Should you invest in the S&P 500, or look for smarter ways to beat the market? Jason Hsu, Co-Founder of Research Affiliates ($159B AUM) and now CIO of Rayliant, explains why simply buying the index or asking “should I invest in ETFs” isn’t enough. In this episode, he breaks down smart beta vs S&P 500, systematic investing, and how factor investing strategies and fundamental indexing can deliver some of the best long-term investment strategies for investors who want to know how to beat the market beyond traditional index funds.Asian markets are less efficient than the US, Jason says. With higher retail speculation, governance risks, and volatility, opportunities open up for quant investing through Asian ETFs, China stock market investing, and emerging markets investing strategies that capture inefficiencies. As CIO of Rayliant, Hsu shows how his team builds factor-based portfolios across China, Japan, Korea, Taiwan, and other emerging markets to turn inefficiency into alpha.We also cover:- How Jason Hsu cofounded Research Affiliates, scaling systematic strategies to manage $159B AUM- Launching the PIMCO All Asset Fund in 2002 and bringing multi-asset investing strategies to retail investors- The origin of smart beta ETFs and why fundamental indexing offers a better alternative to cap-weighted indexes- How the tech bubble exposed flaws in traditional indexing and set the stage for factor investing strategies- Why governance factors and valuation discipline are especially important in emerging markets- Building Rayliant’s smart beta 2.0 products using multi-factor models and machine learning in investing- How factors in investing reveal the “nutrients” of a portfolio for long-term compounding- The difference between risk premia and behavioral biases as drivers of factor returns- Examples of behavioral investing mistakes in Asia and how professionals can capture alpha from retail flows- Why low-frequency quant strategies align better with pension funds and sovereign wealth funds than high-frequency trading (HFT)- The future of quant investing explained: machine learning, non-linear models, and portfolio construction- Jason’s career advice for young professionals navigating the hedge fund and asset management career path00:00 Intro01:42 Founding Research Affiliates and early startup days03:02 Launching the PIMCO All Asset Fund in 200204:26 Smart beta ETFs explained and how they started09:19 Spinning off Rayliant and focus on Asia10:26 Why Asian markets are less efficient than the US11:43 Opportunities from inefficiency and alpha in China13:38 Gambling analogy and retail speculation in Asia16:53 Liquidity challenges in smaller emerging markets20:41 Rayliant’s product offerings and smart beta 2.020:57 What factors reveal about markets and portfolios23:34 Risk premia vs behavioral biases in factors25:39 Governance, valuation, and smart money factors in Asia28:27 Using machine learning in Rayliant’s strategies34:05 Can discretionary managers still have edge today38:39 Poker, luck, and systematic investing advantages41:00 Future of discretionary managers and pod firms42:44 Are high-frequency trading firms sustainable long term46:22 Rayliant’s mission and value to society50:00 Career advice for young finance professionals53:14 Closing thoughts
Can you trade the stock market with AI? Yes: Renee Yao launched Neo Ivy Capital, a billion-dollar** AI hedge fund that uses AI in trading and investing to generate alpha. In this episode of Odds on Open, she explains how she built a quant hedge fund from scratch, scaling to over $1B AUM** with advanced AI hedge fund strategies that adapt to markets in real time and show how to trade stocks with artificial intelligence at scale.Unlike traditional firms that rely on armies of quant researchers and static machine-learning models, Renee (who used to work as a QR Analyst at Citadel and Portfolio Manager at Millennium) reveals how machine learning in trading has evolved into true self-learning AI. She breaks down why most funds still depend on crowded factor bets, and how her fund’s approach delivers uncorrelated returns — a real edge in the hedge fund career path and a blueprint for the future of systematic investing.**Note: According to a recent Form ADV filing, Neo Ivy Capital oversees about $1.02 billion in assets under management. This figure represents total regulatory AUM, which is broader than the ~$313M reported in 13F-disclosed securities and may include additional holdings or leverage.We also discuss...- Citadel hedge fund strategy and risk management lessons after 2008- Why diversification and breadth of edge matter in a quant hedge fund explained- How Neo Ivy uses AI in trading and investing to generate uncorrelated returns- Why machine learning in trading has evolved into true self-learning AI- The three barriers to entry for AI hedge funds: modern AI, infrastructure, portfolio design- Why large funds rely on crowded factor bets while Neo Ivy delivers pure alpha- How fund size impacts scalability and alpha opportunities- What it’s like moving from Citadel and Millennium to founding a fund- How to start a hedge fund and build infrastructure from scratch- How self-evolving AI models adapted during COVID market shocks- The role of modern tools like LSTMs and transformers in AI hedge fund strategies- Career and life lessons from the hedge fund career path and staying disciplined00:00 Intro01:14 Renee Yao’s journey to founding Neo Ivy02:28 Joining Citadel after the financial crisis04:13 Hedge fund diversification and breadth of edge04:45 Why Neo Ivy trades with AI strategies07:50 How self-learning AI adapts to markets09:40 Causation vs correlation in AI hedge funds10:33 Barriers to entry for AI hedge funds14:47 Risks of crowded factor bets explained16:39 Why big funds struggle with AI talent17:29 From PM at Citadel to hedge fund founder18:47 Challenges of launching a quant hedge fund20:25 Biggest constraint for AI hedge fund startups22:08 How AI hedge funds adapted during COVID24:04 Modern AI tools used in quant trading25:13 Building hedge fund infrastructure from scratch26:26 Career advice for aspiring quants and traders28:55 Adapting career goals to changing job markets31:57 Life lessons from trading and risk management32:51 Staying disciplined while running a hedge fund34:38 Obsession and belief in AI hedge funds35:41 Closing thoughts on hedge funds and life
In this episode of Odds on Open, Ethan Kho sits down with Joe Mezrich, Founder of Metafoura LLC and former Managing Director at Nomura Quant Strategies, to reflect on nearly 40 years in quant finance. Joe’s career spans the early days at Salomon Brothers—where he helped pioneer factor models, risk modeling, and even early machine learning in finance—through senior sell-side research roles at Morgan Stanley, UBS, and Nomura.Joe shares how the sell side effectively built modern factor investing, why models like the Barra risk model failed in crises such as the Tech Bubble (2000) and the Quant Crisis (2007–08), and how market-neutral strategies and algorithmic trading continue to shape today’s buy-side. He also explains why interpretability, from CART decision trees to today’s LLMs for trading, is critical for robust risk management.We cover:- Origins of quant finance on the sell side at Salomon Brothers.- Early factor models, the Barra risk model, and portfolio risk modeling.- Use of robust statistics and CART decision trees in machine learning for finance.- Why risk models failed in the Tech Bubble (2000) and Quant Crisis (2007–08).- Growth of market-neutral strategies and interaction between sell-side research and the buy side.- Crisis lessons: liquidity concentration, model speed, and explainability.- Evolution of factor investing into overlays and ETFs.- How quant researchers balance complexity vs. interpretability with LLMs for trading.- Role of alternative data, point-in-time datasets, and data visualization in alpha.- Wall Street culture: Liar’s Poker-era Salomon, Morgan Stanley, UBS, Nomura.- Impact of interest rates, earnings vs. sales growth, and macro regimes on factors.- Sustainability of multi-manager pod shops (Citadel, Millennium) and implications for quants.- Career lessons: curiosity, humility, and finding beauty in quant models.Whether you’re a quant researcher, an aspiring algorithmic trading professional, or an allocator seeking to understand systematic funds, give this a listen.00:00 Intro and Episode Overview00:46 Origins of Quant Finance at Salomon Brothers02:56 Early Factor Models and Barra Risk Model05:51 Robust Statistics and CART Decision Trees08:58 Machine Learning in Finance 1990s Experiments12:06 Why Risk Models Failed in Tech Bubble15:31 Lessons from the 2007 Quant Crisis18:51 Rise of Market Neutral and Sell-Side Research22:26 Evolution of Factor Investing to ETFs26:01 Balancing Complexity and Explainability for Quants29:16 Alternative Data and Point-in-Time Datasets32:46 Wall Street Culture Salomon Morgan UBS Nomura38:08 Interest Rates Macro Regimes and Factor Drivers41:51 Are Multi-Manager Pod Shops Sustainable?46:04 What Makes Exceptional Quant Researchers Last49:26 Curiosity Humility and Risk Management52:56 Finding Beauty in Quant Models and Data56:16 Final Lessons from 40 Years in Quant Finance
Former Cargill Global Trading & Risk Management Director Kristine Engman Hochbaum explains how commodities trading strategies and quant trading strategies really work. On Odds on Open, she breaks down the sources of trading edge and alpha generation in today’s commodities markets, from agriculture trading to energy and metals.We cover why physical vs. financial commodities trade differently, how systematic trading and traditional players (hedge funds vs. ABCDs) approach markets, and the lessons of the 2022 commodities boom. With Ethan, Kristine unpacks real-world risk management strategies around delta, liquidity, forecast accuracy, and headline shocks — from Russia–Ukraine war trading impact to weather, inflation, and supply chain disruptions.Key topics in this episode:- How relationships and brokers create trading edge in commodities- Why capital allocation matters for building positions- Headline risk and the impact of social media on commodity prices- Managing liquidity risk and delta in physical books- Weather risk and forecast accuracy in agriculture and energy trading- The role of supply chain disruptions in commodities markets- Differences in hedge fund vs. ABCD trading strategies- Lessons from the Russia–Ukraine war trading impact and inflation and commodities- Stories from real trades: negative power prices and long oil during crisis- Career lessons: what makes a great trader and how to keep learning commodities marketsAlong the way, she shares what separates good traders from great ones, why inflation and commodities are inseparable, and how to keep learning in fast-changing markets. If you’ve ever wondered what makes a great trader or how to start learning commodities markets, this episode is for you.00:00 Intro01:15 Edge in commodities trading? Relationships, capital, information04:40 Commodities market efficiency, information flows, and AI08:32 Hedge funds vs. ABCDs, commodity trading strategies13:34 When commodities outperform equities, the 2022 boom17:45 Headline risk, social media impact on markets19:08 Risk management strategies in physical commodities trading26:14 Probability, forecasting, and scenarios for trading decisions31:00 What makes a great commodities trader today37:53 Contrarian trading strategies, alpha generation in commodities42:24 Russia–Ukraine war impact on commodity markets, trading45:35 Life and career lessons from commodities trading51:30 Careers, uncertainty, and learning in commodities markets
This week, Ethan Kho sits down with Dr. Ernest P. Chan — former quant at Millennium and Morgan Stanley, and now founder of PredictNow.ai and QTS Capital Management. Ernie is one of the best-known voices in quant finance, author of Quantitative Trading, and a pioneer in systematic trading strategies.
We cover:- When machine learning trading models work in markets — and when they fail- Why financial markets suffer from data sparsity, and how regime shifts and black swan events in finance break models- How quants use AI in trading for risk management and portfolio optimization- The promise of LLMs for financial markets and how generative AI can overcome data scarcity- Semi-supervised learning explained, with real examples from analyst reports and Fed speeches- Where quants can still find alpha generation when new technologies become widely available- How PredictNow helps banks and hedge funds apply AI risk management at scale- Lessons from launching QTS Capital and running independent quant trading strategies such as crisis alpha- The role of alternative data in hedge funds and what actually drives performance post-2008- What it was like working alongside quants at Millennium, Morgan Stanley, Credit Suisse — and how Renaissance Technologies influenced Ernie’s career- The traits that make a great quant, and why creativity still matters in quantitative trading strategies= Advice for students and professionals entering quant finance in the age of financial big data and generative AI- How to spot overfitting in backtests and apply the scientific method in systematic trading strategies- Why risk awareness separates long-term success from blow-ups in post-2008 quant strategies
In this episode of Odds on Open, Ethan Kho sits down with Vinesh Jha, founder of Extract Alpha and former director of PDT Partners, to unpack lessons from the 2007 Quant Quake and how systematic investors can adapt in today’s crowded landscape.We cover:- What really happened inside PDT Partners when the firm lost $500M during the Quant Quake- Why so many quant hedge funds blew up in 2007 — and the key financial crisis lessons still relevant today- Inside the culture at PDT Partners vs the siloed world of other hedge funds- Why Vinesh Jha left the buy side to start Extract Alpha — and how alternative data reshaped quant finance- The rise of earnings transcript models, analyst accuracy signals, and Estimize’s crowdsourced forecasts- Will today’s LLMs and NLP models in finance get commoditized like old factor strategies?- The trade-offs between running a hedge fund and building a data company- How smaller systematic funds can still compete with giants like Citadel, Millennium, and DE Shaw- What it’s really like to work as a quant — and the traits that make a good quantBonus: - How quant hedge funds find alpha using alternative data and NLP- How hedge funds use earnings expectations and post-earnings drift to trade- What lessons can quants learn from market crashes and black swan events?00:00 Intro01:00 Inside PDT Partners during the Quant Quake05:11 How quants decide when models fail08:49 Culture at PDT vs other hedge funds10:38 Why Vinesh founded Extract Alpha15:25 Financial crisis lessons: crowded quant trades16:20 Will LLMs and NLP in finance get crowded?18:53 Best alternative data sets for alpha24:54 Do Estimize crowdsourced forecasts make money?28:19 Can buzzwords like AI predict returns?32:02 Why Vinesh didn’t start a hedge fund35:37 How quants should reinvent mid-career38:51 AI disruption vs creativity in quant finance40:48 Can small funds compete with Citadel, Millennium, DE Shaw?43:30 What makes a good quant stand out46:54 Closing thoughts on longevity in quant financeWhether you’re deep into quant finance, researching hedge fund strategies, or simply curious about what makes a good quant, this conversation offers rare insight into how edge is found—and lost—in modern markets.
What’s it really like working as a quant in fundamental research at Two Sigma—and how will AI, LLMs, and agentic workflows change quantitative trading strategies? Bill Mann, former Two Sigma fundamental researcher and founder of Harmonic Insights, joins Ethan Kho to break down how hedge funds build edge from widely available data and why “hacker” creativity still matters in systematic investing.Bill shares insights from AQR and Two Sigma, including how proprietary data pipelines become alpha generation engines, how to avoid crowding in popular factors, and what makes a great hedge fund strategy and the best work environment for quants. He also explains how LLMs, algorithmic trading, and automated research pipelines will transform research, engineering, and trading—and why mastering data engineering for quant finance is critical for junior quants.We answer questions like:– How do hedge funds find edge using fundamental vs. quantitative analysis?– What is point-in-time data and how does it prevent look-ahead bias?– How do proprietary data pipelines create alpha generation?– How can quants avoid crowding in value factors?– What’s it like working as a quant? What’s the culture like inside Two Sigma’s quantitative trading strategies team?– How do LLMs and AI agents change systematic investing workflows?– Which quant research tasks will be automated first?– What skills will junior quants need in the AI era?– How should aspiring quants practice creativity and problem-solving?– How does algorithmic trading intersect with data-driven investing?– What role will high-frequency trading (HFT) play in the future?– How do fintech startups work with hedge funds?– What’s the *New Barbarians* podcast about?#quantfinance #twosigma #aiinfinance #largelanguagemodels00:00 Intro00:57 Life as a Quant at Two Sigma02:36 Finding Edge in Fundamental Data07:04 Creating a Creative Quant Research Culture11:19 How LLMs Change Quantitative Trading15:52 AI’s Impact on Junior Quant Careers22:56 Using AI Tools for Learning23:57 Harmonic Insights: Advising Fintech Startups30:47 The New Barbarians Podcast Explained33:26 Crypto and Market Makers vs TradFi34:54 Career Advice for Aspiring Quants38:46 Final Takeaways
Can you analyze social media for investment decisions? How do hedge funds use Reddit posts, earnings calls, and SEC filings to find alpha? What’s the role of LLMs for financial analysis in 2025? Chris Kantos, Head of Quantitative Research at Alexandria Technology, joins us to explain how the buy side uses natural language processing (NLP), AI for investing, and text-based sentiment data to generate AI alpha signals across all asset classes—from equities to commodities.We dive deep into how Alexandria builds quantitative trading strategies from unstructured data like Reddit posts, news articles, earnings calls, and 10-Ks. Chris explains how most hedge funds get NLP wrong, why Alexandria’s document-specific classifiers give them an edge, and what makes a good social media for stock analysis dataset in a crowded and noisy world. He also tackles the myth of data commoditization and explains why alpha decay isn’t always inevitable.We answer questions like:– How do hedge funds use Reddit and social sentiment in trading?– What makes a good NLP model for financial data?– Has alternative data become commoditized?– What separates FinBERT from other finance-specific LLMs?– What’s the best way to train a sentiment model for earnings calls?– How is ChatGPT used in finance and investing workflows today?– How do professional quants cut through the noise on Twitter, Reddit, and X?– What’s the future of AI in systematic investing?– How does news sentiment impact trading strategies?– How do hedge funds use alternative data beyond equity alpha?– How do professionals use Reddit data for stock analysis without getting fooled by noise?– What’s the best path to become a quant researcher today?– What skills and experience matter in quant finance careers?Chris also shares quant finance career advice, why he left risk modeling for alt data, and what really happened in the office when Bernie Madoff got caught.
Financial markets are a game, says Grant Stenger. So how do you win in the financial markets? Grant, a former Jane Street intern and current crypto founder, believes markets are the most competitive game on earth—and he's been training to beat them since high school.From card counting in middle school to landing a hedge fund internship at QuantRes before university, Grant shares how a lifelong obsession with games, poker, and mathematical edge led him to build Kinetic—a decentralized crypto exchange and DEX aggregator on Solana trading millions of tokens.We talk about his quant trading internship experiences at QuantRes, Numerai (a crypto hedge fund), and Jane Street, and why he left it all to start his own crypto founder story building next-gen trading infrastructure.He breaks down:- His Jane Street internship experience and why they make you play Figgy- Why gambling and trading the market are similar- Poker and trading—and how poker is better prep than chess for decision making under uncertainty- Numerai’s bet-staking model and encrypted data thesis- How decentralized crypto exchanges actually make money- Trading on Solana vs Ethereum and why Solana is built for high-frequency crypto trading- Building a platform that can support 10 million tokens- Why being early in a new asset class gives you an edge—and how that edge resembles the one in gambling vs trading- And how he figured out how to get a hedge fund internship in high schoolWe discuss the collapse of FTX, the fall of Sam Bankman-Fried, the risks of centralized cryptocurrency exchanges, and why trading isn’t just gambling—but a game of edge in both gambling and trading.
How do you become a solo quant trader and build your own systematic trading business? Robert Carver, ex-head of fixed income at Man AHL—a $63 billion systematic trading hedge fund—shares how he went from managing institutional capital to becoming an independent, full-time quant trader.He reveals the key skills, mindset, and tools needed to succeed in quant trading without working at a big firm, how to create your own quant trading strategies, and why a PhD isn't required to break into systematic trading. Also, he shares how to manage risk and how he runs 200+ futures trading strategies as a solo trader with a small account.He breaks down:- How to become a quant trader without working at a hedge fund- The skills and background needed to succeed in quant trading without a math degree- Whether STEM is required for quant jobs- Why the Sharpe ratio is flawed and what to use instead- What separates top performers: traits of successful quant traders- How to build a quant trading career path solo vs going the institutional route- What investing strategies to use- The best quant trading strategies for individual traders- How to properly backtest trading strategies without overfitting- How to deal with alpha decay and determine when a strategy stops working- Inside the research culture of a top hedge fund strategies team- How to get into hedge funds in today’s competitive environment- What it’s like to work at a quant fund versus a more traditional hedge fundPlus: why most quant trading strategies rely on public, simple rules—and how to apply them profitably with a skeptical mindset.Robert is also the author of several acclaimed trading guides, including Systematic Trading, Advanced Futures Trading Strategies, Smart Portfolios, and Leveraged Trading. They’re what got Ethan (our host) into quant finance to begin with.00:00 Intro00:45 How to Handle Losing $1 Billion04:05 What It’s Like Working at Man AHL06:39 Why Quant Funds Hire STEM Grads08:43 High Frequency vs. Low Frequency Quants10:44 The Most Important Trait in Quant Research12:53 Is Sharpe Ratio a Good Metric?16:41 Geometric Returns vs. Sharpe Ratio17:45 How to Avoid Overfitting in Backtests21:28 Dangers of Implicit Fitting in Models23:02 How Robert Deals With Alpha Decay25:29 Robert’s Current Quant Trading Strategies28:45 Should You Trade Independently or Join a Fund?31:19 Why Trading Your Own Capital Is Hard32:09 Does He Look for Market Inefficiencies?33:45 His Biggest Trading Innovation: Execution35:45 How Hedge Funds Approve New Strategies39:27 Will Big Quant Firms Dominate Forever?42:44 Pod Shops vs. Collaborative Quant Culture43:48 Final Thoughts + His Books on Quant FinanceFind links to Robert's books here:1. http://www.systematicmoney.org/systematic-trading/2. http://www.systematicmoney.org/smart/3. https://www.systematicmoney.org/leveraged-trading4. https://www.systematicmoney.org/advanced-futures
What is edge in trading—and how do hedge funds and quant traders find edge in 2025? According to Agustin Lebron, former quant trader at Jane Street and author of The Laws of Trading, edge is what separates average traders from those who thrive at a top hedge fund. But today, finding edge requires more than just a good model—it demands judgment, adaptability, and a deep understanding of how quant trading firms actually operate.In this episode, Agustin breaks down exactly how quant trading works, how elite firms like Jane Street maintain their edge, and how quant funds make money in both calm and chaotic markets. He shares hard-earned insights on how to become a quant trader, the realities of trading internships at hedge funds, and what it really takes to get hired at Jane Street.He breaks down:– What is edge in trading and how to know if you have it– How elite quant firms like Jane Street develop and defend edge– Why edge is statistical—but also deeply judgment-based– How to get hired at Jane Street and succeed in a Jane Street intern experience– What makes a good trader (hint: it’s not just math)– Why some trading models fail during market crises– How small quant shops compete with large firms– How to stand out in a hedge fund or quant internship– Should I become a quant? Questions every student should ask– Why quant trading firms 2025 will look different—and whether AI will replace traders in some rolesAgustin also shares how young people can find their personal edge in a world transformed by AI, automation, and rising inequality. His advice? Don’t chase status. Follow curiosity. Learn where real value comes from.
If venture capital underperforms the public markets, is it still worth the investment? For Avik Ashar, the answer is yes—but not for the reasons you think. Avik, a Principal at Riverwalk Holdings, an India-focused VC firm, argues that most people misunderstand how venture capital works and how to fairly evaluate VC fund performance.Venture capital, he explains, isn’t just about chasing unicorns or short-term IRRs. It’s a strategic investment tool, especially in Asia, used by family offices and conglomerates not only for returns but also for M&A, R&D, and market expansion. In markets like India, venture capital is helping industrial groups future-proof their businesses while tapping into innovation. He also highlights how India’s maturing public markets and mutual fund sector are making early-stage investing and startup exits far more viable than in places like Southeast Asia, where liquidity is still limited.He breaks down:– Why VC returns vs public markets often look misleading—unless you know how to analyze them– How family offices in India are using venture funding for strategic acquisitions– Why M&A is finally taking off in Asia—and what that means for founders– The key differences between venture capital in India, Southeast Asia, and the U.S.– Why India’s public markets are becoming a critical exit path– How startup exits work in markets without strong IPO pipelines– Why Southeast Asia’s VC boom from 2014–2018 underperformed– What Gen Z needs to understand about building in a noisy, AI-native world– How venture capital vs private equity differs in terms of outcomes, strategy, and timelinesHe also shares an important reminder for our age of endless short-form content: “The most expensive thing you can give today isn’t your time—it’s your attention.”00:00 Intro00:33 Why invest in venture capital?01:01 How venture returns work03:01 Venture as an R&D and acquisition pipeline04:11 The outlier nature of VC returns05:11 Why family offices invest in venture05:52 Examples of conglomerate acquisitions in India09:43 Differences in VC ecosystems: US, Singapore, and India16:29 Do family-backed VCs perform better in India?16:50 Riverwalk Holdings’ experience in India18:22 Maturity of India’s financial ecosystem for startups23:59 Where will venture gains come from?27:51 Indian conglomerates embracing startups28:52 Challenges of building companies in Asia30:22 Advice for young people in an uncertain world32:44 Tech’s share of the US market cap38:04 Staying focused amidst noise40:57 Advice for recent graduates42:24 Outro
Is venture capital dead? Not for Guy Horowitz, who boasts 20+ years in the VC ecosystem, holding the title of partner at firms like DTCP, 33N, and ESH.vc. In this episode of Build to Last, we unpack the changing face of early-stage investing and startup funding—from the 2000s hardware boom to the rise of software unicorns, and now the new frontier: AI-first companies. Guy shares battle-tested insights on identifying founder-market fit, navigating VC cycles, and why understanding capital formation is just as important as finding the next big tech innovation.We also dive into the future of work, education, and the middle class. What happens when generative AI and automation replace white-collar jobs faster than traditional schools can adapt? What happens when AI agents arrive at desks, offices, and boardrooms? With three kids of his own, Guy reflects on raising future-proof talent and what young people today really need to succeed in a world defined by machine learning, venture-backed disruption, and rapid technological change. Spoiler: it's not just about coding bootcamps—it's about curiosity, adaptability, and a willingness to learn from people who aren’t like you.Some key takeaways:– How venture capital has changed in the past two decades– Why "great ideas" don’t matter without the right founders– What makes a great startup exit– The question top VCs like Guy Horowitz ask before writing a check– What NOT to say in a pitch meeting– How DTCP became a breakout fund backed by Deutsche Telekom– Why today’s job market is rigged (and how you can stand out anyway)– Whether AI will make human investors obsolete– Why most white-collar jobs are more automatable than we think– Is now a good time to start a fund? Guy’s honest take– Advice for young people unsure about their futureAlso in this episode, we discuss how to identify REAL startup talent (even if they’re mean) and what makes a great VC (beyond capital). Subscribe for more conversations with founders, builders, and leaders.#venturecapital #investing #ai 00:00 Podcast Intro 00:43 How venture capital has changed 01:55 Guy’s early career at Gemini 05:46 Lessons from being cocky too early 09:15 What makes a great VC investor 12:49 How Guy evaluates founders 18:54 What not to say in a pitch 21:38 The story behind DTCP 27:08 Fundraising success and growth 30:11 Is venture capital dead? 36:41 Raising kids in the AI age 43:44 What happens to the middle class?51:12 Advice to young people 52:42 Outro
How will AI affect education? Minh Tran has a lot to say about the future of learning in the age of large language models (LLMs) and generative AI. As the COO of GoodNotes—one of the world’s leading AI-powered note-taking apps—he’s at the forefront of how AI is changing the education system. For Minh, the future of learning is already here: “We need to rethink WHAT we teach, not just HOW we teach,” he says.Before working in edtech, Minh was the Executive Director at Education First, the global learning company. He also founded Bloom Academy in Hong Kong, literally building a K–12 school FROM SCRATCH during COVID. With experience in both traditional and startup education organizations, Minh shares why AI-first schools are better positioned to thrive, while schools that don’t adapt to AI will fall behind.Some key takeaways:– How Minh started a school from scratch during the pandemic– What today’s students *actually* need to learn in an AI-first world– Why most schools are failing to adapt to ChatGPT and generative AI tools– How Goodnotes became a tech unicorn through remote-first culture– How Goodnotes is winning the tech talent war through flexible work arrangements– The importance of mentorship– How to find a mentor– Why AI experimentation, curiosity, and play are key to raising future-ready kids– How to pivot from education to tech (and how others can break into tech from different industries)– What Gen Z can offer senior leaders at the workplaceWe also dive into Minh’s advice for young people chasing unconventional careers and the secret to building a career with impact. He emphasizes this: put in the hours, and if you’re privileged enough to follow your passion, just do it.Subscribe for more conversations with founders, builders, and leaders.
Is a finance degree still worth it if you want to become a quant? In this week’s Build to Last episode, quant researcher and YouTuber Dmitri Bianco explains why he calls his own finance undergrad “a big mistake” — and why most quant roles today demand far more math, statistics, and programming than students realize. (The short answer to that first question is: no.)Dmitri shares how he went from a misguided finance major to Head of Quantitative Research at Agora Data, a fintech company — after being rejected from top financial engineering programs and racking up $160K in student debt. He now runs a YouTube channel with nearly 60,000 subscribers, where he shares real insights on quant careers, financial engineering, and navigating the industry.breaks down how the world of finance is evolving, and how new grads can break in. A few key takeaways:— Why most finance degrees don’t prepare you for quant roles— How buy-side firms use prestige to exploit junior talent— Why the traditional finance hiring model is broken— How passion and problem-solving beat credentials in quant interviews— Practical advice for students entering quant and finance roles today— What makes a company a great place to work in quant finance— Why career satisfaction matters more than titles or payHe also shares thoughts on AI’s impact on quant careers, what it’s like being a YouTuber with a dedicated hate page (yes, really), and how to build a career you can be proud of (even in a tight job market).#QuantFinance #FinancialEngineering #financejobs Check out Dimitri's channel: https://www.youtube.com/c/dimitribianco
A second passport isn’t just for shady billionaires or globalist elites — it’s actually a legal, fast-growing strategy used by thousands of wealthy families to unlock global freedom. It’s also the core of a booming global industry that Krista Victorio, Partner at Orience, knows inside and out.On this episode of Build to Last, Krista explains how citizenship-by-investment works — and why families from China, Russia, the Philippines, and (most surprisingly!) the United States are buying their way into second passports more than ever before. She also shares how Orience, an investment migration firm, is capitalizing on the rising demand for global mobility. For Krista, investment migration is one of the few ways you can “diversify the accident of birth.” In the episode, she breaks down:- How different programs work — from Caribbean citizenship schemes and one-time-payment Maltese passports to Golden Visas in Europe and the U.S. EB-5 visa- Why investment migration is exploding post-COVID- What wealthy people *really* want from a second passport (Hint: it’s not just taxes)- The top programs in Portugal, Spain, Greece, Malta, and more- Why the traditional career ladder is dead- Why more young people should rethink in an increasingly challenging job market
At no other point in history the #corporate and #academic worlds been so deeply at odds. The former sees the latter as useless bureaucrat over-thinkers, while the latter sees the former as money-driven opportunists placing profit over principle. But Dr. Davide Sola is rallying against this. His experience spans both the #boardroom and the #classroom. Early into his #career, he worked at #McKinsey while simultaneously enrolled as a #Enterprise #Economics PhD student at the University of Torino. "There’s this idea that academic means rigorous but irrelevant. I don’t believe that. You can be both," he says. The mission to bring #academia and #corporate closer together is still something Davide continues today, now almost two decades since his time at McKinsey and Torino. He's currently the #CEO of Strategy in Action ("a strategy solution built to give C-Suite [executives] what they need") and a #business professor at ESCP Europe.Speaking with Ethan on the Build to Last podcast, he discusses the future of #consulting, how to #scale, and the #strategy behind a generations-lasting business.
The startup world is dog eat dog. “Move fast and break things,” so goes the Silicon Valley adage. For Illai Gescheit, this does not have to be the case: “You can still move fast and break things and be a kind person as well,” he says. The unconventional mantra is plastered on his LinkedIn headline. “Kindness is my strategy,” it reads. Indeed, it’s a practice that Illai must commit to in his day-to-day work as a startup advisor. He’s one of the founders of Gescheit & Partners — a global multi-stage venture advisory firm working with bold, visionary and resilient founders. Through Gescheit & Partners, Illai actively nurtures startup founders through the firm’s venture equity firm. He also serves on the boards of Atlas Capital and Barka Fund. Illai shares with Ethan lessons from years as a serial entrepreneur, and in the past working as a Venture Partner at Siemens Energy Ventures and Entrepreneur-in-Residence at BP. On this episode of Build to Last, Illai shares his first journey into the tech industry, the difference between corporate hires and startup hires, and (of course) why kindness is a great business strategy.
We live in a world of noise. Signals are in abundance. There is too much data, too much fuzz and fluff that makes decision-making a real challenge. It’s issues like these that founder Ryan Manuel is trying to solve. As the founder and CEO of Bilby, his main goal is to use AI and machine learning to automate regulatory and government analysis, turning fuzzy policy signals into real, actionable insight for traders and investors. Ryan’s regulatory analysis software covers the regions where government policy is the most nebulous, like China and India. Prior to his foray into the startup world, Ryan was also an Associate Professor at the University of Hong Kong and a Rhodes Scholar at Oxford University.
On the Build to Last podcast, Ryan discusses with Ethan some key trends in AI: how AI is redefining the startup playbook, his expert opinion on Chinese vs. American AI, and “it’s never been a better time to be nimble…” to be the “little guy.” He also shares his views on what young people should do to achieve success.
Every week, it seems that there’s always a new set of questions we’re pondering when it comes to AI: How will AI reshape the job market? How close are we to Artificial General Intelligence? Dr. Seth Dobrin, a General Partner at One Infinity Ventures and the founder of Silicon Sands, is here to give some clarity on these issues. What sets Seth apart in the world of AI investing is his commitment to building tech responsibly. Through Silicon Sands (which is a venture studio under One Infinity), Seth only backs startups that deliver strong returns while meeting top ethical standards.
In an AI space saturated with techno-optimists and accelerationists, Seth has a number of controversial opinions: that LLM capabilities are fully maxed out and that small tech companies are the future of AI industry. On the most recent episode of Build to Last, he discusses with Ethan these hot takes, and where the world is headed.