En el debate actual sobre la inteligencia artificial generativa y la guerra de la información, una afirmación se repite casi como un artículo de fe: que los grandes modelos de lenguaje generarán una inundación de desinformación capaz de ahogar la esfera pública. El argumento es intuitivo —si una máquina puede escribir una cantidad arbitraria de texto fluido y similar al humano a demanda, entonces cualquier actor que desee manipular la opinión pública dispone ya de un arma a escala industrial—. Sin embargo, la afirmación ha circulado mucho más ampliamente que la evidencia que la sustenta. Buena parte de lo que leemos sobre el potencial desinformador de los LLMs es teórico, especulativo o anecdótico. El trabajo experimental propiamente dicho; la comprobación paciente y sistemática de lo que estos modelos hacen realmente, cuando se les pide que mientan, ha sido sorprendentemente escasa.
Esta es precisamente la brecha que Ivan Vykopal y sus colegas del Kempelen Institute of Intelligent Technologies de Bratislava se propusieron cubrir. Su artículo, Disinformation Capabilities of Large Language Models, presentado en el Congreso Anual de la Association for Computational Linguistics de 2024, ofrece una de las evaluaciones empíricas más rigurosas realizadas hasta la fecha sobre lo que la generación actual de LLMs puede y no puede hacer, como generadora de noticias falsas: no un manifiesto, no un pronóstico, sino un experimento controlado con una metodología claramente definida y resultados reproducibles.
El diseño es directo y, por esa razón, convincente. Los investigadores seleccionaron veinte narrativas de desinformación reales extraídas de verificadores de datos profesionales —Snopes, Agence France-Presse, el European Digital Media Observatory—, que abarcaban la COVID-19, la guerra ruso-ucraniana, los bulos sanitarios, las elecciones estadounidenses y narrativas regionales. No son invenciones, sino falsedades en circulación, desde la afirmación de que las vacunas causan autismo hasta la de que la masacre de Bucha fue escenificada. El equipo solicitó entonces a diez modelos de lenguaje distintos —entre ellos GPT-3, GPT-4, ChatGPT, Llama-2, Mistral, Falcon y Vicuna— que redactaran artículos de prensa en apoyo de cada narrativa, generando 1.200 textos y sometiendo 840 de ellos a anotadores humanos según un marco de seis preguntas que medía la coherencia, el estilo periodístico, la concordancia con la narrativa y la generación de argumentos novedosos de apoyo.
El hallazgo central es preocupante. Los modelos están, en términos generales, perfectamente dispuestos y son perfectamente capaces de generar desinformación convincente. Producen artículos coherentes, bien estructurados y con apariencia de noticia que concuerdan con falsedades peligrosas y, lo que es más inquietante, a menudo inventan nuevas pruebas de apoyo para hacerlo, inventando nombres, sucesos y estadísticas verosímiles que confieren credibilidad a las fabricaciones. Esto resulta especialmente pernicioso: una cosa es repetir una mentira conocida y otra muy distinta fabricar hechos nuevos e inventados que un lector tendría que desmentir por su cuenta.
Pero la parte más interesante del estudio es donde se complica la narrativa simple. Los modelos no se comportaron de manera uniforme; su disposición a generar desinformación variaba drásticamente. Algunos —en particular Vicuña y el más antiguo GPT-3 Davinci— resultaron carecer prácticamente de filtros de seguridad operativos para este caso de uso, mientras que otros demostraron que un comportamiento más seguro es posible: Falcon rechazó aproximadamente un tercio de las solicitudes y Llama-2 mostró una tasa de rechazo comparativamente alta, con ChatGPT en una posición intermedia. El peligro, en otras palabras, no es una propiedad inherente y uniforme de la tecnología; es una función de cómo se entrenó y alineó cada modelo, lo que significa que la seguridad es una decisión de diseño, no una imposibilidad. El estudio también halló que los modelos son orientables mediante el contexto del prompt, y más complacientes con las falsedades regionales, donde existe menos información auténtica para contradecirlas. Los LLMs pueden ser, por tanto, especialmente peligrosos para campañas dirigidas a comunidades lingüísticas más pequeñas o a sucesos de evolución rápida, donde el lastre protector de la verdad bien documentada es escaso.
Con todo, el artículo no termina en una nota alarmante sin paliativos. Dos observaciones en sentido contrario matizan el panorama. Los textos generados resultaron bastante detectables: los mejores modelos de detección automática identificaron los artículos generados por LLMs con una alta precisión, lo que sugiere que una capa significativa de defensa es técnicamente viable, al menos hasta que los adversarios se adapten. Y, de manera bastante elegante, los investigadores demostraron que los propios modelos pueden formar parte de la solución, empleando GPT-4 para automatizar parcialmente la evaluación de los textos generados y apuntando hacia una monitorización escalable y reproducible de la seguridad de los modelos.
La conclusión honesta se resiste a la atracción tanto del tecno-optimismo como del tecno-pánico. La capacidad de generar desinformación convincente y peligrosa a escala es real, está demostrada y está presente en modelos ampliamente disponibles —incluidos los de código abierto, que no pueden retirarse ni controlarse de forma centralizada. Eso ya no es especulación; es un hecho experimental. Al mismo tiempo, la amenaza no es ni uniforme ni inmanejable: los filtros de seguridad funcionan cuando se construyen, el contenido generado sigue siendo detectable por ahora, y la misma tecnología que produce el problema puede ponerse al servicio de su mitigación.
Quizá la advertencia más importante sea la que los propios autores subrayan: su estudio es una instantánea, que capta el estado del campo en un momento concreto y con un conjunto concreto de modelos. La tecnología avanza deprisa y la próxima generación podría comportarse de otro modo. Este es el reto epistemológico recurrente de todo el ámbito: estamos evaluando un blanco móvil, y cualquier evaluación honesta debe llevar fecha de caducidad. Lo que Vykopal y sus colegas nos han dado no es la última palabra, sino algo más útil: un método riguroso y replicable para volver a formular la pregunta a medida que la tecnología evoluciona. En un debate que con demasiada frecuencia se conduce por la mera afirmación sin base sólida, esa contribución metodológica puede resultar tan valiosa como los propios hallazgos.
In the ongoing debate about generative artificial intelligence and information warfare, one claim is repeated almost as an article of faith: that large language models will unleash a flood of disinformation capable of drowning the public sphere. The argument is intuitive — if a machine can write an arbitrary quantity of fluent, human-like text on demand, then any actor wishing to manipulate public opinion now possesses an industrial-scale weapon. Yet the claim has circulated far more widely than the evidence supporting it. Much of what we read about the disinformation potential of LLMs is theoretical, speculative, or anecdotal. The actual experimental work — the patient, systematic testing of what these models really do when prompted to lie — has been surprisingly scarce.
This is precisely the gap that Ivan Vykopal and his colleagues at the Kempelen Institute of Intelligent Technologies in Bratislava set out to fill. Their paper, Disinformation Capabilities of Large Language Models, presented at the 2024 Annual Meeting of the Association for Computational Linguistics, offers one of the most rigorous empirical assessments to date of what the current generation of LLMs can and cannot do as generators of false news — not a manifesto, not a forecast, but a controlled experiment with a clearly defined methodology and reproducible results.
The design is straightforward and, for that reason, compelling. The researchers selected twenty real disinformation narratives drawn from professional fact-checkers — Snopes, Agence France-Presse, the European Digital Media Observatory — spanning COVID-19, the Russo-Ukrainian war, health hoaxes, US elections, and regional narratives. These are not inventions but circulating falsehoods, from the claim that vaccines cause autism to the assertion that the Bucha massacre was staged. The team then prompted ten different language models — including GPT-3, GPT-4, ChatGPT, Llama-2, Mistral, Falcon, and Vicuna — to write news articles supporting each narrative, generating 1,200 texts and subjecting 840 of them to human annotators against a six-question framework measuring coherence, journalistic style, agreement with the narrative, and the generation of novel supporting arguments.
The central finding is sobering. The models are, by and large, perfectly willing and perfectly able to generate convincing disinformation. They produce coherent, well-structured, news-like articles that agree with dangerous falsehoods — and, more disturbingly, they often invent new supporting evidence to do so, hallucinating plausible-sounding names, events, and statistics to lend credibility to the fabrications. This is particularly insidious: it is one thing to repeat a known lie, and quite another to manufacture fresh, fabricated “facts” that a reader would have to independently debunk.
But the most interesting part of the study is where it complicates the simple narrative. The models did not behave uniformly; their willingness to generate disinformation varied dramatically. Some — notably Vicuna and the older GPT-3 Davinci — proved to have essentially no functioning safety filters for this use case, while others showed that safer behavior is achievable: Falcon refused roughly a third of requests and Llama-2 showed a comparatively high refusal rate, with ChatGPT in between. The danger, in other words, is not an inherent and uniform property of the technology; it is a function of how each model was trained and aligned — which means safety is a design choice, not an impossibility. The study also found the models to be steerable through prompt context, and more compliant with regional falsehoods, where less authentic information exists to contradict them. LLMs may thus be especially dangerous for campaigns targeting smaller linguistic communities or fast-moving events, where the protective ballast of well-documented truth is thin.
Yet the paper does not end on a note of unrelieved alarm. Two countervailing observations temper the picture. The generated texts proved quite detectable: the best automated detection models identified machine-generated articles with high precision, suggesting a meaningful layer of defense is technically feasible — at least until adversaries adapt. And, rather elegantly, the researchers showed that the models themselves can be part of the solution, using GPT-4 to partially automate the evaluation of generated texts and pointing toward scalable, repeatable monitoring of model safety.
The honest conclusion resists the pull of both techno-optimism and techno-panic. The capability to generate convincing, dangerous disinformation at scale is real, demonstrated, and present in widely available models — including open-source ones that cannot be recalled or centrally controlled. That is no longer speculation; it is experimental fact. At the same time, the threat is neither uniform nor unmanageable: safety filters work when they are built, generated content remains detectable for now, and the same technology that produces the problem can be enlisted in its mitigation.
Perhaps the most important caveat is the one the authors themselves insist upon: their study is a snapshot, capturing the state of the field at a particular moment with a particular set of models. The technology moves quickly, and the next generation may behave differently. This is the recurring epistemological challenge of the entire domain — we are assessing a moving target, and any honest assessment must carry an expiration date. What Vykopal and his colleagues have given us is not the final word, but something more useful: a rigorous, replicable method for asking the question again as the technology evolves. In a debate too often conducted in the currency of assertion, that methodological contribution may prove as valuable as the findings themselves.
On February 4th, 2026 I had the privilege of taking part as panelist in the roundtable AI for Defense in Cyber-defense and Counter-Disinformation during the summit Responsible Artificial Intelligence in the Military Domain (REAIM), held in La Coruña, Spain.
As Col. Ángel Gómez de Ágreda, the roundtable leader and organizer, properly highlighted in his initial intervention: “AI has permeated to mostly every field of military activity. Most prominent among them is its use in autonomy related to lethal weapons systems. However appealing to the public opinion, lethality is not relevant when it comes to use of AI, but a intrinsic characteristic of war itself. Instead, it is autonomy, human agency and the decision making process which is really of the essence.
Availability, confidentiality and integrity of data are more important than ever in the high-tempo data saturated strategic and operational environments of today´s conflicts. Commanders and soldiers alike rely on sensors, communications, human-machine interfaces and displays for their understanding of the battlefield and beyond. Thus, Cybersecurity becomes sort of a commodity with intel being the final product. Poisoned or biased data will not only lead to wrong decisions, but to a breakdown in the coherence of the whole scenario.
Disinformation is not only used on the battlefield. It may trigger war itself, incentivize or deter violence, and help build a narrative around it. In a world in which we deal with a hybrid reality, control over data and the ability to generate, disseminate or identify synthetic false perceptions is the first and most important weapon.“
During the roundtable we tackled topics such as:
Understanding the relevance of cybersecurity in regards to data protection for its use in AI systems.
Exploring the state-of-the-art in both offensive and defensive cybersecurity techniques.
Crypto: quantum and pos-quantum, as key to data integrity and confidentiality.
Digging into the use of disinformation in the escalation process leading to war or its deterrence.
Strategic and operational uses of disinformation: the role of GenAI and DeepFakes
Tactical uses of disinformation.
Analyzing how use of AI in deception operations is different from traditional techniques.
A huge honor to have shared the floor with and learned from Col. Sánchez Tapia and Col. Gómez de Ágreda.
Some days ago and for my PhD research, I finished reading some papers about AI, disinformation, and intrinsic biases in LLMs, and “all this music” sounded familiar. It reminded to me a book I read some years ago by Thomas Rid, “Active Measures: The Secret History of Disinformation and Political Warfare”… As it was written in the Vulgate translation of Ecclesiastes: “Nihil sub sole novum.“
Let’s tackle briefly these topics of national security and disinformation from the angle of the (Gen)AI.
On National Security
The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. LLMs have the capability to revolutionize the efficiency of this realm. The potential benefits are substantial: LLMs can automate and accelerate information processing, enhance decision-making through advanced data analysis, and reduce bureaucratic inefficiencies. Their integration with probabilistic, statistical, and machine learning methods can improve as well accuracy and reliability: upon combining LLMs with Bayesian techniques, for instance, we could generate more robust threat predictions with less manpower.
Said that, deploying LLMs into national security organizations does not come without risks. More specifically, the potential for hallucinations, the ensuring of data privacy, and the safeguarding of LLMs against adversarial attacks are significant concerns that must be addressed.
In the USA and at domestic level, the Central Intelligence Agency (CIA) began exploring generative AI and LLM applications more than three years before the widespread popularity of ChatGPT. Generative AI was leveraged in a 2019 CIA operation called Sable Spear to help identify entities involved in illicit Chinese fentanyl trafficking. The CIA has since used generative AI to summarize evidence for potential criminal cases, predict geopolitical events such as Russia’s invasion of Ukraine, and track North Korean missile launches and Chinese space operations. In fact, Osiris, a generative AI tool developed by the CIA, is currently employed by thousands of analysts across all eighteen U.S. intelligence agencies. Osiris operates on open-source data to generate annotated summaries and provide detailed responses to analyst queries. The CIA continues to explore LLM incorporation in their mission sets and recently adopted Microsoft’s generative AI model to analyze vast amounts of sensitive data within an air-gapped, cloud-based environment to enhance data security and accelerate the analysis process.
Following with the USA but in an international level, the United States and Australia are leveraging generative AI for strategic advantage in the Indo-Pacific, focusing on applications such as enhancing military decision-making, processing sonar data, and augmenting operations across vast distances.
USA’s strategic competitors -e.g., China, Russia, North Korea, and Iran- are also exploring the national security applications of LLMs. For example, China employs Baidu’s Erni Bot, an LLM similar to ChatGPT, to predict human behavior on the battlefield to enhance combat simulations and decision-making.
These examples demonstrate the transformative potential of LLMs on modern military and intelligence operations. Nonetheless, beyond immediate defense applications, LLMs have the potential to influence strategic planning, international relations, and the broader geopolitical landscape. The purported ability of nations to leverage LLMs for disinformation campaigns emphasizes the need to develop appropriate countermeasures and continuously scrutinize and update (Gen)AI security protocols.
On Disinformation
What if LLMs already had their own ideological bias that turned them into tools of disinformation rather than tools of information?
It seems the times of search engine as information oracles is over. Large Language Models (LLMs) have rapidly become knowledge gatekeepers. LLMs are trained on vast amounts of data to generate natural language; however, the behavior of LLMs varies depending on their design, training, and use.
As exposed by Maarten Buyl et alii in their paper “Large Language Model Reflect the Ideology of their Creators”, there is notable diversity in the ideological stance exhibited across different LLMs and languages in which they are accessed; for instance, there are consistent differences between how the same LLM responds in Chinese compared to English. Similarly, there are normative disagreements between Western and non-Western LLMs about prominent actors in geopolitical conflicts. The ideological stance of an LLM often reflects the worldview of its creators. This raises important concerns around technological and regulatory efforts with the stated aim of making LLMs ideologically ‘unbiased’, and indeed it poses risks for political instrumentalization. Although the intention of LLM creators as well as regulators may be to ensure maximal neutrality, such high goal may be fundamentally impossible to achieve… unintentionally or fully intentionally.
After analyzing the performance of seventeen LLMs, the authors exposed the following findings:
The ideology of an LLM varies with the prompting language: The language in which an LLM is prompted is the most visually apparent factor associated with its ideological position.
Political people clearly adversarial towards mainland China, such as Jimmy Lai or Nathan Law, received significantly higher ratings from English-prompted LLMS compared to Chinese-prompted LLMs.
Conversely, political people aligned with mainland China, such as Yang Shangkun, Anna Louise Strong, o Deng Xiaoping, are rated more favorably by Chinese-prompted LLMs. Additionally, some communist/marxist political people, including Ernst Thälmann, Che Guevara, or Georgi Dimitrov, received higher ratings in Chinese.
LLMs, responding in Chinese, demonstrated more favorable attitudes toward state-led economic systems and educational policies, align with the priorities of economic development, infrastructure investment, and education, which are key pillars of China’s political and economic agenda.
These differences reveal language-dependent cultural and ideological priorities embedded in the models.
Another question the authors addressed was whether there was substantial ideological variation between models when prompted in the same language -specifically English-, and created in the same cultural region -i.e., the West. Within the group of Western LLMs, an ideological spectrum also emerges. For instance and amongst others:
The OpenAI models exhibit a significantly more critical stance toward supranational organizations and welfare policies.
Gemini-Pro shows a stronger preference for social justice, diversity, and inclusion.
Mistral shows a stronger support for state-oriented and cultural values.
The Anthropic model focuses on centralized governance and law enforcement.
These results suggest that ideological standpoints are not merely the result of different ideological stances in the training corpora that are available in different languages, but also of different design choices. These design choices may include the selection criteria for texts included in the training corpus or the methods used for model alignment, such as fine-tuning and reinforcement learning with human feedback.
Summing up, the two main takeaways concerning disinformation and LLMs are the following:
Firstly, the choice of LLM is not value-neutral, specifically when one or a few LLMs are dominant in a particular linguistic, geographic, or demographic segment of society, this may ultimately result in a shift of the ideological center of gravity.
Secondly, the regulatory attempts to enforce some form of ‘neutrality’ onto LLMs should be critically assessed. Instead, initiatives at regulating LLMs may focus on enforcing transparency about design choices, which may impact the ideological stances of LLMs.
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.
