With the widespread commercialization of artificial intelligence (AI) and the advent of general-purpose generative AI such as ChatGPT, there has been intense debate over the effect of such automation on labor markets . According to a McKinsey study, current generative AI and other technologies have the potential to automate 60-70% of employee time and, under a technology adoption scenario, half of work activities could be automated by 2045.
The growing ability of AI systems to automate tasks has given rise to numerous studies focused on the effects of AI on the labor market.
Professors Acemoglu and David Autor of the National Bureau of Economic Research (NBER) in the United States have written a number of research papers delving into the details of these automation technologies on capital-labor replacement, labor demand- labor, wages and productivity.
They explained the effect of automation on labor demand as an interaction between the displacement effect and the productivity effect.
The displacement effect reduces the demand for labor by removing workers from automated tasks and confining them to a reduced range of manual tasks, leading to downward pressure on wages.
On the other hand, the productivity effect counteracts the displacement effect by increasing the demand for labor in non-automated tasks, due to the increase in productivity in automated tasks.
Some believe that with a heavy emphasis on technology-enabled automation, we are losing sight of possible new tasks where labor could be employed productively.
If so, what might be the mobility pathways for the workforce in tasks replaced by automation technologies? How can these mobility pathways be activated to minimize the negative impact on employment and wages?
Our research also shows that as companies gradually automate work tasks, workers must compete in an increasingly narrow range of manual tasks.
The overpopulation of workers in these occupations puts downward pressure on wages. Examples of IT services industry jobs with tasks that are easy to automate are software testing and infrastructure management.
In contrast, workers associated with occupations with tasks that are difficult to automate are experiencing upward wage growth, driven by increased demand. Examples of such tasks are IT consulting and software design. These sectors are characterized by high labor productivity relative to capital.
Labor mobility
These effects of deterioration of wage inequalities could be mitigated if displaced workers moved from the first category to the second through professional retraining. Such a mobility pathway structure can facilitate an effective adjustment process.
According to a recent IMF study, in an economy based on automation, investment in education produces the highest welfare gains, such as wage growth and income distribution, compared to d other policy responses such as taxation and infrastructure investments.
An important aspect of finding mobility pathways for displaced workers is upskilling and reskilling. Today’s professionals must constantly be on the lookout for continuing education opportunities to upskill and reskill.
First, start-ups in the edtech market today are enabling such upskilling by offering short-term certificate programs in emerging areas of technology and business. By forging strong alliances and partnerships with Higher Education Institutions (HEIs), edtech platforms such as upGrad, Simplilearn and Great Learning have enabled working professionals to benefit from such upskilling opportunities and adopt career paths. adapted mobility.
Secondly, the National Skill Development Corporation (NSDC), which aims to improve the skill levels of the workforce in line with international standards, also plays a vital role in shaping the mobility pathways of individuals.
With the initial focus on improving skill levels through professional institutes, the NSDC is also exploring ways to further build skills in emerging technology areas such as data science, AI and software development, so as India seeks to become a $1 trillion digital economy by 2025.
Training for the future
Third, the country’s technology and business institutes must train the workforce for the future. Although technologies such as AI and machine learning are evolving rapidly, they sometimes lack veracity and warrant human intervention. Since these technologies affect all humans and society as a whole, they must be built with considerations such as minimizing bias and discrimination in decision-making, proving counterintuitive propositions and assistance, but without taking control of human autonomy, to name a few.
The first steps in this direction were taken in particular by the European Commission by promulgating “Harmonized Rules on Artificial Intelligence (the ACI 2023 law)”. The law strives to balance the socio-economic benefits of AI and possible new risks and negative consequences for individuals or society.
Given the rapid pace of technological change and the challenges associated with it, higher education institutions must prepare tomorrow’s engineers and business leaders through the required multidisciplinary approach. Higher education institutions can lead the way in creating a versatile workforce in the fields of engineering, technology, business, social sciences and management sciences.
Automation technologies using industrial robots and AI are incentivizing businesses and governments to create career mobility pathways for the displaced workforce through education and skills, in order to have a balancing effect on the labor market.
Sridhar is a professor at IIIT-Bangalore and Upreti is an associate professor at PES University. Opinions are personal
