Economic activity declined slightly on average, employment was roughly flat...
2024-02-07 53 英文报告下载
What’s made generative AI so impactful is a convergence of factors. First, advanced hardware—primarily specialized AI chips used in training models—have helped produce more advanced models such as large language models (LLMs). These tools have gone mainstream due to a seamless user experience, enabling even nontechnologists to engage with very advanced models. All this attention has kicked of a gold rush among investors (fgure 3). Investors are pouring money into startups that have generative AI technology at their core, betting that we’re witnessing the dawn of a new paradigm for business technology, one where insights are surfaced automatically, contracts review themselves, and a never-ending stream of content is generated to keep brands in front of their audiences. While there’s been plenty of talk about how AI may threaten jobs, there’s no real indication that business leaders are planning on using it to automate knowledge jobs at any kind of scale. In a survey of leaders, improving content quality, driving competitive advantage, and scaling employee expertise were the most common reasons for deploying generative AI. Reducing headcount was one of the lowest priorities.11 It looks more likely that AI will liberate workers from rote, repetitive tasks and free them to focus on more creative aspects of their jobs. The picture that’s emerging is that AI is coming, and for some, it’s already here. But, as the saying goes, leading businesses know they can’t shrink their way to growth— that is, minimize risks or costs as a path to growth.12 This means the most productive uses of generative AI won’t be about replacing people but instead will focus on arming employees with tools that help them advance and enhance their productivity, knowledge, and creativity—which, in turn, will help drive innovation in the enterprise.