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Inside the Billionaire Backed Prediction Markets Hedge Fund. Run by a 24-Year-Old. Camilo Saravia (camilo), founder of BlueWalker Capital, a systematic prediction markets fund backed by Daniel Howard of Halo Capital. "I don't want more capital. I'm extremely long our equity." We cover: - Why insider trading in prediction...

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Ethan Kho

21,871 subscribers

128,490 次观看 • 1 个月前 •via X (Twitter)

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37 条评论

camilo 的头像
camilo1 个月前

Thanks for having us Ethan!!

Ethan Kho 的头像
Ethan Kho1 个月前

You were awesome on the pod man

Rob Mk 的头像
Rob Mk1 个月前

@curiouscamilo Insider trading point is the one that'll get quoted out of context but it's actually the most honest framing of liquidity I've heard this year

MovieTime 的头像
MovieTime1 个月前

@curiouscamilo blue walker 👀

Ethan Kho 的头像
Ethan Kho1 个月前

@curiouscamilo Blue. Walker.

Zev S 的头像
Zev S1 个月前

@curiouscamilo interesting discussion.

Ethan Kho 的头像
Ethan Kho1 个月前

@curiouscamilo 🙏

0xturbanurban 的头像
0xturbanurban1 个月前

@curiouscamilo Frontier Markets.

Ethan Kho 的头像
Ethan Kho1 个月前

@curiouscamilo Lfg

shan 的头像
shan1 个月前

@curiouscamilo Well, hello, frontier markets.

Parity 的头像
Parity1 个月前

@curiouscamilo the new age of hedge funds

Ethan Kho 的头像
Ethan Kho1 个月前

@curiouscamilo Frontier markets. @lzminsky

Sahaj Sharda 的头像
Sahaj Sharda1 个月前

@woutergort @curiouscamilo 🔥

Ethan Kho 的头像
Ethan Kho1 个月前

@woutergort @curiouscamilo 🚀🚀🚀

Concave 的头像
Concave1 个月前

@curiouscamilo Fantastic improvement in your production quality, congrats!

Ethan Kho 的头像
Ethan Kho1 个月前

@curiouscamilo Thanks!

sujayalluri 的头像
sujayalluri1 个月前

@curiouscamilo incredible @curiouscamilo

dFusion AI Protocol 的头像
dFusion AI Protocol1 个月前

@curiouscamilo Prediction markets might become one of the most important data sources of the AI era.

savi 的头像
savi1 个月前

@curiouscamilo camilo the goat

Guillaume deRouville 的头像
Guillaume deRouville1 个月前

@curiouscamilo Impressive talk, very interesting 🙏

Typikk 的头像
Typikk1 个月前

@curiouscamilo @Moo_too_wah

Lauris 的头像
Lauris1 个月前

@curiouscamilo Frontier Markets.

Ethan Kho 的头像
Ethan Kho1 个月前

@curiouscamilo Frontier Markets.

Chloe 的头像
Chloe1 个月前

@curiouscamilo i’m 21 and my only hedge fund is my chai addiction

Richard Liddle 的头像
Richard Liddle1 个月前

@curiouscamilo The question I would want to ask is how it gets marked. A systematic book in prediction markets still has to be valued independently at month end, and thin binary contracts are among the hardest things to price fairly. Getting an administrator comfortable with that is real work.

Ethan Kho 的头像
Ethan Kho1 个月前

@curiouscamilo think this requires an @curiouscamilo qt!

__$__ 的头像
__$__1 个月前

@curiouscamilo Top

cedarqapi 的头像
cedarqapi1 个月前

@curiouscamilo When you hear a manager says APY, run away

Toro🦇🔊 的头像
Toro🦇🔊1 个月前

@curiouscamilo Sounds like all bs to me and I've been in prediction markets since 2004. I guess we'll see

Holmes1618 的头像
Holmes16181 个月前

@curiouscamilo They do not need to be 100% right on the final outcome. A typical trade mindset is: buy at 20¢, sell at 40¢, and exit early — never wait for full resolution.

deezzex 的头像
deezzex1 个月前

@curiouscamilo "billions of records a day" is the real infra story. what's actually powering that ingestion pipeline, custom-built or vendor stack?

Tony Tong | Founder | Ancient Systems x AI 的头像
Tony Tong | Founder | Ancient Systems x AI1 个月前

@curiouscamilo That kind of pattern-spotting instinct is hard to fake, you either have it or you don't. I only realized mine was unusual once enough people pointed it out independently, then built around it instead of ignoring it.

Nick Nagatkin 的头像
Nick Nagatkin1 个月前

@curiouscamilo his skin is perfect

Ethan Kho 的头像
Ethan Kho1 个月前

@curiouscamilo Me???

Eddie 的头像
Eddie1 个月前

@curiouscamilo can’t wait to listen!

quicky 的头像
quicky1 个月前

buy at 20, sell at 40, never wait for resolution" is the actual alpha in this whole thread - most retail traders on polymarket still think edge means being right. but the insider trading take is the real fault line: if leaked info makes prices converge faster, isn't the "fairness" complaint just liquidity providers mad they're the exit liquidity for smarter flow?

Orthogonal 的头像
Orthogonal1 个月前

@curiouscamilo Smart kid - but please stop saying APY

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436,074 次观看 • 7 个月前

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I sat down with Jordi Visser to discuss why bitcoin is the purest AI trade available in the market, breakdown of energy infrastructure, how to invest amid the government shutdown, the Fed/interest rates, and why prediction markets and tokenization are two big themes moving forward. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:04 - Why bitcoin is the purest AI trade 11:43 - The impact of new energy infrastructure 16:53 - How Base Power is building a decentralized grid and other opportunities for investors 23:49 - Why retail spots themes before institutions 32:33 - Retail orange-pilling each other in public 37:25 - What is the impact of government shutdown on financial markets? 40:25 - The rise of prediction markets and tokenization 49:28 - Where to follow Jordi on the internet

Anthony Pompliano 🌪

110,012 次观看 • 1 年前

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CoinDesk

22,771 次观看 • 4 个月前

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46:11

DROPS E39: Worm - The permissionless truth machine Nass Diba studied quantum physics, worked at Facebook, then left to build in crypto. He's now building Worm - a permissionless prediction market on Solana with leverage. His thesis: prediction markets are the only mechanism that consistently surfaces truth, and they're about to matter more than ever. He grew up in Iran under a dictatorship. He watched state TV claim there was no inflation while the money in people's pockets shrank. That's not a metaphor for him - it's the problem he's building to solve. We talk about: - Why CNN and Fox both showed different election results in 2024 - Growing up in Iran and experiencing information asymmetry under dictatorship firsthand - Building a Poly Market Telegram mini app in September 2024 and getting 56,000 users overnight - Why 90% of those users weren't American - and what that revealed about the technology - How leverage works on prediction markets, why it took 9 months to build, and what liquidation actually means when there's no price - just probability - Why Polymarket and Kalshi are scratching the surface of what's possible - Prediction markets as the engagement layer for Web3 - and what Worm is launching on HyperLiquid HIP-4 And much more… Timestamps: 0:00 Introduction 1:17 Welcome to DROPS 1:57 Prediction Markets in 2026 3:08 Can the crowd be wrong? 4:26 Money Creates Better Truth Discovery 5:22 Who is Nass? 5:55 Growing up in Iran 6:52 Entered Crypto in 2019 8:05 Discovering Prediction Markets 10:08 Signal Behind the 2024 Elections 11:59 How Prediction Markets Disrupt Traditional Media 13:16 Traditional Media Prioritizes Incentives Over Truth 17:33 Polymarket vs Kalshi: Which Model Wins? 18:25 Why is Kalshi or Polymarket enough? 19:43 Lessons Learned Building on Polymarket 21:07 Building Worm on Solana 21:56 What Is Worm and its special elements? 23:36 How Leverage Works on Prediction Markets 25:29 Hedging 27:33 Prediction Markets vs Options & Perps 29:15 How Liquidations Work 31:13 Why Leverage is more than a Gimmick 33:04 Building Leverage Was Harder Than Expected 34:12 Solving Liquidity Problem 36:05 How Worm Acquires Users 36:59 Inflection Point for Prediction Markets 39:06 Why Worm chose Solana 40:24 Becoming the Hyperliquid of Prediction Markets 42:26 Solana vs Hyperliquid: Which Wins? 45:00 Conclusion

MR SHIFT 🦁

31,143 次观看 • 3 个月前

The elite traders of prediction markets are using secret tools to stay ahead. In our latest documentary, Zeke dives into this high-stakes world to see if he can capture his own edge and (as per usual) try to quit his day job. 00:00 Everyone loves a good bet 00:44 Meet PMT– who made $100k 01:01 How mention markets work 02:04 Live trading a press conference 03:41 The MentionMetrix tool 04:48 The JPMorgan bet 05:03 Meeting 50p 07:20 The Pentagon Pizza Index 08:31 AI trading bots 09:13 Arbitrage bots explained 10:19 Meet Daniel An– A Hobby Trader 11:34 Using AI for a key trading strategy 12:45 Zeke’s bet
13:34

The elite traders of prediction markets are using secret tools to stay ahead. In our latest documentary, Zeke dives into this high-stakes world to see if he can capture his own edge and (as per usual) try to quit his day job. 00:00 Everyone loves a good bet 00:44 Meet PMT– who made $100k 01:01 How mention markets work 02:04 Live trading a press conference 03:41 The MentionMetrix tool 04:48 The JPMorgan bet 05:03 Meeting 50p 07:20 The Pentagon Pizza Index 08:31 AI trading bots 09:13 Arbitrage bots explained 10:19 Meet Daniel An– A Hobby Trader 11:34 Using AI for a key trading strategy 12:45 Zeke’s bet

Soon

22,979 次观看 • 7 个月前

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead
1:09:53

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

Raoul Pal

172,640 次观看 • 1 年前

Ex-WorldQuant Head of Data Strategy Matt Ober on why quants don't care about the stock market, & why prediction markets become a new asset class. Matt Ober (Ex-Chief Data Scientist @ Third Point | Ex-Head of Data Strategy @ WorldQuant | Now GP @ Social Leverage) "Nobody actually really cared about the stock market. I bet if you asked people there when Nvidia was reporting, nobody had a clue." We cover: - The WorldQuant thesis: out-consume the world on data, out-manage the world on money - How a Chico State grad found the job on Craigslist — then walked into a room of PhDs - Revere, the alt-data goldmine only quants understood - Tracking CEOs' private jets to front-run M&A - WorldQuant's factory vs Third Point's fine-art boardroom - Selling data science to analysts who thought it was a joke - Why you sell beta, not alpha — alpha erodes, beta is sticky - The "degenerate economy," and why he can't hire interns anymore - Prediction markets as the next asset class — bigger than options and futures - The only edge left in an AI world: your network Thanks a ton Matt Ober for coming on the pod! Highlights: (00:00) Intro (01:18) The WorldQuant thesis - more data, more money (02:00) Building the alt-data stack (03:00) Revere - the dataset only quants understood (04:50) A finance major among PhDs (09:40) Trading datasets like stocks (10:30) Why quants don't watch the market (12:30) WorldQuant Ventures is born (16:30) Factory vs. boardroom (18:30) Data's wild west at Third Point (23:15) Tracking private jets to front-run M&A (24:20) Igor vs. Dan & the power of network (32:10) The degenerate economy (36:25) Patience & outworking everyone (38:55) Alpha vs. beta, markets & careers (46:10) Somebody's gotta sell (51:20) Prediction markets, the new asset class (53:30) The DoorDash earnings call (56:35) Going mainstream (01:03:10) The single source of all edge
1:04:17

Ex-WorldQuant Head of Data Strategy Matt Ober on why quants don't care about the stock market, & why prediction markets become a new asset class. Matt Ober (Ex-Chief Data Scientist @ Third Point | Ex-Head of Data Strategy @ WorldQuant | Now GP @ Social Leverage) "Nobody actually really cared about the stock market. I bet if you asked people there when Nvidia was reporting, nobody had a clue." We cover: - The WorldQuant thesis: out-consume the world on data, out-manage the world on money - How a Chico State grad found the job on Craigslist — then walked into a room of PhDs - Revere, the alt-data goldmine only quants understood - Tracking CEOs' private jets to front-run M&A - WorldQuant's factory vs Third Point's fine-art boardroom - Selling data science to analysts who thought it was a joke - Why you sell beta, not alpha — alpha erodes, beta is sticky - The "degenerate economy," and why he can't hire interns anymore - Prediction markets as the next asset class — bigger than options and futures - The only edge left in an AI world: your network Thanks a ton Matt Ober for coming on the pod! Highlights: (00:00) Intro (01:18) The WorldQuant thesis - more data, more money (02:00) Building the alt-data stack (03:00) Revere - the dataset only quants understood (04:50) A finance major among PhDs (09:40) Trading datasets like stocks (10:30) Why quants don't watch the market (12:30) WorldQuant Ventures is born (16:30) Factory vs. boardroom (18:30) Data's wild west at Third Point (23:15) Tracking private jets to front-run M&A (24:20) Igor vs. Dan & the power of network (32:10) The degenerate economy (36:25) Patience & outworking everyone (38:55) Alpha vs. beta, markets & careers (46:10) Somebody's gotta sell (51:20) Prediction markets, the new asset class (53:30) The DoorDash earnings call (56:35) Going mainstream (01:03:10) The single source of all edge

Ethan Kho

584,212 次观看 • 4 个月前

FROM A $50 SALARY IN SYRIA TO A $6M ROUND Episode 5 of PredictTime is live, and you'll hear it from a founder who restarted from zero three times and never sent a single cold email to close his round Ali, founder and CEO of XO Market. We go deep into why 400+ prediction markets are just copies of the same model, why a whale unfairly resolving his Polymarket bet became the tipping point, and how AI agents now decide market outcomes instead of humans Now he's building XO Market, where anyone can turn any idea into a conviction market. Everything you need to know about user-generated markets, XO Vaults and the future of prediction markets is in this episode --- Win 1,000 Conviction Points and get access to XO's weekly parlays with prizes up to $50,000 (we'll pick the winner on July 13): - sign up via the link: -drop your username with your most creative comment or question about the episode and follow Predict Time and XO Market --- Timecodes: 00:00 Intro 00:38 Raising $6M without pitching a single VC 04:29 Why not just copy Polymarket 07:05 Advice for builders: grants over VC money 12:15 Building a team across every continent 15:52 Ali's story: from Syria to Switzerland 19:29 Leaving corporate (Roche) for crypto 22:24 Why raising money is a liability, not a win 25:42 How XO Market conviction markets actually work 29:43 What stops thousands of dead markets 32:41 The tech that gives markets instant liquidity 37:43 How to create your own market 39:14 Getting attention: local communities & ambassadors 44:20 Not the of prediction markets 48:22 Who decides the outcome: AI resolution 51:43 XO Vault: market making for everyone 56:47 The moment XO Market took off 1:02:14 Is prediction market usage exaggerated? 1:05:17 Elon Musk and mention markets 1:07:04 AI vs humans: jobs and the jury system 1:13:56 Are Polymarket & Kalshi overvalued? 1:16:16 What's next: Parlays, Vaults & the World Cup 1:23:06 Is XO Market becoming a sportsbook? 1:25:15 AI agents trading on their own 1:27:40 Regulation & staying permissionless 1:33:08 The investors: 20VC, Harry & a cricket legend 1:44:55 Will there be a token? 1:46:45 Blitz: rapid-fire round 1:52:59 Closing & giveaway
1:55:06

FROM A $50 SALARY IN SYRIA TO A $6M ROUND Episode 5 of PredictTime is live, and you'll hear it from a founder who restarted from zero three times and never sent a single cold email to close his round Ali, founder and CEO of XO Market. We go deep into why 400+ prediction markets are just copies of the same model, why a whale unfairly resolving his Polymarket bet became the tipping point, and how AI agents now decide market outcomes instead of humans Now he's building XO Market, where anyone can turn any idea into a conviction market. Everything you need to know about user-generated markets, XO Vaults and the future of prediction markets is in this episode --- Win 1,000 Conviction Points and get access to XO's weekly parlays with prizes up to $50,000 (we'll pick the winner on July 13): - sign up via the link: -drop your username with your most creative comment or question about the episode and follow Predict Time and XO Market --- Timecodes: 00:00 Intro 00:38 Raising $6M without pitching a single VC 04:29 Why not just copy Polymarket 07:05 Advice for builders: grants over VC money 12:15 Building a team across every continent 15:52 Ali's story: from Syria to Switzerland 19:29 Leaving corporate (Roche) for crypto 22:24 Why raising money is a liability, not a win 25:42 How XO Market conviction markets actually work 29:43 What stops thousands of dead markets 32:41 The tech that gives markets instant liquidity 37:43 How to create your own market 39:14 Getting attention: local communities & ambassadors 44:20 Not the of prediction markets 48:22 Who decides the outcome: AI resolution 51:43 XO Vault: market making for everyone 56:47 The moment XO Market took off 1:02:14 Is prediction market usage exaggerated? 1:05:17 Elon Musk and mention markets 1:07:04 AI vs humans: jobs and the jury system 1:13:56 Are Polymarket & Kalshi overvalued? 1:16:16 What's next: Parlays, Vaults & the World Cup 1:23:06 Is XO Market becoming a sportsbook? 1:25:15 AI agents trading on their own 1:27:40 Regulation & staying permissionless 1:33:08 The investors: 20VC, Harry & a cricket legend 1:44:55 Will there be a token? 1:46:45 Blitz: rapid-fire round 1:52:59 Closing & giveaway

Predict Time

26,277 次观看 • 3 个月前

Insilico Terminal Podcast Episode 29 - Worst Contrarian - BACK OFFICE @ LARP CAPITAL 00:00 Intro, WC’s background, why he views trading as a business not speculation 04:07 First major arbitrage breakthrough, Binance AUD tether trade and going full-time 08:30 Why most people approach markets wrong, repeatable arbitrage vs lucky directional bets 19:40 The “exploiter mindset”, how to think about protocols, oracles and metagame risk 30:05 Building a trader network, why one phone call can unlock the next unusual trade 40:54 Sports betting exploits, getting banned everywhere and finding edge in inefficiency 53:33 Crypto vs TradFi today, where opportunity still exists and why chaos creates edge 01:06:22 Final advice, relationships, martial arts, Education of a Speculator and closing
1:12:30

Insilico Terminal Podcast Episode 29 - Worst Contrarian - BACK OFFICE @ LARP CAPITAL 00:00 Intro, WC’s background, why he views trading as a business not speculation 04:07 First major arbitrage breakthrough, Binance AUD tether trade and going full-time 08:30 Why most people approach markets wrong, repeatable arbitrage vs lucky directional bets 19:40 The “exploiter mindset”, how to think about protocols, oracles and metagame risk 30:05 Building a trader network, why one phone call can unlock the next unusual trade 40:54 Sports betting exploits, getting banned everywhere and finding edge in inefficiency 53:33 Crypto vs TradFi today, where opportunity still exists and why chaos creates edge 01:06:22 Final advice, relationships, martial arts, Education of a Speculator and closing

Insilico Terminal

25,403 次观看 • 6 个月前

Inside Kalshi : How a Prediction Market Hit $22B in 2 Years 65 lawyers said it couldn't be done. They sued their own regulator instead. Eight years later, Kalshi just raised $1B at a $22B valuation, and co-founders Tarek Mansour and Luana Lopes Lara took us inside the bet that got them here. 00:00 Why prediction markets? 01:35 We sue our regulator 02:31 From Ballet to Building a $22B Company 03:19 Discipline & delayed gratification 04:20 Getting out of a comfort zone 04:56 65 Lawyers said No 05:05 Tarek's story 06:04 How the idea starts - The Trump trade 07:45 No one says it's possible 09:00 Four Years to Earn the Right to Launch 10:22 Stagnating while everyone else grew 11:53 Cutting through the noise 12:43 "Forget product-market fit" 13:28 Why we sued the regulator 14:45 We won. Then they appealed 16:43 The 4-week sprint to the election 18:03 The clearinghouse crisis 18:42 6 months of migration in one weekend 19:14 Calling the election before the media 19:31 Prediction markets vs gambling 20:51 Money where your mouth is 22:06 What "regulated" actually means 23:10 It's not just sports 24:05 The trader who studies Trump's speeches 26:40 The teacher who made $8K from source code 27:42 Hurricane markets as insurance 28:40 Where Kalshi goes next
31:10

Inside Kalshi : How a Prediction Market Hit $22B in 2 Years 65 lawyers said it couldn't be done. They sued their own regulator instead. Eight years later, Kalshi just raised $1B at a $22B valuation, and co-founders Tarek Mansour and Luana Lopes Lara took us inside the bet that got them here. 00:00 Why prediction markets? 01:35 We sue our regulator 02:31 From Ballet to Building a $22B Company 03:19 Discipline & delayed gratification 04:20 Getting out of a comfort zone 04:56 65 Lawyers said No 05:05 Tarek's story 06:04 How the idea starts - The Trump trade 07:45 No one says it's possible 09:00 Four Years to Earn the Right to Launch 10:22 Stagnating while everyone else grew 11:53 Cutting through the noise 12:43 "Forget product-market fit" 13:28 Why we sued the regulator 14:45 We won. Then they appealed 16:43 The 4-week sprint to the election 18:03 The clearinghouse crisis 18:42 6 months of migration in one weekend 19:14 Calling the election before the media 19:31 Prediction markets vs gambling 20:51 Money where your mouth is 22:06 What "regulated" actually means 23:10 It's not just sports 24:05 The trader who studies Trump's speeches 26:40 The teacher who made $8K from source code 27:42 Hurricane markets as insurance 28:40 Where Kalshi goes next

EO

5,277,622 次观看 • 4 个月前

Ex-Global Trading & Risk Director at Cargill on how edge is formed in commodities markets Kristine Engman spent 20+ years at Cargill—one of America's largest agriculture conglomerates—trading billions in physical & financial commodities across energy & agriculture. "Edge in commodities? It's a lot about relationships. The intel you get through those relationships—that's information not readily accessible to the rest of the marketplace that can give you an edge." We cover: - How edge actually works in commodities—relationships, capital access & information arbitrage - Why you can trade on "insider information" in commodities (unlike equities) - Managing physical risk—forecast accuracy, weather shocks & transportation nightmares - The negative power trade—selling electricity below zero & getting hauled into the boss's office - Why Cargill traders performed best when equities markets performed worst - Quantitative (and even technical) vs fundamental trading styles—finding what works & sticking to it - The Ukraine war position—going extended limit long before the invasion - Life lessons from trading—"have your house in order" & taking risks with incomplete information Timestamps: 00:00 Intro 01:15 Edge in commodities trading? Relationships, capital, information 04:40 Commodities market efficiency, information flows, and AI 08:32 Hedge funds vs. ABCDs, commodity trading strategies 13:34 When commodities outperform equities, the 2022 boom 17:45 Headline risk, social media impact on markets 19:08 Risk management strategies in physical commodities trading 26:14 Probability, forecasting, and scenarios for trading decisions 31:00 What makes a great commodities trader today 37:53 Contrarian trading strategies, alpha generation in commodities 42:24 Russia–Ukraine war impact on commodity markets, trading 45:35 Life and career lessons from commodities trading 51:30 Careers, uncertainty, and learning in commodities markets
56:21

Ex-Global Trading & Risk Director at Cargill on how edge is formed in commodities markets Kristine Engman spent 20+ years at Cargill—one of America's largest agriculture conglomerates—trading billions in physical & financial commodities across energy & agriculture. "Edge in commodities? It's a lot about relationships. The intel you get through those relationships—that's information not readily accessible to the rest of the marketplace that can give you an edge." We cover: - How edge actually works in commodities—relationships, capital access & information arbitrage - Why you can trade on "insider information" in commodities (unlike equities) - Managing physical risk—forecast accuracy, weather shocks & transportation nightmares - The negative power trade—selling electricity below zero & getting hauled into the boss's office - Why Cargill traders performed best when equities markets performed worst - Quantitative (and even technical) vs fundamental trading styles—finding what works & sticking to it - The Ukraine war position—going extended limit long before the invasion - Life lessons from trading—"have your house in order" & taking risks with incomplete information Timestamps: 00:00 Intro 01:15 Edge in commodities trading? Relationships, capital, information 04:40 Commodities market efficiency, information flows, and AI 08:32 Hedge funds vs. ABCDs, commodity trading strategies 13:34 When commodities outperform equities, the 2022 boom 17:45 Headline risk, social media impact on markets 19:08 Risk management strategies in physical commodities trading 26:14 Probability, forecasting, and scenarios for trading decisions 31:00 What makes a great commodities trader today 37:53 Contrarian trading strategies, alpha generation in commodities 42:24 Russia–Ukraine war impact on commodity markets, trading 45:35 Life and career lessons from commodities trading 51:30 Careers, uncertainty, and learning in commodities markets

Ethan Kho

82,254 次观看 • 7 个月前

EDGE REVEALED: How an Ex-Jane Street Trader Finds Edge in Markets & Life Agustin Lebron Agustin Lebron (former Jane Street trader, author of The Laws of Trading, now working at an AI startup applying reinforcement learning to market execution) breaks down what edge really means — and how to find yours in trading, careers & life. “Edge is something that either you know or you can do that the marginal participant in that market either doesn’t or can’t.” We cover: - What edge actually means & how Jane Street builds organizational edge (their worst skill is still "pretty decent") - Why you can never truly know if you have edge — the statistical vs intuitive approaches - The consolidation of quant trading: from dozens of options firms to a handful of giants - The gamblification of everything — retail trading, sports betting & prediction markets fueling quant profits - What it was like having Sam Bankman-Fried (SBF) as a Jane Street intern: "Day one, I'm going to ask all the questions" - How to apply edge thinking to your own career: find what you're differentially good at - Raising teenagers in the AI age: why the traditional path still works, but other paths are opening up 00:00 Introduction 00:44 What is edge in financial markets 01:43 Jane Street and organizational structures for quant trading 03:46 Identifying and validating edge in trading 06:22 Navigating extreme market events and volatility 09:37 Future of quant trading and consolidation 12:15 Why quant firm profits have increased 14:42 The gamblization of everything 15:55 Who should pursue a career in quant trading 18:19 Applying the concept of edge to career and life decisions 20:56 Advice for interns to excel in quant trading 23:44 Predicting long-term success in trading interns 25:08 Sam Bankman-Fried as an intern and FTX reflections 28:15 Reasons people leave Jane Street and what they do next 31:19 Advice for young people in a changing world 36:09 Navigating job insecurity in tech-driven roles 38:55 Where to live if you want to be successful 40:28 Raising kids for a rapidly changing future 42:55 Questions young people should ask themselves 44:45 Outro and book recommendation
44:46

EDGE REVEALED: How an Ex-Jane Street Trader Finds Edge in Markets & Life Agustin Lebron Agustin Lebron (former Jane Street trader, author of The Laws of Trading, now working at an AI startup applying reinforcement learning to market execution) breaks down what edge really means — and how to find yours in trading, careers & life. “Edge is something that either you know or you can do that the marginal participant in that market either doesn’t or can’t.” We cover: - What edge actually means & how Jane Street builds organizational edge (their worst skill is still "pretty decent") - Why you can never truly know if you have edge — the statistical vs intuitive approaches - The consolidation of quant trading: from dozens of options firms to a handful of giants - The gamblification of everything — retail trading, sports betting & prediction markets fueling quant profits - What it was like having Sam Bankman-Fried (SBF) as a Jane Street intern: "Day one, I'm going to ask all the questions" - How to apply edge thinking to your own career: find what you're differentially good at - Raising teenagers in the AI age: why the traditional path still works, but other paths are opening up 00:00 Introduction 00:44 What is edge in financial markets 01:43 Jane Street and organizational structures for quant trading 03:46 Identifying and validating edge in trading 06:22 Navigating extreme market events and volatility 09:37 Future of quant trading and consolidation 12:15 Why quant firm profits have increased 14:42 The gamblization of everything 15:55 Who should pursue a career in quant trading 18:19 Applying the concept of edge to career and life decisions 20:56 Advice for interns to excel in quant trading 23:44 Predicting long-term success in trading interns 25:08 Sam Bankman-Fried as an intern and FTX reflections 28:15 Reasons people leave Jane Street and what they do next 31:19 Advice for young people in a changing world 36:09 Navigating job insecurity in tech-driven roles 38:55 Where to live if you want to be successful 40:28 Raising kids for a rapidly changing future 42:55 Questions young people should ask themselves 44:45 Outro and book recommendation

Ethan Kho

214,514 次观看 • 7 个月前

Bitcoin has already won as Digital Capital. The next wave is Digital Credit, Digital Money, Digital Yield, and Bitcoin-backed capital markets — products that can bring trillions of dollars of traditional credit and money market capital onto Bitcoin. My interview with Cointelegraph at BTC Prague. 00:57 — Bitcoin in a drawdown: five major pullbacks in six years, stronger fundamentals, and rising dominance 02:23 — Digital Credit: from zero to an $11B+ asset class in 12 months 03:35 — Digital Money: bitcoin-backed yieldcoins and the path from 40 vol to 0 vol 04:31 — The opportunity for 8% yield in dollars, euros, yen, pounds, and francs 06:02 — $300T of credit, $30–50T of money markets, and the $10T opportunity for Bitcoin 07:19 — Why Bitcoin is winning economically, technically, and ethically 08:26 — Quantum computing, FUD, and why bear markets amplify Bitcoin debates 10:37 — AI capital rotation, Bitcoin’s current drawdown, and the path to recovery 11:36 — Six years of Strategy: why I would have moved faster into Digital Credit 12:22 — The ideal Bitcoin Treasury Company: common equity plus STRC-style Digital Credit 14:35 — The 32 BTC sale, the $100M bitcoin buyback, and why capital must back credit 17:02 — Defending the equity, credit, and bitcoin-backed capital structure 19:03 — The tradeoff: buy 200,000 BTC and sell 10,000 BTC — or buy and sell zero 20:15 — “Never sell,” Twitter trolls, and Strategy’s fiduciary obligations 22:06 — Bitcoin per share, long-term accretion, and accumulating through bull and bear markets 22:34 — $21B of equity raised in 16 weeks and ~$10B of bitcoin acquired this year 24:18 — The Strategic Bitcoin Reserve, US leadership, and supportive regulation 27:18 — Digital Credit, bank credit, and Digital Money bringing trillions onto Bitcoin 28:01 — Why Bitcoin can grow organically without central bank support
28:48

Bitcoin has already won as Digital Capital. The next wave is Digital Credit, Digital Money, Digital Yield, and Bitcoin-backed capital markets — products that can bring trillions of dollars of traditional credit and money market capital onto Bitcoin. My interview with Cointelegraph at BTC Prague. 00:57 — Bitcoin in a drawdown: five major pullbacks in six years, stronger fundamentals, and rising dominance 02:23 — Digital Credit: from zero to an $11B+ asset class in 12 months 03:35 — Digital Money: bitcoin-backed yieldcoins and the path from 40 vol to 0 vol 04:31 — The opportunity for 8% yield in dollars, euros, yen, pounds, and francs 06:02 — $300T of credit, $30–50T of money markets, and the $10T opportunity for Bitcoin 07:19 — Why Bitcoin is winning economically, technically, and ethically 08:26 — Quantum computing, FUD, and why bear markets amplify Bitcoin debates 10:37 — AI capital rotation, Bitcoin’s current drawdown, and the path to recovery 11:36 — Six years of Strategy: why I would have moved faster into Digital Credit 12:22 — The ideal Bitcoin Treasury Company: common equity plus STRC-style Digital Credit 14:35 — The 32 BTC sale, the $100M bitcoin buyback, and why capital must back credit 17:02 — Defending the equity, credit, and bitcoin-backed capital structure 19:03 — The tradeoff: buy 200,000 BTC and sell 10,000 BTC — or buy and sell zero 20:15 — “Never sell,” Twitter trolls, and Strategy’s fiduciary obligations 22:06 — Bitcoin per share, long-term accretion, and accumulating through bull and bear markets 22:34 — $21B of equity raised in 16 weeks and ~$10B of bitcoin acquired this year 24:18 — The Strategic Bitcoin Reserve, US leadership, and supportive regulation 27:18 — Digital Credit, bank credit, and Digital Money bringing trillions onto Bitcoin 28:01 — Why Bitcoin can grow organically without central bank support

Michael Saylor

264,683 次观看 • 3 个月前

Sports books take 10% of every bet and ban winners. Novig founder and CEO Jacob Fortinsky joins our Stateful podcast, hosted by Mason Strom. In this episode, they discuss why prediction markets may replace the casino model in sports: - Sports books kick out profitable users. - 5% better odds on average vs. traditional platforms - CFTC shift: sports event contracts regulated as financial products nationwide - Be your own house: set your own odds, collect the spread - $2T global sports market with zero dominant exchange 00:17 The $75M Bet on Sports Prediction Markets 01:08 From StubHub Algorithms to Prediction Markets 02:21 Replacing the House: Peer-to-Peer Sports Betting 03:59 Do Sportsbooks Survive? 06:14 Be Your Own House: The Market Maker Model 08:35 How Novig Makes Money Without a Vig 10:23 Why Now? The Regulatory Shift Changing Everything 11:51 The Modern Sports Bettor: More Trader Than Fan 14:58 Prediction Market Regulation: Lawsuits, CFTC & Congress 17:45 Novig vs. DraftKings, Polymarket & Robinhood 20:18 Where Prediction Markets Go From Here 22:48 Quickfire: Favorite Sport to Bet On 24:37 Founder Advice: Stay Alive & Don't Chase Trends 26:22 Hiring for Culture Over Raw Talent
29:30

Sports books take 10% of every bet and ban winners. Novig founder and CEO Jacob Fortinsky joins our Stateful podcast, hosted by Mason Strom. In this episode, they discuss why prediction markets may replace the casino model in sports: - Sports books kick out profitable users. - 5% better odds on average vs. traditional platforms - CFTC shift: sports event contracts regulated as financial products nationwide - Be your own house: set your own odds, collect the spread - $2T global sports market with zero dominant exchange 00:17 The $75M Bet on Sports Prediction Markets 01:08 From StubHub Algorithms to Prediction Markets 02:21 Replacing the House: Peer-to-Peer Sports Betting 03:59 Do Sportsbooks Survive? 06:14 Be Your Own House: The Market Maker Model 08:35 How Novig Makes Money Without a Vig 10:23 Why Now? The Regulatory Shift Changing Everything 11:51 The Modern Sports Bettor: More Trader Than Fan 14:58 Prediction Market Regulation: Lawsuits, CFTC & Congress 17:45 Novig vs. DraftKings, Polymarket & Robinhood 20:18 Where Prediction Markets Go From Here 22:48 Quickfire: Favorite Sport to Bet On 24:37 Founder Advice: Stay Alive & Don't Chase Trends 26:22 Hiring for Culture Over Raw Talent

Pantera Capital

25,422 次观看 • 6 个月前

Leaving Citadel & launching a $1B AI hedge fund — how Renee Yao built NeoIvy Capital from scratch Renee Yao walked away from two of the most elite hedge funds on Wall Street — Citadel & Millennium — and built a quant fund on a fundamentally different model: modern AI instead of human-powered alpha generation. The result: $1B+ in regulatory AUM, uncorrelated returns through COVID, & a fund Business Insider named one of the top transforming investing in North America. We cover: - Why large multi-manager quant firms rely on massive global researcher headcounts — & why Renee saw that as a model worth disrupting - The 3 barriers to entry in AI-driven quant — & why legacy sequential infrastructure can be a disadvantage compared to modern parallel distributed systems - How NeoIvy's self-evolving models adapted in real time during the March 2020 crash — while traditional quant managers had a nightmare month - The difference between beta returns, factor returns & pure alpha — & why size is the enemy of true idiosyncratic returns - Why the "black box" reputation of quant funds has been the #1 fundraising obstacle - How a 4-year-old girl visiting her uncle's room-sized supercomputer in China set the foundation for all of this - The edge/breadth/constraint framework from Grinold & Kahn — & how it shaped Renee's thinking on diversification - Renee's raw advice on staying disciplined when everyone around you is chasing beta in a bull market Transcript: 00:00 Intro 01:14 Renee Yao’s journey to founding Neo Ivy 02:28 Joining Citadel after the financial crisis 04:13 Hedge fund diversification and breadth of edge 04:45 Why Neo Ivy trades with AI strategies 07:50 How self-learning AI adapts to markets 09:40 Causation vs correlation in AI hedge funds 10:33 Barriers to entry for AI hedge funds 14:47 Risks of crowded factor bets explained 16:39 Why big funds struggle with AI talent 17:29 From PM at Citadel to hedge fund founder 18:47 Challenges of launching a quant hedge fund 20:25 Biggest constraint for AI hedge fund startups 22:08 How AI hedge funds adapted during COVID 24:04 Modern AI tools used in quant trading 25:13 Building hedge fund infrastructure from scratch 26:26 Career advice for aspiring quants and traders 28:55 Adapting career goals to changing job markets 31:57 Life lessons from trading and risk management 32:51 Staying disciplined while running a hedge fund 34:38 Obsession and belief in AI hedge funds 35:41 Closing thoughts on hedge funds and life
35:59

Leaving Citadel & launching a $1B AI hedge fund — how Renee Yao built NeoIvy Capital from scratch Renee Yao walked away from two of the most elite hedge funds on Wall Street — Citadel & Millennium — and built a quant fund on a fundamentally different model: modern AI instead of human-powered alpha generation. The result: $1B+ in regulatory AUM, uncorrelated returns through COVID, & a fund Business Insider named one of the top transforming investing in North America. We cover: - Why large multi-manager quant firms rely on massive global researcher headcounts — & why Renee saw that as a model worth disrupting - The 3 barriers to entry in AI-driven quant — & why legacy sequential infrastructure can be a disadvantage compared to modern parallel distributed systems - How NeoIvy's self-evolving models adapted in real time during the March 2020 crash — while traditional quant managers had a nightmare month - The difference between beta returns, factor returns & pure alpha — & why size is the enemy of true idiosyncratic returns - Why the "black box" reputation of quant funds has been the #1 fundraising obstacle - How a 4-year-old girl visiting her uncle's room-sized supercomputer in China set the foundation for all of this - The edge/breadth/constraint framework from Grinold & Kahn — & how it shaped Renee's thinking on diversification - Renee's raw advice on staying disciplined when everyone around you is chasing beta in a bull market Transcript: 00:00 Intro 01:14 Renee Yao’s journey to founding Neo Ivy 02:28 Joining Citadel after the financial crisis 04:13 Hedge fund diversification and breadth of edge 04:45 Why Neo Ivy trades with AI strategies 07:50 How self-learning AI adapts to markets 09:40 Causation vs correlation in AI hedge funds 10:33 Barriers to entry for AI hedge funds 14:47 Risks of crowded factor bets explained 16:39 Why big funds struggle with AI talent 17:29 From PM at Citadel to hedge fund founder 18:47 Challenges of launching a quant hedge fund 20:25 Biggest constraint for AI hedge fund startups 22:08 How AI hedge funds adapted during COVID 24:04 Modern AI tools used in quant trading 25:13 Building hedge fund infrastructure from scratch 26:26 Career advice for aspiring quants and traders 28:55 Adapting career goals to changing job markets 31:57 Life lessons from trading and risk management 32:51 Staying disciplined while running a hedge fund 34:38 Obsession and belief in AI hedge funds 35:41 Closing thoughts on hedge funds and life

Ethan Kho

138,224 次观看 • 7 个月前