| [I Raised $300M To Bring AI To Laywers | Winston Weinberg & Harvey](https://www.youtube.com/watch?v=GTU2TyoSRLk) |
You should take AI and basically apply it to X industry. If your company isn’t doing that, I don’t think you’re ambitious enough. The average price of a lawyer in the US is $353 an hour. No one can afford that. This technology is a perfect fit for lawyers and I think it will have a pretty large effect on the average person too. The largest competitor for us is just not moving fast enough. I think that indirectly you want to make sure that you are building a business that is going to survive the next 10 years of model releases. If you are willing to take some pain and to deal with some sacrifice, your impact and what you will learn are just massively compounded more than maybe any generation or any time in tech.
Thanks for doing this. We’re sharing a new podcast room. It’s still coming together, but I haven’t been to this office. I’ve never been to this office, so I’m thrilled to do this. I’m curious. I was thinking you announced the fundraise last week and I probably got, I’m not kidding you, no less than 5 to 10 people on LinkedIn or texting me being like, “Hey, can you introduce me to Harvey? I want to go work at Harvey.” You’re kind of the cool kid on the block right now. That’s good, we need to hire. But I wonder, maybe is it good? That’s the question I have because all of these people are coming out of the woodwork. You see this fancy fundraise, you see these nice acronyms like AI. I wonder if they were to go into the reality of working at Harvey, would they be like, “Oh yeah, this is exactly what I want to do,” or are they kind of like clout chasing?
I think one thing that definitely is happening is there’s just a massive switch right now in just the zeitgeist of application layer companies. There’s going to be a lot of value that accrues there. So, yeah, there might be an argument that there are some people that are like, “Okay, I want to go work in an application layer company.” There are a few that are doing really well or that are in the news, etc. You might get more mercenaries than missionaries, although I will say that there’s also just a lot of folks that didn’t know what we were doing or they thought that we were only selling to law firms, or they thought that we were only a GPT wrapper and that was something that was bad.
I think that there’s a combination of yeah, there might be more folks that are flocking to whatever the next best thing is, but I also think there’s a lot of folks that want to do applied AI. There are a lot of ways to do that, not just from the AI standpoint but the engineering standpoint, from the GTM standpoint across the board. I think people are just starting to understand what those opportunities look like. So maybe a little bit of both.
How do you describe Harvey today? It depends who I’m talking to and I should probably have the same one. I think the best way to describe this, and I’ve talked about this a bit, is like if you were using these models and you aren’t basically saying, “Look, we’re going to take AI and apply it to an industry and transform as much of the industry as we can by partnering with the industry,” I don’t think you’re being ambitious enough.
I really do think that if I was to describe it at like the highest level, it is like domain-specific AI for legal, tax, compliance. That doesn’t sound like, “Oh, this is this small point solution that just does contract review,” or “This is this super small point solution that does patent search,” or something like that. It is kind of a broader mission of saying, “Look, I think this industry really hasn’t adopted much technology in a long time.” This technology is a perfect fit for lawyers; it’ll really make their jobs significantly better.
I do think there are a lot of downstream effects of this. The average price of a lawyer in the US is $353 an hour; no one can afford that. Maybe a lot of people in San Francisco can, but across the country, most people can’t afford that. I do think this technology is not only going to make the lives of lawyers better, the big corporate lawyers, etc., and help them navigate all their issues faster and at like a grander scale internationally. I think it will have a pretty large effect on the average person too.
Something that’s pretty interesting, and you had kind of in the beginning, even when we met at the Series B, is as a vision. Can you unpack that a little bit? The axis to justice? There are a lot of hurdles to getting here. One of the main things is just like the quality has to be so high. When you’re selling to law firms, you have a lot of hierarchical review. I think people missed this when they’re thinking about, “Oh, sometimes these tools make mistakes,” etc. Actually, none of this goes straight to the client.
If you are at a very good law firm, you are doing the first pass of something and then a more senior associate is reviewing that. Then that gets reviewed and that gets reviewed, and then it goes out to the client. At enterprises, it’s the same thing, although a lot of enterprise teams are more leanly staffed. A lot of what you’re doing is also sending stuff internally. There’s a lot less hierarchical review in a large legal team in an enterprise, but it still happens. If you are building something for consumers, the output needs to be 100% accurate. You really have to do this right.
I don’t think you want to give a tool to consumers that gives them bad legal advice. That is a terrible situation. I think that is something that you have to think about when you’re navigating that issue over time. I think it is something that we would be interested in in the future, and there’s a lot of stuff that we are doing right now that would potentially set us up for that. But really our main customers today are attorneys at large firms, medium-sized firms as well, now a lot more in very large enterprises.
Isn’t it weird? We at KP run two CIO groups: one Fortune 100 CIOs and the other tech CIOs. In the Fortune 100, they are as forward-leaning, if not more, into AI and adopting new technologies as the tech CIOs that you would traditionally see. Isn’t that crazy? They take law firms and lawyers to the most logical extreme of that example. It’s massive. I think there’s a lot of reasons for this.
I can give you one reason. One of the more interesting ones to me is I spend a lot of time with chief legal officers (CLOs), which, by the way, is a term that didn’t exist—like a position that didn’t exist for a while. Seriously, it was like general counsel and the chief legal officer. One of the reasons why the term changed is they are business advisers. They are not just what you might think of as a traditional big law attorney or something like that. They are an integral part of advising the direction of the business.
That is what they are doing, and so I think there is not only a massive amount of interest general in AI from the Fortune 500, but especially in legal too. The reason why is a lot of in-house teams are just massively understaffed in terms of how complex their issues have gotten over the past 50 years. How many international mergers happened 50 years ago? Very few, right? How much more complex are all of the regulations and all the compliance checks when you’re operating in 70 different countries than it was even 20 years ago?
What about all of the data laws and things like that? Data processing. I think there’s been so much interest too from the Fortune 500 side just because this is a necessity. They’re getting to a point where they do need help getting around all of these tasks. Do you feel like it’s too magical for them? Is it still in the days of prove it? I don’t believe you; you’re just this tech startup out of Silicon Valley, right? We know these models hallucinate.
Here’s the problem. The best way to get through to folks like that is one of the things that we’ve started doing in the product is we build workflows. You can call these agents. I think a better way to call them is like agentic workflows. What you’re doing is instead of it being kind of like a chat interface or something like that that attaches to different knowledge sources, it is a system that is designed to do a particular task from start to finish.
My point here is if you were designing systems like that, you can get incredibly accurate outputs in a very specific domain that they understand. An example of this is it would be very hard to build a tool that can do every single merger analysis task—like all of the diligence, all of the HSR compliance, all the secondary questions, and all of these different things. But if you build a tool that does each piece of that, that is incredibly useful and it’s very impressive. Folks actually get their hands on it.
For example, we built one workflow that basically you upload the target company’s financials, the acquiring company’s financials, and it just tells you in multiple, like dozens of countries, what you need to file for antitrust. Then we’ll build new features on top of that, but having that output for a merger that your company just recently did and checking that that is correct is a massive moment for them. I think similar to the ChatGPT moment in their particular specialization.
Let me ask you, and feel free to give me the VC answer or not, but did you believe that a company like Harvey comes along and then you work backwards to figure out how can I make this? How can I do this? Or are you in the camp of here’s my mental model of the world, here’s my thesis of how I think all of this is going to play out, and here’s how Harvey fits squarely into that thesis? How do you work backwards from? Are you a prophet or not?
It’s basically pretty clearly not. I think it’s a little bit of both. You have to have a prepared mind on these things. For us, from just an investment thesis perspective, you look at things that are happening. You even look at the ChatGPT experience on the individual consumer level, you get a sense of what is possible. Pretty simply put, we sort of said, “Hey, what are the highest paying jobs out there in the world that really need a co-pilot-like experience?”
Where they have some significant percentage of their data that’s automatable, but still, because of maybe the subjective nature of some of the decisions—like the chief legal officer sort of translating law into business outcomes—you still really need a human that’ll do that work. But that human needs better tools because you can’t pursue all these mergers and downstream effects in a single headspace. Who can build the right kinds of products for them?
Lawyers were sort of at the top of that list, and we obviously knew of Harvey for a while. I think we were a day late to the Series A, which stung quite a bit. Lo and behold, Winston and Gabe walked into our office and said, “Hey, we’re raising a Series B.” So we knew that this was incredibly interesting. Obviously, a lot of it came down to the team construct, having somebody who’s been a practitioner of the craft and somebody who is deeply technical and steeped in research to create the right set of products.
That’s really how it came together. If you think about pretty much any job out there in the world, we will have a sort of co-pilot. You look at some of the deep research stuff that’s come out recently, and you can imagine that for investment teams as well, which is probably the smallest industry you’d want to really automate. It’s pretty obvious that this will happen. Then the question is who are the right folks to build it, and what’s their vision? What’s their path?
So much of the story that Winston and Gabe told us made sense. It was to go after the biggest top AM100 law firms, go after large enterprise in-house legal. Those folks work together. It’s a pretty clear collaboration graph. They can rely on multiple levels of review and having the right tools to answer these very intricate questions. Ultimately, it’s all in service of helping them make that critical business-impacting decision.
We talked to a bunch of customers, and they said, “This is the best thing. This is magic. It’s like magic flying off the shelves.” All the customers wanted to invest in the company, so that’s pretty good signal. I think the collaboration thing is going to be something that’s really interesting. I think that’s one of the largest product problems in the next couple of years.
You know what I mean? Collaboration is just hard to do in a product at all between two humans. But how do you do collaboration between AI agents that are doing parts of the work plus internal teams plus external teams? That’s something that we need to focus on, and we’re spending a lot of iterations on is how do you make the product not just collaborative between folks inside a law firm or inside an enterprise, but between the law firm and the enterprise?
I have a third party that’s involved here too, which is the AI systems that are doing the first pass of this work. So far, one thing that we underestimated in the beginning and that we’re spending a lot of time on is how much in the product you need to have back-and-forth communication between the user and the system. People talk a lot about how good the models are at output and instruction following, but I don’t think people talk enough about how you make it easy for the user to give instructions.
This is something that I think we struggle with a lot as humans, but we don’t pay attention to it. Give me an example. Communication, right? I’ll give you an example: struggling to prompt something. At the end of the day, a lot of it is just how good are you at communication? Seriously, a lot of the very low or entry point into being good at prompting is how good are you at giving all of the context necessary?
My guess is if you went into a law firm and you found the folks that were the best at prompting, my guess is those are the supervisors or managers that people like working with the most. I would actually be very surprised if that wasn’t the case. My point here is there are a lot of things in the product that you need to do to help the user basically give input or give intent. An example of this would be like we have shoulder taps.
A shoulder tap would be like a follow-up question, making sure, “Hey, you’ve uploaded this type of document before. Which type of workflows would you like to run off of that?” Instead of you having to find the workflow button that does XYZ, can you check this output to make sure that the formatting is correct or that we’ve identified the correct parties before we take 10 minutes to run this process?
These are all just things about how we all communicate. I will do a bunch of emails, I will do check-ins. I’m not going to give somebody a task and then say, “Okay, I’ll see you in 6 months, hopefully that goes well,” zero check-in, zero back-and-forth communication. This is going to be something that’s really important for these products because I don’t think we’re going to get to the point—maybe we do, but I don’t think so—in the next couple of years, where you basically just give the models all of the Activision and Microsoft documents, and you just go, “Merge, please,” and then it just does that.
I mean, there are so many different pieces of data; there’s so much context that is specific to what the clients want. There’s context specific to how you structure it for taxes and all of these different things. I don’t think the models are just going to be able to one-shot that. I think a lot of the product service is going to be how do you make it so it’s easier for folks to communicate with the models too—not just model performance but also the other side?
Can you help me reconcile something? Like Ilia calls it a co-pilot, right? You have this idea of an agent which is, in many ways, doing a lot of the work of what somebody would be doing. You see Salesforce that goes out and has their AI cloud or whatever, and then they’re telling customers that they can replace all of their salespeople, and then they go hire a giant sales team to sell their AI cloud. How do you see that world unfolding? Are we just saying that now so we don’t scare away customers that we’re going to replace people?
I think the reality is there is going to be a lot of task automation. Task automation doesn’t mean job automation. That’s something that people miss a lot. You are a transactional attorney; part of your job might be looking in a data room and finding change of control provisions. Is that going to change in the next couple of years? For sure! These systems are going to be able to do that at a level where you are checking, and you’re probably not doing necessarily hands-on all of that work yourself.
Are you an M&A attorney now? That’s it, no. There are so many different pieces, especially to high-knowledge work, that I don’t think these models are just going to be able to automate immediately. I think what you’re going to see is increasing levels of task automation, which again does not mean job automation. Those are different.
Do I think that jobs have to change if 70% of the tasks in the next 3 years of your job are automated? Does that job have to change? Yes, but that doesn’t mean that it happens overnight. It’s not just your entire job is gone; it’s the tasks that are automated over time, and then your job’s going to change. I think that will happen. I do think jobs are going to change.
I mean, I think it’s kind of like the early days of Excel. If you work in finance today and you can’t use Excel, you know you can’t work in finance. But it doesn’t mean the job of finance went away. It’s just you have to use this tool as a core part of your toolkit and be really good at it. I think that’s a good analogy because there are tons of tools like Harvey for financial services, and they’re going to build systems that automate Excel.
Does that mean that everyone that works in private equity is gone? No, right? It’s just like some capital states exactly our work is as subjective as it is. My point here is that the task is going to change, but they’re probably just going to do different things. Also, I think, and this is maybe less about every domain and more about the domain that we work in, a lot of what you want to do as a lawyer are these things that I think you will be able to do much faster in your career.
Fifty years ago, more people were like the CLO—most lawyers were business advisers. They were not basically sitting in data rooms. A legal officer, yeah. I mean, or they were an adviser; they were a strategic partner to businesses. Now, you have to be basically the tippy top of the profession in order to be doing that. Most of what you’re doing is basically giving those advisers all of the insights from all the work you’re doing so that they can advise. That’s what you’re doing, right?
My point here is the more you can automate a lot of those tasks, the earlier on in your career you can start doing the advisory work. I think that’s going to happen, and from a client perspective, I’m a client of a lot of law firms now. My perspective on this has even made me more convinced of this as I’ve been more and more of a client of law firms for that very expert advice.
I would pay more. The reality is, for should you buy this company or should you structure the deal like this, or should you incorporate in XYZ state—things like that are incredibly valuable, right? I would pay more for that than I’m currently paying for if I could pay less for the stuff that AI could automate, if that makes sense.
It makes total sense. It’s also kind of an interesting question to ponder whether that’s ever going to be automatable or managed by a model, right? Because it’s so subjective. I think that’s right. There’s also just like there’s so many pieces. A lot of people have the argument of, “Well, these are the things that no matter what, these models are not going to be able to do because of XYZ.”
I don’t know where I stand on that. I do think that the models can do a lot if they have all the context. But the reality is, are we going to carry a model around with us in our pocket that’s listening to all of our conversations and all, and we’re giving feedback on what our instinctual responses to people are? I don’t know, maybe. I think even with that, there are tons of things that humans do that these models couldn’t do.
But I also will push on the point that if there’s more context for the models, they will increasingly be able to do more things. How’d you pick the name Harvey? This gets a lot of questions. We actually incorporated as Council AI, which is intense. Every time I look at it, I’m like, “What is this?” It’s definitely intense.
Yeah, you do business, like that is a very business name. We had that for a while and it was kind of like Council AI, and we just had people didn’t give feedback as much there. There was something about the element of if we thought if you could give it a name, people would start basically saying, “Harvey did really well at XYZ,” or “I wish Harvey could do D,” or something like that. The other thing that it also ended up doing is—it makes it, people are better at prompting. I swear to God, people got better at prompting. When we changed it from Council AI to Harvey, they got better at prompting. I think going back to my point of prompting is a lot about communication ability. You start thinking about it as a coworker, and so you end up actually prompting the system in a better way.
Now our system doesn’t rely on prompting as much because we have a bunch of routing and we have these knowledge sources and all these different things, so it just doesn’t matter as much. But in the beginning, it was a huge deal anyway, so Harvey definitely was influenced by Harvey Spectre from Suits, for sure. I think another piece of it is Prestige, which is really important in Professional Services. It does kind of sound like Harvard, and I think that had a little bit to do with it, if I’m being honest.
I think there were a couple other characters that were named Harvey that kind of influenced it, and we’ve said it. I remember Gabe, my co-founder, was just like, “This is perfect,” and we went with it. It’s been really good since. There’s also something about the balance of it that is quite good in terms of like the lettering.
I think Suits went back to Netflix right before the Series B. Yeah, it did, so it’s like Peak Netflix got the rights again. So, it was like Peak Suits, Peak Harvey moment. The timing was coming out perfectly; it’s free marketing.
We were talking about brand and maybe hiring people that are involved in brand, and you immediately were like, “I’m never giving up brand. I’m never giving up this component of the business.” It was clear for you. I wonder where that comes from.
For lack of a better explanation, it is respect for the industry. There have been a bunch of companies that have come out of Silicon Valley and basically said, “We’re from Tech and we’re going to completely automate your industry” without tons of respect for it. I think that’s something we care a lot about, and it’s not just for show. I actually think a lot of our companies’ success has come from this, from the product side, from the client’s side, from kind of everything.
You have to partner with these industries. If you are building, you know, earlier I said you should take AI and basically apply it to X industry, like if your company isn’t doing that, I don’t think you’re ambitious enough. If you are doing that, you need to partner with the industry. These industries are incredibly complex. Legal is one of the oldest professions known to man. There are firms that are over 100 years old. There are firms that are hundreds of years old, and having a brand that says, “We are partnering with the industry to transform it,” versus “We are just going to steamroll the industry,” is really important for us.
I think that it’s led to a unique brand where it’s very tech-forward but also caring about tradition. I think we’ve nailed that, and we have really good brand folks at our company. I think the other piece of it is just the importance of making that clear to clients and interacting with clients, helping them define the brand too. That came through in diligence, pretty obviously at the Series B and even more so most recently for the most recent round where you expect to call a decade-old law firm or a multi-decade-old law firm and then say “Hey, you know this tech stuff is pretty cool,” and they were all in. They were like, “This is the future. We got to get on the train.”
If we don’t get on the train, we’re not going to be in the future, and Harvey is truly our partner to help shape that future. We’ve actually rejected VCs because they have had conversations with clients that have been offensive to the client.
The other thing too is it’s created a lot of respect internally at the company. Early on in the company, we sometimes had speakers come in. We had a speaker come and kind of talk a little bit about all the public information about the Dell take-private, and I think that was when our engineers were basically like, “Oh my God, this is incredibly complex work. I understand how important this industry is.” Doing more of that, especially if you are a company that is operating at state-of-the-art technology plus a conservative industry, requires bringing your entire organization together around that, as well as the customers.
When you are an associate at Melvin, were you like, “I’m definitely going to go start a startup one day?”
Not a startup. I knew you wanted to get out of in-house. The reason I actually became a lawyer was I was not a very good student. In the sophomore summer of college, I had an internship at the U.S. Attorney’s Office in the Eastern District of Louisiana. They’re federal prosecutors, so they work with like the FBI and that was so cool. I looked up to them; they all went to really good law schools, etc. I looked up the GPA for getting into those, and I was like, “Oh no.”
Actually, I was like, “Yeah, so I need like a 60 basically for two years in order to do this,” something close to that. But my point here is that was an amazing experience for me and it propelled me forward to do this. Then what happened? How did you actually get there? I think when did you quit? Why did you quit?
So the main motivation was the U.S. Attorney’s Office. Then what ended up happening was I worked at O’Melveny in L.A. and I loved it. It was a great firm. I really liked being a litigator. I think I always wanted to go back to being a U.S. Attorney. My plan was basically to go back, be a U.S. Attorney, and then start up my own firm. I think I had a decent amount of experience with what are the things necessary to start up a firm, and you end up spending a lot of time in the industry thinking about that and talking to a lot of people.
I talked to a lot of people that had spun out into like Houston and Hennington, a famous litigation boutique in Southern California. Up until I met my co-founder Gabe, I had no idea I was going to do a startup at all. I didn’t have a single friend that worked in tech. I didn’t really know anything about the different VC firms or tech companies, and so it was more I had a plan for what I wanted to do in the legal industry. That was entrepreneurial, but I didn’t have a plan to do a startup.
Your co-founder, how did you meet?
We met in San Diego about five years ago. I was still in law school when he was working on an education startup. Then he ended up working at Meta, and I was at O’Melveny. He always wanted to do a startup, and he was talking about a different one. He was doing one at the time, but he wanted to do other ones, and education is hard to get funding for.
I think we kind of talked about this for a bit, and I saw gbd3, which was the public version of gbd3 before gbd4 and anything like this. We started using it; we started doing chain of thought prompting, which was something that was not really a thing at the time, but we called it that at the time. It was basically just like here are the different rules, and based off of this rule, do something else.
After this step, do one of these steps depending on what else happens before it. We did this on a hundred questions from r/legaladvice, which is basically a subreddit where people ask, “Hey, who do I sue for like my neighbor’s dog bit me?” or something like that. We did it on a bunch of landlord-tenant questions, and we gave it, she went into Reddit and took all the questions and answers, fed it into the model.
No, we weren’t doing training at this point. It was just gbd3 with chain of thought prompting. We took those questions, we ran the model over them, and then we basically gave a left and right panel to three landlord-tenant attorneys. We just said, “Here’s the question that a client asked, and here’s a lawyer’s response. Is this good? Would you send this response to the client with zero edits?”
Eighty-six out of 100 of the questions were yes. We literally grabbed those and cold emailed Jason Quan, who is now the CSO at OpenAI. We pitched him on the idea of like “Okay, we should use these models.” We were thinking about doing consumer at first, and there were some reasons for not doing that.
Then we ended up pitching to the C-suite of OpenAI a couple weeks afterward. I think it was actually the morning of July 4th, 2022, which was a vibe. We raised after that.
What did they say when they saw the work you were doing?
When we sent it to Jason for the first time, he was like, “I had no idea these models were this good at legal.” It was really smart of us to send it to a lawyer because one of the things that’s difficult with products, with models, with everything, is if you are building something and the output is just a bunch of text, you have to really get somebody to actually know what that text says.
I know that sounds like a really stupid and superficial thing, but this was a huge advantage for us in the beginning in terms of GTM where we would literally find things that lawyers had filed. We would take those out of the federal court dockets and say, “Make 10 arguments against this,” or “How could you better draft this?”
The lawyer would recognize their work, so they would read it and recognize whether the output is good. Otherwise, it’s how do you do a demo if you have an output that is 10 pages? It’s hard to tell if it worked or not.
When you came to our LP meeting and presented to a bunch of our limited partners, I think you were coming off a plane from India.
That was Singapore. Yeah, that was India. Then my plane got canceled, so I had an 18-hour layover in South Korea. I landed, and then 15 minutes later I presented to you guys. That was a rough one.
Well, you rocked it.
I don’t remember, so I have no idea if I rocked it or not.
Is that an atypical week for you? It seems you’re not an easy guy to pin down because you are everywhere all at once right now.
I’m better now. This is like maybe one of the hardest things for me on a personal level: just figuring out how to scale yourself as the company scales. Last year, I traveled that amount, especially the beginning half of last year. A lot of our GTM motion in the beginning, and it’s much more scalable now, was basically travel to different countries.
We did it in the U.S. We went to all the top law firms in the U.S., and almost all of our first 50 customers were referrals from law firms. We actually weren’t even reaching out to the enterprises; the law firms would intro us to their clients.
My point is we did this in the U.S., and then we did it in different countries. We’d travel to London, and you’d sign one of the firms there, and then they intro you to customers in London. You do the same thing in Germany, do the same thing in France, and my VP of sales did a lot of this. Our early GTM folks did a lot of it; I did a lot of it too in the beginning.
I think I probably did it too long, but that’s something I have struggled with over time. It’s an advantage to make sure you do all of the stuff yourself because then it’s easier to enable a GTM, etc. But I probably did it for too long.
Can you put a finer point on “too much for too long”? What does that actually mean?
It depends on which role. The best way to describe this is I believe founders should do almost every single role at a company for like a month or half a quarter or something like that before they hire for that role, if not a full quarter. If you are doing 95% of that role after one quarter, you probably have not delegated it correctly.
I think that was a problem for me; it’s still a constant problem for me is delegation and making sure that I am hiring six months in advance, a year in advance, etc. It’s something I constantly struggle with because I actually do think I learned a lot from doing a lot of the roles myself in the beginning. I learned a lot, and I think I had better hiring there.
Do you think you’re better at scaling yourself now? You mentioned that things are getting better.
Yeah, what do you attribute that to?
One of the best ways to get better at it is to have a leadership team that holds you accountable for it. Seriously! No, I’m serious; they hold you accountable for not playing ball.
I think there have been a lot of folks on my leadership team that have helped me with this. One of the things that helped the most was that I had someone from my leadership team actually do this fairly recently, which really helped. We have hired a bunch of junior talent that I care a lot about, and there were fights about whether we should hire a more senior person.
We got external pressure for all of those things, but I made a lot of bets on junior hires. I had someone from my leadership team recently say, “Look, if you are spending all this time trying to do all these jobs yourself, you are actually not spending time letting any of that junior leadership team grow, and you aren’t getting better at coaching, teaching, or doing any of those things.”
That was a huge deal for me. I think the argument of, “Oh well, you’re not going to be able to scale yourself out of the heroics,” worked to a degree, but it didn’t convince me that much. I was just thinking, “Oh no, I can work more hours.”
You know, I used to wake up every morning. Most of last year, every morning, I’d wake up, and there’d be a message from Winston at 3 a.m. or 2 a.m. Some sleep guy. I think that stopped, and I got better. Part of it has been just scaling; you just can’t.
How big was the company at the end of last year or how many people did you just finish?
We were 240.
Where did you start at?
About 38.
Yeah, it was a lot. I mean, I think that it’s definitely a combination of hitting scaling limits where you literally have to figure out how to better delegate. You have to hire people, teach people, and enable people. The second piece that worked for me on a personal level was that you were just not giving people a chance. You weren’t giving junior talent a chance to shine here.
What percentage of your team are researchers, and what percentage are lawyers?
We have lawyers across a bunch of different roles. We have internal lawyers that do traditional legal advisory things for the company. We have lawyers that help with go-to-market both on pre-sales and post-sales. These are kind of like your domain experts that help explain how the product works.
We also have lawyers in a unique role, basically product specialists. They’re helping with not only designing a lot of the workflow from a domain-specific area. You have to say, “You have to do this process, then interact with this internal data set, then interact with this external data set, etc.” That data isn’t on the internet anywhere, so you have to collect it from domain experts.
We’re looking at how do you evaluate a merger agreement? We’re looking at how do you transition to autogring? It’s still going to require a lot of human labor for a very long time, I think.
Across there, we probably have around 50 lawyers.
What about research?
That depends on what you call research, but our EPD organization overall is around 85 to 90 people right now. Our engineering organization is a little bit on the smaller side. In there, we have about a third of folks working on applied AI.
When I think about the composition of your company, these aren’t the traditional people that I think of as company builders. Meaning, a lawyer or a researcher at an early-stage startup.
Do you feel like if you’ve been there from 30 to 250, you’re probably… Even if you’ve been at startups, you’re like, “This is insanity.”
Yeah, we definitely have that. We have made so many organizational mistakes, and we will continue to make mistakes. It will get better, and I think to your point, there are two factors we’re trying to navigate. There’s entropy; you have to deal with that. Some of that is you have to just power through it.
Some of that will get better as we scale, and you have to trust leadership that it will do that. The second piece is, yeah, this is a very different type of company. Some stuff is similar, but a lot of how we structure our EPD, we’re definitely thinking about that all the time.
I talked to the CPO of Anthropic and OpenAI a decent amount about how they structure their organizations, and it’s a work in progress that makes me feel a little better. It is different, and I think what we are trying to figure out and getting closer to is creating an organization that does AI patterns.
These are basically the 100, 15, 20, whatever it is, things and systems on the AI side that we need to get better at as a company. This is maybe certain types of retrieval problems, clause extraction, clause comparison, follow-up questions, query routing, and the orchestration layer of our product.
All these different systems. If we have a team focusing on making those systems better, doing research on how to improve those systems, you can then give that to the rest of the organization, and people can incorporate that into the product in different ways.
I think those are two different muscles. It’s very complex to figure out how to do the handoff and all of that.
It turns out revenue solves all problems.
Yeah, I mean, I don’t know. But it does.
I definitely agree. It allows you to make mistakes.
Yeah, it’s a massive work in progress. Can I actually chime in here for one second just to interrupt? I think there’s a very common misconception that these great companies, the canonical companies we know of, don’t make mistakes. In fact, they made as many, if not more than most companies. They just have great product-market fit, and they’re executing pretty well on a rising tide.
But the idea that the best companies don’t make mistakes is such a misnomer. The idea they are perfectly structured from day one, and it’s just always the correct swim lane, and there’s no thrash, that’s the antithesis of what moving fast looks like. Part of this is I think you have to build trust with your team.
There are two ways to do that. One is they have to trust you, and you have to admit when you make a mistake and say, “We messed this up. We’re going to fix it.” That’s one piece that’s really important.
The second piece is I think when you are hiring, it’s important to make that clear. The way I’ve started doing this is like, “We are on an incredibly compressed timeline.” I’ve said this a couple of times: the next 10 years, if you are willing to deal with some thrash, make a lot of sacrifices — and maybe sacrifice is the key word here — if you are willing to take some pain and deal with sacrifice, your impact and what you will learn is massively compounded.
More than maybe any generation or any time in tech. You need that combination of trust plus the willingness to make it very clear that that is what you’re choosing to do as a company. You might make it so that some people aren’t attracted to that type of company, and they don’t want to make that sacrifice.
But I think you have to be transparent about it. I don’t know if I did a good job of being transparent about it in some of my hiring. I think this is something I’m revisiting — making it clear that that is the trade-off you’re making by joining us.
What type of sacrifices do you think you make? What are the trade-offs that you feel like most people don’t see in a given week or a month that are actual sacrifices for you?
This is actually a very good point about sacrifice. So it is not hard to get me to work it. It is not hard to make it so that I do nothing but work like I am obsessed with this. That’s not hard; I don’t know if that’s sacrifice. I actually think that for me part of sacrifice is I hate delegating. It’s hard for me to get good at delegating, right? And so on the surface, you might look at me and you’re like, “Oh wow, all he does is work.” He literally is not taking a day off in this amount of time or whatever, and all these things must be sacrificing a bunch.
I don’t know if I am. I love this; it’s so much fun. It’s easy to be motivated. I’m one of the co-founders, and so you can kind of get away with not actually getting better at the things that are not sacrificing the things that actually are sacrificed to you, right? I think for me, one of the hardest things is delegating and spending more time on system building than just working an extra couple of hours to make sure something gets done and doing it myself.
When I think about sacrifice, I don’t think about it as, I know there’s all this Twitter stuff about being in the office till a certain amount of time and all those. I think that’s important, and I do think that is sacrifice. But I think there are a lot of ways that it can look like sacrifice. I’ll give you another example of sacrifice: dealing with thrash and saying, “Hey, I’m maybe not in exactly the org structure that I wanted,” or this was a very hard thrash. I was put on this project; I don’t really know what the outcome is. Patience might be something that’s sacrificed for a lot of people.
My point here is I think that a lot of folks, and we have a lot of folks at our team that are like this too, that you can kind of… it looks like you are making tons of sacrifices, but in reality, it’s like you’re actually fine with all the things you’re doing. It might be something that’s unique to you that’s hard for you to do. That’s how I would talk about my sacrifice. It’s like the work 24/7 and things like that.
I don’t know if that’s sacrifice; that part’s actually pretty easy for me. It’s these other things that are hard for me. And so it’s like that’s what going on podcasts is, right? Things like that—I’m not a big public speaking or press person. It’s harder for me to go on a podcast than it feels to you like a distraction from work.
But it doesn’t matter. My point is, like, for maybe most people, the podcast is cooler or something like that, whereas working every weekend is like a sacrifice. To me, the podcast is more of a sacrifice, and working every weekend is easy. So I don’t know; it just depends.
Do you feel like there’s such a thing as too fast? When the opportunity is so clear and ahead of us, is there a thing as going from 30 to what are the hiring plans for this year? Yeah, like probably double. You can’t go too fast. I think there’s such a thing as too fast.
I think there are two dimensions to too fast. Just on the org side, you know, the question is how fast can you grow? You can triple, you could quadruple; beyond that, it starts to get pretty tough within a year, mostly because if you think about it, if you’re going from 40 to 240 people in the span of a year, at some point there are more net new folks on the team who have been there less than the existing team. The existing team has been there, and so the energy of the existing team goes towards onboarding and training the people who have just come in, and you distract—obviously, those hours you take away from doing the actual work of growth.
There’s just kind of a natural physics and sort of rate of growth you can have. Now, back to the revenue point: revenue does solve a ton of problems and lets you make mistakes. If you have a high revenue growth, you can definitely scale pretty quickly, but you also need to make sure that those folks are then onboarded, integrated, and on the same page.
Because at the scale of going from, you know, Dunbar’s number at 150, where it’s like the number of people you can keep in your head—you know everybody’s name, you know everybody’s name—but that also relates to organizational context. Below that number, you can pretty easily get the whole team on the same context. Because you can kind of have the relationships.
Beyond that, you really have to work at getting people onto the context of what’s important, what are the priorities, where are we trying to go, here’s a mistake we made, we’re going to change things up. All of that is just a function of communication, and it gets harder and harder, and it’s something you really have to invest in as the company goes.
Then when you get to a bigger scale, it’s easier to double and maybe kind of keep doubling year-over-year because you already have this foundation and kind of institutionalized knowledge and muscle memory for doing it. So that’s one dimension.
I think the other dimension is like there’s infinite opportunity, especially for a company like Harvey. There’s so many things to be done in legal and professional services and enterprises and international. In sort of different types of organizations, different types of subverticals, there’s stuff you can do on the technology side of things, on the collaboration side of things. Just how do you sequence them, and how do you make sure that you actually deliver things sequentially in a way that’s creative to your customer and helps you build your business and not, you know, peanut butter your organization in a way that actually slows you down?
I think that’s kind of the art of it, and that’s what I think a lot of folks who hit hyperscale probably get wrong more than they get right. Your opportunity to do more and your ambition become bigger and bigger as you scale. So how do you actually sequence these things out in a way that’s the optimal trajectory for the company that lets you ultimately deliver the best experience to your customers and really scalably grow your business?
Yeah, the saying no part is really hard. That is something I did a terrible job of last year, and I think I’m doing hopefully a better job of it so far this year. There are so many good ideas, but the good ideas come at the cost of great ideas, and there’s only so many great ideas. I think that’s right.
And it’s like there’s also another thing too: one weird—I mean, great position to be in as a company is there isn’t tons of AI penetration in enterprises, right? And so sometimes our customers will say, “You know, we work with a large Fortune 50 company or something like that,” and they’ll basically say, “There really isn’t like an AI software that we’re doing for this HR workflow,” because you may kind of rotate towards that. “You guys are smart AI guys!”
Yeah, serious, I’m serious. It’s very hard as like a new founder to not be like, “You’re a big logo. Yeah, we’ll do HR too.” Right? Seriously. Then you kind of make an argument that it’s like, “Well, HR is some compliance things that are related to it.” And so that’s still legal, tax, and compliance, etc. But my point is that saying no part is definitely hard for me, and I think it’s something, again, like when you surround yourself with a good leadership team.
This is actually something too that—not just leadership team, but like junior leaders are really good at. I have a bunch of folks that are really good at being like, “That’s come on, Winston, like no, I’m serious.” It’s not necessarily like SE—it’s a lot of like, you know, junior managers and things that are like, “We’re super bandwidth constrained,” and it’s like, “I know you’re excited about this, but like come on.”
I don’t know; I think that’s been really helpful too. I actually have a lot of trust in those junior folks, and I’ve started to listen to them more for those things. Speaking to the point of how do you make sure you scale—it’s like, well, if they can do that with me, they probably can do that with their teams, right? Hopefully.
I think that’s a good way to scale, is getting into the culture of people being like, “Whoa, wait, we should prioritize this instead,” and like asking and requesting to make the priority super clear.
Who is the best you’ve ever seen at saying no at sequencing? Am I asking you to pick a favorite kid? Right now? No, I mean, I don’t think anybody’s particularly good at it. I definitely don’t think anybody’s perfect at it because I think it’s this constant battle of, “Oh, here’s like this awesome thing we can do.” It may not even be like one customer asking; maybe it’s like, “Hey, I’ve had this thing on the whiteboard for when I founded the company; we’re going to do this and we’re going to do that thing, and we’re going to do this other thing.”
Now I have a team, I have resources, I raised capital—let me go do all those, right? That’s the impulse, and I think it’s a little bit of an art because you don’t want to say no to all these things because maybe one of them will actually be the really, really good important one. But you kind of need to know when to say yes to it, and it’s almost like you need your point, this organizational infrastructure that creates enough tension for you to say no to the low conviction things.
And then, when there’s something you’re like, “No, you’re all wrong. I know this is true; I’m going to go do this,” if it makes it past that barrier, I think that’s actually probably the best thing you can do: so build trust, right? If you keep doing this—this is a thing I did last year, and I think it was a mistake. Especially the first half, I think it was better than the second, but it’s like, if you constantly say yes to everything, then that last piece of like, “No, no, no, trust me, we have to do this,” you kind of lose the conviction there, right?
Versus if you keep saying no to a bunch of things for a while and you gain your organization’s trust with that, when it is time to be like, “You’re all wrong. We are doing this. My gut says do this,” you’re going to have full support because it’s like, “Oh wow, you don’t have conviction over everything, and this is like something you want to double down.”
It’s kind of like doing deals in venture capital. If you show up at a partner meeting and everybody is like, “Amazing deal; go do it,” you know, it’s kind of easy. Those happen, but then, you show up and everybody’s kind of like, they’re skeptical, they’re not sure, they’re giving you feedback. It’s the ones where you’re like, “I hear the feedback, but I see it, and I’m going to go do that deal.” Those wind up being pretty exciting.
Yeah, because you really kind of overcome internally all the sort of feedback that you’re getting, and then you have trust with the organization. You can’t do this on every deal; if you do, you lose a lot of money. Exactly, and your partners won’t take you seriously. But you know, you want that filter; you want that narrow band filter for the really, really good things.
On the people allocation and hiring, I had Marissa Mayer on, and she was telling me a story about when Eric Schmidt came to run to be the CEO of Google. Google had the world ahead of it, and they were trying to do everything all at once. It was around the same size that Harvey is.
He went and printed out Larry and Sergey bucks—literally like laminated dollars with Larry and Sergey’s pictures on them—and he gave a certain amount. There was a pool; the N was only a certain number, and each one was a higher. Then he would distribute an equal amount to the leadership team, and they had to basically horse trade for, like, this was the hiring cap, and if sales wanted a product built, then they could give up a Larry and Sergey Buck to the product and engineering team to go build that thing.
To go hire somebody to go build it. Anyway, one thing I really like about that is just the cross-functional collaboration on hiring. So like this seems like a huge problem when you’re scaling really fast. I don’t know which role this is under—whose responsibility? It’s kind of cross-functional. Figuring out who actual responsibility is to hire that role has been hard in some instances.
So you want some Winston and Gab bucks? Yeah, I don’t hate that idea, although I hate the idea of some of our leaders going out and gambling with it. We just have like one small niche part of the org that has like 100 people in it. There’s been like a black market for this; that’s what it turned into. There’s like a straight-up black market.
A couple more on competition: like, it’s interesting like number—and this is for both of you—but number one, I can’t—how many companies are there that are going after this AI for legal? I think I saw a market map a long time ago, and I have to zoom in a lot because it’s just text. But you know, I mean, I’ll let Winston app on this one, but you know, it’s the markets.
It’s not just like there’s going to be one legal tech company; it’s not like one market necessarily, right? There are segments of that market that are really important, impactful, and a good place to start. I think like the—where Harvey is, which is big law firms, big enterprise in-house legal teams, that’s like the juicy right entry point.
That’s a pretty interesting one because it’s, again, very collaborative. I’m sure there’s going to be, you know, AI for legal in a variety of flavors—obviously for lots of parts of the market—but to me at least, this is kind of the biggest media one because it really drives everything else. There’s trust, there’s brand, there’s reference customers—that’s like the place.
I think like one thing that I try to think about a lot is just like what can the models do in like 10 years, right? How do you build a company around that? You know the terra mode, etc. All these things. But I think like one interesting piece about the—let’s go back to like the Microsoft Activision merger, right?
This is work that is at the complete tail end of the models. It can just automate this. In other words, if we’re in a world where GPT can just automate that, most companies are gone. Let’s just be honest. I think that what’s really interesting about going after that type of work is it is incredibly messy; it’s incredibly context specific. The accuracy of it really, really matters.
We are trying to basically put all of our effort into that because we think that is not something that’s going to be solved by the next generation of models. I would say that when I think about competitors, I think that the largest competitor for us is just not moving fast enough. By far, I think that indirectly, you want to make sure that you are building a business that is going to survive the next 10 years of model releases.
That’s how I think about the product, that’s how I think about GTM, that’s how I think about basically everything. Is that the correct play? I think it will be in a while. But you know, we’ve had a lot of revenue growth. To be honest, we’d have much more if we made it self-serve and we kind of did a different type of route.
I think that stuff ends up getting kind of cannibalized by the models. When Gleam was growing in its early days, there was so much pressure on that team to self-serve—just give it away, right? Yeah, and they did it the hard way, similar to how you’re doing it top down, enterprise, get deep into the organization.
Yeah, it’s paying off really well for them. And it’s interesting too because we have a very land-and-expand motion too. We have this weird—like we have PLG. You have to get into the organization for the PLG to start. I think there are a lot of advantages to that.
The nice thing too—and we talked about this kind of early on—is we have the option because we have Fortune 500. You talked about these three parties collaborating; you have internal virality. In other words, at a law firm, you have different lawyers spreading it to each other across different groups in an enterprise.
You know, we’ll sell something to the legal team, and then they’ll tell the tax team about it as well, right? You have virality there internally. Then you have external virality. If you have a bunch of law firms using our product, they tell their clients.
Remember I said they mostly refer to the enterprise customers that we had in the beginning, and vice versa, where the enterprises will tell their law firms, “Hey, we’re doing a deal; are you using Harvey?” “We are!” I think that’s another way to kind of go about expansion. It’s just slower in the beginning, but I think you can still do PLG really fast; it’s just a different kind.
I think the other part here is trust. At the end of the day, if you sort of release something into the ether where people can self-serve, it’s bottoms-up adopted, whether it’s in a big company or a small company. But it’s not hardened yet, and it’s not fully trusted yet, and you haven’t been sitting next to the customer using it.
You’re potentially going to miss out because if you get something wrong, especially in something as critical as legal—that’s kind of a third rail. I think the approach here is really smart because you’re going to build something that is hardened, enterprise-grade, works super well, and then you can kind of let it spread on its own over time.
With Gleam, I think that’s the right way to do it. Yeah, we definitely got some flack for that in the beginning. I think we had a little bit of, “Why are you guys in stealth for so long?” One thing that folks haven’t really realized is we got our first client when we were four people. So a lot of this was we did design partnerships with a lot of our first clients.
It wasn’t like we were trying to stay in stealth to do some sort of marketing blitz. We still kind of just started a marketing org that was a late hire on my end. But it really was because, to your point, we wanted to make sure the product was a certain quality before it gets to the point where it’s like you can’t control it as much, right?
Where you are actually spreading it, people are referring it. Because the reality is, if you mess that up—it’s a big industry from a tech perspective, but it’s actually a small industry from how much people talk to each other. Everyone’s kids go to the same school, all of those things.
You really have to do well with your first impression, or you’re in trouble. So on the model question, are you hot swapping models on the back end, or are you riding a horse all the way? Oh yeah, on a new model. Yeah, yeah. So we’re constantly testing.
No matter what, we’re testing. The way that we’ve structured our product is we’re kind of constantly expanding it and then collapsing it. The expansion is these, whether it’s like knowledge sources—different types of data that you can access—or it’s a specific workflow that’s for a particular, like, very specialized task.
The nice thing about that is you can change the model or replace the model for a particular part of the product without just changing the entire product. It’s like a new model version, right? And so we use a lot of different models and a lot of different—even when I say a lot of different models, I mean a lot of different models from OpenAI.
On the testing side, we’re actually doing this right now; we have folks that kind of do all of the testing constantly. It’s a combination of lawyers plus research scientists, and we’re creating a team that literally just does this. Because there are going to be so many releases, even just this week, right? There are tons that you have to constantly be testing on.
Each different type of piece of your product: is this better or not? We have a bunch of benchmarks. I think those benchmarks over time are really important because the public benchmarks are not useful at all anymore for us. It turns out building a GPT wrapper is really hard.
Yeah, exactly. I mean, it’s just like—the benchmark is just, like, none of them are relevant to doing these tasks. So, wasn’t it frustrating in the beginning? Everyone was like, “Oh, you’re building a wrapper on top of OpenAI,” and now everyone’s like, “Oh, thank God you’re building a wrapper on top of OpenAI.”
One advantage is, I didn’t have any friends in tech before this, so there wasn’t like a prestige meter for me, and so like this bothered me to the extent that it ever affected hiring. That’s it. There were no social circles that this was affected by for me or anything like that.
I think I was a little bit immune to it to a degree. The thing that I did—I think like the place that did impact us a little bit was just that there were folks that, you know, didn’t want to join application layer companies at all. They only wanted to work at labs.
I think that definitely had an impact on us a little bit, although we managed to hire really good people, and a lot of those people were people that wanted applied problems—like real-world problems—rather than being kind of like the 19th, you know, nth person working on XYZ model improvement on a benchmark.
I think we did a really good job hiring a bunch of really good folks in the beginning that were much more—they really wanted to have a direct contact with the product and a direct contact with the client. Then now I think, yeah, it’s become easier and easier to hire folks.
We’re thrilled to be a small part of this and double or triple down. Congrats on the new fund! Doing the math—that’s triple; it’s triple, yeah. Congratulations on where you are so far and on the fundraise; it’s a good milestone.
Thank you. Help you go faster, if that’s possible. Are you hiring massively? Like everything? Bay Area? In New York? Yeah, I mean, Bay Area, New York, and London. I’d say probably one of the areas we’re hiring the most is just on engineering, just across the board—everything.
When you hear the word grit, what do you think of? Probably my point earlier about sacrifice. I think that to that point of what is grit—there are certain buckets that people think these are the things that show grit, whether it’s hours worked or how long you’re in the office or whatever it is.
I think the reality is sacrifice, or grit, or whatever synonym you want to use for it, is very personal to whoever it is that is exhibiting it. I have found that the best employees are the folks that improve themselves, and the way that they improve themselves is they work on the things that they’re bad at.
They don’t avoid the things that are really hard for them, and those things might be easy for everybody else. I think that is the important distinction: that those might be easy for everybody else, or they might be really hard for everybody else. It doesn’t matter. It’s whether that person is willing to work and push through the things that are hard specifically for them. Gentlemen, thank you. Thank you, this is fun.