My guest this episode is Ben Wellington, Head of Complex Feature Engines at Two Sigma.In a modern quant process, one might argue that edge can live in three places: the data you can get your hands on, what you do with that data, and how you forecast from it. Ben lives squarely in the middle layer — feature generation — which also happens to be the place AI is reshaping fastest.So that's where we spend our time. We start with what a "feature" even is, and why, as raw data gets commoditized, the edge increasingly comes from what you build out of it. Then we follow the thread running through the whole conversation: large language models. Ben has a line I keep coming back to — that anything can be language now — and we trace what that unlocks, and whether making feature creation this cheap just democratizes the edge away.We close on what a quant starting out today should be building toward, and which skills compound most in a career where AI is the dominant tool.Please enjoy my conversation with Ben Wellington.

My guest this episode is Peter Hecht, co-head of the North America Portfolio Solutions Group at AQR.Portable alpha is drawing serious attention right now. Long the province of large institutions, it's now being wrapped into mutual funds and ETFs — which means, for the first time, advisors and the individual investors they serve get a seat at the table. So I wanted to sit down with someone who's been thinking about the idea for almost two decades.We start with the basics — what portable alpha actually is, and why Peter insists the core concept is funding, not leverage. From there we work through the real risks, the cautionary tale of 2008, and the design space: how you organize the alpha and the beta, which overlays to choose, and how to combine them without smuggling in hidden tail correlation.Then we turn to what matters most for this new audience — sizing, lifecycle, rebalancing inside a daily-liquid wrapper, and the gap between the line-item experience and the actual portfolio impact.Please enjoy my conversation with Peter Hecht.

My guest today is John Gu, founder and CEO of Caladan, one of the most active market makers in crypto and a firm that has provided liquidity to more than 200 token launches. John's path runs through MIT, AlphaSimplex, Citadel's principal strategies group, and Tower Research before landing in Singapore at the dawn of the ICO era — where what started as a trade on the kimchi premium became the foundation for one of the most active liquidity providers in digital assets.The thesis of our conversation is what I'll call the cold start problem. In traditional markets, every newly listed stock arrives with scaffolding already in place — a designated market maker, a reference price, a universe of comparables, and decades of regulatory infrastructure. Crypto inverts that. A new token can launch with no orderbook, no comparables, and no clear demand curve. Someone has to quote a two-sided market into that void, and how they do it shapes whether the asset becomes a real, tradable thing — or a graveyard of wide spreads and stranded liquidity.John and I dig into how you bootstrap liquidity from zero, how the quoting playbook evolves as a market matures, the economics of token market making contracts, and how that same infrastructure now bridges into structured products and treasury solutions for token foundations.Please enjoy my conversation with John Gu.

In this episode I speak with Faheem Osman, Managing Director and Global Head of QIS Structuring at Macquarie Group.Faheem has spent nearly two decades inside major investment banks — first at Citi, where he spent about ten years on the commodities trading and structuring side, and now at Macquarie, where he's built out their cross-asset QIS business. Commodities, though, remain firmly in the DNA of what Macquarie does, and it's where Faheem cuts his teeth.We spend the bulk of our conversation exploring commodity QIS as a distinct and, in Faheem's view, underappreciated source of systematic returns. Faheem walks us through strategies like curve carry and index congestion, explains why commodities have historically delivered some of the highest risk-adjusted returns of any asset class in QIS, and makes the case for why these premiums haven't simply been arbitraged away. We also dig into commodity volatility selling, the shift toward weekly options, and close with a timely discussion on what the explosion of 0DTE options means for the vol risk premium more broadly.Please enjoy my conversation with Faheem Osman.

Today, I’m speaking with Richard Craib, the CEO and founder of Numerai.If you’ve heard of Numerai before and thought of it as an interesting experiment at the intersection of data science and crypto, it’s worth updating that mental model. Over the last few years, Numerai has quietly grown from roughly $60 million in assets to over $600 million. JPMorgan has invested and secured $500 million of capacity, and Numerai recently raised a Series C at a $500 million valuation led by top university endowments. This is no longer a toy project. It is a real, institutional-scale market-neutral hedge fund with a very unconventional engine.In this conversation, we go deep into how Numerai actually works. Richard walks through the core insight behind Numerai’s design: that crowd-sourced alpha only works if incentives are aligned, not just participation. Simply opening up data and ranking models creates incentives to game the system, not to produce durable signals. That realization led to the introduction of the Numeraire token. By forcing researchers to stake real capital behind their predictions, Numerai shifts from a leaderboard-driven experiment to a capital-weighted signal engine. Instead of rewarding activity, the system rewards conviction, accountability, and uniqueness, creating a self-filtering model that naturally reduces noise and discourages the behaviors that caused earlier crowd-sourced platforms to fail.We also talk about portfolio construction and risk management, including how Numerai neutralizes common factor exposures, what went wrong during the 2023 drawdown, and how those lessons reshaped their approach to diversification and concentration. Finally, we look forward, covering the limits of crowd-sourced modeling, the next frontier for Numerai’s research ecosystem, and how Richard sees AI agents reshaping model development.Please enjoy my conversation with Richard Craib.

Today, I’m speaking with Ruslan Fakhrutdinov, the founder of Extended, a decentralized perpetual futures exchange.Ruslan is the fifth perpetual futures exchange founder I’ve had on the podcast, and that’s very intentional. Flow continues to move toward these platforms, and while trading perps can feel familiar to anyone coming from centralized or traditional exchanges, the way risk is absorbed and resolved under the hood for decentralized exchanges can be very different. In this episode, we go deep on the design of perp DEX vaults and the role they play as a liquidity and risk backstop for the entire exchange. Ruslan walks through how platforms choose between protecting system solvency, safeguarding vault depositor capital, and managing trader losses, particularly during stress events. We also discuss how settlement finality, governance intervention, and liquidation design determine where losses ultimately land.We spend time on Extended’s introduction of vault shares as collateral, why that design can be powerful, and the new risks it introduces if boundaries aren’t explicit. Ruslan lays out the risk-management waterfall: when the vault steps in as a counterparty, when it refuses additional exposure, and when traders are pushed into forced deleveraging or auto-deleveraging instead.We close by connecting this framework to Extended’s next phase, expanding cross-asset margin, and what it takes to design a system that still behaves predictably when markets break.Please enjoy my conversation with Ruslan Fakhrutdinov.

Today, I am speaking with Angana Jacob, Head of the Research Data group within the Enterprise Data business at Bloomberg.We talk about Angana’s career path through quantitative research and data platforms, and how the industry has evolved from a world dominated by bespoke models and backtests to one where many models have become increasingly commoditized. A central theme of our conversation is the idea that while models are easier than ever to replicate, data — how it’s sourced, cleaned, standardized, linked, and delivered — has become the true competitive moat.We discuss what it means to “do data correctly,” how Bloomberg decides which datasets to build or sunset, how modern quants think about their data pipelines and tech stacks, and why aligning research data with production and back-office systems matters more than most people realize. Throughout the episode, we focus on Bloomberg’s goal of shortening a client’s time to alpha, and what that looks like in practice.At its core, this episode is about a simple but powerful idea: when everyone has access to similar models, durable edge increasingly comes from the data beneath them.Please enjoy my episode with Angana Jacob.

A few years ago, I sat down with Moritz Seibert and Moritz Heiden of Takahe Capital to talk about trend following at the edges of the futures markets: places where liquidity is thin, contracts are obscure, and capacity constraint is a feature, not a bug.Since then, despite strong performance, asset growth, and even winning industry awards, they made a very un-industry decision: they shut down their original fund.In its place, they launched a new Global Markets Fund built to stay small, so they can trade calendar and product spreads, niche agricultural markets, and other idiosyncratic contracts at equal risk to more standard markets.In this conversation, we unpack that decision, explore how you systematize trend on markets where liquidity does not exist on screen, and go deep on why spreads represent a fundamentally different opportunity set than outright futures.We also talk about what’s next: from prediction and event markets to new ways of thinking about macro trends and alternative data.I hope you enjoy my conversation with Moritz Seibert and Moritz Heiden.

In this episode I speak with Annanay Kapila, founder and CEO of QFEX, a 24/7 centralized perpetual-futures exchange for traditional financial markets.Before founding QFEX, Annanay worked at Flow Traders and Tower Research, where he was introduced to high frequency trading and market microstructure in both crypto and traditional markets. Insights gleaned during these experiences lead him to the conclusion that the perpetual futures model applied to traditional, so-called “real world asset” markets, like equities, was an inevitable future and one he wanted to build.In this episode, we discuss the ramifications of what that world looks like. First, we discuss what perpetual futures are and their distinguishing characteristics from traditional futures. Then we discuss how perpetual futures can work in markets – like single-name equities – where the underlying do not trade 24/7, and have unique features like corporate actions, opening and closing price auctions, and limit-up/limit-down bounds.A consistent thread throughout the entire conversation is risk management. When leverage is your key feature, it is important to think long and hard about how and when liquidations might occur and the safest way to process them. Finally, we discuss why Annanay believes why perpetual futures will succeed where spot tokenization failed and his view on the current regulatory landscape.Please enjoy my conversation with Annanay Kapila.

In this episode, I speak with Jay Rajamony, Director of Alternatives at Man Numeric.Jay has been with the firm since 2004, giving him a front-row seat to the evolution of quant equity: from simple factor models and broad signals to today’s world of alternative data, model ensembles, and human-machine collaboration.We start with the history: what’s changed in quant over the last two decades, why the 2007 quant quake still matters, and how the definition of “alpha” has shifted alongside new tools and data.From there, we explore the interplay between factors and macro regimes, how sparse datasets are reshaping the research process, and what it means to manage risk in a world where your models don’t always line up with reality.Jay also offers a compelling perspective on how modern quant investing isn’t just about signal breadth anymore—it’s about firm breadth, organizational design, and knowing when to lean in and override the machine.Please enjoy my conversation with Jay Rajamony.