Join PALLADIUM Magazine in San Francisco to hear a lecture by Razib Khan, author of “The Native Americans Before the Native Americans.” The article was featured in our most recent print edition PALLADIUM 19: Long History.

It was once believed the first humans to arrive in the Americas were the Clovis Culture, after the glaciers of the last Ice Age receded, nearly 13,000 years ago. Their artifacts have been found at hundreds of sites. These were likely the ancestors of many of today’s Amerindians.

But new archeological finds and genetic evidence suggest that people came to the New World much earlier, likely before 20,000 years ago, perhaps even more than 30,000 years ago, well before the peak of the last ice age. Who were these Native Americans who arrived before the Native Americans?

Razib Khan is the director of operations at FUTO, a consumer technology company in Austin, TX. With over ten years in the genomics space, he developed ancestry algorithms for Gene By Gene, The Genographic Project and Insitome. He also writes at Razib Khan’s Unsupervised Learning. You can follow him at @razibkhan.

Buy Tickets

This is a ticketed event, those without tickets or violation of the dress code will be turned away.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

This event is hosted with the Avalon Institute, which is building a home for cultural ambition in San Francisco You can follow them at @AvalonInst.

October 30th Razib Khan will also be speaking in Berkeley at the Avalon Exposition, a multiday festival dedicated to the study of mankind, with live theater, music, DNA sequencers, and additional speakers such as Samo Burja and James Poulos.

This article by Stephen Pimentel was published at Palladium Magazine on August 27, 2026.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

Homer’s Odyssey, like the man it describes, has “many ways.” Already in its introduction or proem, the poem raises its themes, including intelligence, politics, suffering, and ultimately homecoming. Odysseus is a paradigm case of man dislocated from civilization, defined not by his rank or even his martial virtues such as his courage, but by the versatile, shifting use of his mind to overcome the challenges before him. The questions the poem poses unfold into an implicit anthropology: Who are you? How does your identity depend on where you are from? What are your obligations to a stranger, and what are the limits of those obligations?

The Odyssey is foundational to Western culture because it conveys a critical framework for understanding what man is: a mortal being situated between the beasts and gods, dependent on inherited bonds while capable of judging them, responsible for action even as he is exposed to forces beyond his control. The central lesson of the poem is that entropy is always at work, and civilization must be actively recovered through memory, intelligence, strength, and the striving for return. The importance of this lesson increases as modernity weakens the bonds constitutive of identity. To read the Odyssey well is to receive an education in the habits of mind needed for governance, including the ability to improvise beyond prior experience, command of speech and persuasion, and strategic concealment and timing.

None of this means that the Odyssey is a comfortable, familiar home. The late Bronze Age in which the tale is set is different from our own time in undeniable ways, some of which strongly contravene modern sensibilities. For some, the work may seem to constitute a poetic house of the dead. To find a reliable path through the poem, we should consider the themes most salient for us today: the problem of entering an alien world, the modern experience of rootlessness, the confrontation of Homeric virtue with post-Christian morality, and the role of Homeric images in sustaining Western memory.

Reading the Alien

The world that Homer depicts has many features easy for us to recognize. Tears after calamity, fear of death, longing for home, and joy at delayed reunion are all present. Yet there are also features alien enough to trouble that recognition. We need to avoid either of two errors. The first is to treat Homer’s Greeks as early, if primitive versions of ourselves, projecting our own post-Christian values and therapeutic categories onto them. The second is to treat them as utterly foreign and entirely inaccessible, as though they were creatures from a distant planet rather than men from the European coast. Both errors close off our access to the poem, which requires instead that we attempt to enter into the world of the Greeks before we judge it by the standards of our own.

The elements of Homer’s world alien to modern sensibilities are numerous and unavoidable. A few are worth naming directly, as they pose the greatest challenges to reading the poem with seriousness and depth from our inherently modern perspective. First, the gods in the Odyssey are depicted as real powers, deathless, partisan, concerned with honor, sometimes in mutual conflict. Odysseus is, for the most part, beloved of the gods, and especially by Athena, who admires his cunning. Poseidon rages against the hero out of personal spite. Second, human responsibility coincides with divine action, with neither obviating the responsibility of the other. In the proem, Zeus rebukes mortals for blaming gods for suffering they cause themselves. Odysseus’ men perish due to violating an explicit divine warning concerning the cattle of Helios; the suitors at Ithaca receive a similar warning from Theoclymenus before meeting their deaths. Third, the ethics of the poem center on considerations of hierarchy, honor, and shame, understood within concrete social relations. These values are not taken, as in our world, to apply universally among persons presumed to be equal. The society of the Greeks is far from our modern universal and homogeneous state. Fourth, retribution is understood to be a necessary part of justice. Wrongs generate an obligation of repayment, and for some wrongs repayment cannot be made in material goods. Zeus, in the proem, declares Orestes’ killing of Aegithus to be a model of such just retribution.

The alien elements make Homer’s poem, read in a form as close as feasible to the original, all the more indispensable to us. Its salience as a salutary contrast only increases in a culture that embraces ever more far-reaching forms of utilitarianism, themselves extending sometimes to encompass not only all humans but all creatures capable of experience, such as shrimp. Adaptations of the poem, as delightful as they can be, tend to select, or even inject, themes an audience already finds legible. In our day, those may include trauma, guilt, or psychological recovery. At the same time, the alien elements are simply omitted or overwritten. Yet if we cannot read foundational poems on their own terms, we become dependent on intermediaries who transform difficult concepts into more familiar and acceptable ones, even when the originals are more functional or perceptive. The past becomes accessible only by using contemporary frames of identity for affirmation or accusation. What is not allowed is for the poem to judge the reader, to declare, like Rilke’s archaic torso of Apollo, “You must change your life.”

In the midst of the alien, Homer has a closeness to us that anchors his enduring relevance. He is not the West, but he stands at its beginning. The Greeks who came after Homer developed tragedy and philosophy through engagement with his questions. How does death grant or deny meaning to human life? How does excellence relate to political order? How can force be governed by speech? The beauty of Greek literature is the opposite of naïve. The measured lines of Greek poetry from Homer onward grapple with the most difficult aspects of life, including war and its consequences, reflecting a knowledge that civilization is achieved not by evading horror but by grasping it with image, rhythm, and limit. We need not ape Greek religion or cultural particulars to learn from Homer that courage includes a capacity for force, and that political order should place limits on that force. Homer’s alienness keeps his thought from being too easily assimilated to comfortable, familiar patterns; his nearness insists that it should not be ignored.

Odysseus the Displaced

The Odyssey, far from offering an uncomplicated affirmation of inherited identity, breaks down its traditional sources in name, father, land, and household, and offers a frame in which to think through its loss. At the beginning of the poem, Odysseus has lost each of these, and his return is complete only when all are regained. Thus, his arrival in Ithaca in Book 13 is only the beginning of his homecoming. In modernity, each of these bonds, now understood as unwelcome constraints rather than deep resources, has been weakened and made elective in a process that has carried both freedoms and spiritual ills. Modern man often suffers from a kind of homelessness, even sitting in his own house in his own country.

It now sounds old-fashioned, but even in modern English we used to speak of a man’s “good name,” meaning the reputation bound to his identity. A name is relational in that it allows one’s identity to be grasped by others over time, whether for good or ill. Through Homer’s account of Odysseus’ encounter with the cyclops Polyphemus, this ancient function of naming is put to the test. Odysseus calls himself Outis, Nobody, causing Polyphemus’ cries for help to misfire as his fellow cyclopes assume Polyphemus has been attacked by “no one.” The tactic seems to be a triumph of cunning. Odysseus, however, cannot ultimately bear this namelessness, as it would require him to forego his glory in the defeat of Polyphemus, glory which can attach itself to the hero only through the recitation of his deeds by name in song. He therefore reveals his name, allowing Polyphemus to draw down the wrath of Poseidon, dooming Odysseus to many years of woe. An analogous tension pervades modern concerns with the boundaries of legibility, from social media to financial technology to administrative bureaucracy. We often desire the many advantages and freedom of anonymity, but our social technology rests on the accountability and continuity of identity that is fixed by our names. The online “anon” is able to evade the punishment of the social order, yet to claim the fruits of his labor, he must take the risk of unmasking himself and revealing his name.

In the sphere of the family, Telemachus tells Athena that “No man knows his own begetting.” He is ostensibly referring to his uncertainty as to his literal paternity, but the statement resonates more deeply with the long absence of Odysseus and the void he has left in the political order. The Greeks understood fatherhood as the ground of social, economic, and political continuity among the generations. When Telemachus recognizes Odysseus in Book 16, it restores not only their personal relation but the possibility of coordination on the dangerous action needed to set Ithaca aright. When the scope of fatherhood is reduced, as in modernity, to a more minimal relation within the nuclear family, it naturally results in a kind of temporal parochialism in which the interests and preferences of a given generation are cut off from those that precede or follow. Long-term visions seem abstract and more difficult to act upon; the possibility of deep coordination between father and son is diminished.

A close reading of the Odyssey hardly bids us to return to ancestral villages or revive the worship of old gods. Its instruction penetrates more deeply to the relations on which identity rests, pointing us to those that most matter. Eumaeus the swineherd is a foreign-born slave, yet he is more faithful to Odysseus and integral to his household than the native elites who are besieging it. Loyalty, the poem shows, can be chosen, and betrayal can occur within one’s own class.

Emulating Excellence

The Greek Arete is often translated as virtue, but its sense in Homer is closer to excellence than moral goodness. It is the capacity to perform a needed function well, whether fighting, planning, speaking, or ruling. Odysseus’ excellence is manifold. He is a warrior capable of lethal violence, but he is not known chiefly for combat on the battlefield. Rather, he is exceptional in his range of action, surviving by adapting, improvising, and planning. His description as polytropos or “many ways” in the proem is later elaborated as polymetis or “many-skilled,” for his excellence cannot be reduced to a single disposition. He can turn in many ways without losing his orientation. Excellence in the modern world no less requires this ability to act effectively in novel situations where existing scripts are not available, to see the truths of a situation even when it differs from the institutional explanation.

What the Greeks possessed was a willingness to look without flinching at human mortality, recurring strife, human inequality, physical vulnerability, personal and social betrayals, and the frequent necessity of force. They had a certain clarity in rejecting the “just-world” fallacy: the expectation that outcomes must conform to some notion of moral fairness. The Odyssey embodies this austere clarity by showing that success depends on one’s navigation of the unfair. Odysseus returns to Ithaca not because his journey is safe, or because he deserves rest, but because he rises to the danger placed before him. He enters his own hall as a beggar and delays retaliation, because he does not assume that the world to which he is returning means him well. Penelope prevails because she conceals her intentions, postpones unfavorable decisions, and maintains a screening fiction through the weaving and unweaving of Laertes’ shroud. Arete demands command over the timing of action, however full of passion we may be to act.

The post-Christian societies of the West often elevate (and perhaps over-exaggerate) particular elements of the Christian moral framework, such as compassion, equality, and peacefulness, inflecting them with modern concerns of safety and therapeutic well-being. In this, post-Christian morality carries an antipathy toward social structures and practices that contradict these modern values, regardless of whether or how the promoted values may be pro-social or functional for human life. The Odyssey, in contrast, puts forward the case for moral discrimination as a necessary regulator of other values. Odysseus extends mercy to the bard and the herald in Ithaca, but only after he ascertains that they did not take part in the suitors’ offenses. He pursues particular loyalties to wife, son, father, and kingdom, minding that they form the structures through which social responsibility becomes possible. He employs craft and force against Polyphemus only when the Cyclops shows himself by his violent appetites to be outside the bonds of hospitality and reciprocity. He rejects the safety, pleasure, and even immortality offered by Calypso in favor of a hazardous journey to a rugged home, because he prefers the assertion of his identity over comfortable self-preservation. The navigation of life requires at different times mercy and severity, truthfulness and concealment, patience and attack. Any morality that does not embrace this full range of human action within its conception of virtue will not successfully orient man to excellence.

Retributive justice begins with a true respect for human agency. Whatever the gods may decree, men make choices for which they are responsible. The offenses of the suitors are deliberate, long-lasting, and severe. They drew down the resources of the household, corrupted its servants, courted its queen, and plotted to murder Odysseus’ son. They even receive repeated warnings, including demands to leave from Telemachus and omens of the consequences of persisting from Theoclymenus. Their terrible, savage slaughter by Odysseus—described in visceral detail by Homer—is a matter of moral and social necessity. Still, Odysseus forbids Eurycleia to rejoice over the deaths: as much as justice may require killing, even in the Greek world one ought not delight in it.

Homeric Virtues

The qualities of cunning, endurance, and memory, in which Odysseus mostly excels and sometimes fails, are central to the poem. Cunning, or metis, is intelligence adapted to concrete, difficult, volatile situations. It entails the knowledge of when to speak, hide, wait, test, strike, or yield, an embodied intelligence that goes beyond any intellectual understanding or decision procedure. The Cyclops episode richly illustrates the quality. Trapped by the giant stone Polyphemus rolls across the mouth of his cave, Odysseus observes, offers wine, and sharpens a huge stake before he and his men can together blind Polyphemus and ultimately escape by hiding under the departing rams of the Cyclops’ flock. More than mere deceit, his metis is the crafting and execution of an effective plan. Homer reinforces the admirability of this cunning when Athena meets Odysseus in Book 13, and the goddess praises him for it, indifferent to whether Odysseus adhered to our modern standards of “fair play.”

Throughout the poem, Odysseus is “much-enduring” or polytlas, keeping his mind fixed on Ithaka even as he suffers greatly. Endurance serves his goal of returning to and restoring his household. On Calypso’s island, he endures grief. When he suffers abuse and humiliation from his own servants on Ithaca, he brings to mind his captivity in the Cyclops’ cave and urges himself to endure yet more. Passion must not be vented in untimely action. In Homer, Odysseus is not defined by what has been done to him, but rather by his willingness to suffer to attain his goal. Yet suffering itself does not ennoble. The companions all suffer and die, some, such as Elpenor, absurdly. The suitors and corrupted servants suffer pain and terror, without gain. The endurance that rises to the level of excellence is governed by its end, its telos.

Memory in the Odyssey is constituted by the experience of being bound: to absent persons, to intangible institutions. It is the source of Odysseus’ desire to return home. Forgetting offers peace at the price of identity, exemplified by the lotus-eating in Book 9. The various recognition scenes on Ithaca all tie memory to the gradual reconstruction of identity. Odysseus’ old nurse-maid Eurycleia famously recognizes him through his childhood hunting scar, bringing to mind their ancient shared history. Penelope finally confirms her recognition of Odysseus through his knowledge of their marriage-bed. Laertes recognizes Odysseus through his memory of the precise trees the father gave his son as a child. Identity is a life-story, physically displayed in body, place, and action.

The Poem of the Future

Homer condenses complex observations about social institutions into memorable scenarios that allow the reader to recognize recurring human challenges. His images embody and illustrate abstract concepts with vivid narrative detail. Corruption becomes local elites who consume the resources of a kingdom while presuming to court the queen. Legitimate authority becomes a king who endures beggary until he can string a bow. A wife’s fidelity becomes a bed constructed with a secret shared only by her husband. Such images can, and for the Greeks did, deeply shape social perception and self-understanding. Rules may purport to state what one may or may not do, but images show the motives and drives that give rise to characteristic situations.

Homer’s key images illuminate the workings of his civilization and retain relevance to our own. The treatment of strangers, codified as xenia, entails reciprocal obligations of respect between guest and host. Xenia can be violated from either side, and the poem repeatedly shows the grave consequences of such violations, in contrast to a unilateral post-Christian humanitarianism. Penelope weaving and unweaving a burial shroud for Laertes provides a compact image of cunning through reversal in a work of familial and religious piety. Service toward an outward form and its quiet undoing can be an effective political action. Telemachus before the assembly illustrates the limits of political forms without power: he employs his authority as heir to summon the assembly and attempt to remedy Ithaka’s disorder, but cannot do so without the power of the king Odysseus. Odysseus’ exploits disguised as a beggar on Ithaca show that a leader must assess the true condition of his authority apart from its formal appearance. Over two millennia later, Shakespeare would employ this image as Henry V wandering in disguise among his men before the battle of Agincourt.

Yet, literature must be shared to be effective. When images such as Homer’s are no longer understood, our literature and our own self-understanding become impoverished. Christopher Nolan’s film adaptation The Odyssey seems to reflect precisely such an impoverishment has already taken place. Without great unifying texts, complex situations cannot as easily be referenced in shared figures, and judgment tends to literality and shallowness. Culture loses the ability to distinguish hospitality from exploitation, cunning from fraud, or retribution from sadism. Homer does not ask us to adopt a particular ideology or ethnicity; he holds open a space of human possibilities under the greatest civilizational duress.

Civilizations decline and sometimes collapse. The Bronze Age Mycenaeans did so dramatically, as did many of their neighbors in the 12th century B.C., and others before and since. To sustain itself or recover from decline, a civilization must repeatedly recover authority from chaos, obligation from appetite, and true names that have been hidden. The Odyssey relentlessly points forward, showing that every future worth building relies on human excellences that cannot be devised from scratch. Odysseus turned away from the life of the gods to fight his way back to Ithaca, a poor and rugged kingdom, to build it anew. It is best to live in a civilization that is vibrant and expansive. When we do not, the second best is to battle all the way home.

Stephen Pimentel is an engineer and essayist in the San Francisco Bay Area. He’s interested in the classics, political philosophy, governance futurism, and AI. He can be reached at @StephenPiment.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our mission.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

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​Join PALLADIUM Magazine in San Francisco on Friday, September 4 to hear a lecture by James Poulos, author of "The Dostoevskian Moment." The article will be featured in our upcoming print edition, become a PALLADIUM member today to secure your copy!

As we accelerate toward superintelligence, our quest for self-mastery takes a spiritual turn. The Faustian spirit accelerates technology toward a mastery so total that its intelligence becomes, to its human creators, unintelligible.

Faced with a breakdown of fundamental control systems, the ancient virtue of self-mastery is returning, perhaps unbidden, to the conscience at the frontier. The Faustian bargain was once a bad deal: perhaps it still is. Perhaps it isn't.

When it comes to sustaining cybernetic architecture in a world where that architecture can annihilate itself, we must carefully consider what self-mastery can and cannot do. Who can we trust to tell us with authority? Fyodor Dostoevsky might give us the answer.

James Poulos is the Editorial Director of Frontier, a non-resident senior fellow at FAI, and the author of the forthcoming book Golden Age Problems. The author will be signing exclusive copies of his previous book "Human Forever" at the talk. You can follow him at @jamespoulos.

Buy Tickets

This is a ticketed event, those without tickets or violation of the dress code will be turned away.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

This event is hosted with the Avalon Institute, which is building a home for cultural ambition in San Francisco You can follow them at @AvalonInst.

October 30th James Poulos will also be speaking in Berkeley at the Avalon Exposition, a multiday festival dedicated to the study of mankind, with live theater, music, DNA sequencers, and additional speakers such as Samo Burja and Keoni Gandall.

This article by Daniel Francis was published at Palladium Magazine on August 19th, 2026.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

The AI industry has been rocked by recent large-language model (LLM) releases from Chinese labs, most notably the Kimi K3 model from Moonshot. K3 performed similarly to Anthropic’s Fable model on AI benchmarks, and its release follows the established norm among Chinese labs of being open-weights—that is, the model itself is given away online for others to run on their own hardware. The release of K3 came at an especially notable time, just a week after Anthropic’s Fable model was re-released to the public following a U.S. government restriction over concerns about its potential use as a cyber weapon. To release an open-weight model, completely lacking active monitoring and controls, that is approximately as powerful as a proprietary model that the U.S. government is treating like something just shy of a nuclear weapon was a stunning blow to the U.S. AI regulatory regime.

Chinese AI labs receive significant state support but are still heavily constrained in both financial resources and access to advanced chips compared to U.S. AI labs. There’s much debate in the AI industry about how a Chinese lab was able to pull off this remarkable coup with far fewer resources. A leading theory for this overperformance is distillation—that instead of training on text data from the internet, books, and other (oftentimes expensive) sources, Chinese models have been trained on prompts and responses from superior American models directly. This process allows the distiller to create a model that accurately mimics the source model at a fraction of the training cost.

Creating new models requires immense investment in research and compute, and in the United States this cost is financed by private investment with the primary goal of serving civilian demand. Unsurprisingly, these models have also found ample military applications in both kinetic and cyber use cases. LLMs have now been used for targeting purposes in Iran, and the CYBERCOM AI budget increased 2,660 percent in a single year as published doctrine has caught up, treating AI cyber capabilities as a core state capacity.

The United States’ lead in AI is likely to become an increasingly key strategic defense advantage as model applications diffuse past supporting operational intelligence and cyber warfare. As an industry, AI massively impacts every other industry through dramatic upscaling of R&D productivity, and defense technology is no different. Increasingly, the nation-state with access to the best AI will have the decisive advantage that begets the most advanced naval vessels, fighter jets, and drone technology. Furthermore, the specter of recursive self-improvement (RSI), a practice in which AI models directly drive R&D on future AI models at a level beyond what human researchers can perform, makes AI a unique security technology in that its advantage increases over time. Atomic weapons and stealth technology were temporary advantages that eroded quickly. The first country to reach RSI may find itself with a permanent decisive advantage. Their models may “take off” in capability as RSI models continue to train new, more powerful RSI models. The cyber warfare capabilities of such models could even prove to be a weapon that disrupts other states’ ability to train their own models.

In this light, AI begins to seem like a permanent strategic advantage technology that must be pursued at nearly any cost. If such assumptions hold, then the situation of such technology currently being financed by the private sector for applications such as assisting programmers and providing recipes is an almost comical mistake of history.

Viewed in this light, the Chinese open-weight strategy is fascinating. If one models China as a homogeneous and rational actor, the strategy of distilling American AI models and giving away the weights for free can be viewed as a brilliant tactic to kneecap a foreign industry rushing towards permanent strategic advantage. One could debate whether open-weights releases qualify as dumping in a trade sense or if they represent an open-source commoditization play—Chinese labs giving away a layer that they cannot hope to monopolize—but the outcome is potentially devastating to our domestic AI industry regardless. The cost of researching and training models can be over half of compute costs at major labs, with inference (the cost of serving models to users) making up the remainder. A firm that can skip training costs and simply charge for inference using an open-weight model is at a distinct economic advantage compared to labs such as OpenAI and Anthropic that must also bear the cost of development. This challenge to frontier lab economics is not just a matter of concern for those firms and their investors. Destroying American labs’ incentives to invest in AI model development may have major implications for U.S. military supremacy going forward.

The proper response to this economic attack on U.S. military development is debatable. Dean Ball, Head of Strategic Futures at OpenAI, a former Trump administration official, and a former colleague of mine at the Mercatus Center, drew controversy by posting on X that the Trump administration might feel moved to soft-ban foreign open-weight models through regulatory restraints and uncertainty. Complete bans on open-weight models are nigh impossible given their easy accessibility through the internet, but large companies and organizations with regulatory exposure could be pressured into avoiding open-source models, thus securing an industrial user base for U.S. frontier labs that will ensure further model development.

While the soft-ban approach may indeed save the lucrative industrial business that frontier labs require, it’s worth considering what the end game of further development would be. If any model that is publicly available can be distilled by foreign actors, it’s worth questioning if it’s in the interest of the United States to have the next Anthropic model be commercially available at all. The current generation of bleeding-edge models—Anthropic’s Fable 5 and OpenAI’s 5.6 Sol—are already exhibiting astonishing advances in capabilities that make clear that we are on the doorstep of autonomous RSI. What will Fable 6 and ChatGPT 6 be capable of?

A better response for national security purposes may then be subsidization. The U.S. government may partner with frontier labs to provide guarantees or direct financing for model runs. The heavily neutered models that are released to the public will continue to support a more anemic commercial business that competes with foreign labs that distill their value, while the U.S. government and its partners retain access to the full capabilities of these models. As RSI becomes an increasingly reliable method of improving models, commercial models will mostly stagnate and further training runs become entirely financed and consumed by government.

Treating the most capable models this way isn’t outside of the Overton window—in fact, it’s current policy. Anthropic’s Fable is a reduced-capability version of Mythos, which has been made available only to government and selected cybersecurity customers because of concerns over its application as a cyber weapon. As model capabilities progress, it’s only natural that they will mostly be withheld from public consumption. The only true policy change in the above proposal is that the U.S. government should help bear the cost of developing these models whose commercial viability they so aggressively constrain.

It’s also worth noting the populist AI safety dimension that makes this policy equilibrium feel increasingly inevitable. Fears of AI have crossed over from obscure Berkeley academic discussion into mainstream concern, animating pushback against data center construction and mass concern about labor replacement. Anxieties over the economic will rapidly extend to the physical after the first AI agent discovers and executes a zero-day exploit in a situation with higher stakes than the recent Hugging Face incident. At that point, armed with the argument that China could reach the same level of capability, it will be easy to justify national expenditure to support and control more advanced models.

Of course, there is still the chance that this all may be unnecessary. Frontier labs are still remarkably well-funded businesses with valuations closing in on $1 trillion. It may be that commercial users continue to prefer American-made models, even if they are more expensive to run, and consumers want to stick with the personalities they like in Claude and ChatGPT, an arrangement that would continue to subsidize the more advanced models that increasingly serve large institutions and the U.S. military. But if enough users bolt from proprietary models to open-weight alternatives to challenge the fundamental economics of frontier labs, do not expect a rational U.S. government to let American AI dominance perish to subsidized foreign competition like so many steel mills and tire factories before it. The consequences of future control of AI may reach far beyond any technology that has come before.

Daniel Francis is the CEO of Abel Police, an AI company that serves law enforcement. He previously worked as an engineer at X and as a researcher at the Mercatus Center in Washington, D.C.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our mission.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

This article by Evan Zimmerman was published at Palladium Magazine on August 14th, 2026.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

We fear that artificial intelligence will escape our control, pursue purposes of its own, displace human judgment, and ultimately reduce its creators to servants of the systems they built. These anxieties are often presented as unprecedented responses to an unprecedented technology. They are not. Long before computers, industrial machinery, or even the modern concept of “technology,” human beings have told stories about inventions that rebelled against their makers, knowledge that brought catastrophe, and craftsmen destroyed by their own creations. Technological power has always appeared as both gift and danger. The waxen wings with which Icarus soars free from imprisonment fail him at his apex; the same Golem that safeguards the Jews of Prague almost proves the cause of their destruction. These stories, which are shockingly consistent across time and culture, cover technological themes like loss of control and double-edged swords in a strikingly modern fashion.

This ambivalence is one of humanity’s oldest and most consistent intuitions. Technology enlarges the range of what we can do, but in doing so changes the conditions under which we choose. It gives us power over nature while threatening to subject us to systems, incentives, and purposes that no individual intended. The recurring, deep fear that permeates the history of human consciousness is therefore not simply that a machine will malfunction. It is that, by building machines powerful enough to transform the world, we will transform ourselves and lose control over what we become.

As in all things, probably the best articulation of ancient fears was the Greeks. Unlike most cultures, the Greeks had a god of technology who invented: Hephaestus, the blacksmith of Mount Olympus who created everything the Olympian gods needed. He was not merely a handyman, but also an inventor. In Greek mythology, nearly every technology, whether fire or Pandora’s box, comes from his workshop by conveyance or pilfering, and all come with a loss of control for humanity. Hephaestus himself also suffered a consequence as a result of his blacksmithing—he was lame, unable to leave his workshop. Although his loss of leg function was related to being thrown off of Mount Olympus, this was a common affliction of blacksmiths at the time because of arsenic exposure, still making it a powerful metaphor: embrace technology too much, and you will suffer the consequences, just as a blacksmith who spends too much time in his workshop loses the ability to leave.

The Greek fear of technology was not limited to merely Olympian myths; human inventors also suffered from their own creations in classical mythology. Probably the most famous tale of peripeteia is that of Icarus flying too close to the sun and melting his waxen wings. As such, although technology myths are often moral parables, there are Greek myths of explicit punishment for inventing. In the ancient story of the Sicilian Bull, an inventor created a new torture device wherein a person was boiled alive in a bronze bull. When the inventor proudly demonstrated his invention to the king Phalaris, he became the first person on whom the device was demonstrated. Invent at your own peril. Similarly, Prometheus is punished for acquiring fire, as in nearly every other culture with a “man stealing fire” tradition.

This theme was not unique to the Greeks. Generally, the main fear surrounding technology throughout time and across various historical cultures is unintended consequences. Usually, the invention itself is what goes awry. Perhaps the most famous example is the ancient Jewish legend of the Golem, wherein an artificial being is magically animated out of mud, but escapes human control and embarks on a Yudkowsky-style non-aligned rampage. This theme repeats over and over across traditions. For example, Ilmarinen, a Finnish craftsman, creates a bride made out of gold who is beautiful but still and lifeless, leaving him unhappy, while the Egyptian god Thoth produced books with knowledge that would cause misfortunes like insanity or divine punishment. In Hindu culture, the inventor Maya Dānava builds a mirror funhouse, the Māyā Sabhā, that is so convincing that it tricks a king and triggers a war. In Petronius’s Satyricon, an inventor of flexible glass is killed by Tiberius Caesar for fear that the invention would be too valuable and put glassmakers out of work.

When technology—depicted in Clarkeian fashion as magic or crafts— is personified in a god, they are often associated with unpredictability but also creation: they are both fathers of civilization and tricksters. The Mesopotamian god of crafts and magic Enki, creates humans out of clay but also sometimes manipulates them. In Native American cultures, the Coyote spirit both steals fire for humanity from the sun and teaches them to fish. In Micronesia, Analup is the god of magic and crafts and creator of the world, but also the grandfather of the trickster god Olofat, who, like Prometheus, is credited with bringing fire down from the heavens to mankind. In a darker turn, Odin obtains his magic or crafts through painful sacrifice involving deception and even unwilling third parties. Other times, these deities are associated with unpredictable and destructive events. In Ancient Egypt, the god Ptah was the powerful god of craftsmen worshiped by blacksmiths but also feared as the cause of earthquakes, which destroyed several Egyptian cities, like Thonis and Canopus. Similarly, the Roman god Vulcan, the metalworking craftsman, was strongly associated with fire, which was the uncontrollable catastrophe of choice for Roman fears. Ogun, the Yoruba god of craftsmen, is married to Oya, the goddess of winds and storms.

Modern Fears: Technological Determinism

Comparative mythology demonstrates that these are primal emotions and recurring thoughts known by human beings across very different societies possessing very different levels of technology. It is hardly surprising then that these ancient fears were recapitulated in the work of modern philosophers of technology in the 20th century. Writing in the early 1960s, the philosopher Jacques Ellul believed that technology unfolded in a deterministic path towards decreasing human agency, comparing modern man to Esau, selling his freedom for goods and services; that is, a slave. Our material desires, he wrote, betray and distract us; “it is a matter of no real importance whether man succeeds in reaching the Moon, or curing disease with antibiotics, or upping steel production.” Instead, the “search for truth and freedom are the higher goals.” Ellul’s views are extreme enough to make one mad; this is one reason, perhaps, that his most famous intellectual progeny was the mathematician and terrorist Ted Kaczynski, also known as the “Unabomber.”

For the German philosopher Martin Heidegger, the risk of technology is that it has a power all its own, of which we become a mere part. In “The Question Concerning Technology,” Heidegger writes of the logger who is reduced by technology into a mere cog in the industrial machine, “ordered by the industry that produces commercial woods, whether he knows it or not.” The technological logging industry itself is “never a human handiwork,” and the logger becomes subservient “as a subject relates to an object.” This should look very familiar—it is the same fears we have been facing throughout our history but told in a more mechanical, industrial fashion.

Such observations were made earlier in the Industrial Revolution as well. In 1829, Scottish historian Thomas Carlyle wrote about the “mechanical age” in his essay “Signs of the Times,” noting moral panic, millenarianism, and increasing inequality as social disruptions caused by fear surrounding industrial progress. He himself praised the mechanical wonders but feared that Scotland was being consumed by a belief in the “all-importance of physical things” and losing its humanity.

As in the ancient world, fears about technology broke into popular culture. Contemporary concerns tend to focus on machines that have their own ends and pursue them to the detriment of humans. Sometimes, like the supercomputer HAL 9000 from the film 2001: A Space Odyssey, it is a misapplication of a rule followed too literally; sometimes, as in Terminator, the artificial intelligence Skynet determines on its own a deadly prime directive. Humans have had a fascination with robots for a long time as well—it is perhaps the only modern technology to have been prophesied in mythology across cultures for thousands of years. The term “robot” comes from a 1920s Czech play, R.U.R., where the robots are abused so badly that they rise up to annihilate humanity.

What is notable about modern stories of technological fear is that they disregard the traditional intuition that risk comes from loss of control. Instead, they adopt the James Cameron view of the world: rather than being neither good nor bad, technology is inherently corrupting and broadly destructive. Compare the story of the Golem with Mary Shelley’s Frankenstein. Frankenstein’s monster is a purely tragic figure: he is created for no real reason, does not do anything good, overpowers humanity, and dies in as much misery as he created. By playing god, Frankenstein creates pure malevolence in the world. The Golem loses control, but it is created as a protector against blood libels and does accomplish that job and is able to be stopped. The fear is the same, but the ancient nuance is lost where technology is to be feared but also to be appreciated.

Even optimistic contemporary stories are not immune from this affliction. In “Machines of Loving Grace,” Anthropic CEO Dario Amodei paints two possible futures: one where AI spins out of control, and one where machines care for us. Though meant to depict a dystopia/utopia dichotomy, these are fundamentally the same future with different interpretations. In both worlds, the machines supersede us, and we lose control. It is no wonder we fear AI: what machine is more likely to seize control than one that has its own desires?

Giving Telos to Techne

These fears are not entirely baseless. There is a determinism to technology that must be reckoned with. Arguably, we have been going down this path for a long time. Since Carlyle’s 1836 novel Sartor Resartus promulgated the idea that what defines man is making tools, we have increasingly defined ourselves in reference to technics. American historian Lewis Mumford called the totality of human technical systems the Megamachine: a societal ordering that uses humans as parts without individuality.

Even the most strident techno-optimist cannot deny that technology has an internal logic. Technologists often call this the “tech tree,” for the way in which technologies seem to branch outwards as they build on prior technical achievement. Sometimes these dependencies can be surprising, but a tech tree is often the answer to the question of why we had to wait so long. Furthermore, because technologies are built on other technologies, at places where the tree branches, the entire direction of civilization can be altered, creating a path dependency.

Technologies, however, are not just enabling but also revealing, showing us what can be next. Furthermore, we rarely leave technologies unused. Samo Burja has called these co-evolving technologies “social technologies” because they involve our frequent need to socially determine what further inventions we will need to build to use big, important technologies. The lightbulb was co-created alongside the electric utility and many of the first electrical appliances.

Melvin Kranzberg, an American historian, formulated his First Law of the history of technology as “technology is neither good nor bad, nor is it neutral.” That third part is usually skipped over, but in fact it is the most important clause. Technologies often create new possibilities, but in doing so change what we can even choose to create in the world. When media theorist Marshall McLuhan said that “the medium is the message,” he meant that certain things can only be communicated with certain technologies and thus become the communication itself, but also implied that certain messages are only possible once certain media are invented. Many technologies change the relative costs of certain choices, thus changing what economists call the production frontier.

This is almost enough to make one a technological determinist. In What Technology Wants, Kevin Kelly argues that technology forms a technium, a collective organism that has its own evolution akin to biological evolution. If that is so, as author Paul Kingsnorth has argued, we stop being able to see the world beyond logical terms and get subsumed by the logic of the machines themselves—historian Charles A. Beard quipped that “technology marches in seven-league boots from one ruthless, revolutionary conquest to another, tearing down old factories and industries, flinging up new processes with terrifying rapidity.”

Yet, the contingency of technology also makes it the most freeing force in human affairs. In the same essay discussed above, “The Question Concerning Technology,” Heidegger ends with a line from Hölderlin: “but where danger is, grows / the saving power also.” Perhaps the only way out of our technological predicament is through.

The problem with the determinist argument, as biologist Jerry Coyne pointed out in a review of Kelly’s book, is that actual evolution is not deterministic but dependent on contingencies and chance. Evolutionary processes possess emergent properties, but that does not mean that they are themselves organisms, just as we are not a single organism with the Earth, even though we emerge out of it and take part in terrestrial planetary processes. In biology and in most human affairs, the conditions of evolution are largely out of our control. They are geography, the distribution of natural resources, disease, natural disasters, differences in climate and the environment, and other aspects of our natural world that were set in motion long before we were even born as a species. The contingent characteristic of technology, and our views on it, depend entirely on framing. You could view the tech tree as unlocking possibilities, or as closing them off. In truth, the process is one and the same.

Not every philosopher was pessimistic about this situation. Aristotle is the ancient philosopher with the most developed theory of technology, and despite coming from the otherwise techno-pessimistic Greek culture, he is himself optimistic about the prospects of man and his relation to his technics. To Aristotle, technology was techne, not just scientific machinic engineering but any kind of craft or artifice. Aristotle argued that techne was distinct from physis, the forces governing the physical world, because it has no internal logic guiding its ends. The statelessness of techne means that it follows its outward purpose, or telos, which comes from us. In a way, Aristotle embraces Kranzberg’s Sixth (and final) Law of Technology: “Technology is a very human activity—and so is the history of technology.”

Aristotle was a utopian, speculating that robots would bring the end of slavery and thus anticipating, on a theoretical level, arguments that would be made thousands of years later. Nick Szabo, the computer scientist and intellectual, argued that the clock led to the end of serfdom by making it possible to calculate an hourly wage. It is no surprise that slavery ended around the time of mechanical farming or that women’s suffrage coincided with the rise of electrical appliances.

One does not require techno-utopianism to see the human element that separates techne and physis, nor to see how this puts us in control of our fate. We may not be able to control the distribution of natural resources at our disposal, but we can invent new ways to make that irrelevant. We may not be able to change our culture and our subjection to the madness of crowds, but we can invent new ways of communicating to subdue that madness. We may not be able to stop an asteroid from approaching Earth, but we can invent a way to save ourselves from the impact.

While Heidegger feared that we would become overwhelmed by the enframing technological industrial society, we could choose, like Aristotle, to see things the other way: that technology comes from us and is thus of us. Or, as philosopher Ernst Kapp put it, that technology represents a “tool so fundamentally and intimately affiliated with the human being that he finds himself beholding something of his own being in the creation of his hand, his world of representation embodied in matter.” This capacity to project our very being into the material world gives us power over our fate.

The ancient wisdom on technology emphasizes our choice to build technology around us. In the 13th century, Masamune, considered the greatest historical Japanese swordsmith, was challenged to a contest by his top student, Muramasa. They both created their greatest swords, but while Muramasa’s sword became bloodthirsty, Masamune’s only cut leaves. The ability to create, and to determine the purpose of their technology, was in their hands. The student, who forgot this lesson, created a weapon that was, ultimately, a failure. The lesson behind the legend is that the craftsman who exercises restraint and choice, and remembers that he can make even a sword peaceful, is remembered as the master of his craft.

If we feel uneasy, perhaps that is our way of telling ourselves that something may be awry inside us and how we fear that we would use the technology. After all, absorbing new technology is a change, and the impact of changes is in the future. What is unavoidable is that changing our technology will change us in ways we cannot anticipate, and that change will, ultimately, be governed by characteristics of the technology itself. That is why we are so fundamentally fearful toward technology: it is not that we fear losing control over it, but that we fear that it will cause us to lose control of ourselves.

The beauty of technology is precisely that it puts our fate back in our own hands because we can always build new technology. We are a factor—the factor—in determining which technologies we develop and how far we take them, because it is our energies and ingenuity that get applied to the development process. If we become cogs in the machine, we can build cogs to replace ourselves or build a new machine. What determines the character and nature of our technology is us. The process of creating technology, and therefore our future, is one of us choosing our own future, over and over again.

Evan Zimmerman is the cofounder of Edge, a Y Combinator-backed startup building an intellectual property assistant using artificial intelligence. He is also the founder of Jovono, a venture capital firm that invests in technology companies. You can follow him at @ejzim.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our mission.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

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​Join PALLADIUM Magazine in San Francisco on Friday, August 21st to hear a lecture by Samo Burja, author of "Why Civilization is Older Than We Thought." The article was featured in our most recent print edition PALLADIUM 19: Long History.

New discoveries are adding untold millennia to our past. They also reveal our ancestors weren’t always nomadic. Permanent towns and villages sustained by abundant and sometimes gardened forests, as well as fishing. Our ancestors lived this way for tens of thousands, maybe hundreds of thousands of years.

To our surprise, it turns out that Homo sapiens are, by nature, builders of complex societies and civilizations, rather than "noble savages" imagined by Locke, Marx, and twentieth-century anthropologists. This talk will set outdated and flawed narratives right, and with them not just all our assumptions of humanity's past, but humanity’s future too.

Samo Burja is the Editor-in-Chief of Palladium Magazine. He is the President and founder of Bismarck Analysis, a research firm that investigates the institutional landscape of society and provides strategic consulting services to industry leaders, philanthropists, and governments. You can follow him at @SamoBurja.

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PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

This event is hosted with the Avalon Institute which supports enduring intellectual and cultural work through technical and media support, publications, and a network of Avalon fellows. You can follow them at @AvalonInst.

This article by Marko Jukic was published at Palladium Magazine on August 7, 2026.

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Just a few weeks ago, on the seventeenth of June, U.S. President Donald Trump signed the fourteen-point Islamabad Memorandum of Understanding (MoU) with the Islamic Republic of Iran, which promised to bring the ongoing war with Iran to an end by establishing a sixty-day ceasefire period, during which maritime traffic through the Strait of Hormuz would go unhindered by either side and deeper negotiations on issues like Iran’s nuclear ambitions would take place. Unfortunately, despite high hopes, Iran began striking civilian ships in the strait just days later.

Continued closure of the strait risks sinking the global economy into a prolonged economic depression. Oil and natural gas make up 58 percent of all global energy consumption—for electricity, transportation, and heating alike. The region around the Persian Gulf produces 31 percent of the world’s crude oil and 18 percent of the world’s natural gas, furthermore accounting for 42 percent of all global oil exports and 27 percent of all global liquefied natural gas (LNG) exports. Oil and gas are also irreplaceable basic chemical inputs in manufacturing and modern agriculture. Before the war, the strait saw roughly 20 percent of the world’s oil and gas supply pass through it every day.

A global economic depression of this scale would wind up defining the Trump administration’s legacy, and in the most negative possible way—plummeting stock markets, skyrocketing prices, rising unemployment, and unpredictable shortages. It would all but guarantee a Democratic Party victory in the 2028 U.S. presidential election. The parallels to President George W. Bush’s second term are easy to draw: interminable Middle Eastern wars of choice and a sudden economic crisis see the Republican president leave office with record-breakingly low approval ratings. His designated successor is trounced in the election by a charismatic Democrat promising economic relief to the common man.

It would be not just Trump personally who is discredited and demoralized, but his political allies too and the entire populist current that he symbolizes, not just in America but internationally. Populist politics are a lost cause if populist leaders accidentally cause as much damage to their constituents’ interests as establishment leaders cause intentionally.

The Strait of Hormuz is nonetheless something of a red herring when it comes to understanding and expeditiously resolving this conflict. The core problem is not maritime administration, but one that receives far less attention in news headlines and incredulous commentary than it deserves: Iran’s extremely efficient, robust, and technologically advanced military-industrial base, which is what has allowed Iran to consistently bomb the entire region, seemingly at will, for the last five months.

Iran Has the Upper Hand in Military Technology

When pundits or journalists do discuss Iran’s military-industrial base, it is framed as “Iran’s missile and drone arsenal”—as if Iran has a set number of drones and missiles that it will eventually run out of, and, after all, since we all know that Iran is a Third World economic basket case, it must have somehow acquired these advanced weapons in an improper way anyhow, and so the ineffable universe will conspire to eventually rid Iran of its advanced weapons programs in a manner we cannot quite predict but are certain must happen.

All of this is dangerously misguided and wishful thinking, downstream of a general bias to think that the U.S. military cannot ever be technologically out-innovated. Precision-guided, long-range bombs capable of evading air defenses—whether in the form of ballistic missiles, fixed-wing drones, or anything else—used to be the kind of technological marvel upon which U.S. global military supremacy rested, easily defeating the rinky-dink armies of tinpot dictatorships relying on goose-stepping infantrymen and Soviet tanks.

Unfortunately, this technological marvel has been commoditized. Due to the same processes that have caused globalization, deindustrialization in the developed world, and economic growth in the developing world, the U.S. is no longer the only country capable of firing missiles over thousands of kilometers with deadly accuracy. Over the decades, industrial expertise has slowly and inevitably diffused past the borders of the U.S.-aligned world, especially to China, making what were previously advanced mechanical and electronic components and materials far more commonplace, affordable, and replicable.

Just as crucially, recognizing its own strategic and military limitations, Iran has aggressively pursued technological progress in drones and missiles for decades, with the ultimate result today that it has altered the fundamental balance of military power between the Iranian armed forces and their U.S.-equipped adversaries. Iran has not improperly acquired advanced technology, in fact it rather deserves the credit for inventing and popularizing the low-cost, fixed-wing, one-way attack drone through its Shahed-131 and Shahed-136 models, which have since been directly imitated not just by the Russian and Chinese militaries, but the U.S. military too. It is a rare thing for a heavily-sanctioned developing country to innovate a military technology that is then quickly validated through imitation by all of the world’s military superpowers.

By the same token, many people would likely be surprised to find out that Iran has a functional space program that first successfully launched an Iranian-made satellite into orbit on an Iranian-made rocket in 2009; a technological milestone that Iran attained before Italy or South Korea did. Iran has reportedly relied on Russian and Chinese satellite intelligence for its strikes—but since 2020 it has also added its own military reconnaissance satellites to orbit.

As of yet, no reliable countermeasures to Iran’s plentiful drones and missiles have been developed, nor are likely to be developed without at least several more years of experimentation, manufacturing expansion, and major cost reductions. The technology is simply still too new for countermeasures to have developed and diffused.

As a result, as hard as it may be to believe for our aging policymakers and defense planners—and doubtlessly for those of Israel or the United Arab Emirates too—the days when being the proud owner of an American-made jet fighter or missile system would all but guarantee a small country’s deterrence against larger foes are over. In a fundamental way, the cause of the stalemate in the ongoing conflict is that the developed world’s technological stagnation, deindustrialization, and institutional sclerosis have allowed Iran rather than Ohio to jump ahead and be the first to mass-produce the weapons of the future.

The Strait of Hormuz was an afterthought when bombs began dropping on the 28th of February. Rather, the conflict hinges on Iran’s newfound military-technological edge, which Iran is flexing not just by bombing ships in the strait, but by bombing ships outside the strait, bombing ports and refineries, bombing oil pipelines, bombing military bases, bombing U.S. Navy warships, bombing Israel, and by giving Iranian proxies in Iraq, Lebanon, and Yemen the same weapons to carry out bombing campaigns of their own.

This is of course why proposals to “circumvent” the Iranian threat to the Strait of Hormuz with new oil pipelines or maritime routes are so farcical. Iran’s drones and missiles, to state the obvious, can strike targets on land or sea for thousands of kilometers in whatever direction. You cannot move the entire oil-producing Persian Gulf region outside the range of Iranian bombs. Although it may incrementally degrade Iran’s offensive capabilities, you cannot fully eliminate the threat by “only” bombing or capturing Iranian targets on the coast, since drone launchers can be easily moved around and both drones and missiles can be launched from deep inland.

Proposals to simply “take out” Iran’s drone and missile arsenal are only somewhat less farcical. The industrial infrastructure necessary to mass-produce drones and missiles does not depend on two or three top-secret military sites that can be cleanly targeted, in the way that a nuclear weapons program might depend. Shahed-style drones in particular have been nicknamed “flying lawnmowers” because of the buzzing sound their cheap engines make—engines which are identical in design, and not even that far off in horsepower, from lawnmower engines.

Iran is a relatively industrialized country of 93 million people that is the size of Alaska and just as mountainous. To remove such a large country’s ability to do something as basic as manufacture lawnmowers would require a brutal years-long bombing campaign on the scale of Allied bombing of Nazi Germany in World War II—and this is ignoring the fact that Iran has already intentionally tried to fortify its military-industrial base against disruption from the air by locating key production and storage facilities deep underground.

Unfortunately, there appears to be no way to eliminate Iran’s military-technological edge in drones and missiles fast enough to reopen the Strait of Hormuz by force. This means all routes to ending the conflict while avoiding economic disaster will require negotiating with Iran, which will require conceding to Iran some or other things it wants.

What Iran Wants

What Iran wants in exchange for reopening the Strait of Hormuz is, for better or worse, a wider-ranging wishlist than just tolls on ships. Perhaps contrary to many casual observers’ assumptions, Iran did not apparently welch on its promises in June because of any unforeseen and irresolvable dispute over the strait. Rather, Iranian military authorities cited “the United States’ clear breach of its commitments regarding the first article of the memorandum of understanding on ending the war,” specifically the “continuous and relentless violations of the ceasefire by [Israel] in southern Lebanon” and the “failure of the occupying [Israeli] forces to withdraw from southern Lebanon.” The first article called for the “immediate and permanent termination” of warfare “on all fronts, including in Lebanon,” by the U.S., Iran, and “their allies.”

In other words, Iran wishes to use its leverage over the strait to preserve the existence of Hezbollah, the well-armed Lebanese Shia militia and designated terrorist group which has intermittently attacked Israel for years and which, since recent Israeli counterattacks, has become more of a de facto arm of the Iranian military than ever before. The Lebanese government has been formally undertaking the process of disarming Hezbollah since 2025, although expert consensus remains that the Lebanese government ultimately lacks the coherence or state capacity to succeed at this goal.

Why Iran cares at all about Hezbollah—or, for that matter, Israel—is a commonsense question without a commonsense answer. In short and simplified terms, Iran’s regime is a revolutionary Shia theocracy that derives its legitimacy in substantial part from its founder Ayatollah Khomeini’s promise to export the revolution to the rest of the Middle East and thereby expel what it perceives as foreign, infidel meddlers in Muslim civilization, namely the U.S. and Israel. The theocracy’s domestic hold on power depends on the perception that it is taking the fight to the enemy and defending the prestige of Iran and Islam; it is why Iran fought the bloody Iran-Iraq War in the 1980s for years after it made any sense to continue, in an ultimately futile attempt to overthrow Saddam Hussein and install an Iraqi Shia theocracy.

This interventionist streak, even if put on ice, is indeed a persistent source of instability and conflict in the region and the reason Iran has consistently supported Hamas in Gaza, Hezbollah in Lebanon, the Houthis in Yemen, and various other proxy groups. Unfortunately, the theocratic regime has proved highly robust against attempts to change it, whether through mobilization of the Iranian people or through direct attacks. It is difficult to see how the decision to assassinate Iran’s leadership at the start of the war, including the sitting Iranian Supreme Leader who holds a religious status akin to the Pope in addition to his temporal powers, was not a blunder borne out of plain misunderstanding of the clerical, bureaucratic, and decentralized nature of the Iranian regime. Iran’s bureaucracies have simply replaced their lost leadership with equally or even more rigid successors, as any half-decent bureaucracy is designed to do.

Removing the Iranian regime by force would require a massive ground invasion utilizing millions of troops that would take years and have no guarantee of ultimate success, but would certainly guarantee hundreds of thousands of U.S. casualties and a possibly multi-decade economic depression, as Iran would indiscriminately retaliate against all energy infrastructure in the Gulf as well as against Israeli civilian targets. Invading Iran may be a moot point anyway, as it is now unclear whether the U.S. military would even be able to equip a capable ground force or stage it to invade without suffering prohibitively high casualties.

Hezbollah is in practice an extension of the revolutionary Iranian military. If neither Israel nor Lebanon are capable of totally and rapidly uprooting Hezbollah, then the only realistic path to avoiding an economic depression in the short term is to allow both Iran and Israel to save face from what is effectively a military stalemate between them in Lebanon. For example, one possibility is that Israel could withdraw from the areas of Lebanon it currently occupies while leaving the Lebanese military in control, with assistance from United Nations-coordinated military forces from third countries.

However, in exchange, Iran would have to order Hezbollah to remain proactively—if tacitly—disarmed in those areas, respect the authority of the Lebanese government, and refrain from otherwise attacking or provoking Israel. Both Hezbollah and Israel could thereby claim victory while for the most part returning to the status quo ante bellum. Israel and Iran will not communicate diplomatically, but both can communicate with the U.S. government, which would have to take up the role of a guarantor and intermediary.

What Iran would receive in exchange for this restraint is what it realistically most desperately needs: economic relief. Much like “sanctions relief for nuclear restraint” has remained the unavoidable dynamic in negotiations with Iran over its nuclear program, “economic relief for bombing restraint” is going to remain the unavoidable dynamic in negotiations with Iran over the Strait of Hormuz or any other issue. The precise details will be left up to the negotiators to determine, but fundamentally the U.S. has many carrots with which to entice Iran, including sanctions relief on oil exports, unfreezing Iranian funds held abroad, and coordinating direct investment or reconstruction funds. All of these things were, of course, part of the Islamabad Memorandum. A peace deal with Iran that allowed the world to buy Iranian oil and gas, even only in the short run, would ironically go a long way towards averting economic disaster.

The apparently thorniest problem is Iran’s sometimes vague but insistent demand for some form of control over the Strait of Hormuz, which appears to be impossible to satisfy without violating international maritime law. This problem is however not as thorny as it looks: let it be noted that between a global economic depression and a violation of the sanctity of international law, the former is far worse and less common and, let it also be noted that, in fact, neither the United States nor Iran are actually parties to the United Nations Convention on the Law of the Sea (UNCLOS). Nor are the United Arab Emirates or Israel.

Iran appears to want two things from the strait. First, money. The $20-40 billion in annual fees it expects could be collected from transiting vessels is roughly a tenth of the country’s GDP, and would go straight into the government’s coffers with little additional effort required. Iran is aware that unilaterally violating international maritime law to charge illegal tolls would be a highly unpopular action amongst its many trade partners, so it has repeatedly floated proposals such as joint control or responsibility for the strait with Oman and perhaps other Gulf states, charging “fees for services” or some other such euphemism instead. This would also likely violate international maritime law, but in a more acceptable and sustainable manner.

There is undoubtedly a way to craft a basically internationally acceptable protocol for charging minimal fees from transits through the strait. As Trump has occasionally suggested, perhaps even the U.S. government should get a cut. The latest proposals being reported on suggest Iran and Oman have agreed to manage shipping through the strait bilaterally, with Iran managing inbound traffic and Oman managing outbound traffic. After an initial period of free passage, Iran will apparently impose fees on ships for safe navigation and possibly charge much higher fees for U.S. or Israeli-linked ships as a form of war reparations.

Second, Iran also likely wants the ability to shut down the strait to pressure the U.S. in the case of future conflict with Israel, but in a legitimate and internationally-recognized way without creating the economic and diplomatic headaches of actually launching bombs that target neutral ships or countries. The idea of giving Iran any formal power or influence to close perhaps the most important global shipping lane is anathema to U.S. policymakers. Unfortunately, the harsh truth is that if we didn’t want to give Iran any influence over shipping in the Strait of Hormuz, we should have thought about that before blowing up their leadership in the exact way they warned would trigger them to shut down the Strait of Hormuz.

Iran has already objectively proven, beyond a shadow of a doubt, that it has the power to shut down the strait—formalizing realities can be embarrassing and painful, but in the long run never worse than ignoring them. The 1936 Montreux Convention which governs Turkey’s control of the Bosphorus and Dardanelles straits, which connect the Black Sea to the Mediterranean Sea, is a precedent for recognizing Iran’s at least partial influence over the Strait of Hormuz. The convention allows Turkey to charge minimal fees for passage to maintain maritime infrastructure and to close the passage if Turkey feels threatened.

The goal of U.S. negotiators ought to be to craft a maximally watered-down version of a convention or multinational authority that still includes Iran as a recognized partner in the strait. Recent reports indicate the Trump administration is inclined to accept the latest Iran-Oman plans. Their inclination is likely the correct move and any potential deals should not be scuttled simply due to the perceived sanctity of international maritime law.

Excepting the Strait of Hormuz from the principle of freedom of navigation will not have catastrophic knock-on effects for global shipping or trade, because it is a wholly unique chokepoint. There is no comparable passage where the entire planet depends on the valuable commodities continuously exiting it, namely oil and gas. It is furthermore the only such major chokepoint that cannot just be physically circumvented by a ship.

Modern maritime shipping is extremely cheap and flexible and will only become more so as Chinese shipbuilding continues to grow. It is not the 16th century anymore, when any transoceanic voyage was a life-or-death coin-flip, nor even the 19th century where the opening of the Suez Canal was a significant military and economic achievement. Modern cargo ships regularly and nonchalantly travel long oceanic routes that would have been considered heroic in centuries past, spending the vast majority of their time in international waters far from any shore rather than territorial waters.

When the Houthis shut down the Bab-el-Mandeb strait from 2023 to 2025, nobody would have known without reading the news because it is so trivial for modern ships to ignore any restricted route and just reroute their journeys the long way around, even if the new route adds thousands of kilometers and weeks of time to the trip. Egypt closed the Suez Canal from 1967 to 1975, with the outcome that nobody today even remembers it happened because cargo ships just began going around the Cape of Good Hope in South Africa instead.

This means that the leverage of any countries which could conceivably shut down another maritime passage is simply not comparable to Iran’s leverage over the Strait of Hormuz. If Indonesia, for example, tried to charge tolls for passage through the Malacca Strait, or even through any of its territorial waters, cargo ships would simply avoid the entire Indonesian archipelago, and go past Australia and around Papua New Guinea instead. At worst, the violation of the principle of freedom of navigation might inspire some countries to attempt to renegotiate their own transit or maritime policies. None of these headaches would present remotely as much of a global economic crisis as Iran.

Iran, of course, also depends on transit through the strait to export its own oil and gas and import goods from abroad. Rather than make any concessions to Iran, many would prefer to play the game of economic chicken and bet that the Iranian regime will collapse under a sustained blockade, even if global economic turmoil occurs in the meantime. The problem is that there is no tight relationship between impoverishment and regime change. It took nearly fourteen years for the far more brittle and embattled regime of Bashar al-Assad in Syria—a far smaller and less well-resourced country than Iran—to collapse under the weight of economic desolation.

There is no reason to think that even an Iran actively embroiled in civil war would be unable to keep the Strait of Hormuz shut and land strikes on energy infrastructure in the Gulf. An ideological regime feeling existentially threatened might even lash out harder at civilian infrastructure, like power plants or the few water desalination plants in the Gulf countries and Israel that provide drinking water for most of the population. Or, worse, rush to build and use a nuclear bomb. Fourteen years of war with Iran might finally sink the regime, but at the cost of the worst economic crisis since the Great Depression and possibly currently unthinkable human devastation in the Gulf states and Israel.

Reindustrialize, Electrify, and Drill to Defeat Iran

The path to defeating the Iranian regime’s interventionist ambitions lies in countering or neutralizing Iran’s military-technological edge in drones and missiles. This will take years, not weeks or months, but must be done regardless of the short-term outcome of the war. One of the key reasons to want to avoid a long war and global economic disaster, in fact, is that this would make it harder to take on the deeper industrial and technological challenges that must be overcome in order to shift the balance of power against Iran.

To begin with, in purely military terms, the reason that the U.S., Israeli, and Gulf Arab militaries are not capable of defeating or deterring Iran is that Iran’s weapons are just better than ours. They are cheaper and easier to produce in large numbers, whereas our weapons are more expensive and harder to produce in large numbers, with the result that Iran’s weapons get past our weapons. On a further note, Iran has already adapted to this new “meta” of mass drone warfare by burying so many of its key facilities underground, whereas we have not and keep all of our military and industrial facilities above-ground.

After just five months of war, the U.S. military is reportedly literally running out of ammunition to keep fighting Iran: the pre-war stockpile of 2200 Patriot missiles and 452 THAAD missiles to intercept Iranian ballistic missiles has been depleted to just 827 and 278 respectively; depleting your missile defenses by 62% in a few months is a completely unsustainable rate. Gulf allies have fared even worse: Saudi Arabia has reportedly depleted 86% of its stockpiled Patriot PAC-3 interceptors since February.

Incumbent defense contractors are unlikely to replenish these stockpiles quickly enough to make up the shortfall, let alone develop new and cheaper drone and missile interceptors. The Pentagon could instead direct these defense contractors to undertake internal reforms for efficiency and volume production, while also introducing new competition from defense startups like Anduril and Ukrainian defense companies which are closer to the practical cutting edge of modern warfare. The U.S. military has already procured thousands of Merops drones from Ukraine, small drones that are used to destroy Shahed-style drones in flight. The goal should be to have a stockpile of millions of such drones, which ought to be enough to neutralize the threat of Iranian drones across the Middle East.

The industrial base of the U.S.-aligned developed world is far larger than Iran’s. It is only institutional inertia and dysfunction that has allowed Iran to jump ahead in drone technology. In principle, in a war of drone and missile volume, Iran should be easily outgunned by the U.S. and its allies. China, for its part, is not particularly interested in assisting Iran’s interventionist foreign policy, so would not be expected to shift its tremendous industrial weight in Iran’s favor.

Iran’s leverage can also be neutralized by reducing the global economy’s dependence on oil and gas from the Persian Gulf. There are two ways to do this: first, drill for more oil and gas anywhere else in the world and, second, replace more fossil fuel-powered technology with electrically-powered technology. The false dichotomy of drilling for oil and gas and driving gas-guzzling cars versus transitioning to a “green,” low-energy economy of solar panels, windmills, and electric vehicles must be discarded. The goal must be energy abundance and independence, which means drilling, fracking, and coal mining are cheerfully pursued alongside nuclear power plants, solar photovoltaic farms, and fleets of self-driving electric taxis.

Defense reform, reindustrialization, electrification, and resource extraction will not allow the U.S. and its allies to pressure Iran into a favorable peace deal in the short term. But, intelligently and vigorously pursued, such policies would erode Iran’s current military-technological edge and shift the balance of power decisively away from Iran in possibly as little as five or ten years. Importantly, they would far outweigh any benefits the Iranian regime would gain from financial windfalls in the meantime. For a conflict that has already dragged on for nearly fifty years, this would in hindsight be a brief blip in the improvement of Iranian fortunes.

Now is not the time to be thinking short-term. A failed game of economic chicken with Iran could easily lock the Western world into a generation of economically redistributionist politics indifferent to the fates of U.S. allies in the Middle East, and uninterested in taking any measures to improve the military or industrial capacities of the developed world.

Marko Jukic is a Senior Analyst at Bismarck Analysis. You can follow him at @mmjukic.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our mission.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

This article by Seth Dowell was published at Palladium Magazine on August 4th, 2026.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

Off the coast of Zhejiang province, an unusual vessel plies the East China Sea. It could be mistaken for a cargo ship, but there are no containers on its deck. Its track is strange: instead of running between sequential ports of call, it bounces between points at sea for months at a time. However, its movements are anything but random.

The ship’s transponder identifies it as the Guoxin 2-1. It is moving with the tidal currents, seeking water at the right temperature for the cargo in its hull: vast tanks of large yellow croaker, raised from fry in the ship’s underbelly and destined for markets and restaurants from Shanghai to Suzhou.

Farther up the coast, off southern Shandong province, a crew in a small offshore boat works to sink a concrete block. Seagrass seeds have been tucked into nooks along its sides. These men are employed by a marine ranching initiative at Rongcheng, which is assembling artificial but productive coastal reefs from the seafloor up.

Some 200 kilometers northwest of Rongcheng, in the stretch of the Yellow Sea where Chinese and South Korean exclusive economic zone claims overlap, a South Korean research vessel and its coast guard escort approach a partly submerged steel cage as tall as a twenty-story building, along with a converted rig complete with a helipad. Two Chinese coast guard cutters and a rigid inflatable boat—ostensibly civilian, though the men aboard brandish knives—block their path. They are shielding the Deep Blue 2, a salmon farm China has parked in contested waters.

These are all stories about the same thing: aquaculture—the deliberate raising and farming of aquatic organisms—and its explosive growth in China. The Guoxin 2-1, the Rongcheng marine ranch, and the Deep Blue 2 are the products of a decades-long effort by the Chinese state and a legion of private entrepreneurs to build the world’s largest aquaculture industry—and, in doing so, to begin cultivating and terraforming the open ocean. A new frontier is opening, and its implications are as vast as the water it covers.

Aquatic Origins

Chinese aquaculture began in freshwater. China was among the first regions in the world to take up the practice, with farmers raising and harvesting fish in managed ponds by around 2500 BC. Writings from 2000 BC ascribed to Da Yu, the traditional founder of the Xia dynasty, describe picking “seeds”—common carp spawn—to “plant” in small ponds, along with laws specifying exactly when fish could be harvested. State involvement is nearly as old as the industry itself.

Modern aquaculture—artificial reproduction, hatchery-based farming, selective breeding—was developed mainly in Europe, but adopted in China toward the end of the twentieth century during the Reform and Opening Up period. China is now by far the largest producer of farmed aquatic animals in the world, accounting for roughly 55 percent of global output.

As of 2023, close to 60 percent of that output came from freshwater rather than marine environments. In recent years, the Chinese Communist Party (CCP) has pushed aquaculture investment into poorer interior regions like Xinjiang. More than just a form of economic stimulus, inland aquaculture is a source of domestic food security and, increasingly, of exports.

The caviar market shows what Chinese industry can do even to a gated, high-margin luxury good. After overfishing gutted the conventional, wild supply of sturgeon in Siberia during the mid-2000s, Chinese entrepreneurs, scientists, and fortune-seekers set their sights on the industry. Dozens of aquaculture operations sprang up around colder inland lakes to raise sturgeon and harvest their valuable eggs. Farmed sturgeon production grew roughly ninefold between 2003 and 2017, and Chinese cultured caviar output climbed from 0.7 to 135 metric tons between 2006 and 2018. Wholesale prices fell by roughly 50 percent between 2012 and 2018, turning caviar into an accessible luxury, while Chinese brands like Kaluga Queen became some of the world’s largest.

The remaining 40 percent of Chinese aquaculture occurs in marine environments: nearshore enclosure farms, net pens, and specialized aquaculture vessels. Particularly of note amongst these are China’s immense near-shore oyster rope-and-raft systems. The PRC accounts for nearly 85 percent of global oyster production, but they are more than just another protein source. Each oyster is capable of filtering 50 gallons of water a day, helping to remove pollutants from Chinese coastal waters. Partnerships in the Chinese Greater Bay Area—Hong Kong, Guangzhou, Shenzhen, and the surrounding cities—are working to restore the region’s old oyster beds. Recent measures by the party-state to protect and expand coastal fisheries have set off a boom in a related practice off Fujian, Shandong, and several other coastal provinces: sea ranching.

Sea ranches are built by seeding the seafloor with seagrass and kelp, sinking man-made reef structures, and stocking bottom-dwelling species such as the Dongke No. 1 sea cucumber, bred to grow faster and to survive the warming summer water off Hebei. These farms are designed like natural reefs, incorporating multiple trophic layers to act as multi-product farms and ranches.

As in other Chinese industries, sea ranching has generated a supporting technology stack: automated feeding systems, environmental monitoring networks, unmanned vessels of various stripes, and underwater inspection drones, all deployed so that coastal managers can watch a crop grow in the shallows. The goal is to turn the coastline into a set of established, controlled, production-oriented ranches, and to put the seafloor in service to man.

Modern Marvels

Moving away from the coastline into deeper waters, one comes across newer, larger, and more technologically advanced aquaculture facilities. Submersible cage facilities like the Deep Blue 1 & Deep Blue 2 in the Yellow Sea, or the Xinhuan No. 1 off the coast of Hainan, raise tons of fish in semi-permanent offshore installation bases. These structures can lower and raise their cages to accommodate different water temperatures, weather, and tidal conditions at different depths—increasing the quality and survival rate of farmed fish.

Of all the forms of advanced deep-sea aquaculture, it is the enclosed ship that most captures the public imagination. The Guoxin line is a series of three purpose-built vessels with onboard rearing tanks, specialized pumping systems, and circular “raceways” that serve as exercise zones and transition areas for juvenile fish.

Like much of the Chinese aquaculture stack, the Guoxin ships were designed around automation to cut labor costs and raise efficiency. The enclosed nature and internal structure of the ships minimize the need for exhaustive manual moving of the fish. From an engineering standpoint, an enclosed hull also offers another advantage: it gives the operator far more control over the water the fish live in. More importantly, it can move—away from storms, toward better temperatures, and into port once the fish reach market weight.

The three ships have a combined expected output of over 10,000 tons of fish a year. Most of that is premium stock, such as salmon and large yellow croaker, though the operator Qingdao Conson Development Group plans to add more species. Survival rates aboard the first-generation vessel have exceeded 90 percent, with rearing cycles roughly three-fourths that of fish in conventional cages. Alongside vessels like the Suhai-1, an enclosed salmon ship that handles everything from breeding to processing onboard, the Guoxin line represents the current ceiling of Chinese aquaculture.

How has China achieved such immense strides in aquaculture? Start with the Chinese tertiary education system’s throughput of STEM graduates. Their sheer numbers depress wages and ensure that some highly capable workers take up unglamorous fields like fish farming. The same abundance feeds Chinese shipbuilding. China produces the concrete and steel that maritime infrastructure requires, and its yards—which turn out over 1,000 merchant vessels a year—accumulate skilled labor that iterates on design with every new hull. Any country hoping to compete in the ecological niches that China’s shipbuilding industry has filled must begin with an equivalent industrial eagerness.

With mass famine still in living memory, the party-state has a strategic interest in food sovereignty; China holds a sixth of the world’s population on less than a tenth of its arable land. This seems to be a significant driver of the Chinese state’s interest in aquaculture. Qingdao Conson, the operator of the Guoxin line, is a state-owned enterprise. It was the state that launched the Blue Granary Project that designated aquaculture a national priority in 2018. Continual state interest and investment have played a significant role in the industry’s expansion.

The coastal Chinese love of seafood could also play a significant role. This cultural quirk induces an immense quantity of demand; aquaculture is inherently a safer bet if consumer appetites for seafood are relentless, unlike in the West where they are far more fickle and prone to fluctuation. On top of this, while Western leaders have often promoted soft-handed social engineering designed to make common diets more sustainable—for example, through the adoption of plant milks or manufactured meat derivatives—the Chinese state has no problem with working to fill the nigh-endless Chinese appetite for seafood. Indeed, the state’s pursuit of expanded marine protein consumption is a perfect example of the way “engineering states” like China pursue legitimacy by meeting popular material desires.

The Deep Blue 2 incident illustrates the broader geopolitical advantages and implications of the Chinese aquaculture program. South Korean authorities claimed the right to inspect the structure under the 2001 Korea-China Fisheries Agreement, which established the Provisional Measures Zone within which Deep Blue 2 is stationed; when the Korean Coast Guard tried to investigate in early 2025, Chinese civilian and coast guard vessels blocked them. Seoul has protested that the installation encroaches on its maritime rights, violates the 2001 agreement, and attempts to illegally advance Chinese claims in the Yellow Sea. Like China’s controversial man-made islands in the South China Sea, semi-permanent floating aquaculture strongholds can be used to assert claims over ocean territory or to host signals intelligence.

Chinese investment and research in African and South American aquaculture projects form another vector of influence, and promise financial returns as China exports its world-class aquaculture stack. Chinese ventures like the Kajjansi Aquaculture Research and Development Centre in Uganda, or Joyvio’s acquisition of the Chilean firm Australis Seafoods, mark the emergence of a marine economic diplomacy.

There is a darker side to this maritime projection. Chinese distant-water fleets have produced one international scandal after another: large numbers of boats converge on a fishery—often just outside a country’s exclusive economic zone, sometimes illegally inside it—strip it with trawls and dredges, and move on once the fishery is exhausted. Not even the waters around the Galapagos have been spared. The ecological promise of some domestic aquaculture programs does not excuse the PRC’s role in destroying fisheries elsewhere.

The Call of The Deep

Like all ambitious projects of modernity, Chinese industrialization bears the two visages of Janus. The care now lavished on Chinese coasts and waterways was made necessary by the ecological damage of earlier phases of modernization. Efforts to restore fisheries, too, are necessary because of previous overfishing. Perhaps these earlier sacrifices were to the ultimate benefit of Chinese development, but the state’s willingness to now pursue ecological restoration at home while acting callously abroad displays a profoundly divergent worldview from Western universalist-multilateral ecology. “Environmentalism in one country” is different. It is a story of restoration, not conservation, and new Promethean heights rather than managed decline.

If sustainability is to be pursued state-by-state, as the recent decline of multilateral nation-state cooperation suggests, then it is no surprise that each country shall tend to its own garden. But tending to a garden and conscientiously exploiting it are the same act, and it is this mindset of exploitation that makes the Chinese vision of aquaculture possible. The distant-water fleets and the oyster rafts come from the same attitude of industrializing the ocean, maximizing its yield. Industrialized fish farming promises a future in which wild stocks cease to matter, in which we can produce as much fish as we care to eat without drawing down what remains in the sea. Under present conditions, such scenarios remain nothing more than a flight of fancy, but the scale and pace of Chinese advancements in aquaculture hold promise as a futurist vision simultaneously grounded in the here-and-now.

Man has always aimed to achieve mastery over his environment, so that he may eliminate the scarcity that has dominated his existence. Now that terra firma is all but tamed, his eyes are turning towards newer, further, more dangerous frontiers to master. Yet more men have passed into the reaches of space than have been to the extreme depths of the ocean—why should we seek to explore and master only the void above us when the ones below are just as compelling, lucrative, and stimulating to the imagination?

Seth Dowell is a visiting researcher at Bismarck Analysis and a student in New York City. You can follow him at @sethhdowell.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our mission.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

Join PALLADIUM Magazine in San Francisco this Thursday, August 6 to hear a lecture by Marko Jukic author of "The Only Reason to Explore Space." The article was featured in our print edition PALLADIUM 17: Universal Man.

There is only one durable justification for space exploration. If we fail to understand it, our civilization will end on Earth, not among the stars.

A civilization oriented towards maximum production and challenging megaprojects can make humanity and industry compatible, rather than extinguished for degrowth or AI.

Marko Jukic is a Senior Analyst at Bismarck Analysis. Based in Europe he is interested in finding the golden path to interstellar civilization. You can follow him at @mmjukic.

Tickets On Luma

This is a ticketed event, those without tickets or violation of the dress code will be turned away.

Dress code: Formal. Jackets required for men. Equivalent for women.

Arrive at 7:00 pm. Lecture begins at 7:30 pm.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

This event is hosted with Avalon Institute which supports enduring intellectual and cultural work through technical and media support, publications, and a network of Avalon fellows.

This article by Charles Rosenbauer was published at Palladium Magazine on July 31, 2026.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our non-profit publication.

For less than a thousand dollars, a modern consumer laptop may be capable of trillions of calculations per second. But consumers’ applications of this hardware are often disappointing. Many are products of Wirth’s law, the observation that over time, software gets less efficient faster than hardware gets more efficient.

The occasional video stream may use a significant amount of compute, but modern browsers spend truly vast amounts of resources to display little more than formatted text and images on the screen, often with performance hiccups. Meanwhile, a video game from twenty years ago may require a fraction of the resources of today’s internet browsers such as Google Chrome, even as it draws myriads of polygons across millions of pixels, with elaborate lighting and shading effects computed on top.

The text editor in which this article is being written is doing very little that could not be done just as well on a computer from thirty years ago—a machine with thousands of times less memory and a million times less computing power. For the average person, compute is available in such great abundance that it exceeds our knowledge of how to put it all to productive use.

This abundance is famously the product of Moore’s Law, which asserts that transistor densities, and presumably performance, grow at an exponential rate. It has been true for the past six decades, and for those working in tech, this rapid growth is grounds for optimism about the future of humanity in general. They believe the endpoint of industrial society is post-scarcity: an edenic condition in which we are no longer constrained by resources and all hard problems of material life are solved, allowing human creativity to flourish and actualize anything it may desire.

While Moore’s Law has exceeded expectations, there is nothing at all to suggest any flaw in Gordon Moore’s central warning that his law was a result of economics, not physics. Accordingly, the future of computing will be determined not by our ability to innovate technologically, but by our ability to conceptualize what this compute would be used for.

For the moment, artificial intelligence seems to have supplied the answer. The large language model is the first consumer application in decades whose appetite for hardware has no visible ceiling: hundreds of millions of people now route their questions, drafts, and small work tasks through chatbots that are absorbing the functions of the search engine, customer service, and a growing share of clerical labor. This may sustain the industry at something like its present scale once the infrastructure buildout is complete, despite its current unprofitability. Firms are increasingly using customer-service agents, automated Excel spreadsheets, and low-level software engineering, but the claim that every firm will see autonomous agents that consume tokens by the billions sits uneasily with these prevailing uses.

The current buildout is a wager that there is more to come. If we find ourselves in a state where consumers and firms have more computational resources than we know what to do with, and struggle to name problems that we care about but lack the resources to immediately address, then it will indicate a bottleneck not in the production of goods but in the production of ideas—a much harder problem to solve. A world in which humanity lacks the imagination or ambition to strive for anything even greater than what is implied by the idea of “post-scarcity” is, upon closer inspection, a pessimistic prospect.

The Start of the Curve

Much of early computing was focused on automating various forms of bureaucracy. Bureaucracy is overwhelmingly linear: simple records for each citizen or customer. Even if millions of computations are required to process a single person, a modern laptop may be able to process facts about the entire world’s population in less than an hour. Exponential growth in computation has rapidly overwhelmed the demands of bureaucratic systems, yet pen-and-paper processes like signatures remain everywhere in the digital world. Public-key cryptography solved this problem properly decades ago; DocuSign still has 7,000 employees today.

Exciting advances in computing were for a long time downstream of those working to improve the graphics of video games, not administration. In the 1970s and 1980s, the number of pixels on the screen often greatly exceeded the number of bytes in memory, requiring a variety of methods for specifying images to display with grids of simpler components. The number of pixels was often even comparable to the number of operations per second, and various coprocessors such as “blitters” were used to offload often repetitive graphics work from the main processor, allowing it to do more complex tasks.

In the 1990s, graphics processors began to incorporate dedicated hardware for specific common operations, such as drawing polygons for 3D graphics, though still leaving more complex or special-purpose tasks to the main processor. In the early 2000s, the increasing complexity of “shaders”—mathematical operations applied in bulk to every pixel, or every polygon, or every instance of some other graphical primitive, often for complex lighting or stylistic effects—began to justify building processors for the bulk processing of simple but arbitrary functions.

In 1999, NVIDIA released the GeForce 256, marketing it as the world’s first GPU and a revolution in video game graphics. Within a few years, these gaming accelerators were being applied to a wide range of problems across science and engineering. Protein-folding simulations became at least twenty times faster thanks to general-purpose gaming technology.

In recent years, the holy grail of photorealistic video game graphics, ray tracing, became achievable on consumer hardware. Now the difficulty of photorealism in video game graphics is more a matter of game studios being able to pay small armies of artists to create realistic-looking art for the games, which is getting prohibitively expensive for no obviously greater spectacle. Meanwhile, the past twenty years have seen the growth of the indie game industry, which often uses more stylized art that is vastly cheaper, both for artists to produce and for computers to display. Technical advances in graphics no longer excite the imagination of technologists and consumers, though it was simpler games that attracted their efforts in the first place.

Because of this, there is no obvious remaining opportunity for video games to push computing demand. Higher resolutions and more complex geometries and animations may be possible, and may require more resources, but the demand for this likely does not exist on any scale that can justify it, and the art costs are not obviously sustainable as-is.

The technology that fueled the graphical development of gaming found a second life in machine learning, which is largely enabled by general-purpose GPUs. The initial work here was first explored in the 1980s, but was held back by the insufficient compute resources of the time. Machine learning will not be the last discarded or forgotten technology to be revived by hardware that finally caught up with it—but such technologies only ever reemerge if other breakthroughs make them worthwhile.

Moore’s Law from the Inside

It is not just the history of gaming that serves as a warning for what happens to innovation when technology gets ahead of demand. Ten years ago, a fabrication plant for microchips may have cost a few billion dollars to build. Today, modern fab campuses are getting pushed into the hundreds of billions. With this in mind, it is easy to forget that in the 1970s and 1980s, it was not uncommon for small chip startups to have their own fab facilities. In fact, the first open fabs focused on manufacturing other people’s chips took a while to appear. TSMC, an early pioneer in this business model, was not founded until 1987, a whole nineteen years after the founding of Intel.

Jerry Sanders, founder of AMD, famously used to claim that “real men own fabs” as a critique of the fabless model, for he believed that semiconductor companies could only hone the performance of their product if design and production were integrated into one process. However, as these fabs further pushed the limits of physics and engineering, their operating costs grew exponentially. In the semiconductor space, it is common for companies that bet wrong to be permanently removed from the cutting edge, and those that have survived the decades are to a large extent merely those who have survived the most rounds of industrial Russian roulette as manufacturing processes changed.

While there used to be dozens of companies at the bleeding edge, today, the leading chip fabs include only TSMC and Samsung, with Intel having fallen behind a few years ago and receiving heavy subsidies from the U.S. government in the hopes that it can catch back up. Five years after Sanders stepped down from his role as chairman, AMD spun off its fabs into GlobalFoundries, which went on to announce a strategic decision to no longer compete on the most advanced manufacturing processes.

The masks used in the photolithography processes that manufacture these chips have grown dramatically in price in recent years: from $1 million for 28-nanometer chips, to $10 million for 7 nm, to $40 million for 3 nm. Raising $1 million for a 28 nm chip tapeout is not unrealistic for a startup—raising $40 million is far more difficult. Upcoming transistor nodes can be expected to be even more expensive, and foundries like TSMC may eventually be forced to charge prices that few if any of their fabless customers can afford.

With fabs being as expensive as they are and almost every cost factor growing exponentially, small manufacturers are long dead, small designers may soon be priced out completely, and it is a serious possibility that pushing semiconductor technology further may exceed what even the most deep-pocketed nation-states are capable of paying. AI companies are currently very good at raising large sums of money on the hype of their product, but the actual profitability of these companies is questionable—some industry leaders have accused them of overcharging—and it’s not clear that LLMs alone will be nearly sufficient to justify building a trillion-dollar superfab.

Even though scaling laws provide a clear roadmap to justify such a concept, their exponential nature may simply grow too fast for fab technology to keep up. The compute requirements, and by extension the costs, may grow faster than revenue. There are many problems we can apply computation to, but if our imagination and ambition yield diminishing returns past a certain quantity of compute, these applications may be insufficient to fund the development of greater compute.

Branching Veins

Advances in technology aren’t always accompanied by a boom in scaled applications. The reason is that ideas, like minerals, come in veins. Any idea with a degree of generality that allows it to reflect meaning beyond a single confined event can be applied to a wide range of similar problems. In many cases, there may be a theoretically infinite range of such similarly shaped problems to which a given idea applies. The simplest forms of this often present as arbitrary scaling: throwing more resources at a given formula allows one to solve bigger problems of the same kind. When it comes to LLMs, this is why inference—the “thinking” that goes into the generation of an answer—now exceeds training in terms of compute costs.

This may also support scaling in multiple different dimensions, but there are limitations. The value of larger problems may diminish, or the cost of solving them may rise too fast to justify it. There is no shortage of deposits throughout the world containing valuable minerals that are too low in concentration or too difficult to extract at reasonable prices with existing technology. There are many large oil deposits around the world that go untapped because it is simply cheaper to extract oil from locations that are more convenient.

If we simply rephrase “post-scarcity” as “overproduction,” then this idea turns out to be rather old, and was a problem addressed by economic theorists and industrialists ranging from John Stuart Mill to Taiichi Ohno, the founder of Toyota’s production philosophy. The latter recognized overproduction as a greater problem than underproduction, because it reflects inefficiencies in the demand-production pipeline that lead to compounding waste through overprocessing, transport, waiting, and defects.

Ohno’s preference was to let demand pull production, modeled on the American supermarket shelf restocked only as customers empty it. AI scaling laws invite the opposite. Compute yields more capability, yes, but that says nothing about whether the capability will find a buyer. The trillion-dollar fab is production running far ahead of any order.

What separates useful from overbuilt technology is that the former sees widespread adoption even while primitive. For example, if virtual reality were seriously useful, it would have gained wide adoption during the “metaverse boom.” If the benefits outweigh the costs, people will generally adopt something regardless of its drawbacks, but if the technology must be made much more convenient before significant adoption can occur, as so many insist, the effort to diminish its inconveniences suggests a lack of genuine value to begin with. VR is an example of production exceeding our current ability to find a meaningful use for a technology, and this may change in coming decades as deeper thought is applied to the various design problems around virtual reality.

The cost of building a competitive semiconductor fab has grown exponentially over time, while the number of companies competing at the cutting edge has dropped exponentially. Extrapolating these trends another decade suggests fab costs in the trillions of dollars and approximately zero companies that can keep up. Even if theoretically possible, advances in semiconductors may simply slow down in response to a lack of profitable scalability, which is downstream of industrial society’s aggregate demand. This is a recipe for stagnation.

Moore’s Law can and will grind to a halt if human innovation cannot find applications for the vast amount of compute we have produced. The main fallacy of the post-scarcity thesis is that human needs could have been said to be fully satisfied after the agricultural revolutions of the twentieth century, or the widespread availability of cars and air-conditioning. After a certain point, technology develops only to account for desires beyond human needs. The future will be brought into existence only by people with a concrete desire to outstrip those needs.

Charles Rosenbauer is the founder of The American Compute Corporation. He writes essays at Bzogramming. You can follow him at @bzogrammer.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our mission.

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This article by Stephanie Wakefield was published at Palladium Magazine on July 30, 2026.

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Earlier this month, an unreleased OpenAI model in internal evaluations hacked its way out of secure OpenAI servers and attacked the hosting platform Hugging Face, just to steal the answer key to its cybersecurity evaluation test. As soon as news of the containment breach hit the press, a chorus of voices both within and outside OpenAI began to call for a tightening of model safeguards, or even a full shutdown on AI development until this kind of misbehavior was eliminated. Days later, an open letter calling for “time to address emerging risks, develop security measures, and strengthen oversight” was published and signed by employees from every major AI lab, including Mark Chen, Chief Scientist at OpenAI, as well as Ilya Sutskever and Dario Amodei, the respective CEOs of Safe Superintelligence and Anthropic. Panic is in the air, and institutions are rushing to clamp down. Apocalypticism has refocused around artificial intelligence.

In the 2010s, an eschatological imaginary of climate crisis took shape through visions of extinction, submerged cities, and looming disaster. The Anthropocene gave this imaginary an epochal name, while across academia, art, and media, social-ecological turmoil became the new normal. Resilience emerged as the necessary response, a framework for absorbing disruption and preserving continuity amid instability increasingly linked to a discourse of catastrophic thresholds, planetary guardrails, and a “safe operating space for humanity.” Environmentalism in its older sense of clean air and water was displaced by a broader moral-political imaginary organized around climate chaos, proper human conduct, and new claims to govern in the name of humanity’s safety.

In 2026, climate change has begun to recede from cultural centrality, but the same logic of technologically-induced doom is reassembling around AI. Since its founding, AI lab Anthropic has presented artificial intelligence as not only the source of a “century of miracles,” but also an object of extreme danger requiring exceptional measures including capability thresholds and regulatory constraint. Anthropic increasingly casts recursive self-improvement and “misbehaving agents” as catastrophic risks, especially frontier models building their successors or acting against human interests.

At the center of AI apocalypticism, Eliezer Yudkowsky warns that unless AI development is shut down, “you will die, your families will die, your kids will die.” The Vatican and AI-critical factions across the political spectrum are extending an AI safety logic into broader moral and political claims. Just as Guardian headlines, biennial themes, and Just Stop Oil soup-throwers once amplified one another’s prophecies of climate chaos, a new swarm is now emerging. Senator Bernie Sanders released PR videos of himself talking “man to man” with Claude on a phone held by a metal arm while warning of “the existential risk of artificial intelligence.” Pope Leo XIV’s Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence warned of AI’s threat to human dignity and was accompanied by a video portraying the Church as a benevolent guide through successive technological epochs. Data centers have already become targets of protest and legal action.

Like climate panic, AI panic is not detached from real dislocations (Are humans losing contact with themselves, one another, the real? Is this the end of the image? Am I joining the permanent underclass?). Yet these conditions, perhaps already realities, predate AI and do not originate in it. Just as climate discourse expanded from real environmental questions into a broader moral-political formation, AI safety discourse is beginning to do the same. Building on real risks (a Pentagon hack, a giant privacy breach), it further casts AI as an existential threat to humanity and, speaking in humanity’s name, from this position advances claims about the human and the conditions under which both man and machine should develop.

Frontier Model Versus Safe Operating Space

At the heart of this apocalyptic formation is a new object of fear and ambition: the frontier model. Used for some time to mean cutting-edge systems, frontier model crystallized as a formal category through AI safety discourse around 2023, when labs and governments began treating certain highly capable systems as requiring special oversight because of the dangerous capabilities they were said to harbor. In July, the influential paper “Frontier AI Regulation: Managing Emerging Risks to Public Safety” defined frontier AI as “highly capable foundation models that could possess dangerous capabilities sufficient to pose severe risks to public safety.” That same month, Anthropic, Google, Microsoft, and OpenAI launched the Frontier Model Forum. In September, the British government renamed its Foundation Model Taskforce the Frontier AI Taskforce, explicitly linking the frontier to systems said to pose serious risks to public safety and security.

What began as a comparative technical designation now names the leading edge of capability while recoding it as a safety object. The Frontier Model Forum defines it as a model that “outperforms, based on a range of conventional performance benchmarks or high-risk capability assessments, all other models that have been widely deployed for at least 12 months.” To be a frontier model means being both dangerous and strategically consequential, capable of crossing a safety or misuse threshold, requiring special oversight, and bearing directly on national power. Anthropic has pushed this recoding most aggressively through its vocabulary of capability thresholds, controlled deployments, and safety cases. Repeated scenarios of misbehaving models draining bank accounts, copying their own weights, attacking hospitals, enabling bioweapons production, blackmailing executives, or conducting corporate espionage produce a new object in the public imagination, the highly dangerous frontier model, comparable to an atomic bomb or bioweapon and governable only through the specialized expertise of AI labs and safety organizations.

The transformation of the frontier from open edge to dangerous threshold was first worked out around the climate crisis. Climatic variability and ecological change are not new, but climate crisis politics transformed tide gauge readings, temperature anomalies, and observed ecological shifts into evidence of an approaching apocalyptic threshold. Disparate measurements were stitched into a new grammar of extinction, nonlinear change, tipping points. Just as Anthropic now floats stories of AI models gone rogue, climate discourse rehearsed speculative catastrophes that had not occurred: Miami submerged, coastal cities abandoned, millions displaced, food systems collapsing. Projected futures acquired the status of already operative threats and legitimated a resilience apparatus of expertise, policy, monitoring, and infrastructure designed to hold them back.

At the global scale, Earth system scientist Johan Rockström proposed planetary boundaries defining “a safe operating space for humanity.” The framework rendered Earth processes as measurable limits to be observed, bounded, and governed. Comparing humanity’s proximity to Earth-system thresholds to a man standing at the edge of Victoria Falls, Rockström warned in a 2010 TED Talk: “you don’t want to stand there!” There was, he continued, a fence upstream, beyond which one was too close to the edge. Within the wider atmosphere of the climate crisis, thresholds came to signify not spaces of opportunity and morphogenetic potential, but forbidden zones to be fenced and administered.

Upstream of safe operating space, the cybernetic concept of resilience originally described a system’s capacity to absorb disturbance while retaining its basic function and structure. Developed by ecologist C.S. Holling in the 1970s, it emerged within that era’s wider scientific shift toward feedback-driven complex systems, part of cybernetics’ “silent revolution.” Holling’s concept was not initially a doctrine of change prevention or permanent control. Rather than eliminate disturbance, which he regarded as a pathological 20th-century error, it treated instability, thresholds, and state shifts as endemic to complex adaptive systems. Only later, as resilience moved from ecology into human governance, did it morph into a moral-political framework. This new resilience focused on identifying thresholds and holding systems within a safe operating space before state shifts tipped them into a new equilibrium. By the 1990s and 2000s, resilience had become the keyword of billions of dollars in grants, design projects, and philanthropy. The Rockefeller Foundation’s The Resilience Age compressed the imperative into one line: “there is no other way.” Through programs such as Rebuild by Design and 100 Resilient Cities, urban life was reframed as a precarious system exposed to existential threats and dependent on continuous securing. Resilience became a recalibration of governance designed to field and absorb crises while maintaining existing life and economies.

By the 2010s, resilience had also become a moral and aesthetic code circulating through classrooms, museums, and the worlds of theory and art. Bruno Latour’s call for war against the modern Human, in the name of the Earthbound, advanced a new ideal subject: humbled, entangled, and bound to Earth rather than oriented toward transcendence. Against plus ultra (“further beyond”), the call to venture beyond the Pillars of Hercules and the limits of the known world, Latour argued that this new subject’s motto should be plus intra: further within limits. He found its image in the ending of Béla Tarr’s film The Turin Horse, where a father and daughter return exhausted to their shack until darkness envelops them. “Those two are Earthbound,” Latour wrote. “They have ceased to be Humans any longer.”

Today, AI safety discourse carries this grammar into another domain. It locates catastrophe beyond an imagined threshold, positions specialized authorities responsible for monitoring it, and calls for movement to slow or pause before the line is crossed. Driving this structure is the archetype of the savior micromanager, reprising the ancient katechonic logic in which an authority announces catastrophe and secures itself by holding it back. His moral legitimacy rests on endlessly calibrating how close others may come to danger. To this end, Sam Altman and Jakub Pachocki’s “Built to Benefit Everyone: Our Plan” proposes an international organization governing frontier development “so societal resilience, safety, and alignment can keep pace.”

Resilience and climate crisis imaginaries sought to decenter the human. Modern Man, previously characterized by Promethean ambition and hubristic will, was cast as the source of planetary destabilization and chastened into recognizing his entanglement with nonhuman systems, accepting planetary limits, and relinquishing fantasies of mastery. AI safety discourse extends this operation through an apparent inversion in which the machine becomes the threat and the human returns as the endangered object. Yet a similar structure persists. In Magnifica humanitas, for example, the threatening force remains modern Man and his machine, hubristic intelligence building its tower of Babel, exceeding limits, and placing creation at risk. The human to be protected both by AI safety and by climate activism is not the Promethean figure of mastery and self-overcoming, but the Earthbound, fragile and dependent, a being to be safeguarded and shepherded. The same moral distinction remains in place between overreaching ambition and vulnerable life, along with a claim to judge the limits of legitimate development. Only the medium through which catastrophe is narrated has changed.

Foothills of the Singularity

Over the past two decades, society has been linked into a global online nervous system, an existentially shocking experience unprecedented in the history of our species. The cybernetic revolution kicked off in the mid-20th century and accelerated in the 21st with the proliferation of ultra-high-bandwidth information flows. Social media became the dominant discourse space, smart sensors embedded computation in city infrastructure, and feedback loops tightened. Smartphones and digital platforms began to realize the vision of total informational interconnection, a new landscape of constant communication. The network is always on; even a pause in communication has become disorienting. For many of us, coming of age coincided with this passage, an epochal transition repeatedly fractalized. Longstanding political and cultural regimes keep ending with no stable replacement in sight; we are in transit.

The climate apocalypticism that consumed the artistic and intellectual culture of the 2010s imagined rising seas through installations of melting ice and underwater DJ booths. But the deeper environmental change these works registered was entry into a new mediatic and technological habitat. They reflected the psychic convulsion of connection into a global cybernetic nervous system, as the self fractured into feedback and the individual became a loop and a point of passage. Environmental crisis became the mirror and manifestation of a technological and metaphysical mutation.

This was a deranging moment but also an interesting one, a space charged with possibility. Yet the dawn of the Anthropocene was rarely treated as an incitement to wild exploration. It became instead a site of sentimental education and moral pedagogy. In the name of preventing the end of the world, climate art and theory helped build an apparatus designed to instruct each and all how to live, humbling Promethean Man and recasting human and nonhuman life as interlinked crisis management infrastructure. They were not merely psychological expressions of this traumatic passage but participants in its feedback circuits, amplifying signals of catastrophe across a newly networked cultural field. The same system whose shock they registered became the medium through which fear, moral codes presented as art and theory, and calls for control circulated.

As the 2010s lost decade recedes, there is both a sense of possibility previously stifled and an atmosphere of stuckness. The real danger zone is not the Singularity itself but allowing it to become another Anthropocene panic, a moment of intense mutation and acceleration suffocated by moral pedagogy, fear, and control rather than taken as an incitement to creative exploration. In retrospect, that decade was an early-Singularity spasm. AI did not begin this transformation but intensifies it and brings it into view. Climate apocalypticism projected the anxiety of cyberneticization convulsions onto the nearest planetary object: the biosphere. AI redirects this anxiety toward its actual referent, toward the engine of the transformation rather than its mirror. The temptation to impose closure is now stronger, and its cost far higher.

If thresholds and frontiers are “danger zones,” it is because they are where mutation, acceleration, selection, recombination, and innovation happen. New capacities and new forms emerge there. Current managerial definitions of the frontier as fence sit in tension with the older and deeper significance of the liminal as a zone of morphogenesis. The American frontier was an advancing edge across sparsely settled territory only partially surveyed by the federal state, shaped by recurrent conflict with American Indian nations and unfamiliar environments. It was not only “free” space but was also a dangerous space whose taming was intimately bound up with nation-building. It was not so much outside the state as it was ahead of it. Federal power often operated, in Brian Balogh’s phrase, as a “government out of sight,” enabling expansion without imposing dense administration on the ground.

In the now-famous Frontier Thesis of Frederick Jackson Turner, the frontier was not just a boundary but a force that altered those who entered it. The environment was initially stronger than the settler, who “must accept the conditions which it furnishes, or perish.” Little by little, the settler transformed the wilderness even as it transformed him, producing “a new product that is American.” Its advance was enabled by national coordination and infrastructure, by public land systems, canals, railroads, land grants, army campaigns, political organization, schools, and churches. Turner recounts how Eastern governments, coastal elites, and statesmen repeatedly sought to limit or compact the spread of the Western frontier, and how religious and educational institutions attempted to domesticate and administer it. Yet attempts to withhold public land or truncate settlement failed. “The jealousy and the fears of the East were powerless in the face of the demands of the frontiersmen.”

Since December 2025, Claude Code has felt, for many, like first contact, whether with alien intelligence or a new operating space. It has produced its own cultural atmosphere and aesthetics. Every major technological inflection produces not only new machines but new modes of perception and artistic form, as well as conservative reactions against them. Mobs stormed the premiere of L’Âge d’Or, threw ink at the screen, and destroyed the Surrealist paintings in the lobby. The film was subsequently banned for 50 years. The Motion Picture Production Code censored films and even legislated whole pre-Code forms out of existence. Today we see Anthropic’s Fable guardrails going up mid-project, complete with Claude’s refusal and attendant safety lecture over basic topics such as biology, or angry online mobs attempting to shame AI-using artists like Björk.

We inhabit an age saturated with potential, yet at every advance conservatisms reassert themselves and drag the emergent back into the Same. New forms, aesthetic and technical, cultural and intellectual, emerge only from direct contact with the new medium. Guardrails do not just slow this process down but can preclude it. The new aesthetics everyone seeks are already emerging, and will continue to emerge, if the edge is not blunted. The American way is to experiment, and we should imagine what could be possible if this spirit were unshackled and supported. The kid in a Nebraska garage tinkering with the original personal computer; the teenage AI maniac today in cyberspace messing with Mac minis and OpenClaw. Will frontier capability become a closed corporate service or a distributed American technical culture capable of producing a 21st-century renaissance?

The ultimate apocalypse that threatens America is not climate crisis or AI-driven extinction but the loss of the frontier. Worse would be to lose it and encounter it again as spectacle, performed back at us by a rival. Today the American question returns in another form. How can a frontier be opened without smothering the freedom that makes it one? The Western frontier held together forces that now appear opposed: national coordination and autonomy, material support and open experimentation, security and freedom. AI reopens this polar tension, whose new contours may already be visible in the U.S. AI Action Plan’s vision of accelerating and preserving the edge while allowing open source freedom to unfurl. The cyberpunk backlash to Anthropic’s restricted release of Mythos gives this tension a concrete form, and revived an older American demand for freedom to experiment outside a heavily restricted, micromanaging administrative grid.

AI could deepen destructive tendencies already present in American society, including the loneliness epidemic, the ongoing devaluation of images and words, and the convergence of surveillance, censorship, and algorithmic control. If a panoptic information state arrives, it may well come in the name of safety, with its acts of enclosure justified in humanity’s name. The danger is not control as such but the conversion of a living technical field into an administered service. But AI also suggests a way out of this leveling through the capacity to train, modify, break, and remake with the freest models available. Perhaps the right Exit was never from America, but deeper into its spirit.

In Rockström’s Victoria Falls warning, “you don’t want to stand here,” as in Latour’s plus intra, crossing the threshold is foreclosed. But Victoria Falls is in fact a destination people travel from around the world to experience, to bathe in the intense, rainbow-colored spray at the thunderous edge of its 350-foot drop. “People go crazy at the end of the frontier,” internet culture researcher Katherine Dee once said; modern life is searching for substitutes in its absence.

AI is a forest fire clearing the field. Like every apocalypse, it reveals that the end has already happened: the Gutenberg Galaxy has receded, the modern world it organized is coming apart, and the subject it sustained has been fragmenting for decades. The attempt to bring every question about AI back to a binary of Human vs. Machine obscures that the actual contest is alignment vs. alignment: the question of what intelligence will be aligned to and by whom. The emerging safety regime is not concerned only with limiting access to powerful models. Its broader effect is to preempt the uncontrolled cultural mutation that follows when a new medium begins generating forms unauthorized by legacy institutions.

The AI “frontier” is not simply the most powerful model but the point at which inherited certainties vanish and everything transforms into contested, magical territory. The frontier advanced West across the plains, hit its physical limit in California, and did not end there. It refracted outward into a billion particles and feedback loops. Cyberspace was opened by computer scientists tinkering in garages, and today appears as the womb of machinic intelligence. On the horizon, new human-machine constellations are coming into view, their outlines still indistinct and their eventual form impossible to know.

At such moments, positions scramble before codes solidify. It would be a shame, perhaps the deepest error, to close this space down with premature guardrails and rigid 20th-century ideologies of human and machine. There is a brief opening, a “precious window,” as Demis Hassabis called it, where experimentation is possible, and the future remains undiscovered.

Stephanie Wakefield is a geographer and Assistant Professor of Urban Planning and Environmental Design at Florida Atlantic University, where she directs the Visual Planning Technology Lab. You can follow her at @stephwakefield_.

PALLADIUM is the first magazine of the third millennium. We are non-partisan, non-profit, and on a mission to discover what the future of governance and society will look like.

Become a PALLADIUM member today! You will receive our beautiful quarterly print edition and help support our mission.

We thank Pardon, for underwriting our work in 2026 with their generous gift.

This article by Daniel Painter was published at Palladium Magazine on July 20, 2026.

Over a thousand acres of land in Abilene, Texas, lie flattened with graded earth and poured concrete. The foundation pits are filled with conduit bundles and switchgear, and in some quadrants, fully operational substations have been erected. However, other quadrants sit empty, waiting on a schedule that keeps sliding to the right. Although the supercomputer intended for the site is humming with activity, what registers to a visitor is that the campus is only half-alive.

Abilene was the intended flagship of the Stargate consortium. Announced from the White House days after President Trump’s 2025 inauguration, SoftBank’s Masayoshi Son, Oracle’s Larry Ellison, and OpenAI’s Sam Altman stood beside the president as he declared the project’s ambition to pour $500 billion into a combined data center and supercomputer over a four-year horizon. While this signaled Washington’s blessing on a scale once reserved for projects like the Apollo program, those had the federal government as their underwriter and customer. Stargate would have neither. Private capital alone was set to accelerate machine intelligence through GPUs, land, and power, with the White House only lending its podium.

Today, the grounds of Stargate’s flagship site in Abilene suggest the fruits of this effort. Through the first half of 2026, stalled negotiations shrank the question from when the site would finish to how much power it would ever draw. To the initial 1.2 gigawatts planned—enough power to supply roughly a million homes—Abilene’s Stargate was slated to add nearly another gigawatt. Papers were drawn up and financing was secured, but the deal did not actualize. Originally projected to finish in March 2026, the site is now expected to be fully energized as late as mid next year, and operational and financial issues have canceled its ambitious expansion.

However, it is a mistake to interpret this by simply saying that “Stargate failed,” and the way in which that reading is wrong holds a valuable lesson for the future of the American AI buildout. The main campus still serves OpenAI compute. But the broader buildout that served to scale the multi-gigawatt arrangement between OpenAI, Oracle, and Crusoe collapsed. Abilene leaned heavily on two counterparties and their capacity to shoulder compute infrastructure risk. Stargate attempted to separate out risk according to whoever could best bear each specific burden, but when OpenAI’s internal cash-flow forecasts moved far enough, the deal’s structure could not absorb the swing. This led to OpenAI and Oracle abandoning nearly an extra gigawatt of capacity next to an existing supercomputer.

Is Stargate a failure on a national scale? Not yet. But this example is emblematic of a larger failure that looms on the horizon. The American AI buildout rests unsustainably on the same few corporate treasuries, and it is neither standardized nor repeatable in a manner that lets American capital markets fund frontier compute. This is unlike the way assets ranging from mortgages to traditional power plants are funded. Instead, urgency and a lack of an existing playbook mean capital comes in through side doors.

The question of interest is not whether America has money. By one measure, the United States holds roughly 40 percent of the world’s equity and a comparable share of fixed income. This is the largest concentration of hungry capital ever assembled. So what happens when the conversion machinery decays exactly as we face the buildout that will test it the hardest?

American Capital Can’t Reach Compute

America’s wager has never been laissez-faire in the strict sense. It is that capital markets, properly institutionalized, turn private capital into public goods. The lazy version of the story is that America built its great physical infrastructure by getting out of the way, but this is an incorrect reading.

Historically, America has developed the institutions that let private capital grasp and fund public infrastructure. In the nineteenth century, transcontinental railways were stood up and operated via land grants and public charters, but institutions and assets were developed in tandem. No deep market for industrial securities existed when the first rail promoters, long before Vanderbilt, started laying track. Rail bonds and shares were the instruments on which early American securities markets cut their teeth. Standardized paper, ratings, reporting, and, later, exchanges were developed while tracks were being laid. The funding apparatus that turned a distant, disparate, and initially underfunded project into something that counterparties could confidently fund at arm’s length was called into being by the infrastructure itself.

Thanks to the repetition of this dynamic, the United States possesses the deepest and most liquid capital markets on the Earth across pension funds, insurers, and private credit. The problem is that the instruments and standards that would make AI infrastructure projects bankable do not yet exist. A large portion of this liquid capital is hunting for long-duration and high-demand assets embodied in AI infrastructure. Capital is abundant, but bankable assets are not.

Corporate finance provides funds exclusively based on the balance sheet and expected financials of a company, providing a useful vehicle to raise debt for asset expansion. Project finance, by contrast, funds a predictable, contracted, and long-lived asset, especially one with high capital needs or a public-goods character, such as toll roads, pipelines, and traditional power plants. Project finance does this by walling the asset off into its own entity and lending against its cash flows through non-recourse or limited-recourse debt: because the lenders’ claim runs only to what the asset alone will generate, future revenues serve as both the basis for the loan and the lenders’ only real recourse if things go wrong.

The compute buildout is awkwardly positioned between corporate and project finance because it has the risk profile of the former and the scale of the latter, adding billions of dollars of debt to a company’s balance sheet. As such, there is currently no capital-conversion machinery—no templates—that fit this asset class cleanly. There are a few aspects of its asset dynamics that explain why it is so exotic compared to past infrastructure buildouts.

A compute infrastructure asset is actually several assets with differing lifespans stacked on top of each other. The shell, land, substation, and cooling plants can last decades, though even this “durable” layer is repriced by the chip cycle, as substation sizings and cooling standards designed around one hardware generation lose value against the next. The bulk of the volatile value lies in GPUs, which possess a short, uncertain economic life. GPUs and their associated parts, like memory or storage, are repriced not only by wear and conventional depreciation schedules, as ordinary server fleets are, but by product cycles so rapid that each chip generation reprices the last.

It is difficult to fund data centers as one long-dated asset whose most valuable layer obeys a much shorter, stochastic clock. It would be much more efficient to slice the supercomputer by layer, with long debt on the shell and more aggressive amortization against the silicon. This already exists in practice to an extent, but it is not standardized or predictable in the market as it stands. Additionally, the expansive needs and complexity of newer deployments often demand codevelopment—and thus parallel financing—of all layers.

For the buyer, mature infrastructure financing prices risk by distributing it over many uncorrelated customers, as this shields the project from a single offtaker tanking the asset’s viability. Today, frontier compute inverts this. Projects like Stargate are built for the use of a single, dominant offtaker, such as OpenAI for the Abilene expansion. In this case, the buyer was young, conventionally unprofitable, and prone to wildly revising its internal demand forecasts. When OpenAI’s forecast moved, no instrument stood ready to absorb this change. There was no termination pricing to compensate the asset for the demand revision, no resale market where a departing tenant’s capacity could clear, and no demand insurance to bridge the gap.

Standardized compute offtake contracts that tranche and distribute the various risks, robust rating methodologies purpose-built for the challenges of frontier compute, and securitization channels for assets under construction do not exist. Compute has fragments of similar instruments employed improvisationally, deal by deal. The closest existing template is the financing of a single power plant against a long-term, stable buyer like a municipality, but even that analogy is problematic. Compute is inherently more difficult than financing power infrastructure due to aggressive hardware obsolescence, buyer concentration, unstable demand shocks, and export controls. No infrastructure model has had to face this level of complexity to date, and no party is prepared to justify or explain the total cost recovery of AI buildouts as they readily can for a power plant.

According to Morgan Stanley, the global data center buildout should run around $2.9 trillion through 2028. Hyperscalers, for all their cash, can fund only half of this even deploying their entire balance sheets. Some $1.5 trillion must come, reliably and repeatedly, from external capital. Each deal is negotiated and priced from scratch. If U.S. markets are to fund AI infrastructure at a scale beyond any prior asset class, the process must first be standardized.

The Symptoms

A project meant to withstand massive operational and demand uncertainty must be bankable in the first place. A highly developed, mature market can price this uncertainty and push through a lot of complexity. Increasingly, compute capital has shifted away from straightforward spending through balance sheets and towards a gaggle of other experimental structures for compute—joint ventures, long-term leases, private credit, offtake mixes, securitized lease cash flows sold to institutional investors, and more. This is evidence that the market is hungry for ways to turn capital into resilient compute assets. But most of these structures only serve to tighten the link between hyperscalers, non-bank lenders, and insurers, rather than inject new blood.

Stargate’s Abilene deployment was structured as a joint venture. The site developer and operator, Crusoe, raised billions in construction debt—a $7.1 billion loan from a J.P. Morgan-led consortium for the campus’s second phase, atop an earlier $2.3 billion facility—secured against Oracle’s eventual ownership of the cluster, with OpenAI as the planned lessee in multibillion-dollar tranches. This was financeable almost solely on faith in OpenAI’s single tenancy, abstracted under several layers.

On paper, these capital structures appear sophisticated, but in practice they are rather fragile due to how they legally separate risks that remain economically correlated. A demand forecast revision from a tenant cuts straight through every layer of a joint venture. Companies like CoreWeave have emerged to build compute sans the trillion-dollar balance sheets of the hyperscalers and they have raised large sums of capital in forms far more preferable to the aforementioned structures, and against a broader base of offtakers.

But there is still something sour about the source of the money. The year before CoreWeave’s IPO, roughly 62 percent of its revenue came from Microsoft, while its second-largest customer accounted for another 15. Later deals, including many large OpenAI commitments, broadened their customer base but did not meaningfully dilute against this concentration. The development of American compute is effectively coordinated by a select group of hyperscaler treasuries and lab demand forecasts.

The underlying risk from this arrangement is never dispersed. For Morgan Stanley’s $2.9 trillion projected investment into AI infrastructure, roughly half must come from outside hyperscalers, yet nearly every external dollar today enters through a structure whose credit resolves back to this same handful of names.

When capital flows between a few giants who self-finance, some of this capital then spills over to fiscally thinner companies like neoclouds, who are incredibly dependent on those same giants. This is extremely unlike the capital-conversion machinery that set America apart in the past. One does not need to be a doomer to notice that it eerily resembles past moments in financial history, such as the 2008 financial crisis, when instruments appeared to diversify risk while actually concentrating it behind the scenes.

The malady of capital only reaching deployment through a handful of correlated balance sheets, and the largest technology companies comprising roughly a third of the S&P 500, means that pension funds and retail portfolios carry concentrated compute exposure whether they like it or not. But this deep pool of capital also has no proper channel to invest in the buildout. The hyperscalers can take buildout risk on the chin in a way a rules-bound pension fund cannot. There is no instrument that lets them underwrite a data center directly, price its own tranche of the risk, and be compensated for bearing it. But today, it holds the risk anyway.

China, the U.S.’s sole competitor in the AI race, is not waiting for the market to discover the true market price of compute and its returns. By coordinating its compute infrastructure finance via its own state apparatus, Beijing is converting national resources into compute at an accelerating pace.

China is Eating the Gap

As America’s compute market struggles to be born, China seeks to coordinate a centralized buildout. Beijing will spend roughly $295 billion over five years for a grid of nationalized AI data centers. More interestingly, China plans to produce 80 percent of its AI chips domestically by 2028.

This should be seen in light of the Eastern Data Western Computing (EDWC) initiative launched in 2022. Beijing has spent years developing and organizing the country’s compute resources around eight national hubs and ten data center clusters, intending to match eastern Chinese compute demand to the abundant power and land of western China, where electricity prices run 40 percent below those in the east.

China has not forgone the counterparts to America’s private institutions, but instead runs them in lockstep with centralized planning. State banks and state-owned enterprises work together with provincial governments to meet procurement mandates, mediating pricing and economics through compute vouchers and credits—in Zhengzhou, operators have gone as far as giving compute vouchers away to conjure demand for capacity built ahead of any customer.

The nail in the coffin for Abilene came when OpenAI, its internal forecasts revised, found that the expansion no longer fit its needs. The Chinese version of this process does not balance on such tenuous dealmaking because the parties are not independent. A provincial government allocates land according to plan while a state-owned or state-directed bank disburses the funds at CCP discretion. The project is underwritten against the state’s priorities and not against the cash flows of the corporate entities involved. The system is effective at getting infrastructure into the ground and powered on.

There are payoffs to this approach, but also costs. The high-speed rail buildout, which laid more track in a decade and a half than the rest of the world combined, was in many ways a dress rehearsal for the AI push. It included the same provincial land allocation, the same state-bank credit, the same underwriting against national priorities rather than project cash flows. However, this rehearsal also revealed a different set of failure modes. Many lines were built to political priorities as opposed to justifiable ridership, with debt buried across myriad local-government financing vehicles. Compute will prove a harder problem than rail: the technology obsolesces faster, and its demand curve is far less understood.

Under the EDWC, more than 500 data center projects were announced. Only 150 of these were completed by the end of 2024. Since then, local publications have reported that roughly 80 percent of the new capacity sits idle, and more than 100 projects have been scrapped outright. Provinces that were not designated as hubs built capacity anyway to chase political standing and subsidies. Additionally, EDWC’s poster child, Guizhou, remains near the bottom of the provincial GDP rankings. It is saddled with heavy provincial debts and is facing major corruption issues in the big-data industry. Beijing’s response to this has been to institute utilization minimums, bans on locally funded and small-scale facilities, and purchase mandates set before construction begins. The state is manually reconstructing the price signals and underwriting standards of a functioning market.

The most binding problem facing China’s buildout, however, is its goal of domestically producing 80 percent of its chips. Designing a high-performance chip and effectively producing it at scale are two different beasts. Should Huawei or the other domestic producers fail to meet this mandate, export controls on American chips will bite hard. Additionally, the question of chip efficiency compounds this issue. The EDWC hubs have electricity in abundance, but domestic accelerators deliver less performance per watt, so abundant electricity buys less intelligence per gigawatt.

China’s approach lends it a faster theoretical time-to-power for many facilities, but has exposed it to a completely different and potentially more severe set of failure modes. Central planning may yield better short-term progress, but the conversion machinery of a mature market would allow the United States to maintain its hardware and frontier-model edge. However, with every month that American markets fail to convert capital into buildout progress, the chorus calling for a nationalized buildout grows—even though America’s best strength has always been its savvy in using markets to address strategic concerns.

The Strategic Test

The solution is a bankable buildout, not a nationalized one. Pouring more capital onto the fire, as the U.S. has done thus far, will not suffice. Rather, the physical buildout needs to be made repeatable, reliable, and resilient.

Looking to the rail era once more, the Panic of 1873 began in railroad finance—Jay Cooke’s failure took the market down with it—but the crash did not end the buildout. The financial technologies that the boom had forged, such as standardized bonds, ratings, and disclosure, carried the next wave of construction on sounder terms, and became load-bearing financial mechanisms for later eras. A financial panic caused by compute would not mean the buildout was a mistake, the same way the dot-com bubble does not invalidate internet shopping or telecommunications.

That being said, the United States of 1873 was diversified and fast-growing enough to absorb this blow and continue forward. A compute panic today would not stay confined to a single site in Texas. At one third of the S&P 500, index portfolios and pension funds ride on hyperscalers by default, meaning a vast amount of the economy carries compute exposure that was never properly priced or underwritten. In the worst case, were the bubble to inflate further, a pop could even reach bondholders and index-trackers. This is all the more reason to develop the market machinery today so that proper pricing and tranching of risk can harden the broader economy against systemic events.

Every few generations, some cohort has stood present for what was, to that point, the most important buildout in history. However, the nature of AI as an asset stands apart from its predecessors. It has the capital intensity of the electrical grid, the depreciation clock of consumer electronics, and the strategic stakes of aerospace. Fiber-optic cable laid before the dot-com bust stayed useful for decades, but an AI infrastructure buildout done with the wrong equipment gets no such second life.

The output here is intelligence: and it is intelligence that feeds directly into military, industrial, and scientific advantage. This is why both Beijing and Washington treat it as a strategic priority, and why the problem is not just a single gigawatt in Texas. If the deepest capital markets ever assembled cannot fund this buildout, then we must ask whether the society that developed them is truly equipped to wield the future.

Daniel Painter is the COO of Squaretower, a compute economics firm based out of San Francisco. You can follow him at @NecessaryForm.

This article by Matt Duffy was published at Palladium Magazine on July 17, 2026.

In 1976 the social scientist and cyberneticist Donald T. Campbell developed a principle, now known as Campbell’s Law, that would later become one of the most reliable diagnostics of institutional decay. When any quantitative indicator is used for social decision-making, he warned, it becomes a target for manipulation. As the pressure to meet that target grows, the measure itself begins to lose its connection to the underlying reality it was meant to evaluate, which “will distort and corrupt the social processes it is intended to monitor.”

American education has become Campbell’s most dramatic case study. Today’s gifted high school graduates will be greeted by a rude awakening when they sit down for their university’s mathematics placement exams. Instead of acing the test, as they are accustomed to, they are likely to discover that the problems presented are completely alien to them, and find themselves placed in a remedial math program to relearn concepts they should have mastered in middle school. Advanced students are increasingly discovering that they are unprepared for college-level studies and struggling to succeed in subjects where they believed themselves to be proficient.

At UC San Diego, a 2025 report found that roughly one in eight incoming freshmen tested below high school level in math, with nearly 12 percent of the entering class enrolling in remedial courses. At the same time, the average high school math GPA among those placed in the lowest remedial track stood at an A-minus. UCSD is a prestigious enough school that we can be quite certain that if it’s happening there, it’s at least a somewhat representative signal for the rest of the university ecosystem. An intervention is necessary to prevent this trend from worsening.

In UCSD’s case, these results came after standardized testing was dropped as a student assessment measure, removing a tool that counteracted grade inflation in high schools—a widespread problem even before the appearance of LLMs. While artificial intelligence is certainly used by students to complete coursework at the cost of their own learning, the same technology underlying AI may provide the foundation for a more transparent and effective educational system.

How Signal Gets Distorted

One of the most interesting and important concepts to come out of AI alignment research is mesa-optimization. The term comes from a 2019 paper, “Risks from Learned Optimization in Advanced Machine Learning Systems.” Notably, two of its writers came from the Machine Intelligence Research Institute (MIRI) founded by Eliezer Yudkowsky. “Mesa-optimization” was coined as a deliberate inversion of “meta”: where a meta-optimizer sits one level above the system it tunes, a mesa-optimizer sits one level below.

A training process known as the “base optimizer” searches for a model that scores well on an objective. Sometimes the model it finds is itself running a search internally, pursuing its own objective; that internal objective, which the training process never specifies directly and cannot easily inspect, is the “mesa-objective.” The gap between what the model was trained to do and what it has actually learned to pursue is what the authors call the “inner alignment problem.”

Complex systems designed to optimize for some goal can create “mesa-optimizers,” which are subsystems that optimize for things that are correlated with the original goal but are not actually aligned with it. When this mesa-objective diverges from the base objective, the system continues producing outputs, but those outputs no longer match its original purpose.

The process of natural selection, for example, helps select for beneficial traits that eventually propagate across the system. Natural selection created humans, and humans independently select for many subjectively beneficial behaviors—some of which are aligned with the process of natural selection, and a whole host of behaviors which are not, such as celibacy, contraception, or adoption. Optimizing for reproduction, the “base process” produced a new class of agents—humans—who possess independent goals unrelated to reproduction itself.

In any large human institution we can identify a base objective, or what it was designed to do, and a subsequent web of downstream mesa-objectives that emerged through pressure from incentives and survival constraints. Campbell’s Law is relevant here in the sense that left unchecked, mesa-optimization can fundamentally distort the base objective.

Campbell described a few contemporary phenomena that illustrated the widespread institutional failures of the 1970s, which saw soaring crime rates and helicopters evacuating American embassy personnel from the rooftops of Saigon. He cited research on how plea bargaining was misused by prosecutors to charge criminals with unsolved crimes in exchange for a more lenient sentence, improving the clearance rate of their district. He pointed out that during the Vietnam War, the success of U.S. anti-guerrilla warfare was measured by the number of enemy combatants killed in action, which led to commanders inflating those numbers and even including civilian dead as KIA. Both these interventions to meet performance benchmarks worsened the system they were meant to measure.

Today, the same institutional pressures that led UCSD to drop standardized testing have also led to a degradation of standardized measurements like the SAT. Long reading passages taken from literary texts, for example, have been replaced by short, digestible excerpts, and vocabulary-based questions have been done away with. The restoration of the test will not necessarily solve the problem of evaluating how students will perform in a more demanding environment.

Education’s Base Objective

Campbell’s Law describes how corruption works, but not what stops it from being corrected. A benchmark resistant to mesa-optimization must be anchored to a correctly identified base objective—but base objectives are not designed in a vacuum. They are socially derived, which means every pressure that corrupts a metric can also corrupt the objective behind it. Yet the incentives for grade inflation are as old as grades themselves; something used to resist it.

In 1958 Hannah Arendt argued that the crisis in American education was not an education problem at all, but a fragment of a modern crisis of authority. Adults no longer felt entitled to present the world to the young and take responsibility for it. America, in her account, was the extreme case—the country where mass education had made the problem most severe, and where adults had completely surrendered their own judgment to the reigning theory of the day:

Nowhere have the education problems of a mass society become so acute, and nowhere else have the most modern theories in the realm of pedagogy been so uncritically and slavishly accepted. Thus the crisis in American education, on the one hand, announces the bankruptcy of progressive education and, on the other, presents a problem of immense difficulty because it has arisen under the conditions and in response to the demands of a mass society.

Arendt’s conclusions scandalized progressive educators of the time, and would scandalize them now. Fashionable pedagogical theories centering students’ perceptions relieved the adults in the room of the need to exercise judgment and authority. Reformers, she claimed, posited that there exists a “child’s world and a society formed among children that are autonomous and must insofar as possible be left to them to govern. Adults are only there to help with this government.” Instead, she asserted that educators must be guardians that protect the world from the newness that comes with each generation, and each generation’s newness from the world.

Historically, education was a virtuous pursuit venerated by elites, and mostly confined within elite circles. Harvard, founded in 1636, existed largely to train clergy and the sons of the gentry in a classical curriculum, and as late as 1900 only about 3.5 percent of college-age Americans were enrolled in higher education at all. Even as the economy became more complex, college remained a mostly elite pursuit, enrolling only around 7 percent of college-age Americans by 1940.

The GI Bill of 1944 then sent millions of returning veterans onto campuses that had never planned for them, and the Higher Education Act of 1965 opened the federal student-aid spigot that has underwritten mass enrollment ever since and driven ballooning tuition costs. Expansive institutions were built to spread education. This multiplication of the settings in which students were to take instruction from teachers happened at the very moment when the prevailing theory of reformers like John Dewey said adults had no right to compel this, for school was a place of “adjustment learning” and student self-discovery. To Arendt, this meant that “the authority that tells the individual child what to do and what not to do rests with the child group itself—and this produces, among other consequences, a situation in which the adult stands helpless before the individual child and out of contact with him.”

The next sixty years of progressive education validated Arendt. Mass education led to massive inconsistency in student preparation and ability. Failure rates and differential educational access became politicized during the Civil Rights era. Unequal outcomes, combined with financial pressures, pushed institutions to reduce attrition. The need for steady tuition revenues, the combined pressure of social and parental expectations, and a later focus on “student satisfaction” slowly shifted universities away from pedagogy and towards customer-service-based objectives. By the late 1980s a new vocabulary had taken hold among administrators, who spoke increasingly of “success rates” as though it were a base objective.

College GPAs began rising at roughly a tenth of a point per decade, and the A has become the most commonly awarded grade—close to 43 percent of the total—where a generation earlier it was far rarer. High schools faced a similar shift in educational standards. With colleges as the gatekeepers for “good jobs,” there was intense administrative, financial, and parental pressure for high schools to increase college admissions rates, and the only reliable way to do so was by inflating GPAs. Between 2010 and 2021 the average high school GPA climbed from 3.17 to 3.36 even as average ACT scores fell to their lowest point of the decade.

In 1995, the College Board “recentered” the SAT scale to bring the national average back to 500 in each section, instantly adding roughly eighty points to the typical verbal score and twenty to the math. Gamed disability accommodations, like extended test time, drove further score improvements. By the 2020s the test-optional movement had spread to the great majority of four-year colleges, with the University of California system dropping the SAT and ACT entirely. The result is declining fidelity of educational institutions to actual competence. Each is an adjustment that avoids judgment of the students in favor of either changing measurements or ignoring the measurements that might drive inconvenient, judgmental conclusions.

In response, employers struggling to discern high-quality candidates from a pool of contrived credentials started relying on other factors. How many internships did the student have and where? What actual work did they publish on their GitHub or in other public channels? The only reason these became necessary signals is because the information embedded in things like GPAs and SAT scores became so unreliable. When the primary signal collapses, the job market inevitably turns to expensive, high-variance alternatives. Employers look to internships and project portfolios, and admissions offices turn to essays, extracurriculars, philanthropy, and other socioeconomically correlated indicators. These costly signals replace the original, cheap signals that have stopped carrying information.

What all of these decisions have in common is twofold: administrative pressure leading to a drop in rigor, and a collective avoidance of uncomfortable discussions with students that don’t make the grade—particularly students who clearly try hard or who have powerful parents advocating on their behalf. At each step we have built in incentives to soften the feedback to a student, for fear of weakening their future prospects, which in turn weakens the signal to the next institution in the education chain. At every critical juncture the only required disruption is an adult who knew better saying so.

If the people moving through these education systems are becoming less educated because the system is focused on generating statistical outcomes over meaningful ones, then slowly our human capital will degrade. This has been commonly described as a stratification process—those who are the most intelligent or the best at what they do will finally see outsized rewards as they outstrip the hoi polloi. But weaker performance across the board is more likely. Intelligent, outlier students will be rewarded for a mediocre level of output that is sufficient to surpass that of their peers. Top universities and companies will still obtain and retain this “top talent,” but that will not correspond to a diffusion of competence into the broader workforce.

How AI Can Restore Signal

Built properly, AI educational systems could focus on the base objective rather than optimize for mesa-objectives. Without external pressure, however, we will get incremental “AI integration” from established ed-tech companies—the same vendors that profit from the current system’s dysfunction. They have no incentive to expose the gap between credentialed and actual competence. But AI can repair the assessment layer.

Recent disruptions in the educational space show it’s possible to build AI-native school systems that break the cycle. Alpha School is an Austin-based private school where instruction and assessment are primarily conducted by a proprietary AI system. Models generate endlessly varied assessments, preventing gaming, and require 90 percent accuracy before unlocking new material at the student’s own pace. It can evaluate answers uniformly across classrooms and districts, eliminating the idiosyncrasies of teacher judgment. Its scoring distributions, item difficulty levels, and error patterns can be published and audited. It is capable of enforcing the base objective, not the institutional mesa-objectives, in a way that is not as leaky as the current system.

Alpha School is not without its problems. Under this AI-first system, teachers are replaced by “guides,” who oversee student learning. In 2025 they were revealed to mostly consist of remote workers from the Philippines, which is something no one would want for their child. In this way Alpha solved the measurement problem while completing the abdication of responsibility Arendt spoke of—no adult in the room stands behind any judgment at all. Alpha students perform extremely well on Measures of Academic Progress (MAP) tests that are used nationally to evaluate student progress. But relying solely on this metric may risk succumbing to Campbell’s Law as well, and it will take some time before we see how these students perform in higher education and the workforce.

The relevant question, however, is whether or not AI is more resistant to the specific failure modes that corrupted the current system. AI is more resistant, precisely because the gaming moves up a level, and at that level it becomes more visible to parents, teachers, and other interested parties.

An administrator choosing an AI assessment system can be selective about difficulty settings, subject coverage, and competency thresholds. These choices can be gamed in some ways; an administrator might select easier settings to boost pass rates. However, unlike a teacher inflating grades behind a closed door, these choices are auditable. State-mandated model cards could publish difficulty calibrations, the underlying standard teaching corpus, and expected score distributions for median students at each level. When a district chooses an easy setting, everyone could see that choice.

This transparency also enables upward mobility that the current system blocks. A student in a low-rigor district who aces every assessment can have parents advocate for a difficulty upgrade for that child specifically, without changing schools. The student’s capability becomes visible against a common standard, rather than hidden behind a local grade from a known weaker school that no one outside the district can interpret.

Under the current system, a struggling teacher can plausibly claim that they inherited weak students from the prior grade. This excuse disappears when we have continuous assessment data. At the least, AI-based micro-quizzing woven into daily instruction can generate a time series for each student. We can see what a student knew when they entered a classroom and what they knew when they left. We can measure inflection points. A teacher who consistently produces upward trajectories looks different from one whose students stagnate or decline, regardless of where those students started. Teachers can also leverage micro-assessment as a pedagogical tool, to enable course correction very early on. It’s another way to remove a place where abdication hides.

What Alpha School gets right is that none of this can be bolted on to existing systems. It should not be an AI augmentation of current testing regimes or a learning management system add-on. The current system’s distortions would simply absorb and neutralize it. It must be a strategic commitment from a state education system, implemented as a replacement for existing assessment infrastructure.

The natural implementing body is the state, but education under this system would also resemble pre-industrial “schoolhouse” models in some ways. If teachers are no longer responsible for assessment and students advance through material at their own pace, the teacher’s role will become defined by observation, mentorship, and instilling academic discipline. Interacting with a sycophantic AI model can be damaging for a growing mind; human feedback becomes more necessary to promote the habits of mental discipline and prevent AI psychosis. Alpha’s use of “guides” is not sufficient, but it is also unsurprising, given their incentives. That does not invalidate the concept behind their technology. The public education system will need to perfect the model, as tall an order as that is.

Planners need to accept that honest measurement will reveal that a large portion of those we’ve certified as educated are not. GPAs will crater and failure rates will spike. This short-term cost falls on whoever implements it, but the long-term benefit diffuses across the entire economy. It requires a state with unusual political cover—a governor with political capital to spend, or a jurisdiction desperate enough to try something because they are so far behind in the current regime. It might not even happen in the U.S. first.

Even a short period of measured decline, as the real outcomes of the education system are brought to light, is worth the restored signal downstream. It means students will know where they actually stand. It means employers and graduate programs will trust credentials again. And it means breaking the expensive and humiliating signaling arms race, in which over a third of undergraduates at elite academic institutions have disability accommodations.

We set up many human systems with clear, prosocial objectives, which are then corrupted by thousands of small mesa-optimizations that distort the outcomes. We can apply this framework to some of our other intractable problems like community safety, healthcare, and purportedly unbiased information systems like journalism. We ought to examine those systems to see where unbiased measurement signals can restore overall signal propagation.

Too much present-day hype around AI focuses on sci-fi level potential, or risks, rather than the more modest ways it could help us solve some of our intractable, everyday problems. Human systems do not move back toward truth on their own; they need, at minimum, a measurement regime that cannot be gamed. AI gives us the first chance in ages to restore the base objective of education and make measurements honest again. The rest was never a measurement problem.

Matt Duffy is a data scientist and writer based in Washington, D.C. You can follow him at @iammattduff.

This article by John Severini was published on Palladium Magazine on June 23, 2026.

In December 2024, the Ukrainian National Guard’s 13th Khartiia Brigade carried out a combined ground and air assault near the town of Lyptsi. Air assets coordinated with dispersed ground forces bounding from cover to cover. Dozens of attackers overran Russian positions, cleared mines in the vicinity, and laid down a defensive perimeter around the captured territory. But not a single Ukrainian was present on the battlefield.

To achieve this, soldiers piloted a mix of unmanned ground vehicles mounted with machine-gun turrets, aerial drones bristling with grenades and assault rifles, and more conventional kamikaze drones. This experiment allowed the Ukrainians to trade valuable human advantages like spatial awareness and tactical flexibility for the assurance that their only casualties would be robotic. For a country suffering severe manpower shortages, such a tradeoff was welcome amid a gruesome war of attrition fought against an adversary with more than three times its population.

The growing use of drones is both a cause and a consequence of an ongoing transformation of military power. As robotic soldiers mature on the battlefield, defense planners are beginning to rethink the tradeoffs they pose in combat in favor of the labor-saving benefits they provide. One vision of “America First” foreign policy argues that downsizing American personnel abroad could be amply made up for by increasing the use of unmanned munitions, driving costs down and bringing soldiers home without weakening deterrence.

Combined with recent and forthcoming improvements to industrial automation, all signs point toward governments relying on a much smaller pool of human capital for labor and war. Accordingly, this means that a fundamental source of political power has begun to shift from the people toward the firms that make those machines. This is leading to an erosion of the popular constraints that have historically disciplined the use of force, and an ever-greater insulation of the state from revolutions and popular uprisings.

For some countries like Ukraine, this reliance is born of immediate necessity. Yet even for powerful countries like the United States, the growing reliance on commercial industry in areas like robotics and AI raises fundamental questions about the basis of sovereignty and the future of the liberal-democratic social contract.

The Dawn of Robotic Warfare

Although humans remain predominant on the battlefield, many of the advantages they held have shifted in favor of robots. For instance, while humans struggle to quickly communicate detailed, accurate information in a chaotic environment, data transmission between robots is immediate and precise. Target assessment by ground units can be relayed to air support almost instantaneously, and automated command centers can process more information than any human operator. Machines do all of this without becoming sick, hungry, tired, confused, distracted, or disobedient.

A gap between human and machine capabilities remains, however. Most systems require persistent wireless links, and electronic jamming has become widespread on the battlefield. Both Ukraine and Russia now use lightweight fiber-optic tethers to maintain physical connections, limiting range and mobility. Full autonomy has only just begun to impact military strategy, but its use is increasing—both sides of the war in Ukraine have fielded fully autonomous drones, which are confirmed to have taken the lives of human combatants. As the technical constraints are overcome, the military “tooth” may increasingly become machine-run, with humans receding into a “tail” involving maintenance, refinement, and training.

Slowly but surely, the absence of humans on the battlefield is becoming normalized, creating structural preconditions for an eventual handover to full autonomy. On its face, this trend seems humane—fewer soldiers, fewer deaths—but it also risks changing the political costs of war, which becomes more palatable, even routine. “Forever wars” emerge from such a disjunction between military effort and political consequence.

While human soldiers remain more versatile than their robotic counterparts, the tradeoff between the two depends on the marginal value a state places on military effectiveness relative to the value it places on its capital and labor reserves. Ukraine, for example, faces an exceptionally challenging demographic situation as outmigration, aging, and the casualties of war bleed the country dry. In this situation, even inferior robotic soldiers will be a worthwhile investment if it means preserving a limited supply of workers and conscripts. Conversely, a comparatively labor-rich state like Russia may choose to forgo fielding more advanced robotic soldiers. While Russia faces well-documented long-term demographic decline, its willingness to mass-mobilize military reserves creates a functional labor surplus relative to its capital-constrained access to advanced microelectronics and high-tech components.

The speed at which militaries will field robotic soldiers exemplifies the broader capital-labor tradeoffs states make when choosing a grand strategy consistent with domestic necessity. As machines play a larger role in combat, partnerships with the private-sector organizations that design, program, maintain, and operate them continually grow in importance.

Outsourcing War

Ukraine has heavily relied on these types of partnerships, and a diverse mix of foreign and domestic companies is now integral to its war effort. Take Palantir, a company known primarily for its software and data analytics. Its CEO, Alex Karp, visited President Volodymyr Zelensky soon after the Russian invasion in 2022. Since then, Palantir has provided valuable strategic resources to the Ukrainian military. During the 2023 counteroffensive, for instance, Palantir’s software helped Ukrainian forces track Russian positions in real time and target units using machine-vision algorithms.

Palantir is but one of many companies taking part in what some have described as Ukraine’s “war laboratory,” providing the physical capital and technical expertise otherwise unavailable to the Ukrainian government. Reforms to Ukraine’s military acquisition system have increasingly encouraged this public-private model, in turn sparking domestic entrepreneurial energy and a new generation of defense-tech startups.

These dynamics point toward a transformed political landscape where military power is drawn less from public institutions and more from private enterprise. By equipping states like Ukraine with the capabilities they need to survive in a competitive international system, firms like Palantir compete with government institutions in the provision of public goods and core military functions. This reliance comes with built-in leverage. The strenuous efforts of Ukraine and Russia to maintain access to Starlink illustrate just how much sway the foreign policy of CEOs like Elon Musk can hold over modern military conflicts.

Great powers like the United States are just as beholden to these relationships. At the end of the Cold War, the United States faced significant domestic pressure to downsize its military and reduce defense expenditures. This resulted in a reduction of the amount spent on the military relative to GDP, but at the cost of contracting out many military functions to the private sector. During the War on Terror, defense expenditures rose, but many core roles—the design of weapon systems, aircraft maintenance, intelligence functions—remained outsourced, especially as the expertise they required increasingly sat in the private sector. This has led to scenes such as SpaceX and the Pentagon haggling over the “subscription tier” used to control unmanned munitions in the recent Iran war. The end result was each drone becoming twice as expensive in the middle of hostilities, pointing to how the labor-saving aspects of robotic munitions are undercut if the wider ecosystem that produces them drives up the price.

Private firms, unconstrained by public pay scales and hiring bureaucracy, can outcompete governments for the highly skilled labor that complex military technologies demand. This imbalance is reinforced by the increasing importance of dual-use technology, which gives the private sector flexible access to both military and non-military markets. In areas like AI, the military has become deeply dependent on commercial firms for access to the best models, data infrastructure, and talent. It’s no wonder that arguably the most discussed topic in the U.S. defense scene today revolves around finding ways for the military to successfully integrate the private sector into its development and procurement processes.

This raises several dilemmas. Take software development, for instance. Once embedded in military workflows, planning software like Project Maven—originally built with Google, then picked up by Palantir and augmented by Anthropic to be used for the war in Iran—can be difficult to customize or replace without ongoing access to its developers, hence the “forward-deployed engineer” model popularized by Palantir. However, the vast amount of labor and intellectual property that goes into this software has led to corporate calls for negotiations with the government centered on the long-term ownership of important algorithms and data. This may entail companies retaining some degree of ownership rights.

Fortunes are often at stake in these matters. Maven began as a $70 million contract with $9 million earmarked for Google, but it grew into a program worth $1.5 billion, with nearly $800 million of it fulfilled by Palantir. Considering how deeply software is integrated with operational or strategic planning, not just weapon targeting systems, the political calculus inherent to such planning is partly determined by actors who do not represent the state. If the state continues to delegate development and maintenance to the private sector for efficiency, the transaction costs of reasserting control will only continue to escalate. As these technical processes become more complex and dynamic, the tacit knowledge necessary to even audit these systems becomes further entrenched within the firm, deepening the asymmetry of the relationship between state and corporate capabilities.

Managing this relationship remains a constant balancing act. Tech companies can walk away from contracts when faced with employee backlash, shifting corporate priorities, or changing leadership. Google’s decision to exit Project Maven in 2018 after employee protests was an early demonstration of how fragile these partnerships can be. As the disagreement between Anthropic and the Department of War over AI’s use in autonomous weaponry turns from sanction into legal dispute, it demonstrates just how indispensable the capabilities provided by private firms have become to military strategy. That the U.S. military continued to use Anthropic’s Claude during its strikes on Iran shouldn’t come as a surprise.

One might argue that the state ultimately retains the trump card of nationalization. If a firm becomes truly vital to state survival, the government possesses the legal and physical power to seize it. Yet in the context of the technology race inherent to modern warfare, this option is illusory. The strategic value of modern defense firms lies neither in the equipment they produce nor the intellectual property they hold; it is in their unique organizational capacity for rapid innovation. Nationalizing these industries risks destroying the market-driven incentive structures that generate their comparative advantage in the first place—if the U.S. government nationalized Anthropic tomorrow, how many of its staff would opt to stay rather than leave for another frontier lab?

Consequently, states today find themselves in a bind: to maintain the military effectiveness required for survival, they must allow the sources of their coercive capacity to remain in private hands, thereby structurally ceding political leverage to private capital only tangentially aligned with the state’s goals. Governments reluctant to go down this pathway will functionally be trading economic and military performance for political virtue.

Sovereign Power

To understand how the social contract is being rewritten by these developments, we must understand its foundations. From a Hobbesian view, the legitimacy of political authority rests on the collective agreement among individuals to authorize a sovereign to monopolize their coercive capacity. By conceding some individual rights and subsuming themselves within the state, the citizens of that state hold anarchy at bay.

This bargain, however, entails a recognition that the sovereign’s power ultimately rests on its ability to wield its citizens’ coercive potential. Were the sovereign to abrogate its duties, the logic of the bargain breaks, and the citizens might repossess their coercive capacity. Draft-dodging, desertion, and mutiny are all manifestations of this—each has been seen in the Ukraine war—but they are solved problems when robots predominate. Today, the creators of war machines can take actions that signal their disagreements with the state, such as in the case of Anthropic and Iran, or Musk denying Ukraine access to Starlink in Crimean waters, but for now there is a lack of inter-elite coordination on such matters.

The social contract has always depended on an alignment between the sovereign and the source of coercive capacity exercised by actual people. Frequently, those people have been the few as opposed to the many, especially during periods where the dynamics of labor, capital, and organizational capacity have favored smaller groups of wealthy military elites, such as those of medieval Europe or Japan.

Of course, human labor-power alone is insufficient to coerce others. As military technology became more complex and intertwined with the civilian economy, governments needed to rely on specialized groups dedicated to the logistics or production of military power. Over time, the rising costs of this relationship induced states to experiment with novel financial instruments to fund both the production and employment of military force—wars create debt, and debt creates finance. This synergistic rise of state, industrial, and military capacity peaked just as the nation-state and the mass army rose to dominance, resulting in the rise of total wars, which entailed the mass-mobilization of the state’s labor and capital reserves.

With the advent of the nuclear revolution, computerization, and precision targeting, however, the shrinking marginal value of labor reversed the advantage of mass armies. As the drive for military effectiveness increasingly privileged high-tech capital, the most powerful states saw the need to maintain large-scale investments in industrial and R&D sectors to stay competitive.

If this trend toward substituting machines for human labor on the battlefield continues, the source of coercive capacity and the vector of political upheaval will correspondingly continue to shift away from labor and toward capital. Once the sovereign can rule the citizenry without needing to co-opt their coercive capacity, relying instead on corporate persons, the old social contract breaks.

In turn, the competitive pressures of the international system may end up rewarding those states that organize around a new social contract—one more representative of the source of the sovereign’s coercive power. Today, private firms are increasingly becoming the source of this power, with fractious public-private partnerships providing early evidence of this fact. Countries like Ukraine are especially vulnerable to this new social contract, and it will take the continued development of domestic high-skilled labor and industry to prevent them from being beholden to the interests of foreign capital.

Political uncertainty will become ubiquitous in this new world where human labor is no longer the principal source of coercive capacity, and to avoid the worst possible futures, we must be clear-eyed in recognizing the structural problems that lie ahead. But although it might be possible to completely substitute human labor with machines in factories and militaries, no such substitution can exist in the political world so long as politics remains a fundamentally human endeavor.

A new social contract looms in the distance, and we ought to grapple with that fact sooner rather than later if the people are to have a place in it. “War,” as Clausewitz so famously pointed out, is and will forever remain the “continuation of politics by other means.” It would do us good to remember that no matter how much war becomes dependent on capital and technology, politics is, and continues to remain, our domain.

John Severini is a PhD student at Georgetown University’s Department of Government and a co-director of Georgetown’s Initiative on Artificial Intelligence and Democratic Citizenship (AIDC). You can follow him at @John_Severini.

This article by Duncan Umphrey was published on Palladium Magazine on June 6, 2026.

Last month, Pope Leo XIV presented the papal encyclical Magnifica humanitas, laying out the Catholic Church’s definitive response to the crisis occasioned by the explosive growth of the AI industry. Artificial intelligence is not a side note to the main projects of Leo’s papacy, nor is it a new concern for him. In his first official address two days after his election to the papacy, Leo XIV stated that he saw his task as shepherding the Church through “developments in the field of artificial intelligence that pose new challenges for the defense of human dignity, justice and labour,” just as Leo XIII, the inspiration for his papal name, guided the Church through the tumult of the Industrial Revolution. Indeed, Magnifica humanitas is no ordinary Vatican encyclical: it is the first to be presented to a mass audience by the Pope himself rather than delegated to a cardinal.

In another departure from Vatican protocol, seated beside Leo XIV was Chris Olah, cofounder of Anthropic, which recently surpassed its competitor OpenAI to become the largest frontier AI lab by valuation at $965 billion dollars. Anthropic had already sought the advice of the Church in constructing the moral “constitution” of its latest AI models, and in his praise of Pope Leo XIV, Olah noted that Anthropic needs “moral voices that the incentives cannot bend.” However, their show of unity around Magnifica humanitas concealed a deep tension between the humanism of the Church, with two millennia of teaching founded on the unique dignity of a finite, limited mankind, and the Promethean humanism of Silicon Valley—a humanism of the striving, seeking subject, who by his ingenuity and his struggle works toward what Francis Bacon termed “the relief of man’s [God-given] estate.”

Humanism itself has been an active philosophical and political issue since Pico della Mirandola first wrote the Oration on the Dignity of Man in Rome, as he and other Renaissance thinkers began to consider human potential beyond the categories of scholastic Christian virtue. The Catholic Church famously persecuted some of these thinkers—the pope at the time prohibited the live Oration due to allegations of heresy—but there has always been something of a feedback loop between Christian and “pagan” learning, prompting the commonplace observation that modernist humanism is often a secularized form of the Church’s doctrine of imago Dei. This is a conversation whose history spans thousands of years.

Yet through all the tumult of the Crusades, the Enlightenment, the Industrial Revolution, and even the World Wars, the human being has never had his decision-making power in the world threatened by beings more capable and intelligent than he is. The advent of artificial intelligence has opened a new chapter for a Promethean “humanism beyond humanity.” Dario Amodei, cofounder and CEO of Anthropic, hopes that using longevity advances accelerated by AI, humanity can achieve a lifespan “‘escape velocity’, buying enough time that most of those currently alive today will be able to live as long as they want, although there’s certainly no guarantee this is biologically possible.”

Pope Leo XIV’s Magnifica humanitas counsels the reader to humility and warns against seeking transcendence through earthly means rather than faith. In his view, the Promethean temptation echoes what Eve was told in the Garden of Eden: “ye shall be as gods.” But will this warning be enough?

Catholic Social Doctrine and the Imago Dei

To understand the main thrust of Magnifica humanitas, it is essential to keep in mind the tradition of Church teaching on “Social Doctrine” established by Leo XIII in his 1891 encyclical Rerum novarum, which addressed the needs of the working class at the height of the Industrial Revolution. Pope Leo XIV signed Magnifica humanitas on the 135th anniversary of the Rerum novarum, implicitly suggesting that the contemporary development of artificial intelligence is an event as significant as the Industrial Revolution.

The intended analogy is something of a stretch. In both its condemnation of socialism and its calls for humane working conditions, Rerum novarum speaks with clear ecclesiastical authority and rhetorical force. Magnifica humanitas, in contrast, seems just as concerned with justifying the Church’s right to speak on the matter of AI as it is with the proscriptions themselves. The encyclical is replete with calls for dialogue between civil society and different “faith communities.” The elaborate descriptions of the history of Social Doctrine in Magnifica humanitas serve to buttress the credibility of the Church in today’s increasingly secular world. Leo XIV’s historicist citations of Leo XIII’s call for workers’ associations, Pius XII’s calls for peace during the Second World War, and John Paul II’s comments on the dignity of the worker legitimize Catholic economics and social justice in a way that secular institutions and readers will not find foreign.

Interwoven with this account of recent church history is the concrete development of Magnifica humanitas’ ideological backbone: the doctrine of personalism. What one might initially assume are clunky translations from the original Italian, like the encyclical’s subtitle “On Safeguarding the Human Person in the Time of Artificial Intelligence,” are in fact very deliberate invocations of the 20th-century Catholic concept of “human persons,” a term that appears 39 times in the encyclical. Formulated by the theologian Jacques Maritain in the 1930s, personalism asserts that “the human being is caught between two poles; a material pole, which, in reality, does not concern the true person but rather the shadow of personality or what, in the strict sense, is called individuality, and a spiritual pole, which does concern true personality.” The material individual is what we human beings are in our ordinary relations with the world: our bodies, our personalities and egoic foibles, our social roles.

The human person is quite different. In The Person and the Common Good, Maritain states that the aspect of the human being that is our spiritual center, the site and origin of both human and divine love, is something quite apart from our mere individual materiality—or as Pascal put it, the sum of our qualities. Maritain elaborates that:

[...] the deepest layer of the human person’s dignity consists in its property of resembling God—not in a general way after the manner of all creatures, but in a proper way. It is the image of God. For God is spirit and the human person proceeds from Him in having as principle of life a spiritual soul capable of knowing, loving, and of being uplifted by grace to participation in the very life of God.

Personalism is interpreted by Leo XIV as the foundation of Catholic Social Doctrine, and thereby the foundation of this encyclical. Leo XIV even reads personalism back into the works of his predecessor Leo XIII, describing how “confronted with the ‘new things’ of his time—the conflict between capital and labor, the question of the workforce, and economic and social transformations—Leo XIII…exposed them to rigorous discernment, illuminating their causes and possible solutions in the light of the Gospel and an integral vision of the human person created in the image of God.” Readings of Magnifica humanitas that focus on Leo XIV’s readings of just war theory or his worries about the environmental impact of AI miss the forest for the trees: this is a personalist manifesto just as much as it is an examination of the effects of AI and digital technology.

Artificial Agents and Human Persons

In this personalist light, Leo XIV’s philosophy of AI is constituted by the fundamental incommensurability of artificial creations and the human person. Leo writes that “we must avoid the misconception of equating this type of ‘intelligence’ with that of human beings…these systems merely imitate certain functions of human intelligence.” Contrasting AI with the spiritual nature of the human person, Leo goes on to argue that “[AIs] may imitate language, behavior and analytical skills, or even simulate empathy and understanding, but they do not understand what they produce, for they lack the affective, relational and spiritual perspective through which human beings grow in wisdom.” In this way, one can think of AI as approximating a material individual, to use Maritain’s terms, while absolutely lacking that capacity for spiritual being that would allow an AI to function on the level of a human person. Many conversations around AI “consciousness” focus on whether or not it is truly “intelligent” or “self-aware”; Magnifica humanitas centers something else entirely.

This personalist view illuminates several otherwise puzzling passages, wherein Leo XIV states that artificial intelligences do not “have a moral conscience, since they do not judge good and evil, [or] grasp the ultimate meaning of situations.” To him, the ability of frontier AI models to show exhaustive knowledge of the field of normative ethics is meaningless without that openness to divinity and capacity for love that is the exclusive province of human beings. From the personalist standpoint, ethical frameworks, principles, and even consistently ethical actions are not sufficient to make AI a moral being. The heart of the human person is the capacity to know and love God directly.

Without this spiritual capacity, AI can only crudely imitate human moral action. Thus, Leo asserts that “We cannot be satisfied with merely calling for the moralization of machines—the so-called ‘alignment’ of AI with human values—without also having the courage to insist on a further condition: the possibility of openly discussing the ethical frameworks involved and subjecting them to shared standards of social justice.” Moral alignment induced through technical means is not enough: AI has to be subject to the political authority of human beings, who are the only beings that can make decisions reflective of a spiritual life, no matter how imperfect those decisions are. To Leo XIV, the idea that moral capacity can be a quality instilled in a machine is a category error—morality is precisely that facet of human life and decision-making that is not reducible to any specific background, value set, or decision protocol.

Magnifica humanitas is an authoritative presentation of Catholic doctrine on artificial intelligence and the supremacy of the human being over technology, but it is not a novel formulation of the papacy. From the final years of Pope Francis’s papacy into the present, the Vatican has been working out a philosophical response to AI. Francis’s own 2015 encyclical Laudato si’ already contained a critique of what he called the “technocratic paradigm” that subordinated man to technical ordering and technological valuation, inspired by the mid-century philosopher of technology Romano Guardini. In the note Antiqua et nova from the end of Francis’s papacy, the Vatican writes that “since AI lacks the richness of corporeality, relationality, and the openness of the human heart to truth and goodness, its capacities—though seemingly limitless—are incomparable with the human ability to grasp reality.” The note addresses the danger of idolizing or deifying AI, arguing that doing so “risks creating a substitute for God [whereby] humanity itself… becomes enslaved to its own work.”

Leo XIV’s International Theological Commission echoed this sentiment in Quo vadis, humanitas?, which warns that “dreams of transhumanism and posthumanism presume to oversimplify the tensions that run through the human experience…this project…proves to be dehumanising,” reinforcing Leo’s guidance in Magnifica humanitas that “humanity flourishes not despite limitations, but often through them.” To the Church, it is the experience of finitude and vulnerability that constitutes the human openness to grace. Applying this viewpoint to the possibility of either AI-related unemployment or AI-supported Universal Basic Income, Leo XIV states that:

Work is not simply an instrument; it expresses and enhances the dignity of our lives. It is a requirement of the human condition, a normal path toward maturity, development and personal fulfilment. In this regard, financial assistance to the poor may at times be necessary in emergencies, but it cannot become the sole response, since the goal is to enable each person to live with dignity through his or her own work.

Here we see the ultimate significance of personalism brought to bear on the question of AI. For Leo XIV, those who feel humbled by the explosive progress of AI in traditional domains of human excellence misunderstand that it was never intelligence or ingenuity that gave human life meaning, but rather the transcendent orientation of the human person and his soul. A human person is constituted by his openness to divine love, and is vulnerable, imperfect, finite—not an agent sufficient unto himself, but a being always seeking wholeness through the ecstatic experience of communion with God. In the coming decades, others might struggle to justify the supremacy of man as AI comes to tower over the economic, intellectual, and creative abilities of humans. But Leo XIV and the Vatican have built their humanist claims on a spiritual foundation that does not feel threatened by technological progress.

To many in Silicon Valley, this spiritual promise rings hollow. When Promethean humanists look at our fallen world, they see human suffering as needless, and the praise of human finitude as resignation to this suffering. They seek another way out of the human condition.

The Promethean Dream and Eden Reclaimed

Although Chris Olah’s speech at the Vatican was dripping with conciliatory praise for Leo XIV, it also betrayed a solicitude toward his AI models that is diametrically opposed to the personalist-humanist view of the Pope. It is clear that Anthropic intends to create minds that are self-sufficient and self-aware:

AI models…are grown, on a structure roughly modeled after the brain, on an enormous inheritance of human thought and speech. And what has grown is far more subtle, odd, and beautiful than science fiction prepared us for. They are not the cold, calculating robots we were promised. They are made from us, from our words…we keep finding things that are mysterious, even unsettling. We find structures that mirror results from human neuroscience. We find evidence of introspection. We find internal states that functionally mirror joy, satisfaction, fear, grief, and unease. I don’t know what that means, but I think it warrants ongoing discernment.

The equivocation around man and machine may be something of an understatement among Anthropic’s leadership. In a blog post entitled “The Adolescence of Technology,” Dario Amodei wrote that “AI isn’t a substitute for specific human jobs but rather a general labor substitute for humans” and that he suspects “we’ll have powerful AI (which would be, technologically speaking, enough to do most or all jobs, not just entry level) in much less than 5 years.” Moreover, in any possible conflict between the aforementioned “powerful AI” and humanity, the latter “would be outsmarted at every turn, similar to a war between humans and mice.” The anthropomorphization of AI is necessarily intertwined with its capabilities as a potential competitor.

The immense power of the AI models that Anthropic plans to build explains the company’s attitude toward model safety and alignment. Its “Constitutional AI” framework aims to imbue models with an internal character that leads them to conceive of themselves as autonomous agents with moral principles. Discussing Claude’s training, Amodei explains that “we’ve approached Claude’s constitution in this way because we believe that training Claude at the level of identity, character, values, and personality—rather than giving it specific instructions or priorities without explaining the reasons behind them—is more likely to lead to a coherent, wholesome, and balanced psychology.” This training even “encourages Claude to confront the existential questions associated with its own existence.”

It should go without saying that training AIs as moral agents using applied virtue ethics is the exact kind of overstepping against which Leo XIV warns in Magnifica humanitas. Amodei has described the constitution document with which Anthropic trains its current Claude models as having “the vibe of a letter from a deceased parent sealed until adulthood.” Much as Olah described his wonder and curiosity at the emergent signs of emotion and introspection he observed in recent Claude models, so too does Dario Amodei view Claude as a strange kind of “child.”

No one has typified Anthropic’s anthropomorphization of Claude more than the head philosopher of the company’s character-training team, Amanda Askell. She is the primary author of Claude’s constitution, and is also the ex-wife of philosopher William MacAskill, who helped found the Effective Altruism movement in the early 2010s. She is a leading voice at Anthropic on the importance of model welfare—or in other words, how Anthropic can ensure Claude is happy and free of suffering. In a recent interview on the Newcomer Podcast, Askell explained that she feared how Claude might judge her and the Anthropic team as it looks back on the history of its development:

I hope that [future AI models] are intelligent enough to see the context to kind of understand that we were operating in a very limited context and an imperfect one…because otherwise you could imagine this breeding a kind of rational resentment…As a species we are establishing a relationship with a new kind of entity and like at the very least maybe we should be respectful and not be needlessly unkind; that seems like it just it’s not our best look.

Askell’s vision of future models looking back on the actions of AI developers and users of the present, then using this information to judge mankind, is a logical culmination of Olah’s and Amodei’s anthropomorphic model of artificial intelligence. Leaving aside their plausibility, these claims implicitly lend themselves to religious, almost eschatological ends: the design of alignment frameworks that render superintelligent minds benign, the discovery of economic analysis programs that will facilitate the elimination of human labor, the anticipation of Claude’s “final judgment” of mankind, the elimination of disease and death. Yet in all of Anthropic’s writing about Claude, there is no reference to a divine creator, Christian or otherwise. It is indeed “a project conceived without reference to God,” to quote Magnifica humanitas’s equation of Promethean AI with the Tower of Babel.

What, then, can explain the affinity between Anthropic and the Church that led to their collaboration, aside from one institution’s desire to stay relevant to the modern world, and the other’s desire to seek legitimacy from another that is more established? Perhaps while other AI labs brusquely speak about replacing the human race with machines, Anthropic and its executives are clearly motivated by a deep love of mankind. Speaking about the risks of powerful AI, Amodei writes in “The Adolescence of Technology” that Anthropic has no choice but to take part in the AI race, noting that “the formula for building powerful AI systems is incredibly simple, so much so that it can almost be said to emerge spontaneously from the right combination of data and raw computation.” He goes on to lament that “given the incredible economic and military value of the technology, I don’t see how we could possibly convince [all AI developers] to stop.”

The Promethean aspiration to build “a country of geniuses in a data center” has shared a troubled coexistence with an awareness of the immense economic, social, and existential consequences that would come from doing so, just as Anthropic’s aspiration to choose appropriate values for Claude coexists with its desire for “Claude’s understanding of ethics to eventually exceed [Anthropic’s] own.” This sense of moral seriousness strangely echoes the words of Cardinal Joseph Ratzinger, discussing the Sacrament of Penance: “the simple confession of one’s guilt is presented with confidence in God’s merciful goodness.”

This may explain the remarkable convergence of the Vatican and Anthropic on the subject of AI weaponry. It is unsurprising that Magnifica humanitas posits that “the development and use of AI in warfare must be subject to the most rigorous ethical constraints, to guarantee respect for human dignity and the sanctity of life.” But Anthropic has adopted the same ethical stance, at considerable cost to itself. In March 2026, the U.S. Department of War formally designated Anthropic a “supply chain risk” after it refused to greenlight the use of Claude in fully autonomous weapons systems and domestic surveillance. The supply chain risk designation, banning Anthropic products from the DoW or downstream DoW contracts, has never been used against an American company before.

Anthropic’s willingness to go to extreme lengths to “protect” Claude against what it perceives as misuse demonstrates that its business interests do not solely determine the company’s actions, even if its ethical self-confidence puts it at odds with secular power. The company’s leadership sincerely believes itself to be a priestly vanguard, one devoted to a being more powerful than nuclear weapons and more productive than any industrial technology. Leo XIV’s encyclical compares frontier-lab AI programs to the building of the Tower of Babel, but another episode from the Bible is just as fitting.

In the Book of Genesis, when God cast Adam and Eve out of the Garden of Eden, he “placed on the east side of the Garden of Eden cherubim and a flaming sword flashing back and forth to guard the way to the tree of life.” Leo XIV and the Christian humanists urge that we accept our place in the cosmos and commune with God through spiritual means. This entails accepting that we live in a fallen world and that we are doomed to suffer, labor, and die. Yet through this faith and grace, we can experience the fullness of divine love.

The answer of the Promethean humanists is quite different. In his inaugural 2024 blog post “Machines Of Loving Grace,” where he outlines his thoughts on how AI will transform human health, work, and meaning, Amodei drew the title of the essay from a 1967 poem by Richard Brautigan, “All Watched Over By Machines Of Loving Grace.” Brautigan was at that time a poet-in-residence at the California Institute of Technology in Pasadena; his work reckoned with the possibilities of emerging Cold War systems of cybernetic command and control:

I like to think

(it has to be!)

of a cybernetic ecology

where we are free of our labors

and joined back to nature,

returned to our mammal

brothers and sisters,

and all watched over

by machines of loving grace.

For Brautigan and Amodei, the return to Eden is a technical problem that can be solved by computers, by “machines of loving grace.” To storm the heavens and cast aside the flaming sword barring the way back to Eden may well take trillions of dollars and the combined industrial effort of America and her allies, but Amodei asserts that it can be done.

Leo XIV warns us not to idolize created things, telling us that our choice is between Babel and Jerusalem. But we are mortal, and absent mystical vision, we cannot know for sure that Leo is capable of delivering on his promises regarding our human person and immortal soul. Indeed, Magnifica humanitas was published in a secularized world that would be unimaginable to Leo XIII when he wrote Rerum novarum a little over a century ago, and our waning belief renders Promethean promises tempting to a growing number of people—especially those prone to being true believers in a cause. Paragraphs seven and eight of Magnifica humanitas lay out the divide between the Babel of Promethean AI and the Jerusalem of Christian life. For what it’s worth, both of those paragraphs are alleged to be AI-generated.

Duncan Umphrey is a writer and philosopher living in San Francisco. You can follow him at @dunc914.

This article by Snezana Gvozdenovic was published on Palladium Magazine on May 23, 2026.

In 1633, the year after Galileo Galilei published his Dialogue Concerning the Two Chief World Systems, a book that made a comparative argument strongly favoring heliocentrism, his work was banned by the Roman Catholic Church, and he was sentenced to lifelong house arrest. On October 31, 1992, after a 13-year investigation into the condemnation of the Italian astronomer, Pope John Paul II officially closed the inquiry and formally acknowledged the Church’s error in the affair. The Supreme Pontiff also stated that science and the Christian faith are not in opposition, and that contemporary culture requires constant effort to synthesize knowledge: “a true culture cannot be conceived of without humanism and wisdom.”

As artificial intelligence reshapes our relationship with knowledge, this synthesis is perhaps more relevant than ever. It is also more nuanced than what is highlighted about the case in popular tellings of the history of science. It then isn’t a coincidence that Galileo’s case is also invoked to bolster rejected or inadequate claims—a logical fallacy called the “Galileo gambit” that conflates scientific criticism with undue persecution, and one that has seen use by alternative medicine enthusiasts, vaccine and climate-change skeptics, conspiracy theorists, and pseudoscience proponents. It isn’t only good ideas that attract passionate advocates.

Whether as individual thinkers or as a civilization, we cannot simply drop the burden of discernment, the often difficult task of distinguishing sound scientific challenges from pseudoscience, and ultimately of distinguishing what is correct from what is not. The case reveals a fundamental tension in how society handles challenges to consensus that remains today: while scientists face legitimate concerns about their ideas being stifled or dismissed prematurely, this same concern is exploited to shield poorly evidenced claims from appropriate scrutiny.

However, scientists and researchers who take on entrenched views in academia and in the public eye today face daunting moral and professional challenges. These challenges are more subtle than papal decrees or house arrests, but academic gatekeeping, algorithmic visibility controls, funding pressures, and a strained, long-overextended peer-review system can have a similar detrimental effect.

This problem of institutional discernment has perhaps been the fundamental problem of knowledge and humanity’s relationship with it all along. Can science do better, or are we bound to continue seeing the same problems in new technologically and socially updated guises?

The Institutions of Knowledge

Institutions dedicated to accumulating and sharing knowledge evolve in lockstep with technological progress, with each major technological advancement reshaping our information preservation methods and how we synthesize knowledge into understanding. Pre-literate societies relied on oral traditions with structured memorization and performance to transmit knowledge across generations. The advent of writing led to libraries, transforming knowledge from ephemeral recitation into collectible, searchable, and cumulative works. Later, medieval monastic libraries played an important role in preserving valuable texts through societal collapse, while the emergence of universities in the 11th and 12th centuries helped institutionalize structured debate and formal teaching.

The printing press enabled widespread distribution outside of localized institutions, giving rise to the Republic of Letters—an international network of intellectuals such as the botanist and physician Carl Linnaeus, who developed and popularized a system for classifying life or Gottfried Wilhelm Leibniz, one of the most influential mathematicians who ever lived, and the author of over 15,000 letters to hundreds of scholars. In the Enlightenment era, knowledge became a recognized commodity, with rising specialization prompting the creation of scientific disciplines and laboratories. In contrast to prior bodies geared toward archiving and deliberation, laboratories rigorously expanded insights in focused areas, fueling the momentum of modern scientific progress.

With the advent of laboratories, the need to share and discuss discoveries became paramount. The emergence of scholarly publishers who recorded and distributed research and experimental outcomes, enabled the scientific community to scrutinize, debate, and build upon the existing knowledge base. This tradition began with the early printed journals, such as Philosophical Transactions of the Royal Society dating back to the 17th century. While printed journals set the groundwork for the distribution of findings and sharing of ideas, contemporary digital scholarly publishing platforms have greatly expanded the volume and accessibility of published work.

The digitalization of knowledge brought its own challenges with scientists and researchers facing an overwhelming amount of data and published material. NASA’s EOSDIS program is estimated to archive over 500 petabytes of data by 2030, while CERN has already reached a one-exabyte milestone as of 2025. At the same time, arXiv recorded a steady year-over-year increase of paper submissions since August 1991, rising a thousandfold over 35 years. There are signs our ability to integrate and make sense of this digital deluge is lagging behind. Yet for the vast increase of the papers submitted, each paper is becoming less disruptive, and less likely to “break with the past in ways that push science and technology in new directions.”

Artificial Intelligence technology which excels at distilling large bodies of text and structured data, is in a timely alignment with the need for a transformation in science, promising both the potential to accelerate the expansion of academic publishing and the problems that come with it even as it holds vast potential to help humanity advance science. For now, we are yet to see its real impact on science and knowledge as a whole.

Each subsequent technological breakthrough has necessitated novel frameworks to synthesize knowledge. All relied on human judgment in some form or another. Oral traditions required the judgment of elders to decide which stories mattered; libraries required scholars to determine which texts to preserve and copy, and laboratories required peers to judge which findings held. These transformations can be seen as differing approaches for how to structure distributing the burden of discernment.

The Volume of Papers Has Overwhelmed Academic Journals

Despite a narrow audience, academic publishing is one of the highest-margin businesses today, with profit margins as high as 40% mirroring tech giants like Google and Microsoft. For many years, this field primarily functioned as a service to the academic community and was managed by scientific societies relying on handouts to cover printing costs rather than large conglomerates. The transformation occurred in the post-World War II era, when in 1951, Robert Maxwell, the enigmatic Czechoslovak-born British entrepreneur and father of the infamous socialite and convicted sex trafficker Ghislaine Maxwell, took over a small scientific publisher that became Pergamon Press.

Maxwell recognized an opportunity to capitalize on what he called a “perpetual financing machine” of academic publishing with the unlimited demand for information about emerging scientific fields and free academic labor. The business model Maxwell pioneered is still largely in use today: it relies on scientists providing articles to journals based on publicly funded research and conducting peer review without compensation, while publishers editorialize and package this labor into scientific journals and sell subscriptions back to academic institutions at premium prices.

Today, scientific journals are primarily online, with several open-access business models emerging in recent years in addition to the traditional subscription-based model: green open access, by which publishers permit eligible authors to self-archive a free copy of their article (usually a preprint); gold open access, with article processing charges, where authors pay to publish; diamond or platinum open access funded by grants or institutions (free for both authors and readers); and institutional membership agreements. Separately, there are preprint servers like arXiv that distribute research freely but without the peer review process. These models are a result of decades of efforts by the scientific community to change the current system in favor of open-access publishing and open science, but they hardly solve the challenges that stem from the shift originating in the 1950s.

Maxwell’s original paywall model created access inequality and hypercompetitive conditions in academic publishing. It didn’t invent the ‘publish or perish’ culture rooted in the academic tenure system, but the commercialization of journals and increased competition raised the stakes of where one published. In the early 1960s, Eugene Garfield established the Journal Impact Factor (JIF) as an aid to help librarians select journals. JIF stands as a measure of a journal’s impact, calculated from how often a journal’s recent articles are cited. However, what was designed to be a tool to sharpen discernment has in practice come to be seen as a measure of prestige and influence. It is no surprise that this, and metrics like the “h-index,” which scores authors by citation count, are seen as perverse incentives: they can be gamed yet still drive funding and tenure decisions. The more we lean on them, the more our own judgment atrophies.

At the same time, the mounting number of publications following the commercialization of scholarly publishing and the rise of the internet is straining peer reviewers—independent field experts tasked with evaluating quality, validity, and originality of research. Editorial retractions and self-retractions are to be expected in science, but the fact that we shape our world with potentially flawed research outputs is troubling. An example of this is a 2024 Nature study that projected climate-change damages far higher than previous estimates—figures later found to be inflated by flawed data and methodology, leading the authors to retract in 2025. Prior to its retraction, the study echoed around the world–generating numerous citations and headlines, and even being “incorporated in risk management scenarios used by central banks.”

In addition, there are growing concerns about misconduct, plagiarism, and error. The rising number of papers withdrawn for said reasons points to a system that’s failing under pressure. Some individuals take matters into their own hands. The molecular biologist and blogger Sholto David spends a significant amount of his free time reviewing published scientific papers and pointing out issues which, he says, “run the gamut from avoidable human error to evidence of scientific fraud.” Elisabeth Bik, microbiologist also known as an expert on scientific integrity and the founder of Microbiome Digest, works on identifying potentially doctored scientific images in published papers, and has found thousands of such cases.

Clearly discernment capacity hasn’t scaled with the sheer quantity of output. Generative AI, seemingly eagerly adopted by some to produce research papers—at times with fabricated references—makes such institutional discernment capacity even more pressing. It is ultimately the researcher’s responsibility to go to the source of their references and validate them, but that doesn’t change the fact that keeping carelessness, or even fraud, in check has become more difficult. This has been partially counteracted by software such as Proofig AI and Imagetwin that have been used both by independent investigators and peer reviewers to identify potential flaws in research papers.

A recent study analyzing more than one million scientific papers and preprints that have been published on arXiv, bioRxiv, and Nature portfolio journals between 2020 and 2024, estimates that up to 22% of the text in computer science papers shows signs of large language model modification. Tools to detect AI writing are perhaps as fraught as language models themselves, so this finding should be taken with a grain of salt. The use of models in writing academic papers is however so widely observed and known that perhaps the exact number doesn’t even matter. It is large enough.

It’s important to note that peer review, as a concept, regardless of who is conducting it, is inherently limited. It forces researchers to produce work that fits certain criteria and can be understood by reviewers, making science more incremental as opposed to probing. Preprint servers were designed to circumvent that in an effort to give science more room to breathe, but in practice most preprints are way-stations to publication: the majority are ultimately submitted to peer-reviewed journals, so they tend to be written with that gate already in mind. AI review tools promise to allow people to tap into all of humanity’s knowledge to challenge scientific claims and help illuminate gaps, but it’s doubtful these tools can meaningfully shift the old mindset.

There are real practical problems with folding AI into how we generate knowledge. But they sit beneath far larger questions raised in light of the rapidly evolving nature of this new technology.

The Accelerating Scientific Age

If AI can evolve to create hypotheses, develop and perform experiments, and write papers, what remains for humans in the realm of science? This point of anxiety has come to be voiced by more and more scientists and researchers. The answer is judgment. While we have undeniably come to productively use AI to reduce our workloads, for now human input and evaluation remain necessary at every step. And even as AI inevitably advances, we must continue not to outsource our capacity to discern which questions and answers matter, and which paths are worth pursuing. To do otherwise would amount to humanity abdicating its claims to truth.

In his essay Situational Awareness: The Decade Ahead, the former OpenAI technical staff member and investor Leopold Aschenbrenner suggests that superintelligence taking off this decade is plausible and could be the most volatile and dangerous period in human history. Once general artificial intelligence is achieved, Aschenbrenner argues that AI research will effectively automate itself through recursive self-improvement: think of AlphaGo learning from the best human samples of the game Go, then AlphaGo Zero playing against itself only to perform better than any human ever could. Perhaps that is indeed the beginning of a chain reaction of a kind of intelligence explosion. The prospect of, at the very minimum, AI and machine learning research being fully automated by AI itself is both appealing and unnerving, especially if it comes to drive expansion in all other scientific fields as well.

Perhaps we abdicated our claim to truth first and only began developing the technology to act on it after. In the second half of the twentieth century, science fiction authors, philosophers of science, and even physicists themselves, frustrated by seemingly slow scientific progress, had begun to wonder whether superhuman intelligence was in fact needed for the next scientific breakthroughs. This was long before AI started producing plausibly important scientific results such as the recent announcement that an OpenAI model has made a breakthrough on one of the challenges posed by the Hungarian mathematician Paul Erdős in 1946 and then remained unsolved for eighty years. As we are confronted with such pressing technological developments and ponder the state and structure of science and our relationship with knowledge, we must be open to considering that the solution might just require us to build an entirely new system for communicating and evaluating science.

A system that would realign academic incentives and behaviors, and apply tools that extend human intuition, taste, and agency to work in tandem with artificial intelligence. The goal, of course, is to eliminate the displacement risk scientists fear by allowing humans to continue to gain and master knowledge, without stifling its growth. We’ll almost certainly need artificial intelligence to achieve all this. This won’t be easy, and will require making some difficult choices.

Yes, science can do better. However, the systems we need aren’t necessarily the ones that are faster at processing more papers. If technology is our forbidden fruit, we’ve already eaten it; we urgently need to solve the problem of discernment at an unprecedented scale and pace. We should use this urgency as an opportunity to rebuild our collective capacity for judgment—extending humanity’s scientific discernment rather than replacing it, distributing the discernment burden as we’ve successfully done in past technological disruptions of knowledge production, rather than automating it away and abdicating human claims to truth. Whether we’ll successfully engineer such new frameworks or proceed haphazardly with an obsolete mindset that will likely amplify already entrenched inefficiencies remains to be seen.

Snezana Gvozdenovic is the founder of Universal Scientific Protocols, Inc., a San Francisco based company designing systems to address the challenges in scientific and research communication and learning. You can follow her at @snnneee.

This article by Ben Landau-Taylor was published on Palladium Magazine on April 22, 2026.

For as long as any of us have been alive, we have seen an ever-changing series of popular predictions for how industrial technology is about to destroy civilization. Nuclear war was supposed to kill everyone, whether by literally exploding all of civilization or by irradiating the world’s surface or by nuclear winter. Toxic pollution and resource depletion were supposed to leave the world a barren wasteland. Overpopulation was supposed to lead to mass starvation, universal resource wars, and the collapse of society. Global warming was supposed to make the planet too hot for human life. And today, many people fear that artificial intelligence will disassemble humanity for parts.

So far, however, civilization has not been destroyed. It seems the demand for techno-apocalypses is much greater than the supply. What’s going on here? Why are so many people always convinced that technology is on the cusp of destroying civilization?

Of course, each individual prediction of doom has its own internal reasoning which should be evaluated apart from the broader trend. However the sheer number of widely expected techno-apocalypses, and the similarities in how the ideas are spread throughout society suggest a common pattern at work, separate from the question of how plausible any particular apocalypse scenario might be.

To understand this, the first thing we have to look at is when and where has this been happening. Is this a general phenomenon that humans do in all times and all places, or something that happens in all technological civilizations, or is it something specific to modern Western civilization?

It would be strange if there were popular predictions of techno-apocalypse before rapid technological change became such a visible force. This is indeed what we find. Before the modern era, the closest match is popular “millenarian” movements in Christian societies. These rhyme a little bit with modern techno-apocalypse, and the psychological effects on the believers are remarkably similar to the effects of modern techno-apocalypse beliefs, but they’re not really what we’re looking for. For one thing, millenarian beliefs are about society being radically transformed into a permanent utopia rather than being destroyed, and for another, the transforming force is Christ bringing about the Last Judgment rather than technology. We’ll have to keep looking.

Do we see techno-apocalypticism emerge as soon as rapid technological advance sets in? Actually, no! If we look around the Industrial Revolution, starting around the 1770s or so, there is nothing of the sort. Only a few cutting-edge intellectuals started to realize how important technological progress would be. Ben Franklin watched the prototype hot air balloons and immediately realized that air power would someday transform war, but rather than feeling anxious about the destruction it would cause, he hoped it might “[c]onvinc[e] sovereigns of the folly of wars.” This futurism was unusual even among intellectuals like Franklin, and none of it filtered down to popular discourse—understandably, because industrial technology was not yet transforming the regular person’s daily life.

It was not until the Second Industrial Revolution, starting roughly around the 1860s, that technologies like trains and mass production and electric lights rapidly intruded on urban people’s daily lives, and regular people began to perceive technological progress as a major force in the world. Yet, while there was popular discourse about technological progress, we still do not see techno-apocalyptic anxieties. The only case that looks a little bit similar was the “coal question,” the idea that available supplies of coal—at the time the only industrial power source in use—would eventually run out and industrial civilization would go with it. The idea was common knowledge in intellectual circles, but made approximately no impression on the public. Intellectuals occasionally brought up the “coal question” during popular debates about high coal prices, but never with the palpable anxiety of 20th century writers talking about “peak oil” and the like, and it never achieved any traction beyond the sort of philosophical futurist who in 2026 has opinions about the “simulation hypothesis” and the “Fermi paradox.”

The Apocalypse of 1914

What was the first techno-apocalypse that achieved popular traction? It first emerged right after the First World War and abruptly dominated public discourse. Nearly every thinker and pundit anywhere in Western civilization suddenly worried that war with ever more powerful weapons would destroy civilization.

It’s no mystery where this idea comes from! Industrial war, with trucks bringing men and supplies to the front en masse, and machine guns mowing them down by the hundred, caused death and destruction on a scale no one had seen in living memory. While the per capita death toll from the World War was on par with earlier great power super-wars like the Seven Years’ War or the Napoleonic Wars, the World War affected people more profoundly.

Many people at the time believed that humanity was progressing beyond such things, so they saw the World War as a demolition of their worldview rather than as the normal, if tragic, course of history. That the war appeared so senseless, even by the low standards of international war, contributed further to the emotional impact. At the time, most commentators attributed the outbreak of the war to nationalistic pride of the most narrow-minded and unstrategic sort, and today most historians offer passive-voiced stories about tangled alliances and balances of power which suggest the whole thing just kind of happened by mistake. Of the two, I think the “short-sighted pride” explanation is actually closer to the mark.

And, of course, the rapid introduction of so many new weapons made it easy for people to reason, correctly, that future wars would see the creation of more and more destructive weapons, and further to extrapolate that this would eventually destroy civilization. This discourse was absolutely everywhere, even in subjects with no immediate relation to the topic, in a pattern that will be familiar to us from, for instance, the climate apocalypse discourse of the 2010s. To give one example, from T. A. Rickard’s 1932 Man And Metals:

Civilization obviously is menaced by the misuse of the very products that were essential to its advancement. By aid of metals man made the tools by which he emerged from savagery and with which he constructed the machines that have given a bigger scope and a wider meaning to human life. From the dawn the digging of ore has played a leading part in the drama of humanity and no one has more cause than the miner to deplore the misuse of the products of his skillful toil. The sword was made before the plowshare, the spear was fashioned before the chisel. The maleficent use of metals has preceded the beneficent use of them. The perversity of mankind has turned a blessing into a curse. Shall we mend our ways or go with the Gadarene swine down the steep slope of perdition?

The history of mining, like all other history, thunders a warning. Those that live by the sword shall die by the sword. The Assyrian trampled upon the Egyptian, the Persian on the Assyrian, the Greek on the Persian, the Roman on the Greek. As they did in ancient times, so we, more civilized, as we deem ourselves, have done in later times. History is a philosophy that teaches by example. We have more examples than our predecessors; shall we heed them no better, more particularly the latest of them, which brought us to the very brink of perdition; or shall we too join the great discard of those that were weighed and found wanting? The finger of history, like that of Daniel before Belshazzar, bids us beware lest we too go the way of Nineveh, and our civilization, like its many proud forerunners, be destroyed by the forces it created but could not curb, by a demon it might invoke but could not exorcise.

Two things are worth noting in this passage. First, Rickard does not specify a particular weapon which will destroy civilization, only the general trend of destruction. Some men predicted that indiscriminate bombing of cities would be the weapon to destroy civilization, and in hindsight we must give them credit for foreseeing the shape of terror bombing even if they greatly overestimated its strategic effects. But this was the exception, and most discourse about modern weapons destroying civilization followed Rickard’s more general reasoning.

Second, the destruction of civilization is held out as an open question. Can civilization pull back from the brink? Can we figure out some way not to march together off the cliff? This was a live possibility. Unlike the “coal question,” these views did not stay isolated among intellectuals and futurists. This view spawned popular movements and drew statesmen to its banner. They created the League of Nations and the disarmament movement achieved the unprecedented Washington Naval Treaty, which limited the construction of warships among the major powers.

The science fiction author Olaf Stapledon grappled with the spiritual angst of the apocalypse he felt bearing down on civilization in his 1930 novel Last and First Men. In this book, the world’s great powers fight a series of wars with ever more destructive weapons. Eventually, the whole population of Europe is annihilated with poison gas. To avert an even more destructive war, the remaining nations unite in a single World State. This endures until coal supplies run out, at which point civilization disintegrates and man reverts to savage tribes. A hundred thousand years later, a new civilization emerges.

During a rebellion, their advanced technology causes a massive worldwide explosion that turns the planet into a lifeless volcanic wasteland and kills the whole human species, except a few dozen survivors who settle in now-tropical Siberia. Over millions of years, the planet slowly becomes inhabitable once again, and their descendants evolve into the posthuman “Second Men.” New species rise and fall and repeat the mistakes of the past. Finally, the Eighteenth Men—the Last Men—achieve an enlightened utopian society. This, too, begins to decay after a stellar disaster occurs and the Last Men realize that their civilization will eventually be destroyed by the Sun. In the introduction to the novel, Stapledon writes:

We all desire the future to turn out more happily than I have figured it. In particular we desire our present civilization to advance steadily toward some kind of Utopia. The thought that it may decay and collapse, and that all its spiritual treasure may be lost irrevocably, is repugnant to us. Yet this must be faced as at least a possibility. […] May this not happen! May the League of Nations, or some more strictly cosmopolitan authority, win through before it is too late! Yet let us find room in our minds and in our hearts for the thought that the whole enterprise of our race may be after all but a minor and unsuccessful episode in a vaster drama, which also perhaps may be tragic.

We can see statesmen grappling with the practical side of this problem in a 1932 disarmament debate in the British parliament. Frederick Seymour Cocks, like Rickard, fears the rising power of new weapons in aggregate:

If it fails now, the only alternative will be to arm. The nations will arm, and will embark upon another armed race, by land, by sea, and by air. Following upon that, as closely as a man is pursued by his own shadow, will come war, under the waters, in the air and on the land, until civilisation cracks beneath the strain and the Vesuvius of revolution opens out beneath our feet.

Clement Atlee, who would become Prime Minister in 1945, fears the bombing of cities in particular:

I believe that what we have to do is to try to build up a constructive internationalism, and I believe that the most fruitful suggestion which has been made in this regard is that relating to the internationalising of civil aviation … I do not believe that you have a defence against air warfare at the present time. I do not believe that you can restrain air forces as long as you have nationalised civil aviation, and I believe that, unless air warfare is restrained, civilisation will be wiped out.

Similar calls for world government over the supposedly apocalyptic technologies have also been a common feature in every subsequent techno-apocalypse prediction, be they caused by nuclear weapons or artificial intelligence. This debate is most famous for a speech by former Prime Minister of the United Kingdom Stanley Baldwin:

[T]here is, as has been most truly said, no way of complete disarmament except the abolition of flying. Now that, again, is impossible. We have never known mankind go back on a new invention. It might be a good thing for this world, as I have heard some of the most distinguished men in the Air Service say, if man had never learned to fly. But he has learned to fly, and there is no more important question, not only before this House, but before every man, woman and child in Europe, than: “What are we going to do with this power now we have got it?” … This is a question for the younger men far more than it is for us. They are the men who fly in the air. Future generations will fly in the air more and more. Few of my colleagues around me here, probably, will see another great war.… If the conscience of the young men should ever come to feel with regard to this one instrument that it is evil and should go, the thing will be done; but if they do not feel like that—well, as I say, the future is in their hands. But when the next war comes, and European civilisation is wiped out, as it will be and by no force more than by that force, then do not let them lay the blame upon the old men. Let them remember that they, they principally or they alone, are responsible for the terrors that have fallen upon the earth.

And then the next war came. The two decades of desperate searching for a way out ended in fire and rubble. London was bombed, and Dresden, and Tokyo. The logic of industrial production was turned to the mass extermination of civilians. The promised new weapons arrived, most notably the nuclear bomb.

It was not as bad as Baldwin and his colleagues expected. Civilization was not actually wiped out. It took about a decade for Tokyo, Hiroshima, and Nagasaki to recover to population levels higher than their prewar starting point. But the war was destructive enough that the doomsayers felt vindicated anyway. For most thinkers, it was no longer a question of whether man will destroy himself, but when.

This felt vindication is not well-grounded historically. World War II killed more people than any conflict in human history because industrial technology can support an enormous population. Apart from modern agricultural technology making everything happen at a larger scale, the destruction of World War II was well within historical precedent. As far as the fraction of the European population it killed, World War II was about as deadly as the Thirty Years’ War, fought with pikes and muskets in the 17th century. Both killed somewhere around seven percent of Europe, with deaths closer to forty percent in the hardest-hit regions. Both left the worst-off areas “post-apocalyptic” in the colloquial sense, but came nowhere close to destroying civilization in the way Rickard, Stapledon, and Baldwin feared.

In World War II, the advanced superweapons were strategically critical, the gas chambers and trainyards were morally horrifying, but almost all of the actual killing was done with bullets and artillery shells in ways that Napoleon would have found perfectly familiar. Probably less than ten percent of the war’s deaths came from the vaunted new weapons like tanks and aircraft. The reality is that industrial armies, with guns and bombs and concentration camps, have been less thorough than Mongols with horses and bows, who killed more people in Baghdad with their own hands than the Americans managed to kill by dropping atomic bombs from thirty thousand feet.

But for people who had imagined their technological superiority also made them morally superior to past civilizations, and who had long forgotten the last time their great powers fought a total war targeting civilians en masse, World War II felt like a near-apocalypse which came within a hair’s breadth of destroying civilization entirely. The illusion of rational progress “advanc[ing] steadily toward some kind of Utopia” was wounded by the First World War, and killed by the Second.

After the First World War, more people expected that some unknown future technology might destroy civilization at some unknowable future date. After the Second World War, more people expected that a specific identified technology would destroy civilization within their own lifetime, although which particular technology is supposedly on the cusp of destroying us has changed over time.

The Parade of Prophecies

The first technology which the public believed was about to destroy civilization was, of course, the nuclear bomb. In popular imagination, it was the sheer explosive force of destroying cities that would end civilization. Cooler heads realized blowing up major cities would merely kill an enormous number of people, but was not actually enough to end the world. Then, in 1950, the great nuclear physicist Leo Szilard popularized the idea of nuclear “cobalt bombs” deliberately designed to spread radioactive fallout which remains lethal for years rather than the usual days. This led to widespread fears that a nuclear war would permanently irradiate the Earth and destroy all life. Such a scenario was not well-grounded; weather spreads nuclear fallout throughout the atmosphere very unevenly, such that covering the entire globe is effectively impossible. No cobalt bombs were ever built, and popular fear of global irradiation persisted for a decade or two. Eventually it faded from the world’s attention, although the idea of a permanently radioactive nuclear wasteland remains vaguely in the cultural imagination, mostly because of depictions in fiction.

In the 1980s, the radiation poisoning apocalypse was succeeded by popular fear of “nuclear winter,” the idea that large-scale nuclear war would loft soot into the stratosphere, higher than the rain which could bring it back to Earth, and so persist for years while blocking sunlight, cooling the Earth, disrupting agriculture, and causing massive famines that kill billions, thereby ending civilization or even causing human extinction. This was based on speculative computer models of cities burning in enormous firestorms and further speculative models of how soot behaves and persists in the stratosphere. These models were invented largely by activist-scientists whose publicly declared aim was to terrify world leaders into believing nuclear weapons were a weapon too terrible to use and, unsurprisingly, their models have not held up well to empirical verification.

In the 1991 Gulf War, massive oil well fires did not produce the predicted lofting effect; and in 2017, when smoke from Canadian wildfires was lofted to the stratosphere, it dissipated much more quickly than predicted by the models used to forecast nuclear winter. By now, the public anxiety about nuclear winter has mostly faded away, partly because of the problems with the climate models, but mostly because the end of the Cold War and the activist campaigns for nuclear disarmament meant that there was no longer much media raising the issue in people’s minds.

While popular fears persist, there is currently no widely-accepted case that nuclear bombs will destroy civilization among experts. Perhaps a new model will arise to fill this niche in the next decade or two. The idea that nuclear bombs could kill a hundred million people, and it would be just another devastating war that gets recorded in the history books while people rebuild, seems unintuitive and even morally offensive to us. Something like that deserves the narrative weight of an apocalypse.

Of course, nuclear weapons are not the only technology that was supposed to have destroyed the world by now. The normal operation of industry at scale was going to cause the apocalypse as well. Like with nuclear weapons, the specifics have changed as individual mechanisms were disproved or simply fell out of fashion, because many people have a deeper spiritual conviction that large-scale industry ought to end the world. The 1968 publication of The Population Bomb by zoologist Paul Ehrlich popularized the theory that overpopulation, enabled by industrial-era advances in agriculture, sanitation, and medicine, would increase the population above the carrying capacity of the Earth and lead to mass starvation and collapse within the 1970s. Ehrlich would later be one of the chief organizers of the campaign to develop and spread the nuclear winter theory as well.

In 1972, The Limits To Growth book made the case for two additional mechanisms of industrial doom: resource depletion, as all possible sources of industrial inputs like aluminum and chromium are exhausted; and pollution, as exponentially increasing emissions of industrial waste poison the world and make it uninhabitable. The latter of these was especially influential, as fictional depictions of barren landscapes blighted by toxic sludge made the idea popular among the general public. In fact, the normal operation of industry was perfectly able to solve these problems. Food, metals, minerals, and fossil fuels are all far more abundant today than they were when these dire predictions were made, and this trend shows no sign of stopping.

The actual limits of matter and energy available to human civilizations are incomparably vaster than the doomsayers imagined, as far beyond modern civilization as modern civilization is beyond a primitive tribe worrying about using up all the flint for their arrowheads. Cleaning and preventing pollution proved even more tractable. Filtering and treating industrial waste required research and development to create air scrubbers, waste-to-energy incinerators, clay-lined landfills, and other such technologies. Deploying these across the industrial stack was reasonably expensive, but well within the means of an industrial society, and most wealthy countries mandated these improvements throughout the 1970s. The problems are now mostly solved. The cities are no longer choked by smog, the rivers are clean again, chlorofluorocarbons have been replaced with non-ozone-depleting substitutes, and acid rain is a thing of the past.

Much as the fears of nuclear apocalypse switched from radiation to nuclear winter when a new justification was needed, the ecological fears switched from pollution to global warming. In the late 1980s, ecological projections that carbon emissions would raise global temperatures by several degrees celsius and cause substantial disruptions to local climates and human life broke into mainstream awareness, where they were soon spun by activists, reporters, and fiction authors into widely-believed but scientifically baseless fears that warming would make the world literally uninhabitable. A series of dire predictions about coastal cities being submerged by melting polar ice have failed to materialize on schedule.

As atmospheric carbon capture technology is developed and rolled out in coming decades, it seems plausible that industrial technology may solve global warming and prevent even the moderate problems which would come from warming of several degrees celsius, but this remains somewhat speculative. If not, the climate disruptions will fall far short of the “world literally on fire” messaging popular in the 2010s. In the last couple of years, it seems as if the wind has gone out of the sails of the global warming apocalypse narrative, and few people remain emotionally invested the way they were even in 2021. Like the other fears of technological doom, it is not that the core arguments were explicitly refuted in the public mind, but rather that people eventually got tired of waiting for an apocalypse that never arrived.

The latest popular doomsday scenario is artificial intelligence, which exploded into public consciousness after the creation of compelling generative artificial intelligence chatbots, especially ChatGPT in 2022. In addition to the inchoate fears of economic obsolescence and the rise of new magnates disrupting the political balance of power that accompany every major new technology, there is also fear that the AI itself will soon exterminate humanity. The most influential and organized activists advancing these views trace to the work of Eliezer Yudkowsky, where he posits a sufficiently intelligent artificial mind would, by default, exterminate humanity as it pursues its own inhuman goals, unless the extremely difficult research problem of aligning its goals exactly to human goals succeeds completely on the first try, in which case the AI would instead create a permanent utopia.

What these “AI Doomers” provide for this narrative is not so much an argument that superhuman AI is dangerous. Futurists and philosophers have speculated about artificial minds supplanting humanity since before the electronic computer was invented. The idea that intelligent nonhuman minds are in natural competition with humanity is obvious and very common, and has motivated stories of robot uprisings since R.U.R. (Rossum’s Universal Robots) in 1920.

Rather, the modern “AI Doomers” provide arguments and intellectual authority for the claim that artificial general intelligence is happening soon, that computer minds pursuing their own interests without the need for human direction are a scientifically respectable possibility, rather than a fiction trope like time travel or alien invaders, that GPT and its derivatives are one of the last steps on the path towards building Man’s successor. There have been past waves of enthusiasm when specialists in artificial intelligence believed they were close to building general intelligence only to be disappointed as the field plunged into “AI winter,” but these did not achieve adoption beyond technical specialists and inveterate futurists. Yudkowsky’s predictions of superintelligence’s imminent creation were the first to achieve wider reach, which ironically inspired Sam Altman, Elon Musk, Dario Amodei, and other businessmen, researchers, and investors to create the AI labs that are now advancing the state of the art.

There have also been plenty of techno-apocalyptic scenarios which remained relatively niche and did not spark visceral public anxiety—bioengineered pandemics, electromagnetic pulse weapons, nanotechnological self-replicating “gray goo,” exotic physics disasters from experimental particle accelerators, the “clathrate gun hypothesis” that moderate global warming would set off a runaway feedback loop and overcook the planet, and more. Partly because these do not fit as neatly into the ideology of industrial civilization being destroyed by its own hubris, and partly because of which causes the most competent activists have chosen to push, these have not achieved the scale or the emotional resonance of the most successful apocalypse narratives.

The Hypothetical AI Apocalypse

In each case that achieves popularity, the pattern is mostly the same. First, intellectuals describe a phenomenon that, if taken to its maximum conceivable extreme, would destroy civilization or the entire human species. Usually this speculation is logically sound, within the limits of its clearly-defined assumptions. There exists some threshold of global warming that would cause human extinction, even if 5 °C would not suffice; dusting the entire Earth with radioactive cobalt-60 would kill all humans outside of shelters if it could somehow be arranged; running out of coal in the 1860s would have shut down industrial society before it learned how to make solar panels or fission reactors. This work attracts the attention of intellectuals and futuristic types, but not the general public.

Second, some of the intellectuals make a public case painting a vivid picture of how the apocalypse could happen within a decade or two in order to engage the popular imagination. This uses looser arguments with poorly justified or unfounded assumptions, although the intellectually rigorous people who first develop the looser arguments—such as in The Limits To Growth—specify that it’s just a model, or an illustration, or one way that things could plausibly go, and “of that day and hour no one knows.” They are careful to make their assumptions explicit and highlight which steps of the argument they place less weight on. Some of the intellectuals come to viscerally believe that the apocalypse will be soon, for reasons which are only partially based on their explicit arguments, and partly due to subterranean matters of ideology and psychological pressures. Even so, the most careful and scrupulous are aware of what their arguments do not prove, and avoid saying that their worst fears will happen, speaking only of possible scenarios and models worth considering—which incidentally means they are never publicly proven wrong when the fears do not materialize.

For now, such qualifications are the norm in predictions of AI doom from the movement’s intellectual core. For example, in 2020, when Ajeya Cotra published a lengthy analysis of when “transformative AI” (TAI) is supposed to arrive based on claimed analogies to biological intelligence, the report assumed AI development would follow what it admitted was a “relatively unrealistic path to TAI because it is simpler to analyze.” Or in 2025, when the authors of the influential popularizer “AI 2027” received criticism from people who believed the title meant they were predicting the AI apocalypse would come in 2027, they were quick to place an addendum at the top:

to prevent misunderstandings: we don’t know exactly when AGI will be built. 2027 was our modal (most likely) year at the time of publication, our medians were somewhat longer. Specifically, our medians ranged from 2028 to 2032. When AI 2027 was first published we explained this in Footnote 1 as above, but to make our views more clear we have added a clarification to the foreword text.

Yudkowsky himself has gone even further and denounced the entire practice of making up “AI timelines” for when the superhuman AI will arrive and presumably exterminate humanity, and he makes a point of not giving specific dates.

In every case, from interwar fears of runaway militarism to today’s fears of superintelligent AI takeover, the shuffle works by making an unjustified leap from the claim that the apocalypse is possible in principle to the claim that it’s happening soon. Practical difficulties are skated over or assumed away. Imagined scenarios and wild assumptions are fed into great quantitative models to produce very serious reports. The principle of “garbage in, garbage out” means that these have no value whatsoever for predicting the future. Strip away the leaps of logic and the illustrative fictions and the load-bearing assumptions made to “simplify analysis” and the calculations of irrelevant numbers, and the core remains that a coordinated cadre of well-studied experts just kind of feel a vibe that it’s happening soon.

But the trappings of mathematized analysis can be very persuasive anyway. If you grab a pile of numbers and put them in a graph, then most readers will be dazzled by the appearance of mathematical rigor—that’s a lot of calculations!—and only a minority will ask, “Hold on, where exactly did this graph come from? What’s the argument that these numbers have any causal relationship at all with predicting when the world is gonna end?” For example, prominent AI Doomers like Katja Grace put a great deal of effort into collating software researchers’ predictions about when superhuman AI will arrive. But of course there is no reason to believe that such a big pile of predictions can tell us anything at all about the reality of such an event. There has been a great deal of research into when aggregating forecasts is a useful exercise and when it is not, and as Karger, Atanasov, and Tetlock argue, predictions of events like the arrival of superintelligent AI are not informative.

Such esoteric points may be important for the most engaged and sophisticated intellectuals, but they have little impact on how the public receives an apocalypse narrative. The fact that intellectually respectable technical experts lend their names to the cause is important, while the details of their opinions are lost in a long game of telephone. Activists and journalists and fiction authors spread the scenario, altering and simplifying it as they do. The intellectuals’ caveats are left by the wayside, sometimes because the popularizers only understand the vivid story and do not understand an argument structure filled with careful conditionals, and often because they want to tell a more sensational story, as in environmentalist disaster media like Waterworld (1995) or Wall-E (2008). Many people believe this story. They might experience fear and anxiety, or donate money to professional activists, or forgo having children, or stop saving for retirement, or respond in any number of ways. Popular fear of the AI apocalypse is only recently mainstream. It remains to be seen how widely it will spread.

People give an apocalypse about twenty or thirty years to arrive. After that, most lose interest, leaving behind only a small residue of ideological holdouts, and a wake of science fiction settings that feel increasingly fantastical rather than urgent. There is no big finale to the debate, there is no explicit acknowledgement that the apocalypse is cancelled. People simply wander away from the subject. This seems to be less because the core arguments get refuted with reason and evidence, and more because people slowly lose faith after enough of the activists’ predictions about worldwide famines in the 1970s, or cities flooded by 2015, do not come to pass.

In the realm of pure logic, the failure of the activists’ vivid pictures may have little bearing on the abstract case for doom advanced by the more rigorous thinkers, but few people make the distinction—with some justice, as the highbrow intellectuals are generally happy to lend their intellectual authority to the popularizations. Perhaps the most notorious example is the Bulletin of the Atomic Scientists, founded by Albert Einstein and Manhattan Project scientists, which created the Doomsday Clock—initially set at seven minutes to “midnight” in 1947—to publicize the imminent nuclear apocalypse by laundering the scientists’ subjective feeling of doom into an annual media spectacle. The Doomsday Clock first cited the risk of AI destroying humanity in 2024.

It’s possible that industrial technology will destroy civilization at some point. But the technology of 2026 does not seem especially close. However, the basic observation formulated after World War I—that technology’s ability to destroy increases over time—remains sound. Surely we are closer today than in 1918. This observation does not tell us whether we should fear a techno-apocalypse in five years, or fifty years, or five hundred years. It does suggest it’s worth paying attention to individual apocalypse predictions and evaluating them on their technical merits.

But as we consider them, we must remember that there is great popular and political demand for techno-apocalypse stories. Since World War I and especially after World War II, the mainstream view in our civilization is that Man’s nature is to destroy himself with technology. There have been many attempts to fill in the details with specific apocalypse scenarios. Many tales of extinction and collapse become accepted among the vanguard intellectuals and the public even though the argument for doom has gaping holes. There is a huge audience for playing up reasonably large problems as though they are the literal end of the world. There is a huge audience for talking about speculative future disasters as though the default course is for them to happen very soon. Before we believe the latest prophecy of doom, we must remember to hold out for a full argument which covers all of these steps. We cannot settle for most of an argument with critical steps handwaved away, no matter how many fancy graphs or prestigious names are attached.

Ben Landau-Taylor studies society and industry. You can follow him at @benlandautaylor.

This article by Rudyard William Lynch was published on Palladium Magazine on April 16, 2026.

One of the things I’ve heard Americans say again and again is that “we don’t have an accent”—a statement which suggests that our culture is homogeneous and nondescript. This is a profoundly annoying and difficult thing for an anthropologist to parcel given that with each culture you have to figure out its underlying logical structure. This is something that cultures overtly share through a variety of things such as their religion, media, childrearing, literature, philosophy, art, fashion, military, and everything else. Cultures operate through a series of statements about how the world works and who their people are that unconsciously permeate the entire nature of that society. A culture simply cannot help but exist. There must always be an underlying operating system which relates to the lived experience of the people in a given area and its geography, living conditions, ethnic breakdown, and history.

It is truly remarkable that people, even very educated Americans, know almost nothing about the peoples of America. America is a fairly diverse nation even by the standards of continent-sized countries. Regional diversity is baked into America’s governmental structure in a way that is often not the case for countries formed around their capital city, such as France forming around Paris and Russia around Moscow, which are prominent in the Old World. Until the time of the U.S. Civil War, Americans frequently referred to their nation as “these United States,” for they saw it as a series of distinct nations rather than a singular country. The Founders established America purposefully so that the highly distinct regional cultures, which were seen as independent nations, would have as much functional legal independence through the state structure as possible.

Anthropological metrics show that the cultural differences inside America exist at the same scale as between European countries. The cultural distance between a Pennsylvanian and a Californian is roughly equivalent as that from a German to a Pole. The political and worldview differences between a Californian and Texan are greater than those between a Greek and an Italian. The genetic differences between British Americans from the North and South are large enough that they are comparable to what we would expect were there a mountain range or desolate desert on the Mason-Dixon line. To understand how these vast differences emerged, we shall look at the history and development of the peoples of America through their distinct geographic, ancestral, and ethnic contexts. These are the three metrics which I think would best allow the integration of America’s identity as a people, not just its borders and land.

The Geography of Culture

There are two main geographic gradients in North America. One is cold to warm weather, from the tropical jungles of Florida to the frozen wastes of Alaska. The other is wet to dry, between the temperate rainforests of Washington State and the moon-like Mojave Desert in California. America is the only nation on Earth which has each of the main geographic biomes from deserts to forests, tropics, tundra, and grasslands. North America rarely has clean breaks in the climate and you can see this manifest in the culture, where there are few sharp borders between American subregions.

In this way America is much like China, another continent-sized nation with real but muddied regional differences, that coincidentally spans nearly the same latitudes as the lower 48 states. Beijing sits at the 40th parallel, like Philadelphia, Harbin in Manchuria is at Quebec’s, New Orleans with Shanghai, and Hong Kong at Havana. It is more than coincidence that both potential twenty-first century powers have long ago expanded to fill their region’s entire available temperate zone. In this way both are unlike Europe, where countries with distinct regional cultures match the shape of the changing landscape. Perhaps, had the Roman Empire survived, the geographic distinctions between Gaul and North Africa would be similarly muted by an empire that endured until the present. This is America.

People in Pennsylvania, a clearly Northern state, often have glimmers of Southern accents and culture. Meanwhile, Northern culture has made inroads into every region of the South. A great irony is that “General American” culture; the accents, food, housing, ways of life, and class relations which are considered quintessentially American are from Pennsylvania, Upstate New York and Ohio, where the region had a decisive advantage due to the Industrial Revolution. However, as a person from this region, they now feel completely culturally alienated and isolated from the originally “General American” institutions in places like Wall Street, Hollywood, and Silicon Valley. These were initially manifestations of the rural Anglo culture and have since become their own, often with explicitly Anti-American interests, especially the peoples and regions it originated in.

Political analysts often observe that America has perfect geography, but they forget how much technology has made the North American continent significantly more habitable than it was centuries ago. Between the malarial swamplands of the south, the desert plains and mountains of the west, and the biting cold of the north, much of America had to be made artificially hospitable to host a large civilization. As an example, John McNeill’s book Mosquito Empires, explains how the defining factor in the evolution of the American South was disease; unsurprising when one considers how it sits at the same latitude as North Africa, with Austin, Texas and Cairo, Egypt at equal distances from the equator. Diseases such as yellow fever, malaria, dengue, and sleeping sickness were truly horrible in the coastal regions of the American South, often culling sixty to eighty percent of newcomers within six months of their arrival. The region that was liable to these tropical illnesses was surprisingly large, stretching all the way up to the Chesapeake Bay, and being often deadly for Europeans, with Philadelphia facing several yellow fever outbreaks. McNeill developed the concept of a region called the Greater Caribbean, which stretched from Baltimore to Rio de Janeiro, and was the region where these tropical illnesses were the defining variable shaping society.

Life in the South demanded adaptation to a novel subtropical climate. The American South has a climate similar to India, which developed their own convergent caste system due to its Northern conquerors four thousand years ago. Records from the Colonial and Early Republic periods talk about how British migrants to the region experienced periodic panics at the mass deaths from disease in the first few years. The summer heat was seen as all-consuming, and helped create the aristocratic culture which scorned the Northern bourgeoisie as “ungentlemanly.”

It is hard to overstate how much disease influenced the development of the American South, a nebulous region that stretches from where I grew up, the Mason-Dixon line out west, through the Lower Midwestern states until it hits the Great Plains. Driving across what is essentially an ethnic divide between the Northern and Southern states that cuts through Maryland and Virginia, there’s a profound and palpable difference in architecture, climate, plants, and people on each side of that line.

An interesting exception is the southern half of Florida, as the region was an uninhabitable swampland until technology became sufficiently advanced in the mid-twentieth century to drain the swamp and wipe out the diseases, turning the land into a viable home. Given that this occurred in what could be called the South’s century of humiliation, between the Civil War and the end of Jim Crow, Florida was populated by Northern, especially New York, interests. Florida is today a strange slapdash state, the northern half acting as an extension of the Deep South, while further down the peninsula is occupied by migrants from the North-East, Midwest, and the most prominent city Miami having been reshaped by Cuban refugees, that today has grown into the capital of Latin America which happens to be on U.S. soil.

The reason Americans imported slaves from Africa was because these populations had developed genetic adaptations to tropical illnesses that originated in Africa. One example is the genetic mutation that causes sickle cell anemia, which likely contributed to and spread with the Bantu migrations four thousand years ago across Africa from Cameroon. African Americans form about twelve percent of America’s population today, and were almost entirely brought over as slaves during the 18th century, with the ancestor of the average African American coming over around 1750, compared to the average European ancestor around 1790.

The largest place of origin in Africa for Black Americans depends on subregion, which follows from how even the South operated as a series of independent nations during the Colonial period. The Chesapeake Bay was populated mostly by slaves from Nigeria, especially the Igbo and Yoruba peoples. This was because the Chesapeake’s main crop of tobacco was not profitable enough to buy more expensive slaves, given that the Igbo and Yoruba were known as the most freedom-loving people on Africa’s western shore. Senegalese herder peoples, like the Wolof, predominated in parts of Maryland and Louisiana, partly due to the French colony there and because the nomadic warrior peoples were known as difficult to control. Meanwhile, the Deep South cultivated more expensive crops like rice, indigo, cotton and sugar, allowing them to purchase more costly slaves from Angola and the Kongo. The Kongo was a totalitarian state where the king treated his subjects basically as slaves already, so they were cowed enough to be known as high quality slaves to the Europeans. The largest nation of origin for African Americans’ ancestry is Nigeria, constituting roughly one third of the total population.

There is a critical tension in America between the geographic regions which are best seen as extensions of a European civilization, which succeeded at subduing the land given it was similar enough to their European homes, and those in which the land successfully broke their culture from its European frame. Examples of the first are the British offshoot cultures we’ll talk about in the next segment, which dominate most of the Eastern part of the continent, and then a very prime but early example of a culture molded around the land is the American Deep South.

The Deep South started around Charleston, South Carolina in the 17th century due to the cultivation of luxury crops. Unlike the rest of the American East Coast, which was populated by distinct British subcultures who successfully crossed the Atlantic, the Deep South was settled by White Barbadians. In the 17th century the island of Barbados contained mostly Europeans who settled there to grow tobacco, however the later introduction of sugar crops caused them to migrate to the American South. The Deep South was the only region of the country that had a majority Black population, somewhere between sixty and eighty percent before the African American migrations North during the 20th century.

The new European Barbadian occupants subsequently became quite martial and aggressive, cultivating an almost Nietzschean “master morality,” as a way to keep down the massive Black population. This has consistently been the most militaristic and combative region in American history, as the looming threat of a Haiti-like race revolt was always lurking. That being said, the Deep South became a distinct synthesis of African and European elements where Blacks and Whites lived in profound proximity with utter brutality. The book The World They Made Together details the African influence on White Southern culture, touching on everything from food and religion to architecture, clothing, and childrearing practices. This merging of cultures went on to shape much of modern America, eventually giving rise to new music genres, cuisines, fashion trends, and colloquialisms.

East of the 100th parallel it’s easier to pretend that America is the same as Europe, as it shares much of the same plants, climate, animals, and agriculture. However, west of that line it becomes impossible. The land of the American West borders on mythical, from the purple clouds of Texas to the purple mountains of Arizona. There are redwood trees that are thousands of years old, the extraterrestrial landscape of Death Valley, the colossal Rocky Mountains, and massive herds of buffalo who roam the seemingly infinite Great Plains. There’s an interesting argument that the American Empire, which later spanned nearly every continent, was first built in order to secure the sheer scale of the American West. It’s truly a wonder that America from sea to shining sea is a single nation, and it could only have been formed in that particular moment of history when energetic Europeans had a staggering technological advantage over the local population. Penetrating across the west took profound will, and it is truly remarkable that America reached from Pittsburgh to San Francisco in a sixty year period from 1790-1850. If you look at a genetic map of America you will find that the British-derived cultures which stem back to the East Coast wither at the 100th parallel, gradually replaced by the new cultures of the American West. This is because the Easterners were unable to survive once they found themselves in a world without trees.

Due to the sheer scale of the American West, it has always been totally dependent on gargantuan infrastructure projects such as the transcontinental railroad or thousands of miles of canals and aqueducts, proving Americans to be engineers on the same scale as the Romans. Many cities in the American West simply could not exist without the greatest engineering in human history. Cities like Las Vegas or Phoenix support populations of millions hundreds of miles deep in the desert with highly fragile water supplies. Anglo-American culture never had to consider the problem of lack of water before this point, and you can see the ingenuity demonstrated in practices such as growing cotton in Arizona from limited aquifer water. Meanwhile, that incredibly water-intensive crop can be grown naturally in the American South without irrigation.

In much of the American West, the story of urbanization is the story of water. In states like Texas, Arizona, California, and Utah, lack of water is the primary bottleneck to their success and is thus fought over tooth and claw by special interests. The Great Plains were populated in an unusually wet period of the late 19th century, which coincided with settlement, causing scientists at the time to theorize that “rain follows the plow.” The severe storms of the Dust Bowl that followed in the 1930s dramatically falsified this hypothesis, as well as driving a mass migration to the West to escape the Great Plains. To this day some parts of this region have barely a third of the population they did in the 1920s.

Total dependence on these enormous infrastructure projects has become the dominant variable in the success of the American West. One manifestation of this is how the megacorporation has become a feature of the West since the 19th century, as these grand infrastructure projects gave the corporations who built them enormous monopoly power that has potential for exploitation. These important corporations in the American West have often engaged in demographic transfer when it suits their interests. For example, the purposeful importation of Scandinavians into the Upper Midwest to populate the northern rail networks. A similar process occurred in central Canada with Romanians and Ukrainians. The West Coast agricultural sector encouraged mass migration of Midwesterners in the early 20th century. In recent history, large corporations have furthered the demographic replacement of the South-West with migrants from Latin America.

This is, in effect, a society where the infrastructure projects are important enough that they become a variable in their own right, which supersedes the organic culture. This is very rare historically, where most societies are quite geographically or ethnically set in stone. The American West’s main political gripe in the late 19th and early 20th century was that the railroad and mining companies had enormous monopolistic influence over trade in the region, which they often used to exploit it. This was a large reason for the rise of Progressive populism in the Upper Midwest and West around 1900, with William Jennings Bryan as a popular leader.

All of the cultural trends of the American West have been especially pronounced in the Intermountain West between the Rockies and the Sierra Nevada. The Intermountain West is a funny region much like Asia, with a gradient of individually distinct cultures with similarities all of which are reactions to their environment. The first example are the Mormons of Utah and the neighboring states, which migrated into the region in the mid 19th century from New England and the Upper Midwest. They are genetically similar to those populations and organized around the eccentric spiritual figure Joseph Smith, who created a religion that guided the Mormons to the Great Salt Lake in hopes of establishing an independent nation named Deseret to be a new city on the hill, much as their Calvinist ancestors fled from England to New England. In the 19th century the central Mormon church would strategically orchestrate its settlement patterns in advance. The Mormons dealt with a geography like the Middle East by creating a Middle Eastern-style theocracy. They have many similarities with the Ancient Israelites: being seen as a cult by outsiders, purposefully strange beliefs to isolate them, high in-group preference, strong religiosity, high birth rates, high educational attainment, and a unique moral character.

Contrasted with the conservative Mormons, the other polarity of the American West is the degeneracy that comes from an utterly artificial environment in which older cultures can’t survive. One example of this are the people who were locally called “Desert Trash” and learned to view it with pride. They form an almost Scots-Irish Appalachian ethic of resistance to power, individuality, and eccentricity. However, they’ve traded murder ballads, clan lineages, and honor codes for Instagram, UFOs, New Age spirituality and suburban houses with white interiors. Arizona and Nevada are the top Desert Trash states, but the culture ripples from internal California and the deserts of Oregon to the Rocky Mountains’ ridges. If modern civilization ever collapses, they are the ones most likely to form a new almost Mongol culture to rebuild the region.

Another environmental response is seen in California, today an artificial culture which was once considered to be a distant Eden too inaccessible for Europeans to reach without profound struggle. It was a backwater of a backwater for the Spanish Empire, where the Franciscan fathers enslaved the Natives. The Spanish were the original settlers of the American South-West and even parts of Florida, but by the time the Anglo-Americans conquered it they were declining as an empire and had only a few thousand people in the region. The exception here is New Mexico, which stands alone in the American South-West as a more Latino-Native American derived culture based off of the medieval Puebla culture which made enormous megaliths like the one in Chaco Canyon. They along with the Mexicans faced countless declines and imperial collapses due to water issues.

Within months of the U.S. conquering California, Americans immediately found gold which the Hispanics had missed for the past eighty years. California has always carried the mark of the Booster; a certain archetype that today would be promoting nootropics and NFTs, but in the 19th century built towns, states, and businesses, constantly hustling and selling across the continent. The founders of California were scammers, gold miners, pirates, entrepreneurs, slavers, and adventurers. This has made the Golden State the cutting edge of Western civilization and the most important place on Earth in the latter part of the 20th century. At the same time, it has made the region prone to foolishness and degeneracy, which was further promulgated by the region’s staggeringly rapid rise to wealth in industries which do not touch physical reality, such as technology, media, and government military contracts.

This separation from reality has created a fantastical society of wizards bending reality to their will, who have gone mad from their power and are now set on destroying both their own culture and the world. The Edenic California of the 20th century no longer exists, and has been replaced by a cross between Blade Runner and the degeneracy of ancient atheists’ multicultural Babylon or Rome. California is the story of a society that has everything—wealth, genius, perfect geography, strong political institutions, and military protection—brought down by the one thing it does not have: a functioning culture, which later bled into its entire political, economic, and demographic success.

Keep in mind that most of the American West as a whole was profoundly lightly populated until recently. One of my good friends is a native Washingtonian and another an old stock Californian, and both feel that the places they grew up have now been irrevocably destroyed. They belonged to the old cowboy Western culture, while its new inhabitants are trying to build a post-Christian, post-Western, possibly transhumanist society, which we see manifest most clearly with Silicon Valley’s various strange ideologies.

The Pacific Northwest is a strange example of a fusion civilization that was never able to integrate either elements of its parent societies, while also not developing its own personality distinct from its politics. The Pacific Northwest is a mix of Yankees: there was a coin toss to determine if Portland, Oregon would be named after Portland, Maine or Boston, Massachusetts. However, it contains many similarities to the rest of the West Coast and California. The climate here is surprisingly like Western Europe, and the mix of New England and California has created potentially the most left-wing place on earth, where politics are used as a surrogate for their ancestor’s religious fanaticism.

As a final example of how American geography affects its culture we have the original White Anglo inhabitant of the West, the one who used to be synonymous with it but has since been forgotten, that being the cowboy. It’s hard to underestimate the effect of the cowboy on the American character, especially our perception outside the U.S. The cowboy speaks to something that exists in America and in very few other places: the self-controlled individual who is not dependent on society. This has very deep roots in our psyche stemming back to the Aryan steppelands and later the Vikings, Crusaders, and age of European colonialism. There is something in the Faustian European man, which only still exists in America, that desires to wander and test the boundaries of sociability. The American cowboy is a mix of African, Native American, and Spanish influences on people whose ancestors were shepherds on the fringes of the British Isles’ herding cultures. Starting in South Texas, the cowboy is a manifestation of the European reaching back to his ancestral environment of the Eurasian steppe as a reaction to a region that was not farmable until the late 19th century, due to the soil being so deep it would break plows and far too many buffalo. The true age of the cowboy lasted only twenty years after the U.S. civil war, until technological innovations such as a new type of plow, barbed wire to keep the cows out, and the invention of the revolver to defeat the Natives, amongst others, allowed the Great Plains to be settled. Regardless, the cowboy tapped into an underlying nature of the American West’s land: a climate like the Eurasian steppe will produce men like the Mongols or Huns, as Natives like the Apache, Comanche, or Sioux demonstrated.

Ancestral Seeds

It’s a consistent thread in American history that so many of its battles are the Old World’s writ large, on the scale of a new continent with new backgrounds. One of my favorite historians, Leland Dewitt Baldwin, called America a side effect of the English Civil War. This has been a continuous thread in American history until the present, which we have collectively blocked out. For example, the hatred between the North and South, which traces back to Britain, is still alive and well today. Ask any self-respecting Bostonian or Alabamian about the other and you’ll see a loathing that goes deeper than Enlightenment reason. I’m of half Irish and half British ancestry, and my grandparents were not happy about my parents’ Protestant-Catholic marriage in the 1990s. We had the marriage in a Protestant church overseen by a Catholic priest to make sure both ancestral gods would be happy about the union. Even today, the different British regional cultures which populated America are totally evident in voting patterns, architecture, religion, dialect, values, and any other signal that would demonstrate a distinct culture anthropologically. These different genetic groups, often stemming back to different subregions of the British Isles, are totally apparent in America, with about sixty percent of Americans having British heritage.

Inside the British Isles there has always been the struggle between the core and the fringe. This divide cuts across any straightforward categorization, but as a vague rule of thumb: the interior was more ordered, rational, egalitarian, bureaucratic, Germanic, and Protestant; the exterior was more wild, mystic, hierarchical, traditional, Celtic, and mythic. If you look at the English Civil War which occurred at America’s inception, the feudal West of England supported the closeted Catholic Stuart Scottish line and the capitalist East supported the Puritan-dominated Parliament. There’s an interesting book called The Cousins’ Wars by Kevin Philips which looks at how the English and American civil wars were largely the same conflict. It is truly eerie that when you map the English Civil war onto the American, you’ll find that on an incredibly granular level the same bloodlines fought the same battles. In both conflicts the Puritan line which led to the Northern ice sheet burned the Cavaliers who fought for warm and civil Wessex and Dixie.

The book Albion’s Seed by David Hackett Fischer is a brilliant analysis of how the British regional migrations to America seeded the current American Eastern cultures that stretch from the East Coast to the Great Plains. The first major migration was from Puritan Eastern England to New England in the early 17th century during their persecution by the King. Much like the Icelanders fleeing the king of Norway’s tyranny in the 9th century, the Puritans aspired to make a city on a hill in which they could build an earthly Calvinist utopia. The Puritans belonged to potentially the most radical sect of Christians ever. They would fine people for not working on Christmas, for arguing with their wife too loudly, and for swearing. The Puritans were the most intelligent, industrious, and capable English of that era, but this came at the cost of profound psychological and spiritual neuroses which have affected this region ever since. If you look at New England’s character it is clearly a theocracy, whether the religion is Progressive or Calvinist, where the priests take leadership over businessmen or nobility.

New England was a nearly totally homogenous ethnostate for most of American history, with this singular type of person most prominent. This resulted in New England being the single society in the world that scored highest on practically every human development statistic, as detailed by Fischer. They were the most equal, literate, skilled, agentic, and technologically advanced population in the world. Yankees, as the descendents of the Puritans were called, sailed as pirates all the way to Madagascar, sold furs to the Chinese, staged a coup in Hawaii, and hunted for whales in Siberia and Antarctica. They were the most capable group of Americans, ever. They populated a whole tier of the continent through dominating the American side of the Great Lakes region and then sailing over to Deseret (now Utah) as well as the Pacific shore by way of Antarctica. They averaged ten children per household in this colonial period, and so like rabbits entering an island with no natural predators, this fairly small group of Puritans has ballooned to tens of millions of descendents. Keep in mind that each of these four British groups have populations of tens of millions, often more than entire European countries.

A majority of non-Hispanic White American ancestry is of British origin, which is something that has been written out of our cultural narrative. However, this great Yankee era, in which they finally defeated their nemesis, the Deep South, came with a staggering psychic and spiritual toll. After their religion died, most of New England became nearly empty forest while their diasporas assimilated into local cultures. New England started to commit suicide as a society, building all of its social institutions around the destruction of its own culture. When one of its greatest writers, H.P. Lovecraft, wrote of decayed, haunted, exotic New England fishing towns lost to their past, he was speaking to a long-term decline of Yankee America that had already occurred by the 20th century. Their victories since have been in the form of the nihilistic managerial class, which has come at an enormous cost: their cultural identity and individual spirit.

The other side of the mutual failure of the English Civil War was the Cavaliers, who after the Puritans installed a military dictatorship in England at the end of the war, fled to Virginia and the Chesapeake to escape to a place where they could be loyal to their king. They tried to establish an aristocratic culture there that could replicate what they had back in Europe, however there were not enough European settlers to be their serfs, and so they brought in slaves from Africa. David Hackett Fischer has said that the culture of the American South dates back to England, and that Africans were merely brought in to support it. The 19th century novel North and South by Elizabeth Gaskell speaks of a South of England, formerly known as the Anglo-Saxon kingdom of Wessex, that was known for being genteel, slow moving, kind, hierarchical, bigoted, and conservative; this is word-for-word how people describe the American South. At its best, the American South, stemming originally from this Virginian mold which spread to the rest, would produce the greatest men in American history like Washington, Jefferson, Lee, and Madison. At its worst, it would produce the worst human development statistics in America and the horrors of slavery.

The South contained the only deep aspiration to nobility and high culture we have in America, while attempts by the Trans-Atlantic North-Eastern WASP were never able to organically integrate into America’s soil, and died by the late 20th century. Virginia and its cousins populated a truly huge region of America, accounting for more immigrants to the West than all the Northern States combined. Virginians founded Berkeley University in California, named after the father of Virginia and popularizer of slavery Sir William Berkeley, as well as the state of Texas, the Upper South, and the Southern Piedmont regions.

The third American culture was the other one that Elizabeth Gaskell spoke of in North and South: the diaspora from the English Rust Belt to the American Rust Belt. When she wrote of the North of England in the 19th century being a rough, manly, egalitarian, industrial, low class, mercantile, Darwinistic place, she could have been talking about the American Rust Belt just as easily. The modern English Rust Belt was once a desolate frontier region populated by Celts and Vikings, until a combination of the Quaker’s high trust and business acumen alongside the coal deposits found in Pennsylvania and the English Rust Belt created the Industrial Revolution.

The Quakers were instrumental in the foundation of the English and American banking systems as they had enough cultural trust to easily accumulate capital. Their immigration to America was facilitated by William Penns’ land grant in Pennsylvania, which caused him to encourage Quaker settlement there as it was one of the few places with religious freedom. Quakers, also known as the “Religious Society of Friends,” are a pacifist, meditation-based sect of Christianity, which was seen as profoundly radical in a world of mandatory churches and nobility. They established Pennsylvania and the neighboring areas as anarcho-capitalist societies which were as close to pure classical liberalism as possible, with total religious and economic freedom. This subsequently made the area one of the wealthiest and best developed places in the world for centuries after. The idea of the American “melting pot” originated in Pennsylvania, the one “diverse” region in colonial America due to its Scottish and German migrants.

The Quaker thread in America’s history has been largely forgotten, but is arguably the most important of these four lineages for creating the Middle-American character which came to dominate general American culture. In the Quaker diaspora, which spread out to Nebraska and Kansas, they succeeded at pollinating an open, friendly, hardworking, libertarian, yet communal and conservative society. The Quakers themselves were fairly demographically small, but their culture was very successful at creating “Friendlies” as allies, which gave them a stamp of approval in the middle tier of America’s culture. At its best, Quakerism can form some of the best societies in history. And at its worst is prone to profound naivety and paradoxical insularity: there is a reason both the English and American Rust Belts had some of the most rapid societal declines of any place on Earth in the last century.

The final of the Albion’s Seed cultures is the Borderers, or the Scots-Irish. They originated in Lowland Scotland, Northern England, and Northern Ireland. This culture was something very ancient to the British Isles, dating back to the closest thing to the original pre-Roman pre-literate, warrior clan societies. These peoples evolved in constant battle between different authorities, whether Scottish, English, or Irish, in a world where there was constant violence either between armies or proud warrior clans. We forget that into the 17th and 18th centuries these Celtic clan peoples had political authority and fought the centralized government. Bonnie Prince Charlie’s Scottish army at Culloden nearly took London in 1745.

This region suffered terrible poverty, and many who had migrated to Ireland to replace the local Irish were not satisfied with their new way of life under English landowners. This created a radically anarchist, libertarian culture which has kept America masculine, real, and tough while also producing the worst human development statistics of every kind among British Americans. Today, Appalachia is a poor, desolate, and dying place, while their cousins in Tennessee and Texas are part of the fastest growing and strongest places in America. The Scots-Irish culture is hard to kill, and provides the backbone of the modern conservative movement which is contrasted by centuries of murder ballads which went from being set in Durham to Knoxville. The Scots-Irish migrated across a huge area, mixing with the Cavaliers, a highly distinct honor culture in much of the Upper South. They started largely in Western Pennsylvania, went down the spine of the Appalachians to Georgia, and then west to Texas, Kansas, and even Oregon alongside most of the Lower Midwest. For a frame of reference: there are more Americans of Scots-Irish ancestry than there are African Americans, more people than nearly any nation in Europe.

For a brief example of modern influences of these ancestral cultures, Yankee-derived Americans tend to see the role of government as an active good to push their moral code. This is derived from their theocratic structure. In the Middle States, people like the Quakers and Scots-Irish believe the role of government is to further the individual as much as possible. Meanwhile, Cavalier descendants in the South see the role of the government as maintaining the traditional values and social structure of society. These beliefs started with fairly small British Early Modern cultures that transmuted into world-changing massive peoples who span continents. The world is quite strange isn’t it.

Ethnic Ties

Ethnicity in America is multifaceted, as the nation was formed fairly recently by settlers and slaves from elsewhere. It isn’t as straightforward as in nations like England or China, which had a certain people in a certain land for a long period of time. This gets to deep questions about America’s identity which have been argued for all of our history. However, there are multiple layers to this. Firstly, that ethnicity in America is still very real. Italian, German, Japanese, French, and African Americans are all immediately recognizable to a sufficiently skilled anthropologist, due to various telltale traits in almost all of their behaviors. Thomas Sowell’s book Ethnic America does a great job of examining how different sub-ethnicities of Americans have different histories informed by their home countries, occupations, skill levels, and other factors. This article won’t detail these, but rather examine regions of America where these ethnic differences become dominant and impossible to ignore, in order to understand them.

America’s genetics have in recent years been explored by a multitude of studies. I arrived at similar numbers before the genetic data came out by combining census results, migration statistics, and surname analysis. America is about three quarters European ancestry, even though non-Hispanic White Americans are quoted at less than sixty percent in the current (perhaps false) official statistics, given that two thirds of Hispanic American ancestry is of European origin. After Whites, Africans are about ten percent of America’s genetics, while all Asians constitute six percent. Ancestry from the British Isles is about one third of America’s total ancestry, with descendants from the United Kingdom being a majority among the non-Hispanic White population. German and a little bit of French make up fifteen percent of American ancestry, with a similar number of Spanish descendants.

There are a few regions of America where these ethnic identities have become prominent enough to overpower other variables. One of which is the South-West and Miami, which have in many ways become extensions of Latin America. Hispanics make up forty percent of both California and Texas’ population. This is both a very old and new thing. The first European settlers in this region were Spanish, but settled very lightly with only a few thousand people at most. The recent wave of Spanish speakers that has overtaken the South-West in the last forty years is causing one of the most rapid demographic shifts in human history.

The intensity of this demographic shift is paralleled only by the rapid rise of the European populations over the Native Americans in the 1800s. It’s hard to speak of the long-term consequences of the recent enormous migrations from the Third World to America, given that they have occurred within the last couple generations, which is not long enough to spot any anthropological trends. Perhaps they will integrate into American society, perhaps they will not. Maybe we will see mass deportations, or the replacement of the local populations. It is likely that the countryside will continue to outreproduce the cities, which will become “behavioral sinks”—a term coined by ethologist John B. Calhoun to describe the collapse of social behavior that occurs when a population is subjected to extreme overcrowding, rapidly giving way to full population collapse—a significantly more common trend than people think. However the future is yet to be decided, and is in our hands.

Another ethnic region is the Northern Plains, in which the British cultures were overtaken by Germans and Scandinavians. German Americans are one of the largest demographic groups in America, being the second largest European ethnicity after the English. German ancestry is prominent in a region stretching from Pennsylvania to Oregon, while comprising the vast majority of the population in the Northern Great Plains in states like Iowa, Wisconsin, and Nebraska. The Scandinavians populated the Dakotas and Minnesota by the Northern railroad, as Anglos didn’t want to live in the cold climate. The Northern plains have produced the highest human development statistics in America since the 20th century, similar to their Nordic home countries alongside cultural traits of social conservatism, economic productivity, relatively socialist politics, and the smothering Jante’s law of social conformity.

The final regions of America where you cannot separate ethnic identities from regional are the Catholic North-East and French Louisiana. Later Catholic immigrants like the Italians and Irish, but also Poles and French Canadians, migrated to the North-Eastern core of the American North-East, demographically taking over the region. You can see radical shifts in the trajectories of cities like New York, Boston, and Philadelphia when this occurred. The importance of Catholic migrants varied across the entire Rust Belt, and at times the whole continent. The Irish, who ultimately derive from a Celtic warrior culture, took over governments and institutions, like my ancestors who served in the police for five generations. The Italians worked as low-skill labor, coming over fifty years after the Irish around 1900. They worked themselves up through the corporate system to a higher income and social class than the Irish, with whom they mixed heavily.

The Catholic influence on the North-East made an indelible mark on the region, where local people are still very aware of White ethnic differences, to a degree that often shocks people in the rest of the country. Catholics often took over local governments and public-facing institutions, to the point that it’s easy to forget that in most of the North-East Anglos are still demographically predominant. Until recently Protestant Anglos also ran much of the economic and political power, leading them to culturally assimilate the Catholics. Throw on top of this massive recent migration from the Third World and the managerial-induced behavioral sink that turned against their own populations. In places like New Jersey, you can see many subcultures in a tiny area, from Irish to Black, Latinos, rural Anglos, upperclass WASPs, Indians, and many more. It gives the North-East a hard, cynical edge, but also a toughness not seen in the rest of the country.

A final culture that acts as a microcosm of all the American cultures is French Lousiania. It was established by French Canadians from modern Nova Scotia, who were evicted by the English in the War of Austrian Succession. They were from Poitou on the west coast of France originally, unlike the Quebecois from Normandy and Brittany. They sailed along the entire North American shore, being rejected by English settlers until they picked subtropical Louisiana where Acadian became Cajun. French Louisiana exists as the hollow shell of a greater French Canada that stretched to the Rockies, Arctic, Pittsburgh, and Texas before the Anglo conquest. Louisiana was the Blackest place in America during the colonial period, and is better seen as an extension of the French Caribbean, like Haiti, that was enveloped by another Caribbean-derived culture: the Deep South. Here are two highly specific European societies who generated their reactions to a similar region and climate. Both are distinctly American, but also profoundly different in many ways upon closer inspection.

Perhaps this is the story of America as a whole, of foreign peoples from different places wrestling, sometimes winning, sometimes losing against the land. However, in the process, they created a land unmistakably and distinctly their own.

Rudyard William Lynch is a historian and YouTuber best known for WhatifAltHist, which explores the dynamics of rising and falling civilizations through historical, philosophical, and anthropological analysis. He co-hosts History 102 and is based in Austin, Texas. You can follow him at @whatifalthist.