 All right. Let's actually get this started. All right. Hi, folks. I'm Josh. I'm a mathematician at Oxford and Stanford and the executive director of MediGov, one of the organizations you see up there. This is going to be a workshop on open problems in Dow Science. How does that mean? Okay. All right. This is actually a series of workshops that have been hosted at events over the past year, beginning with ETH Amsterdam, following up with the researching Web3 workshop, and most recently at the Stanford Dow workshop, part of the Science of Blockchain conference, obviously at Stanford. This is like an exploration or a long-term project to build up this new field of research of Dow Science. Maybe a good framing for this to understand where we're coming from and how this is Dow Science or Dow Research might be distinguished from typical things going on in the industry, is that the framing for this, for the questions that we want to ask, are not three months or six months or even a year down the line. It's really asking a question, what will Dow's look like 10 years from now? Will they still exist? How could they possibly disrupt the existing infrastructure for, let's say, corporate governance or other modes of governance, like non-profit governance? There's a bet that we've released that maybe could help sharpen this. The bet is, imagine by 2032, imagine if Dow's or other digitally constituted organizations controlled and managed over 50% of the assets of the US economy or just the global economy. Just imagine if 50% of the entire economy was managed by Dow's. What would that look like? Could you imagine today's Dow's doing anything like that? Personally, I can't. But that's exactly what we're trying to change through research. Now, I want to share today, we're going to do a little exercise. It's going to spend most of today speaking amongst ourselves in these small groups. You're going to have to be comfortable hanging out with each other for the next 30 minutes. Hope you're okay with that. The idea here is we're going to do a little funding exercise. Over the past several workshops of open problems in Dow's science, we've come up with lists of open problems, lists of problem source not only from practitioners, but also from researchers and academics. Now, what we wanted to do, we've also sourced more concrete research proposals that are derived and synthesized from setting some of these open problems. And now we'd like to present a selection of these proposals to you. And over the next 30 minutes, we're going to talk about, well, will you want what you actually fund them? As practitioners, or sometimes as even newbies to this space, like, do these things like make sense to you? Do they reflect or answer or respond to any of the problems that you care about? And you'll have a chance to sort of allocate, rank some of these proposals, allocate them, and to give us a sense of what are the priorities of the ecosystem? You know, what kind of research should we be doing? And this is really just an exercise. Of course, there are like infinite proposals that you can consider. But we felt this would be a really nice way to kind of interface with some proposals that have been developed by academics and leaders within these ecosystems, and then try to understand, you know, try to engage how would you might be do this better? Or if you're interested in engaging with some of this research, I can also connect you to the, to some of the academics and the researchers who wrote these proposals. Okay, so with that said, I'm going to put down a QR code and everybody can go to that. You can also, if you don't have a phone, just enter that short link down there. It's just bit.ly slash devcon-dow. And that should take you to a spreadsheet and a series of worksheets. Now, I was completely underestimated the amount of people that will be in this room right now. I honestly thought there would be like at most five tables of around like 30 people. I think we actually have capacity here around 120. Okay, which is going to be great because we're going to get lots of data and have lots of great conversations, but I'm going to count down. So just note your table because you're going to be, each table is going to be assigned to a worksheet. And that's, first off, do people have people found the spreadsheet? You can see what's in there. Okay, great. So I'm going to count down the tables from here. So this is going to be table one, table two, three, four in the back, five, six, seven, you're seven, eight, nine, 10, 11, 12, 13, 14. I'm glad I can count because that is exactly the number of sheets in that spreadsheet. And I want you to go through that spreadsheet and you'll find links to these proposals and descriptions and you'll be able to sort of go through the entire thing. So what I would like you to do, well, I think this thing doesn't work. Oh, no, never mind. Okay, so for the first five minutes, and I'm going to break shortly and I'll just walk around and sort of answer questions. But in the first five minutes, I really encourage you to just take a short time to introduce yourselves to the table, see what kind of backgrounds are you like, these are mostly developers, technical people and non-technical social scientists or some, I know there are definitely some researchers and just first in here. Briefly introduce yourselves and then just chat, go through these proposals, try to understand them, talk about what's missing, what's good, what's bad, come up with a ranking and then you can allocate $10 million, as you'll see in there, across these proposals. At the end, at the last 10 minutes or so, we're going to synthesize some of those conclusions and we'll talk about, and we'll have come to an open question and answer session where we can talk about what are the priorities for Dow Science and how can we learn, what kind of new things do we need to learn? All right, with that, I'll break and we'll start these breakouts. One minute, one minute, put in your rankings and your allocations. Oh my God, you're really giving that much money to that? Put in your numbers, see the numbers change. All right, the people who have already put in their things, come on people, there's no need to sabotage Dow's for nonprofits, building public goods. All right, I know, yes, there's a problem, too many people are editing at the same time so it's actually not allowing you to edit. Maybe this is telling you, you should have gotten your rankings early in, earlier on. Okay, so why don't we see what we have right now? And, okay, could you actually, what do we have? Okay, granular primacy permative seems, can you scroll up and down? Just scroll up and down, let's just see what the rankings are. Actually, I'm gonna, okay, let's see. We have, what's highest right now? One is good, one is good. 12 is bad. Okay. Okay, you know what, this is, if you could like slice it, like if you go to rankings in that little dash bar, if you could order the ranking by that, that'd be helpful. Oh my God, granular primacy permatives. All right, okay, so that's interesting feedback. People really like privacy or maybe they just like something like a really solid computer science thing. Allocation 900, almost a million dollars. Okay. I think Dan Bonnet would be very happy with that. Though he has a lot of money, he'll be fine. Let's see, what's next? We have a lot of people in the middle of the pack but it seems when the dows win is fairly high up there. You guys all want dows to win? Or at least avoid the places where they're losing, right? That seems like a good plan. But it's interesting that they are only getting half of as much as granular primacy permatives. So people seem to be putting a premium on these, maybe like CS research, I'm just inferring here. Challenges of digital public infrastructure, decently high ranking, but not much allocation. I don't know why. All right, in this expense that's left, I'll let people explore this on their own. It's kind of like interesting data set. If you, I would really encourage you to, if you're already on these groups, the kind of just refine the data a bit because I'll tell you why. So this data is going to be refined and cleaned up a little bit. And then we're, so right now this is $10 million in fake money or fake, fake money, right? So we will be giving some of this data to a bunch of different funders, the Therian Foundation, Uniswap, the World Bank, National Science Foundation, and trying to actually building together that $10 million grant to fund the future of data science. And this will be directly from the community saying these are our priorities. This is what we care about. At least with this sampling of research proposals that are currently out there. And hopefully this will help guide, if not the next 10 years, at the very least the next two to three years of funding decisions. So I really, really encourage you to go back and at least fix that ref mistake. Come on, who did that? Who did that? I'm gonna find you. I will find you. I like nonprofits. Also just give a little bit of background on this research project. So this is, as I mentioned, this is part of a longer project to build up the infrastructure for Dow Research and for Dow Science. So for those of you who aren't researchers, even for those of you who are, there's a different set of incentives in institutions that you need in order to build an effective research ecosystem. To be able to produce results that have implications, not just three months or six months, but 10 years down the line. And what we're trying to do is working together, I won't pull up the sort of the data set again, but the slides rather. But actually, could you go back to the slides and go to the slides? DevCon 22, that tab? Tab at the top. Anyways, go to dowscience.org. You'll see all this sort of preview there and it has an explainer for what we're trying to do. Basically a collection of some of the main research organizations in the Dow space, including Medigov, the Dow Research Collective and the Smart Contract Research Forum. Basically a bunch of nonprofits are teaming up in order to organize this field of Dow Science and trying to figure out how we can sort of synthesize and bring together practitioners, many of which are represented in this room with scientists. And how do we onboard practitioners more into science and of course attract more scientists, more academics, people from really a large variety of different backgrounds into this space to make it stronger, to make it more vibrant and to ultimately solve some of the problems that we care about. So yeah, all that will be explained at dowscience.org. I can't direct them to point it at it, but just go to dowscience.org. You'll see what's next. And I think with that, we have around three minutes left. Are there any questions? Just if this one, you might want to see what are, but it has so much to do with the proposal and how it was written. Some of them were not clear as to what they really wanted. For example, Dahli proposed a project. They thought it was going to be the conclusion of that, but then I didn't want to give them money. Yeah, so the question was about Dahli and the fact that the Dahli proposal and the fact that it didn't really make sense or it didn't communicate a sense of like, this is the ultimate end, right? This is the broader impact. And the reason we put that in here as part of the sample is because it turns out, well, it turns out funding goes everywhere, but also there's actually a lot of very playful proposals coming out of places like art schools or collaborations between art schools and engineering schools that are much more exploratory, that don't have a sense of like, oh, this is the ultimate benefit to dows, right? And this is actually one of the reasons, I think, it's important to think about Dow research and Dow science apart from standard like VC investment tracks, is that you can fund these kinds of more speculative proposals that don't have any specific impact in mind. They're just plain, right? It's a little bit like art, like Dahli. Any other questions in the back? So taking this. Yeah, yeah. So these proposals will eventually all be synthesized into a big survey paper called Open Problems in Dow Science and it'll be much easier to consume and shorter and have all the citations and you can see, and it'll be like a kind of like a nice concise wave to sort of share that with your Dow, where the factor is in your Dow. The other side of this is that obviously like a, and how do I say it? And there's different ways of being built by different organizations, including as I mentioned, Medica of DRC and SCURF, to figure out ways to, how do I say it? Shorten the pipeline from production of research to application of that research. In science, we call this like translational research, right? And there's efforts to do that. It just takes time and it takes building institutions. This is why it's important to build a research ecosystem because you can't just have researchers like doing random shit or academics spouting theory. You need to have this whole other piece of infrastructure that like pays attention to this stuff and then figures out this is the part that's useful. These are the experiments we're actually gonna, you know, put money into running. Yeah. Yeah, I think one question I had was more like on like, like 10 million is a good example because probably most of those like don't need much money at all, but like below a third threshold, they also couldn't do anything. So I wonder if there could be like something like with more skin in the game, like a GIT coin run on like purely Dow signs, where you for example, like as an active Dow member get higher matching. So actually the people that I actually have an idea of Dow's like have like higher magic, but I think like signaling also like what's the minimum money needed and the maximum. So you're actually almost more like a, I'm not sure if you know, the S process of funding. So I think there's like ways to improve the allocation over this. Yes, yes. I'm gonna answer that very shortly and out of respect for the next speakers, we're gonna end this, but the very shortly the first time we actually ran this exercise was at Stanford with a panel including Guy from A16Z, Aya from the Ethereum Foundation, and Scott from GIT coin. And very much into this idea of yeah, doing something, some sort of quadratic funding infrastructure. And we actually been talking with GIT coin because they recently are trying to make their funding package a little bit more like a protocol. Yeah, doing like a test run of that or maybe just doing like a funding round that specific data towards science, very much exploring that. All right, amazing. Thank you all for coming and sharing your thoughts. Appreciate it.