The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi
- Moshi's positioning is deliberately not “new Bloomberg” — founder Anekha Sokhal's framing is “if Bloomberg and Claude and Netflix had a baby, it would be Moshi.” The Netflix part is literal: an internally built recommendation system that surfaces the “second- and third-order derivatives” of what a trader is watching — e.g., an energy-equities move that indirectly impacts oil — “right now,” which “you're not gonna get from a news stream.”
- The throughput-and-breadth claim: “it takes me four seconds to say this sentence to you, and in the same amount of time, Moshi has learned 300 new things.” The stack spans futures time series and technicals, fundamentals supply/demand, unstructured news/X/podcast audio, and users' private data (internal analysts, LNG ship counts through the Strait of Hormuz), fused into an “N-dimensional graph” that promises “answers in 30 seconds that would typically take you days.”
- Sokhal's sharpest product thesis is that AI made it easy to build dashboards but not to get people to use them: “you're no longer dealing with a problem, you're dealing with a UI problem.” Anyone can now build “your brain in a dashboard,” but getting someone else to use it requires push-based hooks à la Spotify/Instagram — and Moshi's internal bet is that “if you solve for one [UI], you're naturally also solving for the other [the recsys].”
- swyx's buy-side pushback is the episode's best stress test: at Balyasny, he says they were among the first to use the X Firehose; he describes it as costing around $300,000 a month for all the tweets, and questions whether he'd trade on them — “sometimes people just talk random crap, and sometimes people try to manipulate markets.” Sokhal's answer: commodity suppliers and LNG facilities post status updates on X as trusted sources, Moshi attaches confidence scores, and disagreement between Reuters/Bloomberg/WSJ “is a data point” — whether there's an opportunity is unknown.
- On document AI, Moshi has built its own vision-language model for messy PDFs. Sokhal says data privacy is the first consideration when deciding whether to send client data to a third party, while noting that vendors can be useful with clear agreements and off-the-shelf solutions are a good option for teams without engineers. Her team wants to build models; most of it has, in some capacity, worked with NASA.
- Demand signals from her first AIE look real: she extended her New York stay three times, filled a week of post-conference meetings, and got requests to meet in Houston about energy. Hiring is a separate challenge: one job posting open four days drew 2,500 applicants. swyx called most of the applications noise; Sokhal pointed to undercover applicants and people not legally allowed to work, including visa issues. Her priority is meeting clients, not necessarily hiring.
- A recurring buyer persona: AI engineers overwhelmed by management's new default — “We don't need a team. You are the team.” They're being asked to build alone systems “typically done by 10 people,” asking whether they need “a swarm of agents” and what to do when tools they've built go unclicked — a window into the problems they bring to Moshi.
1. From JPMorgan energy derivatives to a Fulbright in the U.S.
- Sokhal studied MORSE (mathematics, operational research, statistics, economics) at Warwick — “what everyone now calls AI.” After internships in investment banking and trading, including at JPMorgan, she started on emerging markets and moved to commodities and energy derivatives. Her trading style foreshadowed the product: breaking markets into their parts and building them up from fundamentals, news, and what was physically happening.
- A founder spark came from a computer-vision startup she side-hustled at university: with her parents in the jewelry business, she explored virtual try-on and wanted to become a founder who could talk about the technology. Around 2020 or 2021, she found Fulbright's data scholarship, which she says was awarded to one person in the whole of the UK. In her interview, she discussed the energy-market crisis and volatile gas prices, which interviewers could relate to through their electricity bills. Fulbright brought her to the U.S., where she did machine-learning research, published at ICLR with Stability AI, and collaborated with NASA before Moshi.
2. What Moshi ingests — and the throughput claim
- The scale framing: “it takes me four seconds to say this sentence to you, and in the same amount of time, Moshi has learned 300 new things.” Sokhal calls it “the program I wish I'd had when I was trading”: real-time insights for commodity, energy, and macro traders.
- The taxonomy she geeks out on: futures time series with technical indicators and moving averages; fundamentals time series for supply and demand; unstructured news, X data, and podcast audio; plus the user's private layer — trusted analysts, subscriptions, internal data, and examples such as tracking LNG ships through the Strait of Hormuz. Moshi cleans and transforms the data, applies statistics and machine learning, tags it under an ontology, and puts it into the “Moshi N-dimensional graph.”
- The output is both push and pull: each time a user opens Moshi, it surfaces something new related to their portfolio, and the LLM can provide answers in 30 seconds that would typically take days.
3. The Bloomberg-Claude-Netflix baby, and why RecSys matters
- Asked whether she's building the “new Bloomberg,” Sokhal demurs — Bloomberg Chat is a “fantastic” feature — and calls Moshi a different type of software: “if Bloomberg and Claude and Netflix had a baby, it would be Moshi.”
- The Netflix piece is the recommendation system. A trader watching one thing may care about its “second- and third-order derivatives” — for example, an energy-equities move that indirectly affects oil. Sokhal says a news stream won't surface that link; a connected graph can show it, and making the connection requires either a human or something mimicking a human, such as a recommendation system.
- swyx says he runs an LLM RecSys track at AIE every year featuring people behind recommendation systems at YouTube, Netflix, Instagram, and TikTok, but engineers often don't appreciate how important RecSys is. Sokhal's diagnosis: “they make it seem so easy.”
- Her adjacent thesis on the post-AI software glut: everyone can build “claw dashboards,” but getting someone else to incorporate one into their workflow is a UI problem. Platforms that push data can hook users; Moshi's internal bet is that solving UI and solving the recommendation system are linked.
4. swyx's buy-side interrogation: noisy tweets, private data, build-vs-buy
- The host's credentials frame the pushback: swyx says he traded currency options in London, then rates, before moving to the buy side. At Balyasny, he says they were among the first to use the X Firehose; he describes it as “stupidly expensive,” around $300,000 a month for all the tweets. He says his eyes glaze over at the noisy data and questions whether he'd trade on it: people “talk random crap,” and some try to manipulate markets. His number-one buy-side question for Moshi: “Are you looking at my data?”
- Sokhal's filter: commodity suppliers and LNG facilities are active on X because it's a quick way to give status updates, and users have people in the industry whose voices they trust. Moshi attaches a confidence score and shows the surrounding conversation. Disagreement between Reuters, Bloomberg, and The Wall Street Journal is itself a data point; whether there's an opportunity is unknown.
- For messy PDFs, Moshi has built a vision-language model to break down uploaded data and build it into its database. On build-vs-buy, Sokhal says the first consideration is not sending client data to a third party without an agreement on how it will be processed. She also says off-the-shelf systems are a good solution for teams without engineers; her team wants to build models, and most of it has worked with NASA in some capacity.
5. Post-talk inbound as a market read: overwhelmed AI engineers, 2,500 applicants
- After the talk, Sokhal was meant to leave New York that day but extended her stay for meetings on Thursday, Friday, and Monday. People also asked to meet in Houston about energy; she says the week after the conference filled up with meetings. The inbound included traders at funds or in consulting, AI engineers, and potential partners.
- The most telling buyer segment: AI engineers “overwhelmed by the pressure from management” because “people now say, ‘We don't need a team. You are the team.’” They're asked to build alone systems “typically done by 10 people,” and some ask whether they need “a swarm of agents” or what to do when a system is in users' email but no one clicks it.
- One job posting open for four days drew 2,500 applicants. swyx says most of the applications are noise; Sokhal points to people applying undercover and people legally unable to work, including visa issues. She says the priority is meeting clients, not necessarily hiring, and that the team is hosting its first webinar because it can't handle all the inbound one-to-one. swyx suggests on-site group meetings; Sokhal likes the idea of scheduled slots so people can meet without traveling to her or her team.
Full transcript
So I’ve actually extended my stay in New York. I was meant to leave today. We’ve now got meetings on Thursday, so I extended it another day. Then we got meetings on Friday, so I extended it another day, and then Monday. Now I’ve got people saying, “Hey, we’re in energy. Let’s talk in Houston.” So we’ve filled up a week of meetings after the conference.
AI engineers were an interesting group, because a lot of them are feeling overwhelmed by the pressure from management.
To do what?
Because people now say, “We don’t need a team. You are the team.” So it’s like, “Hey, I need to build this whole system myself,” but that system would typically be done by 10 people.
Okay, we’re in the studio, last podcast of AIE for me, with Anekha from Moshi. Welcome.
Thank you so much.
Did I get it right, Anekha?
Yes, you did.
Your background caught my interest because I was a former trader, and you’re now a founder. You were also telling me about your experience, and I want to hear about it from you. As far as I know, this is your first AIE.
Mm-hmm.
How did you hear about us? What was the experience like giving your talk, and what happened after?
First of all, thank you to everyone at AIE, because it’s been such a fantastic experience. Thank you for inviting me on the podcast as well.
Of course.
I’m so excited to share my experience. We’re living in such a fast-moving time, and everyone I speak to is very overwhelmed. It’s really helpful to share everything I’ve experienced, because I did my undergrad in statistics and math, which is what everyone calls AI now. It’s really interesting to speak about that and its evolution, and also to speak to a former trader and share war scars from that.
Did you do financial calculus, like stochastic calculus?
Yeah, I did that. My undergraduate degree was mathematics, operational research, statistics, and economics—MORSE—at Warwick.
Ah.
It was what everyone was like, “Oh my God, so nerdy,” and I hated telling people. Now it’s like, “Wow.” Everyone’s like, “Oh my God, machine learning, AI.”
Yeah, yeah. And then derivatives.
My background started at Warwick in England, and I knew I wanted to work in trading. I don’t know what your story was for going into trading, but I wanted something fast-paced and to do with data. I absolutely loved data. When I read about what trading was, I couldn’t imagine myself in any other career, so I was set on doing that.
I got my first internship in investment banking and trading, and then an internship at JPMorgan, where I started on the emerging markets desk. Then I moved to commodities and did derivative trading there. Most of my career has been in energy derivatives. Because I have that data background, I always looked at the market by breaking it down into all the different parts. In commodities, a lot is about the fundamentals, the different news, and what’s physically happening. I was fascinated by building it up from zero.
When I was trading, I saw this shift where it was like data, and then something was going to happen. That’s when I left JPMorgan. I wanted to be an entrepreneur and a founder, but I wanted to have a data company. I got the Fulbright scholarship to come to the U.S. because you have such an incredible venture space.
Just casually got a Fulbright?
Yeah. A funny story: I was doing a yoga class in London. My parents have a jewelry business. I side-hustled a computer vision startup before my startup at university, and I was thinking, “What are the problems in the jewelry space? Virtual try-on.” That led me to want to be a founder. I thought, “How could I be a founder in the best country to be a founder in?” I wanted to be a founder who could talk about the technology.
This was back in 2020 or 2021, and I found that Fulbright had a data scholarship. They gave one in the whole of the UK.
Uh-huh.
I thought, “My way to get here is a Fulbright scholarship, and they only give one.” This is really funny, because I don’t know if you remember 2020, when it was COVID and gas markets were going crazy.
Mm-hmm.
Gas markets had insane volatility. When I did my Fulbright interview, they asked me, “How do you see the future?” I talked about the global issue of everyone having an energy market crisis and how data relates to that. Everyone could relate to what I was talking about because gas prices were crazy and their electricity bills were going crazy. That led me to the U.S.
I came here and was deep in machine learning research. I published at ICLR with Stability AI, worked in collaboration with NASA, and did a lot of really interesting work, which then led me to Moshi.
And what is Moshi?
Moshi is the program I wish I’d had when I was trading. We provide real-time insights for financial traders, starting with commodity and energy markets and macro markets. We process more data than is humanly possible to tell you everything that matters to your portfolio right now.
What kind of data would you be processing? Give us an idea of the types and order of magnitude.
Types and order of magnitude. Let me start with order of magnitude. A fun way I explain it is that it takes me 4 seconds to say this sentence to you, and in the same amount of time, Moshi has learned 300 new things. That’s the volume of data we process.
In terms of the types of data, this comes from geeking out about how I would trade. I’d break the market into all the different types of data a trader looks at to decide what trade to put on and how to manage the risk. I take the market and say, “Well, there’s structured data and unstructured data.” There’s the time series of the futures market and all the technical analysis around it. We’re looking at all these statistical indicators, all the different technical indicators, and all the moving averages. That’s one dataset, and it’s constantly updating.
The next dataset we look at is the time series for market fundamentals. Especially in some commodity markets, there’s a lot of supply and demand. Then we look at unstructured data. News is the obvious one, but there’s also all this alternative data that exists. For example, there’s X data and audio data on podcasts.
Ex-Twitter.
Ex-Twitter, yes. Understanding all these different types of data is what I’d call the publicly available data. Then there’s this whole other type of data, which is the user’s private data.
Mm-hmm.
I’m sure you had this when you were trading: you have the analysts you care about. I can’t come and tell you, “Hey, you should read this person,” because you have the people you need. You’ll have the subscriptions you care about and your internal data. Some of the companies we speak to, if they’re trading energy markets, will have their person tracking the amount of LNG ships going through the Strait of Hormuz.
There’s all this data that’s personal to the user. We take those 2 data points together, do all the data cleaning and data transformation, then look at the statistics and machine learning, tag it, and create our whole ontology on top of it. Then we put it into the Moshi N-dimensional graph and produce the output, which is a piece of software that pushes data to you.
Every time you open Moshi, you see something new related to your portfolio, and you can pull data. With an LLM, I’m going to give you answers in 30 seconds that would typically take you days.
Do people often say “new Bloomberg,” or is that something you disagree with? Do you not like being characterized as the new Bloomberg?
Yeah, that’s an interesting question.
Because obviously, that’s the 800-pound gorilla—or whatever the British version of saying “800-pound gorilla” is.
Yeah, I think Bloomberg has certain features. Bloomberg Chat is a fantastic aspect of the platform.
Yeah. For those who don’t know, traders would transact billions of dollars in Chat, and it has a standardized formula. My entry into NLP was actually parsing Bloomberg Chat into a structured pricing sheet for my logs.
Mm, mm.
And that's how we did option pricing.
Yeah. There's 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. 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. We actually have a recommendation system that we've built internally. If you're a trader looking at one thing, you care about the second- and third-order derivatives right now, which you wouldn't immediately know.
I see.
We've built a whole recommendation system. Let's bring it back to an oil trader: if there's an energy equities company doing something that would indirectly impact oil, you need to know that right now. You're not going to get that from a news stream.
You will get that from a connected graph showing it to you. If those 2 information points are coming out right now, the only way you can do it is either with a human who can connect them, or with something mimicking a human, like a recommendation system that can do that. That's what we try to do, because I wish I had that when I was trading.
Yeah. Well, there's a whole addictiveness element of Netflix that's bad.
There is.
But, yeah, it's very interesting. I think RecSys is one of the most important parts of AI. As a curator of AI events, I actually have an LLM RecSys track every year, and engineers often don't appreciate how important it is. It's not very well attended, even though some of the people powering these things run RecSys for YouTube, Netflix, Instagram, and TikTok. Then nobody shows up, and I'm like, “What do you mean? You're not interested in RecSys?”
You know, I find it really interesting. I think part of it is that they make it seem so easy.
This is the most important system in the entire company.
Exactly. And I'd say there's arguably something linked to it that some companies do really well, like Spotify and Instagram. One thing that's come out of AI is that everyone can now build their own claw 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 to get someone new to incorporate that into their workflow. You're no longer dealing with a problem; you're dealing with a UI problem: how do they interact with it? Because sometimes users don't always use that.
One thing these platforms do really well is push data to you, which hooks you, so you naturally don't even realize you're on the platform. You're like, “Oh, wait, I want that. No, no, I actually want that.” We've been trying internally as a company to understand how UI relates to a recommendation system, because our thesis is that if you solve for one, you're naturally also solving for the other.
I think that's a big thing right now: if everyone's building software, how do you get it to actually be software that people will use?
Yeah.
It's a bit of an interesting take.
Yeah.
Yeah.
Yeah. Bit of a chicken and egg. Well, anyway, I forgot to mention my background, right?
Yes. Yeah.
I used to trade currency options in London, actually, and then I spent some time in rates. I had a very similar career path, where you start with EMEA and then go into maybe G10. At least for currencies, that's what we call it. You ingest a lot of information, and LLMs can probably process it better. We didn't have the tooling back when I did this 10 years ago.
Mm-hmm.
But now I see that. Then I moved to the buy side, where you start paying for alternative data, exactly like you did. We were actually among the very first at Balyasny to do the X Firehose, or Twitter Firehose.
Yeah.
That's a stupidly expensive data thing, like $300,000 a month, that you have to pay for to get all the tweets.
Used to be free.
I don't know if it was ever free. It was free for researchers.
Okay. Yeah.
But if you're a hedge fund, you had to buy that.
Mm.
And you sort of parse them. It's very noisy, and my eyes glaze over because I don't know if I would trade on those things. Sometimes people just talk random crap, and sometimes people try to manipulate markets by saying stuff, and it just doesn't matter that much.
We would also have alternative data like satellite information or credit card information or whatever, right? On the buy side, my number one question is, “Are you looking at my data?” Which you're probably not, but how do you assure people? How do you integrate with systems that you don't necessarily even see?
Yeah. I think there are 2 questions in there. The first one is: if there's so much data available, how do you know what's important? When we're looking at X data—or, let's expand it to any type of data—one thing we've found is that people really have trusted people whose voices in the industry they follow, especially in commodities. There'll be commodity suppliers, LNG facilities—all of these facilities will be active on there because it's the quickest way for them to give a status update.
Wow.
It's interesting because we've found that a lot of our users will tell us about this. They'll say, “Oh, okay, so you find it as a trusted source because you believe this certain individual.”
Sketchy.
Right?
Yeah.
It's one of those things where we've found a way to try to filter out as much as possible. When we show it to someone, we give a confidence score: someone is saying this one thing, but this is everything else happening around the conversation. What's interesting to people is when there are divergent sources. For example, how many ships are going up? How many planes are going above us right now? Reuters, Bloomberg, and The Wall Street Journal may not all agree.
Mm-hmm.
But the fact that they don't agree is a data point, because is there an opportunity there? No one knows. That's how we try to give people information.
The second question is that people's data sits everywhere, and I think there are different types of integration. The first one is their messy PDF data. We've built a vision-language model to understand it. We'll take the data they can upload to their cloud version of Moshi, break it down, and then build it up in our database. There are certain types of data where you just have to connect to their systems, and then that's more of a conversation.
Is it important to own that? I assume you're running this vision-language model yourself. Or would you want to offload it? I mean, there are any number of vendors. Reducto is a known document-processing one.
Yeah, exactly.
The document-processing one. How important is it to have your own models versus just use whatever's off the shelf?
I think that's the question a lot of companies are battling with right now. The first question for us is: if someone's giving us their data, we don't want to be sending it to a third party. There are systems out there that can do these things for you, but you want to make sure you have an agreement on how they're processing that data and what they're doing with it. That's the first question.
The second one is, if you don't have a team of engineers, then that's a great solution. But if you do have a team of engineers like us, a fun fact is that most of the team has, in some capacity, worked with NASA, and they want to build the models.
Mm-hmm.
But not everyone has that. I think it really depends on where you are as a team.
Yeah. Got it. Coming into your talk, you apparently had a lot of inbound from it.
Yeah.
This is our first finance-focused one, so I was genuinely unsure who we could get in the door. Obviously, we pitched it and marketed very heavily for this crew.
What kind of people did you end up talking to?
Yeah.
Yeah.
It was a fantastic opportunity because we’ve been speaking to people back-to-back. We initially thought we knew where the interest would be, but in the past hour, people have messaged me on LinkedIn saying, “Hey, I saw your talk. 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 are a lot of traders, either at funds or in consulting, and AI engineers. That was an interesting one because a lot of them are feeling overwhelmed by pressure from management.
To do what?
Because people now say, “We don’t need a team; you are the team.” So it’s like, “Hey, I need to build this whole system myself, but that system would typically be done by 10 people.” A lot of people have talked to us and said, “Hey, I need to build a system, but do I need a swarm of agents? And what if something breaks?”
Something interesting is when someone says, “Hey, I built this system. People told me they needed it. It’s in their email, but no one’s clicking it. They complain that they have this problem. What do I do?” So that was one area. Then there are partnerships. We’ve been meeting people, and they’re like, “Hey, you’re doing this? That’s interesting, because we’re doing this. Let’s have a conversation after.”
I’ve actually extended my stay in New York. I was meant to leave today, but now we’ve got meetings on Thursday. I extended another day, then Friday; I extended it another day, to Monday. And now I’ve got meetings with people who are like, “Hey, we’re in energy. Let’s talk in Houston.” We’ve filled up a week of meetings after the conference.
Oh my God. Okay.
Yeah. My team was like, “Hey, Anekha, another person signed up. Another person signed up.”
Right, right, right.
So it’s super cool.
Is it interesting to do these one-to-one meetings? I guess they need to ask you things in private. It just seems very inefficient to set up so many meetings when you could be building a business or a product.
Yeah. We try to be as helpful as we can to people. We’ll have an initial meeting, but then we really want to understand what problem you’re solving for and have a plan after that.
Yeah.
We’re doing things now to streamline that. We’re hosting our first webinar because, with the amount of inbound we have, we can’t always do these one-to-ones.
Yeah, exactly. More generally, when I was on the buy side, the banks would set up these meetings for us. They’d come to little rooms like this, with a round table, and I’d meet five at once. Let’s say I’m a CFO of a company and I need to do investor relations: I’m meeting five funds at once, and I would be one of the five funds.
Hmm.
I imagine what we could do for you in the future—and I haven’t got this off the ground yet—is hold the meetings on-site.
That makes sense.
People saw your talk and wanted to speak more with you; they should be able to sign up, and you can do five-on-one. Probably all these questions are the same.
That would be a great idea, actually. Sometimes you don’t know what the person you’re meeting looks like, either.
Okay.
So you’re trying to take pictures of where you are.
Really?
To say, “Hey, meet me here,” and call them. An idea like that would be great.
Ah. So, just an assigned meeting room?
Yeah.
I mean, this whole floor is set up for that, but we had to pre-assign it to other speakers.
Yeah.
We wanted to do some dynamic allocation. I just don’t know how to get that going. I’m trying to feel it out with you.
I think that would be great.
So you’re not having to fly to Houston—or even, if they’re in New York, go to their office. Everyone just gets in here, gets their meetings done, and you’re out.
I actually think that would be a great idea. Have a 30-minute slot for this person, and then they can sign up automatically.
Just whatever you want, right?
Yeah, like smaller one-to-ones. That is a great idea.
Yeah. Anything else that we could do better, or other experiences from the conference that are on your mind?
I think this has been the best-organized conference I’ve been to.
Aw.
No, seriously. The amount of good-quality guidelines I’ve had has been great, and everything was super clear. The speaker team, the breakout rooms, the speaker lounge—I feel like I’ve had—
The lounge is never this good, I have to say.
Okay, it is very nice.
Yeah. Haircut. I know. What did you do?
No, I didn’t get a haircut.
Facial?
I had a facial and a massage.
Yeah.
I know you got your haircut.
I did get my hair cut.
Yeah.
I mean, we do want to spoil our speakers. This was definitely over and above, and that’s thanks to Cognition for doing that. Organizing is one thing, but in terms of business value and efficiency, I think that’s an important element. We want to make this something useful for you, both for hiring and selling. I don’t know how to balance that, because are you even—what’s more important to you, hiring or selling?
We’ve just hired, and we plan to hire. We’re always meeting people, but the priority right now is to meet clients.
Yeah.
That’s the priority, not necessarily hiring, because I think we get continuous inbound from LinkedIn.
Really?
Yeah. We had one job up, and we had to close it. It was open for 4 days, and we had 2,500 applicants. That’s tough to deal with in itself.
Meeting people who want to buy is important. There are people who have the problem but don’t want to buy, and people who have the problem and want to buy. You’re trying to find that match, and I haven’t found anyone who’s done that well. I really like your one-on-one idea.
Yeah. Basically, I’m transferring what I used to have as a hedge fund person over to this market, which is AI and finance. We’ll try to do that next year. We’ll try to be the definitive thing. That’s the buying side, and I think that’s clear. I can definitely help you better there. On the hiring side, I also want to help you.
Okay. Yeah.
2,500 is too much, and most of it is noise, so you need a better filter.
I have a strong opinion.
Okay. Go ahead.
The number of people who are doing it undercover—
They’re shopping? They’re spying?
You have that, but you also have people who are legally not allowed to work. There’s so much noise around that.
Oh.
It’s—
Visas?
Visas. There’s so much noise around that, too. When I first started—as a first-time founder, which I still am—you don’t know those things. You don’t know the market, and there’s a lot of noise related to that.
Yeah. Okay. We’ll try to get you higher signal. We want to be a high-signal conference. I’m just appreciative that you’re spending some time with us, and congrats on your success so far.
Thank you so much.
Yeah.
I have one question: how did you move into AI—
Just decided to start. Well, I left the hedge fund world and moved into tech. Then, when the AI wave happened, I realized I’d better move very quickly to establish this field. So I did the AI Engineering post and started this conference and the media stuff.
Because I’m very much a media person at heart. 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.
Yeah.
Well, thank you. I think we have to go.
Thank you so much. Thanks.
Thanks.