The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Hi, everyone. Thank you so much for coming to my talk. I'm Anekha. I'm the founder and CEO of Moshi, and I'm gonna be talking about a financial investor's context that lives in their head and how we can use that when we're building with machines today. So I wanna do a quick show of hands. How many people in this room are overwhelmed by the amount of data they have when making a decision?

Okay. And how many feel like they've got less attention now today with all the amount of data? And so that's the exact paradox that financial investors have every day. So in 2025 last year, the world made about 175 zettabytes of data. That's expe- exponentially higher than what we had two decades ago. So before we were lucky if we had an internet connection, we had newspapers, books. But

nowadays we have all of that, plus the context that lives in PDFs, subscriptions, lots of different, uh, podcasts, and also a stream of never-ending information coming on X and LinkedIn. So what does that mean for financial investors? Because more data does not equate to more information. It actually just gives everyone the same flood of information, but your attention is the flat line across everything. So for financial professionals who actually are

paid on financial synthesis, what happens between the gap between attention and data? It just gets wider and wider and wider.

So who am I? I've sat on both sides of the table. So I was... Uh, I've spent my career fascinated by data, um, and I've spent my career working in it. So I was a tr- derivative trader for nearly a decade, and then I moved into data science and machine learning. Uh, so I got a Fulbright scholarship to study machine learning, and I got a paper at ICLR, uh, working on machine learning systems with NASA, and now I've married those two. So those two worlds being Moshi. Moshi is your 24/7 quant analyst,

which works watching the markets 24/7 and understanding what is going on and what matters to a financial investor. Having sat on both sides, I can understand what the strengths are for both of them, and that's what I wanna go into deep today.

So I wanna talk about the machine that everyone is trying to beat. So a human brain, to give you context, runs on about 20 watts, which is the power of a dim light bulb. Uh, so it's 86 billion neurons in the brain, and it's making connections that we still can't understand. And that is on the power of a light bulb. And the systems that we are trying to imitate, which we're trying to imitate just a sliver of that power, they

need a whole power plant to, to run. So just in terms of efficiency, the human brain is something like multiple millions, multiple times more powerful than whatever systems that we're trying to... Uh, whatever we're trying to build. And so that actually... What that actually means is it, it's important for where you're trying to point that technology. So at which point in the decision-making, uh, cycle do you point the technology, which is AI, which we're what... We're

building? Because if the most efficient reasoning engine sits between your two ears, then... And it's running on a 20-watt light bulb, where does machine learning actually sit in that? And so do you try and replace it, which is what AI is trying to... What I feel that AI is trying to do every day, or do you actually feed it?

So let's take a few steps back to understand how a trader's brain or a financial investor actually thinks. So what do people actually want? Any investor you speak to is looking for the same thing, edge. What is edge? So edge is in practice just three things. I want to know what's going on before anyone else is. I want to know my blind spots, so tell me the second- and third-order derivatives of what's happening. And also I want to have the conviction on what I'm doing so that I can size up on a trade that I believe in.

All those three together give you edge. So now here's the problem. If you're building in this space and everyone has access to the same models and they have access to the same systems, then what is edge, right? Is it a skills file? Is it a whole bunch of MCP connectors? Is it just a better prompt? Is it a better model? Every con- everyone has access to all of that. So if everyone has access to all of these things, then

what is edge? So the edge is actually what you can't download. It what... It's what sits in an investor's head. And how do they get that? It's decades of working and learning from some of the best traders in the industry and sitting next to them. And here's the concerning part. What happens when you have an investor and everything lives in their head when they move firms or they retire? That IP leaves with them.

So I think before we even start to think about how AI fits into all of this, we have to actually understand what a financial investor is doing. They're keeping up with the markets, which are now moving 24/7. They're trying to understand what is going on and how they relate to each other, so those billions of different data connections. They're trying to compare and understand the historical significance of the event happening right now, what happened in the past, why it happened, and what the market did when it

happened, and trying to piece all of that together and then run it on the models that they have. And then the last part is actually make a decision. So there's a lot that takes place before actually making a financial decision. So I just wanna keep the last bit with you, which is the decision-making. So let's take think about what AI actually changed. So before, if you worked in finance, you'd have a team of quants, data scientists, data engineers, traders

who are all working together to build this decision-making engine. So it's a whole stack of very expensive and very qualified people. However, with AI, these tools made you believe that you actually don't need any of these people. What you need is one person, a few API calls, and you've replaced that whole stack of people. But here's the gap that no one actually talks about. Once you've spent your weekend building a dashboard, the moment you actually want to productionize that and share it with

one more person, you now have to think about security, a database. You have to think about storage, reliability, and the biggest thing in all is credits. How do you actually run the systems? And so what we've seen is pricing has changed. People are no longer pricing by tools, but they're pricing for every API call. So now if everyone is building the same tools, what actually makes it different? And so there's two types of tools in finance. One is the p- tools that you pull data

from, and one, the tools you push data to. And so if you're prompting a model, you are the context. You're explaining to it. You're arguing with a model that's frozen in time, which is telling you, "Hey, go to bed at 2:00 AM. It's late now," when actually it's 3:00 PM on a Wednesday. And all of that, if you think about comparing it to reasoning with a human, it's absolutely no comparison. And then you've got the type of tool which pushes data to you, and that's the tool where it's flooding you with information, and that's a data engineering problem.

So when you have these two tools, and you've got different problems that you're solving for, one is context, and then the other one is the amount of data, you've got to think about what the trader's actually using and what's... what they think about in each of these different tools. And the one thing that it... we're seeing is quietly breaking is that in finance, when you join a bank or you join, join a fund, you sit next to a trader or a team, and you absorb everything that's going on, and you

learn from them, and you just listen to everything. You have one ear on the desk and one ear around everything else that's going on, and it's consistency that pays off. And if everyone is going to be running their own teams as one-person pods, then what happens to that future generation of context that's coming in? And that's the real cost that right now people are not pricing in. And so in finance, you can't just hand the decision-making loop to a team of agents. Why? Because the other side of the trade is a client. If

it's in a commodities trade, it's a cargo, a physical cargo. If it's a company, it's a team of c- people. And so what happens in finance when something goes wrong is you need a human to go and stand up and say, "I made the decision for this reason." If an agent leaks your API key, all it says is, "Oops, I'm sorry. I shouldn't have done that. Please rotate it." And you can't have that in finance because im- like, an agent is just self-correcting, and the one small error is just exponentially getting bigger.

So this is the question that I've been leading to, which is if the brain is the most powerful tool we have, why are we spending everything trying to push it out of the loop? Why don't we flip that problem, and instead of replacing it, why don't we just feed it? And so if you think about what's in a trader's head, they've got everything leading up to the, uh, to the trade itself, but before that, there's a whole bunch of other work that takes place. They're reading. They're gathering information. They're

cross-checking. Uh, they're watching the market. Because we have so much information now, the biggest question people have is what is the truth? What is, what is actually the different sources saying? What is the divergence of information? How can I trust what is out there? And once I've done all of that, then I've actually got the decision. So if we split it into two different parts, we understand that some of it, well, is all the grind, the reading, the gathering, the data cleaning,

and some of it is judgment, which is the decision. And machines are incredible at the grind. They're incredible at cleaning data. They're incredible at gathering data. And then the human is incredible at the decision-making. So why not just split those into two separate parts? And this is what brings us to Moshi, which is what we've built. It's a 24/7 quant analyst for institutional traders, and what that means is we are running a team of six different agents, which are built in

a deter- de- deterministic manner, which are watching the market 24/7, more data than is humanly possible, plus the investor's data, which lives in PDFs, emails, different subscriptions, understanding all of that in a way that is making the t- the trader a better decision-maker. So Moshi has read 2.4 billion data points, and in the four seconds it took you to read that sentence, Moshi has learnt 300 new things.

And in those 300 new things that it's learnt, that's new context for the model. The issue with that is new context is worthless if you don't actually know is it trustworthy, where does it come from, do I even value this analyst's opinion? Because in finance, even if it's a really confident answer, if it's wrong, then it's worth nothing. And so what do we do? We measure it. We measure the hallucination. We measure every claim. We sh- see how it's

traceable, and we also, uh, give the user the assumptions that we've had and how we've led to that decision. Because if an investor asks you about your opinion, they don't just wanna know what you think. They want to know how you came to that reasoning

And so what we do is we leave the decision-making with the human because we believe that that is what humans do best. There's no machine more powerful than the brain. But everything else that you need to run is what we give you. So we believe that every single firm is running these desks of quants, of data engineers, of data scientists, and that's what we give you because it's impossible to run that per human. It's impossible to run your own quant and data science team, and it also doesn't make sense. Economics-wise, it

just is too much to run per person. And so that's what we do. We run an institutional-grade anal- uh, analyst how it works in bigger institutions to actually give you that conviction to make better trades. So what does that actually buy you? It buys you more efficiency, up to 35% more, so that you can actually spend time to have the head space to have more conviction on the trades that you wanna do. So if you speak to a lot of traders, they'll say to you, "Hey, I don't wanna speak to anyone all day. I

just want to trade." So why are you giving these people just LLMs to talk to and to debate, "Hey, is FOMC today or is it tomorrow?" And then the LLM is saying it's tomorrow when it's today. That's just a complete waste of time. And so that's the opposite of what investors want. They want something that can give them the power to reason more and use their brain. So Moshi isn't you. Moshi isn't a model. It's, um-- it is you on steroids, which is the same instinct, but

fed by all the data that lives in different parts so that you can actually spend your time decision-making instead of debating when FOMC is.

In, in terms of the scale, we watch over two point four billion data points, and it's growing every millisecond. And so that's decades of derivatives trading knowledge around the clock, which is what it-- no, no one person can build. And then it's the pushing of the data. So when you're pulling data from an LLM, you are giving it the context on an old model. When you're getting pushed new data, it's forcing your brain to think about new things and make connections in ways that you didn't

initially think about. So that means that you're then thinking about these blind spots which you've been told about, which you are then trying to understand, "Okay, well, I wouldn't have caught this, so what am I gonna do with this new information?" And then it's the deep dive, the ability to deep dive into one thing with a 360-degree view of information that you wouldn't have had before. And so we do that with the big data and all of the specific data.

And so how does it differ from a tool that you're building on the weekend when a tool that you need to build 24/7 on every market on big data, which is running in a way that you can understand the context, which is reliable and trustworthy, that becomes infrastructure. It's no longer a weekend tool because you need it to be something that other people can trust and you can trace back because that's what you have with a human, which is someone being able to stand up and say, "Hey, I made this decision because of XYZ,"

not calling an agent who's never-- who's just gonna apologize to you.

So the one question that I think we haven't answered yet, which is if real edge lives in a human brain, how much of a machine can it act- how much can a machine actually capture? And I don't think we have an answer to that yet, um, and I think whoever answers it is not building a product. They're building a new way of thinking in finance, and that's what we're trying to explore.

So we've been building very quietly with, uh, a few of the biggest players in the trading industry in finance. And so this is the first time we're opening up to talk to the public, uh, because we believe that every single person should have access to institutional-grade analysis without having to run their own data science or quant team. Um, so if that's interesting to you, come talk to me. I love talking about everything to do with data and finance and financial engineering. Um, and yeah, before you trade, we want people

to talk to Moshi. Thank you.