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Automation Disruption

Many professionals claim that we should not worry about automation taking away jobs because it does the opposite. But is this really the case?

They “prove” this theory by relying on the past: technology disruptions can reduce prices, increase supply and demand, and therefore create jobs.

For example, the automated power loom was more efficient than traditional hand weaving so the prices of cloth went down. Thus, there was an increased demand for cloth thereby creating a larger market for it. This larger market meant that more workers needed to be hired. Voila, jobs increased!

Examples that are frequently cited include the automated power loom affecting weavers, the barcode affecting cashiers, and Computer Aided Design affecting draftsman.

However, there is no comprehensive data analysis of how historical examples of automation like the power loom compare to the future predictions for jobs affected by automation today. I believe if we were to compare the situational context in which automation took over in past jobs to how it may take over jobs today, we could more accurately understand how automation will affect job growth or decline.

I would propose at least two factors to provide context on automation disruption: convergence and coverage. Convergence is the speed in which the automation is spread and adopted. Coverage is the quantity of people it affects. A set of measures to quantify these two factors could be:

  • Number of years until this automated technology became the standard practice
  • Percent of people that did have this job displaced by automation out of all related jobs
  • Growth rate of jobs directly (and indirectly) related to the new technology before/during/after the adoption of the technology

As a new paradigm of automation disruption unfolds through data, machine learning, and AI, the data professional inside me wants to understand the trends and predictions through a quantiative lens!