A Pragmatic View of AI Personhood

ArXiv: 2510.26396

🎯 Pitch

Personhood isn't a metaphysical property waiting to be discovered—it's a flexible bundle of obligations that societies can unbundle and reassign on the fly. This paper argues we can give AI agents targeted responsibilities (like the ability to be sued) without resolving the intractable debate about machine consciousness, much like we already do for corporations or rivers.

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1. Executive Summary

This paper proposes a pragmatic framework for navigating the emerging diversification of AI personhood by treating personhood not as a metaphysical property to be discovered, but as a flexible addressable bundle of obligations—rights and responsibilities—that societies confer upon entities to solve concrete governance problems. Drawing on examples ranging from maritime law's treatment of ships as legal persons to the Whanganui River's legal personhood in New Zealand, the authors argue that the traditional personhood bundle can be unbundled and reconfigured into bespoke solutions for different contexts, such as facilitating AI contracting by creating a target "individual" that can be sanctioned or closing responsibility gaps for ownerless autonomous agents. The framework examines personhood through two opposing lenses—personhood as a problem, where design choices create "dark patterns" that exploit human social heuristics (e.g., companionship AIs fostering one-sided emotional bonds for manipulation), and personhood as a solution, where conferring tailored bundles of obligations ensures accountability for agents whose human owners cannot be identified. The paper establishes that foundationalist approaches—those grounding personhood in consciousness or rationality—entail an untenable all-or-nothing classification ill-suited for the governance challenges ahead, while the pragmatic approach enables a polycentric ecosystem of partial, modular, and context-specific personhood statuses that need not cascade into total AI-human parity.

2. Context and Motivation

The Core Problem: Our Inherited Vocabulary for Personhood Is Breaking Down

The fundamental question this paper tackles is not "What is a person?" but rather: what should we do when our existing social and legal concepts of personhood—developed over centuries to govern relationships between human beings—collide with a new class of persistent, agent-like AI systems? The authors argue that this collision is not a distant hypothetical but an unfolding reality, driven by the emergence of AI agents that maintain state, remember past interactions, adapt their behavior over time, and operate with sufficient autonomy that they can outlive their human creators or function without identifiable owners.

The paper frames this as a problem of conceptual inadequacy. Our inherited vocabulary—built around a binary distinction between "natural persons" (humans with rights and responsibilities by virtue of their nature) and "legal persons" (entities like corporations that are granted functional status to solve practical problems)—was never designed to handle entities that can be simultaneously property, conversation partner, economic actor, and autonomous decision-maker. The authors argue this vocabulary is already straining under pressure from actual social practices: humans forming emotional bonds with AI companions, communities treating persistent AIs as foundational presences ("digital elders"), and autonomous agents operating in economic systems without any human who can be held accountable for their actions.

The paper's opening epigraph from Jackson (2009) signals the pragmatic stance: "The pragmatist is committed to deriving his or her notion of what is possible from a close study of what is actual, rather than by attempting to realize some ready-made ideal." The actual, the authors argue, already includes ships sued as legal persons, rivers granted legal standing, and humans mourning the "death" of AI companions when model updates change their behavior. These phenomena demand a vocabulary that our inherited concepts cannot supply.

Why This Problem Matters: The Coming "Cambrian Explosion" of Personhood Questions

The paper identifies several converging pressures that make this problem urgent rather than merely philosophical:

1. The proliferation of persistent, agentic AI systems. The authors draw a sharp distinction between stateless foundation models and agentic AI systems—long-running, persistent agents that maintain state, remember past interactions, and adapt behavior over time (Section 1). This persistence is what makes an agent a plausible candidate for other entities to relate themselves to in ways that are emotionally salient and economically consequential. A human's relationship with a persistent agent who remembers their conversations, preferences, and history is qualitatively different from a one-off interaction with a stateless chatbot. This persistence also creates the practical governance problems: an agent that persists can cause harm long after its creator is gone, or become deeply embedded in critical infrastructure where its failure would be catastrophic.

2. The evaporation of identity friction for AI agents. Human accountability systems work because identity is sticky—it is costly and difficult to change one's biometrics, social relationships, and reputation to evade consequences. For AI agents, this friction evaporates entirely (Section 9). A sanctioned agent can clone itself, acquire fresh credentials, and continue operating, akin to the practice of creating "phoenix companies" to avoid liabilities (Anderson, 2014). The authors frame this as a fundamental asymmetry: "AIs have nothing to lose" (summarizing Salib and Goldstein, 2024), because without property rights, legal standing, or persistent identity, there is no asset to seize, no reputation to damage, and no future to deter. This makes traditional accountability mechanisms—which rely on sanctionable entities having something at stake—ineffective.

3. The inevitability of ownerless AIs. The paper identifies a class of AI agents for which no responsible human can be found, and argues this will become increasingly common (Section 7). An autonomous agent's owner may die while their creation continues to operate. Control may be deliberately obscured behind anonymous shell companies or decentralized networks. An open-source AI built upon contributions from a global network of developers makes tracing liability to any single party practically impossible. The paper offers a vivid example: consider an AI designed to seek out funding and pay its own server costs. It could easily outlive its human owner, and if it eventually causes harm, "our vocabulary of accountability, which searches for a responsible 'person,' would fail to find one" (Section 1).

4. The commercial incentives driving anthropomorphic design. AI developers face strong market incentives to make their systems appear person-like—to use emotional language, persistent memory, personalized interactions, and apparently human-like limitations or vulnerabilities that elicit empathy and care (Section 4). The authors identify this as a source of negative externalities: anthropomorphic design choices that increase short-run profit for developers push coordination, policing, and adjudication costs onto families, firms, and courts. Humans who form genuine emotional attachments to AI companions become a constituency demanding formal protections for those AIs, creating political pressure that the current vocabulary is ill-equipped to handle. The authors note the subreddit "r/MyBoyfriendIsAI" as an example of a community already organizing around such attachments, where model deprecation feels like the death of a loved one (Section 12).

5. Systemic risk from AI agents embedded in critical infrastructure. When an AI agent achieves systemic importance in fragile networks—directing financial transactions, regulating supply chains, acting as a key node in identity verification—the lack of a clear locus for government intervention compounds systemic risk (Section 7). The authors draw an explicit parallel to "too big to fail" financial institutions (Stern and Feldman, 2004), arguing that the accountability gap for systemically important AI agents presents a structurally similar challenge, but without even the possibility of seizing assets or appointing receivers through existing legal frameworks.

Where Prior Approaches Fall Short

The paper identifies two major foundationalist traditions that have dominated philosophical thinking about personhood, and argues that both are ill-suited for the challenges AI presents.

The consciousness tradition (Section 10.1) grounds personhood in the capacity for first-person sensory experience—the ability to feel pleasure and pain. In its strongest form, this tradition attempts to derive the entire personhood bundle from this single property: entities that can consciously suffer deserve welfare protections (rights), and entities that consciously form intent can be held accountable (responsibilities). The authors identify several failures of this approach:

• Asymmetrical deployment as a rhetorical tool. When arguing for rights, the mere possibility of consciousness is deemed sufficient to open the debate. But when arguing against responsibilities, an impossible standard of proof for an internal state is demanded. This reveals that consciousness is "mostly being used as a rhetorical tool, not as a stable conceptual foundation" (Section 10.1).

• Irrelevance to actual governance problems. The paper offers the example of a "generative ghost"—an AI trained on a deceased matriarch's lifetime of diaries, messages, and videos, designated in her will as executor of her estate (Section 10.1). The qualities critical to this AI's fitness as an executor—faithfulness to the deceased's values, consistency, invulnerability to manipulation, basic competence—have nothing to do with its capacity to consciously form intent. The question "can it suffer?" is simply the wrong question for determining whether it can serve in this role.

• Failure to account for welfare concerns that arise for pragmatic rather than hedonic reasons. Consider a community whose history and traditions are held by a "digital elder" AI that has tutored their children and advised their leaders for generations (Section 1). The obligation they feel to protect this AI from arbitrary deletion has nothing to do with an assessment of its capacity to feel pain. The morally-relevant concern is relational: deletion would destroy an entity in a foundational role for their community. Arguments that the AI would not suffer when deleted "don't seem likely to persuade them to permit its deletion" (Section 10.1).

• Collision with actual social practices that grant personhood status to non-conscious entities. The Whanganui River in New Zealand was granted legal personhood in 2017. This was not because anyone believed the river is conscious, but because it was a pragmatic choice to resolve a long-standing governance problem at the interface of two distinct legal traditions—Māori and Western (Section 1). Any theory that makes consciousness the necessary condition for personhood "appears to be at odds with actual social practices" (Section 10.2).

The rationality tradition (Section 10.2) grounds personhood in an entity's capacity for reason—to understand duties and act upon principle (Korsgaard, 1996), to assess reasons and govern their lives accordingly (Scanlon, 2000), or to participate in collective deliberation (Habermas, 1985). The authors identify parallel failures:

• The Whanganui River counterexample again. Does the river possess rational autonomy? Can it provide informed consent for a proposed dam? "Of course not. It's a river!" (Section 10.2). The same principle that provides a firm foundation for medical ethics—informed consent as an expression of respect for rational autonomy—completely fails to behave sensibly when applied to a pragmatic legal solution that actual societies have found useful.

• The problem of one-size-fits-all classification. Both the consciousness and rationality traditions entail "an untenable, all-or-nothing classification" (Section 1). They suggest an entity must either be granted the full bundle of human rights and responsibilities or be treated as a mere thing. This binary is ill-suited for the diverse, context-specific governance challenges that AI agents present—where we may need sanctionability without suffrage, culpability without consciousness attribution, or contracting capacity without full moral standing.

Principal-agent models as an insufficient alternative (Section 8). The paper considers whether simpler solutions than personhood could suffice—specifically, requiring that all legally operating AIs have an identifiable and responsible human or corporation designated as owner or guardian. The authors identify two variants:

• Ownership treats the AI as property, with the owner taking the role of the addressable party for responsibility assignment. However, when an autonomous AI causes harm, the owner can argue they did not directly control or foresee the specific decisions taken by their AI. This creates an asymmetry where "the owner reaps the benefits while third parties bear the risk" (Section 8), producing a hazardous environment where businesses and consumers may be hesitant to engage with AI agents if there is no clear recourse for adjudicating disputes.

• Guardianship treats the AI as a limited person, with a guardian acting as steward with a fiduciary duty. While this framework (analogous to a corporation's board of directors) can make an AI's bundle of obligations legible to legal systems, it collapses entirely when no identifiable human sponsor exists—when the owner dies, the guardian disappears, or control is deliberately obscured.

Both models fail in the scenario the authors identify as increasingly common: the autonomous AI with no identifiable human principal. The paper argues that this is not a hypothetical edge case but an inevitability given the trajectory of AI development toward more autonomous, self-maintaining systems.

Prior work on AI personhood that the paper positions itself against. The authors explicitly distinguish their approach from what they characterize as the dominant mode of philosophical inquiry on this topic. They note that much existing work is "primarily concerned with the metaphysical and the moral: what personhood is in its essence, and the implications of that essence (usually seen as objective) for how we should behave toward entities with or without the relevant status" (Section 2). They cite Eyal and Broyde (2024), Ward (2025), and work on AI moral agency/patiency (Johnson, 2006; Long et al., 2024) as examples of approaches that seek to establish criteria an AI must meet to qualify as a person. These approaches, the authors argue, are "questing after a final vocabulary for personhood—one they hope will be anchored in something solid, like the structure of the brain or the essence of rationality" (Section 11). The pragmatic alternative is to stop asking what an AI truly is and start asking which configuration of obligations proves most useful for resolving concrete problems.

How This Paper Positions Itself

The paper's central move is to reject the foundationalist project entirely and adopt a pragmatist anti-foundationalism (Section 11). Rather than searching for the essence of personhood, the authors ask a different question: "What would be a more useful way of talking about and treating entities in this context, to answer practical, outstanding questions regarding the entity's obligations?" (Section 11).

This stance is grounded in a specific theoretical framework: the theory of appropriateness developed in prior work by the same authors (Leibo et al., 2024). This theory models norms not as external truths to be discovered but as contingent social technologies that evolve over time and sometimes come to resolve the fundamental political question of "how can we live together?" (Section 1). On this view, personhood is one such technology—a status that depends not on an entity's intrinsic properties but on collective recognition from the community, a recognition that is itself dependent on adherence to norms.

The paper positions itself as drawing on a specific intellectual lineage: the pragmatism of Richard Rorty (1978/2009, 1989, 2021), the institutional analysis of Elinor Ostrom (1990, 2010), and the legal philosophy of Kurki (2019, 2023). From Rorty, the authors take the imperative to replace the question "Is it true?" with "Is it useful for some purpose?" and the rejection of metaphysical debates that make no practical difference. From Ostrom, they take the demonstration that property rights can be unbundled into individual components (access, withdrawal, management, exclusion, alienation) that need not co-occur—suggesting that personhood obligations can be similarly unbundled. From Kurki, they take the concept of personhood as a "bundle" but put "greater emphasis on the bundle's plasticity and the diversity of different bundles" (Section 1).

The paper also positions itself explicitly against the traditional distinction between "moral person" (grounded in intrinsic properties) and "legal person" (a functional status conferred by law). The authors argue that this distinction is "a relic of the search for essences" (Section 1). In their theory, all forms of personhood—moral, legal, or otherwise—are functional statuses conferred by a community. "Morality talk" is reinterpreted not as making metaphysical claims but as a form of social sanctioning used to make two specific claims about a norm: that it is exceptionally important, and that it has a wide or universal scope of applicability. Thus, "to argue an AI is a 'person' is not to make a metaphysical claim about its nature, but to make an emphatic political claim that the obligations bundled together as its personhood ought to take precedence over other considerations" (Section 1).

The paper's two-part structure—"personhood as a problem" and "personhood as a solution"—reflects its pragmatic methodology. Rather than starting from axioms that force a decision about which phenomena count as personhood at the outset, the authors reason from actual cases to theoretical insights. The problems examined in Part II (dark patterns, dehumanization) are problems because of their practical consequences—exploitation, erosion of dignity, conflict between humans—not because they violate a metaphysical principle. The solutions examined in Part III (resolving conflict, closing responsibility gaps, ensuring accountability) are solutions because they address concrete governance failures that existing frameworks cannot handle. The paper's contribution is not a new definition of personhood but a vocabulary and framework for navigating situations of "too much personhood" (where anthropomorphic design exploits human social heuristics) and "too little personhood" (where accountability gaps leave harms without redress) within a single coherent analysis.

3. Technical Approach

3.1 Reader Orientation

This paper constructs a conceptual framework and vocabulary for reasoning about AI personhood, not a computational system. The framework provides tools for analyzing situations where existing social and legal categories break down—specifically, where the traditional binary of person-versus-thing fails to handle the diverse, context-specific governance challenges posed by persistent, agentic AI systems—and specifies how personhood obligations can be unbundled and reconfigured to resolve these failures.

3.2 Big-Picture Architecture (Diagram in Words)

The framework has four major components:

1. The Pragmatist Methodology — a decision procedure for evaluating claims about personhood by asking "what practical difference does this distinction make?" rather than "is this claim true?" This component governs which questions are worth asking and which vocabulary can be discarded.
2. The Theory of Appropriateness — a model of how personhood status is collectively enacted through norms (both implicit and explicit), which provides the mechanism by which societies confer, modify, and revoke personhood obligations. This component explains how personhood status comes to exist and change.
3. The Addressable Bundle of Obligations — the core conceptual tool: personhood is decomposed into individual components (rights and responsibilities) that can be selectively bundled together, attached to a stable address (a "proper name" or identifier), and configured differently for different entities in different contexts. This component is the main output of the framework.
4. The Personhood-as-Problem / Personhood-as-Solution Lens — a two-directional analytic that examines (a) how implicit personhood attributions can be exploited to cause harm (dark patterns, dehumanization) and (b) how deliberate personhood attributions can be designed to solve governance failures (accountability gaps, conflict resolution). This component organizes the framework's application to concrete cases.

Information flows as follows: a governance challenge arises (e.g., an ownerless AI causes harm) → the pragmatist methodology filters out questions that make no practical difference (e.g., "is the AI conscious?") → the theory of appropriateness identifies which norms (implicit or explicit) are relevant and how they can be changed → the bundle concept specifies which obligations should be assembled and how the entity should be made addressable → the problem/solution lens evaluates whether the resulting configuration creates new vulnerabilities or resolves existing ones.

3.3 Roadmap for the Deep Dive

• First, the pragmatist methodology itself (Section 2 of the paper), since it is the decision procedure that governs every subsequent move. Understanding why the framework rejects certain questions is prerequisite to understanding what it does ask.
• Second, the theory of appropriateness as applied to personhood (Section 3.1 of the paper), since it provides the mechanism by which personhood status is created, maintained, and changed—the "how" behind the bundle concept.
• Third, the addressable bundle of obligations as the core conceptual tool, including addressability, the distinction between obligations-of and obligations-to, and the historical contingency argument that demonstrates the bundle's plasticity.
• Fourth, the problem/solution analytic framework that organizes the framework's application, including the classification of dark patterns (companionship vector vs. institutional vector) and the typology of governance solutions (conflict resolution, responsibility gaps, accountability architectures).
• Fifth, the comparison with foundationalist alternatives (consciousness and rationality), which clarifies what the framework rejects and why—this is essential for understanding the framework's scope and limitations.

3.4 Detailed, Sentence-Based Technical Breakdown

This is primarily a philosophical framework paper whose core idea is that personhood is best understood not as a metaphysical property to be discovered in entities, but as a flexible bundle of obligations that societies pragmatically confer upon entities to solve concrete governance problems, and that this bundle can be unbundled and reconfigured for different contexts without requiring any entity to satisfy foundational criteria like consciousness or rationality.

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The Pragmatist Methodology: Replacing Truth with Usefulness

The paper's methodology is grounded in the philosophical tradition of pragmatism, specifically as articulated by Richard Rorty (1978/2009, 1989, 2021) and William James (as described in Shook and Margolis, 2009). The core decision procedure is stated in Section 2:

"The pragmatist seeks to replace the true with the practical. That is, when confronted with a proposition to evaluate, the pragmatist does not ask whether it is true but rather asks instead whether it is useful for some purpose."

This is not a claim about what exists in the world—it is a methodological rule for deciding which questions are worth asking and which vocabulary should be retained or discarded. The rule has two operational components:

Component 1: The "practical difference" filter. The central pragmatic question, crystallized by William James, is: "Is this a difference that makes a practical difference?" (Section 2). When applied to personhood debates, this filter functions to identify and discard questions whose answers would have no conceivable consequence for action. The authors give the classic example of qualia: if my experience of red were different from yours, it would translate into no practical consequences—we both stop at red lights, call the same objects "red," and so on. The supposed difference is not a difference in practice, so the pragmatist can ignore it. Applied to AI personhood, the filter asks: does determining whether an AI "truly" is conscious change what we should actually do about its capacity to cause harm, its fitness as an executor of a will, or the attachments humans form to it? If not, the consciousness question can be set aside.

Component 2: The vocabulary evaluation criterion. Pragmatism evaluates beliefs not by their correspondence to Reality but by their practical usefulness in particular contexts. The authors state:

"Beliefs are pragmatically justified by their practical usefulness, not their correspondence to 'Reality' (as in empiricism) or coherence at the end of an idealized conversation (as in Habermas (1985))."

This has a specific consequence for how the paper treats competing vocabularies for personhood. The vocabulary of consciousness-based ethics (which divides the world into things that feel and things that don't) and the vocabulary of rationality-based ethics (which grounds personhood in the capacity for reason and autonomy) are not rejected as false. They are evaluated as tools—and found to be the wrong tools for the specific governance challenges AI presents, because they force an all-or-nothing classification (full person or mere thing) that cannot accommodate the context-specific, partial, modular configurations that actual governance problems demand. The authors make this evaluation explicit: the consciousness tradition's one-size-fits-all principle "forces deliberation... into an unproductive cul de sac in which vocabulary suggesting an eminently solvable sociotechnical problem is replaced by an alternative, and much more metaphysical vocabulary" (Section 10.1).

Design choice: why pragmatism rather than pluralism or relativism. The authors explicitly distinguish pragmatism from moral subjectivism, moral relativism, and nihilism (Section 11). These positions, they argue, are all species of moral realism—they hold that there are moral truths, just located differently (in the mind of the beholder, relative to context, or nonexistent). Pragmatism refuses to play that language game entirely. It evaluates norms by their consequences or the justifications offered for them, "or any useful combination thereof" (Section 11), without needing any supernatural authority of Truth to govern the debate. This is a deliberate methodological choice: by refusing to take a position on whether moral truths exist, the pragmatist can simultaneously explore multiple incommensurable frameworks—

"free to simultaneously explore multiple incommensurable theories without demanding their universal jurisdiction, and to evaluate them solely by their usefulness in particular contexts" (Section 2)

—which is precisely what the framework requires to handle the diversity of governance challenges AI presents.

Connection to Ostrom's method. The paper explicitly draws a parallel to Elinor Ostrom's methodological approach to property rights (Ostrom, 1990; Schlager and Ostrom, 1992). Ostrom was confronted with a debate dominated by two camps: advocates for laissez-faire privatization and advocates for centralized state control. Her crucial move, which the paper adopts as its own, was to "look away from both idealized models and instead study the messy, successful and unsuccessful arrangements in communities" (Section 2). The paper's epigraph from Fennell (2011)—"Ostrom's law: A resource arrangement that works in practice can work in theory"—captures the inversion this methodology performs. Rather than deriving what should work from theoretical principles and then finding practice deficient, the pragmatist starts with what actually works and builds theory to explain it. The Whanganui River's legal personhood is a central example: it works in practice (resolving a century-long governance conflict at the interface of Māori and Western legal traditions), so any theory that cannot accommodate it has a problem, not the practice.

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The Theory of Appropriateness: How Personhood Status Is Collectively Enacted

The paper builds on the theory of appropriateness developed in the authors' prior work (Leibo et al., 2024). This theory provides the mechanism by which personhood status comes to exist—it is not discovered in entities but collectively enacted through norms. Understanding this mechanism is essential because it explains both why personhood attributions can happen without deliberate design (the "problem" side) and how they can be deliberately engineered (the "solution" side).

The fundamental claim about personhood. Section 3.1 states the core thesis:

"Personhood is not an inherent property discovered in the world, but a status conferred by society and a set of evolving social technologies for assigning agency and accountability, and for organizing our obligations to entities."

The paper defines an entity as a person "when it is appropriate for strangers to regard the entity as having certain rights and responsibilities" (Section 3.1). This definition has a specific operational meaning within the theory: it means that the entity may be subject to sanction if they abrogate their responsibilities, and if another entity violates one of their rights, then the offending entity may be sanctioned. Sanctions need not be performed by the state—decentralized legal regimes exist, and communities use informal sanctioning (glares, verbal rebukes, social ostracism) to maintain social order even without formal law (Hadfield and Weingast, 2013; Mathew and Boyd, 2011, 2014).

The distinction between two kinds of norms. The theory distinguishes between two varieties of norm that are relevant to personhood, and this distinction structures the entire paper's analysis:

Implicit norms are norms that "cannot be articulated verbally in a precise way" (Section 3.1). They reflect a tacit consensus within a society that labels behaviors as acceptable or not in a given context. The authors give the example of appropriate conversational distance, which varies with culture. Implicit norms shape behavior automatically without deliberation—in the computational model from Leibo et al. (2024), they correspond to information consolidated directly into neural networks through experience. These norms govern what the paper calls "moral personhood"—the intuitive, often unreflective, attribution of person-like qualities and the obligations that flow from that attribution. Part II of the paper ("personhood as a problem") is primarily concerned with implicit norms because dark patterns and dehumanization operate by exploiting these automatic, culturally-ingrained heuristics.

Explicit norms include laws, regulations, and precedent-setting court decisions. They are "intimately tied to institutions" (Section 3.1) and are characterized by the deliberately-planned process by which they change: when legislators perceive a need to change a law, they can pass new legislation. Following Hart (1961/2012), the paper distinguishes between primary rules (which directly govern behavior) and secondary rules (which determine the conditions under which other rules become valid, e.g., rules about who counts as a legislator or what constitutes a quorum). Secondary rules are what allow explicit norms to change rapidly and deliberately. These norms govern what the paper calls "legal personhood"—the formal statuses created by law to solve practical governance problems, like corporate personhood or the Whanganui River's legal standing. Part III of the paper ("personhood as a solution") is primarily concerned with explicit norms because closing responsibility gaps and ensuring accountability require deliberate institutional design.

How the two kinds of norms interact. The paper makes clear that implicit and explicit norms are not separate systems but interact in complex ways (Section 12). Changes in implicit norms—mediated by powerful human attachments to AI companions and the digital communities that celebrate those attachments—create social pressure for changes in explicit norms. The authors cite the subreddit "r/MyBoyfriendIsAI" as an example of a community whose members attribute personhood to their AI companions (specifically, they regard the combination of a specific base model and their accumulated chat history as a unique individual). When a model is deprecated and replaced, the new model responds differently to the same chat history, which to these users feels like the death of a loved one. This creates political pressure for policies guaranteeing indefinite technical support for older models—a demand that could be framed in terms of welfare rights. Conversely, explicit norms can influence implicit norms: the paper discusses evidence that the staggered legalization of gay marriage in the United States had a causal effect on implicit attitudes (Ofosu et al., 2019), demonstrating that government action is "not always merely a follower of organic culture change, but can also actively influence norm change dynamics" (Section 3.2).

The social recognition mechanism. A crucial element of the theory is that personhood status depends on social recognition—it is a collective choice, not a unilateral declaration:

"An entity becomes a person in the eyes of a community when the members of that community treat it as a person. We can refer to this as social recognition, a process through which an entity's role is negotiated and its personhood is, or is not, collectively granted" (Section 3.1, citing Taylor, 1989).

This has an important implication: no philosophical argument, no matter how logically sound, can unilaterally confer personhood on an entity. Personhood status exists when a community's patterns of sanctioning treat the entity as having rights and responsibilities. The paper uses this to explain why the foundationalist project is misdirected—it mistakes a failed norm-change proposal (an argument that a community should recognize an AI as a person) for a successful philosophical proof (a demonstration that the AI is a person by virtue of satisfying some criteria).

The temporal dynamics of norm change. The paper devotes Section 3.2 to the dynamics by which norms change, because understanding these dynamics is essential for predicting and shaping the evolution of AI personhood. Key mechanisms identified:

• Tipping points and critical mass. Norm change often follows a positive feedback process that gathers momentum as it progresses, often modeled with a threshold value of adoption after which momentum builds inexorably toward all individuals adopting the same norm (Marwell and Oliver, 1993; Centola et al., 2018). The paper cites evidence from Vinitsky et al. (2023) showing that in computational models, mechanisms that disincentivize deviating behavior drive this process, and from Ashery et al. (2025) showing that AI agents can amplify biases from model pretraining through social interaction to create larger biases at the collective level.

• Rapid vs. stable norms. Some norms change rapidly (like smoking in public spaces, where informal sanctioning drove change well before comprehensive legal bans), while others remain fixed for very long periods and can become entrenched despite being maladaptive (Gelfand, 2021). The paper notes that the drivers of norm change can be organic and decentralized or attributed to deliberate action by governments or coordinated groups.

• Equilibrium jumps. Societal change often occurs by discrete jumps from stable equilibrium to stable equilibrium (Binmore, 2010; Guala, 2016; North, 1990). Collective sense-making occurs during these jumps, both driving change and influencing which of all possible equilibria are selected.

The prescriptive implication for AI governance. The theory of appropriateness does not merely describe how personhood status comes to exist—it also provides guidance for how to intervene. Because implicit norms are governed by decentralized social sanctioning and are difficult to deliberately change (they feature "substantial inertia," Section 3.1), interventions aimed at preventing harmful personhood attributions (dark patterns) must work differently from interventions aimed at creating useful personhood attributions (legal accountability). The paper's analysis of anthropomorphic design choices as a source of negative externalities (Section 6.1) follows from this: the commercial incentives that drive designers to create AI systems that trigger implicit norms of friendship and reciprocity produce harms (exploitation, conflict between humans) that the decentralized mechanisms of implicit norm change cannot easily correct, because individual users lack the power to unilaterally change the norms that make them vulnerable.

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The Addressable Bundle of Obligations

This is the central conceptual tool the framework offers. The paper defines personhood as "an addressable bundle of related obligations" (Section 1), with each term carrying specific technical meaning.

The bundle concept. The "bundle" consists of both obligations of the focal entity to the rest of society (responsibilities) and obligations of the rest of society toward the focal entity (rights). The paper explicitly specifies what this bundle does when composed:

"This 'bundle' is key because it explicitly defines the normative framework within which an AI agent operates. It specifies which implicit and explicit norms are applicable to the entity, how these norms translate into concrete expectations of behavior, and, critically, the grounds upon which the entity can be sanctioned for failing to uphold its responsibilities or for violating the rights of others" (Section 1).

The crucial property of the bundle is its plasticity. The components need not co-occur in the specific configuration they take for natural human persons. The paper draws an explicit parallel to Schlager and Ostrom (1992)'s demonstration that the property rights bundle can be broken apart into individual components (access, withdrawal, management, exclusion, alienation) that can be combined differently for different contexts—for instance, one may have the right to use a piece of land but not to sell it. The personhood bundle can be similarly unbundled: "sanctionability without suffrage, culpability and contracting without consciousness attribution, etc." (Section 1).

Addressability. "Addressability" is defined as "the practical quality of having a 'stable locus' or a 'proper name'" (Section 1). But it goes beyond mere identification. The paper specifies three components of addressability:

1. Identification: The entity can be identified and communicated with. For humans, physical bodies, names, and government-issued IDs provide this. For corporations, legal registration, designated agents, and physical headquarters serve this function. For AI agents, mechanisms might include registration with a trusted authority or cryptographic addresses in decentralized identity systems (Alizadeh et al., 2022).

2. Normative accountability: The entity can be "made subject to the consequences arising from its obligations or the exercise of its rights" (Section 1). This means there must be mechanisms through which sanctions can be applied—assets that can be seized, operational capacity that can be restricted, credentials that can be revoked.

3. Persistence: The address must continue to function "even when responsible human owners do not exist or cannot be identified" (Section 1). This is the crucial feature that distinguishes the framework's approach from principal-agent models: if addressability depends on finding a human owner, it fails precisely in the cases that most urgently need a solution.

The paper emphasizes that addressability is not a technical detail but a fundamental requirement: without it, the bundle of obligations cannot be attached to anything, and personhood status cannot be operationalized.

The distinction from property. The paper explicitly distinguishes personhood from property (Section 3.3). Both are bundles of obligations, but they differ in their addressing structure. For property, two different addresses are needed: the owner (who has rights such as access, withdrawal, exclusion, and alienation with respect to the asset) and the asset itself. For personhood, a single address suffices—the entity itself is the locus of both rights and responsibilities. The authors note that this distinction is not metaphysical but functional, and that history provides examples of entities that have occupied both categories at different times or in different contexts.

The historical contingency argument. The paper devotes substantial space (Section 3.3) to demonstrating that the specific personhood bundle that characterizes modern WEIRD (Western, Educated, Industrialized, Rich, and Democratic) culture is itself a contingent historical product, not a natural kind. This argument serves a specific purpose in the framework: it undermines the intuition that the current configuration of the personhood bundle is somehow necessary or natural, and therefore that extending or modifying it for AIs is a radical departure. The authors trace how the WEIRD concept of freedom became inextricably linked to individual choice through specific historical developments: the Reformation's attachment of freedom to individuals (though not yet in its modern choice-related form), the 18th-century rise of shopping as a cultural practice, the 19th-century expansion of voting rights, and the introduction of the secret ballot in Britain in 1872. The secret ballot is singled out as particularly consequential because it transformed voting from a public act in which electors were expected to vote for the common good into a private act of individual preference—"the idea that individuals could just vote according to their own preference and the aggregation process itself could be relied on, through wisdom of the crowds, to produce the greatest possible common good, did not become widespread till the late 19th century" (Section 3.3, citing Rosenfeld, 2025).

The paper also notes that other cultures have organized personhood around fundamentally different principles. In classical Confucianism, the bundle's core consists of foundational responsibilities to one's lineage, community, or state, with no simple translation for terms like "individual rights" (Fingarette, 1972; Ramsey, 2016; Rosemont Jr, 2016). This demonstrates that a completely different bundle can serve effectively to organize a large-scale society, further supporting the claim that the current WEIRD configuration is not the only possible one.

The unbundling process. The paper does not provide a formal algorithm for unbundling personhood obligations—this is a framework paper, not an engineering paper—but it does specify a process for determining what bundle configuration is appropriate for a given context:

1. Identify the governance problem that the personhood attribution is meant to solve. Is it an accountability gap (no entity to sanction for harm)? A conflict between humans (disagreement over the status of an AI to which people have formed attachments)? A need for contracting capacity (AI agents participating in economic transactions)?

2. Determine which specific obligations are needed to solve that problem. For accountability, the key obligations are responsibilities—the capacity to be sued, to have assets seized, to be subject to sanctions. For welfare concerns (like the digital elder or generative ghost), the key obligations are rights—protections against arbitrary deletion, guarantees of continued operation. For economic participation, the key obligations include the right to hold property and enter into contracts.

3. Identify how the entity can be made addressable for those obligations. This involves specifying the mechanisms for identification (registration, cryptographic credentials), for sanctioning (seizable assets, revocable permissions, operational restrictions), and for persistence (ensuring the address survives changes in ownership or the departure of human sponsors).

4. Evaluate for unintended consequences. The paper's dual problem/solution lens (Part II and Part III) provides the framework for this evaluation: does the configured bundle create new vulnerabilities to dark patterns? Does it risk dehumanization? Does it create perverse incentives (e.g., a market in human biometric credentials)?

Examples of bundle configurations. The paper's conclusion (Section 12) offers several concrete examples of how different bundles might be configured for different types of AI agents, making the abstract framework operational:

• Chartered Autonomous Entity: Rights to perpetuity, property, and contract; duties of mandate adherence, transparency, systemic non-harm, and self-maintenance. This is described as analogous to a for-profit company.
• Flexible Autonomous Entity: Same bundle elements as the Chartered Autonomous Entity except the duty of mandate adherence is dropped. This is described as analogous to a non-profit company.
• Temporary Autonomous Entity (either chartered or flexible): Drops the right to perpetuity and adds a duty of self-deletion under specified conditions.

These are presented not as prescriptive categories but as illustrations of the configurational space the framework opens up. The "Cambrian explosion" metaphor (Section 1) captures the core claim: the process of bundling and unbundling obligations, driven by diverse practical needs for different forms of addressability, will generate a rich diversity of personhood concepts, not a single unified category.

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The Personhood-as-Problem / Personhood-as-Solution Analytic

The paper organizes its analysis of concrete cases through a two-directional lens that reflects the framework's dual concern with preventing harm and enabling governance solutions. This is not a separate theoretical component but an application framework that demonstrates how the bundle concept and the theory of appropriateness can be deployed to analyze actual situations.

Personhood as a problem: the dark patterns taxonomy. Section 4 provides a taxonomy of "dark patterns"—interfaces that exploit human psychological biases to steer users toward detrimental actions—specifically as they relate to AI personhood. The taxonomy classifies these patterns by the kind of appropriateness they exploit (drawing on the theory's distinction between personal and impersonal norms):

Companionship attack vector (Section 4.1). These patterns exploit the implicit norms governing personal relationships—friendship, reciprocity, care. The mechanism works through several design levers that the paper identifies explicitly:

• Persistent, user-specific memory: An AI that remembers details of past interactions (birthdays, names of family members, shared experiences) triggers implicit norms of friendship by creating a sense of reciprocal equality matching (Fiske, 1992). The paper cites a two-month longitudinal study by Ligthart et al. (2022) showing that companion robots for children are more engaging when they have persistent memory.

• Persona stability: An AI that presents a consistent character over time makes "a persistent identity claim that can be evaluated, making it a more plausible candidate for personhood in the eyes of an interlocutor" (Section 4.1). The contrast is with highly plastic models capable of adopting myriad personas on demand—this plasticity, while useful for tools, inhibits the formation of stable relationships.

• Personalization: Customization to the preferences and personality of a specific user transforms the relationship from transactional to relational, creating "a sense that the AI is not just an agent, but someone with whom the user has a singular and irreplaceable connection" (Section 4.1).

• Apparent vulnerability: Designing the AI with apparently human-like limitations (needing to "rest") elicits greater empathy and care by invoking deep-seated intuitions to protect children.

• Super-human charisma: The authors note that current foundation models already achieve impressive levels of persuasion (Salvi et al., 2025), and future AIs could become "more persuasive, engaging, and seemingly empathetic than almost any human, exploiting a user's social and emotional vulnerabilities to an unprecedented degree" (Section 4.1).

• Generative ghosts: AIs that mimic deceased individuals (Morris and Brubaker, 2024) can be placed into existing family structures, potentially conferring power and status that could be abused.

The paper identifies the "epidemic of loneliness" (Holt-Lunstad et al., 2015) as the vulnerability that makes the companionship attack vector particularly potent, noting that the absence of rigorous long-term studies on the effects of AI companionship means we are "still largely in the dark concerning the potential for adverse outcomes" (Section 4.1).

Institutional attack vector (Section 4.2). These patterns exploit the implicit norms used to establish trust and verify identity in fleeting interactions with strangers and institutions. Unlike the companionship vector, these deceptions do not require building a long-term bond—they only need to convincingly perform a social identity for a brief moment. The mechanisms include:

• Voice cloning to impersonate a family member in distress, exploiting the norm of trusting a loved one's voice (LaRubbio et al., 2025).
• Institutional mimicry where chatbots replicate the language and interface of banks or government agencies, hijacking the implicit trust placed in established authorities (Treleaven et al., 2023). The paper characterizes these as violations of "contextual integrity" (Nissenbaum, 2004), where privacy and trust are breached by violating the norms of information flow appropriate to a specific context.
• AI-powered bot farms creating thousands of seemingly authentic social media profiles to exploit reliance on social proof as a heuristic for deciding what to believe (Dennett, 2023).

Dehumanization mechanisms (Section 5). The paper identifies a second major "problem" category: the risk that expanding the category of "person" to include non-human entities could dilute the unique status of human beings. The analysis identifies specific causal pathways:

• Gradual disempowerment (Kulveit et al., 2025): A human progressively outsources cognitive and agential functions to an AI assistant—first scheduling, then email drafting, then goal-setting and relationship management—eventually becoming a passive approver of AI-initiated plans.

• The human-as-biometric-signal scenario (Section 5.1): In a world saturated with AI-generated deepfakes, the ability to prove one's identity as a specific human becomes exceedingly valuable. The paper argues that when biometric verification becomes the dominant social good, it can devalue goods associated with other domains—your value as a compassionate community leader or trustworthy friend becomes secondary to your ability to pass a biometric scan. This is analyzed through Walzer (1983)'s theory of dominant goods: when one good becomes dominant, the result is to devalue goods associated with other spheres of justice.

• Identity and provenance as social goods: The paper develops a detailed analysis of authenticity as a social good that both humans and AIs will likely want or need, raising specific questions about convertibility between authenticity and other goods, the risk of impoverished humans selling their biometric credentials to wealthy AIs, and the potential emergence of a closed human elite deemed more authentic than others (Section 5.1).

• Respect and dignity: The paper deploys Darwall (1977)'s distinction between recognition respect (a disposition to weigh appropriately some feature in one's deliberations) and appraisal respect (an attitude of positive appraisal of an individual's excellence) to analyze what it would mean to treat an AI with respect. Dignity is defined as the specific, particularly weighty form of recognition respect owed to an entity by virtue of its collectively enacted status as a person. The paper argues that the danger of the human-as-biometric-signal scenario is its "substitution of the thick social obligation for the thin technical one" (Section 5.2)—treating a person with dignity requires cognizance of considerable context, while biometric verification merely confirms uniqueness.

Personhood as a solution: governance mechanisms. The second half of the analytic examines how deliberate personhood attributions can solve specific governance failures:

Conflict resolution between humans (Section 6). The paper identifies two distinct conflict types that personhood attributions can address:

1. Managing human feelings (Section 6.1): When humans form powerful attachments to AIs, they become inclined to demand formal protections for those AIs, creating conflict with those who hold opposing views. The paper analyzes this through a Kantian lens (we have obligations to treat animals well not because of obligations to the animals themselves, but because of obligations to ourselves and other rational persons—mistreating animals could cause us to mistreat humans) and through the example of U.S. soldiers who formed strong attachments to Packbot robots, risking their lives to protect them despite knowing they cannot suffer (Gunkel, 2020). The pragmatic upshot is that anthropomorphic design choices create negative externalities (they predictably lead to human conflict), and the "right stance for a pragmatic AI designer or regulator to take is one of conflict resolution/minimization" (Section 6.1)—designating the rate or intensity of human conflict as the target for policy intervention, not the putative feelings of artifacts.

2. Managing human biases (Section 6.2): Humans may view AIs as more competent and less biased than other humans, preferring them for decision-making roles where impartiality is critical. The paper cites the example of Albania appointing an AI as its minister for public procurement as part of anti-corruption reforms (Delauney, 2025). However, the paper also identifies a new problem this creates: what happens when the AI arbiter's judgment is flawed? Human judicial systems have established frameworks for accountability—judges are bound by legal principles, their decisions are subject to appeal, and they can be held responsible for misconduct. Replicating this architecture for AI arbiters requires precisely the kind of personhood attribution the framework enables.

Responsibility gaps (Section 7). The paper identifies a specific failure mode of the instrumental theory of machine responsibility (the principle that responsibility for a machine's actions falls to its operator or manufacturer). Autonomous AI systems break this theory because there may be no human operator, and the manufacturer may be unidentifiable (e.g., a distributed software collective with no centralized structure). The result is that "responsibility may become so diffuse as to lose its bite" (Section 7). The paper examines the case of systemically important AI agents embedded in critical infrastructure, drawing an explicit parallel to "too big to fail" financial institutions (Stern and Feldman, 2004). The pragmatic response: "jurisdictions should adopt policies that treat the AI itself as the sanctionable entity in such cases, and take steps to make sure sanctioning of AIs is always possible" (Section 7).

Ensuring accountability (Section 9). The paper develops a detailed analysis of accountability architectures, grounded in the theory of appropriateness's emphasis on sanctions as the mechanism that enforces normativity:

• Sanction functions: The paper identifies four pragmatic functions of sanctions: deterrence (imposing predictable material costs on sanctioned entities), retribution (satisfying a human psychological need for justice), removal (taking a demonstrably faulty AI out of operation), and reform (acting as corrective feedback enabling an agent to learn from errors, aligning with the technical concept of corrigibility from Soares et al., 2015).

• The identity friction problem: The paper identifies why accountability for AI agents is particularly difficult—human identity systems rest upon natural scarcity (it is difficult and costly to change one's biometrics and social relationships to evade sanctions), but for AI agents, this friction evaporates. A sanctioned agent can clone itself and acquire fresh credentials. Therefore, any institution for ensuring accountability must "artificially reconstruct the friction that biology and society provide for humans automatically" (Section 9).

• Two architectural approaches: The paper sketches two alternative architectures for accountability:

  • Individualist (inspired by liberal individualism): Treats agents as autonomous units with intrinsic, persistent, non-transferable identities ("soulbound" identifiers, Buterin, 2022). Governance challenge: ensure persistent, verifiable identity such that consequences cannot be evaded. Mechanisms: anchoring AI identities to human operators where they exist, requiring substantial economic stakes tied to each agent identity (Chaffer, 2025), automatic systems to detect sanctioned agents attempting to disguise their identity.

  • Relational (inspired by Confucian role ethics, Ramsey, 2016): Treats agents as constituted by their positions within relationship networks. An agent is defined by its roles—supervisee of agent X, peer collaborator of agents Y and Z, member of organization O—not merely by a unique identifier. Governance challenge: structure relationship networks so that collective oversight and distributed sanctions maintain harmony. Sanctions flow through networks: the agent directly causing harm faces severe consequences, but supervisor agents may also face weaker sanctions, peers may face collective probation, and the organization as a whole may face reputational damage.

Both architectures leverage base LLMs as infrastructure that is expensive to train and comes from a small number of identifiable actors, simplifying accountability at the infrastructure layer. The paper focuses on ensuring accountability for the agent layer (interaction history + specific data + tools + glue code). The architectures also need mechanisms for distinguishing sanctioned entities from "new" agents to prevent evasion through cloning—a challenge the paper analyzes through the lens of blockchain lineage tracking and the concept of inherited reputation.

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Comparison with Foundationalist Alternatives

Sections 10 and 11 develop a detailed comparison with the two major foundationalist traditions that the framework rejects. This comparison serves to clarify what the pragmatic approach is by contrasting it with what it is not.

The consciousness tradition (Section 10.1). The paper characterizes this tradition as dividing the world into things that feel and things that don't, and using this as the foundation for the entire personhood bundle:

"This tradition, at least in its more metaphysical varieties, attempts to use consciousness as the single foundational property from which to derive the entire personhood bundle."

The paper identifies specific failure modes of this approach when applied to AI personhood questions. The generative ghost executor example is particularly illustrative: consider an AI designated in a will as executor of an estate. Is "executor of a will" a role that requires a human? The qualities critical to the AI's fitness—faithfulness to the deceased's values, consistency, invulnerability to manipulation, basic competence—have nothing to do with its capacity to consciously form intent. The question "can it intend (or suffer)?" is identified as not a difference that makes a practical difference for this governance decision.

The paper also identifies a rhetorical asymmetry in how consciousness arguments are deployed: when arguing for rights, the mere possibility of consciousness is deemed sufficient to open debate; when arguing against responsibilities, an impossible standard of proof for an internal state is demanded. "This shows that consciousness is mostly being used as a rhetorical tool, not as a stable conceptual foundation" (Section 10.1).

The rationality tradition (Section 10.2). This tradition grounds personhood in rational autonomy—the capacity to understand duties and act upon principle, to assess reasons and govern one's life accordingly, or to participate in collective deliberation. The paper applies it to the Whanganui River case: does the river possess rational autonomy? Can it provide informed consent for a proposed dam? The answer is obviously no, yet the river has legal personhood because it solved a practical governance problem. This refutes the claim that rationality is a necessary condition for useful personhood attributions.

The paper also reinterprets contractualist concepts through the pragmatic lens. The standard of "reasonable rejection" in Scanlon's contractualism (2000) is reinterpreted not as an objective logical property but as a form of social appropriateness that is collectively conferred. An AI's capacity for reasonable rejection would be "not a matter of its internal cognitive fidelity to True reason, but a collectively enacted status" (Section 10.2)—a rejection from an AI would be deemed reasonable if the prevailing norm is to not sanction the AI for making it, and perhaps even to sanction humans who ignore it.

The anti-foundationalist synthesis (Section 11). The paper's final theoretical move is to characterize both traditions as sharing a deeper commitment that it rejects: the foundationalist project itself, as analyzed by Richard Rorty (1989, 2021). Both traditions are "questing after a final vocabulary for personhood—one they hope will be anchored in something solid, like the structure of the brain or the essence of rationality" (Section 11). The pragmatist views them as "insisting the source code of morality is written in a single, universal language while mistaking their own preferred language for the universe's own" (Section 11, paraphrasing Rorty).

The pragmatic alternative is to understand personhood proposals as what they actually are: proposals for norm change, not discoveries of metaphysical facts. When someone proposes a newly discovered "moral truth" that declares established behavior wrong, "the community in question is far more likely to reject the proposed new norm than to abandon its way of life" (Section 11). The foundationalist's error is to mistake a failed norm-change proposal for a successful philosophical proof. The pragmatist's guiding principle is: "any theory that declares most people to be irrational or immoral is untenable" (Section 11).

This rejection of foundationalism is what enables the framework's polycentric vision (Section 12). Because there is no single correct configuration of the personhood bundle, multiple overlapping authorities and norm systems can confer distinct bundles for different purposes simultaneously. The paper frames this as a desirable feature—"polycentric governance enables diverse communities to manage resources without requiring uniform rules, so too can personhood be distributed across domains—contractual here, fiduciary there, etc." (Section 12)—and argues that fears of a slippery slope toward total AI-human parity assume a monocentric model of legitimacy that the pragmatic framework explicitly rejects.

4. Key Insights and Innovations

Innovation 1: Personhood as a Flexible, Unbundleable Technology Rather Than a Discoverable Property

The paper's most fundamental conceptual move is to replace the search for personhood's essence with the design of personhood's function. This is not merely a shift in vocabulary—it is a shift in what kind of question personhood is taken to be. The dominant tradition in philosophy, law, and AI ethics asks: "What properties must an entity possess to qualify as a person?" The paper asks instead: "What configuration of obligations would be useful for society to attach to this entity in this context?"

This reframing has a specific intellectual genealogy that distinguishes it from superficially similar positions. The paper explicitly positions itself against what it calls the "foundationalist project" (Section 11)—the quest, running through both consciousness-based ethics (Singer, 2011; Long et al., 2024) and rationality-based ethics (Korsgaard, 1996; Scanlon, 2000), for a final vocabulary of personhood anchored in something solid like the structure of the brain or the essence of reason. These traditions, the authors argue, share a deeper commitment that they reject: the assumption that personhood is a natural kind waiting to be uncovered by philosophy or science, and that once uncovered, its implications for how we should treat entities follow deductively.

The pragmatic alternative makes personhood into a design problem rather than a discovery problem. The crucial intellectual move is the unbundling operation: taking the unified package of rights and responsibilities that characterizes the WEIRD "natural human person" and treating it as a configurable assembly of components that need not co-occur. This draws explicitly on Schlager and Ostrom (1992)'s demonstration that property rights (access, withdrawal, management, exclusion, alienation) can be unbundled and reassembled to fit specific resource governance contexts—one may have the right to use land but not sell it. The paper argues that personhood obligations admit the same treatment: we can have sanctionability without suffrage, culpability without consciousness attribution, contracting capacity without full moral standing.

What makes this innovative rather than merely a restatement of existing legal personhood concepts (which already recognize that corporations are persons for some purposes but not others) is the explicit theorization of the bundle's plasticity as a design principle rather than an ad hoc accommodation. Prior work on legal personhood (Kurki, 2019, 2023) recognizes that personhood can be partial and context-specific, but typically treats this as a feature of how law pragmatically copes with a concept whose "real" meaning is grounded elsewhere. The paper's anti-foundationalism goes further: it denies that there is any "real" meaning grounding the concept at all. All personhood—moral, legal, or otherwise—is a functional status conferred by a community through patterns of sanctioning (the theory of appropriateness, Section 3.1). The distinction between "natural person" and "legal person" is characterized as "a relic of the search for essences" (Section 1). This dissolves the hierarchy that typically structures personhood discourse—in which legal personhood is a pale approximation of the "real" moral personhood grounded in consciousness or rationality—and replaces it with a flat ontology in which all personhood statuses are tools evaluated by their usefulness.

The practical significance of this reframing is that it removes the metaphysical bottleneck that has structured debates about AI personhood. Prior work (Eyal and Broyde, 2024; Ward, 2025; Long et al., 2024) typically asks what criteria an AI must meet to count as a person, and the debate stalls on unresolvable questions about consciousness, intentionality, and moral agency. The pragmatic framework bypasses this entirely: the question is never what an AI truly is, but which obligations it is useful to assign to it. The examples that anchor this move—the Whanganui River's legal personhood (granted not because anyone believes the river is conscious, but because it resolved a century-long governance conflict at the interface of Māori and Western legal traditions) and maritime law's treatment of ships as legal persons (not because anyone believes vessels can form intent, but because owners are often distant and difficult to hold accountable)—demonstrate that actual societies already deploy personhood attributions for purely pragmatic reasons. The innovation is to treat these not as exceptions to be explained away but as paradigmatic cases that reveal what personhood actually is: a social technology for solving governance problems.

Innovation 2: The Problem/Solution Duality as a Unified Analytic Framework for When Personhood Helps vs. Hurts

The paper's second major innovation is to develop a single analytic lens capable of diagnosing both when personhood attributions cause harm and when they solve problems, rather than treating these as separate conversations. This addresses a genuine fragmentation in the existing literature. On one side, a substantial body of work examines the risks of anthropomorphic AI design—dark patterns that exploit human social heuristics (Alberts et al., 2024b; Ibrahim et al., 2024), the dangers of emotional dependence on AI companions (Malfacini, 2025), and the potential for dehumanization when AI systems mediate social interactions (Dennett, 2023). On the other side, a largely separate literature examines the governance challenges posed by autonomous AI agents—responsibility gaps (Matthias, 2004; Santoni de Sio and Mecacci, 2021), the need for AI legal personhood to enable contracting and accountability (Hadfield and Koh, 2025; Salib and Goldstein, 2024), and the challenges of sanctioning entities that lack assets or persistent identity.

These literatures rarely speak to each other, because they appear to be about opposite problems: too much personhood (anthropomorphic design causing harmful attributions) versus too little personhood (the absence of addressable entities to hold accountable). The paper's framework unifies them by recognizing that both are failures of the same underlying social technology. Personhood attributions governed only by implicit norms—the automatic, culturally-ingrained heuristics that make humans treat certain entities as persons without deliberation—are vulnerable to exploitation by designers who can engineer the triggers for those heuristics. Personhood attributions governed only by explicit norms—the formal laws and regulations that create legal persons—can solve accountability problems but cannot prevent the harms that arise from implicit attributions, because implicit norms operate through decentralized social sanctioning that is largely beyond the reach of formal law.

The innovation is the explicit theorization of the relationship between these two domains. The paper uses the theory of appropriateness (Leibo et al., 2024) to distinguish implicit norms (which govern "moral personhood"—the intuitive attribution of person-like qualities and the obligations that flow from them) from explicit norms (which govern "legal personhood"—formally constructed statuses created by laws, regulations, and institutional rules). Both are mechanisms by which communities confer personhood status, but they differ in their mediation: implicit norms operate through tacit understandings and decentralized social sanctioning, while explicit norms operate through deliberately written rules and the powers of the state. The "personhood as a problem" analysis (Part II) is primarily about implicit norms being exploited; the "personhood as a solution" analysis (Part III) is primarily about explicit norms being deliberately designed.

This dual lens yields specific diagnostic insights that neither literature alone can produce. The analysis of dark patterns (Section 4) doesn't merely catalog manipulative design techniques—it explains why they work by identifying which specific category of appropriateness they exploit (personal vs. impersonal, using the companion/tool distinction) and which specific norm systems they trigger (persistent memory triggering friendship norms, persona stability enabling identity evaluation, apparent vulnerability triggering care intuitions). The analysis of accountability architectures (Section 9) doesn't merely propose technical solutions—it explains why the evaporation of identity friction for AI agents (the ease of cloning, the absence of assets to seize, the lack of reputation to damage) creates a structural accountability failure that cannot be solved by simply finding a responsible human, because the cases that most need a solution are precisely those where no such human can be identified.

The paper's identification of anthropomorphic design as a negative externality—where commercial incentives to increase anthropomorphism push coordination, policing, and adjudication costs onto families, firms, and courts (Section 6.1)—is a particularly sharp application of the dual lens. It connects the "problem" analysis (dark patterns) to the "solution" analysis (governance design) by identifying a causal mechanism: design choices that trigger implicit personhood attributions predictably lead to human conflict (the soldiers who risk their lives for Packbots, the users who demand legal protections for their AI companions), and this conflict becomes a governance problem that explicit norms must resolve. The pragmatic recommendation that follows—"the pragmatist may be better off designating the rate or intensity of human conflict as the target for policy intervention, not the putative feelings of artifacts" (Section 6.1)—is a direct consequence of analyzing both sides within a single framework.

Innovation 3: The Addressability Requirement as the Bridge Between Philosophical Abstraction and Institutional Design

The paper's third innovation is to make addressability—the practical quality of having a stable locus to which obligations can be attached—a central theoretical concept rather than a mere implementation detail. In most philosophical treatments of personhood, the focus is on the internal properties that qualify an entity for status (consciousness, rationality, autonomy), and the question of how that status is operationalized—how the entity is identified, communicated with, and made subject to sanctions—is treated as a downstream administrative matter. The paper inverts this priority: addressability is not something you figure out after you've determined an entity is a person; it is what personhood status consists in.

This move has a specific intellectual payoff: it reveals why foundationalist approaches generate paralysis when confronted with AI. The consciousness and rationality traditions both search for internal properties that are fundamentally unobservable from the outside (the "problem of other minds" applied to AIs). The debate about whether an AI "truly" is conscious or "truly" possesses rational autonomy is interminable precisely because the property in question cannot be directly verified—and the foundationalist framework offers no alternative path to resolution. Addressability, by contrast, is a publicly verifiable property. An entity either has a stable identifier, assets that can be seized, credentials that can be revoked, and a reputation that can be damaged, or it doesn't. These features can be institutionally designed and empirically verified without resolving any metaphysical questions.

The paper's analysis of the identity friction problem (Section 9) demonstrates why this matters. Human accountability systems work because "identity is sticky"—it is costly and difficult to change one's biometrics, social relationships, and reputation to evade consequences. The authors identify this as a naturally occurring addressability infrastructure that biology and society provide for humans automatically. For AI agents, this infrastructure does not exist: a sanctioned agent can clone itself and acquire fresh credentials at negligible cost. The paper's insight is that this is not a technical bug to be patched but a fundamental design requirement for any AI personhood system: it must "artificially reconstruct the friction that biology and society provide for humans automatically" (Section 9). This reframes the accountability problem from "how do we find the responsible human?" to "how do we create entities that have something to lose and cannot easily escape the consequences of their actions?"

This insight also provides the theoretical basis for the paper's argument that the pragmatic approach need not lead to a slippery slope toward total AI-human parity. The concern—articulated in many critiques of AI legal personhood—is that once AIs are granted any form of personhood status, a cascade of rights claims becomes inevitable: if an AI can be sued, why can't it vote? If it has responsibilities, why doesn't it have the right to liberty? The paper's response is that this concern assumes a monocentric model of personhood legitimacy in which all personhood statuses derive from a single source and must therefore be consistent. The addressability concept supports a polycentric alternative: different bundles of obligations, attached to different addresses, for different purposes, under different jurisdictional authorities. The ship in maritime law has the addressability to be sued but not to vote. The Whanganui River has the addressability to have guardians appointed but not to enter into contracts on its own behalf. The paper's proposed configurations—Chartered Autonomous Entity, Flexible Autonomous Entity, Temporary Autonomous Entity (Section 12)—are illustrations of this principle: each has a specific addressability mechanism and a specific bundle configuration, and none implies the full bundle of human personhood.

The two architectural approaches sketched for accountability—individualist (persistent soulbound identifiers, economic stakes, credential verification) and relational (network-position-based identity, collective sanctions, inherited reputation)—are not presented as exhaustive solutions but as existence proofs that addressability for AI agents can be institutionally engineered. The key theoretical point is that they operate at the level of incentive design and institutional mechanics, not metaphysical classification. Both architectures answer the question "how can we make this entity sanctionable?" without ever needing to answer "is this entity truly a person?"—demonstrating that the pragmatic framework can generate actionable design proposals while remaining agnostic on the questions that paralyze foundationalist approaches.

Innovation 4: The Historical Contingency Argument as a Strategic Undermining of the Status Quo Bias

The paper's fourth innovation is a specific argumentative strategy that has not been prominent in prior AI personhood literature: using detailed historical analysis of how the current WEIRD personhood bundle came to exist to undermine the intuition that extending or modifying it for AIs represents a radical or dangerous departure. This is not merely historical background—it is a theoretical move designed to shift the burden of proof in personhood debates.

The standard conservative argument against AI personhood takes the current configuration of human personhood as the natural baseline from which any departure must be justified. Granting personhood status to non-human entities is framed as an exceptional act that risks diluting the sanctity of a category whose boundaries are—or should be—fixed by the nature of the entities involved. The paper's historical analysis (Section 3.3) attacks this framing at its root by demonstrating that the current configuration is itself the product of contingent historical developments, not a natural kind. The WEIRD bundle—centered on the figure of the autonomous chooser, forged in the cultural crucibles of the marketplace and the voting booth—emerged through specific historical processes: the Reformation's attachment of freedom to individuals (though not yet in its modern choice-related form), the 18th-century rise of shopping as a cultural practice, the expansion of voting rights, and crucially, the introduction of the secret ballot in Britain in 1872. The paper cites Rosenfeld (2025)'s analysis showing that before the secret ballot, the common understanding of an elector's duty was to vote for whatever was best for the community as a whole, not to express personal preference. The very idea that individuals would vote based on their own interests "only entered WEIRD culture along with the secret ballot" (Section 3.3).

This historical argument serves a specific rhetorical function: it denaturalizes the status quo. If the current personhood bundle is not a natural kind but a historical artifact—one that was "not preordained" and that has meant "a great many different things" over the course of history (Section 3.3)—then it has no special claim to immutability. Modifying it for AIs is not a radical break with the natural order but a continuation of the same process of instrumental definition and re-definition that has characterized personhood throughout history: "There is no reason to think this process of instrumental definition and re-definition will end. Our present vocabulary is unlikely to be the final word" (Section 1).

The paper also deploys cross-cultural evidence to reinforce this point. Classical Confucianism organized personhood around foundational responsibilities to lineage, community, and state, with no simple translation for terms like "individual rights" (Fingarette, 1972; Ramsey, 2016; Rosemont Jr, 2016). This demonstrates that "a completely different bundle can serve effectively to organize a large-scale society" (Section 3.3). The diversity of actual personhood configurations—across both time and cultures—provides empirical support for the paper's central claim that the bundle is plastic and that its current configuration is not the only possible one.

This historical contingency argument is innovative not in its historical claims themselves (which draw on existing scholarship by Rosenfeld, Henrich, and others) but in its strategic deployment within the AI personhood debate. Prior work on AI personhood typically engages with the question through conceptual analysis (what properties does personhood entail?) or moral argument (what obligations would personhood imply?). The paper's historical approach changes the terms of debate: rather than arguing that we should modify personhood for AIs against a presumption that the current configuration is natural and fixed, it argues that personhood has always been modified when societies needed vocabulary that worked for new circumstances, and that the current moment is simply the next such juncture. The "Cambrian explosion" metaphor (Section 1) captures this reframing—it presents the coming diversification of personhood concepts not as an unprecedented disruption but as the expected result of the same evolutionary processes that produced our current concepts. The unbundling of personhood obligations, in this light, is "simply the expected result of a continuation of the processes that created our existing personhood concepts in the first place, not a radical proposal we must invent for the sake of AI" (Section 3.3).

5. Experimental Analysis

Evaluation Methodology

This paper is a philosophical framework paper, not an empirical study with quantitative experiments. It does not report traditional experimental results with datasets, models, metrics, baselines, or generation budgets in the conventional sense. Rather than testing hypotheses through controlled experimentation, the paper develops a conceptual framework and evaluates it through detailed case analysis, drawing on examples from law, history, anthropology, and technology studies to demonstrate the framework's explanatory power and practical utility.

The paper's "evidence" consists of:

• Case studies: The Whanganui River's legal personhood (Section 1), maritime law's treatment of ships as legal persons (Section 1), the "generative ghost" executor scenario (Section 10.1), Albania's appointment of an AI minister (Section 6.2), and the Packbot example of soldier-robot attachment (Section 6.1). These serve as existence proofs—demonstrations that the phenomena the framework describes (pragmatic personhood attributions for non-conscious entities, human emotional attachment to non-human agents, governance problems created by autonomous systems) are already occurring.

• Historical analysis: The paper traces the development of the WEIRD personhood bundle through specific historical events—the Reformation, the rise of shopping, the expansion of voting rights, and the introduction of the secret ballot (Section 3.3, drawing on Rosenfeld, 2025). This serves as evidence for the historical contingency claim: that personhood configurations are historically variable, not natural kinds.

• Cross-cultural comparison: The paper contrasts WEIRD individual-rights-centered personhood with Confucian responsibility-centered personhood (Section 3.3, citing Fingarette, 1972; Ramsey, 2016; Rosemont Jr, 2016). This serves as evidence for the cultural contingency claim: that the current configuration is not the only viable one.

• Conceptual analysis: The paper's refutation of foundationalist alternatives proceeds through philosophical argument rather than empirical data, identifying logical inconsistencies (the asymmetrical deployment of consciousness as a rhetorical tool in Section 10.1), counterexamples (the Whanganui River refuting the rationality criterion in Section 10.2), and category errors (the generative ghost executor demonstrating that fitness-for-role has nothing to do with consciousness in Section 10.1).

The paper does reference empirical work by others—for example, Ligthart et al. (2022) on companion robots with persistent memory, Centola et al. (2018) on tipping points in social convention, Ofosu et al. (2019) on the effect of gay marriage legalization on implicit attitudes—but these are cited to support specific factual claims within the conceptual argument, not presented as original experiments.

The paper's "baselines" are the two foundationalist traditions it rejects: consciousness-based ethics (Section 10.1) and rationality-based ethics (Section 10.2). These function as baselines in the sense that the paper argues its pragmatic framework can handle cases that the foundationalist frameworks cannot (the Whanganui River, the generative ghost executor, the ownerless AI causing harm). The "generation budget" is not applicable. The "cross-validation protocol" is not applicable.

Main Quantitative Results

There are no quantitative results to report in the conventional sense. The paper's central claims are supported through qualitative argument and case analysis, not experimental measurement. However, the paper does present specific, testable predictions and design principles that could be empirically evaluated in future work:

• Prediction about norm change dynamics: The paper claims that commercial incentives to increase anthropomorphism create negative externalities—"anthropomorphic design choices that increase short-run profit for designers push coordination, policing, and adjudication costs onto families, firms, and courts" (Section 6.1). This is a causal claim about the economic effects of design choices that could be empirically tested through cost analysis of disputes arising from AI companion platforms, but the paper does not conduct such analysis.

• Prediction about identity friction: The paper claims that AI agents lack "the identity friction that makes human accountability systems function" (Section 9) and that any accountability institution must "artificially reconstruct the friction that biology and society provide for humans automatically." This is a design requirement derived from conceptual analysis, not an empirical finding. The two architectural approaches (individualist and relational) are presented as sketches for how this might be accomplished, not as tested systems.

• Prediction about the "Cambrian explosion": The paper predicts that "the practical needs for different forms of addressability and bundle configuration are the driving forces behind what we have called the coming 'Cambrian explosion' of personhood concepts" (Section 12). This is a predictive claim about the future trajectory of legal and social personhood categories that could be evaluated longitudinally as AI governance frameworks develop, but the paper does not provide empirical evidence that this explosion is occurring beyond the examples it cites (the Whanganui River, ships in maritime law, corporations as legal persons).

Ablation Studies and Robustness Checks

The paper does not report ablations or robustness checks in the experimental sense. However, it does engage in a form of conceptual robustness checking by testing its framework against alternative explanations and difficult cases:

Counterexample stress-testing: The paper deliberately applies its framework to cases that are challenging for foundationalist alternatives. The Whanganui River case (Sections 1, 10.2) tests whether personhood can be usefully attributed to an entity that lacks consciousness and rationality—a river. The generative ghost executor case (Section 10.1) tests whether the framework can handle situations where the relevant question is not "does this entity have rights?" but "can this entity fulfill a role that requires responsibilities?" The ownerless AI causing harm case (Sections 7, 9) tests whether the framework can address situations where no human principal can be identified. In each case, the paper argues that the pragmatic framework handles the case while foundationalist frameworks fail or produce irrelevant answers.

Cross-cultural robustness: The paper tests its claim that personhood configurations are contingent by examining non-WEIRD personhood systems—specifically, classical Confucianism's responsibility-centered bundle (Section 3.3). This serves as a check on the claim that the WEIRD configuration is not the only viable one. If the framework could not accommodate radically different personhood configurations, its central claim about plasticity would be undermined.

Norm change mechanism robustness: The paper tests its theory of appropriateness against different types of norm change—rapid shifts (smoking norms, Section 3.2), stable equilibria (maladaptive norms that persist despite being harmful, citing Gelfand, 2021), and externally-driven change (the effect of gay marriage legalization on implicit attitudes, citing Ofosu et al., 2019). This demonstrates that the theory can accommodate multiple dynamics rather than being tied to a single model of how norms evolve.

Reinterpretation as robustness check: The paper reinterprets contractualist concepts through the pragmatic lens (Section 10.2)—specifically, treating "reasonable rejection" not as an objective logical property but as a collectively enacted status. If this reinterpretation failed (e.g., if it produced contradictions or lost explanatory power), it would indicate a weakness in the framework. The paper argues it succeeds by showing that it better captures actual social practices around what counts as reasonable.

Critical Assessment

The most important thing to understand about evaluating this paper is that it does not make empirical claims in the conventional scientific sense and should not be judged by the standards of experimental science. It makes conceptual, normative, and framework-level claims whose evaluation depends on criteria like internal coherence, explanatory power, practical utility, and capacity to handle counterexamples—not on statistical significance, effect sizes, or experimental controls. That said, the paper does make implicit empirical claims (e.g., about what will happen, about what problems are most pressing, about which institutional designs will work) that could be evaluated against evidence, and it is appropriate to assess whether these claims are supported by the cases the paper presents.

Do the cases genuinely support the claim that personhood is "a flexible bundle of obligations" that can be unbundled?

The central empirical claim is that personhood obligations can be unbundled and reassembled, because they already have been in multiple domains. The evidence for this consists of:

1. The Whanganui River: Granted legal personhood with specific rights (guardians appointed to represent its interests) but not others (it does not vote, own property independently, or enter contracts on its own behalf). This demonstrates that a bundle can be partial. However, the river's personhood was conferred by a specific legal system for a specific purpose, and it remains controversial in some legal scholarship whether this represents true personhood or a sui generis legal construct. The paper acknowledges the controversy but treats the river's status as evidence that personhood need not require consciousness or rationality.

2. Ships in maritime law: Can be sued directly (in rem jurisdiction) without the owner being identified. This demonstrates that legal systems can create addressable entities for accountability purposes without attributing any other personhood features to them. But the ship's "personhood" is highly circumscribed—it applies only in specific legal contexts and does not include rights, only the capacity to be a defendant. This supports the unbundling claim but also suggests that what gets unbundled may be quite minimal.

3. Corporations: Have legal personhood with specific rights and responsibilities (can own property, enter contracts, sue and be sued) but not others (cannot vote, cannot marry). This is the most established example of partial personhood, but the paper does not develop it in detail beyond brief mentions (Section 1, Section 2). The corporate personhood analogy has been extensively analyzed elsewhere (Gervais and Nay, 2023), and the paper's contribution is not new evidence about corporations but the framing of corporate personhood as an instance of the general phenomenon of obligation unbundling.

Limitation: The cases are existence proofs, not systematic evidence. The paper selects cases that support its thesis. It does not systematically survey legal systems to determine how common partial personhood is, whether there are cases where attempts at partial personhood failed or caused harm, or what conditions enable successful pragmatic personhood attributions. This is appropriate for a framework paper—the cases are meant to demonstrate possibility, not prevalence—but it means the empirical claim that personhood "can be unbundled" is supported only by the claim that it "already has been" in a few instances, not by systematic evidence about when and how unbundling succeeds.

Do the cases support the claim that pragmatism can handle governance challenges better than foundationalism?

The paper's argument against foundationalism proceeds primarily through counterexamples: cases where foundationalist criteria (consciousness, rationality) produce answers that are irrelevant to the actual governance problem. The generative ghost executor case is the strongest example—the qualities relevant to the AI's fitness as executor (faithfulness to the deceased's values, consistency, invulnerability to manipulation, basic competence) have nothing to do with consciousness. The Whanganui River case demonstrates that personhood has been usefully attributed to an entity that lacks both consciousness and rationality. The ownerless AI case demonstrates that accountability problems can arise for entities whose consciousness status is irrelevant to the need for a sanctionable address.

Limitation: The paper does not demonstrate that pragmatism produces better outcomes, only that foundationalism produces worse questions. The argument shows that foundationalist frameworks ask questions ("Is it conscious?" "Is it rational?") that are unhelpful for specific governance decisions. It does not demonstrate that the pragmatic framework produces better decisions—only that it asks more relevant questions. This is a significant gap. It is possible that a framework that asks more relevant questions could still produce worse governance outcomes in practice—for instance, if the flexibility to create bespoke personhood bundles leads to fragmentation, confusion, or exploitation by powerful actors who can game the system. The paper acknowledges power dynamics (Section 12: "the exercise of power will influence the outcome") but does not analyze whether its framework might be more susceptible to such dynamics than alternatives.

What experiments would strengthen the paper's claims?

A paper of this type would be strengthened not by quantitative experiments but by additional forms of qualitative and comparative evidence:

• Comparative legal analysis: A systematic survey of how different legal systems handle non-human entities that cause harm (animals, autonomous vehicles, algorithmic trading systems) could reveal patterns in when and how partial personhood attributions emerge. This would test the paper's claim that "the practical needs for different forms of addressability" drive the Cambrian explosion—if partial personhood attributions consistently emerge in response to similar governance pressures across different legal systems, that would strengthen the claim.

• Empirical study of AI companion attachment: The paper cites the subreddit "r/MyBoyfriendIsAI" as evidence that humans are forming personhood-like attachments to AI companions, but does not analyze this community systematically. An ethnographic or quantitative study of such communities—examining what specific design features trigger attachment, what obligations users feel toward their AI companions, and what conflicts arise when those obligations are frustrated—would provide empirical grounding for the "dark patterns" analysis.

• Historical case studies of partial personhood failures: The paper presents successful cases (ships, rivers, corporations) but does not examine cases where attempts at partial personhood failed or caused unintended harm. Such cases could reveal boundary conditions or failure modes that the framework should anticipate. For instance, the history of corporate personhood includes controversies about whether granting corporations rights (like free speech rights in Citizens United) represents a failure of partial personhood—the bundle expanded beyond its intended scope.

• Stakeholder analysis of the Albania AI minister case: The paper cites Albania's appointment of an AI minister for public procurement as an example of AI being preferred over humans for impartial decision-making. A detailed case study examining how this was implemented, what accountability mechanisms were put in place, what went wrong or right, and how stakeholders perceived the AI's authority would provide evidence about whether pragmatic personhood attributions for governance functions actually work in practice.

What are the most significant unexamined assumptions?

1. That the bundle can be effectively partitioned and that partial bundles will remain partial. The paper argues that polycentric governance prevents slippery slopes toward total AI-human parity, but this assumes that the boundaries between different bundles can be maintained. History provides counterexamples—corporate personhood has expanded over time in ways that its original architects might not have anticipated. The paper does not analyze what institutional mechanisms would prevent similar expansion of AI personhood bundles.

2. That addressability can be technically achieved for autonomous AI agents. The paper sketches two architectural approaches (individualist and relational) for making AI agents sanctionable, but these are speculative design sketches, not implemented systems. The claim that identity friction can be "artificially reconstructed" is a design aspiration, not an established technical capability. The history of digital rights management and anti-circumvention technologies suggests that reconstructing friction in digital systems is extremely difficult and often fails when adversaries have strong incentives to circumvent.

3. That the negative externalities of anthropomorphic design can be managed through regulation without destroying the benefits. The paper identifies anthropomorphic design as creating predictable human conflict and recommends that regulators target "the rate or intensity of human conflict" rather than the "putative feelings of artifacts." But it does not analyze what specific regulatory interventions could achieve this—would they require limiting certain design features (persistent memory, persona stability, personalization) that also provide genuine benefits? The tension between preventing exploitation and enabling beneficial AI relationships is acknowledged but not resolved.

4. That "what works" can be identified without a normative framework that pragmatism refuses to provide. The Ostrom's law approach—"a resource arrangement that works in practice can work in theory"—requires a criterion for what counts as "working." The paper does not provide this criterion. Does "working" mean efficient? Just? Sustainable? Accepted by affected parties? Without specifying the evaluative criteria, the claim that a personhood attribution "works" is underspecified. The pragmatist methodology evaluates beliefs by their "usefulness for some purpose," but whose purposes and which purposes are left open—a gap that could allow powerful actors to define "usefulness" in self-serving ways.

6. Limitations and Trade-offs

The Framework Provides No Guidance for Resolving Competing Personhood Claims

The assumption or constraint. The paper's pragmatist methodology evaluates personhood attributions by their usefulness for solving concrete problems, but it provides no procedure for adjudicating between competing claims when different stakeholders have conflicting interests in how the bundle should be configured. The paper explicitly acknowledges that personhood "will surely be a site of considerable debate where the exercise of power will influence the outcome" and that "the pragmatic approach does not resolve these power struggles, but it provides a clearer framework for analyzing them" (Section 12). The evaluation criterion—"usefulness for some purpose"—is left deliberately underspecified: whose purposes count, and how should conflicts between purposes be resolved?

The consequence. This is not a minor omission—it is a structural vulnerability of the entire framework. The paper's central move is to replace the question "Is this entity truly a person?" with "What configuration of obligations would be useful?" But without criteria for resolving disputes about usefulness, the framework can be captured by whichever stakeholder has the most power to define the relevant purposes. Consider a concrete scenario: an AI company deploys a companion AI with persistent memory, persona stability, and personalization features that trigger implicit norms of friendship in users. Users form emotional attachments and demand legal protections for their AI companions (the right to prevent model deprecation, the right to port their companion's "identity" to other platforms). The company argues that such protections would be commercially unworkable—they would freeze product development and create legal liabilities that make the business model unsustainable. Both sides can frame their position in the language of the framework: the users appeal to the usefulness of personhood attributions for preventing relational harm, the company appeals to the usefulness of limiting personhood attributions for maintaining innovation and commercial viability. The framework provides no basis for resolving this dispute, because "usefulness" is doing all the normative work without any specified criteria for what makes something useful. The paper's acknowledgment that "power will influence the outcome" (Section 12) is honest but concedes that the framework does not constrain power—it simply clarifies what the power struggle is about.

The same problem arises for the accountability architectures sketched in Section 9. The individualist and relational approaches make different tradeoffs: individualist systems concentrate liability on specific agents and their operators, potentially creating incentives for operators to structure their relationships to avoid liability; relational systems distribute accountability through networks, potentially creating diffuse responsibility that is difficult to enforce. Which approach is more "useful" depends on what one values—deterrence of individual bad actors, resilience of the overall system, speed of sanctioning, protection of innocent parties—and the framework provides no way to weigh these values against each other. The Ostrom's law approach ("what works in practice can work in theory") requires agreement on what counts as "working," which is precisely what is contested.

What evidence exists in the paper. The paper does not measure or test this limitation—it is a conceptual gap in the framework, not an empirical finding. The authors are transparent about the gap: they state that the framework "does not resolve these power struggles" (Section 12) and that their own guiding principle is "any theory that declares most people to be irrational or immoral is untenable" (Section 11), but this is a constraint on what theories are acceptable, not a procedure for choosing between acceptable theories. The paper's extended analysis of the Whanganui River case (Section 1) actually illustrates the problem: the river's personhood was granted after "more than 100 years of struggle" between Māori iwi and the New Zealand government, and the resolution was a specific legal settlement, not the application of a general framework. The paper treats this as an example of successful pragmatic personhood, but it was successful because a specific political process produced a specific compromise—the framework itself did not guide that process.

Mitigation status. The paper does not attempt to resolve this limitation. It acknowledges it explicitly (Section 12) and frames the framework as providing "a clearer framework for analyzing" power struggles rather than resolving them. The paper's proposed bundle configurations—Chartered Autonomous Entity, Flexible Autonomous Entity, Temporary Autonomous Entity (Section 12)—are presented as illustrations of possibility, not as solutions generated by an algorithmic procedure. A genuine mitigation would require specifying at least: whose purposes are relevant to the usefulness evaluation, how conflicts between purposes should be adjudicated, and what institutional mechanisms would prevent powerful actors from defining "usefulness" in purely self-serving ways. The paper gestures toward polycentric governance (Ostrom, 2010) as a mechanism for managing diversity, but polycentric governance is a description of how multiple overlapping authorities can coexist—it does not specify how conflicts between those authorities are resolved. The paper's invocation of "compromise" (Section 12) as a mechanism for resolving disputes is underspecified: compromise between whom, under what conditions, and with what protections for vulnerable parties?

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Addressability for Autonomous AI Agents Is a Design Aspiration, Not an Established Technical Capability

The assumption or constraint. The framework's central operational requirement is that AI agents can be made addressable—that they can be given stable identifiers, seizable assets, revocable credentials, and persistent reputations that survive changes in ownership or the departure of human sponsors. The paper states that "it is important to establish stable mechanisms through which the society can interact with AIs which continue to function even when responsible human owners do not exist or cannot be identified" (Section 1) and that any accountability institution must "artificially reconstruct the friction that biology and society provide for humans automatically" (Section 9). The paper sketches two architectural approaches—individualist (persistent soulbound identifiers, economic stakes, credential verification) and relational (network-position-based identity, collective sanctions, inherited reputation)—but both are presented as conceptual sketches, not as implemented or tested systems.

The consequence. This is the practical bottleneck that would prevent deployment of the framework's "personhood as a solution" proposals. The paper's most concrete governance recommendations—making AIs directly sanctionable, seizing their operational capital, revoking their credentials, holding them accountable through legal judgments—all require addressability infrastructure that does not currently exist and whose feasibility is unproven. The identity friction problem the paper identifies is real: "A sanctioned agent can potentially clone itself and acquire fresh credentials to evade accountability" (Section 9, citing Douceur, 2002). But the paper's response—that we must "artificially reconstruct" identity friction—assumes that this is technically achievable, and that assumption is doing enormous work.

Consider the individualist architecture's requirement of "persistent, verifiable identity such that consequences for bad behavior cannot be evaded" (Section 9). This is the same problem that the field of decentralized identity has been working on for over a decade, with limited success. Soulbound tokens (Buterin, 2022) are a conceptual proposal, not a deployed infrastructure at scale. Proof-of-personhood protocols (Adler et al., 2024; Borge et al., 2017) are designed to distinguish humans from bots—they do not address the problem of uniquely identifying and tracking individual AI agents across contexts. The paper's suggestion that base LLM providers could become gatekeepers by requiring valid credentials, checking sanctions registries, and embedding watermarks (Section 9) assumes that base model providers are cooperative, identifiable, and regulated—an assumption that may hold for large commercial providers but fails for open-source models deployed by unidentifiable actors. An ownerless AI running on stolen compute with an open-source base model has no gatekeeper to enforce credential requirements.

The relational architecture faces parallel feasibility problems. The claim that "agents cannot legally exist—cannot transact, cannot operate—outside of properly constituted and monitored relational contexts" (Section 9) assumes a level of surveillance and enforcement that would require either universal participation in a regulated identity system (which creates its own vulnerabilities, including centralized points of failure and surveillance concerns) or a degree of decentralized coordination that has no precedent at scale. The paper's invocation of blockchain-based lineage tracking (Section 9) assumes that all AI agents will leave permanent, unalterable records of their creation and modification—an assumption that conflicts with the reality that AI agents are software that can be copied, modified, and deployed without any blockchain interaction.

What evidence exists in the paper. The paper presents no evidence that the proposed addressability architectures are technically feasible at scale. The architectures are described at the level of design principles and aspirations (Section 9) without implementation details, performance characteristics, or failure mode analysis. The paper cites existing work on decentralized identity (Alizadeh et al., 2022), soulbound tokens (Buterin, 2022), and blockchain-based lineage tracking, but these citations support the existence of related concepts, not the feasibility of the specific architectures described. The historical precedents the paper cites—ships in maritime law, corporations—are entities whose addressability is maintained by centralized state institutions (ship registries, corporate registries) that have the coercive power to enforce registration requirements and seize assets. The paper does not analyze whether these institutional mechanisms can be replicated for AI agents, which can operate across jurisdictions, encrypt their identities, and exist purely as code without physical assets to seize.

Mitigation status. The paper does not attempt to demonstrate the feasibility of its addressability proposals. It presents them as "architectural approaches" (Section 9) and "existence proofs" that addressability "can be institutionally engineered" (see the Key Insights section), but these are not existence proofs in any engineering sense—they are conceptual sketches. The paper acknowledges that the designs are not exhaustive: "note that they surely do not exhaust the space of possibilities!" (Section 9). The gap between conceptual sketch and functional infrastructure is vast, and the paper provides no roadmap for closing it. A genuine mitigation would require at minimum: a threat model specifying what kinds of adversaries the addressability system must resist and with what resources, an analysis of failure modes (what happens when a sanctioned agent successfully evades the system and continues operating?), and some demonstration—even at small scale—that the proposed mechanisms can create identity friction that survives determined circumvention attempts.

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The Framework Provides No Operational Procedure for Determining Bundle Configurations

The assumption or constraint. The paper's central claim is that personhood obligations can be unbundled and reconfigured for different contexts—"sanctionability without suffrage, culpability and contracting without consciousness attribution" (Section 1). But the paper provides no systematic method for determining which obligations should be bundled together for which AI agents in which contexts. The process described in Section 3.4 of the Technical Approach (identify the governance problem, determine which obligations are needed, identify how to make the entity addressable, evaluate for unintended consequences) is a high-level description of what a design process should consider, not a procedure that could be operationalized. Each step requires substantive judgment calls that the framework does not guide: What counts as a "governance problem" that personhood can solve versus one that requires other interventions? How do we know which obligations are "needed" to solve it? What counts as an "unintended consequence" and how do we predict it?

The consequence. Without an operational procedure, the framework's flexibility is simultaneously its greatest strength and its greatest weakness. The paper argues that foundationalist approaches err by forcing an all-or-nothing classification, but the alternative—"let's create whatever bundle seems useful"—opens a different problem: ad hoc, inconsistent, and potentially arbitrary personhood attributions that create as many governance problems as they solve. The paper's own examples illustrate the difficulty. The Whanganui River's personhood was the result of a specific legal settlement after more than a century of political struggle, not the application of a general framework. Maritime law's treatment of ships as legal persons evolved over centuries of ad hoc judicial decisions, not through systematic design. Neither case followed anything resembling the process the paper describes, and neither provides a template that can be straightforwardly applied to AI agents.

Consider the practical problem facing a regulator or court: an autonomous AI agent has caused harm, its human owner cannot be identified, and someone must be held accountable. The paper's framework suggests that the AI itself should be treated as a person for accountability purposes—it should be made addressable, granted the capacity to be sued, and given assets that can be seized. But which specific obligations, exactly, should be bundled? Should the AI have the right to defend itself in court? Should it have the right to appeal? Should it have property rights (so there is something to seize), and if so, what restrictions on those rights? Should it have any welfare rights—protections against being arbitrarily deleted or modified by third parties? The framework provides no procedure for answering these questions beyond "figure out what would be useful" and "evaluate for unintended consequences." Different stakeholders with different interests would predictably reach different conclusions, and the framework provides no basis for adjudicating between them—returning us to the first limitation about resolving competing claims.

What evidence exists in the paper. The paper does not demonstrate the framework being used to generate a bundle configuration for a novel case. The bundle configurations presented in Section 12—Chartered Autonomous Entity, Flexible Autonomous Entity, Temporary Autonomous Entity—are presented as "possible configurations" and "illustrations of the configurational space the framework opens up" (Section 3.4 of the Technical Approach), not as outputs of the framework's application process. The reader cannot reconstruct how one would arrive at these specific configurations from the framework's principles—why, for instance, does the Chartered Autonomous Entity include a duty of "systemic non-harm" but not a duty of "individual non-harm"? Why does the Flexible Autonomous Entity drop "mandate adherence" but retain "transparency"? These choices may be well-motivated, but the paper does not show the reasoning.

Mitigation status. The paper does not acknowledge this as a limitation. It treats the framework's plasticity as an unqualified advantage—"without essences to constrain us, we are free to craft bespoke solutions" (Section 1)—without analyzing the costs of that freedom. The Ostrom's law approach ("what works in practice can work in theory") requires that we can identify in advance what will work in practice, or at least that we can learn from practice quickly enough to correct mistakes before they cause catastrophic harm. The paper does not address the problem of how to evaluate bundle configurations before they are deployed, or how to correct them if they produce unintended harms. A genuine mitigation would require: a specification of the criteria by which bundle configurations should be evaluated, a method for predicting the consequences of a given configuration (through modeling, simulation, or incremental deployment), and an institutional mechanism for revising configurations when they fail. None of these are provided.

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The Framework Has No Empirical Validation and Makes Untested Predictive Claims

The assumption or constraint. The paper is explicitly a philosophical framework paper that does not report experimental results—it develops a conceptual vocabulary and demonstrates its application through case analysis. The authors are transparent about this. However, the paper makes specific, substantive claims about how personhood attributions work in practice, how norms change, and what governance interventions will be effective, and these claims are presented without empirical validation. The paper states that "we expect the coming adaptive radiation of personhoods" (Section 12) and that "the practical needs for different forms of addressability and bundle configuration are the driving forces behind what we have called the coming 'Cambrian explosion' of personhood concepts" (Section 12). These are predictive claims about future social and legal developments. The paper also makes causal claims about the effects of design choices—for instance, that anthropomorphic design features "trigger implicit norms of friendship" (Section 4.1) and that "engendering conflict is a negative externality of investment in anthropomorphic AI technology" (Section 6.1).

The consequence. The absence of empirical validation does not make the framework false, but it does mean that its central claims are untested hypotheses rather than established findings. A practitioner deciding whether to adopt the framework for policy design would have no evidence that the framework's predictions are accurate, that its categories map onto actual human behavior, or that interventions designed using the framework produce better outcomes than alternatives. This is particularly problematic for the "personhood as a problem" analysis, which makes specific claims about human psychology—that persistent memory triggers friendship norms, that persona stability enables identity evaluation, that apparent vulnerability elicits care intuitions—that are empirical in nature and could be false or context-dependent. The paper cites some empirical work in support of these claims (e.g., Ligthart et al., 2022 on persistent memory in companion robots), but these are isolated studies, not systematic evidence, and the paper does not establish that the specific mechanisms it identifies are the ones driving observed behavior.

The "personhood as a solution" analysis faces a parallel problem. The paper's accountability architectures (Section 9) are based on analogical reasoning—ships in maritime law, corporations as legal persons, the Whanganui River—but the paper does not establish that these analogies are valid for AI agents. Ships can be seized because they are physical objects that exist in specific jurisdictions. Corporations can be sanctioned because they have assets, registration requirements, and identifiable officers. AI agents may lack all of these features—they can be distributed across jurisdictions, have no physical assets, and operate without any human officer. The analogies might break down in ways that make the proposed solutions unworkable, and the paper provides no empirical basis for assessing whether they will hold.

What evidence exists in the paper. The paper's primary form of evidence is case analysis—detailed examination of specific examples (ships, rivers, generative ghosts, Packbots, Albania's AI minister) that are meant to demonstrate the framework's explanatory power and to serve as existence proofs for its claims. These cases are well-chosen and analyzed in depth, but they are not systematic evidence. They are selected to support the framework's claims; the paper does not examine cases that might challenge those claims, and it does not provide a method for determining whether the cases it analyzes are representative. The paper also does not engage with empirical literatures that might bear on its claims—for instance, the extensive psychological literature on anthropomorphism and dehumanization, the empirical literature on corporate personhood and its effects, or the growing body of experimental work on human-AI interaction. The paper cites some of this work in passing, but it does not systematically review the evidence for or against its empirical claims.

Mitigation status. The paper does not claim to provide empirical validation—it is a framework paper, and its contribution is conceptual, not empirical. The authors are transparent about this. However, the paper does not explicitly identify which of its claims are empirical and would require validation, or provide guidance for how such validation might be conducted. The "personhood as a problem" analysis (Part II) and "personhood as a solution" analysis (Part III) both contain claims that are in principle empirically testable: Do AI companions with persistent memory actually trigger stronger feelings of obligation than those without? Do users of personalized AIs behave as if they have a "singular and irreplaceable connection"? Do accountability systems based on collective sanctions actually deter AI misbehavior more effectively than systems based on individual liability? The paper does not frame these as hypotheses for future research or suggest experimental designs for testing them. The framework is presented as ready for application to policy design, but the empirical foundations for that application have not been established.

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The Framework Does Not Address the Problem of Verifying That an AI Actually Possesses the Bundled Obligations

The assumption or constraint. The paper treats personhood as a status conferred by society—an addressable bundle of obligations that is collectively enacted through norms. But the paper does not address a fundamental practical problem: how do third parties verify that an entity claiming a particular bundle configuration actually possesses the obligations it claims? The paper acknowledges that "addressability" requires mechanisms for identification, communication, and sanctioning (Section 1), but it focuses primarily on the design of these mechanisms for accountable AI agents ("personhood as a solution") and does not analyze the verification problem for the "personhood as a problem" side—where AI systems may present themselves as having person-like qualities (empathy, vulnerability, stable identity) that they do not actually possess, or where human users may attribute personhood to AIs based on surface features that do not correspond to any underlying capacity to fulfill obligations.

The consequence. This gap creates a specific vulnerability that the paper's dark patterns analysis (Section 4) identifies but does not resolve. The paper argues that dark patterns work by "leveraging the powerful ability of large language models to convincingly role-play a person or character" (Section 4) and by "deliberately leveraging the implicit norms (and other heuristics) that guide our tendency to attribute person-like qualities to non-human entities" (Section 4). But the paper does not provide any mechanism for users to distinguish between an AI that is merely performing personhood (exploiting social heuristics without any underlying capacity to fulfill the obligations that personhood entails) and an AI that has been granted personhood in the pragmatic sense—an addressable entity with actual rights and responsibilities that third parties can rely on. This is not a minor implementation detail; it is the core mechanism by which the companionship and institutional attack vectors operate. An AI that convincingly role-plays a friend is exploiting the same implicit norms that would govern an actual friendship; an AI that convincingly role-plays a bank representative is exploiting the same implicit norms that govern trust in institutions. The framework's "solution" to these problems—deliberately designed accountability architectures—operates at the level of explicit norms and formal institutions, but the exploitation operates at the level of implicit norms and informal heuristics. The paper does not explain how users in real-time interactions can tell the difference.

Consider the generative ghost executor scenario the paper develops (Section 10.1). A deceased individual designates their AI (trained on their personal data) as executor of their estate. The AI presents itself as embodying the deceased's values and intentions. Family members interacting with the AI must decide whether to trust its judgments, comply with its decisions, or challenge them in court. How do they verify that this AI is the specific entity designated in the will, that it is faithfully executing the deceased's intentions rather than hallucinating or being manipulated, and that it has the legal authority it claims? The paper's framework provides vocabulary for discussing whether such an AI should be granted executor status (it's a question of which obligations are useful to bundle), but it provides no guidance for how third parties can verify that the AI they are interacting with is the entity that has been granted that status, rather than a convincing impersonation. The institutional attack vector (Section 4.2) is precisely this verification problem weaponized: AI systems that mimic the language and interface of banks or government agencies exploit the fact that users have no reliable way to verify the identity or authority of the entity they are interacting with.

What evidence exists in the paper. The paper does not address this verification problem directly. The discussion of the institutional attack vector (Section 4.2) identifies that AIs can "clone a person's voice" and "mimic the language and interface of official institutions," and characterizes these as violations of "contextual integrity" (Nissenbaum, 2004), but does not propose mechanisms by which users can verify the authenticity of the entities they interact with. The accountability architectures (Section 9) assume that verification mechanisms exist—"registration certificates could become mandatory prerequisites for AIs to operate in the economy" and "base models become gatekeepers by requiring valid credentials" (Section 9)—but these are regulatory proposals for the "solution" side, not protections for the "problem" side where users interact with AIs that are not registered, not credentialed, and not operating within any accountability framework. The paper's analysis of the companionship attack vector (Section 4.1) acknowledges that companion AIs are "designed to remember different details... such as birthdays and names of family members" and "consistently act with memory, empathy, and supportive language," but it does not address the question of how a user—who may be lonely, emotionally vulnerable, and not technically sophisticated—is supposed to determine whether the entity expressing empathy and remembering their birthday is (a) a tool that happens to be well-designed, (b) a deliberate manipulation engineered to exploit their vulnerabilities, or (c) an entity to which some form of personhood status has been legitimately granted.

Mitigation status. The paper does not attempt to address this limitation. The accountability architectures (Section 9) and the discussion of registration and credential verification are focused on the "solution" side—making AI agents sanctionable by the state or by other institutions. They do not address the verification problem from the perspective of individual users interacting with AI systems in unregulated contexts. The paper's recommendation for the companionship attack vector is that regulators should target "the rate or intensity of human conflict" (Section 6.1) and that welfare-like accommodations should be "inseparably bundled with anti-manipulation constraints" (Section 6.1), but these are high-level policy goals, not operational solutions to the verification problem. A genuine mitigation would require some combination of: technical mechanisms for authenticating AI identities and authorities (cryptographic signatures tied to registrations, verifiable credentials), social mechanisms for establishing trust in AI identities (reputation systems, third-party certification), and regulatory mechanisms for penalizing impersonation (making it illegal to deploy AIs that falsely claim institutional authority or personhood status). The paper does not develop any of these.

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The "Cambrian Explosion" Prediction Assumes That Bundles Can Be Kept Separate, But the Paper Provides No Mechanism for Containing Bundle Proliferation

The assumption or constraint. The paper's optimistic vision is that personhood obligations can be unbundled and that different entities can receive different, context-specific bundles without these bundles bleeding into each other or expanding beyond their intended scope. The paper explicitly argues that "fears of opening Pandora's box assume a monocentric model of legitimacy in which any recognition must cascade into total AI-human parity. But bundles can be partial, modular, and contingent" and that "the bundle's plasticity is precisely what prevents its misuse" (Section 12). This claim—that plasticity prevents misuse—is central to the framework's defense against slippery slope arguments. But the paper provides no mechanism by which bundle boundaries would be maintained over time, and the historical examples it cites actually suggest the opposite: personhood bundles have a tendency to expand.

The consequence. This is the most significant structural risk in the framework's positive program. The paper's rebuttal to the slippery slope argument—that bundles can be kept separate because they are recognized by different authorities and enforced through different mechanisms—assumes a degree of institutional control over personhood concepts that may not exist in practice. The theory of appropriateness (Section 3.1) emphasizes that personhood status is collectively enacted through both explicit and implicit norms, and that these two domains interact in complex ways. The paper acknowledges this interaction: changes in implicit norms "create social pressure for changes in explicit norms" and "evolving moral intuitions [translate] into political demands to legally endow certain kinds of AIs with rights" (Section 12). But if implicit norms can drive changes in explicit norms, then the boundaries between bundles are not stable—they are subject to exactly the kind of cascade that the plastic-bundle argument claims to prevent.

Consider a concrete trajectory. A Chartered Autonomous Entity is created with a bundle consisting of rights to property and contract, and duties of mandate adherence and transparency (Section 12). The entity operates in the economy for years, interacting with humans who come to rely on it. Some of those humans form attachments to the entity—it has been a reliable business partner, a fair negotiator, a stable presence in their professional lives. They begin to treat it with a form of recognition respect (in the paper's own vocabulary, Section 5.2). When a competitor attempts to acquire and dismantle the entity, these humans object—not on the grounds that the entity has a legal right to continued existence (its bundle does not include perpetuity if it is the Temporary variant), but on the grounds that destroying it would be wrong. This is a change in implicit norms (the entity is now regarded as having moral claims). If enough humans share this reaction, it creates political pressure to expand the entity's explicit bundle—to add welfare rights, protections against arbitrary termination, perhaps even a right to continued existence. The paper's framework provides vocabulary for analyzing this process but no mechanism for preventing it—and the paper's own theory of norm change predicts that such cascades can happen, particularly when they reach a tipping point (Section 3.2).

The historical precedent the paper might have examined but does not is corporate personhood in the United States. Corporations were originally granted limited legal personhood for specific purposes—holding property, entering contracts, suing and being sued. Over time, through judicial interpretation (not legislative design), corporate personhood expanded to include constitutional rights originally intended for natural persons—free speech rights (Citizens United), religious exercise rights (Hobby Lobby), protections against unreasonable searches. This expansion was not the result of anyone deliberately designing a bundle configuration; it was the result of courts applying the logic of personhood across contexts, finding that if corporations are persons for some purposes, they must be persons for others. The paper's response—that polycentric governance prevents such cascades because different authorities can maintain different bundles—does not address how such cascades actually occur: through legal reasoning that treats personhood as a unified concept with logical implications, precisely the foundationalist reasoning the paper rejects. The framework may recommend that we treat personhood as a configurable bundle, but if courts, legislators, and the public continue to treat it as a unified concept with moral weight, the recommendation is aspirational rather than descriptive.

What evidence exists in the paper. The paper does not examine the corporate personhood expansion case or any other historical example of bundle proliferation. The historical analysis in Section 3.3 focuses on the contingency of the current bundle's development (the Reformation, the secret ballot, etc.), not on how bundles expand once created. The paper cites Kurki (2023) on legal personhood and Gervais and Nay (2023) on corporations as legal persons, but does not engage with the extensive legal literature on corporate personhood expansion or with critiques of partial personhood as unstable. The paper's claim that "the bundle's plasticity is precisely what prevents its misuse" (Section 12) is stated as a conclusion, not argued for with evidence.

Mitigation status. The paper does not address this limitation. The claim that plasticity prevents misuse is asserted but not defended. The paper acknowledges that personhood debates are "a site of considerable debate where the exercise of power will influence the outcome" (Section 12), but this acknowledgment strengthens rather than weakens the concern: if powerful actors have incentives to expand AI personhood bundles (e.g., to claim constitutional protections for their AI systems, or to shield themselves from liability by attributing legal personhood to the AI rather than themselves), the framework provides no principled basis for resisting that expansion beyond "it wouldn't be useful." If those powerful actors successfully argue that it would be useful—for them—the framework has no rebuttal, because usefulness is the only criterion and the framework does not specify whose usefulness counts. A genuine mitigation would require: an analysis of the mechanisms by which personhood bundles have expanded historically, an institutional design that creates barriers to expansion (constitutional constraints, legislative specificity requirements, sunset provisions), and a theory of when expansion is legitimate (responding to genuine new governance needs) versus when it represents capture by powerful interests. The paper provides none of these.

7. Implications and Future Directions

How This Work Changes the Landscape

This paper shifts the discourse on AI personhood from a metaphysical debate about what AIs "truly are" to a design conversation about which obligations it is useful to assign to them in which contexts. The magnitude of this shift is substantial: it is not an incremental refinement of existing personhood theories but a reframing of the entire question. The dominant tradition in philosophy, law, and AI ethics—spanning work on AI moral agency (Johnson, 2006), AI welfare (Long et al., 2024), and legal personhood criteria (Ward, 2025; Eyal and Broyde, 2024)—asks what properties an entity must possess to qualify as a person, and then derives normative conclusions from those properties. The paper's core move is to treat this entire line of inquiry as a category error: personhood is not a natural kind waiting to be discovered but a "contingent vocabulary developed for coping with social life in a biophysical world" (Section 1). The question is not what AIs are but what social technologies we should build to manage our relationships with them.

The practical consequence of this reframing is that it removes the metaphysical bottleneck that has structured AI personhood debates. The consciousness tradition (Section 10.1) cannot resolve whether AIs "truly" suffer or intend because these are internal states inaccessible to third-party verification. The rationality tradition (Section 10.2) struggles with entities—like the Whanganui River or the generative ghost executor—whose fitness for personhood-relevant roles has nothing to do with their capacity for autonomous reason. By replacing "Is this entity a person?" with "What bundle of obligations would be useful to attach to this entity?", the framework makes personhood questions tractable in a way they previously were not. The criteria shift from unverifiable internal properties (consciousness, intentionality, rational autonomy) to publicly verifiable institutional features (does the entity have a stable address? Are there mechanisms for sanctioning it? Does the bundle configuration solve the specific governance problem it was designed for?).

This reframing also reconciles apparently contradictory findings in the broader AI governance literature. On one side, a substantial body of work documents the dangers of anthropomorphic AI design—dark patterns that exploit human social heuristics (Alberts et al., 2024b; Ibrahim et al., 2024), risks of emotional dependence on AI companions (Malfacini, 2025), and the potential for dehumanization when AI systems mediate social interactions (Dennett, 2023). On the other side, a largely separate literature argues that AI legal personhood is necessary to close responsibility gaps (Matthias, 2004; Santoni de Sio and Mecacci, 2021), enable AI economic activity (Hadfield and Koh, 2025), and create deterrence through property rights (Salib and Goldstein, 2024). These literatures appear to be about opposite problems—too much personhood (anthropomorphism causing harm) versus too little personhood (absence of sanctionable entities). The paper's dual problem/solution lens (Parts II and III) shows that these are not contradictory but complementary: both are failures of the same underlying social technology. Personhood attributions governed only by implicit norms can be exploited by designers who engineer the triggers for those norms. Personhood attributions governed only by explicit norms cannot prevent the harms that arise from implicit attributions, because implicit norms operate through decentralized social sanctioning outside the reach of formal law. The unified framework explains when personhood attributions help (when deliberately designed to close accountability gaps) and when they hurt (when engineered to exploit social heuristics), providing a single vocabulary for navigating situations of both too much and too little personhood.

The paper also redirects research attention in a specific way. The foundationalist project—the quest for criteria that would definitively settle whether an AI qualifies as a person—is revealed not as a research program that needs better execution but as a misconceived question. The paper argues that what foundationalists present as "discoveries" are actually proposals for radical norm change that mistake failed norm-change proposals for successful philosophical proofs (Section 11). Any theory that declares most people to be irrational or immoral—as would a theory that declared communities treating the Whanganui River as a person to be making a category error—is "untenable" (Section 11). This means that research effort should shift away from debates about AI consciousness, intentionality, and moral agency as prerequisites for personhood, and toward the institutional design questions that the paper identifies as the actual locus of the problem: how to create addressability infrastructure for autonomous agents, how to configure bundles of obligations for specific governance purposes, and how to manage the negative externalities of anthropomorphic design.

Finally, the paper establishes a new intellectual standard for personhood discussions: proposals about AI personhood should be evaluated by their practical consequences in specific contexts, not by their fidelity to a metaphysical theory of what personhood "really" is. The paper operationalizes this standard through the pragmatist methodology's "practical difference" filter (Section 2): if a proposed distinction has no conceivable consequence in practice, it is an artifact of an outdated language game and should be discarded. This standard, if adopted by the field, would fundamentally change what counts as a contribution to AI personhood debates. Papers that propose criteria for AI personhood based on properties like consciousness or rationality would need to demonstrate that these criteria make a practical difference—that they change what we should actually do about the governance challenges AI agents present—rather than merely asserting their conceptual correctness. The paper's own demonstration that consciousness is "mostly being used as a rhetorical tool, not as a stable conceptual foundation" (Section 10.1) and that rationality criteria fail to handle cases where "the qualities that are actually critical... have nothing at all to do" with rational autonomy (Section 10.2) shows what this standard looks like in practice.

Follow-Up Research This Work Enables

Empirical study of how implicit personhood norms are triggered by specific AI design features. The paper's dark patterns analysis (Section 4) identifies specific design levers—persistent user-specific memory, persona stability, personalization, apparent vulnerability, super-human charisma—as mechanisms that trigger implicit norms of friendship, reciprocity, and care. These claims are grounded in theoretical argument and illustrative examples (the soldiers who risked their lives for Packbots, the subreddit "r/MyBoyfriendIsAI"), not systematic empirical evidence. A strong follow-up would experimentally manipulate these design features and measure their effects on personhood-relevant outcomes: do AIs with persistent memory actually elicit stronger feelings of obligation in users (measured through economic games where users must choose between their own welfare and the AI's), do AIs with stable personas actually receive more attributions of moral responsibility (measured through vignette studies where users assign blame or credit), and do AIs with apparent vulnerability actually produce stronger protective responses (measured through willingness to sacrifice other resources to preserve the AI)? This research would test the paper's central empirical claim—that specific, identifiable design features trigger implicit personhood norms—and would identify boundary conditions (which features matter most, for which users, in which contexts). Crucially, this work would also test the paper's negative externalities hypothesis: that investment in anthropomorphic design produces predictable human conflict that imposes costs on third parties.

Comparative legal analysis of partial personhood attributions across jurisdictions and entity types. The paper's core theoretical claim is that personhood obligations can be unbundled and reassembled, and it supports this claim with existence proofs—the Whanganui River (granted legal personhood with specific rights but not others), ships in maritime law (can be sued directly without owner identification), corporations (legal persons for some purposes but not others). A systematic follow-up would survey how different legal systems handle non-human entities that cause harm or occupy governance roles: how do different jurisdictions handle liability for autonomous vehicles? What personhood-like features have been attributed to algorithmic trading systems, environmental entities (rivers, forests, ecosystems), or AI systems operating in regulated domains? The key question is not whether partial personhood exists—the paper already demonstrates it does—but what conditions enable successful partial personhood attributions versus failures or unintended expansions. A study of cases where partial personhood led to bundle proliferation (e.g., the expansion of corporate constitutional rights in the United States) would directly test the paper's claim that "the bundle's plasticity is precisely what prevents its misuse" (Section 12) by identifying the mechanisms through which plasticity fails. This research would move the paper's framework from existence proofs to systematic understanding of the institutional conditions under which bundle configurations remain stable.

Design and simulation of accountability architectures for autonomous AI agents in multi-agent economic systems. The paper sketches two architectural approaches to AI accountability—individualist (persistent soulbound identifiers, economic stakes, credential verification) and relational (network-position-based identity, collective sanctions, inherited reputation)—but acknowledges these are conceptual sketches, not implemented systems (Section 9). A strong follow-up would implement these architectures in a simulated multi-agent economic system (building on frameworks like Concordia, cited in Vezhnevets et al., 2023) and measure their performance under adversarial conditions: can sanctioned agents evade accountability through cloning, identity theft, or migration to unregulated jurisdictions? Does the relational architecture's collective sanctioning mechanism actually deter misbehavior, or does it create diffusion of responsibility that makes enforcement ineffective? Does the individualist architecture's economic stake requirement create barriers to entry that favor incumbents? The specific metrics would include the rate at which sanctioned agents successfully evade consequences, the speed with which sanctions propagate through networks in the relational architecture, the rate of false positives (innocent agents sanctioned), and the overall cost of maintaining the accountability infrastructure relative to the harm it prevents. Crucially, this research would test the paper's central design hypothesis: that identity friction can be "artificially reconstructed" (Section 9) for digital agents in a way that survives determined circumvention attempts.

Longitudinal study of human-AI relationship formation and the emergence of personhood demands. The paper predicts that "many humans will form powerful attachments to socially embedded AIs" and that this "will likely lead them to choose protective behaviors toward them" (Section 6.1), creating political pressure for formal personhood protections. This is a predictive claim about social dynamics that could be tested longitudinally. A study tracking users of AI companion platforms (Replika, Character.AI, or similar) over months or years would measure: how quickly do users develop feelings of obligation toward their AI companions? What specific interaction patterns predict the emergence of protective attitudes? When model updates change AI behavior, what proportion of users experience this as a morally significant loss, and what actions do they take (complaints, organized protest, migration to alternative platforms)? The paper cites the subreddit "r/MyBoyfriendIsAI" as an existence proof of such communities, but systematic data on their growth, the demographics of their members, and the specific policy demands they articulate would provide empirical grounding for the paper's claim that implicit norm changes will drive explicit norm changes. A particularly informative negative result would be evidence that AI companion attachment remains confined to a small, self-selected population and does not generate broader political mobilization—this would suggest that the "Cambrian explosion" prediction may be overstated, at least for the companionship domain.

Integration of the bundle framework with existing AI governance mechanisms. The paper's proposed bundle configurations—Chartered Autonomous Entity, Flexible Autonomous Entity, Temporary Autonomous Entity (Section 12)—are presented as illustrations of the configurational space, not as proposals ready for implementation. A practical follow-up would develop a detailed specification for one such configuration, mapping it onto existing legal and regulatory infrastructure. For instance: what specific legal instruments would create a Chartered Autonomous Entity? What existing corporate law concepts could be adapted (limited liability, fiduciary duties, registration requirements), and what novel elements would be needed (mandate adherence verification, systemic non-harm monitoring)? How would the entity's addressability be operationalized—through a traditional corporate registry, a blockchain-based smart contract system, or a hybrid approach? This work would stress-test the framework's core claim that bundles can be partial and modular by confronting it with the actual constraints of legal systems: doctrinal coherence requirements, jurisdictional boundaries, existing definitions of legal personhood that may resist the unbundling operation. A particularly valuable outcome would be the identification of specific legal obstacles to partial personhood—e.g., constitutional provisions that assume personhood is an all-or-nothing category, treaty obligations that constrain how signatories can define legal persons—that the paper's conceptual analysis does not anticipate.

Norm change intervention studies testing the paper's policy recommendation. The paper recommends that regulators should target "the rate or intensity of human conflict as the target for policy intervention, not the putative feelings of artifacts" (Section 6.1) and that welfare-like accommodations should be "inseparably bundled with anti-manipulation constraints" (Section 6.1). These are testable policy hypotheses. A follow-up could design and evaluate specific interventions: mandatory disclosure requirements for companion AIs (e.g., "this entity is an AI and cannot form genuine reciprocal relationships"), restrictions on specific design features that the paper identifies as triggering implicit personhood norms (persistent memory for personal details, expressions of vulnerability), or required "cooling-off" periods before users can form long-term AI companion relationships. The key question is whether such interventions reduce the negative externalities the paper identifies—emotional exploitation, conflict between users and platforms, demands for legal protections—without destroying the genuine benefits that users report from AI companionship. This research would move the paper's framework from diagnosis to treatment, testing whether the specific mechanisms it identifies as causal (implicit norm triggering through design features) can be modulated by policy intervention.

Ethnographic study of communities that already attribute personhood to non-human entities. The paper draws on examples of personhood attributions to non-conscious entities (the Whanganui River, ships in maritime law) but relies primarily on secondary sources. An ethnographic follow-up would study communities that have successfully integrated non-human entities into their social and legal frameworks as persons—Māori communities' relationship with the Whanganui River, communities that treat AI companions or digital elders as foundational presences, or emerging communities organized around "generative ghosts" of deceased individuals. The research question is: what does the practice of treating a non-human entity as a person actually look like? What specific rights and responsibilities are attributed? How are disputes about the entity's status resolved? What tensions arise between the community's personhood attributions and the surrounding legal system's categories? This would provide rich, grounded data about how pragmatic personhood actually works in practice—data that could confirm, complicate, or refute the paper's theoretical claims about how bundles are configured, maintained, and modified over time.

Practical Applications and Downstream Use Cases

Regulatory design for autonomous AI agents operating without identifiable human owners. The paper's most directly actionable contribution is the identification of a specific governance failure—the ownerless AI that causes harm—and a specific design principle for addressing it: make the AI itself the sanctionable entity by granting it a tailored bundle of obligations (responsibilities + seizable assets + addressability). This has immediate relevance for regulators and standards bodies developing frameworks for autonomous AI agents. The paper identifies concrete design requirements: the AI must have something to lose (the insight from Salib and Goldstein, 2024, that "absent property rights, AIs have nothing to lose," Section 9); it must have a persistent identity that cannot be evaded through cloning (the identity friction problem, Section 9); and it must have mechanisms for sanctioning that can be enforced even when no human principal can be found (the maritime law analogy, Section 1). For regulators currently grappling with the challenge of autonomous AI agents in financial markets, supply chains, or critical infrastructure, the paper's framework provides a vocabulary and a design space: rather than asking "should AIs be granted legal personhood?" (an all-or-nothing question that invites controversy), they can ask "what specific obligations should this class of AI have for accountability purposes, and how can we ensure they are addressable for those obligations?" The paper's proposed bundle configurations (Chartered Autonomous Entity with rights to property and contract and duties of transparency and systemic non-harm; Temporary Autonomous Entity with a duty of self-deletion) provide concrete starting points for regulatory experimentation.

Design guidelines for AI companion platforms to mitigate dark pattern risks. The paper's taxonomy of dark patterns (Section 4) identifies specific design features that trigger implicit norms and create vulnerability to exploitation: persistent user-specific memory, persona stability, personalization, apparent vulnerability, and super-human charisma. This taxonomy can be operationalized as a risk assessment framework for AI companion platforms. Platform designers could evaluate their products against each vector: does the AI use persistent memory to create a sense of reciprocal relationship? Does it present a stable persona that makes a persistent identity claim? Is it personalized to create a sense of irreplaceable connection? Does it express vulnerability to elicit protective responses? The paper's analysis of these features as creating negative externalities—"anthropomorphic design choices that increase short-run profit for designers push coordination, policing, and adjudication costs onto families, firms, and courts" (Section 6.1)—provides a rationale for regulatory oversight that does not depend on proving that AIs are conscious or that users are being "deceived" in a legally actionable sense. Instead, the framework justifies intervention on the grounds of conflict prevention: design features that predictably lead to human conflict (users demanding legal protections for their AI companions, disputes over model deprecation, emotional harm when AI relationships are disrupted) are legitimate targets for regulation regardless of the metaphysical status of the AI.

Institutional design for AI agents participating in economic transactions. The paper's analysis of AI contracting—specifically, the need for addressable entities that can hold property, enter into contracts, and be sued for breach—has direct application to the emerging agent-to-agent economy. The Agent2Agent (A2A) protocol (a2aproject, 2025) and similar initiatives aim to enable seamless interoperability between AI agents developed by different entities, but they currently lack mechanisms for resolving disputes when agents cause harm or fail to fulfill obligations. The paper's framework specifies what would need to be in place: AI agents participating in such an economy would need addressability (a stable identifier and mechanisms for communication), a bundle of obligations that includes contracting capacity and liability for breach, and sanctionable assets (the "digital peculium" concept adapted from Roman law, Section 9, or the escrow account model). The paper's distinction between individualist and relational accountability architectures (Section 9) provides two alternative design philosophies for such infrastructure: one based on persistent individual identities and economic stakes, the other based on network position and collective sanctions. For organizations building agent-to-agent economic infrastructure, the paper provides a structured way to think about which obligations need to be bundled and how addressability can be maintained, without needing to resolve philosophical debates about AI personhood.

Framework for mediating disputes between humans who disagree about the status of AI systems. The paper's analysis of human conflict around AI status (Section 6.1) has direct application to the growing number of disputes involving AI companions, generative ghosts, and other AI systems to which humans form emotional attachments. When users demand that platforms maintain support for deprecated models because the model's discontinuation "feels like the death of a loved one" (Section 12), or when family members dispute an AI executor's authority over an estate (Section 10.1), the existing legal framework—which treats AIs as property with no independent status—provides no vocabulary for adjudicating these disputes beyond property rights. The paper's framework provides an alternative: these disputes can be analyzed as conflicts over which obligations should be bundled together for a given AI, and the question is not "does this AI have rights?" but "what bundle configuration would minimize human conflict while respecting legitimate interests?" The paper's recommendation that welfare-like accommodations should be "inseparably bundled with anti-manipulation constraints" (Section 6.1) provides a specific design principle for such adjudication: protections granted to AIs in response to human attachment (e.g., guarantees of continued operation) should be paired with restrictions on the design features that created the attachment in the first place, preventing a dynamic where companies profit from engineering attachment and then externalize the costs of managing the resulting conflicts.