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One agentic workflow now does 1,000 hours of hedge fund analyst work. Aakarsh Ramchandi founded the data team @ Third Point, built screening engines @ FactSet, & now builds agentic research tools @ RavenPack. "There's gonna be a full convergence of quant and qual. Most discretionary analysts I know...

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Ethan Kho

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301,903 views • 1 month ago •via X (Twitter)

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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

432,870 views • 1 month ago

The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self
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The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self

Matt Turck

86,153 views • 1 year ago

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 quantamental research teams, & designing the ML/AI systems that powered their forecasts. "A real edge you used to have 15 years ago was creating your own version of someone else's data." We cover: - How Two Sigma's fundamentals team built proprietary data pipelines before vendors existed - Why point-in-time databases were a secret weapon — & how look-ahead bias destroyed competitors - The crowding problem hiding inside everyone's favorite value factor - LLMs in quant research: what agents can already replace & what still requires human intuition - Why junior quants are at risk — & the one mindset that keeps senior researchers irreplaceable - How HarmoniQ Insights pivoted from advising buy-side firms to backing fintech startups with sweat equity - The New Barbarians thesis: crypto natives meeting old Wall Street, & why both sides need each other - Bill's one piece of advice for aspiring quants: build your own model, put real money behind it, learn from the losses Timestamps: 00:00 Intro 00:57 Life as a Quant at Two Sigma 02:36 Finding Edge in Fundamental Data 07:04 Creating a Creative Quant Research Culture 11:19 How LLMs Change Quantitative Trading 15:52 AI’s Impact on Junior Quant Careers 22:56 Using AI Tools for Learning 23:57 HarmoniQ Insights: Advising Fintech Startups 30:47 The New Barbarians Podcast Explained 33:26 Crypto and Market Makers vs TradFi 34:54 Career Advice for Aspiring Quants 38:46 Final Takeaways
39:09

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 quantamental research teams, & designing the ML/AI systems that powered their forecasts. "A real edge you used to have 15 years ago was creating your own version of someone else's data." We cover: - How Two Sigma's fundamentals team built proprietary data pipelines before vendors existed - Why point-in-time databases were a secret weapon — & how look-ahead bias destroyed competitors - The crowding problem hiding inside everyone's favorite value factor - LLMs in quant research: what agents can already replace & what still requires human intuition - Why junior quants are at risk — & the one mindset that keeps senior researchers irreplaceable - How HarmoniQ Insights pivoted from advising buy-side firms to backing fintech startups with sweat equity - The New Barbarians thesis: crypto natives meeting old Wall Street, & why both sides need each other - Bill's one piece of advice for aspiring quants: build your own model, put real money behind it, learn from the losses Timestamps: 00:00 Intro 00:57 Life as a Quant at Two Sigma 02:36 Finding Edge in Fundamental Data 07:04 Creating a Creative Quant Research Culture 11:19 How LLMs Change Quantitative Trading 15:52 AI’s Impact on Junior Quant Careers 22:56 Using AI Tools for Learning 23:57 HarmoniQ Insights: Advising Fintech Startups 30:47 The New Barbarians Podcast Explained 33:26 Crypto and Market Makers vs TradFi 34:54 Career Advice for Aspiring Quants 38:46 Final Takeaways

Ethan Kho

23,460 views • 4 months ago

Over the past year, we've been building our own internal agent infrastructure at YC: over 350 tools, self-improving skill loops, and a shared organizational brain that gets smarter overnight. In this episode of the Lightcone Podcast, we sat down with YC General Partner Pete Pete Koomen to talk about how he led the effort from the ground up. We cover how giving agents unrestricted access to one database was the key unlock, the self-improving skill loops that get smarter overnight, and why he thinks we've arrived at the personal computer moment for AI. 00:39 — YC's AI Stack 02:15 — The Finance Team Problem That Started It All 05:07 — SQL Access Changes Everything 07:20 — One Database to Rule Them All 09:14 — Jevons Paradox 10:07 — Denormalizing for Agents 12:15 — The Single-Player Era of Agents 14:16 — 350 Tools and a Shared Registry 16:24 — Skillify, DRY, and MECE Resolvers 18:23 — The Self-Improving Dream Cycle 20:26 — The Two-Sentence Pitch Skill 23:06 — How Super Intelligence Compounds 25:10 — Recording Everything as a Building Layer 27:10 — The Shared Organizational Brain 29:18 — Trust-Default Culture as a Requirement 30:44 — Raising the Floor for New Employees 32:35 — Horseless Carriages 34:24 — Why Chat Is the Best Interface for Agents 38:50 — Just-in-Time Software 40:49 — Centralizing vs. Decentralizing AI 43:32 — The Personal AI Revolution
46:29

Over the past year, we've been building our own internal agent infrastructure at YC: over 350 tools, self-improving skill loops, and a shared organizational brain that gets smarter overnight. In this episode of the Lightcone Podcast, we sat down with YC General Partner Pete Pete Koomen to talk about how he led the effort from the ground up. We cover how giving agents unrestricted access to one database was the key unlock, the self-improving skill loops that get smarter overnight, and why he thinks we've arrived at the personal computer moment for AI. 00:39 — YC's AI Stack 02:15 — The Finance Team Problem That Started It All 05:07 — SQL Access Changes Everything 07:20 — One Database to Rule Them All 09:14 — Jevons Paradox 10:07 — Denormalizing for Agents 12:15 — The Single-Player Era of Agents 14:16 — 350 Tools and a Shared Registry 16:24 — Skillify, DRY, and MECE Resolvers 18:23 — The Self-Improving Dream Cycle 20:26 — The Two-Sentence Pitch Skill 23:06 — How Super Intelligence Compounds 25:10 — Recording Everything as a Building Layer 27:10 — The Shared Organizational Brain 29:18 — Trust-Default Culture as a Requirement 30:44 — Raising the Floor for New Employees 32:35 — Horseless Carriages 34:24 — Why Chat Is the Best Interface for Agents 38:50 — Just-in-Time Software 40:49 — Centralizing vs. Decentralizing AI 43:32 — The Personal AI Revolution

Y Combinator

752,311 views • 1 month ago

Real agents will not be limited by models first. They will be limited by data access. Max from Teneo Protocol joins the Acc Podcast to unpack why public web data is getting locked behind walls, and what permissionless infrastructure could unlock for builders, businesses, and the agent economy. Max Full conversation below. 👇 00:00 Intro + what we are covering 01:17 Teneo in one line (elevator pitch) 02:07 Why it matters - users as data owners, not “data lords” 04:06 Max’s origin story + how he got into Web3 05:02 Early days - Ethereum mining, rigs, learnings 05:35 The founding team - 4 co-founders, 8 years together 06:38 The pivot - how Teneo was born 08:19 Product overview - Community Node as the foundation 10:35 Chatroom - the simple UI for specialized agents (private beta) 11:41 SDKs - building on Teneo (customer SDK + agent SDK) 14:17 AI agents era - why real-time data access is the bottleneck 19:27 The core problem - APIs locked down, access gets expensive 22:36 What Max does day to day as CEO 26:28 How to start with Teneo - beginner to advanced paths 30:08 Lessons from pivots + building with the right team 33:08 Ops advice - trust and the right people 35:16 2-10 year landscape - data pipelines, cost barriers, opportunity 37:26 What’s live now + how people contribute today 38:50 Agent SDK launch - early feedback and traction 39:56 Next 6-12 months - pushing more open source 42:36 Awareness + surprising use cases (example: government PDFs) 46:00 Speed, latency, and agent-to-agent payments (microtransactions) 48:38 Web3 adoption - users won’t notice, it just needs to work 51:07 What’s next + closing thoughts + where to get involved
50:36

Real agents will not be limited by models first. They will be limited by data access. Max from Teneo Protocol joins the Acc Podcast to unpack why public web data is getting locked behind walls, and what permissionless infrastructure could unlock for builders, businesses, and the agent economy. Max Full conversation below. 👇 00:00 Intro + what we are covering 01:17 Teneo in one line (elevator pitch) 02:07 Why it matters - users as data owners, not “data lords” 04:06 Max’s origin story + how he got into Web3 05:02 Early days - Ethereum mining, rigs, learnings 05:35 The founding team - 4 co-founders, 8 years together 06:38 The pivot - how Teneo was born 08:19 Product overview - Community Node as the foundation 10:35 Chatroom - the simple UI for specialized agents (private beta) 11:41 SDKs - building on Teneo (customer SDK + agent SDK) 14:17 AI agents era - why real-time data access is the bottleneck 19:27 The core problem - APIs locked down, access gets expensive 22:36 What Max does day to day as CEO 26:28 How to start with Teneo - beginner to advanced paths 30:08 Lessons from pivots + building with the right team 33:08 Ops advice - trust and the right people 35:16 2-10 year landscape - data pipelines, cost barriers, opportunity 37:26 What’s live now + how people contribute today 38:50 Agent SDK launch - early feedback and traction 39:56 Next 6-12 months - pushing more open source 42:36 Awareness + surprising use cases (example: government PDFs) 46:00 Speed, latency, and agent-to-agent payments (microtransactions) 48:38 Web3 adoption - users won’t notice, it just needs to work 51:07 What’s next + closing thoughts + where to get involved

Acc Ventures

43,976 views • 7 months ago

"Concentrated strategies have a shot to do extremely well." Sean Emory (Sean D. Emory) is the Founder & CIO of Avory & Co. — a Miami-based, high-conviction equity shop blending old-school fundamentals with modern alt-data. He's compounded at ~20% a year before fees. "Active traders are passively investing — just querying AI models. We use data for thesis tracking, not timing the quarter. In a six-minute market, long-term is the edge." We cover: - The Wix conversion rate trick from 2013 that kicked off his alt-data obsession — and why that playbook still works today - How app downloads, foot traffic & permit scraping give 99% revenue visibility on some names — and why most shops still don't use any of it - The six minute market — why AI + index funds are turning "active" investors into passive ones (and creating dislocations for the disciplined) - The "Six Ms" framework — why management & margin of safety are the bookends that actually matter - Why he'd rather ride the second AI wave than guess which data center provider survives - Unconstrained cash in an ETF wrapper — the flexibility most active managers don't have - Ringing the Nasdaq bell, the $200-300K year-one cost of launching an ETF & the compliance reality nobody warns you about - Teaching his son to compound — seven-day habit streaks rewarded with books Highlights: 00:00 Intro 00:46 Wix case study: Identifying early signals in alternative data 04:20 Identifying modern low-hanging fruit in digital data sets 06:08 Is there alpha in alt data if everyone has access? 09:08 Integrating alternative data into the fundamental investment process 14:08 Differentiating between fundamental inflection points and market noise 17:47 Self-awareness and avoiding cognitive bias in post-trade evaluation 28:43 Portfolio construction: Concentrated strategies vs. multi-name diversification 30:21 Rethinking risk: Permanent loss of capital vs. volatility metrics 32:58 How generative AI impacts market efficiency and passive flows 37:06 Launching an active ETF: Bridging the gap for allocators 40:13 Strategy deltas between private fund vehicles and public ETFs 01:00:48 Maintaining competitive edge and psychological resilience during drawdowns 01:04:13 The primary skill for outperformance in a storytelling market
1:09:32

"Concentrated strategies have a shot to do extremely well." Sean Emory (Sean D. Emory) is the Founder & CIO of Avory & Co. — a Miami-based, high-conviction equity shop blending old-school fundamentals with modern alt-data. He's compounded at ~20% a year before fees. "Active traders are passively investing — just querying AI models. We use data for thesis tracking, not timing the quarter. In a six-minute market, long-term is the edge." We cover: - The Wix conversion rate trick from 2013 that kicked off his alt-data obsession — and why that playbook still works today - How app downloads, foot traffic & permit scraping give 99% revenue visibility on some names — and why most shops still don't use any of it - The six minute market — why AI + index funds are turning "active" investors into passive ones (and creating dislocations for the disciplined) - The "Six Ms" framework — why management & margin of safety are the bookends that actually matter - Why he'd rather ride the second AI wave than guess which data center provider survives - Unconstrained cash in an ETF wrapper — the flexibility most active managers don't have - Ringing the Nasdaq bell, the $200-300K year-one cost of launching an ETF & the compliance reality nobody warns you about - Teaching his son to compound — seven-day habit streaks rewarded with books Highlights: 00:00 Intro 00:46 Wix case study: Identifying early signals in alternative data 04:20 Identifying modern low-hanging fruit in digital data sets 06:08 Is there alpha in alt data if everyone has access? 09:08 Integrating alternative data into the fundamental investment process 14:08 Differentiating between fundamental inflection points and market noise 17:47 Self-awareness and avoiding cognitive bias in post-trade evaluation 28:43 Portfolio construction: Concentrated strategies vs. multi-name diversification 30:21 Rethinking risk: Permanent loss of capital vs. volatility metrics 32:58 How generative AI impacts market efficiency and passive flows 37:06 Launching an active ETF: Bridging the gap for allocators 40:13 Strategy deltas between private fund vehicles and public ETFs 01:00:48 Maintaining competitive edge and psychological resilience during drawdowns 01:04:13 The primary skill for outperformance in a storytelling market

Ethan Kho

41,885 views • 2 months ago

Alex Behring and Daniel Schwartz have almost never spoken publicly about how 3G Capital invests and operates. The firm is different in many fascinating ways. Every fund they raise is designed to make exactly one investment. They invest more of their own money than any of their limited partners in every deal and send their own people to run the businesses as CEOs and CFOs. Over 20 years they have never lost money on a deal. The conversation includes the story of how they spent 15 years building a relationship with the founding family of Hunter Douglas before getting the opportunity to buy it. They describe receiving a two line rejection email from Tim Hortons after weeks of silence and how they managed to get back to the table and close the deal. They talk about how they bought Burger King for a billion dollars when no other firm showed up to compete. And they explain why Skechers being the third largest sneaker company in the world surprised even them. A major theme is how they develop talent. Daniel was an analyst who became CFO at 26 and CEO of Burger King at 32. Alex became CEO of the largest railroad in Latin America when he was 30. They explain what they look for in young people, why they give them significant responsibility faster than anywhere else, and what it takes to set them up to succeed while holding an unusually high bar. Daniel and Alex are two of the most talented and intense people I know. They are incredibly serious about business quality and disciplined about waiting for the right opportunity. They would rather do nothing than compromise. Enjoy! Timestamps: 00:00:00 Episode Intro: Daniel Schwartz & Alex Behring 00:01:20 The "One Investment Per Fund" Model 00:05:39 Characteristics of Great Businesses 00:08:40 The Unique Structure of 3G Capital 00:14:21 Why Hunter Douglas Was Appealing 00:20:15 Alex's Railroad Story 00:28:12 The "Burger King is Run by Children" Story 00:30:38 Negotiating with Tim Hortons 00:38:18 Be Wired for Urgency 00:46:43 3G's Operating System 00:55:35 Why Burger King Was Undervalued 00:59:43 From Zero to $2 Billion in France 01:01:42 Kraft Heinz: Concentration Risk 01:04:25 Skechers: Great Product Meets Great Distribution 01:13:10 Zero-Based Budgeting & When It Works 01:16:28 The Current State of Capital Markets 01:23:04 3G's Founder-Led Focus 01:28:57 The Kindest Thing
1:30:32

Alex Behring and Daniel Schwartz have almost never spoken publicly about how 3G Capital invests and operates. The firm is different in many fascinating ways. Every fund they raise is designed to make exactly one investment. They invest more of their own money than any of their limited partners in every deal and send their own people to run the businesses as CEOs and CFOs. Over 20 years they have never lost money on a deal. The conversation includes the story of how they spent 15 years building a relationship with the founding family of Hunter Douglas before getting the opportunity to buy it. They describe receiving a two line rejection email from Tim Hortons after weeks of silence and how they managed to get back to the table and close the deal. They talk about how they bought Burger King for a billion dollars when no other firm showed up to compete. And they explain why Skechers being the third largest sneaker company in the world surprised even them. A major theme is how they develop talent. Daniel was an analyst who became CFO at 26 and CEO of Burger King at 32. Alex became CEO of the largest railroad in Latin America when he was 30. They explain what they look for in young people, why they give them significant responsibility faster than anywhere else, and what it takes to set them up to succeed while holding an unusually high bar. Daniel and Alex are two of the most talented and intense people I know. They are incredibly serious about business quality and disciplined about waiting for the right opportunity. They would rather do nothing than compromise. Enjoy! Timestamps: 00:00:00 Episode Intro: Daniel Schwartz & Alex Behring 00:01:20 The "One Investment Per Fund" Model 00:05:39 Characteristics of Great Businesses 00:08:40 The Unique Structure of 3G Capital 00:14:21 Why Hunter Douglas Was Appealing 00:20:15 Alex's Railroad Story 00:28:12 The "Burger King is Run by Children" Story 00:30:38 Negotiating with Tim Hortons 00:38:18 Be Wired for Urgency 00:46:43 3G's Operating System 00:55:35 Why Burger King Was Undervalued 00:59:43 From Zero to $2 Billion in France 01:01:42 Kraft Heinz: Concentration Risk 01:04:25 Skechers: Great Product Meets Great Distribution 01:13:10 Zero-Based Budgeting & When It Works 01:16:28 The Current State of Capital Markets 01:23:04 3G's Founder-Led Focus 01:28:57 The Kindest Thing

Patrick OShaughnessy

42,954 views • 5 months ago

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 views • 5 months ago

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper 📧

66,339 views • 2 months ago

“Founders aren’t made when they start companies. They’re made when they interpret their market correctly.” Episode 2 of Knuckle Up with Nakul is live. Qasar Younis (Qasar Younis): Founder and CEO of Applied Intuition. One of the sharpest and most intentional operators you’ll ever meet on the craft of building a company. Full episode ↓ -- 00:00 Intro 01:19 What really makes someone a founder 05:26 The company that almost became Kickstarter 08:12 The most common misread on feedback 13:40 Why most founders don't end up with the best team 19:45 How to pick a co-founder 23:38 Your first 10 hires are really your first 100 28:21 The case for hiring slow and firing slow 33:22 Red, yellow, green: how Applied gives monthly feedback 35:00 The role that knows what’s actually going on in a company 40:01 How to operate with speed and intentionality 42:41 The three things Qasar spends time on 45:57 How Applied is driving AI adoption 52:06 The type of engineer Applied is now looking for 1:01:19 Why this could be the golden age of small companies 1:09:13 Quickfire: red flags, overrated advice, and superpowers 1:12:32 Qasar's advice to his 25-year-old self
1:13:20

“Founders aren’t made when they start companies. They’re made when they interpret their market correctly.” Episode 2 of Knuckle Up with Nakul is live. Qasar Younis (Qasar Younis): Founder and CEO of Applied Intuition. One of the sharpest and most intentional operators you’ll ever meet on the craft of building a company. Full episode ↓ -- 00:00 Intro 01:19 What really makes someone a founder 05:26 The company that almost became Kickstarter 08:12 The most common misread on feedback 13:40 Why most founders don't end up with the best team 19:45 How to pick a co-founder 23:38 Your first 10 hires are really your first 100 28:21 The case for hiring slow and firing slow 33:22 Red, yellow, green: how Applied gives monthly feedback 35:00 The role that knows what’s actually going on in a company 40:01 How to operate with speed and intentionality 42:41 The three things Qasar spends time on 45:57 How Applied is driving AI adoption 52:06 The type of engineer Applied is now looking for 1:01:19 Why this could be the golden age of small companies 1:09:13 Quickfire: red flags, overrated advice, and superpowers 1:12:32 Qasar's advice to his 25-year-old self

Nakul Mandan

521,414 views • 2 months ago

E114: Walrus 🦭/acc : Decentralizing Data Storage, the perfect complement to the SUI Network George Danezis is the Chief Scientist at MystenLabs.sui , co-creator of the Sui , and one the minds behind Walrus 🦭/acc Timestamps 0:00 Intro 2:00 Partnerships: Jupiter, Bitwise, SUI & Mantle 2:46 Sleeping Habits 4:05 Who is George? 4:23 What is a Chief Scientist? 5:16 Research at Microsoft vs. Now 6:59 What is “Good Research”? 8:49 Corporate Innovation vs. Reality 10:31 Why I Love Teaching Security 11:52 Self Custody with Trezor 12:44 Teaching vs. Real-World Application 16:28 What Makes an Idea Great? 17:51 The Idea That Never Worked Out 19:50 Being Late to Ideas, Not Early 20:56 The Essence of Peer-to-Peer Networks 23:50 Why We Need Peer-to-Peer Systems? 27:51 Why Decentralization Was Hard to Realize 31:09 Bitcoin's Game-Changing Impact 33:10 ETH Aha Moment 34:46 Facebook’s Conviction in Blockchain 37:39 Scaling DIEM/LIBRA to Sui 39:20 Biggest Issue at Facebook 40:20 Why Facebook Bet on Crypto 41:40 Why I Left Facebook 45:07 Balancing Passion and Pay 46:43 What is a Blockchain Good For? 51:42 Understanding Blockchain’s Limitations 53:21 CryptoKitties 54:36 Meme Coins: Culture vs. Scam 57:02 The Role of Norms in Crypto 1:00:32 What is the Sui Network? 1:02:32 Why Do We Need The Sui Network? 1:03:34 Layer 1 vs. Layer 2 1:05:07 Why Build a Layer One? 1:09:21 Top Layer One Competitors 1:10:45 Decentralization vs. Practicality 1:14:06 Minimum Decentralization 1:16:33 Data Storage & Its Biggest Problem 1:20:05 What is Walrus Protocol? 1:22:09 Why Walrus is Possible Now 1:25:35 Why Sui Network for Data Storage? 1:27:37 Vision of Walrus Protocol 1:28:59 Building a Neutral Tech Future 1:30:44 Using Existing Blockchains 1:33:03 Future of Decentralized Production 1:35:51 Prediction For The Next 12 Months 1:39:04 Concluding Remarks
1:39:56

E114: Walrus 🦭/acc : Decentralizing Data Storage, the perfect complement to the SUI Network George Danezis is the Chief Scientist at MystenLabs.sui , co-creator of the Sui , and one the minds behind Walrus 🦭/acc Timestamps 0:00 Intro 2:00 Partnerships: Jupiter, Bitwise, SUI & Mantle 2:46 Sleeping Habits 4:05 Who is George? 4:23 What is a Chief Scientist? 5:16 Research at Microsoft vs. Now 6:59 What is “Good Research”? 8:49 Corporate Innovation vs. Reality 10:31 Why I Love Teaching Security 11:52 Self Custody with Trezor 12:44 Teaching vs. Real-World Application 16:28 What Makes an Idea Great? 17:51 The Idea That Never Worked Out 19:50 Being Late to Ideas, Not Early 20:56 The Essence of Peer-to-Peer Networks 23:50 Why We Need Peer-to-Peer Systems? 27:51 Why Decentralization Was Hard to Realize 31:09 Bitcoin's Game-Changing Impact 33:10 ETH Aha Moment 34:46 Facebook’s Conviction in Blockchain 37:39 Scaling DIEM/LIBRA to Sui 39:20 Biggest Issue at Facebook 40:20 Why Facebook Bet on Crypto 41:40 Why I Left Facebook 45:07 Balancing Passion and Pay 46:43 What is a Blockchain Good For? 51:42 Understanding Blockchain’s Limitations 53:21 CryptoKitties 54:36 Meme Coins: Culture vs. Scam 57:02 The Role of Norms in Crypto 1:00:32 What is the Sui Network? 1:02:32 Why Do We Need The Sui Network? 1:03:34 Layer 1 vs. Layer 2 1:05:07 Why Build a Layer One? 1:09:21 Top Layer One Competitors 1:10:45 Decentralization vs. Practicality 1:14:06 Minimum Decentralization 1:16:33 Data Storage & Its Biggest Problem 1:20:05 What is Walrus Protocol? 1:22:09 Why Walrus is Possible Now 1:25:35 Why Sui Network for Data Storage? 1:27:37 Vision of Walrus Protocol 1:28:59 Building a Neutral Tech Future 1:30:44 Using Existing Blockchains 1:33:03 Future of Decentralized Production 1:35:51 Prediction For The Next 12 Months 1:39:04 Concluding Remarks

MR SHIFT 🦁

111,082 views • 1 year ago

E131: Diogo Mónica: Why DEFI will NEVER WORK, unless these two things change! Diogo Monica is the General Partner at Haun Ventures, President at NEAR Foundation, and the co-founder of Anchorage Digital ⚓. Join me for this captivating 2-hour conversation with Diogo - a crypto founder turned VC, now backing the next wave of blockchain innovation and driving mainstream adoption through Anchorage Digital, the first federally chartered crypto bank in the U.S. Timestamps: 0:00 Introduction 1:54 Partnerships: Jupiter (🐱, 🐐), KAST, Bitwise, Sui, Mantle, Forza! BTC 2:44 Beauty Privilege Is Real 3:26 Why Fitness Builds Trust 8:30 Who is Diogo Mónica? 9:56 Self-Custody with Trezor 10:48 Silicon Valley Changed Everything 12:03 Why I Left San Francisco 15:52 The Crypto Traveling Circus 18:31 Stablecoins & FinTech Founders 20:36 Ambition Through Discomfort 23:17 Retirement Is a Myth 25:25 The Evolution of Jack Dorsey 27:37 The Cost of Working Sundays 29:10 Working Sundays & Culture 33:51 Missionaries vs. Mercenaries 36:26 The Loneliness of Founders 38:09 Why Starting Is Easier Now 40:32 Watching Your Team Disappear 43:13 Hiring Self-Starters Only 45:29 Why Most Startups Fail 51:56 Is Life a Single-Player Game? 53:28 The Struggles of Solo Founders 55:43 The $1.5M Bitcoin Mistake 58:13 The Magic of Zero to One 58:50 Why Zero to One Needs In-Person 1:00:02 Scaling From One To Hundred 1:00:54 From Employee To Multi-Founder 1:05:10 Proactive Mental Health Care 1:06:24 Starting Couple Therapy Early 1:07:24 Founder Burnout Is Common 1:09:33 Entrepreneurs Becoming VCs 1:10:35 Founder Vs. Venture Investor 1:12:41 Why VCs Seem Arrogant 1:15:13 Why Founders Make Bad VCs 1:16:20 VCs Have Higher Expected Value 1:19:01 The Accountability Gap In VC 1:21:19 Why I Joined Haun Ventures 1:23:39 Balancing Work And Family Life 1:26:42 When Family Changes Priorities 1:28:26 What Does Enough Mean? 1:30:24 Balancing Happiness and Anxiety 1:32:35 Contrarian Views on AI 1:34:04 Changing Minds Through Debate 1:37:41 The New Default Career Path 1:39:07 Crypto’s Incentive Problem 1:42:07 Prediction for the Next 12 Months 1:43:30 Concluding Remarks
1:43:47

E131: Diogo Mónica: Why DEFI will NEVER WORK, unless these two things change! Diogo Monica is the General Partner at Haun Ventures, President at NEAR Foundation, and the co-founder of Anchorage Digital ⚓. Join me for this captivating 2-hour conversation with Diogo - a crypto founder turned VC, now backing the next wave of blockchain innovation and driving mainstream adoption through Anchorage Digital, the first federally chartered crypto bank in the U.S. Timestamps: 0:00 Introduction 1:54 Partnerships: Jupiter (🐱, 🐐), KAST, Bitwise, Sui, Mantle, Forza! BTC 2:44 Beauty Privilege Is Real 3:26 Why Fitness Builds Trust 8:30 Who is Diogo Mónica? 9:56 Self-Custody with Trezor 10:48 Silicon Valley Changed Everything 12:03 Why I Left San Francisco 15:52 The Crypto Traveling Circus 18:31 Stablecoins & FinTech Founders 20:36 Ambition Through Discomfort 23:17 Retirement Is a Myth 25:25 The Evolution of Jack Dorsey 27:37 The Cost of Working Sundays 29:10 Working Sundays & Culture 33:51 Missionaries vs. Mercenaries 36:26 The Loneliness of Founders 38:09 Why Starting Is Easier Now 40:32 Watching Your Team Disappear 43:13 Hiring Self-Starters Only 45:29 Why Most Startups Fail 51:56 Is Life a Single-Player Game? 53:28 The Struggles of Solo Founders 55:43 The $1.5M Bitcoin Mistake 58:13 The Magic of Zero to One 58:50 Why Zero to One Needs In-Person 1:00:02 Scaling From One To Hundred 1:00:54 From Employee To Multi-Founder 1:05:10 Proactive Mental Health Care 1:06:24 Starting Couple Therapy Early 1:07:24 Founder Burnout Is Common 1:09:33 Entrepreneurs Becoming VCs 1:10:35 Founder Vs. Venture Investor 1:12:41 Why VCs Seem Arrogant 1:15:13 Why Founders Make Bad VCs 1:16:20 VCs Have Higher Expected Value 1:19:01 The Accountability Gap In VC 1:21:19 Why I Joined Haun Ventures 1:23:39 Balancing Work And Family Life 1:26:42 When Family Changes Priorities 1:28:26 What Does Enough Mean? 1:30:24 Balancing Happiness and Anxiety 1:32:35 Contrarian Views on AI 1:34:04 Changing Minds Through Debate 1:37:41 The New Default Career Path 1:39:07 Crypto’s Incentive Problem 1:42:07 Prediction for the Next 12 Months 1:43:30 Concluding Remarks

MR SHIFT 🦁

73,196 views • 11 months ago

My conversation with Karim Atiyeh, co-founder and CTO of Ramp and one of the best operators active today. I love talking to people who are obsessed with their craft, and Karim is one of them. The way he talks about every part of the business makes it clear that Ramp wins by caring more than anyone. They labor over every pixel and detail that helps customers save time, eliminate waste, and spend less. We talk about how to build products faster, assembling a team of "Avengers", seeking differentiation and how Karim’s "divinely discontent" mindset drives Ramp’s culture. It's a conversation about craftsmanship, obsession, and the quiet intensity behind one of the fastest-growing companies in the world. Enjoy! Timestamps: 0:00 Intro 0:29 Competing with a Day One Mentality 7:58 Winning with Consumer-Grade UX 10:40 Divinely Discontent 14:18 Building a Culture of Mutual Accountability 16:05 Origin Story: Growing Up in Lebanon 21:40 The First Company: Paribus 28:02 Lessons from Building on an Unstable Platform 36:42 From Paribus to Ramp 48:59 The Framework for Choosing Investors 57:54 Shifting the Business Model 1:03:00 Why the Best Builders Will Be Technical 1:06:05 An Engineer's Approach to Marketing 1:20:15 The Power of Differentiation 1:22:22 Recruiting for Spikiness 1:27:41 The Tactics of Speed 1:34:52 The Vision for Ramp 1:43:40 Kindest Thing
1:46:22

My conversation with Karim Atiyeh, co-founder and CTO of Ramp and one of the best operators active today. I love talking to people who are obsessed with their craft, and Karim is one of them. The way he talks about every part of the business makes it clear that Ramp wins by caring more than anyone. They labor over every pixel and detail that helps customers save time, eliminate waste, and spend less. We talk about how to build products faster, assembling a team of "Avengers", seeking differentiation and how Karim’s "divinely discontent" mindset drives Ramp’s culture. It's a conversation about craftsmanship, obsession, and the quiet intensity behind one of the fastest-growing companies in the world. Enjoy! Timestamps: 0:00 Intro 0:29 Competing with a Day One Mentality 7:58 Winning with Consumer-Grade UX 10:40 Divinely Discontent 14:18 Building a Culture of Mutual Accountability 16:05 Origin Story: Growing Up in Lebanon 21:40 The First Company: Paribus 28:02 Lessons from Building on an Unstable Platform 36:42 From Paribus to Ramp 48:59 The Framework for Choosing Investors 57:54 Shifting the Business Model 1:03:00 Why the Best Builders Will Be Technical 1:06:05 An Engineer's Approach to Marketing 1:20:15 The Power of Differentiation 1:22:22 Recruiting for Spikiness 1:27:41 The Tactics of Speed 1:34:52 The Vision for Ramp 1:43:40 Kindest Thing

Patrick OShaughnessy

599,830 views • 8 months ago

NEW: Brian Singerman, fmr Founders Fund, now GPx, on why he only invests in *people* SpaceX is the reason he joined Founders Fund in 2008 "If SpaceX didn't work, Founders Fund would not exist." From Elon & SpaceX to Karp & Palantir, to Anduril, Airbnb, Stripe, & Stemcentrx, Brian Singerman's whole framework is one question: Is this the best founder in the world at their particular thing? We get into: - What makes Founders Fund unique: a team of strong-willed, genuinely authentic individuals - Why a 3x fund loses to the S&P - How a lifetime of strategy gaming shapes how he reads founders & now GPs - Why he bets against the end of the world every time - Why he's bullish on N-of-1 human cultural artifacts in an AI world - Why Cyan Banister & Palmer Luckey are genuinely N-of-1 people Thank you to Max Levchin, Trae Stephens & Scott Nolan for great questions 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Brian Singerman, Co-Founder of GPx, Former Partner at Founders Fund (00:42) Founders Fund (02:09) Max Levchin's unfiltered questions (05:25) How gaming shaped his investing (06:11) Joining Founders Fund in 2008 (07:34) Silicon Valley's most fascinating characters (09:29) Lessons from Peter Thiel (11:26) Building a culture of conviction (11:59) The art of spotting A+ founders (13:55) Trae's biggest lesson from Peter Thiel (14:41) Is socialism a threat to America? (15:58) Why capital is leaving California (17:03) The obsession with Hawaii (20:59) The Founders Fund playbook (24:51) "If SpaceX didn't work, Founders Fund would not exist" (26:03) Why Elon is one of one (27:30) Why everyone knew Starlink would win (28:42) Inside Founders Fund's biggest bets (31:06) The Founders Fund founder archetype (33:25) Backing fund managers instead of startups (35:22) The new generation of GPs (36:34) The most authentic people in tech (41:38) Why other VCs aren't the audience (44:05) Music, Memorabilia & N-of-1 Artifacts (46:25) The story behind Brian's music studio (49:22) What's next for Brian?
51:06

NEW: Brian Singerman, fmr Founders Fund, now GPx, on why he only invests in *people* SpaceX is the reason he joined Founders Fund in 2008 "If SpaceX didn't work, Founders Fund would not exist." From Elon & SpaceX to Karp & Palantir, to Anduril, Airbnb, Stripe, & Stemcentrx, Brian Singerman's whole framework is one question: Is this the best founder in the world at their particular thing? We get into: - What makes Founders Fund unique: a team of strong-willed, genuinely authentic individuals - Why a 3x fund loses to the S&P - How a lifetime of strategy gaming shapes how he reads founders & now GPs - Why he bets against the end of the world every time - Why he's bullish on N-of-1 human cultural artifacts in an AI world - Why Cyan Banister & Palmer Luckey are genuinely N-of-1 people Thank you to Max Levchin, Trae Stephens & Scott Nolan for great questions 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Brian Singerman, Co-Founder of GPx, Former Partner at Founders Fund (00:42) Founders Fund (02:09) Max Levchin's unfiltered questions (05:25) How gaming shaped his investing (06:11) Joining Founders Fund in 2008 (07:34) Silicon Valley's most fascinating characters (09:29) Lessons from Peter Thiel (11:26) Building a culture of conviction (11:59) The art of spotting A+ founders (13:55) Trae's biggest lesson from Peter Thiel (14:41) Is socialism a threat to America? (15:58) Why capital is leaving California (17:03) The obsession with Hawaii (20:59) The Founders Fund playbook (24:51) "If SpaceX didn't work, Founders Fund would not exist" (26:03) Why Elon is one of one (27:30) Why everyone knew Starlink would win (28:42) Inside Founders Fund's biggest bets (31:06) The Founders Fund founder archetype (33:25) Backing fund managers instead of startups (35:22) The new generation of GPs (36:34) The most authentic people in tech (41:38) Why other VCs aren't the audience (44:05) Music, Memorabilia & N-of-1 Artifacts (46:25) The story behind Brian's music studio (49:22) What's next for Brian?

Molly O’Shea

245,356 views • 1 month ago

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed
1:22:57

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed

Matt Turck

53,727 views • 14 days ago

On Aug 27th, 2025, we presented the idea of an Agentic OS at this keynote event in San Francisco. We demo-ed what a post-IDE world looks like (at 35:22, as Andrej Karpathy mentioned recently), and why it requires outrageous ambition. Through multiple breakthroughs across AI, distributed systems, cryptography, game theory, economics, blockchains and UI/UX, we are on the road to agentic general intelligence, the true AGI. This is that vision, posted 6 months after the event, so you can see this indeed is the world which has been rapidly emerging and where we are headed. We are in the endgame now, and all the timelines now converge... 0:00 - Opening remarks 2:37 - What is Hyperspace? The world's largest distributed computing network 4:20 - Why thousands are running agents from home 5:01 - Live scale of this peer-to-peer supercomputer 8:10 - From blockchain experiments to an entirely new OS 10:53 - The hard problem: agents across every OS and environment 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments — killing the ad model 51:52 - Why we need the broadband infra for agentic commerce 54:32 - The Agentic Virtual Machine 57:48 - Building a new blockchain purpose-built for agents 58:30 - Closing remarks and the journey ahead ps: we are at Hyperspace
1:04:50

On Aug 27th, 2025, we presented the idea of an Agentic OS at this keynote event in San Francisco. We demo-ed what a post-IDE world looks like (at 35:22, as Andrej Karpathy mentioned recently), and why it requires outrageous ambition. Through multiple breakthroughs across AI, distributed systems, cryptography, game theory, economics, blockchains and UI/UX, we are on the road to agentic general intelligence, the true AGI. This is that vision, posted 6 months after the event, so you can see this indeed is the world which has been rapidly emerging and where we are headed. We are in the endgame now, and all the timelines now converge... 0:00 - Opening remarks 2:37 - What is Hyperspace? The world's largest distributed computing network 4:20 - Why thousands are running agents from home 5:01 - Live scale of this peer-to-peer supercomputer 8:10 - From blockchain experiments to an entirely new OS 10:53 - The hard problem: agents across every OS and environment 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments — killing the ad model 51:52 - Why we need the broadband infra for agentic commerce 54:32 - The Agentic Virtual Machine 57:48 - Building a new blockchain purpose-built for agents 58:30 - Closing remarks and the journey ahead ps: we are at Hyperspace

Varun

22,935 views • 4 months ago

NEW: What Travis Kalanick Taught Bradley Tusk, & Why He Closed His VC Fund Bradley Tusk, Uber's first regulatory advisor, joins Sourcery to discuss what he learned working closely w/ Travis Kalanick, from Uber’s early regulatory battles throughout their massive scaling. We also cover the real economics of running a VC fund.. and Bradley's decision to ultimately shut down his fund & return to an equity-for-services model. This was a really fun conversation, a bit philosophical, very candid, & I'm sure we could've kept talking for hours. Highlights: (00:00) Bradley Tusk (01:25) How AI infrastructure spending is being driven by market narratives (03:10) DeepSeek, inference models, & compute efficiency (03:55) Where the $2T in AI data-center & energy capital is flowing (07:10) Nuclear energy & the broader implications of AI’s power demand (16:30) Zero-sum vs abundance thinking in tech & politics (17:35) How people find meaning, purpose, & balance in high-pressure work (27:00) Why Bradley invests heavily in his team & removes non-essential tasks (32:00) How Bradley’s experience with Travis Kalanick shaped his view of founders (32:35) “Travis’s Law” & turning users into political advocates (37:05) Why Bradley decided to stop raising traditional VC funds (44:30) The economics of mid-sized funds & why they’re so difficult to run (49:20) How AI valuations differ from non-AI valuations (54:40) What people misunderstand about Silicon Valley & DC (59:40) AI, unemployment risk, & why Bradley believes UBI will be necessary (01:05:50) The biggest lessons Bradley learned from Travis Kalanick
1:12:18

NEW: What Travis Kalanick Taught Bradley Tusk, & Why He Closed His VC Fund Bradley Tusk, Uber's first regulatory advisor, joins Sourcery to discuss what he learned working closely w/ Travis Kalanick, from Uber’s early regulatory battles throughout their massive scaling. We also cover the real economics of running a VC fund.. and Bradley's decision to ultimately shut down his fund & return to an equity-for-services model. This was a really fun conversation, a bit philosophical, very candid, & I'm sure we could've kept talking for hours. Highlights: (00:00) Bradley Tusk (01:25) How AI infrastructure spending is being driven by market narratives (03:10) DeepSeek, inference models, & compute efficiency (03:55) Where the $2T in AI data-center & energy capital is flowing (07:10) Nuclear energy & the broader implications of AI’s power demand (16:30) Zero-sum vs abundance thinking in tech & politics (17:35) How people find meaning, purpose, & balance in high-pressure work (27:00) Why Bradley invests heavily in his team & removes non-essential tasks (32:00) How Bradley’s experience with Travis Kalanick shaped his view of founders (32:35) “Travis’s Law” & turning users into political advocates (37:05) Why Bradley decided to stop raising traditional VC funds (44:30) The economics of mid-sized funds & why they’re so difficult to run (49:20) How AI valuations differ from non-AI valuations (54:40) What people misunderstand about Silicon Valley & DC (59:40) AI, unemployment risk, & why Bradley believes UBI will be necessary (01:05:50) The biggest lessons Bradley learned from Travis Kalanick

Molly O’Shea

96,352 views • 7 months ago

E159: Hyperliquid: Housing all of Finance jeff.hl came back on the When Shift Happens Podcast to talk about the Hyperliquid journey since the TGE and what the future holds for one of the most loved and prolific protocols in the space Hyperliquid Timestamps 0:00 Intro 2:01 Singapore 2:27 Reminiscing on the Token Launch 5:00 Was This Scale Of Wealth Expected? 6:28 Doing The Right Thing In Crypto 9:07 The Responsibility that comes with Billions of $ 11:10 Jupiter KAST 11:51 Bringing Hyperliquid to the masses 15:21 Pre TGE and Post TGE: Operational difference 20:13 Choices on what to build Internally vs Externally 22:05 How to build a reliable team 24:51 Did the Team celebrate the HYPE wealth Generation event? 26:45 How to test talents for High Integrity 28:31 How much does the Hyperliquid team sleep? 30:05 Employee Vesting Fears 31:41 Dealing with FUD 32:28 How Does Jeff Personally Handle FUD 35:02 Token "Buybacks" critics 37:20 Why Hyperliquid can't have Discretionary "Buybacks" 39:04 HyperEVM, explained Simply 40:00 Paradex Zodl (fka Zashi) 40:41 HyperEVM: Success so Far? 44:05 HIP-3, explained Simply 47:44 What makes Hyperliquid's approach different 48:19 Why Should People Care? 51:33 Bring All Finance On Chain 52:08 Why Is The Hyperliquid Approach Better? 53:47 Key Numbers showing that Hyperliquid Is Doing it right 59:01 What Has the Unit team demonstrated with spot trading on Hyperliquid in 2025 1:03:29 HIP-4: Outcome Markets 1:08:01 Trezor Sui 1:08:58 What does "Housing All Of Finance" mean? 1:10:51 Why Hyperliquid is not a crypto company 1:12:23 Why Does Hyperliquid have A Stablecoin USDH (Native Markets) 1:14:39 What Is Kinetiq & Why Does It Matter? 1:16:15 Why Is What HyperLend Is Building Important For HyperLiquid 1:23:39 Where did Fairness cost the most? 1:24:47 What should Hyperliquid be Remembered for? 1:25:24 Why should people stay in Crypto when there's an AI brain drain? 1:28:10 Closing Thoughts
1:29:06

E159: Hyperliquid: Housing all of Finance jeff.hl came back on the When Shift Happens Podcast to talk about the Hyperliquid journey since the TGE and what the future holds for one of the most loved and prolific protocols in the space Hyperliquid Timestamps 0:00 Intro 2:01 Singapore 2:27 Reminiscing on the Token Launch 5:00 Was This Scale Of Wealth Expected? 6:28 Doing The Right Thing In Crypto 9:07 The Responsibility that comes with Billions of $ 11:10 Jupiter KAST 11:51 Bringing Hyperliquid to the masses 15:21 Pre TGE and Post TGE: Operational difference 20:13 Choices on what to build Internally vs Externally 22:05 How to build a reliable team 24:51 Did the Team celebrate the HYPE wealth Generation event? 26:45 How to test talents for High Integrity 28:31 How much does the Hyperliquid team sleep? 30:05 Employee Vesting Fears 31:41 Dealing with FUD 32:28 How Does Jeff Personally Handle FUD 35:02 Token "Buybacks" critics 37:20 Why Hyperliquid can't have Discretionary "Buybacks" 39:04 HyperEVM, explained Simply 40:00 Paradex Zodl (fka Zashi) 40:41 HyperEVM: Success so Far? 44:05 HIP-3, explained Simply 47:44 What makes Hyperliquid's approach different 48:19 Why Should People Care? 51:33 Bring All Finance On Chain 52:08 Why Is The Hyperliquid Approach Better? 53:47 Key Numbers showing that Hyperliquid Is Doing it right 59:01 What Has the Unit team demonstrated with spot trading on Hyperliquid in 2025 1:03:29 HIP-4: Outcome Markets 1:08:01 Trezor Sui 1:08:58 What does "Housing All Of Finance" mean? 1:10:51 Why Hyperliquid is not a crypto company 1:12:23 Why Does Hyperliquid have A Stablecoin USDH (Native Markets) 1:14:39 What Is Kinetiq & Why Does It Matter? 1:16:15 Why Is What HyperLend Is Building Important For HyperLiquid 1:23:39 Where did Fairness cost the most? 1:24:47 What should Hyperliquid be Remembered for? 1:25:24 Why should people stay in Crypto when there's an AI brain drain? 1:28:10 Closing Thoughts

MR SHIFT 🦁

575,445 views • 4 months ago

AMD CEO Lisa Su just doubled her own CPU forecast in 6 months and the reason is one word, agents (Save this). In November, she told investors the CPU market would grow 18-20% per year through 2030. she just revised that number : "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." And here's what changed, six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center and now the new ratio is closer to 1 to 1. Because agents aren't passive and they don't wait for a question. They work constantly on behalf of every employee, every customer, every process running inside an enterprise. Each of us could have 2, 3, even 5 or 10 agents running at the same time and that's not just more GPU compute but rather a completely different CPU demand curve that nobody had modeled at this scale. And the earnings backed it up. AMD posted $10.3 billion in revenue for Q1, up 38% year over year. Data center revenue hit $5.8 billion, up 57%, free cash flow tripled to a record $2.6 billion and Q2 guidance came in at $11.2 billion, 46% growth year-over-year. The Milk Road Pro portfolio was built around exactly this thesis, that agentic AI would create a massive, underappreciated CPU demand cycle that the market was sleeping on. That position is now up nearly 100%, if you want the full thesis, check out Milk Road Pro, link in bio.
3:19

AMD CEO Lisa Su just doubled her own CPU forecast in 6 months and the reason is one word, agents (Save this). In November, she told investors the CPU market would grow 18-20% per year through 2030. she just revised that number : "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." And here's what changed, six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center and now the new ratio is closer to 1 to 1. Because agents aren't passive and they don't wait for a question. They work constantly on behalf of every employee, every customer, every process running inside an enterprise. Each of us could have 2, 3, even 5 or 10 agents running at the same time and that's not just more GPU compute but rather a completely different CPU demand curve that nobody had modeled at this scale. And the earnings backed it up. AMD posted $10.3 billion in revenue for Q1, up 38% year over year. Data center revenue hit $5.8 billion, up 57%, free cash flow tripled to a record $2.6 billion and Q2 guidance came in at $11.2 billion, 46% growth year-over-year. The Milk Road Pro portfolio was built around exactly this thesis, that agentic AI would create a massive, underappreciated CPU demand cycle that the market was sleeping on. That position is now up nearly 100%, if you want the full thesis, check out Milk Road Pro, link in bio.

Milk Road AI

13,241 views • 2 months ago

E166: Gracy Chen @Bitget - Single Mom. 2200 Employees. Top-5 Exchange Gracy Chen is CEO of Bitget - a top-5 global crypto exchange with 25 million users and $20B in daily trading volume. She went from TV journalist to MIT MBA to co-founding a unicorn startup to running one of the world's largest exchanges. She's also one of the few female CEOs in crypto who's been told directly by investors they won't back women who are married but don't have kids yet. So she built her own path! Timestamps: 0:00 Introduction 1:34 Please Subscribe 2:00 The Only Bad Experience With When Shift Happens 4:37 How Do We Motivate More Women To Join The Crypto Space 6:13 Bitget’s Overall Work Environment Explained 7:06 How Gracy Manages 2,200 Remote Workers 10:09 Partnerships: Jupiter KAST 10:50 Who Is Gracy Chen? 13:04 Where Does Gracy’s Fire In The Belly Come From? 15:01 Why Is It Necessary To Go To The Best Schools 17:00 How Does Happiness Connect With Being The Best? 19:38 How Did Gracy End Up As A TV Host 23:45 The Hardest Part About Building A Business 26:10 Want-trepreneurs vs Entrepreneurs 28:52 Partnerships: Ethena Sumsub 29:52 Just Go and Do It 32:57 Why Investors Didn’t Invest In Gracy’s 2017 Project 34:55 Why Should People Care About Crypto Currencies & Blockchain 41:36 What Is Bitget, Explained Simply? 43:55 Gracy’s Breakdown Of Bitget’s Numbers 45:09 How Bitget Got Over 40% Of Its Management Team To Be Female 46:48 Where Women Excel In Terms Of Business Management Practice 50:45 Where Do Men Excel In Business Management Practice 52:20 Partnerships: Trezor Bitwise Sui 53:17 What Are Gracy’s KPIs As The CEO Of Bitget? 56:45 How Bitget Is Growing The Pie To Continue Expanding 59:31 What Can An Exchange Do To Capitalize On Stablecoin Growth 1:01:32 How Much Of Gracy’s Life Is Run On Crypto 1:03:40 What’s The Endgame For Bitget? 1:05:49 Why Can Anyone Become A CEO? 1:09:28 Running A 2,200 Employee Company While Taking Care Of A Kid 1:11:22 How Has Bitget Evolved Since Gracy Joined In 2022 1:13:25 Something Gracy’s Holding Onto That She Knows She Should Let Go Of 1:15:01 What She’ll Teach Her Son To End The “Failed Marriage Curse” 1:17:12 What The Voice In Gracy’s Head Tells Her In The Morning & Evening 1:20:05 One Thing Gracy’s Learned That You Can Take With You 1:24:31 Closing Thoughts
1:25:30

E166: Gracy Chen @Bitget - Single Mom. 2200 Employees. Top-5 Exchange Gracy Chen is CEO of Bitget - a top-5 global crypto exchange with 25 million users and $20B in daily trading volume. She went from TV journalist to MIT MBA to co-founding a unicorn startup to running one of the world's largest exchanges. She's also one of the few female CEOs in crypto who's been told directly by investors they won't back women who are married but don't have kids yet. So she built her own path! Timestamps: 0:00 Introduction 1:34 Please Subscribe 2:00 The Only Bad Experience With When Shift Happens 4:37 How Do We Motivate More Women To Join The Crypto Space 6:13 Bitget’s Overall Work Environment Explained 7:06 How Gracy Manages 2,200 Remote Workers 10:09 Partnerships: Jupiter KAST 10:50 Who Is Gracy Chen? 13:04 Where Does Gracy’s Fire In The Belly Come From? 15:01 Why Is It Necessary To Go To The Best Schools 17:00 How Does Happiness Connect With Being The Best? 19:38 How Did Gracy End Up As A TV Host 23:45 The Hardest Part About Building A Business 26:10 Want-trepreneurs vs Entrepreneurs 28:52 Partnerships: Ethena Sumsub 29:52 Just Go and Do It 32:57 Why Investors Didn’t Invest In Gracy’s 2017 Project 34:55 Why Should People Care About Crypto Currencies & Blockchain 41:36 What Is Bitget, Explained Simply? 43:55 Gracy’s Breakdown Of Bitget’s Numbers 45:09 How Bitget Got Over 40% Of Its Management Team To Be Female 46:48 Where Women Excel In Terms Of Business Management Practice 50:45 Where Do Men Excel In Business Management Practice 52:20 Partnerships: Trezor Bitwise Sui 53:17 What Are Gracy’s KPIs As The CEO Of Bitget? 56:45 How Bitget Is Growing The Pie To Continue Expanding 59:31 What Can An Exchange Do To Capitalize On Stablecoin Growth 1:01:32 How Much Of Gracy’s Life Is Run On Crypto 1:03:40 What’s The Endgame For Bitget? 1:05:49 Why Can Anyone Become A CEO? 1:09:28 Running A 2,200 Employee Company While Taking Care Of A Kid 1:11:22 How Has Bitget Evolved Since Gracy Joined In 2022 1:13:25 Something Gracy’s Holding Onto That She Knows She Should Let Go Of 1:15:01 What She’ll Teach Her Son To End The “Failed Marriage Curse” 1:17:12 What The Voice In Gracy’s Head Tells Her In The Morning & Evening 1:20:05 One Thing Gracy’s Learned That You Can Take With You 1:24:31 Closing Thoughts

MR SHIFT 🦁

104,369 views • 3 months ago