Human, Pending Verification
Since ascending to the papacy, Pope Leo XIV has taken a notably interventionist public posture addressing issues ranging from AI governance to labor, war, human rights, and human identity. However, it is important to note that historically, Pope Leo is not the first outspoken pope. I think what feels historically significant about the Vatican’s current activism is that it has introduced technological sovereignty into the conversation.
In Magnifica Humanitas, Pope Leo repeatedly rejects the idea that AI is a neutral technological evolution unfolding outside moral or political consequence. He describes it instead as a force capable of concentrating power, reshaping labor, distorting truth, altering warfare, and reorganizing the conditions under which human beings are recognized and valued by institutions. This position feels notably different from the dominant language surrounding AI.
It appears that the Vatican recognizes that AI is no longer simply changing the tools we use. It is changing authority. Not only who produces information, but who determines what counts as credible, original, trustworthy, visible, or even human inside these growing automated systems. There is a flattening of thought, creativity, and the unique attributes that are inherently human. And this is what makes the Pope’s intervention significant because his warning is larger than AI as technology, it is more about the framing of the warning itself.
And while his warnings were widely framed as ethical or theological commentary on technology, which is not incorrect and makes sense, since he is the Pope. But, in my opinion, this framing feels too small for what is actually happening, especially when viewed through a governance and legitimacy lens.
What gives me a particular vantage point on this subject is that I do not approach AI as a futurist or speculative commentator. My professional work already sits inside the governance layer AI is beginning to destabilize. The work I do includes translating ambiguous institutional risk into operational behavior across intellectual property, brand governance, rights management, compliance systems, publicity rights, workflow architecture, and what is still consistently referred to as emerging technology oversight.
Much of this work lives inside systems most people rarely think about unless something breaks. These are the quiet structures organizations rely upon to maintain coherence, protect ownership, manage visibility, preserve trust, and move quickly without losing accountability or decision integrity.
I can see that AI is already reshaping the infrastructure my work intersects with on a daily: authorship, ownership, legitimacy, visibility, consent, verification, institutional memory, and risk accountability. Because generative systems no longer only produce content. They are beginning to determine credibility, originality, authority, and human value itself.
What seems to be emerging is the struggle over who governs the systems that steadily govern human value. But we cannot exclude the fact that this infrastructure includes geography, political economy, and a distribution of costs.
Data centers require enormous concentrations of land, water, electricity, cooling infrastructure, mineral extraction, and logistical coordination. And many of those environmental and infrastructural burdens are being absorbed by communities with the least political leverage to resist them. Rural regions. Economically distressed municipalities. Historically neglected industrial corridors.
Recent reporting and energy analysis show that large-scale AI systems require extraordinary levels of electricity and water consumption to sustain computational demand. Researchers, grid analysts, and environmental reporting organizations have begun documenting how hyperscale data centers are placing growing pressure on regional electrical infrastructure, municipal water systems, and local environmental planning, particularly in economically vulnerable regions positioned as favorable sites for rapid technological expansion.
I would be remiss to not also connect the dots of the present, to the future and to the past. The current infrastructure supporting AI resembles earlier industrial models in which wealth, visibility, and technological power concentrated upward while environmental burden dispersed outward into communities positioned furthest from institutional influence. The way railroads reorganized commerce and geography. Financial systems reorganized power, and social media platforms reorganized communication, visibility, and public attention.
But there is another aspect to this “AI revolution” and that is how administrative it already feels.
It is unsettling to realize how optimization has moved beyond technology and into our daily lives. It entered unassumingly through convenience, through recommendation systems, predictive tools, frictionless interfaces, digital assistants, and automated workflows. These platforms are designed to remove uncertainty, pause, friction, or inefficiency from our everyday life.
Over time, this seemingly innocuous optimization stops feeling “technological” and starts feeling normal. Speed now defines competence. Friction is viewed as failure. Even more unsettling is the feeling that institutions themselves are beginning to interpret people through administrative readability: how quickly we respond, how efficiently we produce, and how predictably we behave.
So, this leads me to another question: What happens when administrative readability becomes more than an institutional preference and begins functioning as a cultural value? It is only normal that we begin to adapt ourselves to the systems that allocate legitimacy. This occurs because we learn which behaviors are rewarded, which forms of expression remain visible, and which qualities are easiest for institutions to recognize.
The risk that surfaces is that human traits filtered through these systems begin to be viewed as negative attributes. Human complexity becomes inefficiency. Ambiguity becomes friction. Reflection becomes delay. And qualities that resist quantification become steadily more difficult to justify inside environments organized around optimization.
We are not only normalizing systems that centralize authority while dispersing costs outward into communities with the least power to resist them. We are also normalizing systems that determine which forms of humanity remain visible, recognizable, and legitimate.
