AI Usage Across 4,000+ Tasks and 140 Languages Studied

A snapshot of how people are using AI at work and in their daily lives… There is so much interest and debate around how AI will transform our economy and the way we work, but not enough empirical research to help inform discussions and guide decision making. While I and many believe the potential to benefit society is significant, this is not guaranteed or automatic – so much needs to happen that we much shape collectively, as I discussed at a recent AI and Economy Forum.https://https://lnkd.in/eAvm4WiW A critical element is the need  to understand what’s actually going on in order to collectively shape the outcomes that benefit society. To help, we're sharing Google’s first go at helping to fill in that picture today with our AI & Economy ATLAS (Activity, Task, Landscape, and Adoption Study). We’ve just released the first report, ATLAS v1.0, on how people are using Google’s AI tools at work and in their day-to-day lives. ATLAS’s first dataset is built from 15 million de-identified interactions across the Gemini App, AI Mode, and the Gemini API, and the insights span more than 150 countries, 140 languages, 800 occupations, and 4,000 tasks. If you’d like to learn more about what we observed in this initial snapshot (some of which aligns with conventional wisdom or existing research, some of which is quite novel), my colleagues Zanna Iscenko and Scott Strand share a helpful overview here. https://lnkd.in/ejbGs64s) If you’d like to dig in further into the details, the paper offers a wealth of insights. https://lnkd.in/evWKcnqh So much is changing - AI is becoming more capable, its adoption and use are evolving, and efforts at better understanding all this empirically are still early and incomplete and must also improve – ATLAS v1.0 is a beginning. Also there are many more questions to explore and much work to be done to answer them.

A snapshot of how people are using AI at work and in their daily lives… There is so much interest and debate around how AI will transform our economy and the way we work, but not enough empirical research to help inform discussions and guide decision making. While I and many believe the potential to benefit society is significant, this is not guaranteed or automatic – so much needs to happen that we much shape collectively, as I discussed at a recent AI and Economy Forum.https://https://lnkd.in/eAvm4WiW A critical element is the need  to understand what’s actually going on in order to collectively shape the outcomes that benefit society. To help, we're sharing Google’s first go at helping to fill in that picture today with our AI & Economy ATLAS (Activity, Task, Landscape, and Adoption Study). We’ve just released the first report, ATLAS v1.0, on how people are using Google’s AI tools at work and in their day-to-day lives. ATLAS’s first dataset is built from 15 million de-identified interactions across the Gemini App, AI Mode, and the Gemini API, and the insights span more than 150 countries, 140 languages, 800 occupations, and 4,000 tasks. If you’d like to learn more about what we observed in this initial snapshot (some of which aligns with conventional wisdom or existing research, some of which is quite novel), my colleagues Zanna Iscenko and Scott Strand share a helpful overview here. https://lnkd.in/ejbGs64s) If you’d like to dig in further into the details, the paper offers a wealth of insights. https://lnkd.in/evWKcnqh So much is changing - AI is becoming more capable, its adoption and use are evolving, and efforts at better understanding all this empirically are still early and incomplete and must also improve – ATLAS v1.0 is a beginning. Also there are many more questions to explore and much work to be done to answer them.

There is a lot of talk about how AI is transforming the way we work and live. If you strip away the noise, how are people actually using these tools right now? This week, Google published its first iteration of the AI & Economy ATLAS, an ongoing, large-scale study of how people are using Google’s AI products across 150 countries and 800 occupations. To make sense of this quickly moving landscape, the team used Google DeepMind’s Observation Clustering and Taxonomy Organisation (OCTO). OCTO allowed us to take massive, unstructured text data and distill it into clear, organised trends, all while applying the strictest privacy protections and removing sensitive information. While this is just a snapshot of a moment in time, here are a few things we're learning that challenge some common assumptions: - AI use at work is broad, but shallow: People are using AI selectively. In a typical job, AI is used for only about 21% of tasks. - It's a blue-collar tool, too: We are seeing industrial mechanics and tradespeople use conversational AI as a live collaborator to interpret complex test results or debug wiring. - The "life admin" assistant: A massive amount of AI use (over 86%) happens outside the office, helping people tackle household friction like researching purchases or licensing. Understanding how people actually interact with AI in the real world is one of the most critical steps in deploying it responsibly - and here we have possibly the world's most comprehensive study on AI and the economy. This is also just a starting point, things are evolving quickly and our aim is to share what we’re seeing with the world - take a look: https://lnkd.in/gxVCjPAp

People do not have one clean, rational opinion about AI. They joke about it: “I make sure to say please and thank you. So when they take over the world, they might spare me.” They worry about it: “I worry about the AI bubble popping. I worry that AI is overhyped and it’s taking too much of a role in our economy. I worry about the impact on the environment and how many data centers are being built.” And they feel genuinely more capable because of it: “There have been times when there were things I wanted to do for work, or just creatively, that I just did not have within my own self to do. And being able to use AI to do those things has made me a lot more confident.” That’s the part I keep coming back to. AI is not just an adoption curve. It’s becoming emotional, practical, funny, scary, empowering, confusing, and deeply personal. A tracker can tell you where someone lands on a 1–10 scale. But it usually can’t tell you WHY they feel that way. Or how those feelings change once AI moves from novelty to habit. Or why the same person can be excited, skeptical, dependent, and uneasy all at once. That’s why KADRO partnered with Outset to build AI P.U.L.S.E. Each month, we’re conducting hundreds of in-depth, AI-moderated 1:1 interviews to understand the why behind AI adoption, sentiment, behavior, and usage. Qualitative depth, at a scale and cadence that simply was not possible a few years ago. On July 28, Aaron from Outset and I are walking through Wave 1: what we’re seeing, what surprised us, and why we think this kind of research matters for anyone building for or communicating about AI. 🤲 Come join us: https://luma.com/4mp3wbyu 🧠 Or learn more here: https://lnkd.in/gWneqs-2