AI Is Bad News for the Global South

Artificial intelligence is changing the structure of our global economy, but it’s unlikely that everyone will benefit. Advocates for AI celebrate its potential to decode intractable global challenges and even end poverty, but its achievement in that regard are meager. Instead, global inequality is now set to rise. Those countries that are home to AI development and readily able to incorporate these technologies into industry are set to see rising economic growth. But the rest of the world, which faces critical barriers to adopting AI, will be left further and further behind.

The introduction of new technologies into society has historically brought about economic development and growth. Technologies are often designed to do just this by boosting productivity: The sewing machine or the tractor, for example, enabled textiles to be made or crops to be yielded quicker. Since the turn of the century, digital technologies have been a particularly powerful economic force. In the United States, according to a 2021 study, the internet’s contribution to the country’s GDP has increased by 22 percent a year since 2016. The U.S. digital economy is now worth well over $4 trillion.

AI is a new and powerful force for economic growth. In 2017, PwC attempted to put a price on the value AI would bring to national economies and global GDP. In a seminal report titled “Sizing the Prize,” the consulting firm boasted that by 2030, AI would contribute $15.7 trillion to the global economy. China, North America, and Europe stand to gain 84 percent of this prize. The remainder is scattered across the rest of the world, with 3 percent predicted for Latin America, 6 percent for developed Asia, and 8 percent for the entire block of “Africa, Oceania and other Asian markets,” as PwC termed it.

Following the advent of generative AI technologies such as OpenAI’s GPT series, McKinsey estimated that this new generation of AI would increase the productive capacity of AI across industries by 15 to 40 percent, potentially adding up to $4.4 trillion a year to the global economy. These are broadly considered conservative estimates. The capabilities of the new suite of large language models, of which ChatGPT is a part, are particularly significant for their ability to raise productivity levels, particularly across knowledge economies where language-based tasks form the basis of productive output.

McKinsey’s report also includes a breakdown of the sectors and productive functions set to achieve the most growth—in particular, high-tech industries (tech, space exploration, defense), banking, and retail. In contrast, the industry likely to see the least growth is agriculture, Africa’s largest sector by a long way, and the major source of livelihoods and employment on the continent.

Now, McKinsey’s calculations were early on in the generative AI revolution, when there was limited information about the ways in which AI technologies may improve agricultural production in developing contexts. Today, there are a growing number of use cases demonstrating AI’s value in African agro-industries. In Tanzania, a researcher at Sokoine University of Agriculture is using generative AI technologies to create an app for local farmers to use to receive advice on crop diseases, yields, and local markets to sell their produce. In Ghana, experts at the Responsible AI Lab are designing AI technologies to detect unsafe food. Yet cases like these are still limited in scale and impact. At this stage, it is not clear whether AI will be as transformative in African contexts as its promise holds.

AI’s adoption in developing regions is also limited by its design. AI designed in Silicon Valley on largely English-language data is not often fit for purpose outside of wealthy Western contexts. The productive use of AI requires stable internet access or smartphone technology; in sub-Saharan Africa, only 25 percent of people have reliable internet access, and it is estimated that African women are 32 percent less likely to use mobile internet than their male counterparts.

Generative AI technologies are also predominantly developed using the English language, meaning that the outputs they produce for non-Western users and contexts are oftentimes useless, inaccurate, and biased. Innovators in the global south have to put in at least twice the effort to make their AI applications work for local contexts, often by retraining models on localized datasets and through extensive trial and error practices.

Where AI is designed to generate profit and entertainment only for the already privileged, it will not be effective in addressing the conditions of poverty and in changing the lives of groups that are marginalized from the consumer markets of AI. Without a high level of saturation across major industries, and without the infrastructure in place to enable meaningful access to AI by all people, global south nations are unlikely to see major economic benefits from the technology.

As AI is adopted across industries, human labor is changing. For poorer countries, this is engendering a new race to the bottom where machines are cheaper than humans and the cheap labor that was once offshored to their lands is now being onshored back to wealthy nations. The people most impacted are those with lower education levels and fewer skills, whose jobs can be more easily automated. In short, much of the population in lower- and middle-income countries may be affected, severely impacting the lives of millions of people and threatening the capacity of poorer nations to prosper.

Generative AI technologies threaten the rising middle class in developing contexts. A recent report from the World Bank estimates that up to 5 percent of jobs are at risk of full automation from generative AI in Latin America and the Caribbean and that women are most likely to be affected. In countries where creating formal jobs and economies is a major development priority, AI is set to drive many millions of people into unsecured temporary, gig, or contract work.

In fact, gig economies are rapidly rising. At present, research estimates the gig-economy’s global market share to be 500 billion, but set to rise to almost 2 trillion by 2032. Many millions of gig-workers (an estimated 30 to 40 million) are from across the global south. Workers in platform economies, such as delivery drivers, are oftentimes balancing numerous jobs in order to make just enough to scrape by and certainly not enough to escape a life of poverty. Globally, platform and gig workers have limited labor rights, with the Global Index on Responsible AI finding that only seven countries globally have enforceable laws protecting these workers.

While AI creates uncertainty for the poor, we are witnessing the largest transfer of income to the very top brackets of society. Globally, two-thirds of all the wealth generated between 2020 and 2022 was amassed by the richest 1 percent, according to Oxfam estimates. And the richest of them all is the new class of tech billionaires, equipped with the power, money, and influence to craft the worlds they want to live in. Tech companies are some of the biggest companies in the world. Apple, which ranks among the top five largest companies globally, has a market cap that outweighs the total combined GDP of the African continent.

The wealth of tech companies does not just paint a picture of the stark inequality at the heart of AI; it also creates a barrier for other actors to produce AI technologies. Recently, OpenAI CEO Sam Altman embarked on a campaign to raise $7 trillion to power an AI-driven future. This is the type of sum required to establish the kind of supercomputer infrastructure needed to create frontier AI models. It is not a sport everyone can afford.

Compute, essential for creating AI technologies and applications, is one of the world’s most expensive resources. A major global divide exists in access to compute resources. Collectively, the global south is home to just over 1 percent of the world’s top computers, and Africa just 0.04 percent. At present, with the kind of computing capacity available in Africa or South America, it would take hundreds of years to catch up with the advances that have been made with generative AI in the West and developed East.

The costs for poorer nations to catch up in the AI race are too much. Public spending may be diverted from critical services such as education and health care. While global south governments should be attuned to the AI revolution, decision-makers should closely assess the effects that AI is having on their economies.