Developed (High-income) Countries

Artificial Intelligence is currently disrupting all aspects of the global economy. How countries will be affected by global trends and in their domestic markets is projected to vary tremendously. While difficult to make projections of how economies will fare with increasing AI development, it is likely that high income countries will be in a better position than developing countries. Most developed countries are going to experience to a similar extent the issues and benefits the United States will. McKinsey’s 2017 study on automation predicts that AI could help close the economic growth gap in the 20 largest economies by boosting productivity. Many advanced economies (Australia, Canada, France, Germany, Italy, Japan, South Korea, the United Kingdom, United States) are facing an aging workforce. AI could bring the productivity boost needed to meet economic growth projections. Therefore, these countries will pursue rapid automation adoption. For more information on what the likely scenarios to play out will be in these high-income countries, read U.S. Economic Engagement.


The projections and predicted impacts of AI upon the U.S. economy is largely similar to those of other high-income countries. Independent of which developed countries create powerful and salient AI technologies, the impact on middle and low-income countries is projected to be immense. There are reports and studies that predict largely positive results for these states, but they are either in line with studies that had similar positive outlooks on AI and job growth in developed countries, or are only applicable for the near future. In the long term, improvements and expansions in AI technology will likely result in harmful consequences for developing countries. The comparative advantage in cheap labor held by these countries that has been an integral part of the model to industrialize and improve their economies will be eliminated. This model, the most successful form of development to date, is predicted to be useless. Inequalities across socioeconomic classes, sexes, ethnicities, and the like will worsen. Developing countries do not have the infrastructure or human capital to create and improve industries using AI. These states will have only their domestic markets to focus on, since most of them will lose their international market, their essential tool for economic expansion (there are obviously exceptions, such as countries heavily reliant on tourism). Therefore, developing countries must begin working on new development models and create a welcoming environment for AI inroads. If the proper steps are not taken, these countries will suffer tremendously. The United States will see increased populism, the erosion of democracy, and instability throughout the world. This is a danger to its economic assets, national security, and the liberal world order it leads. It is essential that solutions to the same issues the United States will have to contend with in regards to AI and a new economic model of development are applied and achievable in middle and low income countries.

In my view, the developing countries, where there are large, unemployed populations and which are just about to embark on industrialization, will be hit harder. Although they have the advantage of leapfrogging into the digital economy, the productivity divide between skilled and unskilled [populations] could lead to a bigger gap in income disparities.
— Srikanth Karra

Developing (Middle and Low-Income) Countries

The impact of artificial intelligence on the economies of developing countries could garner positive results. However, most projections for the future are negative, with two different timeframes, one short-term and one long-term.

Short-Term projections

The short term picture offered by automation and other AI technologies provides developing countries with an opportunity to increase labor productivity, create more efficient industry, and expand economic endeavors. AI capabilities will enable increased opportunities for entrepreneurs to develop new businesses. Overall, the expectation is that these technologies will benefit low and middle-income countries through a reduction of transaction costs associated with absences of information and improve the quality of national statistics critical for positive economic planning and policy-making. AI has already been utilized in providing public goods and services, such as simplifying transactions done through government websites, a practice far more common in these countries in contrast to developed economies. Another important sector AI has been used is in combatting public health concerns through actions such as anticipating outbreaks of disease. Other uses and expected increases in usage of AI technologies are happening in improving police coverage, traffic control, environmental initiatives, and the prediction and prevention of natural disasters and their damaging effects.

Artificial Intelligence will also be useful in the strengthening of democracy. As a result of colonialism and imperialism, many of these countries’ governments have English, French, or other colonial languages as their main language. Therefore, citizens have to have a good grasp on these languages in order to fully participate economically and politically. However, many citizens are illiterate or speak an indigenous language of the area and encounter a severe barrier to potential opportunities and fulfilling a civic life. Automated translation and voice recognition systems are slowly eroding the need for these citizens to be fluent in these colonial languages. This could have enormous effects for allowing these groups to fully participate and reap the benefits of their economy and government.

 A report by Accenture has the most positive outlook on the impact of AI on developing economies, particularly as the technologies will play a large role in expanding and improving three large economic sectors. The primary concern for citizens in developing countries is access to food. Since research infrastructure and agricultural extension systems capable of supporting smallholder farmers are lacking, AI can be a simple substitution. Through machine learning algorithms used in drone technology, the planting and fertilization of seeds can be done at a speed beyond human capabilities. This will increase the yield of farmland under tillage in developing countries. An additional application of AI for food management is identification of disease in crops so they can be more easily treated. Researchers at Penn State and the Swiss Federal Institute of Technology (EPFL) have created a system that is able to identify both crops and diseases – from photos – with an accuracy rate of up to 99.35%. UNICEF is leading a similar initiative, testing whether deep learning can diagnose malnutrition through photographs and videos of children. Determining the type and allocation of resources where they are most needed is another area where AI technologies can help NGOs and charities tremendously. These mechanisms reduce the impact of scarcity on a region. An example of a problem this technology could hone in on is predict when a drought could occur, how many people it will likely impact, and what is needed to fix the problem. Scientists at Stanford are using AI and satellite remote-sensing data to project crop yields well ahead of harvest, in an effort to anticipate food shortages. Machine learning technology can also be an effective tool in fighting infectious diseases. These technologies are  understanding the spread of disease, but also providing relief. We are looking at future where machine learning could feasibly identify a disease, develop a cure, locate where the outbreak is likely to strike next, and then transport the cure there in autonomous vehicles, all with minimal human interaction.

Long-term Projections

While current and projected AI technologies are likely to have positive impacts on the well-being of citizens and economies of developing countries in the near future, the long term ramifications of artificial intelligence potentially poses a larger more encompassing threat to developing economies in contrast to developed economies. The amount of jobs at risk of automation is tremendous in developed countries over the next several decades. The worry is even larger for developing countries, although the outcomes will vary highly country by country.

There are two primary reasons it will take longer for developing countries to be negatively impacted by automation and other AI technologies. As mentioned above, the likely implementations of AI technology will be targeted in specific areas that will reap positive outcomes. In addition, these countries will undergo an “adoption time lag”. Most of these low and middle income countries do not have the sufficient communication, energy, or infrastructure that is needed to create and sustain highly automated industries, the main killer of jobs. This does not even touch upon the possible regulations countries will establish that could limit the adoption of said technology.

Unfortunately, this grace period will only last so long. Similar to developed countries, developing states will still experience a significant level of automation, wherein high skilled workers are required and lower skilled workers are pushed out of their jobs. Most countries will go through a similar process as the United States. Job creation will be primarily concentrated in high skilled tasks, while many medium and low-skilled jobs will be lost to automation. This process will happen more slowly in most developing countries. What will put these countries in a worse position than countries like the United States is the current schematic of comparative advantages in the global economy. The projected levels of automation developed countries will inhibit could harm the labor-cost advantage that has become essential to the success and flourishing of developing countries job markets and economies. This comparative advantage has historically been the traditional route to development. A great assessment and breakdown of this phenomenon is given in a paper by Shahid Yusuf of CGD and George Washington University.



"The majority of emerging economies have embraced urban industrialization to promote economic growth. The most successful among the middle-income countries are ones that rapidly deepened manufacturing capabilities, were able to participate in global value chains, and steadily increased their penetration of overseas markets. Among the successful exceptions are exporters of minerals or other resource based products and small economies that have relied on the export of services, primarily tourism. The common thread running through the strategies of all fast developers is structural change that led to the emergence and growth of more productive, export oriented sectors producing manufactures or resource-based products or services, or a combination of the three. Complementing this structural change was rising demand for emerging economy exports from advanced economies. Rapid growth was undoubtedly enabled by sound policies and strategic vision, however the fast movers were also advantaged by lower factor costs, the capacity to assimilate technologies, and by investment in both productive assets and supporting infrastructures. Globalization in its several forms lent added impetus to development underpinned by trade and technology transfers."


Advances in artificial intelligence disrupts these approaches and mechanisms. It will keep developing countries stagnant in their economic growth. The price of labor will slowly become less relevant and the location where capital markets are developed will be the advantage that companies will want to exploit. AI favors capital and skill intensive work, decreasing global value chains among high income countries. The comparative advantage of developed states with regards to manufacturing and services will increase. Developing countries will lose their comparative advantage, face increased divergence in productivity in contrast to developed countries, have little to offer international markets, and experience severe job loss. In a sense, they are facing a perfect storm.

Automation will directly affect certain sectors. This includes manufacturing and call center work. The cruel twist is that jobs that developed countries found easy to outsource and provide to developing countries are also the easiest to automate using AI. Specific demographic groups will be disproportionately affected by these shifts in many countries. Young men looking for work in India are already having a hard time finding a job. Add in AI, and it is difficult to foresee how India will be able to meet the 250 million ADDITIONAL jobs it needs for these men by 2030.

Women in the Workplace

Women across the globe will impacted even more highly. Approximately 14% of women are engaged in full-time formal employment in regions with the highest levels of gender inequality (Middle East, North Africa, Sub-Saharan Africa) compared to 33% of men. While obvious that automation will affect men more, the increasing loss of job opportunities will only exacerbate inequality in these regions as men compete with women for fewer jobs. In addition, specific sectors of the job market facing the largest threats from automation also have the largest share of female employees. Wage inequality may be exacerbated based on the industries that AI technologies enhance. For instance, female-heavy sectors such as care will grow but will likely be low paid. At the same time, STEM jobs will increase in demand, have very high wages, and are mostly made up of men. According to the 2016 WEF Report on gender disparities in industry, if current ratios persist over the 2015–2020 period, nearly one new STEM job will be created for every four jobs lost for men, but only one new STEM job created for every 20 jobs lost for women. There is strong potential for the ‘gender pay gap’ to increase. However, women are not a homogenous group and all of these issues vary tremendously among them and country by country.

Native Industries?

Already a pertinent issue, there is a risk of a ‘brain drain’ of those talented in AI. For individuals working at startups and companies engaged in AI development in low and middle income countries, the higher wage and greater array of opportunities afforded them by high-income countries are strong pull factors, pushing many trained AI engineers to leave lower income countries for greener grass. In McKinsey's "Future of Work" 2017 report, more than 90% of people have moved from their country of origin voluntarily, seeking a better job. Approximately half of these people made the move from developing to developed countries. There is a loss of both needed talent and money, as these engineers are oftentimes trained using government expenses. This inevitably leads to concentrations of people skilled in AI in a small number of large corporations and specific (mostly high-income) countries. The implications of such a scenario are severe. If only several private corporations have most of the AI talent, research in the field will not be as diverse and will ignore centering on issues of social and national importance. Native companies, both involved and not focusing on AI, could be crowded out of the global and their respective domestic market. Aside from the obvious economic concerns this raises, solutions using AI to combat domestic issues will be lacking the developing-world perspective and result in solutions not attuned to the reality of the situation. Lastly, there is a well-grounded concern that middle and low income countries will suffer from exploitation and have the value they produce be extracted by high income countries.


The infrastructure and enabling environment for effective and positive uses of AI are not in place for many low and middle income countries. Access to an immense amount of high quality data is crucial for successful AI systems. Many of these countries do not allow for open data sources, making research difficult. Within sectors that could be tackled by AI technology, so much of the data is paper based that there is nothing useful to implement in an AI system. Sample sizes are insufficient, which is a severe limitation to the success of machine learning systems. As was mentioned earlier, Real problems arise when AI is trained in a way that does not reflect the circumstances and needs of the local population. The lack of local data will reinforce this issue. The primary reason there is some positivity surrounding applications of AI and local date in developing countries is that there is large mobile phone usage. Unfortunately, citizens of developing countries still struggle to connect to the internet from mobile phones, which makes this reason less impactful. A report by Oxford Martin School and Citi provide a good summation of this issue, finding the divergence in penetration rates of technology adoption can account for 82% of the increase in income gaps around the world. The development models that worked for East Asian countries and states with industrialized economies that have relied on the exports of manufactures and participation in global value chains are soon to be rendered irrelevant. The model of low skill, labor intensive industrialization is not sustainable for the developing world. It is possible some states can pursue paths of development through tourism, natural resources, or services. For the others, it is essential that these countries implement the infrastructure to harness AI technologies effectively and create a new viable development model.