is propelling us forward and it's kind of a simple truth. I'm sure you guys are both nodding your heads and especially you, Patrick. Like, is that why you're here? Is there kind of like some mission orientation with like a true scientist as a truth seeker trying to propel humanity forward? Is that why scientists do what they do? Yeah, absolutely. I mean, I think it's about like reducing suffering in the world in general. So kind of what I mentioned before when I was into psychiatry and mental health, like there are a lot of issues in the world that a lot of people struggle with on a daily basis. And having like a repeatable process through which

we can understand what reality is allows us to like manipulate our environment to our own advantage, create, you know, drugs and, like, small molecules that can actually, like, change how you feel and, like, make better your perception of life. And so I think that, like, all technology is based on scientific underpendings, which are based on the process of observing what is true, asking questions about it, and then creating experiments to test hypotheses that underlie the theories of how the world works. Yeah, and I can just add to that, I mean, if you look at all the countries of the world, and they're all kind of trying to think about, you know, how do we grow our economy? How do we have more budget? You know, how do we improve health care and like

all these things? So Matt Ridley and the kind of others have written about this, but if you sort of trace it back to the root cause, right? And it's like, okay, well, how do countries grow their economy so that they have more resources to do more things? I mean, well, you have to have some sort of product that is unique that your country produces that has some unique advantage. It's like hard for people to get it from other countries, right? It's distinct. And so how do you get products that have some advantage, right? Whether it's like the best car or the best rocket or open AI or whatever. Like, well, it usually comes from some technological breakthrough, right? Okay. So how do you get a technological breakthrough in your product? Even going back to like, you know, the Dutch Empire,

like they had the best ships, the caravals or, you know, all these kind of, you can kind of go back and back. Well, where you get the best technological breakthrough is you need to have some sort of scientific breakthrough, right? How do you get scientific breakthroughs? Well, you could probably trace that back to, you know, having great education or having a great immigration program, like to attract the best and brightest people all over the world. And so, you know, essentially like good education produces good science, good science produces technological breakthroughs. Those get commercialized into products, which produce. the prosperity of entire empires. And, you know, sometimes when people think, I don't want to

speak for Patrick, but, you know, I think we're both techno-optimists in that sense. And, you know, I think it's important in a country that you celebrate science and celebrate technological breakthroughs because I think sometimes technology gets a bad name. There's like this tech lash thing, right? People think of, oh, technology is like Instagram. Aren't people like addicted to that and it's kind of frivolous? And it's like, no, technology is much more than that. Like, technology is the shoes on your feet, right? Technology is like fire, humans inventing fire, which led to the internal combustion engine and cars and like, you know, eyeglasses or contact lenses on our face. And, you know, if without technology, we would literally just be like freezing and starving in

the wilderness, like as primitive beings, right? And so, you know, once technology has been around a little while, people don't, like, they don't think of eyeglasses and cars as technology. They think of like open AI and Instagram. But like, they're all part of one. chain of discoveries being built upon another. And I think it's worth celebrating that. Like Uri Milner and a bunch of people they put on this breakthrough prize and they try to actually make scientists like celebrities because they've had this observation that if you look at like the top followed accounts on Twitter and Instagram and stuff or X, like something like 95% of them are musicians and athletes. Right. And so that kind of is like it's sort of an example of

what our society celebrates. And if you ask, like, young people, what do you want to be? Right? A lot of them say, like, I want to be a social media influencer or something. And so, you know, I think it's fine to have some percentage of society be allocated to entertainment, like music and sports and, like, entertainment is great. But maybe it should be like 20% or 30% of our societal, you know, heroes. And like some percent more than like one percent should be the scientists that are achieving these breakthroughs. And so anyway, we want to have like leaderboards. It's like there's a lot we want to do. On Research Hub, it's still very early days.

But, you know, we want to recognize the people who had the best breakthrough in computer science or physics this month or this year and, like, actually give out the Research Hub prize, you know, kind of like the Nobel Prize or something like that, kind of all community-oriented and crowdsourced and voted on. Anyway, lots of stuff there. But, yeah, science is important. We're really passionate about it. I want to add one last dimension to this conversation before we get into some of the details

about, like, what the current modern system of science is and the ways that it is broken or failing us or needs improvement. This last dimension I want to add is the rate of science, which is just like, how much science are we doing, like units of science per time? And I would guess over the arc of humanity that we've been able to do more science over time. And that has been part of the growth. And we have this thing called the scientific method, which I think is just like the first principles of science. And then we have the system of doing science that we've built on top of the scientific method, and that system itself has developed and grown and become more sophisticated over time, that system is a science. And so we're kind of talking about the science of science,

I think. And I think what we're saying is, hey, we are in a phase in humanities arc where we actually can maybe make a newer system of science because our current system of science isn't doing so well. Brian, you use the terms of just a chain of discoveries, and if we're going to put these into blockchain terms. I kind of think of science, maybe a little bit like a blockchain, where we have growing complexity of state being layered on previous knowledge. We have more knowledge being layered on previous knowledge. And then there's like instability in consensus at the tip of the chain, whereas the new knowledge is contested. But over time, we have replication, we have more studies, and things become embedded in knowledge. And that just becomes truth. And the system of

science, the consensus mechanism that we have, really determines the rate of science that we're doing as a species. And I think what we're talking about here is, hey, with crypto protocols, we have newer tools in the tool belt for producing a different system of doing science. We can do more science. Patrick, does this metaphor kind of resonates with what you're doing and inspiring to do at research hub? How would you react to that? Yeah, absolutely. So, like, the science of science is referred to as meta-science. And there are a lot of people who have spent, like, the last 30 or 40 years, kind of understanding how, like, you know, scientific capital turns into new knowledge and sort of the inefficiencies that have existed within an old system in an internet age.

So, yeah, the key innovation that I think we bring to the table with Research Hub is the ability to have modular incentives for creating new scientific research. Kind of traditionally, one of the core problems that's existed in science is if you have capital and you will, want to give it to a scientist, like, how do you decide who to give it to? If you think of it from the terms of, like, a VC investing in startups, like, there are pretty objective metrics that you can use as a VC to determine which startup is the best company to give your money to in hopes of, like, seeing return on that investment. In science, it's a lot harder. Oftentimes, like, the true value of research isn't known for, like, decades after the initial discovery. Like Brian said, I think

CRISPR wasn't truly recognized for its, like, actual, like utility as a product for maybe like 10 to 12 years after the initial discovery was made. And so traditionally, what people with capital have used is this concept of bibliometrics or citation-based metrics. The idea is if you publish a lot in scientific journals and those papers are cited by your scientific peers, then you're likely a good scientist. Unfortunately, this system of using citations as a metric for scientific quality has gained a lot of adoption over the last 20 years, and scientists have begun to sort of optimize their research outputs to game the system in order to generate more publications and more citations. The goal here is to make themselves

more eligible for funding in the future. And so in the last like 20 years, we've seen, I think, like a reduction in the efficacy of our ability to, like, produce new technologies because we have scientists who are more worried about publishing a lot and getting their papers cited a lot than they are with creating verifiable new knowledge. And so with Research Hub, what we want to do is create a new way to financially reward scientists for creating high-quality knowledge. And using like sort of the principles of blockchain, we can come together as a community and democratically decide together what we actually perceive to be like high-value knowledge and set up a reward structure to algorithmically financially compensate people for producing that.

So, yeah, I think traditionally we've sort of relied on these, like, journal-based mechanisms to decide, like, what is quality science? And the internet, and specifically blockchain, gives us new tools to get more granular and actually reward people for doing high-quality research rather than using a proxy metric of citations. It's really fascinating because if you look at, you know, what science is trying to do is it's not all that different from a block. in terms of it's a consensus mechanism. It's a truth-finding tool. And by the way, it can be corrupted. Like, we all know that there are ways to kind of corrupt a blockchain, right? You can do a 51% attack. There are kind of like MEV-type vectors. There are all sorts of ways to kind of corrupt the truth of a chain. And so we're trying to design bottom-up censorship-resistant truth machines that are essentially incorruptible or have a high cost to corrupt. It sounds like

the consensus mechanism that science is used. using is good and has brought us to a certain point, but it has some corruption vectors. Like, crypto listeners will be familiar with this term, gameed, okay? And so, like, it sounds like what you're saying is the citation method, which is basically how truth rises to the forefront, how the consensus machine of science says, okay, this paper is the accurate one. That's been corrupted by influences of capital, which is very interesting. And, like, I'm just wondering if you could kind of define a little bit more flesh out

the modern system of science because you use terms like citations and like journals and that kind of thing. I'll give you like my rough sense of it. It seems like the government, this is a total layman. I know very little about kind of how the system works. So that's why I'm throwing it out there, Patrick, for you to kind of react to it. But it seems like in the early days of science, kind of the main funders of science, you need capital and you have a truth-finding mechanism. But the main capital sources were like patrons, like rich people, lords and, you know, kings and these types of things, right? Now we've moved to this modern age. seems like the main funders of science are public institutions. So it would be like our government,

the NIH. Maybe there's some commercial funding of science involved in there, but largely kind of public. And then our consensus mechanism for finding truth is this citation. So you have scientists that write kind of papers. Those papers are like peer reviewed. And then the more citations you get, it kind of like moves up the page rank algorithm. In fact, I think this is maybe how Larry Page designed, like Google at the very early phases, inspired by the scientific consensus mechanism. And the stuff that rise to the top, well, we call that science. We call that truth.

We call that a peer-reviewed study. And like, that's good. That's the golden stuff. And what you're saying is, in that process, there's crept in some money, some capital, some corruption, some gamifying of the system. So we're not actually getting truth. We're getting some sort of third parties biased form of truth. And they're doing this to bend the rules to their advantage.

because there's some economic advantage. I'm just throwing these out there. Is this kind of like a rough picture of what the science system looks like today? Yeah, I think you absolutely nailed it. And really, there are a lot of great analogies there where I would say that science, you know, within the last like 10 to 20 years,

has done a lot of SEO, where, you know, maybe the initial page rank algorithm doesn't work quite as well as it used to because there's a lot of money on the line. And individuals within this system, you know, want to maximize their ability to receive money in the future. So scientists have done SEO on their work, and they're generating more like clicks and, you know, therefore, citations.

And the result is that, like, the end Google search results are less valuable than they were 20 years ago because of this sort of self-awareness. And really the term is good heart's law. So the idea that a measure ceases to be a good measure when it becomes a target for behavior. So scientists, they're really bright people. And there's this hyper-competitive job market where less than 2% of first-year PhD students become research professors. So in order to succeed as a scientist, you have to do almost anything you can to just be a part of that 2% that can get to the next level. And one of the things you have to do to be there is to optimize your outputs in order to become eligible for funding. So, yeah, I think your page rank algorithm is like a great analogy and is exactly what's happening in science today.

So I'm going to guess, Patrick, does this lead to you? maybe like three outcomes. This is just kind of a guess on my part. But like one is bad capital allocation. So we're funding the wrong things or we're like inefficient in the things that we're funding. They're not actually like achieving good outcomes. Number two, like maybe this also leads to bad science. So actual science that's kind of not truth. And then number three, does this also lead to like poor incentives? So we're not getting our best and brightest, our best and brightest in our society are going and they're working and they're in the Facebook ad department, right? And they're making 500k salaries and they're not scientists competing for that noble prize. Is it those three? Are there

other things? Yeah, I think those are three great categories and I have a lot of stories that kind of embody them. I think the first is like the concept of negative results. Are you guys familiar with this term? The idea is when you're like dreaming up an experiment, you have a hypothesis and like oftentimes you're wrong about the world. Your hypothesis is not supported by the experimental data that you produce. This is considered a negative result. If you look at the entire body of scientific literature, the proportion of papers that convey negative results has gone down from, I think it was like 40%-ish in the 1990s, and now it's only 20%-ish of all papers are actually, like, display negative results. And when you look at this, it, like, actually

comes down to people who are worried about their careers. There have been metascientists who have done studies that have found that, you know, negative results, they just aren't that exciting. So when you publish them, other scientists are like, oh, yeah, cool. Like, I knew this, you know, like no need for me to cite this or share this with my friends. When positive results are oftentimes, you know, just straight up more interesting and more likely to come across. Positive results are sexy. Negative results aren't sexy. Exactly, yeah. And so it turns out that it actually hurts your career as a scientist if you share a negative result. So if you conduct an experiment, it doesn't match your hypothesis, you're actually better off in your career not sharing it because you'll reduce your

ability to receive funding in the future by sharing a negative result. Because you're a narq. Exactly. You're a scientist narc. Exactly. Yeah, they call it the desk drawer problem because you get these negative results and you're just like, whoops, I'll put that in here. You know, nobody needs to know. And kind of the end result is that like, you know, there's a lot of knowledge that's not shared with the world. And it's due to this kind of broken incentive structure. And really, like, as you mentioned, dry and like poor capital allocation that reduces the efficiency of how we as a society are able to convert, you know, $1 to one unit of new knowledge. Right. Yeah.

Well, Brian, that sounds like a broken market and a broken incentive scheme right now. And crypto knows a thing or two about that. What would you add to that, Brian? Yeah, well, I think what we're referring to here is a culture of risk aversion. So, yeah, I've talked to scientists who say, you know, I basically figure out if the thing is going to work. And then I apply for the grant to the NIH or what. whatever, because I need to make sure that the things that I apply for grants demonstrate success so that I can get the next one and the next one. And imagine if you were in, you know, the startup world, in tech startups and, you know, you were going to pitch for seed financing

or your Series A or something, and you were like, well, I know that it has to work. Otherwise, I won't even apply for it because the shame or embarrassment or whatever of having not worked. I mean, most seed investors in tech or Series A investors, they expect two-thirds, 90% of their portfolio to essentially round down to zero. And it's a hits driven business, right? So that's kind of like the culture in tech and startups is you try really ambitious things. You know that most of them are not going to work because you're trying to get breakthroughs. And in science, the culture has become risk averse. And it's basically trying to do incrementalism and dress it up, you know, or shoehorn it into whatever you can get funding for like, oh,

the Department of Defense has a big budget. How can I dress this up to make some argument that it's going to help defense when I actually want to be researching something entirely different? So the incentives are broken. It's also like the people who make these breakthroughs don't get some big upside if it gets commercialized. And so you get people publishing papers on things, which I think, you know, have no hope of ever actually, you know, reducing human suffering or turning into some kind of product. They're just sort of, you know, off in La La Land, like in some of these papers make absolutely no sense why they're getting funded. So anyway, risk aversion, NIH, like if you were going to go in and try to sell something to the government, it's like, you know,

IBM would win that contract or like it's no, you know, people in the government, they're allocating other people's money. So it gets allocated based on citations, tenure, risk aversion, not looking stupid if something blows up, whereas venture capitalists allocate their own money more efficiently. So that's something hopefully we can add into Research Hub over time too and kind of, you know, know, we should be like giving awards for negative results, people who are willing to do that, right? Or one of the features we're trying to think about adding in the future is around funding and allow anybody to come in and propose ideas, get it crowdsourced, like, you know, in terms of peer reviews or upvotes, and people who have capital to allocate, hopefully a lot of people in the

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This means that SELO needs you to weigh in and make your voice heard. Join the conversation in the SELO Forum. Follow at SELOorg on Twitter and visit sello.org to shape the future of Ethereum. Maybe I can try and do my best to recap some of the misincentives that produces misknowledge. Patrick, you were talking about just like it's more safe to try and produce positive results, as in like I have a hypothesis and I did some science and my hypothesis has been proved correct. And that is more incented than equivalent science that's trying to prove something against a hypothesis. It's just like people want confirmation bias, right?

people want to say like here's my idea oh and it's correct there's just like a natural incentive to do that kind of science rather than the inverse and so as a result of that maybe there's like more false positives as we have three different theories go in three different directions and they're all proven true but they actually conflict with each other and no one is taking the other side of that trade for example to put it into like finance terms it's like maybe a market where short selling is banned it's just like as things just get perverse when you don't have the other side of that market being able to express it so And then we've also talked about just, Brian, like, no one's going for the moon shot. Everyone is just trying to keep their cards close to their chest, not trying to take any risks. And how can you blame them? There are kids to pay for school and there are, like, mortgages to pay and job security to keep in mind. It really kind of just seems like a Moloch problem, a Moloch trap, a coordination problem, where it's just we can't actually incentivize trying to find truth, trying to find the North Star. It really seems to always boil back down to just economics at the end of day, capital allocation. capital allocation like three, four, five times so far in this episode. Is it really that simple?

Is it really just boiling down to can we fix the financial incentives to fix science? Is that a reductive way to put this conversation? I think that's one way to put it. Yeah. And Joyce, you should jump in too. But I, you know, I'm a believer in free markets. I'm a believer that people allocate their own capital more carefully than they allocate someone else's capital. And so the market is this beautiful thing that, you know, it has this truth serum baked into it, which is like, if you're going to allocate capital to something. Did you ultimately produce something that generated revenue that paid back? And the people who are good at it get more capital and the people who aren't as good at it get less capital. And the minute we put that into a government institution, I think we break

one of the best mechanisms ever discovered to make progress. One of the arguments against this, by the way, is that there needs to be basic research, right, which would be, you know, how do we discover something about the cosmos or the basic laws of physics, like the Stanford linear accelerator. and like we need to invest in things which have no hope of immediately being commercialized. But, you know, I kind of question that premise a little bit. I think that there are things that can be commercialized even out of basic research. Now, it might take a decade or two, but there's enough private citizens with capital now who are thinking about longer time horizons like that.

You know, if you're like a basic venture firm that's just trying to invest in SaaS companies and have like, you know, three to five extra money in three to five years or something, Maybe you're not going to invest in some of these things. But we've seen people like, you know, Sam Altman or the Collison brothers, like, funded this new Arc Institute, right? We're seeing private sources of capital. In Sam Altman's case, you know, he's funding things like nuclear fusion, right? And like these are in the realm of science fiction. They're research projects. But, you know, or even if you went back to whatever, Elon, you know, with Neurilink or, you know, SpaceX, right? Like, these are the kinds of things that would have normally been in the realm of government. projects or research projects. But it turns out if you have a scientific breakthrough and you can turn it into a product, it's some of the biggest companies on the planet are defined by that. You know, by the way, Coinbase is an example of that too, because it's based on a computer science breakthrough by Satoshi Nakamoto, right? And so it kind of comes back to, like, the best companies are based on scientific breakthroughs. So anyway, I think there's actually a lot more

room to run for private capital being allocated towards this than, you know, a lot of people think. can actually contribute to basic research as well. I think we've defined a lot of patterns and principles and ideas about the broken incentives inside of science. And I think listeners are already starting to kind of get a grip and understanding for like, oh, this is a similar problem to crypto. We have similar tools here. And I think we've done a good job expressing the vibe.

But I'd like to actually kind of drill down and trying to get into some of the specifics of how crypto can actually help. I think we have a lot of ideas in crypto and we have fewer real products than we have ideas in crypto. And so maybe we can kind of drill into some of the ideas more specifically. Like how can crypto and crypto incentives and the tools that we have in the crypto space when we collide the crypto particle with a science particle, what are some of the specifics there? Like how do we actually reconstruct science? Patrick, I'll start that one with you. Yeah. So I think at the end of the value, well, there are a lot of use cases that crypto

couldn't bring to science. And I think there are a bunch of D-Sy companies that are helping to address different aspects of this. But at Research Hub, kind of our main concern is, you know, is setting up an economy where, like, high-quality science is rewarded with economic value. And so, Ryan, I think, as you mentioned earlier, kind of this, like, consensus mechanism of science and, like, how do we all agree what is valuable science? Like, that's, you know, not an easy thing to do. And Brian and I realize that, like, you know, a group of, you know, five to ten people in a room, it's impossible for them to, like, independently reward science in the most effective way.

And so what we want to build with Research Hub is a way to iterate on a reward structure that can, like, in an evidence-based fashion, help to compensate high-quality science over time. And so this kind of takes the format of, like, our current product, which is a Reddit-style forum where users earn the ERC20 token research coin for creating high-quality content. There's, like, a bunch of ways this is happening currently. So, like, if someone shares a paper and creates, like, a great layman summary of that paper for other people to understand, they may earn, like, a tip with RSC for their efforts. We also have a bounty feature where if you need a scientific task completed, you can create a research coin bounty for other scientists to come in and help you. So I think the core innovation that we're driving towards with Research Hub is, like, this ability to give, like, the scientific user base, like control over how research outputs are judged and rewarded with the eventual goal of creating a financial system where we can compensate scientists in an evidence-based fashion for producing

new knowledge rather than being reliant on this sort of like grandfathered-in proxy metric of citations. Okay, so Patrick, I'm going to share Research Hub here and just like to try to make sense of it. This is a screenshot. If you're watching on YouTube, you can kind of see here. So what you're doing at Research Hub, it sounds like, is you're kind of, are you removing the middleman? So, you know, bankless, we call ourselves bankless because we don't have to use banks for banking any longer, right? That's what crypto enables. You kind of delete the middleman. I don't know.

Is this journalist? Is it like no longer does a scientist have to go through this institutional journal type process? They can be journalists and they can do like bottom up peer to peer, finding of capital. and you have created kind of a hub for truth-finding a consensus machine on what's good science and what's not, and you're connecting the scientists to the capital in various ways. Describe this a little bit. What I'm looking at right now is, yes, a Reddit style, I would say upvote, downvote. It looks like a bunch of different papers across all sorts of different categories, right?

It looks like there's some biotech here. Looks like there's a physics paper, a quantized inertia, a physics theory that gets rid of dark matter. That sounds absolutely fascinating. Which current maladies were actually a survival advantage? Anyway, describe a little bit what you're doing here at Research Hub and what we're looking at. Yeah, so here's a Reddit-style feed where users can share existing scientific papers, publish new ones, or create any kind of scientific content. So this quantized inertia post up at the top here, if you click into this, this was actually shared by, I guess, a psychologist within our community who's really interested kind of as a hobbyist in quantum physics. as I think like a lot of people are these days. And so they came across this new kind of understanding of quantum gravity from this

scientist that I actually follow on Twitter, or ex podgies, and he asked a question. So he took some of the research coin that he had earned in the past, created a bounty, and then asked for a physicist who is more familiar with the theory to come in and explain it to him and help him understand whether it might be a viable way to like view the world through a new lens. And so, as Brian said, we're still in very early days. We're trying to play around with how we can use these new financial incentives to create, like, higher quality scientific content than the kind of antiquated traditional system. And so there are a bunch of ways we're going about it.

But right here, I think this is a pretty interesting, like, mechanism for, like, someone who's not a physicist to allocate, you know, relatively modest amounts of capital to another physicist to answer a question that is personally interesting to him. So, yeah, let's us kind of do these, like, sort of micro-grant interactions where people can earn value for creating content or providing knowledge that others with our community perceive as valuable based on the research coin bounty that they place upon the post. This is fascinating. Brian, I love your take on this and what's being built here. And you've got kind of, like, I think, a crypto take and engineers take, right? So, like, you have in the, like, the program, like systems like stack overflow. This kind of reminds me a little bit of that type of a system. Also have, like, open source. so much code is open source and like science is also an open source technology. Anyway, what do you see being built here and what's kind of the vision here? Yeah, so the V1 of the site is really about prioritization and discovery of papers. You know, there's two or three million papers that get

published every year. A lot of the sites that people go to to look at these like archive and bio archive, you know, these are really great efforts. I don't want to disparage them at all, but, you know, the websites are kind of, they feel like they were built 20 years ago. You know, they're usually run out of these universities by a couple of handful of grad students. And so they're just not modern applications that allow you to do, you know, voting and like threaded discussions and things like that. So that was the V1 of like, how do we actually just make sure, you know, as like a modern day journal, let's do the curation, discovery, prioritization of all the massive papers out there. So if you only have a few

hours that week as a scientist, like to come in and check out what's going on in a certain area, you can do that more efficiently. But the product kind of quickly evolved beyond that too, because just prioritization of discovery is only, one small piece of it. Okay, well, how do we get peer reviews incentivized, right? Or people going in there, like Joyce said, to complete some kind of scientific task. Like, if you have a scientific question and you want like a literature summary, which is a common task that scientists do, both in academia and inside companies, you know, what's the current state of the research? So you can now put up bounties to have people go do that. You can have peer review completed and people actually, you know, kind of getting

rewarded for that, whereas in the journal system today, not only are the like big paywalls, and it takes a year to get in the journal and all these kind of things, but the peer reviews are just volunteers doing it. These are like oftentimes like, you know, underpaid grad students. And anyway, actually being allowed to get paid to do peer reviews is like kind of a cool feature on its own. And so we started to add some of those features in. Now, where this is going in the future, I think it's beyond just prioritization, discovery, you know, incentivizing peer reviews, literature summaries, bounties, things like that.

It's how do we actually allow people to get funded more efficiently? How do we fix the funding pipeline? And you can see there's kind of an experiment there in the top right today where people are applying now to get some of their science projects funded or their research projects funded. That's kind of an early feature that is being beta tested today where the community can kind of crowdsource and say, hey, we think this is really cool.

We like this person's background, like whatever it is. And so we're beta testing, getting research funded now. And then where I think this is going in the future is this is going to kind of be, we need to solve the translation of science. Translational science is kind of where you have a breakthrough and how does it get turned into a product and commercialized. And the way that that happens today is super inefficient. There's these things called tech transfer offices within universities.

And essentially, you know, if you're like an entrepreneur and you want to commercialize something based on a breakthrough, you have to call up the tech transfer offices within universities. You have to call up the tech transfer office at Stanford or MIT or whatever. And it's a very bespoke process. You have to like talk to these lawyers. You have to go meet them in person. You have to negotiate all these like custom legal documents. There's high legal bills.

And at the end of it, you might get like a license to go try to do this thing. The universities get a percent of the cut, don't they too, Brian? Aren't they in kind of the negotiating seat in that process? Yeah, that's right. So that's one of the issues. A lot of the revenue goes to the university, not to the person who discovered the breakthrough. so this broken incentives issue. The other issue is that like,

I forget the exact note, it's like 99.9% of all papers never get commercialized into anything. And if you look at the revenue that like Stanford has generated from the tech transfer office, it's almost entirely due to like, I think Google and Genentech

was like the vast majority of it throughout the history of the entire university. So very few things ever get commercialized out, but when they do, they're often big breakthroughs. And so one of the ideas that we had It was like, how do we make it where there's like a one-click license button, you know, on a paper or a scientific breakthrough that someone's put out? And I don't want to go negotiate custom terms like with some lawyer.

Like, it's kind of like the Creative Commons license. Like if it's federally funded research, ideally it would be kind of in the Creative Commons. Anybody could go do it, you know. But it's also, if it's somebody publishing something out of like a company or something where they want to be commercially recognized for it, maybe we can come up with like a. standard, like a safe agreement is a standard agreement that a lot of startups get invested in now. Why don't we make like a safe agreement for science where it's like anybody can click this button. If you go build a product based on this innovation, I get one percent of the revenue for the next 10 years or something like that. A standard terms, or it's like Getty images would be

another example where photographers can put up all their images. There's like standard licensing in terms if you want to license that image off Getty and go do something commercial with it. And so that's kind of where we're hoping to get in the future. with the research hub is make it easy for anybody to commercialize science. And for people who are discovering these really cool things, they don't even have to meet the people. There could be like some company in India, in Europe, in Asia, trying to go build and commercialize cool things off their innovation or their breakthrough that they never even would have thought of. And they just have revenue streams coming in. So that's kind of like where we're hoping to get to in the

future. Just one anecdote. When I started my PhD in Microbiology, I was working in a synthetic biology lab. And between 50 to 70% of any invention that was created from that lab, the IP would be assigned to the university. So the PI would have less than 50% of the equity value of any, like, you know, innovation they created. And then as a grad student, the first thing they do before they even let you in the lab is they make you sign an IP assignment agreement where you receive zero percent equity. And you're kind of at the, you know, whims of your PI, you know, in 1990s record with the artists. That kind of one-sided. It's crazy because it's sort of like people don't fully understand financial incentives

because, you know, as a grad student, why would I invent anything? Right. You know, if there's no way for me to see any value, you know, coming from it. So it's just like sort of these old school grandfathered in, like poor incentive structures that are meant for 50 years ago and don't really resonate with the modern age. What I'm seeing here with Research Hub is a peer-to-peer science platform where, we have an alternative, maybe just like DFI is a parallel financial system to Wall Street, we now have D.Sai, which is a parallel scientific system to trad-sci, institutional science. And so we previously had the gatekeeping of science by like the universities and the institutions.

There's like the anecdote that you just gave us, Patrick. And now what I'm seeing with what Ryan was showing, the research hub and the concept of D-Sai in general, we have a new blank slate of a platform to do science and economically incentivize science. But it's blank. It's unwritten. And the future still needs to be developed. We still need to kind of shape the clay. And so I think we're at the starting line, I think, of what this could be. But there's probably a lot more things to do to make sure this system gets built right. One of the reasons why me and Ryan started bankless is because we wanted to make sure that the ethos of crypto is preserved as it matures, right? Like, we don't want to just bring in a new set of bosses to replace the old bosses.

We want a system to be sustainable into the future. Brian, love what you're doing at Coinbase, but I don't want, you know, all financial activity to route through Coinbase because that would be, like, you know, the same thing that we had. When I see the foundation of Research Hub, and I see, like, oh, it's a bounty platform, I see a new blank slate, but I don't actually know, like, how does the market actually stay balanced? How do the incentives stay? pure. As this system develops, how do we make sure that it also remains uncorrupted? Have you guys thought about any of this perspective or philosophy at all? Yeah, absolutely. And one thing I want to call out first, too, is I do think we have a blank slate, but we're holding it up on the shoulders

of giants, specifically the open science movement. David, you did a great episode maybe a couple months ago with Boris and Mikey, talking about D.I. and Zuzalu. And they did a great job of highlighting that, like, there have been this group of humans who have been going against their own personal financial incentives for the last 30 years in order to advocate for a new way of funding and communicating science. And this is the open science movement. So like sort of innovations like pre-print servers, like open peer review, open access journals in general. There have been a lot of humans who have been working for a very long time on how to create this system. And I think like the really powerful thing that Research Hub brings to the table is the ability to make this like a financially viable decision.

So you don't have to take career risk by practicing open science. You can actually be selfish personally and practice open science on research hub. And I think aligning incentives will really help to accelerate the growth of what the open science movement has accomplished. Does that create a new character class here of like, can you have the scientist entrepreneur? That's something we haven't seen in society, right? Scientists is always like, the person who gives back to the community, that's all great. But like, I also want to see the scientist entrepreneur. What is that character class?

Absolutely. I think kind of as Brian mentioned, you know, at the beginning, like, who are the most famous scientists in the world? Like the person who created, you know, CRISPR, like, is that a household name? No. You know, and why not? Like, I think, you know, one of the things about society is, you know, we're like status-seeking monkeys, right? And, like, we see, like, examples of, like, wealth and success and we kind of want to emulate it. And I think, like, the world has done a poor job of shining a light on some of the, you know, individuals who have been, like, so crucial to our progress as a species. And with research hub, one of the things that we can do is, you know, help people, I guess, like, capture the value that they create in a more efficient way where we might start to see some of the more wealthier people in the world, you know, be earning wealth because of the value they created through research hub. Okay, so we mentioned kind of like an upvote type of tool, which means there's some kind of like a mechanism for a building consensus. I also heard mention of a token. And one popular theme in crypto all this time

has been governance, right? And that's partially what David was getting to, right? With like, what if the old boss replaces the new boss? What if research hub becomes kind of the new scientific journal establishment and becomes corrupted as well? This is called D-Sai. There's got to be a decentralized part of this. And we've talked about the permissionless nature. Any kind of scientists can sort of enter this. But how do you keep it decentralized? And where does governance, where does the token kind of fit into this? Yeah, absolutely. So I think there are kind of like two efforts that we're making here to help decentralized research. The first is that all our code is open source.

So our community can see our source code and fork it if they believe that Brian and I are not, you know, doing a, like, faithful job stewarding the scientific community that we build through Research Hub. So there's some nice governance incentives there to have Brian and I essentially be aligned with our community's desires for the future of the product. The second is that kind of as I mentioned earlier, like, it's impossible for a group of five people to sit in a room in an a priori. or beforehand fashion and decide, like, hey, how are we going to financially incentivize scientific research outputs to come, you know, to end up with the best output? It's just, it's not feasible. And anything that we did do in a priori fashion would end up being gamed, you know, according to Goodhart's law, kind of like the current citation-based mechanism, you know, is being gamed by scientists. So I think one of the innovations that we're trying to

bring about is creating an infrastructure for iteration on a reward mechanism for science. So right now, those who earn Research Coin are able to vote on how rewards are distributed through Research Hub. So we've had some community governance votes around how many Research Coin are rewarded per day on the platform. We've also had community governance around how rewards are distributed to different types of content. In addition to that, we have like this element of reputation on Research Hub that helps to encapsulate a person's like, you know, real world contribution. like their kind of prior publications in conjunction with their activity on research hub. And how this reputation works is also defined by our community.

So essentially we're trying to build a workshop for lack of a better term, where scientists can come together and come to a consensus around like what is good science and how are we going to financially reward that. That's so cool. It seems like you're working to solve kind of this entire coordination problem here. Yeah, Brian, you're going to add something to that? Yeah, well, I mean, very directly, people who come and contribute. to the site or an RSC and then they can participate in these votes. So the more, they have more say,

the more they contribute to the site as decided by the community. I think at this point, about 5% of the RSC supplies being distributed per year. And so you can imagine over time, like, that'll decentralize. And, you know, a bigger, bigger piece of that will come from the community in future Dow votes. Brian, I'm curious. We've talked a little bit about DCI, of course, and David brought up this term, TradSai, right? And the only question I have for you is we're very familiar. and you more than most people about incumbents who resist change. And in crypto famously, we have regulators that like to resist change. There's lots of kind of internal stakeholders that don't want to see crypto take off. They actually don't want it to be successful. And we're on the other side

of that bet, another side of that trade, of course. Is there a similar, is there like a Tad-Sai, like group of institutions that, you know, don't want this to happen? Are you going to have some fights ahead, some giants to slay on the, like, maybe not the regulatory? Toyside. Who's your Gary Gunnerner. Yeah. Yeah. Well, just like anything that's disruptive and innovative, you know, there's that quote about like first they ignore you, then they laugh at you, then they fight you, then you win or whatever. Yeah, I think maybe for better or worse, we're still a little bit in that phase of they're ignoring it, D.Sai. That's not entirely true. I think, you know, the traditional journals are actually relatively tech forward in the sense that I think they're

high level aware of this. They occasionally will do acquisitions of companies that are trying to build innovative tech projects in the scientific publishing space. So my hope is that we can kind of collaborate with them. It's not antagonistic. Who knows, maybe at some point they'll invest in research hub or something and so we can kind of have aligned incentives. I hope it doesn't end up antagonistic. But usually in any given industry, there are some people who are like that, some people who aren't. You know, one big kind of sea change that's on the horizon, which I think will be interesting. So actually, the Biden administration put out an executive order where I believe in 2025, all federally funded research will have to be published in open access journals.

And so this is a sea change event happening that all the TradSai journals, I like that term, I don't know if we just came up with that, are going to have to adapt to this new world where they have, you know, paywalls for people to go access this. And it never really made sense because, you know, our taxpayer money is funding this research. It should be available to the public. We paid for it. Yeah. But just due to legacy reasons and like oligopolis and things like, like that. It's actually like thousands of dollars to go in and read this, some like research paper that, you know, our taxpayer dollars paid for. And so that's all going to be flipped on its head in 2025. And we're really hoping that Research Hub can leave the pack here as the leading kind of open access

journal, a modern day preprint server, modern day journal, if you will. And we have hubs. We call them hubs for many different scientific topics. I think there's like probably 10,000 different hubs technically. but the most popular ones around, you know, physics, computer science, like those kinds of major areas of science. You can also dial into like some really specific, if you just work on one particular protein, like one transcription factor in human biology, like you can probably go find a tag or a hub for that. Or if it's not there, you know, create it. Sign up and create it. So anyway, I hope we can work with the existing journals. I hope it's all collaborative, but I'm sure there will be some people who resist change. And that's just the nature of innovation.

I want to transport us forward maybe like 50 years when D-SI is massively successful because, of course, it will be, just like D-Fi. So D-Fi's working payments at the speed of light, permissionless finance, D-Sai right on its heels. And Patrick, maybe you can kind of create some sort of sci-fi story about what science looks like in 50 years after this D-Sai movement is perfectly successful and achieves all of its wildest hopes and dreams. What does science look like? What does it mean to be a science? scientist, how does science happen? Just like kind of tell us a story. What does that look like? Yeah, absolutely. So the thing that I'm most excited about for 50 years in the future in the world where DC works, and this may be a little bit more boring than like flying cars, but it's auditability. So like if a scientist makes a claim, there's public data, you know, associated with that claim where anyone in the world can audit the data to see if it actually supports the claim and then also replicate the data to see if someone else can find, you know, the same conclusion in an independent fashion. I think this will, like, drastically improve how we're able to innovate in the

world and, you know, will result in, like, all kinds of, like, societally disrupting new technologies. You know, maybe we can, like, teleport from one country to the next, but, like, I try not to, like, speculate about things that I don't know anything about. So, more so I'm interested in, like, ways that we can empower the average individual to participate in the academic world. I think kind of on top of this, another thing that's very achievable for us is to kind of like follow in the footsteps of open source software. For instance, like when I was a medical student, you know, I wanted to make a startup, but you know, no VC would talk to me. Like every engineer I talked to was like, no way, man, like find some money and then I'll work with you. And so what I ended up doing

is I went on GitHub and I found like open source versions of Reddit and I like tried to teach myself some Ruby on Rails and like hacked together like an absolutely atrocious prototype that I could then take to, you know, a financial person in order to like help to describe my vision. And so like I think the beauty of open source software is that like anyone with the internet connection can, you know, go on GitHub and learn how to build something in an autodidactic or like self-learning fashion. And that doesn't really exist in science right now. Like, if you want to contribute to kind of the world's scientific consensus, you almost need to have, like, spent a lot of time in, like, London or Boston or, you know, like Baltimore at Johns Hopkins. And there's, like, a gigantic proportion of the world that's just

not able to get there due to life circumstances. And I think in the future we'll create a system where there are less gatekeepers and anyone can participate, you know, much like GitHub. is done for open source software development. Brian, what about you? What do you imagine in the distant future when D.C.I. achieves all of its hopes and dreams. What are you optimistic about? Yeah, well, just to extend that analogy

about open source software, I mean, there's a lot of cool things about open source software. One is, like, people can fork your stuff and go off in another direction, right? So imagine if you're working on some piece of research. And by the way, let's imagine all that the data sets and the code is just right there. Today, research papers are like a PDF

that's like frozen in time. open source software is a living evergreen document. Like open source software is never done, right? It's you make your first commit, you publish it, you do all the work in the open, then various people come in over time. They might fork it. They might contribute to the master branch. And so imagine if research was happening like that.

Imagine if the data sets and the code was built right in. And then I think the thing I would be most excited about in the future is, yeah, if it's sort of democratized science, people all of, like good ideas could come from anywhere, right? You could have some, the next Einstein is like some kid in Brasilia or Bangalore or whatever, and they could contribute to something. They could publish some idea, the next Bitcoin White Paper or CRISPR. Some of this stuff requires like a laboratory and like, you know, a lot of capital to get into a good place. So I don't want to gloss over that. But good ideas can still come from anywhere. It doesn't have to be in this ivory tower of a few cities in the whole world.

and if that person can then actually we can make that link to how does this get commercialized, could I publish something that's so useful as an idea or a technology that it either has an open access license or somebody could have a one-click button to license it and go commercialize a product. Now I feel like we've really hooked up the power of free markets to scientific research, and that's how we'll really start to accelerate science. So I don't know, that's a bit of a vision of the future of where it could go. And just one question, just how to care. I'm a co-founder of Research Hub. You're also CEO of Coinbase. That's a very big role to fill. What's

your role at Research Hub? What does that look like? What are you doing? What's your time split like? Yeah. So even when Coinbase went public, you know, I kind of, we put some disclosures in there around this, that this was another company that I'm helping out on. But, you know, I really help a lot with the product guidance. But the real work day-to-day is happening with Patrick Joyce and the team there, which are incredible. Obviously, Coinbase is my, you know, full-time job and more, but I can't help myself. I want to see how crypto is going to be used in a variety of ways. That's actually ultimately the potential for crypto. It's got to go beyond trading and payments and financial services to actually be used as Web3. It's the future of the internet.

And so I feel like this isn't a really important way for me to go get my hands dirty, like figure out how it's could actually be used in Web3 to build these new kinds of communities. And so it's been an incredibly fun project for me to work on, but I want to give, you know, all the credit to Patrick Joyce and the team really that are working their hard every day day in and day out. Cool, guys. This has been a fantastic episode. And thanks for giving us a very practical overview of what you're building a research

hub. And also like D. I think I'm leaving this episode incredibly bullish. And also with an increasing recognition about kind of like the kindred quest that we're on with decentralized finance and going bankless and DECI. And, you know, I don't know if this is a quote. Maybe I just made this up. But like the only truth is science and markets.

Everything else is an opinion. That's certainly why I'm in crypto. Why I love finance, why I love markets, right? Is because this is a technology that allows you to figure out what's true or not. There's so many different professions out there where like you don't really know if you're like, if you've arrived at truth or not. But there are some professions like markets, they'll tell you whether it's true or not given enough time. Science also does that. And therefore it's a kind of a kindred technology.

It's like a blockchain for consensus about what's true in nature. And we've got this killer methodology, the scientific method, which is the repeatable experiments of how we verify the blocks on this scientific blockchain. And this position scientist really is almost like the validators of this network. So there's a lot of cool analogies and things that I'm seeing about both. And Brian Patrick, you've been very generous with your time in educating us about this. So best of luck. Like, how can people, I guess, keep in touch with what you're doing?

I don't know how many scientists we have as bankless listeners, but maybe there's a shout-out for, like, anyone who is in the scientific community to do something. What would be the action items you would leave the bankless listeners with Patrick and Brian? Yeah, I mean, I'll go first joys as you jump in, too. But really, if you're interested in science, if you know scientists, I would encourage people to go to research hub, sign up. You know, you can kind of choose which hubs or areas of science that you might be interested in. you can start to kind of just peruse it periodically and see what's happening in there,

but we really are looking for people to come in and participate in the community. So, you know, submit a paper if, like, you hear about it and you think it's interesting, go in there and write a peer review. If you have some kind of a question in science that you're trying to figure out and you want the community to go compile something together for you to try to make it understandable or digestible, you know, post a bounty. And then you can join the Discord. You can follow us on Twitter. at Research Hub, there's a variety of ways to get involved in the community. And I think a lot of people are just going to be consumers of the content, but we actually love it when people sign up and engage

a little bit. And it's early days. The site is still new, but there's a really vibrant early community that's super passionate about it. And whatever, you know, send us feedback too. We know that the site is not perfect. You know, there's a bunch of things that we're working on currently to make it better. So we love it when people send us feedback to you. But Joyce, what would you add? Yeah, so I think one of the, like, most fun parts of working on this project is some of our, like, most active community contributors actually independently thought of the idea for Research Hub and were working on it, you know, like themselves. And then they found us. And they were like, oh, man, I guess I'll just like, you know, meet up with Patrick and Brian and see if I can help contribute. So I'm sure there was someone who will listen to this episode who has been having this idea sort of pop up in the back of their brains. And if they're excited about, you know, trying to fix the problems that exist in science, I would say, say stop by research hub.com and join our Discord and introduce yourself. And we have a lot of very passionate, talented individuals there who'll be excited to chat with you and help to incorporate your ideas and to what we're building. I think at the end of the day, like you guys are right,

like we're trying to decentralize science and we know that we can't do it alone in an a priori fashion. And we need to bring in the voices of those who are most passionate about the problems. So yeah, stop by Research Hub, sign up, join our Discord and help us accelerate the pace of scientific research. It's a fantastic call to action. If anyone doubts the power of this, I mean, we built the entirety of crypto this way. Go check out the Ethereum Research Forms. You know, this is kind of like a mirror of what can be done here. D-Sy, not TradSai. I think we unlocked that in this episode. Guys, thank you so much for joining us, Brian and Patrick. Yeah, thanks for having us. Thanks for having us. Got to end with this. Rest and Disclamers. Wow, I usually say crypto is risky. Is science risky? I don't know. Corruption in any of our truth fighting mechanisms is risky. But we are headed west. This is the frontier. It's not forever. but we're glad you're with us on the bankless journey. Thanks a lot.