How This AI Hedge Fund Updates Itself
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🎤 A Q&A with Aric Whitewood, CEO of XAI Asset Management, on building an evolving AI hedge fund.
INTERVIEW
Aric Whitewood runs XAI Asset Management, a systematic hedge fund built on self-updating AI that trades with minimal human intervention.
In this interview, we discuss:
How his fund “evolves” with markets through a closed-loop, Bayesian framework that updates relationships as new data arrives while keeping human tuning rare.
His background from aerospace and radar systems to leading early ML at Credit Suisse to launching a fully systematic fund.
Why LLMs should be treated as a tool, not the center of “intelligence,” and where they fit alongside time series models, information theory, and neuro-symbolic methods.
His concerns that too little focus on compute efficiency can inflate costs, encourage synthetic data shortcuts, and lead to stretch valuations.
Here’s how the strategy has performed since launch. The emerging manager's XAI Systematic Macro strategy returned 38.6% in 2022, 0.2% in 2023, 16.3% in 2024, and 20.3% so far in 2025. The results reflect a steady level of risk aimed at keeping annual volatility near 15% and are reported in U.S. dollars before fees.
This interview has been edited for clarity and length.
Matt: Tell me about yourself
Aric: I started my career in the aerospace industry, after completing a PhD in radar systems and electronic engineering. I spent a number of years working on drones, back when drones weren’t yet mainstream, studying how swarms fly together. I worked on ship-based sensor systems, very interesting. Also, some jet and helicopter projects.
Then I moved into banking, which was extremely interesting as well. I ran pretty much one of the first machine learning teams at Credit Suisse, and eventually became Head of Data Science across sales, trading, all kinds of different functions.
I moved around—London, New York, then Zurich. And then I left at the beginning of 2017. It was a while ago.
Matt: Tell me about your fund.
Aric: The vision of the firm is to create a kind of multi-strat, but with AI creating all the pods. I know other people have claimed, ‘Oh, we have LLM traders, they do everything for you,’ but I’m not convinced by that. What we have is a real track record—actual trading of real assets and with double-digit returns over multiple years.
We’ve done it for macro assets, to some extent for stocks, and we’re now looking at options and other asset classes. The idea is to have pods, but all powered by a very consistent underlying framework—what I call a causal reasoning platform.
This platform pulls together elements of signal processing from my aerospace days, combined with AI and ML. It handles regime shifts and uncertainty. In fact, it embraces uncertainty. That was one of my early realizations: many quants see uncertainty as a nuisance. They widen their distributions, or they avoid it altogether because it doesn’t fit neatly into an equation.
But in signal processing, uncertainty is everywhere. In radar systems, you’re trying to detect targets with imperfect data, constant noise, and competing signals. Sometimes even your own radar system interferes with what you’re trying to see. In finance, the signal-to-noise ratio is just as bad and worse, it changes over time. That’s the challenge, but also the opportunity.
Our system makes uncertainty a feature, not a bug. It’s fundamentally Bayesian in nature. When you fly drones, you often use Markov decision processes to control them. The environment is uncertain, you never fully know what’s going on, but as you observe more data, you refine your understanding. That’s exactly what we’re doing in financial markets: continuously observing, updating, and adapting as prices come in and regimes shift.
We’re now considering raising VC funding to expand and commercialize this causal reasoning engine.
Matt: How might that work? Applying the structure you have in finance to these other scenarios?
Aric: Yeah, exactly. When you think about drones—and autonomous vehicles more broadly—you can represent their behavior in terms of regimes. For example, a car can be in a regime where it’s approaching a junction and turning, and that turn can have its own subtypes. For a drone, landing is one regime, taking off is another, and there are many others.
The same framework we use in finance, handling uncertain information in a Bayesian way, fits these scenarios naturally.
But the key point is that the underlying representation engine, the way we encode causal relationships and make predictions, can be shared across these domains. It’s a common framework that works whether you’re dealing with financial markets or autonomous systems.
Matt: How would you characterize your mix of AI? It sounds like it’s mostly traditional techniques. Are you using LLMs for some of the causal aspects?
Aric: We use a combination of time series techniques, other ML methods, information theory, compression, all kinds of things mixed together. The LLM work is a bit more recent, but more generally, we’ve done language processing work—data pipelines, creating our own sentiment indicators from high-quality news data. That worked fine. Now we’re experimenting with open-source LLMs to infer information from text.
