Artificial Intelligence and labour

All around the world, there are fears of job losses and increasing inequality resulting from AI and related forms of automation technologies. Developing countries and emerging market economies(EME) have even more reason to be concerned than high-income countries, as their comparative advantage in the world economy relies on abundant labor and natural resources. AI is likely to be resource-saving and labor-saving, devaluing the sources of comparative advantage of many developing countries, deteriorating their terms of trade, and potentially making them worse off in absolute terms. Given all the uncertainties surrounding AI, and applying on steering innovation to economic research the key policy question for developing countries is how they can improve the likelihood of benign outcomes from technological progress.

Student Group Spotlight: CAIS++ Showcases “AI for Social Good” Projects -  USC Viterbi | School of Engineering

The Industrial Revolution that lifted living standards started a bit over two centuries ago and was but a blip in the history of humanity. It is easily conceivable that we are now going into another era. There is even a risk that the terms-of-trade losses generated by progress in AI may push developing countries back into the Malthusian dynamics that have characterized much of our history. But the Industrial Revolution also offers ample lessons on how to manage innovation in a positive way: technological revolutions are very disruptive, but collective action can mitigate the adverse effects and generate an environment in which the gains are shared broadly. The labor-using nature of the Industrial Revolution ushered in an Age of Labor in which the economic gains of workers also shifted political dynamics in their favor, but there is a risk that future labor-saving progress may do the opposite.

However, while it is possible, perhaps likely, that technological progress may hurt labor, it is also possible that technological progress may enhance the prospects of labor, i.e. be labor-using. One example is what is called IA (Intelligence Assistance) rather than AI. Past innovations that fell into this category included telescopes and microscopes that enabled us to see what the naked eye could not; thermometers that enabled us to measure what we otherwise had not been able to detect precisely. These IA innovations enable us to focus human attention where one should. Frequently, automation technologies affect particular tasks but not jobs, which consist of multiple tasks. In the past, automation was associated with highly routine tasks. Going forward, automation is increasingly also affecting tasks that require higher levels of skill. For example, a doctor is engaged in diagnosis but also in explaining the diagnosis to the patient. AI may do a better job in diagnosis – for example, in radiology – but it may not quite replace the doctor in communicating with the patient yet.

The rise of AI and other information technologies may also lead to greater and greater concentrations of market power. As a result, the economy may move to an equilibrium that is more distorted by market power, with greater rents for dominant firms. Actors with sufficient market power who are aware of the impact of their decisions on market prices (including factor prices) are also likely to use that power deliberately to advantage themselves relative to others. The resulting distortions may offset part or all of the benefits of innovation. And this may exacerbate the adverse distributive effects of labor-saving or resource-saving innovation.

There are several reasons why advances in AI intensify market power. First, AI is an information good, and information goods are different from other goods in that they are non-rivalrous – they can be used at close-to-zero marginal cost, implying that a single firm can serve a very large market. Moreover, the creation of AI programs typically involves high sunk costs and/or fixed costs – in a private market, firms need to earn monopoly rents to recoup these costs. In addition, AI applications and platforms typically involve significant network externalities. Some of these arise because firms accumulate vast amounts of data that allow them to train their algorithms better than those of the competition. All of these effects create large barriers to entry and a tendency towards what is sometimes called “superstar” effects or “super monopolies“.

There is a risk that those countries that lead in the advancement in AI may reap all the benefits, becoming “superstar countries” and reaping all the rents associated with the development of AI. The rest of the world, and in particular most
developing and emerging economies, may be left behind. Moreover, the monopolization of knowledge may also impede the catching up process. In the past, advances in technology were driven to an important extent by basic research that was financed by governments in high-income countries and that was freely available to all—including to developing countries. This may change with AI.

There are also a number of broader harms associated with AI that have recently received a lot of attention—the ways in which new technology can affect security (including cybersecurity), privacy, incitement to “bad” behavior, including through hate speech, political manipulation, and, in the economic arena, price discrimination, sometimes exacerbating pre-existing societal divides.

Advances in AI and related technologies may, like the Industrial Revolution, represent a critical turning point in history. Increasing automation in manufacturing may lead to the demise of the manufacturing export-led developmental model, which has had such profound positive effects on many emerging market economies. The worst-case scenario is the unravelling of much of the gains in development and poverty reduction that we have seen over the last half century.

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