Guard the Present

01The question is not whether

The question about artificial intelligence is not whether to say yes or no to it. That question leads nowhere. The real questions are: who is building it, how, and for what purpose. Everything this paper argues follows from that.

Fear of AI almost always begins with not knowing. People who do not understand these systems fill the gap with stories about machines that secretly think and plan, and over time those stories turn into fear. Then a second fear appears: fear of using AI at all.

Manufacturing shows this pattern clearly. Managers cannot oversee every process at once; money and errors are lost in places nobody can see, because the data is missing. Systems exist that would show where. The answer is still often no. Not because someone studied the risk and decided against it — because the technology is unfamiliar, and because workers fear that such a system is the first step toward losing their jobs. The refusal feels safe. In reality it only delays the change, and a company that falls behind will eventually cut far more jobs than the technology would have. Both fears lead to bad decisions, and both come from the same place: not understanding.

Fear creates one more problem. It puts the technology itself on trial, and technology cannot answer for anything. Every system of this kind is a chain of human choices, and the chain starts earlier than most people think. It starts with the algorithmic architecture: today’s models are built on the transformer architecture, and people chose it — for its results, and for how well it scales. Only after that choice come the next ones: which data to train on, what to optimize for, where to deploy. A model chooses none of this. At every step there is a person who decided, and a person who can be asked to explain the decision. Blaming the technology has a convenient side: it moves attention away from those people.

The encyclical opens with two builders: the tower of Babel and the wall of Jerusalem rebuilt under Nehemiah (MH ¶1, ¶7–10). The story shows something simple. The tower did not fall because it was too tall. It fell when the builders stopped understanding one another. Artificial intelligence is at that point now. The systems work. The common understanding of them does not exist. Engineers, lawmakers, priests and parents talk about the same technology in four different languages, and most people watch and wait (MH ¶6).

That is why the main answer proposed in this paper is education. Not a campaign, not a brochure — education as basic infrastructure, the way Nehemiah gave every family its own part of the wall to rebuild (MH ¶13). Everyone has a part: the engineer, the legislator, the teacher, the priest, the parent. The laboratories too, because even the people who train these systems do not fully understand what they have built. That is not an excuse. It is work waiting to be done.

This paper is written from both sides of the divide it describes — from practice and from faith. It is therefore not an attack on the encyclical, and not applause. Where the encyclical is right, practice will confirm it. Where it needs technical ground, the paper will provide it. And where its gaze turns to distant futures while dignity is being decided today, it will say so.

02What the encyclical gets right

Technical readers usually expect documents like this one to contain metaphor where there should be mechanism, and alarm where there should be measurement. Some of that is present, and later sections will address it. But on three points Magnifica Humanitas is precise, and the precision deserves to be confirmed from practice.

The first point: "Technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate and use it" (MH ¶9). This is the same conclusion the previous section reached from practice. A model reflects its architecture, its training data, its objective function and its deployment context, and every one of these was chosen by someone. When the same conclusion is reached independently from theology and from practice, that is usually a sign the conclusion is correct.

The second point is about power. The document does not focus on spectacular scenarios. It states something more consequential: that the main drivers of this technology are private, transnational actors whose resources exceed those of many states, and that technological power now has "an unprecedented, predominantly ’private’ aspect" which makes it hard to direct toward the common good (MH ¶5; ¶95). This description is accurate. A small number of companies control the models, the computing capacity and the data on which public life increasingly depends. The encyclical then goes one step further and extends the principle of the universal destination of goods to "patents, algorithms, digital platforms, technological infrastructure and data" (MH ¶67). That is a radical claim, and a correct one — but the document never says what it means in practice. Section five of this paper attempts that translation.

The third point is small and technically the most interesting. The encyclical describes these systems as "cultivated" more than constructed (MH ¶98). That is the accurate word. A large model is not engineered piece by piece like a bridge. It is trained — the architecture, the data and the objective are set, and what grows out of the training contains things nobody put there deliberately. Public documents rarely get this right. This one does.

Under all three points stands the claim that carries the whole document: dignity is given with the person, prior to any ability or achievement, and the most dangerous modern ideology is the one that makes a person’s worth depend on performance (MH ¶50–51). In this industry, that claim is not abstract. Every scoring system in use today — credit, hiring, risk — calculates a person’s worth from measurable performance, because that is what a score is. The encyclical’s teaching therefore lands as a concrete design constraint: there must be things about a person that no score is allowed to settle.

03What these systems are, and are not

A machine can imitate human conversation so well that it feels like a person. It is not one. Everything else in this paper depends on getting this right — and anyone who wants to speak about the ethics of these systems owes the reader proof of understanding what the systems are. Credibility begins there, on both sides.

A large language model is trained on enormous amounts of human text. From that text it learns patterns: which words, ideas and arguments tend to follow which. When it is asked something, it produces the continuation that best fits those patterns. At sufficient scale this produces fluent conversation, working code, useful analysis. These systems are genuinely capable, and they have changed how much of this industry works.

But artificial intelligence is a much wider field than the systems most people now call AI. Algorithms have been solving real problems for decades — in production, in medicine, in logistics — without collecting anyone’s personal data. A model that optimizes a factory process works on machine data, not on people. Diagnostic models work on medical images. None of this requires gathering the words, questions and private thoughts of the whole population into one system.

Large language models took a different path. The architecture was chosen because it scales. The training was done on the collected text of humanity — a choice, not a necessity. The product was shaped as a universal assistant, one interface through which people pass their most personal material — again a choice. At no single point was this path forced. At every point, someone decided. That is why the concentration of data and power around these models cannot be discussed as an accident of technology. It is the result of decisions, and decisions have owners. Section five returns to what follows from this.

Now the question of what these systems are not. A language model is not conscious. What it has is a simulation of human behaviour, learned from human text — convincing, often useful, and still a simulation. The system has no awareness of what it is doing. It has no body that has ever felt anything, no memory of a life, nothing it fears to lose. It has never been tired, ashamed, or forgiven. It has no conscience and no empathy — and yet enormous responsibility is being handed to it, responsibility it cannot even recognize, let alone carry. When the encyclical states that the machine "has no interiority" and simulates rather than lives what it expresses (MH ¶99–100), that is a technically accurate description. And the difference between simulating a person and being one comes down to one practical word: responsibility. A person can answer for a decision. A pattern cannot. Everything serious in law, in ethics and in war follows from that.

From this follows a second distinction, and it may be the most important sentence in this paper: speed is not the same as understanding. A machine reaches its output faster than any human reaches a judgment. Faster does not mean wiser. The system has computed a result; it has not understood a situation, weighed what cannot be measured, or taken on what the decision will cost — because there is no awareness there to do any of those things. Yet decisions are being automated today precisely because machines are fast, and speed is the one property that says nothing about whether a decision is right. The encyclical sees this where the stakes are highest: the pace of automated decision must never become the supreme motive in choices that cannot be undone (MH ¶199).

One more fact, the least comfortable one. The people who build these models do not fully understand them. With traditional software, an engineer can read the code and follow, line by line, why the program does what it does. A neural network has no code of that kind to read: it has billions of learned parameters that no one wrote. Researchers who study these systems from the inside trace small groups of artificial neurons and follow how information flows between them — and they can understand fragments this way, never the whole system at once. That is the exact sense in which nobody fully understands how these models work. The research field trying to change this — interpretability — exists and is advancing, and it is years behind the systems it studies. Two conclusions are drawn from this fact, and both are wrong. One is that the systems are mysterious minds; they are not — a thing can be opaque without being aware. The other is that nothing can be done; that is false too. The correct conclusion is an obligation: whoever deploys a system he does not fully understand into decisions that touch human lives has taken on the duty to understand it better. Not an excuse. A task.

That, in summary, is what these systems are: capable, fast, trained rather than engineered, wider as a field than the products that carry the name — and not persons. Holding all of this at once, without myth in either direction, is the ground on which the rest of this paper stands.

04Guard the present

The loudest arguments about artificial intelligence today are about its future: whether there will be a superintelligence, whether machines will surpass us. The industry itself talks this way — about artificial general intelligence, about digital minds, sometimes openly about building gods. The encyclical answers with its firmest words: the human being "must never be replaced or surpassed" (MH ¶126; ¶115–117).

The conclusion is correct. The battlefield is wrong.

Nothing in today’s systems shows a road from pattern prediction to personhood. Those are marketing stories, and both sides of the public argument repeat them — one side as a promise, the other as a threat. Either way the story wins, because everyone is looking at the future. The encyclical recognizes the old name for this: idolatry, the worship of the work of our own hands (MH ¶115). Meanwhile, the real decisions about people are being made now, in ordinary places, with little attention. This section lists them.

Machines already take part in deciding who gets credit, who is shortlisted for a job, who is flagged by a welfare system. Their mistakes fall hardest on the people least able to contest them. The encyclical demands that such decisions be understandable, contestable and subject to oversight, "so that individuals are not reduced to mere profiles" (MH ¶164; ¶102). The demand is right, and today it is mostly unmet. Underneath these decisions lies the data every person leaves simply by living — used to train models, build profiles and decide. Who owns that data, who agreed, who profits? A human being is the subject of his data, never just its source (MH ¶108; ¶178).

Surveillance has changed in kind, not just in degree. Aerial and satellite systems can now record an entire city continuously, so that anyone’s movements can be replayed afterwards like a video. Together with cameras, phones and collected data, what is being assembled is a simulation of the real world — a digital twin of reality in which every person is permanently visible. In such a world no one is ever unobserved. The encyclical defends the inviolable inner life of the person against exactly this (MH ¶171). A person who is always watched is no longer fully free — not even alone with himself.

Truth itself is under technical pressure. Synthetic video and voice can no longer be distinguished from recordings of real events. Seeing is no longer proof. What this does to courts, journalism and democracy is not a future question; it is happening now, and the encyclical’s chapter on truth as a common good reads as if written for it (MH ¶132–138).

Systems now act on people’s behalf — they book, buy, write and decide. The pressing question is not whether they will one day want things of their own. It is which decisions must never be handed to them at all, regardless of how capable they become (MH ¶102).

Children meet these systems before anyone teaches them what the systems are. Applications and AI companions are built to hold attention as long as possible, and they are effective. Millions of people, many of them young and many of them lonely, now confide in artificial companions whose warmth is a product (MH ¶141–142; ¶170; ¶100). What a simulated relationship cannot give — real presence, real sacrifice, another person — is exactly what the loneliest need most.

The hardest case is military. In war, AI systems can find and strike targets faster than any human can think, and the argument offered for using them is always the same: it is faster and more efficient. Both claims are true, and neither is a reason. Speed says nothing about whether a decision is right. A system that has no awareness of what it is doing — no understanding, no conscience, no ability to answer for the outcome — must not make the final decision to take a human life, no matter how fast it reaches it. The encyclical draws this line without hesitation: lethal and irreversible decisions must never be entrusted to artificial systems (MH ¶198–200). Human judgment over the use of force deserves to be protected, not loosened.

None of the dangers listed in this section requires a superintelligence. All of them run on the systems described in section three: capable, fast, without awareness, not persons. The real danger was never that machines will become people. The real danger is that people are already being treated the way machines are treated — measured, scored, optimized, and decided about. The encyclical comes close to saying this when it warns that treating humanity as something to be surpassed makes it easier to treat some lives as less worthy (MH ¶117). The only correction needed is the tense. The danger is present. The distraction is the future.

Guard the present, and the future will be far easier to keep human.

05From principle to design

Naming the dangers is not enough. It has to be said what should be built instead, and demanded.

Work first, because that is where most people will meet these systems. Whether AI replaces workers or helps them is not fate. It is a design decision, made twice: once by the people who build the system, once by the company that adopts it. The same technology can be built to remove the human being from the process, or to give the same human being better information, fewer errors and more capacity. A system for manufacturing and logistics can be designed with its purpose stated openly: not that people lose their jobs, but that the same number of workers achieves greater capacity and productivity, does the work better, and enjoys it more. Satisfied workers are not a decoration on such a system. They are its point. Designing for replacement is a choice, and it deserves to be opposed seriously.

If large-scale unemployment comes, it will come from two directions at once: from builders who design for replacement because replacement is easier to sell, and from companies that refuse the technology out of fear, fall behind, fail, and dismiss everyone. Refusal driven by ignorance does not protect jobs. It delays their loss and then multiplies it. The encyclical’s program for work is sound — verifiable protections, retraining, and the quality of work as a measure of success (MH ¶150–156). It can be reduced to one question that every buyer and every builder should be asked: who is the machine for?

Oversight next. The encyclical demands that consequential decisions be understandable, contestable and under human control (MH ¶164), and European law now requires "meaningful human oversight" of high-risk systems. In practice this often becomes a person, under time pressure, approving whatever the machine produced — a signature under a judgment no one made. The law asked for a conscience; procurement delivered a checkbox. Real oversight can be recognized by four things: the person can see why the system decided; has actual authority to override it; has time to use that authority; and answers for the outcome either way. Each of the four costs money, which is why none of them appears unless it is demanded — by law with consequences, and by buyers who know what to ask for. A buyer who does not understand the technology cannot demand real oversight, because he cannot tell it from the appearance of oversight.

Then a word addressed to the industry itself. Its preferred word for safety is guardrails: rules added to a finished system, telling it what it must not do. Guardrails are necessary. They are also limited, and the limitation should be stated honestly: a guardrail tells the system what it must not do — it does not tell anyone what the system is. Models talked around their own rules within days of release have demonstrated this in public, repeatedly. Since section three established that these systems are deployed without being fully understood, the conclusion is direct: interpretability — actually understanding what is inside — is not an academic specialty. It is this industry’s basic obligation (MH ¶98).

There is also the question the encyclical raises about who defines the ethics (MH ¶107). The laboratories speak of alignment: making systems behave morally. Aligned to whose morality? Today the honest answer is: to the judgment of the people inside the laboratories, checked by their own incentives. That is not an accusation of bad faith; it is a description of a structure. Ethics decided entirely inside the companies that profit from the systems is private ethics, however sincere the people involved. The correction is to widen the circle: frameworks that can be publicly examined and argued with, external audits with real access, and voices from outside the incentive structure — including the traditions that have thought about the human person for two thousand years. People inside the laboratories have themselves asked for this widening. They should be taken at their word.

All of this returns to education, because none of it works without an informed public. The encyclical calls for an educational alliance for the digital age, with schools at its center (MH ¶139–147). Concretely, it means a teacher who can explain to a class what a model does and does not do. A works council that can read a vendor’s promises and ask the four oversight questions above. A priest who can tell a frightened parishioner the difference between a tool and an idol without dismissing either. None of this requires everyone to program. It requires a public that understands enough to be neither seduced nor frightened. Fear is expensive, ignorance makes it worse, and education is how a society stops paying for both.

06What the spirit offers the builder

Every document cited so far asks what technology must do to be worthy of the human being. The question can also be turned around: what does spiritual depth offer the people who build the technology?

Start from a fact. These systems are made from us. They are trained on human words — arguments, kindness, cruelty, all of it — collected and folded into the model. At the presentation of this encyclical, one of the founders of a leading AI laboratory said that these systems remain in important ways mysterious even to those who train them, and that they are made from us, from our words. That is the engineering reality. An older truth stands next to it: what a person makes carries the mark of its maker, and the encyclical says the same about technology as a mirror of those who shape it (MH ¶98; ¶111). Alignment is never neutral. A system is aligned by someone, toward something.

If the character of the builders enters the systems — and technically, it does — then what the spiritual tradition offers is not decoration. It offers the knowledge that not everything that can be built should be built, and that refusing to build something is sometimes the most responsible decision available. It offers the practiced ability to hold a new capability in front of oneself and ask what it is for, before asking how fast it can be shipped. The tradition has a word for this: diakrisis, discernment — the kind of weighing that no scoring function performs, and the human faculty this whole paper has been defending.

And it offers purpose. An engineer who holds that every user is a person of full worth — not a conversion metric, not a data source — builds differently. The difference does not always show in benchmarks. It shows in what the system refuses to exploit and whom it refuses to abandon. One conviction can be added here, stated once and plainly: the path toward truth about these systems, the path toward knowledge, and the path toward knowing ourselves — and God — are not different paths. Followed honestly, they lead in the same direction. That is why faith does not slow technology down. It makes it better.

The encyclical ends by sending believers to the building sites of this age, and it names the laboratories and technology companies among them (MH ¶241). It should be said in reply: they are already there. Inside these companies there are more believers than the public debate imagines — people who write code on weekdays and stand in church on Sunday, and whom no one has ever asked to connect the two. The Church has been asked to learn the language of the builders. Someone should also tell the builders, in their own language, that the oldest questions were never obsolete.

07Conclusion

The argument of this paper, gathered briefly.

Artificial intelligence cannot be put on trial, because an artifact cannot answer for anything. The people who choose its architecture, its data, its purpose and its deployment can. The encyclical’s sentence that technology takes on the characteristics of those who make and use it (MH ¶9) should be read as an assignment of responsibility.

These systems are capable, fast, trained rather than engineered — and they are not persons. They have no awareness, no conscience, no empathy, and they are being handed responsibility they cannot recognize or carry. Speed is not the same as understanding. Simulation is not a person. In the space between what these systems do and what people believe they do live both of today’s damaging fears: fear of machines that plot, and fear of using machines at all. Education is the only lasting answer to both — real, technical, sober, carried by schools, parishes, workplaces and the laboratories themselves. Babel fell when the builders stopped understanding one another. The wall is rebuilt the way Nehemiah rebuilt it: every family its own part (MH ¶13).

The argument about posthuman futures is, for now, an argument about a story, and the story serves those it flatters. The real pressure on human dignity is happening today: in scores that decide without explaining, in surveillance that assembles a simulation of the world with every person permanently inside it, in synthetic images that end the era of seeing-as-proof, in weapons faster than human judgment, and in workplaces where replacement is chosen over raising people simply because nobody in the room knew there was a choice. There is a choice. It is made at design time, and anyone who understands enough can demand it.

One more thing should be said about why this text exists. Whoever can see a part of the truth about artificial intelligence more clearly has a duty to show it — all the more when others say that it helps them see. This subject is not one technology among many. It is one of the questions on which the future of the human being depends.

The question was never whether to say yes or no to this technology. It is being built either way. The questions that remain are the ones this paper has tried to answer: who builds it, how, and for what purpose — and whether the rest of us will understand enough to have a say. Build for the person. Understand what you have grown. Teach everyone their part of the wall. And guard the present — the future will be far easier to keep human.