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Friday, August 07, 2026

AI before AI: The legacy of Norbert Wiener’s cybernetics
 (3/3)


British mathematician Norbert Wiener, who founded the field of cybernetics in the 1940s, may deserve a place alongside artificial intelligence founders Alan Turing and John von Neumann. FRANCE 24 revisits this overlooked visionary who was also one of the first to warn against the automation of society.



Issued on: 05/08/2026 -
FRANCE24
By: Sébastian SEIBT


Norbert Wiener, the father of cybernetics, not only influenced research into artificial intelligence but was also one of the first to warn of the risks associated with automation. © FMM graphics studio

The story of the founding fathers of artificial intelligence almost always features British mathematician Alan Turing and his computing machine. John von Neumann is often presented as the mastermind behind programmable computers.
Summer serie: AI before AI

Names from the Dartmouth group – such as John McCarthy or Marvin Minsky – are also sometimes cited as the “inventors” of artificial intelligence.

However, another figure is often overlooked or relegated to the category of second-rate pioneers: Norbert Wiener, the founding father of cybernetics in the 1940s.

ChatGPT, Claude and other AI chatbots can be regarded as the offspring of his ideas.

But it's uncertain whether Wiener – with his particular moral, political and scientific convictions – would claim any credit for these modern large language models.
A young prodigy

Wiener was born on November 26, 1894, in Columbia, Missouri, into a family of Jewish immigrants from Eastern Europe.

His father Leo Wiener, a highly strict professor of Slavic languages of Lithuanian origin, would “behave somewhat like Pygmalion and attempt to mould his son in his own image", said Pierre Cassou-Noguès, a philosopher at Paris 8 University and author of a fictionalised account of the mathematician’s life.

"At least, that is how Wiener describes it in his autobiography,” he added.

Thus, Wiener became “a young scientific prodigy moulded by his father”, agreed Mathieu Triclot, a philosopher specialising in the history of technology at the Belfort-Montbéliard University of Technology.

He learned to read before the age of four, graduated from high school at 11, earned a bachelor’s degree in mathematics at 14 and completed his PhD in mathematics at Harvard at 18.

A few years later, the Massachusetts Institute of Technology (MIT) appointed him as one of its youngest professors.

It was in this role that, after World War II, Wiener developed his ideas which would “have an impact on many fields related to artificial intelligence such as robotics, control engineering and multi-agent systems”, said Philippe Mathieu, an artificial intelligence specialist at the University of Lille.

Weinter laid the foundations for the new field of cybernetics as early as 1943 in a seminal article and further developed his ideas in the book “Cybernetics or Control and Communication in the Animal and the Machine”, published in 1948.

Cybernetics studies “certain phenomena of control and information transmission in the same way in humans, in the animal kingdom and in the world of machines”, Cassou-Noguès said.

In other words, for Wiener and “cyberneticists”, it is possible to draw parallels between the way human and animal brains and machines process information.
Cybernetics, a ‘super-science’ attracting the biggest names in AI

In practical terms, these principles inspired Wiener to work on a new kind of anti-aircraft defence system during World War II.

He aimed to create a system capable of adapting in real time to the movements of missiles or aircraft and of learning from its mistakes. These were the very first tentative steps towards what we would now call “machine learning”.

“The central theme of cybernetics is understanding how an entity adapts to its environment and to the information available. Intelligence is seen as the result of interactions between an entity [living or not] and its environment,” Mathieu said.

“Logic, mathematics and electrical engineering, information theory, brain research and psychology – a whole bundle of scientific disciplines influenced the events that led to AI," writes Rudolf Seising, who specialises in the history of science and technology at the Deutsches Museum in Munich.

"In the first half of the 20th century, interdisciplinary considerations and, above all, transdisciplinary approaches to AI were mainly found under the umbrella of cybernetics, a ‘super science’.”

This “jack-of-all-trades” aspect of cybernetics may also explain why Wiener is less often seen as the “father of AI” than Turing or von Neumann, who had a more direct and immediate impact on the development of computer science.

READ MOREThe Dartmouth Workshop: The $7,500 investment that gave birth to AI (2/2)

But for Triclot, cybernetics’ “indirect influence” on AI is considerable.

A series of ten meetings known as the Macy Conferences on Cybernetics brought together scientists interested in Wiener’s ideas between 1946 and 1953.

Some of the biggest names in the history of AI, including von Neumann and Claude Shannon, took part and identified themselves with cybernetics at the time.

One of the founding members of the Macy Conferences was psychologist Joseph Carl Robnett Licklider, “one of the central figures in the history of American computing”, Triclot said.

He later became the head of the US Defense Advanced Research Projects Agency. Licklider "played a key role in the funding and development of the internet and also took an interest in human-computer interface technologies in the 1960s”, Triclot added.

At MIT, Wiener also supervised the work of Walter Pitts, a researcher and logician who was pivotal to the development of modern AI.

In the 1940s, Pitts laid the foundation for the first neural network model.
Against the Manhattan Project

This work, which is rooted in cybernetics, is essential to understanding today’s large language models.

While cybernetics itself may now seem to be a relic of the past, it paved the way, alongside neural networks, for the emergence of the branch of AI known as “connectionism”, which lies at the heart of the rise of ChatGPT and other 21st-century AI systems.

Wiener not only provided an “intellectual cradle” for AI, but “he was also one of the first to warn of its dangers”, Cassou-Noguès said.

Known for his strong-willed character and notorious outbursts of anger, Wiener understood why certain areas of scientific research were dangerous. Consequently, he refused to take part in the Manhattan Project, which led to the development of the first atomic bomb.

Following the bombings of Hiroshima and Nagasaki, Wiener even published a famous open letter in which he stated his intention to no longer publish research that could be misused for creating weapons of mass destruction.

Albert Einstein supported the move at the time.

But with cybernetics, "as early as 1948" Weiner also foresaw “the possibility of a fully or almost fully automated society – a development which, in his view, could just as easily lead humanity to a world free from the constraints of work or the social hell of mass unemployment,” Cassou-Noguès said.

Wiener concluded that he should continue his work in order to influence the direction research in the field was taking.

Often labelled as politically left-wing – US authorities suspected that he was sympathetic to the Soviet bloc during the Cold War – Wiener was thus one of the first to warn of the risk of social upheaval linked to the advent of machines.

In this sense, “cybernetics is a profoundly political endeavour, preoccupied with the fear of technological unemployment caused by machines that could replace workers”, Triclot said.


Cover image: The Debate © France24
02:02




For him, it is “striking to read this as early as 1947”, a time when computers were still very rare.

But for Wiener, this risk would only materialise “if we fall into the trap of treating human beings inhumanely”, said Triclot.

In other words, according to Wiener, robots would only be able to replace humans if humans were reduced to having the status of robots by denying everything that distinguishes them from machines.


Although the term “cybernetics” seems to have fallen out of use, the ideas of its founder are relevant today, at a time when companies are citing the triumph of AI to justify layoffs.

This article has been translated from the original in French.



















The era of the machine: Silicon Valley shows off humanoid robots

Cover image: Focus © France 24

Issued on: 05/08/2026 - 
05:07 min

For decades, humanoid robots belonged to the realm of science fiction. But today, they're one of Silicon Valley's hottest markets. According to consulting firm McKinsey, automation could transform up to 30 percent of all hours worked across the US economy by 2030. Dozens of companies are already developing robots that can interact with people and even help with everyday tasks. FRANCE 24's Pierrick Leurent and Valérie Defert report, with Wassim Cornet.





Monday, August 03, 2026

The Dartmouth Workshop: The $7,500 investment that gave birth to AI (2/2)

ANALYSIS


Some 70 years before the current frenzy of record-breaking fundraising by modern AI giants such as OpenAI and Anthropic, the Rockefeller Foundation made what may have been the first investment in the history of AI. With a $7,500 grant from the foundation in 1956, four eminent mathematicians organised the Dartmouth Workshop, where the term “artificial intelligence” was first coined.

Issued on: 01/08/2026 
FRANCE24
By: Sébastian SEIBT


The Dartmouth Workshop was where the term "artificial intelligence" was used for the first time. © FMM graphics studio, Chat GPT

In the second part of our summer series “AI before AI”, FRANCE 24 looks back at the early days of artificial intelligence. Click here to read Part I on the forgotten Soviet programme that developed early AI concepts.


Billions of dollars are now pouring into artificial intelligence, and AI start-ups have little trouble convincing investors to back them financially.

But back in 1955, the Rockefeller Foundation rejected a $13,500 request to fund a conference submitted by John McCarthy, a young American mathematician who, although promising, was still at the start of his career.

Rockefeller Foundation administrators balked at approving so much money – the equivalent of more than $160,000 today – to organise a workshop for a field of research that did not yet exist. What was “artificial intelligence”, and why devote two summer months to it at New Hampshire's Dartmouth College?

Nevertheless, McCarthy was backed by some high-profile figures and had teamed up with Marvin Minsky, another young mathematics prodigy. The two rising stars managed to convince other heavyweights to join the project. Nathaniel Rochester, who invented the first commercial computer for IBM, was one of the co-signatories of the funding application. At the time, the few computers in existence resembled large cupboards overflowing with electrical wires and bore little resemblance to today's modern computers.

McCarthy and Minsky also managed to secure the involvement of Claude Shannon, whose reputation as a mathematician was already well established following his seminal 1948 paper on the mathematical theory of communication. Shannon ranks among the names most frequently cited when discussing early AI pioneers alongside Alan Turing and John von Neumann, according to Philippe Mathieu, director of the Multi-Agent Systems and Behaviour team at the University of Lille.

They promised to explore topics at the conference including automatic computers, neural networks, the creation of a computer “programmed to use a language” and even the “self-improvement” of intelligent machines – which might today be described as “deep learning”.
The birth of AI

One man at the Rockefeller Foundation offered a ray of hope: Warren Weaver, director of the Division of Natural Sciences, had already approved funding for several landmark projects of the time, including in genetics, molecular engineering and agriculture. Weaver had also written an analysis of Shannon’s work.

“Weaver was a friend of Shannon's, which is why the application was addressed directly to him,” said Rudolf Seising, who specialises in the history of science and technology at the Deutsches Museum in Munich.

But although Weaver found the proposal interesting, he felt it was focused on how the brain works, and that brain research sounded more like medicine than mathematics, according to Seising. “He therefore forwarded it to Robert S. Morison, the director of medical research at the foundation,” he added.

Morison decided to award $7,500 to this biomathematics project, making the first AI investment in history.

Are we in an AI bubble?
Cover image: PEOPLE & PROFIT © FRANCE 24
11:59



The term “artificial intelligence” was still not yet a fait accompli. In a joint article written with Shannon, McCarthy suggested referring to “Intelligent Machines”, but “Shannon felt the term was much too big,” Seising said. They eventually settled on “Automata Studies”, which McCarthy found less promising.

“‘Artificial intelligence’ was a good term for raising money and to interest the public and policymakers. But it was not purely a marketing decision, as it also served to define a common vision; it was the starting point for a community of researchers on artificial intelligence,” said Hartmut Hirsch-Kreinsen, a sociologist who specialises in technology.

That was the aim of the Dartmouth Summer Research Project on Artificial Intelligence. The group of four – McCarthy, Minsky, Shannon and von Neumann – wanted to bring together mathematicians, philosophers and economists alike to work on the future of AI.

Disappointing results?


In June 1956, the team took up residence at Dartmouth College apartments for five weeks. The true significance of the research programme is debated to this day: was this really the founding moment of AI, or were the results disappointing overall?

The conference itself yielded little of substance. Its organisers had hoped for an exchange of ideas that would in the near future enable them to demonstrate in concrete terms that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”, as McCarthy wrote.

In reality, the various accounts of this historic conference show that most of the participants – the exact number of whom is not known – interacted with one another only very sporadically and were present for just a few days, sometimes simply to make use of the computers.

But the Dartmouth workshop went down in history, and the 50th anniversary of AI was celebrated in 2006.

And at least one breakthrough was unveiled at the conference: the “Logic Theorist”, considered to be the very first AI programme, was presented by its creators, Allen Newell and Herbert Simon, to the Dartmouth assembly.

Many of the workshop's participants went on to have brilliant careers, and several went on to win Turing Awards, the equivalent of the Nobel Prize for AI.

The conference even influenced the silver screen in the 1968 film, “2001: A Space Odyssey”. Director Stanley Kubrick consulted Minsky as he was developing his concept for the HAL 9000 – the hyper-rational, potentially deadly AI computer and centerpiece of the film's spaceship.

This article has been translated from the original in French.




Friday, July 31, 2026

AI before AI: The forgotten Soviet programme (1/2)

ANALYSIS


Beginning in the 1950s, the Soviet Union launched an ambitious programme aimed at developing artificial intelligence. While the history of AI today seems to be shaped by the giants of Silicon Valley, ideas originating from behind the Iron Curtain had a significant influence, although they remain largely overlooked.


Issued on: 30/07/2026 - FRANCE24
By: Sébastian SEIBT

The Soviet Union took an interest in artificial intelligence as early as the 1950s.
 © Studio graphique France Médias Monde


American chess genius Bobby Fischer defeated the reigning world champion Russia's Boris Spassky in 1972 in what has been called "the most exciting world chess championship ever".

But the Eastern Bloc got its revenge on August 8, 1974, when Kaissa, a Soviet computer, checkmated its opponent, securing the very first World Computer Chess Championship title for the Soviet Union. Russian artificial intelligence had crushed its capitalist competitor.

The victory proved to the world that the Soviets also had a say in the field of AI, which had until then mostly been developed at top US universities.

AI, a ‘bourgeois science’?


In the early 1950s, AI – which was not yet known by that name – was viewed behind the Iron Curtain with suspicion. The Soviets viewed what was to become AI primarily through the lens of cybernetics, a field of research that was very much in vogue for studying human-to-machine or machine-to-machine behaviour.

Some saw cybernetics as a “false bourgeois science”, while others thought it held great promise for scientific advancement.

“Cybernetics almost became a sort of official philosophy of scientific research in the USSR,” said Olessia Kirtchik, a sociologist at the European Centre for Sociology and Political Science who has worked on the history of AI in the Soviet era.

The hallways of research institutes and universities were then filled with mathematicians, philosophers and other researchers, tasked with ensuring the communist regime reaped the benefits of cybernetics. While it fell out of favour in the US by the late 1950s, giving way to computer science and what eventually became AI, the Soviets remained committed to cybernetics, particularly the concept of the “thinking machine”.

The gold mine of Soviet AI

One of the leading figures in Soviet AI to go down in history was Alexander Kronrod, a mathematician who was, among other things, in charge of the team that developed the Kaissa chess programme. For some, Kronrod was even the Soviet “father of artificial intelligence”.

But before him, another mathematician had a considerable influence on the development of AI. His name was Dmitry Pospelov.

Pospelov brought prestige to the discipline by “fostering and supporting the artificial intelligence research community for years”, said Kirtchik. This “lobbying” work culminated in the creation of the Russian Association for Artificial Intelligence in 1989.

Pospelov’s pro-AI campaign within Moscow’s corridors of power also illustrates how the field was not immune to the ideological battle of the Cold War era. The Russian mathematician strongly criticised the Western approach to AI, which he deemed too “reductionist”. In his view, American researchers were reducing the human brain to a sort of supercomputer, performing rigorous but disembodied calculations. For him, intelligence had to be conceived as a social activity dependent on its environment, rather than as a purely logical process.

In the Soviet Union, AI was expected to solve practical problems, and the USSR primarily “used algorithms to optimise the management of the socialist economy”, according to Kirtchik.

The oil and gas industries, for example, used it to improve their operations.


Are we in an AI bubble?
Cover image: PEOPLE & PROFIT © FRANCE 24
11:59



Algorithms even proved to be a gold mine for Moscow – quite literally, given that in the 1960s authorities were trying to identify where to dig in Soviet territory to find gold deposits. To achieve this, Yuri Zhuravlyov, one of the most highly decorated mathematicians of the Soviet era, developed an algorithmic approach. Using data on the known locations of gold mines around the world, Zhuravlyov built a programme in 1966 that successfully identified where to find gold within the Soviet Union.

This may have been one of the first practical success of “machine learning”, the ability of an algorithm to learn from the data it is given. Others see it as proof that AI does not necessarily need huge datasets to find the right answers.

“With clever methods, small data can move mountains – or in this case, reveal them,” Valery Manokhin, a machine learning specialist, wrote in a blog post on Medium on the Soviet gold rush.

When Russian AI inspired Apple

Some of Silicon Valley’s big names also have Soviet AI to thank. Apple might never have released its Newton tablet in 1993 without the work of Shelia Guberman, the Soviet mathematician who solved a puzzle that had been troubling many Western AI specialists: how to get machines to recognise handwriting.

“He achieved this by adopting an approach known as Gestalt (a form of pattern recognition), which had been overlooked in the West,” Kirtchik said.

Guberman's solution was adopted by Soviet entrepreneur Stepan Pachikov for the Paragraph company. His handwriting recognition software was used by Apple to develop the Newton personal digital assistant; the technology was subsequently sold to Microsoft and used by the US Postal Service.

Other ideas that were developed in the Soviet Union are now enjoying a resurgence, including the “automaton” conceived by Michael Tsetlin in the early 1960s. These ideas inspired Norwegian computer scientist Ole-Christoffer Granmo, who in 2024 introduced the idea of the “Tsetlin machine”, designed to be a more energy-efficient alternative to today’s large language models like ChatGPT.

But Tsetlin's ideas would likely annoy tech bros of today like Sam Altman and Elon Musk, however. For Tsetlin, “the workings of these automata had to be perfectly transparent, unlike the black boxes that are today’s AI models”, Kirtchik said.

The Soviets were not short of ideas, but as was often the case during the Cold War, they simply did not have the same resources as the West.

The practical applications of Soviet research often remained “marginal", Kirtchik said, "since the computers at their disposal were often incapable of performing the necessary calculations”.

This article was translated from the original in French.



Friday, July 24, 2026

  

Source: Pressenza

For decades, geopolitics was shaped by control over territory, natural resources, maritime routes, and military capability. In the twenty-first century, those factors remain decisive, but a new dimension of power has emerged above them: the capacity to develop, train, regulate, and govern artificial intelligence.

The issue is no longer simply who builds the most advanced models or manufactures the most sophisticated semiconductors. The competition also revolves around who will write the rules that will govern the operation of this technology for the rest of the world.

With that objective, China unveiled an Action Plan on International AI Ethical Governance on July 17, 2026, during the World Artificial Intelligence Conference (WAIC) held in Shanghai. The document was prepared under the coordination of the Ministry of Industry and Information Technology and is explicitly framed within the United Nations Pact for the Future and the Global Digital Compact.

This decision is far from incidental. While much of the international debate continues to focus on technological competition between the United States and China, Beijing seeks to place another issue at the center of the global agenda: the construction of an international architecture for governing artificial intelligence before its development permanently outpaces the regulatory capacity of States.

The distinction is profound. From China’s perspective, artificial intelligence is not merely a strategic industry or a market of immense economic value. It is a civilizational infrastructure destined to transform production, education, medicine, scientific research, public administration, finance, transportation, security, and virtually every sphere of human activity. For this very reason, Beijing argues that its development cannot be determined solely by commercial interests or corporate competition, but instead requires governance capable of safeguarding the global public good.

From this perspective, ethics ceases to be a collection of abstract principles and becomes a political technology. Governing artificial intelligence ethically means deciding how it will be designed, who will bear responsibility when harm occurs, which risks will be considered acceptable, and who will participate in defining those rules.

One of the central concepts of the plan is the establishment of governance throughout the entire life cycle of artificial intelligence. Although the expression may appear highly technical, it represents a significant departure from traditional regulatory approaches. Rather than supervising only finished products, the proposal calls for oversight mechanisms beginning at the very origin of every system. This encompasses data acquisition, training processes, model design, safety testing, deployment, subsequent updates, and even the eventual withdrawal of systems whose risks can no longer be effectively managed.

In practice, this means that responsibility no longer rests exclusively with the end user. Developers, data providers, technology companies, research institutions, and regulatory authorities all share responsibilities throughout the technological development process. Artificial intelligence therefore ceases to be viewed merely as a commercial product and instead becomes a comprehensive chain of shared accountability.

Another fundamental pillar is the classification of risks according to categories and levels. The underlying logic is straightforward: not every artificial intelligence system presents the same potential for harm. A model designed to translate documents does not pose the same risks as one capable of operating critical energy infrastructure, financial systems, medical diagnostics, or military decision-making. Consequently, the plan proposes regulatory obligations proportional to the potential impact of each application, avoiding both insufficient oversight and sweeping prohibitions that could hinder innovation.

The document also introduces the concept of agile governance. China recognizes that technological development advances far more rapidly than traditional legislative processes. A law may require years of parliamentary debate, whereas an artificial intelligence model can evolve within months—or even weeks. In response, the plan advocates regulatory mechanisms capable of continuous adaptation through technical standards, ongoing evaluation, and institutional coordination. Regulation is therefore conceived not as a static legal text, but as a dynamic and evolving process.

Another particularly significant aspect is the strengthening of a collaborative ecosystem. Under the Chinese approach, artificial intelligence is not viewed as the exclusive domain of major technology corporations. Universities, public laboratories, research institutes, manufacturing industries, open-source developers, international organizations, and governments are all considered integral parts of a single innovation chain. This reflects a longstanding characteristic of China’s development model, in which state planning seeks to coordinate capacities across multiple sectors in order to accelerate technological innovation.

Within this context, particular importance is attached to research on explainability, privacy protection, and bias mitigation. Explainability seeks to address one of the most complex questions in contemporary artificial intelligence: is it possible to understand how a model reached a particular conclusion? As AI systems become increasingly sophisticated, many of their decisions function as genuine “black boxes.” The plan therefore identifies as a priority the development of technologies capable of making these internal processes understandable while simultaneously strengthening personal data protection and reducing algorithmic discrimination.

Equally noteworthy is the explicit support for technological exchange and open-source development in areas related to security, transparency, and explainability. At a time marked by trade restrictions, export controls, and intensifying technological competition, this position seeks to project an image of international scientific cooperation while enabling countries with more limited technological capabilities to participate in the development of advanced AI tools.

The plan also incorporates a social dimension extending well beyond engineering. It proposes integrating scientific and technological ethics into the national education system, specifically safeguarding the rights and interests of women, children, older adults, and persons with disabilities, while reducing the digital divide. From China’s perspective, artificial intelligence should not deepen existing inequalities but rather serve as an instrument for expanding human development opportunities.

Perhaps the most significant element from a geopolitical perspective is the plan’s insistence on strengthening cooperation with developing countries. The document proposes expanding regulatory capacities, sharing institutional experience, and facilitating access to technical knowledge so that AI governance does not become concentrated within a small number of advanced economies. This orientation is closely connected to other initiatives promoted by China over the past decade, including the Belt and Road Initiative, the Global Development Initiative, and expanding technological cooperation with Asia, Africa, and Latin America.

This approach contrasts with the trajectory followed by the United States. Although Washington has also developed regulatory initiatives and recognizes the need to address risks associated with artificial intelligence, it has historically assigned a predominant role to private-sector dynamism and the preservation of technological leadership. Major corporations have played a central role in developing the world’s most advanced models, while public policy has combined regulation, innovation incentives, and measures designed to protect strategic capabilities, including export controls on high-performance semiconductors.

China advances a different approach. It argues that international governance should not be built solely around technological competition, but rather through multilateral mechanisms capable of establishing shared rules for the future development of artificial intelligence. From this perspective, regulation is not viewed as an obstacle to innovation but as a necessary condition for ensuring its long-term sustainability and for distributing its benefits more broadly.

Ultimately, the debate extends far beyond technology itself. What is now beginning to take shape is the normative architecture of the twenty-first century. The rules established today will determine who controls data, who sets international standards, who bears responsibility for technological risks, and who participates in the distribution of the benefits generated by artificial intelligence.

The plan presented in Shanghai reflects China’s determination to play an active role in shaping that architecture. Rather than merely responding to immediate technological competition, it seeks to occupy a central position in defining the rules of the emerging international digital order. The debate is no longer simply about who develops the most powerful artificial intelligence. The decisive question is who will possess the authority to define the rules under which that intelligence will ultimately transform the world.

Sources

  • Ministry of Industry and Information Technology of the People’s Republic of China (MIIT). Official statements on the 2026 World Artificial Intelligence Conference.
  • Xinhua News Agency. “Action Plan on International AI Ethical Governance,” July 17, 2026.
  • United Nations. Pact for the Future and Global Digital Compact, 2024.
  • Ministry of Foreign Affairs of the People’s Republic of China. Global AI Governance Action Plan, 2025.
  • World Artificial Intelligence Conference (WAIC) 2026. Official speeches and conference documents.

This article was originally published by Pressenza; please consider supporting the original publication, and read the original version at the link above.Email

Claudia Aranda is a journalist in Pressenza's Chile team.


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The Stories Shaping Africa’s AI Future

Source: Africa is a Country

In the boardrooms of Addis Ababa and the tech hubs of Nairobi, a new worldmaking project is underway. Artificial Intelligence (AI) has become one of the central arenas where African states negotiate their position in an unequal global order. To speak of “AI in Africa” is not simply to describe the arrival of a technology, but to encounter a set of competing discursive frames through which political desires, developmental ambitions, ethical claims, and governance priorities are translated into technical agendas. As national strategies and regional protocols are rolled out, what comes into view is a struggle over how Africa is imagined within this machine age, and what kinds of futures that imagination makes thinkable.

These frames are not simply different ways of describing a new technology. They are competing scripts for what Africa is, what AI is for, and who ought to shape its future. Some cast the continent as a site of leapfrogging and renewal; others as a zone of exposure, extraction, sovereignty, moral repair, or improvised adaptation. Each brings some actors and possibilities into focus while pushing others to the margins. That is why the issue is not only what AI means for Africa, but what pictures of Africa are being called into view through AI discourse.

The first terrain is shaped by promise and protection. One of the most pervasive registers is promissory. In the African renaissance 2.0 frame, visible in the AU Continental AI Strategy, AI appears as a leapfrogging device through which Africa can bypass the chokepoint of traditional industrialization and enter prosperity through technical modernization. Here, the ethical subject is the developmental state or planner charged with delivering growth, inclusion, and renewal. What comes into view is capacity, competitiveness, and beneficial transformation. What recedes from view are the historical and political-economic conditions that make acceleration so attractive in the first place: debt, infrastructural dependence, uneven ownership, and the possibility that AI may deepen rather than overcome inherited asymmetries.

Running alongside this is a protective register. In this vulnerability frame, Africa is not late but exposed to surveillance, bias, exclusion, and systems introduced into settings where safeguards are weak and redress is limited. As the CIVICUS report on AI surveillance in Africa suggests, the issue is not only future risk but already existing forms of technologically mediated harm. The ethical subject here is the harm-bearing population—the data subject, precarious worker, surveilled citizen, and linguistically marginal user most likely to absorb the costs of badly governed AI. This frame is ethically indispensable because it foregrounds rights, harm, and institutional fragility, including the uneven but growing architecture of data protection on the continent. Its limit, however, is that it can cast Africa chiefly as a site to be protected rather than one from which technological futures are actively contested and shaped.

A second terrain is shaped by structure and sovereignty. Alongside these promissory and protective registers sits a more structural critique. In the anti-colonial frame, AI appears as a socio-technical order built through unequal material relations: not only data extraction, but also mineral supply chains, outsourced labor, environmental degradation, foreign-owned infrastructures, and asymmetrical control over the institutions that govern digital life. From this perspective, Africa is not outside the AI order looking in; it is already deeply inserted into it, though often on subordinated terms. The ethical subject is the dispossessed worker, community, or polity whose labor, resources, and data are drawn into AI systems while ownership and value are captured elsewhere. What becomes visible is the political economy of AI—an approach that makes it harder to sustain the comforting idea that Africa’s main problem is simply one of readiness or delayed entry. But the frame has limits. In foregrounding dispossession, it can flatten internal differentiation, externalize agency too completely, and become so totalizing that the uneven realities of negotiation and strategic inhabitation recede from view.

The sovereignty frame shifts the question from extraction to control: AI is cast as a problem of authority over infrastructure, procurement, standards, and rulemaking, a concern visible in South Africa’s National Data and Cloud Computing Policy. Its appeal lies in insisting that African states must govern technological systems in the public interest rather than merely consume them on external terms; the ethical subject is the sovereign polity, imagined above all as the state charged with authoring technological futures on its own terms. But sovereignty should not be romanticized. Declaring authority is not the same as possessing material control, especially where states remain dependent on foreign cloud infrastructures, imported models, consultants, and opaque procurement chains. Equally, this frame can too quickly presume the state to be the vehicle of emancipation.

A third terrain is shaped by ethics and language. Beneath these macro-struggles lie cultural and ethical frames. In the Ubuntu frame, Africa appears as a moral resource: a source of relational vocabularies of dignity, community, interdependence, and solidarity that unsettle liberal individualism in mainstream AI ethics. At its strongest, this frame asks what kind of human subject AI presupposes and produces. Its ethical subject is the person-in-community, understood less as an isolated rights-bearer than as a being constituted through relations of community, solidarity, and mutual recognition. At its weakest, it risks remaining symbolically reparative and romantic rather than politically disruptive: a moral vocabulary that pluralizes AI ethics without redistributing authority over infrastructure, capital, or standards.

The linguistic justice frame relocates the issue to language, depicting Africa as linguistically marginalized. Initiatives such as Lelapa and Masakhane insist that language is not a neutral interface but a site through which recognition and opportunity are distributed in digital life. An AI system that cannot adequately process Yoruba, Wolof, or Zulu does more than inconvenience its speakers; it helps reproduce a hierarchy in which some languages, and the worlds they carry, are made computationally peripheral. The ethical subject here is the speaker, the language community, and the knower whose access to digital life is mediated through systems not built with them in mind. Yet linguistic inclusion alone does not resolve ownership, infrastructure, or power. An AI that speaks African languages can still be embedded in extractive or externally governed systems.

A final terrain is shaped by markets and adaptation. The market pragmatist frame, visible in the African Continental Free Trade Area (AfCFTA) Protocol on Digital Trade, casts Africa as a jurisdictional space to be made legible to capital—a continent whose frictions must be reduced so that innovation, trade, and platform expansion can move more easily across borders through harmonization, regulatory alignment, and investment readiness. Here, the ethical subject is the investor, firm, regulator, and entrepreneurial ecosystem said to require clear rules and scalable markets. What becomes visible is efficiency, interoperability, and growth. What recedes from view is the fact that harmonization is never merely technical. It can distribute advantage, privileging certain actors over others, and can also displace questions of labor and ownership while eclipsing democratic accountability behind the smoother language of market access.

The frugal innovator frame—the jua kali of tech—foregrounds small-scale, improvised solutions that work offline, on cheap devices, and in low-resource settings. Lelapa AI’s InkubaLM is the sharpest contemporary example. This frame’s appeal lies in refusing the assumption that meaningful technical work must begin from abundance. Africa appears as inventive under pressure, capable of repurposing constraints into forms of practical intelligence. The ethical subject is the builder, bricoleur, local entrepreneur, and community improviser who makes systems work where industrial-scale infrastructures are absent. What becomes apparent is creativity, resilience, and situated responsiveness. But it can also romanticize necessity, making scarcity look like authenticity rather than a political condition to be transformed. It can overstate what ingenuity can compensate for and understate the need for public investment, industrial policy, and durable institutional support.

In the end, what matters about these eight frames is not whether any one of them finally tells the truth about Africa and AI. It is that each organizes moral and political attention differently—foregrounding development while muting dependency, naming extraction and harm while narrowing agency, valorizing language and ethics without redistributing material authority. As a constellation of concerns, they reveal less a single African AI trajectory than a struggle over what Africa is imagined and allowed to be in technological modernity.

That is why AI in Africa is not just about chips, code, or competitiveness. It is about who gets to architect the future, under what terms, and in whose interests. The stakes are not simply adoption or regulation, but the representational politics through which certain subjects, institutions, and futures become thinkable while others fall from view.

Yet these frames do not only describe Africa; they help make it where they are taken up. This is the worldmaking with which we began. What a frame brings into view becomes what institutions are built to serve, and what it lets recede goes ignored. Nor do the frames work alone. They overlap and prop one another up: sovereignty talk can leverage the anti-colonial critique, market pragmatism needs the renaissance’s promissory optimism, and the frugal innovator’s ingenuity is enlisted to soften the very scarcity it works within. The future, then, will not be settled by one script defeating the others, but by which come to dominate the mixture—and whose interests that mixture ends up serving—as it hardens into the continent’s legal, institutional, and material infrastructure. Whether this moment marks a structural shift or the repetition of old patterns turns on that.


This article was originally published by Africa is a Country; please consider supporting the original publication, and read the original version at the link above.Email

Anye-Nkwenti Nyamnjoh is a senior research officer at the EthicsLab at the University of Cape Town. His work engages African intellectual archives as a resource for rethinking the ethics and politics of new and emerging technologies.


Opinion

Pope Leo's Eucharistic answer to the age of AI

(RNS) — 'The Eucharist opens us to justice and sharing, with a preferential concern for those who are burdened by poverty or marginalization,' the pope writes.
Pope Leo XIV greets the faithful gathered in the square in front of the Apostolic Palace for the noon Angelus prayer, in Castel Gandolfo, on the outskirts of Rome, Sunday, July 12, 2026. (AP photo/Andrew Medichini)

This is the last of a series of columns by the author on Pope Leo XIV’s first encyclical, “Magnifica Humanitas.” This piece focuses on the Conclusion. For earlier columns, see Chapter 1, Chapter 2,  Chapter 3, Chapter 4 and Chapter 5

(RNS) — In the conclusion to his first encyclical, “Magnifica Humanitas,” Pope Leo XIV proposes what he calls “a sober yet demanding program of Christian life with which we can navigate this epochal change in the light of the Gospel.” The epochal change he refers to is AI, and he writes of a Eucharistic spirituality where we are united with the body of Christ in completing God’s work of creation by living out the gospel message.

This is a challenge and antidote to contemporary culture.

He notes that “our world is filled with attempts to seize control of markets and spheres of influence, often shrouded in reassuring rhetoric and seductive ideologies.” And he contrasts this with the message of the gospel, where the Son of God becomes flesh, poor and vulnerable, like “so many brothers and sisters stripped of their dignity and reduced to silence.”

Peace does not come through power but “is awakened when we allow ourselves to be moved by the tears of the little ones, the fragility of the elderly, the silence of victims and the struggle of those who fight against the evil they do not wish to commit,” he writes. 

While today’s ideologies “urge humanity to overcome limitations through technology, and to rise above others by asserting dominance,” the Son of God offers something quite different: He “takes upon himself our weakness and transforms it into a setting for salvation,” the pope shares. 

This is a demanding message. 



What saves humanity, according to Leo, is “the divine love that descends into the most fragile point of our history and renews it from within.”

“The Incarnation and the Paschal Mystery reveal God entering into our human condition and transforming it through the gift of himself,” Leo explains. In the Eucharist, “the Lord gives himself and gathers the Church together, so that his offering becomes the principle of unity and source of new life.”

The Eucharist is “never simply an act of individual piety,” says Leo, quoting Benedict XVI. In the Eucharist, we “are the Church of Christ, his members, his body. We are brothers and sisters in him.”

As a result, “The Eucharist opens us to justice and sharing, with a preferential concern for those who are burdened by poverty or marginalization.”

This aspect of the Eucharist is missed by those who focus on smells and bells and prefer the traditional Latin Mass.

“While new economic and technological networks can generate exclusion, isolation and dependencies,” reports Leo, “the Church — nourished by the Eucharist — is called to make visible a different paradigm, one that preserves human connections, gives a voice to the invisible and ensures that processes are aimed at respecting people’s dignity.”

Thus, the spirituality proposed by Leo is “committed to building the world for the common good.” It does not take “refuge in spiritual sentimentality or retreating into our own little worlds.” Rather, he says, “We must be faithful to the truth, invest in education, cultivate relationships and love justice and peace.

“Fidelity to the truth,” explains the pope, “requires integrating the possibilities offered by technology within a framework marked by wisdom, which is capable of safeguarding both the dignity of each person and the future of our common home.”

Leo recognizes the need to invest in education to help children and young people use technology “for developing responsible relationships, helping them to recognize the risks and choose what fosters inner freedom.”



Rather than living in our phones, he calls on us to cultivate relationships by cherishing “places and times where physical presence remains crucial, such as shared meals, Christian community gatherings, time spent with the lonely and serving the poor.”

We must also love justice and peace, says Leo. “Every technical or economic decision should include spiritual discernment and be an opportunity for assessing whether the advances in AI are promoting justice and participation or concentrating wealth and power in the hands of a select few.”

Leo wants us to be active in building this world of justice and peace.

In Leo’s view, our vocation is “not to be passive spectators of social and cultural fractures, nor mere commentators on what is crumbling, but men and women prepared to enter the construction sites of history — research laboratories, technology companies, schools, the media, institutions and local communities — in order to rebuild what has collapsed and protect what is threatened.”

Finally, he urges us, especially those who feel helpless, to a life of prayer modeled on Mary, whose “Magnificat” is a hymn of praise and joy.

She sees all of history through the lens of revelation, explains Leo. “The Romans continue to control her land, and her people are still subjugated and humiliated,” Leo admits. “Yet, everything has changed within her, and this allows her to see what is invisible. God has already shown the strength of his arm; he has already scattered the proud, cast down the mighty, lifted up the lowly, filled the hungry with good things and sent the rich away empty-handed.”

“Mary not only teaches us to recognize God’s invisible work,” writes Leo, “but also directs our gaze to the points at which humanity is broken and the world becomes distorted: the contrast between the humble and the powerful, the poor and the rich, the satiated and the hungry, teaching us to look at the world from a lower position: through the eyes of those who suffer rather than the mighty; to view history through the eyes of the little ones, rather than through the perspective of the powerful; to interpret the events of history from the viewpoint of the widow, the orphan, the stranger, the wounded child, the exile and the fugitive.”



Those who think that Catholicism became less demanding after the Second Vatican Council have not been listening. The Eucharistic spirituality proposed by Leo is sober and demanding because it rejects the ideologies of power and wealth in favor of the gospel message of reconciliation, compassion and love.

While the principles of Catholic social teaching are open to all, the spirituality offered here is a challenge to Christians who listen to the Word and break bread together. What they do is not simply for their individual piety; it is to empower them as the body of Christ to build the kingdom of God — one of justice, peace and love.