💡We recently published findings from Google’s new AI & Economy ATLAS (Activity, Task, Landscape, and Adoption Study) v1.0 study, analyzing real-world usage of Google's AI products across 150+ countries and 140 languages. The research yields a clear takeaway: people use AI primarily as a collaborative partner to think, learn, and create. You can read more here 👇 https://lnkd.in/gejnxkPq
Google AI Study Reveals Collaborative AI Adoption
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Google [Blog] AI & Economy Atlas: Understanding the AI economy [23 July 2026] https://lnkd.in/eyaMPTmz [excerpt] ATLAS sheds light on how people are using Google’s AI tools for various tasks at work and in their day-to-day lives. The ATLAS v1.0 report provides an early view of a quickly moving landscape: AI’s capabilities are advancing, its use is evolving, and tools for observing its impact on the economy are still a work-in-progress.
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A super simplified view of how AI is being utilized across. The point about wide AI use but low Automation felt so close to what I see in my profession. A good read. #ATLAS #AI https://lnkd.in/dhJs3Siw
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Fascinating insights from Google on the evolving AI economy! As AI adoption accelerates, understanding its broader economic impact, productivity shifts, and future potential is more crucial than ever for leaders and innovators. Check out the full read here: 👇 #ArtificialIntelligence #AIEconomy #Innovation #FutureOfWork #TechTrends
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.
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Across 15M AI interactions, consumer purchases now make up 5.2% of all conversations, running about 3x ahead of the time people actually spend shopping. Researching purchases has become one of the most common everyday use cases, and comparison shopping is heavily over-represented. More than 86% of all AI usage happens outside of work. It's conversations, not conversions, so read it as direction rather than proof. But the direction is unmistakable: discovery and consideration are moving into AI. And that is exactly where the agentic commerce battle for brands and retailers begins. Thanks to the Google team for releasing this dataset!
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.
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Proud to share this snapshot from the new AI & Economy ATLAS (v1.0) study, led by our brilliant Research leaders James Manyika Scott Strand Andrew Kim . It provides much-needed data on how AI tools are actually being used across 4,000+ tasks and 140 languages. Analyzing real-world usage across 800+ occupations, this is a massive step forward in understanding how people are actually leveraging AI in their work and daily lives today. Worth a read for anyone thinking deeply about the future of work and the economy! 👇
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.
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It's been a while since I shared anything on LinkedIn, so here's something I've been excited to work on recently! 🚀 I recently authored a Microsoft Research article,"Treatment Effect Assessment at Scale: Accounting for Correlated Metrics and Metric Relevance in Modern Experimentation." The article explores a challenge many experimentation platforms face: how to assess overall treatment effects when experiments generate hundreds or even thousands of metrics, many of which are correlated. We also discuss why metric relevance becomes increasingly important as experimentation workflows become more agentic and AI-assisted. Working on Microsoft's Experimentation Platform (ExP), I've had the opportunity to explore the intersection of statistics, experimentation, and AI. This article shares some of the lessons we've learned along the way and the rationale behind our approach to treatment effect assessment at scale. Huge thanks to David Hall, Julie Beckley, and Travis Brooks for their thoughtful reviews, feedback, and support throughout the process. #MicrosoftExP #DataScience #Experimentation #ABTesting #CausalInference #AI
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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
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It's a blue collar tool alright! Seeing the stats for how users are learning to adapt AI into their lives is awesome. I have personally used AI to manifest a backyard mechanic out of my pocket to help me fix an overheating 350Z with a coolant bleed procedure that would work on the fly. Walked be through diagnostics in 15 minutes. I absolutely love it even with the Algorithmic Bias bugs.
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
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AI is moving from experimentation to real-world impact, and that's what excites me most. As someone working in talent acquisition, I spend a lot of time thinking about how technology shapes jobs, skills, and opportunities. What stood out to me in this edition of Spotlight on AI is the focus on who benefits from AI and how we ensure those benefits are broadly shared. The future of work isn't just about AI capabilities. It's about helping people build the skills, confidence, and access needed to thrive alongside these technologies. Lots of interesting perspectives and examples of AI driving meaningful impact across education, research, accessibility, and communities. Read more: https://msft.it/6049a6pml #MicrosoftEmployee #FutureOfWork #TalentAcquisition
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