Will tech wave drown emerging countries?

Even if AI and other innovations seem likely to fuel unemployment and deepen income inequality, no country can simply reject them outright. Instead, policymakers must navigate the complexities of various technologies’ “appropriateness” to their own econom

We are living through humanity’s fourth industrial revolution, which is largely driven by breakthroughs in digital technologies. Some, like the internet and artificial intelligence, are converging and amplifying each other, with far-reaching consequences for economies and societies. For developing countries, the implications are profound, and questions concerning policy choices and the “appropriateness” of new technologies have become urgent.

Even if new technologies seem likely to fuel unemployment and deepen income inequality, no country can simply reject them outright. Instead, policymakers must understand the multifaceted and complex nature of a technology’s appropriateness (or inappropriateness) for development, and then pursue nuanced responses that aim to maximise the benefits and minimise the harms.

In development economics, an appropriate technology is defined as one tailored to fit the psychosocial and biophysical context prevailing in a particular location and period. Such tools are designed with a view to the environmental, ethical, cultural, social, political, and economic aspects of the communities for which they are intended. A technology’s appropriateness for development thus can manifest across many dimensions.

For example, compared to technologies from Europe and the United States, those from China and India tend to be more appropriate for the conditions prevailing in the least-developed countries. Technologies suited to Sub-Saharan Africa, for example, include hand pumps, pharmaceuticals, mobile phones, and solar energy. By contrast, automation technologies designed to address the needs of Japan’s aging society would not be appropriate for low-income countries with massive youth populations in need of work.

The current wave of emerging digital technologies can be grouped into three categories: efficiency-enhancing ones such as AI and robots; connectivity-enhancing ones such as internet-connected devices (from mobile phones to the Internet of Things), digital platforms, and virtual reality; and infrastructural ones such as 5G, cloud computing, and big data.

Let’s focus on the efficiency- and connectivity-enhancing categories, which are the applied technologies directly used by organisations and individual users. My own analysis of their appropriateness reveals a complex picture across many dimensions, including the economic, the technical, the social, the environmental, the ethical, and the cultural. For example, in the cultural dimension, a technology’s appropriateness may hinge on a given society’s expectations of individual privacy. These can differ widely: the expectation of privacy online is significantly lower in China in comparison to that in the European Union (with its “right to be forgotten” law).

While efficiency-enhancing technologies promise increased productivity by reducing labor costs in production, we know that widespread adoption of industrial robots and AI will create serious social and economic challenges in terms of employment and income inequality. Though we have yet to see job replacement at scale, the potential is certainly there. Moreover, developed countries’ reshoring of capabilities newly amenable to roboticisation threatens to close the window of opportunity for less-developed countries to pursue industrialisation through manufacturing. AI and industrial robots also require substantial data-storage capacity, processing power, and analytical capabilities – a high entry threshold that will prevent developing countries from adopting them quickly and catching up. And the large-scale deployment of AI will introduce many ethical challenges as well, meaning that the clock is ticking for policymakers to establish safeguards and other measures to minimise harm.

As for connectivity-enhancing technologies, the economic benefits come in the form of lower access costs and enhanced economies of scale. By lowering entry barriers, these technologies can help to include marginalised communities in value creation, as well as improve access to financial and educational resources and information, and health and other public services.

From the supply side, connectivity-enhancing technologies can create opportunities for widespread adoption by workers and consumers. Easier and more timely access to information can lead to entirely new models of value creation. While these tools require digital infrastructure and basic digital skills, the threshold is lower than it is for AI and big data.

Moreover, innovations like mobile internet give developing countries the opportunity to leapfrog past traditional cabled communication technologies that were unavailable or too expensive and technically difficult to scale up. But, of course, these technologies also raise ethical challenges when it comes to cyber security, social stability, privacy, public trust, and so forth.

New technologies always facilitate new ways of working and consuming. But to map future trends in manufacturing, we should look to where the different categories of digital technologies interact and reinforce one another. How they diffuse and are adopted will define the next phase of technology-enabled productivity.

Two scenarios stand out. First, AI and industrial robots may soon become widely viable. If so, there will be more, and faster, reshoring of manufacturing to industrialised countries, as well as increased concentration of manufacturing in fewer large manufacturing hubs and countries. Manufacturing will remain an important driver of income growth and industrialisation, but it will no longer be the primary engine of job creation. Income inequalities between countries will widen.

Second, few commentators have yet to grapple with the transformative and disruptive potential of 3D printing, which could replace the mass-production model of manufacturing. This technology – which is significantly enhanced by AI – has come a long way, and is now poised to replace the traditional assembly line with more decentralised and bespoke production systems located closer to the consumer. If current trends continue, we could see a dramatic compression of the global value chain into one machine.

Fu is Professor of Tech at University of Oxford Project Syndicate