Invest Like the Best with Patrick O'Shaughnessy
· Jesse Zhang, Patrick O'Shaughnessy
[00:00:00] S1: I think the best ones, I would say they all mean one, they get a very deep understanding of our customers. It's actually funny, our customers have made probably so much money on expert calls because so many investors are hitting them up and I think the best ones can before they even talk to you. They've probably already done quite a bit of research and have a pretty full view on your customers. Unfortunately, I think what we've seen is that there is a lot of noise there because a lot of people just lie on customer calls. And we have people who had said that they've used us and we literally never heard of them. But generally if you do enough research and you're good at it, then you can underwrite the business that way because that gives you the most signal. And the other one is people that index on culture because I think culture is quite important and a lot of good investors know how important that is. And so if they feel you are becoming a place where good talent is congregating, then folks will index on that more. [00:00:00] S0: Let's talk about that. Let's talk about recruiting and culture. Starting with culture. How would you describe it? We talked about the quote on your wall is the first question. So that's part of it, of course. Extremely competitive, extreme bias to action, get things done. What are the other key components of your culture? When you're sitting with a new recruit, what do you tell them about what kind of place it is?
· Jesse Zhang
[00:00:00] S1: I would say there's definitely a level of intensity that's important. And yeah, you definitely want to tell people that upfront because you want them to self-select into this culture. So everyone that has joined Decagon, I would say they want to work hard, they want to be around other people that are really smart and are them and they're motivated by. They view this as maybe the prime of their career where, hey, I'm going to work hard. But we know that because of that, I'm going to build lifelong relationships with other amazing people. I'm going to have good financial outcomes, I'm going to be able to leapfrog steps in my career because the growth is just happening so quickly. So you're attracting people that. I think that alone creates a pretty strong foundation for the culture because you have people that are there to work. We have a lot of people that live right next to the office. And part of that is because we've selected for people that being in the office with other people. Right. We're in the office a lot. Those are the foundations. And then I think what you have to be careful on top of that is we really want our office to be a place where people enjoy coming to work every day because we spend a lot of time in there. It's like you want people to be happy, to be there where they feel like everyone there is supporting them between the organizations. You want people to feel like they're pretty aligned and folks are working towards the same goal. And that's an ongoing problem. I don't think we've solved culture. It's something we just put a lot of thought into and we want to make sure that people feel like they will be very fulfilled by staying here for a long time.
· Patrick O'Shaughnessy
[00:00:00] S0: One of the most interesting subplots of this entire business evolution in and around AI is talent wars. Obviously it's happening in the most extreme cases at the model layer between Meta and Anthropic and OpenAI. And there's great riveting stories to hear about the lengths people will go to secure a great engineer and the amount they'll pay to secure a great engineer or someone really key to the business. Can you give us your perspective on these talent wars? What it's like to be in them? Obviously I'm sure you are too, fighting for the best talent versus lots of other great companies that are being formed. Tell us from the inside what this environment feels like.
· Jesse Zhang
[00:00:00] S1: Definitely feels like talent is a big team effort for anyone you want to hire. You need the whole team to swarm around them to win highly sought after talent and you have to go and do all the things right. There are some parallels to sales, of course. You're trying to convince people that hey, this is the place they want to be. So that involves oftentimes getting to know their families, getting to know their partners, really figuring out what they want out of their own careers and making sure that you can design a role for them that is that. Unfortunately, in the application layer it is not as crazy as the Metas and the OpenAIs. There's only so many top level researchers. We have some very strong researchers on our team. It's a lot more of applied type research for us. It's the same thing that happens. We're going after a lot of people on our team from Harvard and MIT and Stanford. There's only so many of these folks that are in the market anytime who are in SF or going to the office. And so it is a competitive place to be. I think we're kind of fortunate, and now our talent brand has gotten a lot larger than when we were first starting. And so it definitely has gotten easier to hire. But at the same time, the amount of people we need to hire has also gone up constantly, just finding ideas of how to get new people. I mean we just opened up our New York office as you know, and part of the reason for that is that, hey, there's another talent pool over here. We should leverage that.
· Patrick O'Shaughnessy
[00:00:00] S0: One of the questions that I think so many people are interested in for companies like yours is the use of whatever core underlying LLM versus the development of your own models on top of or replacing those underlying LLMs. You are gathering all this incredible data that's just yours. You don't have to share it with anybody else. These models thrive on good underlying data. How do you think about that aspect of all of this where five years from now it's going to be either your own model or your own model plus something else, or just your own data and context on top of the best model? How do you think that will evolve? I'm both curious about this in terms of how it impacts the product, but also how it impacts your business model, your power in your business where you rely or don't rely on GPT, whatever.
· Jesse Zhang
[00:00:00] S1: When we first started, this was about two years ago, people were still figuring out applications. Almost no one was doing fine-tuning. In fact, anyone that was doing fine-tuning, there was a lot of writing at that time was fine-tuning doesn't work because it doesn't really get you that many gains. Another big reason to not do fine-tuning at that time is that the models are changing so fast. You're still kind of figuring out your use case. Why invest so much time in the fine-tuning? It's not reversible. You're going to have to throw it out next time there's a new model release. I think now the open source models, for example, have gotten to the point where they're definitely not smart enough to do everything. But there's a lot of specific use cases where you just don't need that much intelligence. Example would be even the agent. Let's say the first thing the agent does is it just needs to think about, okay, based on what the user said and everything before, what path do I go down? What data do I need? You can make that a fine-tuned model. That model doesn't have to be good at math or coding or anything. It's just you just need a smaller model that's just fine-tuned on that. Nowadays we're seeing much more of that because a lot of the applications have gotten more mature. So you know how your agent is structured, you know the places where you need models to run and you can take smaller fine-tuned models and that improves the entire system both in terms of performance but also latency and so on. So I think over time there's going to be more and more of that. I do think there's still always going to be a huge usage of the OpenAIs, Anthropics of the world because you just need intelligence, you need the best models. So I think there's going to be a balance but at least in the short term there's going to be more and more of the fine-tuning small models that happen.