为大宗商品交易员打造推荐引擎

其实我延长了在纽约的停留，因为我本来今天要走。我们现在周四有会，又延到周五，再延到周一，然后现在又有人跟我说：“嘿，我们做能源的，咱们在休斯顿聊聊。”所以会议结束之后的一周也被会议排满了。然后 AI 工程师这个群体挺有意思，因为很多人觉得管理层给的压力太大了。

So, I've actually extended my stay in New York because I was meant to leave today. We've now got meetings on Thursday, extended 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 filled up a week after the conference with meetings. Then AI engineers was an interesting one because a lot of them are feeling overwhelmed with the pressure from management.

做什么？

To do what?

因为现在有人说：“我们不需要一个团队。你就是团队。”于是就成了：“嘿，我现在得自己搭建整个系统，但这个系统通常需要 10 个人来做。”

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 typically be done by 10 people."

好了，我们在演播室。这是我为 VIA I 做的最后一期播客，嘉宾是来自 Moshi 的 Anekha。欢迎。

Okay, we're in the studio. Last podcast of VIA I for me with Anekha from Moshi. Welcome.

非常感谢。

Thank you so much.

我念对了吗，Anekha？

Did I get it right, Anekha?

对了，没错。

Yes, you did. You did.

你的背景引起了我的兴趣，因为我以前也是交易员，而你现在也是创始人。你刚才跟我讲了你的经历，我想听你自己说说。据我所知，这是你第一次参加 VIA I。你是怎么知道我们的？演讲体验如何？之后又发生了什么？

Um so, your background caught my interest because I was a former trader, and you're also now a founder. And you 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 VIA I. How did you hear about us? What was the experience like giving your talk? And what happened after?

首先，感谢 VIA I 的所有人，因为这次体验太棒了。也感谢你们邀请我来播客。我很兴奋能分享我的经历。而且，我觉得我们生活在一个变化飞快的时代，我交谈过的每个人都感到不堪重负。所以，能分享我的经历很有帮助，因为我本科读的是统计和数学，也就是现在大家所说的人工智能。所以，谈论这个及其演变非常有趣，而且还能和一位前交易员交流，分享那些“战争创伤”。

Yeah, well, first of all, thank you to everyone at VIA I because it's been such a fantastic experience. And thank you for inviting me on the podcast as well. I'm so excited to share my experience. And also, I think we're living in such a fast-moving time, and everyone I speak to is very overwhelmed. So, it's really helpful to be able to 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 and just to share war scars on that.

金融微积分，比如随机微积分？

Financial calculus like stochastic calculus?

是的，我学过，我本科学位是华威大学的数学、运筹学、统计和经济学（MORSE）。

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

好的。

Okay.

当时大家都说：“天哪，太书呆子了。”我讨厌告诉别人。现在却变成：“哇。”你知道，大家都说：“天哪，机器学习、人工智能。”

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."

是啊，是啊，是啊。然后是衍生品。

Yeah, yeah, yeah. And then derivatives.

所以，我学了那个。我的背景始于英国华威大学，然后我知道我想做交易。我不知道你进入交易行业的故事是什么，但我想要快节奏且与数据相关的工作。我绝对热爱数据。当我读到交易是什么时，我无法想象自己从事其他职业。所以我下定决心要做这个。我得到了第一个投资银行和交易的实习，然后在摩根大通实习并开始工作，我先在新兴市场台，然后转到大宗商品。在那里做衍生品交易。所以我职业生涯的大部分时间都在能源衍生品领域。因为我有数据背景，我总是从想要把市场分解成所有不同部分的角度来看待它。例如，在大宗商品中，很多是关于基本面、不同的新闻和不同的实物事件。所以我一直着迷于从零开始构建。当我交易时，我看到了转变，就像数据，然后会发生一些事情，那就是我离开摩根大通的时候，实际上我想成为一名企业家，我创立了公司，但我想拥有一家数据公司。所以我获得了富布赖特奖学金来到美国，因为你们的创业空间太棒了。

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 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. And did derivatives 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 trading, I saw the shift where it's like a data and then something was going to happen and that's when I left JP Morgan to actually I wanted to be an entrepreneur and I founded 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.

拿到了富布赖特？

Got a Fulbright?

是的，我想说个有趣的故事。我在伦敦上瑜伽课，我父母有珠宝生意。我在大学期间副业做了一个计算机视觉初创公司，所以我想，珠宝领域有什么问题？哦，虚拟试戴。这让我想成为创始人。我想，我怎样才能在最适合做创始人的国家成为创始人？我想成为一名能谈论技术的创始人。所以这是 2020 或 2021 年的事。我发现富布赖特有一个数据奖学金，全英国只有一个名额。

Yeah, I guess the way I say I funny story. I was doing a yoga class in London and I was like so my parents have a jewelry business. I side hustled 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.

嗯哼。

Uh-huh.

所以我来到这里的方式是富布赖特奖学金，他们只给一个名额。这很有趣，不知道你记不记得 2020 年新冠时天然气市场疯狂波动。天然气市场波动极大。所以当我进行富布赖特面试时，他们问我你怎么看未来？我谈到了全球问题，每个人都面临能源市场危机，以及数据如何与之相关，每个人都能理解我所说的，因为天然气价格疯狂，电费也疯狂。这让我来到了美国。来到这里后深入机器学习研究，与 Stability AI 在 ICLR 发表论文，与 NASA 合作，做了很多非常有趣的工作，然后引领我来到了 Moshi。

So 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 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. Um 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 super interesting work which then led me to Moshi.

Moshi 是什么？

And what is Moshi?

Moshi 是我们正在构建的一个程序，是我交易时希望拥有的。我们为金融交易员提供实时洞察，从大宗商品、能源市场和宏观市场开始。我们处理的数据量超过人类所能处理，告诉你现在对你投资组合重要的一切。

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.

你们会处理哪些类型的数据？能给我们大概介绍一下类型和数量级吗？

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

类型和数量级。我先说数量级。我向人们解释的一个有趣方式是，我花 4 秒钟对你说这句话，而同一时间 Moshi 已经学习了 300 件新事物。这就是我们处理的数据量。就数据类型而言，这很大程度上源于我对交易方式的痴迷。我会把市场分解成交易员为决定进行什么交易以及如何管理风险而关注的所有不同类型的数据。所以我所做的就是，把市场分为结构化和非结构化数据。有期货市场的时间序列，以及围绕它的所有技术分析。所以我们关注所有这些，比如统计指标、所有不同的技术指标、所有移动平均线。这是一个数据集。显然它在不断更新。我们关注的另一个数据集是基本面市场的时间序列。特别是在某些大宗商品市场，有很多供需数据。然后我们看非结构化数据。

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 4 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 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.

所以，新闻是最显而易见的，但还有所有这些存在的另类数据。例如，有 X 数据。有播客上的音频数据。

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，是的。

X Twitter, yes.

你读 Twitter 来获取信息。

You read Twitter for Okay.

所以，理解所有这些不同类型的数据，我会说这是数据的公开可用性，还有另一整类数据，即用户的私有数据。

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 users' private data.

嗯。

Mhm.

所以，我确定你在交易时也有这种情况，就是你有你关心的分析师。我不能过来告诉你：“嘿，你应该读这个人的东西。”因为你有你需要的人。然后你会有你关心的订阅。你会有你的内部数据，对吧？比如我们交谈过的一些公司，如果他们是交易市场、交易能源市场，他们会有专门的人负责通过霍尔木兹海峡的 LNG 船数量。所以，有所有这些对用户来说是个性化的数据。我们把这两个数据点放在一起，做所有的数据清洗、数据转换，然后我们看统计和机器学习，打标签，在上面创建我们的整个本体。然后我们把它放入 Moshi 的 N 维图。然后我们产生输出，也就是一个向你推送数据的软件。所以，每次你打开 Moshi，你会看到与你的投资组合相关的新东西，你可以拉取数据。所以，LLM，我会在 30 秒内给你答案，而通常这需要几天。

So, if 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." cuz you have who you need. And then you're going to have your um subscriptions that you care about. You'll have your internal data, right? Like some of the companies we speak to, if they are uh 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 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 put 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.

那么，人们经常说新 Bloomberg 吗，还是你不同意？你不喜欢被描述为新 Bloomberg？

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

是的，这是一个有趣的问题。

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

那是 800 磅的大猩猩，或者不管英国版怎么说 800 磅大猩猩。

that's the 800-lb gorilla gorilla, or whatever the British version of saying 800-lb gorilla is.

是的，你知道，我认为 Bloomberg 有某些功能，比如 Bloomberg 聊天，这是平台的一个极好的方面。

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.

是的。对于那些不知道的人，交易员会在聊天中交易数十亿美元。呃，有一个标准化的公式。呃，当我进入 NLP 时，实际上是将 Bloomberg 聊天解析成结构化的定价表，用于我的日志。呃，我们就是这样进行期权定价的。

Yeah. For those who don't know, like traders would transact billions of dollars in chat. Uh and it it it would there's a standardized formula. Uh, when I my entry into NLP was actually parsing Bloomberg chat into a structured uh, pricing sheet for my logs. Uh, for and that's how we did option pricing.

是的，有 Bloomberg 聊天，这是一回事。嗯，我不会说我们是新 Bloomberg。我会说我们是不同类型的软件，这不是我认为最简单的说法。我不知道这有多争议，但如果 Bloomberg、Claude 和 Netflix 有一个孩子

Yeah, there's the there's the Bloomberg chat, which is is one thing. Um, 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. I don't know how controversial is, but if Bloomberg and Claude and Netflix had a baby

Netflix？

Netflix?

那就是 Moshi。

It would be Moshi.

你们有视频？

You have videos?

Netflix。我们实际上有一个推荐系统，是我们内部构建的，如果你是一个交易员，你正在看一件事，嗯，你实际上现在关心二阶和三阶导数，你不会立即知道。

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.

我明白了。

I see.

所以我们构建了一个完整的推荐系统，一个简单的例子，如果你是一个石油交易员，有一家能源股票公司正在做某事，这会间接影响石油，你需要现在就知道。呃，你不会从新闻流中得到这个。

And so we 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.

嗯。

Mhm.

你会从一个连接图中得到这个，它向你展示。因为如果这两个信息点现在出现，你唯一能做到的方式要么是人，能够连接这些，要么是通过模仿人类的推荐系统，可以做到。这就是我们尝试做的，因为我希望我在交易时就有这个。

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.

是的。嗯，你知道，Netflix 有整个上瘾元素，这不好，但嗯，是的，这很有趣。我认为推荐系统是 AI 最重要的部分之一，作为 AI 的策展人，我实际上每年都有一个 L&M 推荐系统 track，工程师们通常不欣赏它有多重要。比如它出席率不高，呃，尽管一些推动这些事情的人，我的意思是，他们为 YouTube 运行推荐系统，为 Netflix、Instagram 和 TikTok 运行推荐系统。然后没人出现，我就想，“你什么意思？比如，

Yeah. Well, uh, you know, there's a whole addictiveness element of Netflix, which is bad, but um, yeah, it's 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 uh, even though some of the people powering these things, I mean, they run RecSys for YouTube, they run RecSys uh for Netflix, for uh Instagram and TikTok. And then like nobody shows up, and I'm like, "What do you mean? Like,

你对推荐系统不感兴趣？

you're not interested in RecSys?

你知道，我觉得这真的很有趣。我认为部分原因是他们让它看起来太容易了。就像，“哦，这太像为什么

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 why

整个公司最重要的系统就是这些东西。

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

正是。我会说有一个与之相关的东西，我认为一些公司做得很好，比如 Spotify、Instagram，那就是人们推出 AI 后，现在每个人都可以构建自己的核心仪表板，对吧？就像，“嘿，我自己构建了这个。”很好。如果你想让别人使用它，那就是你的大脑在仪表板中或你的大脑在某物中。试着让一个新的人将其纳入他们的工作流程。然后你不再处理一个问题，你处理的是一个 UI 问题，即他们如何与之交互？因为有时用户并不总是使用那个。这些平台做得很好的一件事是它们向你推送数据，这让你上钩，所以你自然甚至没有意识到你在平台上，你会说，“哦，等等，我想要那个。不，不，我实际上想要那个。”所以，我们一直在内部作为一家公司理解 UI 如何与推荐系统相关，因为我们的论点是如果你解决了一个，你自然也会解决另一个。我认为这是现在的一件大事，即如果每个人都在构建软件，你如何让它真正成为人们会使用的软件？

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 core 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 lo- no longer dealing with a problem, you're dealing with a UI problem, which is how did they interact with it? Cuz 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, wait, 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?

是的。这是一个有趣的观点，是的。

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

有点鸡和蛋的问题。嗯，好吧，无论如何，我忘了提到我的背景，对吧？我实际上曾在伦敦交易货币期权。嗯，然后我在利率方面待了一段时间。它的职业路径非常相似，你从 EMEA 开始，然后进入可能是 G10。

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

是的。

Yeah.

至少对于货币，我们是这样称呼的。你摄入大量信息，实际上 LLM 可能能更好地处理它。所以，10 年前我做这个的时候，我们没有这些工具。

At least for That's what we call it for currencies. And it's you 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.

嗯。

Mhm.

但现在我看到了。然后我转到买方，你开始为另类数据付费，就像你一样。我们实际上是 Balyasny 最早做 X firehose 或 Twitter firehose 的少数人之一。那是一种愚蠢地昂贵的东西，比如每月 30 万美元的数据，你必须付费才能获取所有推文。嗯，你有点给它定价。它曾经我不认为我不知道它是否曾经免费。它对研究人员免费。

But now 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. We were actually one of the very first few at Balyasny to do the X firehose or Twitter firehose. That's something like a stupidly expensive like $300,000 a month like data thing that you have to pay for to get all tweets. Um and and you sort of priced it. It used to I don't think I don't know if it was ever free. It was free for researchers.

但如果你是对冲基金，你就得买那些数据。而且噪音很大。所以我会警惕，因为我不确定我会不会根据那些东西交易，因为有时候人们就是胡说八道，有时候人们试图通过放话来操纵市场，而那并不那么重要。但我们也会有另类资产数据，比如卫星信息或信用卡信息等等，对吧？所以，在买方，我的头号问题是：你在看我的数据吗？大概没有。但你怎么让人放心？你如何与那些你甚至不一定看到的系统集成？

But then if you're like a hedge fund, you have to sort of buy that. And it's very noisy. So then 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 asset data like satellite information or credit card information and whatever, right? And so, on the buy side, my number one question is: are you looking at my data? Which is probably not. But how would you assure people? How do you integrate with systems that you don't necessarily even see?

是的，我认为这里面有两个问题。第一个是：如果可用的数据这么多，你怎么知道什么重要？所以当我们看 X 的数据时，我们与人交谈时自然会有对话——或者让我们扩展到任何类型的数据。我们发现人们确实会有他们信任的人，他们会关注这些行业声音。尤其是在大宗商品领域，他们会是商品供应商、LNG 设施等，这些设施会活跃在上面，因为这是他们提供状态更新的最快方式。

Yeah, and I think that's two questions in there. 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. One thing we found with people is they really will have their trusted people who they'll follow, these voices in industry. Especially in commodities, they'll be the suppliers of commodities, they'll be the LNG, all of these facilities who will be active on that because it's the quickest way for them to also give a status update.

哇。

Wow.

所以很有趣，因为我们发现很多用户会告诉我们这一点。所以就像，“哦，好的。所以你认为它是一个可信来源，因为你相信某个特定个人。”

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 find it as a trusted source because you believe this certain individual."

有点可疑。

Sketchy.

对吧？所以这是我们找到的一种方法，尽量过滤掉噪音，但当我们向某人展示时，我们会给出一个置信度分数。比如，某人在说这件事，但这是对话周围发生的所有其他事情。因为对人们来说有趣的是，如果有不同的来源。比如，一个例子是现在有多少艘船在航行，有多少架飞机在我们上方飞过。如果路透社在说某事，彭博社在说某事，华尔街日报在说某事，他们可能并不都同意。但他们不同意的事实本身就是一个数据点，因为那里是否有机会，因为没人知道。所以这就是我们试图向人们提供信息的方式。然后第二个问题是，人们的数据无处不在。我认为有不同类型的集成。第一种是他们的杂乱 PDF 数据怎么办？我们已经构建了一个视觉语言模型来理解这些数据。所以我们会获取他们可以上传到 Moshi 云版本的数据。然后我们实际上会去分解它，然后在我们的数据库中构建起来。还有某些类型的数据，你只需要连接到他们的系统。然后那更多是对话。

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, 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, an example would be how many ships are going up, 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. 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 their messy PDF data? That data is what we've built a vision language model to understand. So we'll take the data that they can upload to their cloud version of Moshi. And then we actually go and 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 more of a conversation.

重要的是——我假设你们自己运行这个视觉语言模型。拥有它重要吗，还是你想外包？我的意思是，有很多供应商。Reducto 是一个知名的文档处理公司。拥有自己的模型有多重要，而不是直接用现成的？

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 offload it? I mean there's any number of vendors. Reducto is like a known document processing one. How important is it to have your own models versus just use whatever's off the shelf?

我认为这是很多公司现在正在纠结的问题。对我们来说，第一个问题是：如果有人给我们他们的数据，我们不想把它发送给第三方，因为外面有系统可以为你做这些事情。但你可能想确保你有协议，关于他们如何处理数据？他们用数据做什么？这是第一个问题。第二个是：如果你没有工程师团队，那么那是一个很好的解决方案。但如果你有像我们这样的工程师团队——一个有趣的事实是，团队中大多数人都在某种程度上与 NASA 合作过——那么他们想要构建模型。

I think that's the question that 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 because there are systems out there which can do these things for you. But you may 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.

嗯。

Mhm.

但不是每个人都有。所以，你知道，我认为这真的取决于你作为一个团队所处的位置。

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

是的，明白了。嗯，好的，然后说到你的演讲，你显然从中获得了很多关注。这是我——这是我们的第一个金融 focused 活动。所以我真的不确定

Yeah, got it. Um, okay, and then coming into your talk, you apparently had a lot of inbound from it. Which I'm—this is our first finance focused one. So I was genuinely unsure

好的。

Okay.

能不能进门。显然我们为这群人做了很多推销和营销。你最终和什么样的人交谈？

could get in the door. Obviously we like pitched it and marketed very heavily for this crew. What kind of people did you end up talking to?

是的。我们——这是一个绝佳的机会，因为我们一直在连续与人交谈。我们最初以为的兴趣点，过去一小时有人在 LinkedIn 上给我发消息说：“嘿，我看了你的核心演讲。你在休斯顿吗？我们能见个面吗？”或者，“嘿，你在纽约吗？我们能见个面吗？”我们遇到的人的类型：很多交易员，要么在基金，要么在咨询。然后 AI 工程师是一个有趣的群体，因为他们中的很多人对管理层的压力感到不堪重负。

Yeah. We—it was a fantastic opportunity because we have been 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 on LinkedIn saying, "Hey, I saw your core talk. Are you 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 overwhelmed with the pressure from management.

做什么？

To do what?

因为现在人们说我们不需要团队，你就是团队。所以就像，“嘿，我现在需要自己构建整个系统，但那个系统通常需要 10 个人来完成。”所以很多人和我们交谈，他们说：“嘿，我需要构建一个系统，但是，你知道，我需要一群智能体吗？但然后，如果出问题了怎么办？”有趣的是，“嘿，我构建了系统。人们告诉我他们需要它。它在邮件里。没人点击它，但他们抱怨他们有这个问题。我该怎么办？”所以那是其中一个领域。然后是合作伙伴关系。你知道，我们一直在见人，人们说：“嘿，你在做这个？那很有趣，因为我们在做这个。我们之后聊聊。”所以我实际上延长了在纽约的停留，因为我们现在——我本来今天要走。我们现在周四有会议，延长一天到周五，再延长一天到周一。然后我现在有会议，人们说：“嘿，我们在能源领域。我们在休斯顿谈。”所以我们会议排满了会议后的一周。

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 typically be 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 the email. No one's clicking it, but they complain that they have this problem. Like, what do I do?" So that was that 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—now I was meant to leave today. We've now got meetings on Thursday, extended 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 filled up a week after the conference of meetings.

天哪。

Oh my god.

是的。我的团队说：“嘿，Annica，又有人报名了。又有人报名了。”所以超级酷。

Yeah. And my team was like, "Hey, Annica, another person signed up. Another person signed up." So it's super cool.

做这些一对一的会议有趣吗？我猜是因为他们需要私下问你事情。当你本可以建立业务或构建产品时，安排这么多会议似乎非常低效。

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 a product.

是的，我们尽量对人有帮助，我们会有一个初步会议，但然后我们真的想能够理解你要解决的问题是什么，然后之后有一个计划。

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.

是的。

Yeah.

所以我们现在有一些办法来分流这些需求，因为我们正在办第一场网络研讨会，因为收到的咨询太多了。我们没法总是做这种一对一的会面。

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

更一般地说，我在买方的时候，银行其实会帮我们安排这些会面。他们会来到像这样的小房间。有点像圆桌。我一次见五个人。比如我说我是一家公司的 CFO，我需要做投资者关系。我一次见五家基金。我知道，你就是那五家基金之一。

And more generally, when I was on the buy side, the banks would actually set up these meetings for us. And they'd come to little rooms like this. There'd be sort of a round table. I would meet five at once. So I'd say I'm a CFO of a company and I need to do investor relations. I'm meeting five funds at once. I know, be one of the five funds.

我在想我们未来能为你做什么——这个我还没落地，但我们应该把会面放在现场做。就像你——

And I imagine what we could do for you in future — which I haven't got this off the ground yet, but we should do the meetings on site. Like you've —

这说得通。

That makes sense.

大家看了你的演讲，想多跟你聊聊。他们应该能报名，然后你可以做五对一。而且这些问题大概都一模一样。就像——

People 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 —

那其实会是个很棒的主意。因为另一个——

That would be a great idea, actually. Because another —

因为我们以前在金融行业就这么做，就像——

'Cause we used to do this in finance, like —

那真是个好主意，因为我们有过这种情况，有时候你都不知道你要见的人长什么样。

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

好。

Okay.

所以你会拍下你所在位置的照片，说，嘿，来这里见我，来这里见我，然后给他们打电话。所以这样的点子会很棒。

So you're trying to take pictures of where you are to say like, hey, meet me here, meet me here, and like call them. So an idea like that would be great.

房间，对。

Room, yeah.

就像，嘿，就像——

It's like, hey, like —

我是说，整层楼都在那儿，但我们得把它预先分配给其他演讲者，不过我们想做些动态分配。我只是不知道怎么推动起来。我想我是在跟你试探一下。

I mean, this whole floor is there, but we had to pre-assign it to other speakers, but we wanted to do some dynamic allocation. I just don't know how to get that going. I think I'm trying to feel it out with you.

这样你就不用飞到休斯顿，或者即使他们在纽约，你也不用去他们的办公室。大家到这里来，把会面做完。你就走了。

So you're not having to fly to Houston, or even if they're in New York, you don't even have to go to their office. Everyone gets in here, gets the meetings done. You're out.

我其实觉得那会是个很棒的主意。比如给这个人留一个 30 分钟的时段，然后报名之类的。

I actually think that would be a great idea. Like have a 30-minute slot for this person and then like sign up or something like that.

随你定，对吧？

Whatever you want, right?

对，像更小规模的一对一。那是个很棒的主意。

Yeah, like smaller one-on-one. That is a great idea.

对，对。我是说，还有别的我们能做得更好的地方吗，或者你在这次大会上的其他体验——

Yeah, yeah. I mean, anything else that we could do better, or other experiences from the conference that you're —

我觉得这是我参加过组织得最好的大会。

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

哎呀，你就是这么说说。

Aw, you're just saying that.

说真的。比如我拿到的那些高质量指引，而且非常清楚。

Seriously. Like, the amount of good quality guidelines I've had, and it was super clear.

嗯。

Mhm.

比如演讲者团队、分组讨论室、演讲者休息室，有——我感觉我——

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

休息室从来没这么好过。这点我得说。对，对。所以，对。理发，我不知道，你——你做了什么——

The lounge is never this good. I will have to say that. Yeah, yeah. So, yeah. Haircut, I don't know, did you — what did you —

没有，我没理发。

No, I didn't do a haircut.

做脸？

Facial?

我做了个脸和按摩。

I did a facial and a massage.

对。

Yeah.

我知道你理了发。

I know you got your haircut.

理了发。对。

Get my haircut. Yeah.

我是说，我们确实想宠一宠我们的演讲者。这次绝对是超规格了，这要感谢 Cognition 这么做。但，对，组织是一回事。我确实认为在商业价值和效率方面，那是个重要元素。比如，我们想让这个活动对你在招聘和销售两方面都有用，你知道吗？我不知道怎么平衡这个，因为——

I mean, we do want to spoil our speakers. This was definitely the over and above, and that's thanks to Cognition for doing that. But, yeah, organizing is one thing. I do think in terms of business value and efficiency, I think that's an important element. Like, we want to make this something that's useful for you for both hiring and selling, you know? And I don't know how to balance that because —

对。

Yeah.

对你来说哪个更重要，招聘还是销售？

What's more important to you, hiring or selling?

就——我们刚招了人，也计划招人。我们一直在见人，但现在的优先事项是见客户。

So in terms of — we've just hired, and we plan to hire. We're always meeting people, but the priority right now is to meet clients.

对。

Yeah.

那是优先事项，不一定是招聘，因为我觉得在招聘方面，我们一直能从 LinkedIn 收到源源不断的咨询。

That's the priority, not necessarily hiring, because I think what we have with hiring is we get continuous inbound from LinkedIn.

真的吗？

Really?

对。我们挂了一个职位。我们不得不把它关了。它开放了 4 天。我们收到了 2500 份申请。

Yeah. We had one job up. We had to close it. It was open for 4 days. We had 2,500 applicants.

好。

Okay.

所以光是应付这个就很难了，但见那些想买的人，还有那些——因为我觉得有些人有这个问题但不想买。还有些人有这个问题又想买，而你要试着找到那个匹配。那是——我还没找到任何做到这一点的人。

And so it's like that's tough to deal with in itself, but meeting people who 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 you're trying to find that match. That is — I haven't found any person who's done that.

对。

Yeah.

不过，我真的很喜欢你这个一对一的主意。

Well, but so, I really like your one-on-one idea.

对，对。我是说，基本上，我把我以前做对冲基金时的那套东西搬到这个市场，也就是金融领域的 AI。

Yeah, yeah. I mean, so basically, I'm transferring over what I used to have as a hedge fund person to this market, which is AI in finance.

对。

Yeah.

我们明年会试着这么做。我们会试着做成那个标杆。我觉得在招聘方面——那是买方，那很清楚。我觉得我在那方面肯定能更好地帮你。在招聘方面我也想帮你，对吧？比如 2500 太多了，大部分都是噪音。所以你需要一个更好的筛选器。

We'll try to do that next year. We'll try to be the definitive thing. And I think on the hiring side — so that's buying, and then that's clear. I think I can definitely help you better there. On the hiring side I also want to help you, right? Like 2,500 is too much and most of it is noise. And so you need a better filter.

我有个强烈的看法。

I have a strong opinion.

好，请讲。

Okay, go ahead.

还有很多人是以一种类似卧底的方式在做，不是——是——

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

他们是在摸底，是在刺探？

They're shopping, they're spying?

有那种情况，但也有——他们在法律上不被允许工作。所以那里面噪音很多。

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

签证？

Visas?

签证，那方面也有很多噪音。所以当我第一次——比如我是第一次创业的创始人——嗯，我刚起步的时候，你不懂那些东西。你不了解市场，对吧？而且有很多相关的噪音。

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 — well, when I first started, you don't know those things. You don't know the market, right? And there's a lot of noise related to that.

对，好。我们会试着为你提供更高信号。我们想做一个高信号的大会。我很感激你花时间跟我们聊，也恭喜你取得成功。

Yeah, okay. Well, we'll try to get that higher signal for you. 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.

非常感谢。我很想——我有个问题，就是你怎么——你是什么时候转入 AI 的？

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

就是决定开始。嗯，我离开了高频交易的世界，转入科技行业。然后当 AI 浪潮来的时候，我意识到我最好非常非常快地行动，来建立这个领域。所以我做了 AI 工程方面的岗位，然后创办了这个大会和媒体那块，因为我很大程度上是个媒体人。

Just decided to start. Well, I left the HFT world and moved into tech. And then when the AI wave happened, I realized that I better move very, very quickly to establish this field. And so I did the AI engineering post and started this conference and the media stuff, 'cause I'm very much of a media person.

对。

Yeah.

骨子里是。我确实有点想念交易，但这很有成就感，因为我能见到真实的人，而不是整天盯着屏幕。

At my heart. I do miss trading a little bit, but this is very fulfilling 'cause I get to meet real people instead of stare at a screen all day.

对，我也有同感。

Yeah, I feel the same.

好，谢谢。我想我们得走了。

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

非常感谢。谢谢。

Thank you so much. Thanks.

谢谢。

Thanks.