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.

In this episode I’m joined by Vladimir Novakovski, founder and CEO of Lighter, a decentralized crypto exchange.

To kick off the conversation, we explore Lighter's three big design choices: it’s built as a custom Layer-2 on Ethereum, it relies on zero-knowledge circuits for proving transactions, and it runs with a private sequencer. Don't worry – if that sounds like gibberish, Vlad explains it all. Each of those decisions comes with trade-offs — but also big potential advantages.

We discuss why Ethereum remains the natural home for new rollups, from inheriting its security to tapping into DeFi’s growing composability. We also break down what zk circuits actually are, why they matter for trust and security in a derivatives exchange, and how they’re verified in practice.

From there, we tackle the business side: how you bootstrap liquidity in a brand-new DEX, why Lighter went with an unusual fee model and the key lessons learned during an extended private beta.

Finally, we zoom out to the bigger picture. What might DeFi look like if composability really takes hold? Could specialized rollups like Lighter become the backbone of an on-chain financial system, rather than just another venue for speculation?

Please enjoy my conversation with Vlad Novakovski.

In this episode, I speak with Antti Ilmanen, Principal and Global Co-head of the Portfolio Solutions Group at AQR Capital Management.

Antti has long been one of the most thoughtful voices in the world of expected returns, having written not one, but two landmark books on the subject. But in his latest paper series, he returns to the topic with fresh urgency—probing the difference between objective and subjective expectations, and asking why even rational models can go so wrong in real time.

We explore everything from CAPE ratios and market timing accusations, to why equity investors tend to extrapolate while bond investors expect mean reversion. We dig into how behavioral biases, valuation anchors, and structural shifts collide when forming capital market assumptions—and how Antti and the AQR team try to navigate that mess themselves.

If you’re in the business of long-term forecasting or just curious why markets often act like they’ve never read the textbooks, this is a conversation you won’t want to miss.

Please enjoy my conversation with Antti Ilmanen.