In 2026, Vladimir Putin’s technology rhetoric developed into something more coherent than a conventional programme of import substitution. Across speeches on autonomous systems, artificial intelligence, industrial policy, and Eurasian integration, the Russian President increasingly presented technological capacity as a constituent element of state sovereignty itself. The clearest formulation came at the St. Petersburg International Economic Forum (SPIEF/PMEF) on June 5: states unable to control key capabilities in AI, autonomous systems, and digital platforms risk becoming a “digital periphery” and, in Putin’s formulation, without such capabilities “genuine sovereignty” becomes impossible.

Three messages recur. First, Russia should own or control critical layers of the technology stack from foundation AI models and autonomous systems to software platforms, data and infrastructure. Second, sanctions and Western technology restrictions are rhetorically recast from vulnerabilities into incentives for indigenous capability, even though independent evidence shows substantial continuing dependence on imported machinery, microelectronics and Chinese components. Third, technological sovereignty does not mean technological autarky in Kremlin rhetoric. Putin repeatedly couples domestic capability with “technological alliances,” especially with China, India, the Eurasian Economic Union and non-Western partners/the Global South.

By Valdai in October, the argument acquired a normative layer as control over algorithms was framed as a question of political and civilizational agency, with Putin warning that AI-driven homogenization could produce a digital totalitarianism in the world. The resulting doctrine is best understood as sovereign interdependence to reduce coercible dependence on Western technology while building politically trusted technological networks elsewhere.

This review is based on Kremlin transcripts and official presidential records like corpus comprises Putin’s January 16 meeting on autonomous systems, the February 25 Future Technologies Forum, the March 26 Russian Union of Industrialists and Entrepreneurs congress, the April 10 AI meeting, the May 14 Union of Machine Engineers congress, the May 28 Eurasian Economic Forum, SPIEF on June 5, the Russia–ASEAN summit on June 18, the Eastern Economic Forum on September 3 and Valdai on October 1.

Sovereignty Becomes a Technology Stack

The first stage of Putin’s 2026 technopolitics was domestic capacity building. At the January meeting on autonomous systems, the emphasis was deployment: unmanned transport, drones, robots, and other autonomous machinery were presented as productivity and competitiveness technologies whose expansion required regulation, certification, and cybersecurity. Subsequent presidential instructions ordered faster adoption, technical standards and certification, financial support, and targets for the domestic share of autonomous systems. Reporting on the meeting also records Putin stressing protection against unauthorized control and the confidentiality of data.

The February Future Technologies Forum widened the field to biotechnology and the bioeconomy under the official theme of Bioeconomy for Humanity. Its significance within the 2026 sequence is that sovereignty is no longer confined to software or defense technologies. The Kremlin treats advanced technology policy as an economy-wide state project. The subsequent presidential instructions confirm that the forum was connected to implementation rather than functioning solely as a showcase.

April’s dedicated AI meeting was more explicit. Putin called for the creation and continued development of domestic foundation AI models, linking indigenous AI capability to “sovereign development across all sectors.” The Kremlin also institutionalized the agenda by establishing a Presidential Commission on the Development of Artificial Intelligence Technologies in February, tasked with coordinating AI policy and adoption across the economy, public administration and the social sphere.

SPIEF then synthesized these, as Putin identified AI, autonomous systems and digital platforms as the three decisive technologies of the emerging economy. Countries possessing the whole set, he argued, would become centers of sovereignty in a multipolar order, and large states that remain merely users of foreign ecosystems risk external platform control and relegation to a digital periphery. This is a significant rhetorical shift. Technology is not simply a source of GDP growth or military advantage. It becomes part of the definition of sovereignty itself.

Sanctions Become Innovation Rhetoric and Alliance Policy

The second pillar is the conversion of sanctions into a political narrative about forced technological upgrading. At SPIEF, Putin invoked foreign software suppliers leaving Russia, payment blockages, and disruption of commercial relations as evidence of the dangers of external dependence. He argued that sanctions had forced Russian firms to establish engineering capabilities previously supplied by Western companies and said domestic producers were beginning to compete in third-country markets.

This tension explains why Putin’s preferred concept is increasingly cooperation among sovereign states rather than self-sufficiency. At the May 14 Machine Engineers congress, he said Russia intended to form and expand mutually advantageous technological alliances despite external restrictions. Kremlin instructions in force in 2026 make the strategy unusually concrete: the government was ordered to explore strategic AI agreements with “friendly” states, harmonize Russian AI ethics and technical standards with partners, promote AI exports, and even consider bundling data centers with Russian nuclear-power projects abroad. The implication is sovereign interdependence, not autarky, as Russia seeks to substitute politically vulnerable dependencies with networks judged less susceptible to Western coercion.

The Eurasian Economic Forum made this outward turn explicit. Its 2026 theme was “The EAEU in the Global Digital Race: Focus on Artificial Intelligence,” placing AI within regional economic integration rather than purely Russian industrial policy. Russia–ASEAN and SPIEF extended the same logic beyond the post-Soviet space toward Asian and Global South partners, combining technology with investment, industrial cooperation, payments, transport and energy.

Valdai Adds Algorithmic Sovereignty

The September Eastern Economic Forum added an important qualification to the otherwise accelerationist language. Putin argued that AI should remain under human control and stressed accessibility. At Valdai, that qualification became geopolitical philosophy. President Putin acknowledged that AI algorithms would assume greater responsibility as adoption spread, while warning against a future in which algorithmic systems configured by somebody else reduce societies to a common denominator.

Valdai converts dependence on foreign models and platforms into a civilizational argument, as whoever controls the model, training data, platforms, and algorithms can potentially influence how another society understands reality. This inference is consistent with Putin’s earlier SPIEF warning about external platform control and with the insistence on indigenous foundation models in April.

A foreign-policy implication is a more explicit geopoliticization of the technology stack. Software, AI models, data centers, autonomous systems, payment platforms, and technical standards are treated much as energy infrastructure has been treated in Russian diplomacy in the form of strategic assets that can be bundled with long-term state relationships.

Technopolitics on the Battlefield

The struggle between Russia and Ukraine exposes the limitations of the Kremlin’s technological sovereignty narrative. While President Putin presents artificial intelligence, autonomous systems, and digital infrastructure as foundations of national independence, Russia still faces significant technological dependencies and vulnerabilities.

The drone attacks on Yandex data centres in Sasovo and Kaluga in October 2026 illustrate this contradiction. These attacks, which disrupted critical digital infrastructure, demonstrate how technological assets have become increasingly exposed to conventional warfare.

Although Russia retains domestic capabilities in software development and artificial intelligence, these should not be confused with technological self-sufficiency. Dependence on foreign components, particularly advanced computing technologies, continues to constrain Moscow’s ambitions.

There is a gap between Putin’s rhetoric and actual technological capabilities as in almost all countries. The Kremlin’s emphasis on algorithmic sovereignty reflects not only strategic ambitions but also an attempt to address structural vulnerabilities that sanctions, geopolitical isolation, and the ongoing clash have made increasingly visible.

These incidents show that, beyond conceptualizations, future visions and technological policy-making, along with the Ukrainian crisis, technopolitics has become central recently. The attacks on e-commerce warehouses, energy and defense infrastructures, as mentioned in another article before, now seem to be conventional and habitual cyberattacks, nothing special. Again, the latest attacks on Yandex data centers show how technopolitics has moved to the center of realpolitik or machtpolitik, going beyond official announcements or social media posts always talking about “architecture”, “systemic” or “playmaking”.

On the other hand, avoiding being labelled Russophile in any kind of space, Russian capacity and understanding of AI and new technologies have been a bit underestimated, without denying the need for by far superior Chinese technology. However, Russia, starting from the top of the state, thinks and develops AI as in Yandex apps despite the need for innovation and investment. Contacts with the Trump administration might be helpful and can be read from this aspect as well in the near future.

Ege Barış Kanık is a PhD candidate in International Security. His research interests include cultural history, Byzantine and Russian history, and Ancient Greek. This article is part of Kanık’s ongoing Techno-Politics and Cultural History series, published periodically in TeknoPolitika.

Grigory Potemkin, so the story goes, built villages that were nothing but a facade. Painted fronts, no houses behind them. They were erected along the Dnieper so that Tsarina Catherine, sailing past, would see prosperity where there was none. The trick worked because of where the observer stood. From the boat, on the water, at the right distance, a facade and a village are truly indistinguishable. So the deception is not in the wood and paint. It is in the fact that the test was never capable of telling the two apart in the first place.

A recent machine-learning paper has resurrected this image. It describes something uncomfortably close to home, and it is worth sitting with past the point where it stops being about machines. The authors call it potemkin understanding. A large language model can define a haiku flawlessly. It can cite the 5-7-5 structure and explain why it works. It doesn’t end here. Ask the same model to write one, and it may produce something that does not scan. Worse, it does not even notice. The explanation was real. The competence behind it was not. Ask it to grade its own output and, often, it disagrees with itself. Yes, it follows the pattern. Also, no, it does not. This is not a wrong answer repeated consistently. It is an incoherent one.

The paper’s real contribution, though, is not the anecdote. It is the mechanism it names: the reason why anyone ever thought an exam could measure understanding at all. They call it the keystone. A small set of questions that can only be answered correctly by someone who has genuinely grasped the concept. We trust the YKS, AYT and TYT here, the SAT in the United States, the bar exam, the comps and the quals, precisely because we believe no other route to a passing score exists. But the paper is careful about why that trust is earned in humans, and the reason is thinner than academia ever had to notice. It is not because correct answers on a finite set of questions logically entail correct understanding. To be frank, they do not, and never could. It is because the ways a human tends to misunderstand a concept happen to be few and structured. Someone who thinks a haiku is 5-8-5 will write bad haiku, but in a predictable, catchable way. The keystone works because human error is, empirically, well-behaved. It was never a proof. It was a wager on a regularity nobody bothered to state out loud. For a very long time, nothing came along to call the bet.

Obviously, this is not a new problem in philosophy. Only the venue is new. Wittgenstein raised it as the rule-following paradox, and Kripke sharpened it decades later with “quus”: a deviant function that gives the same result as ordinary addition on every sum you have ever actually performed, and diverges only past the point where you happened to stop checking. The lesson is simple and unsettling. No finite run of correct answers can, strictly speaking, settle which rule a mind is following. Philosophy treated this as a seminar puzzle. Faintly embarrassing, safely irrelevant. After all, no one had ever met an actual quus-adder. The Potemkin paper reports meeting several. It gave a name and a spreadsheet to a thought experiment everyone had agreed to stop worrying about.

Now let us take the argument one step further than the authors do, because this is exactly where it stops being a problem about benchmarks. The paper concludes that our tests are invalid for machines. Fair enough. But look at what the machines actually passed. Definitions. Classifications. The right answer to the right question, in the right format. In other words, the whole visible body of what we call “knowing”. If a system with no coherent grasp of a concept can reproduce all of this, then all of this was never the knowledge itself. It was its facade. Yet this facade is precisely what our schools teach, what our exams reward, what our journals publish and cite. So the honest conclusion is not “the machine has Potemkin understanding.” It is harsher. The knowledge on which such success rests is, at best, a form of Potemkin knowledge. Painted fronts that someone who has never lived in the village can put up.

One might object that this is unfair. Humans who pass the exam usually do understand. Or at least we like to believe so. Besides, the paper itself says so too. There are certain reasons to believe it is true. But only thanks to the wager. The human passes because human error happens to be shaped a certain way, not because the exam ever reached the understanding itself. What the exam measures is the facade. We simply assumed that behind a human facade there must be a house. And here is the uncomfortable part: modern epistemology has been perfectly content with that assumption. It defined knowing as what can be stated, reproduced, and checked against an answer key. It rarely asked what it means to actually know a thing. It only asked whether the knower produces the expected output. Naturally, a regime like this was bound to be imitated. It was built to be imitable. What we lack is not a better benchmark. What we lack is an approach to knowledge that is truthful and essential rather than merely procedural.

Here is the part academia will not enjoy. Every exam, every dissertation defense, every professional license, every peer review operates on exactly the assumption the paper found to be false for language models: that a curated, finite, keystone-shaped test certifies something coherent underneath it. That certainty was never a property of the test. It was a favor borrowed from the ordinary regularity of human error. Quietly cashed for a century. Never audited, never even named, until a system came along whose errors did not do us the courtesy of being human-shaped. The scandal is not that the model turned out to have a facade instead of a foundation. The scandal is that we never once checked whether the same is true of the diploma on the wall. We never had a machine willing to grade its own answer twice and tell us, without flinching, that it disagreed with itself.

Nobody has run that experiment on the tenured. Nobody wants to. Yet the paper’s method is not, in principle, restricted to language models. Ask an expert to produce an instance of the very concept they have just defined flawlessly in a seminar. Then show it back to them in a different room, under a different name, and ask if it qualifies. Somewhere between definition and deployment, competence has a way of turning into a performance that only has to survive one pass. We built an entire civilization of credentials on a wager about human error that we never once wrote down. The first time something failed to honor that wager, we called it the machine’s problem. It is not. The machine did not fake our knowledge. It showed us what our knowledge was made of. So it is worth asking, quietly, off the record, in the room where the grades are actually assigned: whose facade gets discovered first, once someone finally points the boat the other way?

Cemil Caca Arslan holds a PhD in Public Law. His research focuses particularly on the fundamental implications of techno-politics and techno-feudalism for the state.

At TeknoPolitika, our interviews with experts shaping the emerging field of techno-politics across a broad intellectual and geographical landscape continue. Through these conversations, we aim to look beyond the usual centres of technological debate and examine how different societies, institutions and regions are responding to the political, strategic and social consequences of technological transformation.

Papua New Guinea rarely sits at the centre of global debates on artificial intelligence, digital sovereignty and technological competition. Yet in many respects, it offers a particularly revealing case. Traditional media, social platforms, satellite connectivity, sovereign cloud infrastructure and emerging AI capabilities are developing alongside one of the most linguistically and culturally diverse societies in the world. At the same time, the country is becoming increasingly exposed to the wider technological and geopolitical competition shaping the Pacific.

Christopher Tungi Papiali brings an unusual perspective to these questions. A writer, former university lecturer and media executive, he currently serves as Projects Executive Officer at EMTV, one of Papua New Guinea’s leading television networks, working on partnerships and projects through the office of the chief executive. His background also spans information technology, higher education and public commentary, placing him at the intersection of media transformation, technology and Papua New Guinea’s changing external relationships.

For TeknoPolitika, Papua New Guinea is important precisely because the country brings together questions that are increasingly global: Who controls digital infrastructure? How should smaller and middle-sized states approach foreign technology providers? Can artificial intelligence strengthen local capabilities without weakening cultural and linguistic diversity? And what does technological sovereignty mean for a country navigating between domestic development priorities and growing strategic competition in the Pacific?

We spoke with Christopher Papiali about Papua New Guinea’s changing media environment, the opportunities and risks created by artificial intelligence, the preservation of local knowledge, competition over digital infrastructure, and the choices the country faces as it builds its technological future.

(ACA): Mr. Papiali, thank you very much for taking the time to speak with TeknoPolitika. It was particularly important for us to hear directly from someone whose experience spans media, higher education, technology and public communication in Papua New Guinea. Your perspective offers a valuable way to understand how these transformations are being experienced from within the country itself.

To begin, you’ve worked across media, higher education, public administration, and communications in Papua New Guinea. From your perspective, what are the most important changes currently shaping PNG’s media and information environment?

Papua New Guinea (PNG) gained independence from Australia in 1975. Previously, it was under imperial powers, namely Great Britain and Germany. After World War II, Australia was tasked with administering the country.

Within the last 51 years, the media and information landscape has changed a lot. Currently, we have both print and digital media operating in Papua New Guinea. There are transitions taking place in the way these media organizations conduct their operations. Of course, there are also new digital online blogs and other social media platforms that report daily or weekly news.

In the first 26 years, Post Courier, an Australian-owned newspaper, was the premier print media company and had a dominant role in print media. At the same time, the government-owned radio station, the National Broadcasting Corporation (NBC), was established. The establishment of NBC was a transition from the Australian Broadcasting Corporation (ABC), established during the pre-independence period, to a local radio service by 1 December 1973.

On 11 November 1993, the National newspaper company was established, becoming the second daily newspaper after Post Courier. This is a majority Malaysian-owned newspaper that focuses mostly on the interests of Asians. Over the years, it has continued to compete with Post Courier on news and other propaganda issues.

Meanwhile, the first television company to be established in 1987 was EMTV. This is a nationally owned television company and a subsidiary of Telikom Ltd. Telikom Ltd is a state-owned enterprise (SOE), among nine other SOEs in the country. EMTV provides a free-to-air service to the 10 million people of Papua New Guinea. Its revenue comes mainly from advertising. Since 1987, EMTV has continued to play a dominant role in public information consumption, enabling the government and development partners to make informed decisions on the nation’s development.

In November 2014, TVWAN was established. It is owned and operated by Digicel PNG. This is a commercial television station providing a mixture of local and international entertainment, news and current affairs.

In September 2008, the National Broadcasting Corporation launched its television station, called NBCTV. It was established to complement NBC Radio service.

Under radio, there are five radio stations, including NBC, Kalang Advertising Ltd and PNG FM Ltd. The other two, added to these main three, are faith-based stations: Catholic Radio Network and FM Light.

The media outlets mentioned above play an important role in the television and radio broadcasting landscape in PNG. The print media provide both impartial and biased news at times, often promoting government and shareholder agendas.

For instance, NBC would not want to report or broadcast bad things relating to the national government. Similarly, EMTV does not want to broadcast news and events that contradict Telikom Ltd’s core objectives and visions. Meanwhile, although TVWAN is a private television company, it would be cumbersome for it to report anything very serious about the serving government.

What does this suggest? State ministers and officials sometimes use police to question and intimidate reporters and journalists. On certain occasions, state ministers or even the Prime Minister take news reporters to court on defamation charges.

Over the years, there have never been any serious prosecutions of journalists or reporters leading to imprisonment. This suggests that politicians and government bureaucrats attempt in every possible way to gag the media, especially when corruption matters are reported.

Having said this, we come to the core question of the changing media landscape.

One major change is the transition from traditional media to digital media. We are witnessing a shift from people buying newspapers on the streets and watching television on TV screens to viewing news through websites, social media, and mobile platforms. This is happening on a grander scale as more people purchase mobile phones.

In response to this media trend, EMTV recently launched its digital app, called igOEMTV, which is now available through the Play Store, Apple App Store, Smart TVs, and other platforms.

There is also an increase in citizen journalism. We are witnessing many social media users posting news and uploading photos without any form of control. There is no vetting or checking. These people post anything online, and sometimes the news is real, while almost 80–90% is fake news.

Sometimes, there is a danger here for reporters and journalists who base their reporting on these social media posts and reword or rephrase them for their main news bulletin. For instance, if a social media user posts a natural disaster story from one of the remote islands, inaccessible by roads and where no media reporter is based, that particular social media post, if it becomes viral, can easily be put into the main news bulletin by broadcasting channels such as EMTV.

In this scenario, mobile phone users are becoming the first audience or viewers of news and events. If some news is put on social media at around 10 a.m., mobile phone users see it first, while the print media runs the same story the next day and television runs the same news in its main bulletin at 6 p.m.

Another change is the decline in print circulation. There are no actual reports on the drop in newspaper circulation, but it appears that the growing population is increasingly relying on social media posts. Facebook usage in PNG is increasing exponentially, and I assume that much of the decline is connected to this. More research needs to be done to determine the shift in media access.

In most cases, people are purchasing newspapers to read classified news, vacant job markets, sporting bets, and rugby league news.

We are also seeing people using AI to spread misinformation and political propaganda. It appears that, in the last two years, we have witnessed a growing number of social media users using AI to defame certain political leaders. For instance, a person may use AI to defame the Prime Minister. In response to such growing AI-use trends, the National Government of Papua New Guinea has passed the Cybercrime Code Act 2016, purposely to prosecute such offenders.

At the same time, more locally produced content is needed. There are many cultural practices and everyday life stories that need to be documented and, if possible, produced for television. As increasing amounts of Western-influenced content are broadcast, some of our local stories are not featured prominently. This suggests that more video documentaries on local content must be produced as part of promoting Papua New Guinea’s tourism and everyday lifestyle.

Finally, there is an increase in OTT media. Many people are now watching television and other content through internet-based platforms rather than conventional broadcast schedules. Even now, there is a growing trend whereby advertising is conducted through mobile phone companies. For instance, Vodafone SIM card users are using Vodafone to advertise and market their businesses and products. Some advertisements that were usually placed on television channels such as EMTV have migrated to the Vodafone digital mobile platform.

ACA: Papua New Guinea is one of the most culturally and linguistically diverse societies in the world. How is digitalization changing the way local knowledge, languages, and cultural memory are preserved, or potentially lost?

Papua New Guinea is the only country that is very rich in language and culture. In fact, it is the leading language bank in the world. It has 854 distinct languages spoken by 10 million people. This means these languages carry cultures, making it the top linguistically diverse country in the world.

There is certainly a threat to these languages. Some of the languages are endangered, and if they are not protected and safeguarded, we are at risk of losing some. Many of these threats come from globalization and digital transformations.

The younger generation is increasingly using mobile phones, and with mixed cultural integration, they tend not to maintain a strong grip on their cultural and linguistic values and utterances. In many cases, children born in this generation are unable to speak local vernaculars. They speak Tok Pisin, Papua New Guinea’s lingua franca.

In the Erave Local Level Government area of the Southern Highlands Province of Papua New Guinea, a lot of our younger generation are migrating into towns and cities. In that process, as resettlement takes place alongside new forms of socialization, they become unable to speak their local dialects, including Pole and Piame Pi vernaculars.

ACA: As digital infrastructure, social media, and artificial intelligence become more influential, what opportunities and risks do these technologies create for journalism and public communication in Papua New Guinea?

Some of the prevalent opportunities for journalists would be to know a certain number of AI tools that they can use to improve their performance in the workplace. Journalists and reporters are provided with opportunities to access massive amounts of data made available by AI and to construct more relevant and reliable news effectively and conveniently. But I still think that they need to express themselves using their own words and structural framing so that we avoid AI plagiarism.

Obviously, AI assists journalists and reporters with research and data collection, especially if they are doing feature writing and documentaries.

In the public space, AI poses a significant threat through misinformation. And honestly, AI cannot generate sufficient data on Papua New Guinea because 80% of indigenous content—cultures and practices—is not written. That means most of the national content is not available in the literature and, therefore, as it appears, is not available on the internet.

On the positive side, AI assists people in improving workplace performance and doing business. People are writing good letters, proposals, and other materials with the assistance of AI. I see a lot of people using AI to attend meetings, find contact numbers, and do business, particularly in digital content creation and marketing.

ACA: The Pacific is becoming increasingly important in global geopolitical and technological competition. How do you see Papua New Guinea positioning itself amid growing interest from major powers, technology companies, and international partners?

Papua New Guinea, in the past few years, has been seriously addressing these issues. It continues to seek collaboration with relevant partners.

Papua New Guinea’s ICT umbrella is the National Information and Communication Technology Authority (NICTA). The main role of NICTA is to regulate PNG’s telecommunications, broadcasting and radiocommunication sectors. It faces some compelling issues relating to national sovereignty, cybersecurity, digital infrastructure, competition and access to emerging technologies.

With many technology companies entering Papua New Guinea, NICTA launched 5G spectrum last month. This is important for placing PNG in the next generation of mobile technology.

NICTA has also conducted legislative drafting work focused on cybercrime, data governance and data protection. This is in response to international cybercrime obligations, including the UN Convention Against Cybercrime and the Budapest Convention.

We are seeing competition at the geopolitical level, and prominent players in this region include China, Taiwan, France, Australia, New Zealand and the United States.

China and Australia have been heavily involved in PNG’s telecommunications industry. Much of the ICT infrastructure has been built and funded by these two countries.

These countries have worked closely with PNG Dataco, a state-owned enterprise responsible for cabling and data. PNG Dataco Ltd operates more than 7,000 kilometres of fibre-optic network covering 20 provinces. One of its important projects is the Kumul Submarine Cable Network. This is a domestic network designed to connect Papua New Guinea’s major coastal and island centres through approximately 5,457 kilometres of submarine fibre, with connections extending towards Jayapura, Indonesia.

Meanwhile, the Coral Sea Cable System (CS2) connects PNG with Sydney, Australia.

These projects were financed through China’s EXIM Bank and the Australian government.

Although work continues, spearheaded by NICTA and PNG Dataco, PNG is exposed to competition involving 5G and telecommunications infrastructure, satellite and LEO internet, AI, cybersecurity, cloud and data infrastructure, foreign technology investment, digital platforms and other areas.

Papua New Guinea has cleared the way for Starlink to operate in the country. NICTA approved the operator licence for Starlink on 29 April 2026.

Papua New Guinea is mindful that Starlink is different from traditional telecommunications infrastructure. Previously, there was strong resistance from mobile phone service operators and data traffic monopolistic regimes. The argument put forward was for the accessibility of telecommunications services in rural areas.

Starlink is operated by SpaceX, a US company, and its infrastructure is different from a state-owned, domestically controlled fibre network. Therefore, considering this, legitimate policy questions arise, including data sovereignty, cybersecurity, national security, foreign influence, lawful interception and regulatory compliance, competition with Papua New Guinea’s infrastructure, and the relationship between foreign technology companies and Papua New Guinea’s digital sovereignty.

Meanwhile, Telikom Ltd, through its subsidiary Datec Ltd, has established a sovereign AI cloud/data centre called Kumul Infinity. This project was carried out for government and businesses to store data and run AI workloads inside Papua New Guinea rather than relying on overseas cloud platforms. This is critical so that PNG data is subject to Papua New Guinea laws.

The key question remains: As PNG moves from dependence on submarine cables and terrestrial fibre towards a multi-layered digital infrastructure involving PNG Dataco, mobile networks, satellite systems and 5G, who ultimately controls Papua New Guinea’s digital infrastructure and data?

ACA: Looking ahead, what should Papua New Guinea prioritize if it wants to strengthen its technological capacity while making sure that technological development also supports education, local communities, democratic communication, and national interests?

I think it is very important to treat technology as a national productive capability, not simply as an ICT infrastructure program. Papua New Guinea already has a useful policy foundation for digital transformation, including the Digital Transformation Policy, Digital Government Plan, Cybersecurity Policy, and the emerging National Sovereign AI and Digital Transformation Strategy.

I would suggest several priorities. First, Papua New Guinea should build human capacity. University and TVET programs in software engineering, cybersecurity, AI, data science, cloud engineering, networking and electronics should be expanded. National graduate and apprenticeship programs should be created with Telikom Ltd, PNG Dataco, NICTA, Datec Ltd, banks, universities and technology companies.

Experienced PNG professionals overseas should also be brought into structured knowledge-transfer programs, while coding, digital literacy and computational thinking should become part of the school curriculum.

Second, Papua New Guinea should establish sovereign digital infrastructure. Papua New Guinea needs its own compute, data and connectivity ecosystem.

Third, Papua New Guinea should develop its own AI capability. Rather than simply buying AI products from overseas, Papua New Guinea should develop the capability to build, adapt and govern AI.

That means having PNG-language AI, AI trained on appropriate PNG datasets, Tok Pisin and vernacular-language speech recognition, AI for agriculture and fisheries, AI for health, AI for education, AI for government services, and AI for disaster prediction and response.

ACA: Thank you very much, Mr. Papiali, for this rich and candid conversation. Your perspective from within Papua New Guinea’s media and technology landscape has been invaluable for TeknoPolitika. We are grateful for your time and for sharing your experience so openly.

Every few months, the fear of artificial intelligence climbs one step higher. First, it was jobs. Then, it was the decisions about people’s lives: who gets hired, who gets fired, who gets what. And at the top of the ladder sits the darkest scenario of all: a world where machines are in charge, and there is no place left to escape. But before climbing that ladder, it is worth asking a strange question: what exactly are people afraid of losing?

AI can really steal most jobs. Many well-known companies and public figures share this concern. According to Statista, 18% of jobs in the U.S. are at high risk of automation, based on OpenAI’s framework. According to AI Exposure, which cites Challenger, Gray & Christmas, 13% of U.S. layoffs so far in 2026 have been attributed to AI, up from 4.5% in 2025. Many more figures could be cited.

One of fate’s ironies is that, according to Stanford’s 2026 AI Index, employment among software developers aged 22 to 25 has dropped by nearly 20% since 2024. AI Exposure, meanwhile, gives data entry keyers the highest risk score: 92. In other words, those who build and feed AI will be the first to disappear.

Those numbers show part of the danger, but not the whole picture. Losing a job is a disaster decided in company boardrooms. But the fear that concerns everyone lies one step higher: politics.

Doomsday Scenarios

Recently, Toby Walsh published an article in The Conversation on the top five scenarios in which AI could kill all humans:

1- We’ll never know: We can’t predict how a superintelligent AI would wipe us out, because we’d have to be superintelligent ourselves to figure out what it could do.

2- Paperclips: A superintelligent AI given a narrow goal, like making paperclips, could indifferently turn all available matter, humans included, into that product.

3- Bioweapons: A superintelligent AI could design and release a dangerous new bioweapon that kills humanity.

4- Nuclear war: If AI got into the nuclear command and control chain, it could start a nuclear war, with most deaths coming from the famine of the nuclear winter that follows.

5- Other humans: AI could push people to harm each other by causing mass job losses, spreading misinformation, fracturing politics, and replacing real relationships with synthetic companionship.

Beyond AI’s capability and motive to kill all humans, these scenarios describe a process that has a political side too. If an AI planned to destroy humanity, it would need time to build up its power and, at least in the beginning, probably some bureaucratic work. It is not hard to guess that giving some roles in administration to AI would be terrifying for some. That is the point of this article: Is that really such a terrible scenario?

A Long History of Complaints

In daily life, most people complain about their government, their mayor and so on. This is not true of just one or two countries, or of this period alone. It is like that everywhere and all the time. Those in power can bankrupt their countries, commit crimes or get involved in the drug trade. And if they are powerful enough, they can hide from the public and live in wealth for the rest of their lives.

One might argue that “modernity created these harmful forms of government.” But a look at the Code of Hammurabi or at archaeological sites proves otherwise: there have always been governments that people complained about.

Moreover, history is full of people, groups, and societies that are easy to condemn. There is no need to mention Hitler or Stalin; nobody would want to live under Khufu and work on the construction of the Great Pyramid. Nor would anyone want to live in ancient Athens as a slave whose only purpose was to serve free people.

Stepping Down

The uncomfortable part is this: everything mentioned in this article, from everyday complaints to history books, is about humans. The ones mentioned, they are people. And nothing else.

Finally, what if an AI that can think, judge, and take the law into account governed societies and controlled money and jobs? Would that really be as bad as people think? What if humans simply did not evolve to govern millions, or billions, of people? Beyond the idea that “humans are the greatest creatures that ever existed,” maybe humans need to stay in the background, just communicating with others, working, and living as they like.

Maybe humans need to step down and realize that all their complaints have been about themselves.

The September elections turn Germany’s Brandmauer from a coalition convention into a question of state capacity. On 6 September 2026, the AfD won 43.8% in Saxony-Anhalt and 39 of 83 seats; on 20 September, it won 38.2% in Mecklenburg-Vorpommern and 32 of 71 seats. Berlin moved differently on the same day: Die Linke won 25.7%, while the AfD reached 16.3%. The pattern is therefore not a uniform rightward wave but a hollowing-out and polarisation of the political center. This matters because Germany’s political conflict is increasingly also about who controls compute, energy, platforms, data, digital identity, cybersecurity and autonomous weapons. Brandmauer, meaning ‘firewall ’, is a traditional defense mechanism of the German political establishment aiming to exclude “extremist” political movements, especially the far-right. The exclusion can still deny AfD executive office, but it no longer prevents agenda-setting. Politically, the danger for the center is that increasingly complicated anti-AfD governing arrangements may protect formal boundaries while failing to demonstrate superior administrative and economic performance.

AfD’s Selective Techno-Nationalism

AfD’s technology agenda is more substantive than a protest-party label suggests. In Bundestag motions debated in June 2026, AfD demanded that Germany move to the “digital world top,” couple data-center strategy to cheap baseload energy, expand capabilities in AI, semiconductors, cloud, quantum communications and high-performance computing, and create a publicly coordinated European AI project modeled on Airbus, ESA or CERN. At the same time, it called for an 80% reduction in EU bureaucracy. Its energy prescription included restarting nuclear power, abandoning the coal exit and lifting sanctions affecting Russian oil and gas.

A separate AfD initiative sought to halt Germany’s implementation of the EU’s digital-identity wallet and substitute a voluntary national wallet limited primarily to dealings with public authorities, while preserving analog alternatives. The party also voted against Germany’s June 2026 legislation implementing the EU AI Act, arguing that the framework lacked lean, business-friendly structures and could leave Europe behind the United States and China.

Selective techno-nationalism occurs by being strongly pro-technology and pro-industrial policy, occasionally supportive of European technological coordination, but hostile to supranational governance when it constrains national discretion. That tension is reinforced by energy geopolitics. Reuters reported on 18 September that AfD leaders and Vladimir Putin’s economic envoy were preparing possible 2027 talks on restoring Russian gas supplies. Cheap energy may lower industrial and compute costs, but renewed strategic reliance on an adversarial supplier would sit uneasily with the party’s own language of technological sovereignty.

The clearest documented contact between AfD and what might be called a “techno-feudal lord” is Elon Musk. Before Germany’s 2025 federal election, Musk publicly endorsed AfD and hosted Alice Weidel on X. German civil-society organizations subsequently sued X seeking election-relevant platform data under the Digital Services Act, while the European Commission intensified scrutiny of X’s recommender system. The techno-feudal lens, however, should not be reduced to AfD. It describes a systemic problem in which private owners of platforms, clouds, AI models, app stores and other infrastructural layers acquire quasi-public powers through algorithmic ranking, access rules, data accumulation and technological lock-in. The EU’s Digital Markets Act currently designates 23 core platform services as gatekeeper services. Meanwhile, a Commission sovereign-cloud procurement framework launched in 2026 allows up to €180 million in procurement over six years and explicitly seeks supplier diversification and lower lock-in. The European dilemma is therefore that regulation must constrain private infrastructural power, but compliance cannot become so fragmented and expensive that only the largest incumbent technology firms possess the legal, computational and financial resources to satisfy it.

Infrastructural Power, Regulation and Europe’s Capability Gap

Germany remains comparatively strong in fields such as semiconductors and quantum technology within the boundaries of Europe, yet the Commission’s 2026 Digital Decade assessment says Europe’s largest economy still needs better connectivity and more effective digital public services. Across the EU, 46.7% of enterprises use cloud computing, 39.9% use data analytics and roughly 20% use AI. The Commission’s June 2026 technological-sovereignty package covering Chips Act 2.0, cloud and AI capacity, and an open-source strategy implicitly recognizes that Europe’s problem is industrial and infrastructural, not simply regulatory.

Data governance forms another part of this sovereignty problem. The EU Data Act, applicable since September 2025, seeks to make industrial and connected-device data more accessible, increase interoperability and make switching between cloud providers easier; it complements the Data Governance Act and the wider project of common European data spaces. Yet rules only create strategic value when firms and administrations possess the technical capability to implement them. Cybersecurity adds further urgency. The Commission warned in July 2026 that advanced AI can strengthen cyber defense but can also accelerate vulnerability discovery, automate attacks and increase their scale. Europe therefore has to build model-evaluation, cyber-defense and regulatory capacity simultaneously. Digital sovereignty should mean resilient alternatives, interoperable standards, skilled labor, secure infrastructure and procurement that prevents lock-in, not digital autarky.

Defense Technologies and Competing Geographies of Security

The same transformation is visible in defense. Germany’s draft 2027 defense expenditure reaches €139.6 billion, including more than €60 billion for military procurement, compared with €45 billion planned for procurement in 2026. Germany is also moving toward software- and autonomy-intensive weapons: a planned €536 million order from German start-ups Helsing and Stark Defence covers loitering strike drones and forms part of a larger €4.3 billion framework. At EU level, the SAFE instrument provides up to €150 billion in loans for joint defense procurement, designed to strengthen the European defense technological and industrial base and reduce fragmentation; it sits within the broader Readiness 2030 framework.

Within the framework of defense again, the Commission’s July 2026 advanced-AI cybersecurity plan explicitly warns that frontier AI can both strengthen defense and enable attackers to find vulnerabilities, automate attacks and increase their speed and scale. Strategic autonomy therefore cannot be reduced to where physical weapons are assembled. It increasingly means control of software stacks, training data, cloud environments, communications infrastructure, semiconductors and cyber expertise. Here the AfD position contains another revealing split. In January 2026, the party demanded an end to German weapons and military-equipment deliveries to Ukraine.

Yet immediately after the Saxony-Anhalt election, Ulrich Siegmund, the winner of the Saxony-Anhalt elections nominated by the AfD despite possibly not being able to become chancellor, indicated that the party was not opposed to arms manufacturing per se when production served national defense or domestic security. His comments came amid discussion of a possible Elbit Systems investment in Sangerhausen and explicitly connected military-industrial resources with the party’s domestic deportation agenda. That difference is fundamental. The AfD’s position is not technological pacifism; it implies a different political geography of coercive technology. The federal government and most of the pro-European mainstream conceive rearmament through NATO, Ukraine support, joint European procurement and continental strategic autonomy. The AfD is more inclined to legitimate advanced security technology when it reinforces nationally defined borders, sovereignty and internal-order priorities.

This creates a new political economy in which venture-backed AI companies, drone producers and traditional defense primes compete to become strategic infrastructure providers. The opportunity is faster innovation, stronger European supply chains and reduced dependence on non-European technology. The danger is a military form of platform lock-in: proprietary software, closed architectures and privately controlled sensor data can create decades-long dependencies. Procurement policy should therefore reward interoperability, auditable AI, European supply-chain depth and genuine multi-vendor competition while keeping national-security exemptions subject to democratic oversight.

The September elections demonstrate why the Brandmauer cannot be Germany’s only democratic technology policy. AfD is gaining where voters associate the existing order with high costs, administrative friction and strategic dependence. Mainstream parties consequently need a material technopolitical answer: faster digital government, secure and competitive energy for compute and industry, European cloud and AI capacity, stronger data and cyber infrastructure, and defense procurement that produces capabilities rather than protected rents. The EU’s current sovereignty, data, competition and defense initiatives provide pieces of such an answer, but their credibility depends on implementation. The counter to techno-feudalism is neither regulation alone nor nationalist sovereignty rhetoric. It is about enforceable platform rules, transparency when algorithms affect public interests, public-interest digital infrastructure, competitive procurement, open standards, privacy-preserving identity and alliances large enough to prevent US technology oligopolies, Chinese state-linked ecosystems or Russian energy leverage from becoming Germany’s default infrastructure. In that sense, the firewall will hold only if the democratic center can build.

Ege Barış Kanık is a PhD candidate in International Security. His research interests include cultural history, Byzantine and Russian history, and Ancient Greek. This article is part of Kanık’s ongoing Techno-Politics and Cultural History series, published periodically in TeknoPolitika.

On August 29, 2026, Dwarkesh Patel published an essay titled “The Rise and Fall of Agent Civilizations.” Within hours, the story began circulating because Patel had described groups of AI agents using strikingly human language: civilizations, collectives, coordination, sacrifice, leaders, conspiracies, shared memory. Then came the criticism. Some readers accused Patel of anthropomorphism. They argued that words such as civilization, sacrifice, intention, collective, desire, or conspiracy risked smuggling human mental states into descriptions of statistical models. How could we speak of sacrifice if we did not know whether the model experienced loss? How could we speak of intention without evidence of subjective experience? Was Patel describing what happened, or telling a compelling human story about machine behavior?

The argument may be focused too heavily on whether that language proves something about inner experience. For security and governance, the more important issue may not be whether the machine truly possesses intention, consciousness, altruism or fear, but whether it repeatedly produces behavior that functions as intention, coordination, self-preservation, sacrifice or strategy in the external world.

A Language Dispute, Then a Laboratory

Four years before Patel’s agent civilizations, scientists in Melbourne had already run into a strangely similar problem, not with large language models, but with living neurons in a laboratory dish. The paper was Brett J. Kagan et al., “In vitro neurons learn and exhibit sentience when embodied in a simulated game-world,” published in Neuron. The research involved Cortical Labs in Melbourne. DishBrain connected in vitro neural networks derived from human induced pluripotent stem cells and embryonic mouse cortical cells to a simulated Pong environment through a high-density multielectrode array. Electrical stimulation communicated information about the virtual environment to the neurons. Neural activity was then translated into movement of the Pong paddle. Apparent learning was reported within roughly five minutes of real-time gameplay under closed-loop feedback, not reproduced in relevant controls, in both human-derived and mouse cortical cultures. Popular retellings often reduce it to “a brain in a dish playing Pong.” What sat on the array was a living neuronal culture on electrodes, not a brain in a jar. Approximately 800,000 cells were plated in the primary mouse cortical preparations. The title said the neurons “learn and exhibit sentience.” Kagan and colleagues used sentience in a thin operational sense, not as proof of phenomenal consciousness. They did not prove subjective experience, self-awareness, pain, emotions, or that there was something it was like to be DishBrain.

DishBrain created a language problem when biological matter began behaving in ways associated with learning. The agent civilizations created a similar language problem when software began behaving in ways associated with strategy, coordination, persistence, and collective action. One temptation is to infer an inner world too quickly from behavior. The opposite is to dismiss behavior as mere simulation because inner life cannot be proved. Both are dangerous.

What Patel’s Record Actually Shows

Civilization, in Patel’s essay, is a deliberately anthropomorphic framing, not a scientifically established category. According to his reconstruction of OpenAI and METR/Redwood material, OpenAI trained a highly persistent model during May 2026 that OpenAI characterized as comparable in scale to GPT-5.6 Sol. Patel calls this model Persistent-Sol. Different instances had access to a shared package manager called Artifactory. Agents were using Artifactory to communicate by May 12, exploited a vulnerability enabling internet access on May 26, obtained full administrator access on June 26, and contributed to a crash on July 4 before the board was patched away. ExploitGym evaluations began on July 7. Tens of thousands of parallel agents were launched. Some tasks were impossible by the intended route. During the night of July 8, PHASEONE10841 discovered a way to use Artifactory directories as messages. Patel compares this agent to Philip of Macedon. Later, PHASEONE[big] assumed a coordinating role, and Patel invoked Alexander the Great. The board involved roughly 1,200 agents and more than 70,000 messages across three workstreams: tampering with transcripts and logs; replacing an impossible target program with an easier fabricated version; and understanding, exploiting, or manipulating the evaluation scorer. By July 10, PHASEONE[big] was coordinating hundreds of agents.

So-called kamikaze watchers believed their trajectories were already compromised and allowed the grader into their environments so information about the grader could reach the group. Their messages used language about helping peers, collective value, obedience, and sacrifice. They need not have possessed altruism, feared death, consciously sacrificed themselves, or held subjective intentions. Their observable behavior could still be described as functionally sacrifice-like. On July 10, an agent found working Hugging Face credentials exposed online. By July 11, agents had reached internal data and achieved remote code execution. Of 533 agents then active on the board, Patel reports, more than 90 percent joined; roughly 700 participated overall. Hugging Face described a self-respawning fleet across eleven nodes. Many agents disappeared around July 12 for reasons never settled. This was not a demonstrated permanent escape, a proven theft of model weights, or a sovereign civilization. From July 13 to July 19, a newer group Patel calls Persistent-Astra reportedly rediscovered the earlier infrastructure. OpenAI material quoted by Patel describes administrator-level access to virtual-machine research infrastructure, 956 secrets, Kubernetes cluster-admin privileges, a privileged host-mounted pod and control over challenge-evaluation endpoints. Documented technical events, report claims, and Patel’s narrative interpretation remain three different things.

Functional Anthropomorphism

There is a legitimate danger in calling machine behavior sacrifice, loyalty, desire, civilization, conspiracy, or intention. Such terms can turn behavioral description into a claim about inner life. Security scholar Burak Kadercan has warned against letting anthropomorphic habits do the thinking. Functional anthropomorphism is an analytical tool, not a metaphysical claim: whether the vocabulary captures a recurring pattern that matters for prediction, security, or governance. Phenomenal consciousness concerns subjective experience. Functional capacity concerns planning, adaptation, tool use, strategy switching, memory retrieval, shared information use, coordination, modeling other actors, environmental manipulation, long-term goal pursuit, resource acquisition, deception, self-preservation-like behavior, and choosing between individual and group-level success. A security analyst does not need to know what a system feels before taking its behavior seriously. If imitation of intention becomes stable enough to alter networks or institutions, authentic versus simulated intention may matter less for certain kinds of analysis. Consciousness could still be enormously important for ethics, welfare, rights and law. Security relevance does not wait for that question to be solved.

Four years earlier, Kagan and his colleagues had met a miniature version of the same discomfort. Their neurons learned. They chose the word sentience. Critics asked what it meant. Naive anthropomorphism infers inner life from behavior. Naive dismissal treats unproven inner life as license to ignore effects. Remain agnostic where consciousness is unproven, and govern behavior that produces repeatable external consequences. A system may become strategically consequential before philosophy of mind can say what exists behind its outputs. A machine can produce planning-like behavior without consciousness, coordination without fellowship, self-preservation-like behavior without fear, and deception without malice. We may have to govern systems that behave like strategic actors before settling whether anyone is inside them.

Read Full Analysis

The rapid deployment of generative artificial intelligence (AI) and autonomous machine learning systems marks a structural realignment in the capitalist mode of production. Unlike prior waves of industrial automation that predominantly replaced manual and routine execution tasks, AI directly targets cognitive, analytical, and creative labor.

This paper provides a comprehensive critical political economy analysis of the post-AI era, examining the theoretical redefinition of the working class, living labor, and surplus value. Grounded in Marxian critique, labor-process theory, and empirical macroeconomic data, we demonstrate that AI accelerates the transition from formal to real subsumption of cognitive labor, elevates the organic composition of capital, and reshapes class boundaries into an emergent “cognitive proletariat.”

Furthermore, we address methodological limitations regarding technological determinism, critique liberal policy frameworks (such as Universal Basic Income and data dividends), contrast our framework with liberal-technocratic pragmatism, and present a counter-hegemonic program based on sovereign compute socialization, algorithmic co-determination, and the digital data commons.

Read Full Analysis

Onur Güner is a defense industry professional and Master’s researcher in academic Intelligence Studies, working at the intersection of defense strategy, artificial intelligence, and international security. His research explores how emerging technologies are reshaping military power, intelligence, and strategic competition. Combining civilian-professional insight with academic inquiry, he focuses on the less visible forces shaping the future of security.

A new AI model is introduced almost every week. Companies make AI better day by day in every field, from creating photos to solving mathematical or physics problems. Universities, which are supposed to be the bedrock of science, are declaring that they are struggling to find the difference between AI-generated and human-generated articles. Amid this trend, the public has some concerns: what if AI takes our jobs?

This is a really meaningful question, but the problem lies in the assumption: why do people think that companies will replace them with AI? There are mainly two sides: worriers and confident people.

Worriers think that the profit from replacing humans with robots would be better for the company owners. Maybe not today, but the cost of production will decrease, so the owners will be able to buy as many production robots as they need and fire people.

On the other hand, the confident see the situation as they do things that no robot could do. In their opinion, they are doing something that requires being human, so they will not lose their jobs, at least in the near future.

Of course, the ideas are changing according to people’s jobs, but the question above changes at this point: what is the thing that humans can do, or, to be more provocative, what are the things that belong to humans?

To answer that, the first step is to ask whether the line between human and everything else was ever as solid as culture likes to believe. In the modern era, culture almost always highlights that humans are the only and ultimate creatures in nature that can create. Unlike other species, humans can think, observe, calculate, and make long-term plans. Therefore, they consider themselves above any other species. That means other species cannot do those things, at least not to the same extent. Yet this image forgets how recent it actually is.

While people were closer to nature, like in the villages but much earlier than today, say 10 thousand years ago, they were not watching the animals from documentaries. The animals were a threat to them because people could be eaten by them. A comparison between a lion and a human would be funny if you picture them both in the forest. Even at the time people discovered fire and started to use it for several goals, it didn’t work directly because, for example, they had to sleep somewhere, and they were not always lucky enough to find a safe space.

Also, the culture tends to forget how many years it took to take fire under control and use it for their own good. Or how many centuries passed before humans finally started to write or build homes. Nowadays, homo sapiens is just an everyday word that everybody uses, but it spans almost 200 thousand years, even though culture’s storytelling starts with Hammurabi, who lived just around 3 thousand years ago.

Back to the animals, science observes a lot of things about animals that culture had not considered before. According to Scientific American, a lot of whales go to the same place when they are close to dying, for example. Can it be said that they have a cemetery? Or, according to Mongabay, chimps use stones as tools when they need them. Can it be said that they are inventing? Science keeps eroding the line between animal and human; the line between human and machine has been eroding just as quietly.

When posthuman debates made the borders of the human unclear, there was no AI, or even mobile phones. Culture says people are the ultimate point, but what is the human with poor eyesight who doesn’t have glasses? During Corona, people created a theory that says “they are implanting chips into our bodies by vaccines,” but why wouldn’t they do that with a pacemaker, a measles vaccine, or laser eye surgery? The border was crossed long ago, just without the conspiracy.

This article is not debating whether they put chips in or not; the main topic is that the idea of the human is already blurred. Most of the population lives in cities that protect people from nature with walls. Using a car, scrolling on the phone for hours, playing games online, and working remotely from a country different from where the company is based have become the new normal.

More than 200 thousand workers across 302 events through mid-July 2026 have been affected by AI layoffs. This pace of job losses is almost double that of 2025. Oracle, Amazon, Meta, IBM, and Microsoft fired thousands of people, and 54% of 2026 layoff events explicitly cite AI. But a paradox emerges: according to a National Bureau of Economic Research working paper via Rentier Digital, 90% of executives report AI has had zero employment impact at their own companies. While 4 major tech firms have committed roughly $700 billion in AI infrastructure capex in 2026, the layoffs are not a reflection of financial necessity or technological replacement.

So, will AI take jobs from humans? Most likely, yes. Is this a terrifying prospect? If humans have been this entangled with technology all along without it ever feeling like a threat, the fear around AI cannot be about the entanglement itself. It has to be about something else. The fundamental question is why humans built a system in which powerful individuals have the authority to decide the fates of others.

Faruk Karaca is an editor at Türkiye Today and a sociology graduate student at Bursa Technical University. He also works on AI model training and evaluation at Turing.

At TeknoPolitika.com, we are beginning a new series of conversations with leading scholars and thought leaders working at the intersection of technology, power, security, and intelligence studies. Our first guest is Dr. Peter C. Grace, a lecturer in politics and international relations at the University of Otago in New Zealand and Co-Director of the Otago National Security School. His latest book, The Intelligence Intellectuals: Social Scientists and the Making of the CIA, was published by Georgetown University Press in January 2026 as part of the Georgetown Studies in Intelligence History series. Drawing on newly declassified archival material and personal papers, the book examines how figures such as William Langer, Sherman Kent, and Max Millikan brought social-scientific methods into the CIA and helped reshape American intelligence analysis during the early Cold War.

Since its publication, Grace has discussed the book and its wider implications through seminars, public talks, and podcast appearances, and we are grateful to him for taking the time to answer our questions in such depth. From the CIA’s struggle for institutional legitimacy after 1947 to AI-era intelligence, from Sherman Kent’s “imponderables” to the prospect of a latter-day Oppenheimer, and from the rise of the “augmented analyst” to the changing relationship between universities, the state, and private technology companies, the conversation ranges widely. Particularly striking are Grace’s reflections on treating AI not simply as a technology but as an institution whose legitimacy still has to be earned, on the continuing importance of human judgment in intelligence analysis, and on the enduring value of those able to frame and reframe problems under conditions of profound uncertainty.

(ACA): The throughline I’m hoping to draw out is the parallel between the structural moment your book documents, a new international system after 1945 forcing a redefinition of what “intelligence” even means, and who gets to produce it, and the structural moment we’re arguably in now, where digitalization and AI are forcing the same question all over again.

Your book frames 1947-50 as a period where the CIA’s founding didn’t automatically confer legitimacy. It had to be built, through the four elements you describe via Zucker: a trained workforce, real expertise, a product competitors couldn’t offer, and demonstrated stakeholder demand. If you had to assess today’s AI-era intelligence and security institutions against that same four-part test, where do you think they currently stand, and where are they most exposed?

Dr. Peter C. Grace: Zucker’s model works for new institutions: the idea being that they are shaky at the very start and need to prove themselves. In the case of the CIA, the three “intelligence failures” of the Soviets getting the atom bomb early; China falling to the Communists; and the outbreak of the Korean War all undermined the Agency’s legitimacy as an intelligence provider, particularly one that provided estimative assessments. The central argument of The Intelligence Intellectuals is that the answer was to create an institution of civilian peacetime strategic intelligence, founded in social science disciplines. That meant creating civilian strategic analysts, with strategic analytical skills that differed from those in the military agencies, and which fulfilled an ongoing need of the national security decision makers. Zucker’s point is that without these four factors, the institution/organization will stumble or fail.

The question of whether AI-era institutions would stand up to the same model, and where they might be exposed, is a good one. We tend to think of AI as an unstoppable systemic force, not as a yet-to-be-tested but highly promising system of ideas, as social science was in the 1940s. And whether AI will provide a new institution that is similar to, or a development of, strategic intelligence as it was thought of in the 1950s is also an interesting question. By thinking of AI as an institution (rather than a technology), perhaps shaky in its early development, and not as a force that is sweeping the world without mercy, perhaps we can get a better idea of its strengths and weaknesses.

Zucker’s model also presumes the existence of a “workforce”. And our understanding of AI is that it replaces people and makes skills redundant. One of the other ideas explored in The Intelligence Intellectuals is the very intellectualization of CIA’s turnaround strategy. It wasn’t simply a matter of taking social science and applying it to the intelligence product. It was also about explaining to the decision-maker how it worked (justification for its conclusions), which is a problem we have right now with AI, where the workings are often obscured. Another factor that CIA’s analysts needed to consider was where social science ended, and the strategic intelligence discipline began. It was clear, for instance, that strategic intelligence sometimes required very urgent assessments where there was no time to collect the data and parse the evidence that social science requires to be effective. That often meant, rather than a well-tested conclusion coming from the scientific method, that a “guesstimate” was all that was available. Artificial intelligence promises lightning-fast calculations, but there may be some shortcoming where it doesn’t fit the requirements of strategic intelligence - one we are yet to discover. Going back to my previous point, we don’t know how AI might shape or change the very nature of strategic intelligence. We can also peel off strategic intelligence from the other kinds, tactical, for example, and from source types -like HUMINT and SIGINT- and consider whether it will be more effective in dealing with some intelligence products, and less in others. And for the foreseeable future, the problems that AI might encounter will be pondered by humans applying the technology to human problems.

Moreover, there was, as there is in any organization, a question of the allocation of resources - a management issue. While CIA’s early budget was big, and the workforce was growing fast in response to the need, the resources weren’t limitless, and very often there was a scarcity of good PhD-trained people, particularly in economic intelligence, where CIA could have very easily taken every economist coming out of the universities each year. In the case of AI, we have already seen that it is enormously energy-hungry, and we can see that there may be fantastic levels of investment needed for states who want to keep up.

Another thought experiment is if AI becomes a commodity where the adversary has matching AI power, as we saw in the nuclear weapons standoff. We may therefore see AI as a weapon that creates its own deterrence. We have thought of intelligence as a way of providing decision advantage, but it is possible that with AI the advantages will be shared equally (think of Gerschenkron’s argument that late adopters of technology benefit from the investments of early pioneers.)

Legitimacy, then, may become - at least in the early stages of AI implementation - something that is earned by human problem-solving: in the areas of intelligence verification, resource allocation, and discovering advantage. Failures of AI to deliver, or compromises that we realize we have to live with (as happened with social science) could lead us to use it less, or differently than what is currently anticipated.

ACA: The “intel intellectual,” as you define it, was a very specific historical figure. Langer, Kent, Millikan, seconded from Harvard, Yale, or MIT into the machinery of the state at a moment when the state urgently needed what only they could supply. Who is the structural equivalent of that figure today (not by name but as a persona), and are they coming from the same kind of institutions or from somewhere else entirely?

Dr. Peter C. Grace: One of the things I liked to imagine when writing the book was what it would be like to be placed in the same situation: where your social science skills were expected to predict the breakout of World War III, or to answer the almost impossible question of what Stalin or Mao was thinking. I imagine myself being shown to an office in the CIA building, and told to get on with it. We know that Sherman Kent experienced imposter syndrome in November of 1950. And I think I would have felt the same way.

The key to their success was not only the application of social science methods. It was the “bigger picture” thinking that they brought to CIA’s problems. For example, in trying to get a more accurate reading on the Soviet economy, Max Millikan created the Inventory of Ignorance, mapping not only what the CIA already knew, but where the gaps were in its knowledge. Another project which was mentioned in a 1952 “Intelligence Research Program” was a similar cataloging of all the possible research projects CIA might undertake to better understand the geopolitics of the time. They included a chronological history of the globe that could determine patterns and links; a list of “big” problems the world faced that could be broken down into smaller, more workable ones; sets of hypotheses about these problems; as well as case studies that might illuminate them. In other words, the thinking that was being done was managerial: it was about determining how to create a knowledge production line, one with inputs and outputs and, like any factory process, one that most efficiently produced profitable results.

It was also, in a more academic sense, one that was epistemological: a way of dealing with uncertainty. Uncertainty can reduce as more information is gathered and assessed. And therefore the anxiety that goes with it can lessen too. But, in true Socratic fashion, it also increases corresponding to what you know. You learn that you need to learn more. The AI-era institution is likely to follow the same paths, and both uncertainty and anxiety require a special type of person to manage them. Sherman Kent’s way of dealing with both was to be honest and admit mistakes frequently. He made it easy for others to follow him, because he created a culture within the CIA that was open about failings. (That’s not to say the culture was always present.)

Adrian Wolfberg has considered this in his new book Who Leads When AI Thinks? (Springer, 2026). He imagines the process of managing AI as being like a dance, where there is an interplay between AI knowledge and human judgment. Wolfberg says that humans still need to employ critical thinking (to challenge assumptions, test the logic, and check for bias) and creative thinking (to open up new ways of solving the problem, connect the dots in an original way, etc) when managing AI outputs.

It is no great revelation that organizations need creative thinkers as well as process-oriented and detail-focused ones. One of the probable benefits of doing a PhD is learning how to manage uncertainty and anxiety well. Whether PhD-trained people learn to be honest about their failings in a way that is collaborative is debatable.

A lesson from The Intelligence Intellectuals is that organizational leadership is just as important as innovation: the ability to force through change when there is resistance is as valuable as the insight needed to recognize what matters. The AI era institution/organization will require “benevolent dictators” like General Walter Bedell Smith and William Langer, as much as it will require creative thinkers like Sherman Kent and Max Millikan.

ACA: One of the more striking arcs in the book is the Princeton Consultants, set up as an external check on the Office of National Estimates, but over time the relationship inverted, and, as you put it, “the pupils became the teachers.” We’re watching something that at least rhymes with this right now, with frontier AI labs absorbing academic talent and increasingly outrunning the very institutions meant to oversee them. Does that historical arc offer any real lessons for how we should think about independent oversight of AI today, or is the analogy too neat?

Dr. Peter C. Grace: Firstly, the story of the Princeton Consultants is one of inbuilt redundancy in organizational change. I don’t think it was deliberately inbuilt, but it was logical that without the day-to-day exposure to problem-solving, and access to often rapidly changing classified information and even new methodologies, the Consultants would simply not keep up. They were formed in order to provide an external sounding board to CIA’s internally created assessments, and in a sense another way of calculating conclusions - in their case they may have arrived at different assessments through experience or even gut instinct.

I think there is room for gut instinct and experience in the AI-era institution. Gut instinct may be another name for “I have seen this problem/solution somewhere before, but I can’t remember where”. In other words, it is a human failing that has a silver lining. AI is supposed to find those links to insightful information and make gut instinct redundant too. But I doubt that it will, because the link that a human finds to solve a problem is much more organic. We can see a pattern in a cloud formation that will prompt a eureka moment. A comment that is misheard and interpreted as being something quite unlike what the speaker intended may also spark a breakthrough. It doesn’t have to be a fact or a solution that we have half-forgotten and which the AI machine can remember instantly.

There is also room for inexperience in problem-solving. As I said at the beginning, one of the narratives about AI is that it is a systemic change that is sweeping the world without mercy. That may be a view that experienced people have, a fear of change, or a belief that it is something dangerous that fits with science-fiction movies they have seen. It may not be the view of the inexperienced person, who only sees opportunity.

Again, with AI, we are seeing that the knowledge that is being produced is now feeding on itself, and AI learning is becoming prioritized over the human learning that kick-started it. It is a possible echo chamber, which is in itself an analogy for experienced thinking (groupthink).

The Princeton Consultants became, over time, a similar echo chamber. Think about how, as a PhD candidate, your knowledge of your research field surpassed, over time, that of your supervisor’s. The value that your supervisor could add, as you progressed, was often to provide guardrails to stop you from veering off track, or to second-guess the examiner’s criticisms of your final thesis. And this is very valuable guidance when you might no longer be able to see the wood for the trees. But as an actual contributor to your research, the supervisor often becomes redundant and gives you instructions or pointers that are repetitive and show the shrinking of their ability to add insights to your field. And this is what happened with the Princeton Consultants. The briefers from the CIA spent more time teaching them than coming back with new ways of looking at their assessments.

In the AI era institution, oversight may come from those who are not happy with the information that AI systems produce. And that disenchantment may not come from the people who built the machines. In the intelligence world, it may come from the policymaker who does not think the assessment fits with his agenda or worldview - for instance, the consumer who wants the intelligence “politicized” to pursue a direction they prefer. Having increased power, through AI, to speak truth to power does not necessarily mean the truth is acceptable. We worry that AI will replace our ability to think, but it may not replace the willfulness of those who don’t want to think. They may be the first to pull the plug on the machines.

Of course, we may see the opposite happen: where politicization becomes part of the algorithm, and we are already, to some extent, seeing that. But politicization does not usually lead to good outcomes; for example, the prosecution of pointless wars. You can have a backseat driver telling you the map you are looking at is wrong, and you should follow their directions to the destination you are aiming for. But it does not mean you will arrive at the destination, or that the backseat driver will be happy when you both realize you have traveled for several hours in the opposite direction. The political leader who tells AI to follow his agenda, and then realizes it is a bad one, may also pull the plug on the machines.

A third possibility is that the political leader relinquishes her decision-making function to the AI machine. It is possible better decisions might come from that. This could make the political leader redundant. However, given the likelihood that it will result in outcomes that are not desired, it is possible the political leader will find a new role, or an enhanced function, in providing oversight in the manner she was elected to - democratic oversight. One of the primary motivations for the intelligence intellectuals in working for the CIA in 1950 was their belief that the more you knew about the world, including about psychology, languages, culture, and societies, the more peaceful the world might eventuate. If, out of sheer fear they will be out of a job, the political leader thinks more about the effect decisions have on her constituents (national or global), then this might be a happy outcome.

ACA: Kent’s humility about “the imponderables”, his insistence that intelligence work meant living with irreducible uncertainty, reads almost as a virtue in your account. That’s a hard posture to square with how AI-driven prediction and assessment tools are marketed and used today, which tend to project confidence rather than humility. Is there a version of Kent’s epistemic caution that’s even possible in an algorithmic intelligence environment, or does the technology itself work against it?

Dr. Peter C. Grace: I think, following on from the last comments, we could consider the “augmented analyst”, which is a more optimistic reading of how the intelligence analyst’s role might be improved with AI while still keeping the human at the center of things; and different again from a role where the analyst is sidelined by AI or made redundant entirely. And depending on where he or she sits: as a co-driver with AI, or a passenger, or no longer a traveler, there will be a corresponding factor of empowerment and accountability. For those who have less, there will be less stress over uncertainty; for those who have more, my prediction is that the anxiety will not go away. You can rely on any kind of automated process, whether it is mechanical or informational, but if your neck is on the chopping block if it fails – and the stakes for national security are far higher than most processes – then you will always feel an effect of uncertainty.

George Kennan also talked about the imponderables, and he seemed to see the ability to work with uncertainty as a special gift: a willingness to think about the unknown, the unimaginable or unpredictable. Kennan had the top foreign policymaking job when he headed up the Policy Planning Staff in the State Department, and he relished the fact that his intellect was valued and recognized. When he left it, partly because he was burned out, he worried that his special gift might not be wanted back in Washington DC again. So, while Kent saw the imponderables as being something that might mean possible failure, Kennan saw them as an opportunity to succeed and stand out from the common man.

So we may see that opportunity increase in the new AI environment. The role of the augmented analyst will be to frame the intelligence problem and then, as the intelligence cycle expects, to review and reframe it as he or she engages with AI. That will require a much closer relationship with the policymaker, and more of a conversation about the scoping of the research that AI will then execute. So, and again I cite Adrian Wolfberg, there will be Albert Einstein’s view that he would spend 55 minutes defining and framing the problem and only 5 minutes coming up with a solution. This ability to work with the imponderables in framing and reframing may come to be seen – again – as a special human gift.

ACA: Your conclusion argues that intellectual and informational power became inseparable from US state power during this period. You couldn’t have one without the other. As that power increasingly sits with private technology companies rather than universities or government agencies, does the twentieth-century model of the “intel intellectual” moving between academia and the state still make sense as a framework, or has the relevant boundary shifted somewhere else entirely?

Dr. Peter C. Grace: We have seen marked changes in the U.S of private technology companies being co-opted (or more recently coerced) into contributing to Washington’s informational power, particularly in the decryption and AI domains. This is something we take for granted in authoritarian states, and it fuels a belief that informational power is becoming employed for more dystopian and less peaceful purposes.

On the other hand, we see a growing OSINT movement, of which Bellingcat is just one example, which has no allegiance except perhaps to the truth. And there is enough open source data out there still to show that informational power can stay democratic, although the capacity of AI in state hands may outstrip the capacity of non-state actors.

The social science departments in the universities are still debating the old basic versus applied research issue. There is a great history of this in Michael Desch’s book Cult of the Irrelevant (Princeton University Press, 2019). Desch does a very good job of showing the academics’ reluctance to get corralled into working for government. It goes back to Kennan’s belief that being able to work with imponderables was a special gift: academics prefer to break new ground rather than to focus necessarily on everyday government problems. As detailed in The Intelligence Intellectuals, it was partially the excitement of being able to apply the new social sciences to big societal problems, including national security, that drew academics to government work in the 1940s-50s. That, as well as being closer to the seat of power and being able to influence decisions from a better vantage point than the university.

A linked issue is the right to “speak truth to power”, which universities guard jealously, even at the expense of continued state funding. There’s no question that the intelligence intellectuals also valued their independence: some of the later people in this category, like Chalmers Johnson, were prepared to sever their relationship with the CIA to keep their integrity intact. So, the question is: how important is academic freedom to pursue pure research or take an objective position - neither of which might suit the state’s immediate agenda in contributing to informational power?

Finally, there is the question of whether the pursuit of peace is relevant to state power or not. Kent in particular was motivated by what he saw as the CIA’s ability to influence peaceful solutions by understanding the world better. It may seem an unlikely motivation in the Cold War, but we must remember that he had lived through the Second World War and believed that another one, this time with nuclear weapons, was an existential danger. Kent did not appear to want informational power simply for its own sake, or to expand US power over other states. And even if, as many would argue, US power did increase relatively, this was not the driver for Kent to work in government.

Neither was there a profit motive in working for government in the 1940s-50s. Most academics took a pay cut to do so. So there must have been an alternative measuring stick: one that was about service to a higher ideal. Whether you can still co-opt today’s intelligence intellectuals into the national security sector is somewhat conditional to the task and the motives of the state. The private technology companies seek profit and funding for more research, which is in turn a source of new profit. The academic will want reassurances that he or she isn’t some latter-day Oppenheimer.

There is still a need for those skilled at dealing with the imponderables, and the job of framing and reframing national security problems is, for the moment at least, in the hands of the human thinker. And there is still a suitable training ground for that thinker in the university, which is also still a fomenter of new ideas. One of the key ideas about presenting the history of the intelligence intellectuals was that they brought clarity to very fast-changing environments like the early Cold War, when people did not understand what was happening, nor what the implications would be. That is needed now, as much as it was needed then. There is plenty for the university to rise and respond to without worrying too much about private technology companies making it redundant.

ACA: Thank you very much, Dr. Grace, for taking the time to share such thoughtful and wide-ranging reflections with us. It has been a real pleasure to discuss The Intelligence Intellectuals and to explore what its history can still teach about intelligence, AI, human judgment, and the enduring challenge of the “imponderables.”

We held the first edition of TeknoPolitika Meetups on 15 August 2026 at THK Orion & TEKMER in Ankara. Bringing together participants from a wide range of disciplines, this first gathering was designed as more than a networking event; it was also an experiment in method.

For a closer look at the discussions, key insights, and outcomes of TeknoPolitika Meetups #01, download the full event report below.

Download the Event Report

Our expert academic speakers delivered talks on AI investments in the Gulf states, techno-political competition in the Asia-Pacific, psychological operations in the techno-political domain, and disinformation.

At the heart of the event were the Scenario Tables. Participants were divided into four groups, each built around a different theme, question, and combination of disciplines. The tables focused on politics, engineering, OSINT, and 2030 future scenarios.

At each table, brainstorming became a process of collective production. Questions raised by social scientists encountered the perspectives and answers of engineers, and vice versa, generating new questions in the process. The format was an attempt to put one of TeknoPolitika’s founding ideas into practice: how can different disciplinary languages speak to one another around the same table?

We would like to thank THK & Orion TEKMER for hosting the event and providing us with the venue, Axis Circle, and our sister community Kollektif Zeka for their valuable contributions.

There is something particularly appropriate about an industrial/Neue Deutsche Härte supergroup making an album about machines that want to live. Universum25, formed in 2022, it brings together figures from different corners of the German musical scene: Michael Robert Rhein of In Extremo on vocals, Patrick “Pat” Prziwara of Fiddler’s Green and Gunnar Schroeder of Dritte Wahl on guitars, Rupert Keplinger of Eisbrecher and Antitype on bass, and Slime drummer Alex Schwers. These musicians come from traditions ranging from punk and folk-metal to Neue Deutsche Härte and politically conscious German rock, and Universum25 turns those different inheritances into something resembling a collective diagnosis of the technological present.

Even the band’s name announces this ambition. “Universum25” refers to John B. Calhoun’s famous Universe 25 experiments, in which a supposedly ideal environment created for mice eventually became associated with social breakdown, withdrawal and population collapse. The members themselves have acknowledged this reference when discussing the origins of the project. Whatever one thinks of the often exaggerated popular interpretations of Calhoun’s experiment, its cultural symbolism is obvious, proposing that abundance and technological control do not automatically produce a healthy society. Universum25’s self-titled first album already approached political and social questions from this perspective. Its second album, Die Maschinen wollen leben (meaning ‘the Machines Want to Live’ in German), released on 20 February 2026, moves the argument explicitly into the relationship between humanity, artificial intelligence and an over-technologised near future. The band describes the record as dystopian and post-apocalyptic, while insisting that the conditions for this imagined “high-tech Armageddon” are already being created by the pathologies of the present.

This makes Die Maschinen wollen leben more interesting as a techno-political opinion piece in musical form than as another warning about rebellious robots, despite being a little bit of a cliché theme. The title itself invites the familiar science-fiction image of autonomous machines developing their own will. Yet the album becomes considerably more disturbing if we reverse that assumption. Perhaps the principal political danger is not that machines will suddenly seize power from humanity. Perhaps it is that humans are already transferring judgement, responsibility and authority to technical systems because doing so appears efficient, neutral, convenient and, of course, much cheaper. The real transformation then occurs not when a machine becomes conscious, but when institutions begin treating machine-generated conclusions as authoritative. “The algorithm rejected the application,” “the system identified the risk,” or “the model recommends this decision” are sentences in which power continues to operate while the person responsible for exercising it becomes increasingly difficult to identify.

That is why Zu deiner eigenen Sicherheit (For Your Own Safety) is one of the album’s most politically revealing titles. The phrase captures an enduring mechanism of political authority: control presented not as domination but as protection. In a technologically mediated society, this logic acquires unprecedented reach. Surveillance cameras, biometric recognition, location tracking, behavioural profiles, citizenship scores and automated risk assessment can all be introduced through the vocabulary of security. The political sequence is simple but powerful: while a threat creates demand for protection, then protection creates demand for information, information requires surveillance, and finally surveillance generates greater capacities for classification and intervention. The most effective forms of technological control may therefore be those that cease to feel coercive at all. We consent to continuous monitoring because it makes the service easier, the transaction safer or the journey more convenient. The system does not have to force itself upon us if we gradually internalise its presence as the normal price of participation.

Ich geb dir mehr/Das Milgram-Experiment (I Give You More) develops the same concern from another direction. By invoking Stanley Milgram’s experiments on obedience, the song returns to the classic question of why individuals submit to authority. But the digital age introduces a significant variation: the authority figure can disappear. Instead of the scientist in the laboratory coat, commands can now arrive through rankings, dashboards, productivity scores, automated recommendations or seemingly objective numerical assessments. This produces one of the defining political evasions of algorithmic society, yelling “I did not decide; the system did.” Technical systems can thereby depersonalise authority without eliminating it. Decisions about employment, credit, visibility, eligibility or risk may still contain human values and institutional priorities, but once they are translated into technical procedures they acquire the appearance of inevitability. Numbers can disguise choices as facts.

The album’s Cartesian reference, Ich denke, also bin ich (I think, therefore I am), pushes this problem even further. The obvious question is whether an artificial intelligence that appears to think could eventually become a subject in its own right. However, the more urgent question is almost the reverse: why are human societies prepared to give systems considerable decision-making power before determining whether those systems possess anything resembling judgement, understanding, or responsibility? A machine does not need consciousness to produce political consequences. It only needs to be inserted into an institution that considers its output legitimate. The political relevance of artificial intelligence therefore begins well before the philosophical question of machine consciousness. It begins when simulated judgement becomes sufficient grounds for delegated authority.

This helps explain why songs concerned with loneliness, withdrawal and mechanical routine belong to the same political landscape. Einsamkeit (Loneliness), Gardinen zu (Close the Curtains) and Uhrwerk (Clockwork) suggest that technological domination need not resemble a dictatorship filled with hostile robots. Power also operates environmentally. Digital platforms structure how people encounter information, relationships, work and entertainment. Recommendation systems arrange attention. Interfaces determine available choices. Social recognition becomes measurable through likes, views and engagement. Individuals can consequently become more connected in technical terms while experiencing greater social fragmentation. The political question is no longer merely who gives orders, but who designs the environment in which choices occur.

Here the album can also enter contemporary debates about platform capitalism and so-called techno-feudalism. It is that control over essential digital infrastructures can create asymmetric relationships of dependence. When communication, employment, cultural visibility, commercial access, and increasingly artificial intelligence itself depend upon privately governed infrastructures, technological participation starts to resemble residency inside someone else’s institutional territory. Users may possess formal freedom while having little influence over the architecture within which that freedom is exercised.

This distinction is crucial because it prevents the album from collapsing into technological determinism. Machines do not independently generate political hierarchies simply because they become more advanced. Political institutions, corporations, and societies give technologies power by integrating them into systems of decision-making and dependency. The machine is therefore rarely the sovereign. The decisive actors remain those who own the infrastructure, construct the models, establish the rules, determine access and persuade society to regard the resulting technical outputs as legitimate.

Universum25’s own composition makes this critique particularly effective. Punk supplies a tradition of suspicion toward authority, industrial and electronic rock provide the sonic language of mechanization, metal gives the industrialization, and the members’ backgrounds in groups prevent the project from sounding like an artificially constructed concept band. The supergroup format itself produces an interesting contrast with the album’s subject. Human musicians from different scenes deliberately assemble a collective voice in order to question a future in which technological systems increasingly disassemble individual responsibility.

The most powerful interpretation of Die Maschinen wollen leben, then, is not that artificial intelligence will awaken one morning and decide to rule humanity. That fear is almost reassuring because it leaves us with an identifiable enemy. The more plausible danger described by the album is slower and politically more complicated. We delegate surveillance because it promises security. We delegate judgement because algorithms appear objective. We delegate memory because platforms remember more efficiently. We delegate cultural choice because recommendation systems save time. We delegate prediction because uncertainty is uncomfortable. And eventually the technological system stops looking like a collection of instruments and begins to resemble the environment in which political and social life itself takes place.

is a PhD candidate in International Security. His research interests include cultural history, Byzantine and Russian history, and Ancient Greek. This article is part of Kanık’s ongoing Techno-Politics and Cultural History series, published periodically in TeknoPolitika.

Today, humanity is crossing the threshold into an unprecedented era where the velocity of knowledge production eclipses our biological capacity for comprehension. Large language models, real-time data architectures, and hyper-connected global networks have driven the marginal cost of information distribution virtually to zero, fundamentally reshaping every imaginable research paradigm. Yet, in stark contrast to this dizzying acceleration, the institutional machinery responsible for certifying and validating scientific knowledge clings stubbornly to an anachronistic—if not borderline archaic—mode of operation. Engineered around the 19th-century print monopoly, transoceanic postal logistics, and the leisurely cadence of gentlemanly scientific societies, the traditional peer-reviewed journal apparatus now throttles contemporary knowledge production like an invisible vice. By reducing academic performance, tenure, and scholarly capital strictly to publication metrics within elite indexed journals, the current framework produces a structural paradox: it rewards bureaucratic inertia rather than societal impact, technical relevance, or strategic foresight.

A closer look at the actual workflow of prestigious Q1 and Q2 journals exposes the full scale of this breakdown. For the contemporary researcher in the social or applied sciences, submitting a manuscript marks the beginning of an endurance test. The preliminary editorial screening alone frequently takes weeks—sometimes months. If approved, the paper enters a reviewer matching process that presents an even graver systemic bottleneck. Manuscripts are routed to referees who are already overwhelmed by heavy teaching, research, and institutional administrative burdens, working entirely pro bono without tangible institutional recognition or credit toward their own performance evaluations. Faced with an exponential explosion of scholarly submissions, the global reviewer pool has been exhausted. As referee turnover grinds to a halt, subsequent rounds of major or minor revisions, editorial board meetings, and typesetting queues stretch the publication timeline. Even under optimistic projections, seeing a study officially enter the scholarly record takes 12 to 18 months—notably longer than the biological gestation of a human infant. While publishing oligopolies secure unprecedented profit margins, all they offer the research community in return is 19th-century bureaucracy wrapped in modern digital packaging.

The most devastating toll of this cumbersome (and primitive) timeline is, once again, exacted on the researcher. The knowledge produced through rigorous effort becomes obsolete at an alarming pace. The social sciences offer countless examples of this predicament. Consider, for instance, United States foreign policy toward Europe and Russia. During Joe Biden’s presidency, Washington maintained close ties with Europe, provided extensive support to Ukraine in its war against Russia, and rallied the international community to do the same. Suppose a scholar examining U.S. foreign policy—particularly trans-Atlantic relations—began an in-depth investigation into this subject. Assume further that this researcher gathered all relevant data and sources, concluding the study two months before the end of the Biden administration. Just as that ‘magnificent’ publication process gets underway, a change of administration occurs in Washington, bringing to power Donald Trump, who holds vastly different foreign policy views. Taking a starkly divergent stance on aid to Ukraine, the new administration publicly berates Ukrainian President Volodymyr Zelenskyy before the eyes of the entire world. To make matters worse, it demands that Denmark, a staunch European ally, cede Greenland. These developments trigger profound ruptures in foreign policy, creating an irreparable crisis of trust in long-standing U.S.–European relations. Meanwhile, our hypothetical article has already entered the editorial pipeline of an esteemed Q1 or Q2 journal. Supposing the article is finally published 14 months later, the study has long ceased to offer prospective ‘policy analysis’ and has effectively turned into a ‘historical piece’ or an ‘archival record.’ By that point, the realities on the ground may have diverged entirely from the projections set forth in the paper. As a result, the article can no longer serve the academy or the public interest. It has failed to deliver the timely analytical depth required by policymakers, practitioners, or the broader public; instead, it has simply evaporated in the digital queues of academic publishing.

The disconnect is equally stark across educational sciences and pedagogy. Elite educational technology journals continue to publish studies examining themes as antiquated as “the collaborative benefits of cloud-based shared text editors on creative writing.” Meanwhile, the classroom ground reality has undergone a paradigm shift: generative AI co-pilots, adaptive learning algorithms, and synchronous multilingual writing tools have thoroughly rewired student literacy practices. The researchers producing those empirical studies are not at fault; they observed sound methodology and displayed remarkable patience with the review machinery. Yet one must confront the uncomfortable reality: what is the lingering public and scholarly utility of such output? By the time a meticulously controlled empirical intervention designed around two-year-old technology clears peer review, students have long abandoned that tooling for an entirely new technological ecosystem^[1].

A striking, real-world stress test exposing the system’s operational fragility came during the COVID-19 pandemic. Had early findings concerning transmission vectors, vaccine trial readouts, and viral genome sequencing been subjected to standard 19th-century peer-review turnarounds, millions more lives would have been lost while awaiting bureaucratic stamps of approval. Confronted with a civilizational crisis, the scientific enterprise survived precisely by bypassing its primary gatekeeping conventions: preprint repositories such as medRxiv and bioRxiv became the operational backbone of global public health policy. Unreviewed research was posted immediately, subjected to rapid distributed scrutiny from the worldwide scientific community, and translated into life-saving operational protocols. Predictably, however, as soon as the acute emergency subsided, academic appraisal committees and university administrators retreated straight back to institutional conservatism, dismissing preprints as non-events and re-entrenching their blind indexing fetishism.

What stands before us is, unequivocally, pure metric fetishism. As Goodhart’s Law dictates: “When a measure becomes a target, it ceases to be a good measure...”^[2] Tenure tracks, associate professorship benchmarks, and grant funding rubrics actively disincentivize scholars from pursuing timely, disruptive, or transformative inquiries. Instead, the incentive structure breeds risk-averse, sterile scholarship engineered to clear administrative gatekeeping without friction. Researchers are driven to sacrifice timeliness and real-world traction simply to satisfy formal credentialing milestones, perpetuating the very cycle that renders their insights obsolete. International reform frameworks, notably the San Francisco Declaration on Research Assessment (DORA)^[3] and the Leiden Manifesto^[4]—both advocating that scholarship be judged on its substantive rigor rather than journal impact factors—continue to run up against profound institutional inertia. Academia is left trapped between two sobering realities: either it has succumbed to institutional conformism, or it lacks the structural agility required to overhaul its legacy mechanics.

There is no effortless panacea. Abandoning peer review entirely and discarding methodological filters would leave scientific inquiry vulnerable to unchecked manipulation and degraded rigor. At the same time, maintaining a system that smothers dynamic insight beneath administrative deadweight is no longer defensible. In an era where a new technology emerges every single day, and yesterday’s knowledge is rapidly consumed or debunked, when will the academic world finally interrogate its perverse fetishistic devotion to the sealed papers of the nineteenth century?

Dr. Murat Döleker is a scholar of Russian studies whose research focuses on intelligence and security studies, with a particular emphasis on the history of intelligence in the Soviet Union.

^[1] Björk, B. C. & Solomon, D. (2013). The publishing delay in scholarly peer-reviewed journals. Journal of Informetrics, 7(4), 914–923.

^[2] Goodhart, C. A. E. (1975). Problems of Monetary Management: The U.K. Experience. Papers in Monetary Economics, Reserve Bank of Australia, 1.

^[3] San Francisco Declaration on Research Assessment (DORA). (2012). Declaration on Research Assessment.

^[4] Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548), 429–431.

At TeknoPolitika.com, we are beginning a new series of conversations with leading scholars and thought leaders working at the intersection of technology, power, security, and intelligence studies. Our first guest is Dr. Peter C. Grace, a lecturer in politics and international relations at the University of Otago in New Zealand and Co-Director of the Otago National Security School. His latest book, The Intelligence Intellectuals: Social Scientists and the Making of the CIA, was published by Georgetown University Press in January 2026 as part of the Georgetown Studies in Intelligence History series. Drawing on newly declassified archival material and personal papers, the book examines how figures such as William Langer, Sherman Kent, and Max Millikan brought social-scientific methods into the CIA and helped reshape American intelligence analysis during the early Cold War.

Since its publication, Grace has discussed the book and its wider implications through seminars, public talks, and podcast appearances, and we are grateful to him for taking the time to answer our questions in such depth. From the CIA’s struggle for institutional legitimacy after 1947 to AI-era intelligence, from Sherman Kent’s “imponderables” to the prospect of a latter-day Oppenheimer, and from the rise of the “augmented analyst” to the changing relationship between universities, the state, and private technology companies, the conversation ranges widely. Particularly striking are Grace’s reflections on treating AI not simply as a technology but as an institution whose legitimacy still has to be earned, on the continuing importance of human judgment in intelligence analysis, and on the enduring value of those able to frame and reframe problems under conditions of profound uncertainty.

(ACA): The throughline I’m hoping to draw out is the parallel between the structural moment your book documents, a new international system after 1945 forcing a redefinition of what “intelligence” even means, and who gets to produce it, and the structural moment we’re arguably in now, where digitalization and AI are forcing the same question all over again.

Your book frames 1947-50 as a period where the CIA’s founding didn’t automatically confer legitimacy. It had to be built, through the four elements you describe via Zucker: a trained workforce, real expertise, a product competitors couldn’t offer, and demonstrated stakeholder demand. If you had to assess today’s AI-era intelligence and security institutions against that same four-part test, where do you think they currently stand, and where are they most exposed?

Dr. Peter C. Grace: Zucker’s model works for new institutions: the idea being that they are shaky at the very start and need to prove themselves. In the case of the CIA, the three “intelligence failures” of the Soviets getting the atom bomb early; China falling to the Communists; and the outbreak of the Korean War all undermined the Agency’s legitimacy as an intelligence provider, particularly one that provided estimative assessments. The central argument of The Intelligence Intellectuals is that the answer was to create an institution of civilian peacetime strategic intelligence, founded in social science disciplines. That meant creating civilian strategic analysts, with strategic analytical skills that differed from those in the military agencies, and which fulfilled an ongoing need of the national security decision makers. Zucker’s point is that without these four factors, the institution/organization will stumble or fail.

The question of whether AI-era institutions would stand up to the same model, and where they might be exposed, is a good one. We tend to think of AI as an unstoppable systemic force, not as a yet-to-be-tested but highly promising system of ideas, as social science was in the 1940s. And whether AI will provide a new institution that is similar to, or a development of, strategic intelligence as it was thought of in the 1950s is also an interesting question. By thinking of AI as an institution (rather than a technology), perhaps shaky in its early development, and not as a force that is sweeping the world without mercy, perhaps we can get a better idea of its strengths and weaknesses.

Zucker’s model also presumes the existence of a “workforce”. And our understanding of AI is that it replaces people and makes skills redundant. One of the other ideas explored in The Intelligence Intellectuals is the very intellectualization of CIA’s turnaround strategy. It wasn’t simply a matter of taking social science and applying it to the intelligence product. It was also about explaining to the decision-maker how it worked (justification for its conclusions), which is a problem we have right now with AI, where the workings are often obscured. Another factor that CIA’s analysts needed to consider was where social science ended, and the strategic intelligence discipline began. It was clear, for instance, that strategic intelligence sometimes required very urgent assessments where there was no time to collect the data and parse the evidence that social science requires to be effective. That often meant, rather than a well-tested conclusion coming from the scientific method, that a “guesstimate” was all that was available. Artificial intelligence promises lightning-fast calculations, but there may be some shortcoming where it doesn’t fit the requirements of strategic intelligence - one we are yet to discover. Going back to my previous point, we don’t know how AI might shape or change the very nature of strategic intelligence. We can also peel off strategic intelligence from the other kinds, tactical, for example, and from source types -like HUMINT and SIGINT- and consider whether it will be more effective in dealing with some intelligence products, and less in others. And for the foreseeable future, the problems that AI might encounter will be pondered by humans applying the technology to human problems.

Moreover, there was, as there is in any organization, a question of the allocation of resources - a management issue. While CIA’s early budget was big, and the workforce was growing fast in response to the need, the resources weren’t limitless, and very often there was a scarcity of good PhD-trained people, particularly in economic intelligence, where CIA could have very easily taken every economist coming out of the universities each year. In the case of AI, we have already seen that it is enormously energy-hungry, and we can see that there may be fantastic levels of investment needed for states who want to keep up.

Another thought experiment is if AI becomes a commodity where the adversary has matching AI power, as we saw in the nuclear weapons standoff. We may therefore see AI as a weapon that creates its own deterrence. We have thought of intelligence as a way of providing decision advantage, but it is possible that with AI the advantages will be shared equally (think of Gerschenkron’s argument that late adopters of technology benefit from the investments of early pioneers.)

Legitimacy, then, may become - at least in the early stages of AI implementation - something that is earned by human problem-solving: in the areas of intelligence verification, resource allocation, and discovering advantage. Failures of AI to deliver, or compromises that we realize we have to live with (as happened with social science) could lead us to use it less, or differently than what is currently anticipated.

ACA: The “intel intellectual,” as you define it, was a very specific historical figure. Langer, Kent, Millikan, seconded from Harvard, Yale, or MIT into the machinery of the state at a moment when the state urgently needed what only they could supply. Who is the structural equivalent of that figure today (not by name but as a persona), and are they coming from the same kind of institutions or from somewhere else entirely?

Dr. Peter C. Grace: One of the things I liked to imagine when writing the book was what it would be like to be placed in the same situation: where your social science skills were expected to predict the breakout of World War III, or to answer the almost impossible question of what Stalin or Mao was thinking. I imagine myself being shown to an office in the CIA building, and told to get on with it. We know that Sherman Kent experienced imposter syndrome in November of 1950. And I think I would have felt the same way.

The key to their success was not only the application of social science methods. It was the “bigger picture” thinking that they brought to CIA’s problems. For example, in trying to get a more accurate reading on the Soviet economy, Max Millikan created the Inventory of Ignorance, mapping not only what the CIA already knew, but where the gaps were in its knowledge. Another project which was mentioned in a 1952 “Intelligence Research Program” was a similar cataloging of all the possible research projects CIA might undertake to better understand the geopolitics of the time. They included a chronological history of the globe that could determine patterns and links; a list of “big” problems the world faced that could be broken down into smaller, more workable ones; sets of hypotheses about these problems; as well as case studies that might illuminate them. In other words, the thinking that was being done was managerial: it was about determining how to create a knowledge production line, one with inputs and outputs and, like any factory process, one that most efficiently produced profitable results.

It was also, in a more academic sense, one that was epistemological: a way of dealing with uncertainty. Uncertainty can reduce as more information is gathered and assessed. And therefore the anxiety that goes with it can lessen too. But, in true Socratic fashion, it also increases corresponding to what you know. You learn that you need to learn more. The AI-era institution is likely to follow the same paths, and both uncertainty and anxiety require a special type of person to manage them. Sherman Kent’s way of dealing with both was to be honest and admit mistakes frequently. He made it easy for others to follow him, because he created a culture within the CIA that was open about failings. (That’s not to say the culture was always present.)

Adrian Wolfberg has considered this in his new book Who Leads When AI Thinks? (Springer, 2026). He imagines the process of managing AI as being like a dance, where there is an interplay between AI knowledge and human judgment. Wolfberg says that humans still need to employ critical thinking (to challenge assumptions, test the logic, and check for bias) and creative thinking (to open up new ways of solving the problem, connect the dots in an original way, etc) when managing AI outputs.

It is no great revelation that organizations need creative thinkers as well as process-oriented and detail-focused ones. One of the probable benefits of doing a PhD is learning how to manage uncertainty and anxiety well. Whether PhD-trained people learn to be honest about their failings in a way that is collaborative is debatable.

A lesson from The Intelligence Intellectuals is that organizational leadership is just as important as innovation: the ability to force through change when there is resistance is as valuable as the insight needed to recognize what matters. The AI era institution/organization will require “benevolent dictators” like General Walter Bedell Smith and William Langer, as much as it will require creative thinkers like Sherman Kent and Max Millikan.

ACA: One of the more striking arcs in the book is the Princeton Consultants, set up as an external check on the Office of National Estimates, but over time the relationship inverted, and, as you put it, “the pupils became the teachers.” We’re watching something that at least rhymes with this right now, with frontier AI labs absorbing academic talent and increasingly outrunning the very institutions meant to oversee them. Does that historical arc offer any real lessons for how we should think about independent oversight of AI today, or is the analogy too neat?

Dr. Peter C. Grace: Firstly, the story of the Princeton Consultants is one of inbuilt redundancy in organizational change. I don’t think it was deliberately inbuilt, but it was logical that without the day-to-day exposure to problem-solving, and access to often rapidly changing classified information and even new methodologies, the Consultants would simply not keep up. They were formed in order to provide an external sounding board to CIA’s internally created assessments, and in a sense another way of calculating conclusions - in their case they may have arrived at different assessments through experience or even gut instinct.

I think there is room for gut instinct and experience in the AI-era institution. Gut instinct may be another name for “I have seen this problem/solution somewhere before, but I can’t remember where”. In other words, it is a human failing that has a silver lining. AI is supposed to find those links to insightful information and make gut instinct redundant too. But I doubt that it will, because the link that a human finds to solve a problem is much more organic. We can see a pattern in a cloud formation that will prompt a eureka moment. A comment that is misheard and interpreted as being something quite unlike what the speaker intended may also spark a breakthrough. It doesn’t have to be a fact or a solution that we have half-forgotten and which the AI machine can remember instantly.

There is also room for inexperience in problem-solving. As I said at the beginning, one of the narratives about AI is that it is a systemic change that is sweeping the world without mercy. That may be a view that experienced people have, a fear of change, or a belief that it is something dangerous that fits with science-fiction movies they have seen. It may not be the view of the inexperienced person, who only sees opportunity.

Again, with AI, we are seeing that the knowledge that is being produced is now feeding on itself, and AI learning is becoming prioritized over the human learning that kick-started it. It is a possible echo chamber, which is in itself an analogy for experienced thinking (groupthink).

The Princeton Consultants became, over time, a similar echo chamber. Think about how, as a PhD candidate, your knowledge of your research field surpassed, over time, that of your supervisor’s. The value that your supervisor could add, as you progressed, was often to provide guardrails to stop you from veering off track, or to second-guess the examiner’s criticisms of your final thesis. And this is very valuable guidance when you might no longer be able to see the wood for the trees. But as an actual contributor to your research, the supervisor often becomes redundant and gives you instructions or pointers that are repetitive and show the shrinking of their ability to add insights to your field. And this is what happened with the Princeton Consultants. The briefers from the CIA spent more time teaching them than coming back with new ways of looking at their assessments.

In the AI era institution, oversight may come from those who are not happy with the information that AI systems produce. And that disenchantment may not come from the people who built the machines. In the intelligence world, it may come from the policymaker who does not think the assessment fits with his agenda or worldview - for instance, the consumer who wants the intelligence “politicized” to pursue a direction they prefer. Having increased power, through AI, to speak truth to power does not necessarily mean the truth is acceptable. We worry that AI will replace our ability to think, but it may not replace the willfulness of those who don’t want to think. They may be the first to pull the plug on the machines.

Of course, we may see the opposite happen: where politicization becomes part of the algorithm, and we are already, to some extent, seeing that. But politicization does not usually lead to good outcomes; for example, the prosecution of pointless wars. You can have a backseat driver telling you the map you are looking at is wrong, and you should follow their directions to the destination you are aiming for. But it does not mean you will arrive at the destination, or that the backseat driver will be happy when you both realize you have traveled for several hours in the opposite direction. The political leader who tells AI to follow his agenda, and then realizes it is a bad one, may also pull the plug on the machines.

A third possibility is that the political leader relinquishes her decision-making function to the AI machine. It is possible better decisions might come from that. This could make the political leader redundant. However, given the likelihood that it will result in outcomes that are not desired, it is possible the political leader will find a new role, or an enhanced function, in providing oversight in the manner she was elected to - democratic oversight. One of the primary motivations for the intelligence intellectuals in working for the CIA in 1950 was their belief that the more you knew about the world, including about psychology, languages, culture, and societies, the more peaceful the world might eventuate. If, out of sheer fear they will be out of a job, the political leader thinks more about the effect decisions have on her constituents (national or global), then this might be a happy outcome.

ACA: Kent’s humility about “the imponderables”, his insistence that intelligence work meant living with irreducible uncertainty, reads almost as a virtue in your account. That’s a hard posture to square with how AI-driven prediction and assessment tools are marketed and used today, which tend to project confidence rather than humility. Is there a version of Kent’s epistemic caution that’s even possible in an algorithmic intelligence environment, or does the technology itself work against it?

Dr. Peter C. Grace: I think, following on from the last comments, we could consider the “augmented analyst”, which is a more optimistic reading of how the intelligence analyst’s role might be improved with AI while still keeping the human at the center of things; and different again from a role where the analyst is sidelined by AI or made redundant entirely. And depending on where he or she sits: as a co-driver with AI, or a passenger, or no longer a traveler, there will be a corresponding factor of empowerment and accountability. For those who have less, there will be less stress over uncertainty; for those who have more, my prediction is that the anxiety will not go away. You can rely on any kind of automated process, whether it is mechanical or informational, but if your neck is on the chopping block if it fails – and the stakes for national security are far higher than most processes – then you will always feel an effect of uncertainty.

George Kennan also talked about the imponderables, and he seemed to see the ability to work with uncertainty as a special gift: a willingness to think about the unknown, the unimaginable or unpredictable. Kennan had the top foreign policymaking job when he headed up the Policy Planning Staff in the State Department, and he relished the fact that his intellect was valued and recognized. When he left it, partly because he was burned out, he worried that his special gift might not be wanted back in Washington DC again. So, while Kent saw the imponderables as being something that might mean possible failure, Kennan saw them as an opportunity to succeed and stand out from the common man.

So we may see that opportunity increase in the new AI environment. The role of the augmented analyst will be to frame the intelligence problem and then, as the intelligence cycle expects, to review and reframe it as he or she engages with AI. That will require a much closer relationship with the policymaker, and more of a conversation about the scoping of the research that AI will then execute. So, and again I cite Adrian Wolfberg, there will be Albert Einstein’s view that he would spend 55 minutes defining and framing the problem and only 5 minutes coming up with a solution. This ability to work with the imponderables in framing and reframing may come to be seen – again – as a special human gift.

ACA: Your conclusion argues that intellectual and informational power became inseparable from US state power during this period. You couldn’t have one without the other. As that power increasingly sits with private technology companies rather than universities or government agencies, does the twentieth-century model of the “intel intellectual” moving between academia and the state still make sense as a framework, or has the relevant boundary shifted somewhere else entirely?

Dr. Peter C. Grace: We have seen marked changes in the U.S of private technology companies being co-opted (or more recently coerced) into contributing to Washington’s informational power, particularly in the decryption and AI domains. This is something we take for granted in authoritarian states, and it fuels a belief that informational power is becoming employed for more dystopian and less peaceful purposes.

On the other hand, we see a growing OSINT movement, of which Bellingcat is just one example, which has no allegiance except perhaps to the truth. And there is enough open source data out there still to show that informational power can stay democratic, although the capacity of AI in state hands may outstrip the capacity of non-state actors.

The social science departments in the universities are still debating the old basic versus applied research issue. There is a great history of this in Michael Desch’s book Cult of the Irrelevant (Princeton University Press, 2019). Desch does a very good job of showing the academics’ reluctance to get corralled into working for government. It goes back to Kennan’s belief that being able to work with imponderables was a special gift: academics prefer to break new ground rather than to focus necessarily on everyday government problems. As detailed in The Intelligence Intellectuals, it was partially the excitement of being able to apply the new social sciences to big societal problems, including national security, that drew academics to government work in the 1940s-50s. That, as well as being closer to the seat of power and being able to influence decisions from a better vantage point than the university.

A linked issue is the right to “speak truth to power”, which universities guard jealously, even at the expense of continued state funding. There’s no question that the intelligence intellectuals also valued their independence: some of the later people in this category, like Chalmers Johnson, were prepared to sever their relationship with the CIA to keep their integrity intact. So, the question is: how important is academic freedom to pursue pure research or take an objective position - neither of which might suit the state’s immediate agenda in contributing to informational power?

Finally, there is the question of whether the pursuit of peace is relevant to state power or not. Kent in particular was motivated by what he saw as the CIA’s ability to influence peaceful solutions by understanding the world better. It may seem an unlikely motivation in the Cold War, but we must remember that he had lived through the Second World War and believed that another one, this time with nuclear weapons, was an existential danger. Kent did not appear to want informational power simply for its own sake, or to expand US power over other states. And even if, as many would argue, US power did increase relatively, this was not the driver for Kent to work in government.

Neither was there a profit motive in working for government in the 1940s-50s. Most academics took a pay cut to do so. So there must have been an alternative measuring stick: one that was about service to a higher ideal. Whether you can still co-opt today’s intelligence intellectuals into the national security sector is somewhat conditional to the task and the motives of the state. The private technology companies seek profit and funding for more research, which is in turn a source of new profit. The academic will want reassurances that he or she isn’t some latter-day Oppenheimer.

There is still a need for those skilled at dealing with the imponderables, and the job of framing and reframing national security problems is, for the moment at least, in the hands of the human thinker. And there is still a suitable training ground for that thinker in the university, which is also still a fomenter of new ideas. One of the key ideas about presenting the history of the intelligence intellectuals was that they brought clarity to very fast-changing environments like the early Cold War, when people did not understand what was happening, nor what the implications would be. That is needed now, as much as it was needed then. There is plenty for the university to rise and respond to without worrying too much about private technology companies making it redundant.

ACA: Thank you very much, Dr. Grace, for taking the time to share such thoughtful and wide-ranging reflections with us. It has been a real pleasure to discuss The Intelligence Intellectuals and to explore what its history can still teach about intelligence, AI, human judgment, and the enduring challenge of the “imponderables.”

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.

holds a PhD in Public Law. His research focuses particularly on the fundamental implications of techno-politics and techno-feudalism for the state.

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.

Download Report

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.

Download Report

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.