Future of jobs predictions refer to the likely loss of current jobs and the availability of jobs in the future. There have been two significant types of future of jobs predictions. The first one: the future will likely be the past—linear extrapolation or repetition at a scale. Like before, technology creates high-paying white-collar jobs and removes manual production jobs. The next one is about massive job loss predictions. Technology like artificial intelligence (AI), robotics, and automation will render mass-scale job loss.
In retrospect, these two schools of thought appear grossly wrong. For example, unlike in the past, in the middle layer of corporations, we have witnessed a relatively high rate of loss of jobs. These are white-collar jobs requiring Codified knowledge and skills delivered through formal education.
Contrary to predictions, people with little or no education doing manual jobs on the factory floors face very little job loss. On the other hand, massive job loss due to AI is yet to happen. Such high-level contradictions in the future of job forecasts create future career prediction complexity, resulting in a decision Dilemma. Notably, students suffer from career life prediction. They keep wondering about the forecast of future professions. Hence, it makes us intrigued about the underlying cause of gross deviations.
Review of future of jobs predictions
A review of more than 20 future job predictions indicates that the global economy will likely lose hundreds of millions of positions due to technology. According to CNBC reporting, nearly 25% of jobs in 2023 are set to be disrupted by 2028. Goldman Sachs predicts that fast-growing technologies like AI may kill or degrade as many as 300 million jobs. Some other predictions claim that during 2023-2033, AI could take the jobs of as many as one billion people globally. Consequentially, this process may make 375 million jobs obsolete. Predictions in 2023 claim that by 2030, AI will likely create 97 million jobs.
Along with improving productivity and contributing up to $15 trillion to global GDP by 2030, automation will likely displace between 400 million and 800 million individuals in jobs. As a result, as many as 375 million people may need to switch occupational categories.
These and many other future job loss predictions are scary indeed. But is the reality telling the same job loss scenario? Besides, what is the underlying reason for Job loss? How do technologies like AI, Robotics, and automation always take jobs from humans? Do they just show up and take away jobs?
Future of Jobs reports admit contradiction
In 2020, according to the World Economic Forum’s (WEF) Future of Jobs Report 2020, humans performed 67 percent of jobs, leaving only 33 percent to machines. Based on the survey opinion, the report concluded that by 2025, humans and machines will have equal shares of jobs. However, WEF’s Future of Jobs Report 2023 finds that only machine has taken over an additional 1% of jobs over three years, leaving 66% to humans. Hence, such a minor shift does not indicate that machine’s shares of jobs will rise from 34% in 2023 to 50% in 2025. Besides, new jobs have been created for humans for advancing machines. Hence, the future of Jobs Report 2020 suffers from a significant contradiction in future job prediction. Consequentially, it has caused confusion about future career planning, education, and training.
The future of jobs report of WEF and similar reports predicted the complete elimination of millions of jobs due to artificial intelligence. However, there has been no indication of mass disruption in the job market due to AI. Besides, the job loss prediction of the Oxford Martin Programme on the Impacts of Future Technology has also been found to be grossly incorrect.
Consequence of gross contradictions on future career predictions
Due to gross job loss prediction errors, recent reports have been downgrading the number. But that does not help future job seekers. Instead, it has been intensifying confusion. Will those predictions go up once job loss starts growing? Hence, future career prediction and planning has become a big issue for students, job seekers, and development professionals.
For example, in the middle of the 2010s, there was wide prediction that autonomous vehicles would experience mass-scale deployment by 2020. As a result, professional driving jobs will be lost. Hence, American and European youths shied away from pursuing professional driving careers. But the reality has been quite the opposite. By 2023, there has been no sign of autonomous trucks populating the highways. Hence, there has been a truck driver shortage.
Similarly, predictions were made about the massive loss of jobs requiring physical and manual labor. Notably, reports produced by ILO and many other organizations rang alarm bells. They predicted that the labor-intensive export-oriented manufacturing sector of Bangladesh, Vietnam, and many other countries would suffer from massive job loss due to automation. However, the reality significantly differs from those predictions. Consequentially, in WEF’s Future of Jobs Report 2023, it has been opined that physical and manual labor is highly immune to automation. Instead, automation finds white-collar jobs relatively easy to kill. But such a reality has raised questions about the investment that less developed countries have made in education and training to prepare their workforce for the future.
Prediction methods about the future of jobs and their efficacy
The predominant prediction method has been an opinion survey. Respondents predict the future of jobs by extrapolating their experience and recently acquired perception. Hence, they suffer from structural flaws. First, extrapolation of the past runs the risk of deviation if technology does not grow linearly. Due to the S-curve-like life cycle, the implications of technology on jobs also experience a similar pattern. Besides, upon showing early demonstration, technology risks suffering from premature saturation, resulting in gross error in the extrapolation of perception gathered through initial demonstration.
For example, during 2010—2015, the rise of the demonstration of autonomous vehicles gave the impression that by 2020, millions of professional driving jobs would be lost. However, before crossing the threshold level, autonomous driving technology has suffered from premature saturation—getting caught in the chasm. As a result, the future of job loss in the automobile industry has suffered from gross contradictions.
Furthermore, the complexities of automating all kinds of human abilities are different. Contrary to common belief, automating manual jobs is far more challenging than automating knowledge-intensive jobs. However, before 1950, the scenario was the opposite. Hence, extrapolating the belief gained from the experience has been a primary source of contradiction in the future of jobs predictions.
Remedy of improving the accuracy of future job predictions
The future of jobs is a big concern. Technology has been a driving force in transforming jobs. It has been creating, killing, and redefining jobs. As a result, the human-machine frontier has been continuously getting refined. For sure, AI, Robotics, and Automation have been a significant driver in changing human-machine boundaries. Hence, we have accepted. The challenge has been in predicting the future unfolding job scenario so that we can prepare accordingly. Notably, for students and development planners, having reasonably good accuracy in predicting the future of jobs is highly important. However, despite paramount importance, opinion-based surveys have been found to be highly erroneous in predicting the future of employment. In the previous section, the underlying cause has been explained.
We need to focus on underlying technology uncertainty to overcome gross future job loss prediction errors. Besides, we need to perform task content analysis in figuring out the roles of human capabilities like innate, codified, and tacit and their relative complexities of automation. Such exercise will offer more significant insights leading to errors in the future of jobs prediction.