Automation Is a Myth

In this short and engaging book, Luke Munn challenges the view in the automation literature that the technological displacement of human labor is a universal, inevitable phenomenon. Recent improvements in artificial intelligence and robotics have induced labor economists to produce forecasts about future employment effects of automation and infer the power of new labor-displacing technologies from the logic of Moore’s law. Moore’s law states that the number of transistors on microchips would double every year. Presumably, the compounding effects of improving algorithms would allow for the ever-quicker replacement of workers, slowly at the beginning but much faster in later periods. Munn argues that these economists’ predictions of fast and universal automation are just as wrong as the prediction by left-wing thinkers about a ‘‘postcapitalist world without work’’ and ‘‘fully automated luxury communism’’ (p. 15). The argument of Automation Is a Myth is that automation is a limited, localized, and socially specific phenomenon, and it echoes Gray and Suri’s (2019) point that automation discourse neglects ‘‘ghost workers’’—that is, the many invisible workers who are needed to train the algorithm or to fix the kinks and flaws in technology deployment (p. 30). Quoting Tesla founder Elon Musk’s tweet ‘‘humans are underrated’’ (p. 17), he contends that the main problem plaguing the modern workplace is not too much automation, but insufficient automation, given its imperfections. Tesla was incapable of fixing the inconsistencies in assembly tasks that require human judgment, which resulted in less productivity. Amazon warehouses are filled with shelf-moving robots, which may have reduced the amount of walking among warehouse workers but has also increased physical injuries based on monotonous but fast-paced body movements (p. 94). Furthermore, the more robots Amazon is introducing, the more reliant they are on skilled workers who can fix and maintain the robots. For technological systems to function, workers must internalize the logic of the system and perform their activities such that the algorithms recognize them (p. 25). Rather than producing a world without work, the new work in algorithmically controlled environments could be low quality: social media content moderators must identify violent or pornographic content that the algorithms cannot detect on their own, resulting in psychological trauma (p. 38). Munn also argues that technologies are adopted in a cultural context, noting that East Asian cultures are more likely to trust automation than Middle Easterners (p. 56). However, the dark side in the Chinese context is the use of surveillance technology to control daily lives and forced labor in cotton picking among the Uyghur Muslims, a repressed minority group that the Chinese government forces to assimilate to Han majority culture. In line with the main thesis, the surveillance technology, focused on facial recognition algorithms and cameras, is not fully automated but requires heavy police, lab work, and neighborhood watch presence (p. 69). Cotton picking is very labor intensive despite commercials about smart cotton harvesters (p. 74). Munn also argues that automation has a racial and gender dimension: American warehouse workers tend to be poor and black, which exposes them to the indignity of stressful work while it exists and precarity if parts of the work became automated. Women tend to do housework, which has historically been uncompensated, and their work is unlikely to be automated because men, who dominate high-tech sectors, focus their innovation on paid work for which there is a market. Female coders are unfortunately pushed out of the field due to sexual harassment and bullying in the industry. In the book’s conclusion, Munn advocates for desirable, non-alienating forms of automation that are also ecologically desirable (p. 127). He lists initiatives like Data for Black Lives and Maori Data Sovereignty Network as examples of social organizations that focus Reviews 359