In a recent blog I explored Mckinsey’s report on the AI impact for women in the workplace. As the hype around AI subsides, a clearer picture emerges. The “robots coming to replace humans” picture fades. Instead, the more realistic picture is one where AI automates distinct tasks, changing the nature of occupations rather than replacing them entirely. Failure to understand this important distinction will continue to fuel the misinformation on this topic.
A Novel Approach
In this blog, I want to highlight another source that paints this more nuanced picture. The MIT-IBM Watson released a paper last week entitled “The Future of Work: How New Technologies Are Transforming Tasks.” The paper was significant because of its innovative methodology. It is the first research to use NLP to extract and analyze information on tasks coming from 170 million online job postings from 2010-2017 in the US market. In doing so, it is able to detect changes not only in the volume but also in job descriptions themselves. This allows for a view on how aspects of the same job may change over time.
The research also sheds light on how these changes translate into dollars. By looking at compensation, the paper can analyze how job tasks are valued in the labor market and how this will impact workers for years to come. Hence, they can test whether changes are eroding or increasing income for workers.
With that said, this approach also carry some limitations. Because they look only at job postings, they have no visibility into jobs where the worker has stayed consistently for the period analyzed. It is also relying on proposed job descriptions which often time do not materialize in reality. A job posting represents a manager’s idea for the job at that time. Yet circumstances around the position can significantly change making the actual job look very different. With that said, some data is better than perfect data and this researches open new avenues of understanding into this complex phenomenon.
Good News: Change is Gradual
For the period analyzed, researches conclude that the shift in jobs has been gradual. Machine learning is not re-shaping jobs a neck-breaking speed as some may have believed. Instead, it is slowly replacing tasks within occupations over time. On average, the worker is asked to perform 3.7 less tasks in 2017 as compared to 2010. As the researchers dig further, they also found a correlation between suitability to machine learning and faster replacement. Tasks more suitable to machine learning do show a larger average of replacement, at around 4.3 tasks while those not suited for machine learning show 2.9 average replacement. In general, jobs are becoming leaner and machine learning is making the process go faster.
This is good news but not necessarily reassuring. As more industries adopt AI strategies the rate of task replacement should increase. There is little reason to believe what we saw in 2010-2017 will repeat itself in the next 10 years. What the data signal demonstrates is that the replacement of tasks has indeed started. What is not clear is how fast it will accelerate in the next years. The issue is not the change but the speed in which it happens. Fast change can be de-stabilizing for workers and it is something that requires monitoring.
Bad News: Job Inequality Increased
If the pace is gradual, its impact has been uneven. Mid-income jobs are the worst hit by task replacement. As machine learning automate tasks, top tier middle income jobs move to the top income bracket while jobs at the bottom of the middle income income move to the low income jobs. That is, occupations in the low tier of the middle become more accessible to workers with less education or technical training. At the top, machine learning replace simpler tasks and those jobs now require more specialized skills.
This movement is translating into changes in income. Middle jobs has seen an overall erosion in compensation while both high and low income jobs have experienced an increase in compensation. This polarizing trend is concerning and worthy of further study and action.
For now, the impact of AI in the job market is further exacerbating monetary value of different tasks. The aggregate effect is that jobs with more valued tasks will see increases while those with less value will either become more scarce or pay less. Business and government leaders must heed to these warnings as they spell future trouble for businesses and political unrest for societies.
What about workers? How can these findings help workers navigate the emerging changes in the workplace? That is the topic for my next blog