PredICTing the Future

A critical analysis of what is and what out to be skilled work, labor, and automated assemblages extending human capabilities

“A small sliver of humanity is currently materializing their imagination in our digital structures, and the rest of us have to live inside their imagination as our reality.” ~ Ruha Benjamin (2021)


Technological visions of the future generally come in one of two flavors. In a utopian dream, technology seamlessly integrates into the fabric of everyday life. On the other end of the spectrum lie visions of dystopia, often centered around the havoc a sentient artificial intelligence can cause when it inevitably determines that humans are our most significant threat. This essay attempts to illuminate a bridge between what is and what ought to be through a critical analysis of automation and technological innovation. We trace efforts to deskill labor, from early mechanization through current efforts to design a “future-proof” smart city. To do this, we examine automation through Haraway’s cyborg lens, the postmodernist assemblage of contradictory components. Who is benefitting from automation? Who is harmed by it? In following with the theme of our essay, we also follow up by asking, who ought to? To explore this question, we review efforts to build economic infrastructure from the bottom-up in a process that emphasizes upskilling rather than deskilling labor.

Sex, Drugs, and Cyborgs

Before Haraway’s famous essay, an exciting vision for human-computer symbiosis was proposed by JCR Licklider, saying, “Men will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations. Computing machines will do the routinizable work that must be done to prepare the way for insights and decisions in technical and scientific thinking” (Roy, 2004). That same year, Kline and Clynes presented a similar vision at a military conference on space medicine (Kline & Clynes, 1961). The cyborg offers a path through which cybernetics could provide an organizational system. Where issues best left to computers and robots are taken care of automatically and unconsciously, leaving the human free to think, feel, and explore. Initially, the term cyborg meant “an exogenously extended organizational complex functioning as an integrated homeostatic system unconsciously” (Clynes & Kline, 1960, p. 27).

Haraway’s (1991) postmodern reinterpretation defines the cyborg as “a cybernetic organism, a hybrid of machine and organism, a creature of social reality as well as a creature of fiction.” For Haraway, the cyborg is an apt metaphor because it has no real origin story in Western civilization. And yet, a man in space is the ultimate expression of white male transcendence of nature. It is at this point where the boundaries between the two begin to break down. Our notions of what separates humans from animals are frayed. Technologies become more ubiquitous and embedded in our everyday lives so that we start to lose a sense of exactly where we end and our machines begin.

Our language imprisons us, shackling us to the past and limiting our ability to communicate beyond the dualisms of human/animal, human-animal/machine, and the physical/non-physical. Moreover, though these boundaries are blurring, the language we use to label and classify each other remains the same, vestiges of eroding patriarchal imaginations. Haraway’s essay serves as a wake-up call to recognize and break the shackles of tradition that our language has laid upon us.

It is with this lens that we look to the past. Before the language of the cyborg was spoken. Before man transcended Earth, in the early days of industrial mechanization, human labor supported and extended the work of machines. Is it still this way today? If so, could it be that Licklider’s vision simply has yet to be fulfilled?

Automation’s last mile

Gray & Suri (2019) explore the history of the human labor required to extend the capabilities of the very machines engineered to replace human labor. The authors refer to this gap as automation’s last mile. Gray and Suri draw on this concept to expose the history of piecework, the labor which could not fit into mechanical processes. Through piecework, factory owners were able to draw from cheap labor pools, such as newly freed Blacks, European immigrants, as well as women and children on both the literal and figurative fringes of society. Exploiting these labor sources offer elites, namely the makers of the machines and those who can afford to buy them, an opportunity for rapid economic growth driven by technological innovation in what became known as the Gilded Age. Today parallels between the information and industrial age signal a new Gilded Age (Wheeler, 2018). Job seekers are increasingly being pushed into lower-wage, precarious work (Dillahunt et al., 2021), as jobs have trended towards deskilling human labor through technological innovation (Eglash et al., 2020).

“Each moment of technological innovation that is highlighted shows how political leaders, economic power brokers, labor advocates, and the social norms of the day reproduced divisions between skilled professional work (meaning what is beyond the capacity of machines) and unskilled work (meaning contingent labor headed for automation).” (Gray & Suri, 2019, p. 39)

According to Gray and Suri, both Marx and Smith could see how machines deskilled human labor. However, whereas Marx saw automation as dehumanizing workers, Smith maintained a utopian vision like that of Licklider, that through automation, humans would come to better know and understand ourselves (Gray & Suri, 2019, p. 58). Through the cyborg lens, we see early piecework as a kind of exogenously extended organizational complex as a human-machine hybrid of the order of Kline and Clynes’ cyborg, but in reverse. In this case, the human pieceworker serves as the exogenous extension to the machines on the factory floor.

Similarly, Noble (1978, p. 345) quotes a 1971 article about wage incentives appearing in the Manufacturing and Engineering Management Journal, describing automation as prioritizing the machine while the worker’s role diminishes. However, there is a paradox here because while the machine’s capabilities serve to “deskill” the machine operators, the operators themselves are crucial to optimizing the machine’s output, which continues to pose a problem for management (Noble, 1978).

Automation’s last mile paved with ‘bullshit.’

Anthropologist David Graeber opens his original essay On the Phenomenon of Bullshit Jobs: A Work Rant, with a utopian vision offered by John Maynard Keynes in 1930, that by the dawn of the 21st-century technology would be advanced enough in the United Kingdom and the United States to allow for a 15-hour workweek (Graeber, 2013). By 1935, with the passage of the Wagner Act, the United States began to manifest a labor culture that values and prioritizes full-time employment, while corporate culture began to see full-time employees as a liability (Gray & Suri, 2019). Per Noble (1978, p. 346), a machine tool operator succinctly summarized automation as meaning, “our skills are being downgraded and instead of having the prospect of moving up to a more interesting job we now have the prospect of either unemployment or a dead-end job.” Haraway notes, “deskilling in an old strategy newly applicable to formerly privileged workers” (Haraway, 1991, p. 39).

For Haraway, there was more to automation and the growing cottage industry (the phrase she uses to discuss piecework) than large-scale deskilling. It was also an indication of a new level of the market, home, and factory integration. This integration is made possible by, rather than caused by technological innovation. So, piecework is about command and control as much as, if not more than economic efficiency through automation. In his famous essay, Winner (1980) presents the case of Cyrus McCormick, a factory owner who used machines operated by unskilled workers in the 1880s to manufacture an inferior product at a higher cost for the expressed purpose of union-busting. McCormick’s case demonstrates how control can take precedence over economic efficiency.

However, let us be clear about who controls and who is controlled because this is a critical component of automation—protecting the status quo for white men. Take, for example, the ad from a 1957 Mechanix Illustrated (see Appendix A). In a recent presentation on The New Jim Code for the Anti-Eugenics Project, Benjamin (2021) describes how the Civil Rights Movement began in 1954 and that by 1957 white men were seeking to automate their service staff. Implicit in the message is that the “you” they are referring to is a white man who used to own slaves, even if only through lineage with other white men, and “you” will again (Benjamin, 2021). Only this time, according to the ad, no one is going to take your slaves away from you.

Graeber describes the myth of neoliberal rhetoric in prioritizing economic efficiency over any other values. He contrasts this with the reality that the very free-market policies intended to unleash the marketplace have slowed economic growth as well as science and technological innovation (Graeber, 2018, p. 12). He notes that younger generations practically everywhere except India and China can expect to be less prosperous for the first time in centuries than their parents. Data from the Urban Institute supports this, indicating that the average net worth for adults in the United States between 20-28 increased an average of only $1700 between 1983 and 2010 (Kalish, 2016). Even as meaningful work is automated away, we privileged folk appear to be working more than ever. Why?

According to Graeber (2018, p. 111), governments have crafted economic policy on the premise of full employment, offering that in the Soviet Union, the joke was, “We pretend to work; they pretend to pay us.” In capitalist nations like the United Kingdom and the United States, Graeber documents the rise of the service economy, or more specifically, information work. Elsewhere studies have shown that the number of information workers increased from 37% in 1950 to 59% in 2000 (Wolff, 2006). Wolff similarly finds this growth driven by the substitution of information workers for goods rather than a shift in demand for information-intensive goods and services. Between 1950 and 2000, this growth may correlate with investment in computing technology and computer operators in the FIRE sector (finance, insurance, real estate). Nevertheless, as tech companies in Silicon Valley learned how to monetize their products with ad targeting, user data has become the “new oil,” leading to what some describe as the coding elite, or those who can harness technology to exploit users through their data (Burrell & Fourcade, 2020; Van’t Spijker, 2014).


As mentioned earlier, Haraway saw the proliferation of the cottage industry as deepened integration between the factory, market, and home. Similarly, McCord and Becker do not mince words when they say information communication technology (ICT) has become a foundation of dominating cultures and economies (McCord & Becker, 2019). The declared beneficiaries of the Sidewalk Toronto project include current and prospective residents of Toronto from all income levels and walks of life; in reality, the goals of the project come from its most powerful stakeholders: Sidewalk Labs and Waterfront Toronto. These stakeholders seek to organize a “dense cluster of skilled labor” for employer access. The beneficiaries are subject to the imagination of these stakeholders.

In the case of a smart city, who owns and controls the technological infrastructure, who is responsible for data storage, and who gets to decide how it is used and by whom? According to McCord & Becker (2019), much of the community involved in smart city sustainability research has focused on technological solutions. Researchers and policymakers attempt to explain sustainability either through the lens of social or technological determinism. The social determinists suggest humans have agency over their impact and just need better tools to become more sustainable. On the other hand, technological determinists see sustainability as primarily driven by access to certain technologies or information.

McCord and Becker offer a framework for sustainability projects such as Sidewalk Toronto through Critical Systems Heuristics. Their goal is to provide a means of seeing beyond the narrow viewpoint of stakeholder needs, which tends to view human activity through the reductionist myth of Homo economicus (Fleming, 2017). Suppose this kind of thinking shapes design decisions for smart cities, with capitalism being the foundation upon which we leverage humanity’s purported greedy nature for the benefit of all. In that case, we might see such smart cities optimizing for the tragedy of the commons (Ostrom, 2008), so long as it served business interests.

If automation deskills labor, then why should a smart city prioritize employer access to skilled labor? Given the evidence presented here, one could argue that employers need skilled labor to support the machines through automation’s last mile. A smart city can optimize the cottage industry. Which begs the question, who truly benefits from the design and development of smart cities?

Bottoms-up for sustainability and satisfaction

Eglash et al. (2020) take a different approach to automation and the future of work. While the authors agree that automation and mass production leads to deskilling labor, they add that automation typically optimizes the alienation of labor and ecological value. The authors note that mass production and the deskilling of labor produces jobs so tedious that it causes physical and mental health issues. Recall the measures Foxconn took at its factories, installing nets on the exterior of the building to prevent workers from committing suicide by jumping out of the windows (Reuters, 2010).

Graeber (2018) agrees, documenting what he refers to as the spiritual violence of working in a bullshit job. Decision-makers generally draw this underlying economic calculus that humans will always tend to seek their best advantage. In this framework, obtaining a steady income by sitting at a desk all day or standing in place performing repetitive tasks would seem like a great way to get the most benefit for the least expenditure of time and effort. In reality, as Eglash et al. (2020) point out, the features commonly linked with “good work,” such as self-esteem and interest, are associated with craftwork (Luckman, 2015). Ocejo (2017) points out that while many “good” jobs are typically associated with knowledge and technology, there is a trend among educated and culturally-savvy young people to move into such craftwork as bartending, barbering, butchering, and others. If this is true, why does this shift stand in contrast to our theories of human nature? Graeber argues that our theories of human nature are wrong (Graeber, 2018, p. 61).

Eglash et al. (2020) point to a strong correlation between job satisfaction and job decision authority, which they find diminished in mass production. Gray & Suri (2019) document a concept they refer to as the “double bottom line.” In business, the bottom line refers to net profits after the tabulation of all expenses and earnings. Some companies, particularly those technology companies using gig-work to bolster their software as a service platform, organize their businesses around prioritizing workers. In this case, the double bottom line refers to “making a profit while pushing for social change” (Gray & Suri, 2019, p. 141).

Even in the case of a double bottom line, Gray and Suri show how this goal is complicated by technical, social, and political challenges involved in creating a sustainable business model that does not simply convert workers into another revenue stream. To develop a sustainable “future-proof” smart city, Waterfront Toronto uses the “triple bottom line.” This approach attempts to balance economics, environmental, and social issues in the “3Ps”: people, profits, and the planet (McCord & Becker, 2019, p. 4). The bottom line is about striking a balance, and striking a balance often comes with making tradeoffs between competing concerns. In the case of a bottom, double bottom, or triple bottom line, who gets to make those tradeoffs? Furthermore, which bottom line are they prioritizing?

Economic theorists such as Marx and Smith, factory owners like McCormick and Foxconn, politicians like Wagner, and organizations like Sidewalk Labs and Waterfront Toronto all have something in common; they are taking a top-down approach of imposing their vision on the masses. Eglash et al. (2020) stand in contrast to these approaches. Rather than suggesting et another top-down framework to achieve a desired bottom line, they offer a path to the future of work that draws on generative traditions sustained in Indigenous practices that work from the bottom up. Instead of deskilling labor, they suggest we strive to find the “sweet spot between ease of use and skills development” (Eglash et al., 2020, p. 600). This requires using automation to invest in upskilling people rather than deskilling the work they perform and relying on networks of people rather than monopolies funneling alienated labor and materials through pipelines and down assembly lines.

The bottom-up generative approach presented by Eglash et al. (2020) attempts to bridge the gap between automation as it is with automation as it ought to be. They point to research that suggests that when an artisanal value chain is composed of other artisans versus, for example, having to purchase supplies from a corporation or comparatively wealthy entrepreneur continually, their labor value can circulate unalienated. Additional examples describe how agroecology circulates ecological value unalienated and the need for unalienated social value to prevent a tragedy of the commons. They suggest that all of this is not only possible but demonstrable as a common feature of Indigenous life. Automation for an artisanal economy is not about competition but rather collaboration.

Through Haraway’s cyborg lens, Eglash envisions human and machine artisanal hybrids, where people can assemble their repertoire of components and become a node in the artisanal economy. Importantly, this is not in the same vein as the utopian vision of Licklider. Eglash deals in reality and spends considerable time exploring issues of scale. It is not enough to present a utopian vision without working out the steps to get there. For Eglash, those steps begin with thorough collaboration and consideration of Indigenous groups and the knowledge they are willing to contribute.

The micro, meso, and macroscale refer to three different levels of production that we need to consider. The microscale focuses on the details of labor and other features at the site of production. The mesoscale refers to the point of interface at the organizational level. Finally, the macroscale is about the policies, infrastructure, and cultural dynamics that shape success metrics. As shown, even if one has the best intentions by accumulating more bottom lines to accommodate the microscale, such efforts can quickly be overshadowed at the macroscale.


In this essay, we have attempted to illuminate a bridge between what is and what ought to be through a critical analysis of several works documenting the history and potential futures of automation and technological innovation. We traced efforts to deskill labor from piecework in early mechanization through recent efforts to design a “future-proof” smart city. Employing Haraway’s cyborg metaphor, we asked who benefits and who is harmed by technological innovation. We found that elites benefit from such innovation by utilizing technology to optimize efficiency in extracting value from labor, society, and the environment as a whole. We then asked who ought to benefit from such innovation. Drawing on the work of Eglash et al., we argue for a bottom-up approach to the design and implementation of automation technologies that considers each of the three scales of production: 1) the microscale; 2) the mesoscale; 3) the macroscale. This framework emphasizes upskilling rather than deskilling and finds a reasonable middle ground between utopian and dystopian visions to present possibilities for the future of work and automation, grounded in reality.


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1957 Mechanix Illustrated – You’ll own slaves again – O.O. Binder (see header image)

Published by Matthew Garvin

UX Research | Culture | Information | Human-Computer Interaction

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