Jean-Jacques Pluchart In a collective work entitled ‘Nouvelles réflexions sur la richesse des Nations. Les leçons de Turgot et de Smith’ (‘New Reflections on the Wealth of Nations. The Lessons of Turgot and Smith’), published in 2025, the Club Turgot examined the legacy of Adam Smith’s ideas in recent works on political economy written in French. Les leçons de Turgot et de Smith’, published in 2025, the Club Turgot examined the legacy of Adam Smith’s ideas in recent French-language books on political economy. The Turgot Club’s conclusion was that the ideas put forward in Smith’s seminal work, published in 1776 and entitled ‘An Inquiry into the Nature and Causes of the Wealth of Nations’, were still relevant today. In 1776, England and France were in transition from an agricultural society to a pre-industrial world. These countries were entering an era of institutional, economic and social transformation. At that time, Smith observed that the drivers of prosperity did not stem primarily from land, gold or the state, but rather from the organization of labour. He argued that, through the division of labour, the manufacture of goods became more efficient. He cites the well-known example of a worker who, on his own, could only make a few pins a day, whereas a production line organized according to the division of labour could make thousands. This pioneering vision remains relevant in most industries today. Today, global supply chains, made up of digital platforms and specialized companies, operate on the same principle.  Furthermore, the specialization of suppliers and subcontractors fosters technical and organizational innovation. The ‘invisible hand of the market’ ensures coordination between producers and consumers, who, while pursuing their own particular interests, contribute to the public interest through competition and the dual interplay of supply and demand. Today, this market mechanism is even more efficient thanks to Artificial Intelligence and new information and communication technologies.   However, Adam Smith opposes uncontrolled market freedom.   He entrusted the state with the roles of regulating competition, guaranteeing the right of co-ownership, punishing price manipulation, and defending the domestic market against external threats. In particular, he opposed the formation of monopolies, the granting of subsidies or the setting of excessively protectionist customs tariffs, believing that these measures hindered the free market. He also tasks the state with promoting trade through appropriate infrastructure, in line with the state’s current initiatives to develop digital networks, electricity infrastructure and research activities. Smith therefore opposes mercantilism, which regulates the market through customs duties and interventions that run counter to the international division of labour and harm a country’s prosperity. 250 years after its publication, ‘The Wealth of Nations’ remains much more than a historical document; it is neither an ideology nor a scientific theory; it is a rational principle and an intellectual logic that lie at the heart of today’s and tomorrow’s political and social debates.

Jean-Jacques Pluchart Since its creation in 1987, the Turgot Club has observed a recurring statistical imbalance between male and female authors in the publication of French-language economic and financial works. Is this inequality attributable to the French educational guidance process or to other factors of a more sociological nature?  A recent survey by the Chair for Women’s Employment and Entrepreneurship (Sciences Po Paris) on gender diversity in ‘science and technology’ courses – and in economics in particular – sought to answer this question. The results of this survey were published by the Well-Being Observatory of the Centre for Economic Research and its Applications (Cepremap). The survey complements the latest government initiatives to promote gender diversity in all higher education programmes. It follows on from the ‘Filles et maths’ (‘Girls and Maths’) action plan, launched by the Ministry of National Education, Higher Education and Research in May 2025.  This plan aims to support growth in high-potential sectors while reducing inequalities, particularly in terms of pay. The survey was conducted among a sample of 1,400 final-year pupils applying to enrol in public or private higher education via the Parcoursup platform in 2025.  The results clearly show that girls are less likely than boys to choose science-related courses: boys account for around 70% of applications for science and technology courses (including economics), while girls account for 75% of applications for courses in health, humanities and social sciences, literature, languages and the arts. More male students than female students reported that they only liked science subjects at secondary school (29% of male students compared to 14% of female students). These disparities can be explained by multiple factors – such as gender stereotypes, early rejection of mathematics, the attractiveness of better-paid jobs for men, or the pursuit of more diversified educational pathways for women – but these factors alone are not sufficient to account for such disparities. The survey reveals that the majority of women prefer to forgo well-paid careers in order to pursue their interests in health, social or cultural fields. These preferences on the part of girls are reportedly encouraged by their parents during their secondary education, whereas parents are said to encourage boys more to pursue courses that will ultimately be more lucrative. Paradoxically, the lack of parental guidance on girls’ choices may explain why they are more likely to follow their passions and why they subsequently find themselves more constrained  in the labour market. So, how can we attract women to science and technology? The authors of the study argue in favour of greater diversification of these courses and more interactive teaching methods in order to foster greater enthusiasm among pupils, particularly girls. Highlighting the contributions of these sciences and technologies to the success of the ongoing and future digital, energy, environmental and social transitions would be one of the drivers for achieving greater gender diversity in science education.

We are entering a war economy. Not a military economy, but an economy where the priority is no longer simply to optimize costs but to guarantee business continuity. For thirty years the dominant model was built on speed, globalization, and the constant reduction of safety margins. The low-cost model delivered efficiency gains, but it relied on a simple assumption: a stable world. That world no longer exists. Geopolitical tensions, supply chain disruptions, industrial dependency and competition between economic blocs are reshaping the rules. Companies must now secure before they optimize. Economic models should not be opposed because they complement each other. The low-cost model remains necessary to stay competitive, simplify offerings and control costs. The frugal model brings profitable resilience, doing better with less, cooperating rather than over-competing, relocating rather than over-globalizing, regenerating rather than over-consuming. The war economy adds a third dimension: security. It requires protecting supply chains, cash flow, margins, leadership and productive assets. Economic performance no longer comes from a single model but from the ability to combine efficiency, robustness and security. This shift directly transforms corporate financial management. The low-cost model aimed to reduce working capital needs by minimizing inventories and accelerating capital turnover. The war economy does the opposite. It encourages strategic inventories, supplier diversification and operational redundancy to avoid disruption. This mechanically increases cash requirements and puts short-term financing back at the center of the system. At this point one reality becomes clear: in the coming war economy, short-term financing becomes an issue of economic sovereignty. The figures confirm this trend. Corporate lending in France represents roughly €1.4 trillion in outstanding loans, including nearly €300 billion in short-term cash financing according to Banque de France and French Banking Federation data. At the same time, the Banque de France has observed a slowdown in short-term lending, around –3% year-on-year in late 2025. This means that needs are increasing while credit supply is tightening. This tension is structural. The clearest example is the Military Programming Law. The 2024-2030 program represents €413 billion in investment. Behind this budget figure lies a major industrial reality. If we assume conservatively that 40% flows directly into private production, this represents around €24 billion per year for industry. With an average industrial cycle of four months, this creates roughly €8 billion in permanent cash-flow needs. Industrial ramp-up will therefore depend not only on long-term investment but on the ability to finance the operating cycle. In this new environment one discipline becomes essential: dependency analysis. Business leaders must understand the financial strength of their suppliers, measure concentration risks and anticipate possible disruptions. A company can look strong on paper yet remain fragile through its supply chain. Banks must do the same. Financing a client without analyzing its ecosystem becomes an incomplete risk assessment. In a war economy risk travels through economic chains. Security needs translate into concrete banking solutions. – Securing supply chains involves documentary credits, bank guarantees and short-term lines dedicated to strategic inventories. – Securing cash flow relies on overdraft facilities, revolving credit lines, structured short-term credit, factoring and reverse factoring to accelerate liquidity from receivables. – Securing margins requires financing capable of absorbing cost gaps and appropriate hedging tools. – Securing the business leader means protecting the private sphere through protection insurance and key-person coverage to ensure decision continuity. – Securing productive assets relies on property and casualty insurance and business interruption coverage, since any disruption immediately becomes a liquidity risk. In this context the role of banks is changing fundamentally. For years they mainly financed growth. Tomorrow they will have to finance continuity. This requires faster decisions, an industrial understanding of business models and dedicated short-term financing envelopes. Short-term financing is no longer a simple banking product. It becomes a strategic tool. The question of risk remains central. In an uncertain environment banks may be tempted to reduce exposure. That would be a collective mistake. As during the Covid crisis, a public reinsurance mechanism for short-term credit could be introduced. The principle is simple: banks distribute financing quickly, the State guarantees part of the risk, and Bpifrance coordinates and mutualizes the system. Such a framework would support industrial ramp-up without blocking financing to the real economy. Bpifrance would then play a central role as coordinator, sharing risk, guiding sector priorities and securing the overall system, while commercial banks maintain proximity to businesses and execution speed. The war economy does not replace the previous economic model; it corrects it. Low-cost brings efficiency, the frugal model brings resilience, and the war economy imposes security. Real performance will no longer be measured only by growth speed but by the ability to endure. In this new environment short-term financing returns to the core of the economic system because it determines whether companies can continue to produce. Ultimately, the economy that is emerging reminds us of a simple truth: resilience comes before performance. Business continuity, control of dependencies and the ability to finance the short operating cycle become conditions of economic sovereignty. As Jacques Rueff wrote: “Order, and order alone, ultimately creates freedom. Disorder creates servitude.” In the coming war economy, that order also depends on our collective ability to finance and secure the real economy. Benoit Frayer

Jean-Jacques PluchartEditor-in-Chief  ​Clubturgot.com celebrates its 100th issue. ​​Since March 2024, the leading French- and English-language newsletter on economic and financial literature has published over 300 articles, book reviews and tributes to the works of leading economists. ​​Over the weeks, in order to better meet the expectations of their readers, the newsletter’s editors have published their articles first in French and then in English, focusing on the work of theorists and the insights of practitioners in the increasingly numerous and complex fields of economics and finance.​Every week, in just a few minutes, the 30,000 readers of clubturgot.com are thus able to learn about the key economic and financial events of the day. The authors of the articles published on clubturgot.com are Turgot Prize winners, representatives of partner associations and members of the Club Turgot, which pre-selects the books submitted to a jury of distinguished figures chaired by Jean-Claude Trichet.​ ​​Since the Turgot Prize was established in 1987, the Club has read around 5,000 books and reviewed nearly 4,000, and the jury has awarded, first in the halls of the Senate and then at Bercy, 39 Grand Prizes, 41 Jury Prizes, 102 honourable mentions and 120 Special Prizes (for collective works, educational books, French-language books, young authors, the DFCG and AF2i). ​​Together with Prize winners brought together in the Cercle Turgot, the Club Turgot has also published 22 collective works on key economic and managerial issues. ​​The reviews of the award-winning books have been compiled into three volumes: La pensée économique française (French Economic Thought, 2 volumes) and Les leçons de Turgot et de Smith (The Lessons of Turgot and Smith). For its 100th issue, clubturgot.com presents:  ​​- An original review by Jean-Jacques Pluchart on the impact of Artificial Intelligence on the banking profession, – a presentation by Philippe Alezard on the work of Norbert Wiener, a leading mathematician in the field of finance, – an analysis by Sophie Ffriot of Eric Weil’s book Retraites, un blocage français (Pensions, a French Impasse).

Phlippe  ​Alezard According to classical economic theory, the price of an asset is determined by the interaction between supply and demand: between a seller wishing to dispose of the asset and a buyer wishing to acquire it. ​​The stronger the demand, for various reasons, the higher the price of the asset will be pushed. ​​Indeed, in theory, demand can be almost unlimited, whereas the asset, by definition, is limited in number. ​​Conversely, when demand is low, the seller seeking to dispose of their asset will tend to lower the price in order to find a buyer. ​​We can therefore understand that, at any given moment, the price corresponds to the point of equilibrium between supply and demand.All of this is true in an ideal, theoretical world. ​​In reality, a wide range of events can occur at any time: geopolitical, climatic, informational, economic or financial events. ​​These events trigger emotional reactions – panic, rumours, euphoria – which in turn lead to human decisions, as well as algorithmic positioning in one direction or another. ​​This multitude of random shocks affects the behaviour of economic agents and creates erratic and unpredictable movements in the short term. ​​It is precisely these random fluctuations, this constant uncertainty, that mathematicians have sought to model in the form of stochastic processes. The first person to have this insight was Louis Bachelier. ​​In his now famous thesis[1] ‘Théorie de la spéculation’ (‘Theory of Speculation’), submitted in 1900, he introduced the use of probabilities to describe price movements and showed that price changes can be represented as a sequence of independent, identically distributed random variables. ​​He went even further by constructing histograms that showed that these variations were distributed according to a bell-shaped curve, in other words, a Gaussian distribution. ​​In this way, Bachelier laid the initial foundations for finance based on Brownian motion, a diffusion process, and the normal distribution.However , the history of Brownian motion begins long before finance. ​​In 1827, Robert Brown[2] used a microscope to observe the persistent agitation of pollen particles suspended in a fluid. ​​This phenomenon can be explained by the incessant and random impacts of the fluid molecules on the particles. ​​The individual movement of each molecule is negligible, but the combined effect of all these impacts produces a completely erratic overall movement. ​​The Brownian motion of a particle can therefore be modelled as a stochastic process characterised by a succession of independent increments, with a mean of zero, whose magnitude and direction vary unpredictably.In finance, the pollen particle becomes the price of an asset suspended in a market. ​​The molecules of the fluid are replaced by the multitude of buy and sell orders, which are themselves driven by an infinite amount of information, events and sometimes conflicting decisions. For a process to be classified as standard Brownian motion, three properties must be satisfied: 1. ​​ ​​ ​​ ​​ ​​ All trajectories start at the origin, or more precisely, in the probabilistic sense, the probability that the trajectory starts at zero is equal to one. 2. ​​ ​​ ​​ ​​ ​​ Each increment of the process is independent of the previous one: future changes do not depend on the past. ​​Brownian motion has no memory. 3. ​​ ​​ ​​ ​​ ​​ The distribution of the increments P(t+1) – P(t) at each instant follows a normal distribution with a mean of zero, whose variance (t+1) – t is proportional to the elapsed time. It was precisely these properties that Bachelier had already observed when studying the prices on the Paris Stock Exchange. ​​However, it was Norbert Wiener who would provide this phenomenon with its rigorous mathematical formalisation. Born in 1894 in Columbia, Missouri, Wiener came from a Russian Jewish family who had immigrated to the United States. ​​His father, Leo Wiener, a translator of Leo Tolstoy’s complete works and later a professor of Slavic languages at Harvard, personally oversaw his son’s education, employing innovative teaching methods. ​​A child prodigy, Norbert received his primary education at home, entered secondary school in 1903 and obtained his equivalent of the baccalaureate in 1906, at the age of twelve. He then attended Tufts University before moving on to Harvard, where he defended a thesis on mathematical logic. ​​At the age of just eighteen, he became the youngest doctoral graduate in the history of this prestigious university. ​​After his thesis defence, he travelled to Europe: in Cambridge, he attended Bertrand Russell’s lectures; in Göttingen, he studied with David Hilbert, one of the greatest mathematicians of the 20th century. Back in the United States, after several temporary positions, Wiener joined MIT in 1919, where he would spend the majority of his career. ​​There, he developed a remarkably diverse body of scientific work, spanning the fields of mathematical analysis, probability, engineering and the philosophy of science. Brownian motion had been known since Brown’s observation in 1827. ​​In 1900, Bachelier had applied it to price fluctuations. ​​In 1905, Albert Einstein published his theory of Brownian motion, while Marian Smoluchowski [3] independently developed an approach based on the random collisions of molecules. These works provided a physical interpretation of Brownian motion. However, a fundamental question remained: how could a continuous random trajectory over time be rigorously defined in mathematics? By 1923, discrete random walks, such as those resulting from a game of heads or tails, were already well understood. ​​At that time, a finite number of random variables were used. ​​However, the transition to continuous time posed a major conceptual challenge: how could a probability be defined over a non-countable infinity of random variables? In his article ‘Differential-Space [4]’, Wiener proposed an elegant solution. ​​He considered the set of possible trajectories as the points of a functional space of infinite dimension. ​​To construct a probability measure on this space, he begins by discretising time by subdividing the interval [0,1] into n segments: 0 = t0 < t1 < t2 … ​< tn =1 He then studied only the increments: X1 = J(t1) – J(0) X2 = J(t2) – J(t1) …. Xn = j(tn) – J(tn−1)  ​​ Three points are crucial: 1. ​​ ​​ ​​ ​​ ​​ It is not the successive positions that are independent, but the displacements over each interval;2 . ​​ ​​ ​​ ​​ ​​ Each increment follows a normal distribution, the variance of which is proportional to the length of the interval.3 . ​​ ​​ ​​ ​​ ​​

Jean-Jacques Pluchart The spectacular advances in AI – and in particular in generative AI since 2022 – are disrupting the strategies, organisational structures and practices of an increasing number of industries, particularly in the banking and insurance sectors. ​​The scale and speed of these transformations can be seen in the fluctuations in the margins, earnings and share prices of listed institutions. ​​The most erratic fluctuations in certain stock prices reflect the uncertainty felt by savers and investors regarding the ability of banks and insurers to adapt their value creation chains and rebuild their business models.  ​​ Banking businesses are built on the secure management of personal data and the coverage of risks of various kinds. ​​Traditionally, banking businesses are divided into retail banking and wholesale banking. ​​However, they are becoming increasingly differentiated according to the bank’s predominant strategy, which may focus on volume or on the differentiation of its products and services. ​​In the former case, they cover ‘document-intensive’ functions, and in the latter, ‘high-responsibility’ functions. The former encompass the administration of day-to-day operations, the generation of contracts, customer relationship management (CRM), accounting and financial analyses, etc. The latter involve trade-offs between transactions, the issuance of credit, legal, tax and financial arrangements, and strategic decisions, etc. The former can increasingly be replaced by automated processes. ​​The latter can only be supported by dedicated AI-based models for recognition, classification, simulation, projection, correlation, etc.  ​​ ​​ ​​ ​​Distinguishing between these two types of activities is becoming increasingly difficult due to the rapid progress of AI and LLMs, which are based on the massification of data, the acceleration of data processing, the proliferation of specialised AI agents and, above all, the ability to quickly code new programs using natural language (machine learning or automatic encoding). ​​As a result, new functions are being ‘augmented’ by AI: the development of more sophisticated chatbots for interacting with prospects and customers, the security of data and data processing, the systematisation of securities rating, the automation of compliance (due diligence), the optimisation of securities settlement and delivery, etc., as well as the enhancement of the reliability of forecasting models (predictive trading) and the simulation of credit and market risks. ​​Functions that were previously performed by specialists with rare skills are thus becoming ‘commodities’ provided by standard applications (benchmarks). Advances in AI and LLMs are leading to the disintermediation of value creation chains, the reconfiguration of banks’ business models, and the reshaping of their ecosystem. ​​These shifts can be observed in the changes in the margins and stock market prices of banking institutions and their subcontractors. ​​SaaS software outsourcing licences are gradually being replaced by proprietary models generated through ‘Vibe Coding’ at low marginal cost. ​​This transition to token-based pricing has already led to a drop in the MSCI USA Software index. For example, the share prices of Salesforce, Thomson Reuters and LegalZoom have been affected.  ​​The downward trend in margins and valuation multiples is beginning to affect software publishers, property and personal insurance companies, and financial and non-financial rating agencies. Insurify’s launch of a purchasing agent capable of instantly comparing millions of policies caused a sharp drop in the value of Willis Towers Watson and Aon. ​​In the fields of accounting (auditors, analysts) and credit rating, the same phenomenon has affected certain agencies, such as S&P Global, Moody’s and FactSet.​ Retail banks, which focus on providing advice and credit to individuals and SMEs, are directly exposed to a loss of competitive advantage unless they demonstrate their ability to adapt quickly to the changes brought about by AI. In contrast, investment or merchant banks benefit from barriers to entry based on the personalisation of client relationships (i.e., on trust and personalised historical data), on financial, legal and tax structuring (M&As, major projects, etc.), particularly at the international level, on wealth management, on the securitisation of receivables, on the management of derivatives, on strategic decisions, and even on certain functions related to shadow banking (management of investment funds or tax avoidance schemes, etc.). ​​The quality of the banking relationship creates value when it is developed during a monetary and financial crisis or simply in a volatile market environment. ​​The involvement of a human adviser provides the client with ‘mental and emotional well-being’ and greater confidence in the future. This transformation of banking models prompts us to revisit the lessons taught by Michael Porter since the 1980s, which distinguish between corporate strategies based on volume and those ​​ based  ​​on service differentiation. ​​It appears that, in the wake of advances in AI, these lessons are once again becoming increasingly relevant. ​​Banks are being  ​​compelled to adopt strategies that focus on innovative and phygital activities, combining these two approaches.