麦肯锡:模拟人工智能对世界经济的影响(英文 64 页)

麦肯锡:模拟人工智能对世界经济的影响(英文 64 页)


摘要

人工智能具有促进全球经济活动的巨大潜力。但是,需要管理各国,公司和工人之间差距的扩大,以最大限度地发挥效益。

人工智能(AI)工具和技术在商业和全球经济中的作用是一个热门话题。这并不奇怪,因为人工智能可能会引发人们生活和工作方式的激进 - 可以说前所未有的变化。人工智能革命尚未处于起步阶段,但其大部分经济影响尚未到来。

麦肯锡全球研究所的新研究试图模拟人工智能对世界经济的影响。首先,它建立在对公司行为和各部门动态的理解的基础上,以形成如何采用和吸收人工智能技术的自下而上的观点。其次,它考虑了国家,公司和工人在向AI过渡时可能遇到的中断。在这个过渡期间很可能会有成本,并且需要将它们纳入任何估算中。该分析研究了如何在公司,员工和国家之间分配经济收益和损失,以及这种分配如何可能阻碍人工智能效益的获取。第三,

应根据现阶段可获得的最佳知识,将分析视为人工智能潜在经济影响的指南。主要调查结果如下:

  1. 人工智能有很大的潜力为全球经济活动做出贡献

  2. 一个关键挑战是采用人工智能可能会扩大国家,公司和工人之间的差距


INTRODUCTION

The role of artificial intelligence tools and techniques in business and the global economy is a hot topic. This is not surprising given recent progress, breakthrough results, and demonstrations of AI, as well as the increasingly pervasive products and services already in wide use. All of this has led to speculation that AI may usher in radical—arguably unprecedented—changes in the way people live and work.

This discussion paper is part of MGI’s ongoing effort to understand AI, the future of work, and the impact of automation on skills. It largely focuses on the impact of AI on economic growth. Our hope is that this effort helps us to broaden our understanding of how AI may impact economic activity, and potentially touch off a competitive race with major implications for firms, labor markets, and economies. Three key findings emerge:

AI has large potential to contribute to global economic activity. 

AI is not a single technology but a family of technologies. In this paper, we look at five broad categories of AI technologies: 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. Others may be bolder, adopting all five and then absorbing them across their entire organization. For the sake of our modeling, we define the first approach as adoption and the second as full absorption. Between these two poles will be many companies at different stages of adoption; the model captures partial impact, too. By 2030, our average simulation shows, some 70 percent of companies may have adopted at least one type of AI technology, but less than half may have fully absorbed the five categories.

The pattern of adoption and full absorption may be relatively rapid—at the high end of what has been observed with other technologies. However, several barriers may hinder rapid adoption. For instance, late adopters may find it difficult to generate impact from AI because AI opportunities have already been captured by front-runners, and they lag behind in developing capabilities and attracting talent. Nevertheless, at the average level of adoption implied by our simulation, and netting out competition effects and transition costs, AI could potentially deliver additional global economic activity of around $13 trillion globally by 2030, or about 16 percent higher cumulative GDP compared with today. This amounts to about 1.2 percent additional GDP growth per year. If delivered, this impact would compare well with that of other general-purpose technologies through history.5 Consider, for instance, that the introduction of steam engines during the 1800s boosted labor productivity by an estimated 0.3 percent a year, the impact from robots during the 1990s around 0.4 percent, and the spread of IT during the 2000s 0.6 percent.

The economic impact may emerge gradually and be visible only over time.

 The impact of AI may not be linear, but may build up at an accelerating pace over time. AI’s contribution to growth may be three or more times higher by 2030 than it is over the next five years. An S-curve pattern of AI adoption is likely—a slow start due to substantial costs and investment associated with learning and deploying these technologies, but then an acceleration driven by the cumulative effect of competition and an improvement in complementary capabilities. The fact that it takes time for productivity to unfold may be reminiscent of the Solow Paradox.7 Complementary management and process innovations will likely be necessary to take full advantage of AI innovations.8 It would be a misjudgment to interpret this “slow-burn” pattern of impact as proof that the effect of AI will be limited. The size of benefits for those who move into these technologies early will build up in later years at the expense of firms with limited or no adoption.

A key challenge is that adoption of AI could widen gaps between countries, companies, and workers. 

AI could deliver a boost to economic activity, but the distribution of benefits is likely to be uneven:

— Countries. AI may widen gaps between countries, reinforcing the current digital divide.9 Countries may need different strategies and responses because AI adoption levels vary. AI leaders (mostly in developed countries) could increase their lead in AI adoption over developing countries. Leading countries could capture an additional 20 to 25 percent in net economic benefits compared with today, while developing countries may capture only about 5 to 15 percent. Many developed countries may have no choice but to push AI to capture higher productivity growth as their GDP growth momentum slows, in many cases partly reflecting the challenges related to aging populations. Moreover, wage rates in these economies are high, which means that there is more incentive than in low-wage, developing countries to substitute labor with machines. Developing countries tend to have other ways to improve their productivity, including catching up with best practices and restructuring their industries, and may therefore have less incentive to push for AI (which, in any case, may offer them a smaller economic benefit than advanced economies). This does not mean that developed economies are set to make the best use of AI and that developing economies are destined to lose the AI race. Countries can choose to strengthen the foundations, enablers, and capabilities needed to reap the potential of AI, and be proactive in accelerating adoption. Some developing countries are already being ambitious in pushing AI. For instance, China, as we have noted, has a national strategy in place to become a global leader in the AI supply chain, and is investing heavily.


— Companies. AI technologies could lead to a performance gap between front-runners on one side and slow adopters and nonadopters on the other. At one end of the spectrum, front-runners (companies that fully absorb AI tools across their enterprises over the next five to seven years) are likely to benefit disproportionately. By 2030, they could potentially double their cash flow (economic benefit captured minus associated investment and transition costs), which implies additional annual net cash flow growth of about 6 percent for more than the next decade.11 Front-runners tend to have a strong starting digital base, a higher propensity to invest in AI, and positive views of the business case for AI. Although our simulation treats front-runners as one group, in reality, this category is not homogeneous. Some current AI innovators and creators have big starting endowments of data, computing power, and specialized talent. Other early adopters may not engage in creating these technologies but may be innovative in how they deploy them. At the other end of the spectrum is a long tail of laggards that do not adopt AI technologies at all or that have not fully absorbed them in their enterprises by 2030. This group may experience around a 20 percent decline in their cash flow from today’s levels, assuming the same cost and revenue model as today. One important driver of this profit pressure is the existence of strong competitive dynamics among firms, which could shift market share from laggards to front-runners and may prompt debate on the unequal distribution of the benefits of AI.

— Workers. A widening gap may also unfold at the level of individual workers. Demand 

for jobs could shift away from repetitive tasks toward those that are socially and cognitively driven and others that involve activities that are hard to automate and require more digital skills.12 Job profiles characterized by repetitive tasks and activities that require low digital skills may experience the largest decline as a share of total employment, from some 40 percent to near 30 percent by 2030. The largest gain 

in share may be in nonrepetitive activities and those that require high digital skills, rising from some 40 percent to more than 50 percent. These shifts in employment would have an impact on wages. We simulate that around 13 percent of the total wage bill could shift to categories requiring nonrepetitive and high digital skills, where incomes could rise, while workers in the repetitive and low digital skills categories may potentially experience stagnation or even a cut in their wages. The share of the total wage bill of the latter group could decline from 33 to 20 percent.13 Direct consequences of this widening gap in employment and wages would be an intensifying war for people, particularly those skilled in developing and utilizing AI tools, and structural excess supply for a still relatively high portion of people lacking the digital and cognitive skills necessary to work with machines.

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