A Papal Document as Organizational Diagnosis

On Monday, Pope Leo XIV released his first encyclical, titled "Magnifica Humanitas," framing artificial intelligence as a civilizational governance problem rather than a purely technical one. The document's most structurally significant claim is not theological. It is organizational: Leo argues that ownership of AI data must not be left solely in private hands, and that unconstrained technological power poses risks of "domination, exclusion, and death." Tech leaders, politicians, and public intellectuals responded quickly, largely sorting themselves into predictable camps. What I find more interesting than the debate itself is what the encyclical reveals about a specific failure mode in how organizations are currently thinking about AI coordination.

The Private Ownership Claim Is Actually a Coordination Argument

Leo's insistence on distributed AI data ownership is easy to read as a political or ethical position, and it is both of those things. But it is also, underneath, a claim about coordination structure. When a single firm controls the data infrastructure through which millions of workers, institutions, and citizens interpret and respond to their environments, the coordination mechanism is no longer a platform in the traditional sense. It becomes what Rahman (2021) calls an "invisible cage" - a structural constraint that shapes behavior without being legible to those inside it. The encyclical uses the phrase "profoundly human" to gesture at something that organizational theory can articulate more precisely: competencies developed inside algorithmically-mediated environments are endogenous to those environments. When the environment is privately owned and opaque, the competencies that develop are calibrated to the owner's incentives, not to the participant's interests.

Why the "Data Ownership" Framing Misses the Deeper Problem

The public debate following the encyclical has focused almost entirely on the question of who owns the data. This is understandable but analytically incomplete. Ownership is a legal and economic concept. The deeper organizational problem is about schema availability. Kellogg, Valentine, and Christin (2020) demonstrate that algorithmic systems at work systematically obscure the structural logic of their own operation. Workers develop what I have elsewhere called folk theories - individual impressions about how a system works - rather than accurate structural schemas. The distinction matters enormously for governance. You can redistribute data ownership without making the structural logic of AI systems any more legible to the people coordinating through them. If the encyclical is correct that AI poses risks at a civilizational scale, then data ownership reform addresses the symptom while leaving the underlying coordination failure intact.

The Awareness-Capability Gap Appears at the Institutional Level

Most of the commentary responding to Leo's document acknowledged that AI governance is underdeveloped. This is now a consensus position across a wide ideological range, from papal documents to congressional hearings to corporate ESG reports. What is less often acknowledged is that awareness of a problem and capacity to respond to it are not the same thing. This is precisely the awareness-capability gap that the ALC framework identifies at the level of individual platform workers, and it appears to operate at the institutional level as well. Gagrain, Naab, and Grub (2024) find that algorithmic media use increases awareness of algorithmic influence without producing corresponding improvement in outcomes. Institutions naming the AI governance problem are in an analogous position: they have developed awareness without developing the structural schemas necessary for effective response.

What a Governance Schema Would Actually Require

Gentner's (1983) structure-mapping theory offers a useful frame here. Effective transfer of understanding across domains requires identification of relational structure, not surface features. Current AI governance discourse is heavily focused on surface features: job losses, bias in outputs, data privacy, liability for errors. These are real problems. But they are topographic rather than topological, in the sense I use that distinction in my research. Knowing where the terrain causes specific problems is different from understanding the shape of the constraint system that generates those problems. Leo's encyclical gestures toward the topological question when it frames AI governance as fundamentally about power concentration and the structural capacity for exclusion. Whether one accepts the theological framing or not, the organizational observation is sound: a coordination mechanism that concentrates schema production in private hands while distributing schema-dependent participation across millions of workers and citizens has a structural instability that surface-level governance interventions will not resolve.

The Organizational Research Question This Opens

The most actionable implication of the encyclical, read through an organizational theory lens, is this: effective AI governance requires institutions capable of producing and distributing structural schemas about AI systems, not just procedural rules about their use. Hatano and Inagaki (1986) distinguish routine expertise, which is procedure-dependent and brittle under novel conditions, from adaptive expertise, which is principle-dependent and transfers across contexts. Current regulatory proposals are almost entirely proceduralist. They specify what organizations must not do. They do not build the adaptive expertise that would allow institutions to respond to the next configuration of AI coordination that current regulations do not anticipate. That is the gap the encyclical names but does not fill, and it is where organizational research has real work to do.