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

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4.7 • 6.4K Ratings

🗓️ 12 February 2026

⏱️ 44 minutes

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Summary

Alan Rozenshtein, research director at Lawfare, speaks with Justin Curl, a third-year J.D. candidate at Harvard Law School, and Arvind Narayanan, professor of computer science at Princeton University and director of the Center for Information Technology Policy, about their new Lawfare research report, “AI Won't Automatically Make Legal Services Cheaper,” co-authored with Princeton Ph.D. candidate Sayash Kapoor.

The report argues that despite AI's impressive capabilities, structural features of the legal profession will prevent the technology from delivering dramatic cost savings anytime soon. The conversation covered the "AI as normal technology" framework and why technological diffusion takes longer than capability gains suggest; why legal services are expensive due to their nature as credence goods, adversarial dynamics, and professional regulations; three bottlenecks preventing AI from reducing legal costs, including unauthorized practice of law rules, arms-race dynamics in litigation, and the need for human oversight; proposed reforms such as regulatory sandboxes and regulatory markets; and the normative case for keeping human decision-makers in the judicial system.

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Transcript

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0:00.0	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 X-Sat 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
0:22.3	or winning at trial, it takes much more work and much more outputs to reach that exact same
0:28.0	outcome. It's the Lawfare podcast. I'm Alan Rosenstein, Associate Professor of Law at the University
0:34.0	of Minnesota and Research Director at Lawfare. I'm talking to Justin Curl, a third-year JD candidate at Harvard Law School, and
0:41.3	Arvin Naranian, Professor of Computer Science at Princeton University and director of the
0:45.5	Center for Information Technology Policy.
0:48.2	Judges make law a lot of the time, and exercising human judgment about what we want the world to look like, that's the
0:59.1	perfect example of what I would want humans to be doing in a world where all conceivable
1:04.6	labor can be automated. Today, we're discussing their new Lawfare Research Report, co-authored with
1:10.4	Princeton PhD candidate
1:11.5	Sayash Kapoor, arguing that, despite AI's impressive capabilities, structural features of the legal
1:16.7	profession, from guild regulations to adversarial dynamics, mean that the technology may not
1:21.5	deliver the dramatic cost savings that many predict.
1:24.7	So I'm excited to get into the paper that you and your co-author Sayash Kapoor have
1:29.5	written about the effect of AI in the legal profession and specifically why it might not provide
1:34.6	the sort of cost savings that everyone is predicting, and at least some people, that perhaps
1:40.2	not lawyers, are hoping for. But before we get into that, I want to take a moment to talk
1:44.5	about the broader framework of how you're all thinking about this, which draws on this broader
1:49.8	project that you, especially Arvin and your collaborator, Syash, have thought about and I've written
1:56.6	a great book about and done a lot of great writing about, which is this idea that AI is a normal
2:01.6	technology. So just before we get into the law part of this, just sketch out what you mean and
2:06.7	particularly what you mean by a normal technology. Definitely. Let me start with a historical example.
	...

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