The Law of Software
Fall 2026 Readings
Please note that these topics are not necessarily in the order we will discuss them, and we will not cover all of these topics this semester. See the schedule for an authoritative list of which topics we will cover when.
Introduction
- Readings: Lawrence Lessig, The Law of the Horse: What Cyberlaw Might Teach, 113 Harvard Law Review 501 (1999)
- Notes: More than any other single work of scholarship, Lawrence Lessig’s The Law of the Horse created and defined the field now known as Internet law. Lessig himself has moved on to copyright policy, Creative Commons, and campaign-finance reform, but Internet law still shows his influence. This article provides a framework for the question this course asks: how does law change when software is involved?
- Questions:
- What kind of argument is Lessig making? It is a technical argument about how the Internet works? A doctrinal argument about how existing law treats the Internet? A policy or normative argument about how law should treat the Internet? A conceptual or jurisprudential argument about how to think about the nature of Internet law?
- If your answer to the first question is “more than one of the above” (hint: it should be), how do the pieces fit together?
- What does the slogan “code is law” mean?
- Why was Lessig’s argument so revolutionary in the late 1990s?
- How well does the article’s theoretical framework hold up? How about its case studies?
- Additional Resources:
- Frank H. Easterbrook, Cyberspace and the Law of the Horse, 1996 U. Chi. Legal Forum
207 (1996). This is the piece that Lessig uses as a jumping-off point. It’s interesting now to look at the substantive portions of Easterbrook’s argument to see how well they hold up.
- Lawrence Lessig, Code: And Other Laws of Cyberspace (Basic Books 1999). This is the book-length version of The Law of the Horse. It was revelatory in 1999 and remains one of the best books in the field. If you want to engage seriously with Lessig’s theory of modalities of regulation, this is essential reading, especially the appendix. Lessig revised Code for a second edition in 2006, but I think it loses some of the focus of the first edition.
- Joel R. Reidenberg, Lex Informatica: The Formulation of Information
Policy Rules through Technology, 76 Texas Law Review 553 (1998). This is the other canonical article that made a similar argument to The Law of the Horse at a similar time.
- James Grimmelmann, Regulation by Software, 114 Yale Law Journal 1719 (2005). This was my student note in law school; it explores Lessig’s metaphor of software as architecture and tries to draw out some general lessons about what software does well and poorly.
- Jonathan Zittrain, The Future of the Internet—And How to Stop It (2008). This is one of a small handful of academic books in technology law to have comparable impact to Code. It is also the one that most directly carries on Lessig’s scholarly approach.
- James Grimmelmann and Paul Ohm, Dr. Generative or: How I Learned to Stop Worrying and Love the iPhone, 69 Maryland Law Review 910 (2010). This review of The Future of the Internet recaps the “architecturalist” tradition of legal scholarship that both Lessig and Zittrain are writing in. Have a look at this if you are wondering about that tradition’s theoretical commitments.
- Bryan H. Choi, The Anonymous Internet, 72 Maryland Law Review 501 (2013). Another response to Zittrain’s book; Choi circles back to the issues of zoning and anonymity from Law of the Horse.
Legal Scholarship
- Readings:
- Notes: The goal of this class is to understand what makes legal scholarship distinctive from other forms of scholarship, especially in technical fields like computer science. We will also discuss research tools, particularly scholarly databases and search engines.
- Questions:
- What types of scholarship that Minow and Tobin describe strike you as the most interesting and important?
- Where does Lessig’s The Law of the Horse fit in Minow and Tobin’s taxonomy?
- What struck you as most novel or unusual about Lessig’s article as a piece of scholarship, compared with other work you have read? Do the readings for today explain why it had those features?
- Think of a research idea. Would it work as a law-review article? Would it work as an article in a different field? How much do the genre expectations of various academic publication formats drive the substantive content of research?
- How would you determine whether your research idea is novel? Where would you look and what would you look for?
- Is the law-review submission system as described by Galle functional or dysfunctional?
- Additional Resources:
- Paul Ohm, Computer Programming and the Law: A New Research Agenda, 54 Villanova Law Review 117 (2009). An influential argument for the connection between law and computer science. Ohm argues that software is relevant to legal scholarship in general, not just to Internet law in particular.
- The authoritative collection of legal scholarship is Hein Online, which contains PDFs of almost everything published in U.S. law reviews. Most law reviews post articles online, as do many law schools and individual scholars, but coverage of older work is spotty and articles are often lost to linkrot. SSRN is widely used by U.S. legal scholars to post their work; it contains drafts and preprints not yet available and has a lot of work from recent decades but also has numerous gaps. When you are downloading a published article, be sure to get the actual published PDF with finalized page numbers.
- I recommend doing keyword searches in Hein, in Google Scholar, and on SSRN (either using its own search engine or your favorite general search engine with the keyword “site:ssrn.com”). AI systems, including general-purpose ones like ChatGPT and legal-specific ones like Harvey, can be useful, but require care. We will discuss some pointers and pitfalls in class.
- For additional research pointers, see my resources for scholars page.
Software Patents
- Readings:
- Ben Klemens, Math You Can’t Use: Patents, Copyrights, and Software (2006), chapters 3 and 4 (on Canvas)
- Alice Corp. v. CLS Bank, 573 U.S. 208 (2014). The most important passages are Part I.A (describing the invention at issue) and Part III (the legal analysis).
- The question of whether software can be owned forces us to ask what software is. Ben Klemens is a computational social scientist, rather than a lawyer or legal scholar. His argument against software patents is representative of the views of many computer scientists, and also well within the range of mainstream views among legal scholars. Alice is the Supreme Court’s most recent word on the subject.
- Questions:
- What is software, according to Klemens?
- What is the difference between software and hardware? How sharp is the distinction?
- Why does Klemens argue that software can’t software be patented? Is this a technical argument? Doctrinal? Policy? Conceptual?
- Does Justice Thomas’s opinion in Alice adopt Klemens’s reasoning? Reject it? Ignore it? Is the line it draws more or less coherent than the one Klemens draws? Does it have better or worse policy consequences?
- In what sense, if any, is software something that exists in the physical world? What is the significance, or insignificance, of this physicality to Klemens and Thomas?
- Additional Resources:
- Pamela Samuelson, Benson Revisited: The Case Against Patent Protections for Algorithms and Other Computer Program-Related Inventions, 39 Emory Law Journal 1025 (1990). A thorough, careful, and early statement of the argument against software patents, from the leading scholar of software IP.
- Kevin Emerson Collins, Patent Law’s Functionality Malfunction and the Problem of Overbroad, Functional Software Patents, 90 Washington University Law review 1399 (2013). Similar diagnosis, different prescription.
- Mark A. Lemley, Software Patents and the Return of Functional Claiming, 2013 Wisconsin Law Review 905 (2012). A critique of how software inventions are claimed that focuses on the difference between what software does and how it does it.
- Athul K. Acharya, Abstraction in Software Patents (and How to Fix It), 18 John Marshall Review of Intellectual Property Law 364 (2019). Another modern take.
Software Copyright?
- Readings:
- Notes: CONTU’s recommendations were largely adopted by Congress; the present Copyright Act (as amended) reflects its proposal that software should be protected as a literary work. If there is an orthodox statement of how U.S. copyright law thinks about software, this is it. Richard Stallman (but see) was the driving force behind the creation of the free-software movement, and his essay is a rejection of this orthodoxy. He argues that the entire concept of owning software is a mistake.
- Questions:
- What is software (“computer programs”), according to CONTU?
- Why does CONTU recommend legal protections against the copying of software?
- Why does CONTU recommend treating software as a literary work (the same category used for poetry and novels)?
- Where does Commisioner Hersey (the author of Hiroshima, among many other books) disagree with the CONTU majority? Does his understanding of software differ from the majority’s?
- Where does Stallman disagree with the CONTU majority? Does his understanding of software differ from the majority’s?
- Is Stallman’s argument the same as Klemens’s from last time? Do they rhyme?
- Additional Resources:
- Eben Moglen, Anarchism Triumphant: Free Software and the Death of Copyright, First Monday (Aug. 1999). This is a longer, more detailed, and delightfully sarcastic articulation of the case against software copyright.
- Samir Chopra and Scott D. Dexter, Decoding Liberation: The Promise of Free and Open Source Software (2007). An extended and more academically rigorous exploration of the philosophical case for free software.
- Pamela Samuelson, CONTU Revisited: The Case Against Copyright Protection for Computer Programs in Machine-Readable Form, 1984 Duke L.J. 663. Samuelson’s work towers over the field of software copyright, and I could easily have filled the semester’s reading list with her work. This piece was one of the most significant early critiques of CONTU.
- Pamela Samuelson, Randall Davis, Mitchell D. Kapor, and J.H. Reichman, A Manifesto Concerning the Legal Protection of Computer Programs, 94 Columbia Law Review 2308 (1994). A canonical article on software copyright, combining technical precision with a thoughtful economic analysis. Even decades later, it remains one of the most thorough and careful treatments of the subject.
- David Stein, Hot Apps: Recalibrating IP to Address Online Software, 2024 Wisconsin Law Review 1014. David Stein was a software engineering manager before going to law school and entering academia. This article argues that the shift from apps that run on users’ computers to apps that run as cloud services fundamentally changes the economic argument for software copyright.
Software Copyright.
- Readings: Charles Duan, What is Copyrightable in Software?
- Notes: Charles Duan, who did his postdoc work at Cornell Tech, was a computer-science major in college and remains an active programmer. The previous class was about whether software should be copyrightable at all. But even if it is, there remains a hard question as to which aspects of it are copyrightable. Duan’s article attempts to draw that boundary.
- Questions:
- What is software, according to Duan? How does his description compare to CONTU’s, Hersey’s, and Stallman’s?
- Is Duan’s distinction between the communicative and functional elements of software technically sound?
- Is the distinction consistent with the Copyright Act and caselaw interpreting it?
- Is the distinction consistent with CONTU’s economic arguments for software copyright?
- Additional Resources: There is a vast literature on software copyright, so I can only recommend a few highlights. If you look through the footnotes in Duan’s article, you will find many of the usual suspects. The following are not necessarily the most important pieces, but they are ones that I think are particularly rewarding reads.
AI and Authorship
- Readings: Dan L. Burk, Thirty-Six Views of Copyright Authorship, by Jackson Pollock, 58 Houston Law Review 263 (2020). The late Dan Burk was known for his intellectual tenacity, his personal warmth, and his shrewd sense of humor. All three are on display in this piece. It tackles the question of who (if anyone) should own the copyright to computer-generated works. (This piece is formally inventive. It works here, but this kind of unconventional structure is easy to get wrong. Do not try this at home, unless you are very sure you know what you’re doing.)
- Questions:
- What is a computer-generated work?
- How is a work created using generative AI different, if at all, from a work using analog tools like paintbrushes? From a work generated using digital tools like Adobe Illustrator?
- Do the technical details matter in deciding whether a computer-generated work is copyrightable? Or is it irrelevant how the computer works, because the only important fact is that it is not a human?
- Here is a proposal: computer-generated works are copyrightable, and the copyright is owned by whoever first publishes them. Is this consistent with copyright theory? Would it lead to good results in the real world? What would Burk say?
- This article was written shortly before the recent explosion of generative AI. How well does it hold up?
- Additional Resources:
- Jane Ginsburg and Luke Ali Budiarjo, Authors and Machines, 34 Berkeley Technology Law Journal 343 (2019). This is a long article, but careful and thorough. It covers the same ground as Burk, but more methodically and in much greater depth.
- Pamela Samuelson, Allocating Ownership Rights in Computer-Generated Works, 47 University of Pittsburgh Law Review 1185 (1986). It should be no surprise that Samuelson addressed this question early, or that her work holds up well.
- James Grimmelmann, There’s No Such Thing as a Computer-Authored Work – And It’s a Good Thing, Too, 39 Columbia Journal of Law and the Arts 403 (2016). I argued that “the computer is the author” is a bad answer to the hard and case-specific questions that computer-assisted authorship raises. I still think so, but not as confidently as I did then.
- Ryan Abbott and Elizabeth Rothman, Disrupting Creativity: Copyright Law in the Age of Generative Artificial Intelligence. This is one of the best statements of the case that computers should be treated as authors.
- Bruce Boyden, Emergent Works, 39 Columbia Journal of Law and the Arts 377 (2016). This is the article that made me understand why AI authorship is an intrinsically hard question.
- Katherine Lee, A. Feder Cooper, and James Grimmelmann, Talkin’ ‘Bout AI Generation: Copyright and the Generative-AI Supply Chain, Journal of the Copyright Society of the USA (forthcoming). A more recent take. The computer-authorship material is in Part II.A, but there is also a detailed description of how modern generative-AI systems work in Part I, which you may find useful.
- Carys Craig and Ian Kerr, The Death of the AI Author, 52 Ottawa Law Review 33 (2021). A very different take on AI authorship, one that focuses on the social role of “author” rather than the mental and physical processes that results in a work.
AI and Infringement
- Readings: Matthew Sag, Copyright Safety for Generative AI, 61 Houston Law Review 295 (2023).
- Notes: Matthew Sag is a copyright scholar whose interest in bulk technological uses led him to AI early. This is one of the most important articles on fair use and AI training from the modern era, i.e., after the launch of ChatGPT. It is not a comprehensive overview of the infringement issues involved in generative AI training and deployment; instead, it focuses on some of the most important ones.
- Questions:
- How are (potentially) copyrighted works used in AI training?
- Why would copyright law have a problem with this?
- If an AI output is similar to a work that the AI might have been trained on, how should a court decide whether the similarities are due to copying or coincidence?
- How much should AI companies have a responsibility to implement “guardrails” that limit what users can generate with their systems? Does the answer to this question affect whether training should be allowed?
- Additional Resources:
- Benjamin L.W. Sobel, Artificial Intelligence’s Fair Use Crisis, 41 Columbia Journal of Law and the Arts 45 (2017). An early, and some might say prescient, article about AI and copyright. Then again, Ben did his post-doctoral fellowship at Cornell Tech, so I’m biased.
- Amanda Levendowski, How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem, 93 Washington Law Review 579 (2018). Another early article about copyright and AI training, but one that framed the problem a little differently: copyright stands in the way not of AI innovation but of AI fairness. Compare Amanda Levendowski, Resisting Face Surveillance with Copyright Law, 100 North Carolina Law Review 1015 (2022), in which Levendowski argues that copyright should be used to limit some forms of AI training in the name of other important social values.
- Mark A. Lemley and Bryan Casey, Fair Learning, 99 Texas Law Review 743 (2021). A particularly clear and well-written take on fair use and AI training.
- James Grimmelmann, Copyright for Literate Robots, 101 Iowa Law review 657 (2016). My own piece on bulk technological uses of copyrighted works applies to AI, but is not entirely about AI. It’s a little more polemical than the other readings here.
- Benjamin L.W. Sobel, Elements of Style: Copyright, Similarity, and Generative AI, 38 Harvard Journal of Law and Technology (forthcoming). A more recent piece by Ben, which focuses on substantial similarity of AI outputs rather than on fair use for AI training.
- Mark A. Lemley, How Generative AI Turns Copyright Upside Down, 25 Columbia Journal of Law and the Arts 21 (2024). Another Lemley piece, one that gets at the subtle and tricky relationship between AI prompts and AI outputs.
- A. Feder Cooper and James Grimmelmann, The Files are in the Computer: On Copyright, Memorization, and Generative AI, 100 Chicago-Kent Law Review 141 (2025). Is a model a “copy” of the works it was trained on? We think the answer can be “yes,” at least sometimes—when a generative model has memorized a work, in the sense that it can produce a substantially similar version of that work as an output.
Software and the First Amendment
- Readings: Andrea M. Matwyshyn, Hacking Speech: Informational Speech and the First Amendment, 107 Northwestern University Law Review 795 (2013).
- Notes: Andrea Matwyshyn is jointly appointed in law and engineering; her work engages both with the institutional, regulatory side of the field and with the chaotic world of hackers and cybercriminals. The first major debates over whether “code is speech”—i.e., whether the First Amendment protects the creation, distribution, and/or use of software—took place in the 1990s, when the U.S. government tried to restrict the export of encryption software. Now there are new versions of these debates involving social media (think the TikTok ban), AI (think the Claude bans), 3D printers (think ghost guns), and much more. Matwyshyn wrote this article at the tail end of the lull in between: how useful a guide does it offer for today’s challenges?
- Questions:
- What is the best argument you can give that software is inherently expressive and protected by the First Amendment?
- What is the best argument you can give that software is inherently functional and unprotected by the First Amendment?
- Think about the sale and use of exploits. Does your thinking about how the First Amendment applies to software change when you know that this is the specific case at stake?
- Identify as many other settings as you can where a First Amendment argument could at least be raised to challenge a law regulating software. Can you think of five? Ten?
- Is the line between expressive and functional the same for the First Amendment as it is in copyright? Should it be?
Additional Resources:
- The classic literature on the code-is-speech question from the 1990s includes Lee Tien, Publishing Software as a Speech Act, 15 Berkeley Technology Law Journal 629 (2000). Tien litigated the issue in one of the leading cases, and this article uses speech-act theory to make its argument. Robert Post, Encryption Source Code and the First Amendment, 15 Berkeley Technology Law Journal 724 (2000) is an influential reply by one of the world’s leading First Amendment scholars.
- Derek Bambauer, Copyright = Speech, 65 Emory Law Journal 199 (2015). A provocative analysis of the relationship between copyright and the First Amendment.
Legal Citation
- Readings:
- Notes: In some ways, legal citation is a citation system like any other—a set of mechanical rules for how to describe a source. But it has some unusual properties that are worth paying attention to. First, it is a more elaborate system than any other I am aware of, with a variety of unique conventions for what information must be included and how it should be presented. Second, because it is used by practicing lawyers as well as scholars, and because the nature of authority in law is different than in other fields, it serves some goals (such as rapidly indicating why a source is being cited) that other citation systems do not. Peter Martin is a former dean of Cornell Law School and deeply knowledgable about the history and practice of legal citation.
- Questions:
- How is legal citation different from citation in other fields?
- What are the goals of a citation system? How well do the citation systems you have used meet them?
- Regardless of how citations are formatted, why does legal scholarship use so many of them?
- Additional Resources:
- Anatomy of Basic Legal Citations. A nicely-formatted cheat sheet for the most common types of legal citations.
- Orin S. Kerr, A Theory of Law, 16 Green Bag 2d 111 (2012). This is a joke, but also maybe not quite a joke.
- There is a small cottage industry of articles attacking the Bluebook. Some call for reforming it to be less detailed, some call for throwing the whole thing out. Notable examples include Paul Gowder, An Old-Fashioned Bluebook Burning , 1 Northwestern Law Journal Des Refusés 1 (2024); Richard A. Posner, Goodbye to the Bluebook, 53 University of Chicago Law Review 1343 (1986); James Ming Chen, Something Old, Something New, Something Borrowed, Something Blue, 58 University of Chicago Law Review 1527 (1991); David Ziff, The Worst System of Citation Except for All the Others, 66 Journal of Legal Education 668 (2017)
- For background on the history and evolution of the Bluebook and legal citation, see Fred R. Shapiro and Julie Graves Krishnaswami, The Secret History of the Bluebook, 100 Minnesota Law Review 1563 (2016)
- Peter Martin’s Citing Legally blog features some outstanding deep dives on the nuances of legal citation.
- There have been numerous attempts to automate legal citation. Many of them are badly incomplete, make basic and easily spotted mistakes, or both. The only two software packages that I can recommend are Juris-M (a Zotero fork with a Word plugin) and Hereinafter (a LaTeX package). I have used both, currently use Hereinafter, and can provide pointers and advice on getting started if you would like.
Algorithmic Speech
- Readings: Stuart Minor Benjamin, Algorithms and Speech, 161 University of Pennsylvania Law Review 1445 (2013)
- Notes: Stuart Benjamin’s scholarship is an interesting combination of patent law, administrative law, and telecom law. This article was written when the main issue on the table was search-engine regulation, rather than AI.
- Questions:
- Why isn’t the question of when algorithmic outputs are protected by the First Amendment fully settled by the answer to the question of whether software itself is protected by the First Amendment?
- Does First Amendment coverage for algorithms depend on how predictable the algorithms are?
- Is turning a light switch on or off protected by the First Amendment?
- Do computer systems themselves have First Amendment rights? What about their creators? Their users?
- Additional Resources
- There is a rich literature from the late 2000s and early 2010s on how the First Amendment applies to search results. A few highlights are Oren Bracha and Frank Pasquale, Federal Search Commission: Access, Fairness, and Accountability in the Law of Search, 93 Cornell Law Review 1149 (2008); Eugene Volokh and Donald M. Falk, Google First Amendment Protections for Search Engine Search Results, 8 Journal of Law, Economics, and Policy 883 (2012); and James Grimmelmann, Speech Engines, 98 Minnesota Law Review 868 (2014).
- Tim Wu, Machine Speech, 161 Pennsylvania Law Review 1495 (2013), from the same symposium as Benjamin’s article, takes a more generally skeptical view.
- Eugene Volokh, Crime-Facilitating Speech, 57 Stanford Law Review 1095 (2005), is a more general (too general?) analysis of speech at the expression/conduct border.
- James Grimmelmann, Speech In, Speech Out, in Ronald K.L. Collins & David M. Skover, Robotica: Speech Rights and Artificial Intelligence 85 (2018), is my take on the light-bulb hypothetical.
AI Defamation
- Readings: Eugene Volokh, Large Libel Models? Liability for AI Output, 3 Journal of Free Speech Law 489 (2003).
- Notes: Eugene Volokh is one of the preeminent First Amendment scholars working today, and he has a long-standing interest in technology and Internet law, dating back to his extensive experience as a programmer. Note that this is a big and slightly sprawling article; many doctrinal articles are, because they attempt to cover all of the relevant issues that could arise in a lawsuit. (I’ve certainly been guilty of this myself!) As you read, try to pin down which issues are fundamental and raise genuinely new challenges, and which are likely to become non-issues over time.
- Questions:
- Should we treat the outputs of AIs as potentially defamatory? Aren’t they obviously the results of purely digital processes, with no semantic meaning?
- Relatedly, why should anyone take what ChatGPT outputs seriously? Should the legal system presume that everyone should know that generative-AI chatbots hallucinate all the time?
- Can an AI system have actual malice? What would it take to conclude that ChatGPT emitted an output “with knowledge that it was false or with reckless disregard of whether it was false or not”?
- Should OpenAI have a duty to prevent ChatGPT from hallucinating defamatory lies? Should it have a duty to investigate and fix ChatGPT after someone points out a defamatory lie that it emitted?
- Additional Resources:
- Another doctrinal analysis is Leslie Y. Garfield Tenzer, Defamation in the Age of Artificial Intelligence, 80 NYU Annual Survey of American Law 135 (2024).
- Toni M. Massaro and Helen Norton, Siri-ously? Free Speech Rights and Artificial Intelligence, 110 Northwestern University Law Review 1169 (2016), and Toni M. Massaro, Helen Norton, and Margot E. Kaminski, Siri-ously 2.0: What Artificial Intelligence Reveals About the First Amendment, 101 Minnesota Law Review 2481 (2017) are listener-oriented takes on AI speech, written before ChatGPT upended everything.
- Dan Burk, Asemic Defamation, or, the Death of the AI Speaker, 22 First Amendment Law Review 189 (2024) is the most pungent argument that AI outputs aren’t even meaningful enough to be defamatory. Another take arguing that AIs lack the intentions required in law for certain kinds of liability is Ian Ayres and Jack M. Balkin, The Law of AI is the Law of Risky Agents without Intentions, University of Chicago Law Review Online (forthcoming).
- James Grimmelmann, The Defamation Machine (38th Annual Silha Lecture 2023) is my take on whether AI outputs have meaning and whether AI can have actual malice. It has pictures!
- Lawrence B. Solum, Artificial Meaning, 89 Washington Law Review 69 (2014), is a philosophically sophisticated take on where the meaning, if any, in AI outputs comes from.
- Peter Henderson, Tatsunori Hasimoto, and Mark Lemley, Where’s the Liability in Harmful AI Speech?, 3 Journal of Free Speech Law 589 (2023). Another recent paper, from a mixed CS+law team of authors.
Layering
- Readings: Christopher S. Yoo, Protocol Layering and Internet Policy, 161 University of Pennsylvania Law Review 1707 (2013).
- Notes: Christopher Yoo is a law-and-technology scholar who has played a leading role in establishing law-CS collaborations, including the ACM Symposium on Computer Science and Law. This article is interesting because it engages with attempts to make a technical principle normative. The scholars Yoo is responding argued that the architecture of the Internet was layered and that regulators should take steps to lock layering in place. This is an interesting kind of argument! Remember that Lessig said law can change architecture–these scholars, including arguably Lessig himself, could be read as saying that architecture should drive law.
- Questions:
- What is “layering”? Is it something that we only see on the Internet, or can other systems also be layered?
- Was the Internet actually layered in the 2000s? Is it layered now in 2026?
- Just because the Internet is layered now, does it follow that it it should be layered? Are there counterarguments? Better arguments that they fail to make?)
- Does the layers principle imply network neutrality?
- Have you seen this type of attempt to make technical principles normative anywhere else? Are those other attempts persuasive?
- Additional Resources:
- Lawrence B. Solum and Minn Chung, The Layers Principle: Internet Achitecture and the Law, 79 Notre Dame Law Review 815 (2004). This is one of the articles to which Yoo is responding; it is extremely detailed.
- Mark A. Lemley and Lawrence Lessig, The End of End-to-End: Preserving the Architecture of the Internet in the Broadband Era, 48 UCLA Law Review 925 (2001) (Cornell Hein link, public draft, blurry scan). Another argument for preserving a technical architecture; this one is much lighter on the technical details. Contrast Tim Wu, Network Neutrality, Broadband Discrimination, 2 Journal on Telecommunications and High Tech Law 141 (2003), which is based on policy considerations rather than architectural ones. (Yes, this is where the term comes from.)
- The Cursed Computer Iceberg Meme. Computers are deeply weird. We talk about them using logical and well-structured abstractions, but sometimes—often—those abstractions break down. This is a compilation of incredible, and frequently hilarious, stories about times when those abstractions broke down.
- Eric Wustrow, Scott Wolchok, Ian Goldberg, and J. Alex Halderman, Telex: Anticensorship in the Network Infrastructure, Proceedings of the 20th USENIX Security Symposium (USENIX Security ‘11) (2011). An interesting technical proposal to help users circumvent governmental censorship, which depends in a fundamental way on violating the layers principle. See also the authors’ companion sites for Telex and its descendant Refraction Networking and my blog post about Telex, Planet Telex.
- Larry Patterson and Bruce Davie Computer Networks: A Systems Approach (Morgan Kaufmann 6th ed. 2021). This is my personal favorite networking textbook; it presents the standard model of the Internet. It is useful to read while pondering the question of where the layers principle comes from.
- Barbara van Schewick, Internet Architecture and Innovation (MIT Press 2010). A thorough and detailed analysis of the role that the layers principle and the related end-to-end principle play in Internet policy. The literature review is exceptional; if you are going to do serious research on Internet architecture and law, this is an essential reference.
- J[erome] H. Saltzer, D[avid] P. Reed, and D[avid] D. Clark, End-to-End Arguments in System Design, 2 ACM Transactions on Computer Systems 277 (1984). This is the paper that introduced the end-to-end principle; it remains highly readable and a canonical reference.
- Jerome H. Saltzer and M. Frans Kaashoek, Principles of Computer System Design: An Introduction (Morgan-Kaufmann 2009). This is a full-on computer-science textbook about system design. Part I is only legally available in print, but Part II is free online and includes a chapter about layers and modularity in networks. A good place to look to understand how system designers think about abstractions.
- Pamela Zave and Jennifer Rexford, The Real Internet Architecture (Princeton University Press 2024). This book gives a more modern model of Internet architecture, including developments like VPNs, firewalls, and CDNs. It is useful to read while pondering whether the Internet today obeys the layers principle.
- Tejas N. Narechania and Scott Shenker, How to Save the Internet, 41 Berkeley Technology Law Journal 65 (2026). Narechania is a legal scholar; Shenker is a CS scholar. This is a modern take on Internet architectural issues, one that takes into account developments over the last few decades.
Protocols
- Readings: Eric J. Feigin, Architecture of Consent: Internet Protocols and Their Legal Implications, 56 Stanford Law Review 901 (2004)
- Notes: Eric Feigin is a career DOJ attorney who has argued numerous Supreme Court cases, including Chatrie v. United States; this article was his student note. It is another paper on the legal consequences of technical principles. But where Yoo focuses on the consequences for regulators of the fact that the Internet is designed in a particular way, Feigin focuses on the consequences for private parties and judges. Given how Internet protocols work, Feigin argues, people who use those protocols in particular ways should be treated as having consented (or not) to particular conduct. This is also an interesting kind of argument, but be clear that it is a different kind of argument than we discussed last time.
- Questions:
- What is a protocol?
- Was Feigin’s description of Internet protocols accurate in 2004? Is it accurate now in 2023?
- Is Feigin right that use of an Internet protocol is a kind of consent? If so, is it the kind of consent that can be withdrawn by an explicit statement to the contrary?
- What kind of technical information do you need about a protocol to attribute legal consequences to its use? What else do you need to know?
- Could someone define a new protocol that is like IP or TCP or HTTP but which does not have these consent-granting features?
- Additional Resources:
- Joshua A.T. Fairfield, “Do Not Track” as Contract, 14 Vanderbilt Journal of Entertainment and Technology Law 545 (2012). Another article arguing that the use of a particular protocol has legal consequences.
- James Grimmelmann, Consenting to Computer Use, 84 George Washington Law Review 1500 (2016). My view on what constitutes “consent” in fact and in law when it comes to computer systems.
- Orin S. Kerr, Norms of Computer Trespass, 116 Columbia Law Review 1143 (2016). Not a consent-based argument, but not not a consent-based argument, either.
Abstract Writing Workshop
- Readings: Randall Munroe, Up Goer Five, XKCD.
- Notes: For class today, write the first part of your paper using only the ten hundred words people use most often. It may help to use this box you can type in to check your work.
- Additional Resources:
- Randall Munroe, Thing Explainer: Complicated Stuff in Simple Words (2015). Up Goer Five in extended book form. Highly enlightening.
- Guy L. Steele, Growing a Language (1998). Another example of how to build up big ideas from small pieces.
- Patrick Winston, How to Speak (2018) (transcript). I saw Winston give this talk live when I was at an impressionable age, and it has shaped my presentation style. I depart from his advice in a few ways, but there is a lot in here to learn from.
Law as Code
- Readings: Sarah Lawsky, Form as Formalization, 16 Ohio State Technology Law Journal 114 (2020).
- Notes: Sarah Lawsky is a tax scholar with a Ph.D. in logic. This is one in a series of articles on formalizing tax law. It draws on one of the two major branches of AI—formal methods and deductive reasoning, sometimes called “good old-fashioned AI” (or “GOFAI”)—to draw logically rigorous conclusions about statutes, and discusses the implementation of this kind of reasoning in tax forms and tax software.
- Questions:
- What does it mean to “formalize” a problem?
- Is law formal? Can it be? Should it be?
- Is there anything about tax law that makes it easier to formalize?
- What is the connection between formalization and software?
- How do you know whether a formalization is correct?
- Additional Resources:
- Form as Formalization was part of an Ohio State Technology Law Journal symposium on AI and tax law. I also highly recommend Joshua D. Blank and Leigh Osofsky, Legal Calculators and the Tax System and Susan C. Morse, Do Tax Compliance Robots Follow the Law?.
- The article is part of Lawsky’s larger research agenda on formalization and law. Other notable articles in this line of research from Lawsky include Sarah B. Lawsky, A Logic for Statutes, 21 Florida Tax Review 60 (2017); Sarah B. Lawsky, Coding the Code: Catala and Computationally Accessible Tax Law, 75 SMU Law Review 535 (2022); Sarah B. Lawsky, Reasoning with Formalized Statutes: The Case of Capital Gains and Losses, 43 Virginia Tax Review 361 (2024); and Sarah B. Lawsky, Direct File as Formalization, 23 Pittsburgh Tax Review 283 (2026). I wrote a short review of Lawsky’s work in James Grimmelmann, When Law is Code, Jotwell: Technology Law (July 31, 2024).
- Shrutarshi Basu, Nate Foster, James Grimmelmann, Shan Parikh, and Ryan Richardson, A Programming Language for Future Interests, 24 Yale Journal of Law and Technology 75 (2022). This is my own effort to formalize a fragment of law. Don’t miss our interactive interpreter for propery conveyances.
- James Grimmelmann, Programming Languages and Law: A Research Agenda, CS&Law (2022). I have been involved with the ProLaLa research community, and this was my attempt to describe what legal scholarship and programming-language theory can teach each other.
- The Catala project, with which Lawsky is affiliated, is developing a programming language tailored for legal formalization. The main ideas were described in Denis Merigoux, Nicholas Chataing, and Jonathan Protzenko, Catala: A Programming Language for the Law, and a particularly ambitious application of these ideas is Denis Merigoux, Raphaël Monat, and Jonathan Protzenko, A Modern Compiler for the French Tax Code.
- The OG articles on legal formalization are Layman Allen, Symbolic Logic: A Razor-Edged Tool for Drafting and Interpreting Legal Documents, 66 Yale Law Journal 833 (1957); from a generation later L. Thorne McCarty, Reflections on TAXMAN: An Experiment in Artificial Intelligence and Legal Reasoning, 90 Harvard Law Review 837 (1977); and M.J. Sergot et al., The British Nationality Act as a Logic Program, Communications of the ACM, May 1986, at 370. The field is vast, but see particularly Kevin D. Ashley, Artificial Intelligence and Legal Analytics (2017), for an overview and John Horty, The Logic of Precedent (2025), for an influential formal model of judicial precedent.
Smart Contracts
- Readings: Gregory Klass, How to Interpret a Vending Machine, 7 Georgetown Law Technology Review 69 (2023).
- Notes: Gregory Klass is primarily a contract theorist, and this article is in some ways a deliberate attempt to avoid getting bogged down in technical details. In my mind, at least, he builds on our discussion of Yoo and Feigin, because this is yet another paper about how technical facts produce legal effects.
- Questions:
- What is a smart contract?
- Was Klass’s description of smart contracts accurate in 2023? Is it accurate now in 2026?
- Critique the following argument: “People who use a smart contract intend to have it enforce their transaction rather than have the legal system do it. Therefore, the legal system should always defer to the smart contract and should never, under any circumstances interfere with it.”
- Critique the following argument: “People who use a smart contract intend to enter into an enforceable agreement. Therefore, a smart contract is also a legal contract. A court should enforce the legal contract and ignore whatever the smart contract does.”
- What should happen when a smart contract has a bug?
- What should happen if A convinces B to enter into a smart contract by lying about what the smart contract does?
- Additional Resources:
- J.G. Allen, Wrapped and Stacked: ‘Smart Contracts’ and the Interaction of Natural and Formal Languages, 14 European Review of Contract Law 307 (2018). More intricate and detailed than Klass, but also meticulously precise.
- Shaanan Cohney and David A. Hoffman, Transactional Scripts in Contract Stacks, 105 Minnesota Law Review 319 (2020). A more technically detailed discussion of whether, when, and how smart contracts are legal contracts. If you read it, ask whether, when, and how the technical depth makes a difference to the argument.
- James Grimmelmann, All Smart Contracts Are Ambiguous, 2 Journal of Law and Innovation 1 (2019). My take on the interpretation problem for smart contracts.
- Karen E. C. Levy, Book-Smart, Not Street-Smart: Blockchain-Based Smart Contracts and The Social Workings of Law, 3 Engaging Science, Technology, and Society 1 (2017). A good statement of the point that for legal contracts, ambiguity can be a feature rather than a bug.
Legal Interpretation
- Readings: Yonathan Arbel and David Hoffman, Generative Interpretation, 99 NYU Law Review 451 (2024).
- Notes: Yonathan Arbel and David Hoffman are legal scholars who have done significant empirical work. In a sense, this article is two legal empiricists taking a new toy out for a test drive to see what it is capable of. That new toy comes from the other major branch of AI, which uses statistical methods to inductively learn patterns in data.
- Questions:
- Compare and contrast generative interpretation with the formal methods discussed in the previous class.
- In Snell v. United States Specialty Insurance Co., 102 F.4th 1208 (4th Cir. 2024), a child was injured on an in-ground trampoline and her family sued the landscaping contractor who installed it. The contractor’s insurance policy covered it from liability that “arises from” “operations” in which “[the] Insured performs landscaping.” The contractor and the insured disputed whether installing a trampoline was “landscaping.” In a concurrence, Judge Kevin Newsom suggested that the issue might be a good one for LLMs. Having read Arbel and Hoffman, how would you actually test this out? When you get an answer from the LLM, do you need to confirm that it is correct? If so, how?
- Arbel and Hoffman focus on contract interpretation. Could the same approach work for other kinds of legal interpretation and reasoning, such as statutory interpretation, constitutional interpretation, or applying caselaw?
- What kinds of empirical validation would LLM interpretation need to be considered reliable in general? In a specific case?
- How would you feel about having a lawsuit in which you are party resolved by using LLM interpretation? Does it matter what kind of case it is and how high the stakes are?
- What is similar about LLM interpretation to human interpretation? What is different?
- Additional Resources:
- Yonathan A. Arbel and David A. Hoffman, Generative Gap Filling (draft 2026). A follow-up paper that reports on experiments asking LLMs to fill in deleted terms from actual contracts.
- Judge Newsom’s entire concurrence in Snell is well worth reading. So is his follow-up in United States v. Deleon, 116 F.4th 1260 (4th Cir. 2024). Another interesting case in which the majority and dissent debated the use of LLMs is Ross v. United States, No. 23-CM-1067 (D.C. Ct. App. Feb. 20, 2025).
- Appellate litigator Adam Unikowsky has written a number of enthusiastic blog posts about LLMs for legal reasoning and legal writing. See In AI We Trust (June 8, 2024); In AI We Trust, Part II (June 16, 2024); Sunny or Melon? (August 10, 2024); Automating Criminal Appeals (September 18, 2024).
- Christoph Engel, Experimental Comparative Law 2.0? Large Language Models as a Novel Empirical Tool (draft 2024), finds that “merely giving the LLM the otherwise identical vignette in different languages leads to strongly different results.” (Is this a disturbing finding that LLMs are influenced by arbitrary and irrelevant factors, or an encouraging finding that LLMs have learned the different legal contexts in which speakers of different languages typically work?)
- James Grimmelmann, Benjamin L.W. Sobel, and David Stein, Generative Misinterpretation, 63 Harvard Journal on Legislation 229 (2026). A response to Arbel and Hoffman, Newsom, Unikowsky, and other LLM interpretation proponents.
Software Liability
- Readings: Bryan H. Choi, Software as a Profession, 33 Harvard Journal of Law and Technology 557 (2020)
- Notes: Bryan Choi majored in computer science before getting a JD, and his recent work focuses on the regulation of software development. In this article, he argues that software development is a profession, and that software developers should be treated as professionals for liability purposes. Professional status is a double-edged sword: in some ways the law is harder on professionals (stronger duties to their clients, higher standards of competence), and in some ways it is more forgiving (deference to the customary standards in the profession).
- Questions:
- How does the way that software is developed affect the quality of the resulting software?
- Was Choi’s description of software development accurate in 2020? Is it accurate now in 2026?
- Does the legal category of “professionals” track the everyday usage of the term?
- What are the reasons for treating professionals differently than others when it comes to tort liability?
- Is software development a profession in this sense? How does it compare to other legally recognized professions, like medicine, law, engineering, and plumbing?
- If developers really are professionals, are there other consequences – e.g., could the practice of software development require a license, the same way that the practice of medicine does?
- Additional Resources:
Dangerous Software
- Readings: Deirdre K. Mulligan and Aaron K. Perzanowski, The Magnificence of the Disaster: Reconstructing the Sony BMG Rootkit Incident, 22 Berkeley Technology Law Journal 1157 (2007)
- Notes: This is a post-mortem of a famous case of allegedly harmful software. We’ll ask what made it so bad, and what the law can do about it. Deirdre Mulligan is a privacy scholar who served in the Biden administration; her work pays close attention to the technical and organizational details of how institutions do and don’t protect privacy. Aaron Perzanowski is a privacy and IP scholar who has written about the right to repair, digital ownership, tattoos, and clown eggs. No, seriously.
- Questions:
- What is DRM?
- What is a rootkit? Is it appropriate to describe the Sony BMG DRM this way?
- Why did Sony BMG put this software on CDs it sold in 2005?
- What kind of process should companies follow when deciding whether or not to add “features” like this to consumer software?
- Critique the following argument: “People can install whatever software they want on their computers. They chose to install this software. It is not the government’s place to intervene.”
- Critique the following argument: “Sony BMG’s software was badly coded, but there is nothing wrong with the general idea of DRM. But Mulligan and Perzanowski want a world in which DRM is unworkable because computer owners can always use their own software to crack the DRM.”
- How do you listen to music and watch movies on your computer now? Does it involve DRM? Who laughed last?
- Additional Resources:
Reidentification
- Readings: Paul Ohm, Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization, 57 UCLA Law Review 1701 (2010)
- Notes: Paul Ohm is yet another computer scientist turned law professor; he created a highly-influential course on programming for lawyers. This paper is a classic in the genre of arbitraging technical scholarship into law-review scholarship. We’ll ask whether it deserves that celebrated status, and if so, what Ohm does right.
- Questions:
- Why does privacy law treat “personally identifiable data” (PII) differently than other kinds of data?
- What defines whether data is PII?
- What is reidentification, and how big a problem is it?
- What should privacy law do, in light of the reidentification techniques Ohm discusses?
- How does Ohm present and summarize the technical material?
- How does Ohm organize the legal material?
- How does Ohm relate the technical material to the legal material?
- Additional Resources:
The Fifth Amendment
- Readings: Orin S. Kerr, Compelled Decryption and the Privilege Against Self-Incrimination, 97 Texas Law Review 767 (2019)
- Notes: Orin Kerr is a leading criminal procedure scholar and the undisputed authority on digital issues in criminal investigations. His article is a nice window for talking about encryption. I think it gives us a concrete way to talk about what users, companies, and governments can do to use computers to hide or access data without getting dragged into the full encryption-wars debate.
- Questions:
- What is encryption?
- What are the different ways to read encrypted data?
- What does the Fifth Amendment say about secret information? Why?
- What is the foregone conclusion doctrine? How does Kerr think it applies? Do you agree?
- When should government be able to compel decryption by demanding assistance from the suspect? From third parties?
- Is Kerr’s doctrinal argument in Parts I and II clever, or too clever by half? Discuss.
- Additional Resources:
- Orin S. Kerr and Bruce Schneier, Encryption Workarounds, 106 Georgetown Law Journal 989 (2018). A useful big-picture piece, co-written with a leading security technologist, that asks what the stakes in compelled decryption cases actually are, in light of the goverment’s other options.
- Aloni Cohen, Sarah Scheffler, and Mayank Varia, Can the Government Compel Decryption? Don’t Trust — Verify?. An interesting technical paper that asks what is truly a ``foregone conclusion’’ at the technical leve
- Laurent Sacharoff, What Am I Really Saying
When I Open My Smartphone? A Response to Orin S. Kerr, 97 Texas Law Review Online 63 (2019). A brief but thoughtful response to Kerr.
- Orin S. Kerr, An Equilibrium-Adjustment Theory of the Fourth Amendment, 125 Harvard Law Review 476 (2011). This is the larger theoretical framework within which Kerr works.
- Charles Duan and James Grimmelmann, Content Moderation on End-to-End Encrypted Systems: A Legal Analysis, 8 Georgetown Law Technology Review 1 (2024). A real for-the-nerds-only deep dive on how encryption and wiretapping law interact in cutting-edge group communication settings.
Geofence Warrants
- Readings: Haley Amster and Brett Diehl, Against Geofences, 74 Stanford Law Review 385 (2022)
- Notes: Not every student note is this ambitious, but it’s a good illustration that you don’t need seniority and a fancy job title to write good scholarship.
- Questions:
- What is a geofence warrant? Why do law enforcement authorities use them?
- How do companies like Google store location data?
- How do companies search their databases to extract the information demanded by a warrant for a specific account? For a geofence warrant?
- Are geofence warrants constitutional under existing doctrine?
- Should geofence warrants be allowed as a policy matter?
- Additional Resources:
Blockchains
- Readings: Adam J. Levitin, Not Your Keys, Not Your Coins: Unpriced Credit Risk in Cryptocurrency, 101 Texas Law Review 877 (2023)
- Notes: This one may be doubly tricky, because it involves both technical complexity and also bankruptcy law, which is notoriously dense. Do your best; we will discuss how well Levitin does at explaining both the technology and the law.
- Questions:
- What is a cryptocurrency? A cryptocurrency wallet? A cryptocurrency exchange?
- What happens if a grocery store goes bankrupt? A bank?
- What goes wrong, according to Levitin, when we try to map cryptocurrency exchanges onto this existing bankruptcy system?
- Critique the following argument: “Crypto is just gambling anyway; you shouldn’t invest in it unless you’re prepared to lose all your money.”
- What would Levitin say a properly investor-protective regulatory regime for cryptocurrency exchanges should look like? How likely are we to get it?
- Additional Resources:
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