Ahead of our Funds of the Future Summit, Hedgeweek® spoke with Timothee Consigny about his role as CTO at H2O Asset Management. In the conversation, Consigny shared insights into the evolving AI landscape, real-world use cases, and his advice for fellow hedge fund managers. He’ll be speaking at the summit during The Smart Stack: Levelling the Field with AI and Data workshop, where he’ll explore how emerging hedge funds can leverage AI, data analytics, and advanced technology to compete with established players, streamline operations, and scale efficiently.
How did you end up as CTO at H2O?
I’ve always been an engineer at heart – I like building things. I started as a quant, but I’ve always been more drawn to development and systems than pure modeling. I joined H2O AM when they launched in London 15 years ago.
We’ve always been a discretionary shop and have viewed technology as a means to empower our staff, not replace them. We build tech around our investment process – not the other way around. Asset management is one of the few industries where it makes sense to have a dedicated team of devs working on tools used by just a handful of PMs.
Over time, my role naturally evolved. Today I run the tech, though I still enjoy getting my hands dirty with code when I can.
How is AI changing fund management and the buy side as a whole?
Over the past 70 years in finance, plenty of resources have gone into developing better models to manage money. The problem is, when you quantify something, you lose information. If you reduce companies to balance sheets – or view economies only through GDP, inflation, and unemployment – you get data, but not the whole story.
On the other hand, if you want a more qualitative approach, that takes time, experience and skill. And, because you’ve got a human being who has to do that, you will probably have some bias.
GenAI bridges that gap. It’s both qualitative and quantitative. And crucially – it has no ego. It won’t argue, won’t defend bad calls, and has no problem telling you when you’re wrong. That kind of blunt, bias-free feedback loop is gold for discretionary investors.
Can adoption of advanced AI and data analytics bridge the gap between emerging and big shop hedge funds? Do you see tech helping emerging managers get more competitive in terms of both performance and scalability?
Absolutely – if you’ve got the right culture.
The challenge with GenAI isn’t the tech. It’s getting your senior people – your star PMs with 30 years of experience – to use it. That’s a lot easier in a boutique where the CIO sits next to the quant team and innovation happens organically.
Governance also plays a big role. Before we kicked off our GenAI journey, we asked ourselves: What do we want to use this for – and just as importantly, what don’t we want to use it for? We drafted a simple AI manifesto: always keep a human in the loop, don’t use AI for client-facing tasks, pricing, or live trading signals.
In my experience, I’ve found the process of engaging with AI more valuable than the output. It forces you to ask better questions, rethink your assumptions, and be clearer about what you’re trying to achieve.
The Goliaths have unlimited tech budgets. What’s the David strategy for emerging funds to compete technologically without breaking the bank?
The actual cost of the technology is very low – what’s more costly is the internal development and the time you’ll spend on governance. The key is knowing the difference between what’s cool and what’s useful.
Start small. Take something like document summarisation. Use GenAI to condense a 60-page research note into bullet points and use prompting/grounding to explore the document.
Then go one step further: feed in ten reports from different banks. Ask AI to find the consensus and the outlier. Out of that, the AI may tell you that nine banks agree on something, and one bank has a different stance. Then, as a PM, you get on the phone with an analyst and find out why that one bank differs.
You’re no longer drowning in research – you’re zooming in on what matters.
If you had to pick just three technologies for a launch-phase hedge fund to invest in today, what would they be and why?
The first, which might seem a bit boring, is about getting access to market data from trading venues – that’s non-negotiable.
The second is your Portfolio Management System (PMS). You need a golden source for your data – where your positions, risk metrics, and performance distribution are all centralised.
Now, the last one is the part I find most interesting. You need a system for sharing information. Start with internal communication tools – whether it’s Slack, Teams, whatever works. The key is to have a space where everyone in the company can communicate openly.
From there, build on it. Start integrating data, workflows, processes and evolve it into a sort of knowledge hub. Once that’s in place, plug it into a large language model.
That combination – structured communication plus integrated intelligence – is where the real potential lies.
Anything else you would like to add?
Risk is always the elephant in the room. Yes, GenAI can hallucinate. But the bigger risk? Shadow IT. If you don’t offer an internal solution, people will access random LLM on their phones and send your data who-knows-where.
That’s why we treat GenAI like a junior hire: you train them; you check their work; you give feedback; and you don’t blame them if you missed a mistake.
Handled right, GenAI is not here to replace anyone. It’s here to help us be better at being human – more curious, more consistent, and more aware of our blind spots. If the user knows what they’re doing, the risk with this technology is limited.
For more on the event, see here.
