Analysis Of The Article Unequal Competition Among Video
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| Analysis Of The Article Unequal Competition Among | Sep 04, · The analysis examines how economic gains and losses are likely to be distributed among firms, employees, and countries and how this distribution could potentially hamper the capture of AI benefits. Third, the research examines the dynamics of AI for a wide range of countries—clustered into groups with similar characteristics—with the aim of Author: Jacques Bughin. Growing through Competition: The Reduction of Entry Barriers among Chinese Manufacturing Firms Helu Jiang, Yu Zheng, Lijun Zhu. Competition for Attention in the ETF Space Itzhak Ben-David, Francesco Franzoni, Byungwook Kim, Rabih Moussawi. Apart but Connected: Online Tutoring and Student Outcomes during the COVID Pandemic. Unequal Power. There’s an inherent imbalance of bargaining power between employers and employees. Here's how it impacts your workplace—and our democracy. Policy Agenda. Policy choices have tilted the playing field toward the rich and corporations. Here's how to tilt it back. Tax & Spending Explorer. |
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A key challenge is that adoption of AI could widen gaps among countries, companies, and workers
Please click "Accept" to help us improve its usefulness with additional cookies. Learn about our use of cookies, and collaboration with select social media and trusted analytics partners here Learn more about cookies, Opens in new tab. The role of artificial intelligence AI tools and techniques in business and the global economy is a hot topic. This is not surprising given that AI might usher in radical—arguably unprecedented—changes in the way people live and work. The AI revolution is not in its infancy, but most of its economic impact is yet to come. First, it builds on an understanding of the behavior of companies and the dynamics of various sectors to develop a bottom-up view of how to adopt and absorb AI technologies.
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Second, it takes into account the likely disruptions that countries, companies, and workers are likely to experience as they transition to AI. There will very probably be costs during this transition period, and they need to be factored into any estimate. The analysis examines how economic gains and losses are likely to be distributed among firms, employees, and countries and how this distribution could potentially hamper the capture of AI benefits.
Third, the research examines the dynamics of AI for a wide range of countries—clustered into groups with similar characteristics—with the aim of giving a more global view.
The analysis should be seen as a guide to the potential economic impact of AI based on the best knowledge available at this stage. Among the major findings are the following:. The McKinsey Global Institute looked at five broad categories of AI: computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning. Companies will likely use these tools to varying degrees. Some will take an opportunistic approach, testing only one technology and piloting it in a specific function an approach our modeling calls adoption.
Analysis Of The Article Unequal Competition Among might be bolder, adopting all five and then absorbing them across the entire organization an approach we call full absorption. In between these two poles, there will be many companies at different stages of adoption; the model also captures this partial impact.
Bythe average simulation shows that some 70 percent of companies might have adopted at least one type of AI technology but that less than half will have fully absorbed the five categories. The pattern of adoption and full absorption might be relatively rapid—at the high end of what has been observed with other technologies.
For instance, late adopters might find it difficult to generate impact from AI, because front-runners have already captured AI opportunities and late adopters lag in developing capabilities and attracting talent. This amounts to 1. If delivered, this impact would compare well with that of other general-purpose nAalysis through history. A number of factors, including labor automation, innovation, and new competition, affect AI-driven productivity growth. Micro factors, such as the pace of adoption of AI, and macro factors, such as the global connectedness or labor-market structure of a country, both contribute to the size of the impact. Our simulation examined seven possible channels of impact.
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The first three relate to the impact of AI adoption on the need for, and mix of, production factors that have Akong impact on company productivity. The other four are externalities linked to the adoption of AI related to the broad economic environment and the transition to AI. We acknowledge that these seven channels are not definitive or necessarily comprehensive but rather a starting point based on our current understanding and trends currently under way Exhibit 1. The impact of AI might not be linear but could build up at an accelerating pace over time. Its contribution to growth might be three or more times higher by Analysis Of The Article Unequal Competition Among Ampng is over the next five years.
An S-curve pattern of adoption and absorption of AI is likely—a slow start due to the substantial costs and investment associated with learning and deploying these technologies, then an acceleration driven by the cumulative effect of competition and an improvement in complementary capabilities alongside process innovations. The size of benefits for those who move early into these technologies will build up in later years at the expense of firms with limited or no adoption. Potentially, AI might widen gaps between countries, reinforcing the current digital divide. Countries might need different strategies and responses as AI-adoption rates vary.
Leaders of AI adoption mostly in developed countries could increase their lead over developing countries.]
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