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This Essay compares a judicial revolution that is happening to one that is not. Both the change and the status quo are being managed by the current Supreme Court. That Court has, when it comes to administrative law, shown a capacity to revisit everything. But when it comes to securities regulation, it has resisted change. What is the explanation for this divergent approach between general regulation, which the Court has sought to police, and securities regulation, which the Court has left alone? Some scholars have argued that the Supreme Court is simply uninterested in securities regulation, but the Court now hears proportionately more securities cases than it once did. Others dispute the premise that the Court supports corporate America. And, of course, the Roberts Court could change its approach to securities regulation in time. But I think the divergence suggests that the Court wants to police public rights and rights against the state but is less interested in reformulating the standards for private disputes, such as disputes between shareholders and managers.

Artificial intelligence (AI) has the potential to alter the interpretation of the duties of care, skill, and diligence. As these duties form the foundation for the BJR and equivalent provisions, the development of AI is also expected to impact the BJR. There is a broadening importance, in an increasingly data-driven business environment, of the requirement to gather sufficient information before making a decision and to use information in a valid manner. Changes are both quantitative (how much information to collect) and qualitative (which types of information to collect). The changes also relate to the methods of decision-making, including the role of measures and statistics over intuition.

This Essay argues that the increasing prevalence and sophistication of artificial intelligence (AI) will push securities regulation toward a more systems-oriented approach. This approach will replace securities law’s emphasis, in areas like manipulation, on forms of enforcement targeted at specific individuals and accompanied by punitive sanctions with a greater focus on ex ante rules designed to shape an ecology of actors and information.

Critics of the Supreme Court’s equal protection jurisprudence despair that the Court conceives of discrimination as the mere classification of individuals on forbidden grounds, such as race and sex, rather than systemic patterns of subordination. On the Court’s anticlassification theory, affirmative action, which relies on overt racial or gender classifications, is generally forbidden. Such context-insensitive anticlassification rules could, in principle, extend to individuals who are members of groups often regarded with hostility and suspicion, such as transgender people. Indeed, this is how most trial courts have approached recent laws that classify individuals based on sex to exclude transgender people. However, appellate courts have refused to take anticlassification rules seriously. This Article argues that all sex classifications, like all race-based ones, ought to trigger heightened constitutional scrutiny. It draws support from the principles undergirding anticlassification rules announced by the Roberts Court, most recently in its university affirmative action decisions.

Catalyzed by the #MeToo movement, states have adopted a spate of laws restricting secret settlements. In 2018, California led the charge with the Stand Together Against Non-Disclosure (STAND) Act, which targets secrecy in the resolution of sex discrimination, harassment, and abuse cases. Transparency advocates hail these reforms as a major win for victims. Critics, meanwhile, warn that the reforms will hurt those they intend to help. Nested within this debate sit a raft of confident, conflicting—and eminently testable—claims about what exactly happens in the wake of reform. Will defendants still settle, even if secrecy isn’t on offer? Will case filings disappear? Debate over these questions has raged since the 1980s, and, over these decades, the debate has always centered on fervent predictions regarding each. Our findings tell a clear and consequential story. Contrary to critics’ fears, the STAND Act did not yield a sharp increase or decrease in case filings. Nor did the Act appear to significantly prolong cases or amplify their intensity. The upshot: cases still settle even when secrecy isn’t on offer. Perhaps most importantly, it appears that positive effects did come to pass.

In the last few years, the Supreme Court has upended its doctrine of religious freedom under the First Amendment. Now, the government must treat religion equally with respect to providing public benefits. But it must also grant special exemptions from regulations that burden religion. We refer to this regime as structural preferentialism. This Article offers an external, political account of changes in Free Exercise and Establishment Clause jurisprudence by analyzing them as if they were the result of political conflicts between competing interest groups. Focusing on the role of religion in political polarization, rapid disaffiliation from denominations, and shifting strategies to fund religious schools, this political perspective has explanatory and predictive power that extends beyond conventional legal arguments about text, history, and precedent. Applying this approach, we predict that structural preferentialism will transform First Amendment doctrine and provide material grounds for its own entrenchment.

Chapter 11 was widely viewed as a failure in the first decade of the Bankruptcy Code’s operation, the 1980s. While basic bankruptcy still has its critics and few would say it works perfectly, the contrast with bankruptcy today is stark: bankruptcies that took years in the 1980s take months in the 2020s. Multiple changes explain bankruptcy’s success and we do not challenge their relevance. But in our analysis, one major change is missing from the current understanding of bankruptcy’s success: bankruptcy courts and practice in the 1980s rejected market value; today bankruptcy courts and practice accept and use market value. This shift is a major explanation for bankruptcy’s success. We argue that valuation improvements explain much of the increased speed and efficiency of Chapter 11 practice over the decades. We provide evidence that valuation conflicts narrowed and that the corporate reorganization process accelerated. The switch to market thinking across the bankruptcy spectrum—in bankruptcy transactions, in judging, and in lawyering—goes far in explaining why.

This Essay explores the two holy grails of AI and the law: predicting court decisions and predicting contracts. While there is some overlap between the two, because in order to draft contracts one needs to know the law, both issues can be functionally distinguished. These two areas, and their importance in the context of increasing AI development, are explored more deeply within the context of corporate insolvency law.

This Essay focuses largely on structural responses to AI pricing in antitrust, outlining the bulk of its argument in the context of merger law but also considers monopolization law and exclusionary conduct. It argues that the relationship between the strictness of the law and the sophistication of AI pricing is not straightforward. In the short run, a stricter approach to merger review might well make sense, but as AI pricing becomes more sophisticated, merger policy ought to become less strict: if anticompetitive outcomes are inevitable with or without a merger because of highly sophisticated AI pricing, antitrust interventions to stop mergers will not affect pricing and instead will create social losses by impeding efficient acquisitions. This Essay considers similar questions in the context of monopolization. It concludes by observing that the rise of AI pricing will strengthen the case for antitrust law to shift its focus away from high prices and static allocative inefficiency and toward innovation and dynamic efficiency.

Causal AI is within reach. It has the potential to trigger nothing less than a conceptual revolution in the law. This Essay explains why and takes a cautious look into the crystal ball. Causation is an elusive concept in many disciplines—not only the law, but also science and statistics. Even the most up-to-date artificial intelligence systems do not “understand” causation, as they remain limited to the analysis of text and images. It is a long-standing statistical axiom that it is impossible to infer causation from the correlation of variables in datasets. This thwarts the extraction of causal relations from observational data. But important advances in computer science will enable us to distinguish between mere correlation and factual causation. At the same time, artificially intelligent systems are beginning to learn how to “think causally.”