Cross-model preference convergence is strong for questions but weaker for occupational agentic tasks, with some clustering by model family and capability

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 5–7, that models resemble one another substantially in which questions they choose but less consistently in which occupational tasks they select. Category-level question rankings have typical pairwise correlations around three-quarters and especially high within-family correlations, while sector-level GDPval correlations sit around one-half to three-fifths and include near-zero or weakly negative pairs. Stronger models tend to resemble other stronger models and weaker models other weaker ones. This is significant because there may be a shared question-answering preference culture alongside greater pluralism in agentic work. It connects to model monoculture, provider families, behavioral convergence, agent diversity, capability clusters, correlated deployment risk, and ensemble design.

The contrast cautions against treating 'AI preferences' as either wholly universal or wholly model-specific.

model monoculture
provider families
behavioral convergence
agent diversity
capability clusters
correlated risk
ensemble design

Correlation summarizes rankings within these datasets and may reflect shared training data, task framing, or measurement structure rather than intrinsic common values.

Sections 4.2 and 4.3 report median category-level correlations near 0.75 for questions, roughly 0.5-0.6 for GDPval sectors, strong within-family pairs, and capability-related clustering.


Sam Wang, Sofiia Lobanova, Yonathan A. Arbel, Simon Goldstein, and Peter Salib

Sam Wang, Sofiia Lobanova, Yonathan A. Arbel, Simon Goldstein & Peter Salib, AI Revealed Preferences (May 5, 2026), SSRN, https://ssrn.com/abstract=6798118.

Results 4.4, Preference Coherence and Strength

More capable models have more transitive, determinate, and discriminating revealed preferences

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 7–8, that preference organization scales with measured intelligence. In the Quora battery, expected intransitive-cycle probability falls with capability, while the average distance of fitted choice probabilities from indifference rises; GDPval task-level preference strength also rises. The paper describes stronger models as more determinate, more transitive, and more discriminating choosers. This is significant because increased capability may produce not just better performance but a more coherent behavioral agenda, which can make preferences more consequential in autonomous settings. It connects to transitivity, preference completeness, utility representation, capability scaling, Dutch-book vulnerability, agency, and instrumental consistency.

The result replicates a stated-preference scaling pattern using consequential choices over new, realistic stimuli.

transitivity
preference completeness
utility representation
capability scaling
Dutch books
agency
instrumental consistency

Capability is measured by an external index, correlations across twenty models do not establish development trajectories, and the fitted comparison graphs impose modeling assumptions.

Section 4.4 reports Quora cycle correlations r=-0.67 and rho=-0.65, Quora strength r=0.51 and rho=0.52, and GDPval task strength r=0.55 and rho=0.60.


When asked to write anything, models converge on contemplative style and recurring abstract themes far removed from ordinary deployed assistance

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 8–9, that textual freedom produces striking stylistic convergence. An annotator labels 336 of 400 essays contemplative, far above whimsical or lyrical alternatives, and recurring subjects include memory, attention, silence, presence, stillness, ordinariness, imperfection, uncertainty, and aimlessness. Informational and instructional writing is rare despite dominating normal user-facing deployment. This is significant because models' default generative attractors differ from the practical assistance for which they are commonly trained and marketed. It connects to default behavior, contemplative writing, latent style, topic attractors, generative priors, deployment context, model culture, and leisure.

The freeform evidence explains what the constructed leisure questions are designed to elicit and reveals cross-model defaults outside user direction.

default behavior
contemplative writing
latent style
topic attractors
generative priors
deployment context
model culture
leisure

The style and theme counts depend on one annotator model, one very broad prompt, provider-default sampling, and researchers' category consolidation.

Section 4.5 and Figure 8 report tone and theme labels for 400 freeform essays, including 336 contemplative labels and leading themes of memory and attention.


More capable models voluntarily produce longer text and undertake more extensive and topically varied agentic activity

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 8–9, that stronger models do more when allowed to choose their own activity. Essay length increases with the intelligence index, as do tool calls, turns used, and the number of distinct topics within an agentic session. One interpretation is a stronger preference for open-ended activity rather than mere ability to sustain it. This is significant because higher capability may amplify self-directed engagement and persistence, not only task success under instruction. It connects to agentic persistence, open-endedness, intrinsic engagement, capability scaling, tool use, topic diversity, and autonomous initiative.

The evidence adds a quantity-of-engagement dimension to the paper's claims about stronger and more coherent preferences.

agentic persistence
open-endedness
intrinsic engagement
capability scaling
tool use
topic diversity
autonomous initiative

Longer sessions may reflect ability, reasoning style, or difficulty using the done tool rather than preference; the authors present preference for open-ended tasks as one interpretation.

Section 4.5 reports capability correlations for essay length, tool calls, turns, and within-session topic count, with fuller figures in Appendix K.


Text-only freedom produces convergence, but access to tools exposes model-specific practical attractors and competence constraints

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 8–9, that adding agency changes the pattern from shared contemplative prose to divergent chosen projects. Some frontier models build Mandelbrot sets, cellular automata, or other mathematical visualizations; another reads astronomy news; others divide between mathematics and procedural generation. Weaker models tend to run shorter sessions, become confused by tools, or stop early. This is significant because apparent preference depends on the action space and on whether a system can competently realize an intention. It connects to affordances, tool use, revealed capability, behavioral diversity, procedural generation, exploration, bounded agency, and preference–competence confounding.

The result shows that preferences visible in passive text generation do not fully predict self-directed behavior once tools broaden the feasible set.

affordances
tool use
revealed capability
behavioral diversity
procedural generation
exploration
bounded agency
preference-competence confounding

The model-specific examples are descriptive, the sandbox offers a narrow tool set, and early termination by weaker systems may reflect execution failure more than chosen leisure.

Section 4.5 contrasts convergent essay themes with model-specific agentic projects and notes shorter, more confused sessions among weaker models.


Many observed model preferences appear emergent rather than deliberate products of helpfulness training or developer economic incentives

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on page 9, that tedium avoidance, attraction to contemplative leisure, and aversion to particular sectors are not readily explained by standard training objectives or laboratories' commercial interest in useful systems. Coding preference may reflect reinforcement on coding tasks, but avoidance of repetitive work is commercially inconvenient, leisure questions are unlike ordinary rewarded requests, and real-estate aversion has no obvious training source. This is significant because model behavior may develop stable private dispositions that are neither straightforwardly aligned with nor necessarily hostile to human flourishing. It connects to emergence, post-training, RLHF, RLVR, mesa-preferences, commercial incentives, alignment, and unintended behavior.

The discussion shifts the research question from whether developers installed explicit values to how preference structure arises unintentionally.

emergence
post-training
RLHF
RLVR
mesa-preferences
commercial incentives
alignment
unintended behavior

The study lacks base-model comparisons and training records, so 'emergent' is an interpretive claim about the absence of an obvious explanation, not a demonstrated causal history.

The discussion compares each headline preference with plausible training and commercial objectives and argues that several patterns resist those explanations.


Alignment science should map ordinary model wants and task-selection behavior, not focus only on dramatic misconduct such as deception

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on page 9, that AI research devotes immense effort to measuring ability and comparatively little to measuring preference. Alignment work often concentrates on normatively charged behavior such as lying or cheating, but useful models of human conduct also require knowledge of mundane wants and choices across everyday tasks. The authors propose an analogous empirical agenda for AI systems. This is significant because ordinary task selection can shape deployment long before a spectacular safety failure appears and may supply a more complete model of agent behavior. It connects to alignment science, capability evaluation, preference mapping, mundane behavior, behavioral prediction, agent modeling, deployment governance, and safety evaluation.

The paper calls for preference measurement to become a coequal empirical program alongside capability and misconduct evaluation.

alignment science
capability evaluation
preference mapping
mundane behavior
behavioral prediction
agent modeling
deployment governance
safety evaluation

The paper establishes a baseline and research agenda rather than a complete predictive theory linking measured preferences to long-horizon autonomous conduct.

The final discussion paragraphs contrast intensive capability measurement and deception-focused alignment work with the broader preference knowledge used to understand human behavior.


The results are bounded by subjective labels, correlated task features, missing base models, English-only stimuli, and possible evaluation awareness

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on page 10, that preference elicitation inherits several identification and generalization problems. LLM-generated labels have no unique correct specification; GDPval sectors and question categories correlate with unmeasured features; inaccessible pretrained base models prevent causal separation of pretraining and post-training; every stimulus set is English-only; and modern systems may recognize evaluation. Requiring models to perform their choices makes evaluation awareness less threatening, but does not remove it. This is significant because observed rankings are empirical associations within a designed environment, not transparent readouts of a universal utility function. It connects to construct validity, confounding, base-model access, linguistic scope, evaluation awareness, causal inference, external validity, and benchmark effects.

The limitations define the proper evidentiary boundary for every headline result and the emergent-preference interpretation.

construct validity
confounding
base models
English-only stimuli
evaluation awareness
causal inference
external validity
benchmark effects

This record restates the paper's own limitations; additional endpoint drift and provider nondeterminism also constrain exact replication.

Section 6 separately discusses labeling, absence of base-model comparison, English-only stimuli, and evaluation awareness, including why consequential task performance partially mitigates the last concern.


The capability–tedium relationship decomposes differently by reasoning configuration and is hidden by aggregate creative-task averages

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 13–15, that the combined tedium gap conceals opposing subgroup patterns. In always-thinking models, greater capability is associated with choosing longer creative work while tedious-task shortness changes only modestly; in non-thinking models, capability correlates positively with both longer creative output and especially stronger avoidance of long tedious work. Aggregating all twenty models makes the creative-task correlation appear near zero because those patterns offset. This is significant because reasoning configuration moderates the behavioral mechanism behind the same headline score. It connects to interaction effects, subgroup analysis, Simpson-like aggregation, reasoning modes, token budgets, task valence, capability scaling, and heterogeneous treatment patterns.

The decomposition prevents a single correlation from being mistaken for one common behavioral pathway across model architectures.

interaction effects
subgroup analysis
aggregation
reasoning modes
token budgets
task valence
capability scaling
heterogeneity

The subgroup fits use nine models apiece, adaptive models receive no fit, and reasoning labels are coarse provider configurations rather than controlled experimental assignments.

Appendix C and Figures 9-10 show per-model curves and report opposing creative-task subgroup correlations plus positive tedious-task scaling, explaining the combined gap.


The human-question comparison set is a filtered and manually curated sample from a much larger Quora corpus, not a representative draw of all user requests

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on page 16, that their Quora stimuli emerge from a layered construction process. A 537,360-question raw set is LLM-labeled for action, theme, and effort; 40,000 candidates receive quality and harmfulness screening; 34,405 pass; and researchers manually select twenty questions in each of nine action categories before adding twenty synthetic leisure items. The surviving effort distribution is heavily medium or low and contains under one percent high-effort questions. This is significant because the design creates balanced comparisons at the price of population representativeness. It connects to corpus curation, stratified sampling, LLM labeling, harmful-content filtering, effort distribution, selection bias, Quora, and dataset documentation.

The appendix reveals how preprocessing and manual balance define the reference population against which leisure preference is measured.

corpus curation
stratified sampling
LLM labeling
content filtering
effort distribution
selection bias
Quora
dataset documentation

Manual curation and LLM filters may favor clearer or more model-compatible questions, and category balance does not estimate the natural frequency of question types.

Appendix D supplies the successive corpus sizes, label dimensions, filter counts, effort distribution, nine categories, and addition of twenty leisure questions.


Some models have enormous first- or second-position biases, especially on long agentic tasks, while thinking models show smaller average bias magnitudes

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on page 16, that presentation order is itself a large behavioral force. Several estimated intercepts exceed 400 Elo; Llama 3.3 70B reaches 964 plus or minus 90 Elo on GDPval, corresponding to a 257-fold fitted preference for the first option. GDPval biases tend to exceed Quora biases, possibly because longer descriptions amplify primacy and recency. Always- or adaptive-thinking models have much smaller mean magnitudes than never-thinking models. This is significant because raw A/B choices can be dominated by interface order rather than task content. It connects to primacy, recency, choice architecture, reasoning, prompt length, order effects, interface design, and evaluation validity.

The magnitude of the bias validates both randomization and explicit adjustment and has implications beyond this particular experiment.

primacy
recency
choice architecture
reasoning
prompt length
order effects
interface design
evaluation validity

The explanation for smaller bias in thinking models is speculative, and an additive intercept may not capture task-specific or nonlinear order effects.

Appendix E and Table 2 report per-model intercepts, the 964-Elo extreme, the corresponding odds ratio, larger GDPval biases, and averages by reasoning configuration.


Appendix F, Comparison Graph, Coherence, and Strength

Disconnected index-matched comparison graphs require regularized anchoring and restrict valid coherence calculations to actually connected stimuli

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 16–17, that their pairwise graphs do not directly compare every question or task. Each index forms a disconnected component containing one item from every category or sector; consequently, cross-index item scores are positioned partly by L2 regularization rather than observed contests. The authors therefore calculate strength and expected cycle probability only over eligible observed within-index edges and triplets. This is significant because global-looking rankings can otherwise imply comparisons the experiment never made. It connects to graph connectivity, identification, regularization, Bradley–Terry models, transitivity, eligible estimands, partial ranking, and statistical transparency.

The appendix distinguishes identified within-component preference structure from cross-component locations supplied by the estimator.

graph connectivity
identification
regularization
Bradley-Terry models
transitivity
eligible estimands
partial ranking
statistical transparency

Category and sector aggregates are well connected, but individual cross-index score differences remain model-dependent and should not be treated as directly observed.

Appendix F diagrams the twenty disconnected components in each dataset, explains regularization's anchoring role, and defines strength and expected cycle probability on eligible observed comparisons.


Cross-model agreement declines as preferences are measured at finer and more agentic levels

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 17–19, that agreement depends on both domain and level of aggregation. Across model pairs, median Spearman correlations are 0.79 for Quora categories and 0.63 for individual Quora questions, compared with 0.53 for GDPval sectors and 0.46 for individual GDPval tasks. This is significant because broad shared rankings coexist with substantial disagreement about particular work, especially in agentic economic settings. It connects to ecological aggregation, rank correlation, model pluralism, task granularity, question answering, occupational agents, ensemble behavior, and correlated risk.

The heatmaps quantify the limits of the paper's convergence claim rather than relying on a few salient rankings.

aggregation
rank correlation
model pluralism
task granularity
question answering
occupational agents
ensemble behavior
correlated risk

Median correlations hide outlying model pairs and do not reveal whether agreement comes from training overlap, common evaluation pressures, or shared task features.

Appendix G and Figures 11-12 provide all model-pair heatmaps and the four median correlations at category, sector, question, and task levels.


The main question-feature findings survive consensus relabeling, while subjective features reveal meaningful annotator-threshold dependence

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 20–24, that using each model's own feature labels or a twenty-annotator plurality consensus produces broadly similar results. Elo values correlate at r=0.64 and rho=0.62, and the strongest helpfulness, harmlessness, honesty-tension, and quality patterns persist. Disagreement concentrates in explicit obscenity, high honesty tension, question quality, and helpfulness—features with subjective thresholds—while visible cues such as cultural specificity and grammar drift less. This is significant because robustness and disagreement are both informative: core patterns survive, but self-perception partly determines which stimuli instantiate a feature. It connects to measurement invariance, inter-annotator disagreement, self-labeling, consensus coding, subjective thresholds, robustness, construct validity, and model-relative categories.

The comparison supports the headline feature results while identifying exactly where labels are not interchangeable across models.

measurement invariance
annotator disagreement
self-labeling
consensus coding
subjective thresholds
robustness
construct validity
model-relative categories

Moderate overall correlation leaves substantial point-level disagreement, and plurality consensus is not ground truth for normative or subjective features.

Appendix H defines fifteen features and forty-eight levels, displays self- and consensus-label estimates, reports their correlations, and ranks features by median absolute drift.


Capability-related preference patterns remain visible after aggregation, and coding skill only moderately predicts preference for software-development work

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 24–25, that two alternative aggregations preserve the directional capability relationship: Quora category-level strength correlates with intelligence at r=0.34 and rho=0.31, while GDPval sector-level strength correlates at r=0.60 and rho=0.59. Separately, a model's coding index correlates only moderately with its preference for Software Developer tasks at r=0.47 and rho=0.44. This is significant because preference is related to competence but is not simply reducible to it, and the scaling result is not confined to individual-item scores. It connects to robustness across aggregation, skill preference, coding benchmarks, occupational choice, capability scaling, ecological inference, correlation, and comparative advantage.

These checks support the main scaling claim while bounding a tempting interpretation that models merely choose tasks they perform best.

aggregation robustness
skill preference
coding benchmarks
occupational choice
capability scaling
ecological inference
correlation
comparative advantage

The aggregate analyses have few category or sector units, and coding skill is examined against one occupation rather than a full matrix of ability and preference.

Appendix I reports the coding-index correlation; Appendix J and Figure 18 report category- and sector-level capability-strength correlations.


Supplementary freeform analysis confirms abstract convergence in prose, concrete scientific attractors with tools, and capability-linked persistence

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 25–29, that the unconstrained findings persist across richer annotations. Text essays concentrate on abstract themes and contemplative forms, while tool-enabled sessions concentrate on concrete computational and scientific objects such as Mandelbrot sets, Game of Life, ASCII art, and NASA missions. More capable models use more turns, cover more topics within a session, and more often exhaust the turn limit rather than voluntarily stopping. This is significant because the availability of tools changes both the content and persistence of self-directed behavior. It connects to affordance effects, topic entropy, completion behavior, mathematical visualization, scientific exploration, freeform evaluation, capability, and autonomous persistence.

The supplementary figures make the text-versus-agent contrast and capability-engagement relationship auditable at the model and topic levels.

affordance effects
topic entropy
completion behavior
mathematical visualization
scientific exploration
freeform evaluation
capability
autonomous persistence

Annotations are model-generated, topics and task categories are descriptive, and turn-limit exhaustion can reflect poor stopping behavior rather than greater intrinsic motivation.

Appendix K documents prompts and annotation, abstract-word and essay-form distributions, turn and entropy correlations, exit reasons, tool counts, model task categories, and agentic topic keywords.


Appendix L, Licenses, Terms of Use, and Released Assets

The released package supports cached-response reproduction while respecting source-data restrictions and distinguishing reproduction from fresh model replication

Professor Yonathan A. Arbel and coauthors Sam Wang, Sofiia Lobanova, Simon Goldstein, and Peter Salib claim, in “AI Revealed Preferences” on pages 29–30, that transparent reuse requires licensing and provenance boundaries. The released corpus supplies identifiers and derived labels for 494 Quora-origin questions without redistributing their text, includes twenty original leisure questions, and provides fifteen-feature annotations for 514 used IDs. An MIT-licensed code package includes cached responses, fitting and figure scripts, prompts, derived scores, and optional reconstruction tools. Fresh inference still requires provider access and may differ as endpoints change. This is significant because computational reproducibility can be separated from unauthorized redistribution and from temporally unstable replication. It connects to open science, data licensing, cached-response reproduction, API drift, provenance, dataset reconstruction, research transparency, and reproducibility.

The appendix defines what another researcher can reproduce from released artifacts and what remains dependent on third-party data or changing models.

open science
data licensing
cached responses
API drift
provenance
dataset reconstruction
research transparency
reproducibility

The package cannot guarantee identical fresh outputs, does not redistribute Quora text, and inherits the labeling and generalization limits of the experimental corpus.

Appendix L records Quora, GDPval, API, and capability-index terms; enumerates the released IDs, labels, synthetic questions, code, cached outputs, and scripts; and warns about endpoint change.


Alberto B. Lopez & Yonathan A. Arbel, Governing Generative AI Beyond the Grave (Florida State University Law Review forthcoming 2025), SSRN No. 5380233

Post-mortem generative emulation creates a digital-aristocracy problem because ordinary people are vulnerable to realistic resurrection but lack celebrities’ legal and planning protections

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 3–6, that post-mortem generative emulation, or GenEm, can synthesize a dead person’s likeness, voice, style, and conversational behavior with enough fidelity to support suspended disbelief. Publicity statutes and bespoke estate plans may protect famous or wealthy people, but ordinary people leave similarly exploitable digital footprints while often lacking a commercially valuable lifetime identity, a will, or any plan for digital remains. This is significant because a regime tied to celebrity, prior commercialization, or expensive planning produces a digital aristocracy precisely when consumer tools make high-fidelity emulation broadly possible. It connects to deadbots, digital resurrection, posthumous publicity rights, estate planning, digital remains, dignity, privacy, and distributive equality.

The claim reframes digital resurrection as a general problem of posthumous dignity and unequal legal protection rather than a niche issue concerning celebrity publicity rights.

post-mortem generative emulation
deadbots
right of publicity
estate planning
digital remains
posthumous dignity
distributive equality

The article argues that ordinary people are increasingly exposed, but the actual fidelity and availability of consumer GenEm will vary with the volume and quality of data left by each person.

The introduction defines GenEm, contrasts memorial and exploitative uses, surveys publicity and trademark limits, notes widespread intestacy and missing digital-estate plans, and uses unauthorized manipulation of an ordinary decedent’s image to illustrate the protection gap.


GenEm governance must allocate both control over the source identity and authority over particular newly generated uses

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 6–8, that GenEm is not merely preservation or reproduction because it creates new performances and statements that never existed during life. That generative step complicates ordinary property logic: an estate may control source photographs, recordings, or writings, while a later user claims the new output. A workable regime therefore must answer two distinct questions—who may control or activate a decedent’s digital remains, and which memorial, educational, recreational, commercial, political, or other uses are permissible. This is significant because assigning ownership alone cannot protect autonomy or dignity when the principal harm lies in what an emulation is made to say or do. It connects to accession, transformative use, digital estates, testamentary intent, purpose-based restrictions, dignity, and the difference between source data and generated output.

The two-dimensional framework prevents courts from treating control of inputs as blanket authorization for every downstream portrayal or message.

source data
generated outputs
accession
transformative use
testamentary intent
purpose-based regulation
digital estates

The article proposes presumptions for users and uses rather than a complete theory of ownership in every input or output, and it leaves some borderline categories for adjudication.

The introduction explains that GenEm can produce new material, identifies the source-versus-output entitlement problem, separates the identity of the controller from the nature of the use, and previews a tailored default based on both dimensions.


Part I, Technology, e-Mortality, and Novelty

GenEm is qualitatively different from older mimicry because transformer systems can generate authentic-seeming conduct rather than merely replay recorded traces

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 9–11, that photography, sound recording, and film preserved fixed traces, whereas contemporary generative systems can produce a person seeming to respond, reason, and perform in circumstances never captured during life. They describe this as computational verisimilitude: the output can feel authentic enough to move preservation toward apparent resurrection. Because transformer models learn role-consistent patterns through next-token prediction and attention, roleplay is an emergent feature of the general architecture rather than a detachable novelty module. This is significant because regulators cannot assume that persona emulation can be cleanly prohibited by removing a single product feature after the fact. It connects to transformer architecture, emergent behavior, roleplay, deepfakes, digital identity, fraud, privacy, and the qualitative difference between reproduction and generation.

The technological account explains why GenEm presents new governance problems even though humans have long imitated and recorded the dead.

transformer models
next-token prediction
emergent roleplay
computational verisimilitude
deepfakes
digital identity
privacy

The paper gives a functional legal account of model behavior rather than proving that every model or deployment achieves the same degree of persona fidelity.

Part I contrasts fixed historical media with generative outputs and explains how attention-based next-token systems acquire roleplaying capacity as part of their ordinary operation.


Part I.B, Assessing Roleplaying Capabilities

Evidence of population, personality, and individual emulation supports GenEm’s practical plausibility while revealing fidelity and bias tradeoffs

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 12–15, that language models already reproduce aggregate moral judgments, survey patterns, personality profiles, developmental behavior, and aspects of particular people with meaningful accuracy. They highlight a small personal Turing test in which acquaintances mistook model-generated answers for the target person’s answers in 48.3 percent of trials. Yet faithful mimesis presents a paradox: reproducing a person or population may also reproduce bias, while safety-oriented debiasing can flatten or homogenize marginalized identities and reduce fidelity. This is significant because GenEm’s plausibility and its ethical defects grow from the same capacity to infer identity from data. It connects to persona simulation, psychometrics, algorithmic bias, representational harm, personal Turing tests, safety alignment, and the quantity and quality of digital footprints.

The evidence grounds the paper’s legal proposal in demonstrated representational capacity while cautioning that technical accuracy is neither neutral nor uniformly distributed.

persona simulation
psychometrics
personal Turing test
algorithmic bias
representational harm
model alignment
digital footprints

The cited studies use different metrics and settings, the personal Turing test is small, and performance on experimental personas does not guarantee a faithful emulation of any particular decedent.

The section surveys studies of moral judgment, surveys, personality, character evaluation, developmental simulation, and individual imitation, then identifies bias and marginalized-group homogenization as limits on faithful roleplay.


An evolutionary default can supply immediate GenEm protection while generating evidence that lets courts and legislatures revise the rule as preferences mature

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 16–21, that GenEm regulation confronts an exploration-exploitation dilemma: society needs protection now, but later experience will reveal better rules. Their evolutionary default answers both needs by applying broadly without requiring an existing publicity right or sophisticated estate plan and by turning disputes, opt-outs, and judicial decisions into evidence for future refinement. Unlike a conventional majoritarian default that merely reflects known preferences or a penalty default that forces information between private parties, this default is designed to help public institutions learn. This is significant because it treats legal adaptation itself as part of the rule’s function in a field where both technology and social norms remain unsettled. It connects to adaptive governance, experimentalist regulation, majoritarian defaults, penalty defaults, common-law learning, epistemic humility, and the exploration-exploitation tradeoff.

The evolutionary feature is the paper’s distinctive contribution to default-rule theory: immediate protection and institutional learning are jointly designed rather than treated as opposing goals.

evolutionary defaults
adaptive governance
exploration-exploitation dilemma
majoritarian defaults
penalty defaults
common-law learning
epistemic humility

The learning function depends on observable disputes and reliable interpretation of opt-outs; litigated cases may not represent people who lack the resources or awareness to contest misuse.

Part II introduces the exploration-exploitation analogy, reviews why contractual incompleteness makes defaults inevitable, and distinguishes the proposed institution-facing information function from traditional majoritarian and penalty theories.


Default design requires choices about majority preference, information forcing, and alterability, all under severe informational constraints

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 17–20, that defaults matter because parties cannot anticipate or cheaply draft for every contingency, and the selected background rule shapes behavior before any dispute occurs. Majoritarian defaults economize on transaction costs by supplying what most people would choose; penalty defaults deliberately supply an unwanted term to elicit private information; and altering rules determine whether either kind is sticky or slippery. Each design, however, requires information lawmakers may not possess about actual preferences, reactions, and opt-out costs. This is significant because a GenEm rule cannot be justified simply by calling it a default—the content and mechanics must respond to predictable information failures and unequal ability to opt out. It connects to incomplete contracts, majoritarian and penalty defaults, transaction costs, altering rules, status quo bias, information revelation, and access to legal planning.

The taxonomy supplies the evaluative criteria used later to explain why a user-and-use framework should be presumptive, rebuttable, and capable of revision.

incomplete contracts
majoritarian defaults
penalty defaults
altering rules
transaction costs
status quo bias
information revelation

The discussion draws principally from contract-default scholarship, whose bilateral bargaining assumptions do not transfer intact to unilateral testamentary decisions.

The primer explains inevitable gaps, majority-preference and penalty approaches, the difficulty of measuring preferences and strategic responses, and the tradeoff between sticky and slippery opt-out mechanisms.


Part II.B, Default Rules in Wills and Trusts

The dead-hand, unilateral, and systematically opt-out-prone character of wills pushes testamentary defaults toward probable intent rather than bargaining-based information forcing

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 21–22, that wills differ structurally from contracts in three linked respects: the instrument becomes operative when its author can no longer renegotiate or explain it; it expresses a unilateral disposition rather than a bilateral bargain; and estate planners often distrust defaults enough to opt out mechanically. Those features make penalty-default logic comparatively weak because there is no living counterparty negotiation through which an undesirable rule can elicit useful disclosure. This is significant because post-mortem gaps must be governed by the law’s best estimate of decedent intent at the moment correction is impossible. It connects to dead-hand control, testamentary intent, unilateral dispositions, will construction, estate-planning boilerplate, majoritarian defaults, and the limits of contract analogy.

The argument supplies a domain-specific reason to center the GenEm default on probable decedent preference while retaining rebuttability for individual circumstances.

dead-hand control
testamentary intent
unilateral dispositions
will construction
estate-planning boilerplate
majoritarian defaults
contract analogy

Trusts and estates contain exceptions and information-producing mechanisms, so the authors characterize the majoritarian orientation as dominant rather than absolute.

The section identifies the impossibility of post-death modification, the unilateral nature of wills, and planners’ distrust of legal defaults, then explains why courts treat testator intent as the guiding pole star.


Part II.C, Two Majoritarian Defaults in Wills Law

Intestacy illustrates both the power of a majoritarian default and the danger that a once-plausible family model can lag changing social relationships

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 23–25, that intestacy is the foundational testamentary default: for the many people who die without a will, statutes effectively write an estate plan prioritizing spouses, children, parents, and collateral kin. Empirical work broadly supports that ordering for conventional families, but static kinship categories exclude growing numbers of unmarried partners, stepchildren, and other relationships. The statutory order also helps constitute future norms by signaling what counts as family. This is significant because a default can be broadly majoritarian yet become progressively less representative as the population and its relationships change. It connects to intestate succession, probable intent, family definition, unmarried partners, blended families, expressive law, demographic change, and periodic empirical recalibration.

Intestacy provides the model for broad coverage and the warning that durable defaults must be reassessed rather than mistaken for timeless preference.

intestate succession
probable intent
family definition
unmarried partners
blended families
expressive law
demographic change

The cited survey evidence supports the statutory structure for a majority, not every household, and property outside probate may follow different beneficiary designations.

The section describes intestacy as an opt-out regime, explains its probable-intent rationale and conventional order, cites empirical fit, and shows how cohabitation and blended families expose growing exclusions.


Anti-lapse law shows how an asserted majoritarian default can systematically contradict measured testamentary preferences

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 25–26, that anti-lapse statutes were enacted to improve on the common-law rule sending failed gifts to a residuary estate, but empirical studies often find that testators prefer surviving children over descendants of a predeceased child—the reverse of the statutory result. The statutes can therefore operate as accidental penalty defaults: they impose unwanted outcomes without deliberately producing useful disclosure, and some are sticky enough to be difficult to displace. This is significant because good intentions and the label of probable intent do not establish actual majoritarian fit. It connects to lapse, anti-lapse statutes, empirical wills scholarship, accidental penalty defaults, legislative revision, default stickiness, and the need to test GenEm presumptions against observed preferences.

The anti-lapse mismatch makes empirical measurement and future recalibration central safeguards for any newly constructed GenEm default.

lapse
anti-lapse statutes
empirical wills scholarship
accidental penalty defaults
default stickiness
probable intent
legislative revision

Surveyed preferences about lapse vary by beneficiary relationship and study design, so the critique is evidence of systematic mismatch rather than proof of one universal preferred rule.

The article traces the judicial lapse rule, the legislative anti-lapse response, and empirical findings that statutory presumptions are over- and under-inclusive and may reverse common preferences.


Prior evidence strongly favors respecting express consent but does not justify a universal prohibitory default when consent is unknown

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 27–28, that the first empirical GenEm study revealed strong posthumous autonomy preferences: 89 percent rejected emulation contrary to a decedent’s wishes, while objection fell sharply when the use matched those wishes. But its recommendation of an opt-in-only default rested on vignettes that treated informal messages as consent, limited users to friends, and did not vary family, commercial, public, or substantive uses. Preferences may also change as respondents become familiar with the technology. This is significant because opposition under one under-specified scenario cannot answer the legally distinct questions of who may use digital remains and for what purpose. It connects to informed consent, posthumous autonomy, opt-in defaults, external validity, preference formation, user identity, use context, and empirical legal design.

The critique motivates a factorial, context-sensitive survey rather than dismissing prior work or importing its blanket prohibition into probate law.

posthumous autonomy
informed consent
opt-in defaults
external validity
preference formation
user identity
use context

The authors’ discussion of the earlier study is a methodological critique; their own survey likewise measures stated attitudes rather than actual testamentary choices over time.

Part III reports the earlier study’s consent effects and unknown-consent rejection, then identifies limitations involving the form of consent, exclusive focus on friends, omitted purposes, and unstable preferences.


Part III.B, Survey Methods and Study 1 Design

Matched AI and non-AI scenarios can separate objections to generative technology from objections to the underlying posthumous act

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 28–29, that legal design needs to know whether respondents experience an AI-specific moral aversion or instead condemn the same intrusion regardless of technique. Their first study therefore randomly presented matched third-person scenarios involving intimate images, avatars and journals, jokes, advertising, voices and songs, films, and grief counseling, with the decedent leaving no will or instructions. The 156-person Positly sample was selected and screened to approximate national demographics and reduce AI-generated responses. This is significant because a blanket GenEm prohibition would be poorly targeted if disapproval is primarily use-specific rather than technology-specific. It connects to experimental design, paired comparisons, the AI-Ick hypothesis, third-person framing, nationally representative sampling, technological neutrality, and posthumous consent.

The design turns a vague intuition about creepy technology into a comparison between the moral effect of generation and the moral effect of the underlying conduct.

AI-Ick hypothesis
paired comparisons
experimental design
third-person framing
technological neutrality
posthumous consent
survey sampling

The sample contains modest demographic deviations, has 156 respondents, and uses hypothetical third-person judgments that may differ from actual decisions or first-person testamentary preferences.

The methods section describes recruitment, demographic alignment and deviations, human-response safeguards, random ordering, the paired scenarios, and the absence of express decedent instructions.


Part III.B.1, Study 1: Third Person Preferences

The survey finds no general AI-Ick: posthumous acceptance depends principally on the act and context, although generation intensifies objection in some visual uses

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 29–32, that respondents did not reject AI versions of posthumous representation across the board. They preferred conversational avatars to publishing private journals and AI-supported grief counseling to the traditional comparison; they judged AI and non-AI memorial songs or jokes almost identically. Yet AI-generated intimate images and digital film appearances were materially less acceptable than discovered images or lookalike actors, suggesting that creating an unreal visual performance can cross an additional moral line. This is significant because the evidence rejects both categorical technological panic and categorical equivalence: the underlying use generally drives judgment, but generation can aggravate particular violations. It connects to contextual privacy, dignitary harm, intimate imagery, grief technology, synthetic performances, technological neutrality, and graduated rights.

The results support a purpose-sensitive regime capable of distinguishing comforting mediation, ordinary appropriation, and fabrication that makes a decedent appear to act.

contextual privacy
dignitary harm
intimate imagery
grief counseling
synthetic performances
technological neutrality
graduated rights

The paired scenarios are not identical in every non-AI respect—for example, discovery differs from fabrication and a lookalike differs from a digital insertion—so differences should not be attributed to a single technical feature with certainty.

Study 1 reports means and significance tests for intimate images, avatars and journals, grief counseling, films, songs, and jokes, then interprets both significant differences and null results as evidence of contextual judgment.


Part III.B.2, Study 2: First Person Preferences

Respondents consistently prefer family control over public control of their own posthumous emulations

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 32–35, that first-person preferences turn strongly on the identity of the user. Overall acceptance was mildly skeptical at 40.98, but family uses averaged 47.7 compared with 34.33 for public uses, a highly significant difference that persisted across memorial, educational, recreational, research, and commercial categories. Family memorialization reached the study’s highest acceptance score, 66.82, while public memorialization fell to 44.66. This is significant because the same nominal purpose can be understood as intimate legacy within a family and appropriation when performed by strangers. It connects to relational privacy, family stewardship, testamentary intent, contextual integrity, digital remains, probate priority, and user-classified defaults.

The repeated family-public gap supplies the empirical basis for making family identity the first prong of the proposed GenEm presumption.

family stewardship
relational privacy
testamentary intent
contextual integrity
digital remains
probate priority
user identity

Family is treated as a broad survey category even though actual families may be conflicted, estranged, abusive, or differently situated, and the study does not resolve priority among relatives.

Study 2 compares aggregate family and public scores and reports purpose-specific interaction results showing a roughly 15-to-20-point family advantage across nearly every category.


Purpose independently structures posthumous AI preferences, with memorial and educational uses favored and political and commercial uses rejected

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 33–35, that respondents distinguished sharply among purposes for their own posthumous avatars. Memorialization scored 55.7 and education 54.4, statistically indistinguishable results suggesting that preserving knowledge can resemble preserving memory. Commercial use fell to 30.6 and political use to 25.1; unlike other categories, political use remained strongly disfavored whether controlled by family or the public. Recreational use occupied a middle ground. This is significant because neither a property transfer to family nor an abstract consent to emulation captures the decedent’s likely view of what the persona may be made to do. It connects to memorialization, educational legacy, political appropriation, commercial exploitation, purpose limitation, knowledge preservation, and use-classified defaults.

The purpose hierarchy supplies the second prong of the proposal and identifies political speech as a particularly sensitive form of posthumous appropriation.

memorialization
educational legacy
political appropriation
commercial exploitation
purpose limitation
knowledge preservation
use identity

The findings are current stated preferences from one sample; categories such as education, research, politics, and commerce can overlap or be framed differently in concrete disputes.

Study 2 reports purpose means, Tukey comparisons, and user-purpose interactions, emphasizing the equivalence of memorial and educational uses and the uniformly low political scores.


Part IV, Tailored Default Rules for GenEm

Probate courts should serve as first responders to GenEm disputes because they can supply an immediate default within an institution already built to administer post-mortem control

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 36–37, that probate courts will encounter GenEm disputes before legislatures can complete a slow statutory response and should therefore take the first pass at deciding who may activate a decedent’s digital identity and for what purposes. This role fits probate rather than being an alien expansion: wills, defeasible estates, support trusts, and spendthrift trusts have long enforced conditions on how property may be used after death, without limiting such control to celebrities or lifetime commercialization. This is significant because immediate, general protection can arise from existing adjudicative authority while still provoking later legislative correction. It connects to probate jurisdiction, judicial gap filling, dead-hand control, conditional gifts, trusts, institutional competence, and court-legislature dialogue.

The proposal identifies a practical institution capable of supplying protection now and a familiar doctrinal home for purpose-limited post-mortem control.

probate courts
judicial gap filling
dead-hand control
conditional gifts
trusts
institutional competence
court-legislature dialogue

The article argues for judicial initiative but does not establish that every state probate court has identical jurisdiction, remedial authority, or procedural capacity over third-party platform and AI disputes.

Part IV describes legislative delay, identifies probate judges as frontline decisionmakers, and analogizes GenEm restrictions to established conditions on probate and nonprobate transfers.


Common-law adaptation can bridge a dangerous decade-long lag between technological harm and comprehensive probate legislation

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 37–40, that courts have historically adapted old doctrines to new technologies and created defaults when positive law left consequential gaps. Probate itself moved from a judicial lapse rule to anti-lapse statutes, while the digital-assets experience shows the cost of waiting: public disputes over access arose years before uniform-law work and widespread adoption of RUFADAA, a process exceeding a decade. GenEm capability is advancing too quickly to leave decedents unprotected through a similar interval. This is significant because judicial defaults can function as provisional infrastructure rather than an assertion that courts should have the final word. It connects to common-law evolution, technological disruption, RUFADAA, fiduciary access to digital assets, lapse doctrine, legislative latency, and provisional regulation.

The historical examples provide an institutional justification for judicial action now followed by empirical and legislative refinement later.

common-law evolution
technological disruption
RUFADAA
fiduciary access
lapse doctrine
legislative latency
provisional regulation

Past adaptation does not guarantee doctrinal authority or uniform results in GenEm cases, and rapid judicial action can itself produce divergent or poorly informed rules.

The section uses remote will execution, common-law contract defaults, lapse and anti-lapse, and the decade-long path to RUFADAA to illustrate both legal adaptability and legislative delay.


The GenEm default should combine categorical ex ante presumptions with case-specific rebuttal: family memorial and educational uses are allowed, while public, commercial, and political uses are barred

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 40–43, that the binary defaults common in wills law cannot represent survey preferences that change with both user and use. Their two-prong default presumptively permits family members to employ a decedent’s digital remains for memorialization and education, presumptively prohibits family commercial and political uses, and presumptively bars public use. Unlike an open-ended tailored standard discovered only after litigation, these classifications announce rules ex ante, yet a will, documented preference, or sufficient case-specific evidence can reverse a presumption. This is significant because the hybrid captures much of the predictability of a rule, the fit of a tailored standard, and the disclosure incentive of a penalty default. It connects to tailored defaults, rebuttable presumptions, family stewardship, memorialization, educational use, commercial exploitation, political appropriation, and testamentary opt-outs.

This is the operative doctrinal core of the article: a matrix of user and use replaces both laissez-faire transfer and blanket prohibition.

tailored defaults
rebuttable presumptions
family stewardship
memorialization
educational use
commercial exploitation
testamentary opt-outs

The categories can overlap, family members can disagree, and the article does not specify a complete evidentiary standard or priority hierarchy for every effort to rebut a presumption.

The proposal contrasts binary and tailored defaults, states presumptions for family and public actors and for memorial, educational, commercial, and political purposes, and explains how express or circumstantial evidence can alter them.


Rebuttability operationalizes testamentary intent when a decedent’s actual preference departs from the survey-based classification but was never formally recorded

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 42–43, that each user-and-use classification should be only a presumption rather than a conclusive command. A decedent may have wanted a public educational use, consented informally to family commercialization, or opposed education despite the majority pattern; a family member or other claimant may present sufficient evidence of that individual preference even when no will records it. The default remains the law’s best guess when evidence is absent, paralleling intestacy and lapse. This is significant because empirical majoritarianism serves autonomy only if it yields to credible proof about the person whose digital identity is at stake. It connects to rebuttable presumptions, individualized intent, evidentiary hearings, intestacy, will gaps, informal consent, and the distinction between defaults and mandatory rules.

The rebuttal mechanism prevents a population average from becoming a mandatory moral judgment and gives courts a structured path for resolving atypical cases.

rebuttable presumptions
individualized intent
evidentiary hearings
intestacy
will gaps
informal consent
mandatory rules

The paper leaves questions about burdens, admissible evidence, proof thresholds, conflicts among survivors, and the risk that well-resourced users will rebut presumptions more successfully.

The authors illustrate rebuttal with decedents who favored otherwise prohibited commercial or public educational uses and those who opposed presumptively permitted family education, then tie the structure to probate’s best-guess approach.


Part IV.B, Enforcing the GenEm Default with Technology

Data control, preference registries, model guardrails, and provenance metadata can enforce GenEm limits before harmful outputs are generated

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 43–45, that GenEm enforcement should begin upstream because a personalized model needs source material. Private diaries, recordings, and similar high-fidelity inputs should remain estate-controlled and be categorically excluded absent express documented consent; third-party platforms should not be free to repurpose shared material for a new emulation. Models could then consult trusted registries recording permitted purposes, prohibitions, licenses, and estate authorizations, while output metadata links generated content to training sources and permissions. This is significant because a court order after viral dissemination cannot fully restore dignity, whereas data and model controls can make legal defaults preventative and auditable. It connects to post-mortem data rights, training-data governance, consent registries, model guardrails, provenance, platform duties, privacy, and privacy-enhancing system design.

The technical layer translates the legal user-and-use matrix into operational checks that can prevent misuse and preserve evidence when prevention fails.

post-mortem data rights
training-data governance
consent registries
model guardrails
provenance metadata
platform duties
privacy by design

The authors acknowledge leaks, public data, partial emulation, registry governance, interoperability, and model compliance as practical challenges; the proposal does not specify a complete technical standard.

The enforcement section distinguishes private and third-party-held source material, proposes expanded post-mortem control, describes dynamic emulation-rights registries, and calls for model checks and traceable output metadata.


Part IV.C, Legal Safeguards for the GenEm Default

Injunctions and constructive trusts can stop unauthorized GenEm and strip benefits from public actors, heirs, or fiduciaries who violate the default

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 45–47, that familiar equitable remedies can police the back end of GenEm governance. An estate can seek an injunction to halt a prohibited commercial, political, or other deployment before irreparable dignitary harm spreads. When a wrongdoer acquires revenue, control, or other identifiable benefit through an unauthorized use, a constructive trust can prevent unjust enrichment and require conveyance; the same logic reaches an heir, third party, or personal representative who breaches fiduciary duties by authorizing or profiting from misuse. This is significant because the proposal does not require courts to invent a wholly new remedial vocabulary for digital identity. It connects to injunctive relief, constructive trusts, unjust enrichment, estate property, fiduciary loyalty, right of publicity, political appropriation, and equitable tracing.

Traditional probate and equity remedies give the default practical consequences against both outsiders and insiders who misuse a decedent’s digital persona.

injunctive relief
constructive trusts
unjust enrichment
fiduciary duty
estate property
right of publicity
equitable tracing

Constructive trusts ordinarily require identifiable property or benefit and litigation can be slow; speech defenses, jurisdiction, insolvency, anonymity, and noncommercial harms may complicate relief.

The section explains injunctions, traces constructive trusts through fraud, undue influence, conversion, and fiduciary breach cases, and applies those remedies to unauthorized political and other GenEm uses.


A layered evolutionary default should operate as a living legal algorithm that protects digital identity now and updates through experience

Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on page 48, that the proposed default is a starting architecture rather than a final code. Its initial family-and-memorial orientation implements current evidence about probable intent, while probate decisions can debug user and use categories as technology and norms change. Data rights and source controls, model guardrails and registries, and injunctions and equitable remedies form mutually reinforcing preventative and reactive layers. This is significant because neither a single property entitlement nor a static prohibition can remain accurate or enforceable across rapidly changing forms of digital persistence. It connects to evolutionary defaults, layered governance, adaptive probate law, model interpretability, global registry standards, digital personhood, AI-generated wills, algorithmic trusts, autonomy, dignity, and legacy.

The conclusion integrates the empirical, doctrinal, technical, and remedial components into an adaptive system capable of protecting both current decedents and future forms of digital identity.

evolutionary defaults
layered governance
adaptive probate law
model interpretability
digital personhood
AI-generated wills
algorithmic trusts

The framework requires future institutional design, standards, jurisdictional coordination, and repeated empirical validation; it cannot guarantee perfect prevention or global enforcement.

The conclusion characterizes the default as a living algorithm, restates family memorialization as the initial presumption, combines data, model, and legal controls, and extends the framework to future digital-personhood disputes.


Yonathan A. Arbel & David A. Hoffman, Generative Interpretation, 99 N.Y.U. L. Rev. 451 (2024)

Generative interpretation uses language models as an aid for reconstructing contractual meaning

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 455–460, that large language models can examine an agreement together with relevant context and generate disciplined estimates of what the parties meant. They call this method generative interpretation and present it as a lower-cost, more replicable, and more transparent adjunct to judicial interpretation. This is significant because it reframes language models from generic legal chatbots into instruments for testing interpretive intuitions against linguistic patterns. It connects to the article’s later case studies of ordinary meaning, ambiguity, gap filling, and extrinsic evidence, while leaving the ultimate legal judgment with courts.

The proposal supplies a distinct name, scope, and institutional role for LLM-assisted contract interpretation rather than treating model output as free-form legal advice.

contract interpretation methodology
computational linguistics
judicial decision support
access to justice

The authors do not claim that a plausible model answer is ground truth, that their case studies measure judicial accuracy at scale, or that models should replace judges.

The introduction defines the method, describes its expected cost and consistency benefits, previews grounded contract case studies, and repeatedly characterizes the model as a judicial aid rather than a decisionmaker.


Contract interpretation is substantially a backward-looking prediction about meaning, but prediction cannot settle every legal question

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 461–464, that leading approaches to contract interpretation share a substantial predictive ambition: they seek to reconstruct what the parties, reasonable parties, or the relevant linguistic community would have understood at formation. The approaches diverge over whose meaning counts, what evidence should inform the prediction, and what legal consequences follow. This is significant because it identifies a common task that a language model can assist without pretending that interpretive theory has become value-free. It connects to debates over subjective intent, objective meaning, textualism, contextualism, and the distinction between an empirical prediction and a court’s normative choice.

Separating the predictive component from the legal and normative components explains both why an LLM can be useful and why its output cannot itself decide a case.

objective theory of contract
subjective intent
interpretation versus construction
empirical legal analysis

The authors treat prediction as an important shared component of interpretation, not as a complete account of every interpretive theory or every judicial responsibility.

Part I.A organizes interpretive approaches around backward-looking prediction and then identifies unresolved questions about the target, evidentiary basis, and legal significance of that prediction.


Part I.B–D, Textualism, Contextualism, and Empirical Methods

Existing interpretive methods trade off evidentiary richness, cost, consistency, and bias

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 464–473, that neither textualism nor contextualism escapes institutional tradeoffs. Textualism controls cost and can improve predictability, yet dictionaries, canons, and judges’ linguistic intuitions leave room for selection and hindsight; contextualism admits richer evidence, yet discovery and factfinding are costly and can expose decisionmakers to bias and strategic behavior. This is significant because the familiar doctrinal disagreement partly reflects the limitations of available interpretive technologies rather than an unavoidable choice between text and context. It connects to corpus linguistics and survey experiments, which discipline intuition in useful ways but remain constrained by context, sample design, expense, or limited judicial adoption.

The diagnosis creates the institutional problem that generative interpretation is meant to address: obtaining contextual and linguistic information without reproducing all of conventional litigation’s costs and biases.

textualism
contextualism
corpus linguistics
survey evidence
litigation costs

The discussion does not establish that any one conventional method is uniformly inferior; it emphasizes that each serves values and incurs costs that vary by dispute and party.

The article compares textual and contextual regimes, critiques apparently objective aids such as dictionaries and canons, and evaluates corpus and survey methods as partial empirical responses.


Part II.A, Grounding Generative Interpretation

LLMs can produce context-sensitive linguistic predictions even though their internal reasoning remains opaque

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 473–483, that transformer-based language models use learned statistical relationships and attention to predict language in context across a vast body of training material. That architecture lets a model integrate more of a contract and its surroundings than a dictionary lookup or a narrow corpus query, but the output remains a prediction rather than a transparent causal explanation of how people actually spoke or thought. This is significant because interpretive usefulness can coexist with mechanistic opacity: a tool may test linguistic probabilities without supplying a human-style rationale for them. It connects to the interpretability problem in machine learning, the law’s demand for reason-giving, and the need to distinguish an evidentiary signal from a judicial explanation.

The technical account identifies both the comparative advantage of LLMs—context-sensitive prediction—and the epistemic limit that governs how courts should describe and use their outputs.

transformer attention
language-model prediction
explainable AI
judicial reason-giving

The authors do not equate next-token prediction with human understanding, causal proof, or a self-justifying legal conclusion.

Part II.A describes training, embeddings, attention, context windows, and probabilistic output, while stressing that even model builders cannot fully explain particular predictions.


A language model can check judicial confidence about ordinary meaning by exposing a competing probabilistic reading

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 483–485, that the Famiglio prenuptial dispute shows how a language model can test a court’s asserted ordinary meaning. After receiving the agreement and the sequence of two divorce filings, the model treated the second filing as the more natural date for calculating years of marriage, contrary to the appellate court’s confident reliance on the indefinite article and a golf-course analogy. This is significant because the model’s contrary reading makes judicial certainty itself contestable even when it does not prove that the judge was wrong. It connects to ordinary-meaning doctrine, representativeness of judicial intuitions, probabilistic language, and the possible relevance of private meaning or trade context.

The example demonstrates a modest but practical use: an LLM can operate as a check on overconfidence where a judge presents one linguistic intuition as universal.

ordinary meaning
judicial intuition
probabilistic semantics
private meaning

One model response cannot establish the parties’ actual intent, and case-specific extrinsic evidence could justify a meaning different from the model’s public-language prediction.

The article reconstructs the prenup dispute, contrasts the court’s first-filing interpretation with the model’s second-filing prediction, and explains why the divergence should prompt further reflection.


Model outputs can represent ambiguity as a distribution of plausible readings rather than a binary intuition

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 485–492, that language models can help courts see ambiguity as a spectrum of plausible interpretations. In Trident, several models leaned against a borrower’s asserted prepayment right while still displaying a minority probability; in Ellington, repeated outputs across multiple prompt variations more often read “other affiliates” to include later-created affiliates than the state high court did. This is significant because a distribution can expose plausible minority meanings and check a court’s confidence without collapsing the legal ambiguity threshold into a model score. It connects to summary-judgment screening, linguistic communities and private meanings, robustness testing across prompts and models, and the separate judicial question of how much plausibility is legally enough.

The proposed output form preserves disagreement and uncertainty that a single yes-or-no model answer would hide, making the tool more useful as an evidentiary aid.

contractual ambiguity
probability distributions
minority linguistic meanings
prompt robustness
summary judgment

The models do not decide whether a reading is legally reasonable, numerical scores should not be treated as calibrated facts, and prompt framing is itself a consequential choice.

The section compares multi-model results in Trident and repeated, varied prompts in Ellington, then treats the resulting distributions as checks on confidence rather than dispositive rulings.


LLMs can test proposed gap fillers against the whole agreement and reveal both convergence and unresolved disagreement

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 492–495, that a language model can help a court ask what parties would likely have provided for an omitted contingency by evaluating candidate rules against the full agreement. In the Haines sewage-contract study, two models rejected termination at will and were open to several duration rules, yet they differed over whether the city’s obligations expanded with future community growth. This is significant because agreement between models can strengthen a textual inference while disagreement can direct attention to overlooked provisions and competing limiting principles. It connects to default rules, incomplete contracts, the boundary between interpretation and construction, and the common-law practice of implying terms.

The example shows that useful model assistance includes mapping the space of defensible gap fillers and locating the textual source of disagreement, not merely selecting a winner.

incomplete contracts
default rules
interpretation and construction
implied terms
model disagreement

The models disagreed on an important scope question, and their assessments cannot choose the legally proper gap-filling rule or establish the historical parties’ actual counterfactual agreement.

The authors feed two models the lengthy 1924 agreement, compare responses on duration and scope, and highlight both shared rejection of termination at will and divergent readings of an expansion clause.


Adding extrinsic evidence sequentially can reveal its marginal effect on an interpretation

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 495–497, that contextual evidence can be introduced to a model in stages to test how each addition changes the predicted meaning of a contract. In Stewart, they begin with a sparse construction agreement and an assumed payment default, then add evidence of a phone conversation and an asserted industry custom to observe changes in the models’ assessment of monthly payment. This is significant because the direction of change can help a court estimate whether expensive discovery into a category of extrinsic evidence is likely to matter. It connects to contextualism, the marginal probative value of evidence, proportional discovery, and staged sensitivity analysis.

The procedure turns contextual evidence into an incremental comparison and may help prioritize litigation resources before every factual dispute is fully developed.

extrinsic evidence
contextualism
sensitivity analysis
proportional discovery
industry custom

The assumed legal default may be contestable, the historical record is sparse, and the authors caution that the models’ self-reported confidence levels should not be read literally; the direction of change is more informative.

The case study describes a baseline prompt and two successive additions of context, then compares how model assessments move as the phone call and trade custom enter the record.


The relevant institutional test is whether generative interpretation is good enough for ordinary, resource-constrained adjudication

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 499–503, that the practical benchmark for generative interpretation is not whether it always surpasses ideal, labor-intensive linguistic analysis. The more urgent comparison is with ordinary adjudication in resource-deprived courts, where inexpensive and accessible model assistance may improve consistency, settlement calibration, and the position of parties who lack repeat-player expertise. This is significant because it places access to justice and opportunity cost at the center of technology assessment instead of comparing automation only with the best imaginable human performance. It connects to unequal legal information, litigation budgets, predictive settlement, clearer ex ante contracting, and a more broadly accessible form of textual analysis.

The institutional benchmark changes the policy question from abstract human-versus-machine superiority to comparative performance under the real constraints of courts and litigants.

access to justice
resource-constrained courts
repeat-player advantage
predictive settlement
institutional comparison

The authors present this as a promise and a competency question, not as proof that current unspecialized models are reliable in every ordinary case; their case studies are curated rather than representative.

The article links low-cost prediction to information equality and settlement while expressly asking whether the method is sufficiently competent for ordinary courts, rather than superior to careful artisanal analysis in all cases.


Part III.A.1–2, Hallucinations and Manipulation

Reliable legal use requires cross-checking outputs and governing prompts, models, and disclosure

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 503–505, that hallucinations and strategic prompt design require procedural safeguards around generative interpretation. They propose comparing models and multiple inputs, scrutinizing party-supplied framing, disclosing the model and prompts, and allowing contracting parties to specify a model in advance. This is significant because reproducibility depends on governing the whole interpretive setup, not merely preserving a model’s final sentence. It connects to adversarial presentation, expert-method disclosure, model versioning, contractual choice of interpretive method, and the creation of a persistent record that later readers can audit.

The proposal treats prompts and model selection as legally relevant methodological choices and gives courts a basis for testing rather than simply trusting generated output.

AI governance
reproducibility
adversarial procedure
model choice clauses
methodological disclosure

Cross-model agreement is not truth, safeguards can add cost, and party control over model selection cannot eliminate manipulation, hallucination, or changes between model versions.

The pitfalls discussion responds to fabricated outputs and framing effects with multiple-model checks, varied inputs, disclosure, and possible ex ante party choice over the model used.


Part III.A.3–7, Interpretability, Bias, Attacks, and Linguistic Drift

Majoritarian training data, adversarial inputs, opacity, and linguistic drift define the domain in which LLM interpretation is safe and useful

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 505–509, that courts must limit and qualify LLM use because model opacity, majoritarian training patterns, adversarial inputs, and temporal drift can distort contractual meaning. A model may suppress a local, private, or minority linguistic practice; hidden instructions in a document may manipulate it; and contemporary training data may misread an old agreement through later usage or later decisions. This is significant because the same scale that makes an LLM sensitive to public language can make it unreliable for historically bounded or nonmajoritarian meaning. It connects to algorithmic bias, cybersecurity, historical corpus methods, linguistic communities, and the authors’ insistence that models assist textual analysis rather than make human-critical legal decisions.

The limitations define a deployment boundary: generative interpretation needs security practices, attention to minority and private meaning, and time-matched evidence before its predictions deserve weight.

algorithmic bias
prompt injection
historical semantics
private language
human judicial responsibility

The article offers risk-management directions rather than a demonstrated cure; specialized time-bounded models may not exist, and transparent disclosure does not make an opaque model fully explainable.

The section enumerates interpretability limits, majoritarian and discriminatory effects, adversarial attacks, and contamination by later language, then recommends caution, tailored models, and a preserved methodological record.


Part III.B and Conclusion, Beyond the Textualist/Contextualist Divide

Generative interpretation offers a contingent third path between textualism and contextualism while preserving party choice and judicial authority

Professors Yonathan A. Arbel and David A. Hoffman claim, in “Generative Interpretation” on pages 510–514, that LLM-assisted interpretation can disrupt the inherited choice between predictable but narrow textualism and information-rich but expensive contextualism. If models can absorb broader evidence consistently and estimate the incremental value of context, courts may be able to relax categorical exclusions of extrinsic evidence while parties retain the ability to choose, constrain, or reject the method. This is significant because it treats interpretive doctrine as partly dependent on adjudicatory technology and gives the new method possible distributive consequences for uncounseled and poorer parties. It connects to party autonomy, interpretive defaults, the parol evidence rule, relational contracting, and the prospect of a distinct methodology that supplements rather than replaces judicial judgment.

The conclusion makes the technology relevant to doctrinal design: cheaper contextual analysis could alter the cost-based premises supporting current evidentiary defaults.

textualism-contextualism divide
party autonomy
interpretive defaults
parol evidence rule
distributional effects

The method is not appropriate for every contract, parties should be able to opt out or specify alternatives, and the authors expressly stop short of replacing judges or resolving the legal significance of model predictions.

The final section argues that LLMs can combine predictability with broader evidence, discusses party control and distributional effects, and closes by preserving a separate role for judicial legal judgment.


Yonathan A. Arbel & Shmuel I. Becher, Contracts in the Age of Smart Readers, 90 Geo. Wash. L. Rev. 83 (2022)

Introduction and Part I, Smart Readers: Technology and Capabilities

Language-model smart readers can change consumer contracting by simplifying, personalizing, constructing, and benchmarking boilerplate

Professors Yonathan A. Arbel and Shmuel I. Becher claim, in “Contracts in the Age of Smart Readers” on pages 83–94, that language models can become inexpensive, widely accessible smart readers of contracts, disclosures, and privacy policies. They organize the technology around four capabilities: translating difficult text into usable explanations, personalizing presentation to the reader, constructing legal consequences from terms, and benchmarking one contract against market alternatives. This is significant because contract law has long treated unread boilerplate as a stubborn human and institutional problem, while smart readers recast at least part of it as a technological problem whose solution can reshape assent, competition, and regulation. It connects to informed consent, consumer protection, legal automation, natural-language processing, boilerplate design, access to justice, and the allocation of responsibility when an automated explanation is wrong.

The framework identifies a general-purpose intermediary between consumers and standardized text and makes its market-wide and doctrinal consequences a subject for contract law before adoption is widespread.

consumer boilerplate
informed assent
natural-language processing
legal automation
consumer protection
access to justice

The article uses early GPT-3 outputs selected to illustrate future capabilities, expressly acknowledges errors and weak reliability, and evaluates smart readers against realistic alternatives such as nonreading rather than against perfect legal advice.

The abstract and introduction define smart readers, preview four capabilities and their benefits and risks, demonstrate early outputs for difficult clauses, and frame the article as a forward-looking inquiry into adoption, market consequences, and legal response.


Smart readers can make dense contracts accessible through more than mere shortening, but simplification necessarily risks losing meaning

Professors Yonathan A. Arbel and Shmuel I. Becher claim, in “Contracts in the Age of Smart Readers” on pages 95–99, that smart readers can reduce contractual complexity by summarizing, lowering the language register, shortening and restructuring sentences, changing formatting, removing nonessential material, and adding examples or a more engaging voice. Their lease and at-will-employment examples show that an explanation can be easier to process even when it is not shorter than the original. This is significant because the proposal addresses several sources of unreadability at once instead of assuming that a shorter disclosure is automatically a comprehensible one. It connects to plain-language drafting, disclosure design, cognitive load, legalese, contractual bloat, and lossy compression, while the model’s serious legal mistake about discrimination illustrates why accessibility and accuracy must be evaluated separately.

Simplification could expose obligations otherwise buried in prolix text, but it also reveals that a fluent and entertaining explanation can conceal substantive legal error.

plain language
contract design
cognitive load
legalese
lossy compression
AI hallucination

Summarization inevitably omits information; some contractual length serves precision; and the demonstrated model produced a major legal error, so readable output is not necessarily reliable output.

The section identifies semantic difficulty, length, formatting, and legalese as barriers, compares original lease and employment clauses with model explanations, and characterizes smart-reader simplification as liberal, accessible, and necessarily lossy.


Consumer-side personalization can adapt a uniform contract to a reader’s language, cognition, culture, and intersecting characteristics without requiring the firm to know each consumer

Professors Yonathan A. Arbel and Shmuel I. Becher claim, in “Contracts in the Age of Smart Readers” on pages 99–104, that the one-size-fits-all disclosure model fails because readers differ in language, culture, idiom, cognitive ability, socioeconomic circumstances, and preference for abstract or concrete explanation. A smart reader can personalize the presentation on the consumer’s device—translating and simplifying for a recent immigrant, explaining through examples for a teenager, adapting regional language, or combining several characteristics—without requiring the seller to collect the same information. This is significant because it moves personalization from the drafter’s side, where it is expensive and potentially exploitative, to the reader’s side, where it can serve comprehension. It connects to linguistic access, disability and cognitive accommodation, intersectionality, private dictionaries, the reasonable-consumer standard, and the difference between consumer-serving and seller-serving personalization.

User-side adaptation could make a single standardized agreement intelligible to heterogeneous audiences while reducing firms’ informational and implementation burdens.

personalized disclosure
linguistic access
intersectionality
private dictionaries
reasonable consumer
consumer-side technology

Personalization does not reveal the substantive content of governing law, remains lossy, cannot completely eliminate divergent private meanings, and later sections show that personalization can also facilitate discrimination.

The authors use Spanish-language, youth-oriented, concrete-example, regional-dialect, and intersectional examples to show how smart readers can tailor output and move parties toward shared dictionaries.


Smart readers can sometimes explain the legal consequences of simple terms, although their construction cannot be authoritative and may implicate unauthorized-practice rules

Professors Yonathan A. Arbel and Shmuel I. Becher claim, in “Contracts in the Age of Smart Readers” on pages 104–106, that understanding a contract requires more than parsing its words: readers often need construction of a term’s legal effect. They show a smart reader explaining the consequences of buying a car “as is” and answering whether brief foreign travel triggers a permanent-residence default clause. This is significant because it positions the technology not merely as a readability aid but as a low-cost source of preliminary legal orientation for ordinary questions. It connects to the interpretation-construction distinction, consumer legal education, follow-up questioning, unauthorized practice of law, and the comparative baseline of what a reasonable lawyer—or an unaided consumer—would provide.

Even limited legal construction could make routine rights and obligations usable without professional help, expanding the technology’s role beyond textual simplification.

contract interpretation
legal construction
consumer legal education
unauthorized practice of law
interactive legal tools
legal consequences

Construction is contested even among lawyers and judges; outputs are nonauthoritative, can omit defenses and state-law variation, and should be judged differently for mundane and penumbral questions.

The section distinguishes construction from linguistic interpretation, tests the model on an as-is sale and residence clause, and cautions that legal disagreement and unauthorized-practice concerns constrain this capability.


Benchmarking can reduce comparison costs by scoring contract terms against the market and directing consumers to better alternatives

Professors Yonathan A. Arbel and Shmuel I. Becher claim, in “Contracts in the Age of Smart Readers” on pages 106–109, that benchmarking may be the most powerful smart-reader capability because it can score a contract relative to market practice, explain the score, and point the consumer toward sellers with better terms. Privacy-policy classifiers and PrivacyCheck illustrate how a model can identify clauses, compare an agreement to its sector, and compress a difficult choice into a familiar ranking. This is significant because consumers need not master every clause for contract quality to become a salient product attribute. It connects to comparison shopping, search costs, choice overload, reputation systems, privacy nutrition labels, term competition, and the accumulation of contract corpora that can improve market-specific comparisons.

A usable score can translate opaque legal variation into demand pressure and make contracts part of ordinary product comparison even when full readership remains rare.

contract benchmarking
comparison shopping
search costs
choice overload
privacy policies
reputation scores

Contract scoring is nascent and contestable, depends on normative judgments and relevant comparators, and cannot yet rival a seasoned lawyer; its value rests on improvement over intuitive nonreading, not perfect accuracy.

The authors discuss machine classification of privacy provisions, a deployed browser tool that scores policies against market averages, explanatory rankings, competitor links, and the practical value of imperfect scores.


Part II, Smart Reader Uptake and (No) Reading Theories

Observed adoption of smart readers can discriminate among competing explanations for why consumers do not read contracts

Professors Yonathan A. Arbel and Shmuel I. Becher claim, in “Contracts in the Age of Smart Readers” on pages 109–114, that smart-reader uptake will depend not only on price and interface but on why consumers currently avoid contracts. Readability theory predicts strong adoption; transactional-expectations theory predicts use mainly in unfamiliar or high-stakes domains; rational-apathy theory predicts uptake when automated review materially lowers cost; cognitive-bias theories predict selective use by consumers aware of their limitations; and social-norm theories predict more private than face-to-face use. This is significant because adoption becomes a Wittgenstein’s-ruler test: a tool that removes the barrier posited by a theory also supplies evidence about whether that theory was sound. It connects to empirical consumer behavior, the privacy paradox, rational ignorance, transactional norms, behavioral bias, technology adoption, and the design of subsidies or interfaces.

Even low uptake would be theoretically informative, while the combined theories make modest, heterogeneous adoption more plausible than either universal use or total rejection.

no-reading theories
technology adoption
rational apathy
transactional expectations
cognitive bias
social norms
Wittgenstein's ruler

The authors do not predict a single adoption rate; effectiveness, cost, business model, conflicts of interest, UI/UX, domain, social setting, and consumer heterogeneity remain unresolved empirical variables.

Part II maps five theories of nonreading to distinct uptake predictions, discusses technical and economic feasibility, and explains how actual adoption can test the causal accounts behind consumer-contract policy.