Flirting with Models

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.