In this episode of AI and the Future of Law, Jen Leonard and Bridget McCormack are joined by Nick Fleisher, co-founder and CEO of Sandstone, an AI-native platform built for in-house legal teams.
Nick discusses how corporate legal departments are using AI to manage intake, triage work, gather business context, and move from reactive service providers to more proactive strategic partners. He explains why legal AI is often a data and context problem, how AI systems can learn legal judgment and team preferences over time, and why lawyers should focus first on automating administrative work rather than complex legal decision-making.
The conversation also explores the rising pressure on in-house teams as businesses generate more AI-assisted work, the role of outside counsel in a changing legal economy, what “agents” really mean for lawyers, and why implementation, adoption, and human-in-the-loop workflows will determine whether AI creates lasting value.
Key Takeaways
● In-house legal teams need better systems for intake, triage, and business context.
● AI adoption depends on clean data, connected systems, and the right institutional knowledge.
● Legal teams may become more proactive as AI helps spot issues before they become bottlenecks.
● Lawyers should distinguish between automating administrative workflows and automating legal judgment.
● Agentic workflows can be useful and low-risk when paired with human oversight.
● Successful AI adoption requires implementation support, behavior change, and trust in the underlying data.
Final Thoughts
This episode offers a practical look at how AI is changing the work of in-house legal teams. Nick Fleisher makes the case that the future of legal AI is not just about faster drafting or redlining, but about building better infrastructure for how legal departments receive work, understand context, make decisions, and collaborate with the business.
Transcript
Intro + AI Aha!
Jen Leonard: Hi, everyone, and welcome back to AI and the Future of Law, the podcast where we explore all of the dynamics around artificial intelligence and consider what it means for the legal profession.
I'm your co-host, Jen Leonard, founder of Creative Lawyers, here as always with the wonderful Bridget McCormack, president and CEO of the American Arbitration Association.
And Bridget, I know I'm really excited to meet a connection of yours, Nick Fleisher, today. I'm going to toss it over to you to introduce everyone to Nick.
Bridget McCormack: Excellent. It’s great to see you again, Jen. I’m really excited about this conversation, and the fact that you and Nick didn't know each other until today. Your lives are both better already because you now know each other.
But I'm thrilled to have with us Nick Fleisher, who is the co-founder and CEO of Sandstone, which is an AI-native platform built specifically for in-house legal teams.
It is based in Brooklyn, and it launched publicly in January of 2026 with a $10 million seed round led by Sequoia.
Sandstone uses AI to pull in context from across the business, not just within the legal department. And that’s, I think, part of what makes Sandstone kind of exciting.
Nick is an engineer by training, and he spent several years at McKinsey, where he led the firm's legal tech service line in New York and focused on AI and automation for law firms, corporate legal teams, and legal tech companies.
He worked closely with QuantumBlack, which is McKinsey's AI unit, including, fittingly for us today, on a project with the AAA. He was our McKinsey lead in developing the AI arbitrator for construction disputes, the platform that we're now extending to other dispute types and other use cases.
And working with Nick was a complete pleasure.
But before we learn more about Sandstone, Nick, we always ask our guests to give us an AI Aha! — anything they're using AI for lately. It doesn't have to be in your professional role. It could be in your personal life, something you're finding kind of fun and exciting.
Nick Fleisher: Thank you both for having me and for the great intro.
I have so many AI Aha! moments in my daily life. I think my whole life is AI. I realized last month that I actually talk to AI more than any individual human that I work with today, which could be seen as really good or really bad.
For me, my biggest AI Aha! moment is actually using AI avatars and people to prepare for calls and meetings. This was actually almost a year ago. My fiancée was complaining about prepping for a big Zoom call, and so I built a tool where you can do a mock presentation on Zoom or a fake Google Meet, and there are all these AI avatars that give you live feedback.
They yell at you, and sometimes it will crash randomly, which is built in.
And that has been the most helpful way to prepare for speaking events and for big meetings and presentations. So that has been a surprise AI Aha! moment: you can actually create twins, or fake versions of people, to interact with.
Bridget McCormack: That’s amazing.
So do you really train the individual AI personas for a specific meeting, or do you have a bunch of them trained and ready to go and say, “I’ll take these two or three because they’re close enough to the personality types I’m meeting with”?
Nick Fleisher: It’s still quite generalist. I have the annoying boss who’s making a bunch of side comments. You have the side talker who’s talking during the meeting. And then you have the distracting messenger who’s dropping messages in the chat.
But I think long term, the goal would be: could we even have a meeting recording bot that lives on your actual meetings and learns your preferences and gives you guidance?
And I think some of the tools, like Granola, are getting there, where you can actually be improving yourself.
And then, as you probably know from the world around arbitration and judges, now the big thing is digital twins. Could I run a presentation on a fake version of myself a hundred times and see which one performs best?
But that’s defeating the fun of it, so I don’t want to go too far out into the future.
Bridget McCormack: All right. That’s a really exciting one. I’m going to see if I can work on that. I could use that.
Why In-House Legal Teams Need AI-Native Infrastructure
Bridget McCormack: Let’s talk about Sandstone. Tell us just a little bit more about what it does and why people want it.
Nick Fleisher: Sandstone helps in-house legal teams manage intake, work execution, triage, and knowledge across their department.
If you think about the start of an item that hits the desk of any lawyer, it is something from the business or a counterparty that comes in over email or Slack or Jira or one of these other sources.
We centralize that all into one place. We then help triage it to the right person or the right team within the legal department.
And then behind each of those, we actually go out and gather relevant context.
So imagine you're a commercial lawyer. Someone on the sales team has sent in a contract over Slack. It gets routed to your inbox, it hits your desk, you open that, and we're able to surface: How important is this deal in our CRM, like Salesforce? What are all the notes from that?
The tool has already gone back and forth with the sales rep to understand all the context on the deal that matters, and the tool will go pull all your past contracts and negotiations with that counterparty.
It then takes that context and uses it to help you either draft an answer, review an agreement, or draft a contract.
And so that's kind of how we think about this context and knowledge layer over time. The idea is that, as you triage more work through the system, we see more on your counterparties and the operations of your team. We basically build that knowledge layer.
Today, most legal teams are operating across all these different systems, whether it's Slack, whether it's your CLM, whether it's Salesforce. And we want to consolidate that into a single knowledge-layer context graph that is kind of the source of truth for your legal department.
Bridget McCormack: I always think one of the benefits of having a tour of duty at McKinsey is that you see such a broad view of a particular profession you're working with. And I think that must have been true for you within legal.
Why did you decide that in-house teams were where you wanted to build? Why did you decide on that?
Nick Fleisher: I think two main reasons.
One, I think law firms have seen earlier adoption and excitement over AI just because of the ability that they have to spend on every kind of tool under the sun, no matter how early it is or how late it is in that tool actually becoming useful.
And I think the other thing that I saw in working with the biggest law firms at McKinsey was that it's actually very hard to get high adoption rates and to get everyone to buy into a single system or tool.
That's why a lot of the existing tools that people are using are very chat- and Word-heavy, because it's very hard to convince 2,000 Big Law partners to go use the same infrastructure and the same workflows.
Then with my in-house clients, it felt like if you got them aligned on one vision or one way of working, there was real opportunity for upside where everyone could leverage something that would be valuable.
But it felt like there was still very little being built on the in-house side.
We kind of had these historic CLMs, or contract lifecycle management tools, which are like repositories of contracts. And then we had redlining point solutions. And there was no in-between that said: Let's tackle the actual issue around how people in the business interact with legal.
Let's streamline that so it's easier for people in the business to get the things that they need from legal and go back and forth. And let's build a platform that doesn't just connect contracts to counterparties, but rather every document to every person, to every company, so that when we're actually using AI, we always can surface the right context immediately.
I also think, like you asked about my engineering background at the start, in engineering we have tools like Linear and Jira that are staples of the way that we work.
When I wake up as an engineer, I check what's going on in Linear, and it tells me, "Hey, the sales team complained about this feature in Slack. And by the way, you got an email that we need to work on this bug and we need to prioritize it."
There was nothing comparable in legal.
When I needed something from the lawyers that I worked with closely at McKinsey, I would just Slack them. And then a couple of days later, they'd finally get to it, and then they'd pass it to the right person. It was just completely manual.
So we learned a lot from how engineering teams work and engage with the rest of the company, and wanted to bring that to legal.
Bridget McCormack: One of the things that strikes me is that the incentives are aligned for in-house teams to figure out these solutions. And so that probably gives them a bit of an advantage.
Maybe that's part of why we see, when we present to law firms but also to in-house teams, that in-house teams are moving in a lot of ways faster than others.
But I wonder if there are any lessons that you think regulators or courts, or maybe parts of the profession that still haven't gotten around to figuring out what AI is going to do to them, could learn from what you're seeing inside companies that are pursuing pretty sophisticated AI transformation.
What are the lessons for the rest of the profession? Are there any lessons for regulators or courts?
Nick Fleisher: I think two big things that we've seen that I would say are somewhat surprising and apply across all of legal.
One is that most organizations don't realize that the core of getting AI right is just a data problem. If you don't have your data in order, and you don't have the right context to pull from, the AI is not going to look very different from what happens if you ask ChatGPT or Claude.
That's an enormous issue, and it's not an easy one to solve. You really have to think about each use case and what type of data you need and where that data lives.
And that applies everywhere.
I think it's most pronounced in-house because the data typically doesn't just live in a DMS like in law firms. It's actually across all these different business systems. But if you imagine all parts of the legal value chain, they're trying to access data from many different systems.
And so that's one core learning and challenge that I think people need to actually start with, rather than thinking about, "How do I get the chatbot up and running?"
I think the second one is part of why in-house teams have been racing to adopt AI.
Obviously, one is the potential to save on costs. But I think, more importantly, if you adopt AI well on the in-house side, your legal team becomes more proactive rather than reactive.
What I mean by that is today, most legal functions in companies just react to whatever hits their desk. A question comes in. A contract comes in. Great. Now it's legal's turn to review it, which means that legal inherently becomes a bottleneck and is seen as a bad thing.
And I think you could argue the same in many court systems or broader justice systems, where there's a vast backlog or delay on things.
With AI, what we see some of our clients doing is you have a bot or a tool that lives on top of Slack or on top of Salesforce or your procurement tool or even on top of Jira, and it starts to spot things before they happen.
So it says, "Hey, we see that we're about to launch a new product, and we only have one product counsel, and they're already at full capacity. And so in two weeks, we're about to become the bottleneck. How do we handle that? Let's either deploy AI to help prep work or get on the low-level stuff, or we actually need to change how we think about staffing and our team."
I think that's the real promise of AI: if you can get to the point where it's spotting things before they happen so that we're actually able to move faster and buy ourselves more time.
That might seem super future-state and meta, if you will, but I think that it's where we start to see real value from some of these tools.
Legal Judgment, Decision-Making, and the Limits of Automation
Jen Leonard: One of the things that seems really difficult to me is the process of extracting from a lawyer's mind all of the judgment that they have, all of the intuition and wisdom and nuance that they understand about law and advising their clients, and incorporating that into an AI system.
So how do you, or how does anybody, try to capture that information without missing out on that nuance or without carrying it forward?
Nick Fleisher: The one thing I will say is, when you're building tools that try to capture context and past decisions and precedent, the answer won't be solved day one.
What I always tell people is that we're going to set up a couple integrations and try to pull in as much context from past work as possible, but the first couple of things you do are not going to feel like you. There's still going to be a gap.
And as you use a tool like ours, or many of the others across different industries, we start to learn those patterns and see what you do and ask for feedback.
An example is that we have a lot of clients who use Sandstone to think about when an item or a work item or a request, whether it's a contract or a question, hits the desk of legal: Who does it need to go to? Who needs to approve it? And what are the right playbooks and pieces of information to pull in?
Usually, for the first couple of weeks of using the tool, we can actually ask you to do that manually the same way you do today, but do it while our tool is there on top of Teams or Slack or Word or email.
And then we'll learn your preferences over time. Allowing the tools to sit there and observe and gain that preference is just a flywheel where it starts to get much better.
I also think there's a world where we start to see within legal departments not just, "What are my individual preferences?" but, "What are the preferences of the team?"
So if I'm a commercial lawyer, what are my preferences versus the other lawyers reviewing sales and procurement agreements? And how do those differ?
If you can actually start to track this in one place, you'll see people also learn their biases and potentially improve just by seeing how they compare to the rest of the folks in their organization. And we're already seeing some of that at scale.
Jen Leonard: Could you talk for a second, Nick, a little bit about the bias?
I think when a lot of people think about bias and AI, they think from a social justice and equity lens. But you're talking about a different kind of bias. Could you walk us through what that looks like and maybe offer an example?
Nick Fleisher: I think the bias could come from that lens, for sure. But the more common one would be routinely falling back to a certain provision or clause that the rest of the company doesn't, or that is not market standard, just because it's something that you've done so many times over.
Or maybe because the name brand of the counterparty is something that you know very well, you assume they have more bargaining power, and so you give in on certain things. But in reality, they have a very small legal function, and this deal greatly favors your company, and they need the deal to go through.
So if you can pull in that context and surface, "Hey, this is how the rest of the team does similar things," those kinds of decisions start to even out.
Maybe it's not actually thinking about it in terms of decision bias, but rather decision correctness or decision difference to the mean of what the rest of your peers are doing.
Jen Leonard: Nick, you are very tech-forward. You come from a consulting background. You're an engineer, so you bring to the challenge a lot of mindsets that work well for the moment.
And you're also working with a famously change-resistant profession, and many of the people who populate it have some pretty stubborn views about things.
So what do you think is the most stubborn assumption that lawyers hold that you think AI is about to make obsolete? And what's one thing you think will never be automated in legal?
Nick Fleisher: I don't think lawyers hold that many stubborn assumptions.
I think, largely within the world, we've jumped very quickly into AI, especially in legal, given how much money and fundraising has gone into the market. I think everyone has the right to be stubborn and to push back and to ask questions.
I do think that there's a lot of work that happens in legal that is not legal work that lawyers were trained to do.
This is the same for every profession, right? There's so much admin and operational overhead, which people actually, in many cases, do like. They like managing people and the small decisions about who gets assigned what work or what type of information we go retrieve. Sometimes people like those small admin-type moments. And so I think that's a place where we see some stubbornness in-house.
I would push back and say everyone should really focus on spending their time doing the real, deep legal work and let the AI, as much as possible, take care of the admin things.
I think we've jumped too quickly to saying, "Let's go build and do AI redlining," which is one of the more complex parts of the day-to-day of many commercial in-house lawyers, versus: What about all the triaging and managing who's doing what work? Why don't we take that off our plate? That, to me, is one place where we've seen some change resistance, which is surprising.
I think there's a lot that will never be automated. As much as I run an AI company, I would say I'm somewhat bearish overall on just how much we can really do with AI.
I think, in almost every work item that we see in-house where any legal decision has to be made, whether it's as simple as, "Should we agree to this counterparty's redline on a basic NDA?" all the way through to complex litigation, there's always going to be some reasoning on the in-house side. For us, it's: Does the business outcome potential outweigh the risk of whatever the legal thing is on our plate right now?
That is a very hard thing for AI to weigh. AI can give you both sides, but for it to accurately make the decision is hard. And so I do think that there will always be some level of last-mile decision-making that lawyers will be making, and we're not going to fully automate that.
Now, in some low-volume or low-stakes work, I think we can start to automate it. You see this in the lowest-value arbitration cases. I do think there's a world where AI can fully automate some of it. That is because the cost tradeoff is worth it. In those cases, many times, we wouldn't even get to it. And so that's where I think the value makes sense.
But when we get to much more high-stakes, high-value work, that's where, from an ROI perspective, you still want a human making those final judgment calls.
Agentic Workflows and the Future of Legal Team Adoption
Jen Leonard: I have sort of a related question, Nick, which is touching back to the idea of the proactive lawyer in-house and having capacity freed up to be more strategic and be anticipating challenges more upstream.
What do you think that will take on the human side, to have such a change in the way that they view their work or they’ve been conditioned to work?
Nick Fleisher: Because of AI, we see organizations are doing a lot more. They’re generating a lot more agreements. They’re negotiating more things. They’re buying more stuff.
And so I think that will inherently create a lot more work for lawyers.
If you don’t become proactive, meaning you don’t learn to use AI to sit in on every meeting or to read all the different channels, there’s a world where you fall behind if AI becomes as prevalent as we expect it to, and as it is becoming across the business.
To get there, you have to lean into not being in every conversation, but rather relying on AI to help you do that.
What I mean by that is you need to trust that AI can have a quick conversation on Slack with someone to gather information for you, or it could sit in as the note taker on your meeting, or it could go run a quick report across several different business systems to help you gather context, which you’re going to use and rely on and trust to make a decision down the line.
I think leaning into that is quite hard, even for me in my day-to-day. I can’t attend every meeting, but I trust that the sales note taker on all the sales calls is giving me enough context and a good summary every day that I know where to jump in when it matters.
And I think legal will have to go there, just because I was talking to a GC the other day and they were like, “Honestly, we’re probably seeing 30 to 40% more work just because of how much sloppier the things are that are coming out of product and procurement and all these different places because everything’s AI-generated.”
Every day, they get sent some contract that someone reviewed or looked at or drafted with Claude. And I think that’s only going to create more work.
Bridget McCormack: That’s so interesting.
The courts are seeing increased filings, not only from people who can’t afford lawyers, but also from lawyers. Lawyers are just filing significantly more motions and significantly more pages per motion, and it’s AI in the background in both places.
But I hadn’t really focused on the fact that within a corporate legal team, they’re probably getting the same increased input from their partners across the business.
I was thinking about where in-house teams are being influenced, and I assumed, obviously, their clients view AI as a way for them to save money. That one feels obvious.
But this is another one: their clients are now sending them more work, or sometimes just work that takes more to get across the finish line, but probably also more work in terms of volume.
At the same time, they probably have relationships with their outside counsel that push them in certain directions. And you must have some insight into that. They might be caught in the middle of a lot going on right now.
I’m sure they’re not all the same. It depends on the particular company and the particular culture and their particular partners.
But would you say that in-house legal teams are under more pressure than ever as a result of AI, even though there are these tremendous benefits that eventually, once we get through the messy middle, they might get from it?
Nick Fleisher: There are a lot of competing forces that I think make in-house teams today under more pressure than ever.
The first one is that legal is now seen as a top place for AI adoption and for early AI usage. So lawyers are now expected to turn things around faster.
We see the same in engineering. People are expected to code and build faster because of AI. So immediately off the bat, you have a difference in expectation.
Then, turning to the law firms and outside counsel, most GCs I talk to say that they ask their outside counsel about AI usage, and they say they’re using it, but they don’t see any change in either delivery times or actual billed hours and total cost.
You would hope that would be a place of leverage, but it’s not yet.
So if you take those two things — outside counsel is the same cost and roughly the same speed, and we’re expected by the business and counterparties to move faster — that’s where you start to create this pressure. And we see it with almost every single client.
And now, with AI being this great thing in legal, we see CEOs telling GCs, “You need to do more with less. We’re going to grow the business by three or four X this year, but you can’t grow legal, or you can only add one person.” So yes, I think there’s a lot of pressure on in-house teams today because of these things.
I do think, though, that we are close to a breaking point where outside counsel will have to start offering more alternative fee arrangements and at least having some impact on either cost or time to deliver. Because I haven’t really seen anything come to fruition at scale there yet. And so that will be an interesting one to watch play out over the next year or two.
Jen Leonard: What you just talked about really resonates with me.
I was having lunch a few weeks ago with a GC, and as Bridget said, we present frequently to law firms and work with law firms, and they have their perspective.
And then I was talking to her and she said, “What my firms don’t understand is that this is an existential moment for me as well and for my department. It’s not just about their revenues and us nitpicking them over the bills and trying to get them lower. It’s because we have pressure from the C-suite to reshape how we spend our resources.”
Nick Fleisher: Historically, for most in-house legal teams, the vast majority of spend is internal people and outside counsel. It’s not tech. And they’re not like-for-like.
There is some AI that maybe, at full scale, can really help you do the work that a junior lawyer can. But today, they are not like-for-like. Even if you’re talking about legal ops and paralegals and non-lawyer roles, it’s still not at that level yet.
And so that, to me, is one of the core problems with saying, “Hey, let’s go cut legal’s budget today by 20%,” because you are probably going to see at least a 10% reduction in work output.
But your business is probably not shrinking 10%.
Bridget McCormack: You know, it’s 2026, and we haven’t said anything about agents yet. And you describe Sandstone as taking a legal team’s institutional knowledge and turning it into agentic workflows.
Can you demystify what agents are for all the lawyers who are listening right now?
Nick Fleisher: To start with the fear point, there are definitely types of agents and AI agents that exist out there that can be very scary and can go do crazy things.
I think in legal, most of the use cases of agents today are AI-enabled tools that can complete or conduct actions across your systems or the types of work that you do.
If you have an agent that can send a Slack to someone to gather context and then take that and use it to improve a redline, that’s totally fine as long as that person you’re Slacking was expecting the message and you’re still signing off and reviewing the redline.
I think people generally think agent means autonomous, but it may not mean that. You can still have human-in-the-loop steps around the work that agents are doing.
So if we think about, on the in-house side, an agentic workflow, this might mean that when a request comes in from someone in the business to legal, there can be an agent that goes back and forth with the person in the business just to gather context and understand the situation better.
That’s pretty low-risk. It’s not like they’re responding to a counterparty yet or sending anything to outside counsel. That, to me, is a very low-risk agent, and we should see those deployed at scale across many different organizations.
There are definitely higher-risk agents. You could have an agent that autonomously negotiates your contract back and forth with the counterparty.
I think in those, you want to see more human-in-the-loop steps, where you have to press approve on anything that the agent is going to go do. And that’s kind of what we’re seeing today as the norm for leading organizations.
But I think everyone has a right to be scared around some of the potential definitions of what an agent is.
I think we are a very long way out, if not completely out, from in-house teams having fully autonomous tools that are going and completing end-to-end workflows without them touching them or being a human in the loop.
But I do think there are very safe applications that people are doing today.
Jen Leonard: You mentioned that we talked about human change elements and personality adjustments and workplace adjustments.
It seems like a lot of this era right now in AI is taking the technology, as powerful as it is, and figuring out how to execute on it, integrate it into people's work, and help people adopt the tech more quickly.
And your company recently partnered with a legal ops group, LECG, to handle implementation.
What were you seeing about the gap between purchasing or securing AI solutions and actually getting them to produce value inside an organization? And why is the collaboration necessary?
Nick Fleisher: As we've talked about, a lot of the benefits of building a more proactive legal department using agents are on things that are not necessarily the raw legal judgment, but rather all the things around it that get you to the point where you need to come to a decision around something that is legal-related.
And so as we think about transforming the parts of the legal department that include gathering requests and taking in work items from the business, or going and retrieving information from five different business systems, people are very used to the way that they do those things today.
In most businesses, if I'm a commercial lawyer who redlines agreements for most of the day, I'm very used to going and taking in the requests from Salesforce, reviewing the materials there and the context there, then researching the counterparty, pulling up our template, and then comparing the document to that. That's a very specific way of working.
And I think the hardest thing that we see with getting people to adopt new tools is rewiring the way that they think about actually doing the work and getting them to say, "Okay, I don't need to do this 30 or 40% of the work that is very admin-intensive. I can spend all my time focusing on the more complicated parts." That just requires time and learning.
I also think there's another portion, which is, as we always talk about, AI is only as good as the data that you give it.
So we spend a lot of time with our clients initially, when we first read all of their data from these different systems and from their existing repository. We use AI to help us clean it and improve it and extract valuable metadata.
It's not always perfect. Sometimes people say, "Well, I didn't actually want to include that folder," or, "That playbook is outdated. We actually don't care about those provisions or those types of documents."
If you don't do that upfront, you just get much worse outputs on the back end. And I think people don't trust the AI as much. That's where implementation and having folks who understand the implications of the inputs that you give the tool is just super important.
We partner with LECG, but we partner with many others, and we have a big function internally, which is largely former lawyers as well as former heads of legal ops who have seen these challenges before and can embed themselves in organizations and understand what's actually helpful here, and how do we make sure that people are using the things properly.
We do a lot of office hours, just teaching people what works and what doesn't.
I think even in the early days on the AI arbitrator work, a lot of it was just teaching arbitrators what's possible and what's not, so that they know where to even start.
Bridget McCormack: It feels like the frontier model companies are all lately announcing these enormous investments in forward-deployed engineers, right? And I think legal teams definitely can use that.
It might really be kind of the secret sauce that gets them across the finish line to seeing how they can focus on that high-value work that is significantly more enjoyable, and why I think Jen and I have a largely optimistic view about how this will all work out for lawyers eventually, once they get through the messy middle part.
It’s been so great having you here today, Nick. We’re really excited to watch Sandstone and you and your team, and to see everything that you accomplish. You’ll have to come back and tell us how it’s going in a little bit.
Nick Fleisher: I appreciate it. Thank you for having me.
Jen Leonard: Thanks, Nick. It was so great to meet you.
And thank you to all the listeners out there for tuning in to this session of AI and the Future of Law. We look forward to seeing you on a future podcast. And until then, be well.