Dean George Triantis explains how Stanford Law is preparing students for lawyering in the age of AI.
How should law schools respond when AI begins to reshape not only the tools of lawyering but the profession’s core responsibilities?
In the debut episode of AI Sidebar, host Irene Liu sits down with Stanford Law School Dean George Triantis, who lays out Stanford Law’s overall approach to AI: prepare students to use the technology fluently and responsibly, preserve the human judgment at the heart of lawyering, and help legal institutions adapt to a rapidly changing world. From the launch of Stanford Law’s AI Initiative to the rise of AI agents, legal tech tools, and AI-native law firms, Dean Triantis explains why adaptability, ethics, and trust will remain central, even as the tools of the trade change rapidly. Together, they discuss how Stanford Law is preparing students for an AI-enabled profession, why “human in the loop” thinking is essential, and what legal education must get right as AI transforms the practice and purpose of law.
Learn more about the Stanford Law School AI Initiative
George Triantis, Richard E. Lang Professor of Law and Dean of Stanford Law School
Irene Liu, Executive Director of the Stanford Law School AI Initiative
Chapters:
(00:01:10) A Career in Legal Education
(00:04:14) Why AI Matters to Law Schools
(00:06:26) Launching Stanford Law’s AI Initiative
(00:08:45) Preparing Students for an AI-Enabled Profession
(00:14:04) From Westlaw to AI Agents
(00:16:49) Preserving Foundational Legal Skills
(00:20:03) What Will Distinguish Excellent Lawyers
(00:23:02) AI in the Classroom
(00:27:38) What Law Firms and Alumni Are Saying
(00:32:26) Looking Five Years Ahead
(00:34:33) The Future of the AI Initiative
(00:37:38) Conclusion
AI Sidebar - Ep 1 - George Triantis
[00:00:00] Irene Liu: Welcome to AI Sidebar. I'm Irene Liu, your host and executive director of Stanford Law's AI Initiative. For our very first episode of Stanford Law School's new podcast, we're so honored to be joined by our very own dean, Dean of Stanford Law School, George Triantis. We'll be discussing law schools in the age of AI.
[00:00:20] Under Dean Triantis' leadership, Stanford Law has launched important work around AI, including its AI Initiative. So today, we'll talk about why Stanford Law created its AI Initiative, how law schools should prepare students for an AI-enabled profession, what core legal skills still matter most, and how deans and faculty are thinking about AI in the classroom.
[00:00:43] So with that in mind, let's jump in and hear directly from Dean Triantis.
[00:00:52] Dean Triantis, thank you so much for joining us for our very first episode of AI Sidebar. We're so honored to have you here to talk about this topic of law schools in the age of AI.
[00:01:03] George Triantis: It's an honor to be here, and it's an honor to work with you, Irene, at the law school with the Stanford Law AI Initiative.
[00:01:10] Irene Liu: Thanks so much. And so, before we dive into everything about AI, I'd love to just step back and talk about your career. You've had such a fascinating one, and you started out as an academic scholar, professor, and now dean at Stanford Law School, and you stepped into this role two years ago. And so, can you tell us about what drew you to legal education and now leadership?
[00:01:34] George Triantis: I'd love to, and I'll try to keep it brief. But decades ago, when I graduated from law school, I started work with a law firm, and my practice was a transactional practice, so I became enmeshed in negotiation and agreements and contracts.
[00:01:51] But the, but the routine of practice didn't really give me time to look into some fundamental questions, like how do negotiations get translated into agreement? What part of agreements are actually embodied in a contract? I was very curious about collaboration. What is the role of contracts in encouraging and facilitating collaboration? And I just needed time to think about some of these fundamental issues, and that's why I became, a legal scholar, and most of my focus, since then has been on contracts, on commercial law, and also bankruptcy, which is, when you think about debt restructuring, most of it is a multi-party negotiation.
[00:02:32] Then when I came to a university, I was very interested about the interplay between contracts and institutions and the institutional structure of universities. And particularly when I came to Stanford, it was interesting to study how Stanford is structured. How does it succeed so widely in promoting interdisciplinary study?
[00:02:54] And any chance I had to be involved in strategic decision-making and advice in other parts of the university, I, I seized on on it as a result of my curiosity. So, I did a number of things around the university, and I had the opportunities to do it because, lawyers tend to, be asked to get involved whenever there's committee work or to chair a committee, and I think it's the attention that we pay to process and to legitimacy and to outcomes and to listening.
[00:03:25] So I was invited to do a variety of different things. Most recently, I was a senior associate vice provost for research that looked into research policy. And then when the opportunity came up to become dean, I, I was very enthusiastic about doing it because in the decades that I've been in legal academia, I don't think there's a more exciting and transformational time for law schools, than the present, given what's happening to higher education and also to, to the legal profession.
[00:03:57] Irene Liu: Yeah, and it's such a transformative time, not only, in education in general, but because of AI that's transforming, education as a whole. So how is, how is AI, how did you decide that AI should be a priority at Stanford Law School?
[00:04:14] George Triantis: Well, I, I, I'm certainly not unique in that respect in the sense that who isn't focused on right- AI right now? If you look at, if you pick up any newspaper from the popular press to much more technical or industry specific, media, most of the attention is on AI and the implications of AI.
[00:04:36] So at the law school, AI is pervasive. It, it affects what we research and how we research and also what we teach and how we teach it. And I think what's distinctive about, thinking about AI at a law school like Stanford Law School it stems from our two missions. Our first mission is to prepare our students for legal practice and also for a variety of other career paths that they'll take after they graduate. And law is, to a significant extent, a knowledge-based, profession, but not exclusively, and I hope we'll have a chance to talk about that a little bit, a little bit later. But to the extent that it is knowledge-based, you can see how, AI is going to transform or is transforming the legal profession.
[00:05:25] The other mission that we have is to study and to contribute to knowledge at the intersection of legal institutions and society. And as AI profoundly affects all sorts of social phenomena, we have to, we have to follow it, and we have to think about how the law is going to adapt and how it's gonna lead and how it's gonna create incentives, looking forward across the three branches of government and across all levels of government. The study of law is inherently interdisciplinary, and at Stanford, we have a long history at the law school of interdisciplinary study.
[00:06:04] We also have a long tradition of applied work, which is working with partners outside the law school, and particularly, particularly government agencies. So, building on that, it was natural for us to turn, in both of our research and our educational missions to incorporate, AI, and we've been doing it for some time.
[00:06:27] Irene Liu: And not only did you prioritize AI, but you also created this new initiative called the AI Initiative, which I'm now the executive director for. But what inspired, the formation of such an initiative, and what were you trying to achieve with the AI Initiative?
[00:06:44] George Triantis: As I mentioned earlier, we, we do have a, a substantial history of thinking about AI and its impact on different systems and legal responses to it. And so, in various parts of the school, there are faculty and labs and, and clinics that are addressing the impact of AI.
[00:07:06] If we were doing nothing in the area two years ago, I think we would have established a center for AI. But we deliberately thought of creating the initiative as a hub, as a hub that, that that helps in the communication of different parts of the law school that deal with different aspects of AI, that aligns the activities, and also that amplifies and builds on them.
[00:07:32] There are a couple of other things that I think are unique about AI that we really wanted to address through the initiative. The one is that it is rapidly changing, and therefore, we need some flexibility, and we need some adaptability, and I think the AI is built in order to, in, in order to have that flexibility.
[00:07:51] And the other is, as much as any other phenomenon that law schools have engaged with, AI really requires engagement outside of the law school. With, not just aligning the different parts of the law school working with AI, but also across the university, in the Bay Area, with industry, with government, globally as well, and I think the initiative will help us to, to amplify.
[00:08:20] Irene Liu: And what I found really surprising when I joined Stanford Law's AI Initiative was how many centers, labs, and programs, that we have already in existence that work on AI. So obviously, this is the hub for it, but we've had the RegLab, the liftlab, the Rhode Center, and we also have the library that's working on a lot of, with a lot of efforts to help train students on AI as well too.
[00:08:45] And so which leads me to the next question, like, how are we preparing our law students, for this generation where AI is such a transformative part of their practice, an important part of their practice?
[00:08:58] George Triantis: Our students will go out and do a variety of different things in their careers. many of them will become leaders, and we wanna make sure that they understand not only how to use AI themselves, but also what it means to lead an organization where AI plays a big part.
[00:09:17] So, there are many ways in which our students are exposed and learn about AI. I can start within individual classes. There is scarcely an area of the law or a subject in law school that isn't affected by AI. If you think about copyright and disputes over copyright of material that are used to train, AI, models. If you think in an area that I'm focused on, in contracts, about the capacity of AI agents to contract. In torts, the liability for harm that's caused by AI.
[00:09:53] So students are being exposed to the impact that AI has in their systems that they're studying almost everywhere in, in, in every course, and there's a continuing adaptation of courses, that reflects that.
[00:10:07] I think that it may be that the question that you're asking is about how we are training them on the use of AI, and particularly the effective and responsible use of AI. We know that they're graduating into a market in which they're expected to be fluent in the use of, of AI, but also one where the technology's gonna be rapidly changing.
[00:10:30] So our focus, when you think about the education across the school, our focus is on adaptability, which means that they are exposed to multiple different AI tools, not just prompt engineering, which might have been the focus two years ago, but also using the foundational models to build AI agents. And thinking creatively about how to explore new tools, how to learn how to use them, and also how to evaluate them, which is, extremely important.
[00:11:04] So, the law school has a multifaceted approach given the pervasive nature of AI technology and how rapidly it is developing. We have grown expertise in our law library, which has been really important because after all, it's always been, the locus for the preservation, the dissemination, and the flow of information. And our librarians have been extraordinary in both keeping up with the technology and also thinking of creative ways to share their insights with the students.
[00:11:39] So, they have created courses on AI essentials for some of the legal AI tools that are built on top of foundation models. And they also-- we also have courses on the building of AI agents. They have created an AI learning hub and a drop-in AI curiosity corner, which can answer questions for the students as they arise, that they can, that they can drop in. They've created workshops for, for other AI skills.
[00:12:10] Our first-year legal research and writing has been transformed to teach the responsible use of AI in the third quarter, in the spring quarter, and they pay particular attention to the importance of the human in the loop, the evaluative, function, of the human. And we have upper year courses in AI literacy as well.
[00:12:32] And Irene, thanks to your leadership, we have a speaker series, that brings in people, especially from industry, but also from government agencies and the legal profession. And our location within Stanford, the contribution of our graduates, in this industry, and being in the Bay Area certain certainly helps. But we can tell the interest in the students by the attendance, in these events, which have been, extraordinary.
[00:13:00] Irene Liu: Yeah. And one of the things that's so fascinating is the level of software and gen AI tools that the students have access to. I, I thought it was really interesting when I joined Stanford a few months ago to see that we have specific legal, LLM tools like Harvey, Legora, LexText AI. And we also give students access to Stanford's AI Playground, which has general purpose LLM tools that range from ChatGPT all the way to DeepSeek as well too, which is fascinating because the AI Playground incorporates Gemini, ChatGPT, Claude, DeepSeek, and so many different softwares that students have access to far more tools than I had even access to when I was practicing. And so, I just thought it was really interesting.
[00:13:43] And if you think about the days back when you started practice, I'm sure there was Westlaw, and there was LexisNexis, and those were probably the two main tools. So, do you see any parallels here with this generation that now have access to so many tools, to how when you started out your legal career?
[00:14:04] George Triantis: I do, Irene, and I see some parallels, but I-- And then I see some really important differences of the, of the type that you mentioned.
[00:14:10] So the parallels were that Westlaw and Lexis were really transformative when it came to legal research. We were, we were supposed to be tentative at the time because they made mistakes. So, when it came to Shepardizing, when I was in law school, we were told that we needed to go look at the hard copies in order to verify what Westlaw or Lexis was telling us about whether a case had been, overruled or overturned.
[00:14:39] One important difference is the one that you referred to, which is, if I recall correctly at the time, we were being trained in Westlaw and Lexis, that these were tools that it was anticipated that we would be using these tool, one or both of these tools- for the rest of our career, I think, at that time.
[00:15:01] Our approach here is not to train our students on specific tools, but to give them general exposures and to train them in how to adapt to the new tool and also how to evaluate tools. So, in legal research and writing, when they're given an assignment, they are asked to use three tools, see what the responses are, and then evaluate them, which what are the relative strengths? What are the relative weaknesses? What are some of the hazards of relying on them?
[00:15:29] So the important message is the human has to be in the loop, evaluation is critical, and using multiple tools is helpful, whether it's at one time or whether it's over time as these tools, as these tools develop. And now, we're not just asking them or inviting our students to learn about the specific, legal tools that are built on the foundational models, but also using the foundational models in order to build their own agents, which is an important, step that they're taking.
[00:16:02] Irene Liu: And what's so interesting is when, when you were saying that even with Westlaw and LexisNexis, you had to go back to Shepardizing and actually using, finding the books to Shepardize, because there were errors. And in the same way, with all of these tools today, we're still training the lawyers and law students of today to make sure you go to the sources and cite check because there has been hallucinations and errors.
[00:16:25] So, it's sort of interesting to see how there are parallels from technology that was launched way back to now technology that's launched today. But one of the things that people are concerned about with AI though is that law schools, if they lean too hard into AI, will they be able to preserve foundational skills like legal reasoning, writing, judgment, and advocacy? Is that a concern that you have, or, and how are we preserving it at Stanford Law in our curriculum?
[00:16:57] George Triantis: It is a concern, or at least it's important part of our, of our goal to make sure that these foundational skills are continued to be taught and that they are preserved in our curriculum.
[00:17:08] I wanna mention why the found, what are, what is foundational, is actually a foundational question right now-
[00:17:16] Irene Liu: Yeah, yeah ...
[00:17:16] George Triantis: for us, and it's changing, I think, with the development of AI tools. But before I get into that, let me say that doctrinal analysis and statutory analysis, the things that are the bread and butter of law school education and of legal practice, they continue to be crucial, in law school.
[00:17:36] And part of the link is what we were discussing before, which is about the importance of evaluation. In order to be able to evaluate the outcomes of a tool or a model, you need to know something, you need to know a lot- about the, the, the underlying, the underlying analysis, doctrinal, statutory, and so on. So, those courses will continue to be, foundational for us, at the law school, even as AI becomes a bigger part, of the, of the education.
[00:18:10] With respect to the other skills that people mention, the human skills of judgment, of creativity, of writing, I I think it's more, complicated, because while, knowledge work is being automated and execution of decisions are being automated, the concern is that judgment, decision-making, management, those are things that AI's gonna have more trouble with.
[00:18:35] But one needs to be careful not to make a blanket statement. I mean, even now, AI models do provide some support with respect to judgment and decision-making. You need a human in the, in the loop, but some of the judgment work, if you will, or analysis is being, being assumed by AI. And so, it calls for a, a deeper question as to what kind of judgment is gonna remain with, with, with humans as opposed to, being, as opposed to being undertaken by, AI tools.
[00:19:12] When we think about what kind of judgment is gonna remain human, I think a critical part is what the legal profession, especially in the legal profession, is what it focuses on, which is the ethical and professional responsibilities that are owed to the client, to one's client, as well as to society as a whole through the administration of justice. As you know, AI agents are driven by goals, and I think it's very important that when they are used in law, whether it's legal practice or in legal institutions, those goals need to be set and overseen by lawyers that are trained in the ethical norms and the and the responsibilities of the profession.
[00:19:53] And that part of legal education, I think, will, if anything, become even more important in the, shall we say, AI-enhanced era of, of, of legal practice.
[00:20:03] Irene Liu: And in this AI-enhanced era, what, what will distinguish excellent lawyers from everyone else?
[00:20:13] George Triantis: I think that's an excellent and essential question, and I want to return to the core role of a lawyer as a trusted advisor and a fiduciary. And trust is not something that AI can establish, and it'll probably be a considerable amount of time before it can. And it continues to be a human quality, along with self-awareness, empathy, skills of collaboration. I think also lawyers understand the importance of legitimacy in a, in a process, and ethics.
[00:20:50] And these are all things that will, I think, distinguish a really excellent lawyer from everybody else. The pace of AI development that we've talked about, and the pace of change in the world, whether it's geopolitical, whether it's climate change, all sorts of things, is such that adaptability and a learning mindset is really important, and those are also, human skills.
[00:21:16] Now, how to help our students develop these skills so they will be the distinguished lawyers is something that we give continuous attention to in our curriculum, but also in our extracurricular, programs at the law school. We're lucky that the traditional Socratic method actually does quite a lot as an effective means to convey those skills.
[00:21:39] In a Socratic method, in a Socratic dialogue in class, students are required to consider the strongest arguments on the other sides. They're the, they're, they're called upon to listen to the arguments on the other sides, and also to argue perhaps the side of a case that they don't-- that doesn't match their beliefs.
[00:22:00] And these are skills that I think are, are, are very important. Experiential learning is also important, and I think perhaps even of greater importance in the new world that we're entering into because of these human skills. so, whether it's through our full-time clinics, that students can take over, over a quarter, or through simulation exercises or case studies, those are other places where we can help them develop, the human skills.
[00:22:32] Irene Liu: And also, the AI skills potentially through those clinics as well as the simulation exercises, because now you can do more simulations as well with AI.
[00:22:41] George Triantis: Absolutely. I think that the, the teaching of human skills- needs to be integrated with the teaching of AI and the use of AI. It'd be very problematic if students saw two types of courses or two types of programs, one that focuses on human skills and the other that focuses on AI fluency. They need to be integrated.
[00:23:02] Irene Liu: Yeah. One of the things that you said around this, age of AI is that adaptability is key. And so how... Are you seeing adaptability among the students and the faculty, especially when it comes to AI? What is the sentiment? Are they asking for more training, clearer rules, more experimented, more experimentation or more caution?
[00:23:24] George Triantis: Well, I have read about, the anxiety and the anger that, many in the Gen X, generation have towards AI, and to be honest, I haven't seen it, at the, at the law school. Perhaps it's because law firms have still not changed their hiring, even if it might be in the future.
[00:23:46] But our students are definitely aware of AI, and they do want to be prepared, and we can see that in the enrollment in training courses as well as attendance at your speaker series. So generally, I think there's a broad awareness among the students that this is a period of rapid change and experimentation, but it does give rise to certain types of anxiety.
[00:24:08] One relates to what are the norms for AI use. You know, even, in, even the use of AI outside of the law school, if you think about writing an email to a friend or sending birthday wishes to a friend, or, can you use AI or can AI can correct, your grammar when you are, when you are writing, an email or a message?
[00:24:35] And then maybe more seriously, how to use it in class. Our students are increasingly coming to law school with already a habit or, or some expertise in using AI, and there's a growing number of apps that they can use in order to help them with their studying and their performance in class. And until there are some broad, very clear norms across the law school, they're gonna be nervous as to whether they are violating, any rules and policy.
[00:25:09] At Stanford, what we've done so far is instead of having a clear one-size-fits-all across the school, the, each instructor and each faculty decides on what the policy is in their course, depending on the nature of the course and also their pedagogic goals. And we insist, and our faculty are careful in making sure that this is explicitly stated, what is allowed and what's not allowed.
[00:25:37] Very few of them exclude the use of AI altogether, but they, allow some uses and not other uses, and sometimes they rely on disclosure, as a way of doing it. So, we're in a period, as we've said before, of rapid change, of experimentation, and we'll see how that all shakes out in the years to come
[00:25:58] Irene Liu: I really like that point about giving the flexibility to faculty to decide how AI should be used in the classroom. So, for example, our, AI Initiative faculty co-director, Nate Persily, Professor Persily, the way he allows students to use AI is that they can use AI in the classroom as long as, and on exams, as long as they disclose how it's being used. But if he finds any errors or citation issues, then it's an automatic fail.
[00:26:28] And so it really requires the students to continue to also remain diligent and preserve their human skills as well, too. And it also allows him to see how students are using AI. And I think it'll be really interesting to see, especially as we have our first, seniors are the first AI native class that's graduating. So, it'll be interesting to see how they, when they enter law school, how their adaptation of AI evolves as well, too. and so, it should be a really interesting phenomenon within the law schools, too.
[00:27:00] George Triantis: Yes, and there, in fact, there's another, there's at least one other faculty member that, provides the students in an exam with the AI-generated answer to the hypothetical question or the question based on the hypothetical, and then asks the students to improve on it.
[00:27:17] And the theme in both of what Professor Persily is doing and in that class is that you will be using AI, but you need to stay in the loop. You need to monitor it and do your diligence and make sure that you evaluate the, the results. And I think that that's something that's going to become more pervasive over time.
[00:27:38] Irene Liu: And are you hearing, any sentiments from alums and law firms around AI? Are they concerned or are they excited?
[00:27:45] George Triantis: They are requiring some AI fluency, a growing amount of AI fluency. I think they also anticipate that they will not need as many associates in the future, although, as I said earlier, it hasn't impacted their hiring of Stanford, graduates, at least for now.
[00:28:03] And as they see the apprenticeship period for associates shrinking because more and more of the tasks that associates traditionally provide will be done by AI, they would like the graduates that they hire to be more prepared with the human skills- ... to, business development skills, client relations, collaboration skills. They, they look to, graduates who have motivation, who are resilient.
[00:28:33] And so I think our graduates need to be more ready- with those skills at the time that they start with firms than they might have been, for instance, when I, when I graduated a, a long time ago.
[00:28:47] Irene Liu: What about the sentiments of alums? Are they excited or are they concerned about where legal education is headed?
[00:28:53] George Triantis: You know, we're in, we're still in a relatively early period of AI adoption- ... so there is a there's a wide range of, of reactions to AI. Some of them are concerned. They're concerned about their own practices. They're concerned about the world and society as a whole-
[00:29:12] Irene Liu: Yeah
[00:29:12] George Triantis: ... And the threats of AI, but others really have embraced it. And if we were to stereotype the Stanford graduate, the Stanford Law graduate, you would say it's someone who is entrepreneurial, future-looking, gets excited by new things, and there's definitely a, a, a significant portion of our alumni that are like that across different ages.
[00:29:39] As I also, as I mentioned earlier, it, it's not just that they are thinking about how they individually are gonna use AI, but how is AI changing their organization, the structure, their operations? So, we have some alumni who have left their firms and started a new firm. And they started new firms that are based on AI, that are AI native, and that's something that they can do as entrepreneurs, which they might, which would've been more difficult to do in, in a, in a big firm. So, there's a lot of excitement as well as some apprehension, depending on whom you talk to.
[00:30:17] Irene Liu: Yeah, and for law students as well as alums, there's many new job opportunities as a result of AI as well for the legal profession. Traditionally, it used to be you go to a law firm for most, majority of the students and alum as well, but now you can actually go to a legal tech company. You can run their sales team. You can become a legal engineer. You can be part of their customer success team. You can actually be part of their product team. And so, it's opened a lot of different avenues, and even within law firms, like you said, there's AI native law firms that are hiring talent as well, too.
[00:30:48] So it's a really interesting time from a job opportunity perspective for the students as well as the alums, and I know some Stanford students are also building their own startups as 1Ls and 2Ls, which is fascinating as well, too.
[00:31:02] George Triantis: It is. I think that one of the things about Stanford Law graduates that is, that may be surprising, to your listeners, is that many of them, even in the past, our past graduates are not in the practice of law. They are CEOs of companies. They lead organizations. They are leaders of government agencies. Of course, they're politicians. They're in civil society and international organizations, not practicing law. In fact, some of them aren't even, aren't even thinking a lot about law, but they're bringing skills from law school to these leadership positions.
[00:31:37] And there's something about thinking like a lawyer, about the communication, about the ability to see all sides of an issue, the ability to talk to and to interact with experts from different domains, that the, the attention to process that really helps them in these roles. So, it's always been the case that, especially when you look beyond five or ten years after graduation, our graduates have been doing a wide variety of things. But now, as you, as you suggested, Irene, I think that potential, in other words, the breadth of possibilities, is, is just gonna expand given the changes in technology, and also the, the entrepreneurial opportunities that are available for our graduates two, three years out of law school.
[00:32:26] Irene Liu: Yeah. And just looking ahead, like you said, five years. Let's say we look ahead five years from now. What would you hope that Stanford Law and legal education more broadly has gotten right about AI?
[00:32:38] George Triantis: Just as I know that our students and our graduates, need to learn to be resilient and adaptable to changes in technology, as an institution, we have to be the same way.
[00:32:52] That we, we need to be aware that the technology's gonna change, the impact of technology's gonna be deep, but constantly changing, and so we have to be, adaptable as an organization. And so, looking back five years from now, I hope that we can say that we were flexible in that way, and, and that we did adopt a mindset of learning, adaptation, and experimentation, even if occasionally it leads us down the, the, the wrong path or we have to, backtrack a little bit.
[00:33:29] As a, as a law school, and given our, our history, our research, much of our research has been focused on how to make sure that legal institutions remain robust. And these are challenging times for legal institutions, both within the, the nation and also internationally. When you layer over that what's happening with AI, AI creates opportunities for increasing the robustness, for increasing the effectiveness, and in-indeed the, the, the fairness of our legal institutions, but it also creates some very significant challenges.
[00:34:06] And I'd I'd love it if five years from now we looked back and we said that, AI provided opportunities for us to, to do even more for the robustness of legal institutions, and we helped them through partnerships, through collaborations with government across the three branches, across the different levels to, to, to meet the challenges of, of AI.
[00:34:33] Irene Liu: So, since we're looking ahead, if you were to look five years ahead and looking at this AI Initiative that was just created this year, what would you hope that it would have accomplished in the five years, when you're looking back in 2031?
[00:34:48] George Triantis: I, I hope that it has, met some of the goals that you and I have talked about and have established for it. One is to make sure that as decentralized as we are with different areas of research and teaching that, engage with AI, can we make sure that we are rowing in the same direction, that we know what different parts are doing, that we learn from each other. And I think, the AI Initiative as a hub is gonna play a very important role doing that.
[00:35:18] The second goal that we have is to make sure that we are fully engaged as much as possible with the outside world, with industry, with government, with civil society, with international organizations, because we need to be, we need to be involved with the debate outside of our, outside of our campus. And the AI Initiative is going to be absolutely essential, in doing that. But let me turn it to you, Irene.
[00:35:45] Irene Liu: Yeah.
[00:35:46] George Triantis: What other hopes do you have for the initiative?
[00:35:49] Irene Liu: Yeah, I mean, I think what will be really great is, you know, I came in and there were a number of centers and labs and, programs already in existence. I would love to see that flourish. I mean, it can continue to be some of the existing programs can be, become bigger, and I'm sure there will be other types of labs and, and research that will be done. And if we can, touch on those other areas. As we speak right now, I mean, agents has become a thing that was talked about but is real today.
[00:36:18] I mean, two years ago, people kept saying it's the year of the agents, it's the year of the agents, but really twenty twenty-six has really become the year of the agent. So, what can we be doing more around research around agents, for example, or even, issues, for example, of, all these other types of IP related issues.
[00:36:35] There are so many different issues that are coming up each day, especially with AI, that we're flourishing with more research ideas and that we're leading the way in terms of providing that type of thought leadership and scholarship that this, the industry is craving. in many ways, they're looking at institutions like Stanford for neutral perspective, neutral academic perspective, and I hope we can provide that as a, as a hub right in Silicon Valley that has access and collaboration with industry, that we can provide that type of research and knowledge, to share with the greater world.
[00:37:10] George Triantis: I couldn't have put it better myself. I think that's, I think it's terrific. I, you know, five years from now, given-
[00:37:16] Irene Liu: Yeah
[00:37:16] George Triantis: ... how quickly everything is moving, a lot will happen, and most of it we can't really, we can't really predict.
[00:37:24] Irene Liu: But it'll be fun if we did this again, hopefully before five years, but it will definitely be fun to do it in five-year anniversary to look back on this interview to see what has changed and what actually materialized from our predictions, so.
[00:37:37] George Triantis: It, it's a deal.
[00:37:38] Irene Liu: Yeah. Sounds great. Thank you so much, Dean Triantis, for joining us for our first episode. Really appreciate it.
[00:37:44] George Triantis: Thank you.
[00:37:49] Irene Liu: Thanks again to our Stanford Law School Dean George Triantis for joining us today. We're so grateful for his time and insights. And thank you for tuning into our first episode of The AI Sidebar. If you found value in today's conversation, please share with a friend. Until next time, keep learning.