Over the last decades, technology has increased at a pace never experienced ever before and with the introduction and development of Artificial Intelligence (AI), the rhythm is moving even faster. Although technology has undoubtedly transformed the education and job's sectors (something especially visible during this pandemic, since many students and employees are able to continue studying and working from their homes) still, many people worldwide fear that AI is going to replace humans in the job market.
One could falsely assume that these fears come from older generations and not by the so called 'digital natives', but a survey conducted in 2019 reached the astonishing result that 37% of American workers between the ages of 18 and 24 are worried about new technology eliminating their jobs (Douglas, 2019). But the question still remains, should we be afraid of AI and it's impacts on the job market?
A key aspect to mention in this discussion is the fact that this isn't the first time digital operating models have threatened the 'old norms' in businesses. A clear example is the impact the invention of photography had on the demand for paintings, and consequently, the rise of digital photography. Although the degree of economic repercussions differed in both cases, it is fair to say that they also brought new opportunities, since this technology would eventually lead to the creation of global firms with new business models such as Facebook, Snapchat or TikTok (Iansiti and Lakhani, 2020). These companies employ thousands of people worldwide and also affect indirectly in the growth of many other businesses that use their platforms. With the rise of AI over the past few years, should this time be any different?
This topic still generates debate to this date, due to the variety of opposing stances in one corner and the other. A research involving 1500 companies that use AI actively, reached the conclusion that firms achieve significant performance improvements when machines and humans work together. Hence, they state that human-machine collaboration is beneficial for both parties, since humans can train, explain and sustain machines while in turn, these can amplify, interact and embody human skills to expand our own capabilities. (Wilson and Daugherty, 2018)
Nonetheless, although human-machine collaboration seems like a viable way to move forward, much of the fear of replacement people have nowadays, revolves around autonomation processes, where human interaction won't be needed at all. This fear has been named as "automation anxiety". Until now, automation had been mainly used in the realm of predictable or routine, repetitive tasks, but with the rise of AI and machine learning, automation has now made its way into high-productivity jobs, which used to be in the domain of humans; clear examples being self-driving cars and diagnosing diseases. Nowadays, less than 5% of occupations are entirely automated, but 60% of occupations could at least automate half of their current tasks, which implies the potential automation has in the near future. (Kletzer, 2018) Therefore, should we worry? The classic economic idea of comparative advantage seems to disagree. Even if robots have an advantage over humans in everything (being more efficient than we are), they would only be involved in jobs where they possess the greatest relative productivity advantage (Kletzer, 2018). This means for example, if robots can build automobiles 20 times faster per day than we do, but they build planes only twice as fast, it would be efficient for them to focus 100% on building automobiles while leaving the task of building planes for humans.
But before humans can effectively work alongside machines or new job opportunities by the advances of AI can be seized, some challenges need to be overcome first. In this article author's opinion, one of the most relevant involves the 'methodical overload problem'. This issue occurs when learners are expected to master both the new and old methods of some particular job. If time isn't provided alongside the learning process, the most highly outcome is that learners don't end up mastering neither of the methods. Furthermore, the use of AI in the workplace is impacting negatively in aspects such as shadow learning1; a direct example being that trainees' involvement in the risky or complex portions of their work is being reduced due to the introduction of intelligent machines, ultimately diminishing the firm's future capability. Even so, trainees are actively engaging in ways to adapt and prevail over, but the firms' redesign should occur in conjunction with this. (Beane, 2019)
Overall, it is crucial to consider that AI is likely going to impact the job market in diverse ways. Even so, this isn't a "black or white situation", with clear winners and losers. With AI being actively involved in a larger number of tasks that used to be executed by humans, new opportunities will arise, either creating or improving these "machines", working side by side with them or even keeping old jobs in which, these aren't so efficient. However, challenges will also emerge alongside these opportunities, so the way people manage themselves to overcome them, will also play a key role in determining the future of the job market.