Jean-Jacques Pluchart The many stakeholders involved in Artificial Intelligence are questioning the ability of generative AI models and LLMs (large language models) to generate profits for their designers and productivity gains for their users. The latest report from the  MIT (MassachusettsInstitute of Technology) entitled “The GenAI Divide: State of AI in Business 2025″, notes that 95% of generative AI programmes launched since 2022 have still not achievedtheir profitability targets among publishers and American user companies.  It is true that global spending on AI peaked in 2026, with investment projects estimated at €2.5 trillion worldwide, having more than tripled since 2023.  After sharp price increases, the fear of severe stock market corrections is intensifying, and the most recognised experts seem to be unable to predict the timing and extent of the corrections.   However, the stock market outlook differs depending on the links in the AI value chain: the stock prices of semiconductor equipment manufacturers (Nvidia in the USA, TSMC in Taiwan, Samsung in Korea) and those of electricity suppliers are on a positive trend; those of data, computing, storage and rental (cloud) centers are uneven; software publishers, such as the GAFA companies, are more uncertain, as are those of European equipment manufacturers (ASML and STMicroelectronics). The AI ecosystem seems to be threatened by the “Thucydides trap”, whereby a rivalry between a leader and its followers often leads to takeovers and sometimes conflicts. The trap threatens industry leaders such as Open AI and Anthropic, who are competing with several application developers, but also American leaders who are being challenged by Chinese designers such as MiniMax M3 and DeepSeek V4 Pro.  Western and Asian companies are increasingly testing Chinese applications, which are less expensive in terms of tokens and more energy-efficient. They are also striving to diversify their sources of supply for equipment and applications, for fear of higher fees and a US or Chinese embargo on certain applications. Finally, in Europe, designers and users may be taxed by the governments of the Member States in order to contribute to public investments in the infrastructure  (electricity, water, access routes) of digital platforms. For all these reasons, the prospects, particularly the financial ones, of the AI ecosystem can be described as a “black swan” in the sense of Taleb.

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

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

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.

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

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

Philippe Alezard With Bachelier, finance had found its founding insight: stock market prices can be thought of as random movements and options as rights whose value depends on the probability of future prices. With Wiener, this intuition is given a rigorous mathematical foundation: the Brownian motion becomes a continuous process, with independent and Gaussian increments, capable of giving shape to uncertainty. Finally, with Markowitz, uncertainty is no longer merely described; it becomes the subject of a rational decision, organised at the level of a portfolio. However, a decisive question remained: if chance governs prices, is it possible to give a rigorous price to a contract that relates precisely to this chance? This is the question that Fischer Black, Myron Scholes and Robert C. Merton answered in the early 1970s. Their contribution is not just about producing a famous formula. It transforms the very nature of financial valuation. Before them, an option essentially appeared as a bet: the right to buy or sell an asset at a fixed price, in an uncertain future. After them, the option became an object that can be replicated, hedged and valued based on arbitrage reasoning. The price is no longer just an opinion about the future; it becomes the logical consequence of a dynamic hedging strategy. The intellectual and financial context of the 1960s and 1970s is essential to understanding this breakthrough. In the United States, finance was then in the process of becoming an independent academic discipline. Business schools were moving closer to the markets, price databases were developing, and Markowitz’s portfolio theory had introduced variance and covariance into the language of investors, while the CAPM (Capital Asset Pricing Model) of Sharpe, Lintner and Mossin sought to establish a balanced relationship between expected return and systematic risk. At the same time, the options markets, long dominated by over-the-counter transactions, were undergoing an institutional change. In April 1973, the Chicago Board Options Exchange opened its doors and offered, for the first time, an organised market for standardised stock option contracts. The publication of the Black-Scholes model came in the same year, almost at the exact moment when the market needed a common language to price, compare and hedge these new instruments. Fischer Sheffey Black was born in 1938 in Washington D.C. His career path was less linear than that of traditional academic economists. He first studied physics at Harvard before turning to applied mathematics and computer science. He obtained a doctorate from Harvard in 1964 in applied mathematics with a thesis devoted[1] to what we would now call artificial intelligence, at a time when this discipline was still in its infancy. Nothing in this initial career path would have predicted that he would become one of the founders of modern finance. However, this interdisciplinary background, spanning physics, calculus, logic and dynamic systems, undoubtedly explains his ability to view markets as formalisable mechanisms. After his studies, Black worked, among other places, at Arthur D. Little, a consulting firm where he met Jack Treynor[2], one of the pioneers of modern portfolio theory and the equilibrium model of financial assets. This meeting was decisive. Treynor introduced Black to financial problems and encouraged him to think about the links between risk, return and market equilibrium. Black then developed a very personal approach to finance: he was less interested in institutions than in the abstract forces that must govern prices if arbitrage opportunities are eliminated. His mind is that of a theoretical engineer: he seeks the hidden constraint, the necessary relationship, the equation that must be true if the market is consistent. Myron Scholes was born in 1941 in Timmins, Ontario, Canada, to a family with deep ties to the business world. In his Nobel autobiography, he emphasises the importance of this family environment: from a very young age, he was interested in trade, accounting, probability and risk. An eye operation during his adolescence disrupted his schooling and forced him to develop special working methods based on listening, memory and conceptualisation. This personal constraint would play an important role in his way of thinking: Scholes was not only a calculation technician, he was attentive to the economic structure of problems. He continued his studies at McMaster University, then at the University of Chicago, where he obtained his doctorate in 1969. Chicago was then one of the most powerful centres of the new financial economy. Eugene Fama was working there on market efficiency, Merton Miller on corporate finance, Milton Friedman on monetary theory, and the university’s intellectual tradition valued equilibrium reasoning, price consistency and the discipline imposed by competitive markets. There, Scholes received an economic education that was very different from the more institutional European tradition: the objective was not only to describe the markets, but to deduce what prices should be in a world where agents exploit all the possibilities of arbitrage. Robert Cox Merton was born in 1944 in New York. His father, Robert K. Merton, was one of the most influential sociologists of the 20th century, known in particular for his work on “self-fulfilling prophecy” and “unintended consequences”. Young Robert therefore grew up in an exceptional intellectual environment, but quickly chose a different path. He first studied mathematical engineering at Columbia in 1966, then applied mathematics at the California Institute of Technology, where he obtained his MS in 1967. He then joined MIT for his PhD in economics, under the supervision of Paul Samuelson. This point is crucial: Samuelson is one of Bachelier’s successors in American finance. Through him, Merton inherited a tradition in which mathematical physics, probability and economics could be brought together in a single analytical architecture. Merton had a more advanced technical mastery of stochastic calculus than most financial economists of his time. Where Black and Scholes construct a highly powerful arbitrage intuition, Merton gives the model its mathematical generality. His 1973 article[3], “Theory of Rational Option Pricing”, published in the Bell Journal of Economics and Management Science, broadens the framework, clarifies the conditions of validity, establishes general restrictions on option prices and embeds

Jean-Jacques Pluchart Is the rise in crude oil by more than 70% over the last six months changing the equation of the global energy transition? The replacement of fossil fuels (oil, gas, coal) by alternative energies (solar, wind, biomass) is in principle governed by their differential prices. The level of $105-110 reached in May 2026 by a barrel of crude oil (Brent) should in principle make most new energy sources competitive, and the risks of shortages caused by the closure of the Strait of Hormuz should encourage European and Asian countries to promote their domestic energy sources. Despite these incentives, the decision-makers of the companies that consume the most energy still seem hesitant to make the long-term investments needed for each link in the value creation chain of the various energy sources: production, mass transport, distribution, consumption. The stock market prices of the stakeholders in these chains are still undervalued compared to those of the dominant groups in the fossil fuel sectors. The governments of consumer countries are slow to implement massive plans to support the development of alternative energies. There are various explanations for this hesitation. The volatility of crude oil and natural gas prices is due to the uncertainty surrounding the outcomes of the Ukrainian and Iranian conflicts. A significant drop in prices following their settlement would compromise the profitability of projects undertaken in alternative energy sectors. Are investors subject to game theory and its law of minimum regret? In the most indebted countries, such as France, the room for manoeuvre to support energy supply and demand remains limited. The financing of green investments remains hampered by the uncertainty surrounding their long-term profitability, the foreign sources of supply of certain components and their supply infrastructure, as in the case of electric cars and solar panels. For all these reasons, while the latest conflicts are helping to change the strategies of the private and public stakeholders involved in the energy transition, their unpredictable outcomes and the inconsistency of public policies mean that it is not yet possible to see all the effects.

Nadia ANTONIN  In a study published on 16 April 2026, INSEE reveals that the French redistribution system reduces income inequalities but locks beneficiaries into a structural dependency on public authorities. This reduction in inequality is mainly the result of four factors: 1) individualisable social transfers in kind; 2) cash social benefits in the form of replacement income; 3) collective expenditure; 4) levies (income tax and wealth tax).  The INSEE study report also shows that, between the richest 10% and the poorest 10%, the income gap decreases from 1 to 20 before redistribution to 1 to 3.7 after the application of social welfare mechanisms.  Finally, the INSEE analysis reveals that in 2023, more than one in two French people received more from the system than they contributed to it. Some claim that social welfare benefits in France trap people in an ‘inactivity trap’. Having examined the concept of poverty, we shall demonstrate that ‘work is indispensable to man’s happiness; it elevates him, it consoles him; and the nature of the work matters little’ […] (Alexandre Dumas fils).  Examination of the concept of ‘poverty’ Ms Marie Lecerf from the European Parliament’s Directorate-General for Parliamentary Research Services points out (‘Poverty in the European Union’, March 2016), ‘there is no consensus on the definition of poverty, which is often defined by other concepts, such as well-being, basic needs, income or social exclusion, rather than by poverty itself’.  According to INSEE, ‘a household and the individuals who make it up are considered to be poor when the household’s standard of living is below the poverty line, which is most often set at 60% of the median standard of living’. Like Eurostat and other European countries, INSEE measures income poverty in relative terms, whereas other countries (such as the United States or Canada) adopt an absolute approach. Absolute poverty refers to people who are unable to meet their basic needs (food, housing, etc.).  The consequences of the decline in the value of work: living on state benefits  Welfare dependency is over-developed in France. International comparisons reveal a ‘distinctive French exception’. Some refer to France as ‘the land of a thousand and one benefits’! The Directorate for Research, Studies, Evaluation and Statistics (DREES), the statistical department of the Ministry of Health and Social Solidarity, has entitled its presentation of the social protection accounts for 2024 ‘The French: Champions of Social Protection’. In 2024, social welfare expenditure reached €932 billion, i.e. 31.9% of GDP and an average of €13,650 per capita. In the EU-27, this expenditure accounts for an average of 27.3% of GDP.  With redistribution having reached its structural limits, ‘France must be put back to work’.  Work is the best way out of poverty  In his book entitled ‘Celui qui ne travaille pas ne mange pas’ (‘He who does not work shall not eat’), Régis Brunet, a professor at the Catholic University of Louvain, points out that ‘from Benedictine abbeys to Bolshevik soviets, and from the Calvinist Reformation to capitalism’, St Paul’s dictum has continued to resonate: ‘He who does not work shall not eat’. This aphorism, adopted by Lenin during the Russian Revolution, expresses a social contract based on the ‘value of work’.  In Candide, Voltaire writes: “Work keeps three great evils from us: boredom, vice and want.” Work is beneficial to human beings. According to Immanuel Kant, ‘the best way to enjoy life is through work: it is a profound deliverance that fulfils human beings, enables them to flourish in their freedom, rescues them from boredom and leads them to a deep understanding of practical interest, invigorates their reason, and ultimately brings them joy’.  Escaping poverty through work certainly requires stability, fair remuneration and a favourable labour market, but also other essential conditions.  Creating the conditions to enable individuals to succeed and progress through their work In his book ‘Development as Freedom’ (1992), the economist Amartya Sen proposes understanding poverty not in terms of insufficient income levels, but in terms of individuals’ ability to fulfil themselves: freedom of expression, dignity, self-respect, and participation in social life in general (what he calls ‘capabilities’).  In order to restore work to its rightful place, we propose the following guidelines: – Reduce the tax burden on labour and productive capital. According to the OECD, France remains Europe’s champion of taxation. In 2024, the share of compulsory levies in France stood at 45.3% of GDP, compared to 40.4% for the EU as a whole (source: Eurostat). – Recognising work, i.e., identifying, assessing and rewarding each person’s merits. We must condemn bonuses for incompetence, nepotism and cronyism. In addition to the lack of recognition of merit, some people rightly complain about the devaluation of qualifications, particularly the PhD. – Combat envy, which is not confined solely to professional relationships. Described at the time as ‘social jealousy’, it lies at the root of a certain political ideal that advocates egalitarianism and reliance on welfare rather than financial prosperity earned through work.  To conclude on the assertion that we can combat poverty through work, let us consider the Christian victims of the genocide perpetrated in 1915 in the Ottoman Empire. Having arrived as stateless persons, they found their place in society through their hard work. They received no help and held out with tenacity. They worked 16 hours a day, seven days a week. Hard-working and extremely dignified, they enabled their second-generation children to rise to the top of the hierarchy.

Column: Florence Anglès  In the previous column, the focus was on verifying the project’s viability. But once this point is clarified, the question shifts. It’s no longer about whether value can be created, but whether it can be sustained. In a constantly evolving environment, the challenge is understanding when the equilibrium might become fragile. Sustaining Value: From EBITDA Volatility to NPV Sensitivity After verifying the structure’s solidity, the focus shifts to the sustainability of value rather than its mere existence. The question then becomes: do the projected cash flows remain stable in a realistic operating context, considering all technical constraints? Unlike traditional infrastructure, BESS construction projects are heavily dependent on revenues generated in the market. This makes them sensitive not only to price fluctuations but also to profound changes in market dynamics. At this stage, the aim is to assess risks to transform operational uncertainties into financial uncertainties, and thus measure how the investment reacts to key assumptions. Fragility of Revenue Streams and Risk of Market Saturation BESS projects often rely on multiple revenue streams, such as arbitrage, frequency response, voltage support, and sometimes capacity mechanisms. This diversity can reduce risk, but it also leads to exposure to market fluctuations. These markets are often highly competitive and can change rapidly. When new capacity is added, revenue margins tend to decrease, and past performance is no longer a guarantee of future results. In this context, the main risk is not the fluctuation of short-term earnings, but rather the gradual decline in margins over time. The key questions are: Weak revenues primarily affect EBITDA levels, but due to discounting and leverage effects, they can have a much greater impact on internal rate of equity. A project evaluated using overly optimistic growth assumptions, without considering downside risks, demonstrates financial vulnerability rather than genuine strength. Degradation, Efficiency, and Integrity of Long-Term Cash Flows Technical assumptions significantly influence financial results. In battery energy storage (BESS) projects, factors such as degradation, efficiency, and system condition directly impact the sustainability and reliability of cash flows. Battery degradation plays a significant role in energy production capacity, the duration of revenue generation, the need for capacity expansion, and the remaining asset value. Overly optimistic degradation assumptions may lead to inflated final cash flow projections, potentially resulting in an excessively high net present value (NPV). Other technical assumptions follow a similar trend: Even small deviations in these assumptions can have significant financial consequences. In some cases, they may be enough to push the project’s IRR below the investor’s cost of capital. The key question is: at what level of technical degradation or underperformance does the project become unprofitable?  If small deviations in technical performance have significant consequences for returns, robustness should be prioritized over optimization. Schedule-related risk: commissioning delays and discounting effects Time is a critical but often underestimated risk factor. Delays in commissioning not only postpone revenue generation but also reduce the net present value. Initial cash flow plays a more significant role in net present value (NPV). An initial cash flow lag increases the risk to NPV, particularly in capital-intensive projects. A six-month lag may have a limited nominal impact but a disproportionate effect on NPV due to the discounting effect. From an investor’s perspective, the question is not only whether the lag can occur but also what lag the project can withstand without impacting its returns. Furthermore, a delay in commissioning can lead to various side effects, including: In leveraged structures, schedule risk can directly impact debt repayment capacity and refinancing conditions, thereby amplifying its effect on return on equity. The question, therefore, is not whether the project will ultimately be operational, but rather: how significant is the value erosion caused by an execution delay? Leverage, Coverage Ratios, and Resilience to Risk Ultimately, a project’s “bankability” is tested in a crisis, not in baseline scenarios. A project may appear robust under optimistic assumptions but prove fragile under more realistic adverse conditions. Stress tests must assess the robustness of the investment project under adverse scenarios, such as: At this stage, it is essential to make the following distinction: A project with a stable NPV under adverse scenarios is considered investable despite fluctuating short-term performance. A project with a marginal NPV under moderate crisis scenarios is considered inherently fragile. Conclusion Ultimately, the goal of risk assessment in battery energy storage (BES) mergers and acquisitions is not to create a perfect model or to account for every possible scenario. It is to answer a few practical questions, even if the answers are not always clear or perfectly structured. First, is the project feasible in a safe and realistic way? This is the starting point. Grid access, permits, regulatory compliance: if any of these elements are uncertain, the projected revenues become meaningless. The project may look promising on paper, but it simply won’t materialize. Then, assuming it does materialize, the question is whether its value will hold over time. BESS assets do not operate in a stable environment. Performance fluctuates, equipment ages, and market conditions change. What matters is not the base-case scenario, but the project’s behavior when conditions are less favorable. There is also the question of execution. Delays or friction, even relatively minor ones, can have disproportionate effects, not necessarily because they significantly alter the project, but because the time factor may not be as secondary as one might think. Costs skyrocket, revenues fluctuate, and the overall balance sheet changes. Over time, operational stability becomes equally crucial. A project unable to maintain consistent performance tends to accumulate problems. Individually, these problems may seem manageable. Together, they can become far more significant. Finally, there is resilience. A project should not be evaluated solely under favorable conditions. The real test is observing its reaction to pressure, declining revenues, rising costs, or less favorable market conditions. This is where the distinction becomes clearer. Some projects remain largely intact even when assumptions change. Others do not. This isn’t always immediately apparent, but ultimately, it’s what makes an investment worthwhile. Therefore, risk assessment