# openai-work-at-the-frontier-task-crossover-2026-07-27

## Veille

Post and report from **OpenAI Economic Research** published on **July 27, 2026**, the first installment in the **Work at the Frontier** series, analyzing **more than 800,000 messages from US ChatGPT users**. **Coined concept**: ***task crossover*** — *« work historically associated with one occupation appearing in the AI use of people in another »*. **The headline figure is actually two figures, and that's the point coverage loses**: **16.8% of work-related messages** concern tasks associated with another occupation, and **43.5% of occupation-specific messages**. The funnel explains the gap: **61.5% of usage is generic** (writing, summarizing, planning — too widely shared to count as evidence of crossover) and is excluded; of the **remaining 38.5%**, **43.5% fall outside the occupation** and 56.5% are *« inside **or near** »* — so the upper bound is calculated on a reduced base, while the lower bound is calculated on the entire professional usage. **By occupation** (share of occupation-specific messages pointing to an external task): customer experience **77%**, design **75%**, HR **69%**, legal **56%**, marketing **53%**, sales **40%**, finance **40%**, engineering **28%** — *« a majority in five of eight groups »*. **Two distinct directions of circulation**: design **imports** (35.2%) and **exports** almost nothing (1.7%); engineering does the opposite (imports 18.5%, exports 7.4%); **marketing does both** (imports 24.3%, exports **8.9%**, the highest outward share in the sample). **Two tasks appear in the top 3 of borrowings for the other seven groups**: **financial calculation** and **technology troubleshooting**. **The heatmap, absent from coverage, is the richest object**: it gives the full distribution of tasks by user occupation, and its diagonal is striking — engineering retains **53%** of its own work while customer experience retains only **11%**, HR **10%** and design **12%**. **Size effect**: the outside-occupation share drops from **18.9%** (2-5 employees) to **16.3%** (>100 employees) — **but only « among average users »**, OpenAI specifying that *« among the heaviest users, we do not see the same monotonic pattern »*, and concluding conditionally: *« AI **may be** especially useful as a generalist tool where specialist resources are scarce. »* **Claimed status**: an **early signal**, visible *« before firms rewrite job descriptions or create new job titles »*. **Structural caveat**: OpenAI measures OpenAI's own usage, on US ChatGPT users only, and presents this position as an asset — *« our unique window into how the world of work is changing »*.

## Titre Article

How AI is expanding what people do at work (Work at the Frontier, rapport 1)

## Date

2026-07-27

## URL

https://openai.com/index/how-ai-is-expanding-what-people-do-at-work/

## Keywords

OpenAI Economic Research, Work at the Frontier, task crossover, task overflow, occupational porosity, 800,000 messages, ChatGPT, US users, work-related messages, occupation-specific messages, generic tasks, calculation base, methodological funnel, inside or near, customer experience, design, human resources, legal, marketing, sales, finance, engineering, task import/export, heatmap, diagonal, occupation retention, financial calculation, technology troubleshooting, marketing-material creation, organization size, headcount, average users, heavy users, non-monotonic pattern, generalist, specialized resources, AI Jobs Transition Framework, occupational reorganization, early signal, job descriptions, job titles, labor-market statistics, division of labor, source bias, provider-side measurement

## Authors

**OpenAI Economic Research** — équipe de recherche économique d'OpenAI ; la page crédite simplement *« OpenAI »* et la classe sous les tags *Economic Research* et *2026*. Le billet est la porte d'entrée d'un **rapport PDF** (`work-at-the-frontier-report.pdf`) et s'adosse à un cadre antérieur de la même équipe, l'**AI Jobs Transition Framework**, dont il reprend la thèse que de nombreux métiers vont **se réorganiser** plutôt que disparaître.

**Position à connaître** : l'éditeur mesure l'usage de son propre produit et en fait explicitement un argument — *« Using our unique window into how the world of work is changing, we will offer regular data-driven insights based on evidence to guide policy and practice. »* La visée est déclarée : **orienter la politique publique et la pratique**.

## Ton

**Profile**: institutional research post, sober and methodical register, serving as a public summary of a PDF report. Neither a product announcement nor an op-ed — a **findings note** structured in four sections whose headings are the conclusions (*« Nearly half of occupation-specific AI use crosses job boundaries »*, *« Some tasks travel farther than others »*, *« More task crossover in small businesses »*, *« The task list itself is changing »*).

**Style**: **the methodological counterpoint set out from the start** is the structuring move. *« Many studies of AI and work begin with a fixed list of tasks associated with a given occupation and ask whether models can perform them. Our evidence suggests that AI is also changing who takes on which tasks. »* It doesn't measure an occupation's substitutability but the **observed redistribution** — a claimed shift of object, and this is what gives the paper its value.

**Notable trait: caution is built into the sentence, not relegated to a footnote.** The qualifiers are set at the moment the result is stated — *« our new research **suggests** »*, *« **Among average users**, the outside-occupation task share falls… »*, *« Among the heaviest users, **we do not see the same monotonic pattern** »*, *« AI **may be** especially useful as a generalist tool »*, *« usage patterns **may** provide an early signal »*. The text almost never asserts without bounding.

**Illustration through concrete cases before the figures**: three vignettes open the piece — the small-business owner who writes their copy, reviews a contract and runs a financial analysis; the salesperson who explores a customer dataset *« that might once have gone to an analyst »*; the marketer who fixes a website *« without waiting for a developer »*. The phrase that sums them up is the thesis: *« AI changes not just how work gets done, but who does what. »*

**Marker phrases**: *« task crossover »*, *« who does what »*, *« some activities that once required a handoff can now be done by the person who first encounters the need »*, *« these occupations are "borrowing" these tasks »*, *« before firms rewrite job descriptions or create new job titles »*.

## Pense-betes

- **Date / source**: **July 27, 2026**, OpenAI Economic Research, first installment in the *Work at the Frontier* series, based on more than **800,000 messages** from US ChatGPT users.
- **Key framing**: the paper doesn't ask whether the model can perform a fixed list of tasks, but **who does what** — *« Our evidence suggests that AI is also changing who takes on which tasks. »* It measures an observed redistribution, not a theoretical substitutability. ### The two headline figures, always to be given together **16.8%** of work-related messages **and 43.5%** of occupation-specific messages. The funnel: **61.5%** of professional usage is generic (writing, summarizing, planning — *« shared too broadly across occupations to be evidence of crossover »*) and removed from the calculation; of the remaining 38.5%, 43.5% fall outside the occupation. The 43.5% is thus calculated on 38.5% of usage, and citing it alone nearly doubles the impression of scale. Additional nuance: the complement is not "within the occupation" but *« inside **or near** a user's occupation »* — the measured boundary is fuzzy by construction. ### By occupation, outside-occupation share Customer experience **77%** · design **75%** · HR **69%** · legal **56%** · marketing **53%** · sales **40%** · finance **40%** · **engineering 28%**. *« Outside occupation work is a majority in five of eight groups. »* ### The heatmap, the richest object in the report Rows = user's occupation, columns = task's occupation of origin; diagonal in bold. | User ↓ / Task → | CX | Design | Eng. | Fin. | HR | Legal | Mktg | Sales | |---|---|---|---|---|---|---|---|---| | **Customer experience** | **11** | 5 | 20 | 14 | 6 | 6 | **26** | 11 | | **Design** | 6 | **12** | **28** | 10 | 4 | 4 | **28** | 8 | | **Engineering** | 4 | 4 | **53** | 9 | 4 | 4 | 20 | 2 | | **Finance** | 6 | 4 | 22 | **23** | 4 | 8 | **25** | 7 | | **HR** | 9 | 5 | 18 | 16 | **10** | 10 | **23** | 9 | | **Legal** | 5 | 4 | 17 | 12 | 7 | **31** | 18 | 6 | | **Marketing** | 10 | 6 | 17 | 11 | 4 | 4 | **36** | 12 | | **Sales** | 11 | 5 | 18 | 15 | 4 | 6 | **29** | **12** | Three readings the text doesn't offer. **The diagonal is the real result**: engineering retains 53% of its own work, customer experience 11%, HR 10%, design 12%, sales 12%. **For four occupations, the marketing column exceeds the diagonal** — a designer does more marketing tasks (28%) and engineering tasks (28%) than design tasks (12%). **Two columns absorb everything**: marketing (18 to 36% everywhere) and engineering (17 to 53%). Caveat: a low diagonal doesn't mean these employees no longer do their own job, but that their non-generic messages are classified elsewhere, on a base that already excludes 61.5% of usage. The tasks specific to these occupations may be precisely the ones classified as "generic." ### Directions of circulation | Occupation | Imports | Exports | |---|---|---| | Design | **35.2%** | **1.7%** | | Engineering | 18.5% | 7.4% | | Marketing | 24.3% | **8.9%** — *« the highest outward share in the sample »* | Marketing is the only one that does both. Two tasks are universally borrowed — **financial calculation** and **technology troubleshooting** appear in the top 3 of borrowings for the other seven groups; marketing-material creation appears among five other groups. ### The asymmetry of engineering Engineering is both the **least porous** occupation (28%, imports 18.5%) and a **major exporter** (7.4%), with the highest diagonal (53%). The celebrated porosity is thus largely one-way toward the technical side: other occupations do troubleshooting and technical-systems work, the reverse is rare. AI doesn't dilute engineering into other occupations, it **spreads engineering tasks everywhere else** while leaving engineers on their core work. ### The size effect is weaker than reported elsewhere The shift from **18.9% to 16.3%** holds *« among average users »* only, and *« among the heaviest users, we do not see the same monotonic pattern »*. OpenAI offers two possible explanations — moderate users at small organizations turn to AI when they encounter work belonging to another function; heavy users have stabilized workflows or use AI more intensively **within** their core occupation. The conclusion is conditional: *« AI may be especially useful as a generalist tool where specialist resources are scarce. »* Correction to [[sfeir-ia-frontieres-metiers-skill-based-organisation-2026-08-01]], which treats these 2.6 points as a clean result and the generalist conclusion as an assertion: the source is more cautious than its commentary. ### Claimed epistemic status An **early signal**: this data makes it possible to see recombinations *« before firms rewrite job descriptions or create new job titles »* and *« may provide an early signal of occupational change that conventional labor-market statistics will capture only later »*. A leading indicator, not an employment measure — it says what people are attempting, not what organizations have ratified. The paper draws on the same team's **AI Jobs Transition Framework**, whose thesis is that many occupations will be reorganized rather than disappear. ### Underlying caveats
- **OpenAI measures OpenAI** — a single assistant, its own users only, claimed as an advantage (*« our unique window »*). What is measured is ChatGPT usage, not AI usage.
- **US users only**, a self-selected population not representative of the workforce.
- **The occupation classification is done by a model**, on messages, with no external validation described. The eight categories and the generic/specific boundary are design choices that mechanically determine the results — this is where the robustness of the 43.5% is actually decided.
- **Stated aim**: *« to guide policy and practice »*. An actor that produces the data on its own impact and aims to shape public policy.

## RésuméDe400mots

First installment in **OpenAI Economic Research**'s **Work at the Frontier** series (July 27, 2026), based on more than **800,000 messages** from US ChatGPT users.

**The concept.** ***Task crossover*** refers to *« work historically associated with one occupation appearing in the AI use of people in another »*. The methodological counterpoint is set out from the start: exposure studies begin with a fixed list of tasks and ask whether the model can perform them; here the question is **who does what**. *« AI changes not just how work gets done, but who does what. »*

**The figures, and there are two.** **16.8%** of work-related messages and **43.5%** of occupation-specific messages concern a task from another occupation. The gap comes from the funnel: **61.5%** of usage is **generic** (writing, summarizing, planning) and is excluded; of the remaining 38.5%, 43.5% fall outside the occupation, with the rest being *« inside **or near** »*.

**By occupation**: customer experience **77%**, design **75%**, HR **69%**, legal 56%, marketing 53%, sales and finance 40%, **engineering 28%** — a majority in five of eight groups.

**Two directions of circulation.** Design **imports** (35.2%) without exporting (1.7%); engineering does the opposite (18.5% / 7.4%); marketing **does both** (24.3% / 8.9%, the highest outward share). Two tasks appear in the top 3 of borrowings for the other seven groups: **financial calculation** and **technology troubleshooting**.

**The heatmap** gives the full distribution, and its diagonal is the most striking result: engineering retains **53%** of its own work, while customer experience retains only **11%**, HR **10%** and design **12%** — for these three occupations, marketing tasks outweigh their own.

**The size effect is more fragile than it appears.** The outside-occupation share drops from 18.9% (2-5 employees) to 16.3% (>100 employees) **among average users only**: *« among the heaviest users, we do not see the same monotonic pattern »*. The conclusion remains conditional — *« AI **may be** especially useful as a generalist tool where specialist resources are scarce »*.

**The claimed status** is that of an **early signal**, visible *« before firms rewrite job descriptions or create new job titles »*.

OpenAI measures usage of its own product, on its US users only, and presents this position as an asset.

## GrapheDeConnaissance

- OpenAI Economic Research —publie→ Work at the Frontier (DOCUMENT, 0.98)
- Work at the Frontier —mesure→ 16,8 % des messages liés au travail et 43,5 % des messages métier-spécifiques portent sur une tâche d'un autre métier (MESURE, 0.97)
- Work at the Frontier —mesure→ 61,5 % de l'usage professionnel est générique et écarté du calcul du crossover (MESURE, 0.95)
- task crossover —est_instance_de→ l'apparition, dans l'usage IA d'un métier, de travail historiquement associé à un autre (CITATION, 0.97)
- OpenAI Economic Research —s_oppose_à→ les études qui partent d'une liste figée de tâches par métier pour tester la substituabilité du modèle (AFFIRMATION, 0.93)
- Work at the Frontier —mesure→ part hors métier par fonction : expérience client 77 %, design 75 %, RH 69 %, juridique 56 %, marketing 53 %, vente 40 %, finance 40 %, ingénierie 28 % (MESURE, 0.96)
- Work at the Frontier —mesure→ le design importe 35,2 % de tâches extérieures et n'en exporte que 1,7 % (MESURE, 0.95)
- Work at the Frontier —mesure→ l'ingénierie importe 18,5 % de tâches extérieures et en exporte 7,4 % (MESURE, 0.95)
- Work at the Frontier —mesure→ le marketing importe 24,3 % et exporte 8,9 %, la plus forte part sortante de l'échantillon (MESURE, 0.95)
- Work at the Frontier —mesure→ l'ingénierie retient 53 % de ses propres tâches, contre 11 % pour l'expérience client et 10 % pour les ressources humaines (MESURE, 0.92)
- calcul financier —observé_dans→ le top 3 des tâches empruntées par les sept autres groupes de métiers (AFFIRMATION, 0.94)
- dépannage technologique —observé_dans→ le top 3 des tâches empruntées par les sept autres groupes de métiers (AFFIRMATION, 0.94)
- Work at the Frontier —mesure→ la part hors métier passe de 18,9 % à 16,3 % selon la taille de l'espace de travail, chez les utilisateurs moyens seulement (MESURE, 0.95)
- OpenAI Economic Research —affirme_que→ la tendance liée à la taille de l'organisation n'est pas monotone chez les utilisateurs les plus intensifs (CITATION, 0.95)
- OpenAI Economic Research —affirme_que→ l'IA pourrait être particulièrement utile comme outil généraliste là où les ressources spécialisées manquent (AFFIRMATION, 0.9)
- données d'usage —permet→ de voir la recomposition des métiers avant la réécriture des fiches de poste et des intitulés (AFFIRMATION, 0.94)
- Work at the Frontier —est_basé_sur→ AI Jobs Transition Framework (DOCUMENT, 0.92)
- AI Jobs Transition Framework —affirme_que→ de nombreux métiers vont se réorganiser, leurs tâches quotidiennes pouvant changer substantiellement (AFFIRMATION, 0.92)
- Work at the Frontier —est_basé_sur→ plus de 800 000 messages d'utilisateurs américains de ChatGPT (AFFIRMATION, 0.97)
- OpenAI —affirme_que→ sa position d'observation unique sur le monde du travail lui permet d'orienter la politique publique et la pratique (CITATION, 0.9)

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Canonical: https://www.thekb.eu/en/fiches/openai-work-at-the-frontier-task-crossover-2026-07-27/