From Astrophysics to AI: Building the future AI Data Stack — with Sarah Nagy of Seek.ai

**Alessio Fanelli** (0:09)
Hey everyone, welcome to the Latent Space Podcast.
This is Alessio, Partner and CTO in Residence and Decibel Partners. I'm joined by my co-host, Swix, Brother and Edder of LSPACE Diaries.

**Swix** (0:19)
Today we have a special guest, Sarah Nagy from Seek AI. Welcome, Sarah.

**Sarah Nagy** (0:23)
Yeah, thank you so much for having me.

**Swix** (0:25)
Awesome, I like to introduce guests on your behalf so that you don't have to always introduce yourself, and then also you can get a chance to correct me. So you were an astrophysics major at UCLA, and then you were a masters in finance at Princeton. You spent something like, it looks like 10 years in quantitative trading, which is fun because I also was briefly a quant in a hedge fund. And you were most recently a Citadel before you started Seek.ai.
Anything in your bio that people should know about you that people don't find on LinkedIn?

**Sarah Nagy** (0:53)
Sure, there's a lot of hobbies I had throughout the years, things I did for fun. So, I mean, that's not on my LinkedIn.
I actually played classical piano for over 10 years and actually was a DJ for a little while, just DJing around Williamsburg and Lower East Side. Just had random hobbies here and there. I also used to do improv comedy. Maybe I'll talk about that later, like why it's relevant to ChatGPT, because there's actually some similarities. Besides that, I mean, pretty much everything's on my LinkedIn. So what you said, that's pretty much my background, like you mentioned, started out doing astrophysics.
I was working at UCLA and Caltech, doing a lot of research using data from the Hubble Space Telescope. But like you mentioned, I saw a lot of my colleagues going into quantitative finance, and so that's what brought me out here, and that was my background before starting Seek.

**Swix** (1:49)
Yeah, that's super cool. Actually, I don't mind going into improv. What got you into, this is a little bit off topic, what got you into improv?
I assume ChatGPT is like a yes and, and in terms of how it agrees with every question that you ask it.

**Sarah Nagy** (2:04)
Yeah, I actually, I was kind of a theater kid in high school. So, you know, I had actually been doing musicals since I was in like middle school.
And I just was really, really bad at improv in high school. Like I auditioned for comedy sports like two or three times and just never made it on the team. And then later as an adult, I started taking improv classes. And after working hard enough at it, I ended up becoming a lot better. So that's kind of why I chose to do it. But yeah, I think it's actually really helpful to use that to describe ChatGPT because what I realized, you know, working with these models for actually, I can almost call it several years now.
In improv, if you have to pretend to be a character, for example, a doctor, when you're on stage, you can just talk about, you know, things that sound believable to the audience, but they may not necessarily have to be factual. And so I found that talking with these large language models, they can say things that sound believable, but you know, they're not necessarily true. So, you know, I just think it's kind of an interesting analogy that I noticed.

**Swix** (3:24)
It is, it is. So then that's an interesting lead-in to using GPT-3 and large language models to be a source of truth of data.
So maybe we should just set the context. What is Seek and how do you explain it today?

**Sarah Nagy** (3:40)
Yeah, so Seek AI is a natural language interface that anyone in a business can use to ask questions about the data within the business and get the answers that they need much faster than it would take talking to the data team.
So to tell you a little bit about why I started Seek, it really kind of arose from this pain point that I just kept encountering pretty much everywhere I was working, which was, I'd want to focus on projects that could really help the business. What really excited me about so-called big data was being able to just unearth all of these insights.
And especially in the quantitative finance world, it gets really exciting when you can put together these novel trading strategies, and you're the first one to discover them, and they can make the company a lot of money. That's just an example of the type of value that excited me about becoming a data scientist and a quant. But what I kind of kept seeing was that my less technical colleagues really didn't have the right tools to be able to answer their own questions about the data.