 My name is Alayma Bebel. I am co-founder and the Chief Operating Officer of Impact Alpha. And Impact Alpha is the leading impact investing media platform, and I believe some of you are subscribers, so thank you for your for your support. In, um, sorry. Data is at the core of Impact Alpha, and from the beginning we've tied our editorial coverage to data. And it's this emphasis on data that has allowed us to identify the trends and gaps in the impact space and across different industries and social and environmental objectives. If you are familiar with Impact Alpha's coverage, you also know that our coverage on climate is focused on solutions. And we do this by following breakthrough business models, innovative financing mechanisms, and high impact deal-making. In an effort, for us to go deeper in our climate solutions, we had the opportunity to partner with Eric Berlow from Vibrant Data Labs, a social impact data science company. Eric, together with his team and a few other partners, have developed the technology behind the climate finance tracker that you'll see today. So before I move further, go farther, we are going to spend the next 15 to 20 minutes actually exploring the data that Eric has discovered and that he will share that with us. And then we'll open up the room for questions from the audience. If you have the SoCAP app, you can use the Q&A feature, and if you don't have that, that's okay. We'll have people around the room with mics, and then we'll come over to you and ask you the questions that you have. I also like to introduce some other people from the Vibrant Data Labs team. Annie Garnes over there. She's a product manager, so she will be able to answer some questions as well. And we also have other team members from Impact Alpha. We have David Bank, CEO and editor, and Dennis Price, our chief impact officer. So you can ask any of us questions. So with that, I'll pass it over to you, Eric. Thank you. Is this live? Oh, wait, okay. If you guys want to scoot in, it might be easier to see the screens if you feel like it'd be great to be a little bit more cozy. It's a large, wide room. But anyway, thanks so much. And I'm just really excited to be partnering with Impact Alpha on this tool. Oh, cool. It's on. So the big existential question that we are all facing is one of them, is can we actually avert a climate crisis? And one of our partners, OneEarth, just published a really detailed, rigorous model that makes a solid case for yes we can. Yes, we can solve this and limit warming to 1.5 degrees within one generation. This was, it's a 500-page report published by Nature Springer Peer Reviewed, a lot going on. But the core essence of it is that there's these three pillars of solutions that if we achieve them, we can do it in one generation. And the energy transition, 100% transition to clean renewable energy, nature conservation, protecting and restoring 50% of Earth's lands and oceans, and a full-scale 100% shift to regenerative food systems. Those three pillars in the model, like without inventing anything new, we can achieve it. So to achieve that, however, is trillions of dollars of investment decisions that are going to need to be made to tackle those three pillars. And to help inform those solutions to be smarter and more strategic, we've been working together to create this open source tool called the Climate Finance Tracker that is meant to help everybody follow the money to climate mitigation and adaptation efforts on the ground. Now, there's many reports that try to just track where big bucks of money are going to what kind of broad categories of things. And here, we're trying to actually track the flows of money to the companies and organizations on the ground that are implementing solutions. This started in part because I was doing some work with a large foundation that did both impact investing and philanthropic grant making. They did not have a history of working on climate-related things like renewable energy or electric cars. But they're interested in understanding those other reports, convinced them of the need to do something or to think about it in their strategy, but they didn't actually have... It wasn't part of their history to work on those kinds of technologies and things like that. So they want to know where do we sit? And part of the question was, we don't know who's already doing what. We don't know who to talk to. Our network is focused on other things like human rights or whatever, and we don't know outside that network who should we talk to. How do we not duplicate efforts? So they asked me, could we try to see the big picture of who's funding what where that spans climate mitigation and adaptation, private investments and grants? And I thought, wow, that sounds really difficult. So let's try. Just for context, I am not a climate finance expert. I'm an ecologist. I'm a complexity scientist, a data scientist. I do a lot of work with high-dimensional data. So what you see here are my learnings in literally just like nine months of work of diving into climate finance data. So we're going to talk to you a little bit about how we did it. And then I want to walk through three gaps that I discovered in doing so that are... Can we get the... There we go. Three high-impact gaps that address these, that are in these three areas that are also opportunities for social capital to fill them. And so that's coming from the data. So I'm just going to give you a little... That's what I hope you walk away with at the end. Jason, hey. And but at first I need to tell you kind of what it is and how we did it. So we started from the ground up. We partnered with Crunchbase for Investment Data Candidant, which is Foundation Center, GuideStar together for grants data. And then we just searched for any company or nonprofit that mentioned any one of about 150 climate-relevant search terms in their profiles. The way we did that was by leveraging existing reports. So these search terms span a wide range of topics that covered mitigation adaptation using one-Earths reports, project drawdown, regeneration. We even scraped like hundreds of Wikipedia pages that mentioned climate mitigation adaptation just to pull out like what are the terms we should be looking for that mean climate relevant. When we did that, we found over 6,000 US-based companies and nonprofits that received over 200 billion in grants and investments in the past five years that were called climate-relevant entities. For each one of those, we gathered descriptions of how they describe what they do, how they describe their work, both from Crunchbase and Candidant, as well as we scraped LinkedIn and also their websites. And from that language, we partnered with a machine learning company that natural language processing called Primer, and we used that language to extract a taxonomy of climate-relevant keywords like energy storage, energy independence, microinverters here, whatever. And that taxonomy was generated from natural language from 6,000 companies and nonprofits. And then we also guided those machines through existing taxonomies like this one earth taxonomy. So remember I mentioned the three main solution pillars. They also broke it down into about 72 different solutions within those. So they had the taxonomy that we used to kind of guide the construction of this bottom-up ontology of climate-relevant keywords. We also searched for intersectional themes in the language around like climate equity, social justice, et cetera, as well as water security. And also for terms about how are they doing what they're doing, whether it's policy, law, technology, et cetera, activism. So from those keywords, why do that? So once we can tag every organization up with keywords, we have not just a list of who got funded, but now we can have a queryable database of who's doing what and who's solving what problem that comes from their language. And then we can let them self-organize by the things that are similar huddled together. And then they form their own topical clusters like this group in renewable energy or electric vehicles or natural disasters. The way that works, let me just kind of do an instant replay on that, is imagine every dot is an organization and every organization describes what they do and they have a bunch of keywords that have a signature of climate-relevant keywords. Then we link organizations if they're similar in their keywords and then that creates a network of organizations that are linked if they're similar. I'm a network scientist and ecologist, so this is where I'm just kind of leveraging old skills to do that. Then they self-organize because the links act like springs that pull organizations together if they're similar and then they identify their own little clusters that then you can auto-label by the most commonly shared terms in the group which creates a theme like renewable energy or electric vehicles. So that's a little schematic of how it works. This is what it looks when you have a bigger set of them. This thematic distillation of how the money is flowing comes not from what the funders say they're doing, not from any predefined categories, but from the language bottom up of how everybody on the ground, the doers are describing what they do. I think it's the most inclusive way of defining the problem from the bottom up and it includes co-associated language that those people are using. So every bubble is a theme and it kind of self-organizes. It'll change over time. The other really cool thing that excites me is that we developed a novel machine learning algorithm to lay them out in 2D space where companies that are similar clump together in space and the bubbles that are more similar to one another also clump together in space. So you already see like a few agriculture related and food bubbles in the bottom, nature ones on the left, energy ones in the upper right. So even that one earth solutions taxonomy of these three pillars of energy, nature and agriculture just came bottom up out of the language. It wasn't like predetermined. So that was kind of cool. So in addition to being a pretty picture, which gets me excited in its own right, it's actually, it's not just a visualization, it's a visual database. So it's fully interactive. It's kind of like, I would call it a visual Rolodex of US private climate finance where you can answer very quickly questions like who's already funding what on regenerative agriculture or energy storage? Who should I talk to because you can summarize and see who are all the investors, the donors, the founders, the executives and who should I convene around a topic to bust down silos and understand the future. So that's the visual Rolodex part of it but it's also a map to identify gaps and opportunities. Gaps are critical because all these things need to happen and so I work on complex systems. The most important thing is the thing that's missing in all this stuff because everything needs to happen. So whatever is missing is the most important thing you need to fill. So identifying gaps is key. And so I wanna show you how we just played with the data to poke around to identify a few gaps in these three high priority areas. So one key part of the data is we've got both grants and investments which is I've never seen before. You can go to Pitchbook to find a thing or Crunchbase you can go to Candid to find philanthropy data but having it together really makes a difference because one contextualizes the other. So already, so we got the green dots are venture back the redish dots are grant funded. We can already see a concentration of venture in one side of the map which is mostly focused on technology around electric vehicles, renewable energy, ag tech and even plant-based food like food tech. At the same time, because we have the language of how everybody's describing what they're doing, we can search for mentions of anything related to equity and tag all the organizations that mention explicitly anything related to climate equity in their work which is also non randomly distributed on the map. And so these were, we extracted about 150 terms like poverty, inequality, accessibility, underserved, low income, et cetera that came from the language of how the organizations describe their work. So now if we put the two together we can let, oops, we can, let's do that again. We can let the bubble sort of sort themselves so that, oh well, so that there's more venture back if the bubble has more green dots it's to the far right more venture back companies in the theme and higher up if it had more explicit mentions of climate equity terms it's higher up on the Y axis and so already, so remember so each bubble is a theme self organized, the more green it is the more venture back, the more red it is the more grant funded and the higher up it is the more they explicitly mention equity. There's already a big gap, there's a huge gap in the upper right where there never any venture dominated themes that commonly mention equity in their language which is no surprise because typically the venture model is you build for the rich and hope that it eventually trickles down the poor, it's like the trickle down economics approach. But it's a huge gap and I think once you identify the trend you can find the outliers. So let's just look at the energy transition question that's pillar number one, we need to do 100% transition to renewable energy. Well we can't do it if only 1% of the people have access to those technologies. So one huge opportunity here is to scale the renewable energy and mobility access to 100% of the people. And again it's not, in retrospect it makes sense that there's not a lot of equity language in that tech. But once you have a trend then you can see the outliers. So within there we can see these positive deviance of energy equity organizations that are trying to scale renewable energy to low income communities. And here's an example like you can drill down to each one the renewable, the rural renewable energy alliance explicitly in their description makes solar energy accessible to everyone in order to address energy poverty. So that's in their language. So that's how we're using the language to pull these things out. So one solution or opportunity is to basically support more energy equity nonprofits to just get the tech out to scale it. So that would move the bubble a little bit up into the left. The other option would be it's simultaneously to actually design the tech upfront for the bottom of the pyramid so that maybe it makes that job easier so you have more equitable venture back technology. So that's opportunities in the energy transition. The second area pillar is nature conservation which is near and dear to my heart as an ecologist. The goal here being to protect and restore 50% of Earth's lands and oceans. Here the interesting gap which is again kind of makes sense in retrospect but I didn't see it ahead of time. All the nature-based bubbles are often the lower left here where their grant dominated themes for conservation, restoration, biodiversity but they rarely mention equity related terms which is really interesting to me. And it actually is a very common thing while these efforts fail because they protect the land and then kind of screw the people locally and then it creates conflict and poaching and then it doesn't really work. So a big opportunity here is to look for the outliers. There are a group of nature conservation organizations that explicitly mention low income livelihoods and poverty within there. They're the exception but again the trend helps you find the outliers which allows us to think about with the opportunity here is to scale conservation restoration more efficiently by actually simultaneously addressing low income livelihoods on the ground locally and using those outliers as models for how to do that. The final area is regenerative agriculture where again our goal is 100% transition to net zero food and fiber production. In here there's three gaps and opportunities. One is ag tech again is not designed for smallholder farmers. It's more for large scale ag to be more efficient. So making that tech more accessible actually to all the farmers would make it help for all our food producers. The second one is the food tech plant-based diets is also, it was kind of came out to me surprisingly but these companies that are working on food tech typically are geared towards high income livelihood like wealthy lifestyle things and not like how do we get the whole nation on plant-based diets. So that was a little enlightening to see that in here. So how do you make that actually accessible 100% of the people? And then finally there's a lot of regenerative ag in this bubble which is a grant dominated bubble with a lot of equity mentions. There's a lot of red in here but there's also a lot of, there's some green dots in here too. There's these outliers which are venture-back regenerative food companies that are kind of rare and so our challenge here is how do we nudge this bubble to the right to help them develop more investable business models so that we can scale those ventures and this is kind of like where electric cars was 20 years ago is probably mostly grant dominated with a few sprinklings of investments and so how do we get regenerative food to be as hot of an area as electric cars are now? So those are those three different areas. High impact gaps and opportunities in the energy transition to scale access to the technology or design the tech so that it's actually for the bottom of the pyramid from the start. Protecting and restoring 50% of Earth's lands and oceans by actually supporting low income livelihoods so I'd call that nature-based development. The first one would be called low carbon development and then the final area in regenerative agriculture is helping scale access to that technology to low income or smallholder farmers and also helping develop investable business models for that emerging market so I would call that regenerative development because we can't do it if everybody can't participate otherwise we're screwed. Like this is a case where equity is the answer it's not just the right thing to do. So that's kind of what I found in there that's the climate finance tracker. I again I knew nothing about climate finance nine months ago like nothing and so I'm hopeful that so that's one story I found so that's why we partnered with Impact Alpha because they actually know a lot about it and so we're trying to work together to find and tell these stories and hoping to partner with others like Prem Coalition and others who are doing amazing work who are actually experts in that field. Right now we are focused on the United States like these are all US based companies because for the data we're better as for this prototype but we're really trying hard to expand our geographic reach. We're just working on prototype with a few others like RTI International on Africa but expand the geography, expand the funding types to include public sector funding, corporate funding, et cetera, research grants and then also everything we're doing is open source it's been philanthropically supported and also through contracts where we keep the licensing open source. So we're trying to make the pipeline for how we did this, the keyword ontology development, the everything accessible to other efforts to support them because it doesn't all have to just be this visualization we just wanna help the whole field so that's what we're doing. Yeah so that's the tracker. Just wanna leave you with this, like our take is the climate crisis is an all hands on deck problem and when I first started this and hearing from the group I was working with it's surprising to me that all the hands don't know what the others are doing, they all need to see what the others are doing and so our goal is to help those hands see what everybody's doing. So yeah, so here's our partners currently, a wide group of folks who are helping out and if you wanna check it out, here's the main landing page at Impact Alpha where there's also associated climate finance news. We were, we wanted to have a, basically there's like a session to walk through it with you to help you know, but somehow there's no way to connect the computer here so Annie's actually in the back there where she can kind of drive it if you want if you guys have questions because we're trying to learn how the interface can be more usable by others, there's a lot more we can do with it, this is a prototype that, I mean the first version of this was just me and a couple people and I know there's other efforts that's like two years and 35 people to like make a report so there's a lot more we could be doing and so we're actively trying to get focal groups for feedback and figuring out ways that we can expand as we're looking for support to build it out next year or two. Yeah, thanks. I have a question on, it was so interesting the way when you were walking us through like where the clusters are and the venture versus grants then there were those outliers and now what I wanna know is are those outliers gonna pan out over time? Will those companies die and will they be creating a whole new opportunity for others to follow or are they gonna follow the trend it was a one-off and then they didn't survive? Yeah, that's a really good question because we don't in here all we have is who funded whom and then we go from there to what are they doing with the language and then we try to put some order to that language and the idea, but we don't have measures of like how effective are those organizations at doing what they say they're doing and how successful were they? We are, we definitely are hoping to track these over time so that we could kind of get data on that but more importantly because we, because it's really, like I said, it's like a visual Rolodex so because you can really easily circle that, summarize them, find out who are the investors, who are all the co-investors that maybe you didn't realize who are the founders, whatever. At their minimum, it could spark some very simple conversations to ask, you know, convene those people and say, well, what's working, what's not, are these organizations successful what they're doing? My worry about, because Cisco Foundation was an early supporter of a prototype and they were hoping we could add an impact metric layer on top of it but the data, the data is not there for everything so then that means that we only have impact data for a small set that is a skewed set and there's a lot of problems to solve that don't have like immediate short-term easy metrics on them so we don't have that kind of layer yet, yeah. Yeah. What about just up feasible, is it to have over time data? I don't know. No, it's super feasible, yeah. Yeah, so this is a snapshot of today, right? Correct, yeah. And then how often is it updated and what happens? Right, right, so it's a good question. So we're trying to make it so that we could update it every quarter and that we, right now again, it's like a bunch of engineering just to get the versioning of it, you know, solid and as the clusters change over time, what does that look like? So we're putting a little bit of deep thought into how to do that because it's non-trivial but I have some really, really good collaborators who are excited to tackle it but yeah, we can track changes over time and that's what we're trying to do and just for context, like I know the Climate Policy Initiative is known for like the big reports they put out in the state of global finance and it is like a two-year process of like 35 economists and analysts would put it together for one thing and we were trying to get it so that within a week or two weeks we could do an update, you know, for every quarter and then also, you know, have a rubric for tracking changes over time and who blinked out and who didn't and is that blinking in and out non-randomly distributed on the map, that kind of thing, right? So we could, yeah, it's a really good idea and these kind of questions are what we need because I wouldn't have thought to do it to think about it in that way and so to get these questions from people like you who are actually trying to figure this shit out helps us know, like, oh, we could add that, yeah, you know? So that's a really good question. Just a quick reminder that you can use the Q&A feature on the app to ask questions and we have a question from there asking what is the best way for entrepreneurs and investors to interact with the map and add more data. So if you actually go to the landing page for the climate tracker, we do have a section for you to add your email. It's in your view, subscribe for updates. You can also add your email so you can stay up to date with the latest news and we also have the feedback. So you can tell us what you're looking for, what kind of data you have and then we'll reach out back out to you. That feedback button down there because one thing is for sure is this map, this is a map, but it's not a perfect map. There's companies in your portfolio that are missing because somehow they didn't come up in our search or they weren't in Crunchbase or whatever it was. There's companies that are in there that are a little on the fringe because they met the search but then they're a little bit kind of on that edge. So we want feedback on that and also this is all just what we were able to gather from publicly available data and so we are really open to trying to add other custom portfolios and that kind of thing. So this is just to get the conversation going. That said, because even though it's not complete, I'm pretty solid that the trends are very solid. Like we can, and again, I'm used to working with messy data all the time in ecology. Like we can subsample it a thousand times and see whether we get the same trend and how robust it is to errors in the data. So I think the trends, it's a sample, not a census, but it's a very good sample. So that's my... We have a few other questions, Eric, from the audience. Could this tool be potentially be adapted for other focus areas beyond climate? Oh, absolutely, yeah. So the framework we're building is generic to be applied to other things and we've done one prototype for adolescent mental health for one organization. The challenge is building the ontology of keywords from the language and also identifying like what's in and what's out and so it takes a while to get it set up and there is a lot of expertise involved in that, but no, it's absolutely being trying that the framework is pretty generic to do for other things, yeah. Because everybody should know what's going on is my take, yeah. Any other questions from the audience today? Maybe we just spend a little bit, oh, please. I'll give you the mic. So as a data nerd, I'm worried about data quality because so you're scraping the information in for each bucket and that makes a lot of sense, but typically more than one funder will come together to fund a project and each of one of those funders will be, let's say, posting their participation, their participation in the project online on their own website, right? So you could be double, triple, quadruple counting all of those impacts. Oh, I'm sorry, maybe you misunderstood the methods so we're not getting data from the funder's websites. We're just getting, for the organizations that were funded or the investees that were invested in, we go to their summary of what they do. So if your organization makes new batteries, we go to your website and your LinkedIn and your description of like, how do you describe your own company? And then we compile that all into one description of, so the entity that's getting tracked is just like the investee or the grantee, right? So not like 10 different funders saying that they did X, Y, and Z. And how do you get the typology about what's been venture funded or what's been grant funded? So the venture data came from CrunchBase so we're just looking at who got funded there, like just a list of companies and then the grants data came from Candid which is GuideStar and Foundation Center. And so we're just looking for mentions of climate relevant terms in the descriptions of the companies or their profiles or the grants that they got. So, and then sometimes that means that we got, like somebody might have mentioned climate but in the wrong context, like in this political climate. So they met the search and then we just got like 24,000 companies. So what we did there was we cast a wide net and then we randomly grabbed 10% of all of it and then we manually went through and labeled them as like now this is irrelevant, like it's the wrong context or this is irrelevant, relevant. So we labeled 10% of them and then we trained the machines to read the descriptions and learn that and then apply it to the rest so we could filter out the chaff, so to speak. So that's why it's not perfect. There's still some things that made it through that weren't relevant so maybe there's over counting there but each entity is only in there once in theory. Yeah, it's a very good question. We have more questions from the audience. These are more from the app. Can the data be accessed via an API? So the data cannot, so unfortunately right now the code to do this is all open source and like I said, it's generic that we could point it to other topics. Any data that's like who funded whom, how much, what type of funding and what did they do? But right now the data has come from a partnership with Crunchbase. They're providing data, the access for free and a partnership with Candid. And the licensing does not allow us to share the raw data. So there is no, we could add export data, like you could just query something, filter it down and export it. We could add that button but they won't allow us to do that right now anyway. And so I'm trying to figure out ways that we could do it where we just let you download a derived slice of that data, like just a list of who are all the funders or whatever it is, but not all the raw data. But that's a constraint right now unless we have other open data sets. And then there's other, we're hoping to partner with others who can fill in the gaps. But a lot of those data are totally confidential, like a lot of philanthropy data, which I just hope that efforts like this could help make those data more public because it's just so essential for everybody. Thanks, so that also answered would we be able to access the data behind? Another question is, could this be used to project trends of where we expect the most VC funding to go based on historic grant to VC funding? Yeah, it's an interesting question. Well, first just going back to the data access, you can, this is a really uniquely liberal license we have from French Basin Candid that you can drill down to the details of every individual company in there and see who is their funders, who, how much do they get, et cetera. So they are letting us, it's like kind of taking their whole database and making it visually browsable in a way, which is really unique. But for the predictions, yeah, as we start to get more data over time and we start to see where things were grant dominated with a sprinkling of investments and then over time became more and more, market-based solutions, for sure we could try to build predictive models on that. Yeah, and again, I think the putting together the grants with the investment data is like the synergistic thing where it provides more information than either one can do alone because to know that an investment is sitting in a sea of grants is really useful because it's knowing like, wow, that's interesting. What's going, it's unusual. Or a grant in a sea of investments is trying to make the tech accessible to low-income communities. It's like, oh, wow, there's only a couple of those. Maybe they could be useful for scaling this tech to more broadly or maybe we should talk to them about is our tech designed properly to make their job easier? The related question too to that was, are you tracking data through time? At some point can we track how investments change through time? So related to that? Yeah, no, these are great questions and we're, this is the first prototype. I mean, our first goal was like, can we do it? And so that's what this snapshot is. And then now we're like, oh, now we got to version it and we got to figure out how to track trends, how to track multi-dimensional trends in time, et cetera. And then here, is the map weighted by size of funding? Bigger bubbles for bigger funding? So right now the bubble, each dot, the bigger the dot, the more funding. It's on like a normalized scale because otherwise it's like one giant dot. Like it's a very skewed distribution. But the topical bubbles are sized more by how many entities are within them. But that's all configurable so we could change that. And similarly, when you, so I don't know if you wanted to show any, like if you wanted to see, let's say like, who's funding regenerative agriculture? You could go to the keywords and just search for regenerative in the keywords. Maybe look up there. And regenerative agriculture. So you can click on it and then really quickly just go to summarize that group. So I can't see how many there are but you could summarize that group. And then you can see, okay, here's a subset that mentioned their tag with regenerative agriculture. And already you can summarize who are the top investors like tech stars, et cetera. On the left, if you scroll down, you can see who are the top donors into those entities. So it's easy to kind of query and drill down. And you can see what are the funders. But what you can't do right now is then summarize that and say how much money was, went to grants versus venture versus whatever. You can sort by the number of entities, but not by the total amount that they got. But again, this is just, the more we learn we could add that to it so that you can summarize better by like how much money went to this versus that as opposed to how many entities are this versus that. But, and then yeah, you can always then flip and see it in the list view and be able to browse through the individual details of every entity as a list. So the general idea is like, how can we help you to see the big picture and at the same time drill into the details and do it so quickly and easily that you're more likely to ask a question that you didn't think to ask because it was so easy to answer in an informal way. So we have one more question. Oh, is there one over there? Go ahead. Implications for public policy legislation and whatever it's being made to kind of get this rich database. Yeah, well, for sure. I mean, right now one thing that we really want to do is add in government funding in here too to understand like where again, the more layers the more context for everybody. So where are you going? And I know like there's some work in like the, what are the modern monetary theory or whatever it's called where, where you can infuse government capital into an economy as long as it's not conflicting with the markets as long as it's complimentary to them. And so I think that seeing a landscape of like where, like we hear a lot of buzz about investments in climate related things, but when you look here, it's a real, it's a non-random corner of the problem that has clear market solutions. And so I think that helps guide policy to understand where like, let's not compete with the markets there. Let's figure out like what are the needs to support how it scales that needs alternative capital. So I think that's the idea of putting it all together to know where can that money be complimentary rather than in competition with, right? But so I think that's kind of part of the modern monetary theory idea of like the public money. You can print money if it's actually synergistic with the private sector, not in competition with it. And so you can only see that if you can kind of see where the non-random distribution is. Okay, sorry to cut you off. We have one more question. And I did also, I just thought we should, a note for folks in the room is that this technology currently is desktop only. So if you're looking on your phone right now. It doesn't work on your phone. But please go explore on your desktop because we do want to hear your feedback, but it won't work on your phone today. And so one more question we have is, could a funder map their own portfolio to see how they are or are not filling gaps? Yeah, so already, well there's two things. One is already, if you just went to any funder there like Techstars or something, you can just click on them. You can already, that's their portfolio. That's a Techstar footprint right there. And if you summarize it, you can see that. And now already next to Techstars, you can see who are all their co-investors, right? Just by summarizing that one thing. So you might see people, funders that you didn't think about before. So that's a really quick one for what's in there. But for an individual portfolio, absolutely, I mean we're happy to work with anybody to even if it's like a, maybe like a private data that you can't share or to like help you contextualize your portfolio in this map. And if, or if there's parts of your portfolio that are not in here, we would love to add them. So you could just click on your name and then see where it is easily. So yes. And then one last thing was, please do just go and then click on that feedback or improve this map button. There's a little form and then you could just sign up for if you wanted to like an online, there's a tutorial where we just get together on a Zoom call and walk through things or if you have questions or suggestions, just please click on that. It's just a simple thing and send us an email. Yeah. Thank you. Well thank you, thank you Eric. Thank you all for being here today. Their presentation, thank you. As Eric mentioned and we mentioned a few times already, we do need your input. We wanna know what you want to see, what you don't wanna see, the data that you have available that you can share with us and share with others. So do please go to the website, enter the feedback form, also come to impactapa.com, get all of your climate coverage from there and tell us the stories that you're seeing and the solutions that you're working on so that we can do this better together.