Nearly half of America’s billion-dollar startups were founded by people born outside the US. Sidedoors documents them one by one, and what makes each of them great.

This profile is built from Karol’s lectures and interviews, what his co-founders and investors have said on the record, and Pi’s own research papers. It will tell you:

• Why four of robotics’ biggest names, from Berkeley, Stanford and Google, bet their careers on a company with no product, no revenue and no rush to get either

• Why Karol pointed his team at folding laundry, a task they called “impossible,” and how Pi landed its first paying customer in 18 months. He had guessed it would take five years.

In 2021, Karol Hausman gave a lecture wittily titled Disordered Thoughts of Machines. Back then, he still had a bit of time on his hands: he led a robotics team at Google Brain and was not yet a father or a founder. The invitation came from the University of Warsaw, where the Polish-born engineer had once studied philosophy. Would he give the lecture that opens the Polish academic year?

Smiling, Karol agreed, and set out to make the case for chaos.

His lecture starts with “the philosopher Socrates,” who walked around Athens asking experts to define their own fields, and showed every single one of them that they couldn’t. Then he tells the story of the first AI researchers, who tried to hand-write the rules of intelligence the same way Euthyphro, Athens’ religious prophet, tried to define piety to Socrates: fact by fact, rule by rule. It didn’t work, and the researchers got an AI winter for their trouble. Intelligence, he tells the audience, can’t simply be written down and handed over.

“Intelligence is learned by experiencing the mess and chaos of the real world.” _{Karol Hausman}

Two years later, Karol followed his own advice. He left Google Brain to start his company: a big bet, a leap of faith, a dive into chaos (in the philosophical sense of the word).

The company is called Physical Intelligence, or π, or Pi. Its grand vision: build a single brain that can control any robot to do any task. Over the past few years, OpenAI has taught machines to work with words. Physical Intelligence wants to teach them to work with their hands and bodies: fold, cook, pack, build… work. If Pi succeeds, labor will come at the flip of a switch, like electricity.

Since March 2024, Karol has raised over two billion dollars for his chaotic curiosity, from an all-star roster that includes Jeff Bezos, OpenAI, Thrive, Alphabet and Sequoia. Just last month, an organizer of a Silicon Valley founders’ dinner summed up the mood around Karol and his company:

“Honestly, it felt like the moment we hosted Dario Amodei in 2022. Something big is happening, we all believe it is almost here, and it is not yet consensus.”

It isn’t consensus, and much of the robotics field is betting on other approaches. When Karol thinks about what could kill the company, the first thing he names isn’t a rival. “We think our greatest competition is science itself.”

Physical Intelligence, quickly

The company: Physical Intelligence came out of stealth in March 2024 with one indecently large objective: build a single model that can control any robot to do any task. The company calls itself π. Its website is pi.website. pi.com is for sale but, in Karol’s words, “super super expensive. We would rather spend that money on compute.”

The seven co-founders: Karol Hausman (CEO), Sergey Levine (UC Berkeley professor), Chelsea Finn (Stanford professor), Brian Ichter and Quan Vuong (both from Google), Adnan Esmail (formerly SVP of engineering at Anduril) and Lachy Groom (early Stripe employee, then an angel investor in Figma, Notion and Ramp).

The bet: Pi trains a single model on data from many different machines (one arm, two arms, wheels, whatever partners send over), on the theory that a generalist beats specialists, the way large language models ended up beating years of specialized translation software.

Today: Pi has roughly one hundred employees. It has no disclosed revenue and, by design, no commercialization timeline. First deployments: folding laundry in rental homes and folding cardboard boxes at Dandelion Chocolate, a San Francisco chocolate maker. Robot makers such as Weave (laundry) and Ultra (warehouse packing) already run their machines on Pi’s models. Pi still describes itself as research-first.

The believers: Pi first announced a $70 million seed round in March 2024 led by Thrive Capital, and joined by Khosla, Lux, Sequoia, OpenAI, and Greenoaks. Eight months later (November 2024), Pi raised another round at a $2.4B valuation, led by Thrive and with participation from Jeff Bezos and Bond Capital. Alphabet's CapitalG led the company's $600M round one year later, and Index Ventures (Nina Achadjian) joined the cap table. And earlier this year, Physical Intelligence raised their latest round at an $11.5 billion valuation led by Situational Awareness and Lux, in which Founders Fund, Thrive Capital, Kleiner Perkins, Accel and Iconiq also participated.

Pi vs the competition: Skild (Pittsburgh, founded in 2023 by Carnegie Mellon professors Deepak Pathak and Abhinav Gupta) is chasing the same goal, one brain for all robots, from the opposite end: sell first, learn from the deployments. It already sells to security, warehouse and manufacturing customers, has booked about $30M in revenue, and was valued at $14B in January 2026. Meanwhile, Tesla, Figure and 1X are betting on the perfect humanoid body. Karol’s position on all of it: “The biggest bottleneck to the entire field of robotics is intelligence. It has always been intelligence.”

1.

Karol Hausman is a LeBron James fan.

This may be surprising for a European, until you remember his years in Los Angeles as a PhD student at the University of Southern California. The love goes back further than that, to Poland, where he grew up watching his local team, AZS Koszalin. When a Koszalin news site asked him in 2016 why he’d moved to the US, one of his answers was a childhood dream: to see an NBA game live.

In the summer of 2010, LeBron left Cleveland to play in Miami with two stars good enough to lead teams of their own: Dwyane Wade and Chris Bosh. That was the NBA’s first modern super-team. They went to four straight Finals and won two.

Founding Physical Intelligence was robotics’ version of that summer. Here’s the starting lineup:

1. Sergey Levine. A UC Berkeley professor and one of the most cited researchers in history. At Google, he ran the “arm farm”.

2. Chelsea Finn. Sergey’s PhD student at Berkeley, she invented MAML, one of the best-known methods for teaching a model a new task from a handful of examples. Her thesis won the ACM Doctoral Dissertation Award, the top prize for a PhD in computer science.

3. Brian Ichter. Karol’s teammate at Google, where they worked on the projects that first plugged language models into robots (SayCan, RT-2).

4. Quan Vuong. Another Google teammate, and with Karol and Brian, one of the people behind RT-X, which pooled more than a million recordings of 22 different kinds of robots into what Google called “the most comprehensive robotics dataset of its kind.”

5. Adnan Esmail, who had shipped hardware at scale at Tesla, then as senior vice president of engineering at Anduril.

6. Lachy Groom, the wildcard from Australia, who had never worked in a research lab.

Karol started Pi with six co-founders, most of whom had spent the previous decade citing, co-authoring, advising and occasionally out-publishing each other. Getting one star researcher to leave a safe job is hard. Getting five of them to bet their careers on the same company, and agree on who runs it, is in a league of its own.

If LeBron joining Wade and Bosh made Miami a super-team, Pi was closer to the Dream Team: Jordan, Magic and Bird teaming up on a franchise they had started themselves.

What made our Magnificent Seven all take the leap into chaos in 2024? According to Karol, it was the science. In 2022 and 2023, working at Google with Brian and others, Karol watched language models and robots start to connect. The two still recall, with fondness, awe and a little adorable self-deprecation, how a one-armed robot moved a Coke can onto a photo of Taylor Swift. “That was one of those moments where it was like, holy shit this is going to work,” Karol remembers.

For fifteen years, nothing in robotics had really added up. Robots had to learn everything from their own experience: one lab, one robot and one task at a time. Progress in one place rarely helped anywhere else. It felt, Karol says, like “they’re not adding up to each other.” The Coke-can robot had never seen Taylor Swift, but the internet had. If robots could borrow what the internet already knew, they wouldn’t have to learn the whole world from scratch. Now there was, in his words, a light at the end of the tunnel. “Once you see that light, you can’t unsee it.”

Once Karol saw that light, he decided the problem couldn’t be solved as “priority number 20 in another organization.” It needed its own company, built like OpenAI: a lot of capital, a top team, one focus and real urgency.

He had the top team. Like any super-team, Pi also needed a patient owner: an investor happy to wait, who would let the company start as a research lab with no pressure to make money early. He found Josh Kushner and Philip Clark at Thrive, who led the $70 million seed in March 2024 and came back eight months later for a $400 million round at a $2.4 billion valuation. Today, Karol says he made the right bet.

Every super-team also needs a front office, someone to run everything besides the research. That job fell to the wildcard of the lineup, Lachy Groom. He’d sold his first company at thirteen and joined Stripe straight out of high school, as one of its early employees. Over the next few years, Lachy led Stripe’s launches in Singapore, Hong Kong and New Zealand. Then he went out on his own and became one of Silicon Valley's best-known angel investors, with early checks in Figma, Notion and Ramp.

On paper, he was an unlikely match for a room of robotics professors. But like any good angel investor, Lachy had seen it coming. He’d been reading papers out of their labs for years, and telling friends that if Chelsea, Sergey or Karol ever started a company, they should connect him. That happened. The researchers pitched him as an investor, and by the end of the pitch he wanted to join. "It was just one of those meetings where you walk out and it's like, This is it," Lachy told TechCrunch.

Karol says Lachy understood the research-first idea within the first minute. Most investors before him had heard a research project and tried to make it smaller and faster: pick a market, find revenue, ship something.

Karol had watched that road swallow robotics companies before. They begin with a broad vision, dive into the market with the biggest TAM, cut corners for that one customer, and very quickly end up, in his words, as "an application company, like a warehouse pick and place company." For him, Karol says, that would be "heartbreaking."

He had always been the one arguing for the biggest version of the idea. Lachy was the first to argue for an even bigger one.

In early 2024, the team was lined up.

2.

Karol is from Koszalin, a city of about 100,000 people near Poland's Baltic coast, where his father was a mechanic and his mother ran her own business. He grew up fast, helping to support the family from a young age. His escape was competitions (math, physics, and chemistry), and Star Wars: the books, the films, again and again. What stayed with him were the robots: so many kinds, with different bodies and different jobs, good ones and bad ones, all living next to people. If you could build something that human-like, he thought, "it would allow us to understand ourselves a little bit better."

He would chase that idea for about fifteen years, across three countries and four universities, perfectly happy even though for a long time it "felt like it's not really possible to do."

In high school, he wanted to be an astrophysicist. He applied to physics and math programs for university, but when his friends chose engineering in Warsaw, he followed them. They were smart, and he wanted to stay with them. He didn’t worry much about the right template for a career. He trusted his friends to point him somewhere good, which they did.

At the Warsaw University of Technology he studied mechatronics and finished first in his class. On the side, he spent a year studying philosophy at the University of Warsaw, and finished first there too.

Philosophy taught him that even the great names “made many, many mistakes along the way,” and were sure the world worked a certain way “when it’s now very clear that it doesn’t.” As we’ll come to learn, Karol is very comfortable with being wrong.

His favorite class was ontology, the study of being: what exists, and what things really are. Logic, the other class he remembers, had been easy. He already knew the math, and every rule made sense. Ontology was something else. Every philosopher used the same words differently, nothing fit together, and he had no idea what was happening from one class to the next. He loved it. You just “sit and contemplate reality,” he says, even when nobody can tell you what is right and what isn’t. It was the class where he had the most fun, and where he felt “much closer to the truth” than in any other.

Robotics, meanwhile, was a letdown. At the time, the dream job for a Warsaw robotics student was on the production line of the Avon cosmetics factory outside the city. Karol went to see it once. “It was the most uninspiring thing I’ve ever seen.” What bothered him was that nobody seemed to be working on robots that could actually think.

So he went to Germany for his master's, on a scholarship to study Robotics, Cognition, Intelligence at the Technical University of Munich (TUM). On his first day, the metro got stuck and he missed his very first class, so he roamed the corridors looking for something to do. As if guided by a higher power, he stumbled on two big white doors with a mechanical sound coming from behind them. "I don't know why, but I just decided to open them."

We'll call it a cosmic intervention. Inside, two human-size robots were at work. One was making popcorn. The other was spreading butter on toast. (The lab's two robots were called Rosie and James. Around that time, a video of them making popcorn and sandwiches made the news.)

After years of searching, Karol had found hope for physical intelligence. He frantically knocked on the office next door and asked for a job. He did not know the standard robotics software. He did not know Linux. He barely knew how to program. But Dejan Pangercic and Professor Michael Beetz, who led the group, were presumably charmed by his enthusiasm, or impressed by his pedigree and intelligence. They told him to come in on Monday, and he spent the next year and a half as a research assistant in their group. Karol still counts each of them as "yet another person that I'm really grateful to."

After Munich came America. Karol picked the University of Southern California for his PhD: for its robotics group, for his professor, Gaurav Sukhatme, and, in his own words, for “the weather and the palm trees.” His topic was interactive perception: robots that learn about an object by poking it, pushing it and moving around it. He was, as he later put it, “quite good at it,” and his results were promising.

Two years in, a postdoc candidate came through Karol’s lab for an interview. He worked at Berkeley with BRETT (the Berkeley Robot for the Elimination of Tedious Tasks), a two-armed machine best known for folding towels at roughly twenty minutes a towel.

Cue cosmic intervention number two. Karol presented his work to the visitor, who listened to all of it and did not hold his tongue. Do a 180, the visitor told him: scrap two years of work and switch to an entirely different method called deep learning. Karol was unfazed. "I just thought to myself that he had no idea what he's talking about," he remembers, "and he probably didn't listen to me."

But Karol’s curiosity takes the lead, and that same afternoon he goes to hear the visitor’s lecture anyway. Somewhere in the middle of it, he thinks back to his ontology class in Warsaw, and "all this wonderful mess and chaos" that made him feel closer to how the world really is. The lecture gives him the same feeling. By the end, Karol has made up his mind to throw out two entire years of work. He starts working with the visitor, rebuilds his PhD around deep learning, and “never looked back.”

The visitor was Sergey Levine.

Betting on deep learning that early didn’t make our duo popular. The method had real problems back then: nobody could fully explain how the networks worked, and they needed enormous amounts of data. Machine learning conferences didn’t want robotics papers, and roboticists didn’t want neural networks. “We were a small group of renegades,” Karol says.

According to Karol, only one place really embraced their view: Google Brain. He applied for an internship there and got rejected. Twice. So he interned at DeepMind in London instead, and in 2018, straight out of his PhD, Google Brain finally let him join the ranks.

Karol spent the next six years proving the renegades right. He worked on RT-X, a project that asked 34 labs around the world for their latest robot datasets, trained one generalist model on all of them, and sent it back to each lab to compete against the specialist method that lab had built for its own robot. The generalist beat the specialists by more than 50% on average. For once, the results were adding up.

3.

When Karol started Pi, he split the long climb ahead into three camps, the way mountaineers split Everest.

The first camp was capability: could a robot do a hard task at all? Every robotics company starts with the same big decision: which task do you teach your robots first? The classic move had always been to pick the biggest market ready for automation (manufacturing, warehouses), move in, and master that one task. But Karol the Renegade thought this road was too slow: you have to learn a customer’s process, get on someone’s schedule, ship in their parts. Instead, he pointed his team at … folding laundry.

When they set the goal, he says, you could see the fear on people’s faces. “Laundry. That’s impossible.”

Laundry is brutally hard for a robot, because every shirt lands in a different shape, and one bad grab ruins every step after it.

“If you can fold laundry, you can probably assemble iPhones.” _{Karol Hausman}

Karol had expected a good result to take three or four years.

Pi released π0 eight months in. It was one model running eight different kinds of robots, and it could fold laundry, clear tables, pack groceries and build boxes. The recipe was the one that made ChatGPT work: pre-train on a huge mix of data, about 10,000 hours of robots doing everything, then post-train on a small amount of excellent data, about 20 hours for a specific job.

Karol worries much less about a rival copying Pi than about nobody solving the problem at all, in which case keeping your model secret just gets you a fence around a desert. So he made the counterintuitive call to open-source π0. The weights and code went online as openpi, and within months, people had put π0 on surgical robots, cars and drones. One side effect: top researchers now want to work at the company whose model they already use.

The second camp was generalization: getting robots to work in places they have never seen before. Almost every robot video ever is shot in the room where its training data was collected, with the same light, the same table, the same objects. Move the robot to your laundry room at home, and it falls apart. When the Pi team started, they argued about how many homes a robot would need to see before it could handle a new one. A million? A billion? The answer, it turned out, was about one hundred.

In April 2025, the team rented a bunch of Airbnbs around San Francisco to train and test π0.5. In one video, a researcher asks a robot to clean a bedroom it has never been in. It puts the clothes away, drops the trash in the bin, and makes the bed. Nice!

After training in a hundred homes, a robot does about as well in the hundred-and-first as a robot trained in that exact home, with task progress above 80%. Karol called it “a plot that I’ve been waiting for for a very, very long time.” His theory about why: “Maybe the world is not as diverse as we thought.”

The third camp was performance: getting robots to do the job well, every single time. Nobody in robotics has made it there yet. In mid-2025, Karol said the models “still fail a lot. They’re more like demo ready than they are like actually deployment ready.”

To solve performance, Pi brought back an old friend: reinforcement learning, where the robot tries, gets feedback on whether it succeeded, and does more of what works. It had never worked at scale on real robots, because a robot starting from zero almost never succeeds by accident.

In November 2025, π*0.6 folded laundry for four hours straight. It also made espresso on a commercial machine for thirteen hours, cleaning up after itself, with no cuts (a miracle! a scientific breakthrough!). Early on, it tamped the coffee so hard it almost lifted itself off the table (lol). But roughly fifty small human corrections taught it to be gentle.

To date, performance has been the hardest part. Language models have a scaling law: add more data and computing power, and the model gets better by a relatively predictable amount. That predictability is a big reason investors were willing to spend billions on each new model. Robotics has no such rule yet, partly because nobody agrees on what “better” should even be measured in: success rate, speed, the number of tasks? “We don’t even know what the Y-axis of that scaling law should be,” Karol admits.

The espresso and laundry experiments showed that Pi’s models could perform for hours without falling apart. The next test was harder: could that reliability survive outside the lab? Karol had guessed it would take the company five years to get its first real ‘paying gig.’ It took 18 months. Dandelion Chocolate in San Francisco hired Pi’s robots to fold the cardboard boxes its chocolate ships in, and two more companies have ‘hired’ them since. At Weave, which folds laundry in homes and laundromats across the Bay Area, Pi’s model cut human interventions per laundry load by half. At Ultra’s warehouse, Pi packs real e-commerce orders, and ran a full shift at 96.4% autonomy.

Philip Clark at Thrive, who backed Pi from its seed round, has been surprised by the pace too. “We’re probably today 18 months into the company where I thought we would be 3 or 4 or 5 years into the company,” he says. “They’ve moved something like two to three times faster on that front than I would have thought possible in my more optimistic expectations.”

Pi hasn’t reached the summit yet - not even close. But by Karol’s own account, it has passed the milestone that matters most: its models are good enough to earn money doing real work, and every robot on a deployment sends back data that makes the next one better, at a cost he calls “basically negative”. On October 13 2026, Pi opened its models to outside robotics teams, whose machines already harvest strawberries and assemble servers. Starting a robotics company should be much easier than it is today,” Karol wrote that day.

The view from here is better than anyone at Pi expected, but it also shows how much is left to climb. Sergey Levine has a line for this, and he credits it to Karol: only from the top of one mountain can you see how high the next one really is.

4.

At Pi, they let science lead the way.

It shows in how Pi works, starting with who gets hired. Karol hires people “who can take new evidence and change their opinions,” and who will try an idea even when they don’t believe in it. People, in other words, a lot like him. From the inside, Lachy found that this looked very different from the academic culture he had imagined.

“The researchers and engineers here feel surprisingly close to the engineers that I worked with at Stripe. There is an obsessiveness and obsession with the end goal and getting there as quickly as possible.” _{Lachy Groom}

He describes Pi as having “a real intensity to the culture,” along with “a desire to search for truth” and “an ambition.” Whatever conception he had of working in a research organization, he says, “was largely wrong.”

The team at Pi runs a lot of experiments, tries many ideas, and doubles down on whichever ones stick, whoever came up with them. Sometimes that means dropping Pi’s own plans, sometimes ignoring the rest of the field. Originally, the company planned to buy all its hardware; now builds more and more of it in-house. Many roboticists consider touch sensors essential for handling anything delicate. Pi decided they were too fragile and expensive, and put small cameras on the robots’ wrists instead. Same thing for simulation: most robotics teams train their robots in virtual worlds, because it’s faster than using real machines. Pi learns from the real world, and mostly uses simulation for tests. If simulation ever becomes the better teacher, Karol says, “we’ll switch everything.”

All of this leaves Karol with a tough call: which problems deserve the time, money, people and robots? "It's hard work," he says. In research, he believes, the real failure is choosing badly:

"A research failure is not a matter of failing to solve a problem, but rather that you picked the wrong problem to solve." _{Karol Hausman}

His second, unofficial job is to plan for a future in which science has led the way, Pi has won the game, and true physical intelligence is part of our reality. Karol Hausman has been at it since he was a kid watching Star Wars on repeat.

In 2016, the Koszalin news site asked him whether his work could one day lead to a Terminator scenario. He wasn’t worried then. True artificial intelligence was still “very far” away, he said. A computer could beat a human at one specific task, but that wasn’t even a tiny part of the general intelligence of an average two-year-old. “I’m not afraid, rather I’m full of hope.” Robotics, he believed, could completely change the way we live, and if it did, he wanted to add “at least a small brick” (małą cegiełkę) to making that change a good one.

Ten years later, Karol has laid brick upon brick and is still full of hope. “Right now the only unit of labor we know is a human” he says. “But imagine when that’s not the case and labor is just freely available to anyone.” With populations aging, he sees it as the only way to care for the elderly and keep economies running.

He is also worried, as anyone sensible would be. He’s uneasy about the “accumulation of power, the change being too rapid, how we’re going to deal with this as a society.” In 2016, true AI was “very far” away. Now, “I don’t think many people realize how fast this is coming.”

Last year, with a one-year-old daughter at home, Karol wanted a robot for the bottles, the dishwasher, the cleaning, “just to spend more time with her.” An interviewer asked whether she would get a chores list when she’s older. “That’s a good point,” Karol said. “I need to think about that.”

5.

In December 2023, a few months before Physical Intelligence came out of stealth, Karol took the time to finish The Worlds I See, Fei-Fei Li’s memoir. She arrived in New Jersey from China as a teenager with less than $20, worked in restaurants and in her parents’ dry-cleaning shop, and went on to build ImageNet, the giant photo dataset that helped set off the deep learning boom. (The same boom, a few years later, sent Karol the Renegade on a 180.)

The book brought back feelings he had long forgotten, he wrote when he finished it: “immigration, feeling lost and lonely, passion for physics, appreciation for teachers who believed in me from a young age, thoughtful mentors and guides who were gracious with their time and wisdom, falling in love with a discipline that I didn’t know much about.”

“It was a long journey from Poland through Germany to the US,” he says, “and at every single stage of the journey there was someone kind of rooting for me and helping me.” You’ve met a few of them already: the friends he followed to Warsaw, the lab in Munich that hired a student who didn’t know Linux, and the Berkeley visitor who told him to scrap two years of work and start over.

So he wrote to some of them, to thank them “for everything they did for me.”

A few months later, Karol would leave Google to start Pi.

Nearly half of America’s billion-dollar startups were founded by people born outside the US. Sidedoors documents them one by one, and what makes each of them great.

This profile is built from conversations with early employees, investors, and board members of Kalshi, every recorded interview Luana has given, and the CFTC’s own paper trail. It will tell you:

• Why twenty-five people with Citadel résumés stayed at Kalshi through two public failures, layoffs, and no revenue in sight.

• Luana’s answer to legal battles in nineteen states, cease-and-desist across a dozen states, and criminal wagering charges.

• The cost of being the most optimistic person in the building, as told by her co-founder who spent years arguing with her about it and then stopped.

It’s September 2024, and we’re in Kalshi’s office in SoHo, New York.

Ali Partovi, one of Kalshi’s earliest investors (his fund Neo also backed Cursor and Ramp), happens to be there to see Luana Lopes Lara. It’s a terrible day for a meeting. A federal court in D.C. is about to rule on KalshiEx v. CFTC, the lawsuit Kalshi filed against its own regulator. Lose, and the CFTC gets a permanent veto over anything Kalshi ever wants to launch. Lose, and the company dies (at least according to its investors).

The ruling could drop any minute and, for a full hour, Luana never checks her phone. She is, Partovi says, “cool as a cucumber.”

Down the hall, her co-founder Tarek Mansour picks up a call from their litigator and his hands start to shake. They won. The room erupts. Employees jubilate. Chairs fly. Twenty minutes later, everyone is back at work.

Let’s unpack what just happened. A tiny 25-person startup with roughly $20M in monthly trading volume sues the federal agency that holds its license. The same agency that had spent two years blocking Kalshi’s most important product: election markets. Alfred Lin, who led Sequoia’s investment and sits on the board, told the founders that even if they won one day, they would die getting there.

Tarek, a self-described paranoid risk manager, tried to call the whole thing off the night before they filed. Luana’s response: “Are you fucking kidding me?”

She convinced the team to stick to the plan. The win meant that it was the first time in a century Americans could legally trade on their own elections. Tarek has never been vague about whose win it was: “If it were up to me and Alfred, we probably wouldn’t have sued, to be honest. She’s the one who wanted to sue.”

Two months later, on election night, Kalshi’s market called the Trump election while the polls still had the race tied. A million new customers arrived in a fortnight. The company 100x’d overnight on a team of about 25. Twenty months later, Kalshi was worth $22B and Forbes had named Luana the youngest self-made woman billionaire in the world. She was 29.

I’d bet money on what you’re picturing: someone hard. You’d lose the bet. The people who work for her describe “a ball of sunshine”.

Relentlessly optimistic, soft-spoken, warm, smiley, “the heartbeat of Kalshi”. These are the same people who watched her spend years chewing glass and staring into the abyss, without once slowing down. Her investors say the same thing. “What is striking about Luana is her unshakeable optimism that everything will work out, which I think is underrated for any entrepreneur starting out” says Matt Huang, who led the $1B Series E out of Paradigm and sits on Kalshi’s board.

Kalshi, quickly

Founded: idea born 2017-18 at MIT and during finance internships; Y Combinator Winter 2019 batch; Kalshi spent its YC batch reporting “regulatory traction” while everyone else in the batch (Deel included) reported user growth. Public launch July 2021. Kalshi means “everything” in Arabic; they picked the name in a 30-second Messenger exchange, partly because the domain was cheap.

The moat: the first federally regulated US prediction market, and for four years the only one, in a category that had been an academic dream for 30 years (the only legal alternative was a nonprofit with an $850 bet cap). CFTC designation granted on November 3, 2020 after two years of filings. Polymarket only got a US license in 2025, by buying a company that had one.

The inflection: beat the CFTC in court in September 2024 and opened election markets. Roughly half a billion traded on the presidential race, 2M new customers in two weeks. Launched sports in all 50 states in January 2025 on one federal license, while DraftKings and FanDuel (the big US sports-betting apps) pay state gaming taxes of up to 33%.

Today (September 2026): roughly breakeven at enormous scale, over $4B annual revenue, $400B in annualized trading volume, more than 8x in a year. By growth rate, arguably the fastest-growing company in the world outside AI, run by roughly 170 people, of whom about 130 report directly to the two founders. Luana, COO, runs everything internal. Tarek, CEO, runs the outside world. Lately, Kalshi’s marketing has been serving pure genius: a Messi campaign timed to land two days before his first match of the last World Cup he will play, a Timothée Chalamet spot shipped the week the internet crowned him, live odds piped onto Times Square billboards. Kalshi becaome the exclusive prediction market partner of the US Open in August 2026. The federally regulated derivatives exchange behaves, in public, like a culture brand.

The believers: Michael Seibel of Y Combinator took the first bet. Justin Mateen, co-founder of Tinder, invested at seed after a five-minute phone call (March 2019). Kevin Carter of Night Capital backed the team early. Ali Partovi led the Seed round for Neo in early 2020. Alfred Lin, who made his name on paradigm-shifting bets like Airbnb, led the $30M Series A for Sequoia in 2021. The angel investor list reads like a history of American finance: Charles Schwab, Henry Kravis, Ron Conway. From 2025 onwards, the company raised four rounds in about a year, valuation roughly doubling each time ($2B → $5B → $11B → $22B and reportedly raising at $40B today). Matt Huang’s Paradigm led twice (the $2B round in June 2025 and again the $11B round that December). Huang has sat on the board since, and called prediction markets “a new asset class on a path to trillions” the week Kalshi was worth $2B.

Kalshi vs Polymarket: Polymarket launched fast and offshore, ate a $1.4M CFTC fine, blocked US users, and became the brand the world’s media quotes. Kalshi took the hard path: years of legal battle, and now a claimed 95% US market share. The rivalry is undecided: ICE, owner of the New York Stock Exchange, committed up to $2B to Polymarket. Luana: “It might sound cocky, but we’re now six to 12 months ahead of everyone, including Polymarket.”

1.

Roll the dice on Luana Lopes Lara’s life a hundred times, and in ninety-nine of them she is a very rich, very anonymous quant trader.

Luana grew up in Minas Gerais, Brazil, about as far from the San Francisco ecosystem as it is gets. At MIT she took two entrepreneurship classes and enjoyed neither. She wanted to do math, and she aimed herself squarely at the prestige track of finance: Bridgewater, Five Rings and Citadel Securities. Her co-founder Tarek did the same: Goldman, then Citadel.

That’s where “the idea for Kalshi found them”. They had both watched the same thing happen from different desks. In 2016, Tarek sat at Goldman while clients tried to work out how to trade Brexit. There was no instrument for that, so they bought expensive structured products over the counter, or built clumsy proxies out of stocks and options and hoped the correlation held. It usually didn’t; you could be right about the referendum and still lose money on the trade.

Then at Five Rings in the summer of 2018, Luana and the other interns played a game called ‘market maker’: someone quotes a probability on anything at all, everyone else tightens the spread or trades against it. I think this is what the finance interns call “fun”. One afternoon, Luana took a large position on some Trump-related outcome (Luana likes her positions large), the whole floor argued against her, and she was right. Walking home that evening she could not shake the question. Every firm played this game in disguise. Why was there nowhere to play it for real?

They stayed up all night talking about it, then sat on it for six months, then flew to a Y Combinator hackathon in Mountain View two weeks past the application deadline. They had emailed the organizers an excuse and been let in anyway. They landed from the East Coast with a janky stablecoin mock-up and walked into a room of 250 hackers. Luana still remembers what the guy they pitched said first. “The first thing he said was, that’s illegal.”

That ‘guy’ was Michael Seibel, and they came third in the hackathon, winning office hours with him. Seibel’s own verdict on taking them: “This sounds crazy, but these two MIT kids are really driven. Let’s give them a shot.” From there, our duo started taking their own company seriously.

When the founders incorporated, they settled the org chart in one conversation: Tarek CEO, Luana CTO. Partly because she preferred product and engineering to sales. Partly for a much less glamorous reason: the US-born Tarek could employ the foreign-born Luana. The founding structure of a future $22B company was, among other things, a visa workaround. (Immigration law: quietly one of the most consequential corporate governance frameworks in Silicon Valley.)

They entered YC W19 and proceeded to have the least YC experience in YC history. The entire methodology (launch fast, talk to users, grow weekly) was unavailable to them, because their product was illegal until a federal agency said otherwise. While their batchmates shipped, Kalshi appeared to stand perfectly still.

So they went looking for a lawyer. They built a spreadsheet of sixty-five securities and derivatives attorneys and called them, by some accounts all in a single day. Every one said a version of the same thing. “People have been trying this since the 1980s.” “The CFTC will never allow it.” “It cannot be done.”

Nobody said why.

The no’s were unanimous and they were unreasoned. It could be manipulated, said some. Manipulation is a solved problem, said Tarek, we solved it for equities. It’s gambling, said others. “Then why is a grain future not gambling?” Nobody could say. To sixty-five professionals this was a closed question.

To Luana it was a mis-priced asset with no one bidding.

The first piece of encouraging news for Kalshi came on January 29, 2019, when Luana and Tarek met Jeff Bandman, a former senior CFTC official. After the 65th lawyer said no, the path to the one man who said maybe ran, in Bandman’s own telling, through “absurd degrees of separation: Luana knew a lawyer, who had a cousin, whose spouse knew a regulator, who worked for a judge, whose college roommate knew a lawyer I had worked with.”

He spent their first meeting, a Thursday, trying to frighten them off. There was no guarantee it would work. The result would not look like the vision. To be designated a contract market they would have to satisfy twenty-three Core Principles, aka the government’s checklist for “can this exchange be trusted with people’s money”. Can your prices be manipulated? How would you catch a cheater? Is customer money safe if the company goes under? Are your contract rules precise enough that every trade settles without a fight?

Little did Luana know that half of her working hours for the next decade would go to regulation.

Four days later, a weekend among them, Bandman opened his inbox and found a twenty-page memorandum on how Kalshi could satisfy every core principle of the Commodity Exchange Act. As a kind reminder to the reader, this was years before ChatGPT launched. “For kids, they were oddly enamored with derivatives law.”

At the W19 YC Demo Day, Justin Mateen, co-founder of Tinder, watched 175 companies pitch and wrote exactly two personal checks. One went to Deel. The other went to Kalshi, at a $10M post-money valuation, after a five-minute phone call. He has since called it lightning striking twice. What sold him was a pairing he almost never sees: two founders who were obviously brilliant, and obviously naive, in the specific way that only brilliant people can afford to be. He tried to take as much of that round as he could.

What followed was maybe the least sexy zero-to-one in modern startup history: a two-year regulatory email thread. The CFTC sent Kalshi twenty concerns. Kalshi answered each with data and legal argument. The agency came back convinced on three and with follow-up questions on the other seventeen. Then it happened again. Luana has a name for this phase and it deserves to enter startup language. "It's a regulatory-market fit, in a way." Instead of iterating on a product until users say yes you iterate on proof until the agency runs out of objections.

The two-year crossing of the regulatory desert got more monotonous still. Luana stripped her days of every choice that didn’t matter. She wore the same thing every day: two pairs of Levi’s, a company t-shirt, one of three sweaters. Her favorite was her father’s, bought in some Brazilian shop when he was young. She ordered from the same few restaurants, most often rice, beans and steak.

After two years of that, on November 3, 2020, KalshiEX LLC became the first federally regulated exchange for event contracts in American history. The vote was unanimous and bipartisan, taken in the middle of COVID. The founders printed that date on a trucker hat.

To Luana and Tarek, and to anyone in startup land watching them do it, those two years had felt like a lifetime. The CFTC veterans Bandman spoke to afterwards had the opposite reaction entirely. They were amazed anyone had pushed this through that quickly. Two years, in their world, is practically a rush job.

2.

It is worth asking what kind of person is actually built for spending two years, and millions of dollars, on an email thread with a federal agency.

Well, Luana had been training for this battle since she was a little girl, and had become an expert in delayed gratification.

At eleven, at a festival in Joinville, she noticed the Bolshoi Ballet auditioning for its only school outside Russia and walked in. She passed on the first try, into a system that in one documented year took forty children from twenty-four hundred. Getting in turned out to be the easy part. The school runs eight years, on its Moscow curriculum with mostly Russian faculty, and on full scholarship. Of the original eighty children accepted on Luana’s year, only about ten graduated.

Her childhood reads like something a screenwriter would be told to tone down. She spent eight hours a day at the Bolshoi. Students weighed their food before rehearsal, down to a quarter of a strawberry. Teachers held a lit cigarette under her thigh while she raised one leg to her ear, to see how long she could hold it there without burning.

In parallel, she went to a technical school, harder in the sciences than the one most Bolshoi students attended, and found time to win gold at the 2013 Brazilian Astronomy Olympiad and bronze at the regional mathematics olympiad.

She has called those ballet years the most intense years of her life, more intense than MIT, more intense than Kalshi. She also says she loved every minute. Ask her what ballet gave her and she does not say discipline. “There’s nowhere to hide,” she says. “You need to be completely on, you need to give it your all, all the time.” Ballet is a sport and an art form that pushes the body toward the absolute: you execute the movement exactly, and you smile through the pain. Perhaps the Bolshoi set Luana’s bar, for herself and for her team.

There is a delightful 2014 Brazilian TV segment of 17-year-old Luana walking through this schedule. The double life sounds brutal on paper; but on camera, behind her ballerina physique, her ease, and a smile running under every sentence, she makes it look light. She claims that she didn’t live the life of an isolated prodigy that her resume implies, and that she was fully able to enjoy parties on weekends and long stretches of playing The Sims. Luana insists all of it, ballet included, was pleasure and never obligation.

Some details of her childhood do however hint that Luana was likely going to be up to something larger-than-life. As a little girl, she kept a list beside her bed. She added a name to it every time she heard about someone great at school. Alexander the Great. Winston Churchill. “I’d write their name and think, one day, maybe I’ll try to do something even remotely similar to what they did. I was a weird child in that way.”

Nothing in her background explains the ambition. Hers was an ordinary (but very smart) middle-class Brazilian household. Catholic, Mass on Sundays, full of engineers: both of her parents had STEM backgrounds, and her older sister did a chemical engineering PhD at Carnegie Mellon. Luana still brags about her big sister in interviews, with her adorable admiration fully intact.

3.

Kalshi went live in July 2021 with about thirty markets. Inflation, COVID, the Oscars. Volume hovered near $1.5 million a month, which was fine but definitely not explosive.

Then Thomas Peterffy, the electronic-trading pioneer who built Interactive Brokers into a $50B business, made Luana and Tarek an offer for the company. Life-changing money, for a business with no users and no name. They turned it down. On the numbers at the time, the decision was insane. Tarek explained it in one sentence: “We didn’t build Kalshi to build a company. We built a company to build Kalshi.”

The market they wanted, the holy grail, was elections. Twice, in 2022 and 2023, Kalshi had formally asked the CFTC to let Americans trade on their own elections. Each attempt cost a year of the company’s life: filings, Washington meetings, and the payroll of a team working sixteen hours a day, six days a week. Both ended in an outright “no” and a collapse in morale. Layoffs followed. People walked, investors openly stopped believing in the strategy, and the board asked for a plan that didn’t involve elections.

Almost nobody inside the company was bearish. Not because the odds looked good (they were terrible) but because it never occurred to anyone that Luana and Tarek would stop. The question was never whether approval would come. It was when. Luana never once looked like she was considering quitting, which is why twenty-five people with Citadel-grade resumes stayed at a company with barely a product, barely any users, and two public failures in the rearview mirror.

By the summer of 2023, before the second rejection had even landed, Luana was already drafting the lawsuit. The tiny startup Kalshi would sue the Commodity Futures Trading Commission: the federal agency that grants, and can revoke, Kalshi’s license to exist.

The “adults in the room” were unanimous: it was a bad call. Alfred Lin told them what everyone told them: you are a tiny startup, the government can pull your clearinghouse out from under you, bury you in audits, and kill you before the ruling ever arrives. Ali Partovi’s summary is shorter. “All of the other investors in the company said that that would be a terrible idea.” The founders went ahead anyway. They buried the decision in the last paragraph of the last page of a board memo, as though it were housekeeping.

The night before Kalshi sued the United States government, in his mostly empty office in SoHo, Tarek got cold feet. He rehearsed all the ways his company was about to die. Tarek is good at this. Cataloguing catastrophe is roughly his job, and it is the thing that covers Luana’s blind side caused by her extreme optimism. So that night, Tarek proposed the reasonable thing. Maybe the company shouldn’t sue. Maybe they should step away from elections and go build financial products. “We don’t need to really bet the farm on this.”

Luana wouldn’t budge.

Her reaction was not just about nerve. In April 2022, two and a half years earlier, an interviewer asked her what she had learned about investing. She was twenty-five, the company had no traction, and she gave what sounds now like a prepared defense of a decision she had not yet made. “The bets that seem ridiculous at first, that seem like there’s no way it’s going to work, are usually the ones that achieve the large outlier results.”

Her philosophy isn’t about bets with good odds. It’s about bets where being right pays out vastly more than being wrong costs. Spend two years on a license nobody has been granted in forty years: lose, and you have wasted two years of your twenties; win, and you own the only legal door into the category. Sue your regulator: lose, and you were dying anyway on $1.5 million a month; win, and Americans trade their own elections for the first time in a century, with you as the only venue. It’s called an asymmetric bet.

The agency’s retaliation came as predicted. Audits that normally take ten days stretched to nine months. Tarek called it “death by a thousand paper cuts.” And then they waited. Nine months of it, with the clock running on the one election the whole company had been built to trade. On September 12, 2024, Judge Jia M. Cobb of the District Court for the District of Columbia ruled in Kalshi’s favor.

The team barely had time to celebrate. They had the right to run an election market, and the presidential election was … 22 days away. Kalshi went on the sprint of a lifetime. Tarek stopped leaving the building and showered roughly every fourth day.

They made it work.

Two million customers arrived in a fortnight, carrying $2 billion in trades onto rails built for one thousandth of that. On election night, with the polls still calling it a coin flip, Kalshi’s market went to Trump and stayed there. CNN read the odds on air.

4.

Kalshi has a company value: “Climb the steeper mountain”.

Luana and Tarek are nowhere near the top of theirs. The company they described to each other in 2018 was not a betting app. It was an exchange where every question about the future carries a price: what inflation will be, what compute will cost next quarter, who will win an election. A market that tells you what is likely to happen, continuously, in public, because people have money on it.

Yet, so far in 2026, more than eighty percent of Kalshi’s volume comes from sports.

Dan Schwarz, who designed Google’s internal prediction market, puts the bear case in one line: “It’s really a sports gambling site with a thin layer of prediction market on top.”

This is not a stupid thing to say. Kalshi pays no state gaming taxes, operates in California and Texas where sportsbooks are illegal, and takes accounts from eighteen-year-olds in a country that mostly caps betting at twenty-one. It self-certified sports on January 22, 2025. Three days earlier a new administration had taken office, with Caroline Pham as acting chair. Pham had abstained from the 2023 vote that rejected them and was known to be friendly to prediction markets. The ninety-day review window passed without incident. Kalshi is now in litigation with more than ten states, holds cease-and-desist orders from at least eleven, and is defending suits from eight tribal nations and a stack of consumer class actions consolidated in Manhattan. Eight bills before Congress would undo the 2024 ruling. None has passed a chamber. Bloomberg’s read is that this ends up at the Supreme Court within two years, with nine justices asked to define gambling.

Luana’s answer to all of this is not a denial. “Regardless of whether people like that some people bet or dislike that some people bet, people bet.” What she disputes is the model, not the demand. A sportsbook’s revenue is its customers’ losses, which is why it bans winners and sends bonuses to losers. An exchange takes a fee from both sides, which is why she wants the sharpest traders on her book.

Sports is what pays for the building. What is being built inside it is a derivatives exchange with an unusually generous definition of what can be traded. The evidence for that is in who has started showing up on Kalshi. Institutional volume, meaning hedge funds and trading firms rather than people on their phones, is up eightfold in six months. Kalshi has also started running block trades: single large orders negotiated privately between two institutions and then printed to the exchange, rather than worked through the public order book. Block trades are unglamorous and they are the thing serious markets run on. Nobody builds that machinery for a sportsbook.

Which changes the comparison. Not DraftKings at $12 billion. CME at $100 billion, ICE at $90 billion: the two exchanges that clear America’s futures and equities, each of which took the better part of a century to get there. Matt Huang’s version of the bull case is larger still: “Prediction markets are a superset of every other market. Maybe even bigger. I truly think they are uncapped.”

When Luana and Tarek talk about the future of Kalshi, they talk in decades.

Luana’s half of that is everything internal: product, engineering, design, operations. Thirty of Kalshi’s 170 people do not report directly to a founder, an org chart most management theorists would classify as a war crime.

It somehow works.

Start with product, which is Luana’s territory. In five months of 2026, the team shipped seven launches: Kalshi Klear, its own clearinghouse, so it no longer needs anyone else to settle its trades. Contracts on GPU rental prices, so you can hedge the cost of compute. A commodities hub: oil, gold, lithium. KPOW, a live price on American political power. The first US-regulated crypto perpetuals, which are the most heavily traded instrument in crypto and had never legally existed here. Finally, Kalshi Pro, a Bloomberg terminal for the people trading the most size.

Seven launches in five months, from a company that spent its first two years shipping nothing at all. Note that not a single one is a sports product.

Kalshi’s mobile squad is five people. In a single quarter they shipped 42 releases, including the first perpetual futures in the United States, and put the app at the top of the App Store for most of a month. And then, at night, they rewrote the entire thing. Four thousand commits and a million lines later: peak CPU usage from 369 percent down to 15. Install size from 315MB to 95MB, against Robinhood at 834MB. External dependencies from 94 to 11 and test coverage from 17 percent to 100.

Marketing is Tarek’s department, and the numbers are just as silly. In July 2026, EDO, the firm that scores TV advertising by measurable consumer engagement, ranked all 1,200 ads that ran during the FIFA World Cup. Kalshi’s “Dentist,” an absurd thirty seconds of Timothée Chalamet in which essentially nothing happens, finished first. A derivatives exchange with fifteen people in marketing beat Nike, Adidas, Toyota, Meta and Verizon at the largest sporting event on earth. Fifteen is less than one percent of Nike’s marketing team.

Both founders hire for the full spectrum of a job. Someone on the marketing team should be able to buy the ad, think about the ROI, write the copy and shape the storyline. They have deep respect for people who do the work with their own hands, down to the details, and get excellent at it. Luana is world-class at this herself. She moves between levels of abstraction without friction: from the product details, down to the copy, or the color and design of a button, all the way up to understanding how the market is structured and will change, whether it makes sense to partner or build a tech in-house.

Ask people who work at Kalshi what it’s like and the answers converge on “Low-ego, high agency, shipping-first.” “One of the highest talent density teams I have ever been around.” “People here care A LOT. Everyone pushes HARD. People regularly choose the harder path if they think it leads to a better outcome long term.”

The most endearing thing about Luana is that she appears not to have noticed she has won anything at all.

A company holding the only license in its category still behaves like it is losing. She still works and talks like an underdog, at $22 billion+ valuation, with 95 percent market share, from inside a moat she spent four years digging by hand.

5.

Ask Tarek, who has sat next to her for eight years and thinks about downside risk for a living, what actually drives her, and he cannot tell you either.

“Luana is very faith-based” Tarek says. “Sometimes I would say super irrationally optimistic. She sometimes doesn’t really do the math on how something is gonna work out, on why it’s gonna work out. And that’s continuously happened at Kalshi. And I mean, for years it frustrated me. I was like, this really doesn’t make any sense.”

And then, having thought about it a while longer: “Luana’s approach to things, I now believe that given enough time horizon, it’s gonna end up being true no matter how irrational it sounds. It’s kind of amazing.”

Every morning, before any of it, Luana gets out of bed right foot first. It is a Brazilian thing. Start on the right foot and the day goes well.

So far, it has, roughly.