How could AI impact developing economies?

While the internet has changed many things, the past year’s developments in AI suggests that accessing knowledge may have simply been a prelude. Once this knowledge has been ingested into machine learning models, it can be automatically synthesized.

What impact might AI have on developing economies, and on the world’s poor?

If AI is as important a breakthrough as mobile phones for these countries, then it could increase efficiencies across existing industries (Jensen 2007), alter political mobilization (Manacorda and Tesei 2020), and improve welfare by a decent share of GDP (Björkegren 2022). But if, as some are considering, it is as important as electricity or the printing press, then it could completely alter the structure of industry, politics, and production.

Developing countries have already seen widespread adoption of some early applications of machine learning. In addition to general applications that are common across societies (e.g., search engines, algorithmic ranking on social media), there are specific applications like digital credit scoring for people who are unbanked. Other applications are emerging, like digitally targeting aid during crises, or analyzing satellite images for policy. But those traditional ML systems are much more constrained. What we are facing is a new animal.

What can AI do and how might it affect these economies?

These technologies are evolving quickly, and their adoption across societies is likely to lead to complex dynamics. But here are some initial observations of what could be on the horizon.

Cheap, tailored expertise

Many of the poor have little access to experts, on topics ranging from physical and mental health, agriculture, and entrepreneurship. Hiring experts can be transformative: Bloom et al (2013) found that management consultants substantially improved production at textile factories in India. Many envisioned that the internet would level access to various forms of expertise, allowing entrepreneurs in Delhi and farmers in Western Kenya equal access to the world’s knowledge. But much of the poor’s knowledge is not written, and much of the world’s knowledge is not written for the poor. As anyone who owns a dusty textbook knows, raw information is not enough. Much of written knowledge is in English, some uses technical jargon, and has metaphors and references that make sense to people in Los Angeles but not in Lagos. AI has the potential to better synthesize local and global knowledge, and thus to unlock insights. The newest generation of AI chatbots can not only translate between languages, but also change reading levels, change jargon, and rewrite to use local customs and metaphors. These chatbots also allow you to have a conversation about a topic, allowing you to ask for clarification for specific parts, or specify that your needs differ from what the system had assumed. While these systems sometimes make mistakes, their quality, and ability to translate is improving quickly. And they will get better as local knowledge is increasingly digitized. They may allow almost zero cost access to the tailored expertise that would otherwise require hiring experts that would be prohibitively costly for the poor. This tailored expertise might improve business processes across developing economies for motivated people. Some startups are already developing targeted advisors for specific tasks, like choosing between schools (ConsiliumBots). Similar advisors could also help deliver medical advice to rural populations.

AI may alter pathways of development

AI has the potential to alter which industries lead to growth, and comparative advantage.

For one, there is a set of ‘high value’ industries that were seen as pathways to widespread good jobs which may no longer be, if automation replaces some outsourcing (Korinek and Stiglitz 2021). For example, software development is one job that is increasingly being transformed by AI: AI can write code to meet a specification, correct bugs, and describe code in everyday language. These impacts are likely to spread to other desk jobs, and may alter the returns to some jobs enabled by the internet (Hjort and Poulson 2019). Tasks for which a human can assess correctness of the end result are likely to be more affected.

At the same time, AI can provide some advantages to developing economies. AI models tend to have worse knowledge about lower income consumers, which will initially inhibit applications targeted towards the needs of the poor. However, these models have good knowledge about wealthy foreign consumers. This could help lower income countries to develop products for export. An entrepreneur in Lagos can query an AI to learn about the needs of consumers in Los Angeles: cheap market research. Additionally, strong grasp of ‘correct’ English may not be as important for engaging with the global labor market. This may diminish some of the returns to learning English, relative to other major languages. These systems can polish text to make it professional, which evens the advantage across clear thinkers who speak different languages.

A tutor for every child?

AI’s ability to reshape knowledge can also improve the quality of education. While there has been a dramatic increase in school attendance in developing countries over the past generation, quality has been a struggle, and many classrooms rely on rote learning. Having a good teacher can have a profound impact on learning and lifetime earnings (Chetty et al. 2011), but good teachers are hard to find. In developing countries, equipping relatively untrained teachers with standardized lesson plans and school procedures can lead to dramatic gains (one study saw students gain 3-3.5 years of learning relative to two years in status quo schools, in a randomized control trial; though that implementer has had ethical breaches). 

AI tutors have the potential to improve on standardization by personalizing learning. Many education technologies have underdelivered, but well implemented ones have already produced large gains in learning, such as one in India (Muralidharan, Singh, and Ganimian 2019). Given the right setting, personalized AI tutors could result in far more learning, could be engaging, and iteratively learn the pedagogy that leads to the highest gain. They have the potential to give every child access to a tutor with good pedagogy. That could revolutionize the next generation. It’s unlikely that these modes can be simply set into existing education systems, so one large question is how to develop the physical and institutional environment that will best empower learning. Current systems can act as good tutors, and may be sufficient for advanced and motivated students. But younger ages will need engagement layers and supervision. Coming AI systems will generate video and interactive experiences, which could engage students (in ways both educational and not). Tutoring systems can also help adults: because they can tailor knowledge, they make it easier for a person trained in one field to learn a new field: AI chatbots can tailor the explanations to the mental model they already have.

Simultaneously, the needs of education systems may shift as economic activity shifts. Such changes have occurred in the past: as cognitive skills became more remunerative than memorization, parents invested more in children’s critical thinking (Hermo, Päällysaho, Seim, and Shapiro 2022). This is one reason that IQ has been rising over the past generations (the Flynn effect). These new innovations may also shift the returns to different skills. It’s unclear what skills will be most needed. For example, with current language models, figuring out tricks in how to phrase your request (“prompt engineering”) can help you get much more useful information, for example asking how a particular smart person would answer a question (Cowen and Tabarrok 2023). But this is likely to be more a symptom of these models not yet meeting our needs; as models improve, this skill may become less necessary. However, these systems are likely to produce some output that is incorrect, a defect which is likely to endure in some form. So, an ability to critique output and think critically will likely continue to be valuable.

While many of the first integrations of AI are into systems that knowledge workers use (e.g., create slides from revenue numbers), in the medium term this could change organization structure. These systems may enable a few decisionmakers to directly surface the information needed to make the decision. This may make it possible to produce more output of higher quality in smaller ventures. In developing countries, relying on AI rather than humans may help get around some of the contracting frictions that inhibit the growth of midsize ventures. 

A challenge: AI knows less about poor consumers

Many of the poor in developing countries do not regularly access the internet, and are not well represented in the data used to train models. In particular, in Africa very few people have full computers, and smartphone adoption is still low among the poor (Milusheva, Björkegren, Viotti 2021). This has two implications. First, it will be difficult to access AI tools until smartphones are adopted. (Though, if AI tools are sufficiently valued, they could drive adoption.) Second, these tools will be less tailored to the needs of the poor.

Initially, AI tools will tend to make lower quality decisions for the poor. That will arise from two factors. First, the poor produce much less public content. Fewer smartphones mean fewer photos, and less writing about the topics important to the poor. This problem is more severe for minority languages. Additionally, much of the digital content that low income consumers create is posted to closed networks (like Facebook or WhatsApp groups) rather than public, annotated archives (like GitHub, Stack Overflow, or Wikipedia), which are the most useful for training machine learning models. Second, in the process of training an AI system, users provide feedback about whether a response is acceptable. So far, most of that training is being done by or for wealthy Western consumers, and as a result they will produce answers tailored to their preferences. People in other societies have different preferences (Falk et al. 2018), so some of these recommendations will fall short. For example, AI models tailored to the U.S. may recommend greeting consumers by first name in emails or encouraging customers to leave tips, techniques that may backfire in other settings. Yet, even untailored AI may be valuable for some tasks. And these weaknesses can improve. In particular, the presence of AI will increase the social returns to digitizing the knowledge of the poor.

What next

Over the past 20 years, developing societies have built out mobile phone networks and internet connections. This infrastructure already allows the fluid movement of information. It may increasingly also provide forms of cognition. This transition is likely to be disruptive—and could have a substantial impact on the world’s poor.

This post has benefited from feedback from a few people, who I thank without implicating: Jeff Berens, Mike Björkegren, Tyler Cowen, Avi Goldfarb, David McKenzie, Chris Neilson, and Paul Novosad. The cover image was generated by AI (Midjourney, with the prompt, ‘a manager of a high tech factory in Lagos looks over the factory floor holding a smartphone, in a vibrant cartoon style’).



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