With Black, Scholes and Merton, modern finance had found one of its most powerful formulations. The price of an option could be deduced from arbitrage reasoning, a dynamic hedging strategy and a differential equation derived from Brownian diffusion. Financial randomness thus became an object that could be calculated, neutralised and almost domesticated. Where Bachelier had introduced the random movement of prices, where Wiener had given a rigorous structure to Brownian motion, where Markowitz had organised portfolio selection under uncertainty, Black, Scholes and Merton took a further step: they showed that a contingent right could be valued on the risk itself. But this construction was based on a particular representation of chance. Price changes are continuous, returns are assumed to be sufficiently close to a normal distribution, and volatility is considered constant or, at the very least, controllable within the framework of the model. The world thus described is unstable, but with a regular instability; uncertain, but with a disciplined uncertainty; random, but with a randomness that is sufficiently smooth to be integrated into an equation. It is precisely this representation that Benoît Mandelbrot disrupts. His contribution to finance does not consist in proposing a new valuation formula comparable to that of Black-Scholes. It is more profound and, in a way, more unsettling. Mandelbrot asserts that financial markets are not only uncertain: they are rough. They do not always fluctuate according to a smooth and continuous geometry; they experience breaks, accelerations, discontinuities, concentrations of volatility, and extreme events more frequently than the Gaussian model predicts. With him, finance can no longer be content with thinking of risk as a regular dispersion around an average. It must learn to think about irregularity itself. Benoît Mandelbrot was born in Warsaw on 20 November 1924 into a Jewish family of Lithuanian origin. His father was a businessman and his mother a doctor. The Mandelbrot family belonged to that world of Central Europe where scientific culture, languages, migration and historical uncertainty were closely intertwined. Very early on, the young Benoît was shaped by a demanding intellectual environment. His uncle, Szolem Mandelbrojt[1], was a renowned mathematician, a professor at the Collège de France, a specialist in analysis and close to the Bourbaki group[2]. This family presence played an important role in his intellectual orientation, even though Mandelbrot would always remain at a distance from established schools and mathematical orthodoxies. In 1936, faced with the growing dangers in Eastern Europe, the family left Poland for France. Benoît Mandelbrot arrived in Paris at the age of eleven. This geographical and cultural break was decisive. It exposed him both to the richness of the French intellectual system and to the violence of history. During the Second World War, the family had to take refuge in the provinces to escape persecution. These war years interrupted normal schooling, but they developed a very particular form of visual and intuitive intelligence in Mandelbrot. He would later say that he often learned better through images, shapes and geometric analogies than through traditional linear demonstrations. This uniqueness made him stand out very early on. After the war, he entered the École Polytechnique, where he benefited in particular from the teaching of Gaston Julia[3], one of the great names in the theory of complex functions. From the beginning of the 20th century, Julia had studied mathematical objects that would later become central to fractal theory. At the time, these shapes were still largely considered as analytical curiosities, or even as mathematical monsters: sets that were difficult to visualise, irregular, and beyond the reach of classical geometric intuition. Mandelbrot would later find in them one of the profound sources of his work. After the École Polytechnique, he continued his studies in the United States, notably at the California Institute of Technology, then returned to France before working at the CNRS. But he did not fully identify with the dominant style of post-war French mathematics. The Bourbaki group, to which his uncle was close, promoted very abstract, axiomatic, structural mathematics, detached from immediate applications. Mandelbrot, on the contrary, was attracted to concrete phenomena, irregular shapes, empirical data, and misclassified objects. He was interested in linguistics as much as in turbulence, the geography of coastlines, the distribution of income, transmission noise in telephone lines or variations in financial prices. He does not seek only to demonstrate; he seeks to see. This intellectual freedom explains his departure for the United States. In 1958, he joined the IBM laboratories, where he remained for several decades. This choice was fundamental. IBM offered him a rare environment: the opportunity to work at the frontier of mathematics, computer science, physics and economics, without being confined by the constraints of a traditional university department. Above all, IBM gave him access to powerful computers, which would play a decisive role in the visualisation of fractal objects. For Mandelbrot, the computer was not just a calculating machine; it became an instrument for geometric exploration. It made it possible to make visible shapes that classical mathematics had foreseen but could not yet fully represent. The word “fractal” would not appear until later, in the 1970s. Mandelbrot coined it from the Latin fractus, which means broken, fragmented, irregular. A fractal is a shape whose complexity is repeated at different scales. A coastline, a cloud, a mountain, a vascular network or a fern all have this property: when you zoom in on a part, you find patterns that resemble the overall structure. This is self-similarity or scale invariance. These shapes cannot be described correctly by classical Euclidean geometry. A line, a circle, a triangle or a sphere are suitable for representing an ideal, smooth and regular world; but the real world is often rough, fragmented and uneven. Mandelbrot’s great insight was to show that this roughness is not pure disorder. It has a structure. It can be measured, described, compared. The apparent chaos conceals a form of order, but an order that is different from that of classical geometry. Where Euclid describes simple shapes, Mandelbrot seeks to describe
THE ETHICAL APPROACH OF AI
In order to limit the effects of the biases that taint the practices of sustainable AI oriented towards ESG, “coalitions” made up of institutions, companies, scientists and think tanks, strive to define ethical frameworks – in the form of codes and charters – based on philosophical, sociological and psychological reflections, but this framework provokes debates between regulators and libertarians. The Foundations of AI Ethics Ethical deliberations on AI practices follow three main approaches: universal, normative, and applied. The first – of an axiological nature and inspired by Kant and Rousseau – is based on the principles and values that underpin life in society: respect for man, truth, justice, nature, etc. The second – called legalistic or prescriptive – covers moral judgments and social values, such as true or false, good or bad, fair or unfair, etc. The third – of a praxeological nature – measures the consequences, externalities or impacts of a system, behavior or object on the economy, nature, society or the person. It is most often applied to new technologies, in particular AI, and to management, in particular sustainable management (Pistilli, 2024). AI ethics frameworks In order to limit their negative impacts on collective and individual thoughts, decisions or behaviors, guides or ethical charters published by companies and normative codes, references and/or regulations issued by regulators (international organizations, nation-states, associations), aim to frame the design of software and the use of their data and results by companies (Constantinides & al, 2024). Codes generally do not have an ethical or moral dimension, unlike charters, guides or, in France, the “raisons d’être” of companies. International institutions such as the OECD, the UN, UNESCO and the G7 have been working since 2019 to establish a “normative ethics” and “global governance of AI”. The Vatican, inspired by the work of Bonanti (2018), who inspired several codes, advocates the advent of an “algorithmic ethics” (or ethics of algorithms) based on the principles of “transparency, social inclusion, responsibility, impartiality and reliability”. Overall, according to Menecoeur (2020), the 126 documents on the ethics of AI identified worldwide are divided between public codes (national and international) and private guides (companies, universities and associations). But the rulers of the United States, the People’s Republic of China and the European Union, as well as the leaders of their digital and/or ESG-oriented companies, have applied rules, codes and ethical guides that respond to often different approaches. The ethical relativism of AI and ESG European texts – and in particular the AI Act – are the subject of intense lobbying, in particular by GAFAM, in order to avoid open source AI and the downgrading of certain generative AI software. Most European think tanks advise strengthening the regulation of AI, such as the Montaigne Institute, which launched the Objectif IA operation for digital training, the CSR Observatory, which strives to put AI at the service of the application of ESG standards, and the Louis Bachelier Institute, which has launched the Good in Tech program to measurethe impact of AI on society. But a collective of 30 global AI leaders denounced the European approach, stating that “Europe has become less competitive and less innovative compared to other regions, and it now risks falling even further behind in the AI era due to inconsistent regulatory decisions”. These reactions show that AI codes and guides are subject to a form of “ethical relativism”, because they depend on both technological and economic factors, but also – and increasingly – on geopolitical and cultural considerations. They are the subject of different readings according to the disciplines, professions and ideologies of the actors of AI and ideology. In the United States, under the influence of the Federal Guidelines for Sentencing Organizations (1991), practical guides (guidelines) are more common among American companies than among European or Chinese firms. The GAFAM have initiated a Partnership on AI which recommends the application of general principles and a collective commitment: “We are committed to conducting open research and dialogue on the ethical, social and economic implications of AI” (Hern, 2016). But the interpretation of these principles differs from one society to another. Google focuses on social criteria (including non-discrimination). Apple displays a charter based on honesty and respect for stakeholders. Meta only declares that it applies the professional standards in force. Amazon takes up the fundamental principles, but paradoxically states that “AI drives humans”; Microsoft and Open AI display “Codes of trust reflecting their cultures and values; they recognize “the potential for bias in algorithms and strive to mitigate their impact”. In Europe, reflections on the ethics of AI were launched in 2015 and led to a regulation aimed at the protection of personal data (Data Governance Act) published in 2018 (but applied in 2023), then a white paper on AI (2020), a directive on microprocessors (Chips Act, 2023) and a directive on artificial intelligence (AI Act) passed in 2024, (but applicable in 2026). These texts strive to manage the risks induced by AI and to promote a “trustworthy AI” based on compliance with laws (Lawful AI), ethical values (ethical AI) and technical skills (robust AI). The principles – borrowed from bioethics – relate to human autonomy (respect for the rights of citizens), the prevention of any infringement (protection of people and property), equity (between users) and explainability (of software). These principles were then broken down into “requirements”: systems (data, software, results) must “remain under human control”; they must be “robust, safe and transparent”. Compliance with the requirements is controlled by so-called “technical” methods: audit of “trustworthy architectures” (Trustworthy AI), control of the application of design standards (X-by-design), methods of explanation, tests, validations and implementations. Non-technical methods complete this system: regulation by codes, charters and guides, certification of systems, orientation of AI training and AI research. Among these methods, the AI Act prioritizes so-called “foundational” AI systems according to three levels of risk to public and private lives: models classified as “unacceptable”, leading to intrusive and discriminatory uses of AI, are prohibited; systems classified as “high risk”, which may affect
Roch Cyriaque GALEBAYI, La modernisation des systèmes éducatifs africains. Étude comparée et solutions, Eds L’Harmattan, 2026, 263 pages.
The author notes that African countries are more than ever faced with the challenge of modernity in order to hold their place in the community of nations. He argues that African modernity can only be based on growth, consumption and technological innovation, which are the foundations of Western modernity. He is aware that “the lights of Western reason will not be enough” to reform African institutions and practices, particularly in the field of education and training. The African education system must indeed train elites capable of responding to the multiple challenges of the contemporary world. To fulfil its mission, the system must acquire true “educational sovereignty”. But he notes that the idea of revolutionising education systems faces multiple political and bureaucratic obstacles in Africa. In some countries, academic freedom is “manipulated” by the ideologies and interests of the social groups in power, by religious or ethnic traditions, and by the “symbolic violence” and “economic imperialism” of Western capitalism. The author courageously denounces certain “patronage” practices used in universities, which favour the selection of students, the appointment and promotion of professors, the setting of teaching programmes and the organisation of examinations. In particular, the author analyses the procedures that contribute to the reproduction of the elites and their alignment with the practices of the groups or coalitions in power. According to the author, African universities must provide spaces for freedom of teaching, research and innovation. In order for the education system to reflect a country’s cultural identity and serve the deepest aspirations of its population, the new system must partially break free from the colonial education model and better integrate the African socio-cultural paradigm. The author is aware that, following the Western model, each African state will have to deconstruct certain traditions, modernise its administration, reform its institutions and rethink its governance. The role of schools and universities will be essential in implementing this reconstruction, through realistic diagnoses of problems, effective support for decision-making, implementation and monitoring of execution. He courageously advocates for a reform of African education systems that aims to break free from colonial models and integrate the continent’s cultural particularities, but his well-documented plea shows the extent of the path still to be travelled by the African continent. Roch Cyriaque GALEBAYI is a graduate of the IEP of Aixen Provence and holds a doctorate in history. He is a trainer in African military schools.
Geopolitical shocks and contagion between risk “silos”: the failure of recognition and the illusion of governance
Prepared by Michelle Thomson, QRD®, Chair Florence Anglès, Vice-Chair Contributors DCRO Emerging Topics Subcommittee: Joy Albright; Chukwunomnso Anyichie, QRD®; Cesar Chalhoub; Aku Odinkemelu; Brian Prentice; Mai Shuaibi; David Streliski; Benson Uwheru, Secretary. Originally developed through the DCRO Emerging Topics Subcommittee. Submitted to Club Turgot | July 2026. Geopolitical crises do not stay where they start. What begins as a regional event spreads rapidly through commodity markets, supply chains, financial systems, regulation and corporate strategy. For boards, the defining challenge is not the initial shock — it is recognising how quickly its consequences migrate across the organisation before any single committee has seen the whole picture. Geopolitical crises have become a permanent feature of the global business environment. They rarely remain confined to their point of origin. What begins as a regional event can quickly spread through commodity markets, supply chains, financial systems, regulation and corporate strategy. For boards, the defining challenge is not the initial shock itself but recognising how rapidly its consequences migrate across the organisation. The paper shows that the principal governance challenge is recognition rather than prediction. Boards oversee risk through specialised committees, each performing its mandate effectively. During systemic crises, however, information becomes fragmented. Every committee sees part of the problem; none sees the complete picture. By the time directors assemble an integrated view, cross-silo contagion is often already underway. This structural weakness is described as the Governance Illusion. Drawing on the 1956 Suez Crisis, the 1973 OAPEC Oil Embargo, the 1979 Iranian Revolution and the 2022 Russian invasion of Ukraine, the paper identifies a recurring pattern: geopolitical disruption, energy shock, supply-chain disruption, inflation, tighter financial conditions, sovereign stress and long-term structural adjustment. Across all four events, the common governance failure was delayed recognition rather than lack of information. Three analytical tools support earlier recognition. The Potential Cross-Silo Contagion Pathways helps directors anticipate how risks migrate across business functions. The Committee Lag Map explains why committees receive signals at different moments, delaying enterprise-wide understanding. The Asymmetry Map identifies the regions and structural conditions where contagion is most likely to emerge first, enabling boards to focus on leading rather than lagging indicators. Each tool is presented as a visual framework in the full paper. The historical evidence also highlights three recurring governance failures: concentrating on immediate impacts while overlooking second- and third-order effects; assuming geographically distant crises will remain contained; and underestimating how individually manageable risks interact to create systemic disruption. These structural weaknesses are reinforced by behavioural biases including confirmation bias, threat rigidity and bounded rationality. Recommendations Three recommendations emerge. First, establish an Integration Function capable of consolidating information across board committees into a coherent view of systemic risk. Second, appoint a Designated Challenger responsible for questioning prevailing assumptions, exploring alternative scenarios and ensuring weak signals receive appropriate attention. Third, complement these structural changes with behavioural discipline through regular cross-committee dialogue, explicit consideration of second-order effects and continued vigilance during periods of apparent stabilisation. Geopolitical shocks cannot be prevented, but governance failures can. Boards that integrate information, challenge assumptions and recognise cross-silo contagion early will be better positioned to protect long-term value and strengthen organisational resilience in an increasingly interconnected world. The full paper, including the analytical frameworks and historical case analyses, is available at
THE STRANGE DEFEAT OF FRANCE IN 1940 AND OF EUROPE IN 2026
Jean-Jacques Pluchart Now that Marc Bloch has just been interred in the Panthéon and is thus held up as an example to the French, it is advisable to read or reread his book entitled “L’étrange défaite” (The Strange Defeat), in which he examines the reasons for the debacle of the French army and the discouragement of the French in 1940. The reader cannot help but be confused by the current resonance of his observation. The France of yesterday, invaded by German mechanical force, seems to foreshadow the Europe of today, threatened by American digital supremacy and Chinese commercial domination. As a captain in the 1st Army in 1939, Marc Bloch clearly observes the errors of the strategists, the indecision of the tacticians and the disarray of the troops. He notes that the general staff, which is surrounded by “too many agencies”, retains, in the face of the enemy, “the cult of beautiful paper” and “bureaucratic reflexes”. He observes “a whole network of cronies around the rulers who redouble their devotion and intrigue”. He confesses “the bitter aftertaste left by this war, which was badly conducted and ended even worse”. After criticising the “diplomacy of the Treaty of Versailles and the invasion of the Ruhr”, he deplores the spirit of Munich and the political instability that drives the French political class. He welcomed “the attempt of the Popular Front”, but regretted “that it succumbed because of the follies of some of its supporters”. He criticised the role of the press, dominated by a “bourgeois elite concerned with its own interests”. He mocked the “old “preachers, who over time have amassed a whole arsenal of verbal patterns to which their intelligence clings like rusty nails”. He lamented a “resignation of the elites” and a renunciation of effort by the French. Marc Bloch went further by attributing the cause of the defeat to the “government of old men” in France in the 1930s. He attributed much of the country’s ills to the teaching at the École de Guerre, which focused on the tactics of Napoleon’s armies and the trench plans of the Great War. He advised his sons to “reflect on the faults of their elders”, adding “that he would not have the presumption to draw up a programme for them”. He criticises the Vichy government for proposing “only a return to the land and to the values of yesteryear, elevated to the status of virtue”. like the amercan Republicans, the reader of Marc Bloch cannot help but think that today’s Europe, the “land of arts and culture”, is perpetuating the Europe of the 1930s, which Marc Bloch described as a “museum of antiquities”. Marc Bloch, a history professor at the Sorbonne, argued that countries in difficulty should learn from the past. Through his book, 83 years after his death, he gives us his final lesson.
Serge LATOUCHE, La décroissance. De l’utopie à la (re)construction d’un monde commun, eds Edisens, 2026, 163 pages.
The author develops the theory initiated by Karl Polanyi in his seminal book entitled The Great Transformation, which argues in favour of a “re-embedding of the economy in society”. Serge Latouche strives – following Castoriadis and Lefort – to go beyond this thesis by showing that degrowth is less economic than political and social, more “frugal and cultural” than material and financial. He denounces the excessive importance taken on by the neo-liberal economy founded in the 18th century and on the English enclosure movement denounced by Thomas More, then on the research of Hayek, Friedman and the Mont Pèlerin Society. He proposes a project for society based on sharing and living together. He criticises the “neo-libertarian narrative of growth”, by contrasting it with the “counter-narrative” of the “grand narrative of degrowth”, which is based on plans to develop common goods (resources and public services) and the common good (shared values). The aim of the project is to put an end to the failures of the state and the market economy. The author notes that, despite the calls for vigilance from eco-socialists, the growth economy, which is based on “sad passions” such as “the thirst for wealth and power”, continues to prosper, in particular thanks to the “post-truth” maintained by AI. The author considers the most suitable political regime for initiating the degrowth project, and concludes that “direct and decentralised democracy” is preferable to a “plutocratic technocracy”, the former transforming society from below and the latter from above. He advocates “local action, the eco-region and the urban village”. He argues that the growth society destroys the common goods (natural resources) and the common good (the societal project and collective values), and that it is based on a one-dimensional man: homo economicus. He comments on the debates between the different schools of degrowth thought and criticises the fact that most eco-socialists call for the advent of degrowth through catastrophic discourses , such as those of the IPCC. The reader of the book will better understand the themes and language codes of the degrowthers, but will also appreciate the impasses of some of their projects. Serge Latouche is Professor Emeritus at the University of Saclay and author of numerous books and articles on the Social and Solidarity Economy. Jean-Jacques Pluchart
The ethical approach to AI: towards sustainable AI
Jean-Jacques Pluchart The encyclical Magnifique Humanité published in May 2026 (see clubturgot.com 114) has rekindled the debate on the ethical principles of AI. Various movements, made up of institutions, companies, scientific laboratories and think tanks, are striving to define ethical frameworks – in the form of codes and charters – based on philosophical, sociological and psychological considerations, but this framework is sparking debates between regulationists and libertarians. The foundations of AI ethics Ethical deliberations relating to AI practices follow three main approaches: universal, normative and applied. The first – of an axiological nature and inspired by Kant and Rousseau – is based on the principles and values that underpin life in society: respect for people, truth, justice, nature, etc. The second – known as legalistic or prescriptive – covers moral judgements and social values, such as true or false, good or bad, fair or unfair, etc. The third – of a praxeological nature – measures the consequences, externalities or impacts of a system, behaviour or object on the economy, nature, society or the individual. It is most often applied to new technologies, in particular AI, and to management, in particular sustainable management (Pistilli, 2024). Ethical frameworks for AI In order to limit their negative impacts on collective and individual thoughts, decisions or behaviours, guides or ethical charters published by companies and normative codes, frameworks and/or regulations issued by regulators (international organisations, nation-states, associations), aim to provide a framework for the design of software and the use of its data and results by companies (Constantinides & al, 2024). The codes generally do not have a deontological or moral dimension, unlike charters, guides or, in France, the “raisons d’être” of companies. International institutions such as the OECD, the UN, UNESCO and the G7 have been working since 2019 to establish a “normative ethics” and “global governance of AI”. The Vatican, notably inspired by the work of Bonanti (2018), who inspired several codes, advocates the advent of an “algo-ethics” (or ethics of algorithms) based on the principles “of transparency, social inclusion, responsibility,impartiality and reliability”. Overall, according to Menecoeur (2020), the 126 documents on AI ethics identified worldwide are divided between public codes (national and international) and private guides (from companies, universities and associations). But the leaders of the United States, the People’s Republic of China and the European Union, as well as the leaders of their digital companies, apply in practice rules, codes and ethical guides that often reflect different approaches. The ethical relativism of AI European texts – and in particular the AI Act – are the subject of intense lobbying, particularly by GAFAM, in order to avoid open-source AI and the decommissioning of certain generative AI software. Most European think tanks recommend strengthening the regulation of AI, such as the Institut Montaigne, which launched the Objectif IA operation in favour of digital training, the Observatoire de la RSE, which is striving to put AI at the service of the application of ESG standards, and the Institute Louis Bachelier, which has initiated the Good in Tech program to measure the impact of AI on society. But a collective of 30 global AI leaders has denounced the European approach, stating that “Europe hasbecome less competitive and less innovative compared to other regions, andit now risks falling further behind in the AI era due toinconsistent regulatory decisions“. These reactions show that AI codes and guidelines are subject to a form of “ethical relativism”, because they depend on both technological and economic factors, but also – and increasingly – on geopolitical and cultural considerations. They are interpreted differently depending on the disciplines, professions and ideologies of the AI stakeholders and ideology. In the United States, under the influence of the Federal Guidelines for Sentencing Organisations (1991), practical guides (guidelines) are more common among American companies than among European or Chinese firms. The GAFAM companies have initiated a Partnership on AI which recommends the application of general principles and a collective commitment: “We are committed to conducting open research and dialogue on the ethical, social and economic implications of AI” (Hern, 2016). But the interpretation of these principles differs from one company to another. Google focuses on social criteria (in particular non-discrimination). Apple has a charter based on honesty and respect for stakeholders. Meta only states that it applies the professional standards in force. Amazon adopts the fundamental principles, but paradoxically states that “AI guides humans”; Microsoft and Open AI display “Codes of trust reflecting their cultures and values”; they recognise “the potential for bias in algorithms and strive to mitigate its impact”. In Europe, reflections on the ethics of AI were launched in 2015 and led to a regulation aimed at the protection of personal data (Data Governance Act) published in 2018 (but applied in 2023), then a white paper on AI (2020), a directive on microprocessors (Chips Act, 2023) and a directive on artificial intelligence (AI Act) passed in 2024 (but applicable in 2026). These texts seek to manage the risks induced by AI and to promote a “trustworthy AI” based on compliance with the law (Lawful AI), ethical values (ethical AI) and technical skills (robust AI). The principles – borrowed from bioethics – relate to human autonomy (respect for citizens’ rights), the prevention of any harm (protection of people and property), fairness (between users) and explainability (of software). These principles were then broken down into “requirements”: the systems (data, software, results) must “remain under human control”; they must be robust, reliable and transparent “. Compliance with the requirements is monitored by so-called “technical” methods: auditing of “trustworthy architectures” (Trustworthy AI), monitoring of the application of design standards (X-by-design), methods of explanation, testing, validation and implementation. Non-technical methods complete this system: regulation by codes, charters and guides, certification of systems, guidance on AI training and AI research. Among these methods, the AI Act prioritizes so-called “foundational” AI systems according to three levels of risk to public and private life: models classified as “unacceptable”, leading to intrusive and discriminatory uses of AI, are prohibited; systems classified as
Céline MARANGE, La guerre d’Europe a commencé, Les Arenes, 2026.
On 5 March 1946, Winston Churchill delivered a speech in which he said, “I do not think that Soviet Russia desires war; what it desires are the fruits of its power as well as of its doctrines. Our difficulties and dangers will not disappear if we turn a blind eye, if we wait to see what happens, or if we practise a policy of appeasement.” These reflections from another time are strikingly relevant today. The Kremlin wants to become once again the feared power it was in the past by reasserting its rights to lands it considers ancestral. Faced with the rise of these dangers, it is imperative to have clear ideas about the state of the threat, the adversary’s modes of action, our interpretation biases and our level of preparedness. There is also a much more pernicious danger to be feared: that of the alteration of democratic systems, as artificial intelligence will allow targeting with rare precision and the manipulation of information with unsuspected sophistication. For Ukraine, it is a war of national liberation, a struggle for the survival of the state and a fight for freedom, since the Russian president denies the existence of the Ukrainian nation. The war has forged a new Ukraine, tested, exhausted but united and resolute. The Kremlin is using the war in Ukraine to stage its confrontation with Western countries and accentuate the divisions in the world. What is certain is that the current confrontation is not only about the survival of Ukraine and the security of Europe, but also about the sustainability of liberal democracy, which is now under attack from all sides. Faced with a danger that is certainly imprecise but blatant, dealing with the most pressing issue is a necessity; imagining the worst is a categorical imperative. Without this effort of imagination and a surge of willpower, there is a great risk of being caught off guard. Because Russia is actively preparing for the possibility of the war spreading across Europe. Several indicators show that Russia is preparing for a prolonged war: The defence budget, which represents 38% of Russia’s budget, shows that the Kremlin’s priority is to continue the war. It is rearming at full speed while organising itself to improve its supplies and armaments: it manufactures 300 tanks per year – France, which is the best equipped, has 215 Leclerc tanks. Russia is increasing the size of its army: 600,000 soldiers compared to 150,000 before the war in Ukraine. Lifting the sanctions would give the Russian economy a breath of fresh air. A militarist regime feeds on war; there are many actions that are already affecting Europe and France in particular, there are also attacks on the integrity of public debate aimed at influencing general opinion, and above all there are attacks on national cohesion. Europe must prepare for a long-term confrontation where anything goes. Céline Marangé is a researcher on Russia, Ukraine and Belarus at the Strategic Research Institute of the École Militaire, and an associate member of the Research Centre inSlavic History (Université Paris 1 Panthéon-Sorbonne) Reading summary prepared by Michel Gabet
Marlène Benquet, La finance aux extrêmes – Enquête sur le capitalisme autoritaire en France, La Découverte, janvier 2026, 256 pages, 22 €.
Sociologist Marlène Benquet’s work, the result of ten years of research, aims to define how a “second finance” is constructed—a novel mode of capital accumulation by financial actors fully aware of their “value,” but with political consequences that favor the development of a libertarian-type regime through authoritarian capitalism. One of the main characteristics of this second finance is its detachment from the market driven by traditional financial actors, who collect savings, such as banks, insurance companies, pension funds, and household savings. In short, the market considered as the only way to invest in listed companies. Second-tier finance develops outside the market but without truly severing ties with it, through over-the-counter transactions. This allows it to be less regulated while retaining considerable freedom to invest in unlisted assets. Consequently, it enables the transformation of assets previously considered ineligible (real estate, unlisted companies, infrastructure, derivative financial products, pension funds) into assets. Second-tier finance develops outside of traditional markets. Its purpose is therefore not to promote competition. It encourages privatization in order to seize control of certain sectors, such as healthcare. The accelerated concentration of clinics since the early 2000s is a clear example. Marlène Benquet also points out that numerous examples support this argument, citing a window of opportunity created by the Commission for the Liberation of Growth (2008), which specifically encouraged third-party investors to acquire stakes in pharmacies: “A Health Europe Directive, driven by the European Union, which also encouraged third-party investment, particularly from financial players, in pharmacy capital, has created assets in the healthcare sector that previously escaped financialization. These assets have become highly profitable and are guaranteed by the State, since payment, pricing, usage, and profits are all guaranteed by the national health insurance system.” The workings of this mechanism are perfectly adjusted. Continuing the demonstration leads to an inescapable conclusion: (1) no competition, (2) a quasi-rent-seeking situation, (3) a level of service guaranteed by the State. Zero risk, maximum profit. The book is based on genuine fieldwork conducted using an indisputable research methodology (some sixty interviews with financial players, who were “more accessible than expected,” and access to archival material comprising 2,600 documents). This on-site , in-vivo observation of financial players in their daily lives is transcribed in a rich verbatim style from the very first chapters. The conclusion—which appears to be the author’s true motivation—reveals a kind of unwritten strategy employed by this secondary financial sector, paving the way (or perhaps even providing a mediated platform?) for an authoritarian libertarian ideology widely shared abroad and in France. The actors (agents of normalization), described as conservatives, are explicitly mentioned by the author when establishing a link between these “extreme” political alternatives and their economic influence. Sociologist Marlène Benquet is a research director at the CNRS and a member of the Interdisciplinary Research Institute in Social Sciences (IRISSO). Her work focuses on mass retail and the world of international finance. Alain BRUNET
How does the company apply AI? The example of banking.
Are the canonical observations of Abernathy and Utterbach (1975) on the transition from experimental innovation to applied innovation still valid half a century later? Will the example of AI applied to banking activities make it possible to verify this? Since the 2020s, banks have been engaged in a vast process of AI-driven innovation of their products, services and systems, as well as of their relationships with their customers, their staff, their partners and regulators. These innovations aim to personalize products and services according to customer segments (households, businesses, local authorities, governments), their risk-return profiles, and the types of services provided (payment, credit, investment, etc.). They are seeking to diversify the current banking interface and integrate the payment function into various objects (telephones, homes, vehicles, glasses, watches, etc.). Their aim is to adapt credit and insurance offers to each purchase, and to automate investment management according to the risk profiles of savers and their ESG scoring requirements. They aim to ensure that each transaction is supported by a “robot advisor” or an AI-“augmented” banking advisor, capable of carrying out prospecting, projections, simulations and training. Thanks to AI, they are striving to better support start-ups in their “valleys of death” and industrial or financial groups in their merger and acquisition projects (especially cross-border), with the help of structured multi-disciplinary networks. In the structured products market, AI applications already allow for the automatic selection of counterparties, an analysis of the levels of protection and barriers offered, the coupons proposed, the pricing of embedded options, the strength of balance sheets and the rating of issuers, and the depth and quality of the secondary market. But above all, AI makes it possible to ensure better security for customer data, processing, transactions, settlement-delivery and securities custody. Achieving these objectives already requires the most advanced AI applications, such as biometric identification, automatic flow traceability, scheduled data dissemination, the systematisation of smart contracts (in MNBC and tokens), the control of loans financing investments with ESG impact (such as Tree Token), the development of “complementary currencies” (such as Bancor) and micro-payments promoting the inclusion of unbanked people (such as Arcadia Blockchain), etc. The staff of each bank must therefore demonstrate “innovism” (Phelps et al., 2020), so that their bank is not a “follower” but a “pioneer” in the application of AI. Innovation encompasses the ability to anticipate new, abnormal or crisis situations by setting up “innovation laboratories”, creating a financial “imaginarium” and stimulating the “desire to create” among staff at all levels. It therefore seems that, in the light of this example, the technological acculturation of companies is both more comprehensive and faster than it was during the “thirty glorious years”. Abernathy, W. J., & Utterback, J. M. (1975). A Dynamic Model of Process and ProductInnovation. Omega, 3(6), 639-656. Phelps, E., Bojilov, R., Hoon, H. T., & Zoega, G. (2020). Dynamism: The Values ThatDrive Innovation, Job Satisfaction, and Economic Growth. Cambridge, MA: Harvard University Press. Jean-Jacques Pluchart