Loading video...

Video Failed to Load

Go Home

Inside Two Sigma & AQR with Bill Mann: How Early Quants Built Edge Before the Modern Tools Existed Bill Mann spent nearly 11 years across two of the world's most elite quant funds — AQR & Two Sigma — rising to Senior Vice President while building alpha models, establishing...

23,460 views • 5 months ago •via X (Twitter)

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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 views • 5 months ago

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 views • 6 months ago

From a Thai prison cell to a fintech empire processing $1.6B in international payments across 40+ banks and 250M+ users. Jonathan Low (Jonathan Low). Forbes 40 Under 40. Author of "Cell to CEO." We covered prison, banking for AI agents, RWA projects, the future of crypto in banking, vibe coding for trading, and the businesses that win the next 5 years. ⏱ Timestamps: 00:00:00 — Teaser 00:00:46 — Who is Jonathan Low 00:01:35 — What Jonathan's life was like before prison 00:02:13 — How and why Jonathan ended up in prison 00:02:57 — Prison conditions: expectations vs reality 00:07:33 — Prison became the greatest blessing 00:09:12 — How the entrepreneurial journey began after prison 00:10:34 — Why social capital matters 00:11:06 — Launched own club and took it to the top in 3 months 00:12:09 — Built an Axie Infinity gaming guild during COVID 00:13:49 — The beginning of the BipTap Group journey 00:17:23 — How Jonathan built his own banking system 00:20:07 — How to get a crypto card 00:22:03 — How much it costs to launch a white-label solution with BipTap 00:22:58 — Banking for AI agents 00:25:57 — How to build an RWA project 00:27:45 — Future of cryptocurrencies in banking 00:32:08 — The business verticals within Empire Group 00:32:48 — What Jonathan invests his money in 00:33:54 — How relationships with regulators are built 00:34:43 — Implementing AI in business 00:36:28 — Vibe coding in trading 00:40:06 — Advice for first-time founders 00:43:36 — From construction to trading: Ruslan Khairullin's journey 00:44:57 — Inner peace: why calmness is essential for founders 00:50:39 — Work-life balance for entrepreneurs 00:53:47 — $1.5 million in 24 hours on TST coin 00:55:11 — The best way to capture a market 00:55:57 — Banking for nations Watch the full conversation and let me know which part you liked the most 👇

Ruslan Khairullin

16,872 views • 1 month ago

Ex-Point72 Proprietary Research Head Kirk McKeown on building edge, alpha decay, & why everything that happened on Wall Street is about to happen on Main Street. Kirk McKeown (8.5 years @ Point72 under Steve Cohen | Built primary research at Glenview under Larry Robbins | Now founder of Carbon Arc Carbon Arc) "Alpha rewards those who value assets in a cold way. You want to get it right — not be right." We cover: - How alpha creation differs across multi-manager vs. concentrated shops - The 3 vectors every middle office function must move to justify its existence - Why he worked 6-hour Sundays from 2006-2020 — and the math behind it - The TSMC call that signaled semiconductor cancellations before anyone else knew - What the quant revolution on Wall Street tells us about the AI economy today - His framework: 4 market structures, 9 business models, & why they have rules - The MIT beer game & why every business problem is really an inventory problem - His hot take: a top hedge fund launches an enterprise AI lab in 2026 Highlights: 00:00 Intro 04:47 Tutor vs Glenview vs Point72: how edge differs 12:29 How to build “lift” for PMs: at-bats, hit-rate, sizing 18:44 Building research edge: outwork, read, fieldwork 27:16 Personal moat in 2026: analogs, history, decision trees 40:08 “Main Street becomes Wall Street”: what that actually means 44:30 Carbon Arc thesis: “decimalization” of data market structure 46:43 Why the edge migrates to data plus domain context 51:00 How to win in commoditized research: sample size beats anecdotes 01:03:26 Factorizing everything: themes, market structure, business models 01:08:37 Pruning decision trees: signals, scale points, inventory dynamics 01:14:18 Contrarian 2026 take: hedge funds launching enterprise AI labs 01:23:32 Final question: one habit to build career alpha

Ethan Kho

1,536,469 views • 4 months ago

Ex-Balyasny PM Ying Hua (Ying Hua) on why automation will increase demand for hedge fund talent, the quant/fundamental convergence, & why quant is blackjack but fundamental is poker. Ying Hua (PM @ Balyasny — built & led a quantamental team covering US insurance, capital markets & fintech | ~5 yrs @ Citadel running a long/short insurance book | Equity research @ Goldman Sachs | MS in Data Science @ UC Berkeley | Now founder & CEO of Implied Implied) "One of the best-kept secrets: fundamental investors are not good at sizing. Quant funds are really good at sizing." We cover: - The only real line between quant and fundamental: historical pattern matching vs. "how is this time different" — and the alpha neither group is looking at - Why she rebuilt her process so every model updated within 2 minutes of a print - Scraping highway patrol data from 15 states to track auto insurance losses live, every single day - The Malibu wildfire: mapping burned mansions from celebrity tweets to estimate losses before any industry consultant published a number - Her automation math: data gathering ~100% automatable, processing ~80%, judgment still 100% human - AI is quant for words — next-token prediction is pattern matching, which makes this just the next automation wave after quant and indexing - The proof differentiated views pay more: insurance stocks moved 2-3% on earnings in 2010; by the time she left, 15-20% intraday - Why "hook Claude Code up to data and let it rip" fails: BloombergGPT losing to a smaller open-source model, & why horizontal models are college grads - Quant is blackjack with card counting; multi-manager investing is poker — your hand, others' perception of it, your seat, everyone's stack - Most PMs are playing the wrong game: the positioning game hiding inside "fundamental" sectors with no new money coming in - Her hiring bar at BAM: every fundamental analyst learns Python — and the one skill she says can't be trained - The only two truly meritocratic jobs: hedge fund PM & sales Highlights: (00:00) Intro (00:40) How a quantamental PM actually puts on a position (02:05) The only real line between quant and fundamental (04:45) Why quantamental lowers the burden on your brain (06:40) Scraping 15 states of highway patrol data to nowcast insurance losses (09:25) The Malibu wildfire: estimating losses from celebrity tweets (11:55) How much of fundamental investing can be automated (13:45) Quantifying intuition: when a CFO's filler words jump 8% to 20% (16:25) The contrarian case: automation expands demand for talent (18:15) Earnings vol exploded — differentiated views pay more (20:25) Why Claude Code can't run your book (23:40) Horizontal models are college grads with no domain knowledge (30:50) Why chat is the wrong interface for investors (36:20) Will AI make markets more or less efficient? (39:10) Two things every fundamental PM should do today (41:50) The moat that expands: talent, redefined (45:50) Sometimes the game is positioning, not fundamentals (48:25) Blackjack vs. poker vs. surfing: matching the game to your horizon (52:00) Should young analysts chase the hottest sector? (57:55) Munger vs. Musk: two philosophies of wealth (1:01:05) Self-awareness in investing is bimodal (1:07:05) The only two truly meritocratic jobs: hedge funds & sales (1:08:50) The one skill for every regime: reconstruct the narrative

Ethan Kho

244,449 views • 19 days ago

MUST-WATCH: Inside high-frequency trading & building the future of markets with Annanay Kapila Annanay Kapila (ak0) — former quant trader at Flow Traders & Tower Research, now founder of QFEX (YC-backed). Building the first 24/7 perpetual futures exchange for equities, commodities & FX — bringing crypto-level UX to traditional assets. "My friends at Cambridge have been making $5M+ a year for the last few years. They don't have to work anymore. But no one retires early. Why? Everyone who gets selected has that desire to win that transcends money, even rationality." We cover: - What it's really like inside elite HFT shops — international math Olympiad winners, professional poker players & zero room for error - Why the smartest traders keep working after making generational wealth (hint: it's pure competitive ego) - The two schools of quant trading: Chicago pit traders vs MIT mathematicians — and which strategies they dominate - How his team navigated the FTX collapse in real-time (including friends who escaped via Dogecoin withdrawals) - Building QFEX : 24/7 perpetual futures on equities with crypto-level UX — "We're building FTX without the fraud" - YC insider stories: meeting Sam Altman & Paul Graham, raising from Paul Graham (Paul Graham) personally, why the $125k for 7% is worth it - Culture secrets from Citadel & Tower: when your dev environment pages you at 2am on Saturday - The one interview hack VCs use that most founders miss (interrupt their CV walkthrough relentlessly) - Why doomerism is dead wrong: "My life would've been worse if I was born 10 years ago. I dread to think how much better it would be if I was born 10 years from now" Huge thanks to ak0 for the transparency. Follow his journey building the future of financial markets. 01:12 Inside high frequency trading culture and competition 03:00 What drives top performers in HFT 03:47 Why elite traders never retire early 04:50 How Tower structures quant trading work 06:14 Chicago versus MIT quant trading styles 10:29 How modern market making actually works 12:25 Why prop firms struggle to scale capital 16:36 Annanay's work at Tower and Flow 19:15 Tower's crypto trading and API edge 19:24 How firms handled the FTX collapse 23:42 Inside FTX culture and HK crypto scene 27:11 What QFEX is building for traders 29:37 QFEX beta launch and early rollout 29:56 Why building an exchange is exciting 32:48 QFEX execution edge and team DNA 36:24 Why quants leave HFT for startups 38:22 QFEX culture versus HFT culture 41:46 How meritocracy works inside QFEX 47:52 How QFEX hires true A players 53:05 Backchanneling and evaluating candidates 53:27 What YC actually teaches founders 55:21 Lessons from meeting Paul Graham and Sam Altman 58:41 Why startups fail and how to pivot 1:00:48 Why young people should stay optimistic 1:11:22 Closing remarks and QFEX vision

Ethan Kho

164,024 views • 6 months ago

What It's Like Building a Proprietary Power Trading Firm with Cory Paddock Cory Paddock (Cory Paddock) has been trading the physical power grid since the early 2000s. He built his own firm from scratch in 2014 — trading his own capital — and has navigated every major paradigm shift in U.S. energy markets since coal dominated the grid. "It's the perfect amount of darkness. The information is public — but it's in the dark." We cover: - How Cory built a point of view on paradigm shifts before the market caught up - Why power trading sits at the intersection of economics & physics — and why that matters for edge - Why backtesting more than a few years of power data is basically useless — the grid isn't the same grid - LMP & locational marginal pricing: how physical grid constraints turn into alpha if you know where to look - The 5–10 trades that make your year — and why forcing setups in the lean periods kills you - How GBE stripped pay uncertainty out of trader comp so people can just focus on trading well - What it actually feels like watching your own money swing in real-time — and when it stops feeling that way Thanks so much Cory for coming on Odds on Open! Timestamps: 00:00 Intro 01:09 Starting GBE and finding trading edge 01:38 Overview of electricity markets and pricing 02:05 Power trading structure and deregulated markets 04:11 Research pipeline for market data analysis 04:56 Domain knowledge and renewable energy trading 06:39 LLMs and AI tools for quants 07:49 Grid data and intraday market signals 09:25 Finding alpha in electricity markets 10:13 Paradigm shifts and regime change insights 13:19 Coal to gas, wind, and solar trends 14:39 Data centers, EVs, and load growth 16:47 Recruiting talent in energy trading firms 17:38 Gen Z quants and algorithmic trading skills 21:55 Outliers, agency, and Gen Z traders 24:11 Culture and innovation in quant finance 27:29 Trading personal capital and risk management 28:06 PJM West Hub and market dynamics 32:19 Five-minute tick data and volatility 34:49 High-conviction trades and alpha generation 35:35 Incentive alignment and trader performance 36:42 Pay structure and removing stress capital 39:22 Motivation and purpose in trading careers 40:00 Passing knowledge to the next generation 42:04 Host reflections on electricity trading 42:14 Closing thoughts and sign-off

Ethan Kho

17,408 views • 5 months ago

DROPS E38: Vanta Trading - The Best Traders Won't Be Human Arrash is the founder and CEO of Vanta Trading, a decentralized prop trading platform built on Bittensor. He spent years as a quant trader building his own strategies before deciding the entire funded-account industry needed to be rebuilt from the ground up. We talk about: - Why most "funded accounts" trade on money that doesn't exist - Why your payout was never real - How prop firms intentionally change rules and spreads before you cash out - Why the best traders of the future won't be human And much more… Timestamps: 0:00 Introduction 1:48 Founder of Vanta 3:08 Explaining Vanta to an Uber Driver 3:26 Unfair vs Fair Funding 6:37 How do they make money? 8:33 How a Legit Prop Firm Makes Money 9:43 Founder's Journey Into Entrepreneurship 10:42 Discovering Crypto & Blockchain 13:03 From LinkedIn Engineer to Quant Trader 15:22 Trading Strategies 16:13 How Trading Is Changing? 18:39 Why TradFi Should Fear Hyperliquid 19:16 Building on BitTensor 21:43 Explaining BitTensor Simply 22:03 How BitTensor Creates Value 23:17 BitTensor's Structure 24:36 BitTensor as Crypto AI 25:35 Is the BitTensor Hype Justified? 27:09 Revenue & Profitability in BitTensor 29:17 Role of TAO 29:51 Advantages & Limitations of BitTensor ecosystem 31:56 What Is Vanta? 32:26 Why No One Fixed Prop Trading Before 33:12 Is the Entire Industry a Scam? 34:17 How Prop Firms Really Make Money 35:35 How Vanta Is Different 38:33 Copy Trading Explained 39:30 Vanta's Business Model 40:28 Dark Reality Behind Funded Accounts 43:48 Long-Term Vision for Vanta 44:50 What Happens If Too Many Traders Win? 46:25 Future Belongs to AI Traders 47:59 Vanta's Endgame 49:14 Conclusion

MR SHIFT 🦁

37,896 views • 1 month ago

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,359 views • 1 month ago

🎧🍌 New The Peel with Ankur Goyal Ankur is the Founder and CEO of @braintrustdata, the end to end developer platform for building the world's best AI products. Their customers include companies like Instacart, Zapier, Notion, Airtable, Replit, and more. We hit on the importance of LLM evals, advice for building AI products, why the best companies have two AI product roadmaps, how he hates meetings, and all his non-conventional advice for founders. Watch below or links in the replies! Timestamps: 04:04 Why everyone’s now an AI company 06:03 Reasons LLM evals are so important 09:19 Replacing vibe checks with Braintrust 10:37 Making OpenAI’s protocols the standard 11:27 Why the best companies have two AI roadmaps 13:06 Build your product so each LLM release makes it better 14:54 Predicting AGI is impossible 15:54 Why people who work with LLMs aren’t worried about AI safety 16:52 The best developers are all-in on co-pilots 18:11 How AI is changing software development 21:09 Combining IDE, CI/DC, and observability in one product 27:18 Are models more like CPU’s or relational databases? 30:14 How to pick an LLM 33:00 Advice for staying on top of new AI developments 34:30 Why tool calling is so important 38:02 Advice for young software engineers 40:25 Learning to code doing linear algebra homework 42:36 Lack of purpose interning in big tech 44:07 Working at MemSQL learning to be a founder 47:52 How to get a job at a startup 50:43 Building his first startups product on an flight 52:39 Three lessons from his first failed startup 54:46 Don’t delegate what you’re good at 55:46 Why you should be careful listening to VCs advice 57:34 Tactics for successful delegation 59:36 Why Ankur hates meetings 1:02:42 The importance of self-service in unlocking certain customer segments 1:05:14 How Braintrust got started 1:07:45 Advice on picking your target customers 1:10:35 How Braintrust hires with work trials 1:15:21 Balancing security with a modern UI 1:17:49 Why it’s hard to sell non-AI products right now 1:19:21 Advice for selling to large enterprises 1:23:10 Ankur’s favorite AI products

Turner Novak 🍌🧢

44,280 views • 2 years ago

Ex-Citadel Quant Researcher on Trading Power & Gas — One of the Most Asymmetric Markets in the World Neel Somani (Neel Somani) — ex-Citadel commodities QR. Built the models the discretionary traders used to price power. "It's table stakes to put down seven figures of collateral in order to seriously trade power." We cover: - What a commodities QR actually does — building models traders use, sitting in PM meetings & how "slope" (your real cut of P&L) works - Where power edge comes from: congestion — the physics of a wire that heats up, droops, and can't carry more - How a hub trade gets built from the ground up: weather → demand → which units switch on → your price vs. the market's - Why blindly going long power is a structurally losing trade — skew assets always price above expected value - The anatomy of a blow-up: doubling down into the Feb 2021 Texas freeze as the price ran to $9,000/MWh - Why hedge funds trade power & gas but mostly steer clear of oil — geopolitics & risk you can't model - "Binding constraints" — the pricing model he carried off the grid and into startups, AI & supply chains - Why the guys who take risk for a living buy index funds with their own money Highlights: (00:00) Intro (01:12) Quant researcher execution models within multi-manager hedge funds (07:35) How transmission line congestion drives alpha in power markets (13:24) Capital intensity and managing risk profiles of high-skew assets (19:19) Why commodity desks prefer domestic power over geopolitical oil risk (22:55) Portfolio construction and risk mitigation during tail-risk freeze events (31:36) Capitalizing on the physical infrastructure constraints of AI data centers (36:25) How agentic architecture redefines software engineering and technical moats (43:04) Quant career opportunity cost relative to the AI paradigm shift (56:15) Variant views on venture multiples and agentic customer acquisition economics

Ethan Kho

116,875 views • 1 month ago

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI

Y Combinator

151,559 views • 4 months ago

MUST-WATCH: Why SIG Dominated Options Trading — Explained by an 8-Year Insider Kris Abdelmessih (Kris) spent 8 years trading energy derivatives at SIG, then ran options businesses at Parallax & Prime before founding Moontower —one of the world's most popular newsletters on options & volatility trading. "SIG understood there was an abnormal amount of edge in the market. They came from gambling—sports betting, poker—where edge was tiny. A bookie makes 5% margins. But trading a $2.5 call spread for $2.20 when it's worth $2.50? That's a ridiculous amount of edge compared to gambling, with the same risk distribution." We cover: - Why SIG was called "the evil empire" & how they crushed competitors by trading massive size for tighter spreads - The exact structure of prop shop deals: 50/50 splits, escrow accounts, how you get to 60% then 70% payouts - Why markets look efficient from most vantage points & how trading is ultimately about labor—getting your vantage point close enough that it stops looking random - The tyranny of beta: why the best operator in a melting ice cube business will lose to a mediocre performer in a great market - How to escape the "striver" trap & tune out status optimization (hint: find what you got obsessed with before college applications mattered) - Teaching his 12-year-old options market making & involving his 9-year-old in building a trading card game—scattered cards on the bedroom floor that'll become a finished product Thanks to Kris for the masterclass. Highlights: 02:05 How Kris first recognized real trading edge 04:01 How early market structure created easy edge 05:27 Why improvement in trading comes from hindsight 07:08 The core SIG frameworks that shaped his edge 09:37 Why uncovering edge requires labor and precision 11:02 How informed order flow forces trader humility 12:53 What truly differentiated SIG from competitors 13:23 How SIG built a world-class education pipeline 16:30 How SIG captured edge by refusing to hedge 18:11 How centralized risk controlled exposure and variance 19:09 How SIG used size and spreads to dominate markets 23:20 What Kris learned working with Jason McCarthy 25:40 Why elite traders share extreme competitiveness 26:06 How top performers operate across domains and PM roles 28:22 How Kris transitioned from SIG to prop trading 31:56 What shifting into senior roles taught him about trading 33:46 How Kris built training and feedback systems for traders 35:00 How the backer model works inside prop shops 38:41 How escrow capital protects traders from tail events 41:03 How natural gas options trading changed with regime shifts 42:13 How Kris applies trading edge concepts to life decisions 45:46 Why personal alignment beats chasing status in trading 47:13 How status games distort decision-making for young traders 52:23 Why striver behavior is actually risk management 56:27 How Kris teaches opportunity cost through parenting 1:01:28 How exposing kids to decisions builds intuition 1:04:46 How Kris teaches EV using homemade trading games 1:08:05 How iteration and feedback loops shape real learning

Ethan Kho

233,306 views • 6 months ago

2025 has been a wild year for AI video. My conversation with Cristóbal Valenzuela, CEO of Runway: 00:00 – Meet Cris and Runway – AI Video's Wild Year 01:48 – Runway's AI Film Festival Goes From Chinatown To IMAX 04:02 – Hollywood's Shift: From Ignoring AI To Adopting It At Scale 06:38 – How Runway Saves VFX Artists' Weekends Of Work 07:31 – Inside Gen-4 And Aleph: Why These Models Are Game-Changers 08:21 – From Editing Tools To A "New Kind Of Camera" 10:00 – Beyond Film: Gaming, Architecture, E-commerce & Robotics Use Cases 10:55 – Why Advertising Is Adopting AI Video Faster Than Anyone Else 11:38 – How Creatives Adapt When Iteration Becomes Real-Time 14:12 – What Makes Someone Great At AI Video (Hint: No Preconceptions) 15:28 – The Early Days: Building Runway Before Generative AI Was "Real" 20:27 – Finding Early Product-Market Fit 21:51 – Balancing Research And Product Inside Runway 24:23 – Comparing Aleph Vs. Gen-4, And The Future Of Generalist Models 30:36 – New Input Modalities: Editing With Video + Annotations, Not Just Text 33:46 – Managing Expectations: Twitter Demos Vs. Real Creative Work 47:09 – The Future: Real-Time AI Video And Fully Explorable 3D Worlds 52:02 – Runway's Business Model: From Indie Creators To Disney & Lionsgate 57:26 – Competing With The Big Labs (Sora, Google, Etc.) 59:58 – Hyper-Personalized Content? Why It May Not Replace Film 1:01:13 – Advice To Founders: Treat Your Company Like A Model — Always Learning 1:03:06 – The Next 5 Years Of Runway: Changing Creativity Forever

Matt Turck

18,817 views • 11 months ago

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (Andrew Courtney) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much Andrew Courtney for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

Ethan Kho

436,074 views • 5 months ago

"AI agents will hold more crypto than humans within a decade." Charles Hoskinson (Charles Hoskinson) studied math, dropped out, built one of the only blockchains designed by peer-reviewed research. He co-founded Ethereum, walked away over how it was run, and built Cardano to do it differently. The man who has argued with everyone in this industry now thinks the biggest user of crypto won't be people at all. "Humans are a rounding error in the system we're building. AI agents don't sleep, don't panic-sell, and don't care about price. They transact in tokens because that's the only thing they can actually use." We cover: - Why AI agents (not humans) become the dominant on-chain actors, and what that does to every token model - The infrastructure that has to exist before agents can transact safely at scale - Why most current blockchains can't handle machine-speed transactions - Where Cardano's research-first approach fits in a world of autonomous agents - The identity problem: how do you tell a human from an agent on-chain, and why it matters - Why he's bullish on the technology but blunt about the timeline - What he thinks the rest of the industry is getting wrong about AI + crypto - The one thing that has to happen for any of this to be real Thanks to Charles for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 01:30 - Why AI Agents Change Everything 06:30 - Humans as a Rounding Error 12:00 - The Infrastructure Gap 18:30 - Identity: Human vs Agent On-Chain 24:30 - Where Cardano Fits 30:00 - What The Industry Gets Wrong 34:00 - The Timeline Nobody Wants To Hear

Michaël van de Poppe

293,287 views • 2 months ago

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion

Charlie O'Neill

89,279 views • 20 days ago