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Tickets and applications now live for AIE CODE - the last flagship AIE of the year. When Anekha Sokhal traded energy derivatives, staying on top of the market meant tracking more information than any person could read. Now, as CEO of Moshi, she is building the tool she wished she had: a recommendation engine that connects market information to what matters for a trader’s book. At AI Engineer New York, Anekha joins swyx to discuss her path from mathematics and JPMorgan to founding Moshi, and why useful AI needs to fit the way people actually work. She describes Moshi as what might happen if Bloomberg, Claude and Netflix had a baby: market data, an interface for working with it, and recommendations tailored to the trader. They also talk about trusted sources, document extraction, building versus buying models, and the pressure on AI engineers who are told, “you are the team.” Anekha shares what she learned from customer conversations at the conference and why meeting users comes before hiring. Watch the episode Watch Anekha Sokhal on YouTube Show notes Moshi — Anekha’s company Anekha Sokhal — background and work GIQ research paper — research discussed in the interview Reducto — document extraction Timestamps 0:00 — AI Engineer New York and “you are the team” 0:54 — Meet Anekha Sokhal 2:09 — From mathematics to energy derivatives 3:46 — The Fulbright and becoming a founder 5:45 — What Moshi does for traders 6:10 — Processing a flood of market data 9:03 — Bloomberg and market workflows 10:03 — Bloomberg, Claude and Netflix 12:03 — From dashboards to workflows 15:03 — Trusted sources and signal 17:13 — Build or buy models and document extraction 18:20 — AI Engineer New York 20:32 — Customer meetings and community 23:54 — Hiring and talking to customers 25:55 — swyx’s path into AI Transcript Automated transcript; wording may contain errors. Speaker labels are omitted. 0:00 So I’ve actually extended my stay in New York, because we’ve like now, I was meant to leave today. We’ve now got meetings on Thursday, extended it another day, Friday, extended it another day, Monday. And then I’ve got now meetings where people are like, hey, we’re in energy. Let’s talk in Houston. So we’ve like filled up a week after the conference of meetings. AI engineers was an interesting one, because a lot of them are feeling like overwhelmed with the pressure from management. To do what? Because people now say, we don’t need a team, you are the team. And so it’s like, hey, I need to now build this whole system myself. But that system would be typically done by 10 people. Okay, we are in the studio. Last podcast of AIE for me with Anekha from Moshi. Welcome. Thank you so much. 1:01 Did I get it right, Anekha? Yes, you did. So your background caught my interest because I was a former trader and you’re now a founder. Yes. And you also were telling me just about your experience. I kind of want to hear about it from you. As far as I know, this is your first AIE. How did you hear about us? What was the experience like giving your talk and what happened after? Yeah, well, first of all, thank you to everyone at AIE because it’s been such a fantastic experience. And thank you for inviting me on the podcast as well. I’m so excited to just share my experience. And also, I think we’re living in such a fast moving time and everyone I speak to is like very overwhelmed. So it’s really helpful to be able to kind of share everything that I’ve had in my experience because I did my undergrad in statistics and math, which is now what everyone calls AI. And so it’s really interesting to speak about that and the evolution of that and also to always speak to a former trader as well. 2:02 And just to share like war scars on that. Did you do financial calculus, like stochastic calculus? Yeah, I did that. And I so my undergraduate degree was mathematics, operational research, statistics and economics, Morse at Warwick. And it was what everyone was just like, oh, my God, so nerdy. And I hated telling people. And now it’s like, wow. Yeah, everyone’s like, oh, my God, machine learning, AI. Yeah. Yeah. And then derivatives. So I did that. So my background was started in that at Warwick in England and then knew I wanted to work in trading. I don’t know what what your story to go into trading was, but I wanted something which was fast paced and to do with data, like absolutely love data. And it was when I read about what trading was. I could not imagine myself being in any other career. So I was just set on doing that. So I got my first internship in investment banking and trading and then got an internship at JP Morgan where I started. 3:04 And I started on the emerging markets desk and then moved to commodities and did derivative trading there. So most of my career has been in energy derivatives. And because I have that data background, I always looked at the market from I wanted to break it down into all the different parts. So, for example, in commodities, a lot is about the fundamentals and about the different news and the different the physical happening. And so I was always fascinated by like building out from zero. And so when I was trade trade trading, I saw this shift where it’s like a data and then something was going to happen. And that’s when I left JP Morgan to to actually I wanted to be an entrepreneur and and a founder. But I wanted to have a data company. So I got the Fulbright scholarship to come to the US because you guys have such an incredible venture. Just casually got a Fulbright. 4:06 Yeah, I guess the way I say a funny story, I was doing a yoga class in London. I was like, so my parents have a jewelry business. I side hustled a computer vision startup before my startup at university. And so I was like, oh, what are the problems in the jewelry space? Oh, like virtual try on. So that led to me wanting to be a founder. And I thought, how could I be a founder in the best country to be a founder in? And I thought, what I want to be a founder who can talk about the technology. So this was back in like 2020, 2021. And I found Fulbright had a data scholarship and they gave one in the whole of the UK. So I was like, my way to get here is a Fulbright scholarship and they only give one. And this is really funny because I don’t know if you remember 2020 when it was COVID and gas markets were going crazy. So there was there was like gas markets had insane 5:06 volatility. And so when I did my Fulbright interview, they asked me, how do you like, how do you see the future? And I talked about the like global issue of everyone is having the energy market crisis and how data relates to that. And everyone could relate to what I was talking to because like gas prices were crazy. Their electricity bills were going crazy. And so that led me to the US, came here, was deep in machine learning research, published at ICLR with Stability AI, worked in collaboration with NASA, did a lot of a lot of super interesting work, which then led me to Moshi. And what is Moshi? So Moshi, we are building the program that I wish I had when I was trading. So what we do is we provide real time insights for financial traders, starting with commodity and energy markets and macro markets. So what we do is we process more data than is humanly possible to tell you everything that matters to your portfolio right 6:09 now. What kind of data would you be processing that give us an idea of maybe types and an order of magnitude? Types and order of magnitude. So let me start with order of magnitude. So a fun way I explain it to people is it takes me four seconds to say the sentence to you. And in the same amount of time, Moshi has learned 300 new things. So that’s the type of volume of data that we process. In terms of the types of data, this very much comes to me like geeking out about how I would trade. I would break the market up into all the different types of data, which a trader looks at to decide what trade to put on and how to manage the risk. So what I do is I take the market and I say, well, there’s structured data and unstructured data. There’s the time series of the futures market and there’s all of the technical analysis around it. So we’re looking at all of this like statistical indicated, all of the different technical indicators, all of the moving averages. 7:11 That’s one data set. And obviously that is updating constantly. The next data set we look at is the time series for fundamentals markets. So especially in some commodity markets, there’s a lot of demand and supply. And then we look at the unstructured data. So the news is the obvious one. But then there’s also all this alternative data that exists. For example, there’s X data. There’s audio data on podcasts. X Twitter. X Twitter, yes. You read Twitter. OK. So understanding all of these different types of data. And that I would say is the like public availability of data. And there’s this whole other type of data, which is the user’s private data. So I’m sure you had that when you were trading, which is you have the analysts you care about. I can’t come and tell you, hey, you should, you know, read this person because you have who you need. And then you’re going to have your subscriptions that you care about. You’ll have your internal data, right? 8:11 Like some of the companies we speak to, if they are trading markets, trading energy markets, they will have their person for the amount of LNG ships going through the Strait of Hormuz. And so there’s all this data which is personal to the user. So we take those two data points together, do all of the data cleaning, data transformation. We then look at the statistics and machine learning, tag it, kind of create our whole ontology on top of it. And then we put it into the Moshi, like n-dimensional graph. And then we produce the output, which is a piece of software which pushes data to you. So every time you open Moshi, you see something new related to your portfolio. And you can pull data. So LLM, I’m going to give you answers in 30 seconds, which would take you typically days. So do people often say new Bloomberg or is that something that you disagree? You don’t like being characterized as new Bloomberg? Yeah, that’s an interesting question. 9:13 Because obviously that’s the 800 pound gorilla or whatever the British version of saying 800 pound gorilla is. Yeah, you know, I think Bloomberg has certain features, which the Bloomberg chat, it’s a fantastic aspect of the platform. Yeah, for those who don’t know, like traders would transact billions of dollars in chat. And there’s a standardized formula. When my entry into NLP was actually parsing Bloomberg chat into a structured pricing sheet for my logs. And that’s how we did option pricing. Yeah, there’s the Bloomberg chat, which is one thing. I wouldn’t say we’re a new Bloomberg. I’d say we’re a different type of software, which is not. I think this is the easiest way to say it. And I don’t know how controversial this is. But if Bloomberg and Claude and Netflix had a baby. Netflix. It would be Moshi. You have videos? Netflix. 10:14 We actually have a recommendation system, which we’ve internally built, which is if you’re a trader and you’re looking at one thing, you actually care about the second and third order derivatives right now, which you wouldn’t immediately know. I see. And so we’ve built a whole recommendation system where a simple thing, if you’re, let’s bring it back to like an oil trader, and there’s an energy equities company that’s doing something which would indirectly impact oil, you need to know that right now. You’re not going to get that from like a news stream. You will get that from a connected graph, which is showing that to you. Because if those two information points are coming out right now, the only way you can do it is either a human, which is being able to like connect those, or by a mimicking human, sort of like a recommendation system, which can do that. But that’s what we try and do, because I like wish I had that when I was trading. Yeah. Well, you know, there’s a whole addictiveness element of Netflix, which is bad. 11:15 There is. But yeah, it’s very interesting. I think like RecSys is one of the most important parts of AI. And as a curator of AIEs, I actually have every year an LLM RecSys track. And engineers often don’t appreciate how important it is. Like it’s not very well attended, even though some of the people powering these things, I mean, they run RecSys for YouTube, they run Netflix for Netflix, for Instagram and TikTok. And then like nobody shows up. And I’m like, what do you mean? Like you’re not interested in RecSys? You know, I find it really interesting. I think part of it is they make it seem so easy. And it’s like, oh, this is so like... This is the most important system in the entire, you know, company. Exactly. And I’d say there’s arguably something linked to it, which I think some companies do really well, like Spotify, Instagram, which is one thing that people have come out with AI 12:15 is everyone now can build their own Claude dashboards, right? It’s like, hey, I built this myself. Great. Great. If you want to get someone else to use it, that’s your brain in a dashboard or your brain in something. Try and get someone new to incorporate that into their workflow. You’re then no longer dealing with a problem. You’re dealing with a UI problem, which is how did they interact with it? Because sometimes users don’t always use that. One thing that these platforms do really well is they push data to you, which like hooks you. So you naturally don’t even realize you’re on the platform and you’re like, oh, I want that. No, no, I actually want that. And so we’ve been trying to internally as a company understand how does UI relate to a recommendation system? Because our thesis is if you solve for one, you’re naturally also solving for the other. And I think that’s a big thing right now, which is if everyone’s building software, how do you get it to actually be software that people will use? It’s a bit of an interesting take. 13:19 Yeah, a bit of chicken and egg. Well, anyway, I forgot to mention also my background to this. I used to trade currency options in London, actually. And then I spent some time in rates. And it had a very similar career path where you start with EMEA and then you go into maybe G10. Yeah. At least that’s what we call it for currencies. And you ingest a lot of information and really probably LLMs can process it better. So we didn’t have the tooling back when I did this 10 years ago. But now I see that. And then I moved to the buy side where you start paying for alternative data exactly like you did. We were actually one of the very first few at Balyasny to do the X fire hose or Twitter fire hose. Yeah. That’s something like a stupidly expensive, like $300,000 a month data thing that you have to pay for to get all tweets. It used to be free. And you sort of parse them. 14:19 I don’t think I don’t know if it was ever free. It was free for researchers. Okay. But then if you’re like a hedge fund, like you have to buy that. And it’s very noisy. And so my eyes go up because I don’t know if I would trade on those things because sometimes people do just talk random crap. And sometimes people try to manipulate markets by saying stuff. And it just doesn’t matter that much. But we would also have alternative data like satellite information or credit card information or whatever. And so on the buy side, my number one question is, are you looking at my data? Which is probably not. But how do you assure people? How do you integrate with systems that you don’t necessarily even see? Yeah. And I think there’s two questions in there, which is the first one is, if there’s so much data available, how do you know what’s important? So when we’re looking at X data, it’s a natural conversation when we speak to people or let’s even expand it out to any types of data. 15:20 One thing we found with people is they really will have their trusted people who they’ll follow of these voices in industry, especially in commodities. There’ll be the suppliers of commodities. There’ll be the LNG, all of these facilities who will be active on there because it’s the quickest way for them to also give a status update. Wow. So it’s interesting because we found that a lot of our users will tell us about this. So it’s like, oh, OK, so you find it as a trusted source because you believe this certain individual. Sketchy. Right. And so it’s one of those things where we have found a way to try and filter out as much as possible. But when we are showing it to someone, we give a confidence score of, OK, someone is saying this one thing, but this is everything else happening around the conversation. Because what’s interesting to people is if there are divergent sources, like an example would be how many 16:21 ships are going, how many planes are going above us right now. If Reuters is saying something or Bloomberg is saying something, a Wall Street Journal is saying something, they may not all agree. But the fact that they don’t agree is a point of data because is there an opportunity there? Because no one knows. So that’s how we try and give people information. And then the second question is, you know, people’s data sits everywhere. And I think there’s different types of integration. The first one is what about like their messy PDF data? That data is what we’ve built is like a vision language model to understand it. So we’ll take the data that they can upload to their cloud version of Moshi. And then we actually go and we break it down and then build it up in our database. And there are certain types of data where you just have to connect to their systems. And then that’s like more of a conversation. Is it important to, I assume you’re running this vision language model yourself, is it important to own that? Or would you want to lay out, I mean, there’s any number of vendors, Reducto 17:24 is like a non-documenting processing one. How important is it to have your own models versus just use whatever’s off the shelf? I think that’s a question that a lot of companies are battling with right now. The first question for us is we, if someone’s giving us their data, we don’t want to be sending it to a third party. Because there are systems out there which can do these things for you. But you want to make sure that you’ve got that agreement on how are they processing that data? What are they doing with it? That’s the first question. The second one is, if you don’t have a team of engineers, then that is a great solution. But if you do have a team of engineers like us, a fun fact is most of the team has in some capacity worked with NASA, then they want to build the models. But not everyone has that. And so, you know, I think it really depends like where you are as a team. Yeah, got it. Okay, and then coming into your talk, you apparently had a lot of inbound from it. 18:28 This is our first finance focused one. So I was genuinely unsure who we could get in the door. Obviously, we like pitch it and market it very heavily for this crew. What kind of people did you end up talking to? Yeah, we, it was a like fantastic opportunity because we have been like back to back speaking to people. And what we initially thought would be the interest, we’ve had people like, in the past hour messaging me on LinkedIn saying, hey, I saw your court talk. Are you’re in Houston? Can we get a meeting? Or hey, are you in New York? Can we get a meeting? The types of people we’ve had a lot of traders, either at funds, consulting, and then AI engineers was an interesting one, because a lot of them are feeling like overwhelmed with the pressure from management. To do what? Because people now say, we don’t need a team, you are the team. And so it’s like, hey, I need to now build this whole system 19:31 myself. But that system would be typically done by 10 people. So a lot of people have talked to us and they’re like, hey, I need to build a system. But like, you know, do I need to have a swarm of agents? But then like, what if something breaks? Something interesting is, hey, I built the system. People told me they needed it. It’s in their email. No one’s, no one’s clicking it. But they complain that they have this problem. Like, what do I do? So that was one area. And then partnerships. You know, we’ve been meeting people and people like, hey, you’re doing this. That’s interesting, because we’re doing this. Let’s have a conversation after. So I’ve actually extended my stay in New York, because we’ve like now, I was meant to leave today. We’ve now got meetings on Thursday, extended it another day, Friday, extended it another day, Monday. And then I’ve got now meetings where people are like, hey, we’re in energy. Let’s talk in Houston. So we’ve like filled up a week after the conference of meetings. Oh my god. Yeah. And my team was like, hey, Anekha, another person signed up. Another person signed up. So it’s super cool. 20:32 Is it interesting to do these one to one? I guess because they need to ask you things in private. It just seems very inefficient to set up so many meetings when you could be doing building a business or building the product. Yeah. We try and be as helpful to people where we will have an initial meeting, but then really we want to be able to understand what the problem is that you’re solving for and then have a plan after that. So we have got things that we’re doing now to stream that because like we’re hosting our first webinar because of the amount of inbound that we have. We just can’t always do these one to ones. And so more generally, when I was in the buy side, the banks would actually set up these meetings for us. Yeah. And they’d come to little rooms like this. There’d be sort of a round table. I would meet five at once. So like I’d say I’m a CFO of a company and I need to do investor relations. 21:33 I’m meeting five funds at once and I would be one of the five funds. And I imagine what we could do for you in future, which I haven’t, I have not got this off the ground yet, but we should do the meetings on site. Like you’ve, that makes sense. People like saw your talk. They want to speak more with you. They should be able to sign up and you can do five on one. And probably all these questions are the exact same. And like, that would be a great idea actually, because another, because we used to do this in finance, like that, that is such a good idea. Cause we had one where, um, sometimes you like don’t know what the person you’re meeting looks like as well. Okay. So you’re trying to like take pictures of where you are. Really? It’s like, Hey, meet me here, meet me here and like call them. So an idea like that would be great. So just like an assigned meeting room. Yeah. It’s like, Hey, like. I mean, this whole floor is that, but we had to pre-assign it to other speakers, but, um, we, we wanted to do some dynamic allocation. I just don’t know how to, you know, get that going. I’m trying to fill it out with you. So like, you’re not having to like fly to Houston or even like, even if they’re in New York, you don’t even have to go to their office. 22:34 Everyone just gets in here, gets their meetings done. You’re out. I actually think that would be a great idea. Like have a 30 minute slot for this person and then like sign up. Just like whatever you want. Yeah. Like smaller one, one. That is a great idea. Yeah. Yeah. Uh, I mean anything else that like we, I guess we could do better or like other experiences from the conference that you’re, I think this has been the best organized conference I’ve been to like, no, no, seriously. Like the amount of good quality guidelines I’ve had, and it was super clear, like the speaker team, the breakout rooms, um, the speaker lounge having, I feel like the lounge was never this good. Uh, yeah. So haircut. I know. I didn’t do a facial. I did a facial and a massage. Yeah. I know you got your haircut. I did get my haircut. Yeah. Um, I, I, I mean, we do want to swallow speakers. Uh, this was definitely, uh, over and above and that’s thanks to the cognition for doing that. But, uh, yeah, I mean, uh, organizing is one thing. 23:38 I do think like in terms of business value and efficiency, I think that’s an important, uh, element. Like we want to make this, uh, and you know, something that’s useful for you for both hiring and selling, you know, and I don’t know how to balance that because yeah, are you even, what’s, what’s more important to you hiring or selling? So in terms of we’ve just hired and we plan to hire, um, we’re always meeting people, but the priority right now is to meet clients and that’s a priority, not necessarily hiring. Um, because I think what we have with hiring is we get continuous inbound from LinkedIn. Really? Yeah. I had, we had one job up. We had to close it. It was open for four days. We had 2,500 applicants. Okay. Um, and so it’s like, that’s tough to deal with in itself, but meeting people who like want to buy and people who, because I think there’s people who have the problem, but they don’t want to buy. And there’s people who have the problem and want to buy. And then you who are trying to find that match that is, 24:39 I haven’t found any person who’s done that. Um, well, but so I really like your one-on-one idea. Yeah. Yeah. I mean, so basically I’m, I’m transferring over what I used to have as a hedge fund person to this market, which is AI and finance. Yeah. Um, well, we’ll try to do that next year. We’ll try to be the definitive thing. Um, and I think on hiring side, so that that’s buying and then that’s clear. I think, I think I can definitely help you better there. Uh, on the hiring side, I also want to help you. Right. Okay. Like 2,500 is too much and most of it’s noise. And so you need a better filter. I have a strong opinion. Okay. Go ahead. The amount of people who are also doing it in a way where it’s like undercover, not it’s they’re shopping, they’re spying. You have that, but you also have, it’s that like legally not allowed to work. So there’s so much noise in that. It’s like visas and there’s so much noise around that as well. And so when I was a first time, like I am a first time founder, when I first 25:41 started, you don’t know those things. You don’t know the like market. Right. And there’s a lot of noise related to that. Yeah. Okay. Well, we’ll, we’ll try to get that higher signal for you. We want to be a high signal conference. Um, I’m just appreciative that you’re spending some time with us and congrats on your success. Thank you so much. I’d love, I have one question, which is how you went, you moved into AI. Uh, just, just decided to start. Uh, well, I, I left, uh, the hedge fund worlds. I moved into tech and then when the AI wave happens, I realized that I better move very, very quickly to, uh, establish this field. And, um, so I did the AI engineering post and, uh, started this, this conference and the media stuff because I’m, I’m very much of a media person. Yeah. Uh, at my heart. Um, I do miss trading a little bit, but this is very fulfilling because I get to meet real people instead of stare at a screen all day. Uh, yeah, I feel the same. Well, thank you. I think we have to go. Thank you so much. Thanks. Thanks.
Tickets and applications now live for AIE CODE - the last flagship AIE of the year. When Anekha Sokhal traded energy derivatives, staying on top of the market meant tracking more information than any person could read. Now, as CEO of Moshi, she is building the tool she wished she had: a recommendation engine that connects market information to what matters for a trader’s book. At AI Engineer New York, Anekha joins swyx to discuss her path from mathematics and JPMorgan to founding Moshi, and why useful AI needs to fit the way people actually work. She describes Moshi as what might happen if Bloomberg, Claude and Netflix had a baby: market data, an interface for working with it, and recommendations tailored to the trader. They also talk about trusted sources, document extraction, building versus buying models, and the pressure on AI engineers who are told, “you are the team.” Anekha shares what she learned from customer conversations at the conference and why meeting users comes before hiring. Watch the episode Watch Anekha Sokhal on YouTube Show notes Moshi — Anekha’s company Anekha Sokhal — background and work GIQ research paper — research discussed in the interview Reducto — document extraction Timestamps 0:00 — AI Engineer New York and “you are the team” 0:54 — Meet Anekha Sokhal 2:09 — From mathematics to energy derivatives 3:46 — The Fulbright and becoming a founder 5:45 — What Moshi does for traders 6:10 — Processing a flood of market data 9:03 — Bloomberg and market workflows 10:03 — Bloomberg, Claude and Netflix 12:03 — From dashboards to workflows 15:03 — Trusted sources and signal 17:13 — Build or buy models and document extraction 18:20 — AI Engineer New York 20:32 — Customer meetings and community 23:54 — Hiring and talking to customers 25:55 — swyx’s path into AI Transcript Automated transcript; wording may contain errors. Speaker labels are omitted. 0:00 So I’ve actually extended my stay in New York, because we’ve like now, I was meant to leave today. We’ve now got meetings on Thursday, extended it another day, Friday, extended it another day, Monday. And then I’ve got now meetings where people are like, hey, we’re in energy. Let’s talk in Houston. So we’ve like filled up a week after the conference of meetings. AI engineers was an interesting one, because a lot of them are feeling like overwhelmed with the pressure from management. To do what? Because people now say, we don’t need a team, you are the team. And so it’s like, hey, I need to now build this whole system myself. But that system would be typically done by 10 people. Okay, we are in the studio. Last podcast of AIE for me with Anekha from Moshi. Welcome. Thank you so much. 1:01 Did I get it right, Anekha? Yes, you did. So your background caught my interest because I was a former trader and you’re now a founder. Yes. And you also were telling me just about your experience. I kind of want to hear about it from you. As far as I know, this is your first AIE. How did you hear about us? What was the experience like giving your talk and what happened after? Yeah, well, first of all, thank you to everyone at AIE because it’s been such a fantastic experience. And thank you for inviting me on the podcast as well. I’m so excited to just share my experience. And also, I think we’re living in such a fast moving time and everyone I speak to is like very overwhelmed. So it’s really helpful to be able to kind of share everything that I’ve had in my experience because I did my undergrad in statistics and math, which is now what everyone calls AI. And so it’s really interesting to speak about that and the evolution of that and also to always speak to a former trader as well. 2:02 And just to share like war scars on that. Did you do financial calculus, like stochastic calculus? Yeah, I did that. And I so my undergraduate degree was mathematics, operational research, statistics and economics, Morse at Warwick. And it was what everyone was just like, oh, my God, so nerdy. And I hated telling people. And now it’s like, wow. Yeah, everyone’s like, oh, my God, machine learning, AI. Yeah. Yeah. And then derivatives. So I did that. So my background was started in that at Warwick in England and then knew I wanted to work in trading. I don’t know what what your story to go into trading was, but I wanted something which was fast paced and to do with data, like absolutely love data. And it was when I read about what trading was. I could not imagine myself being in any other career. So I was just set on doing that. So I got my first internship in investment banking and trading and then got an internship at JP Morgan where I started. 3:04 And I started on the emerging markets desk and then moved to commodities and did derivative trading there. So most of my career has been in energy derivatives. And because I have that data background, I always looked at the market from I wanted to break it down into all the different parts. So, for example, in commodities, a lot is about the fundamentals and about the different news and the different the physical happening. And so I was always fascinated by like building out from zero. And so when I was trade trade trading, I saw this shift where it’s like a data and then something was going to happen. And that’s when I left JP Morgan to to actually I wanted to be an entrepreneur and and a founder. But I wanted to have a data company. So I got the Fulbright scholarship to come to the US because you guys have such an incredible venture. Just casually got a Fulbright. 4:06 Yeah, I guess the way I say a funny story, I was doing a yoga class in London. I was like, so my parents have a jewelry business. I side hustled a computer vision startup before my startup at university. And so I was like, oh, what are the problems in the jewelry space? Oh, like virtual try on. So that led to me wanting to be a founder. And I thought, how could I be a founder in the best country to be a founder in? And I thought, what I want to be a founder who can talk about the technology. So this was back in like 2020, 2021. And I found Fulbright had a data scholarship and they gave one in the whole of the UK. So I was like, my way to get here is a Fulbright scholarship and they only give one. And this is really funny because I don’t know if you remember 2020 when it was COVID and gas markets were going crazy. So there was there was like gas markets had insane 5:06 volatility. And so when I did my Fulbright interview, they asked me, how do you like, how do you see the future? And I talked about the like global issue of everyone is having the energy market crisis and how data relates to that. And everyone could relate to what I was talking to because like gas prices were crazy. Their electricity bills were going crazy. And so that led me to the US, came here, was deep in machine learning research, published at ICLR with Stability AI, worked in collaboration with NASA, did a lot of a lot of super interesting work, which then led me to Moshi. And what is Moshi? So Moshi, we are building the program that I wish I had when I was trading. So what we do is we provide real time insights for financial traders, starting with commodity and energy markets and macro markets. So what we do is we process more data than is humanly possible to tell you everything that matters to your portfolio right 6:09 now. What kind of data would you be processing that give us an idea of maybe types and an order of magnitude? Types and order of magnitude. So let me start with order of magnitude. So a fun way I explain it to people is it takes me four seconds to say the sentence to you. And in the same amount of time, Moshi has learned 300 new things. So that’s the type of volume of data that we process. In terms of the types of data, this very much comes to me like geeking out about how I would trade. I would break the market up into all the different types of data, which a trader looks at to decide what trade to put on and how to manage the risk. So what I do is I take the market and I say, well, there’s structured data and unstructured data. There’s the time series of the futures market and there’s all of the technical analysis around it. So we’re looking at all of this like statistical indicated, all of the different technical indicators, all of the moving averages. 7:11 That’s one data set. And obviously that is updating constantly. The next data set we look at is the time series for fundamentals markets. So especially in some commodity markets, there’s a lot of demand and supply. And then we look at the unstructured data. So the news is the obvious one. But then there’s also all this alternative data that exists. For example, there’s X data. There’s audio data on podcasts. X Twitter. X Twitter, yes. You read Twitter. OK. So understanding all of these different types of data. And that I would say is the like public availability of data. And there’s this whole other type of data, which is the user’s private data. So I’m sure you had that when you were trading, which is you have the analysts you care about. I can’t come and tell you, hey, you should, you know, read this person because you have who you need. And then you’re going to have your subscriptions that you care about. You’ll have your internal data, right? 8:11 Like some of the companies we speak to, if they are trading markets, trading energy markets, they will have their person for the amount of LNG ships going through the Strait of Hormuz. And so there’s all this data which is personal to the user. So we take those two data points together, do all of the data cleaning, data transformation. We then look at the statistics and machine learning, tag it, kind of create our whole ontology on top of it. And then we put it into the Moshi, like n-dimensional graph. And then we produce the output, which is a piece of software which pushes data to you. So every time you open Moshi, you see something new related to your portfolio. And you can pull data. So LLM, I’m going to give you answers in 30 seconds, which would take you typically days. So do people often say new Bloomberg or is that something that you disagree? You don’t like being characterized as new Bloomberg? Yeah, that’s an interesting question. 9:13 Because obviously that’s the 800 pound gorilla or whatever the British version of saying 800 pound gorilla is. Yeah, you know, I think Bloomberg has certain features, which the Bloomberg chat, it’s a fantastic aspect of the platform. Yeah, for those who don’t know, like traders would transact billions of dollars in chat. And there’s a standardized formula. When my entry into NLP was actually parsing Bloomberg chat into a structured pricing sheet for my logs. And that’s how we did option pricing. Yeah, there’s the Bloomberg chat, which is one thing. I wouldn’t say we’re a new Bloomberg. I’d say we’re a different type of software, which is not. I think this is the easiest way to say it. And I don’t know how controversial this is. But if Bloomberg and Claude and Netflix had a baby. Netflix. It would be Moshi. You have videos? Netflix. 10:14 We actually have a recommendation system, which we’ve internally built, which is if you’re a trader and you’re looking at one thing, you actually care about the second and third order derivatives right now, which you wouldn’t immediately know. I see. And so we’ve built a whole recommendation system where a simple thing, if you’re, let’s bring it back to like an oil trader, and there’s an energy equities company that’s doing something which would indirectly impact oil, you need to know that right now. You’re not going to get that from like a news stream. You will get that from a connected graph, which is showing that to you. Because if those two information points are coming out right now, the only way you can do it is either a human, which is being able to like connect those, or by a mimicking human, sort of like a recommendation system, which can do that. But tha
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