This brings up the same debates about wealth distribution that have existed for centuries, but in a new light, as the inequality scenario could be even more extreme. From Universal Basic Income (UBI) to taxation of robots, many ideas have been proposed to address these issues. The feasibility of UBI is uncertain and there are reasons to think it might generate other problems, such as mental health issues (Siau and Wang, 2019) or discouragement from seeking retraining and reemployment, as well as causing those still paying high taxes to flee the countries that implement UBI (Re-educating Rita, 2019). So policies to facilitate labor market flexibility and mobility, as well as education and retraining appear to be the most straightforward solutions (Clifford, 2018), assuming that new jobs or human-robot cooperative opportunities will be created. It has been theorized that 65% of children will have jobs that do not exist yet (Kasriel, 2018). This could imply the necessity of a whole new education structure for misplaced people being able to remain competitive in the job market through lifelong learning opportunities (Card et al., 2018; OECD, 2017). For this, the financial benefits reaped from automation should be used to fund continuous education. Therefore, an important challenge for policymakers and educational institutions is the identification of complementary skills for future work, emphasizing scientific and mathematical abilities, as well as soft skills such as communication, flexibility, creativity, and adaptability. 

But in the long run, there will be a need for broader redistribution policies. In Anthony Atkinson’s ‘Inequality, What can be done?’ (2015), the first proposal refers to the direction of technological change, calling for policymakers to encourage innovation which increases the employability of workers and human-centered provision of goods and services, while also guaranteeing public employment. Another proposal refers to the implementation of a capital endowment (minimum inheritance) to be paid to all citizens upon reaching adulthood, so that everyone is an owner of the current and potential technologies, and the technological dividends are distributed more evenly throughout the population.

For emerging economies, particularly those in Africa and Latin America and the Caribbean, the automation process makes it harder to achieve economic growth, so inequality between countries may increase too. As mentioned, LDCs (Least Developed Countries) offer cheaper labor which enables them to avoid job automation, but automation has significantly decreased the need for companies to outsource their production to LDCs (Re-educating Rita, 2019), which may make growth more difficult. What’s worse is that these countries may need to rely on the AI systems created in developed countries, which may not be the most adequate for the conditions in developing economies. Either way, given a decreasing pattern of RTC (Routine task content) in education, and as most literature in LDCs finds, there is no evidence for polarization in the labor market as pronounced as what is found in developed economies. Maloney and Molina (2016) suggest some possible reasons for this, including different initial occupational distributions, impact of off-shored jobs and the effect of new technologies in fostering sectors that create middle-skill jobs. However, more research is needed to understand these factors. For instance, in Latin America and the Caribbean, the automation process is more likely to affect the structure of employment, with unskilled and semi-skilled workers likely to bear a disproportionate share of the adjustment costs compared to skilled workers, making automation more of a threat to income equality than overall employment (Brambilla et al., 2021).

Humanity needs to be proactive, rather than reactive, in managing the consequences of technological advancement. But are decision-makers thinking strategically about the forces of disruption and innovation shaping our futures? It seems like they are too often trapped in traditional, linear thinking, or too absorbed by the multiple crises demanding their attention to fully prepare for this massive shift that will affect most of the world’s population in some way (Chuah et al., 2018). For the rest of us, as stated by Sam Harris, the first step would be to just start thinking about these issues, given that a technological explosion seems to be unavoidable.