Lawfare Daily: Why AI Won't Revolutionize Law (At Least Not Yet), with Arvind Narayanan and Justin Curl

Summary

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The Lawfare Podcast: Why AI Won't Revolutionize Law (At Least Not Yet)

Host: Alan Rosenstein, Lawfare Institute
Guests: Arvind Narayanan (Princeton University), Justin Kurl (Harvard Law School)
Date: February 12, 2026

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Episode Overview

This episode dives deep into the recent Lawfare research report by Arvind Narayanan, Justin Kurl, and Syash Kapoor. Despite the hype around AI's potential to overhaul legal practice, the guests argue that structural features of the profession — such as adversarial dynamics, regulatory barriers, and the necessity for human judgment — will prevent truly transformative cost savings or radical change in the near term. The episode frames AI as a "normal" (rather than exceptional) technology, requiring substantive organizational adaptation to be impactful.

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Key Discussion Points & Insights

1. AI as "Normal Technology"

(03:41–06:50)

• Four-Stage Framework: Arvind outlines a model inspired by historical technological adoption (e.g., electricity) with four stages:
  1. Improvements in capabilities.
  2. Translation into domain-specific products.
  3. Worker adoption and adaptation.
  4. Organizational, legal, and normative changes.
• Transformative, But Slow: "People systematically overestimate what can be done in the short term... but underestimate what's going to happen in the long term." (Alan Rosenstein, 05:49)
• Role of Agency: AI’s impacts aren’t inevitable; reform and institutional change are critical for broad benefits.

2. Why Law Is Expensive — Before AI

(10:34–14:44)

• Credence Good: It’s hard for clients (even lawyers) to evaluate service quality—market mechanisms don’t work as with TVs or cars.
• Relative vs. Absolute Value: Service value is often zero-sum, depending on the other party’s moves (especially in litigation).
• Professional Regulation: Licensing laws (Unauthorized Practice of Law or UPL) restrict who can provide legal advice, making innovation difficult.
  • “Anytime you apply legal knowledge to specific facts, you might be engaged in the practice of law. If you do so without permission... that's actually a felony.” (Justin Kurl, 12:50)
• Vague Boundaries: “It's an almost Kafkaesque situation... there is such a thing as the unauthorized practice of law... but no one will actually tell you what constitutes [it.]” (Alan Rosenstein, 13:25)

3. AI Capabilities vs. Legal Impact

(16:13–18:58)

• Not Skeptics of Capabilities: Paper concedes AI can already do many discrete legal tasks and may soon outperform most lawyers at set pieces (e.g., briefs).
• Feedback Loops in Law: True legal skill requires nuanced judgment—hard to build in data for AI. Unlike math/coding, learning is slow: “That feedback loop is extremely slow... Nevertheless, that's not where our skepticism comes from.” (Arvind Narayanan, 17:32)

4. Bottlenecks Blocking AI’s Transformative Promise

a. Regulatory Barriers

(18:58–22:24)

• Law Firm Ownership: Only lawyers can own firms, stifling innovative models.
• Administrative Tasks: Small-firm lawyers spend most time on non-billable work. AI can help here when regulations allow.
• Where Regulation Isn’t a Barrier: “You can actually use AI to make it much more efficient... because there aren't those regulatory barriers.” (Justin Kurl, 21:46)

b. Adversarial Dynamics (The "Arms Race")

(27:46–31:58)

• Zero-Sum Productivity: If both litigation sides have AI, they each do much more work, but the outcome is unchanged; costs may rise, not fall.
  • "[Lawyers are] now doing 100x [what they used to] in all those relevant domains... but the outcome... is still settling favorably or winning at trial." (Justin Kurl, 28:10)
• Discovery Example: Digitization created more discovery work, not less cost.
• Non-Litigation Law: Adversarial dynamic bleeds into transactional work too, e.g., contract negotiations.

c. Human Involvement as Bottleneck

(33:17–37:59)

• Judgment Can't Be Removed: Even if AI can do much, there's constitutional and moral weight in decisions (e.g., sentencing) that society expects humans to bear.
  • “If you're making a decision about whether someone has 10 years in prison... I would want a human being to be involved in that.” (Justin Kurl, 36:38)
• Parallel Tracks: For low-stakes matters, maybe AI-enabled arbitration; for high-stakes or "agency" decisions, keep humans in the loop.

5. Reform Proposals & Their Tensions

(41:08–48:48)

• Reform UPL Rules: Liberalize who can provide legal services; e.g., regulatory sandboxes (Utah, Illinois), regulatory markets (Gillian Hadfield’s proposal).
• Consumer Protection Tension: Current UPL rules are ineffective at ensuring quality and mainly restrict access.
  • “70% of people are losing by default because they didn't actually respond... they couldn't afford a lawyer.” (Justin Kurl, 46:26)
• A Moment for Innovation: “Maybe this moment of upheaval around AI is a time when we can have a lot of innovation around the way we regulate different professions...” (Arvind Narayanan, 47:20)

6. Philosophical and Normative Stakes

(39:46–41:00)

• Human Agency vs. AI: “This is what it means to be in control of our own civilization... Not killer Terminator robots. [It boils down to] moments where we put the course of humanity in the hands of machines... a line we should not cross.” (Arvind Narayanan, 39:46)

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Notable Quotes & Memorable Moments

• On AI’s slow burn:
  “People systematically overestimate what can be done in the short term, but then they tend to underestimate... what's going to happen in the long term.” — Alan Rosenstein (05:49)

• On regulatory uncertainty:
  “It's an almost Kafkaesque situation... there is such a thing as the unauthorized practice of law... but no one will actually tell you what constitutes [it.]” — Alan Rosenstein (13:25)

• On zero-sum arms races:
  “If you give both sides access to AI and you're sort of locked in this zero sum process, the amount of work that each side does could essentially just go up... But the outcome clients ultimately care about is... unchanged.” — Justin Kurl (28:10)

• Why humans in the loop matter:
  “This is what it means to be in control of our own civilization... a world in which we leave these kinds of decisions up to AI is not a world I want to live in.” — Arvind Narayanan (39:46, 41:00)

• On reform imperative:
  “I view AI as partially a way to fix those problems, but also as a way to sort of push through or motivate the reforms that we've needed for a long time that aren't actually about AI.” — Justin Kurl (44:11)

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Timestamps for Important Segments

• AI as Normal Technology: 03:41–06:50
• Why Law is Expensive (Pre-AI): 10:34–14:44
• Capability Skepticism?: 16:13–18:58
• Regulatory Barriers: 18:58–22:24
• Adversarial/Arms Race Dynamics: 27:46–31:58
• Human Judgement Bottleneck: 33:17–39:46
• Normative Stakes: Human Agency: 39:46–41:00
• Should We Welcome or Resist AI in Law?: 41:08–44:57
• Reform Proposals and Regulatory Innovation: 44:57–48:48

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Conclusion

The episode offers a sobering, nuanced take: AI’s raw cognitive power will not sweep away the deep structural features that define legal practice. Real gains require changes at organizational, regulatory, and philosophical levels — and much of the drama may play out in adjusting the rules of the legal “game” rather than replacing its human players. Both reform and caution are needed, with continual focus on access to justice, consumer protection, and societal consensus on the human role in decision-making.

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Transcript

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[Justin Kurl]

The amount of work that each side does could essentially just go up because now both sides are being hyper productive with AI. Instead of writing like one motion or writing five pages or looking at 100 cases, they're now doing 100x that in all of those relevant domains. So the amount of outputs has increased, but because the outcome that clients ultimately care about is settling favorably or winning at trial and it takes much more work and much more outputs to reach that exact same outcome.

[Alan Rosenstein]

It's the Lawfare Podcast. I'm Alan Rosenstein, Associate professor of Law at the University of Minnesota and Research Director at lawfare. I'm talking to Justin Kurl, a third year JD candidate at Harvard Law School and Arvind Naranian, professor of Computer Science at Princeton University and Director of the center for Information Technology Policy.

[Arvind Narayanan]

Judges make law a lot of the time and exercising human judgment about what we want, the world to look like. That's the perfect example of what I would want humans to be doing in a world where all conceivable labor can be automated.

[Alan Rosenstein]

Today we're discussing their new Lawfare research report, co authored with Princeton PhD candidate Syash Kapoor, arguing that despite AI's impressive capabilities, structural features of the legal profession, from guild regulations to adversarial dynamics, mean that the technology may not deliver the dramatic cost savings that many predict. So I'm excited to get into the paper that you and your co author Syas Kapoor have written about the effect of AI in the legal profession and specifically why it might not provide the sort of cost savings that everyone is predicting and at least some people, though perhaps not lawyers, are hoping for. But before we get into that, I want to take a moment to talk about the broader framework of how you're all thinking about this, which draws on this broader project that you, especially Arvind and your collaborator Saish, have thought about and have written a great book about and done a lot of great writing about, which is this idea that AI is a normal technology. So just before we get into the law part of this, just sketch out what you mean and particularly what you mean by a normal technology.

[Arvind Narayanan]

Definitely. Let me start with a historical example. Way back when electricity became a thing, there was a lot of hope that it would enable factory owners to rapidly achieve a lot of cost savings by, as I understand it, replacing those big messy steam boilers with electricity. But there's a great analysis of this by economic historian Paul A. David, and it turns out when they first started doing that, it didn't really seem to help. And it took 40 years, something like that, to really figure out how to gain the benefits of electricity. And that's by taking advantage of the fact that electricity is a much more portable kind of technology and you can move it and generate it wherever you want. So that required restructuring the whole layout of factories, going towards the logic of the assembly line, changing how firms hired and paid and trained workers, that sort of thing. And these kinds of downstream innovations, again took a period of decades. And really the insight of AI as normal technology is that we can and should learn from these past general purpose technologies. And AI is not exceptional in that way. And a lot of the discourse starts from rapid improvements in AI capabilities to drawing a straight line to societal effects or effects on any particular profession or the economy. And our view is, no, that's not going to happen. There are various stages in this pipeline. So we take this existing framework from the theory of diffusion of innovations and apply it to AI specifically. And we have four stages. The first stage is improvements and capabilities. The second one is how those get translated into better products in law or any other particular domain. The third stage is workers starting to adopt these products and learn to use them. And the fourth stage is really the hardest one. That's where you need to make organizational changes, changes to laws, norms, business models, et cetera, which we get into in this paper. But really the overall framework is about looking at all of those four stages in order to understand the speed and nature of AI impacts on any particular profession, as opposed to merely looking at what is the latest performance of GPT 5.2 or whatever.

[Alan Rosenstein]

That's great. And let me just dig in a little bit before I turn to Justin to sort of set up the law part of this to be clear. And I try to make sure that I'm understanding the argument correctly. When you say normal, you don't mean non transformative because of course, electricity was quite a big deal. Automobiles were a big deal. The Neolithic revolution was a big deal. I mean, a lot of these things are big deals. It sounds like what you're saying, though, is just takes a lot longer than people think. I think there's a quote, I think it's often associated with Bill Gates and maybe it's one of these quotes that's associated with many people, which is that people systematically overestimate what can be done in the short term, but then they tend to underestimate because people are bad at compound math. They tend to underestimate what's going to happen in the long term. Is it fair to say that you are at least open to the possibility that in the longish term AI will have a deeply transformative effect, even if it takes quite a long time and there are roadblocks and reforms and a lot of messiness that, as you pointed out, the latest frontier math accomplishment that GPT 5.2 Pro or whatever is going around X does not really capture.

[Arvind Narayanan]

That's exactly right. We are pretty optimistic about the effects of AI in the long run. We do have much longer timelines than a lot of people talking about this. But also we emphasize the agency that's involved. I think these effects are not preordained. I think we need to get a lot of things right in terms of reforming our institutions in order to be able to take advantage of these benefits.

[Alan Rosenstein]

What interested you in particular? Because Justin's a law student, I'm a law professor, sort of. We are professionally invested in the legal field. You are a computer Scientist. So there are any number of fields that you could and I'm sure have in your research applied this to. I'm curious if you think that the legal field in particular is a good test case or if it has some unique elements relative to other possibilities for you in particular. What was interesting about the legal angle here?

[Arvind Narayanan]

Yeah, lots of things. So let's contrast a few different fields. On the one extreme I would say is software engineering, which like law is a purely cognitive field. And unlike, let's say medicine, which I'm going to put on the other extreme, although it's not really an extreme, I want to look at the range between these three fields. So in software engineering being a purely cognitive field, we're starting to see the impacts of AI very quickly. But an important way in which it's different from law is that it's, that's not professionalized. There aren't a whole bunch of regulations about who can do software engineering, how software engineering can be done, various kinds of liability, none of that stuff. And so the impacts are starting to hit very, very quickly. On the other hand, in medicine, like law is very professionalized. But also you can't as an individual just start using a model for self diagnosing yourself. You can, but you very quickly run into roadblocks.

[Alan Rosenstein]

Yeah, I think tens of millions of people might disagree with you there, Arvind.

[Arvind Narayanan]

Yes, for sure. Yeah, they the problem is you can't prescribe yourself something. You hit a roadblock in terms of interacting with the system. Law is very nicely in the middle. It is purely cognitive and so there is clearly a lot of great potential. But at the same time it is very professionalized. There are all these regulations that we discuss in the paper. And the reason it's so interesting to look at this profession that's in the middle of these two extremes is that you can really imagine and spell out what are the kinds of reform that will be needed in order to really take advantage of this tremendous possibility. And those are things that the profession can start doing now. It's much harder to articulate that in medicine. So an example is people say, oh, we're going to be able to dramatically speed up drug development. Well, the problem is the hard part of drug development is not discovering new molecules, it's testing them. The human trials, which take 10 or 15 years, highly regulated, et cetera. It's really hard to articulate how you can compress that down to, let's say a few months without throwing away what we've learned about testing drugs safely. But when it comes to law, it's not that kind of thing. You can actually imagine and spell out the reforms. And I think that's what Justin has tried to take the lead in doing in this paper.

[Justin Kurl]

I'll just jump in briefly. I think one way that I also think about the AI as normal technology framework is just as a prescription of where we should be focusing our efforts. I think it's really easy to sort of view AI as this like all encompassing tsunami where there's nothing any individual can do to fight back against it or can intervene or shape its development. But AI as normal technology tries to identify systematically what those organizational and societal bottlenecks are so that you can know where you should focus your efforts if you're trying to ensure that sort of AI diffusion is positively impacting people.

[Alan Rosenstein]

So I think one way of looking at your paper is an exploration fundamentally less about AI and more about why law is so expensive. Right. Why the practice of law, why the products of law are so much more expensive, and why we don't see the productivity gains in law that we do with, I don't know, flat screen TVs or many other things. And so you identify sort of three structural reasons that legal services are expensive even before AI enters the picture. So you talk about law being a credence.

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[Alan Rosenstein]

You talk about how the value of a legal service is often relative rather than absolute. And then of course there are these professional regulations. So can you just sketch out for sort of the non lawyers in the audience, what is it about law that makes it so expensive, sort of ex ante before we even start talking about any technological developments like AI?

[Justin Kurl]

Yeah, of course. And I think here it's really important to sort of nod towards Gillian Hadfield's work. A lot of this comes directly from papers she's written over the past two decades about this. They're excellent. I recommend people check them out, starting with the first sort of reason about credence goods. I think one reason law is unique is because it's very hard to evaluate the quality of legal services, even for lawyers and experts in the field. You could imagine, if I'm engaged in this complex year long trial, it's hard to know whether a particular motion filed or a particular decision in one sentence about how to frame a topic is the reason why the client reached the outcome they want. And so instead of being able to directly assess the quality of legal services, you're sort of forced to rely on reputation or things like how prestigious was the law school that someone went to? Or things like that. And so that makes it very hard to have a functioning market when you think about legal services. The second reason about the value of legal services being relative is also it's hard to have, like, an understanding of legal services in the abstract. It's not like I can look at that and be like, that's a seven. Legal services. If I'm engaged in litigation, oftentimes whether I'm able to achieve the result I care about depends on what the other side is doing. So if my contract term is really good, might depend on what the other side is doing and how they're thinking about it. And then the final sort of reason that, again, Professor Hadfield identifies is there's a very complicated regulatory framework. There's two types of, I think, regulations that are relevant here. The first is upl, or Unauthorized Practice of law. Regulations which limit who is allowed to provide legal advice. That is defined incredibly broadly. So anytime you apply legal knowledge to specific facts, you might be engaged in the practice of law. If you do so without permission or you're not a licensed attorney, that's actually a felony in a lot of jurisdictions. So that's already one reason why maybe some of these companies, when their chatbots are providing things that look like legal services, they might actually start to run into some liability for it and also.

[Alan Rosenstein]

Jump in and just add to that. So I am a lawyer, believe it or not. Despite being a law professor who I would not recommend hiring for legal advice, I am technically a lawyer. I'm barred in the fine state of New York. And when I moved here to Minnesota at some point, I was just curious, okay, well, what does legal practice look like? Because I don't intend to really practice law anymore, but I don't want to accidentally practice law and not doing the right thing. And so I looked at the New York bar Rules, and what's amazing is there was all these paragraphs about what the practice of law might be, but then they specifically refuse to say what the practice of law actually is and that they actually also won't tell you. So it is an almost Kafkaesque situation where there is such a thing as the unauthorized practice of law. It's quite a big deal to do it, but no one will actually tell you what constitutes the unauthorized practice of law, which I have to assume just has sort of a chilling effect on this whole industry.

[Justin Kurl]

I think there's two really good examples actually connected to that that we talk about in the paper. I think one is if you look at what the New York Bar association has said, They've been like, well, chatbots. They might be the practice of law. It seems like it's getting close, and so I don't really know what I'm supposed to do with that. If I'm a lawyer thinking about, or even a consumer thinking about using AI.

[Alan Rosenstein]

What units is close measured in? I'd be very, very curious how many GPT units is close in this context?

[Justin Kurl]

Yeah, that's also what I want to know.

[Arvind Narayanan]

Can I ask you if either of you know if there have been any lawsuits against the major chatbots? I mean, I use it all the time for little things like reviewing contracts, and I assume that many people out there are doing that. So presumably these chatbots, you know, at least in my case, are providing legal advice that's tailored to my situation. So I wonder if people have been trying to sue them.

[Alan Rosenstein]

I don't personally know of any of these. I mean, obviously there are other. The automated legal services is a thing that predates these chatbots. And I think there have been some legal, at least regulatory challenges to some of these. I don't know if it's. LegalZoom has faced these challenges, but sites like that. There are also these countervailing First Amendment considerations where you couldn't get. The guild could not get too aggressive about this because people also have First Amendment rights to talk to a chat bot about their legal issues as well. But I'm curious, Justin, if you've heard of anything.

[Justin Kurl]

Yeah, I haven't seen anything focused on AI chatbots specifically, but I think LegalZoom is a good example where over the past two decades, they've been sued countless times, and they've had to rework the actual way that they provide legal services because they've had to reach very expensive settlement agreements. And what's interesting about that is who the plaintiff is. You ultimately need a plaintiff to bring the lawsuit. And so sometimes that's the attorney General of a state, but sometimes that's actual individuals who have received the legal services. So maybe if ChatGPT gives bad legal advice and someone's upset, we might see a new lawsuit about it.

[Alan Rosenstein]

So I want to get into the bottlenecks that you all go through in your paper. But before I do, I want to address sort of one thing that's not in the paper that actually might be surprising if you're reading a paper about skepticism, that AI will automatically lower the cost of legal services. And that is what none of you are arguing is that AI won't be able to do the actual individual cognitive tasks. There are a lot of people that think that AI is quote unquote, fancy autocomplete or stochastic parrots or just a giant plagiarism machine, there are lots of ways of dismissing that. And that'll never have the skill or the creativity to be a really good lawyer. Am I right? And let me ask you, Arvind, since you also have sort of a broader sense of the AI landscape across different cognitive domains, that's not what you're arguing. It seems like your paper is happy to concede the possibility. And maybe you all actually believe this. I certainly do. But I'm not a computer scientist that already today, and certainly within several years on any discrete, even large scale task like write me a Supreme Court brief, GPT7 may actually be quite capable of outperforming all but the absolute elite lawyers. And even for those elite lawyers at a tiny fraction of the cost that it takes to hire Paul Clement to argue your case. And so all these bottlenecks are actually totally separate from the raw capabilities. Is that a fair articulation?

[Arvind Narayanan]

That's mostly where we land. That's right. We're not capability skeptics. So let's divide it into two ways of looking at it. One is some of the current limitations, such as hallucinations or not really having access to all of the documents that it would need in order to do a good job in your case, that sort of thing. These are all easily fixable in our view, especially when you consider the long term of AI development. They're going to get fixed. But then you do get into some gray areas. So what it means to write a good Supreme Court brief is not something unlike, let's say, coding, where there are correct and incorrect answers. People are going to disagree about that. These are matters of judgment. So we do think there are some limits there in terms of how good AI can get, because, you know, it learns from feedback. And it's not going to be that easy to learn from millions of cases of feedback where AI creates an argument and then that brief is submitted and then you get to learn from what was the result in that case. That feedback loop is extremely slow. And so you're not necessarily going to see the kind of rapid capability progress that you see in, let's say, math or software engineering. Nonetheless, that's not where our skepticism comes from. We are acknowledging that there might be a day where AI is able to do any precisely specifiable cognitive task that most lawyers are able to do.

[Alan Rosenstein]

All right, so let's now then jump into the first bottleneck which is what you were talking about just recently, Justin, this question of regulatory barriers. So explain how that could get in the way of AI really revolutionizing and lowering the cost of legal services especially. And pictures, that's maybe a bit of a counterargument. Even if at the end of the day, the legal landscape, if you go to law school in 2029, 2030, even if the legal landscape superficially looks very similar, there are a bunch of law firms, some are very big, some are medium, some are small. If all of these law firms are using AI integrally, if these law firms are essentially kind of wrappers around these models, why isn't that enough to really have AI revolutionize legal practice?

[Justin Kurl]

Yeah, and I think it's important to distinguish between two ways that someone could receive legal services. I think the one that you just mentioned with law firms, that more directly implicates the entity regulations, piece of professional regulations of the law. And so that limits who can own equity in a legal services business. So I think it's no surprise that all of the law firms are owned by lawyers, because you have to be a lawyer in order to own a law firm. What again Gillian Hadfield points out, is that this can create very inefficient business models. In a lot of the smaller practices serving individuals and small businesses, lawyers work eight hours a day. But of those eight hours, only about 2.3 of them are actually doing billable work. The other six hours of that is just doing administrative tasks and like sourcing clients, things like that. And so even if AI is very advanced and capable of performing a lot of legal work, the way that AI is integrated into the business might actually be a lot less efficient because of these constraints on how those businesses are run.

[Alan Rosenstein]

But let me actually push back on that a little bit because there's a whole cottage industry right now about using multi agent Claude swarms to go out and find your clients and to do all your invoicing and all of that sort of stuff, it seems be to to me that one of the things that AI could do, in fact this is certainly how I try to use these AI tools, which is to sort of automate the administrivia of my life, whether it's as a teacher or a researcher or a consultant or whatever the case is. So I mean, I do wonder if there's a possibility here for these AI systems to be exactly actually the thing that a mid sized firm that is run because of guild rules by lawyers who, God bless whatever skills we have, management is often not one of them. Maybe what we need is Claude code, actually just for that purpose. Why isn't that an answer to the management inefficiency problem?

[Justin Kurl]

Yeah, so I actually think this is a very good application of AI, in part because I think it is a nice niche that is not necessarily covered by the unauthorized practice of law rules. So if I'm outsourcing clients, very few people think that that counts as practicing legal services. So this regulatory barrier actually wouldn't really cover those set of applications. And so one great way to make a lot of smaller firms much more efficient is to go out and automate a lot of the tasks that are taking up their time so that they can spend more time providing legal services. And so I think this is a great application of AI and maybe actually helps prove the point because it shows that when there aren't those regulatory barriers, you can actually use AI to make it much more efficient.

[Arvind Narayanan]

I wonder even in some of these administrative tasks, if there are competitive dynamics, certain kinds of paperwork, certainly there's a fixed amount to get done. But something we say later on in the paper is that one of the big barriers to productivity improvements actually translating to a better version of legal services is that there are kinds of arms races. We talk about arms races between plaintiffs and defendants, and I'm sure Justin will say more about that. But one of the kinds of arms races that can happen even in the more management kind of work, is you talked about going out there and finding clients. Well, these are going to be tools that every firm is now going to be using to kind of level up how effectively they can do that. So what's going to be the end result of that process? That seems hard to anticipate. And we're seeing in other cases, for example, in scientific peer review, for instance, there are these arms races between authors using LLMs to try to improve their productivity and reviewers using LLMs to try to automate some of the aspects of reviewing. And it's leading to some very unhealthy kinds of equilibrium, or not an equilibrium. Perhaps it's leading to a kind of death spiral. So we should be careful about things that at first appear to be productivity improvements, but can in fact upset existing kinds of balance and end up removing certain useful kinds of friction from the process.

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[Arvind Narayanan]

Ooh, where'd you get your windshield wipers? Ebay has all the parts that fit my car.

[Alan Rosenstein]

No more annoying, just beautiful.

[Justin Kurl]

Millions of finds, each with a story. EBay things people love. That new thing?

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[Alan Rosenstein]

Well, so that's great. And actually let's use that to then pivot to the second bottleneck, which is this sort of adversarial point. Arvind teed it up. But Justin kind of riff on that. I mean, I think everyone has sort of an intuition that law is a somewhat adversarial profession. But talk more about that and how that might lead to AI being sort of largely awash when it comes to the provision of legal services.

[Justin Kurl]

Two things on this, I think the first is it's important to understand when are we ending up in this world where this becomes the predominant bottleneck. I think even if we're in a world where AI is being used very widely and it's being used to make lawyers much more productive, this is still a constraint. Because if you give both sides access to AI and you're sort of locked in this zero sum process, the amount of work that each side does could essentially just go up. Because now both sides are being hyperproductive with AI. Instead of writing like one motion or writing five pages or looking at 100 cases, they're now doing 100 xat in all of those relevant domains. So the amount of outputs has increased. But because the outcome that clients ultimately care about is settling favorably or winning at trial, it takes much more work and much more outputs to reach that exact same outcome. And so although AI has made both sides more efficient, you end up doing a lot more work. The second thing on this, and maybe the historical analogy here, is the discovery process. A lot of people thought that digitization was going to make discovery way, way easier because you can now just control F for documents, so it's much easier to find the relevant documents. What they didn't expect was now both sides, there's just much more documents being created. Digitization means you can now request a lot more documents and share a lot more documents and the net Result is now discovery consumes like half the time that first year associates spend doing. And they're also. It's become one of the most expensive parts of litigation. And litigation costs have not actually come down. They've if anything, gone up in a lot of the complex cases.

[Alan Rosenstein]

How much of this is about is a litigation story versus a sort of general law story. Right. So again, I think most people when think of law, they think of litigation and that's obviously a large part of it. But litigation is only one. I'm not even sure it's the plurality, frankly of legal practice. I suspect transactional work is actually right, especially when you include smaller scale stuff like wills and things of that nature is probably get, if not the majority, then the plurality of legal work. And then of course is a bunch of in house stuff. So how much of this adversarial kind of arms race problem is a litigation story and how much of it also bleeds into, let's say, transactional work?

[Justin Kurl]

I think some of it definitely bleeds into transactional work. I think it again depends on the dynamics within transactional work. When you touched on wills, to me that it doesn't seem like there's a clean adversarial process because the goal is just sort of to match the intent of the person who wrote the will.

[Alan Rosenstein]

It's not clear who's on the other side. Exactly. God gets it all in the end.

[Justin Kurl]

And then in some sort of transactional context though, like say you're negotiating a merger between two parties, that to me starts to seem a lot more adversarial. Like oftentimes transactional lawyers distinguish their work from litigators by saying, no, no, no, we're much more positive sum, it's much more collaborative. But at the end of the day, if how you draft your contract provisions, what you choose to include, what you disclose to the other side, there's a very fine line between what is and is not okay. And how you skirt that line can actually translate into advantage for your side. And so you may end up using AI to take advantage of that.

[Alan Rosenstein]

Arvind, let me actually go back to you. You mentioned sort of earlier that obviously law is not the only place where you have these arms races. You gave some examples. The examples I was thinking about was actually, for example, trading, which seems like a perfect example of, of this, right? We've already seen this before, AI, where you have these sort of massive high frequency trading outfits that are spending God knows how much money, but it's sort of not clear that they're making Anything necessarily that much better because there's just someone else on the other side. I'm curious again, zooming out and from your work, thinking about AI as a normal technology across the entire economy, how much of the economy, how much of a productive economic worth is vulnerable to these sorts of adversarial conditions where the result of AI is not really lower cost, it's just everyone using AI more to sort of try to beat each other.

[Arvind Narayanan]

Yeah, it really comes up everywhere. Trading is, of course, a perfect example. I remember 10 years ago there were proposals for exchanges in the middle of the ocean, something like that, because the speed of light was becoming a constraint in high frequency trading. And so that's an example where I don't know if they ever ended up actually building it in between London and New York in the Atlantic Ocean. But it's a perfect example of sinking a lot of money into something that brings benefits that are purely relative if neither side has access to it, you haven't lost out on anything. Your trades are a fraction of a second slower. You can't argue that that's actually a benefit to society to build these things in the middle of the ocean. These dynamics come up literally everywhere. We just talked about peer review, but there's this great book called Bullshit Jobs by David Graeber. And he has, I think, five different categories of bullshit jobs, but one or two categories of them are all about how so many different jobs in every different occupation are not necessarily about providing the service better, but doing it better than your competitors. And so better on that dimension doesn't actually translate to better service for consumers. So this is not specific to law. It comes up really all across the board.

[Alan Rosenstein]

Yeah, I remember that book. I think the essay from which it comes from is even better because it's a nice tight read. And I do recall Corporate Lawyer was one of the main examples that he gives. All right, so let's now turn to the third bottleneck, which is this need for human involvement. So, Justin, what is this need for human involvement in the law? Why can't we just have robot lawyers arguing in front of robot judges while I sip daiquiris on the beach?

[Justin Kurl]

Well, okay, so you definitely could have that world. I personally would not really want to live in that world. I think even the most sort of AI pilled people out there are still hesitant about the idea of turning over judges in society to AI. I think there's also compelling constitutional reasons not to do that, namely Article 3. But putting that aside, I think the human element is assuming we want human beings to be involved. This is. There is a limit to how quickly judges can process cases. So if you imagine sort of this going back to this litigation example, both sides are producing a bunch more work, they're writing much more sophisticated briefs, they're citing more cases. Also it probably becomes easier to file lawsuits. So there's just a lot more lawsuits. As that happens, the new bottleneck becomes the time it takes for judges to adjudicate those cases. Going over to the transactional side, I think the new bottleneck, as contract provisions get longer and these negotiations become more complex, I think the bottleneck becomes the ability for human lawyers to actually understand what's going on on behalf of their clients. I for one, would want to be in a world where corporations know what they're signing up to, when they're signing up for contracts. And so I think having someone inside of the corporation who understands this is sort of the final bottleneck. So even though AI makes things a lot faster, there is still this thing of how quickly can human beings work?

[Alan Rosenstein]

So the normative question about do we want human judges, human decision makers, the legal question of is a constitutional crime. That's interesting. Let's put that to aside for a second because I think the psychological assumption or the empirical assumption that is a matter of human psychology as a matter of what is sometimes called sociological legitimacy, which is whether or not the system is a good one, do people perceive it to be a good one. Whether that requires human decision makers, I'll admit, and maybe this just shows how out of touch I am with actual human beings and that I should log off Claude code more. It is not obvious to me actually that there is going to be such a demand for human decision makers outside, let's put criminal law, for example, to aside, but outside sort of the most high salient context. I mean, it certainly seems to me that the sort of story of modern human sociability over the last 20 years is the increasing replacement of sort of human connection and human engagement with digital connection and digital engagement. And again, that may be a bad thing, right? That may be quite possible, but it still appears to me to be. To be a thing. So I'm curious for your thoughts, Justin, about whether that might be a possibility in the legal sphere. And then also zooming out, Arvind, for your thoughts about how we might think about that in other domains. Right. Because you could presumably tell the same story about education or mental health therapy, but at the same time, I don't know, maybe it's possible that in 10 years we'll all be using chatbots as the bulk of mental health therapy. And people just got sort of used to that because humans are malleable creatures. So let me start with Justin, and then I'll move to Arvind.

[Justin Kurl]

I know you said put the normative considerations aside, but I have to fight the hypothetical on this one just because I do think if you're making a decision about Whether someone has 10 years in prison or not, that is such an important decision that it carries such moral weight that I would want a human being to be involved in that.

[Alan Rosenstein]

Sure. But again, criminal law is still relatively small percentage of legal practice. And I think the sort of economic story that you're talking about is also more relevant to commercial litigation and the commercial practice of law than the criminal practice of law, which is why I'm sort of curious about the sort of like, 90% of legal disputes that are not as high stakes or salient.

[Justin Kurl]

Yeah. And so I think this is where one of our reforms actually is to have sort of parallel tracks. And so you could imagine you have judges for the context in which human involvement is most important, and then for those less important things, you have a parallel track, such as through arbitration. And there are a lot of problems with arbitration about whether it's like, actually people are consenting into it properly, but assuming that they are, you can have sort of these AI judges are using it as a way to make the process more efficient in contexts where you're less worried about the stakes, and the stakes do seem lower. And so that might be a way to sort of. If there is a finite pool of human attention and human time that we can allocate to these tasks, we should allocate it in a way where it's most needed.

[Arvind Narayanan]

So let me share a couple of thoughts, both specifically for judging, but also, like you said, zooming out. Alan. One thing I'd say is even in some world where AI could make these decisions, what do we want the humans to be doing in that world? To me, you know, judges make law a lot of the time, and exercising human judgment about what we want the world to look like. That's the perfect example of what I would want humans to be doing in a world where all conceivable labor can be automated. I mean, I think that should be literally the last thing to get automated, again, for normative reasons, regardless of whether that can or can't be automated.

[Alan Rosenstein]

Well, let me ask about that because I'm curious to sort of push on that intuition. Where is that normative commitment coming from? Because you can imagine a lot of arguments for you. You could say, well, it's because I think that humans will always do a better job, at least in some cases. Right. In exercising that judgment than AIs do. In which case I would say we did earlier, however, kind of stipulate that at least in a lot of domains, AIs are getting quite, quite good. And then you'd have to have a, I think, a somewhat rosy view of how good human decision makers are. At least kind of the median human decision maker. Or you might say, I worry that if we outsource those decisions to AIs, we ourselves will kind of have an almost moral deskilling. We will lose the capabilities. Or you might say, human psychology just requires something carbon based to pass judgment on me and it'll just rebel and society will fall apart if we have this done by computers. But I don't know, I'm curious to sort of push you on, just to clarify where that intuition is coming from.

[Arvind Narayanan]

Yeah, it's a simple answer. I think this is what it means to be in control of our own civilization. I mean, all those debates about AI safety, this is actually what it boils down to for me. Not killer Terminator robots. These kinds of moments where we put the course of humanity in the hands of machines. I think that's a line we should not cross. I mean, historically, look at any cases that had a significant impact on how society functions. Let's say Brown v. Board of Education. Is that something we would want to have been decided by a robot? I don't think it's a matter of accuracy. There are no accuracy standards by which you can claim that AI is doing a better or worse job than a human judge. It's purely a normative question. It does not have an empirical component. And, you know, I would say that a world in which we leave these kinds of decisions up to AI is not a world I want to live in. And hopefully the majority of people.

[Alan Rosenstein]

And just to clarify. And it's not, it sounds like. Because you don't think I could have written Brown versus Board of Education, it's just that the whole point was that humans decided like that. The whole point of democracy. Right. Is not that we come, not necessarily that we come to the best decisions, but they are fundamentally our decisions. Is that the intuition? I'm not pushing back on it, but I think it is useful to clarify where those intentions come from.

[Arvind Narayanan]

Yeah. This is what it means to have agency as a species. These are the biggest decisions that we make about the course of our Societies.

[Alan Rosenstein]

Okay, so we talked about the impediments to the broad diffusion of AI, the unauthorized practice of law issues, the adversarial dynamics and just the need for humans. And therefore humans will be a bottleneck. Let's talk briefly about some of the reforms and the solutions that you all propose. But before we get into them, I do want to ask a kind of a meta question. It sounds like at least as I read the paper, you all do think that we should have more AI in legal service. I mean, a lot of these reforms are meant to facilitate the spread. But one could, I think just as easily look at your analysis and say, oh thank God we have these roadblocks. Right? We want there to be these really strong unauthorized practice of law rules. I'm not sure anyone would say we want to have these bullshit jobs where people are just creating costs. But certainly, I mean, Arvind, I think you just very eloquently set out your argument for why humans being in the loop is just kind of a fundamental axiom of what it means to be human and have a human led society. Why isn't the last third of your paper a thank God and we should do everything we can to keep AI out of the legal profession? Yeah, Arvind, why don't you start?

[Arvind Narayanan]

Sure. I mean, I think there's going to have to be some line drawing exercise. I think in every profession people will argue about what kinds of aspects of what it means to do that job are fundamentally human and which ones can be delegated to a machine. Certainly in the legal profession and really any other profession, there are lots of things we've chosen to delegate and which we're comfortable with. I think in a way law is starting from a really good place because there are all of these restrictions currently. And so it's kind of opting in to AI. We have to choose to allow AI to be used for certain things. And it's not like by default AI is going to replace judges and lawyers. So in a way that's good and so that is something that I celebrate. But I don't think the current equilibrium is the optimal one. I think there are a lot of things that make sense to delegate and just a simple example being access to justice for people who can't afford a lawyer, being such a scarce thing. And if AI can be used to enable public defenders to be more productive, that would be a big win. So it's an existence proof that there are some tasks. Where that line is is not for me to say. But I don't Think the current equilibrium is the optimal one?

[Alan Rosenstein]

Well, let me ask the same question to Justin. And I think your perspective is particularly interesting here because you're a third year law student, you're graduating in a few months and you're entering a world, a legal profession, that is radically changing. And I suspect you can see that even more than many of your classmates. Now, I think you've made a very clever bet in focusing on AI, because even if legal profession goes away, there will always be, I think, demand for your expertise in thinking about AI in the legal profession. But I'm just curious from your perspective. I mean, I could imagine you as a student saying, I do not want AI interfering with my future job prospects. And so I'm curious where you come down on this point.

[Justin Kurl]

Yeah, I actually think it takes us back to the beginning of the conversation where ultimately the AI as normal technology is a prescription about where we should focus. And I view sort of these reforms and these bottlenecks as opportunities of where we should be allocating our time and attention. Because ultimately, one thing I think you'll learn going through law school is just there's so much that needs to be fixed and there's so much like there are so many problems with our current legal system. And I view AI as partially a way to fix those problems, but also as a way to sort of push through or motivate the reforms that we've needed for a long time that aren't actually about AI. Like, there's a lot of problems with our access to justice system that aren't really AI problems. But maybe now that people are thinking critically about how should we redesign our system in light of AI, we can start having a better system generally.

[Alan Rosenstein]

So let me end the conversation then with one of the reforms or kind of grouping some of the reforms under one category, which at least to me jumped out as the most interesting, and that is really changing these unauthorized practice of law rules. And so just talk me through what that would look like and then also how you'd respond to some of the concerns that, well, the reason we have unauthorized practice of law is the same reason we have unauthorized practice of dentistry. Right. We do want some consumer protections around this. And so why doesn't that mean that sort of the proposals that you all suggest, the regulatory sandboxes that are in places like Utah and Chicago, what those are, or your paper mentions Gillian Hadfield's idea of regulatory markets where you have sort of the government regulators certifying private regulators, and it's those private regulators, then in a Kind of market competitive sense, then regulate the individuals almost in a way that the government recognizes certain accreditation bodies and those accreditation bodies then accredit schools. You know, all of this is very clever, but it ultimately is in the service of weakening unauthorized practice of law regulations. And someone might argue, I'm not sure I would argue that because it worries, I worry that it's just guilt capture. One might argue that that all is just making customers of legal services more vulnerable. So let me start with Justin and then I'm curious, Arvind, actually, how you think about those issues more broadly. Because as I mentioned, very similar issues can come up in other professions and even in non professional settings like software engineering.

[Justin Kurl]

Yeah, I think ultimately if the purpose of unauthorized practice of law regulations sort of in their strongest form is to protect consumers from sort of unethical practitioners that are giving bad legal service, I just am not very compelled that they're actually doing that good of a job of it right now. It seems to be making things much more expensive. And there's some people who've passed the bar who give horrible legal services. And then if you think about in the debt collection context, 70% of people are losing by default because they didn't actually respond to the lawsuit because they didn't afford a lawyer. If you look at some of the most consequential sort of legally relevant decisions in our life, like whether you're getting divorced or whether you're getting evicted, people just don't really have access to lawyers. And so I just don't think that UPL rules are serving their intended purpose right now. And so that's sort of why I'm for changing them in some way.

[Arvind Narayanan]

I'm trying to say something useful by comparing it to other domains like software engineering, without making it sound like I'm giving advice to lawyers on how to run their profession, because that's not for me to say. Okay, so let me say this. Certainly it's easy to understand the motivation behind unauthorized practice of law rules, but I think as Justin said, they're currently not that great at serving their intended function and they seem to have all of these unintended consequences, guild capture, as you might put it, that are deeply problematic. I think there could be other ways of ensuring that consumers are not harmed. For me, as a non lawyer, not a legal scholar, it's not really for me to say what those are. But maybe this moment of upheaval around AI is a time when we can have a lot of innovation around the way we regulate different professions and what institutional and organizational structures we put in place. Software engineering is one example of a field, like I was saying earlier, is not professionalized, but still has a mix of various kinds of checks in place to ensure that horrible outcomes don't result. So maybe there's something to look at from different fields. Maybe we don't have to put all of our weight into unauthorized practice of law rules.

[Alan Rosenstein]

I think it's a good place to leave it. Arvind Justin, thanks for coming on the show and for writing a great paper. It's very interesting and I do hope that both optimists and skeptics of AI in the legal profession get a chance to read it.

[Justin Kurl]

Thank you.

[Arvind Narayanan]

Thank you, Alan. This has been really fun.

[Alan Rosenstein]

The Lawfare Podcast is produced by the Lawfare Institute. If you want to support the show and listen ad free, you can become a Lawfare material [e-mail] support supporters also get access to special events and other bonus content we don't share anywhere else. If you enjoy the podcast, please rate and review us wherever you listen. It really does help. And be sure to check out our other shows including Rational Security, Scaling Laws, Allies, the Aftermath, and Escalation. Our latest Lawfare Presents podcast series about the war in Ukraine. You can also find all of our range written [e-mail] the podcast is edited by Jen Patya with audio engineering by me. Our theme song is from Alibi Music and as always, thanks for listening.

[GNC Drop Host]

That new thing yeah, we've got it. The Drop by GNC Bringing you all the newness that matters. Hand picked by the pros who actually know what's up and what's proven to work. We keep you on top of the trends and dialed into what's next. Whether you're crushing it at the gym, leveling up your game or thriving every day, the Drop by GNC is where the latest solutions in health and wellness land first nonstop innovation and fresh finds daily explore what's new and what's next on the Drop by GNC.