“你就是整个团队”：专访 Moshi 创始人 Anekha Sokhal，AI 如何深度融入交易员工作流

本年度最后一场旗舰大会 AIE CODE 现已开放门票与申请通道。

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当 Anekha Sokhal 还在从事能源衍生品交易时，时刻掌握市场动向意味着必须追踪远超人类阅读极限的海量信息。如今，作为 Moshi 的 CEO，她正在构建那款自己当年梦寐以求的工具：一个能够将庞杂市场信息与交易员投资组合核心关切精准串联的推荐引擎。

在 AI Engineer 纽约峰会上，Anekha 与 swyx 展开深度对谈，分享了她从数学专业到摩根大通、再到创立 Moshi 的历程，并阐述了为何好用的 AI 必须深度契合人类的实际工作流。她将 Moshi 形象地描述为 Bloomberg、Claude 与 Netflix 结合的产物：既有市场数据，又有与之交互的界面，还具备专门为交易员量身定制的内容推荐能力。

他们还探讨了可信数据源、文档提取技术、自研模型与采购模型的抉择，以及当下 AI 工程师在被管理层告知“你就是整个团队”时所承受的巨大压力。此外，Anekha 分享了在大会期间与客户交流的心得，并阐明了为何与用户沟通必须先于团队招聘。

在 YouTube 上观看 Anekha Sokhal 的访谈

Moshi — Anekha 创立的公司
Anekha Sokhal — 个人背景与工作经历
GIQ 研究论文 — 访谈中提及的研究
Reducto — 文档提取平台
AI Engineer New York — 峰会官方页面

0:00 — AI Engineer 纽约峰会与“你就是整个团队”
0:54 — 走近 Anekha Sokhal
2:09 — 从数学专业到能源衍生品交易
3:46 — 获得富布赖特奖学金并开启创业之路
5:45 — Moshi 为交易员解决什么问题
6:10 — 处理洪流般的市场数据
9:03 — Bloomberg 与市场工作流
10:03 — Bloomberg、Claude 与 Netflix 的结合
12:03 — 从仪表盘转向工作流
15:03 — 可信数据源与有效信号
17:13 — 模型的自研与外购抉择及文档提取
18:20 — 回顾 AI Engineer 纽约峰会
20:32 — 客户会议与社区建设
23:54 — 团队招聘与用户沟通优先级
25:55 — swyx 的 AI 转型之路

以下为自动转录文字，措辞可能存在误差，发言人标签已略去。

0:00 我其实延长了在纽约的逗留时间，因为日程已经排满了，我本来今天就该离开的。但现在我们周四有会，延了一天；周五有会，又延了一天；周一又有会。然后还有人跟我说：“嘿，我们也是做能源领域的，来休斯敦聊聊吧。”所以大会结束后的整整一周，我们的会议全部排满了。这次见到的 AI 工程师群体很有意思，很多人都感到快被来自管理层的压力压垮了。为什么呢？因为现在很多人会说：“我们不需要一个团队，你一个人就是整个团队。”于是情况就变成了：“嘿，我现在必须一个人把整套系统搭出来。”但那种规模的系统在过去通常需要 10 个人来做。好的，我们现在在录音室里。这是我这届 AIE 的最后一期播客，嘉宾是来自 Moshi 的 Anekha。欢迎你！非常感谢。

1:01 我念对你的名字了吗，Anekha？是的，完全正确。你的背景深深吸引了我，因为我也曾是一名交易员，而你现在是一名创始人。对。你刚才也跟我聊起了你的经历，我很想听你亲自讲讲。据我所知，这是你第一次参加 AIE。你是怎么知道我们的？在台上做演讲体验如何，之后又发生了什么？好的，首先非常感谢 AIE 的所有工作人员，这次参会体验太棒了。也谢谢你邀请我来录播客。我非常兴奋能分享自己的经历。同时我觉得我们正处在一个飞速发展的时代，我聊过的每个人都感到应接不暇。因此能够分享我所经历的一切非常有帮助，我本科读的是统计与数学——也就是现在大家所谓的 AI。所以能聊聊这个方向的发展演变，再和一位同样做过交易员的前辈交流，真的很有意思。

2:02 还能互相倒倒当年的苦水。你当时学过金融微积分吗，比如随机微积分？是的，我学过。我本科在华威大学读的是 MORSE 专业（数学、运筹学、统计学与经济学）。当时大家听了都觉得：“天呐，太书呆子气了。”我以前甚至很讨厌告诉别人我学这个。而现在大家的反应则是：“哇塞，机器学习！AI！”对吧。然后就是衍生品。我最早就是在英国华威大学读这个专业的，当时就很明确自己想从事交易行业。我不知道你是怎么进入交易领域的，但我当时一心想要快节奏、且与数据打交道的工作——我极其热衷于数据。当我读到交易的定义时，我觉得自己不可能从事其他任何行业了。所以我一心奔着这个方向去，拿到了第一份投行与交易的实习，随后又获得了摩根大通的实习机会，并在那里正式开启了职业生涯。

3:04 我最初在新兴市场交易台，后来转到了大宗商品部门，做那里的衍生品交易。所以我职业生涯的大部分时间都在做能源衍生品。正因为我有数据背景，我总是习惯把市场拆解成各个不同的组成部分来看待。比如在大宗商品领域，很大程度上取决于基本面、各类新闻动态以及现实物理世界的运作情况。所以我一直对从零构建体系非常着迷。在做交易的过程中，我察觉到了一种趋势转变：先是数据层面的异动，随后市场必然会发生反应。正是在那时，我决定离开摩根大通，去创办一家数据公司、成为一名创业者。后来我拿到了富布赖特奖学金来到美国，因为你们这里的风投环境实在太不可思议了。就这么随随便便拿了个富布赖特奖学金？

4:06 哈哈，说来挺逗的，我当时在伦敦上瑜伽课。我父母是做珠宝生意的，在大学创业前，我还利用业余时间做过一个计算机视觉类的初创项目。当时我就想：“珠宝行业有什么痛点？比如虚拟试戴。”这让我燃起了当创始人的念头。但我琢磨着，怎么才能去最适合创业的国家创业呢？而且我希望能成为一名懂底层技术的创始人。那是 2020、2021 年前后，我发现富布赖特有一个数据专项奖学金，而且在全英国只给一个名额。我心想：“去美国唯一的路径就是拿下这个奖学金，虽然只录一人。”这事特别巧，不知道你还记不记得 2020 年疫情期间天然气市场有多疯狂。当时天然气市场出现了不可思议的波动。

5:06 极端的波动率。所以在参加富布赖特面试时，他们问我：“你如何看待未来？”我就讲到了全球正在面临的能源市场危机这一重大议题，以及数据如何与之紧密相关。每个人都能对我说的话产生共鸣，因为当时天然气价格飙升，大家的电费账单也都疯涨。于是我来到了美国，深入钻研机器学习研究，在 ICLR 上发表了论文，与 Stability AI 以及 NASA 展开过合作，做了大量极具深度的工作，最终顺理成章地催生了 Moshi。那么 Moshi 到底是做什么的？Moshi 正在打造的就是我当年做交易时梦寐以求的那套系统。我们为金融交易员提供实时洞察，切入点是大宗商品、能源市场和宏观市场。我们所做的事情，是处理远超人类处理极限的数据量，实时提炼出对你当前投资组合至关重要的一切信息。

6:09 你们处理的到底是什么样的数据？能否具体讲讲数据类型和量级？数据类型与数量级。先说说数量级吧。我常打一个生动的比方：我说完刚才这句话大概需要 4 秒钟，而就在这 4 秒里，Moshi 已经学习并吸收了 300 条新信息。这就是我们处理数据吞吐的量级。至于数据类型，这完全源于我对当年交易方式的技术拆解。我会把市场切分成交易员决定建仓和管理风险时所参考的所有数据维度。具体而言，我把市场分为结构化数据与非结构化数据。一方面是期货市场的时间序列数据及其周边所有的技术分析指标。我们监控着所有的统计指标、各类技术指标以及各类移动平均线。

7:11 这是其中一个数据集，显然它在持续不断地更新。我们关注的下一个数据集是基本面市场的时间序列。尤其是在某些大宗商品市场中，供需数据极其庞大。接着我们看非结构化数据。新闻资讯显然是最直接的一种，但除此之外还存在大量的另类数据（Alternative Data）。比如 X（Twitter）上的数据、播客里的音频数据。X 即 Twitter。对，X Twitter。你们会读取 Twitter。是的。所以关键在于理解所有这些截然不同的数据类型。刚才提到的这些，我认为属于公开可获取的数据。除此之外，还有完全属于另一类的数据——用户的私有数据。我相信你当年做交易时深有体会：你有你自己信赖的特定分析师。我不可能跳出来对你说：“嘿，你应该读这个人的报告”，因为你早有自己的信息渠道。此外你还会有自己的付费订阅，有属于你们内部的专有数据，对吧？

8:11 比如在我们接触过的一些交易能源市场的公司里，他们会有专门的专员负责统计穿过霍尔木兹海峡的 LNG（液化天然气）运输船数量。这类数据对该用户而言是高度私有的。因此，我们将公开与私有这两类数据融合在一起，完成所有的数据清洗与数据转换；接着运用统计学与机器学习方法进行打标，构建出贯穿其上的整套本体论（Ontology）；随后将它们灌入 Moshi 的 N 维图谱中；最终输出一款以软件形式呈现的产品，主动向你推送数据。每次打开 Moshi，你都会看到与自己投资组合高度相关的新动态。同时你也可以主动检索数据——底层 LLM 可以在 30 秒内给出以往你需要耗费数天才能整理出的答案。那大家会不会常说你们是“新版 Bloomberg”，还是说你不同意、不喜欢被贴上“新版 Bloomberg”的标签？嗯，这是个很有意思的问题。

9:13 因为 Bloomberg 显然是行业里无人能撼动的绝对庞然大物。是的，你看，我认为 Bloomberg 具备一些非常优秀的特性，比如 Bloomberg Chat，那是该平台上极其出彩的功能。确实，对于不了解的人来说，交易员们经常直接在 Chat 里面敲定数十亿美元的交易，而且有标准化的格式暗语。我当年踏入自然语言处理（NLP）领域的契机，其实就是去解析 Bloomberg Chat 中的内容，将其结构化为交易日志里的定价表，我们就是用这种方式来做期权定价的。对，Bloomberg Chat 是它的一大王牌。但我不会说我们是“新版 Bloomberg”，我们是一种完全不同形态的软件。我觉得最贴切的表述——虽然不知道会不会引发争议——那就是：如果 Bloomberg、Claude 和 Netflix 生了个孩子，那它就是 Moshi。Netflix？你们还提供视频？Netflix。

10:14 我们内部专门构建了一套推荐系统。作为交易员，当你正在关注某一个标的或事件时，你真正关心的是当前正在发生的二阶甚至三阶衍生影响，而这些是你无法立即凭直觉意识到的。我明白了。所以我们搭建了一整套推荐系统。举个简单的例子：假设你是一名原油交易员，某家能源资产相关的公司做了一个举动，会对油价产生间接冲击，你需要立刻知道这个信息。这种关联你不可能仅凭看普通的新闻滚动流看出来，只有通过一张实时展示关联关系的知识图谱才能捕捉到。因为如果这两个信息点同时爆出，要串联它们，要么靠一个经验丰富的人脑在瞬间完成联想，要么靠一个模拟人类认知能力的推荐系统来完成。这就是我们努力要做的事，因为当年做交易时我真的太渴望有这种能力了。哈哈，不过 Netflix 可是有让人上瘾的一面，这可不算什么好词。

11:15 确实有这方面。但这真的很有意思。我认为推荐系统（RecSys）是 AI 最重要的组成部分之一。作为 AIE 大会的策划人，我每年都会设置一个专门的“LLM + 推荐系统”分论坛。但工程师们往往意识不到它有多关键——那场分论坛的参会人数总是不算多，即便来分享的嘉宾是执掌 YouTube、Netflix、Instagram 和 TikTok 核心推荐引擎的业界大牛。台下居然没坐满几个人，我都纳闷：“搞什么？你们难道对推荐系统不感兴趣吗？”我觉得这很微妙。一部分原因在于，这些平台把推荐呈现得太丝滑自然了，以至于大家都觉得：“这看起来好简单……”

Tickets and applications now live forAIE CODE - the last flagship AIE of the year.

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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 Anekha Sokhal on YouTube

Moshi — Anekha’s company

Anekha Sokhal — background and work

GIQ research paper — research discussed in the interview

Reducto — document extraction

AI Engineer New York

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

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.