Shashank Yadav on Why AI Agents Will Replace Hedge Funds

**SPEAKER_1** (0:00)
This is AI Supercycle, Episode 31, presented by NIR. And today we've got our friend Shashank from Fraction AI XYZ. We're gonna be talking about autonomous AI trading agents and everything happening in the space as Fraction AI recently announced.
And so Shashank, congrats on the announcement and welcome to the show.

**Shashank Yadav** (0:24)
Yeah, thanks a lot for having me, really appreciate it.

**SPEAKER_1** (0:27)
Absolutely. Well, I'd love a bit of background on yourself, how you ended up as a founder of Fraction AI. And then we'll get into Fraction AI and how you're viewing the entire AI and agentic space nowadays.

**Shashank Yadav** (0:40)
Yeah, definitely. I started in AI back in 2014 in college. It used to be called machine learning back then. We barely had any deep learning models.
So we were doing research in computer vision and our goal used to be identifying things from images. So if given an image of cat, the model has to say it's a cat or a dog, like super simple models. And so I've seen the growth, how industry has progressed over the years. And Transformers was barely a thing back then. And especially in natural language processing, we used to believe non-neural models were actually better at prediction because generation itself was super bad, wasn't possible at all. And the kind of growth we have seen in the industry over time, it's just fascinating. After college, my first job was at Goldman Sachs as an AI researcher. There I worked with Professor Charles Elkin. He was head of machine learning at Goldman. So our team was called Core Machine Learning Team, and we were embedded into several different finance teams. So I got to learn how finance actually, how AI actually embeds into finance at the world's best investment bank firm.
So yeah, that was that. After that, I worked for an early stage startup, a hedge fund, and finally decided to start Fraction AI, because I saw how quickly AI was moving, how quickly it was improving, and it was necessary to ensure that everyone had equal access to AI, equal access to agents so everyone can have their own agents that creates value just for them and not just in the hands of few companies.

**SPEAKER_1** (2:23)
Absolutely, and I think this is a general trend from those that we're seeing, even with the larger labs companies, the idea that intelligence should be ubiquitous among everyone on the planet, I think this is something that we all share. Obviously, some of them are going about it different ways, and two of them, Elon and Sam, are in court right now battling it out about how it should be done. Obviously, we here believe in open source, we believe in a lot of these pillars for open source intelligence and AI.
If we're talking in particular about the AI trading agents here, I'm curious, before we really dig into a fraction, what was it that you saw at Goldman Sachs during your time there? As much as you can share, I'm sure they've got trade secrets and things, NDAs and whatnot, but what was your experience at Goldman? How were you using AI in the market-making and trading business at the largest scale, and what were some of your most important takeaways from that experience?

**Shashank Yadav** (3:33)
Trading is a major part of Goldman's business. Market-making is mostly done by HFT firms, so it's not that relevant.
They create very exotic products and directly sell it to high net worth individuals. You have to create engines to manage that risk. So for example, there's a portfolio of watches, paintings and several other exotic stuff. It is very difficult to identify a true value and sell it at a price where you can actually hedge the risk with respect to your entire firm.
So what you learn from there is managing risk itself is way more important than the returns curve. So yeah, that kind of got stuck there. And regarding agents for finance, perps are like the biggest road drivers in crypto right now.
In due time, possibly all assets, all RWAs, equities, commodities would be there in perps. It's just a better way to trade. Options are super difficult. You have to know a lot of maths to actually do options right. But with perps, it's super simple. Anyone can do it. So I believe perps is going to be a big road driver, and agents for perps is how it's going to play out. Because the way agents can trade, it's way more efficient than humans ever can. However, there has to be intelligence behind how trading happens. So if you read Marcos Lopez de Prado, he has been the authority on using ML in finance. He talks about how you shouldn't directly try to fit models onto the data. Because the model will always fit on noise. Since in finance, there is very high noise to signal ratio. Most of it is noise, very little signal. So he talks about how you should always start with an idea and then try to validate that idea through data. And we used to do that at Goldman, and that's what we have done with our new product index.