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

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 winning Links: 📌 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=LuanaLopesLara https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=LuanaLopesLara ⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/𝕏 : ⁠⁠⁠⁠https://x.com/siliconvalleymm⁠⁠⁠⁠ https://x.com/siliconvalleymm 🔗 Instagram: ⁠⁠⁠⁠⁠https://www.instagram.com/siliconvalleygirl/⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/ 💼 LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/marinamogilko⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko 📌 My Companies & Products: ⁠⁠⁠⁠https://Marinamogilko.co https://Marinamogilko.co

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:02Speaker 00

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• 0:30Speaker 01

  Flowing ad budget on metrics that look great till the CFO sees them? That's bull spend. And marketers are calling it out in Dashboard Confessions.

• 0:39Speaker 02

  I remember telling my boss, "It'll be good for the brand," when leads were slow. Yeah, it wasn't.

• 0:47Speaker 01

  Cut the bull spend. LinkedIn lets you target by company, job title, and more. Advertise on LinkedIn. Spend $250 on your first campaign and get a $250 credit. Go to LinkedIn.com/campaign. Terms and conditions apply.

• 1:00Marina Mogilko?

  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?

• 1:08Speaker 04

  Even with 5,000 volume, we already see convergence to a very calibrated number. So that number should be trusted, for sure.

• 1:14Marina Mogilko?

  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 OpenAI? Who takes the house in November? More tech layoffs in 2026 than in 2025? Can we make some predictions?

• 1:35Speaker 04

  Let's do it.

• 1:35Marina Mogilko?

  What actually changes for a normal person by the end of this year?

• 1:38Speaker 04

  I still think that by the end of the year, we're gonna see work change the most.

• 1:42Marina Mogilko?

  Do you think 2026 is gonna feel lighter or heavier for majority of people?

• 1:47Speaker 04

  That is a tricky question.

• 1:49Marina Mogilko?

  Do you see any jobs suddenly becoming safer?

• 1:52Speaker 04

  For now, I would actually claim that...

• 1:56Marina Mogilko?

  Luana, welcome.

• 1:57Speaker 04

  Oh, thank you.

• 1:57Marina Mogilko?

  Thank you so much for doing this.

• 1:59Speaker 04

  Of course.

• 1:59Marina Mogilko?

  I'm really happy when I have women on my podcast because my podcast is AI in business. And mostly, most of the times, guys building and like guys watching as well. I think we're 70% male.

• 2:11Speaker 04

  That's all right.

• 2:12Marina Mogilko?

  It makes me really happy to interview one of the youngest self-made... Are you the youngest self-made billionaire?

• 2:18Speaker 04

  I think so. I hate the title, but yeah.

• 2:20Marina Mogilko?

  This is very, very impressive. And you're an immigrant. I would love to talk to you about the future.

• 2:25Speaker 04

  Let's do it.

• 2:26Marina Mogilko?

  Because where you're building at Kalshi, you're basically making a ton of predictions about different markets. And I want to talk to you about what you're seeing. Is there something that you see at Kalshi that we're not talking enough about?

• 2:40Speaker 04

  One example that actually my co-founder loves giving is the Citrini scenario. I don't know how to say this. 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.

• 3:04Marina Mogilko?

  For the doomsday scenario?

• 3:05Speaker 04

  It's... 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 it impacts our life so much. I think it was a very big report that came out a couple of 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 30... I think it's 37% chance they're gonna 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, have data for, and try to frame the right market that gets to that question.

• 3:50Speaker 04

  Because not every question is a very simple yes/no.

• 3:53Marina Mogilko?

  Yeah.

• 3:53Speaker 04

  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 we need to find it. But a lot of our job is doing that, so it's very fun.

• 4:03Marina Mogilko?

  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 were 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?

• 4:16Speaker 04

  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?"

• 4:28Marina Mogilko?

  Gemini Claude is still trending.

• 4:31Speaker 04

  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 will pan out. And I think 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 requests that we get.

• 5:04Marina Mogilko?

  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.

• 5:16Speaker 04

  It's one of the smaller markets, but still, like, we've done a lot of research. We have an arm of the company called Kalshi Research that looks at the markets. And even with like, I think, 5,000 in volume, we already see kind of convergence to like a very calibrated number. So that number can be trusted, for sure.

• 5:32Marina Mogilko?

  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...

• 5:39Speaker 04

  It looks likely that it would be again, yeah.

• 5:41Marina Mogilko?

  So, 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." And 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 like how AI impacts the day-to-day decisions? Are more people asking about stable job or business? I don't... What kind of bets can you make?

• 6:05Speaker 04

  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 for other things as well. Like, for example, like, big, you know, math problems being solved. There are 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

• 6:47Speaker 04

  is try to price these kind of unknown innovation in 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.

• 7:21Speaker 00

  The action's on the court. And as the official beer sponsor of the NBA, Michelob Ultra is getting fans closer to the game with a chance to win courtside seats, merch, and more. Visit michelobultra.com/courtside to enter. Michelob Ultra Courtside, 26 to 27. No purchase necessary. Open to U.S. residents 21+. It begins on October 1st, 2026 and ends on June 30th, 2027. Multiple entry periods. See official rules at michelobultra.com/courtside for free entry, entry deadlines, prizes, and details.

• 7:48Speaker 01

  Flowing ad budget on metrics that look great till the CFO sees them? That's bull spend. And marketers are calling it out in Dashboard Confessions.

• 7:58Speaker 02

  I remember telling my boss, "It'll be good for the brand," when leads were slow. Yeah, it wasn't.

• 8:06Speaker 01

  Cut the bull spend. LinkedIn lets you target by company, job title, and more. Advertise on LinkedIn. Spend $250 on your first campaign and get a $250 credit. Go to LinkedIn.com/campaign. Terms and conditions apply.

• 8:18Marina Mogilko?

  So, I was listening to some of your podcasts. 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 gonna take your job, and it takes your job, you can bet against that on Kalshi. So you can have some insurance payment.

• 8:35Speaker 04

  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 gonna run a promotion that basically was, "Whoever comes in, we're gonna 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 gonna see, that's my forecast there, is like, at the beginning, everyone was also like not sure what was going on, what are 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 gonna see them starting to understand the other use cases, like hedging and all of that, that we're really seeing growing on small business side,

• 9:18Speaker 04

  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."

• 9:31Marina Mogilko?

  Exactly. Because insurance wouldn't work if something happened.

• 9:33Speaker 04

  Exactly.

• 9:34Marina Mogilko?

  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?

• 9:42Speaker 04

  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 a forecast of different things. So basically, what you just did, to look at their 70% chance that AI will be the main reason for job cuts in May. They're gonna come and kind of digest all that information in the morning, from sports to culture to, you know, who's gonna 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 like billions of people really is with the data that we're bringing. And I think that that's kind of the best use case. That, and also, like, obviously, if you want a forecast for something,

• 10:25Speaker 04

  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 the, almost the main use case for people, so.

• 10:35Marina Mogilko?

  What are you looking at? Every morning.

• 10:36Speaker 04

  I look a lot on the economy stuff and I look a lot of the election stuff. I'm very, I love, uh, you know, American politics. I love politics in general. I'm from Brazil, uh, so I like Brazilian politics too. Uh, and in an election year, we've been looking a lot at that, especially we launched on. And, 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 races, senate races, uh, 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, but it's always like, there's one seat here. How do we think about that? The house is another way.

• 11:19Speaker 04

  What we really wanted to create was a number that you can look at that will kind of track.

• 11:23Marina Mogilko?

  Well, like an AI assistant, now that I'm thinking, i- if I just ask, what's the sentiment about AI today?

• 11:28Speaker 04

  Right.

• 11:28Marina Mogilko?

  And it runs all the.

• 11:30Speaker 04

  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 ma- market forecasts and try to create kind of like one number that is the sentiment or the index for something. So we released the Kalshi Power, American Power Index, uh, which is we call K-Power, which is basically tracking. Is the country more Republican, more Democrat, based on current state of the world and our forecast?

• 11:55Marina Mogilko?

  What does it say?

• 11:56Speaker 04

  Last I checked was like point two, like plus two for the Republicans.

• 12:00Marina Mogilko?

  Mm-hmm.

• 12:01Speaker 04

  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 news side. So now, before I used to look at race by race, there are 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 of days, I just open, you know, Kalshi.com research. It's on our research tab and then, uh, and then see the number there. But I try to look at, I, I'm looking at the markets the whole day. That's kind of my job, so.

• 12:30Marina Mogilko?

  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 betting on. Can we make some predictions?

• 12:39Speaker 04

  Let's do it.

• 12:40Marina Mogilko?

  AI, what actually changes for a normal person by the end of this year?

• 12:43Speaker 04

  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 Kalshi, 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 twenty cloud agents and all those things. Uh, 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 Kalshi AI agent. That's kind of like everyone has their agent. But anyway, we, we're investing a lot in like how to, how to solve a lot of classic company problems with AI. Like communicate, as a 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?

• 13:25Speaker 04

  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 over for me for two days.

• 13:40Marina Mogilko?

  What are you using for that?

• 13:41Speaker 04

  Um, I just use JGPT for that, so that maybe.

• 13:43Marina Mogilko?

  So you just give it whatever you're thinking of and it gives you suggestions?

• 13:47Speaker 04

  Yeah.

• 13:47Marina Mogilko?

  But then you still go and book yourself?

• 13:49Speaker 04

  I still go and book myself. Maybe I should, maybe that's, yeah.

• 13:52Marina Mogilko?

  Oh, you know, I did that yesterday and I was talking to my husband and I'm like, how is it possible? Two thousand twenty six, I'm still booking every hotel myself. I'm clicking all the buttons.

• 14:00Speaker 04

  Right, i- 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, 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, is valid. I just feel like I'm more of an optimist than a pessimist, if that makes sense.

• 14:16Marina Mogilko?

  What about money in general? Do you think, uh, two thousand twenty six is going to feel lighter or heavier for majority of people?

• 14:23Speaker 04

  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 like most people would think about gas prices as kind of like a big, uh, dependent on that.

• 14:32Marina Mogilko?

  But I like how you think about that. So if somebody has a concern about money, they can go to Kalshi.

• 14:37Speaker 04

  Exactly.

• 14:37Marina Mogilko?

  And see what people are betting on.

• 14:38Speaker 04

  And we have markets on all these things, like recession and inflation and all of that. I would probably say it's neutral. Uh, that, that would be my forecast if we want to go with that, but.

• 14:47Marina Mogilko?

  Well, with the summer travel, I already feel like I'm, I don't know, thirty percent poorer because of the.

• 14:52Speaker 04

  Oh, that's.

• 14:54Marina Mogilko?

  Prices. They're crazy.

• 14:55Speaker 04

  That's fair, right? Because it's crazy how many things are impacted by, by gas prices or oil prices at the end of the day. It's kind of.

• 15:01Marina Mogilko?

  Yeah.

• 15:02Speaker 04

  We even forget about that.

• 15:03Marina Mogilko?

  Absolutely. Do you see any jobs suddenly becoming safer?

• 15:07Speaker 04

  I was at the gym the other day and I was actually thinking that, for example, trainers are continue, going to continue. I think everything that's more physical in nature are going to continue. Uh, we'll see how, 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, that people thought were safer before. I think it's kind of clear that, uh, they're going to be less safe. So yeah, I would say anything that's more like craft and physical, probably.

• 15:33Marina Mogilko?

  When it comes to, um, trusting those numbers that you see on Kalshi, how many of the bets? So for example, if people are betting for something like seventy percent 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?

• 15:52Speaker 04

  I think the most important thing to think about is that Kalshi, what we give are probabilities, right? There's not an answer. So even if it's ninety-nine, it's still like, if you think about probability as like the frequencies, like you still have one in a hundred that it's, it's not going to happen, right? So for example, the Pope, the American Pope, he was around one percent at Kalshi the whole time. And the news were all like, oh, the Kalshi markets were wrong, the Kalshi markets were wrong. And I mean, one is not zero, right? You still have one percent 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, to forecast from the outside. But we've done a lot of analysis and research on our calibration. So basically like if, if a market says a seventy percent chance, is it actually seventy percent chance? So we can actually plot, like do some calibration math.

• 16:37Speaker 04

  And the calibration is actually very, very, very good. And I think that even like the, there's a Fed paper that came out about prediction markets. They how it's much better than any other forecast. Um, but I think the core of it is understanding that seventy percent is not a hundred.

• 16:49Marina Mogilko?

  Is there a number where predictions are right? Like an average percentage?

• 16:55Speaker 04

  That really depends on the time to expiration and the type of market. So for example, for an entertainment market is actually different from, than from a politics market. And even in a politics market, if you see like one week before, I think you'd need like maybe one or three thousand dollars 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 ten thousand. I'm not as accurate on the numbers there, but, um, but I think it depends on a lot of things. I still think though, that it's like the, 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, in, in like good information. Because people are incentivized to do their research, because if they're right, they make money.

• 17:39Marina Mogilko?

  Because yeah, if they're putting their money, that means.

• 17:41Speaker 04

  Exactly.

• 17:41Marina Mogilko?

  They put some thinking behind.

• 17:43Speaker 04

  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, you're not going to get a better answer than an alternative. But we've actually way less than what people expect, you start getting there.

• 17:58Marina Mogilko?

  So we touched upon some agents, um, and I really like that topic. Uh, can you talk to me about how agents have transformed your life as the founder?

• 18:06Speaker 04

  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, one seventy people, um, 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 this, from, 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. Um, 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.

• 18:48Speaker 04

  We think about, um, you know, number of mistakes, but also like listing latency, determination latency, coverage, all of those things that, that we, 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 AI. 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 on, 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 I need them to do.

• 19:21Marina Mogilko?

  Can you talk to me about a couple of agents that you built for yourself? Something that anyone who's a knowledge worker could deploy for themselves as well.

• 19:28Speaker 04

  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. Um, that I think it's like, how do we get updates from the entire company? Track from what the updates were from the week before, flag what's.

• 19:44Marina Mogilko?

  Well, what, what hasn't been done?

• 19:45Speaker 04

  What hasn't been done.

• 19:46Marina Mogilko?

  How do you collect all the data? Do you use any tools? Does every employee have their agent? Like, how do you collect it all inside one database?

• 19:53Speaker 04

  Yeah, that, that is a great question. And I think that we should, we should ask our, our, 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, 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 uh 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 uh 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.

• 20:39Speaker 04

  Everyone, what was done, what we need to do the next week, look at all the metrics and all that, that's all I do on Sunday. And now I'm actually able to like have brunch on Sunday because I'm like, I, I, I have a lot more time uh to think about things. But um 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 overpromised, underdelivered. These are the things, like these metrics are not.

• 21:01Marina Mogilko?

  I'm trying to build something for my, like that for myself, but what I've 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.

• 21:13Speaker 04

  Yeah, 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 operation is a great one, but for example, design, um design is something we didn't use a lot of AI for and now we're kind of.

• 21:26Marina Mogilko?

  Quad design or?

• 21:27Speaker 04

  Quad design.

• 21:29Speaker 04

  Yes, but we're trying to also figure out a lot better on like, how do we also empower almost everyone to be a designer in a better way? Uh 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 ju- we're, 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 and experiment very quickly. But how do we actually like do it in a great way from the start? Because I feel like that's the point of design, right? You can, you can just.

• 21:54Marina Mogilko?

  Like built-in reviews or something?

• 21:56Speaker 04

  Yeah, but also like engineers are very good at like, if you wanna 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 is good, 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. Um and we're trying to figure out how can we actually not need that loop anymore by make, yeah. A lot of things we're thinking about.

• 22:24Marina Mogilko?

  That's interesting. So you have that agent running, um 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 wanna 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.

• 22:42Speaker 04

  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 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, we were talking about this yesterday, it's like a bug. Something to, to, for that extent, to like, we'll track everything immediately, prioritize them, see what's live and not live without adding engineering burden. Um 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. We, there are a lot of companies we tested for on the QA side, nothing was great, so we tried to figure. Anyway, we're trying to put a lot of time.

• 23:25Marina Mogilko?

  A lot of burning a lot of tokens from what I'm hearing. Um is there anything that you've built yourself uh for yourself?

• 23:32Speaker 04

  Not really, to be honest. I think, I think that both Tarek 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 Tarek and I, we should be always be 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, the big differentiator for Kao Shi, that people used to think of as 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.

• 24:08Marina Mogilko?

  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 that doesn't make mistakes. Well, or at least the mistake rate is like three percent, then you have to hire someone.

• 24:33Speaker 04

  Because I also, I'm a big believer, I think, I think it's a Peter Thiel that maybe said that, that is, 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 already very spread thin. And if I was to say, I'm going to put five percent of my time into trying to do something, it's just not, it's not gonna 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 as possible. So AI 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 AI will create so many more opportunities for us to do more and more things, right?

• 25:13Speaker 04

  Like we just announced uh Perps, which are a big new product, first time that we're going outside of prediction markets. So it's, it's a perpetual future. So it's basically what you can do now is you take a like a long and a short, for example, on Bitcoin. Uh 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 just, 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.

• 25:44Marina Mogilko?

  AI is something where you can short or long AGI or super intelligence.

• 25:49Speaker 04

  Exactly. That's exactly kind of the direction we wanna 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?

• 25:56Marina Mogilko?

  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?

• 26:05Speaker 04

  And that's why we wanna 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 for forever, up until you wanna say, I don't wanna be long.

• 26:14Marina Mogilko?

  And that's your alternative uh to investing in tech companies, right?

• 26:20Speaker 04

  Because even if it, it, that's one of the reasons we started Kao Shi. It's so hard, like if you're a long AI, like you can say, okay, I'm gonna buy Nvidia stock, I'm gonna do. But it's very hard, there's other, so many other factors that impact all of these stocks. And what prediction markets or, or what we built, that what we are excited about and what Kao Shi is about, is that we want whatever your thesis is, you're going to be able to get that.

• 26:41Marina Mogilko?

  Yeah, not like trying to diversify among this data centers or.

• 26:45Speaker 04

  Exactly. So you're just able to do uh, to do uh 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 com- 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.

• 27:15Marina Mogilko?

  How are you hiring these days? How has it changed from the last year?

• 27:18Speaker 04

  We like being very lean, uh so we are one seventy people um at the moment. And people that work very well at Kao Shi, they, 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. Uh and that kind of like cultural side is very important for us. But realistically, the two things that matter the most is just working really hard and having like a commitment to, to work above everything else. But when I say commitment to work, it's more about um when we ask you to do something and we trust you have 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.

• 27:57Marina Mogilko?

  Is it about AI as well?

• 27:58Speaker 04

  It is about, so in the engineering side, in the engineering interview, we put a lot of time into, in, in, in kind of like. Now you can use AI in the interviews and it's completely fine, um and, 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 uh of that. Because now a lot of the things that we used to look at like two years before, 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 um in design now, I told you that we're, we're trying to get more and more on the, eh, 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?

• 28:37Marina Mogilko?

  So it's spreading to design. What about knowledge work?

• 28:39Speaker 04

  Less so. We need to do, uh, 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. Yeah. Cases and 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, um 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, to do it? Because we have, as again, like our engineering team, what we're doing is that we're kind of putting them, the, the AI group in like design. And then they're gonna 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, and the answer is like, what they used, how they used to work is not the way that we're going to work at Kao Shi and the world's going to do.

• 29:23Speaker 04

  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, it's like 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. Um 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.

• 29:55Speaker 00

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• 30:22Speaker 01

  Blowing ad budget on metrics that look great till the CFO sees them? That's bull spend. And marketers are calling it out in Dashboard Confessions.

• 30:32Speaker 02

  I remember telling my boss, "It'll be good for the brand," when leads were slow. Yeah, it wasn't.

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• 30:52Speaker 04

  I'm ready to do it.

• 30:53Marina Mogilko?

  Well, that makes total sense. You sound really smart. What you built is 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?

• 31:08Speaker 04

  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 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. And, but I told them I wanted to do it. And they were like, "All right, like, let's...

• 31:53Speaker 04

  Sounds 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 ballet, also doing ballet for so long, taught me is just, "You can do things. You just need to work very hard for them. You're not old to anything. But if you work really hard, good things happen." I think that that's kind of like the main thing about my upbringing is just like, teaching me that hard work is 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.

• 32:20Marina Mogilko?

  Is that your work principle? The main work principle, work hard?

• 32:23Speaker 04

  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 Cauchy 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 sue the government. And it was a very painful decision.

• 32:52Marina Mogilko?

  Sounds very crazy, especially as an immigrant.

• 32:54Speaker 04

  Yeah, it was crazy. And also, like, we were a small company 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 yet, and we should try it. And I think that's kind of like this, um, this thing. I think it's impacted Cauchy 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.

• 33:25Marina Mogilko?

  Yeah.

• 33:26Speaker 04

  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, 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 a lot of ways. And I think that having that mentality has helped Tarek and I a lot, too.

• 33:49Marina Mogilko?

  Okay, my last question. Can you give advice to women trying to build something?

• 33:53Speaker 04

  It might not be the best advice. But I think it's like focusing less on the fact that you're a woman.

• 33:59Marina Mogilko?

  Mm-hmm.

• 33:59Speaker 04

  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 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, 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 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

• 34:44Speaker 04

  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 see is just, it's just very sad and upsetting. But I think it's just a matter of focusing on what we can control.

• 34:56Marina Mogilko?

  Thank you so much. So impressive. And congratulations on all your success.

• 35:00Speaker 04

  Thank you.

• 35:01Marina Mogilko?

  It's a huge inspiration for all the immigrants as well.

• 35:03Speaker 04

  Oh, thank you. Thank you. Thank you so much.

• 35:05Speaker 01

  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.

• 35:17Speaker 05

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• 35:32Speaker 00

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