# dgtresor-ia-effets-emploi-2026-06-30

## Veille

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).

## Titre Article

L'intelligence artificielle, quels effets sur l'emploi ?

## Date

2026-06-30

## URL

https://www.tresor.economie.gouv.fr/Articles/895c28b4-dca6-4ca6-b03b-88058fc4ce87/files/a0c9e9a1-6c53-4f2e-ad5d-f603fd5c456d

## Keywords

AI and employment, generative artificial intelligence, displacement effect, productivity effect, substitution, complementarity, task-based approach, AI exposure, price elasticity, Jevons paradox, J-curve, biased technical progress, creative destruction, youth employment, labelling, AI layoffs, youth unemployment 15-24, LLM, AI adoption, Global AI adoption index, exposed sectors, finance IT services, professional retraining, bridge occupations, support policies, training, Osez l'IA plan, France 2030, Académie de l'IA, competitive decline, sovereignty, Trésor-Éco, DG Trésor, Acemoglu Restrepo, Autor, Brynjolfsson, Richmond OpenAI, Arquié, 2025 digital barometer

## Authors

**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 ».

## Ton

**Profile**: institutional economic analysis note (*Trésor-Éco* series), **academic and cautious** register, aimed at public decision-makers and the economic community. Deliberate neutrality, no advocacy: the note **lays out the mechanisms and the uncertainty** rather than settling the question.

**Style**: canonical three-part structure (aggregate volume → heterogeneous effects → long term + policies), theses set out as subheadings. **Rigorous sourcing**: 43 footnotes, three panel-estimate tables (Table 1: aggregate effect; Tables 2-3: by qualification and by experience), exposure charts. Economist's vocabulary (Acemoglu-Restrepo's displacement/reinstatement effect, *task-based* approach, price elasticity, Jevons paradox, "J-shaped" curve, biased technical progress, creative destruction). Marked **epistemic honesty**: "empirical studies do not allow the total effect to be determined," repeated emphasis on the **short time horizon**, the **dispersion of results**, and the risk of **"labelling"** (AI as a pretext for layoffs). Framing formula: uncertain *ex ante* effects, a call for **resolute public policies**.

## Pense-betes

- **Key thesis**: at this stage, **no measurable aggregate effect** of AI on employment (insufficient hindsight + limited adoption), but **two opposing channels** structure the analysis — **displacement** (substitution) vs. **productivity** (complementarity). AI is neither a "job killer" nor neutral: it is **empirically undetermined**, and it will depend on policies and the speed of diffusion.
- **Perception ≠ measurement**: **62% of French people** see AI as a risk to employment (2025 digital barometer), while the macro effect remains undetectable. The note clearly distinguishes social anxiety from the data.
- ***Task-based* approach** (Acemoglu-Restrepo, Autor): an occupation is broken down into tasks, and **exposure** is measured, BUT **exposure ≠ effect** — one must determine **substitutability** (AI replaces) vs. **complementarity** (AI augments the worker, who refocuses on judgment/validation/relationship). **Cognitive** tasks = more exposed to LLMs; **physical** = less so (for now); **relational** = intermediate.
- **Price elasticity decides the sign** (Chart 2): if productivity gains lower costs and **demand is elastic** (graphic designers, developers, elasticity > 1), employment can **grow** (Jevons paradox); if it is **inelastic** (firefighters, executives, ~0), employment can fall. → technical substitutability does not prejudge the employment effect.
- **Proven individual productivity gains**: **+14%** for customer service agents, **+26%** for software developers (targeted experimental studies, probably **underestimated** since obtained on models that are already outdated).
- **Very wide exposure range**: between **5% and 60%** of total employment depending on the study (cover chart). France estimates: **Arquié et al. (2026)** — **3.8%** of work content at risk of automation *today*, up to **16.3% within 2-5 years**. Richmond (2026, OpenAI) US: **18%** of jobs at high risk, 24% will see their tasks recomposed, 12% growing, 46% little affected.
- **"AI" layoffs to be put in perspective**: in 2025, cuts attributed to AI = **4.5 to 6.2%** of announced US layoffs, diluted within larger hiring flows. **Risk of "labelling"**: **59%** of US companies admit invoking AI to justify hiring freezes → AI serves as a **pretext** masking structural/post-Covid reasons (Josh Bersin case).
- **Concerns for young people** (the most concrete point): **Brynjolfsson et al. (2025)** — employment among **22-25 year-olds** in the most exposed occupations **−16%** (Nov. 2022 → Jul. 2025, US), corroborated by **Massenkoff & McCrory (Anthropic)** and Hosseini-Lichtinger. In **France** (Insee), unemployment among **15-24 year-olds +2 pts** (19.1% → 21.1%, Q1 2025→Q1 2026) — but **with no established causality** (economic conditions, overrepresentation in temporary contracts). Issue: automation of the **codified tasks** that train juniors → risk to entry-level integration and **senior renewal**.
- **Long term = an open bet**: the **massive substitution** scenario (if agentic/physical AI becomes widespread — Arquié: >40% of occupations would exceed 30% automatable tasks) VS **creative destruction** (4-decade review: innovations created more jobs than they destroyed; **Autor: 60% of workers hold jobs that did not exist in 1940**). Reminder: **Frey & Osborne 2013** forecast 47% of US jobs automatable — **which did not materialize**.
- **Transition costs**: the slowness of reallocation (skills obsolescence, geographic frictions) **cuts by 40%** the benefit of robotization in France (Bocquet 2026) → the role of **bridge occupations** and training policies.
- **Strategic (policy) imperative**: **investing in AI = protecting employment** via competitiveness. France = **intermediate** position (adoption **18%** OECD 2025 vs. EU average **20%**, but catching up **+8 pts** 2024-2025 vs. +6 pts EU). Measures: the **"Osez l'IA"** plan (2025, **15 million professionals trained by 2030**, **Académie de l'IA**) and **France 2030** ("Compétences et métiers d'avenir").
- **To link**: a macro-institutional counterpoint to the productivity/employment cluster of the watch (SFEIR AI4IT [[girard-sfeir-ai4it-vs-ai4business-budgets-2027-2026-06-24]], the announced disappearance of developers [[bfmtv-tech-co-business-ia-developpeurs-disparaissent-2026-05-05]], human+agent headcount accounting [[sternfels-mckinsey-60000-people-20000-agents-officechai-2026-01-14]]). Here, the reading is **cautious and aggregate**, in contrast to firm-by-firm experience reports.

## RésuméDe400mots

This **Trésor-Éco n° 391** note (DG Trésor, June 2026) offers a cautious, well-sourced review of the economic literature on **the effect of AI — mainly generative — on employment**. Starting point: AI capabilities have progressed sharply (LLMs, generative AI), fueling concern (**62% of French people** see it as a risk to employment), but economic analysis calls for distinguishing perception from measurement.

**1. Aggregate effect, weak for now.** AI acts through two opposing channels: the **displacement** effect (substitution of automatable tasks) and the **productivity** effect (proven individual gains: +14% in customer service, +26% for developers; complementarity, lower costs, increased demand). Recent empirical studies **do not identify a significant aggregate effect**, for lack of hindsight and because adoption remains partial (≈20% of EU firms in 2025). The absence of a macro effect does not mean an absence of localized destruction: "AI" layoffs account for 4.5-6.2% of announced layoffs in the US in 2025, with a risk of **"labelling"** (AI invoked as a pretext — 59% of US companies).

**2. Heterogeneous effects.** Via the *task-based* approach, **exposure** varies by task (cognitive > relational > physical), but **exposure ≠ effect**: everything depends on the degree of **substitutability/complementarity** and the **price elasticity** of demand (Jevons paradox — a substitutable occupation with elastic demand can see its employment grow). **Biased technical progress** could disadvantage certain segments, with **marked concerns for young people**: −16% employment among exposed 22-25 year-olds in the US (Brynjolfsson 2025), a rise in unemployment among 15-24 year-olds in France (19.1%→21.1%) — with no established causality. By sector, finance, IT and business services are the most exposed.

**3. Uncertain long term.** Two scenarios coexist: **massive substitution** (if agentic/physical AI becomes widespread) or **creative destruction** (past revolutions created more jobs than they destroyed; 60% of workers today hold jobs that did not exist in 1940). The transition will generate **costs** (slow reallocation: −40% of the benefit of robotization in France), to be smoothed by **training** and **bridge occupations**.

**Public policy conclusion**: support the transition (the "Osez l'IA" plan, training 15 million people by 2030, the Académie de l'IA, France 2030) and **invest resolutely in AI** to avoid **competitive decline** — France sitting in an intermediate position (18% adoption, catching up) amid international competition.

## GrapheDeConnaissance

- DG Trésor —publie→ Trésor-Éco n° 391 « L'IA, quels effets sur l'emploi ? » (DOCUMENT, 0.99)
- Trésor-Éco n° 391 —affirme_que→ l'IA n'a pas d'effet agrégé mesurable sur l'emploi à ce stade, faute de recul et d'adoption (AFFIRMATION, 0.95)
- IA générative —permet→ des gains de productivité individuels de +14 % (service client) à +26 % (développeurs) (MESURE, 0.9)
- IA générative —réduit→ la demande de travail via l'effet de déplacement (substitution de tâches) (CONCEPT, 0.88)
- effet de productivité —s_oppose_à→ effet de déplacement (CONCEPT, 0.9)
- approche task-based —mesure→ l'exposition d'un métier à l'IA par décomposition en tâches (AFFIRMATION, 0.9)
- élasticité-prix de la demande —permet→ de déterminer si les gains de productivité augmentent ou réduisent l'emploi d'un métier (AFFIRMATION, 0.87)
- Trésor-Éco n° 391 —affirme_que→ l'emploi des 22-25 ans exposés à l'IA a reculé de 16 % aux US (nov. 2022 - juil. 2025) (MESURE, 0.85)
- Trésor-Éco n° 391 —affirme_que→ l'IA sert parfois de prétexte (« labellisation ») pour justifier des licenciements et gels d'embauche (AFFIRMATION, 0.88)
- destruction créatrice —soutient→ l'hypothèse que l'IA créera à terme plus d'emplois qu'elle n'en détruira (AFFIRMATION, 0.8)
- Trésor-Éco n° 391 —affirme_que→ 5 à 60 % de l'emploi total serait exposé à l'IA selon les études (MESURE, 0.85)
- Arquié et al. —mesure→ 3,8 % du contenu du travail à risque d'automatisation aujourd'hui, jusqu'à 16,3 % d'ici 2-5 ans (France) (MESURE, 0.85)
- Trésor-Éco n° 391 —recommande→ accompagner la transition par la formation et les politiques de mobilité (Osez l'IA, France 2030) (AFFIRMATION, 0.93)
- Trésor-Éco n° 391 —recommande→ investir dans l'IA pour soutenir la compétitivité et éviter un décrochage face à la concurrence internationale (AFFIRMATION, 0.93)
- plan Osez l'IA —permet→ de former 15 millions de professionnels d'ici 2030 (Académie de l'IA) (MESURE, 0.85)
- approche task-based —est_basé_sur→ travaux d'Acemoglu et Restrepo (DOCUMENT, 0.9)

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Canonical: https://www.thekb.eu/en/fiches/dgtresor-ia-effets-emploi-2026-06-30/