Summary
In Episode 4, Jen and Bridget dive deep into the world of data, explaining the crucial differences between structured and unstructured data. They also explore the intriguing concept of agentism in generative AI, where AI systems exhibit autonomous, goal-oriented behavior.
For solo practitioners and small firms looking to harness the power of AI, Jen and Bridget offer practical advice on getting started. They highlight platforms like Claude and ChatGPT, and emphasize the importance of experimentation with publicly available documents. Engaging with technology in various ways, leveraging social media, and joining online communities are key strategies they discuss for building support and confidence in AI adoption.
Key Takeaways
- Start with Free Tools—Not Legal Tech Giants: Solo and small firm lawyers can get started with free or low-cost versions of Claude or ChatGPT by simply visiting their websites—no IT department needed.
- AI Use Cases Are Already Within Reach: Examples include: summarizing court opinions, simulating oral arguments, improving client communications, translating text, and drafting creative content like FAQs or video scripts.
- Agentic AI and Reinforcement Learning Are Reshaping Usefulness: GenAI is moving beyond passive Q&A tools toward “agentic” systems that can autonomously complete tasks. It learns from feedback (reinforcement learning), just like humans.
- Structured vs. Unstructured Data Is a Legal Game-Changer: Tools can now extract insights from unstructured data (like case documents, transcripts, or client notes), which previously required manual review or conversion.
- Mindset Beats Mastery: A growth mindset matters more than technical expertise. The most effective adopters are curious, open, and willing to experiment—no matter their age or background.
Transcript
Jen Leonard: Welcome back, everybody, to our podcast series, 2030 Vision, AI and the Future of Law. I am your co-host, Jen Leonard, founder of Creative Lawyers, and I am privileged and honored always to co-host this series with Bridget McCormack, who is President and CEO of the American Arbitration Association.
The reason we started this podcast is because the two of us together have the opportunity to present to lots of different groups in the legal profession, and the opportunity to co-teach courses to law students on generative technology. But we realized that many people in the day-to-day busyness of their lives don’t have the chance to really slow down and try to understand what the technology is, what its fundamental principles are, some helpful definitions and use cases. So we wanted to create a space to do just that for the legal profession. So we’re thrilled to be here today. Hi, Bridget, it’s great to see you.
Bridget McCormack: Great to see you too. I’m excited about our conversation today—as always, so much going on.
Jen Leonard: So on today’s episode, we’re really excited to talk to our audience members who are practicing in solo firms or small practices. The questions that I’ve been hearing are very different from the ones that I hear when I talk with people from Big Law or legal tech communities. So we thought it might be valuable to share some of the FAQs that we’re getting, and we’ll continue to do that in future episodes as well, because it’s probably a community that does not have as much access to some of the most cutting-edge technology or the conversations about it.
Let’s get started in the way we usually do. We always have a Gen AI moment that each of us has experienced since our last recording—that moment where we sort of recognize how magical this technology can be (and very strange and mysterious). And so, Bridget, I’m curious: what your Gen AI moment has been since our last recording?
Gen AI Moments
Bridget McCormack: Yeah, this one is—again, I’m pulling in my team here—but my team has been experimenting with a product called HeyGen, which is a generative AI video generation set of tools that integrates with ChatGPT. And we are using it, like probably many others are, for both internal training videos and also—I’m going to loosely call it marketing videos, but really short videos to sort of tell the public or our users about some of our services and some of the specific tools and products that we offer. And so we have lots of different people across the organization. (Again, you heard from our conversation in the last episode that I’m a believer in pulling people from across your organization into using this technology.) And we’ve done that with HeyGen as well. And the different team members—none of them are at all experienced in video creation. And that’s what’s magical about this particular platform. And again, maybe there’ll be many others like it; we’re not here to endorse certain products, but we are using this HeyGen platform. And it’s really incredible how it can turn a text script into a video. It has these avatars you can use, it can translate into whatever language you need, and it’s so user-friendly that you don’t have to have any background in being able to make a little video and you can just create these really effective tools.
Just for one example, you can imagine some of the obvious educational and marketing use cases, but one of our team members—who is working on getting our data in good shape for making sure we can use it for lots of different use cases down the road—she’s working on making a video to explain to staff (case managers, for entering data) why it’s important to enter it in a particular way. It’s a much more fun way to interact about a topic that might feel kind of boring when you can make a fun little video about it.
Jen Leonard: I’ve heard about HeyGen and other video generation technology, and I’m curious because I haven’t tried it myself at all, and I haven’t seen its output. So if I am trying to create a training video—or I’m imagining, you know, teaching law students and I want to have some sort of asynchronous content that I post on Canvas for them—what would I do? Would I draft a script of what I want to say and then feed the script into the HeyGen technology?
Bridget McCormack: So yes, you could do that, and that’s what I think is kind of amazing—if you just want it to make a short video for you based on the content that you were able to put in writing. But it has lots of other features as well that are also easy to use, and they’re interactive, and it gives you choices about a particular voice you might want, language you might want, avatar you might want. And it just has translation features, a streaming feature that’s kind of amazing. It’s an unbelievably user-friendly tool. I think the best advice I can give you is to just go get a demo and play around with it (which is what I think we say about almost all of these tools), because by playing around with it, you’ll see what we mean. Unlike you, I’m basically like a middle-aged, non-tech-savvy person.
And the thing about this technology that’s showing up in all these different use cases is it’s making it available to all of us. And that’s the big difference here, right? That’s what we keep coming back to.
Jen Leonard: Which languages have you used? Have you tried different languages so far in your work? And how do you assess the translation?
Bridget McCormack: I haven’t, and I would not be the person to be able to assess the translation frankly. But from team members who are experimenting with the translation—because we do have users who need to be able to understand some of what we’re putting out there in their native language—they say it’s excellent.
Jen Leonard: Really, really interesting. Do you see, you know, in your role now (you’re leading a huge organization that has marketing and training material for lawyers and judges in particular), how would you imagine they might use it?
Bridget McCormack: When I was thinking about this particular platform for this podcast, it just seemed to me to be a way for courts in particular to be able to put out lots of information kind of quickly and easily and in high quality—and in lots of languages—that right now people are trying to respond to in this one-to-one service model kind of way. That’s true for lawyers as well, right?
You could imagine firms being able to provide information about the kinds of things they can do, not in the one-to-one service model that we’re all used to, and therefore reach far more people than we can reach right now. For courts, that’s critical, right? Courts just never get through all the questions that they have from the public, and a public who, for the most part, can’t afford lawyers. So I think it’s a tremendous tool for an innovative court administrator who says, “I really want to be able to make sure our court can answer any question anybody has in our community about any service.” It could be a wonderful way to really serve the public for a court that was focused on that.
Jen Leonard: Cool. And I could imagine combining, you know, if you took FAQs—if you're a court administrator and you have FAQs on hand for procedural rules, administrative things, especially with self-represented litigants—and you fed it into a video generation platform, and then you created a kiosk in the, you know, lobby of the courthouse where people could pick the language that they speak and need to know the answer in, or on the court's website. That could be amazing and transformative.
Bridget McCormack: Yeah, I mean the kiosks are important because sometimes people think they have to go to court to figure these things out. If courts put it on their websites and figure out a way to, like, get that information into people’s hands before they have to come to court, even better, right? And I think this technology gives you an opportunity to do that. I think it’s a pretty great platform and I think people are going to find great use cases with it
Jen Leonard: That’s very cool. And I can also see the flip side of everything we talk about is the training model and the different ways that we can create differentiated content for people who learn in different ways. I could imagine educational and training offerings that meet different populations of learners in a way that they really prefer by having different videos present to them. I don’t even know what that would look like, but that was the first thing I thought.
Bridget McCormack: Yeah, I know you’re absolutely right. Our education team is playing with it and they think it’s so much more user-friendly for the video content that they’re creating. But you’re right, it’s also going to be a lot easier to put together individualized learning modules. It just makes it really simple and that’s exciting.
Jen Leonard: Very cool. Okay, so that was a great Gen AI moment for this week. And as you were talking about translation, I realized that my Gen AI moment this week came from an experience with a language I don’t speak. My good friend is from Germany. She and I speak English to one another, but her family speaks to her in German. And she sent us a text from a family member in German, and I had no idea what it meant. So I typed in, you know, the German into generative AI—into Claude—and it immediately translated it. But I wanted to know from her whether it translated it correctly. And she wrote back that it had. It had translated it perfectly.
So I thought that was very cool. And to imagine traveling around the world to lots of different places where I don't speak the language and being able to take a screenshot of a menu or a sign in a train station and be able to instantly translate it is incredible to me.
And the other example I have—I have a few, but I’ll save a few for our next edition—but this is a very silly example. I never cook on my own outside of meal delivery kits, but I was trying to make béchamel sauce for pasta and I was all excited. But then I realized I didn’t have any dairy milk on hand. But I did have almond milk, and I wasn’t sure whether almond milk would be something I could use to make béchamel.
So I asked Claude, and I expected it just to say, “Yes, you can use it as a substitute.” But instead, it said, “Yes, you can,” and here’s a revised recipe for how you would incorporate almond milk instead of dairy milk. I made it with almond milk. I don’t know if it was the best béchamel sauce I’ve ever had, but it was definitely usable and edible.
But I thought that next step—providing me with the recipe—was super interesting. It adjusted the times and the things that I would have to do, and it talked about the difference between the almond milk and the dairy milk in preparation. So I thought that was pretty cool.
Bridget McCormack: And are you using Claude now? Like, is that your first go-to nowadays?
Jen Leonard: Claude’s always my first go-to, except for tech support. I find it’s not very good at tech support.
I use a platform called Buffer for scheduling social media posts, and it doesn’t allow you to tag people. So you draft it, and it automatically sends it when you schedule it. But it doesn’t allow you to tag people in your post. So when it goes up, it looks a little silly because it just has that “@” sign when you’re trying to tag someone.
So I went into Claude and asked why that was the case, and it didn’t have any answers. It just said, “I don’t know anything about LinkedIn.” But I went into ChatGPT and asked it, and it always has the answers for my tech questions. And the answer in that case was that it’s not a feature that you can actually enable because of the interface, I guess, between these external platforms and LinkedIn.
How about you? Are you using Claude or ChatGPT exclusively or for different purposes?
Bridget McCormack: So I think I use them both a lot. I do almost always start with Claude, though, nowadays. We’ll see when the next model of GPT comes out—maybe that’ll change again. It feels like it’s a constant horse race.
But I often go to both. And sometimes I’m much more satisfied with an answer on one than the other, and sometimes they’re pretty similar. But they’re both pretty good. But I find myself starting with Claude.
In your German text case, did you feed it right in from your phone? Or did you copy and paste it into your browser? How are you actually doing it? I think this is a question you get on the road sometimes.
Jen Leonard: Yeah, I was on my computer when the text came in, and I have a Mac, so the texts come through to my screen. I took a screenshot, but I was careful to eliminate any identifying data because I don’t want to ever put in anybody’s information. And I thought about that a lot, and I think we’ll have to continue thinking about that.
So I just captured the German piece of it and took a snapshot and pasted that snapshot into Claude. And that’s how I did it. I have not tried to use the Claude app, like uploading anything to Claude.
Bridget McCormack: I haven’t either. That’s why I was wondering if you had done that. I want to try. Although on the other hand, I guess if the new iPhone is going to have all of it built in and we could just get the new iPhone this year, maybe that makes it all easier. I don’t know. We’ll have to play with it.
Jen Leonard: That is the thing that I’ve been wondering about with this whole arms race around advancing technology. And I was listening to a conversation the other day about: what if one of these companies develops the most advanced form that you can, and then just gave it away for free—would that end the arms race, essentially, and capture the market? I’m not sure. But for now, for me, Claude is the language-focused version that I use. I think the writing that comes out of Claude is incredible as compared with GPT at the moment. Okay, so great Gen AI moments.
Definitions: Agentism, Reinforcement Learning & Structured vs Unstructured Data
Jen Leonard: The other thing that we try to do on our podcast is to define some key terms that are bandied about in the LLM and AI world. And so, a couple terms each episode. Two that we have today— the first one is “agentism”. So Bridget, did you want to describe what “agentic” or “agentism” refers to?
Bridget McCormack: When we talk about agentism in the context of generative AI, we’re talking about the idea where the AI system is designed to behave or operate autonomously, at least in some ways—like an agent would (hence agentism)—and that it has some level of decision-making or goal-oriented action in what it does. So it’s not just responding to your question (which is something that probably everybody by now has had an experience with AIs doing), but actively seeking to get something done for you, like a task or a goal, using techniques that it knows how to use (like reinforcement learning and other forms of behavior that the AI has been taught to do).
I think it’s a concept that makes a lot of people uncomfortable, but it’s also a concept that seems to me—following where this market is headed—like one we’re going to be talking more and more about in the coming months and years, at least as I understand it.
Jen Leonard: One question I have—just another definition—is you mentioned reinforcement learning, which is another term that comes up in these conversations. Could you describe what “reinforcement learning” means?
Bridget McCormack: You know, it’s sort of like fine-tuning. That’s what my engineers say. They spend a lot of time teaching it “yes” or “no” in what it’s doing, so it learns how to do it better on its own—like teaching it how to learn on its own.
Jen Leonard: I mean, it’s one of the ways that AI really mimics humans, I think—we also learn through reinforcement learning, being given feedback and trying to get better over time and eventually being taught how to do it on our own. I don’t want to over-anthropomorphize the technology, but I think it’s part of what makes it different from other technology.
Jen Leonard: And the other thing I was going to add onto your great definition of agentism is, you know, say your Gen AI produces a travel itinerary and instead of you having to then go on and make all the reservations, it would get your authorization and then go ahead and do those things for you. Which is really advanced.
On a more simple level, they are already agentic in the sense that they’re pre-trained. Their training period cuts off at a certain point, but they are now able to go out to the internet and search for things and come back with more advanced understanding. So that is also agentism that already exists. And I think at the very beginning, when ChatGPT came out and you would ask it a question, it might respond, “My training data cut off in…,” you know, whatever it was, January 2022: “I don’t have any more current information.” But that’s no longer the case, because it’s already demonstrating some agentism.
But that doesn’t mean its training is current, if that makes sense. It’s going to get new information and fold it into its response. Is that how you understand it?
Bridget McCormack: Yeah, that is how I understand it. I mean, I think—we’ve talked about this example before of GPT-4 teaching itself to code, and I didn’t really understand that that wasn’t something that the technologists who built GPT-4 had taught it to do. But when I read the Atlantic piece on Sam Altman and I learned he was surprised when it started coding, I guess that’s an example of it, you know, going and learning how to do things on its own and maybe doing those things in service of—well, not “pleasing” the user (because it doesn’t really aim to please; we’re anthropomorphizing the technology a lot today)—but in service of the user asking it to do things.
So, how about structured versus unstructured data, which again feel like terms that, two years ago, I don’t think I might have heard once a year, and now I feel like I hear every other day. What do we mean by those?
Jen Leonard: Yeah, and I was even more ignorant about it—I don’t think I ever really thought about it. And the first person I heard talk about it was Darth Vaughn, when he came and did a presentation for us about why generative technologies will change the legal profession. It was sort of like one of those light bulb moments for me.
My understanding is that “structured versus unstructured data” refers to—we used to have all these data entry roles where you were taking data and entering it into fields for spreadsheets, for example. And until you created structure around the data that you were using, it was unusable. And unstructured data is the reverse, which is sort of the way that we normally communicate in documents and text-heavy files.
One of the reasons that Gen AI could be such a breakthrough is that we can actually use that unstructured data now to make predictive outcomes from it or ask it questions and talk to it, which was impossible to do before.
Bridget McCormack: I think of structured data as data that’s organized in a predefined format. You think of Excel spreadsheets and databases, like you were saying, and they’re easily searchable and you can analyze them. You can see why that data is easy to train an algorithm. And so you can imagine financial records or inventory data, or even in the legal context, you know, court opinions are somewhat structured, right? They follow a format. Statutes are somewhat structured, and Westlaw and our other legal overlords have structured them even more so.
Bridget McCormack: Right? They’ve really put them in a format that makes them searchable. And I think of unstructured data as—you know—emails and audio recordings and social media posts and the ways in which we live our lives in words, either in writing or in speaking. All of a sudden, that unstructured data is now valuable in the context of this technology.
Jen Leonard: Yeah, I’m going to oversimplify it even more and take us to my favorite store, which is The Container Store. I think of structured data as like the little jewelry organizers that have a tiny little spot for every single thing you own, and unstructured data is like the kids’ toy organizer, where it’s just a big bin where you could throw everything in and make use of it and keep it organized. That’s how I think of structured versus unstructured data: we could throw a whole lot of stuff in there, but it no longer means that it’s a complete mess. It’s actually something usable.
Main Topic: Harnessing Generative AI for Solo Practitioners and Small Firms
Jen Leonard: Okay, so our next section—another thing that we do is, we get a chance to talk to lots of different groups, most of whom are too busy in their day-to-day to be thinking about generative AI but recognize that it’s important in the legal field.
So we wanted to share some questions that we get on the road and our answers to those. And one: I was presenting recently, and these were solo practitioners and small firms where there is not an IT team in-house thinking about these things or doing training on them. It tends to be people who are working really, really hard all day with the technology they have and haven’t yet even thought about these things.
And after an hour-long discussion about all these different topics—and I was talking about Claude this and Claude that—they said, “Where do you actually access these things? Like, where is Claude? Where is ChatGPT?” So, where can we find them, Bridget?
Bridget McCormack: It’s a great question. And the answer is: you know, you can literally go to Google and put in “Claude” and you will come up with the Claude website and you will be able to use it for free or buy a subscription (mine comes in at like $21.79 a month now, I feel like), which I highly recommend. I think the subscription version is incredible and really, really gives you the kind of introduction to the technology—if you haven’t played with it yet. But yeah, go right to your browser and type in “Claude.” You will absolutely find it.
Same with ChatGPT: you can’t try to not find it; you will find it just by using your Google machine. I will tell you, I now use Claude and ChatGPT the way I used to use Google. My habits have changed completely, and I start questions—when I’m trying to figure out something or putting something together for various tasks—I start now there and not in Google. But because you, dear audience member who needs to know where to find Claude, haven’t done that yet: go to Google, put in “Claude,” get your Claude account started and start playing.
Jen Leonard: And the other question during the same conversation was: for solo practitioners and small firms (large firms may have resources to be buying legal-specific technology like Harvey, like CoCounsel), what would your advice be to small firms and solo practitioners who don’t have those resources and they don’t have technical expertise in-house? Where do they get started?
Bridget McCormack: One of the things that I think is really exciting about this moment is everybody is basically in the same place right now. So I think, first of all, even if you are in a small firm or a solo practice, you will learn quickly how using the technology saves you time. I know you feel like you have no time because you have so much to do in a small firm when you’re doing all of it yourself—you don’t have a marketing department or an IS department—but you will quickly see how much time these tools save you.
And then there are these resources that Jen and I have talked about (and we will continue to post about) where you can learn pretty quickly kind of updates week to week. I don’t know… what do you tell people in small firms or solo practice who are looking to figure out how they can at least get competent?
Jen Leonard: I mean, my first piece of advice is: don’t try to buy Harvey or CoCounsel. A) You’re not going to be able to afford it. B) I think the cost of all of these technologies will come down over time. So I think it’s less important to learn legal-specific technologies and more important to develop just a muscle for understanding what they can do currently.
And like you’re saying, I’ll just offer an example of downloading the most recent Supreme Court case and opinion—something that’s publicly available, nothing sensitive about it. Read it yourself as a lawyer, understand it as a lawyer would, and then upload it to one of these platforms, and talk to it. Ask it questions, ask it to summarize the opinion, see if you agree or disagree with the summary. I find increasingly they’re pretty accurate, at least in summary.
And then if you have extra time, pretend you’re preparing for an appellate argument about that case and ask the technology to serve in the role of a judge who’s challenging you on how this applies to your case. You can change the personality of it, you can engage with it in lots of different ways, and just play around with it that way.
And then I think for the administrative side of your work and the non-sensitive pieces of your work—like marketing or drafting an email to somebody that’s not privileged or confidential—find ways that you can adjust the tone to make it more diplomatic, to make it funnier. I mean, just playing around with it, I think at this stage is just as valuable a use of time—probably more—than trying to figure out where it integrates in your legal practice right now.
Bridget McCormack: I remember us talking about a small firm, maybe even a solo practitioner, who had an appellate argument in a state appellate court, who fed the publicly available documents (the briefs that were filed with the court) up into—I think it was just ChatGPT—and had the LLM ask him questions as if it were the appellate court. And he answered the questions and had it write a decision. And it guided him in how he made his formal presentation, and he said it was an enormous help. And here’s where it’s still democratized, because you’re right that the legal tools still have some pricing issues.
And I think that will change. But right now, Catherine Forrest (who’s a partner at Paul Weiss, who’s a real, I think, leader in technology and generative AI) does the same thing when she has an argument. So you can be a solo practitioner in Pennsylvania doing an appellate argument before a state appellate court, or you can be Catherine Forrest preparing an argument for the Ninth Circuit, and you have access to the same tools. I think starting to figure out the ways in which it can already help you is the first piece of advice.
Jen Leonard: I agree. And yes, that example you’re recalling—I remember that lawyer saying that he can’t prove that ChatGPT helped him win oral argument, but he did win at oral argument. And he did say that it was much more helpful in terms of preparing him and thinking through some things that he wouldn’t have thought through before—some new questions, that kind of thing.
Bridget McCormack: One question I’m curious about, Jen, is whether you have any advice for solos or small firm lawyers about whether there are resources or communities where they could go for crowdsourcing information about this technology that might be especially useful for their practices. What kind of advice would you give to them if they were looking for that information?
Jen Leonard: Yeah, I think if I were a small firm practitioner, I would start with my local bar association and see what conversations are happening and if there are any educational resources available. I know some are starting to have panel discussions with leading thinkers locally about how to use the technology.
And also leverage social media—I actually think social media is a much better way to learn about this tech than, you know, waiting for the next edition of whatever legal resource you consume periodically. Instead of that type of resource, hop on LinkedIn and find some people in the legal tech community. Bob Ambrogi is a great resource to follow around legal tech. Niki Shaver is another great resource, and I follow her. Are there people on LinkedIn and social media that you follow that might be helpful for anybody getting started?
Bridget McCormack: We’ve mentioned Jordan Furlong before, but Jordan Furlong is really worth a follow if you’re thinking about the future of the legal profession and legal tech and legal education, frankly. I’m not sure it’s especially helpful for small firms and solos—although I think a lot of what Jordan is thinking about and writing about is very hopeful for small firms and solo practitioners, given all the changes out there. You know, I know everybody is short on time, but I would subscribe to Jordan’s Substack and follow Jordan.
And I’ve said this before as well, but I find that following people who are not necessarily in legal is quite useful with this technology, because it’s surprising to me how much there is to learn from other disciplines that are maybe diving into use cases a little faster, and it turns out they’re not necessarily that different from us. You know, we say every week that following Paul Roetzer and Mike Kaput is worth doing. I would always follow Ethan Mollick to sort of be learning what’s happening with the technology.
Jen Leonard: Yeah, I would say the other thing is: I know some groups can get very overwhelmed pretty quickly with the technology, and we’ve talked before about how this is an intelligent, educated community and we shouldn’t feel overwhelmed. But just to counter that or temper it a little bit, I would also say that I expect that this era where we’re all trying to find the different platforms in their most recent iterations—and which one compares to the others—will end at some point (or at least recede in its importance). Because a lot of this technology will become infused and integrated into the tech that you’re already using in your practice.
So while I think it’s really important to think about how it could transform your practice—and I think for solos and smalls the transformative power is huge; the ability to grow the service you provide, to open up new markets for you, is enormous (and we’ve talked about that before).
I wouldn’t necessarily try to boil the ocean and learn everything you can about generative AI, because I think it will arrive to you in a more consumable way sooner than later.
Bridget McCormack: I think that’s right. The products are going to settle out, and so you don’t have to necessarily figure out the legal-specific products. I would be learning as much as you can about what the possibilities are. That’s, I think, probably more important than figuring out which products are going to work for your practice.
Jen Leonard: And we’ve talked about tools and resources, but mindset is really key. I met a woman last week—I thought she was amazing. She was 80 years old, a psychologist and a lawyer by training, and she was taking all of this handwritten material that she had drafted throughout her entire career that was not sensitive and trying to figure out how to use it to create guides for other people who wanted to have a career like that. And I think to be 80 years old and not only not dismissive of the technology, but actively embracing it… That’s what I love to see. I just think that’s so inspiring.
So I think just having an open mind about it and spending less time sort of debating whether technology will change the world—it always does—and more time figuring out how you can make sure you harness it for good.
Bridget McCormack: Yeah, and you have the power to do that no matter what your practice is, right? It’s like, you can put your head in the sand, you can hold on with both hands to what we all have today, or you can have a growth mindset. And I think people who have a growth mindset right now are the ones who are going to have a more satisfying and probably more successful practice tomorrow. So growth mindset is the whole ballgame.
Jen Leonard: Plus, it’s just so much fun to learn every day. So next time, Bridget, I’m really excited—especially given your deep background as a judge—to dive into some topics that are emerging in the courts and for judges, for clerks, for court administrators, and for lawyers who practice before those tribunals. And I think we’ll do at least two episodes on some of those topics, maybe more, because they keep proliferating. But we hope you’ll join us next time for some of those great conversations and Bridget’s wise expertise and thoughts on that area.
Bridget McCormack: Yeah, looking forward to that. That’ll be fun.