The Exchanges

Q You are obviously not focused just on property law now. Like, how do you think about the mission or scope of Harvey today?

A Yeah, um, so mostly we're, we're developing it for legal overall, but I would say that what we're building is the AI platform for legal and professional services, right? And if that sounds vague or it sounds like there aren't incredibly defined use cases for, you know, the small areas that we're building, that's on purpose. Like, the reality is If you are using these tools and you don't think that you can take basically AI and apply to X industry and transform the entire industry, I don't think you're thinking ambitiously enough, right? And I think it's really hard because, you know, these models can't just one shot all of these really complex legal tasks or, or in these other domains like tax and provide other professional services. And so what you have to do is you have to build a platform that is kind of constantly expanding and constantly collapsing. And so what I mean by that is you need to build specific features and maybe agentic workflows, et cetera, that can do parts of a task. And then you need to combine them all together. So the UI is simple and you don't have this like tentacle monster of a platform.

AI assessment note: “what we're building is the AI platform for legal and professional services”

Q What is the end-to-end task you're most excited about that you think Harvey will be able to do this year?

A Yeah, filing an S-IV, um, I think is something that is, the reason I like that process is it is a combination of external data, internal data, and a million steps, right? Um, and I think of workflows as basically a bunch of agentic systems that need to combine together, right? And if you think about knowledge work, professional services, legal, honestly, any swath of that, What you are doing is manipulating things based off of your internal context, external context, whether that's external data, whether that's data from your, whoever your customer is, your client is, et cetera, and then process of this is how, you know, this is how you do an LBO. This is how you do side letter compliance. And then there's another step of that, which is this is how you do side letter compliance for this particular private equity firm, right? This is market for this particular clause. And the more complicated the workflow is, the more you have to combine all of those different elements together.

AI assessment note: “filing an S-IV, um, I think is something that is”

Q And expectation of quality from, let's say, like, an associate or a model is very high. Like, how do you, how did you go about building that trust because the, like, you didn't start at the capabilities you have today?

A Yeah, I'll start from one kind of, like, high-level distinction between two pieces. So if you go back to the productivity versus, like, specific specialized output, right? On the productivity side, the minimum viable quality of that output can be lower because you're selling seats, And at the end of the day, there are multiple people reviewing it, right? Um, and so what you want to do in that state is have just show your work, right? Like that is the most important thing. You want to mimic exactly how a senior associate reviews the work of a junior associate. So you say, this is why I did this. This is the information that I pulled. And here's an inline citation to the literal sentence that I pulled it from. Is this correct or not? Right.

AI assessment note: “what you want to do in that state is have just show your work”

Q The legal industry is one of reasoning. How much has the growth of models and interest in developing models that, um, scale test time inference impacted you guys?

A Massively in a good way. Um, the best way to think about this is if you are building a system where you are trying to basically break down every single problem into a sub problem because the models can't quite do it, that just unlocks different pieces of it. So let me give you an example of this. We go back to that antitrust example, right? Where the first step of it is we're trying to take all of the, you know, target financials, your acquirer's financials, and just say in all these different countries, Yeah, this is what you need to file. Well, the next step to that would be, can you help it actually do all of the filings, right? And we were having trouble figuring out how to do that. Now with reasoning models, you can start unlocking those steps, right? And so the best way to think about this is we are constantly building all of the steps that we can and being on the cutting edge. And then when a model improves, that just pushes our ability to go out the next cutting edge more. Um, and then the other thing too, is just cost going down is incredible for us, right? We try, you know, we're not optimizing for cost at all times right now. We're optimizing for quality. And if the prices go down, that makes it so that we can increase our quality across every single user base much faster.

AI assessment note: “Massively in a good way. Um, the best way to think about this”

Q Uh, I'm sure that's a learning experience. You feel like you got a focus on agency, right? And, you know, hiring and the business, like, what did you get wrong?

A Yeah, I mean, I don't think we have, like, 12 hours to do this. Um, a lot, a lot of things. I think if I had to say there's one thing that I got wrong over everything else, it is figuring out when to scale yourself. Um, I have a certain kind of, like, tendency for how I work, and there are some things I, I want to keep. So one of the lessons that I definitely want to keep is I do think you should do every single role For a certain amount of time before you hire for it. Almost all of my mishires were because I did not understand what that role was. And maybe it's a lack of my experience. I don't know, but there's a hands-on piece. Having said that you can't scale a company by wanting to be hands-on in everything at all times. Right. Um, and I think that I didn't spend enough time transitioning from, I mean, the beginning of last year, we were 40 people transitioning from everyone knew what was going on. Everyone had all the context because They were working with me directly. Right.

AI assessment note: “one thing that I got wrong over everything else, it is figuring out when to scale yourself”

Q Yeah. Um, where does, where does the, like a drive come from?

A I mean, as simple as possible, like this is the most fun thing ever. Um, I don't think, you know, I started this company when I was 27. Um, there was nothing in the 27 years leading up to there that was even close to as much fun as this is. I, I think that it is just so much, you get so much energy from things moving so quickly and you being able to actually have the agency to come up with an idea and then see it built. That is a crazy experience. And I think that you can do that faster than you used to be able to, and it is addicting. Like it is incredibly addicting. Um, and so I actually think that it's, it's less like, where do you get the drive or where do you get the inspiration? I think that most people that I've met that have been successful in this space, they just have it. Like, it's just naturally they love the moment or the like very compressed timeline of you can have a very large impact.

AI assessment note: “as simple as possible, like this is the most fun thing ever.”