This chapter serves as a primer for understanding the sweeping implications of generative AI (GenAI). It begins by establishing a clear terminological foundation, distinguishing modern GenAI from its predecessors, good old-fashioned AI (GOFAI) and machine learning. While GOFAI and machine learning solved complex, specific problems, GenAI’s ability to generate novel outputs, coupled with its cheap, scalable, and versatile nature, has rapidly woven it into the fabric of society. The text emphasizes that for every potential beneficial use, there are equally foreseeable harms, such as the spread of deepfakes and the weaponization of biases. The chapter argues that GenAI is not a neutral tool and that its deployment is often driven by corporate interests that externalize risks onto society. To foster a more informed public discourse, the chapter introduces Neil Postman’s seven questions for evaluating new technology, urging readers to move from being passive subjects to active participants in the democratic oversight of GenAI’s development.

This chapter dissects the intense, seemingly irrational boom in generative AI (GenAI), moving beyond superficial hype to reveal the complex, structural incentives driving billions of dollars into an industry with uncertain returns. It argues that the current boom is propelled not purely by altruistic motives or technological inevitability, but by ten powerful forces. These forces – namely, shareholder primacy, financial imperatives, the pursuit of first-mover advantages, insatiable data-collection needs, self-serving safety justifications, fear of losing relevance, the pursuit of prestige, fear of missing out (FOMO), a belief in “manifest destiny,” and regulatory entrepreneurship – collectively prioritize speed, growth, and market capture over caution, sustainability, and societal benefit. By examining these systemic pressures, the chapter provides the framework for understanding the breakneck pace of AI development and for shaping its ultimate deployment.

This chapter provides two practical frameworks for navigating the pervasive hype surrounding generative AI (GenAI). The first is a consumer handbook for critically reading AI-related news and press releases, urging skepticism toward unsubstantiated skill claims, proprietary benchmark scores, and “demo-ware,” while prioritizing independent third-party review. The second framework introduces “GenAIuflecting” – the unwarranted deference governments show to AI companies – and offers practical tests (the evidence, substitution, beneficiary, accountability, and precedent tests) to identify and resist policy proposals that favor corporate profit over public interest. Ultimately, the chapter argues that the true stakes are not winning an “AI race,” but securing democratic control over technology’s role in society.

The advent of generative AI (GenAI), which can produce billions of words daily, is forcing a profound reexamination of the First Amendment’s protection of “freedom of speech.” Written in 1791 to safeguard human expression, this constitutional right is now being claimed by AI companies seeking immunity from liability for the harmful outputs of their products.

The central question is whether the algorithmic output of a large language model (LLM) – a statistical calculation rather than a human expression of belief or intent – qualifies as “speech” under the First Amendment. Examining the traditional justifications for protected speech – self-expression and the “marketplace of ideas” – the chapter argues that neither rationale neatly applies to content produced by nonsentient machines. It then explores two proposed legal standards: the “speech certainty” standard, which grants protection only when the content is predictable and controllable by a human author, and a human-centric approach requiring intentionality, self-awareness, and human expression. Granting full First Amendment protection could immunize AI companies from liability for a range of harms.

This chapter challenges the notion that generative AI (GenAI) outputs qualify for First Amendment protection as “speech.” It argues that under both speech certainty and human-centric legal frameworks, GenAI outputs – being probabilistic, statistically driven, and devoid of human understanding, sentience, or intent – should be legally categorized as nonexpressive conduct or utilitarian data processing, not intentional communication.

The chapter dismantles the common counterargument that a user has a constitutional “right to receive” an AI’s output, asserting that if no speech is created by the speaker (the AI model), there is no speech to receive. This classification determines the government’s regulatory authority. If AI outputs are deemed speech, regulations face the nearly insurmountable barrier of “strict scrutiny.” If they are nonexpressive conduct, however, the government retains the power to regulate GenAI for safety. The chapter warns that extending constitutional free speech protections to nonhuman, nonintentional algorithms would jeopardize product liability, consumer protections, and democratic self-governance.

This chapter discusses the contentious world of web scraping and its essential, yet problematic, role as the lifeblood of generative AI (GenAI) model training. It highlights the vast scale of data required and the subsequent reliance on the internet’s reservoirs: the public, deep, and dark webs. The central tension explored is the conflict between technological capability and creator consent, revolving around legal and ethical dilemmas.

The chapter scrutinizes the ethical implications of this “finders keepers” approach, the use of torrenting to acquire copyrighted material, and the economic imbalance created when AI models compete with the original content creators. Finally, the chapter examines the complex debate surrounding content opt-out mechanisms and concludes by stressing the impending challenge of diminishing data, which will likely force a greater reliance on synthetic data. Ultimately, the chapter argues that the current approach has created a crisis of legitimacy that requires a reevaluation of the terms under which the internet should be scraped.

This chapter argues that the second core justification for traditionalism concerns the mechanisms through which the American people become bound to their Constitution and the civic affection necessary to sustain the American democratic republic. This emotional side of the Constitution has been largely ignored by constitutional theorists, but it is critical to sustaining it. Traditions give the people a sense of agency and ownership over their foundational charter of governance. Related justifications for traditionalism include its capacity to foster democratic self-governance and its cultivation of a healthy populism in Americans’ regard for their Constitution.

Chapter 3 investigates the evolution of institutional shareholder activism, focusing on how a diverse range of investors – including pension funds, asset managers, hedge funds, and index funds – have moved from passive holders to more assertive and strategic actors in corporate governance. It traces the theoretical roots of shareholder activism, from the exit-versus-voice framework to the economic constraints and tactical shifts that define modern engagement. Using a typology of firm-, portfolio-, and system-level activism, the chapter explores a broadened strategic repertoire – illustrating a shift from adversarial tactics to more collaborative, coalition-based engagement – and examines how activist goals have expanded from financial returns to encompass ESG and systemic concerns. Drawing on a hand-collected dataset of UK activism campaigns (2010–20), it maps evolving trends in actors, agendas, and tactics, and analyses how ownership concentration and investment horizon influence outcomes. The chapter concludes by situating shareholder activism within policy debates, contrasting ‘engaged’ and ‘transient’ investors, and reframing shareholder voice within a pluralistic model of stewardship.

AI technologies are increasingly influencing decisions in high-stake domains, where biased outcomes can harm vulnerable groups, erode trust, and conflict with societal values. This chapter examines fairness in AI, which seeks to ensure that AI systems make equitable decisions without reinforcing injustices. We begin this chapter by explaining what fairness means and why it is important in AI. Then, we explore the challenges of achieving fairness in AI. We highlight the difficulties posed by biases present in training data, the ambiguity in defining fairness across different contexts, and the trade-offs that may arise with other Responsible AI principles. Next, we elaborate on how fairness is applied in practice. We discuss existing methods and tools and highlight real-world examples. Finally, we outline what is missing to make fairness work in AI. We emphasize the need for clearer guidelines, interdisciplinary collaboration, robust frameworks for transparent reporting, continuous monitoring, and iterative improvement.

This chapter charts the author’s personal and professional journey from an evangelist for AI to an AI critic. It begins by exploring the binary visions of AI’s future: the utopian promise of solving global challenges (curing cancer, reversing global warming) against the existential danger of narrowly optimized superintelligence. The narrative details the author’s tenure at the Allen Institute for Artificial Intelligence, where initial excitement over a commitment to “AI for the common good” and ethical development turned to disillusionment as core ethical initiatives were deprioritized, licenses were changed, and the organization’s focus seemed to shift, and his time as a lecturer on AI, law, and ethics at the University of Texas at Austin. This experience propelled the author into a deep dive into the burgeoning legal and ethical challenges of generative AI (GenAI). The chapter concludes by arguing that GenAI is the decade’s most consequential technology, yet its rapid, legally ambiguous rise is largely outside public consciousness. It makes a forceful call for greater AI literacy and public involvement to ensure that society, rather than a handful of companies, shapes how this technology transforms our world.

Chapter 4 reconceptualises investor stewardship by tracing its historical, conceptual, and economic roots and advancing a theory of stewardship as delegated, relational power. It unpacks the evolving meanings of stewardship – from early moral connotations to contemporary use in corporate governance and investment management. Rejecting a narrow principal–agent lens, it reframes stewardship as a multidimensional practice embedded in complex delegation structures and layered accountabilities. The chapter introduces a tripartite model of stewardship – as power exercised by institutional investors, on behalf of clients and beneficiaries, and for the benefit of wider, often unseen, stakeholders – and a four-part relational model: client stewardship, end-investor stewardship, asset stewardship, and sustainability stewardship. These relationships expose the plural and sometimes conflicting responsibilities investors bear within a fragmented investment chain. It also considers the economic rationale for investor stewardship, highlighting incentives, constraints, and portfolio dynamics. Finally, it introduces the enlightened steward as a pluralistic figure balancing private mandates with systemic effects.

This chapter offers a sobering assessment of artificial general intelligence (AGI), which remains frustratingly difficult to define. The debate is polarized between optimistic “effective accelerationists,” who foresee boundless abundance, and “doomers,” who warn of existential risk from misaligned systems. The chapter discusses my skepticism of imminent AGI and explores indicators suggesting that current capabilities are being significantly oversold. It concludes that the AGI narrative primarily serves to attract investment and deflect accountability. Therefore, law and policy should regulate AI based on its actual capabilities and current harms, rather than speculative future potential.