Is AI displacing white-collar jobs? How are people actually using AI in their daily lives and work? Google DeepMind and Google's Chief Economist's Office jointly released the first large-scale empirical study report, ATLAS (Activity, Task, Landscape, and Adoption Study) v1.0. Based on 14,653,926 de-identified real interactions across Google AI Mode (over 1B MAUs), Gemini App (over 900M MAUs), and Gemini API, this report provides detailed data and methodology addressing key industry assumptions.
The Underlying Mechanics of ATLAS: DeepMind's OCTO Privacy Engine and Key Taxonomies
Traditional industry surveys often rely on a few thousand subjective questionnaires, which can be swayed by vendor marketing and fail to reflect how massive user bases interact with AI. Google ATLAS v1.0 differs by using statistical classification directly on real human-AI interactions.
To process these 15 million text interactions, Google DeepMind developed the OCTO (Observation Clustering and Taxonomy Organisation) data processing pipeline:
- DLP PII Scrubbing and Random UUID Unlinking: Data Loss Prevention (DLP) filters automatically strip names, contact details, financial, and health information. Original log IDs are replaced with irreversible random UUIDs to prevent user tracking.
- Two-Layer Summarization and Cluster Extraction: The pipeline discards full conversation transcripts, first summarizing individual conversations and then aggregating related conversation summaries into clusters, discarding raw text.
- Alignment with Official Statistical Taxonomies (ONET-SOC and ATUS): For workplace interactions, OCTO maps conversations to BLS SOC occupations and ONET 30.2 tasks (spanning 800+ occupations and 4,000+ tasks); for non-work interactions, it maps to the ATUS (American Time Use Survey) framework.
- K-Anonymity Protection: Any cluster representing fewer than 10 unique users is automatically excluded from analysis.
OCTO Classification and Privacy Pipeline
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DLP PII Scrubbing
Automatically filters and strips names, contacts, financial, and health data.
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Random UUID Unlinking
Replaces system log IDs with irreversible UUIDs to break tracking linkages.
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Two-Layer Summarization
Summarizes individual conversations and then cluster-level summaries, discarding raw text.
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Taxonomy Mapping and K-Anonymity
Maps to O*NET-SOC and ATUS taxonomies, discarding clusters with fewer than 10 users.
To validate classifier accuracy, the research team generated synthetic ground-truth benchmark datasets seeded with O*NET-SOC and ATUS taxonomies, evaluating inter-rater agreement and randomizing prompt option order to mitigate LLM classification bias.
Empirical Analysis of Workplace Usage: Occupational Diffusion, Task Depth, and Skill Levels
Based on taxonomy classification across 15 million real interactions, ATLAS v1.0 reveals four key insights about workplace AI adoption.
Core ATLAS Empirical Metrics
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14.65M
Analyzed 14.65M de-identified real interactions globally.
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68%
Covered 68% of detailed occupations representing 90% of US workforce.
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21%
Identified 21% median task saturation within covered occupations.
Myth 1: White-Collar Replacement and Task Saturation
The report shows that AI usage spans 68% of detailed occupations globally (representing 90% of total U.S. employment). However, looking within specific occupations reveals a nuanced picture.
Among occupations with meaningful AI usage, workers use AI for a median of 21% of their occupation's constituent tasks. Meanwhile, 29% of detailed occupations show zero task saturation (no tasks attempted by >=25 users), concentrated in manual roles like food prep and material moving. In OECD economies, AI usage is concentrated in Computer/Math and Business/Finance, whereas non-OECD countries see higher usage shares in Office/Admin and Arts/Design.
Myth 2: Task Automation Reality and Collaborative Intent
Across work-related AI interactions, attempts to fully automate tasks end-to-end represent less than 10% of total conversations.
Over 90% of workplace AI interactions focus on collaborative tasks such as ideation, strategy, drafting, refinement, review, information retrieval, and learning. Under the Autor & Thompson (2025) task framework, non-routine cognitive tasks represent 65% of AI work conversations in ATLAS (versus 35% in the overall economy). Users rely on AI as a thought partner rather than an automated surrogate.
Myth 3: Manual Trades and Multimodal AI Diagnostic Use
While AI is often viewed as a tool for office knowledge workers, ATLAS data shows that workers in manual trades, such as auto technicians and industrial mechanics, actively use conversational AI for on-site troubleshooting.
Because field maintenance involves complex wiring diagrams, equipment wear, or obscure error codes, workers in technical trades are 2x more likely to use multimodal AI capabilities (image and video) than standard occupations. They photograph machinery for diagnosis and use AI to interpret test logs. Furthermore, workers in non-OECD countries generate work-related images and videos at twice the rate of advanced economies.
Myth 4: Skill Level Distribution and Upskilling Effects
At an aggregate demographic level, higher-income and higher-education individuals exhibit higher overall AI adoption rates. However, breaking down analysis by task expertise levels reveals a subtle dynamic.
Across task expertise classifications, tasks requiring lower-to-middle levels of expertise see relatively higher AI usage than highest-expertise tasks. This indicates AI functions as an upskilling tool for entry-level and mid-level tasks, helping workers bridge skills gaps in unfamiliar workflows.
Non-Work Usage Analysis: Household Non-Market Welfare Unlocked by 86% of Interactions
In the ATLAS dataset, over 86% of total user interactions occur outside of market work settings.
- Navigating Administrative Friction: Assisting users with government paperwork, licensing, tax guidance, and administrative inquiries;
- Household Troubleshooting and Decision Support: Diagnosing home appliance issues, comparing products, and managing household budgeting;
- Personal Learning and Wellness: Answering educational questions and supporting personal health planning.
Standard GDP frameworks measure market labor while omitting non-market household time savings. Mapped to the ATUS framework, ATLAS demonstrates how AI reduces friction in daily life, unlocking substantial household non-market welfare.
Global Diffusion and Multilingual Patterns: English Accounts for Only One-Third
Regarding international adoption, concerns exist that non-English speakers might be forced to adopt English for complex tasks. ATLAS data clarifies global usage patterns:
- Multilingual Usage: Across 150+ countries and territories, English represents only 33.3% of global AI conversations. Users consistently retain their native languages for complex and specialized tasks.
- GDP Correlation and Exceptions: Normalizing for country-level Google app penetration, per-capita AI usage tracks GDP per capita globally. However, middle-income countries in Latin America (e.g., Brazil, Colombia) and the Middle East match high-income adoption rates.
This finding highlights both policy challenges in bridging the digital divide and significant opportunities for middle-income countries to leverage AI for digital leapfrogging.
Report Limitations and Future Research Directions
While ATLAS v1.0 provides detailed empirical evidence, Google notes key limitations: the dataset excludes enterprise usage via paid Gemini API on Google Cloud and inline Workspace AI tools (Docs, Gmail); the data captures a two-week snapshot from April 6 to April 19, 2026.
Click to View Study Limitations and Sampling Window
The dataset excludes paid Gemini API enterprise usage on Google Cloud and inline Workspace tools (Docs, Gmail); the data captures a two-week snapshot from April 6 to April 19, 2026.
Google's Chief Economist's Office states that ATLAS is an ongoing research project designed to provide an empirical basis for understanding AI's evolution.
Data Sources: 1. Google Official Blog: Understanding the AI economy (2026-07-23); 2. Google DeepMind & Chief Economist's Office: ATLAS v1.0 Report (100 pages, 2026-07).