We live in times of endless crisis alarms, a general state of uncertainty, combined with an orchestrated and intentional undermining of established academic institutions. Science and academic research are under questioning around the world, and universities’ position as a legitimate source of knowledge and critical thinking are under attack. Adding to this sense of instability and uncertainty, there is an ongoing digital and technological development that some argue has become a major risk for technical unemployment – fortelling the “end of work” (Brynjolfsson & McAfee, 2014; Danaher, 2017).

Automation, history and current threats

Historically, we have seen how new technology has changed the way we work and our everyday work practices. Machines have revolutionised and increased efficiency in agriculture and industrial production, and reduced the number of workers demanded in the process. In light of today’s ever-growing improvements in computer power, artificial intelligence and robotics, the gloomiest predictors are now once again convinced that we are moving towards a jobless future (Brynjolfsson & Mcafee, 2014). This time, technology is substituting more cognitively advanced and emotionally demanding jobs – ones previously performed by professionals in technical and managerial professions, including university teachers (Autor, 2015).

However, the past two centuries of automatisation and technological change have not made human labour obsolete. Even though unemployment rates have fluctuated cyclically, there has been no long-run increase in unemployment (Autor, 2015). Through governmental programs of re-education and reorientation, most exempted workers have been able to move to other forms of labour, and new areas of work have opened up in the wake of technological transformation. In previous technological automation processes the focus have been on replacing the most dangerous, bodily harming, and repetitive tasks, and in doing so contributing to a more human friendly labour market.

The digital transformation of academic work

University teachers have seen their work tasks and everyday work practices changed dramatically with digitalisation. Through digital aids and tools, they book rooms, coordinate and organise lectures and seminars, examine and grade students digitally, do research, apply for research funding and ethical approval, publish in academic outlets, manage conference bookings, calculate budgets for research proposals and develop data management plans. These are just a few examples. Their everyday work practices include a significant amount of digital administration, interacting with large numbers of different digital platforms. In the name of efficiency, an increasing number of tasks that have been previously assigned to administrative employees, have gradually been reassigned to the university teachers.

However, when trying to understand the consequences of technological changes of the everyday work situation for university employees, it is important to also take into consideration that during the same time these organisations have also been subject to New Public Management (Thomas & Davies, 2002). Which has also entailed increases in the number of students with diverse needs, less preparation time for teaching, and continuous monitoring of performance through audits and performance evaluations. In result, this left each university teacher with less time to do research due to increased demands and shrinking resources. Consequently, this has spurred even more administrative work as researchers constantly need to apply for research grants in highly competitive, complex and time-consuming funding processes, resulting in additional time and resource-consuming processes. This is also important to take into consideration when trying to understand the implications of genAI on the future of university teachers, illustrating the importance of moving beyond a techno-deterministic understanding (Lindberg et al., 2022). Automation and digitalisation are often presented as neutral, a consequence of technological progress, and as something inevitable. The ideological and material consequences remain hidden (Lindgren, 2024).

AI’s role and data dependency

We have to understand what distinguishes AI technology from previous types of technology, and in doing so, understand what consequences it will have on the everyday practices of university teachers. First, how can they use AI in their everyday work? What tasks do they have that could be suitable for genAI tools? Tasks such as compiling large amounts of text or getting an overview of a new research field for teaching or research, writting summaries, and polishing research applications, compiling CVs and creating concise bios, conducting  text analysis of ethnographic materials, supporting peer-review and expert processes, when assessing exams and essays, supporting development of lectures, conference and seminar presentations. The possibilities are endless. In the modern universities that the New Public Management have constructed, with its endless rounds of evaluations, constant applications and assessments, genAI tools can function to support and facilitate the everyday administrative work, making the work practices more manageable. However, it is also important to keep in mind that AI needs large amounts of data to be able to learn from the environment in which it will operate. To be able to help the teachers in their professional practices, genAI needs access to information and data, and the employees must assist and train the systems. Algorithmic systems depend on humans performing a certain kind of digital work, data labeling and moderation, breaking down the work into smaller components for autonomous decisions (Lindgren, 2024). This work is not always visible or even seen as actual work (Moore & Woodcock, 2021).

Hidden labour and digital capitalism

In digital capitalism, we all are involved in generating this type of data. When we move around in digital environments, we perform a lot of work for free that contributes to generating profits for the system, often without us being aware of it. As users we contribute to training the AI systems. Those who are involved in creating content online leave behind data traces, and it is these traces that the large digital media giants (Meta, X, etc.) exploit and capitalise on. The work that people do in the borderland between AI and society is often hidden (Lindgren, 2024; Moore & Woodcock, 2021; Taylor, 2018). For example, when you order a pizza for home delivery via an app, you might perceive it as a digital process. However, the actual physical work behind is invisible. Someone is standing and making the pizza. Another person is delivering it to your home. Foodora and Deliveroo’s apps are part of the complex socio-technical ecosystem of digital society. The pizza delivery people use their own bicycles to deliver the pizza. Foodora does not own the bicycles. Therefore, the company is not responsible for them. The companies claim to offer “flexible and free work”. The couriers can work whenever they want. The work is clearly fragmented, and the workers are interchangeable. The work schedule is individualised. The workers have to deal on their own with all the challenges, including icy roads, angry customers, unclear directions, and other issues. The digital platforms pay for the result, not for the time in-between. In many ways, these working conditions resemble those at the beginning of industrialisation, before union mobilisation, labor protection, sick leave pay, and the right to vacation (Ilsøe & Söderqvist, 2023). What is presented as high-tech and new, is in fact a regression in labour law. Historically, we have seen how every technological leap favors the emergence of armies of marginalised workers, who would take jobs that are not considered jobs anymore. In this respect, automation processes are often much less impressive than the big tech companies and large digital platforms want us to believe. Some tasks may disappear and wages will be reduced, though people continue working alongside the machines for lower pay or even sometimes without pay (Taylor, 2018).

To understand work under digital capitalism, we need to go back to the basic question formulated within  the socialist feminist tradition “What is work?” (Ferguson, 2020). How digital capitalism has not only survived but prospered while certain types of work have been hidden and unpaid (Fraser, 2016). The unrecognised work performed by most of us in the borderline between genAI and society, mirrors digital capitalism’s historical and current approach to reproductive work (Jarrett, 2018). The indispensable affective and material labour, mostly cast as “women’s work”, often performed without recognition and pay. In other words, a work that is not regarded as work and is not considered as having any social or economic value. This work in practice is extremely important, while ideologically seen as completely unimportant. Departing from this reasoning, we can conclude that the capitalist system have an inherent desire to devalue and hide socially important work. As participants of the digital capitalism, we often ignore the work that takes place behind the applications, and buy the myth of “success”. This way, we give automation more credence than it deserves. We ignore the work that lies behind the shiny facades of digitalisation. Making the machines appear smarter than they are (Taylor, 2018).

If the discussion about technology continues to focuse only on the narrative that technology drives humanity’s development forward and humans have to keep up, there is an imminent risk of missing the social contexts in which these technical devices are created. When considering the consequences of genAI on university teachers and their daily work, it is important to understand that it is not the technology that will make the teachers obsolete. Technology is developed within a specific economic and social system, where certain resourceful organisations and individuals invest in developing technology that will benefit their personal interests, including control, power, and immense financial returns.  The technology is designed to replace human labor to some extent, but it is developed within a digital capitalism that thrives on making people feel that they are constantly replaceable and vulnerable.

Conclusion and final reflections

To conclude, will genAI make university teacher obsolete? There is a need to acknowledge both the advantages and disadvantages of these tools. With the current university climate orchestrated by the New Public Management with its constant demands for auditing, evaluations, counting, and compiling information, daily tasks of university teachers might become more manageable with the help of genAI. In a better of world, genAI tools might be used to feed the insatiable New Public Management system, freeing up time for research, ciritical thinking and teaching. On the other hand, AI needs data and other resources to be able to learn from the environment in which it will operate. When university teachers participate in training the algorithmic systems, the digital capitalism thrives. This work is often not recognised as work. Digital capitalism wants us to believe that technological development is unstoppable and that we need to accept that our work is exploited. As educators and citizens, we need to be aware that there is an inherent mechanism in the system that actively benefits from hiding work tasks and treats them as non-work.

References

Autor, D. H. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

Danaher, J. (2017). Will Life Be Worth Living in a World Without Work? Technological Unemployment and the Meaning of Life. Science and Engineering Ethics, 23(1), 41–64. https://doi.org/10.1007/s11948-016-9770-5

Ferguson, S. J. (2020). Women and work: Feminism, labour, and social reproduction. Between the lines.

Fraser, N. (2016). Contradictions of Capital and Care. New Left Review, 100, 99–117.

Ilsøe, A., & Söderqvist, C. F. (2023). Will there be a Nordic model in the platform economy? Evasive and integrative platform strategies in Denmark and Sweden. Regulation & Governance, 17(3), 608–626. https://doi.org/10.1111/rego.12465

Jarrett, K. (2018). Laundering women’s history: A feminist critique of the social factory. First Monday. https://doi.org/10.5210/fm.v23i3.8280

Lindberg, J., Kvist,E., & Lindgren, S. (2022). The Ongoing and Collective Character of Digital Care for Older People: Moving Beyond Techno-Determinism in Government Policy. Journal of Technology in Human Services, 40(4), 357–378. https://doi.org/10.1080/15228835.2022.2144588

Lindgren, S. (2024). AI – ett kritiskt perspektiv (Upplaga 1). Studentlitteratur.

Moore, P. V., & Woodcock, J. (2021). Augmented Exploitation: Artificial Intelligence, Automation, and Work. For Work / Against Work. Pluto Press. https://onwork.edu.au/bibitem/2021-Moore,Phoebe+V-Woodcock,Jamie-Augmented+Exploitation+Artificial+Intelligence,Automation,and+Work/

Taylor, A. (2018). The Automation Charade. Logic(s) Magazine. https://logicmag.io/failure/the-automation-charade/

Thomas, R., & Davies, A. (2002). Gender and New Public Management: Reconstituting Academic Subjectivities. Gender, Work & Organization, 9(4), 372–397. https://doi.org/10.1111/1468-0432.00165

Keywords:

Technological unemployment, genAI, work, gender, digital capitalism, university teachers, reproductive work