Most studies about the impact of artificial intelligence (AI) on jobs and the economy have focused on developed countries such as the United States and Britain. Through my work as a scientist, technology executive and venture capitalist in the US and China, I have come to believe that the gravest threat AI poses is to emerging economies.
In recent decades, China and India have presented the world with two different models on how countries can climb the development ladder. In the China model, the nation leveraged its large population and low costs to build a base of blue-collar manufacturing. The country then steadily worked 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 software testing and business process outsourcing. 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.
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Unfortunately for emerging economies, AI thrives at performing precisely that kind of work.
AI 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, it will do those better.
Robots that examine your iPhone for scratches do not take holidays for Lunar New Year; AI customer-service agents do not demand pay rises.
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.
Increasing desperation in the developing world will be in stark contrast to 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. Of the US$15.7 trillion in wealth that AI is forecast to generate globally by 2030, a full 70 per cent will accrue to those two countries alone, according to a study by consulting firm PwC.
Indeed, according to PwC:
- Artificial intelligence (AI) can transform the productivity and GDP potential of the global economy. Strategic investment in different types of AI technology is needed to make that happen.
- Labour productivity improvements will drive initial GDP gains as firms seek to “augment” the productivity of their labour force with AI technologies and to automate some tasks and roles.
- Our research also shows that 45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. This is because AI will drive greater product variety, with increased personalisation, attractiveness and affordability over time.
- The greatest economic gains from AI will be in China (26% boost to GDP in 2030) and North America (14.5% boost), equivalent to a total of $10.7 trillion and accounting for almost 70% of the global economic impact.
Why AI stealing our jobs may be a blessing in disguise – if we can rethink what meaningful work is. So, what is an emerging economy to do? The first step is to recognise that the traditional paths to economic development – represented by the China and India models – are no longer viable. China is likely to 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 will require a two-pronged approach to address education. For the large body of less-educated workers, countries must look to build up unique, human-centred service industries. Even the best robots cannot give travellers the feeling of warmth and hospitality of staying at a unique bed-and-breakfast. Industries such as tourism, call centres and elderly health 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. A microlending algorithm developed using the credit reports of American consumers would be useless in an agricultural country like Ethiopia, where borrowers do not have credit cards or traditional mortgages.
To seize on that gap, governments need to fund the AI education of their best and brightest students, with the goal of building local companies that employ AI. Maths and engineering prodigies should be discovered early, trained vigorously and sent to top global universities to study AI.
China’s AI industry gets the most funding, but lags the US in key talent, says Tsinghu. Neither of those tasks will be easy. Fostering a million small businesses is far more difficult than building a hundred mega-factories, while paying for top students to study abroad is a daunting task for countries still dealing with malnutrition. But if developing countries can strike that balance, AI can 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 and not a global burden, its benefits will need to be shared.
