Silicon Valley Girl: AI, Tech and Career Growth - $22B Co-Founder: What the Market Predicts for Your Job in 2026 | Luana Lopes Lara

Episode Date: August 11, 2026

Luana Lopes Lara is the world's youngest self-made woman billionaire, by Forbes' count. In 2018, she co-founded Kalshi, a platform where anyone can look up the odds on things that haven't happened yet. This May, the company raised $1 billion at a $22 billion valuation. Her entire job is watching what people bet real money on.Luana Lopes Lara sits down with Marina Mogilko, Silicon Valley Girl, to discuss what the board already knows. What actually changes for a normal person by the end of this year? Will 2026 feel lighter or heavier for most people? Will any jobs suddenly become safer? And how much should you be relying on the public's opinion versus reality?They cover:- The exact odds the market is giving AI as the #1 reason for job cuts this month- The doomsday AI scenario the market is quietly pricing in — and the five conditions behind it- Which jobs Luana thinks are actually getting safer, and which ones people are wrong about- Her 2026 forecast for money, and the one variable it hangs on- How Kalshi ships product with 170 people and an agent for every new hire- Why the "solo founder with agents" story is wrong- Perps: Kalshi's first product outside prediction markets, and what it lets you bet on for the first time- The principle from her Brazilian middle-class upbringing that got her from a US college application to suing the US government — and winningLinks:📌 Subscribe to my free newsletter where I go deeper on AI tools, career strategies, and building with AI: ⁠⁠⁠⁠https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=LuanaLopesLara⁠⁠⁠⁠⁠⁠⁠𝕏 : ⁠⁠⁠⁠https://x.com/siliconvalleymm⁠⁠⁠⁠🔗 Instagram: ⁠⁠⁠⁠⁠https://www.instagram.com/siliconvalleygirl/⁠⁠⁠⁠💼 LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/marinamogilko⁠⁠⁠⁠📌 My Companies & Products: ⁠⁠⁠⁠https://Marinamogilko.co

73% chance that AI will be number one reason for job cuts. Like, how much should I be relying on public's opinion versus reality? Even with 5,000 in volume, we already see convergence to a very calculated number. So 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 Kalshi, a $22 billion company where anyone can look up the odds on things that haven't happened yet. Anthropic 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? And I still think that by the end of the year, we're going to see work change the most. Do you think 2006 is going to feel lighter or heavier for majority of people? That is a tricky question.

Do you see any jobs suddenly becoming safer? For now, I would actually claim that... Well, Anna, welcome. Oh, 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, guys building and like guys watching as well.

I think we're 70% male. 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 title of you. I think so. This is very, very impressive. And you're an immigrant.

I would love to talk to you about the future. Because where you're building at Calci, 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 Kalshi that we're not talking enough about? One example that actually my co-founder loves giving that is the Cittrini scenario.

I don't know how to say this is Chitrini or C-Treni scenario, which is kind of like a little bit of a doomsday scenario for AI. And I think there's five conditions on, you know, unemployment levels and all of that. And actually the odds 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 if

three out of five of them hit, the market will pay out to yes. And the odds are a lot higher than what people think. And it's very liquid. The market's traded like millions of dollars. And that's a market we look at a lot because obviously impacts our life so much. I think 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 Knicks, there's a, 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 the people mean by saying. AI did this or, you know, the economy is in this position and then really define 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, of 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 I be able to do that? which model will be better than which model. Gemina, Claude 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. So, like, tech layoffs and, like, just unemployment in general and kind of, like, how that side would pan 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.

And 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 that we get. 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 an arm of the company cultured 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 numbers can be trusted for sure. 73% chance that AI will be number one reason for job cuts. And that was the truth for April and March. So it looks like... It looks likely that it 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. Now it's actually having some impact on my job, not for everyone. but for a lot of tech workers? Is there anything else you see in terms of how AI impacts the day-to-day decisions? Are more people asking about stable job or a business?

I don't know. What kind of bets can you make? Yeah. On the AI front, I think that I would still 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, how can we define a market of the AI impact on 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 are 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. And now that we give interest on positions and dollar that you have in the account, 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. Those are some of our favorite markets. I was listening to some of your podcasts. Some people are hedging their risks with AI.

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 CalShish. So you can have some insurance payment. Exactly. We actually just had yesterday, not on the AI side, but 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 if the Knicks win. and they were very concerned because they were like, we might be down like $10,000, $20,000. So then they bought a hedge that way. And I think that one of the kind of like the prediction market adoption curve, I think a lot of what we're going to see, that's my forecast there. It's like, at the beginning everyone was also like not sure what was going on, what our prediction markets, all of that.

Then 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 see growing on small business side, but also beginning of hurricane season now in Florida. 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, like, you know, 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 Kalshi? 70% of our users actually don't trade on anything. They're just coming to ingest, like to just look almost like the news. They're just coming to see what is the forecast of different things. So basically what you just did to look at their 70% chance that AI would be the main reason for job cuts in May.

They're going to come and kind of digest all that information in the morning from sports to culture to, you know, who's going to win Love Island and all of that. And that's the vast 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 markets are really 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 the 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, like, get answers on the spot as well. But I would say that that's almost 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 election stuff. 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 a lot of that, especially we launched on, and that's something that, for example, driven by the use case of the forecasting and kind of getting information, right? We have thousands and thousands of election markets for the midterms, all the primaries, all the house raises, Senate raises, 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's moving this way, but it's always Like, there's one seat here. How do we 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. Well, 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. Exactly. And that's a lot of what we're 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 Cowshy Power American Power Index, which is 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 point two, like plus two for the Republicans yesterday. And we want to do more and more of that because I think it really helps and adds on the, 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 race. they look at Maine. You can look a lot of the California races are very interesting, but now you can look at one number that do it. So like now the past couple days I just open, you know, couch.com research. It'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. That's kind of my job. 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. Can we make some predictions? Let's do it.

AI, what actually changes for a normal person by the end of this year? work is one of the angles that's going to change the most. But I still, I don't think when I look at like Kaushi, for example, I don't think there's any role that we've completely just switched. 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 those 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 Kaushi AI agent that's kind of like everyone has their agent. But anyway, we're investing a lot on like how to solve. a lot of classic company problems with AI. As the company grows, communication and context is 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 stay? Whatever now I'm just like plan this whole thing for me for two days. What are you doing for that? I just used Chachabit for that.

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 that's, yeah. You know, I did that yesterday and I was talking to my husband. I'm like, how is it possible? Two thousand 20s, I'm still booking every hotel myself.

I'm clicking all the buttons. Right. Exactly. And I think that it's like a lot of these are menial tasks 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 in their work is valid. I just feel like I'm more of an optimist than an pessimist.

What about money in general? Do you think 2026 is going to feel lighter or heavier for majority of people? That is a tricky question. I think it depends a lot on the direction of the war. I would be honest because I think that most people would think about gas prices is 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 a call sheet 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 of that. Well, with the summer travel, I already feel like I'm, I don't know, 30% poorer because of the ticket prices. That's crazy. Oh, that's fair, right? Because it's crazy how many things are impacted by gas prices or price at the end of the day.

It's kind of even forget about that. Absolutely. Do you see any jobs suddenly becoming safer? I was at the gym the other day. And I was actually thinking that, for example, trainers are continuing to continue. I think everything that's more physical in nature are going to continue. We'll see how all the robots kind of like the optimist 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 CalShite, 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? Like, how much should I be relying on public's opinion versus reality? I think the most important thing to think about is that Kaosur, 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 frequencies, like you still have one in 100 that it's not going to happen, right? So for example, the Pope, the American Pope, he was around 1%. at Kaushu the whole time. And the news were all like, oh, the Kaushi markets were wrong. The couch 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 a market says a 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, say how it's much better than any other forecast. But I think the core of it is understanding that 70% is not 100. Is there a number where predictions are right? Like, an 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. And even in a politics market, if you see like one week before, I think you 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 in 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 is that people are putting money where their mouth is.

It's a system that's from the start designed to incentivize truth in information, like good information because people are incentivized to do their research because if they're right, they make money. Because 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 start you're going to have like if there's $1 trade and

you're not going to get a better answer than an alternative. But we've actually way less than what people expect to 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 like a lot of every single part of the company and a lot of it. We are like as of, I think yesterday, we want 70 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 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. 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, you know, number of mistakes, but also like listing, latency, determination, latency, coverage, all of those

things that we kind of, you'd 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 top of via because 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 all 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 of 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 agents that you build for yourself? Something that anyone who's 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 was from the week before, flag what's, what hasn't been done? What hasn't been done? How do you collect all the data?

Do you use 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 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, slack, everything. 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, 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 it 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 it was done, what we need to do the next week, look at all the metrics and all 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 way that I expect. But I would say is like really looking at like for the past X number of weeks This person has overpromised, underdelivered. These are the things.

Like, these metrics are not. I'm trying to build something like that for myself, but what I realized, 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. Yeah. And that's the thing is also, it's like we're kind of putting engineers in every single part of the company to kind of figure this problem out. Because obviously, market operation is a great one.

But for example, design is something we didn't use a lot of AI for. And now we're kind of cloud design or cloud design. 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 just, 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 just, like built-in reviews or something. Yeah. But also, like, engineers are very good. Like, if you want to just test a new module on a page, right, you can very easily put it out. But what we have at the companies, they put it out, and then we get, like, we test it,

and we're like, okay, this was good, direction to go, let's ship it. And then when we ship, 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 by making, yeah. A lot of things we're thinking about. 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 and you need to collect it. And you want to make sure the agent knows what's a 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 these two, like basically this channel is like product feedback, right? That we put a tweet that user is complaining or a user from Discord or internal, like everyone puts stuff 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 run, it's almost like a, we were talking about

this yesterday. It's like a bug. Something to, for that extent, to like, we'll track everything immediately, prioritize them, see what's live and not live 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 try to figure, anyway, we're trying to put. a lot of time. Burning a lot of tokens from what I'm hearing. Right.

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 for me. 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 build the most efficient company that we can that will keep, I think the big differentiator for Kau Shia,

that people used to think it was a regulatory piece. I really think the actual real differentiator is how fast we've moved and like how good our product has been by how 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 here, 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 try to build something and something's working, but if you want to do really something sophisticated, that 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 Peter the other that maybe said that, that 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 happens with me and I think it happens my co-founder as well, that we are already very spread thin. And if I was to say, I'm going to put 5% of my time into trying to do something, it's just not going to be great. 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 as possible. So I need 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 AI will create so many more opportunities for us to do more and more things. Right. Like, 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 basically what you can do now is you take like 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, but there's no end date anymore. So you can just express your opinion in like a simple way. So crypto is what we launched,

but we're looking at a lot of different things. Even when we talk about... AI is something where you can short or long AGI or superintelligence. Exactly. That's exactly kind of the direction we want 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 what actually is. Because I saw some of the predictions that 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 be long. And that's your alternative to investing in tech companies, right? It's kind of my favorite future along. Yeah, because that's one of the reasons we started at Kaoshi.

It's so hard. Like, if you're a long AI, like you can say, okay, I'm going to buy Nvidia stock. I'm going to do. But it's very hard because there are other, so many other factors that impact all of these stocks. And what prediction markets or what we view, what we're excited about and what Kaoshi is about, is that we want whatever your thesis is, you're going to be able to get that. Yeah, not like trying to diversify among this data centers or... Exactly. So you're just able to do that. So for example, launching perpetuals was,

would it have impossible 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 and 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 it changed from the last year? We like being very lean. So we were 170 people at the moment and people that work very well like Kauci, they are very low ego and willing to learn a lot. I think 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 change. shooting things that matter the most, it's just working really hard and having like a commitment to work above everything else. When I say commitment to work is more about when we ask you to do something and we trust you if something, we can trust that it's going to be done great. It's not about a number of hours.

It's not about these things. Is it about it about AI as well? It is about so in the engineering side, in the engineering interview, we put a lot of time into it and 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 on the figure out how to use AI in a better way in design. In our design interviews, we're starting to be like has this person used a lot of AI before design. So it's spreading to design. What about knowledge work? Less so. We need to do.

One thing actually, funnily enough, in the legal team, we're starting to do that a lot too, to be like, because we have so many cases and litigation, 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, and it kind of adds, 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, What we're doing is that we're kind of putting them the AI group in like design. And then they're going to go into 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 they used to work is not the way that we're going to work at Kashi 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, oh, 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 think a lot of what Tarek and I think about so much is you always have that feeling of, you know, that people say you obviously have the feeling you're 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 it's like someone

full time thinking about it which obviously we cannot afford to do well that makes total sense you sound really smart where you build 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, my biggest privilege in 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 like, I mean, there's this, this, this, this, this whole view of like, you know, like, it's not about entitlement at all. It's not about like, I deserve or I, 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 U.S. 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 U.S. to study. But I told them I wanted to do it. And they were like, all right, like, sounds hard, but let's try to figure it out. And they supported me so much.

And I think it's kind of this thing that ballet also doing ballet for so long thought 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 is 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 and 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 funnily enough that's a very kousy 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 is when 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 to the government. And it was very painful.

Sounds very crazy. Especially as an immigrant. Yeah, it was crazy. And also, like, we were small companies suing our own regulator. Like, what are we doing? But it was that thing of, like, we should do everything that we can. And there is this option that we didn't. try it and we should try it. And I think that's kind of like this, this thing, I think it's

impacted Kau Shia 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 of 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. And I want to be able to have that kind of like to rest at night and be like, I've done every single thing that I can. And a lot of it obviously is very correlated with working really hard. But it's not just that, it's about hiring great people. It's about being 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 a lot of ways. And I think that having that mentality has helped to Ragan I a lot. 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 like 0.005 they're actually

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 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 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 in 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 upsetting. But I think it's just a matter of

focusing on what we can control. Thank you so much. 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. 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.