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$22B Kalshi Co-Founder: How Life Changes in the Next 12 Months

📌 Transkriptor records your calls, splits them by speaker, and hands you the decisions and action items — 300 free minutes on a work email: https://transkriptor.com/?utm_source=youtube&utm_medium=midroll&utm_campaign=siliconvalleygirl2 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. So I spent this conversation asking her 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 of course, how much should I be relying on the public's opinion versus reality? *Timestamps:* 0:00 — Intro 1:16 — The market Luana's team watches most right now 3:04 — What people ask to bet on now vs a year ago 4:05 — The layoffs market and why 73% was a number you could trust 6:20 — How a New York bar used a market as insurance 7:34 — Why 70% of Kalshi users never place a trade 8:50 — One number for the sentiment of a whole country 11:32 — What actually changes for you by the end of this year 13:10 — Will 2026 feel lighter or heavier financially? 14:00 — Which jobs get safer from here 14:26 — The 1% chance on Kalshi that came true anyway 16:51 — How agents changed the way she runs 170 people 18:22 — The weekly planning agent you could copy tomorrow 19:57 — Why she put engineers inside design and legal 23:27 — How long can you stay a solo founder with agents? 26:12 — What Kalshi looks for in job interviews now 28:50 — What years of ballet taught her about building Kalshi 30:17 — How suing the U.S. government helped Kalshi grow 31:48 — Luana's advice for women building something hard *Links:* 📩 Follow my Newsletter: https://siliconvalleygirl.beehiiv.com/ 🔗 My Instagram: https://www.instagram.com/siliconvalleygirl/ 📌 My Companies & Products: https://partnerships.marinamogilko.co

Marina MogilkohostLuana Lopes Laraguest
Aug 11, 202633mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 1:16

    Intro

    1. MM

      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?

    2. LL

      Even with 5,000 in volume, we already see convergence to very calibrated numbers. So that number-

    3. MM

      Yeah

    4. LL

      ... should be trusted, for sure.

    5. MM

      This is Luana Lopes 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? [laughs]

    6. LL

      Let's do it.

    7. MM

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

    8. LL

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

    9. MM

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

    10. LL

      That is a tricky question.

    11. MM

      Do you see any jobs suddenly becoming safer?

    12. LL

      For now, I would actually claim that-

    13. MM

      Luana, welcome.

    14. LL

      Oh, thank you. Thank you for having me.

    15. MM

      Thank you so much for doing this.

    16. LL

      Of course.

    17. MM

      Uh, I'm, I'm really happy when I have women on my podcast. Uh-

    18. LL

      [laughs]

    19. MM

      ... 'cause my podcast is AI and business, and, uh, mostly, most of the time it's guys building and, like, guys watching as well. I think we're at 70% male. But-

    20. LL

      It's all right. [laughs]

    21. MM

      [laughs] It makes me really happy to interview one of the youngest self-made...

  2. 1:16 – 3:04

    The market Luana's team watches most right now

    1. MM

      Are you the youngest self-made billionaire?

    2. LL

      I think so. I hate the title, but yeah, I think so. [laughs]

    3. MM

      That's very, very impressive, and you're an immigrant. I would love to talk to you about the future. Because-

    4. LL

      Let's do it

    5. MM

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

    6. LL

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

    7. MM

      Mm-hmm

    8. LL

      ... scenario, which is kind of like the, a little bit of a doomsday scenario for AI. And they're think- I think there's five conditions on, you know, unemployment levels and all of that, and actually the odds are around, like, I think 26 or 30%, which is extremely high, uh, if you think about it.

    9. MM

      For the doomsday scenario.

    10. LL

      For... I- it's... So there are five conditions, and I don't know them all by heart, but there's... The, the market is if three out of five of them hit, the market will pay out to yes. Um, and the odds are a little 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, uh, so much. I think it was a very big report that came out a couple months ago that, that just got so much attention. So we have a lot of markets on the AI side, obviously on, on sports. Um, I mean, we're in New York, so the Knicks, uh, [laughs] there's a 30... I think it's 37% chance they're gonna win, um, 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 wanna know forecasts for and what they wanna know, um, you know, have data for, and try to frame the right market that gets to that question, 'cause not every question is a very simple yes/no.

    11. MM

      Yeah.

    12. LL

      Right? You have to actually figure out what do people mean by saying AI did this-

    13. MM

      Mm

    14. LL

      ... or, you know, the economy's in this position, and then really define it. Um, but a lot of our job is doing that, so it's very fun.

  3. 3:04 – 4:05

    What people ask to bet on now vs a year ago

    1. MM

      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.

    2. LL

      Right.

    3. MM

      And then you decide what goes on the platform.

    4. LL

      Right.

    5. MM

      Is there a trend in anything related to AI that you're seeing?

    6. LL

      A year or two years ago, most of the markets proposed were about AI capabilities. Like, they... People were interested in, like, will AI be able to do this, will AI-

    7. MM

      Mm

    8. LL

      ... be able to do that? Uh, which, uh, model will be better than which model? All of those things.

    9. MM

      Gemma, Claude is still trending, uh, [laughs] in the AI section.

    10. LL

      Right, exactly. And that was kind of a big thing. Nowadays actually, a lot more of the requests that we get are more on the im- the impact of AI.

    11. MM

      Mm-hmm.

    12. LL

      So, like, tech layoffs and, like-

    13. MM

      Yeah

    14. LL

      ... just unemployment in general and kind of like how, how that side will pan out. And I think it, like, it's interesting because we see a lot of what people request of markets kind of show a shift also in, 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.

    15. MM

      You have

  4. 4:05 – 6:20

    The layoffs market and why 73% was a number you could trust

    1. MM

      it monthly where you ask about tech layoffs.

    2. LL

      Mm-hmm.

    3. MM

      And, uh, for May is, will AI be the number one reason for job cuts in May? And that's... Well, it's 30,000 volume.

    4. LL

      It's one of the smaller markets-

    5. MM

      Mm-hmm

    6. LL

      ... 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, to, like, a very calibrated-

    7. MM

      Mm

    8. LL

      ... number. So that number should-

    9. MM

      Yeah

    10. LL

      ... can be trusted, for sure.

    11. MM

      73% chance that AI will be number one reason for job cuts, and that was-

    12. LL

      Yeah

    13. MM

      ... the truth for April and March. So it looks like-

    14. LL

      It looks likely that it will be again, yeah.

    15. MM

      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." Now it's-

    16. LL

      Yeah

    17. MM

      ... now it's actually having some impact on my job, not for everyone-

    18. LL

      Right

    19. MM

      ... but for a lot of tech workers.

    20. LL

      Right.

    21. MM

      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? [laughs] I don't... What, what kind of bets can you make?

    22. LL

      Yeah. On, 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, uh, 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 electional- electoral thinking around, um, AI there?" And the other side is just really, like, capabilities and all of that.

    23. MM

      Mm-hmm.

    24. LL

      But we have a lot of markets in 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, 'cause 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, and tech things that we look at, like future of AI or the future of, you know, uh, 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-

    25. MM

      Mm

    26. LL

      ... because you actually get paid on that, uh-

    27. MM

      Mm-hmm

    28. LL

      ... the, the interest. So we're seeing more activity on, on those. But those are some of our favorite markets. [laughs]

    29. MM

      I was listening to some of your

  5. 6:20 – 7:34

    How a New York bar used a market as insurance

    1. MM

      podcasts. The- some people are hedging their risks with AI. Like-

    2. LL

      Right

    3. MM

      ... w- the, the example that I heard was floods, but now that I'm thinking, like, if you're fearing that AI's gonna take your job, and it takes your job, you can bet against that on Kalshi-

    4. LL

      Exactly

    5. MM

      ... so you can have some insurance payment.

    6. LL

      Exactly. We actually just had yesterday, uh, not on the AI side, but on the, on the sports side, um, a bar, I think in the Upper East Side here in New York, that, uh, 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, 'cause they're like, "We might be down like [laughs] 10, $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, uh, that's, that's my forecast there, is like, how at the beginning, everyone's 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, but also beginning of hurricane season now in Florida. The amount of people coming in saying, like, "Can we have a hurricane market for this specific part of Florida I live in, 'cause I wanna like, you know, be able to hedge my, my, uh, deductibles or, or this or that."

    7. MM

      Exactly, 'cause insurance wouldn't work if something happened.

    8. LL

      Exactly.

  6. 7:34 – 8:50

    Why 70% of Kalshi users never place a trade

    1. MM

      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?

    2. LL

      70% of our users actually don't trade on anything.

    3. MM

      Mm.

    4. LL

      They're just coming to ingest, like to just look at... Almost like the news. They're just coming to see what is the forecast of different things.

    5. MM

      Mm-hmm.

    6. LL

      So basically, what you just did, to look at, there's 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, 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 what- how we get to impact the, like, billions of people really is with the data that, 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, 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.

    7. MM

      What are you looking at every morning?

    8. LL

      I look a lot on the economy stuff, and I look a lot at the election stuff. I'm ve- I love, uh, you know, American politics. I, 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

  7. 8:50 – 11:32

    One number for the sentiment of a whole country

    1. LL

      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, m- 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'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-

    2. MM

      Or like an AI assistant, now that I'm thinking. I- if I just ask, "What's the sentiment about AI today?"

    3. LL

      Right.

    4. MM

      And it runs all the-

    5. LL

      Exactly, exactly.

    6. MM

      Yeah.

    7. LL

      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 mar- market forecasts, and try to create kind of like one number that is the sentiment or the index for something.

    8. MM

      Yeah.

    9. LL

      So we released the Kalshi Power American Power Index, uh, which is, we call KPA, which is basically tracking is the country more Republican, more Democrat, based on current state of the world and our forecasts.

    10. MM

      What does it say?

    11. LL

      Last I checked was like .2, like, plus two for the Republicans-

    12. MM

      Mm-hmm

    13. LL

      ... yesterday. And we wanna do more and more of that, 'cause I think it really helps and adds on the, on the forecasting side. And we wanna 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 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. [laughs]

    14. MM

      Yeah. 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.

    15. LL

      Right.

    16. MM

      Every conversation you have disappears the second it ends, a client call, a podcast interview, a team meeting, gone unless someone wrote it down. Transkriptor records it, transcribes it, and hands you a structured summary with action items. Automatically, no scribbling notes, no follow-up email asking, "Wait, uh, what did we decide?" You can paste any YouTube link to your own episodes, an interview you want to research, a talk you missed, and get a full transcript split by speaker in seconds. We're going to show you that on screen right now. Want to go deeper? Search across every past recording. Ask who said what, which decisions got made, and when. It's all archived and searchable. It connects to your AI agents through MCP, so it plugs straight into the automation workflows we've been talking about in this episode. Link is in the description. Sign up with your work email, and you get 300 free

  8. 11:32 – 13:10

    What actually changes for you by the end of this year

    1. MM

      minutes. Can we make some predictions? [laughs]

    2. LL

      Let's do it.

    3. MM

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

    4. LL

      Work is one of the angles that's gonna 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, so we don't need this role, we have AI.

    5. MM

      Mm-hmm.

    6. LL

      All the roles have been, like, augmented by AI, so like an engineer now has like 20 cloud agents and all those things.

    7. MM

      Yeah.

    8. LL

      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're, we're investing a lot on, like, how to, how to solve a lot of classic company problems with AI, like co- communi- as a company grows, communication and context a big deal, right? Like, someone that just joined doesn't have the context to make decisions.

    9. MM

      Yeah.

    10. LL

      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 gonna see work- ... changed 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," and it's done.

    11. MM

      What do you use for that?

    12. LL

      Um, I just use ChatGPT for that, so maybe, yeah-

    13. MM

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

    14. LL

      Yeah.

    15. MM

      But then you still go and book yourself?

    16. LL

      I still go and book myself. Maybe I should- that- maybe that's... Yeah.

    17. MM

      Oh, well, you know, I did that yesterday, and I was talking to my husband, and I'm like, "How is it possible, 2026-

    18. LL

      [laughs]

    19. MM

      ... I'm still booking every hotel myself? I'm clicking all the buttons."

    20. LL

      Right. Right. E- 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-

    21. MM

      Yeah

    22. LL

      ... pessimist,

  9. 13:10 – 14:00

    Will 2026 feel lighter or heavier financially?

    1. LL

      if that makes sense.

    2. MM

      What about money in general? Do you think, uh, 2026 gonna feel lighter or heavier for majority of people?

    3. LL

      That is a tricky question. I think it depends a lot on the direction of the war, I would be honest-

    4. MM

      Mm

    5. LL

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

    6. MM

      But I like-

    7. LL

      Yeah

    8. MM

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

    9. LL

      Exactly

    10. MM

      ... and see what people are betting on.

    11. LL

      And we have markets on all of these things, like recession and inflation and all of that. I would probably say it's neutral, that, that would be my forecast, if you wanna go with that, but [laughs] I'm not sure.

    12. MM

      Well, with the summer travel, I already feel like I'm, I don't know, 30% poorer because of the [laughs] -

    13. LL

      Oh, that's true

    14. MM

      ... plane ticket prices.

    15. LL

      That's-

    16. MM

      They're crazy.

    17. LL

      Oh, 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-

    18. MM

      Yeah

    19. LL

      ... we even forget about that. Yeah.

    20. MM

      Absolutely. Do you see any jobs suddenly

  10. 14:00 – 14:26

    Which jobs get safer from here

    1. MM

      becoming safer?

    2. LL

      I was at the gym the other day, and I was actually thinking that, for example, trainers are continu- going to continue. I think everything that's more physical in nature are going to continue. Oh, we'll see how, how all the robots, kind of like the Optimas 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 gonna be less safe. So yeah, I would say anything that's more, like, craft and

  11. 14:26 – 16:51

    The 1% chance on Kalshi that came true anyway

    1. LL

      physical probably.

    2. MM

      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 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?

    3. LL

      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-

    4. MM

      Mm

    5. LL

      ... as, like, the frequency, it's like you still have 1 in 100 that it's, it's not gonna happen, right? So for example, the pope, the American pope, he was at around 1% 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 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, 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 70% chance, is it actually 70% chance? So we can actually plot, like do some calibration math, and the calibration's actually very, very, very good, and I think that even, like, the... There's a Fed paper that came out about prediction markets, saying how it's much better than any other forecast.

    6. MM

      Mm.

    7. LL

      Um, but I think the core of it is understanding that 70% is not 100.

    8. MM

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

    9. LL

      That really depends on the time to expiration-

    10. MM

      Mm

    11. LL

      ... and the type of market. So for example, for an entertainment market, it's 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 1 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, 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. So-

    12. MM

      'Cause, yeah, if they're putting their money, that means that-

    13. LL

      Exactly

    14. MM

      ... they put some thinking behind-

    15. LL

      Exactly.

    16. MM

      Yeah.

    17. LL

      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 gonna have... Like, if there's $1 traded, you're not gonna get a better answer than an alternative. But we've actually way l- less than

  12. 16:51 – 18:22

    How agents changed the way she runs 170 people

    1. LL

      what people expect to start getting there.

    2. MM

      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?

    3. LL

      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, 170 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 built AI systems from the s- from, from, like, bottoms up of, like, how we were thinking about engineering, how we were thinking about market operations, how we were 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 threads 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. 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, 'cause it's very complicated to think about a lot of these things-

    4. MM

      Yeah

    5. LL

      ... in the context of, like, markets. So yeah, I think it's, like, on, all on the metrics 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, 'cause I can just have my quad agents kind of, like, do everything I need them to do. [laughs]

    6. MM

      Can, can you talk to me about a couple agents that you built for yourself?

    7. LL

      Yeah.

    8. MM

      Something that anyone who's a, a knowledge worker could deploy for themselves as

  13. 18:22 – 19:57

    The weekly planning agent you could copy tomorrow

    1. MM

      well.

    2. LL

      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 was from the week before, flag what's-

    3. MM

      W- w- what hasn't been done, what has to-

    4. LL

      What hasn't been done-

    5. MM

      How do you collect-

    6. LL

      ... trends

    7. MM

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

    8. LL

      Yeah. That, that is a great question, and I think that we should, we should ask m- 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-

    9. MM

      Slack

    10. LL

      ... Slack-

    11. MM

      Everything. Mm-hmm

    12. LL

      ... 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, the, of, 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-

    13. MM

      Yeah

    14. LL

      ... 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, everyone, what was done, what we need to do the next week, look at all the metrics and all that. It's all I do on Sunday. And now I'm actually able to, like, have brunch on Sunday because [laughs] 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 moving-

    15. MM

      I'm trying to build something

  14. 19:57 – 23:27

    Why she put engineers inside design and legal

    1. MM

      for my... like that for myself.

    2. LL

      Yeah.

    3. MM

      But what I've realized, we need to hire someone. So we try to build internally, and my team is, like, creative producers-

    4. LL

      Right

    5. MM

      ... and they didn't. Now we hired someone with an engineering background-

    6. LL

      Oh, nice

    7. MM

      ... to do that. 'Cause-

    8. LL

      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, 'cause 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-

    9. MM

      Claude Designer

    10. LL

      ... Claude Designer. [laughs]

    11. MM

      Yeah. [laughs]

    12. LL

      Well, y- 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 j- we, we're still defining it, but it's not just that the f- 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?

    13. MM

      Mm-hmm.

    14. LL

      'Cause I feel like that's the point of design, right? You can, you can just-

    15. MM

      Like built-in reviews-

    16. LL

      An engineer can-

    17. MM

      ... or something.

    18. LL

      Yeah.

    19. MM

      Yeah.

    20. LL

      But also, like, e- 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 company is they put it out, and then we get, like, we test it, and we're like, "Okay, this was good. Directionally good. Let's, 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 it was just like it didn't look awful, but it wasn't great.

    21. MM

      Mm-hmm.

    22. LL

      Um, 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.

    23. MM

      Interesting.

    24. LL

      We'll see how [laughs] that goes.

    25. MM

      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-

    26. LL

      Right

    27. MM

      ... and you need to collect it.

    28. LL

      Yeah.

    29. MM

      And you wanna make sure the agent knows what's a priority, what's not, 'cause otherwise it's a very long email of-

    30. LL

      Yeah

  15. 23:27 – 26:12

    How long can you stay a solo founder with agents?

    1. MM

      hire someone.

    2. LL

      'Cause I, I also, I'm a big believer, I think, I think it's, uh, Peter Thiel that maybe said that, that it's you need to have one person doing one thing if you want it to do it very well. And I think that, uh, 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 5% of my time into trying to building things"-

    3. MM

      To building agents. Yeah

    4. LL

      ... 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? 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 is, basically what you can do now is you take a, like, a long and a sh- or a short, for example, in 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-

    5. MM

      Mm-hmm

    6. LL

      ... where there's no end date anymore. So just, you can just express your opinion in, like, a simpler way.

    7. MM

      Interesting. Mm-hmm.

    8. LL

      So crypto is what we launched, but we're looking at a lot of different things. Even when we talked about-

    9. MM

      AI is something-

    10. LL

      Exactly

    11. MM

      ... where you can short or long AGI or super intelligence. [laughs]

    12. LL

      Exactly, 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.

    13. MM

      Yeah, exactly. 'Cause 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-

    14. LL

      Right

    15. MM

      ... before that time.

    16. LL

      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 anymore."

    17. MM

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

    18. LL

      Right.

    19. MM

      It's kind of-

    20. LL

      It is an alternative

    21. MM

      ... if you're long. [laughs]

    22. LL

      Yeah. Because even if it, it, that's one of the reasons we started Kalshi. It's so hard. Like, if you're long AI, like, you can say, "Okay, I'm gonna buy Nvidia stock. I'm gonna do..." But- It's very hard. There's, uh, other, so many other factors that impact all of these stocks. And what prediction markets or, or what we built that what we're excited about and what Kalshi's about, is that we want whatever your thesis is, you're going to be able to get that, right?

    23. MM

      Yeah.

    24. LL

      So we could, yeah.

    25. MM

      Not like trying to diversify among this-

    26. LL

      Exactly

    27. MM

      ... data centers or-

    28. LL

      Exactly.

    29. MM

      Yeah.

    30. LL

      So you're just able to do, uh, to do, um, that. So for example, launching perpetuals was, would have impossible 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 and so many more products, and hopefully become a way bigger company because we are AI first than we are about, like, hiring less people. That's just not how we're thinking about it at all.

  16. 26:12 – 28:50

    What Kalshi looks for in job interviews now

    1. MM

      year?

    2. LL

      We like being very lean. Uh, so we are 170 people, um, at the moment, and people that work very well at Kalshi, 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. And when I say commitment to work, it's more about, um, when we ask you to do something, and we trust you with something, we can trust that it's going to be done great.

    3. MM

      Yeah.

    4. LL

      It's not about number of hours. It's not about these things. It's about-

    5. MM

      Is it about AI as well?

    6. LL

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

    7. MM

      So it's spreading to design.

    8. LL

      It's spreading.

    9. MM

      What about knowledge work?

    10. LL

      Less so. We need to do, uh, one thing actually, funnily enough, in the legal team, we are starting to do that a lot too, to be like-

    11. MM

      Okay

    12. LL

      ... 'cause we have so many-

    13. MM

      Yeah

    14. LL

      ... 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, our engineering team, what we're doing is that we're kind of putting them, the, the AI group in, like, design, and then we're, 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 Kalshi, and the world's going to do, and we just need them to be open-minded-

    15. MM

      Yeah

    16. LL

      ... 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, as like people say, you always 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-

    17. MM

      Yeah

    18. LL

      ... which obviously we cannot afford Tarek or I to do.

    19. MM

      Well, that makes total sense. You sound really

  17. 28:50 – 30:17

    What years of ballet taught her about building Kalshi

    1. MM

      smart. What you built-

    2. LL

      Oh. [laughs]

    3. MM

      ... is amazing.

    4. LL

      Thank you.

    5. MM

      As, as a mom who's raising two daughters-

    6. LL

      Oh

    7. MM

      ... can you share some of your principles or something that you think was there in your upbringing that brought you here?

    8. LL

      I joke I have the, my biggest privilege in life is having my parents. They're, 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 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, to study here in the US, it was, 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. Um, and, but I told them I wanted to do it, and they're, they were like, "All right." Like, "Let's, sounds hard, but let's try to figure it out," and they supported me so much. Um, and I think it's kind of this, this thing that bal- ballet also, um, doing ballet for so long taught me is just you can do things. You just need to work very hard for them. You're, you're not owed 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'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-

    9. MM

      Is, is that your work principle, the main work principle,

  18. 30:17 – 31:48

    How suing the U.S. government helped Kalshi grow

    1. MM

      work hard?

    2. LL

      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 Kalshi thing because we took three to four years to get regulated, and then we had to sue the government to, 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 very painful to see-

    3. MM

      Sounds very crazy, especially as an immigrant. [laughs]

    4. LL

      Yeah, it, it was, it was, 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, um, Kalshi 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'd 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.

    5. MM

      Yeah.

    6. LL

      And I want to be able to, 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, in a lot of ways.

    7. MM

      Yeah.

    8. LL

      And I think that having that mentality has helped, uh, Tarek and I a lot too. [laughs]

    9. MM

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

    10. LL

      It might not be the best

  19. 31:48 – 33:10

    Luana's advice for women building something hard

    1. LL

      advice, but I think it's, like, focusing less on the fact that you're a woman.

    2. MM

      Mm-hmm.

    3. LL

      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 wanna do, and I'm not gonna listen to the noise. And obviously, like, look, it, it, a lot of things suck, and I think it's a lot harder. You see the numbers of, of, of women start... It, 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, NVCs 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-

    4. MM

      Yeah

    5. LL

      ... um, in, in a, in a lot of ways, and a lot of the numbers that we see is just, it's just very sad and, and, and upsetting, but I think it's just a matter of focusing on what we can control.

    6. MM

      Thank you so much.

    7. LL

      Of course.

    8. MM

      So impressive, and congratulations-

    9. LL

      Oh, thank you. Thank you

    10. MM

      ... on all your success.

    11. LL

      I appreciate it.

    12. MM

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

    13. LL

      Oh. Oh, thank you. Thank you. [laughs]

    14. MM

      Thank you.

    15. LL

      Thank you so much.

    16. MM

      If you want to stay ahead in the AI era, subscribe to this channel. New episode every week on AI careers and how not to get left behind. Thank you for your support.

Episode duration: 33:11

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