In-depth opinion piece published on **sfeir.com** on July 23, 2026, signed by **SFEIR** (the firm's editorial voice). It is a **strategic commentary on Trésor-Éco note No. 391** from the DG Trésor (June 2026 — see [[dgtresor-ia-effets-emploi-2026-06-30]]), read through SFEIR's doctrine of « **amplifying AI rather than enduring it** ». The article praises Bercy's **cautious economist's tone** (mechanisms plus uncertainty rather than a prediction) and draws from it a **three-part thesis**: (1) **no measurable aggregate effect** at this stage (two offsetting forces — displacement vs. productivity — EU adoption ~20%); (2) a **single solid empirical signal, on juniors** (−16% employment among exposed 22-25 year-olds in the US); (3) a **long-term danger that shifts the question** — **competitive lag** (non-adoption), not job destruction. The analytical core SFEIR retains: **price elasticity** determines the employment effect (the **Jevons** paradox applied to code) → the argument is **structurally pro-employment for developers**. The article **dismantles the "AI layoffs" narrative** (4.5-6.2% of US layoff announcements, "labeling" at 59%) and points to the note's **blind spots** (the agentic scenario relegated to a footnote; diffusion speed not discussed; OpenAI/Anthropic having become sources for Bercy = an unflagged source bias). **SFEIR's operational translation** (for CIOs/CTOs): value migrates toward intent/architecture/control, training **augmented engineers** (**AI Champions** programs), and avoiding rushed adoption (**workslop**, technical debt) through **context engineering** and governance.
#AI and employment#competitive lag#non-adoption
**SFEIR** — ESN française « AI Only » (~850 ingénieurs, 8 agences France & Benelux). Voix éditoriale du cabinet (byline « SFEIR »). Positionnement de la maison sur la transformation IA des DSI ; ce texte prolonge la ligne éditoriale portée notamment par Didier Girard (cf. [[girard-sfeir-ai4it-vs-ai4business-budgets-2027-2026-06-24]]).
Analysis note **Trésor-Éco n° 391** (June 2026) from the **Direction générale du Trésor** (Ministry of the Economy), authored by **Martin Chopard, Elisa Cotet, Tristan Gantois and Eloïse Villani**. Institutional economic literature review on **the effect of AI (mainly generative) on employment**. **Three-part thesis**: (1) AI affects employment volume via **two opposing channels** — the **displacement** effect (substitution of automatable tasks) vs. the **productivity** effect (complementarity, lower costs, increased demand) — but the **aggregate effect remains, for now, weak/unmeasurable**, for lack of hindsight and adoption (≈20% of EU firms in 2025); (2) **heterogeneous effects** appear depending on **occupations** (exposure ≠ effect: everything depends on the degree of substitutability/complementarity and the **price elasticity** of demand), **workers** (biased technical progress, concerns for **young people**) and **sectors** (finance, IT, business services the most exposed); (3) in the **long term, the net effect remains uncertain** — between massive substitution (if agentic/physical AI becomes widespread) and **creative destruction** (lesson from past revolutions: innovations created more jobs than they destroyed). **Public policy** conclusion: support the transition (training, mobility — the "Osez l'IA" plan, France 2030) and **invest in AI to avoid falling behind** in international competition. Extensively sourced corpus (43 footnotes, estimate panels in Tables 1-3).
#AI and employment#generative artificial intelligence#displacement effect
**Martin Chopard · Elisa Cotet · Tristan Gantois · Eloïse Villani** — économistes de la **Direction générale du Trésor** (DG Trésor) · Ministère de l'Économie · des Finances et de la Souveraineté industrielle · énergétique et numérique. Directrice de la publication : Dorothée Rouzet. Le document engage la DG Trésor mais « ne reflète pas nécessairement la position du ministère ».
In-depth opinion piece (point of view) published on **sfeir.com** on June 24, 2026, by **Didier Girard** (Managing Director, SFEIR). **Central thesis**: in 2024 everyone was betting on **AI4Business** (AI in business processes) as the great value reservoir; by 2026 the picture has **reversed** — it is **AI4IT** (AI to produce the information system: code, SDLC, software factory) that is creating **measurable** value. The article *grounds* this thesis in the firm's tech watch: AI4Business disappointment (the MIT study "95% of pilots without ROI," contested but revealing; an **organizational** blockage / Mollick's Hayekian problem) versus quantified AI4IT evidence (Salesforce, Intercom, Raiffeisen, AWS/Bedrock, Atlassian, DORA). Mechanistic explanation: **code verifies itself** (compilation, tests, CI) whereas business processes have neither a compiler nor an immediate feedback loop. **2027 budget consequence**: a **CapEx→OpEx** shift, token price dynamics (rising peak — Fable 5 at 2× Opus — vs inference ÷280 and downward pressure from open weights/desktop), and **AI FinOps** driven by **cost per outcome**. Closes with **4 recommendations for the COMEX**.
#AI4IT#AI4Business#reversal
**Didier Girard** — Managing Director (CTO / DG) de **SFEIR** · ESN française (~1 000 personnes, France · Belgique · Luxembourg · Suisse). Auteur de l'article ; voix éditoriale du cabinet sur la transformation IA des DSI.
Continuous Delivery as the non-negotiable foundation of AI-assisted development — Dave Farley, on his channel *Modern Software Engineering*, argues that without CD, AI is not an accelerator but a trap (theory of constraints and Jevons paradox applied to generated code, ATDD/BDD as a safeguard, deployment pipeline as quality arbiter).
#Continuous Delivery#Generative AI in the SDLC#ATDD (Acceptance Test-Driven Development)
Dave Farley (Modern Software Engineering — YouTube channel)