Science emerged in Christendom because God was transcendent, not pantheistic. Now materialism reigns as a hidden theology – Epstein-funded, preached by ambiguous atheists.

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There is possibly more than one correct answer to the question of why science emerged in Western Christendom, rather than appearing first in one of the many Eastern societies with formidable technology, or within the fold of Greco-Roman paganism.

The Benedictine epistemologist Stanley Jaki offers a detailed answer that is intrinsic to Christianity: as we read in Christ and Science, the distinguishing factor is that Christians are not prone to falling into pantheism. Among Greco-Roman pagans, the cosmos was called “only-begotten” – a title later assumed by Jesus in Christianity, and only in Christianity. In the world of Aristotle, Western Christendom’s favorite pagan, bodies move of their own volition (fire seeks to rise, earth seeks to sink, etc.), and the Prime Mover is part of nature rather than transcending it. In Judaism and Islam, figures such as Spinoza (beloved in the 20th century by Einstein and Sagan, and in the 21st by Sam Harris) and Averroes (with his universal intellect) demonstrate how easily God can be conflated with nature itself. It was in medieval Christendom that the world came to be conceived as a vast clock with inanimate parts adjusted by an intelligent Creator, whose laws we must discover. This way of thinking about the universe, which we associate today with Newton, is medieval. And this worldview, specific to Christendom, is what made the invention of science possible.

Here in this SCF column, I once ventured a more modest answer of a cultural nature. I wrote that the West has been rationalist since before it was Christian; and this fosters not only the emergence of science but also the willingness to uphold it in the event of a conflict with faith. Sunnis, who constitute the majority of Muslims, saw a form of Quranic literalism put an end to the spectacular flourishing of the sciences during the Middle Ages. Jews, who devoted themselves to the exegesis of religious texts, only became scientists following emancipation and secularization. This cultural characteristic helps explain why science emerged in Western Christendom rather than in the East, given that both the Roman and Greek Churches shared the belief in the only-begotten Jesus. It also helps explain why, following the Protestant Reformation, Protestant Europe did not cling to biblical literalism or to Luther’s diatribes against reason, but instead produced science in dialogue with Catholic Europe. This ultimately shifted the locus of Truth from religion (where each group held its own version) to science (to be accepted by all), paving the way for that recent Western invention known as scientism.

Now, an idea intrinsic to Christianity that I wish to highlight is its absolute intolerance of relativism regarding knowledge. In the pagan world, philosophy and religion were disconnected matters. Plato reflected on the good, the beautiful, and the just without investigating Olympus. Cicero, as a philosopher, wrote against the public soothsayers of the Roman religion; yet, as a politician, he showed no intention of altering state religious practices or inventing secularism. Even Aristotle, who wrote on every conceivable discipline, did not write about Zeus, Hera, or Athena. He did create a “theology,” but it referred not to the gods, but rather to the Prime Mover. Zeus, Hera, and Athena were not the objects of any discipline.

It was during the Middle Ages that the Catholic Church invented the university with the aim of studying the universum – that is, everything: metaphysics, physics, politics, theology… Knowledge was organized cohesively, based on the presumption of an objective reality. Thus, physics could not teach something that contradicted theology, and vice versa. (Although the Galileo affair stemmed from the Aristotelianism embraced by the Church through Thomas Aquinas, rather than from a literal reading of the Bible.) This cohesion might seem obvious, yet today the discipline of physics claims that glass is a liquid, while the discipline of chemistry claims that glass is a solid. And that is not even the greatest problem: who would dare ask a university for an unequivocal definition of “man” or “human”? In practice, relativism reigns in the university – and did so even before the postmodernists took over everything.

This fragmentation of knowledge originated in the scientific model that emerged during the Renaissance. Outside the universities, humanists sought out ancient texts that medieval Catholics had discarded, such as De rerum natura by the materialist and Epicurean Lucretius. Since antiquity conferred authority in those days, the occult tradition formed by the fusion of Hermeticism and Kabbalah enjoyed particular credibility among the learned class. Hermeticism derives from Hermes Trismegistus, whom Renaissance thinkers believed to be the teacher of Moses, Plato, and Pythagoras, though in reality, he was a fictional figure dating from slightly after the time of Christ. An etymological trace of Hermeticism’s importance to science is the adjective “hermetic,” used by alchemists to describe their tightly sealed vessels. As for Kabbalah, it stems from a religion that predated Christianity – which is enough for it to be regarded as ancient, even though its principal text, the Zohar, originated in medieval Spain. As I have argued in greater detail here, Renaissance occultism influenced modern science and is responsible for its utilitarian bias. If individual disciplines decide that a certain concept “works,” contradictions between them pose no problem; what matters is not knowledge itself, but practical utility. It is a science inspired by the methods of the sorcerer, who consults ancient books and tests spells to see which ones work.

Thus, the status quo of knowledge within universities is one of practical relativism, resulting from the institution’s lack of a unified vision. On an individual level, however, it is humanly impossible to conduct science without a coherent worldview – even if that worldview is constructed a posteriori based on the “spells” that proved effective, much like the alchemists of the Renaissance. Today, it is quite clear that the scientific mainstream is atheist, but that tells us little. It would be more accurate to say that the mainstream is materialist.

The nineteenth century was the century in which materialism became consolidated within science. It was the century of Darwin’s triumph – Darwin being the first (and perhaps only) great scientist in history that was unequivocally atheist. Before Darwin, the prevailing worldview was the medieval one of God as the “watchmaker,” or of nature as a book written by God in the language of mathematics (to use Galileo’s metaphor). Alternative worldviews began to emerge during the Renaissance. By the Enlightenment, this movement had taken the forms of deism and materialism – concepts that are not mutually exclusive. Deism is the idea that God exists but is indifferent to humanity. This is a very vague notion, as it encompasses the possibilities that: (1) God is a distant architect (as for Voltaire, a “creationist” and admirer of Newton); (2) God is matter itself, or the universe itself (pantheism); or (3) gods exist but are made of atoms, just like the rest of nature – a view found in De Rerum Natura that results in practical atheism. Since this conception of God is psychologically difficult to sustain, it is hardly surprising that, by the nineteenth century, the public face of materialism had become that of atheism.

Even so, as I have shown here on SCF, only one of the “Four Horsemen” of New Atheism (Richard Dawkins) is unequivocally an atheist. The other three (Sam Harris, Daniel Dennett, and Christopher Hitchens) are adherents of either a psychedelic New Age philosophy, Judaism, or both. Sagan was another atheist with this ambiguous profile.

I believe, then, that it is useful to distinguish between these two opposing scientific worldviews: one, which gave rise to science, is that of the transcendent God who acts as the cosmos’s engineer; then there is the materialist view, according to which everything that exists is matter moving randomly, generating at times chaos and at others order. Within this latter framework, one can be an unequivocal atheist – like Dawkins today or Baron d’Holbach in the 18th century – or a New Age type like Sam Harris. Matter itself can also be divinized by crystal-obsessed wine moms and potheads. An ambiguous figure is none other than Epstein; this patron was a Jew religious enough to want to create a GMO kosher pig so he could eat bacon, yet he simultaneously funded Sam Harris’s atheist club. As I have shown here, Epstein was such a committed materialist that he sought to identify the specific part of the brain corresponding to the soul.

One final intrinsic trait of Christianity worth mentioning is its exotericism – that is, its public, non-initiate nature. This characteristic is reflected in universities and libraries: the entire repository of knowledge is open to anyone wishing to study. In the case of materialism, England has maintained a solid tradition of private and secretive scientific societies, often with commercial aims, dating back to the 18th-century Lunar Society. This aligns with the esotericism of the Renaissance. Furthermore, in the 21st century, the Epstein files brought to light discreet societies of materialists, such as the one involving Sam Harris that was funded by the pedophile.

Given that the pedophile also funded university research, it is plausible that this materialism – bordering on both pantheism and atheism – constitutes a hidden “theology”: one that is always presupposed in contemporary science yet never made explicit to the uninitiated.

The more technology distances the actor, the more responsibility must increase.

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From person to target

What becomes of humanity when technology becomes capable of recognising, classifying, tracking and ultimately killing human beings without those carrying out the strikes ever having seen them? My friend Mohammad Reza Dehshiri asked this very question, and his answer is a powerful one. The danger, he writes, is not artificial intelligence but artificial inhumanity – that is, the possibility that machines will become more accurate precisely as humans cease to feel responsible for what those machines do in their name.

With this article, I am launching a series that takes Dehshiri’s analysis as its starting point and attempts to take it further. If the problem is ‘artificial inhumanity’, the answer cannot lie in a ‘more ethical’ AI, as if ethics were a module to be installed as an afterthought to a system that has already been designed. We need to reclaim a category that technological jargon has rendered almost unmentionable: human intelligence, understood not as a remnant to be protected amidst the complexities of automation, but as a form of knowledge with its own distinct characteristics, which no increase in computing power can replace.

I shall call it Human Intelligence, and the ambiguity is deliberate. In intelligence service jargon, HUMINT refers to the oldest of the intelligence disciplines: that which relies on people, sources, languages and the patient frequenting of places. In philosophical terminology, the expression denotes the faculty of understanding. The thesis of this first contribution is that these two meanings now converge, and that their convergence has geopolitical implications that extend far beyond the debate on the ethics of weaponry.

We can describe the operational chain of contemporary warfare in seven stages: detect, identify, classify, predict, prioritise, target, strike. Each link can be technically refined without the sequence, taken as a whole, being morally refined. The resulting phenomenon is called the ‘datafication of human life’: the individual reduced to an identity, a location, a behavioural pattern, a threat score.

This is no abstraction. In April 2024, an investigation by Yuval Abraham for +972 Magazine and Local Call described ‘Lavender’, a system used by the Israeli armed forces in Gaza to assign tens of thousands of people a probability of affiliation with armed groups. According to internal sources cited in the investigation, the human verification of each name often amounted to no more than twenty seconds – the time needed to ascertain that the target was male. The Israeli Defence Forces have disputed this account and described Lavender as a database designed to support analysts. Even if one accepts the official version, however, the crux of the matter remains: a human operator was present, and their presence was not sufficient to transform the system’s recommendation into a judgement.

Cognitive psychology has a name for this phenomenon, automation bias: the tendency of those working with an automated system to treat its recommendations as a substitute for their own verification, and to fail to seek out the information that might contradict them. Back in 1976, Joseph Weizenbaum – who had built one of the first conversational programmes in history – already distinguished between the ability to calculate and the ability to judge, and argued that there are tasks that should not be entrusted to a computer even if the computer were capable of performing them. Half a century on, this distinction has become even more pressing. Computation works on what has already been translated into data. Judgement must decide whether that translation has been faithful.

Dehshiri drives the point home with four distinctions: technical precision is not ethical precision; accuracy is not justice; automation is not responsibility; technological superiority is not moral superiority. I would add a fifth, of an epistemic nature, upon which the others depend: correlation is not understanding. A machine-learning system recognises statistical patterns in a dataset. It does not know what a school is, because it does not know what a child is, and it does not even know that it does not know this.

L’iraniano cites an interesting case study: On 28 February 2026, in the early hours of the war, the Shajareh Tayyebeh primary school in Minab, in Hormozgan province, was struck several times during school hours. The figures provided by the Iranian authorities range between 150 and 175 deaths, the vast majority of whom were girls, along with dozens of teachers and a few parents; the district prosecutor put the final death toll at 156. Washington has never directly acknowledged its responsibility nor published the findings of the Pentagon’s investigation. Amnesty International has described the incident, at best, as a serious intelligence failure.

There is one detail, revealed by satellite imagery, which shifts the analysis from the moral to the factual plane. According to Western media, the school stood next to a naval base belonging to the Revolutionary Guards and had previously formed part of it; among the explanations circulating in US circles is that of out-of-date targeting data. If this hypothesis were confirmed, Minab would not be a case of a machine making a mistake, but of a database that had ceased to correspond to reality. The building had changed. The data had not.

No classification algorithm, however sophisticated, can detect that an old military building has become a school if no one has recorded the change. Everyone living in Minab knew this: the parents who took their daughters to school every morning, the schoolbus driver, the teachers. This is precisely the kind of knowledge that HUMINT gathers and that remote warfare tends to regard as superfluous, because it is slow, costly and difficult to scale. Based on publicly available sources, US responsibility appears highly probable, whilst the exact circumstances remain unclear and confidence on this point is low: a database error, a classification error, or a conscious acceptance of the risk. The three hypotheses, however, all converge on the same question. Who, prior to the strike, checked whether there were people in that building?

Beyond ‘human-in-the-loop’

The international debate has long been content with the principle of human-in-the-loop, whereby a human being is part of the decision-making loop.

This formula is insufficient: a person may be present in the loop yet merely rubber-stamp the algorithm’s recommendation. He proposes replacing it with a more demanding principle, which he calls humanity-in-command: the effective ability to question, reject, override and halt the automated process.

The history of the Cold War offers two examples that clarify what this means in practice. On 26 September 1983, the Soviet early-warning system Oko signalled the launch of five ballistic missiles from the United States. The officer on duty, Stanislav Petrov, dismissed the alarm as a false alarm and did not report it as an ongoing attack, reasoning that a first American strike would never consist of just five missiles. He was right: the satellite had mistaken the sun’s reflection off the clouds for missile launches. Twenty-one years earlier, on 27 October 1962, in the Caribbean Sea, the Soviet submarine B-59, isolated and under fire from signal depth charges, was on the verge of launching a nuclear-tipped torpedo. The launch required the consent of the three senior officers on board, and Vasily Arkhipov refused to give it.

In neither case did the man approve a recommendation from the system. He contradicted it. And he did not do so because he had more data: indeed, the data were the problem. He did so because he knew how to ask himself a question that no procedure required him to ask, namely whether what he was seeing made sense in the context of everything else he knew about the world. Petrov was familiar with American nuclear doctrine; Archipov knew the difference between an attack and a threat. It is this ability to compare the signal with the bigger picture, the detail with what is plausible, that I call Human Intelligence. It is worth noting that the Dena was sunk by a submarine. We do not know what its officers were asked, nor what scope they had for judgement.

This proposal needs to be clarified, as there is a risk of reducing it to a generic humanistic appeal. Human Intelligence, as I understand it, is structured across three distinct levels, each of which marks a structural – rather than contingent – limit of artificial intelligence.

The first level is epistemic. The scholastic tradition distinguished between intellectus, the act by which the mind immediately grasps a truth, and ratio, the discursive process of moving from one element to another: for Thomas Aquinas (Summa Theologiae, I, q. 79, a. 8), the two are not distinct faculties but aspects of a single intellect, in which reasoning begins with an understanding and returns to an understanding. Current machine learning systems realise a ratio of unprecedented power, capable of inferring, combining and predicting on scales inaccessible to the human mind. They lack the aspect of intellectus, that is, a grasp of the meaning of what they process. This is a philosophical thesis and must be presented as such, but it has precise practical consequences: a system devoid of understanding cannot know when its own model of the world has become false.

The second aspect is practical. Prudentia, in the Thomist definition, is right reason applied to action, and its specific task consists in applying the universal principle to the particular case. The machine generalises: its value lies in mapping the individual case back to a class. Prudence does the opposite, because it must recognise what, in this particular case, falls outside the class. War, which is the realm of the singular and the unforeseen, is the arena where this difference carries the most weight. The Vatican’s Antiqua et nova note of January 2025 took up this line of thought, emphasising the embodied and relational nature of human intelligence and its irreducibility to a mere processing function.

The third level is relational, and it is here that philosophical meaning meets that of the intelligence services. Sherman Kent, who effectively founded the field of intelligence studies in the post-war United States, defined it first and foremost as knowledge, even before organising or activity. HUMINT is the form of this knowledge that passes through the other as a person: someone who speaks their language, knows their customs, can read a neighbourhood, and can distinguish a festival from a rally. Over the last three decades, the major powers have progressively shifted resources towards technical disciplines, from signals intelligence to satellite surveillance, right through to data fusion platforms. Minab illustrates the cost of this imbalance.

None of this implies a rejection of technology. Human Intelligence does not stand in opposition to AI in the same way that a manual stands in opposition to a mechanic. It establishes a hierarchy: the algorithm as a tool subservient to a judgement it cannot produce on its own. And from this it derives a principle, formulated by Dehshiri, which deserves to become an operational criterion: the more technology distances the actor from the consequences of their own actions, the more the conscious responsibility of the decision-maker must increase, not decrease.

Geopolitically speaking

So far, the issue might seem to be one of moral philosophy. It is not merely that. The distribution of algorithmic warfare capability is a distribution of power, and the rules governing it will be written – if they are written at all – by those with an interest in limiting them as little as possible.

The state of international law confirms this. The group of government experts established under the Convention on Certain Conventional Weapons has been discussing lethal autonomous weapons systems since 2017 without having reached a binding instrument. In November 2023, the United States promoted a political declaration on the responsible military use of AI, which is by definition non-binding; in December of the same year, the United Nations General Assembly adopted the first resolution dedicated to these systems, whilst the International Committee of the Red Cross has been calling since 2021 for a ban on unpredictable autonomous systems and those designed to target human beings. In June 2024, speaking at the G7 summit in Borgo Egnazia, Pope Francis called for no machine ever to be placed in a position to decide on a person’s life. The gap between these positions and the reality on the battlefields of 2026 is measured in casualties.

There is also a deeper asymmetry. Those who control the data decide who is a target, and the victims of algorithmic warfare are largely found outside the West: in Gaza, Iran, the Sahel and Yemen. For states that do not produce the platforms or control their databases, human intelligence thus becomes a matter of sovereignty. A country that maintains linguistic expertise, regional knowledge and human intelligence networks retains its autonomy of judgement; a country that entrusts its worldview to systems designed elsewhere – often by private companies – surrenders it along with its data. In an order tending towards multipolarity, this distinction will separate the actors who judge from those who are judged.

This leads to a concrete proposal, which I shall develop in future articles. Alongside the general principle of meaningful human oversight, international humanitarian law could codify a specific obligation for human verification of the civil status of every fixed infrastructure included on a list of targets, with an expiry date beyond which the information becomes invalid and must be renewed using direct sources. This is a minimum standard. Applied to Minab, it would have required someone to ask whether that building was still what the records stated it to be.

Technology can determine how a weapon reaches its target, whilst it is up to humanity to decide whether that target should be struck. The question underlying all this is who, in practical terms, exercises that humanity, with what tools of knowledge and within which institutions. In Minab, someone should have known that there was a school there. The fact that nobody knew, or that nobody asked, is the point from which we must start again.