AI-generated editorial illustration; not a documentary photograph.
Google has launched an interactive, open-access version of its AI & Economy ATLAS, allowing users to explore reported AI use by occupation, country and activity. The company also published ATLAS-based research with Google DeepMind and MIT FutureTech on how scientists use AI and where productivity gains encounter bottlenecks.
Adoption varies by region
Google’s data points to different occupational patterns across markets. In India, arts, design and media occupations account for 19% of work-related AI usage, which Google says is 1.6 times the global average. In the U.S., computer and mathematical occupations represent 30% of work-related AI use, twice the share reported for the rest of the world.
Across OECD countries, computer and mathematical work, along with business and financial operations, leads reported AI usage. In non-OECD countries, office and administrative support, arts and media, and education and library occupations are among the leading categories. Google also identifies Brazil and the UAE as having adoption rates higher than their GDP per capita would predict.
A new way to explore the data
The open-access interactive experience expands access to millions of data points, including occupation-level usage from electricians to purchasing managers, household use and country-level adoption.
Scientists save time, then face backlogs
The accompanying study analyzes 2,600 specialized AI models and a survey of more than 600 scientists in the U.S. and U.K. Google reports that nearly half of surveyed scientists use some form of AI every day, combining large language models with specialized systems for different tasks. Large language models such as Gemini are used across fields, while specialized models are more common in health and life sciences and domain-specific prediction, generation and simulation.
Surveyed scientists report saving just under seven hours per week with AI. The research also identifies time spent validating outputs and growing backlogs of hypotheses awaiting testing. Physical experiments and clinical validation are described as bottlenecks, meaning reported time savings have not automatically translated into faster discoveries.
Source: New insights from Google’s AI & Economy ATLAS
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