Rossana Merola, International Labour Organization (ILO)
The current wave of technological change based on advancements in artificial intelligence (AI) has created the sentiment of vastly accelerating technological change that is feared to disrupt labour markets in yet unforeseen magnitude. Many analysts are warning that advances in both robotics and AI over the next few decades could lead to significant job losses or job polarization and hence widen income and wealth disparities (Korinek and Stiglitz, 2017).
Three main factors are deemed to have triggered the rapid increase in AI patent applications across the world. First, the drop in computing costs has led to an explosion in installed computing power and storage capacity. Second, the development and wide-spread adoption of the internet and other forms of digital communication has led to a significant increase in the supply and storage of digital information. Finally, the drop in capital costs for digital technologies has significantly lowered barriers of entry for start-ups.
This paper aims at addressing this knowledge gap to gain a better understanding of the economic and social implications of AI. In order to do so, it suggests to start from a granular analysis of how previous waves of automation have changed occupations and employment opportunities in the past. Specifically, we look at experiences of advanced and emerging economies with the automation of physical tasks through the rise in robotization. This approach can shed some light on the likely impact that the development and wide-spread diffusion of AI might have on employment, incomes and inequality through the automation of mental tasks. We also look at offshoring as much as it affects the role that AI can play in the structural transformation in developing countries. The questions this paper then tries to answer are twofold: First, to what extent is the current digital transformation through the rise in AI labour-augmenting rather than labour-saving? Moreover, what will be the implications for productivity and inequality given the specific, digital nature of AI applications? In particular, can we expect an acceleration in productivity and earnings growth thanks to wide-spread diffusion of AI in areas that so far have not yet been subject to large-scale automation? Or, on the contrary, should we be afraid of technological rents arising from AI to be appropriated by the lucky few?
The answer that this paper gives to these questions is moderately optimistic. New, AI-based digital technologies will allow larger segments of the labour market to improve their productivity, to access better paying occupations and, thereby, will help promote (inclusive) growth. This requires, however, the adoption of a certain number of policies aiming to support the necessary shift in occupational demand, maintain a strong competitive environment and keep up aggregate demand to support structural transformation. At the same time, AI applications rise the potential for productivity growth for interpersonal, less technical occupations and tasks, leading to higher demand for such work, which is likely to dampen inequality trends observed over recent decades. A particular challenge arises for developing countries when they are part of a value chain that forces them to adopt capital-intensive technologies despite an abundance of underutilised labour. Here, AI-driven automation might further drive up informality unless governments ensure a wide-spread adoption and diffusion of digital technological change beyond the value chain sectors. In other words, the productivity enhancing potential of AI is real, but the specific characteristics of this new technology require policy responses that differ from those given during previous waves of technological change in order to generate shared benefits for the world of work.
To develop our argument, this paper in Section 2 will start by looking at the historical perspective of automation. It will argue that the rise in educational attainment has led to an increasing skill-biased nature of technological change, bringing fewer benefits for productivity but increasing inequality; it is against this background that the introduction of AI needs to be assessed. Section 3 will shift the focus on tasks and away from jobs to help understand the implications this had for employment and the organisation of production. Section 4 discusses the particular experience that advanced and emerging economies have made during the recent wave of robotization. In section 5, our focus will then turn towards AI and the various aspects that this new technology brings for job growth, earnings dynamics and firm productivity. In section 6, we will develop possible policy answers that can help address the issues that AI brings in order to allow for a proper sharing of technological rents both within countries but also between advanced and less developed economies. A final section concludes.