The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi

Anekha Sokhal, CEO of Moshi, joins swyx to discuss building a recommendation engine for commodity traders, turning market data into useful signals, and designing AI that fits users’ workflows. Watch: YouTube · Moshi · Anekha 0:00 AI Engineer New York a...

• 0:00Speaker 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. And AI engineers was an interesting one because a lot of them are feeling like overwhelmed with the pressure from management.

• 0:29Speaker 01

  To do what?

• 0:30Speaker 00

  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.

• 0:54Speaker 01

  Okay, we are in the studio, last podcast of AIE for me with Anekha from Moshi, welcome.

• 0:59Speaker 00

  Thank you so much.

• 1:00Speaker 01

  Did I get it right, Anekha?

• 1:02Speaker 00

  Yes, you did.

• 1:03Speaker 01

  So your background caught my interest because I was a former trader and you're also, you're now a founder.

• 1:10Speaker 00

  Yes.

• 1:11Speaker 01

  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?

• 1:22Speaker 00

  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, 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, to speak about that and the evolution of that. And also to always speak to a, a former trader as well and just to share like war, war scars on, on that.

• 2:05Speaker 01

  Did you do financial calculus, like stochastic calculus?

• 2:08Speaker 00

  Yeah, I did that. And I, so my undergraduate degree was mathematics, operational research, statistics and economics, Mors at Warwick.

• 2:16Speaker 01

  Okay.

• 2:17Speaker 00

  And it was what everyone was just like, oh my God, so nerdy. And I hated telling people. And now it's like, wow, you know, everyone's like, oh my God, machine learning, AI, yeah.

• 2:27Speaker 01

  Yeah, yeah. And then derivatives?

• 2:29Speaker 00

  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 and I started on the emerging markets desk and then moved to commodities.

• 3:09Speaker 00

  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 it 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.

• 3:53Speaker 00

  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 space.

• 4:02Speaker 01

  Just casually got a Fulbright?

• 4:06Speaker 00

  Yeah, I, I guess the way I, where I say, a funny story, I was doing a yoga class in London. And 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, 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.

• 4:49Speaker 00

  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 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.

• 5:28Speaker 00

  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.

• 5:45Speaker 01

  And what is Moshi?

• 5:46Speaker 00

  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 now.

• 6:10Speaker 01

  What kind of data would you be processing that give us an idea of maybe types and then order of magnitude?

• 6:16Speaker 00

  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 learnt 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.

• 6:57Speaker 00

  There's, 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 indicator, all of the different technical indicators, all of the moving averages. 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, yes.

• 7:40Speaker 01

  UV Twitter, okay.

• 7:41Speaker 00

  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? 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.

• 8:23Speaker 00

  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 like 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.

• 9:03Speaker 01

  So do people often say new Bloomberg or is that something that you disagree, you don't like being characterized as new Bloomberg?

• 9:11Speaker 00

  Yeah, it's, it's, that's an interesting question.

• 9:13Speaker 01

  Obviously, that's the 800 pound gorilla or whatever the British version of saying 800 pound gorilla is.

• 9:19Speaker 00

  Yeah, you know, I think, I think Bloomberg has certain features, which the Bloomberg chat, it's a fantastic aspect of the, of the platform.

• 9:29Speaker 01

  Yeah, for those who don't know, like traders would transact billions of dollars in chat.

• 9:36Speaker 01

  And there's a standardized formula. When I, 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.

• 9:48Speaker 00

  Yeah, there's the, there's the Bloomberg chat, which is, is one thing. I wouldn't say we're a new Bloomberg. I'd say we're a different type of software, which is.

• 10:02Speaker 00

  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.

• 10:10Speaker 01

  Netflix?

• 10:10Speaker 00

  It would be Moshie.

• 10:12Speaker 01

  You have videos?

• 10:13Speaker 00

  Netflix. We actually have a recommendation system, which we've internally built, which is if you're, if you're a trader and you're looking at one thing, um, you actually care about the second and third order derivatives right now, which you wouldn't immediately know.

• 10:30Speaker 01

  I see.

• 10:30Speaker 00

  And so we've built a whole recommendation system where a simple thing, if you're, um, 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. Uh, you're not going to get that from like a news stream.

• 10:48Speaker 01

  Mm hmm.

• 10:48Speaker 00

  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. That's what we try and do because I like wish I had that when I was trading.

• 11:10Speaker 01

  Yeah. Well, uh, you know, there's a whole addictiveness element of Netflix, which is bad.

• 11:15Speaker 00

  There is.

• 11:15Speaker 01

  But, um, yeah, it's very interesting. I think like Rexis is one of the most important parts of AI. And as a curator of A I E's, I actually have every year an L N Rexis track. And engineers often don't appreciate how important it is. Like it's not very well attended. Uh, even though some of the people powering these things, I mean, they run Rexis for YouTube. They run Netflix, uh, for Netflix, for, uh, Instagram and TikTok. And then like, nobody shows up. And I'm like, what do you mean? Like, you're not interested in Rexis?

• 11:46Speaker 00

  You know, I, I find it really interesting. I think part of it is it, they make it seem so easy. And it's like, oh, this is so like.

• 11:56Speaker 01

  This is the most important system in the entire, like, you know, company.

• 12:02Speaker 00

  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 is everyone now can build their own Claude dashboards, right? It's like, hey, I built this myself. 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 not, no longer dealing with a problem. You're dealing with a UI problem, which is how did they interact with it? Cause sometimes users don't always use that. One thing that these platforms do really well is they push data to you,

• 12:46Speaker 00

  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 it's, we've had, 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, get it to actually be software that people will use?

• 13:16Speaker 01

  Yeah.

• 13:16Speaker 00

  It's, it's a bit of an interesting take. Yeah.

• 13:19Speaker 01

  Bit of chicken and egg. Um, well, anyway, I, I forgot to mention also my background to this, right? I, I used to trade, um, currency options in London, actually. Um, and, uh, then I, I spent some time in rates. Uh, and it had a very similar career path where you start with, with EMEA and then you go into maybe G10.

• 13:37Speaker 00

  Yeah.

• 13:37Speaker 01

  Uh, at least for, that's what we call it for currencies. And, um, it, it's, uh, you, you ingest a, a lot of information and really probably L L Ms can process it uh, better. Um, so we didn't have the tooling back when I did this 10 years ago.

• 13:53Speaker 00

  Mm hmm.

• 13:53Speaker 01

  But, uh, now I, I see that. And then, then I moved to the buy side where you start paying for alternative data, exactly like, like you, like you did. Uh, we were actually one of the very first few at, at Ballyasney to do the X fire hose or Twitter fire hose.

• 14:07Speaker 00

  Yeah.

• 14:08Speaker 01

  That's like something like a stupidly expensive, like $300,000 a month, like, uh, data thing that you have to pay for to get all tweets.

• 14:16Speaker 00

  It used to be free?

• 14:19Speaker 01

  It used, I don't think, I don't know if it was ever free. It was free for researchers.

• 14:22Speaker 00

  Okay.

• 14:22Speaker 01

  But then if you're like a hedge fund, like you have to sort of buy that. Um, I, I, and it's very noisy and, and some, and so then my, my eyes goes out 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.

• 14:39Speaker 00

  Yeah.

• 14:40Speaker 01

  Saying stuff and it just doesn't matter that much. But we, we would also have, um, alternative asset, uh, data, like satellite information or credit card information or whatever, right? And so my, on the buy side, my number one question is, are you looking at my data, right? Like, which is probably not. But how would you assure people? How do you integrate with systems that you don't, don't necessarily even see?

• 15:02Speaker 00

  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, let's even expand it out to any types of data. One thing we've 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 L N G, uh, the, the, all of these facilities who will be active on there because it's the quickest way for them to also give a status update.

• 15:40Speaker 01

  Wow.

• 15:40Speaker 00

  So it's interesting because we found that a lot of our users will tell us about this. So it's like, oh, okay. So you, you find it as a trusted source because you believe this certain individual.

• 15:52Speaker 01

  Sketchy.

• 15:53Speaker 00

  Right? And so it's, it's one of those things where we have found a way to uh, try and filter out as much as possible, but when we are showing it to someone, we give a like confidence score of, okay, 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, uh, uh, an example would be, um, how many ships are going, how many planes are going above us right now? If Reuters is saying something or Bloomberg is saying something and Wall Street Journal is saying something, they may not all agree.

• 16:29Speaker 01

  Mm hmm.

• 16:29Speaker 00

  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,

• 16:41Speaker 00

  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? Um, 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 Moshie. 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. Um, and then that's like more of a conversation.

• 17:13Speaker 01

  Is it important to, um, I assume you're, you're running this vision language model yourself. Is it important to own that? Or would you want to off lay up? I mean, there's any number of vendors. Uh, Redactor is like a, a non-document processing one. Um, how important is it to have your own models versus just use whatever's off the shelf?

• 17:32Speaker 00

  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, 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. Um, but you make, you want to make sure that you've got that agreement on how are they processing that data? What are they doing with it? Um, 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, um, a fun fact is most of the team has in some capacity worked with NASA. Um, then they want to build the models.

• 18:12Speaker 01

  Mm hmm.

• 18:13Speaker 00

  But not everyone has that. And so, you know, I think it really depends like where you are as a team.

• 18:20Speaker 01

  Yeah, got it. Um, okay. And then coming into your talk, you, uh, apparently had a lot of inbound from it.

• 18:26Speaker 00

  Yeah.

• 18:27Speaker 01

  I'm, this is our first finance focused one.

• 18:30Speaker 00

  Okay.

• 18:31Speaker 01

  So I was genuinely unsure.

• 18:33Speaker 00

  Okay.

• 18:33Speaker 01

  Who we could get in the door. Uh, obviously we like pitched it and marketed it very heavily for, for this crew. Uh, what, what kind of people did you end up talking to?

• 18:41Speaker 00

  Yeah, we, um, 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 message me, 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, um, 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.

• 19:21Speaker 01

  To do what?

• 19:22Speaker 00

  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 ten people. So a lot of people have talked to us and they're like, hey, I need to build the 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 complained that they have this problem. Like, what do I do? So that was that one area. And then partnerships, you know, we, 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.

• 20:07Speaker 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.

• 20:26Speaker 01

  Oh, my God.

• 20:27Speaker 00

  Yeah. And my team was like, hey, Anekha, another person signed up, another person signed up. So it was super cool.

• 20:32Speaker 01

  Is it interesting to do these one to one? I guess because they, they need to um ask you things in private. It just seems very inefficient to set up so many meetings when you could, you could be doing, building a business or building a product.

• 20:46Speaker 00

  Yeah, we've um, we, 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, after that.

• 21:02Speaker 01

  Yeah.

• 21:02Speaker 00

  So we have got things that we're doing now to stream that because uh, 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.

• 21:16Speaker 01

  And, and so more generally, uh, when I was in the buy side, um, the, the banks would actually set up these meetings for us. Right? 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, let's say I'm a CFO of a company, uh, and I need to do investor relations. I'm meeting five funds at once and I'll be one of the five funds. Um, 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 onsite, like you've.

• 21:45Speaker 00

  That makes sense.

• 21:46Speaker 01

  People like, sorry, 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.

• 21:54Speaker 00

  That would be a great idea, actually, because another.

• 21:58Speaker 01

  Because we used to do this in finance, like.

• 21:59Speaker 00

  That, that is such a good idea because we had one where um, sometimes you like, don't know what the person you're meeting looks like as well.

• 22:08Speaker 01

  Okay.

• 22:08Speaker 00

  So you're trying to like, take pictures of where you are.

• 22:11Speaker 01

  Really?

• 22:12Speaker 00

  It's like, hey, meet me here, meet me and like call them. So an idea like that would be great.

• 22:17Speaker 01

  Meeting room, yeah.

• 22:17Speaker 00

  It's like, hey, like.

• 22:18Speaker 01

  I mean, this, this whole floor is that, but we, we had to preassign 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.

• 22:27Speaker 00

  I think that would be great, yeah.

• 22:29Speaker 01

  So like, you, 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. Everyone just gets in here, gets their meetings done, you're, you're out.

• 22:38Speaker 00

  I actually think that would be a great idea. Like have a thirty minute slot for this person and then like sign up.

• 22:43Speaker 01

  Just like whatever you want, right?

• 22:45Speaker 00

  Yeah, like smaller one one, that is a great idea.

• 22:47Speaker 01

  Yeah, yeah. Uh, I mean, a, anything else that like we, I guess we could do better or like other experiences from the conference that you're?

• 22:55Speaker 00

  I think this has been the best organized conference I've been to.

• 22:59Speaker 01

  Oh.

• 23:00Speaker 00

  Like, no, no, seriously, like, the amount of good quality guidelines I've had and it was super clear.

• 23:08Speaker 01

  Mm hmm.

• 23:09Speaker 00

  Like the speaker team, the breakout rooms, um, the speaker lounge, having, I feel like I've.

• 23:14Speaker 01

  The lounge was never this good. Uh, I will have, yeah, yeah. So, yeah, haircut, I know, did you?

• 23:21Speaker 00

  No, I didn't do a haircut. I did a facial and a massage.

• 23:23Speaker 01

  Yeah.

• 23:24Speaker 00

  I know you got your haircut.

• 23:25Speaker 01

  I did get my haircut, yeah. Um, I, I, I mean, we do want to swallow speakers. Uh, this was definitely uh, the over and above and that's thanks to the cognition for doing that. But uh, yeah, I mean, uh, organizing is one thing. 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.

• 23:51Speaker 00

  Yeah.

• 23:52Speaker 01

  Are you even, what's, what's more important to you, hiring or selling?

• 23:55Speaker 00

  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.

• 24:04Speaker 01

  Yeah.

• 24:05Speaker 00

  And that's a priority, not necessarily hiring. Um, because I think what we have with hiring is we get continuous inbound from LinkedIn.

• 24:13Speaker 01

  Really?

• 24:14Speaker 00

  Yeah, I had, we had one job up, we had to close it. It was open for four days, we had two thousand five hundred applicants.

• 24:20Speaker 01

  Okay.

• 24:20Speaker 00

  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, I haven't found any person who's done that. Um, well, but so I really like your one on one idea.

• 24:45Speaker 01

  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.

• 24:54Speaker 00

  Yeah.

• 24:54Speaker 01

  Um, well, we'll try to do that next year, which try to be the definitive thing. Um, and I, 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?

• 25:07Speaker 00

  Okay.

• 25:08Speaker 01

  Like twenty five hundred is too much and most of it is noise. And so you need a better filter.

• 25:12Speaker 00

  I have a strong opinion.

• 25:14Speaker 01

  Okay, go ahead.

• 25:16Speaker 00

  The amount of people who are also doing it in a way where it's like undercover, not, it's.

• 25:24Speaker 01

  They're shopping, they're spying?

• 25:26Speaker 00

  You have that, but you also have, it's, they're like legally not allowed to work. So there's so much noise in that.

• 25:33Speaker 01

  Uh, visas.

• 25:33Speaker 00

  Visas and there's so much noise around that as well. And so when I was a first time fa, like I am a first time founder, when I first started, you don't know those things.

• 25:42Speaker 01

  Yeah.

• 25:43Speaker 00

  You don't know the like market, right? And there's a lot of noise related to that.

• 25:47Speaker 01

  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 so far.

• 25:55Speaker 00

  Thank you so much. I'd love, I have one question, which is how you, when you moved into AI.

• 26:01Speaker 01

  Uh, just, just decided to start. Uh, well, I, I left uh, the hedge fund world, 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 generating post and uh, started this, this conference and, and the media stuff. Because I'm, I'm very much of a media person.

• 26:20Speaker 00

  Yeah.

• 26:21Speaker 01

  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.

• 26:28Speaker 00

  I, yeah, I feel the same.

• 26:30Speaker 01

  Well, thank you, I think we have to go.

• 26:31Speaker 00

  Thank you so much, thanks.

• 26:32Speaker 01

  Thanks.