For quite some time now, we have been living through a moment of almost unrestrained enthusiasm surrounding artificial intelligence. Big Tech companies that own the major large language models, together with governments and large corporations making multi-billion-dollar investments in generative AI, promise — and expect — spectacular productivity gains, extraordinary returns on investment, significant cost reductions, and a radical transformation of economic growth. The dominant narrative seems clear: AI will become the great engine of prosperity for the next decade.
However, if we want a more rational perspective on what is actually happening, it is worth revisiting Daron Acemoglu’s -winner of the 2024 Nobel Prize in Economics and professor of economics at MIT- paper The Simple Macroeconomics of AI. Dense and published a couple of years ago, its arguments and analytical framework remain perfectly applicable to today’s AI landscape.
Acemoglu invites us to view these expectations with far greater caution. His central thesis is both simple and uncomfortable: the macroeconomic effects of AI depend fundamentally on two very concrete variables — what real percentage of tasks AI will actually be able to transform, and how much cost reduction or productivity improvement it will generate in those tasks. And once the available data are analyzed within his framework, the numbers turn out to be far less spectacular than current discourse often suggests.
Using current estimates of occupational exposure to AI and observed productivity improvements in specific tasks, Acemoglu concludes that aggregate total factor productivity growth could remain below 1% over ten years. That is a long way from the almost revolutionary narratives dominating much of today’s technological and financial debate.
One of the paper’s most interesting contributions is its distinction between “easy-to-learn” and “hard-to-learn” tasks. AI performs particularly well in activities where objectives are clearly defined and there are objective metrics of success: basic programming, information classification, text generation, or structured customer support. But much of valuable human work — diagnosis, creativity, contextual decision-making, expert judgment — remains far more difficult to replicate.
Acemoglu also reminds us of something fundamental that is often forgotten amid technological euphoria: every major technology generates enormous organizational adjustment costs. Companies do not transform automatically simply because they adopt a new tool. Processes, structures, incentives, and human capabilities must evolve as well — and that process is usually slow and expensive. Drawing on classic research on digitalization, the author reminds us that productivity gains often follow a J-curve: long initial periods of adaptation before meaningful benefits materialize. Greenwood, Yorukoglu, and Brynjolfsson, among others, already estimated that, in the case of digital technologies, the lower part of that curve could last at least 20 years. If the same pattern holds for AI, even today’s cost-saving estimates may be significantly overstated for the next decade.
Be careful with the siren songs and the inflated numbers. Spreadsheets can justify almost anything.
Ángel Gómez de Ágreda is one of the most solid intellectual references in Spain and Europe for understanding the intersection amongst geopolitics, disinformation, and generative artificial intelligence. A retired Colonel of the Spanish Air and Space Force, engineer with a PhD, strategic analyst, and public intellectual, his work stands out for connecting philosophical reflection on truth and knowledge with the technological and military transformations of 21st century.
In 2025, together with Enrique Martín Romero, he published Inteligencia artificial y defensa. El impacto en los ejércitos (Artificial Intelligence and Defense: The Impact on Armies). This year, 2026, he has released Un mundo falaz. El nuevo orden global en la era de los algoritmos y la manipulación ((Fake New World. The New Global Order in the Age of Algorithms and Manipulation). The central idea around which both works revolve is the following: global power is no longer measured solely by the economic or military capabilities of states, but by their ability to shape the perception of reality for millions of people.
Gómez de Ágreda’s thesis begins with an essential observation: technology does not create our weaknesses; it simply amplifies those that already exist. Politics, digital platforms, and now generative AI exploit the human tendency to accept narratives that emotionally align with our prior beliefs. The German philosopher Markus Gabriel defines this condition as post-reality: a stage in which societies no longer merely manipulate others, but actively participate in their own collective self-deception. The phenomenon goes beyond classic post-truth; it entails the gradual replacement of facts with narratives designed to be shared, viralized, and emotionally effective.
Social media first, and generative AI later, have accelerated this process to unprecedented levels. Gómez de Ágreda argues that we have delegated not only cognitive tasks to machines, but even the search for knowledge itself. What once required comparing sources and developing personal judgment is now resolved through an instant query to an LLM. Large language models function as digital oracles whose authority is perceived as neutral and infallible, despite the fact that no AI can ever truly be neutral, impartial, or objective. Algorithms are only as neutral as the programmers behind them.
The problem is that, in a context of cognitive saturation, people tend to accept automated responses with little questioning. This is where the transition occurs from technical manipulation to emotional manipulation: the moment when machine dominance ceases to operate over what we think and begins to operate over what we desire. Machines allow us, in a sense, to “want to want.” They give us reasons to want to fall in love, to want to love. More than satisfying the need to receive affection, they address our impulse to offer it. This connects with the Spanish philosopher José Antonio Marina’s accurate description of the contemporary individual as “credulous, passive, gregarious, isolated, and anti-Enlightenment.” The result is an individual incapable of withstanding the pressure of the surrounding environment.
This social and ontological transformation has direct consequences for contemporary geopolitics. For Gómez de Ágreda, the classical concept of sovereignty must evolve into the idea of cognitive sovereignty: the ability of a country or community to preserve interpretive autonomy against external manipulation campaigns. Disinformation ceases to be a marginal phenomenon and becomes a strategic resource aimed at shaping emotions, altering perceptions, and conditioning collective decisions. In this scenario, the true battlefield is no longer confined to physical borders, but lies within societies themselves.
Contemporary military doctrines reflect precisely this evolution. The author cites Russian analyst Dmitri Trenin to explain how current strategies no longer necessarily seek territorial occupation, but rather internal chaos and psychological destabilization. The Gerasimov Doctrine and the concept of reflexive control aim to alter the adversary’s perception of reality. Cognitive warfare therefore seeks not merely to control information, but to directly influence the mental processes of entire populations. As Gómez de Ágreda reminds us, while information warfare operates on content, cognitive warfare targets the human brain itself.
Generative AI exponentially multiplies the scope of these operations. The ability to produce synthetic texts, audio, images, and videos that are virtually indistinguishable from reality radically transforms the information environment. Unlike traditional propaganda, messages can now be tailored to each psychological profile, disseminated on a massive scale, and adapted in real time according to audience reactions. Gómez de Ágreda describes disinformation as functioning through an organized chain of actors: activators, amplifiers, legitimizers, dissemination bots, and relaunchers. Generative AI drastically reduces the cost and time required to deploy such campaigns, making them practically ubiquitous.
One of the most disturbing examples cited in Un mundo falaz is GoLaxy, a system already operating in China that can generate highly realistic artificial avatars capable of emotionally interacting with real users. These synthetic identities can operate simultaneously on a massive scale, without arousing suspicion, while adapting psychologically to each interlocutor. Manipulation is no longer confined to the ideological sphere; it shifts into the emotional domain. Machines no longer condition only what we think, but also what we desire.
China appears in both books as the geopolitical actor that has best understood the strategic potential of AI. Beijing has developed an ambitious roadmap to make this technology the core of its economic, industrial, and military development. According to the official Chinese document Opinions of the State Council on the Deep Application of the R&D Initiative, published in August 2025, the goal is to achieve 70% penetration of intelligent terminals and AI agents across six key sectors by 2027: science and technology, industry, consumption, social welfare, government, and global cooperation. By 2030, penetration is expected to reach 90% across the entire economy. By 2035, AI should become as universal as electricity is today, equivalent to what the internet represents in our era. By 2037, industries themselves are expected to be created with AI as both their foundation and guiding principle. Just as a new economy emerged around the internet, the report proposes that the next industrial system will be built around algorithms.
The United States, fully aware of this technological competition, has responded by accelerating its own military generative AI programs. In 2023, OpenAI, Google, Anthropic, and xAI received multimillion-dollar contracts from the Department of Defense to develop intelligence and combat simulation applications. At the same time, Washington has imposed restrictions on U.S. investments in AI technologies directed toward China, aiming to slow the development of Chinese military AI and preserve the West’s technological advantage. The geopolitical rivalry of the 21st century is now being fought in the domain of semiconductors, data centers, and algorithms.
However, Gómez de Ágreda warns that the impact of AI is not limited to the balance between great powers. Recent conflicts show how this technology is also transforming conventional warfare. The war in Ukraine and the earlier Nagorno-Karabakh conflict have demonstrated that small autonomous systems, inexpensive drones, and accessible AI capabilities can create enormous asymmetries against vastly more expensive weaponry. The battlefield of the future will be hybrid: physical, digital, and cognitive simultaneously.
Yet perhaps the author’s deepest warning is philosophical in nature. In a world saturated with information, the primary threat is not merely technological, but epistemological. If every act of understanding necessarily involves interpretation, as philosopher Hans-Georg Gadamer argued in his book Truth and Method, then the struggle to control interpretive frameworks becomes a struggle to control reality itself. That is why Gómez de Ágreda insists on recovering critical thinking and philosophical reflection as tools of democratic defense. The great battle of 21st century will not be decided solely in AI laboratories or military arsenals, but in societies’ ability to preserve their cognitive freedom against a technological ecosystem designed to influence, seduce, and manipulate.
Since it seems we are developing AI models gradually more intelligent -probably owing to this quantum leap that GenAi has meant-, let’s raise the level: what about their sentience? I.e., their capacity for feeling or perceiving consciousness.
Last week I have the pleasure to talk to my good friend Gregory about AI, ethics, the future of work, AI and geo-politics… and he recommended to me the book “The Edge of Sentience” by Jonathan Birch. I do appreciate his recommendation. There is a chapter devoted to LLMs and the gaming problem. Let’s analyze what this problem is about.
According to Birch, sentience does not require or imply any particular level of intelligence. Yet intelligence and sentience are related: intelligence can make sentience easier to detect. The AI case, however, shows us that intelligence of certain kinds can also make it more difficult to assess the likelihood of sentience. For the more intelligent a system is, the more likely it will be able to game our criteria. What is it to ‘game’ a set of criteria? Gaming occurs when systems mimic human behaviours that are likely to persuade human users of their sentience without possessing the underlying capacity. No intentional deception is needed for gaming. It could happen in service of simple objectives, such as maximizing user-satisfaction or bettering interaction time. When an artificial agent is able to intelligently draw upon huge amounts of human-generated training data (as in LLMs), the result can be gaming of our criteria for sentience.
The gaming problem initially leads to the thought that we should ‘box’ AI systems when assessing their sentience candidature: that is, the AI model must be denied access to a large corpus of human-generated training data. However, this would destroy the capabilities of any LLM. According to the author, what we really need in the AI case are deep computational markers, not behavioral markers. We could use computational functionalist theories -such as the global workspace theory and the perceptual reality monitoring theory– as sources of deep computational markers of sentience. If we find signs that an AI system has implicitly learned ways of recreating them, this should lead us to regard it as a sentience candidate. Nevertheless, the main problem with this proposal is that we currently lack the sort of access to the inner workings of LLMs that would allow us to reliably ascertain which algorithms they have implicitly picked up during training.
Some years ago I wrote about the following paradox in AI: Is an infallible machine really intelligent? Echoing Turing’s approach, it couldn’t be expected a machine infallible and intelligent at the same time. Instead of building infallible computers, fallible machines should be developed, which could learn from their own mistakes; i.e., a sort of reinforcement learning, in which the AI model learned an optimal (or near-optimal) course of action that maximized the reward function. Maybe we should follow this deeply human approach to “teach sentience” to machines: by the end of the day, human beings learn through testing and we replicate those actions that bring us reward. In this case, the reward could be a profound feeling of self-assurance and happiness but how could we encode that in a, for instance, Monte Carlo simulation?
As mentioned in our post “China: Techno-socialism Seasoned with Artificial Intelligence“, in its aim of gaining a global leadership role, China launched the Belt and Road Initiative in 2013: a global infrastructure development strategy to invest in more than 150 countries and international organizations. The BRI was composed of six urban development land corridors linked by road, rail, energy, and digital infrastructure and the Maritime Silk Road, linked by the development of ports.
In 2015, the Chinese government published the “Vision and Actions on Jointly Building Silk Road Economic Belt and 21st Century Maritime Silk Road”, introducing the concept of “Information Silk Road” as a component of BRI -later to be rebranded as “digital’ to encompass its broader aspirations. In 2017, during the BRI Forum in Beijing, Xi Jinping stated the use of AI and big data would be incorporated in the future of BRI as well, further illustrating its broad and ever-evolving nature. The DSR is an important component of China’s Belt and Road Initiative (BRI); it covers a wide array of areas, ranging from telecommunications networks, to ‘Smart City’ projects, to e-commerce, to Chinese satellite navigation systems, and of course AI.
The DSR aims at the global expansion of Chinese technologies to markets in which western players have previously dominated, or in developing countries that are only now undergoing a technological revolution. The implementation of China’s DSR has mainly covered the developing countries of Africa, Asia, Latin America, the Middle East, and Eastern Europe. China presents the DSR as a tool for development, innovation, and technological evolution. However, in its ambitions and impact, the DSR is also a question of geopolitics, as it facilitates China’s attempt to establish itself as a major global power across a growing number of technical and research fields, and regions.
With the growing prominence of the DSR, some Western countries have voiced their concerns about the potential risks related to Chinese technology and involvement in sensitive sectors. Both the US and EU have taken steps to counter the rising influence of the DSR. As a tool to contest the Chinese initiative, the US launched the ‘Clean Network’ initiative. Said that, the EU does not have a unified stance on cooperation with China on the DSR. Among 27 members, there are ‘champions’ of the pushback against China, especially among Central and Eastern European countries like Czechia, Slovakia, Slovenia, and Romania, that have aligned with the US’ initiative. Others, like France, have not introduced outright bans but have de facto decided to exclude “untrusted vendors”, and to focus on the European companies and equipment due to security concerns. Germany, on the contrary, is still considering the inclusion of the Chinese companies in the construction of its 5G infrastructure, for instance.
Western Balkans is a region that has been often seen as a springboard by China regarding its presence in Europe. Chinese efforts to include Serbia in the DSR have been more than welcomed and hence Serbia has become a main stop for the Chinese initiative in the region.
Serbia has developed extensive and strategic relations with China over the past decade. The partnership has also included cooperation within the framework of the DSR. Serbia and China signed the Strategic Agreement on Economic, Technological, and Infrastructural cooperation in 2009. That agreement was a starting point for the development of the contemporary relations between two countries and a cornerstone for future joint projects. During the visit of Chinese leader Xi Jinping to Belgrade in 2016, the two countries established a Comprehensive Strategic Partnership.
DSR has reached Serbia and made it the focal point in the Western Balkans. However, cooperation could come with a price. If Serbia relies too much on China in its technological development and does not differentiate partner companies and suppliers, it may become too dependent on its Chinese partners. The absence of diversification can jeopardize the sustainability of the system and the possibility of further improvements of the system in the future. The need of not being dependent on foreign technology is a lesson perfectly learned and practiced by the Chinese authorities concerning AI.
Chinese Non-dependency Policy Regarding GenAI / LLMs
For China and Chinese companies, developing indigenous LLMs is a matter of independence from foreign technologies and also a matter of national pride. Since August 2023, when China’s rules on generative AI came into effect, 46 different LLMs developed by 44 different companies were approved by the authorities. The legislation requires companies to ensure that the models’ responses align with the communist values and also undergo a security self-assessment, which has, however, not been defined until recently. Besides the afore-mentioned approved models, it is estimated that there are more than 200 different LLMs currently functioning in China.
The first wave of models approved in August 2023 was predominantly general LLM models developed by the biggest players in China’s technological market – Baidu, Tencent, Alibaba, Huawei, iFlytek, SenseTime, and ByteDance. Besides these companies, Chinese research institutions, namely the Chinese Academy of Sciences and Shanghai Artificial Intelligence Laboratory, received approvals for their models. In the following batches, models with specific applications started to appear: models designed for recruitment purposes -ranging from CV formatting to providing recommendations; models designed to help companies with cyber security assessments and risk prevention; models designed for readers to interact with their favorite literary characters; models aimed at video content generation based on an article or an idea description; and models providing recommendations to customers and serve as AI assistants.
In March 2024, China’s National Information Security Standardization Technical Committee (TC260) published its basic security requirements for generative AI, which qualifies as a technical document providing detailed guidance for authorities and providers of AI services. This text sets measures regarding the security of training data. Providers must randomly choose 4,000 data points from each training corpus and the number of ‘illegal’ or ‘harmful’ information should not exceed five percent. Otherwise, the corpus may not be used for training. Developers are also required to maintain information about the sources of the training data and the collection processes, and acquire agreement or other authorization to use data for training when using open-source data. This document also provides detailed guidance regarding the evaluation of the model’s responses. Providers are required to create a 2,000-question bank designed to control the model’s outputs in the case of areas defined as “security risks.” -everything which might mean a violation or threat to the communist values.
Importantly and as final note concerning the willingness of being independent from foreign technical developments, the newest AI rules stipulate that Chinese companies are not allowed to use unregistered third-party foundation models to provide public services. This means that access to LLMs developed outside China becomes even more limited and some of the Chinese AI companies who have built their applications based on ChatGPT or LlaMa, for instance, will need to find other solutions.
More than ever the geopolitical battlefield is played mainly on the technological / AI realm.
Artificial intelligence has become a genuine instrument of power. This is as true for hard power (military applications) as for soft power (economic impact, political and cultural influence, etc.). Whilst the United States and China dominate the market and impose their pace: Europe, lagging behind, is trying to respond by issuing new regulations; Africa has become a battlefield for the new digital empires, and Ukraine has turned into the test-bed for AI-based military innovations and developments.
In September 2017 Vladimir Putin, speaking before a group of Russian students and journalists, stated: “Artificial intelligence is the future. . . Whoever becomes the leader in this sphere will become the ruler of the world.” Sharp and accurate. AI is a more generic term than it seems: in fact, artificial intelligence is a collective imaginary onto which we project our hopes and our fears. The rapid progress of AI makes it a powerful tool from the economic, political, and military standpoints. AI will help determine the international order for decades to come, stressing and accelerating the dynamics of an old cycle in which technology and power reinforce one another.
Nowadays we are witnessing to the birth of digital empires. These are the result of an association between multinationals, supported or controlled to varying degrees by the states that financed the development of the techno-scientific bases on which these companies could innovate and thrive. These digital empires would benefit from economies of scale and the acceleration of their concentration of power in the economic, military, and political fields thanks to AI. They would become the major poles governing the totality of international affairs, returning to a “logic of blocs.”
It would be tempting to think that AI is a neutral tool but not at all, indeed. Artificial intelligence is not situated in a vacuum devoid of human interests. Big data, computing power, and machine learning -the three foundations behind the rise of AI- in fact form a complex socio-technical system in which human beings have played and will continue to play a central part. Thus, it is not really a matter of “artificial” intelligence but rather of “collective” intelligence, involving increasingly massive, interdependent, and open communities of actors with power dynamics of their own. Let’s explain this framework:
Teams of engineers construct vast sets of data (produced by each and all: consumers, salesmen, workers, users, citizens, Governments, etc.), design, test, and parameter algorithms, interpret the results, and determine how they are implemented in our societies. Equipped with telephones and ever more interconnected “intelligent” objects, billions of people use AI every day, thus participating in the training and development of its cognitive capacities.
For the majority of these companies, the product is free or inexpensive (for example, the use of a search engine or a social network). As in the media economy, the essential thing for these platforms is to invent solutions that mobilize the “available human brain time” of the users, by optimizing their experience, in order to transform attention into engagement, and engagement into direct or indirect incomes. In addition to concentrating on the attention of the users, the big platforms use their data as raw material. These data are analyzed to profile and better understand users in order to present them with personalized products, services, and experiences at the right time.
Even if their products and services have unquestionably benefited users worldwide, these companies (Apple, Alibaba, Amazon, Huawei, Microsot, Xiaomi, Baidu, Tencent, Facebook, Google…) are also engaged in a zero-sum contest to capture our attention, which they need to monetize their products. Constantly forced to surpass their competitors, the various platforms depend on the latest advances in the neurosciences to deploy increasingly persuasive and addictive techniques, all in order to keep users glued to their screens. By doing this, they influence our perception of reality, our choices and behaviors, in a powerful and as yet completely unregulated form of soft power. The development of AI and its worldwide use are thus constitutive of a type of power making it possible, by non-coercive means, to influence actors’ behavior or even their definition of their own interests. In this sense, one can thus speak of a “political project” on the part of the digital empires, commingled with the mere quest for profit.
The development of AI corresponds to the dynamics of economies of scale and scope, as well as to the effects of direct and indirect networks: the digital mega-platforms are in a position to collect and structure more data on consumers, and to attract and finance the rare talents capable of mastering the most advanced functions of AI. As Cédric Villani wrote in Le Monde in June 2018: “These big platforms capture all the added value: the value of the brains they recruit, and that of the applications and services, by the data that they absorb. The word is very brutal, but technically it is a colonial kind of procedure: you exploit a local resource by setting up a system that attracts the value added to your economy. That is what is called cyber-colonization“.
National actors are increasingly aware of the strategic, economic, and military stakes of the development of AI. In the past 24 months, France, Canada, China, Denmark, the European Commission, Finland, India, Italy, Japan, Mexico, the Scandinavian and Baltic region, Singapore, South Korea, Sweden, Taiwan, the United Arab Emirates, and the United Kingdom have all unveiled strategies for promoting the use and development of AI. Not all countries can aspire to leadership in this sphere. Rather, it is a matter of identifying and constructing comparative advantages, and of meeting the nation’s specific needs. Some states concentrate on scientific research, others on the cultivation of talent and education, still others on the adoption of AI in administration, or on ethics and inclusion. India, for instance, wants to become an “AI garage” by specializing in applications specific to developing countries. Poland is exploring aspects related to cybersecurity and military uses.
Today, the United States and China form an AI duopoly based on the critical dimensions of their markets and their laissez-faire policies regarding personal data protection. The same as USA, China has also integrated AI into its geopolitical strategy. Since 2016, its “Belt & Road” initiative for the construction of infrastructures connecting Asia, Africa, and Europe has included a digital component under the “Digital Belt and Road” program. The program’s latest advance was the creation of a new international center of excellence for “Digital Silk Roads” in Thailand in February 2018.
And Europe? Just falling far behind China and the United States in techno-industrial terms. The European approach seems to consist in taking advantage of its market of 500 million consumers to provide the foundations of an ethical industrial model of AI, while renegotiating a de facto strategic partnership with the United States.
Private investment is the key element and Europe lags really behind. The US is leading the race (€44 billion) in 2022, followed by China (€12 billion), and the EU and the United Kingdom (UK) together attracting €10.2 billion worth of private investment, according to 2023 AI Index of Stanford University. The AI revolution is perceived in Europe as a wave coming from abroad that threatens its socio-economic model, to be protected against. The EU is searching for model of AI that ties together the reclamation of sovereignty and the quest for power with respect for human dignity. Balancing these three desiderata will not be easy: by regulating from a position of extreme weakness and industrial dependency in relation to the Americans or the Chinese, Europe is likely to block its own rise to power.
Africa – The great and not anymore forgotten battlefield
The African continent is practically virginal in terms of digital infrastructures oriented towards AI. The Kenyan government is to date the only one to develop a strategy in this respect. However, Africa has enormous potential for exploring the applications of AI and inventing new business and service models. Chinese investments in Africa have intensified over the last decade, and China is currently the primary trade partner of the African nations, followed by India, France, the United States, and Germany. Africa is probably the continent where cyber-imperialisms are most evident. Examples of the Chinese industrial presence are numerous there: Transsion Holdings became the first smartphone company in Africa in 2017. ZTE, the Chinese telecommunications giant, provides infrastructure to the Ethiopian government. CloudWalk Technology, a start-up based in Guangzhou, signed an agreement with the Zimbabwean government and will work in particular on facial recognition.
A powerful cyber-colonialist phenomenon is at work here. Africa, confronted with the combined urgencies of development, demography, and the explosion of social inequalities, is embarking on a logical but very unequal techno-industrial partnership with China. As the Americans did to Europe after the war, China massively exports its solutions, its technologies, its standards, and the model of company that goes with these to Africa, while also providing massive financing. Nonetheless, the American AI giants are mounting a counterattack. Google, for example, opened its first AI research center on the continent in Accra. Moreover, GAFAM is multiplying startup incubators and support programs for the development of African talent.
Ukraine – The Test-bed of AI-based military developments
Early on the morning of June 1, 2022, Alex Karp, the CEO of Palantir Technologies, crossed the border between Poland and Ukraine on foot with five colleagues. A pair of Toyota Land Cruisers awaited on the other side to take them to Kyiv to meet the Ukrainian President Volodymyr Zelensky. Karp told Zelensky he was ready to open an office in Kyiv and deploy Palantir’s data and artificial-intelligence software to support Ukraine’s defense.
The progress of this alliance has been striking. In the year and a half since Karp’s initial meeting with Zelensky, Palantir has embedded itself in the day-to-day work of a wartime foreign government in an unprecedented way. More than half a dozen Ukrainian agencies, including its Ministries of Defense, Economy, and Education, are using the company’s products. Palantir’s software, which uses AI to analyze satellite imagery, open-source data, drone footage, and reports from the ground to present commanders with military options. Ukrainian officials state thez are using the company’s data analytics for projects that go far beyond battlefield intelligence, including collecting evidence of war crimes, clearing land mines, resettling displaced refugees, and rooting out corruption. Palantir was so keen to showcase its capabilities that it provided them to Ukraine free of charge.
It is far from the only tech company assisting the Ukrainian war effort. Giants like Microsoft, Amazon, Google, and Starlink have worked to protect Ukraine from Russian cyberattacks, migrate critical government data to the cloud, and keep the country connected, committing hundreds of millions of dollars to the nation’s defense. The controversial U.S. facial-recognition company Clearview AI has provided its tools to more than 1,500 Ukrainian officials. Smaller American and European companies, many focused on autonomous drones, have set up shop in Kyiv too.
Some of the lessons learned on Ukraine’s battlefields have already gone global. In January 2024 the White House hosted Palantir and a handful of other defense companies to discuss battlefield technologies used against Russia in the war. The battle-tested in Ukraine stamp seems to be working.
Ukraine’s use of tools provided by companies like Palantir and Clearview also raises complicated questions about when and how invasive technology should be used in wartime, as well as how far privacy rights should extend. Human-rights groups and privacy advocates warn that unchecked access to this tool, which has been accused of violating privacy laws in Europe, could lead to mass surveillance or other abuses. That may well be the price of experimentation. Ukraine is a living laboratory in which some of these AI-enabled systems can reach maturity through live experiments and constant, quick reiteration. Yet much of the new power will reside in the hands of private companies, not governments accountable to their people.
Summing up, AI is indeed an instrument of power right now, and it will be increasingly so as its applications develop, particularly in the military field. However, focusing exclusively on hard power would be a mistake, insofar as AI exercises indirect cultural, commercial, and political influence over its users around the world. This soft power, which especially benefits the American and Chinese digital empires, poses major problems of ethics and governance. The big platforms must integrate these ethical and political concerns into their strategy. AI, like any technological revolution, offers great opportunities, but also presents —overlapping with these— many risks.
