Developing markets and AI
India’s call-centres and China’s factory floors will bear brunt of such automation
India’s call-centres, China’s factories will bear brunt of such automation |
Most studies of the impact of artificial intelligence on jobs and the economy have focused on developed countries such as the US and Britain. But the gravest threat AI poses is to emerging economies.
In recent decades, China and India have presented the world with two different models for how such countries can climb the development ladder. In the China model, a nation leverages its large population and low costs to build a base of blue-collar manufacturing. It then steadily works its way up the value chain by producing better and more technology-intensive goods.
In the India model, a country combines a large English-speaking population with low costs to become a hub for outsourcing of low-end, white-collar jobs in fields such as business-process outsourcing and software testing. If successful, these relatively low-skilled jobs can be slowly upgraded to more advanced white-collar industries.
Both models are based on a country’s cost advantages in the performance of repetitive, non-social and largely uncreative work — whether manual labour in factories or cognitive labour in call centres. Unfortunately for emerging economies, AI thrives at performing precisely this kind of work.
Artificial intelligence is dramatically accelerating the automation of factories and taking over routine tasks such as customer service or telemarketing. AI does such jobs cheaper than the low-wage workers of the developing world and, over time, will do them better. Robots examining your iPhone for scratches don’t take vacations for Chinese New Year; AI customer-service agents don’t demand pay raises.
Without a cost incentive to locate in the developing world, corporations will bring many of these functions back to the countries where they’re based. That will leave emerging economies, unable to grasp the bottom rungs of the development ladder, in a dangerous position. The large pool of young and relatively unskilled workers that once formed their greatest comparative advantage will become a liability — a potentially explosive one.
Self-perpetuating cycle
Increasing desperation in the developing world will contrast with a massive accumulation of wealth among the AI superpowers. AI runs on data and that dependence leads to a self-perpetuating cycle of consolidation in industries: The more data you have, the better your product. The better your product, the more users you gain. The more users you gain, the more data you have.
We’ve seen this phenomenon play out with purely online products such as Google Search, and it will soon be replicated in other AI-intensive industries such as self-driving cars. The result will be an unprecedented concentration of productive capacity and wealth in the hands of the elite AI companies, almost all of which are located in the US and China. According to one study by the consulting firm PwC, of the $15.7 trillion in wealth AI will generate globally by 2030, a full 70 per cent will accrue to those two countries alone.
So, what is an emerging economy to do? The first step is to recognise that the traditional paths to economic development — the China and India models — are no longer viable. China will likely be the last large country to climb out of poverty through factory work. The next wave of emerging economies must chart a new course.
That requires a two-pronged approach that addresses education. For the large body of less-educated workers, countries must look to build up unique, human-centred service industries. Even the best robots can’t give travellers the feeling of warmth and hospitality of staying at a unique bed-and-breakfast. Industries such as tourism, culture, hotline call centres and elderly care can bring poorer nations into a complementary relationship with the AI superpowers.
At the same time, developing countries need to carve out their own niches within the AI landscape. Factory robots can work anywhere in the world.
But, a micro-lending algorithm developed using the credit reports of American consumers would be useless in an agricultural country such as Ethiopia, where borrowers don’t have credit cards or traditional mortgages.
To seize on this gap, governments need to fund the AI education of their best and brightest students, with the goal of building local companies that employ AI. Math and engineering prodigies should be discovered early, trained vigorously, and sent to top global AI universities to study.
Neither of these tasks will be easy. But if developing countries can strike that balance, AI can also offer them an invaluable new opportunity: the chance to improve livelihoods and grow an economy without having to suffer exploitative sweatshops or environmental degradation.
Larger and more resourceful countries, such as the US and China, can help. Access to education and training may prove to be even more valuable than financial support. If AI is to be a boon not a global burden, its benefits will need to be shared.