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Now that more machine learning-based AI has been deployed in more places, human skills are being replaced in finer slices with new automation technologies. What has been observed in traditional blue collar work is that not all AI is good enough to increase the value of the output.
The traditional view of automation and labor is that automation increases the value of labor by increasing the productivity of a chain of tasks. Now that more machine learning-based AI has been deployed in more places, human skills are being replaced in finer slices with new automation technologies. What has been observed in traditional blue collar work is that not all AI is good enough to increase the value of the output.
According to Daron Acemoglu, an influential economist at the Massachusetts Institute of Technology, if the automation is “so-so” (just good enough to be adopted but not so much more productive than the labor being replaced), productivity will not increase, the cost of the product or service at the end of the chain will not decline (so no demand increase), and the incentive is then to remove more human labor. An example of this phenomenon in action is a supermarket self-checkout, arguably the ultimate example of “shitty automation.” Automation since the 1980s has not been especially “human-friendly;” Acemoglu estimates it explains somewhere between 50 and 70% of the variance of changes in wages between 1980 and 2016.
And this is all before AI, which has only accelerated in adoption since 2016. AI changes things again because it impacts human decision-making and judgment more profoundly. Many AI opportunities are in activities where humans are quite skilled (vision, conversation, pattern recognition) which means the impact of so-so automation—human-unfriendly AI—is potentially even greater.
The Artificiality Weekend Briefing: About AI, Not Written by AI
