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.