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

AI Engineer · 16 min · 187 sentences · from YouTube's caption track

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00:01[music]
00:12Hi everyone.
00:13Thank you so much for coming to my talk.
00:15I'm Annika.
00:16I'm the founder and CEO of Moshi and I'm going to be talking about a financial investors context that lives in their head and how we can use that when we're building with machines
00:26today.
00:28So I want to do a quick show of hands.
00:30How many people in this room are overwhelmed by the amount of data they have when making a decision?
00:37Okay.
00:38And how many feel like they've got less attention now today with all the amount of data?
00:47And so that's the exact paradox that financial investors have every day.
00:51So in 2025 last year, the world made about 175 zettabytes of data.
00:57That's exponential higher than what we had two decades ago.
01:02So 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,
01:12lots of different podcasts and also a stream of never-ending information coming on X and LinkedIn.
01:21So what does that mean for financial investors?
01:23Because more data does not equate to more information.
01:27It actually just gives everyone the same flood of information, but your attention is the flat line across everything.
01:35So 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.
01:47So who am I?
01:48I've sat on both sides of the table.
01:49So I was uh I spent my career fascinated by data um and I've spent my career working in it.
01:55So I was a derivative trader for nearly a decade and then I moved into data science and machine learning.
02:01Uh so, I got a full bite scholarship to study machine learning, and I got a paper at ICLR uh working in machine learning systems with NASA.
02:10And now I've married those two.
02:12So, those two worlds being Moshi.
02:14Moshi 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.
02:26Having sat on both sides, I can understand what the strengths are for both of them.
02:30And that's what I want to go into deep today.
02:34So, I want to talk about the machine that everyone is trying to beat.
02:37So, a human brain, to give you context, runs on about 20 W, which is the power of a dim light bulb.
02:46Uh so, it's 86 billion neurons in the brain, and we're it's making connections that we still can't understand.
02:53And that is on the power of a light bulb.
02:57And 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.
03:08So, 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.
03:20And so, that actually what that actually means is it it's important for where you're trying to point that technology.
03:26So, at which point in the decision-making uh cycle do you point the technology, which is AI, which we're what we're building?
03:35Because if the most efficient reasoning engine sits between your two ears, then and it's running on a 20 W light bulb, where does machine learning actually sit in that?
03:45And 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?
03:56So, let's take a few steps back to understand how a trader's brain or a financial investor actually thinks.
04:01So, what do people actually want?
04:03Any investor you speak to is looking for the same thing, edge.
04:07What is edge?
04:09So, edge is in practice just three things.
04:12I want to know what's going on before anyone else is.
04:15I want to know my blind spots.
04:17So, tell me the second and third order derivatives of what's happening.
04:20And 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.
04:26All those three together give you edge.
04:30So, now here's the problem.
04:32If 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?
04:41Is it a skills file?
04:43Is it a whole bunch of MCP connectors?
04:46Is it just a better prompt?
04:48Is it a better model?
04:51Every Everyone has access to all of that.
04:53So, if everyone has access to all of these things, then what is edge?
04:59So, the edge is actually what you can't download.
05:01It what It's what sits in an investor's head.
05:04And how do they get that?
05:05It's decades of working and learning from some of the best traders in the industry and sitting next to them.
05:12And here's the concerning part.
05:14What happens when you have an investor and everything lives in their head when they move firms or they retire?
05:20That IP leaves with them.
05:24So, I think before we even start to think about how AI fits in into all of this, we have to actually understand what a financial investor is doing.
05:34They're keeping up with the markets, which are now moving 24/7.
05:38They're trying to understand what is going on and how they relate to each other.
05:42So, those billions of different data connections.
05:45They're trying to compare and understand the historical significance of the event happening right now.
05:50What happened in the past, why it happened, and what the market did when it happened.
05:55And trying to piece all of that together, and then run it on the models that they have.
06:00And then the last part is actually make a decision.
06:03So, there's a lot that takes place before actually making a financial decision.
06:06So, I just want to keep the last bit with you, which is the decision-making.
06:12So, let's take think about what AI actually changed.
06:15So, 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.
06:28So, it's a whole stack of very expensive and very qualified people.
06:33However, with AI, these tools made you believe that you actually don't need any of these people.
06:39What you need is one person, a few API calls, and you've replaced that whole stack of people.
06:46But, here's the gap that no one actually talks about.
06:48Once you 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,
06:57a database, you have to think about storage, reliability, and the biggest thing in all is credits.
07:03How do you actually run the systems?
07:06And so, what we've seen is pricing has changed.
07:09People are no longer pricing by tools, but they're pricing for every API call.
07:14So, now if everyone is building the same tools, what actually makes it different?
07:19And so, there's two types of tools in finance.
07:22One is the tools that you pull data from, and one the tools you push data to.
07:26And so, if you're prompting a model, you are the context.
07:29You'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 a.m. It's late now."
07:36when actually it's 3:00 p.m. on a Wednesday.
07:38And all of that, if you think about comparing it to reasoning with a human, it's absolutely no comparison.
07:45And then you've got the type of tool which pushes data to you.
07:48And that's the tool where it's flooding you with information, and that's a data engineering problem.
07:53So, 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.
08:03You've got to think about what the traders actually using, and what what they think about in each of these different tools.
08:11And the one thing that it we're seeing is quietly breaking is that in finance, when you join a bank or you join a fund, you sit next to a trader or a team
08:20and you absorb everything that's going on.
08:23And you learn from them and you just listen to everything.
08:26You have one ear on the desk and one ear around everything else that's going on.
08:30And it's consistency that pays off.
08:32And 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?
08:42And that's the real cost that right now people are not pricing in.
08:46And so in finance, you can't just hand the decision-making loop to a team of agents.
08:51Why?
08:51Because the other side of the trade is a client.
08:53If it's in a commodity trade, it's a cargo, a physical cargo.
08:57If it's a company, it's a team of people.
09:00And 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."
09:08If an agent leaks your API key, all it says is, "Oops, I'm sorry.
09:12I shouldn't have done that.
09:13Please rotate it."
09:14And you can't have that in finance because um like an agent is just self-correcting and the one small error is just exponentially getting bigger.
09:25So, this is the question that I've been needing 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?
09:35Why don't we flip that and instead of replacing it, why don't we just feed it?
09:41And 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.
09:49But before that, there's a whole bunch of other work that takes place.
09:53They're reading, they're gathering information, they're cross-checking, uh they're watching the market.
09:58Because we have so much information now, the biggest question people have is what is the truth?
10:03What is what is actually the different sources saying?
10:06What is the divergence of information?
10:09How can I trust what is out there?
10:11And once I've done all of that, then I've actually got the decision.
10:15So, 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.
10:28And machines are incredible at the grind.
10:30They're incredible at cleaning data.
10:32They're incredible at gathering data.
10:35And then the human is incredible at the decision making.
10:38So, why not just split those into two separate parts?
10:43And this is what brings us to Moshi, which is what we've built.
10:46It's a 24/7 quant analyst for institutional traders.
10:50And what that means is we are running a team of six different agents, which are built in a deterministic manner, which are watching the market 24/7, more data than is humanly possible,
11:02plus the investors' data, which lives in PDFs, emails, different subscriptions, understanding all of that in a way that is making the the trader a better decision maker.
11:14So, Moshi has read 2.4 billion data points.
11:18And in the 4 seconds it took you to read that sentence, Moshi has learned 300 new things.
11:25And in those 300 new things that it's learned, that's new context for the model.
11:30The issue with that is new context is worthless if you don't actually know, is it trustworthy?
11:36Where does it come from?
11:38Do I even value this analyst's opinion?
11:42Because in finance, even if it's a really confident answer, if it's wrong, then it's worth nothing.
11:48And so, what do we do?
11:49We measure it.
11:50We measure the hallucination, we measure every claim, we see how it's traceable, and we also give the user the assumptions that we've had and how we've led to that decision.
12:01Because if an investor asks you about your opinion, they don't just want to know what you think, they want to know how you came to that reasoning.
12:11And so, what we do is we leave the decision-making with the human because we believe that that is what humans do best.
12:18There's no machine more powerful than the brain.
12:20But everything else that you need to run is what we give you.
12:24So, 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.
12:33Because it's impossible to run that per human.
12:36It's impossible to run your own quant and data science team, and it also doesn't make sense.
12:40Economics-wise, it just is too much to run per person.
12:44And so, that's what we do.
12:45We run an institutional-grade analyst how it works in bigger institutions to actually give you that conviction to make better trades.
12:54So, what does that actually buy you?
12:56It buys you more efficiency, up to 35% more, so that you can actually spend time to have the headspace to have more conviction on the trades that you want to do.
13:05So, if you speak to a lot of traders, they'll say to you, "Hey, I don't want to speak to anyone all day.
13:11I just want to trade."
13:12So, why are you giving these people just LLMs to talk to and to debate, "Hey, is FOMC today or is it tomorrow?"
13:20And then the LLM is saying it's tomorrow when it's today.
13:23That's just a complete waste of time.
13:25And so, that's the opposite of what investors want.
13:29They want something that can give them the power to reason more and use their brain.
13:34So, Mosaic isn't you.
13:35Mosaic isn't a model.
13:37It's It is you on steroids, which is the same instinct but fed by all the data that lives in different parts.
13:44So, that you can actually spend your time decision-making instead of debating when FOMC is.
13:52In In terms of the scale, we watch over 2.4 billion data points, and it's growing every millisecond.
13:59And so, that's decades of derivatives trading knowledge around the clock, which is what it No No one person can build.
14:07And then it's the pushing of the data.
14:09So, when you're pulling data from an LLM, you are giving it the context on an old model.
14:14When you are 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.
14:23So, 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 going to do with this new information?
14:34And then it's the deep dive, the ability to deep dive into one thing with a 360° view of information that you wouldn't have had before.
14:43And so we do that with the big data and all of the specific data.
14:50And 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
15:01in a way that you can understand the context, which is reliable and trustworthy, that becomes infrastructure.
15:07It'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."
15:20Not calling an agent who's never who's just going to apologize to you.
15:25So, 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 actually how much can a machine actually capture?
15:36And 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,
15:45and that's what we're trying to explore.
15:49So, 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
15:58uh 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.
16:08Um so, if that's interesting to you, come talk to me.
16:10I love talking about everything to do with data and finance and financial engineering.
16:15Um and yeah, before you trade, we want people to talk to Moshi.
16:20Thank you.