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4 papers
- May 202624 min readconstitutional-lawadministrative-lawstate-ai
Augmentative or Substitutive?
A Constitutional Typology of State Artificial Intelligence
The scholarly and policy debate over state deployment of artificial intelligence has matured around a familiar tripartite critique: algorithmic systems in government are insufficiently transparent, inadequately auditable, and disparately impactful. The remedies proposed — explanation requirements, impact assessments, procurement standards, algorithmic due process — share the implicit premise that all state AI deployments occupy a single normative category, distinguished from one another only by accuracy, transparency, and disparate impact. This paper argues that this premise is mistaken and that the failure to draw a categorical distinction within state AI is responsible for both the under-protection of citizens in some contexts and the over-restriction of beneficial deployments in others. I propose a typological distinction between augmentative state AI — systems that extend the capacity of a human decision-maker while preserving the human as the locus of decision — and substitutive state AI — systems that displace the human decision-maker as the locus of decision, whether formally or functionally. The distinction is not engineering-internal. It is constitutional. Augmentative systems implicate the ordinary law of administrative rationality, professional competence, and tort. Substitutive systems implicate due process, non-delegation, and the procedural conception of democratic legitimacy. The two categories therefore call for fundamentally different regulatory regimes, litigation strategies, and design constraints. The paper develops the typology formally, situates it in constitutional and administrative doctrine, and confronts its hardest case: the nominal–functional gap between systems that are described as advisory but operate substitutively in practice. I propose an empirical methodology — override-rate analysis combined with deviation-cost analysis — for distinguishing nominal from functional locus of decision, and I argue that the constitutional category should be assigned by functional rather than nominal classification. The paper closes with doctrinal implications for litigators, procurement officials, legislators, and agency designers.
Holden Zerega - May 202648 min readadoptionstrategyreal-options
The Bifurcation Hypothesis
Why Proactive AI Will Diffuse Asymmetrically in the Firm
The dominant narrative about artificial intelligence in the firm is one of broad, accelerating diffusion. Consultancies forecast double-digit annual growth in enterprise adoption; survey instruments document rising deployment counts across functional areas; the popular and trade press describe a wave that will, within a decade, transform every layer of the corporation. This paper argues that the narrative is incomplete in a specific and predictable way. Proactive AI — algorithmic systems that initiate action, monitor state, and operate on schedules or triggers without per-instance human authorization — will not diffuse uniformly across the functional architecture of the firm. It will diffuse asymmetrically: rapidly and with little contestation in operational and transactional layers where errors are small, recoverable, and rarely litigated, and slowly, haltingly, and reversibly in strategic and adjudicative layers where errors are large, irreversible, and routinely litigated. The mechanism driving the asymmetry is not technical capability. It is liability exposure interacting with Knightian uncertainty about agent behavior in the tail of the deployment distribution. Where tail outcomes carry tort, fiduciary, or regulatory exposure that loads on the deployer rather than the agent, the deployment becomes a real-options problem in which the option value of waiting is high — high enough, in many contexts, to dominate the expected productivity gain from immediate deployment. The paper develops the bifurcation hypothesis into four testable predictions with stated directional signs, situates the argument within the technology adoption literature (Rogers, David, Bresnahan and Trajtenberg, Acemoglu and Restrepo), specifies three empirical strategies in detail sufficient for execution, generates sector-by-sector predictions across finance, healthcare, legal services, insurance, manufacturing, and professional services for a five-year observation window, and confronts the most serious rival hypothesis — that competitive pressure will force uniform adoption against the structural logic — with the specific empirical pattern that would distinguish bifurcation from competition-driven convergence. The paper closes with the observation that bifurcation, if it obtains, has implications beyond firm strategy: it predicts the institutional sites at which the political contest over AI's penetration into authority structures will be most intense, and it identifies the legal regime — tort and fiduciary law — that will functionally regulate proactive AI long before any AI-specific statute does so.
Holden Zerega - May 202652 min readeconomic-historyautomationdelegation
Delegation Is the Novelty
Why Proactive AI Is Not Just Another Wave of Automation
Every wave of technological change in the modern era has provoked predictions of unprecedented dislocation, and almost every such prediction has been wrong in the same way: the predicted dislocations occurred, but on timescales and through institutional mediations that absorbed the technology into familiar forms of economic and political life. Mechanization did not abolish work; it reorganized it. Electrification did not abolish the firm; it restructured production within the firm. Computerization did not abolish white-collar employment; it reshaped its content. Against this record, the contemporary alarm about artificial intelligence is, on its face, ahistorical. The historical-precedent counterargument — that proactive AI is the latest in a sequence of automation waves, and that the institutional adaptations of the past will, mutatis mutandis, suffice for the present — deserves serious engagement rather than dismissal. This paper argues that the historical-precedent counterargument, taken seriously, ultimately fails, and that it fails for a precise reason: proactive AI is the first technology in the modern industrial sequence to automate not production, not power, not information processing, but delegation itself — the transfer of authority to act on behalf of a principal. Mechanization automated motion. Electrification automated power transmission. Computerization automated symbolic calculation. Each of these prior waves required the human principal to invoke the technology in order for it to do anything. Proactive AI, by contrast, operates on authorization rather than instruction: the principal grants a scope of authority, and the system acts within that scope without per-instance invocation. This is not a quantitative difference in the speed or sophistication of automation. It is the difference between a tool and an agent. The paper develops the definitional distinction with care, conducts a comparative-historical analysis of three prior automation waves and one important partial exception (the rise of the modern corporate hierarchy in response to railroad-era scale), engages directly with Acemoglu and Johnson's Power and Progress (2023), and generates four falsifiable predictions about institutional adaptation. The argument is not that proactive AI is uniquely dangerous, but that it is categorically novel in a way that disables the standard historical-adaptation reassurance.
Holden Zerega - May 202661 min readprincipal-agentfiduciarycorporate-governance
Delegation Without an Agent
Extending Principal–Agent Theory to Non-Sanctionable Actors
The principal–agent literature is among the most successful frameworks in modern economics. From Jensen and Meckling's reformulation of the firm to Holmström's resolution of the moral-hazard problem under risk aversion, the framework has supplied the analytic vocabulary for the study of delegation across corporate, regulatory, fiduciary, and political settings. The framework has a presupposition that has rarely required defense because it has rarely been false: that the agent can bear sanction. Reputational damage, financial loss, professional discipline, criminal liability, and the prospect of dismissal jointly compose the sanction set on which the framework's incentive logic depends. When the principal cannot directly observe the agent's action, the contract substitutes the prospect of conditional sanction for direct observation; the agent internalizes the incentive because the agent can be made worse off. Proactive artificial intelligence systems — systems that initiate action, monitor state, and operate on schedules or triggers without per-instance human authorization — are agents in the economic sense and non-agents in the legal and reputational sense. They have no reputational capital, no financial position, no professional license, no criminal capacity, no continuity of identity across the institutional boundaries that make reputation and discipline coherent. The standard principal–agent solution menu — incentive contracts, monitoring with stochastic verification, fiduciary duty, the threat of termination — degenerates when applied to such agents, and the degeneration is not subtle. The optimal contract under standard assumptions becomes degenerate (any contract is "optimal" because none is binding); monitoring becomes diagnostic rather than disciplinary; fiduciary duty has no incident on which to attach; the threat of termination is a threat against the deployer, not the agent. This paper develops a formal extension of agency theory to non-sanctionable agents. It shows how the standard solutions degenerate, characterizes the residual sanction-bearer as the deployer, and identifies the conditions under which deployer-as-residual-bearer reproduces the desirable incentive properties of the classical framework and the conditions under which it does not. It engages Balkin's information-fiduciary proposal as the most fully developed legal attempt to address the same problem from the fiduciary side, identifying the beneficiary-identification, remedy, and conflict-of-interest problems that limit its reach in the proactive case. It extends the multitask analysis of Holmström and Milgrom (1991) to the non-sanctionable case and identifies new pathologies. It translates the resulting framework into the vocabulary of the alignment literature on corrigibility, scalable oversight, and assistance games, and argues that the two literatures have been addressing one problem in two languages. The paper closes with a research agenda for the institutional economics of non-sanctionable delegation.
Holden Zerega