With the arrival of ChatGPT, economic prognosticators across the country are talking about what AI could do for the economy.
A recent poll by the University of Chicago’s IGM forum (the inspiration behind Scioto Analysis’s Ohio Economic Experts Panel) found only 2% of panelists disagreeing with the statement that artificial intelligence will have a big impact on incomes over the next few decades.
One large impact of artificial intelligence on the U.S. economy could be to transform middle-class jobs. Artificial intelligence could make jobs easier or even eliminate the need for certain jobs by fulfilling the key functions of these jobs. Many of these jobs are those currently in the middle class.
For our purposes here, I will define “middle class jobs” as jobs among the most common in the 20th to 80th income percentiles. I picked these from a great NPR analysis from 2014 that presents American Community Survey data on the most common job categories per income percentile.
Primary School Teachers
Primary school teacher is arguably the most common middle- and upper-middle-class job, making up the most common job category in the 50th to 60th and 60th to 70th income percentiles and coming just behind managers in the 70th to 80th income percentiles.
A 2020 study by McKinsey estimated that the average K-12 teacher spends 22 hours a week on preparation, evaluation and feedback, and administration, 10.5 of which could be reallocated through AI and new technologies. Student instruction and engagement, coaching and advisement, and behavioral-, social-, and emotional-skill development, takes an average of 24.5 hours, only 2 of which can be reallocated. This suggests that AI could free up time teachers spend on preparation, evaluation, and administration in favor of more face time with students.
Seeing as this study also found that 70% of teachers identify lack of time as a primary barrier to personalizing learning, this could also help teachers tailor learning more toward students.
Managers
Managers are the top employment category for the 70th to 80th percentile of incomes and right behind primary school teachers for the 60th to 70th percentile.
Managers are often the people who will have to make decisions about when artificial intelligence is employed or not. According to a Wharton School blog, AI could be relevant to managers who spend a lot of time on administrative tasks, screening resumes of job candidates, customer service, marketing, and merchandising.
Truck Drivers
Truck drivers might be the jobs that are most threatened by artificial intelligence technology. Trucking is a top 3 profession in every income decile from the 20th to the 70th percentile in the United States.
Autonomous trucking could functionally eliminate the need for millions of trucking jobs across the United States. Goldman Sachs projects that in 20 years, the United States will lose 300,000 trucking jobs a year to automation. Given that the average trucker in the United States is 47 years old, it is probably not a good time for young people to get involved in the trucking industry.
Secretaries
Secretaries are the cornerstone lower-middle-class employment category in the United States. They are the most common employment for jobs at the 30th to 40th and 40th to 50th percentiles of income and are only behind nursing aides for the 20th to 30th percentile of income.
While a lot of the duties of secretaries (scheduling, managing records) are being automated, there are other roles that could be harder to automate. As long as managers and professionals need someone with a human touch to assist them, secretaries will be in demand.
What I take away from this list is that the idea that AI will take away middle class jobs is a bit simplistic. While trucking is almost sure to suffer as an area of employment, I don’t see primary school teachers, managers, or secretaries being quickly automated away. If anything, their jobs will get easier and free them up from tedium in order to carry out more mission-critical work. I also wonder which jobs could arise from AI: will AI open opportunities for people to do things they weren’t able to do before?
It seems that the best way to be realistic, looking at the data available, may be to be optimistic.