$22B Co-Founder: What the Market Predicts for Your Job in 2026 | Luana Lopes Lara

73% chance that AI will be number one reason for job cuts. Like how much should I be relying on public subpoenae versus reality? Even with 5000 in volume, you already see convergence to a very calibrated number. So if that number should be trusted for sure. This is Luana Lopez-Lara. Forbes calls her the youngest self-made woman billionaire in the world. She built Kalshe, a $22 billion company where anyone can look up the odds on things that haven't happened yet. On the topic going public before open AI, who takes the house in November? More tech layoffs in 2026 than in 2025? Can we make some predictions? Let's do it. What actually changes for a normal person by the end of this year? I still think that by the end of the year, we're going to see work change the most. Do you think 2016 is going to feel lighter or heavier for majority of people? That is a tricky question. Do you see any jobs suddenly becoming safer? So now I would actually claim that Luana, welcome. Thank you. Thank you so much for doing this. Of course. I'm really happy when I have women on my podcast, because my podcast is AI in business and mostly most of the time is guys building and like guys watching as well. I think we're 70% there. But it makes me really happy to interview one of the youngest self-made, are you the youngest self-made billionaire? I think so. I hate the time to be. I think so. Very, very impressive. In my grant, I would love to talk to you about the future, because where you're building at CalShi, you're basically making a ton of predictions about different markets. And I want to talk to you about what you were saying. Is there something that you see at CalShi that we're not talking enough about? One example that actually my co-founder loves giving that is the Citrini, Citrini scenario. I don't know how to say this with Citrini or Citrini scenario, which is kind of like a little bit of a Doomsday scenario for AI. And I think there's five conditions on unemployment levels and all of that, and actually the odds are around like I think 26 or 30%, which is extremely high if you think about it. For the Doomsday scenario. So there are five conditions, and I don't know them all by heart, but the market is three out of five of them hit. The market will pay out to yes. And the odds are a little higher than what people think, and it's very liquid. The market is treated like millions of dollars. And that's a market we look at a lot because obviously impacts our life so much. If it was a very big report that came out a couple months ago, that just got so much attention. So we have a lot of markets on the AI side, obviously, on sports. I mean, we're in New York. So the next, I think it's 37% chance they're going to win the finals. There's a lot of very interesting markets, and I think a lot of our job is figuring out what are the big questions out there in the world that people want to know, forecast for and what they want to know, you know, have data for and try to frame the right market that gets to that question. Because not every question is a very simple yes, no. Right. You have to actually figure out what do people mean by saying AI did this or, you know, the economies in this position and then redefined it. But a lot of our job is doing that. So it's very fun. Some of the requests I can actually see them in my app, they're already public, but there are a lot of requests that you are seeing privately, right? What people are asking for. And then you decide what goes on the platform. Is there a trend in anything related to AI that you're seeing a year or two years ago? Most of the markets proposed were about AI capabilities. People were interested in like, will AI be able to do this? Will AI be able to do that? Which model will be better than which model? Gemini's quad is still trending. That was kind of a big thing. Nowadays actually a lot more of the requests that we get are more on the impact of AI. To like tech layoffs and like just an employment in general and kind of like how that side will pen out. And I think it's like, it's interesting because we see a lot of what people request of markets kind of show a shift also. And like I think there was a lot of excitement for AI at the start. It wasn't like mainstream that everyone knew what AI was. Nowadays they do it. I think you can see that kind of like vibe shifting to a more conservative, more skeptical vibe. And we see that in the market request. So again, you have it monthly where you ask about. Tech layoffs and for May is will AI be the number one reason for job cuts in May? And that's, well, it's 30,000 volume. It's one of the smaller markets, but still like we've done a lot of research. We have a arm of the chemical calcium research that looks at the markets and even with like I think 5,000 in volume, you already see kind of convergence to like a very calibrated number. So that number can be trusted for sure. And that was the truth for April and March. So it looks like it looks likely that would be again. Yeah. Yeah. And it's what you mentioned is very interesting a year ago, people were still trying to figure out what AI is. And now with all the headlines, they're like, Oh, okay, interesting. That was it. And that was actually having some impact on my job, not for everyone, but a lot of tech workers. Is there anything else you see in terms of like how AI impacts the day-to-day decisions are more people asking about stable job or business that don't go what kind of bets can you make? Now on the AI front, I think that I would still quote, like divide the world of the AI markets between the impact that they have in jobs and, you know, government, even in elections. I think there's a lot of people asking, well, how can we define a market of the AI impact on election or electoral thinking around AI there? And the other side is just really like capabilities and all of that, but we have a lot of markets and for other things as well, like for example, like big, you know, math problems being solved or a lot of things that we're doing more on the kind of like FDA drug approval trials and timings for those, those markets we're getting a lot of interest in now. It's interesting because if you look at the history of prediction markets, right, a lot of the most important things that prediction markets do is try to price these kind of unknown innovation and tech things that we look at, like future of AI or the future of, you know, a lot of different drugs or the future of crypto or quantum computing. So we really try to have as many markets as we can for those in now that we give interest on positions and dollar they have in the account, so you can actually, it makes sense for you to invest in something that's like five years down the road because you actually get paid on that, the interest. So we see more activity on those, but those are some of our favorite markets. I was listening to some of your podcasts that some people are hedging their risks with AI. Like the example that I heard was floods, but now that I'm thinking like if you're fearing that AI is going to take your job and it takes your job, you can bet against that on calcium. So you can have some insurance payment is that we actually just had yesterday, not on the AI side, but on the on the sports side, a bar I think in the Upper East Side here in New York that was going to run a promotion that basically was whoever comes in, we're going to pay for the entire tab of the next win. And they were very concerned because they're like, we might be down to $20,000, so they bought a hedge that way. And I think that one of the kind of like a prediction market adoption curve, I think a lot of what we're going to see that's my forecast there is like, at the beginning, everyone's was also like not sure what was going on, what our prediction markets, all of that, that there was a lot of skepticism. And now that people are starting to really understand what they are, you're going to see them starting to understand the other use cases, like hedging and all of that. That we really seeing growing on small business side, but also beginning of hurricane season. I went forward to the amount of people coming and saying like, can we have a hurricane market for this specific part of Florida I live in because I want to be able to hedge my deductibles or this or that. Because insurance wouldn't work if something happened. Exactly. For a person like me, I'm not into betting, I don't have time for that, I know some people do it professionally. What do you think is the use case for me as a user of CalShade? 70% of our users actually don't treat on anything. They're just coming to just like to just look at almost like the news, they're just coming to see what is a forecast of different things. So basically what you just did to look at their 70% chance that I would be the main reason for job cuts in May, they've got to come and kind of digest all that information in the morning from sports, to culture, to who's going to win, love Ireland and all of that. And that's the best majority of the use case. Obviously, like, look, we make money on transaction fees. So we make money when people come in and trade. But at the end of the day, what prediction market is already good at and how we get to impact the billions of people really is with the data that we're bringing. And I think that that's kind of the best use case data. And also like, obviously, if you want to forecast for something, if you want the data for something that we don't have the market for, you can suggest we can add it. And then you can kind of get answers on the spot as well. But I would say that that's the always the main use case for people. So what are you looking at every morning? I look a lot on the economy stuff, and I look a lot of the elections. If I'm there, I love American politics, I love politics in general. I'm from Brazil. So I like Brazilian politics too. And in an election year, we've been looking at a lot of that, especially we launched on, and that's something, for example, driven by the use case of the forecasting and kind of getting information. We have thousands and thousands of election markets for the midterms, all the primaries, all the House races, Senate races, all those things. But it's actually pretty complicated to digest all of this into like one number of, like, how is the country leaning, right? Because you can look at the Senate and it's like, the Senate is moving this way. And it's always like, there's one seat here, how do you think about that? The House is another way. What we really wanted to create was a number that you can look at that will kind of track like an AI assistant now that I'm thinking, if I just ask, what's the sentiment about AI today? Right. And it runs all the. Exactly. And that's a lot of over working on now, which are these like indices of how do we aggregate a lot of data about the world, but also all of our market forecasts and try to create kind of like one number that is the sentiment or the index for something. So we released the cow-sheep power, American power index, which is what we call K-POW, which is basically tracking is the country more Republican, more Democrat based on current state of the world and our forecast. What does it say? Last I checked was like 0.2, like, plus 2 for the Republican. And we want to do more and more of that because I think it really helps and adds on the forecasting side. And we want to build more and more on the kind of new side. So now, before I used to look at race by race, there's some key Senate races that you look at like Maine. You can look a lot of the California races are very interesting. But now we can look at one number that do it. So like now the past couple days, I just open, you know, cash.com research. She's on our research tab and then, and then see the number there. But I try to look at, I'm looking at the markets the whole day, this kind of my job. So it's fascinating. It's another way you consume news, but it's not from a particular news outlet. It's basically what people are trading on and getting on. Let's do it. AI, what actually changes for a normal person by the end of this year? Work is one of the angles is going to change the most. But I still, I don't think when I look at like, how she, for example, I don't think there's any role that we've completely just switched. So we don't need this role. We have AI. All the roles have been like augmented by AI. So like an engineer now has like 20 cloud agents and all these things. And I think we will see more and more changes in other things. So for example, we are investing a lot in having kind of this Kowshi AI agent that's kind of like everyone has their agent. Anyway, we're investing a lot on like how to, how to solve a lot of classic company problems with AI. Like, as a company grow, it's communication and context a big deal, right? Like someone that just joined doesn't have the context to make decisions. They don't really know what they have to do. So how can we use AI to solve that? And I still think that by the end of the year, we're going to see work change the most. For example, for me, one thing that changed the most also with AI is like travel planning. I was traveling for a weekend and before I used to have to be like, oh, where should I say whatever. Now I'm just like, plan this whole thing for me for today. What do you think about that? I just use JGPT for that. So the baby. So you just give it whatever you're thinking of and it gives you suggestions. Yeah. But then you still go and book yourself. I still go and book myself. Maybe I should, maybe that's yeah. But you know, I did that yesterday and I was talking to my husband and I'm like, how is it possible? In 2020, since I was still booking every hotel myself, I'm clicking all the buttons. And I think that it's like a lot of these are minotasks that people don't actually like doing, that I think that that would be. But I still think that the biggest impact would be work. And I think that the concerns that people have with the impact of in their work is valid. I just feel like I'm more of an optimist than it is. What about money in general? I think 2016 is going to feel lighter or heavier for majority of people. I think it depends a lot on the direction of the war. I would be honest because I think that like most people would think about gas prices. It's kind of like a big dependent on that. But I like how you think about that. So if somebody has a concern about money, they can go to college and see what people are betting on. And we have markets on all these things like recession and inflation and all of that. I would probably say it's neutral. That would be my forecast. We want to go with that. Well, with the summer travel, I already feel like I don't know, 30% poorer because of that. Oh, that's crazy. That's crazy. That's fair, right? Because it's crazy how many things are impacted by gas prices are all priced at the end of the day. It's kind of we even forget about that. Absolutely. I was at the gym the other day. I was thinking that for example, trainers are going to continue. I think everything that's more physical in nature are going to continue. Oh, we'll see how all the robots kind of like the optimists and all those things develop. But I think it's like for now, I would actually claim that the engineering roles and all of those roles that people fall were safer before. I think it's kind of clear that they're going to be less safe. So yeah, I would say anything that's more like craft and physical probably. When it comes to trusting those numbers that you see on Kalshi, how many of the bets? So for example, if people are betting for something like 70% agree and then the reality is completely different and it's flipped, how often do you see that happen? I think the most important thing to think about is that Kalshi what we give off are probabilities, right? There's not an answer. So even if it's 99, it's still like if you think about probability as like the frequency is like, you still have one in a hundred that it's not going to happen, right? So for example, the Pope, the American Pope, he was around 1% at Kalshi the whole time. And the news were all like, although Kalshi markets were wrong, the Kalshi markets were wrong. And I mean, one is not zero, right? You still have 1% chance of something happening. And I think that that's kind of like the best example of a completely closed information system, which is a conclave and there's no information that gets out how hard it is to forecast from the outside. But we've done a lot of analysis and research on our calibration. So basically like if the market says 70% chance is it actually 70% chance? So we can actually plot like do some calibration math. And the calibration is actually very, very, very good. And I think that even like there's a fed paper that came out about prediction markets, they have it's much better than any other forecast. But I think the core of it is understanding that 70% is not a hundred. Is there a number where predictions are right? Like a average percentage? That really depends on the time to expiration and the type of market. So for example, for an entertainment market is actually different from them from a politics market. Even in a politics market, if you see like one week before, I think you'd need like maybe one or $3,000 for it to be extremely accurate if it's one week before. But if it's six months before an election, then I think you need a lot like more on the like tens of thousands, maybe 10,000. I'm not exactly sure on the numbers there, but I think it depends on a lot of things. I still think though that it's like the whole point of a prediction market that people are putting money where their mouth is. It's a system that's from the start designed to incentivize truth and information, like good information because people incentivize to do their research because if they're right, they make money. So if they're putting their money, that means they put some thinking behind. Exactly. So that's kind of how we really see it as kind of like directionally from the start is a better system. It doesn't mean that from the from the start you're going to have like if there's one dollar traded and not going to get a better answer than an alternative, but we've actually way less than what people expect you start getting there. So we touched upon some agents and I really like that topic. Can you talk to me about how agents have transformed your life as the founder? I think it transformed it like a lot of every single part of the company and a lot of it. Very like as of I think yesterday, 170 people at the company and I think that we're able to do everything that we do a lot because we kind of just build AI systems from the from like bottoms up of our how we were thinking about engineering, how we're thinking about market operations, how we're thinking about all of those things. And I think what it's helped me the most is like able to get context on things a lot faster and I'm able to know what's going on a lot faster. So I'm able to manage a lot more threats and a lot more people in a way more effective way. We are very like metrics driven in the company kind of everywhere. Obviously, for example, a great example is market operations, right? Like the way that we think about market operations is almost the same way as you think about a factory. We think about number of mistakes, but also like listing latency, determination, latency, coverage, all of those things that we kind of you think about it in a factory. And kind of how to define these metrics, how to get these metrics in real time. And all of that is kind of all built on the way I guess it's very complicated to think about a lot of these things in the context of like markets. So yeah, I think it's like on the metric side and how like communication flows and is aggregated in the company. It's kind of all like that and it becomes a lot simpler for me to do my job because I can just have my cloud agents kind of like do everything. Can you talk to me about a couple of agents that you build for yourself? Something that anyone has a knowledge worker could deploy for themselves as well. Well, one thing that I think is useful for a lot more people maybe is on kind of like weekly planning and kind of like state of things. That I think it's like how do we get updates from the entire company track from what the updates wish from the week before flag once. Well, what hasn't been done? What hasn't been done? How do you collect all the data? Do you send any tools that every employee have their agent like how do you collect it all inside one database? Yeah, that is a great question. And I think that we should we should ask our engineers would know better because I'm very lucky that they can build a lot of the things for me. In terms of that, like it's connected to everything that we do, emails, docs, all of that. We actually have an AI team now that is actually building. We obviously have a very very good like engineering side of the AI equation is very good. But we're trying to build kind of like every new employee should get an agent that's kind of like the biggest problem we have there that we're trying to figure out is how to figure out like permissions in the right way. We need to make sure that for example, we have a lot of legal work or like surveillance and all of that that has to be very, you know, just some people have it and how do we think about it that way. But I would say that like planning, organizing and collecting information Sundays are very like heavy days for me because it's like when I stop and I look at the entire week, everyone what there was done what we needed to do next week. Look at all the metrics and all of that's all I do on Sunday. And now I'm actually able to like have brunch on Sunday because I'm like I have a lot more time to think about things. But because a lot of it is kind of done in the in the way that I expect. But I would say is like like really looking at like for the past X number of weeks this person has over promise under deliver these are the things like these metrics are not trying to build something for my like that for myself. But whatever realize we need to hire someone so we try to build internally and my team is like creative producers and now we hired someone with an engineering background to do that. And that's the thing is also it's like we are kind of putting engineers in every single part of the company to kind of figure this problem out because obviously market operations a great one. But for example design design something we didn't use a lot of AI for and now we're kind of cloud design or cloud design. Yes, but we're trying to also figure out a lot better on like how do we also empower almost everyone to be a design in a better way. Obviously we have a design system and all of those things, but if an engineer just wants to ship something like how do we actually build something that it's not we're still defining it but it's not just like you can right now you get a design system you kind of can ship an experiment very quickly. But how do we actually like do it in a great way from the start because I feel like that's a point of design right you can you can just. Reviews or something yeah, but also like engineers are very good at like if you want to just test a new module. on a page. You can very easily put it out. But what we have are the companies, they put it out, and then we get, like, we test and they're like, "Okay, this was good. Direction to go, let's ship it." And then when we ship it, we actually go back to design, and then the design team actually makes it good. Because before, I was just like, it didn't look awful, but it wasn't great. And we're trying to figure out how can we actually not need that loop anymore. Yeah, a lot of things we're thinking of. Interesting. That's interesting. So you have that agent running, giving you all the information, something that I'm trying to build, I can relate to a lot, because also, like, information is all over the place. Right. And I need to collect it. And you want to make sure the agent knows what's the priority, what's not. Because otherwise, it's a very long email of all the things you need to do. One thing we struggle a lot with is that on Slack, we have this two, like, this, basically, this channel is like product feedback, right? That we put a tweet that users complaining, or a user from Discord, or internal, like, everyone puts up there. And it's very, very tough to prioritize track what's done or not done. And we have all those, like, linear integrations and all those things. But what we want, it's almost like a, we were talking about this yesterday, it's like a bug, something to, for that extent, to like, we would track everything immediately, prioritize them, see what's live in our life without adding engineering burden, that I think would, would be very helpful. Obviously, like, on the QA side as well, we're trying to figure something out so that it's a lot better. There are a lot of companies we tested for on the QA side, nothing was great, so we tried to figure it. Anyway, we're trying to put a lot of things out, burning a lot of tokens. Right. What I'm hearing. Is there anything that you've built yourself for yourself? Not really, to be honest. I think that both Tark and I, we're kind of very lucky to have a great team that does a lot of these things, because a lot of, like, what we think about is like, obviously, Tark and I, we should be always trying to be as productive as we can, and be more and more productive, but I think it's more about, for us, our biggest question is how do we view the most efficient company that we can, that will keep, I think that the big differentiator for Kyle Shade, that people use to think it was a regulatory piece. I mean, I think the actual real differentiator is how fast we've moved, and like, how good our product has been, they have fast we're moving, and for us, that's the biggest question. A lot of people are talking how a founder can be a solo founder now, because he can deploy, he or she can deploy so many agents. What I'm hearing from you is completely different. You still, you're hiring more engineers to build those things for you, and this is what I'm experiencing myself. Yes, we tried to build something, and something's working, but if you want to do really something sophisticated, right, doesn't make mistakes, well, or at least the mistake rate is like 3%, then you have to hire someone. Because I also, I'm a big believer, I think it's a bit of a deal, then maybe you said that it's, you need to have one person doing one thing if you wanted to do it very well. And I think that my question is more, like, I think even it happens with me, and I think it happens with my co-founder as well, that we are ready very spread thing. And if I was to say I'm going to put 5% of my time into trying to build some things, it's just not, it's not going to be great, right? If we really want to be, we want the company to be as efficient as possible, and as fast as possible, and the best product is possible. So yeah, it needs to be a core part of that. So we need people that are amazing at this, they're going to be doing this, and they're going to be doing this full time. And that's why like, I was saying I'm very optimistic about things. I think that, yeah, I will create so many more opportunities for us to do more and more things. We just announced perps, which are a big new product, first time that we're going outside of prediction markets. So it's a perpetual future. So it's basically what you can do now is you take a long or short, for example, on Bitcoin. So you're like long Bitcoin, you don't need to worry about for how long you can get leverage in that position. You can short Bitcoin very easily, which is very hard to do. So basically you can think about a future, where there's no end date anymore. So you can just express your opinion in like a simple way. So crypto is what we launch, but we're looking at a lot of different things. Even when we talk about AI is something where you can short a long AI or super intelligent. Exactly. That's exactly kind of the direction we're going to go to. And it's more of a matter of like how do we define, we're back to like how do we define why exactly because I saw some of the predictions are really well structured. I'm like, oh, this is not a yes, no, this is something. Does it happen before this day or this amount before that time? And that's what we want to take out the component of time. So that for example, if you're long AI and we define it as like what really that is, you can just be long forever up until you want to say, I don't want to belong. And that's your alternative to investing in tech companies, right? It's kind of it. It is. That's one of the reasons we started cows. It's so hard. Like if you're long AI, like you can say, okay, I'm going to buy Nvidia soccer. I'm going to do, but it's very hard. There's so many other factors that impact all of these stocks. And we'll prediction markets or what we built them, what we're excited about and what cowsheets about is that we want whatever your thesis is, you're going to be able to get that. We're not like trying to diversify among this data centers or is that so you're just able to do to do that. So for example, launching perpetuals was, would it have been possible if we didn't have AI at the state that it's now probably not without hurting the core product a lot more by resources or hiring a lot more people. So I think that the way that we think about it is more we hope to be able to do so much more and grow so much more so many more products and hopefully become a way bigger company because we're AI first and we are about like hiring less people. That's just not how we're thinking about it at all. How are you hiring these days? How has the change for the last year? We like being very lean. So we were 170 people at the moment and people that work very well, a cow, she they are very low ego and willing to learn a lot. I mean, we're very direct culture. We really like being efficient with time. So that means like feedback is like, I don't like something you did. I'll tell you right now, and I'll be honest about it and you have to be in that kind of like cultural side is very important for us. But realistically, the tune things that matter the most is just working really hard and having like a commitment to to work above everything else. I when I see commitment to work is more about when we ask you to do something and we trust you something we can trust that is going to be done great. It's not about a number of hours. It's not about these things. Is it about AI as well? It is, but so the engineering side in the engineering interview, we put a lot of time into it and in kind of like now you can use AI in the interviews and it's completely fine. And all of that and actually in a lot of the systems review that we do interviews on systems review or like previous project review is kind of a big component of that because now a lot of the things that we used to look at like two years before of like, oh, can someone actually do this or do that? But now like whatever, like that's just not relevant. We are actually talking about in design now, I told you that we're trying to get more and more in the figuring out how to use AI in a better way in design. In our design interviews, we're starting to be like, has this person use a lot of AI for design? So it's spreading to design. What about knowledge work? Let's so we need to do one thing actually funny enough, in the legal team, we're starting to do that a lot too, to be like, because we have so many cases in litigation, we're trying, we're starting to be a lot more like how have you used AI for this? How would you use AI for that? It's less about any kind of ads goes back to the willingness to learn. I think it's less about them having the answers or having used it to do something amazing before, but more like, are they willing to do it? Because we have, again, like our engineering team, what we're doing is that we're kind of putting them the AI group in like design and then we're going to legal and try to kind of like, how do we help them to do it? And we just want people to be open-minded and the answer is like, how do they use to work? It's not the way that we're going to work a couch and the world's going to do and we just need them to be open-minded and have like low ego to figure out like, all this thing that I thought I was very good at is actually I don't need to do anymore. But yeah, it's funny because I've been a lot of what dark and I think about so much is you always have that feeling of, you know, is that people say that you're obviously definitely not working hard enough? For us, it's more like, we're not using AI enough, we need to sit down and like think about kind of how to do it. And that's why it was important for us to have this team in the company doing this. So then it's like someone full-time thinking about it, which obviously we cannot afford. Well, that makes total sense. You sound really smart, were you built? Oh, it's amazing. As a mom who's raising two daughters. Can you share some of your principles or something that you think was there in your upbringing that brought you here? I joke, I have the biggest privilege of life is having my parents, they're perfect. My parents always kind of taught me that I could do or be or whatever, whoever I wanted. And it's less about like this, I mean, there's this whole view of like, you know, like, it's not about entitlement at all. It's not about like, I deserve or it's more about like, if I want to do something, I am capable of doing it. And my parents always like kind of like really believed in me and kind of like have this kind of like respect for what I wanted to do or when I was in Brazil and I wanted to study here. In the US, it was kind of a crazy idea. Like, I'm from a middle class background. I'm like, it's not like, no one is applying to come to the US to study. And but I told them I wanted to do it. And they were like, all right, like, let's sound hard, but let's try to figure it out. And they supported me so much. And I think it's kind of this, this thing that Belly also doing Belly for so long, taught me is just you can do things. You just need to work very hard for them. You're not old anything, but if you work really hard, good things happen. I think that that's kind of like the main thing about my bringing. It's just like teaching me that the hard work's very valuable and doing things that matter are very important. And you should be proud of yourself and like work really hard and try to do things. Is that your work principle? The main work principle work hard? I want to make sure always that I did everything that I could. And I think that that's kind of how I think about it. And it's funny enough, that's a very cowshoe thing because we took three to four years to get regulated. And then we had to sue the government to get election markets, which after that, this one we just started growing. And at the time, we engaged with the government for two years before we were able to launch the election markets. And it got to a point that we realized they weren't going to let us do it. And the only last thing that we could do was sue the government. And it was very painful. It was very crazy, especially as an immigrant. Yeah, it was, it was, it was crazy. And also like we were small companies, swing out on regulated like what are we doing but it And it was that thing of like, we should do everything that we can. And there is this option that we didn't try yet and we should try it. And I think that's kind of like this, this thing, I think it's impacted, um, cow, she a lot too. But it's more about let's do everything that we can. So it's like, if I, if the company, I remember thinking about this when we were a couple years ago, I never want to think that the company didn't work or a product didn't launch or something didn't go well, but I personally could have done something different. Yeah. And I want to be able to, to have that kind of like, to rest a night and be like, I've done everything I can. And a lot of it obviously is very correlated with working really hard, but it's not just that right. It's about hiring great people. It's about being like nice to the people around you and making sure the employees are happy because if the employees are not happy, it's like, that's on me in, in a lot of ways. And I think that having that mentality has helped, uh, Targan, I love to. Okay. My last question. Can you give advice to women trying to build something? It might not be the best advice, but I think it's like focusing less on the fact that you're a woman. And the reason for that is like when you're trying to do something very, very, very hard, the odds of you doing that are already like 0.01%, right? The difference of 0.01 from my 0.005, they actually have a very big difference. But in the grand scale of things, they're both very, very hard. And I think that it's better mentally to just focus on, that's my goal and that's what I want to do. I'm not going to listen to the noise. And obviously like, look, it, a lot of things suck and I think it's a lot harder. You see the numbers of women starting, it's just obviously, it should be a lot better. And I, and I really hope it is. And I think that the world and like investors and VCs need to hire more women and invest in more women and all those things need to be fixed. But I think from a woman being a founder and trying to build something, I think it's better to just focus on that in a lot of ways and a lot of the numbers that we say is just, it's just very sad and, and upsetting, but I think it's just a matter of focusing a lot of control. Thank you so much. I'm so impressive. And congratulations on all your success and it's a huge inspiration for all the immigrants as well. Oh, thank you. Thank you so much. If you want to stay ahead in the AI era, follow Silicon Valley Girl podcast on your favorite platform, new episode every week on AI careers and how to not get left behind.