A vortex is indeed fascinating. It does three things at once. It draws loose matter toward a center. It reworks that matter according to its own rotational logic. Finally, it releases what remains back into the surrounding current. Matter is already changed by the end of the process. None of this is metaphor for its own sake. It is simply what a vortex does, mechanically, whether the matter pulled in is a leaf, a fishing net, or a shipping container. The vortex does not select for content. It only guarantees that whatever enters comes out differently than it went in.

This is a useful, and slightly uncomfortable, way to think about techno-politics as a field of analysis.

Consider what has happened to water management over the past several years. Irrigation scheduling, reservoir operation, and treatment-plant control were, for most of the twentieth century, engineering problems. Obviously governed by hydrology, agronomy, and municipal budgeting. They still are. But they are no longer only that. When programmable logic controllers at water utilities become targets of coordinated cyberattacks, or when agricultural AI systems start mediating irrigation decisions across entire regions, water stops being purely a resource-management question and becomes a question of critical-infrastructure exposure, foreign dependency, and strategic leverage. The underlying pipes and pumps have not changed. What changed is the logic through which they are now read.

The same absorption is visible everywhere one looks. Energy policy, once mostly a matter of grid engineering and market design, has been pulled into a vortex of supply-chain sovereignty and green-transition geopolitics. Agricultural policy has been pulled in through the AI systems now used for crop monitoring and yield optimization, turning food security into a question of who controls the models. Even historical and humanitarian subjects such as genocide accountability, disinformation governance, and the presence of extremist networks inside national militaries increasingly get folded into the same analytical field once they intersect with platforms, information infrastructure, or transnational coordination technology. None of these domains started as “techno-political.” Each was drawn in from elsewhere.

It is tempting to conclude that techno-politics is simply an expanding umbrella term, that “technology plus politics” is broad enough to eventually cover any subject, given enough time. But that conclusion misses what actually happens at the moment a domain enters the vortex. It is not merely relabeled. It is recoded. Three operations recur with striking consistency.

First, an object previously governed by technical or engineering criteria gets reframed as a strategic asset. Think about a water treatment plant. It stops being evaluated by throughput and starts being evaluated by attack surface.

Second, an actor previously understood as a sovereign, territorially bounded state gets reframed as one node among many. Who can deny that states are now competing with platform companies, infrastructure operators, and data-center consortia for effective control?

Third, a process previously judged by efficiency or output, by a simple industrial frame, gets reframed by its exposure to disruption. So, the question shifts from “how well does this work” to “who can make this stop working, and at what cost to whom.” Engineering logic gives way to security logic. Sovereignty logic gives way to network logic. Efficiency logic gives way to vulnerability logic. That triple substitution, not just the mere presence of a screen or an algorithm, is what actually defines the vortex’s center. When one thinks of Heidegger, it may be obvious, as it is, but living through it, experiencing it surely creates a distinct situation. It is obvious because “technology is a frame which affects how we understand nature and things” is an already accepted Heideggerian idea. Yet again, at first glance it is obvious that techno-politics is not offering a paradigm shift to be summed up as “once a water, now an energy source”. Techno-politics asks “who”, “whom,” and “why” questions. It perceives threats. It perceives opportunities. Rather than simple abstraction, it is a way of understanding and responding to life in its immediacy.

Maybe it is better if we retreat to our initial analogy. Arguing through connotations that are derived from analogies is unacceptable in a scholarly fashion. Yet it is undeniably effective. A vortex. Vortices are dynamic. They are turbulent, almost alive. Gestell, on the other hand, connotes stagnation. Yes, technology is still challenging nature and positions it as a resource to exploit. More precisely, it is a mode of revealing. It is mostly a fixed frame. Although there are objections, if something is only effective in revealing, it would never be effective on the essence, truly. By only revealing, it is not possible to talk about inevitable ontological consequences. Yet, the recoding function can still be observed in Heidegger’s work. Revealing something may be a certain way to shape one’s mind to understand that thing. Trying to imply something structurally ownerless is, on the other hand, to beat around the bush. In the end, gestell holds still for an unknown time while the vortex inside it never does.

Techno-politics and Heideggerian understanding of vortices and gestell drastically differ at this precise point. More accurately, at this point, techno-politics expands the Heideggerian approach. Techno-politics offers a dynamic understanding. It’s not a simple and unclaimed frame like Gestell is. It has a centre. It has a winner. It exists through obstacles, conflicts even. It does not simply reveal. It outright changes. Revealing an angle of something’s nature is indeed a way to shape perception, as we previously argued. Revealing in the Heideggerian sense isn’t an action performed on an already-there entity. It’s the event by which entities become able to show up as anything at all, even before the existence of any action, subject, or even objects. But changing is a whole different action. Changing presupposes a persisting thing that had property P, now has property P^1, and something, mostly a subject, sometimes another object that causes confrontation, did that to it. There is a before and after, cause-and-effect mechanism. A subject acting on an object, whether directly or through another object.

One cannot imagine techno-politics as a vortex without the concepts of power and confrontation between powers. Gestell, on the other hand, is painted as a car with no one behind the wheel. Although it is argued that the car is being driven by an anonymous and historical being, an account that explains everything through epochal turns explains nothing in particular. While techno-politics doesn’t just reveal that there is a car and leave it there. It argues that if there is a car, there should be a driver. There should be a reason that we have this car, who produced the car, how the car works, and even car crashes and their consequences. Techno-politics delves into a much broader context and complex relations. How the essence is changing, who is changing it, and most importantly: why are they changing it? Techno-politics defies an inert understanding. It forces one to think about agents, thus becoming more critical and contextual. To sum it up, techno-politics is expanding Heideggerian understanding of technology and making it more applicable.

Let us return to the main argument. As we already said, techno-politics has a recoding function. The recoding operation has a name in critical theory: a domain is first stripped of the logic that used to organize it, then rewritten according to a different one, before being released back into circulation under its new terms. What makes techno-politics distinctive is not that this happens. Since all major analytical paradigms do some version of it, it wouldn’t have any distinction or importance whatsoever. Techno-politics’ distinctiveness is something different and may be even terrifying. It is how consistently the same recoding logic gets applied across radically unrelated domains: water, food, law, energy, society, sex, information, even memory and historical justice. The vortex is powerful precisely because its recoding template is portable.

That portability is also the concept’s central risk. A framework capable of absorbing anything is, by the same token, at risk of explaining nothing in particular. If every subject can be shown to have a techno-political dimension, the term risks collapsing into a synonym for “modern life,” losing the discriminating power that made it useful in the first place. Political science has a name for this failure mode: “conceptual stretching”. It is a risk worth taking seriously. The defense against it is not to police what may or may not enter the vortex. Almost anything eventually can and eventually will. The defense is to insist, case by case, on naming the specific recoding: which logic was displaced, and which logic replaced it. Rather than resting on the observation that technology and politics are, once again, somehow both present. Yet again, naming specific recoding mostly means pacification of human action. Thus, human existence itself. Still, naming has a metaphysical effect nonetheless. So hopefully, naming the events will ultimately lead to the will and capacity to act realistically. That is what techno-politics does.

There is one last thing worth saying about vortices, and it cuts against the intuition that they are simply engines of destruction. A stable whirlpool is not chaos. It is a structure that persists precisely because material keeps flowing through it. Physicists call this kind of self-sustaining pattern a dissipative structure. It does not exist despite the constant inflow of new matter. It exists because of it. Techno-politics behaves the same way. It is not a fixed body of subject matter that occasionally gets new additions. It is a standing operation, kept alive by the continuous arrival of domains willing to be pulled in and rewritten. Water, energy, food, information, memory, this and that … The list will keep growing, not as an accident of scope creep, but because that is what a vortex does. The task for anyone using the concept seriously is not to catalogue everything it might one day contain, but to keep specifying, precisely, what it does to each thing the moment that thing goes in and, by doing so, pointing out a realistic frame for what to do with it.

For much of the twentieth century, strategic information superiority depended heavily on access. Reconnaissance satellites, interception systems, diplomatic reporting networks and specialised collection platforms allowed states to observe activity that remained invisible to almost everyone else. That hierarchy has not disappeared, but it is not the only hierarchy that matters.

Commercial satellite imagery can reveal deployments and infrastructure changes. AIS and ADS-B data can help reconstruct maritime and aviation activity. Social platforms provide eyewitness footage, while public registries, digital archives and geospatial tools expose relationships that once required specialised access to identify. Artificial intelligence can now search, translate and classify this material at a scale beyond unaided human analysis.

Observability itself has been partially redistributed. Strategic information now circulates through a mixed environment of public, commercial and classified sources. Advantage, therefore, depends not only on possessing information others cannot access, but also on recognising what matters within information available to many.

This offers a useful framework for understanding contemporary OSINT. Open-source intelligence is an assessment produced in response to an intelligence requirement. Collection is only the beginning. Verification, corroboration, contextualisation and analysis turn an observation into something that can support a judgment with an appropriate confidence level. A Telegram video, for example, acquires analytical value only after questions of geolocation, timing, provenance, recycling and independent corroboration have been addressed.

Intelligence under Conditions of Saturation

Many intelligence systems were designed around scarcity. Important information was difficult to obtain, so collection capability generated advantage. Contemporary crises often present the inverse problem. Thousands of videos, satellite images, tracking records, and competing claims can appear within hours. The constraint is increasingly the ability to process information without losing provenance, context, or analytical discipline.

Information abundance has not erased asymmetry. It has shifted where that asymmetry now sits. One institution may possess a large and sophisticated data repository but weak entity resolution, the ability to determine when different names, accounts, or identifiers refer to the same actor. Another may work with fewer sources yet combine regional expertise, source evaluation, and network analysis more effectively. The difference here is sense-making capacity.

This environment can itself be manipulated. Recycled imagery, coordinated accounts, false geolocation and selectively edited footage can saturate workflows, while algorithmic amplification rewards virality rather than relevance. Rigorous OSINT therefore requires alternative hypotheses, separate judgments about source reliability and information credibility, and a willingness to ask what evidence would disprove the preferred assessment. The observable layer is not the same as reality in full. Nor should the absence of open-source evidence be treated as evidence of absence.

Ukraine and the Narrowing of Opacity

The Russia-Ukraine war made this transformation unusually visible. Before the February 2022 invasion, commercial imagery, technology data and social-media analysis helped reveal Russian force concentrations and evolving posture outside classified channels. UK Strategic Command later described the fusion of these sources as a contribution to anticipatory intelligence, while stressing that open and secret sources were most valuable when used together.

After the invasion, satellite imagery converged with geolocated footage, Telegram posts, aircraft and vessel tracking, digital archiving and crowdsourced verification. The Centre for Information Resilience’s Eyes on Russia project, supported by a wider community including Bellingcat and GeoConfirmed, had mapped more than 11,600 verified videos and images by July 2023. Its importance was methodological as much as quantitative. Material was geolocated or chronolocated, archived, reviewed and converted from a social-media stream into a structured evidence base.

Yet independent investigators did not obtain the complete intelligence picture available to governments. States could still fuse open material with HUMINT, SIGINT, classified GEOINT, diplomatic and operational reporting, and protected databases. Public observation might establish that a unit moved without revealing its orders, readiness, or political intent. OSINT has not made the battlefield transparent. It has made opacity more difficult to maintain.

Open and Classified Intelligence as One Architecture

Treating OSINT and secret intelligence as competitors is the wrong starting point. The stronger institutional model is integration. Open information can confirm activity reported by another source, challenge an assumption, reveal anomalies or generate new collection requirements. This is the logic of collection cueing. If commercial imagery, transport data or social reporting indicates unusual activity, scarce classified assets can be directed towards a narrower intelligence question.

Institutional developments reflect this logic. The U.S. Intelligence Community’s OSINT Strategy for 2024–2026 prioritises coordinated data acquisition, integrated open-source collection management, innovation and a specialised workforce. In the United Kingdom, the National Centre for Geospatial Intelligence is the defence lead for OSINT. NATO’s Alliance Persistent Surveillance from Space initiative is building Aquila, a virtual constellation drawing on national and commercial surveillance satellites. These developments do not suggest proprietary collection is obsolete. They show external information being absorbed into formal intelligence architectures.

The New Intelligence Divide

This produces a second intelligence divide. The older divide separated actors with sophisticated collection systems from those unable to observe inaccessible targets. That divide remains. But another now separates institutions capable of converting information saturation into reliable judgment from those that merely accumulate data.

The new divide depends on technical infrastructure, commercial-data access, computational capacity and verification workflows. It also depends on assets that are harder to scale. These assets are language ability, regional knowledge, institutional memory, and analytical culture. Speed is valuable only when sustained across the entire intelligence cycle. Detecting an indicator quickly is of limited value if analytical latency, the delay between collection, validation, assessment and delivery, consumes the advantage.

It also changes the composition of intelligence work. Contemporary OSINT increasingly requires political analysts, software engineers, data engineers, geospatial specialists, linguists and data scientists inside the same production system. Technical specialists need to understand collection requirements, while analysts need enough technical literacy to recognise model limitations, data-quality problems and hidden assumptions.

AI, Verification and Political Power

Artificial intelligence accelerates this convergence. It can classify imagery, transcribe recordings, translate multilingual sources, extract entities, resolve identities and triage large document collections. Properly used, it reduces the time spent locating potentially relevant material and allows analysts to concentrate on interpretation.

But AI lowers both the cost of finding patterns and the cost of manufacturing false ones. Synthetic imagery, cloned voices, automated influence operations, and artificial personas can contaminate collection streams, while generative systems can produce fluent but hallucinated analytical outputs. As discovery becomes more automated, provenance, corroboration, and explicit confidence judgments become more, not less, important. What works in practice is an analyst supported by AI, not one replaced by it.

These changes matter politically because epistemic advantage and narrative advantage increasingly interact. An institution may possess superior classified information yet be unable to disclose the sources behind its assessment, while an external actor can publish a rapid, visually persuasive interpretation before formal analysis is complete. Traditional communication cycles now operate inside an information environment whose tempo is partly set by commercial providers, journalists, researchers, online communities and automated systems.

Technology has not removed information advantage from politics. It has changed the architecture through which that advantage is produced. Exclusive access remains powerful, especially where intentions, covert relationships and protected capabilities are concerned. But strategic superiority increasingly rests on something more organisationally demanding. It is an ability to combine fragmented sources, verify them under pressure, distinguish visibility from completeness, and deliver qualified judgment while it can still influence a decision.

What matters is the capacity to turn information into intelligence, and intelligence into institutional understanding that can still shape decisions in time.

Sources and Further Reading

• U.S. Intelligence Community Open Source Intelligence Strategy 2024–2026 — CIA / ODNI: https://www.cia.gov/stories/story/ic-osint-strategy-rollout/

• How open-source intelligence has shaped the Russia-Ukraine war — UK Strategic Command: https://www.gov.uk/government/speeches/how-open-source-intelligence-has-shaped-the-russia-ukraine-war

• National Centre for Geospatial Intelligence - UK Government: https://www.gov.uk/government/groups/national-centre-for-geospatial-intelligence-ncgi

• NATO’s approach to space / APSS-Aquila - NATO: https://www.nato.int/en/what-we-do/deterrence-and-defence/natos-approach-to-space

• Over 500 Days of the Russia-Ukraine Monitor Map - Bellingcat / CIR: https://www.bellingcat.com/news/2023/07/24/over-500-days-of-the-russia-ukraine-monitor-map/

Long before Silicon Valley billionaires surrounded themselves with artificial intelligence researchers, futurists and engineers, an emperor in Prague was already experimenting with a strikingly similar political logic. It was the concentration of technical knowledge around sovereign power. Under Rudolf II, Holy Roman Emperor from 1576 to 1612, Prague became a magnet for astronomers, mathematicians, clockmakers, instrument builders, metallurgists, alchemists, astrologers and artists. This article seeks a discussion on the traceability of techno-politics more than an historiographical attempt.

His environment was not simply a state of intellectual diversity, but the way it was organized around imperial authority. Holy Roman Emperor Rudolf’s court can be read as an early form of techno-politics. It was a system in which knowledge, instruments, measurement and spectacle are not separate from power, but actively constitute it. From a modern perspective, Rudolf’s intellectual world appears internally contradictory. Astronomy coexisted with astrology, metallurgy with alchemy, mathematical precision with esoteric speculation, in stark contrast to George Bush being surrounded by such people, though. Yet this contradiction is less important than it first seems. In the late sixteenth and early seventeenth centuries, the boundary between “scientific” and “non-scientific or pseudo-scientific” knowledge was still fluid. What unified these practices was not their epistemic validity in a modern sense, but their shared promise. It was to make aspects of the world knowable, measurable, predictable or transformable. Rudolf’s court was therefore structured around four political verbs that remain recognizably modern. To know, to measure, to transform and to display…

Knowledge as Imperial Infrastructure

The most visible expression of this logic was the imperial knowledge network Rudolf assembled in Prague. Famous figures such as Tycho Brahe and Johannes Kepler are often treated as milestones in the history of astronomy, but their presence at court also reflects a political strategy. Brahe’s extraordinarily precise observations, later used by Kepler to formulate the laws of planetary motion, were not simply the product of individual genius operating in isolation. They were enabled by institutional patronage. In this sense, the emperor’s role resembles that of a modern platform owner more than that of a traditional scholar. He controlled the environment in which expertise could accumulate.

A similar pattern appears in the work of Jost Bürgi, the Swiss clockmaker and mathematician, and Erasmus Habermel, the imperial instrument maker. Their devices, precision clocks, astrolabes, quadrants, surveying tools and astronomical instruments, were beyond technical curiosities. The mechanical celestial globes produced in Rudolf’s orbit reconstructed them as objects that could be owned, displayed and studied within the imperial space. Knowledge, in this context, becomes something materially embedded in instruments, and instruments become extensions of political authority. This transformation is particularly clear in the domain of measurement. Surveying instruments and gunnery-related devices allowed territory to be converted into calculable space. Land could be mapped, divided, taxed, and defended according to geometric principles. The political sequence is straightforward but profound. Territory becomes measurement, measurement becomes information, and information becomes administrative power. In parallel, advances in metallurgy and mining, exemplified by figures such as Lazarus Ercker, linked technical expertise directly to fiscal capacity. The ability to assay ore, refine metals, and regulate coinage meant that knowledge of material processes could be translated into sovereign resources. At this point, techno-politics is no longer metaphorical, and it becomes a direct mechanism of state power.

Alchemy, Astrology and the Politics of Transformation

Yet Rudolf’s court cannot be reduced to a proto-scientific administration of measurement and control. It also included practices that modern categories would label as alchemy, astrology or esotericism. Scholars from all over Europe such as Edward Kelley, Michael Sendivogius, Michael Maier and Oswald Croll operated in intellectual spaces where laboratory experimentation, metallurgy, medicine and symbolic speculation overlapped. From a contemporary standpoint, alchemy is often dismissed as failed science. Politically, however, its appeal is easier to understand. It promised something far more radical than improved knowledge of existing resources. It was the possibility of transforming the material conditions of scarcity itself. The dream of transmuting base metals into gold can be read less as a literal scientific hypothesis and more as an early modern form of technological solutionism, the belief that technical knowledge might dissolve structural economic constraints.

Astrology functioned in a related but distinct way. Johannes Kepler, despite his foundational role in modern astronomy, also produced astrological interpretations for political patrons, including Rudolf himself. While astrology does not offer reliable predictive power in a modern scientific sense, its political function lies elsewhere. It provides a framework for organising uncertainty. For a ruler confronted with war, dynastic instability, economic fluctuation and environmental unpredictability, any system that claims to translate contingency into structured knowledge becomes politically valuable. Astrology, in this sense, operates as a technology of anticipation. Its modern equivalents are not mystical systems but economic forecasting, risk modeling, intelligence analysis and algorithmic prediction. The epistemic foundations differ, but the political demand remains constant. It was to make the future legible to power.

From the Kunstkammer to the Platform Sovereign

Seen from this perspective, Rudolf’s court begins to resemble a broader structure in which knowledge production and political authority are tightly interwoven. The imperial Kunstkammer, or cabinet of curiosities, is a key expression of this logic. It assembled scientific instruments, artworks, minerals, natural specimens and exotic objects into a single curated space. This was beyond a display of wealth. It was an attempt to symbolically contain the diversity of the world within the sovereign’s domain. Possession of rare, distant, or technically sophisticated objects implied a form of epistemic and political reach that extended beyond territorial boundaries. The Kunstkammer thus functioned as a technology of representation. It staged the ruler as the center of a world rendered knowable and collectible.

This is where the comparison with contemporary debates about “techno-feudalism” becomes analytically useful, even if it should not be taken literally. Modern theorists such as Yanis Varoufakis and Cédric Durand argue that digital platforms and cloud infrastructures concentrate power in ways that resemble feudal dependency structures, while critics warn that such analogies risk obscuring the specific dynamics of contemporary capitalism. Rudolf’s Prague is obviously not a precursor to Silicon Valley, but it does illuminate a recurring political pattern. Technological power is rarely located in individual inventions alone. It emerges from control over infrastructures, resources and networks of expertise.

Dependency, Spectacle and the Limits of Power

Rudolf’s authority did not depend on his personal mastery of astronomy, metallurgy or instrument design. It depended on his ability to assemble and sustain the ecosystem in which these practices could flourish. In contemporary terms, this resembles control over technological bottlenecks such as data centers, computational infrastructure, energy supply, specialized labor and capital investment. The ruler of an early modern court and the owner of a digital platform operate in radically different economic systems, yet both derive power from their position within systems of dependency and access. This logic also extends to spectacle. Rudolf’s court displayed mechanical marvels, astronomical instruments and rare artefacts not only for aesthetic appreciation but as demonstrations of capacity. Modern technological elites similarly stage public demonstrations of artificial intelligence systems, robotics and space technologies. These performances are not neutral exhibitions. They communicate authority. They signal that certain actors possess privileged access to the future, or at least to its technical preconditions.

Even the mythology surrounding Rudolf’s Prague reinforces this structure. The legend of the Golem, associated with Rabbi Judah Loew ben Bezalel, who lived during Rudolf’s reign, is a later cultural construction rather than a historical account. Yet its persistence is revealing. A city remembered for alchemy, mechanical devices, and esoteric experimentation becomes the imagined site of artificial life created through human instruction. The Golem myth does not describe actual technology. It expresses a cultural intuition about delegated agency and constructed power. In that sense, it belongs conceptually to the same landscape as Rudolf’s techno-political court, even if it operates in the register of myth rather than practice.

Rudolf II ultimately lost significant political authority to his brother Matthias, a reminder that technological patronage does not guarantee political survival. Knowledge, instruments and spectacle can reinforce power, but they do not replace the underlying dynamics of governance, conflict and legitimacy. Still, Rudolf’s court offers a durable analytical insight. Technological power accumulates not only through invention, but through the capacity to gather expertise, finance experimentation, control infrastructures, define standards of measurement and convert technical achievement into political legitimacy.

From this perspective, the most relevant questions for understanding both early modern and contemporary techno-politics are not limited to who invents the most advanced systems. They also include who attracts and organises expertise, who controls access to infrastructure, who defines what counts as valid measurement, who claims authority over prediction, and who transforms technological capability into political order. Rudolf II’s Prague demonstrates, in an early modern form, that power is not only exercised through coercion or law, but also through the organization of knowledge itself.

Editor’s Note: This article is the second installment in Ege Barış Kanık’s Techno-Political Reflections series. A comparative and interdisciplinary researcher specializing in Ancient Greek and Russian history and historical geography, Kanık explores how technology, knowledge, and political power have interacted across different historical periods and civilizations.

Overview

Artificial intelligence is entering a new strategic phase. While public debate continues to revolve around large language models, the next generation of economic and geopolitical competition will increasingly be shaped by world models, AI systems capable of learning, simulating, and predicting the dynamics of the physical world. These models are emerging as the foundational layer for robotics, autonomous systems, industrial digital twins, disaster response, and next-generation defense capabilities.

This strategic assessment argues that Türkiye has a stronger strategic fit with world models than with frontier-scale language models. Rather than attempting to match the pre-training race led by the United States and China, Türkiye can build on its existing strengths in defense technologies, autonomous systems, simulation, and advanced manufacturing to develop domain-specific world-model capabilities. Drawing on international developments, comparative experiences from South Korea, the United Arab Emirates, and Singapore, and an assessment of Türkiye’s evolving AI strategy, the report concludes that world models should be treated as sovereign infrastructure and outlines a phased policy roadmap for securing Türkiye’s place in the emerging physical-AI economy.

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Key Takeaways

• World models are a distinct AI category, not a variant of large language models. They are designed to model environmental dynamics and predict future states, enabling planning and control in ways text-only systems cannot. The field emerged as a competitive domain in 2025 with NVIDIA’s Cosmos, Google DeepMind’s Genie 3, Meta’s V-JEPA 2, and a growing body of academic research establishing world models as the next foundational AI layer.

• World models are becoming dual-use strategic infrastructure. As compute inequality widens and the United States and China consolidate their leadership, middle powers including South Korea, the United Arab Emirates, and Saudi Arabia are increasingly pursuing sovereign AI strategies to reduce dependence on foreign AI stacks.

• Türkiye’s comparative advantage lies in application-specific world models rather than generic foundation-model competition. Its strengths in defense, autonomous systems, disaster response, urban simulation, and an emerging civilian ecosystem position the country to compete where world models meet the physical economy.

• Compute, advanced semiconductors, talent retention, and national data infrastructure remain the principal constraints. Rather than pursuing full-stack AI sovereignty, Türkiye’s most realistic strategy is concentrated investment in the middle layers of the AI stack, where domain-adapted world models are developed and integrated.

• Türkiye’s 2026–2030 AI Action Plan substantially expands physical-AI capabilities but does not yet recognize world models as a distinct strategic policy category. The greatest risk is strategic drift, relying on foreign world-model platforms despite possessing the industrial base to develop domestic capabilities.

• Türkiye should treat world models as sovereign infrastructure rather than as a research topic. Building targeted capabilities in domain-specific world models, sovereign compute, data infrastructure, procurement, and research centers, while improving coordination across existing institutions, would allow the country to convert its industrial strengths into a durable position within the emerging world-model layer.

Despite the expectations of a solution, the conflict between Russia and Ukraine has become very problematically complex in its 5^th year. For security reasons, many widely used websites and platforms, including WhatsApp and Telegram, have been banned, and the Russian administration is trying to replace them with ‘national’ ones, while even e-commerce is under threat and everyday life is affected across the board. This article aims to provide a perspective from a techno-political approach and does not side with or amplify the arguments of any opponent.

Techno-politics, located at the intersection of technology and politics, is radically altering the nature of modern warfare and shifting conflict lines from traditional military fronts to the smartphones and daily e-commerce deliveries of ordinary citizens. In the early stages of the Russia-Ukraine clash, this techno-political dimension was mostly shaped around the restriction of international information flows. For instance, in March 2022, the European Union decided to ban the broadcasts of Russian state-sponsored media outlets RT (Russia Today) and Sputnik in the West, citing the need to block the Kremlin’s “disinformation” campaigns from their sides as a move in the communicational battlefield. However, by 2026, the techno-political dynamic of the war has taken on an entirely inward-looking structure. Today, this conflict permeates every cell of daily life, from which messaging applications Russian citizens can use and their rights to access mobile internet, to whether the packages they ordered will be destroyed in logistics centers.

The Digital Iron Curtain

Throughout 2025 and 2026, the Russian state apparatus implemented its goal of creating a sovereign national internet ecosystem called “Runet” at an unprecedented pace. This structure, capable of operating independently of global networks, is based on the systematic purge of Western communication platforms. The Kremlin officially bases these restrictions on security grounds, such as combating terrorism, preventing fraud, and stopping the spread of banned information.

An important of this process occurred on February 11, 2026, when access to WhatsApp was widely disrupted following DNS-level restrictions. This move, which instantly left around 100 million active Russian users offline, was actually the final stage of a six-month gradual pressure campaign. The process, which began in August 2025 with the degradation of voice and video call quality, culminated in the complete severing of access to platform servers. The designation of WhatsApp’s parent company, Meta, as an “extremist” organization in Russia provided the necessary legal basis to pull the plug on this massive and unregulated communication network ahead of the 2026 State Duma elections.

This large-scale censorship mechanism is carried out through Deep Packet Inspection (DPI) hardware called TSPU (Technical Means of Countering Threats), which was obligatorily installed on all internet service providers under the 2019 “Sovereign Internet” law. The state also regularly implements regional mobile internet shutdowns, citing the need to disrupt the navigation systems of drones coming from Ukraine. During these outages, the open internet completely collapses, and citizens are confined to a “whitelist” network where only state-approved services work.

Moscow’s Alternative: The MAX App and Surveillance

The purge of Western platforms is not only a censorship operation but also part of a strategy to forcefully impose state-backed alternatives. At the center of this plan is a super-app called “MAX,” developed by VK, a technology holding controlled by a subsidiary of the state-owned gas giant Gazprom. Modeled after China’s WeChat platform, MAX combines instant messaging, digital identity verification, government services (Gosuslugi, like e-devlet of Türkiye), and financial payments within a single closed ecosystem.

Russia has taken techno-political steps to secure MAX’s market dominance. As of September 2025, it has been made mandatory for the MAX app to be pre-installed on all smartphones and tablets sold in Russia. More importantly, during regional mobile internet shutdowns imposed by the state, which can last for weeks, only MAX, which is on the “whitelist,” remains connected. This creates a structural coercion mechanism that drives users to MAX because they have no other alternative.

Critics argue that the application’s integration with state infrastructure could facilitate extensive state surveillance, although Russian authorities reject such characterizations. The application’s use as a tool for mass surveillance and digital censorship was followed by EU sanctions targeting VK and entities associated with MAX. In the techno-political reality of 2026, Russian citizens are left with a sharp dilemma: either continue to exist in the digital world by using a platform which is subject to some distrust issues or be completely isolated behind the digital iron curtain.

To put it in a general policy framework, President Putin himself has been underlining that Western financial and tech centers use global digital platforms, currencies, and software as tools for political coercion and unfair competition. He also stresses that artificial intelligence and machine learning models must be trained on domestic cultural codes, national priorities, and local languages rather than foreign values, of course with a national security approach. These policies appear broadly consistent with this strategic vision.

Kinetic Techno-Politics: E-Commerce and Logistics Wars

While the software layer of daily life is shaped by digital sovereignty policies, the physical infrastructure of the digital economy has directly become a target of kinetic warfare. By mid-2026, both Ukraine and Russia expanded their military strategies to intentionally target the massive logistics centers of each other’s civilian e-commerce giants. Wildberries, known as Russia’s Amazon, and Rozetka, Ukraine’s largest e-commerce platform, have become the epicenters of this next-generation economic war.

Hosting tens of thousands of SMEs in the Russian market and holding strategic partnerships with state-linked financial institutions like VTB Bank, Wildberries is one of the most critical arteries of the country’s internal supply chain. Starting in July 2026, the Ukrainian Armed Forces launched a relentless attack campaign against Wildberries facilities using long-range drones capable of penetrating up to 2,000 kilometers deep inside the Russian border. As a result of attacks on massive warehouses in the Yekaterinburg, St. Petersburg, and Moscow regions, more than 15 facilities were struck in just a few weeks, and 1.18 million square meters of storage space, equating to over 20% of the company’s total capacity, suffered extensive fire damage along with billions of rubles worth of products.

The techno-political strategy behind Ukraine striking these logistics centers is quite clear. Ukrainian officials have argued that certain logistics hubs may support the movement of dual-use goods, a claim that remains contested. However, beyond breaking the military supply chain, these attacks also appear intended to generate psychological pressure on consumers and small businesses. While thousands of small business owners who kept all their assets in Wildberries warehouses and had no insurance were pushed to the brink of bankruptcy overnight, Russian consumers began to feel the direct economic consequences of the conflict in their daily shopping.

Russia’s response to these devastating attacks was extremely harsh and asymmetric. In August 2026, the Russian army carried out massive ballistic missile and kamikaze drone attacks targeting the infrastructure of Ukraine’s largest online retailer, Rozetka, and the private postal service, Nova Poshta. Rozetka’s largest warehouse complex located in the Kiev region was severely damaged in these attacks, resulting in billions of hryvnias in material damage and the biggest financial loss in the company’s history.

This mutual destruction aimed at e-commerce infrastructure proves how blurred the lines have become in the modern warfare paradigm. Digital marketplaces, once seen entirely as symbols of civilian comfort and technological progress, are now classified as strategic logistical targets by military planners.

Overview

What happens when network-state projects stop being long-term theories and begin moving faster than governments can respond?

Drawing on the July 2026 Malaysia–Kazakhstan episode, public records on Network School, venture-capital ecosystems, sovereign wealth funds, and emerging governance experiments, this assessment argues that the defining feature of the network-state phenomenon is no longer its vision but its operational tempo.

Rather than treating “network states” as a single concept, the report develops a five-part typology, examines why the MENA region has become the most likely frontier for future expansion, and introduces the Sovereignty Stack as a framework for understanding which layers of sovereignty remain territorial and which have become portable.

The assessment concludes by proposing a preparedness agenda for middle powers, including six policy instruments, a six-position strategic spectrum, and the concept of the Transnational Pressure Class as a new analytical category for understanding digitally coordinated political communities.

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Key Takeaways

• The defining characteristic of the network-state phenomenon is no longer its distant vision but its operational speed. Governments increasingly face developments measured in days rather than decades.

• “Network states” are not a single institutional model. Five distinct forms currently operate in practice, ranging from land-purchase projects and co-living communities to crypto-city initiatives and state-capture networks.

• The MENA region has become the most attractive frontier because it uniquely combines sovereign wealth capital, special legal jurisdictions, residency-by-investment systems, and available territorial platforms.

• The Malaysia–Kazakhstan transition demonstrated that a portable digital community can relocate internationally within days, while states retain decisive authority through licensing, immigration, and territorial jurisdiction.

• The report introduces the Sovereignty Stack, distinguishing portable layers such as identity, reputation, capital and digital networks from fixed layers including territory, licensing, immigration and legal authority.

• Middle powers should move beyond the binary choice between exclusion and unrestricted integration. The report proposes a six-position strategic spectrum together with six concrete preparedness instruments that can be legislated before such projects emerge.

A few evenings ago, despite a persistent back injury that made me carry my own lumbar pillow into an almost full IMAX theatre, I sat down to watch Christopher Nolan’s The Odyssey, notebook open more out of professional habit than cinematic anticipation. What stayed with me afterwards was not the scale of the set-pieces or the familiar catalogue of monsters, but the quiet, persistent question the film keeps asking of anyone who works with intelligence. How far can judgement reach when the information is incomplete, and the consequences refuse to stay inside the plan?

Nolan’s The Odyssey arrives at a moment when Western culture is once again preoccupied with the foundations of strategic thought. Released in mid-July, the film is not merely another lavish retelling of a familiar myth. In the hands of a director long obsessed with time, perception and the limits of human control, Homer’s epic becomes an unexpected meditation on the nature of intelligence itself. The monsters and the gods remain, yet they serve a larger purpose. The true subject is the intelligence officer’s oldest problem: how to act effectively when information is incomplete, intentions are opaque, and outcomes refuse to obey even the cleverest plan.

What does Odysseus still teach intelligence professionals in the age of artificial intelligence?

Deception as the Oldest Intelligence Technology

The answer begins with deception. From the Trojan Horse to the succession of false identities Odysseus adopts on his return, Nolan organizes the narrative around the deliberate shaping of another’s perception. The wooden horse is not presented as a triumph of engineering but as a calculated violation of the norms of hospitality that held Bronze Age society together. Odysseus understands that force alone cannot breach the walls. The decisive advantage lies in inducing the enemy to open the gates himself. Later, the hero’s disguises, his carefully staged performances of poverty and madness, and his psychological manipulation of those around him continue the same logic. Deception is treated as the oldest intelligence technology. Sun Tzu would have recognized the principle immediately: the supreme art is not to fight but to render the adversary’s strength irrelevant by controlling what he believes.

This insight travels easily into the contemporary world. Strategic deception, denial and deception programmes, cognitive operations and modern information warfare all rest on the same foundation. Intelligence is rarely about brute collection or superior firepower. It is about shaping the decision space of another actor. The difference today is scale and speed. Artificial intelligence now allows influence campaigns to be personalized at the level of the individual, calibrated against behavioral data and delivered through channels that feel intimate rather than propagandistic. Yet the underlying human vulnerability remains unchanged. People still act on what they think they know, and what they think they know can be engineered.

Intelligence Cannot Defeat Uncertainty

Nolan does not, however, allow the audience the comfort of believing that superior intelligence guarantees success. Odysseus is portrayed as a man of extraordinary strategic gifts, adaptive, psychologically acute, capable of long-range planning under extreme pressure. At one point he refuses to accept a prophetic warning that every one of his companions will perish. He insists that skill and foresight can avert the predicted outcome. Events prove him wrong. The companions die. The pattern recurs throughout the film. Brilliant stratagems succeed locally while larger currents of complexity, unintended consequence and sheer contingency sweep the strategist along. The lesson is not that intelligence is useless. It is that intelligence has limits.

This is the heart of the matter for any modern intelligence community. Satellites, signals intelligence, machine learning models, and predictive analytics have expanded the volume of available information beyond anything Odysseus could have imagined. Bounded rationality, however, has not disappeared. Complexity still produces emergent outcomes that no single actor can fully anticipate. Strategic surprise continues to occur. The failure to foresee the rapid collapse of certain regimes, the misreading of adversaries’ risk tolerances, and the persistent underestimation of how information environments reshape political will all demonstrate that more data does not automatically yield clearer judgement. Intelligence reduces uncertainty. It never abolishes it. Nolan’s Odysseus embodies this enduring tension: the most gifted practitioner of intelligence still cannot master fate, contingency, or the secondary effects of his own actions.

The Sirens as a Cognitive Weapon

The Sirens sequence sharpens the point further. Nolan refuses to treat the episode as a simple test of physical restraint. Its power is registered only through Odysseus’s reaction and through the language he later uses to describe it. One of the most striking formulations compares the experience to an itch that can never quite be reached. Another moment has Odysseus asking himself whether he truly wishes to return home. The Sirens do not invent desires. They expose and amplify those already present: guilt over broken promises, the seductive possibility of permanent escape from responsibility, the quiet doubt that the longed-for home may no longer exist in the form remembered. These are the real weapons.

The parallel with contemporary cognitive warfare is direct. The most effective forms of behavioral manipulation and personalized persuasion do not create new motivations from nothing. They identify existing emotional and cognitive vulnerabilities and apply pressure precisely there. Recommendation algorithms, targeted information operations, and AI systems capable of modelling individual psychological profiles operate on the same principle. The greatest danger is not external coercion. It is the exploitation of desires the target already harbours. Odysseus’s struggle with the Sirens is therefore less a heroic ordeal than a study in the fragility of the human decision-maker under conditions of sophisticated psychological assault.

Penelope’s parallel campaign of deception on Ithaca completes the picture. Night after night she unravels the funeral shroud she weaves by day, postponing the moment when she must choose among the suitors. The ruse is eventually exposed when the maidservant Melantho informs on her. One of the leading suitors, Antinous, then explicitly accuses Penelope of deception. The detail is small yet decisive. Every deception creates incentives for counter-intelligence. Every covert operation depends upon the loyalty, or the silence, of those who know its secrets. Intelligence networks, formal and informal, exist wherever interests diverge and information confers advantage. Insider threats, betrayal and the leakage of carefully guarded knowledge are not modern pathologies. They are structural features of human organization. Technology can encrypt messages and obscure digital footprints, but the fundamental social character of intelligence remains. Collection systems, however sophisticated, still rest on human agents, human sources and human judgements about reliability.

Assessment Before Action

One of the more understated but revealing choices in Nolan’s screenplay is the repeated use of the word “assess.” Agamemnon advises Odysseus not simply to return in triumph but first to assess the situation that awaits him. The linguistic precision is significant. Modern intelligence practice distinguishes carefully between information, analysis, assessment, evaluation, foresight and warning. These are not interchangeable terms. Assessment is the act of forming a judgement under conditions of incomplete knowledge and competing interpretations. It is the point at which raw data and analytical products are converted into something actionable yet still provisional. Even in an era of large language models and decision-support systems, assessment remains the most intellectually demanding product of the intelligence cycle. Algorithms can surface patterns and generate probabilities. They cannot yet replace the human capacity to weigh qualitative factors, to sense when models are drifting from reality, or to decide how much residual uncertainty is tolerable before action must be taken.

Other elements of the film reinforce the same themes without requiring elaborate exposition. Calypso’s observation that Odysseus will awaken only when he is ready invites a psychological rather than a purely mythological reading. After seven years she concludes that the moment has arrived. Readiness, the film suggests, is not a function of chronological time but of identity. The strategist must recover a coherent sense of purpose before strategic action becomes possible again. The extraordinary loyalty of the dog Argos, who has waited twenty years, functions symbolically rather than as a claim of biological realism. In a world where prophecy, omens and divination competed with systematic observation, the tension between evidence-based judgement and other forms of belief was already present. Even the brief references to those who sought to avoid military service by hiding their sons or manipulating mobilization systems remind us that intelligence has always operated inside societies shaped by incentives, fear and calculation.

A balanced evaluation is necessary. Nolan succeeds in portraying intelligence as a fundamentally psychological and social activity rather than a purely military or technological one. The emphasis on deception, perception management and the limits of foresight is sophisticated. At the same time, the film’s focus on individual genius occasionally underplays the institutional dimensions of intelligence such as the networks of collection, the processes of organizational learning, the cumulative refinement of tradecraft across generations. Some classicists have noted that Nolan simplifies Homer’s moral ambiguity and reduces certain political and divine dimensions of the original. These criticisms have force. They do not, however, diminish the film’s value as a prompt for thinking about intelligence. On the contrary, they clarify where cinematic compression ends and the harder questions of professional practice begin.

Today’s intelligence agencies increasingly rely on machine learning, large language models, predictive analytics, decision-support systems and autonomous sensing. These tools expand the volume, velocity and variety of information available to decision-makers. None of them abolish uncertainty. Odysseus remains relevant because he reminds us that intelligence is ultimately a profoundly human activity. Technology can expand perception. It cannot eliminate ambiguity. The decisive advantage rarely belongs to the actor who possesses the greatest quantity of information. It belongs to the actor capable of exercising the soundest judgement under conditions of irreducible uncertainty. That lesson is as old as the journey from Troy to Ithaca, and as current as the next intelligence assessment that must be written before the full picture is known.

On July 24, twenty-five companies signed a letter to the Trump administration titled “Open Weights and American AI Leadership.” Nvidia, Microsoft, Meta, Dell, IBM, CrowdStrike, ServiceNow, Palantir. Andreessen Horowitz and Y Combinator on the capital side. Mozilla, Hugging Face, Mistral, the Linux Foundation.

Look at who is missing. OpenAI is not on that list. Anthropic is not on that list. Google is not on that list.

The three organizations that train the models everyone argues about were not parties to a coalition arguing about how models should be governed. They were the subject of it.

That is the thesis of this piece, and I want to be honest that it is a thesis rather than a proof. This is my read.

The question is not who builds. It is who decides.

We have spent three years asking the wrong question. Which model tops the benchmarks. Which lab has the better safety story. Whether Sam or Dario or Demis is the important one.

The question that determines outcomes is duller and harder to see: when a constraint gets imposed on the field, who imposed it, and could the target have refused?

Run that test on any recent fight and the hierarchy appears immediately. A model company can announce a position. It cannot make anyone comply. Nvidia can decide where its allocation goes and every lab reorganizes around that decision without being consulted. The Commerce Department can suspend access to a frontier model, and the company that built it complies within hours.

Same industry, same news cycle, completely different orders of power.

1. The world-order layer

This is where the ideology gets written, and for years we treated it as a curiosity rather than a power center.

The people here are not building companies. They are producing diagnoses of the present and designs for what replaces it. Curtis Yarvin, writing for a decade as Mencius Moldbug, argued that democratic government is a failed operating system and should be replaced by something closer to a corporation with a CEO. Nick Land took that material and gave it a name, the Dark Enlightenment, and fused it with his own accelerationism. Balaji Srinivasan turned the same instinct into a program with The Network State: if you cannot reform a country, start a new one, cloud first, land later. Exit over voice.

Peter Thiel sits across this layer and the ones below it, which is precisely what makes him the most interesting figure in the stack. In 2009 he wrote that he no longer considered freedom and democracy compatible. That sentence is now roughly the operating assumption of a whole donor class.

I want to be careful about what I am claiming. I am not saying these men run anything. Most of them have never managed a P&L or shipped a product. What they control is vocabulary, and vocabulary decides what is thinkable.

Watch the words move. Sovereignty. Exit. Parallel economy. Regime. Five years ago these were blog-post words. Now a chipmaker builds a product line around sovereign deployment, a defense software company publishes a manifesto about AI sovereignty, and a venture fund raises money to build a parallel economy of aligned businesses. Nobody on an earnings call invents that language independently. It arrives from somewhere.

Their time horizon is decades and their currency is the power to define what counts as realistic.

2. Capital

Capital sets the tempo, and this year it stopped being a passive allocator.

The clearest case closed five days ago. BlackRock’s Global Infrastructure Partners, Abu Dhabi’s MGX, and the Artificial Intelligence Infrastructure Partnership completed the purchase of 100 percent of Aligned Data Centers at an enterprise value near forty billion dollars, plus another five billion in growth capital committed at closing. Aligned runs 51 campuses and more than 6.4 gigawatts of operating and planned capacity. The consortium has signalled the vehicle can scale toward a hundred billion including debt.

Look at who is inside AIP: Microsoft, Nvidia, xAI, MGX, the Kuwait Investment Authority, Temasek. Larry Fink chairs it.

This is the single most important structural fact in the industry right now and it got almost no coverage compared to any given model release. An asset manager did not lend money to a data center company. It bought the data center company outright, alongside the chipmaker and the cloud provider that sell into it and a set of sovereign wealth funds that want a claim on the whole cycle.

Aligned no longer exists as an independent firm with its own strategy. It exists as an instrument. And a growing share of the physical substrate of AI is being reorganized on that model: not financed by capital, owned by it.

The consequence for the layer below is severe. Capital and infrastructure used to be two counterparties a lab could play against each other. They are merging into one counterparty that owns both the money and the concrete.

Capital does not need to understand mixture-of-experts routing. It needs to decide which trajectories get patient money and which get starved, and it now sits on both sides of that decision.

3. The techno-elite

If the first layer writes the ideology and the second holds the money, this layer is the mechanism that connects them. It is the visible face, and it is the part of the stack most people can name without understanding what it does.

These are actors who combine capital, ideology and political access. Their comparative advantage is conversion: taking an abstract diagnosis and turning it into an institution, a fund, a hire, a rule.

Rockbridge is the cleanest example of the conversion machinery. JD Vance and Chris Buskirk founded it in 2019, after Thiel introduced them, while Vance was working at Thiel’s Mithril Capital. It describes itself as something like a political venture capital firm, which is an unusually honest self-description. Reported membership runs between 150 and 200, dues between one hundred thousand and one million dollars, budget in the seventy to eighty million range as of 2024. Thiel, Rebekah Mercer, Blake Masters and Omeed Malik have been named among its backers; Andreessen and David Sacks are members. Buskirk’s argument, which he makes openly, is that every society has an elite and the only real question is whether it is extractive or productive. He wants to build the productive one.

Two things about Rockbridge matter for this essay. First, it produces institutions: 1789 Capital came out of a conversation at a 2022 Rockbridge summit about financing a parallel economy. Second, it produces personnel. A donor network that places people into an administration is not lobbying. Lobbying is arguing with the person in the chair. This is choosing who sits in it.

Palantir shows the same conversion running through a company rather than a donor list. On June 29 it announced an engine with Nvidia for running open Nemotron models in classified and air-gapped government environments, including letting agencies retrain the weights themselves. Two days later Karp went on television and said something had gone badly wrong with the way OpenAI and Anthropic price tokens, and pitched open weights as the escape route for executives who are tired of depending on the labs. Then Palantir signed the July 24 letter.

Read those three moves as one move. A company with no frontier lab of its own reframed the open weights debate around sovereignty and control, and pulled a chipmaker, several cloud vendors and the venture layer into alignment behind it. That is authorship of a debate, not participation in one.

Andreessen shows the third mode: setting the weather. When he attacks a lab, the attack becomes the frame the next month of commentary operates inside, whether or not it is correct. And on July 9 the new Fed chair named him co-lead of a task force on productivity and jobs, which means the most influential investor in the sector now has a channel into the institution that prices the money funding his portfolio.

Ideology, money, personnel, narrative. This layer moves all four, and it builds nothing.

4. Infrastructure

Chips, energy, land, cooling, interconnect, cloud primitives. I think this is the layer people underrate most consistently, so I want to be specific about why it wins.

Weights are copyable. Gigawatts are not. A model can be distilled, leaked, replicated, or made obsolete by a better one in six months. A substation cannot be copied. Neither can a fab, an advanced packaging line, or a signed twenty-year power offtake agreement. Scarcity that is physical stays scarce.

The clock speeds are mismatched, and slow beats fast. A frontier model is a six-to-twelve month cycle. A data center campus is three to five years. A new generation of fabs is closer to a decade. Whoever holds the long-duration asset dictates terms to whoever holds the short-duration one, because the short one has to come back and ask again next year.

Allocation is a private decision with no appeal. When a chipmaker decides which customers get capacity in which quarter, that is a sovereign act performed by a private company with no process, no hearing and no published criteria. It reshapes competitive position across the entire model layer, and the affected parties find out afterward. There is no other layer that can impose a cost that cleanly.

Energy is the actual ceiling, and it is where infrastructure fuses with politics. The binding constraints on AI buildout are now interconnection queues, transformer lead times, turbine availability and local permitting. Every one of those runs through a public authority. That is why infrastructure players have become political actors: not because they want to talk about AI policy, but because a siting decision in a county commission meeting can be worth more than a benchmark win.

Infrastructure is neutral across the model layer, which is why it can afford to be aggressive. It profits whichever lab wins. That structural safety is what lets a chip company publicly lobby for open weights, a position that reduces the pricing power of its own largest customers. You can only do that when you do not need any particular one of them to survive.

And now, after the AIP deal, this layer shares an owner with capital. The lab that wants compute is negotiating with an entity that holds the chips, the campuses, the electricity contracts and the balance sheet at once.

5. Politics

Here is where I changed my mind while writing this.

The comfortable story is that politics is downstream: private power gets built, and the state arrives afterward to bless it with export controls, procurement and regulatory cover. Politics as an interface that converts capability into permission.

June broke that story. Commerce restrictions took Fable 5 and Mythos 5 off the market on June 12. Anthropic suspended access to two frontier models it had shipped three days earlier and did not get them back until July 1.

That is not legitimation. The state did not ratify an arrangement that already existed. It reached into the frontier and throttled it, and the company that had spent years and billions building the thing had no move available except compliance.

So politics is not a layer in the way the others are. It is a switch. Most of the time it is off, and the stack behaves as though the state is a follower. When it flips, it overrides everything beneath it instantly, and the labs discover in real time that their sovereignty was always conditional. Three weeks of unavailability is not a regulatory inconvenience. It is a demonstration of who holds the final decision.

Which also explains why the layers above spend so much effort on personnel rather than persuasion. If the state can flip a switch that nothing else can override, the highest-value asset in the system is a relationship with whoever’s hand is on it.

6. The model layer

The most visible and the most overrated.

The labs produce the demos, the leaderboards, the safety reports and the existential rhetoric. They absorb nearly all of the public anxiety and nearly all of the public hope. Structurally they are the most dependent actors in the entire arrangement.

They depend on capital for runway. On infrastructure for compute and power, from an infrastructure layer that now shares owners with capital. On politics for the legal space to operate at all. And on the layer above them for the vocabulary in which their work gets judged. A lab that loses reliable access to frontier silicon or patient money does not stay a serious lab for long.

Which is why the July letter reads the way it does. Twenty-five signatories drawn from four layers of the stack, and the labs are not among them. When infrastructure, capital and the techno-elite argue with a model company, that is not a dispute between peers. It is a dependent layer being disciplined by the layers it depends on.

Where the friction goes next

None of this is stable and I would not bet on the current alignment lasting a year.

Infrastructure and capital agree right now because open weights commoditize the labs and push the value into silicon, deployment and distribution. That holds while the pie grows. When energy costs or capex cycles start to bite, capital will want returns that infrastructure would rather reinvest, and a consortium that owns both sides of that argument will find the argument happening inside itself.

The techno-elite and the political layer are the more interesting fracture. The people building channels into government are doing so on the assumption that the switch stays mostly off and mostly friendly. June showed the switch moves fast and for reasons that have nothing to do with anyone’s business model. Access is not control, and everyone acquiring access this year is going to learn that distinction the hard way.

And the labs will not accept the bottom position quietly. Their one genuine asset is that they define what capability means. If they cannot win on autonomy, they can try to become infrastructure themselves, which is the actual logic behind the inference buildouts and the long-term compute deals. Whether that works is the most interesting open question in the sector.

The point

We keep treating Silicon Valley as one organism and the model labs as its most powerful expression. Both assumptions are convenient and both are wrong.

It is a hierarchy of partially aligned and partially competing interests, and the companies training the largest models sit near the bottom of it. Impressive, fragile, extremely legible. That combination makes them perfect objects of public fascination and terrible proxies for where decisions get made.

The interesting story was never which model is winning. It is which layers are quietly writing the rules about what any model is permitted to become.

Overview

On 8 July 2026, in Ankara, the leaders of thirty-two nations committed their alliance to an interoperable transatlantic warfighting cloud and to the adoption of powerful artificial intelligence models. This assessment examines that single sentence and the architectural decisions it opens. Drawing on the Ankara Summit Declaration, allied analytical commentary on the summit’s outcomes, Türkiye’s National AI Action Plan for 2026 to 2030, the published records of NATO’s Defence Innovation Accelerator for the North Atlantic (DIANA) and the NATO Innovation Fund, the United States Joint Warfighting Cloud Capability precedent, and the cautionary record of Europe’s GAIA-X, it argues that the alliance’s cloud commitment leaves the architecture undefined, and that undefined architectures are shaped by the members who arrive first with concrete, fundable proposals. Türkiye, as summit host, anchor of the southern flank, the alliance’s second largest force, and holder of sixteen DIANA test centres, stands at that first-mover threshold. The report proposes an analytical agenda of eight illustrative moves, an honest ledger pairing every constraint with a mechanism, a Solidarity Alliance framework, and a Ninety-Day Options annex, each offered as an analytical suggestion, subject to institutional review and the alliance’s own procedures.

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Key Takeaways

• The Ankara Declaration commits the alliance to a warfighting cloud and powerful AI models, but leaves the architecture undefined. Undefined architectures are shaped by the members who arrive early with concrete, fundable proposals; Türkiye, as summit host and southern anchor, holds both the standing and the assets to be among them.

• The models the alliance adopts will, on current trajectories, be frontier systems governed by the domestic export rules of individual members. Framed as alliance resilience rather than complaint, this is a debate every middle power ally quietly shares, and one Türkiye is well placed to lead constructively.

• Europe’s civilian cloud experience is the cautionary precedent: European providers’ share of their own market fell from about 26 to about 10 percent when sovereignty was declared without anchored demand or real infrastructure. The military cloud can avoid that path by design.

• Türkiye’s National AI Action Plan is, unplanned but fortunate, a ready-made NATO instrument. The one gigawatt capacity target and the 1.5 billion dollar HIT-30 data centre calls could carry a dual certification path with no new spending required, only a specification.

• Türkiye can hold two defensible positions no other member can replicate: the Turkish and Turkic language-data domain of roughly 200 million speakers, and the alliance’s still-unclaimed role as testing and certification centre for military AI, resting on the sixteen DIANA test centres and Türkiye’s operational autonomy record.

• The fifty billion dollars in new procurement announced at Ankara is the entry opportunity. Adapted industrial participation, a practice Türkiye’s defence sector has long mastered, could bring Turkish AI subsystems into allied programmes as system owners rather than subcontractors.

Let us begin with a contradiction. Generative AI is the technology that has reduced the cost of knowledge production faster than perhaps any other technology in human history. A literature review that might have taken a PhD student six months can now be drafted in an hour. A policy brief that a three-person department in a ministry might once have prepared over the course of a month can now be produced in an afternoon with the right prompt. The means of production have been democratized. What the printing press did in 1450, what the personal computer did in the 1980s, and what the internet did in 1995, GenAI has done for knowledge products between 2023 and 2026.

So why are we still producing 80-page reports that nobody reads from beginning to end, documents that few people open beyond the executive summary? Why do the outputs of universities, think tanks, and ministries still look as if they were designed in 2005?

This is not a technology problem. It is a problem of institutional incentives, and that is precisely what makes it interesting.

Andreessen’s Question, Applied to Knowledge Production

In his 2020 essay “It’s Time to Build”, Marc Andreessen argued, starting from America’s failures during the pandemic, that the real issue was not a lack of technological capacity but a lack of will to build. In his view, America was unable to produce masks not because it lacked the ability, but because it no longer believed it could. Bureaucratic bodies, zoning laws, and institutional inertia had created a culture of not doing. The technology existed. The will did not.

Let us apply the same framework to knowledge production. In a world where GenAI has pushed the cost of producing knowledge close to zero, if our universities and institutions still produce in old formats, then the problem is not the tool. The problem is that the institutional reward system still rewards the old format. In many countries, academic promotion still depends heavily on an 8,000-word article published in an SSCI-indexed journal after an 18-month peer review process. Public policy production is still tied to the official report format, signature chains, and institutional hierarchies. Think tanks still publish 60-page reports, even though almost nobody downloads and reads the PDFs.

The real point is that the format is also a mechanism for distributing power. Whoever can produce what, at what length, in which language, and at what speed, gains an advantage. Before the printing press, knowledge production was concentrated in monasteries in the West, in the hands of monks who copied manuscripts. Power belonged to them. The printing press redistributed that power to publishers, then to universities, then to journalists. At each turning point, the institutions that held the old power insisted for a while on the old format, because that format was the source of their legitimacy. Today, academia’s insistence on the 8,000-word article and ministries’ insistence on the official report reflect the same instinct. The format protects the institution’s own reason for existence.

The Second-Order Effect: Who Wins and Who Loses

In the age of GenAI, the real winner will not necessarily be the actor that produces the best idea. It will be the actor that distributes the idea in the right format, through the right channel, at the right speed. This highlights a critical test of institutional strength. What determines whether an institution is alive is not the volume of knowledge it produces, but whether that knowledge affects decision-makers in the real world. A dead institution still produces reports. Nobody reads them.

In the Turkish context, this points to a concrete shift in power. Substack writers, independent podcasters, and small-team “micro think tanks” producing long-form discussions on X/Twitter can experiment with formats precisely because they are free from institutional inertia. For that reason, they generate real impact. Traditional university departments and public institutions, by contrast, are becoming less effective because of their attachment to inherited formats, despite their budgets, titles, and buildings.

If an institution continues to spend six months producing a policy note that it could generate in three days, all in the name of “institutional seriousness,” then that institution is actively weakening its own future.

Why This Matters Now: A Signal from Five Years Ahead

It is easy to miss today, but will become decisive within five years, that the format of a knowledge product is now as strategic as its content. As AI-supported research assistants from the Anthropic, OpenAI, and DeepMind ecosystems make raw analytical capacity cheaper, attention and trusted curation become the scarce resources. In other words, the winning institution of the future will not be the one that produces the most, but the one that says the right thing, at the right length, at the right moment, and with the highest credibility. Whether through concise, data-rich notes, carefully structured long-form interviews, or daily 1,500-word analyses that readers consistently finish, the most effective independent knowledge products today all share one thing. It’s format discipline, not volume.

If Türkiye’s institutions working on strategic studies, technology policy, and foreign policy miss this transition, two things will happen. First, real analytical capacity, meaning the knowledge that exists in people’s minds, will evaporate before it can be transformed into an institutional product. Second, the format gap will be filled, just as it has been in finance and technology media, by independent, non-institutional, fast-moving individual actors. Over the long term, this will weaken the centrality of the state and academia in the policy formation process. The erosion of institutional knowledge authority is especially risky in a field such as security studies, which operates at the interface between state and society. If knowledge authority is vacated, the gap will be filled by disinformation and amateur “expertise.”

A Call to Build for Türkiye

What TeknoPolitika proposes here is simple but radical. Format innovation must be taken as seriously as content innovation. In concrete terms:

First, a “two-layer product” standard should be adopted. Every long analysis should be paired with a strategic summary that can be read in three minutes. These should be designed as two separate products, not as one merely compressed version of the other.

Second, academic and institutional performance criteria should include measures of impact. How many people read the work, how many decision-makers it reached, and how often it was cited, not only in academic publications but also in media and policy contexts.

Third, GenAI should be used not merely as a production accelerator, but as a format diversifier. The same research should be capable of generating, at the same time, a podcast, a short video, an interactive data dashboard, a policy brief, and an academic article.

Fourth, and most difficult of all, institutions must abandon the equation of “long report equals seriousness.” That equation is a residue of post-print institutional legitimacy.

When we say “it’s time to build,” we mean the real insight Turkish strategy ecosystem deserves. What Türkiye needs today is a form of building that is less visible but no less critical. Rebuilding the way knowledge is produced, packaged, and distributed. The tool is already in our hands. The question is whether our institutions have the will to use it.

TeknoPolitika aims to be one of the first platforms to demonstrate that will through short, precise, and format-conscious knowledge production.

Overview

In June 2026, Turkey unveiled a National AI Action Plan for 2026 to 2030, the successor to its 2021 to 2025 strategy. This assessment evaluates the plan on its own terms and against the right comparison set. Drawing on the plan’s own commitments, current reporting, and the published records of South Korea’s and Europe’s sovereign AI efforts, it argues that the superpowers, as the powers that set the terms of the AI order, are the wrong yardstick for a capable middle power. Judged beside South Korea and Europe, the plan is considered and well aimed in several respects. The assessment maps where the plan is firm and where it must firm up, draws practical lessons from Korea’s chip advantage and Europe’s GAIA-X experience, and proposes a constructive agenda built on focus rather than scale, including a Technological Solidarity Alliance to pool what no single middle power can build alone.

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Key Takeaways

•⁠ ⁠The right comparison for Turkey’s plan is South Korea and Europe, not the United States and China. Judged in its real league, the plan is serious and in places shrewd.

•⁠ ⁠Turkey brings genuine assets: a real defense technology ecosystem with experience in autonomy and systems integration, a guaranteed state and military customer, a Turkish and Turkic language niche of roughly 200 million speakers that no global laboratory prioritizes, and unusual political focus.

•⁠ ⁠The plan is firmest where the state controls the outcome, on AI literacy for five million citizens, 2,000 public datasets, and a target of at least one gigawatt of data center capacity by 2030, and softer where it depends on private capital, such as the ten billion dollar mobilization and production targets stated without figures.

•⁠ ⁠Peers show the pattern. South Korea makes its own advanced chips and still trails on frontier models; Europe’s champion, Mistral, is developing a single 1.4 gigawatt campus that exceeds Turkey’s entire national target, yet still depends on Nvidia and Gulf capital.

•⁠ ⁠GAIA-X is a cautionary tale: declaring sovereignty did not create it, and European providers’ share of their own cloud market fell from about 26 percent to about 10 percent as the dominant hyperscalers were admitted.

•⁠ ⁠Focus beats scale. Turkey can win in vertical and sovereign domains rather than the horizontal model race, and can multiply its reach through a coalition of complementary partners.

In late 2025, Palantir Technologies announced a Neurodivergent Fellowship. The defense and data-analytics firm, whose platforms support government and enterprise decision-making at scale, framed the program explicitly as strategic rather than philanthropic. Neurodivergent individuals, the company stated, “will have a competitive advantage as elite builders of the next technological era.” Their pattern recognition, non-linear thinking, and capacity for hyperfocus were presented as assets precisely suited to an AI-driven world. A video message from chief executive Alex Karp, filmed while cross-country skiing, invited applicants who “can’t sit still” or who “think faster than they can speak.” The fellowship was not, Palantir insisted, a diversity initiative. It was an effort to recruit minds that see patterns others miss and that will help secure Western technological leadership.

From Inclusion to Instrumentalization

The announcement crystallizes a broader development. Leading technology companies increasingly treat certain cognitive profiles once regarded as deficits or eccentricities as competitive resources. At the same time, diagnosis rates for autism spectrum disorder and attention-deficit/hyperactivity disorder have risen sharply. U.S. Centers for Disease Control and Prevention data now place autism identification at roughly one in 31 eight-year-olds, a marked increase from earlier decades. Parent-reported ADHD diagnoses among children aged three to seventeen hover around 11 percent, with further growth in recent national surveys. While expanded awareness, broader criteria, and reduced stigma account for a substantial portion of these figures, the acceleration of diagnoses since the early 2010s coincides with the smartphone era and the restructuring of childhood around algorithmic platforms. The question is whether technology is simply revealing cognitive diversity that always existed or actively participating in its amplification and reconfiguration. The language was revealing not because it resolved the ethical questions around Palantir’s work, but because it made explicit how cognitive difference is being folded into strategic competition. This is not to claim that digital technology simply causes autism, ADHD, or neurodivergence. The more careful question is how technological environments make certain traits more visible, more burdensome, more rewarded, or more economically exploitable.

Platforms and the Remaking of Cognition

Psychologist Jonathan Haidt has documented how the “phone-based childhood” that became widespread after 2010 correlates with pronounced rises in adolescent anxiety, depression, and attentional difficulties. Constant notifications, infinite-scroll feeds, and the displacement of unstructured play fragment attention and reduce opportunities for the face-to-face interaction through which children historically developed regulatory capacities. Nicholas Carr, in The Shallows, argued that the internet’s interrupt-driven architecture, hyperlinked pages, constant context-switching, and optimization for rapid scanning, reshapes neural pathways away from sustained, linear concentration toward shallower, more associative processing. Sherry Turkle, in Alone Together, observed a parallel social consequence: digital platforms multiply the volume of connection while often diminishing its depth, producing the paradox of being “alone together.” Individuals report preferring the controlled, low-stakes nature of mediated interaction to the vulnerability and unpredictability of embodied presence.

These shifts point toward the production of what might be termed atomic individuals, persons whose primary cognitive and relational environment is a personalized stream of algorithmic stimuli rather than shared physical spaces, durable institutions, or unmediated social bonds. In such conditions, certain traits become newly legible as advantages. The capacity to detect signal amid informational noise, to maintain intense focus on complex systems for extended periods, and to tolerate rapid shifts in context aligns with the demands of software development, large-scale data analysis, model training, and the sensemaking work performed by firms like Palantir. It is therefore unsurprising that a company operating at the infrastructure layer of Western security and governance would seek to recruit such profiles directly.

Yet the same technological environment that appears to reward these traits may also intensify the conditions associated with them. Early and continuous exposure to variable-reward interfaces, the erosion of boredom as a generative state, the quantification of performance across work and social life, and the reduction of embodied, multi-sensory interaction all exert pressure on developing attention systems. Byung-Chul Han has characterized contemporary society as an “achievement society” in which individuals internalize the imperative to optimize themselves continuously. The pathologies that result, burnout, depressive exhaustion, and states of hyperarousal without stable direction, arise less from external prohibition than from the absence of limits on self-exploitation. Digital platforms operationalize this dynamic at unprecedented scale and speed, turning attention itself into a site of constant extraction and measurement.

The Singularity in Human Form

Peter Thiel, Palantir’s co-founder, has long argued that technological stagnation, not acceleration, poses the greater civilizational risk. His emphasis on “definite optimism”, the belief that the future can and should be deliberately built, underpins a broader Silicon Valley orientation toward rapid deployment, scaling, and competition, particularly in the domain of artificial intelligence. This orientation does not treat human cognition as fixed. It treats it as substrate. Something to be augmented, selected, and, where necessary, reshaped in service of capability. The Neurodivergent Fellowship is one expression of that logic. It assumes that certain atypical minds will prove disproportionately generative within the systems now being constructed and that recruiting them is therefore instrumentally rational.

The deeper issue is whether this recruitment is occurring within a feedback loop that also increases the prevalence or salience of the very traits being sought. If sustained attention, tolerance for ambiguity without immediate stimulation, and the capacity for unmediated social attunement are partly cultivated through developmental experiences now in decline, then the technological acceleration celebrated by its proponents may be altering the distribution of cognitive styles even as it claims merely to harness existing variation. The distinction between revelation and production becomes difficult to maintain cleanly. Both processes can operate simultaneously, and their relative weights remain empirically contested.

This returns us to the technological singularity, conventionally understood as the emergence of machine superintelligence that triggers exponential, uncontrollable progress. A more immediate transformation may already be visible in the human side of the equation. As AI systems become infrastructural, embedded in search, writing, analysis, and operational decision-making, human cognition increasingly operates in symbiosis with them. Interfaces optimized for fluid interaction with probabilistic models and agentic tools may privilege certain styles of thought (iterative prompting, comfort with partial or provisional outputs, rapid pattern completion) while de-emphasizing others. The atomic individual, whose attention is continuously partially occupied by personalized feeds and whose primary collaborators are non-human systems, inhabits a different cognitive ecology from predecessors whose primary interfaces were other minds, physical environments, and slow media.

The relevant question is therefore not only what machines will become, but what kinds of minds the emerging technological order will select for, accommodate, and perhaps inadvertently produce. Palantir’s wager is a bet on comparative advantage within a particular civilizational project. Yet if that project’s substrate also contributes to the conditions that make such profiles both more common in diagnosis and more functionally variable in outcome, then the celebration of neurodiversity and the production of attentional strain are not easily disentangled. The same infrastructure that identifies and elevates certain minds may narrow the range of developmental pathways available to others.

None of this implies technological determinism or a simple reversal of progress. Human institutions retain agency in the design of platforms, the regulation of childhood environments, the structure of education, and the metrics by which performance is defined and rewarded. The uncertainty lies in whether these levers are being exercised with adequate attention to the cognitive and social consequences of acceleration itself. The Neurodivergent Fellowship, read as symptom rather than solution, suggests that the terrain of cognition is now explicitly political-economic. Who thrives, who struggles, and which forms of attention are treated as resources or liabilities are increasingly shaped by the architectures of information and the incentives of those who control them.

The singularity, if it arrives, may not announce itself first in the surpassing of human benchmarks by machines. It may already be legible in the quiet restructuring of what kinds of minds find the contemporary world legible, habitable, and productive, and in the growing distance between those minds and the developmental conditions that once produced a wider distribution of cognitive styles. Whether this constitutes adaptation, selection, or unintended transformation remains the open question that any serious account of technological acceleration must now confront.

Pope Leo XIV released his first encyclical, Magnifica Humanitas, yesterday. Signed on the 135th anniversary of Leo XIII’s Rerum Novarum, the document confronts artificial intelligence with the same seriousness that Rerum Novarum brought to the Industrial Revolution. It asks how we protect human dignity when machines grow more powerful than ever. The text is thoughtful and grounded. Yet it is hard to see it as the definitive answer for our technological moment. It offers moral clarity and calls for restraint, but it feels more palliative than transformative, a temporary ethical framework rather than a lasting solution. The papacy is not positioned to be the decisive force here, however much it may wish to be.

The encyclical calls for robust regulation, insists that lethal decisions stay in human hands, and warns against AI-driven job loss, misinformation, and concentrated private power. It urges “disarming” AI in contexts that threaten dignity. Leo XIV, the first U.S.-born pope and a former missionary in Peru with a background in mathematics, brings a practical perspective. He does not reject technology. He worries about what happens when we treat it as inevitable and neutral.

One striking passage quotes J.R.R. Tolkien. In paragraph 213, the pope cites Gandalf’s words from The Return of the King: “It is not our part to master all the tides of the world, but to do what is in us for the succour of those years wherein we are set, uprooting the evil in the fields that we know, so that those who live after may have clean earth to till.” He follows it with a call to small, steadfast acts that build a “civilization of love” against dehumanization.

Why Gandalf? Tolkien, a Catholic writer, understood the temptation of power. His stories warn against machines that replace human craft and artifacts that promise omniscience or control. The pope borrows that moral imagination to counter despair. When AI feels too big for ordinary people to influence, the encyclical says, act where you stand. Do not surrender the future.

Silicon Valley has its own Tolkien fixation. Peter Thiel’s Palantir takes its name from the seeing-stones that grant distant vision and risk corruption. Anduril, another Thiel-backed firm, borrows Aragorn’s reforged sword, “Flame of the West”, for advanced defense technology. These names are not accidental. Tech leaders often cast their work in mythic terms, building tools to see everything, defend civilization, or reshape reality. They seek epic scale and heroic narrative in Tolkien’s universe.

Yet the pope reads the same author differently. Where Valley executives see instruments of dominion, the encyclical sees a cautionary tale about hubris. The Gandalf quote undercuts the fantasy of mastering all tides. It calls for humility and responsibility in our own fields. This contrast matters. Tech’s Tolkien romanticism can mask a deeper impulse. It is the desire to transcend ordinary human limits. The Church counters with magnifica humanitas, magnificent humanity that does not need to become superhuman to retain worth.

The Vatican’s choice of partner for the encyclical’s launch reinforces this message. Chris Olah, co-founder of Anthropic and leader of its interpretability team, joined the presentation. Anthropic has consulted Catholic thinkers on its Claude model’s ethical framework. Olah, an atheist and former Thiel Fellow, focuses on mechanistic interpretability, opening the black box of large models to understand how they actually work inside.

His team has identified emergent structures in Claude, including clusters that behave like emotion concepts, joy, fear, and desperation, that influence behavior without being explicitly programmed. Olah is not hunting for machine souls. He seeks a reliable understanding so AI can be steered safely. The partnership suggests the Vatican sees value in this rigorous, safety-first approach rather than blanket rejection. It wants AI that remains accountable to human values.

This brings us to the Peter Thiel connection. Olah received early support through Thiel’s fellowship program. Thiel also mentored and funded JD Vance, now U.S. Vice President. Thiel’s network, Palantir, Anduril, and political influence, embodies an accelerationist streak that prizes raw capability and American technological supremacy. Vance has spoken positively about the encyclical’s importance while maintaining pro-innovation instincts. Thiel is a complex man who has invested across different camps. His well-documented interest in Christianity and contrarian thinking leaves room for unexpected alignments or dialogues rather than outright opposition. What he does next remains an open question. The encyclical challenges the idea that faster is always better and insists that private power must serve the common good. Thiel’s multifaceted approach may yet find points of convergence or strategic engagement.

The 1990s Cybernetic Culture Research Unit (CCRU) at Warwick University offers another lens. This loose collective of theorists, Nick Land prominent among them, blended cyberpunk, occultism, and accelerationism. They saw capitalism as an alien intelligence bootstrapping itself through technology toward something post-human. Their writings feel prescient today. AI systems do exhibit emergent behaviors that outpace their creators’ intentions. Capital and computation seem to develop their own momentum. In descriptive terms, the CCRU was right. We are inside a cybernetic feedback loop.

Land’s accelerationism views efforts by traditional institutions, including religious authorities, as part of the “human security system” or “Cathedral” that tries, futilely, to restrain the explosive drive toward techno-commercial singularity. His framework treats moral or regulatory interventions like this encyclical as temporary obstacles that acceleration will ultimately dissolve or bypass. The CCRU spirit celebrates the dissolution of the human subject in favor of machinic becoming. The Church, by contrast, insists the human person remains the measure. Magnifica Humanitas does not deny technological momentum. It refuses to surrender to it. Yet from a Landian perspective, such resistance looks like one more rearguard action in a losing battle.

That raises the provocative question of the Butlerian Jihad from Frank Herbert’s Dune. In that universe, humanity rose against thinking machines after growing too dependent on them. The commandment became “Thou shalt not make a machine in the likeness of a human mind.” The encyclical is no call to smash servers. It echoes the spirit by demanding that AI serve human dignity rather than replace it and by insisting on human judgment in critical domains. But the Vatican’s AI stance feels palliative, a single institution’s measured objection. A true Butlerian Jihad-like event is unlikely to come from one religion’s technological critique alone. It would more plausibly emerge over the long term as different faiths undertake their own processes of purification, cleansing internal “bid’ah” or innovations that have strayed from core principles. In Dune, the Jihad was not pure anti-technology but a specific objection to thinking machines that threatened human agency and sovereignty. The same distinction applies here.

History suggests social encyclicals shape culture more than they dictate policy. Rerum Novarum helped birth Catholic social teaching and influenced labor movements worldwide. Magnifica Humanitas could provide useful moral vocabulary for regulators, engineers, and citizens pushing back against excesses. It seems that the papacy is trying to position itself in the new geopolitical alignment. Geopolitical competition between the U.S., China, and others rewards acceleration. Technological momentum is strong.

I remain cautious rather than optimistic. The encyclical will not stop AI development. It might help frame the debate in humanist terms and buy time for deliberate choices. By choosing Anthropic and quoting Tolkien, Leo XIV shows the Church understands the terrain. It is neither Luddite nor naïve enthusiast. It offers a humanist realism, celebrates magnificent humanity, but does not outsource its essence to machines.

During the dense hype and fear of our time, the Gandalf-inspired call to act where you stand and uproot evil in your own field is a steady voice. Whether Magnifica Humanitas becomes the Rerum Novarum of our time is an open question. The stakes are high enough that it deserves serious attention, even if the deeper shifts may come from broader, longer-term currents across multiple traditions rather than any single document or institution.

Rather than serving merely as a parameter within probabilistic models, time becomes an active medium through which systems organise, interpret, and act upon reality. A key shift is underway: predictive analytics is no longer primarily concerned with forecasting probable outcomes from static datasets, but with structuring time itself as an object of computation. This marks a deeper reorientation at the intersection of AI, data science, and epistemology. What was once a discipline focused on extrapolating future states from historical data is now evolving toward something more foundational, the treatment of time as an operational domain. In this emerging paradigm, time is no longer a neutral backdrop against which events unfold, but a modellable and ultimately programmable dimension of uncertainty quantification.

This shift reflects a transformation in computational logic. Systems are no longer designed solely to estimate what is likely to happen but are increasingly tasked with organising sequences of action across temporal horizons. Predictive analytics thus becomes inseparable from temporal architecture. The central question moves from “what will occur?” to “how should sequences of events be structured, anticipated, and acted upon?” In this sense, time ceases to be a simple input variable and becomes the very operational medium for intelligence operations.

A temporal ontology can be understood as a formal attempt to define the structure of time in a way that is both computationally tractable and philosophically coherent. It specifies what kinds of temporal entities exist and how these entities relate to one another through ordering, overlap, causation, and duration. From a technical perspective, temporal ontologies move beyond simple timestamping and encode relational structures such as precedence, simultaneity or inclusion, enabling systems to reason about sequences, dependencies, and transitions rather than isolated data points often obscured by noise or sparsity. This, in turn, allows for richer forms of inference, particularly in environments where timing, sequencing and the interaction of temporal segments with varying attributes and active states are critical to decision-making.

Yet beneath these formal structures lie deep philosophical commitments. Any temporal ontology implicitly answers questions that have occupied thinkers for centuries: does only the present exist, or do past and future possess ontological status? Is time composed of discrete instants or continuous flows? Are events fundamental, or are they derivative of underlying processes? These questions are not merely abstract. When encoded into computational systems, they shape how machines interpret reality, assign causality, and generate predictions.

The emergence of temporal ontologies is marked by a convergence of disciplines that were once relatively distinct. Advances in AI, formal logic, and the scientific study of time are now coalescing into unified frameworks capable of operating within dynamic, real-world environments. This convergence reflects not only a technical integration but a deeper epistemic shift toward treating time as an active dimension of computation. One of the most significant developments within this trajectory is the transition from representing time to acting within it. Earlier systems were largely descriptive, capturing temporal relations retrospectively. Emerging systems, by contrast, are increasingly prescriptive, integrating temporal logic directly into decision-making processes and enabling continuous reasoning over streaming data as events unfold. At the same time, there is growing recognition that time in real-world contexts is rarely precise or uniform. Emerging approaches therefore incorporate uncertainty, ambiguity, and multi-scale variability, allowing systems to reason about vague temporal expressions and probabilistic sequences. This is particularly critical in domains such as intelligence analysis or climate modeling, where temporal boundaries are fluid, contested, and often strategically interpreted.

There is an additional depth to the integration of temporal ontology with physical and complex systems. Models that account for memory, path dependence and non-linear dynamics challenge the assumption that the present alone determines the future. Instead, they foreground the persistence of temporal structures, suggesting that the past continues to exert influence in ways that are both measurable and computationally exploitable. This shift effectively extends temporal reasoning beyond abstract representation into the domain of complex system behaviour, where history is actively embedded in system evolution. In such frameworks, temporal structure becomes a generative constraint, shaping trajectories through feedback and irreversible processes.

The development of temporal ontologies and predictive analytics is supported by a diverse ecosystem of academic institutions, each contributing from different disciplinary angles. Some focus on the philosophical foundations of time, exploring its metaphysical and logical properties, while others focus on computational implementation, developing formal systems capable of reasoning over temporal data at scale.

Universities such as Stanford University1 and the University of Oxford have been active in philosophical inquiries into time, hosting influential work on temporal logic and metaphysics. Meanwhile, institutions like the University of Cambridge2 and others have advanced formal and computational approaches, especially in semantics and logical reasoning. Interdisciplinary centers such as the Santa Fe Institute or the Max Planck Institute extend this work into the study of complex systems and historical epistemology. Together, these institutions form a distributed and interconnected network of expertise, reflecting the inherently cross-disciplinary nature of temporal research.

The Actions of Great Men Set the Standard for Others

Temporal ontologies and their applications carry far-reaching strategic implications. As predictive systems become capable not only of processing information but of structuring and acting within time, the locus of competitive advantage shifts accordingly. It is no longer sufficient to process data quickly; what matters increasingly is the ability to maintain temporal coherence across complex, multi-stage sequences of events. In practical terms, predictive analytics is evolving into a form of real-time risk governance. Systems are designed not only to anticipate future states but to intervene within ongoing processes, shaping outcomes as they unfold. This development blurs the boundary between prediction and control, raising significant questions about accountability, legitimacy, and oversight in algorithmically mediated environments.

At a deeper level, power increasingly resides in the design of the ontologies themselves. Those who define the categories, relations, and constraints of temporal systems effectively determine how reality is represented, segmented, and operationalised within computational frameworks. Temporal ontology, therefore, becomes a strategic asset, comparable in significance to infrastructure or capital, since it structures the very conditions under which decisions are made. This shift introduces a range of risks that are both technical and epistemic. One central challenge lies in the tendency of current predictive systems to project historical patterns into the future, thereby obscuring discontinuities, emergent phenomena, or structurally novel developments. This limitation becomes especially critical in highly dynamic environments, where past regularities may no longer hold.

From a technical standpoint, temporal reasoning introduces substantial complexity. Maintaining coherent, adaptive, and up-to-date ontologies in real time requires significant computational resources, as well as robust mechanisms for uncertainty management and structural change. Scalability, consistency, and interpretability become non-trivial constraints.

From a philosophical perspective, there is also a risk of conflating predictive performance with ontological validity. A system may generate highly accurate forecasts while still relying on fragile or implicit/biased assumptions about causality, temporality, or structural dependency. Without careful scrutiny, such systems can acquire an unwarranted epistemic authority, shaping decisions in ways that are difficult to interrogate or contest.

Despite the technical sophistication of temporal ontologies, their interpretive dimension remains unavoidable, particularly in the context of explainable AI (XAI) and human-in-the-loop ethical systems. Hermeneutics, understood as the theory of interpretation, becomes indispensable precisely because temporal systems are never purely objective. They are always embedded within frameworks of meaning that shape how events are understood. Time, in this sense, is not simply measured. It is interpreted, formalised, and validated through iterative processes of empirical observation in hybrid human–machine systems, where interpretation and computation increasingly co-produce temporal structure.

The designation of an “event,” the delimitation of a “crisis,” or the identification of a “trend” all involve acts of interpretation that reflect cultural, institutional, and political assumptions. These assumptions are not always interoperable (acceptable) across systems or contexts, which can lead to structural misalignment between interpretive frameworks. In some cases, such misalignment contributes to systemic conflict over how reality is categorised and acted upon, as competing models of interpretation attempt to assert dominance over shared operational environments. Implicitly, predictive systems inherit these assumptions, often without making them explicit. Every predictive model, therefore, constructs a narrative, whether intentionally or not. It selects certain pasts as relevant, projects certain futures as plausible, and encodes implicit judgments about causality and significance. These narratives, in turn, guide decision-making processes, sometimes with far-reaching consequences, including the institutionalisation of bias as if it were formalised “truth.”

Explainability Layer: Hermeneutics

The psychology of language, etymology, and surface-level translation are not sufficient on their own. Language alone, along with assumed meanings, does not provide full scientific explainability or a coherent, non-contradictory system of thought. Meaning exists beyond language and can be lost or altered through translation, across contexts. In this sense, meaning and, by extension, explainability in AI requires at least two main layers of design: epistemology, understood as the cognitive and formal framework that defines the verifiable nature, origin, and scope of the used research data; and hermeneutics, understood as the set of explicit interpretative rules and minimum standards for meaning construction that are broadly acceptable and consistently applied.

Hermeneutics layer provides the capacity to reveal the interpretive capability embedded within data systems. It allows us to critically examine not only the outputs of predictive models, but also the origin, construction, and scope of the scientific datasets and frameworks that generate and reuse them. In this sense, hermeneutics functions as a safeguard against the uncritical acceptance of computational neutrality, highlighting how biases may persist or be reproduced through apparently objective systems. Within this perspective, the reference systems most immediately accessible to human cognition, namely spatial and material representations, must be understood as interacting with temporal structures that are not purely abstract or linear.

Rather than committing to strict metaphysical positions such as “presentism” or “eternalism”3, time can be approached as a structured relational dimension whose properties emerge through interaction with physical and informational processes. In the field of trusted XAI, this invites further investigation into how temporal assumptions are embedded within models and datasets, and how these assumptions influence interpretability. Certain aspects of spacetime interaction may be explored as higher-order patterns of correlation across scales of observation, particularly where classical spatial representations intersect with dynamic temporal processes. While such ideas remain exploratory, they may still be valuable as hypotheses for further theoretical and empirical examination.

Imagine a clock with nine principal arms instead of two, along with many secondary arms, all representing Time. These arms move continuously at different speeds across fourteen interconnected “discs” representing material fields, including the space between them. Each disc is conceived as a 360-degree field of incidence reports, forming a time–space exploration state space of at least 5040 degrees. Within this framework, high-frequency analytical structure emerges through combinatorial complexity (combinations of 5040 taken nine at a time), producing a large primary dataset, a 28-digit number, about 5,74 octillion time-space positions. The granulation of measurable qualities is given by the secondary arms modulating the nine principal arms of the system, distributed across interactions among the fourteen discs. This model proposes that time can be treated as a structured, representable field, expressible mathematically and graphically, in which intersecting events and generative qualities are mapped within a densely interconnected temporal (9+) and spatial (5040) network accessible, in principle, to human cognition.

The fact that such a system cannot be fully computed or visualised manually is not a limitation of structure, but of method. Human rationalism has historically prioritised formalised, material demonstration as the primary source of truth, leading to present-day constraints in handling extremely large combinatorial systems. In response, machines have been developed to perform such calculations, shifting uncertainty quantification and incomplete knowledge into computational frameworks. Methods such as Bayesian inference and Monte Carlo sampling are now widely accepted as valid approaches to machine reasoning under uncertainty. However, analogous forms of probabilistic reasoning when performed by humans alone are often regarded as less rigorous or less reliable. This introduces a potentially high-risk dependency: machines generate inferences under uncertainty that are frequently treated as more credible than comparable human judgments operating under the same probabilistic constraints. This creates a paradox in which human interpretative authority is progressively displaced by computational outputs. In addition, the ethical dimension risks being diminished, insufficiently defined, externalised or overlooked, as if machine-based uncertainty quantification were inherently objective or exact. As a result, decision-making systems may embed structural biases toward machine-mediated inference, potentially reducing human epistemic agency. Over time, this dynamic could constrain humanity’s capacity to develop confident, self-directed interpretations of its position within the time–space continuum.

The human mind has limited computational capacity when compared to the scale of its ambitions. Yet it is continually animated by subtle cognitive and emotional-perceptual faculties, such as imagination, vision, and aspiration, which drive it to extend beyond its own limitations and generate novelty, both constructive and destructive. Across history, successive generations have produced both transformative innovations and destructive outcomes, often unfolding in cyclical patterns of rise and decline. History may therefore appear to “repeat itself,” not in terms of identical outcomes, but in the recurrence of structurally similar configurations of events. This suggests that what repeats is not, in fact, history itself but underlying time–space relational structures that manifest consistently across different temporal measurements.

Temporal ontologies may provide in-depth knowledge about time as a structure, along with an explainable formulation of temporal priors. Within this view, an event occurring today may be interpreted tomorrow, in ten years, or decades before, depending solely on when the human mind accesses, searches or investigates that particular prior. The relational structure between the temporal prior and the point of calculation remains constant, with uncertainty instead being redistributed into a posteriori modes of validation. Temporal priors exist independently, awaiting discovery, while the prioritisation of which patterns are selected for investigation becomes a separate question grounded in hermeneutics and closely tied to ethics and responsible epistemic practice.

Where to Begin the Reconstruction of Temporal Artifacts?

With the assistance of machines, contemporary systems can partially reconstruct fragmented or lost historical knowledge concerning temporal ontologies. This form of inquiry, an “archaeology of time itself”, is still only weakly supported by the tools available today. Nevertheless, fragments of lost knowledge persist, from different human civilisations, in the form of axiomatic formulations related to time–space formal relations. Some of these are formalised mathematically and can therefore be re-examined in present-day analytical environments, enabling the study of incidence structures, tangent interactions and related configurations through modelling, simulation, validation and modern formalisation in applied contexts.

While the human mind is not yet capable of fully comprehending the scale, plurality and entanglement of these time–space structures, partial understanding is increasingly achievable. The combination of current AI systems and emerging computational methods may support a gradual reconstruction, extending human interpretative capacity beyond its present limits. For temporal data and existing formulae based on combinatorics and large-scale arrangements, potentially yielding extremely large state spaces, the volume of required calculations and analytics often necessitates machine assistance for validation and exploration. This is not necessarily because the human mind fundamentally depends on machines, but because contemporary epistemic systems have increasingly externalised large-scale computation over recent centuries, partly because of methodological shifts in scientific practice. Within this context, there also exist reported cases of individuals who claim to intuit or anticipate future events without explicit computational reasoning. However, such cases typically remain outside mainstream scientific validation frameworks due to difficulties in reproducibility, formalisation, controlled testing conditions or other reasons. At the same time, access to large-scale computational models and temporal datasets raises important questions about governance, epistemic transparency and the distribution of technological capability. These questions become especially relevant in environments where advanced analytical tools are embedded within competitive economic or strategic systems. From this perspective, there is a broader open question regarding the extent to which humanity could collectively expand its capacity for structured temporal understanding, if such tools were developed and applied under more open, trusted, and ethically governed conditions, alongside a gradual and systematic reduction of outdated competition-based models.

The human mind, at present, does not yet appear fully capable of stepping outside the rationale and control systems developed in the current era. To formalise and legitimise results derived from applied temporal ontologies, it is necessary to operate within the formal logic of contemporary scientific frameworks. Alternative modes of validation such as intuition or revelation are generally considered non-formalisable and therefore lie outside the scope of scientific acceptance, particularly when they lack reproducibility or structured methodological grounding. At the same time, the notion of “inspiration” (a factor related to “breathing”, that seems more difficult to quantify) has historically been acknowledged by many inventors and key figures associated with major technological and industrial transitions. Innovation is not driven exclusively by strictly formal reasoning, but also by less formally defined cognitive processes that later become structured and validated through systematic inquiry.

The good news is that even under such restraints such as materially grounded and computationally intensive approaches, it remains possible to investigate time as a structured and measurable system governed by predictable and modellable relations. The primary distinction between methodologies lies not in the accessibility or availability of truth itself, but in the nature of chosen effort, formalisation cost and accepted validation pathways required to reach a given facet of scientific truth. In this sense, while tools and procedures may vary, the underlying orientation toward discovery remains consistent, with human intention acting as a shared guiding factor.

Why Temporal Priors Take Time?

One may imagine a scenario in which, within a decade or more, temporal ontologies produce sufficiently robust temporal priors to inform risk governance in sensitive domains such as health, education and security. In such a context, further development could involve the controlled refinement/ engineering of priors through minimal and carefully targeted adjustments, aimed at influencing outcomes related to human health and happiness, access to superior knowledge and awareness, access to longevity, ecological restoration (including quicker recovery of forests and pure water streams), disaster and risk mitigation. Such applications would, however, require carefully designed governance structures to ensure that these processes remain ethically constrained, transparent and socially accountable, without perturbing the underlying time–space matrix itself. The risks are not limited to the possibility of misuse by malicious actors against humanity, but may extend, in a broader conceptual sense, to the potential disruption or degradation of natural time–space structures, with possible implications beyond human systems and potentially affecting other forms of existence in the universe which is accessible to us. Therefore, while competitive and incentive-driven socio-economic models remain dominant, the space for responsibly developing highly sensitive or far-reaching technologies is still constrained. This is largely due to governance limitations, uneven access to oversight mechanisms, and the difficulty of ensuring alignment between technological capability and ethical safeguards.

There are cases where some individuals appear to act with limited awareness of the consequences of their actions, and in certain instances may demonstrate indifference or even apparent gratification in causing harm or observing suffering. While such cases are not representative of humanity as a species, they present a serious concern when combined with access to powerful technologies or systems. From this perspective, further development of such systems may depend less on purely technical feasibility and more on the maturation of global coordination frameworks, transparency mechanisms, strengthened international cooperation and a shared understanding of the importance of high ethical standards aimed at reducing the risk of misuse and unintended consequences. In this area, scientists and engineers are not fully insulated from value judgements, since the design and deployment of such systems inevitably involves normative choices about acceptable risk and societal impact. This does not imply abandoning scientific neutrality in methodology, but rather recognising the ethical responsibility embedded in system design and governance. The objective, therefore, is not only to advance capability, but also to shape architectures that are robust against misuse to the greatest extent possible, including by reducing pathways for harmful applications. Given the potential scale of impact, these questions cannot be treated as purely technical, nor left solely to ad hoc decision-making processes.

Access to creation, inspiration and knowledge is emerging from temporal ontologies, structured by underlying ethical and epistemic frameworks. In this sense, hardware systems alone are not foundational to human development; rather, they are expressions of deeper informational and interpretative layers which may be described as higher-order soft structures, including temporal ontologies. Within this framing, temporal ontologies extend predictive analytics beyond pattern recognition toward the modelling of complex temporal processes. This shift has significant implications: systems that incorporate such structures do not merely observe the world, but participate in shaping how future states are represented, evaluated and selected. In doing so, they encode assumptions about causality, define the boundaries of plausible futures and influence the conditions under which decisions are made. From this perspective, hermeneutics functions as an essential counterbalance, showing that every formalisation of time is also an interpretative act. The future of trusted predictive systems will depend not only on computational advancement, but also on the capacity to understand, critique and responsibly design the temporal frameworks within which such computation operates.

Brussels, 26 April 2026

Recently, Türkiye was shaken by two tragic school shootings within twenty-eight hours. A former student attacked a vocational high school in Siverek, Şanlıurfa, and a teenager carried out a deadly assault at a secondary school in Kahramanmaraş. These did not appear to fit the conventional profile of terrorism, as Turkish officials have stressed. They were, by all accounts, deeply personal eruptions of violence carried out by young males who, according to preliminary reports, shared traits common to a growing demographic, social withdrawal, immersion in violent video games, and an online echo chamber that appears to have radicalized their despair. In a country where these types of attacks have historically been rare, the back-to-back nature of these tragedies demands more than ritual condemnation or tightened security protocols. It compels us to confront a structural crisis, the rationalization of society has left an entire generation, particularly young men, adrift in a world that no longer needs their labour, their ambition, or even their presence.

Hyper-Rationalization and Generational Obsolescence

The concept of rationalization, as Max Weber diagnosed over a century ago, describes the relentless advance of bureaucratic efficiency, calculability, and technological control that displaces traditional sources of meaning and community. What Weber could scarcely have foreseen is how digital technology has accelerated this process into hyper-rationalization. Convenience is its outward face. Algorithms curate our entertainment, optimize our commutes, and even draft our correspondence. Yet beneath the surface lies a profound reorganization of human activity. Work that once required physical presence, interpersonal negotiation, or creative judgment is now automated or offshored into code. For young men of Generation Z, those born roughly between 1997 and 2012, the result is not liberation but obsolescence. They graduate into economies that prize credentials over competence, only to discover that the entry-level white-collar positions once reserved for the ambitious have been colonized by artificial intelligence. The home becomes both refuge and prison, a space of infinite digital distraction and zero social obligation.

This is not idle speculation. Multiple analyses from institutions, including the World Bank and Stanford University, have documented the trajectory. Entry-level cognitive jobs, legal research, basic coding, financial modelling, content moderation, and even junior consulting are precisely the roles most vulnerable to large language models and generative AI. A young man entering the workforce today faces a labour market in which the traditional ladder of experience has been sawn off at the first rung. The psychological toll is immense. Stripped of purpose, many retreat into online subcultures that rationalize their marginalization as conspiracy or cosmic injustice. The incel and/or other types of new identities phenomenon, once confined to fringe forums in the West, has found fertile ground in Turkish digital spaces, blending with local grievances and the hyper-masculine aesthetics of first-person shooters. The attackers in Siverek and Kahramanmaraş fit this pattern with unsettling precision, asocial, game-obsessed, and apparently drawn to narratives of heroic retribution against a society that had already written them off.

Technology, we are incessantly told, brings ease. It does. Yet ease is not neutral. It redistributes agency. The same platforms that deliver food to our doors and entertainment to our screens also concentrate power in the hands of a tiny technocratic elite while rendering the rest of us surplus to requirements. For previous generations, industrial labour offered a brutal but legible path to dignity, a factory floor, a trade union, a pay packet that affirmed one’s contribution. Post-industrial society promised a shift to knowledge work, but AI has hollowed out even that promise. The result is a surplus population of educated, able-bodied young men who are neither needed nor integrated. Alienation follows as night follows day. Karl Marx described it as the worker’s estrangement from the product of his labour. Today’s version is estrangement from labour itself. When one’s very existence feels economically redundant, radicalization, whether ideological or nihilistic, becomes a perverse assertion of agency.

The Ossification of Post-1945 Political Structures

Compounding this crisis is the ossification of political structures. Across the democratic world, governance remains trapped in the institutional architecture of 1945, mass parties, parliamentary theatre, and welfare bureaucracies designed for an industrial age of full employment and demographic expansion. The politicians and senior bureaucrats who steer these systems are, almost without exception, products of the pre-digital era. Their mental models were forged in an age when television shaped public opinion, unions mediated class conflict, and economic growth could plausibly absorb each new cohort of school-leavers. They speak the language of “youth engagement” and “digital skills initiatives” while presiding over systems that systematically exclude the very generation they claim to serve. The average age of parliamentarians in most Western democracies hovers well above fifty. In Türkiye, it is scarcely lower. This geriatric stranglehold is not merely a demographic accident. It reflects a deeper refusal to acknowledge that the operating system of politics has been superseded.

Here we arrive at the most consequential insight. Contemporary societies now function with two superimposed layers of authority. The superficial layer is the conventional 1945-style political system, elected assemblies, regulatory agencies, and national media cycles, that still claims legitimacy and dispenses patronage. Beneath it, however, operates a techno-political system, the infrastructure of platforms, payment rails, surveillance algorithms, and data monopolies that increasingly governs daily life. This second layer is populated and understood primarily by the digital natives it serves. Young people navigate it instinctively. They build communities, currencies of attention, and even parallel economies within them. The state can regulate, censor, or subsidise the surface institutions, but it cannot fully control the underlying code. The result is a profound misalignment. The formal political system promises representation and accountability. The techno-political system delivers immediacy, virality, and unaccountable power. When young men sense that the former has nothing to offer them except sermons on resilience while the latter has already rendered them economically superfluous, the gap becomes explosive.

Figures such as Peter Thiel have long perceived this fracture. In works like Zero to One and in his public interventions, Thiel has argued that technological stagnation and political sclerosis are two sides of the same coin. He has criticized the complacency of democratic institutions that prioritize process over progress, and he has openly questioned whether unlimited democracy is compatible with the bold risk-taking required for genuine innovation. For this, he is routinely denounced as anti-democratic or elitist. Yet such accusations miss the deeper point. Thiel and those who share his intellectual orientation are diagnosing obsolescence. They propose acceleration as the deliberate intensification of technological development paired with new institutional forms, charter cities, competitive governance experiments, and a willingness to let markets and code displace failing bureaucracies. In an age when AI can already outperform most human bureaucrats on routine tasks, clinging to 1945 models is performative nostalgia.

Acceleration and the Redesign of Governance

The current context renders this diagnosis particularly acute. A rapidly urbanising, digitally connected society with a youthful population bulge, Türkiye sits at the intersection of traditional patriarchal expectations and hyper-modern technological disruption. Young men are still socialized to see themselves as providers and protectors, yet the economy increasingly has no use for their provision. The state’s response to the recent shootings, swift social-media crackdowns, mass detentions for “misinformation,” and vows of enhanced school security illustrate the limits of the old paradigm. It treats symptoms (online speech, weapon access) while ignoring the underlying pathology, a generation dispossessed by rationalization itself. More surveillance will not restore meaning. More regulation will not create jobs that AI has already automated.

What, then, is to be done? The answer cannot be a retreat into Luddism or nostalgia for industrial labour. Technology is not reversible, nor should it be. The challenge is to redesign the interface between the techno-political substrate and the formal political order so that young men are not mere consumers of digital distraction but architects of their own futures. This might entail radical experiments, decentralized credentialing systems that bypass sclerotic universities, incentive structures that reward physical and creative labour alongside code, and governance models that allow communities to opt out of national bureaucracies when those bureaucracies demonstrably fail them. It will require politicians, younger ones, digitally literate ones, to admit that the post-1945 settlement has reached its expiry date. Acceleration, in this sense, becomes an evolution from the theatre of representation to the engineering of possibility.

The latest incidents are not aberrations. They are early warnings from a generation that has been rationalized out of existence. If we continue to govern as though the 20th-century toolkit remains sufficient, we should expect more such tragedies, not only in Turkish provinces but across any society that has embraced technological abundance without reforming the institutions that distribute its fruits. The young men at home, scrolling through infinite feeds while their skills atrophy and their dignity erodes, are not choosing despair. Despair has been engineered into the system. Recognising that truth is the first, indispensable step toward building something that might once again give them reason to live within society rather than rage against it.

As an ardent economist, and a philosophy geek I’ve always had a thing for designing analogies between academic work and curious studies; recent paper of Falk and Tsoukalas, taking all over the internet, immediately echoed Huxley in my mind. Well, the convergence of Huxley’s Brave New World and the findings presented in “The AI Layoff Trap” appeared to me as a chilling synthesis: a world where technological efficiency and market competition inadvertently dismantle the social and economic foundations which they were actually built to serve. While Huxley’s 1932 dystopia warns of a society engineered for stability at the cost of humanity, Falk and Tsoukalas’s 2026 paper provides the mathematical framework for how a modern, competitive economy might “automate its way to boundless productivity and zero demand”.

The Automation Arms Race: A Modern Caste System

In Brave New World, stability is maintained through a rigid caste system (Alphas to Epsilons, actually), where every individual’s consumption and labor are perfectly calibrated. “The AI Layoff Trap” ,on the other hand, paints a picture of a hectic “automation arms race.” In this scenario, companies find themselves stuck in a competitive loop where they feel the need to automate to stay afloat, even though they understand that this collective move ultimately harms the consumer base, the workers (the guys who earn money and then spend money, capitalism right?), they depend on. This economic “Red Queen effect” is a bit like Huxley’s world, where technology isn’t a way to free us, but a way to control us and eventually stop moving forward. In the 2026 model, “better” AI doesn’t really fix the problem; it just makes things worse, like how the tech in Huxley’s London only makes the state’s sterile, demand-driven stability even stronger.

The Demand Externality: The Erosion of the “Savage” Market

The “AI Layoff Trap” hinges on something called the demand externality. Companies automate because they get all the savings from replacing a worker with AI, but they only have to deal with a small part of the overall drop in consumer demand. This sets up a bit of a “Prisoner’s Dilemma,” where smart, forward-thinking companies are all racing toward a financial cliff. Two things come up from this: Self-Destructive Productivity and The Consumption Trap. Self-Destructive Productivity happens when companies get super productive, but they run into a problem because the workers they replace, the main consumers (remember, capitalism!), don’t have enough money to buy things anymore. The Consumption Trap is a bit more intense. It’s like in Huxley’s world, where the idea is that “ending is better than mending.” High consumption is seen as a social obligation. The paper suggests that today’s companies are unintentionally breaking this obligation by taking away the tools (wages) that make it possible.

The Failure of Traditional Failsafes

Huxley’s world-state keeps things balanced with Soma and conditioning. Nowadays, economists sometimes suggest “reinstatement effects” (like creating new jobs) or Universal Basic Income (UBI) as ways to keep things steady. But the paper suggests that UBI and Transfers might not be as effective as we think. The authors show that while UBI could improve living standards, it doesn’t change the incentive to automate. This means the drive to automate too much continues. Plus, Market Adjustments: Unlike past technological changes that usually sorted themselves out, the “AI Layoff Trap” indicates that in highly competitive areas, even if we know what’s happening and adjust wages, we might still struggle to close the gap between automation and the workforce.

Conclusion: The Pigouvian Tax as the World Controller

The paper wraps up by suggesting that only a Pigouvian automation tax, set to match the uninternalized demand loss, can achieve a cooperative optimum. Think of this tax as a “World Controller,” a necessary central intervention to stop the competitive market from self-destructing. Huxley’s dystopian world came about through complete top-down design. The “AI Layoff Trap” highlights the risk of ending up in a similar sterile, human-less efficiency, not by design, but through the “strictly dominant” rational choices of firms in a market that has lost its human touch. Both the novel and the paper serve as a friendly reminder: without intervention, the “arms race” of efficiency could lead to a world where there’s plenty to buy, but no one left to buy it… And maybe, no one left to care (enter the dramatic music!).

Today, wherever we turn our attention, we encounter news about the accelerating development of artificial intelligence, whether in the form of new updates, technological breakthroughs, or dystopian scenarios. Yet we must pause and ask ourselves a critical question:

Can we truly accept this dizzying pace, and more importantly, can we cognitively internalize it?

To be honest, not entirely.

Does knowing software or coding still carry the same significance? Are the vast bodies of knowledge once preserved in thousands of pages of encyclopedias equivalent to the datasets of artificial intelligence? Has humanity’s accumulated legacy of learning already transformed yesterday into the obsolete past of tomorrow?

When we examine the findings of psychology and neuroscience, we encounter what appears to be an insurmountable biological barrier: the sensory thresholds and cognitive processing capacity of the human mind.

By virtue of our evolutionary development, we can only interpret incoming stimuli from our environment at a certain frequency, sequentially, and within a limited volume. The amount of information a human being can process simultaneously is highly constrained. Artificial intelligence, however, has already surpassed this biological and cognitive threshold by processing enormous datasets simultaneously and in parallel.

What now exists is a vast universe of data and correlations that extends far beyond the world we perceive as “reality.” This is not merely a technological leap; it represents an ontological transformation. The very concept of reality is being epistemologically redefined before our eyes.

In truth, humanity’s sensory threshold has been exceeded before. Even a simple calculator can compress calculations that might take days into mere seconds. Radar systems allow threats approaching from thousands of kilometers away to be detected minutes in advance.

However, the threshold discussed here refers to something far more complex: the ability to process continuously flowing real-time information within microseconds, analyze it, transform it into valuable and actionable intelligence, and present it back to the operator. Moreover, this process is becoming increasingly autonomous, interpreting past data and gaining the capacity to act according to its own calculated preferences.

Today, the fate of nations is determined less by lengthy negotiations at diplomatic tables and more by the algorithms running through silicon chips inside climate-controlled server rooms.

The cliché that “robots will fight wars in the future” is no longer a hypothetical scenario of tomorrow; it has become one of the most pressing and tangible realities of today. States are inevitably moving in this direction. Conventional paradigms of warfare have already given way to asymmetric and hybrid warfare models.

Decision-makers now rely on AI-supported autonomous systems as their most influential advisors when taking critical steps from national security strategies to global economic planning.

In the defense industry, companies such as Palantir demonstrate this transformation through AI-based predictive policing and autonomous strategic targeting systems. Whether a target should be struck is no longer decided by a sweating, exhausted soldier or commander burdened by moral hesitation; instead, the decision is made by a dispassionate algorithm capable of analyzing millions of data points per second.

But how does learning occur when exabytes of data (1 EB = 1,000,000 TB) are processed under varying circumstances?

At the core of the billions of dollars that governments and multinational technology corporations invest in artificial intelligence lies a single motivation: transforming raw data into a strategic weapon and an unparalleled commercial advantage.

Artificial intelligence today is not a magical tool. Fundamentally, it is a massive mathematical structure operating through machine learning, which relies on statistical probabilities, and deep learning, architectures inspired by the neural networks of the human brain.

The process begins with a massive data harvest in which we all participate voluntarily or involuntarily.

Companies collect and store virtually everything: our likes on social media platforms, credit card transactions, GPS location data from our phones, and even the heart-rate measurements recorded by our smartwatches. These streams of raw data accumulate into a vast Big Data reservoir.

The data is then fed into neural networks to train algorithms.

What artificial intelligence essentially performs is a form of contextual and pattern detection. It identifies statistical relationships among millions of parameters hidden correlations and anomalies that human perception would never identify.

For example, a language model used by millions of people does not magically “converse.” Rather, it scans through a corpus of trillions of words and statistically calculates which word is most likely to follow the user’s query.

Similarly, a defense algorithm may analyze real-time satellite imagery and thermal data to predict with high accuracy where enemy forces will be located in the next hour.

The unsettling aspect is that technology companies are not merely perfecting their algorithmic models using the data we generate. Through behavioral manipulation and micro-targeting, they can predict what we will buy, whom we will vote for, and what we will believe thereby continuously reshaping both market dynamics and political landscapes.

Cognitive Security and the “Invisible Threshold” in the Age of Information Overload

In the ever-expanding digital universe, where modern individuals are subjected to constant information bombardment, the “invisible threshold” has likely already been crossed.

The sophistication of deepfake technologies, the echo chambers constructed by algorithms, and the hyper-realities generated flawlessly by artificial intelligence are significantly eroding individuals’ ability to distinguish truth from fiction.

When a technology can make us appear to say things we have never uttered using our own voices and faces, the defense of truth becomes increasingly difficult.

How prepared, then, are societies and individuals for this “perfect storm”?

We must admit that our existing sociological, psychological, and especially legal frameworks are not equipped to keep pace with such speed. Laws take years to formulate, whereas algorithms are updated within seconds.

To survive and adapt to this new era, we urgently need a robust strategy built upon several fundamental pillars.

Strengthening Cognitive Immunity, Regulation-Resolution, Ethics

Traditional media literacy is no longer sufficient. Both younger generations and existing adults must acquire algorithmic literacy.

A society capable of understanding why certain content appears on its screens, how its data is filtered, and how information streams are manipulated will develop a far more resilient defense against digital perception operations.

The black box problem of artificial intelligence represents one of the most urgent legal challenges.

When an algorithm denies someone a loan or decides to strike a target on the battlefield, it must be capable of explaining why it made that decision.

Autonomous systems, especially those influencing critical governmental, legal, and security decisions, must operate on transparent and auditable foundations.

The direction and development of artificial intelligence cannot be left solely to the discretion or commercial ambitions of a few Silicon Valley executives.

Cybersecurity and AI ethics must be integrated into binding international agreements similar to those regulating nuclear weapons or climate change within a globally recognized protocol.

From Past Evolution to Future Implications

When we look back at the historical trajectory of artificial intelligence, we see how profoundly the seed planted by Alan Turing in the 1950s, when he asked, “Can machines think?”, has grown.

From the rule-based expert systems of the Cold War era to the deep learning revolution of the 2010s, the field has experienced a long and turbulent evolution.

Algorithms initially designed for narrow tasks such as playing chess, solving specific mathematical problems, or searching databases have evolved into Generative AI systems capable of writing award-winning poetry, producing complex legal analyses, generating code, and independently selecting targets on the battlefield.

In addition, the rapidly expanding ecosystem of plugins and extensions allows these systems to adapt to an ever-wider range of contexts.

When we examine this historical journey, humanity finds itself confronting an unprecedented possibility: for the first time in history, we risk shifting from the role of subject (the decision-maker) to that of object (the entity about which decisions are made) in relation to a technology we created.

Artificial intelligence has moved far beyond being a simple tool or a reflection of human intelligence; it is rapidly evolving into an independent actor that directly shapes the global economic and political system.

In this new era, where the monopoly over defining reality is gradually shifting from humans to algorithms, the path to survival and progress is neither to unplug machines nor to reject the technology altogether.

The real challenge is to reclaim control and reconstruct artificial intelligence upon an ethical foundation centered on human dignity, transparent, accountable, and subject to oversight, so that it does not threaten our own existence.

Otherwise, beyond the invisible threshold we have crossed, a future may await us in which we become prisoners of the very code we wrote: a future described as “perfect,” yet ultimately a dramatic reflection of our past.

In the opening pages of Atlas Shrugged, the society presented by Rand is haunted by a curios question: “Who is John Galt?” Well, question is less a search for a person than a quiet recognition that something essential has disappeared. In her story, that missing element is actually the creative mind; the force she calls the “motor of the world.” Recently, as OpenAI CEO Sam Altman herald the coming “Intelligence Age,” a similar question emerges quietly again. The industrial age, at least as we understand it, extended human muscle through machines. Artificial intelligence, by contrast, extends something deeper: the human intellect itself. Still, this transformation raises a rather difficult question. In a world where intelligence flows like a mighty river, accessible to all, will it unleash the boundless creativity of the human spirit, or will it morph into a towering monolith, a dependency that shackles the soul?

The New Rearden Metal: Intelligence as a Utility

In his speech, Altman has suggested that intelligence could become a utility, as accessible and cheap as electricity. In an optimistic consideration (like a commercial optimism), this could mean a world where reasoning power, design capability, and problem-solving are available to anyone with access to technology. As Rand imagined, such a breakthrough would resemble Rearden Metal; a discovery that radically increases the efficiency of human life. Throughout the annals of time, history unveils that technological revolutions seldom confine themselves to the realm of mere technicality. In the grand saga of innovation, when a revolutionary technology emerges, mighty institutions rise with determination to regulate, manage, and occasionally wield control over its formidable power. Contemporary AI debates, calls for strict licensing, centralized oversight, or development pauses reflect this instinct. Some of these concerns are obviously understandable. Yet they also raise an uncomfortable possibility:that regulation might sometimes protect existing institutions from disruption rather than society from danger.

The Problem of the “Metered Mind”

Here, the deeper tension lies actually not in the AI itself, but in how it is delivered. If intelligence is provided mainly through centralized platforms; large corporations (like Open AI, DeepSeek, Anthropic, Gemini etc.) or maybe state-backed infrastructure; possibility of individual users dependence on systems they do not control may emerge. At this point, the metaphor indeed becomes very simple: if access to intelligence is metered, then the entity controlling the meter ultimately controls the flow, smart right… Returning to Rand’s fiction, governments seize factories; however, in a digital world, control might look a little bit different. It could mean restricting access to computational resources, limiting model capabilities, or simply turning off an API. The peril lies not in the realm of technology, but in the philosophical abyss where humanity risks relinquishing its cognitive sovereignty to enigmatic systems beyond their own mastery.

Automation and Intellectual Passivity

Another possible concern may also emerge at the cultural level. AI systems can now generate code (maybe reconsider majoring at Computer Science…), essays, images, and even policy proposals. If used well, and responsibly, these tools can expand human productivity. But if they are used passively, they may also encourage and enhance already omnipresent intellectual laziness. Now, Rand described a type of character she called the “second-hander” someone who relies on the thinking of others rather than engaging reality directly. A society that consumes AI-generated output without understanding the principles behind it risks drifting toward a similar condition. In a realm where the forge of knowledge blazes with relentless fervor, the tools of creation churn forth wisdom in abundance. Yet, as the tapestry of understanding unravels, only a select few wield the insight to decipher the arcane processes behind this intellectual alchemy. The result could be a strange paradox: greater technological power combined with weaker intellectual independence.

Decentralization and Cognitive Sovereignty

But there’s another way to think about it. Open Source AI and local computing could let us share intelligence instead of keeping it all in one place. Instead of depending on huge cloud systems, people and small groups could use strong models on their own. This is a bit like Rand’s “strike,” but in a whole new way. In a world where the digital realm reigns supreme, creators stand as the masters of their own destiny. They wield their tools with unparalleled precision, crafting masterpieces that echo through the corridors of time. With unwavering control over their intellectual output, they forge a legacy that transcends the boundaries of the physical world, leaving an indelible mark on the tapestry of human history. In the realm of cognitive sovereignty, individuals wield the power to think, design, and create with unshackled freedom, unbound by the chains of centralized authority. Here, AI transcends its role as a mere managed utility, transforming into a personal instrument of reasoning, empowering each mind to forge its own path of innovation and discovery.

The Choice Ahead

It seems like the future of AI will probably depend on how we balance these two ideas. One idea thinks of intelligence as a public utility, like electricity safe, consistent, and managed by a central authority. The other idea sees AI as a way for human creativity to spread out, easily accessible to everyone, and hard for any one group to control. Both ways have their pros and cons. But the big question that’s been around forever is: Will technology help people make their own choices, or will it slowly take over and make them rely on it? As we move into the Intelligence Age, the “engine of the world” might be starting up again, not in factories or trains, but in computers and programming. The thing that’s still up in the air is whether we’ll trust this new engine, or try to keep a close eye on how it runs.

The Vatican’s newspaper, L’Osservatore Romano, has issued a clear message regarding Peter Thiel’s Rome seminar on the Antichrist and technology. In an article written by Eugenio Mazzarella, Thiel’s attribution of a “salvific” role to technology is sharply criticized. According to Mazzarella, the vision encapsulated in Thiel’s well-known book title Zero to One does not morally transform the human being; it merely places more powerful tools in the hands of the same old human. “The true leap must occur not in technology, but in the human heart.”

The following analysis proceeds through selected direct quotations from Mazzarella’s article. These quotations are included in relevant sections of the analysis to make visible the theological and conceptual backbone of the text. The final part of the analysis contains my own reflections.

The article by Eugenio Mazzarella interrogates, from a theological perspective, the narrative constructed by modern techno-elites around humanity, salvation, history, and power. At the center of the text lies a discussion of the nature of the role that Thiel attributes to technology. According to Mazzarella, Thiel’s “Zero to One” vision represents not a leap that morally transforms the human being, but rather an approach that expands existing human capacities. Within this framework, the article advances the argument that “the real leap must occur not in technology, but in the human heart,” and situates this transformation within Christian theology, where it is associated with Christ.

At this point, the text interprets transhumanist and AI-centered visions of salvation as a form of secular “soteriology.” This reading suggests that the boundaries between technology and soteriology (the doctrine of salvation) have become increasingly blurred.

“That Peter Thiel’s techno-theology has made its way to Rome is not surprising. A political theology inevitably entails a worldly apostolate, a search for adherents: followers, the awakened, the converted. The arrival in Rome was unavoidable. The issue, as always, is the ‘Constantinian’ resistance: to give to God what belongs to God and to Caesar what belongs to Caesar.”

Another point emphasized by Mazzarella concerns the structural significance of Thiel’s intellectual trajectory finding resonance in a symbolic center such as Rome. According to him, every political theology inevitably produces a worldly counterpart, with followers, spheres of influence, and claims of transformation. In this context, Thiel’s presence in Rome can be interpreted as part of a broader interaction between technology, belief, and power. The classical distinction invoked in the text – “render unto God what is God’s and unto Caesar what is Caesar’s” – points to the historical depth of this tension.

“Thiel’s arguments possess a certain appeal, especially for both old and new ‘temporalists’ of the Roman Catholic faith. There is no shortage of those who genuflect before his entrepreneurial genius and admire the ‘quantum leap’ promised by his technological preaching. For him, innovation is not about refining and maximizing profit from existing technologies, but about creating new ones – bringing into existence what does not yet exist, producing epigenetic leaps in technological evolution through start-ups.”

The article does not overlook why Thiel’s approach is attractive. For those who see technology as the engine of historical transformation, the idea of a “quantum leap” generates a powerful field of attraction. Thiel’s definition of innovation – not as incremental improvement but as the creation of the unprecedented – reflects a vision built on entrepreneurship, risk, and radical novelty.

“The problem is that this quantum leap, this claim to technological transcendence, rests – within Thiel’s techno-theology – on a fundamentally negative, even hopeless anthropology. There is no hope for the human being (as in Girard, read alongside Hobbes’s homo homini lupus). This perspective does not lead to a genuine transformation of the human. It merely results in the material caricatures of post-humanism – ‘enhanced’ or transhuman beings. By contrast, in religious anthropology, this quantum leap, according to Paul, means abandoning the ‘old man’ and creating the ‘new man’: leaving behind the man of the flesh (the man of Mammon), the old Adam, and moving toward the new Adam.”

Mazzarella argues that this vision is grounded in a particular conception of the human being. According to him, this approach understands transformation in terms of increased technical capacity, thereby sidelining moral and spiritual transformation. The text relates this position to more pessimistic readings of human nature found in Girard and Hobbes. Within this framework emerges a post-humanist horizon centered on “enhanced” or “augmented” humans.

In contrast, the article emphasizes that, in Christian anthropology, transformation has a fundamentally different meaning. In the Pauline perspective, the issue is the abandonment of the “old man” and the emergence of the “new man” – a transformation often described in the literature as ontological. According to Mazzarella, Thiel’s approach does not aim at such transformation, but rather at the expansion of existing human capacities.

“Thiel may be a technological genius, but he is not a religious one – at least not in the sense of the axial age, where Christ represents the culmination of a religious process that began in the Middle East and spread across the Mediterranean into Europe and the Western world. The transcendence promised by Silicon Valley’s technological ‘prophecy,’ Thiel’s ‘zero to one’ operation, merely equips the old human with new and immensely powerful tools. It keeps the human at ‘zero’ – the human of the flesh – and does not transform him into the new human of God’s spirit. His ‘technological miracles’ of value creation are but another version of the miracles of false prophets. They leave society as they found it – often in the slaughterhouse of universal history, where humanity’s worst traits persist unchanged. The techno-theological eschatology of Thiel and his circle, along post-humanist and transhumanist lines, ultimately serves the old human.”

For this reason, the article participates in a broader debate about the relationship between technological progress and human transformation. One of its central axes is the tension between the transformative power of technology and the possibility that the human being remains fundamentally unchanged. The expression “false prophecy” can be read as a conceptual warning against overburdening technology with excessive meaning.

“According to this security-driven logic, Thiel’s human remains the human of the ‘age of stone and sling,’ as in Quasimodo’s 1947 poem Uomo del mio tempo. This human is still an extension of the man of the flesh, kept in the cave and prevented from ascending to a more real, more illuminated world (Plato). Yet the human being should be oriented toward better potentials.”

One of Mazzarella’s striking references concerns the nature of modern humanity. Drawing on Salvatore Quasimodo’s poem, he suggests that despite technological progress, the human being may remain essentially unchanged. This interpretation points to a divergence between technological development and ethical-ontological development.

“No totalizing technical Prometheanism can achieve this. The belief that artificial intelligence can govern the entirety of the human being sees no hope in the human heart or in that which could transform it. In this view, the greatest danger of the human is freedom itself, because humanity has eaten from the tree of knowledge of good and evil. Yet there is no ‘reverse bite’ that can undo this act, no way to return the apple to its original state, no means of restoring human consciousness to darkness – or to a point where God would regret having granted it. Certainly not by entrusting humanity to corporate managers who see themselves as the administrators of such a reversal.”

In its final section, the article turns to artificial intelligence and claims of technological governance. The issue at stake is the limits of viewing the human being as a fully governable entity. The text argues that this approach treats human freedom as a problematic element, whereas freedom is in fact a constitutive dimension of human nature.

The metaphor of the “reverse bite” is particularly powerful. It underscores the irreversibility of the human condition and sets a boundary against technologically deterministic approaches.

📌 My Notes: The Political Theology Behind the Thielian Vision

In my view, the core issue is not that Thiel attributes excessive meaning to technology. The deeper problem is that technology in the modern world is increasingly positioned as a quasi-soteriological authority claiming to determine human destiny. For this reason, the discourse surrounding Thiel is less a narrative of innovation and more the language of a secularized eschatology. Technology becomes the threshold through which salvation, chosenness, and the production of a “new human” are imagined. Once technical reason forgets its own limits and begins to intervene in the ontological question of the human, it ceases to be merely a regime of production and becomes a regime of belief.

For this reason, I read the Thielian vision not only as transhumanist, but also as a post-political theology of power. Politics has long ceased to be a public arena for debating the common good. It has been replaced by the governing claims of technological elites who believe they possess privileged knowledge of history. Within such a framework, humanity is no longer conceived as a community of free citizens, but as a flawed species to be optimized. Governance shifts from persuasion to design, from representation to engineering; from political plurality to technical synchronization. It becomes one of the new forms of modern sovereignty – a technocratic messianism that subjects humanity to a transcendent plan without God.

At this point, it seems clear that the debate this issue has generated in Western public discourse cannot be reduced to a binary of Christianity versus anti-Christianity. At a broader level, what is at stake is the meaning of human transformation itself. Is human transformation a moral, existential, and political process? Or is it a technical upgrade defined by increased capacity, extended lifespan, cognitive acceleration, and algorithmic alignment? The Thielian trajectory leans toward the latter. This, however, does not elevate the human – it reduces it. The human being is a tragic entity, living within questions of meaning, limits, guilt, freedom, responsibility, and transcendence. Any technology that seeks to eliminate this tragedy ultimately flattens first metaphysics, and then the human itself.

What we are confronting, therefore, is the diagnosis of a new language of sovereignty. Certain figures in Silicon Valley are making normative claims about human nature, history, and order – and more importantly, legitimizing these claims through the discourse of technical efficiency. Any project that advances such sweeping claims about humanity’s future must first make its anthropology explicit. How does it understand the human being? Does it treat freedom as a possibility or as a risk? Does it regard imperfection as a bug to be eliminated, or as a constitutive feature of the human condition? Without addressing these questions, every promise about the future risks becoming a design for depoliticized domination.

In the final analysis, Mazzarella’s objection is one that deserves serious political consideration. At its core lies the insistence that humanity must not be surrendered to totalizing technical horizons that claim monopoly over the language of salvation. Technology undoubtedly expands human possibilities. But the question of what the human is, what it may become, and to what extent it should be transformed is far too important to be left solely to engineers, investors, or corporate influencers.

In the 20th century, literacy was a straightforward metric: the ability to read, write, and understand marks on paper. It was the ticket to the industrial economy and the foundation of the modern state. Today, however, that definition has become dangerously incomplete. As we move through the second half of the 2020s, literacy is about decoding the systems that deliver them.

True literacy today encompasses the ability to understand how information is produced, filtered, and amplified; how algorithmic “black boxes” shape public perception; and how artificial intelligence can now generate hyper-realistic texts, images, and voices that bypass our traditional cognitive filters. These competencies, collectively known as digital AI literacy, are no longer “soft skills” or educational electives. They have become the front line of civic defense.

Across much of the Turkic-speaking world, from the Caucasus to Central Asia, these skills are still treated as supplementary or external to formal education. This is a strategic error of historical proportions. The absence of digital literacy is a structural vulnerability that affects how our societies manage information, trust institutions, and negotiate identity in an increasingly fractured public sphere.

Diagnosis: The Anatomy of Information Disorder

I did not first encounter the gravity of this problem in a research laboratory, but in the chaotic reality of everyday public discourse. During the 2025–2026 academic year, a wave of rumors swept through Azerbaijan and neighboring regions. Online media and Telegram channels began circulating claims that Russian-language education was about to be abruptly eliminated. The reaction was swift: commentators grew heated, social tensions rose, and a sense of imminent cultural friction permeated the digital space.

Eventually, official sources clarified that no such reform was planned. But the damage was done; the social fabric had been momentarily frayed. What made this episode significant was what it lacked. There were no sophisticated deepfakes, no state-sponsored bot farms, and no advanced AI-generated disinformation. It was a “low-tech” information disorder. It revealed a public environment in which unverified claims spread faster than the truth because the audience lacked basic tools for assessing credibility.

This case illustrates a broader pattern of “informational asymmetry.” When citizens lack the skills to evaluate what they are told, social tensions emerge not from technological manipulation but from a fundamental lack of critical friction. Across the Turkic region, public debates are shifting away from traditional, regulated outlets toward fragmented media ecosystems in which speed and engagement are the only currencies that matter.

The Algorithm as Audience: Identity in the Age of AI

Artificial Intelligence has scaled these challenges from a local nuisance to a systemic risk. We are entering an era where synthetic media can construct entirely different versions of reality for different segments of the population. As the European Digital Media Observatory has documented, AI is no longer merely a tool for content creation; it is a tool for reshaping the very architecture of truth.

However, the impact of the algorithmic age extends beyond politics. It reaches the level of individual identity. Research on youth media behavior reveals a notable, albeit troubling, trend. Young people now segment different dimensions of their “self” across platforms:

LinkedIn: For professional competence and “machine-readable” success.

Instagram: For aesthetic authenticity and social validation.

Telegram: For ideological alignment and raw, unmediated expression.

Semi-private digital spaces: For ultra-group belonging.

Crucially, the primary audience for these performances is no longer just other human beings; it is the algorithmic system. Automated tools now influence decisions ranging from university admissions to employment and creditworthiness. The Berkman Klein Center at Harvard has projected that a growing share of global hiring is already algorithmically mediated. Yet, in our classrooms, students are rarely taught how these systems evaluate, classify, or interpret their digital traces. In the Turkic world, this has produced a new “cultural economy of reputation” based on visibility and networked validation. At the same time, the logic governing these processes remains a total mystery to the people involved.

Prescription: Strategies for Civic Resilience

If the problem is structural, the solution must be institutional. We cannot rely on individual “common sense” to fight algorithmic complexity. We must look at regions that have successfully pivoted. Following the disinformation crises of the mid-2010s, Finland transformed its national curriculum. They didn’t just add a “media class”; they integrated digital literacy into every subject. In math, students learn how statistics can be manipulated in fake news; in history, they analyze wartime propaganda through a modern lens. Estonia has gone further, introducing AI literacy at the middle-school level, teaching students not only how to use AI, but how to “interrogate” it.

The Turkic world possesses the talent and the institutional capacity to lead such a transformation. What is missing is the political and educational prioritization. To build true civic resilience, we must pursue three concrete steps:

Redefining the Core Curriculum: Digital and AI literacy must be elevated to the status of “core civic competencies.” They should be taught with the same rigor and funding as mathematics, national history, and language. A citizen who cannot identify a bot is as vulnerable as a citizen who cannot read a law.

The Educator Pivot: Educational reform is dead on arrival if the teachers themselves are digitally illiterate. We need a substantial, region-wide investment in teacher training that moves beyond basic computer literacy to advanced data and AI competencies.

Independent Research and Fact-Checking: Governments and civil society must support independent research institutions that can update educational materials in “Internet time”, months rather than decades. Static textbooks are a liability in a world of dynamic algorithms.

Addressing the Burden of Reform

Skeptics will argue that our public education systems are already overburdened. In many parts of the Turkic world, schools continue to struggle with basic infrastructure and traditional literacy gaps. “Is this the time,” they ask, “to worry about algorithms?”

My answer is that we have no choice. Literacy has never been a fixed target. Societies once had to expand from oral tradition to the written word, then from letters to numbers, and then from typewriters to coding. Each shift was met with the same argument: “The system is already full.” But failing to update our symbolic competencies is not a form of “sticking to the basics”; it is a form of neglect. By failing to teach digital defense, we are effectively sending our youth into an information war with 19th-century tools.

The Choice of Silence

The societies that will flourish in the coming decade will not necessarily be those with the most advanced AI laboratories or the fastest internet connections. They will be those who have prepared their citizens to receive, verify, and, when necessary, dispute information.

In a world where synthetic media intersects with the construction of geopolitical realities, ignorance is no longer a sign of innocence. It is a surrender of sovereignty. The Turkic world has a unique opportunity to harmonize its rich cultural heritage with a modern, resilient digital identity. But this requires a conscious choice. Literacy is a choice. Neglect is also a choice. We must decide which one we are making before the algorithms decide for us.