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Creative Labour in the Age of Automation: Artists’ Lived Experiences in the Fourth Industrial Revolution

Abu Yousuf Pranto ORCID: https://orcid.org/ Department of Fine Arts in Drawing & Painting Faculty of Fine & Performing Arts Shanto-Mariam University of Creative Technology Dhaka, Bangladesh

Prof. Dr Kazi Abdul Mannan Department of Business Administration Faculty of Business Shanto-Mariam University of Creative Technology Dhaka, Bangladesh Email: drkaziabdulmannan@gmail.com ORCID: https://orcid.org/0000-0002-7123-132X   Corresponding author: Abu Yousuf Pranto: aypranto@gmail.com

Percept. motiv. attitude stud. 2026, 5(2); https://doi.org/10.64907/xkmf.v5i2.pmas.4

Submission received: 2 April 2026 / Revised: 20 May 2026 / Accepted: 25 May 2026 / Published: 29 May 2026

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Abstract

The Fourth Industrial Revolution has significantly transformed creative labour through the integration of artificial intelligence, automation, and platform-based systems. This study examines artists’ lived experiences within this evolving landscape, focusing on how technological change reshapes creative practices, labour conditions, authorship, and professional identity. Employing a qualitative research design based on secondary data, the study draws on academic literature, artist interviews, and institutional reports to identify key thematic patterns. The findings reveal that while automation expands creative possibilities and enables new forms of experimentation, it simultaneously intensifies labour precarity, economic instability, and dependence on platform infrastructures. The study also highlights a reconfiguration of authorship, where creative agency is increasingly distributed across human and non-human actors. Emotional responses among artists are marked by ambivalence, combining enthusiasm for technological innovation with concerns about obsolescence and loss of control. By integrating perspectives from critical political economy, posthumanism, and cultural labour theory, the study provides a nuanced understanding of creative labour in the digital age. It concludes that the future of artistic work will depend on balancing technological advancement with equitable labour practices and supportive policy frameworks.

Keywords: creative labour, artificial intelligence, automation, digital art, precarity, authorship, platform economy

1. Introduction

The Fourth Industrial Revolution (4IR) has ushered in a period of rapid technological transformation, fundamentally altering the nature of work, production, and human creativity. Defined by the integration of artificial intelligence (AI), machine learning, robotics, and data-driven systems, the 4IR extends beyond traditional industrial automation into domains once considered uniquely human, including artistic and creative practices (Schwab, 2017). This expansion of automation into the cultural sector raises critical questions about the future of creative labour, particularly regarding the role of the artist, the meaning of creativity, and the socio-economic conditions under which creative work is produced.

Historically, creative labour has been associated with human imagination, originality, and emotional expression. Artists have been positioned as autonomous creators whose work reflects subjective experiences and cultural contexts. However, the increasing use of algorithmic tools and generative systems challenges these assumptions. Technologies such as generative adversarial networks (GANs), large language models, and computational design platforms can now produce artworks, music, and texts that rival or even surpass human outputs in complexity and aesthetic appeal (Elgammal et al., 2017; Manovich, 2019). As a result, the boundaries between human and machine creativity are becoming increasingly blurred.

This transformation is not merely technical but deeply socio-economic. The integration of AI into creative workflows has implications for labour structures, income stability, and professional identity. Creative workers are increasingly required to adapt to new tools and platforms, often without corresponding institutional support or regulatory frameworks. The proliferation of digital platforms has facilitated broader access to creative tools and global audiences, yet it has also intensified competition and contributed to precarious working conditions (Gill & Pratt, 2008; Srnicek, 2017). In this context, artists must navigate a complex landscape characterised by both opportunity and uncertainty.

The concept of “creative labour” provides a useful lens for examining these dynamics. Creative labour encompasses not only the production of artistic outputs but also the affective, cognitive, and symbolic processes involved in cultural creation (Hesmondhalgh & Baker, 2011). Unlike industrial labour, which is often standardised and repetitive, creative labour is characterised by variability, personal investment, and intrinsic motivation. However, these very qualities can render creative workers vulnerable to exploitation, as passion and self-expression are often leveraged to justify low pay and unstable employment (Banks, 2017).

Within the context of the 4IR, these vulnerabilities are further amplified by automation and platformization. Algorithmic systems play an increasingly central role in determining visibility, distribution, and value within cultural markets. For example, social media platforms and digital marketplaces rely on opaque algorithms to curate content, often privileging certain styles or trends while marginalising others. This dynamic can shape artistic production itself, as creators adjust their work to align with platform logics to gain visibility and income (van Dijck et al., 2018).

At the same time, automation introduces new possibilities for creative experimentation and collaboration. Many artists embrace AI as a tool that expands their creative capacities, enabling them to explore novel forms and processes. In this sense, the relationship between artists and machines is not purely antagonistic but can be understood as collaborative or co-creative. This perspective aligns with posthumanist theories that emphasise the distributed nature of agency and creativity across human and non-human actors (Braidotti, 2013).

Despite these opportunities, the integration of automation into creative labour also raises profound ethical and philosophical questions. Issues of authorship, originality, and intellectual property become increasingly complex when creative outputs are generated through algorithmic processes. Who owns an artwork created by an AI system? To what extent can such work be considered original? These questions challenge established legal and cultural frameworks, necessitating new approaches to understanding creativity in the digital age (McCormack et al., 2019).

This study seeks to explore these issues by focusing on the lived experiences of artists operating within the context of the 4IR. Rather than treating automation as a purely technical phenomenon, the research examines how artists perceive, interpret, and respond to technological change. By centring the perspectives of creative practitioners, the study aims to provide a nuanced understanding of how automation reshapes not only artistic practices but also the broader conditions of creative labour.

The research is guided by three primary questions: How do artists perceive the impact of automation on their creative processes and outputs? In what ways does automation influence labour conditions, including precarity, income stability, and professional identity? How do artists negotiate issues of authorship and agency in the context of human-machine collaboration? These questions are addressed through a qualitative analysis of secondary data, including academic literature, artist interviews, and digital platforms.

By addressing these questions, this study contributes to ongoing debates about the future of work and the role of technology in cultural production. It also highlights the importance of considering the human dimensions of technological change, particularly in fields where creativity and expression are central. As automation continues to reshape the landscape of creative labour, understanding the experiences and perspectives of artists becomes increasingly essential for developing equitable and sustainable frameworks for the cultural sector.

2. Literature Review

The concept of creative labour has been extensively examined within the field of cultural studies, particularly in relation to the cultural and creative industries. Creative labour is often characterised by its reliance on intellectual, emotional, and symbolic forms of production, distinguishing it from traditional forms of industrial labour (Hesmondhalgh & Baker, 2011). This type of labour involves not only the creation of artistic products but also the generation of cultural meanings and social value.

Scholars have emphasised the dual nature of creative labour, which combines elements of autonomy and exploitation. On one hand, creative workers often experience a high degree of personal fulfilment and self-expression. On the other hand, they frequently encounter precarious working conditions, including irregular income, lack of job security, and limited access to social protections (Gill & Pratt, 2008). This paradox has been described as the “precarious autonomy” of creative work, where the freedom to create is accompanied by economic instability.

The notion of immaterial labour further expands this understanding by highlighting the production of intangible goods, such as knowledge, affect, and cultural content (Lazzarato, 1996). In the digital age, immaterial labour has become increasingly central to economic production, as cultural and informational goods circulate through global networks. Artists, in this context, are not only producers of aesthetic objects but also contributors to broader systems of cultural and economic exchange.

2.1 Automation and Artificial Intelligence in Creative Practices

The integration of automation and AI into creative practices has generated significant scholarly interest, particularly in relation to the nature of creativity itself. Advances in machine learning and generative algorithms have enabled the production of artworks that challenge traditional distinctions between human and machine creativity (Elgammal et al., 2017).

Manovich (2019) argues that AI-driven cultural production represents a new paradigm in which creativity is increasingly mediated by computational processes. Rather than replacing human creativity, these technologies reshape the conditions under which creative work is produced. Artists must learn to navigate complex systems of data, algorithms, and interfaces, which can both enable and constrain their creative agency.

McCormack et al. (2019) further explore the implications of AI-generated art for concepts such as authorship, intention, and authenticity. They suggest that creative agency in such contexts is distributed across multiple actors, including programmers, datasets, and algorithms. This distributed model of creativity challenges traditional notions of the artist as a singular, autonomous creator.

At the same time, some scholars caution against overly deterministic narratives that portray AI as either a threat or a panacea. Instead, they advocate for a more nuanced understanding of human-machine collaboration, emphasising the role of human intention and interpretation in shaping creative outcomes.

2.2 Platform Capitalism and Digital Labour

The rise of digital platforms has fundamentally transformed the organisation of creative labour. Platforms such as social media networks, online marketplaces, and streaming services serve as key intermediaries in the production, distribution, and consumption of cultural goods. These platforms operate within a broader system of platform capitalism, characterised by data extraction, algorithmic governance, and network effects (Srnicek, 2017).

Van Dijck et al. (2018) highlight the ways in which platforms shape cultural production through their underlying architectures and algorithms. Visibility and success within these systems are often determined by metrics such as engagement, popularity, and user behaviour, which can influence the types of content that are produced. As a result, artists may feel compelled to adapt their work to align with platform logics, potentially compromising their creative autonomy.

The gig economy further exacerbates these dynamics by promoting flexible but unstable forms of employment. Creative workers are often required to engage in continuous self-promotion and content production to maintain visibility and income. This constant demand for productivity can lead to burnout and emotional strain, as artists struggle to balance creative expression with economic survival (Standing, 2011).

2.3 Precarity, Inequality, and Creative Work

Precarity is a central theme in the literature on creative labour, particularly in the context of digital economies. The term refers to conditions of uncertainty, instability, and insecurity that characterise many forms of contemporary work (Standing, 2011). In the creative sector, precarity is often intensified by factors such as oversupply of labour, lack of institutional support, and the commodification of cultural production.

Banks (2017) argues that creative labour is shaped by moral and ethical considerations, as workers navigate tensions between artistic integrity and economic necessity. These tensions are further complicated by structural inequalities related to gender, race, and class, which influence access to resources and opportunities within the cultural sector.

The introduction of automation adds another layer of complexity to these dynamics. While AI tools can enhance productivity and reduce barriers to entry, they can also contribute to labour displacement and devaluation. For example, automated systems may reduce the demand for certain types of creative work, leading to increased competition and downward pressure on wages.

2.4 Authorship, Ownership, and Posthuman Creativity

The question of authorship has become increasingly complex in the context of digital and AI-driven art. Traditional models of authorship, which emphasise individual creativity and ownership, are challenged by collaborative and algorithmic forms of production.

Posthumanist theories provide a useful framework for understanding these shifts. Braidotti (2013) argues that human agency is always embedded within networks of relationships that include non-human actors. In this view, creativity is not the product of isolated individuals but emerges from interactions between humans, machines, and environments.

This perspective has important implications for intellectual property and legal frameworks. Current systems of copyright and ownership are often ill-equipped to address the complexities of AI-generated content. As a result, there is an ongoing debate about how to define and regulate authorship in the digital age (McCormack et al., 2019).

2.5 Emotional and Affective Dimensions of Creative Labour

In addition to economic and structural factors, the literature also highlights the emotional and affective dimensions of creative labour. Artists often invest significant emotional energy in their work, which can be both rewarding and taxing (Hesmondhalgh & Baker, 2011).

The integration of automation into creative practices can amplify these emotional dynamics. On one hand, artists may experience excitement and curiosity as they explore new technologies. On the other hand, they may feel anxiety and uncertainty about their future roles and identities. This emotional ambivalence reflects broader tensions between technological innovation and human values.

3. Theoretical Framework

This study adopts an interdisciplinary theoretical framework to critically examine creative labour in the age of automation. The framework integrates three complementary perspectives: critical political economy, posthumanism, and cultural labour theory. Together, these approaches provide a multi-layered understanding of how technological transformations intersect with power, identity, and lived experience in the context of the Fourth Industrial Revolution.

3.1 Critical Political Economy of Creative Labour

The critical political economy perspective emphasises the structural dynamics of power, capital, and labour within cultural production. Rooted in Marxist traditions, this approach examines how economic systems shape the conditions under which creative work is produced, distributed, and consumed (Mosco, 2009). In the context of digital capitalism, creative labour is increasingly commodified, with artistic outputs functioning as both cultural expressions and economic goods.

Automation and artificial intelligence play a central role in this transformation. These technologies are not neutral tools but are embedded within systems of capital accumulation. Platform-based economies, in particular, rely on data extraction, algorithmic governance, and network effects to generate value (Srnicek, 2017). Artists who participate in these platforms contribute to the creation of value through their content, often without receiving proportional compensation.

From this perspective, automation can be understood as a mechanism for restructuring labour relations. While it enables new forms of production, it also facilitates the displacement of labour and the intensification of competition. Creative workers are required to continuously adapt to technological changes, often bearing the risks associated with innovation. This dynamic reflects broader trends in neoliberal economies, where flexibility and self-entrepreneurship are emphasised at the expense of stability and security (Standing, 2011).

Moreover, the commodification of creativity raises questions about the ownership and control of cultural production. Intellectual property regimes and platform policies often favour corporate interests, limiting the agency of individual artists. By situating creative labour within these broader economic structures, the critical political economy framework highlights the systemic inequalities that shape the experiences of artists in the digital age.

3.2 Posthumanism and Distributed Creativity

While critical political economy focuses on structural conditions, posthumanism provides a conceptual framework for understanding the changing nature of creativity and agency in the age of automation. Posthumanist theory challenges anthropocentric assumptions by recognising the role of non-human actors, such as machines, algorithms, and networks, in shaping human experience (Braidotti, 2013).

In the context of AI-driven art, creativity is no longer the exclusive domain of human individuals. Instead, it emerges from interactions between humans and computational systems. Algorithms can generate novel forms, patterns, and ideas, often drawing on vast datasets that exceed human cognitive capacities. This shift necessitates a rethinking of authorship and intentionality, as creative outcomes are co-produced by multiple agents.

Manovich (2019) describes this phenomenon as a new cultural paradigm in which software becomes a key medium of artistic expression. Artists engage with algorithms not merely as tools but as collaborators that influence the direction and outcome of creative processes. This perspective aligns with the notion of distributed creativity, where agency is shared across human and non-human entities (McCormack et al., 2019).

Posthumanism also invites a reconsideration of the boundaries between subject and object, creator and creation. In AI-generated art, the distinction between these categories becomes increasingly fluid. For example, generative systems can produce outputs that evolve, responding to user inputs or environmental data. In such cases, creativity is not a fixed attribute but an ongoing process of interaction and transformation.

However, it is important to note that posthumanism does not imply the erasure of human agency. Rather, it emphasises the relational nature of agency, where humans and machines are interconnected within complex networks. This perspective provides a nuanced understanding of how artists navigate their relationships with technology, balancing control and unpredictability in their creative practices.

3.3 Cultural Labour Theory and Lived Experience

Cultural labour theory complements the previous perspectives by focusing on the subjective and experiential dimensions of creative work. This approach examines how individuals perceive and navigate the conditions of their labour, emphasising issues such as identity, motivation, and emotional investment (Hesmondhalgh & Baker, 2011).

Creative labour is often characterised by a strong sense of personal attachment, as artists invest their identities and values into their work. This emotional dimension can be both empowering and burdensome. On one hand, it provides a sense of purpose and fulfilment. On the other hand, it can lead to self-exploitation, as individuals are willing to accept unfavourable conditions in pursuit of their creative goals (Banks, 2017).

In the context of automation, these dynamics are further complicated by technological change. Artists must not only adapt to new tools and workflows but also renegotiate their professional identities. The integration of AI into creative practices challenges traditional notions of skill and expertise, requiring artists to develop new competencies while maintaining their artistic vision.

Cultural labour theory also highlights the importance of community and collaboration in shaping creative experiences. Artists often rely on networks of peers, institutions, and audiences for support and recognition. In digital environments, these networks are mediated by platforms that influence visibility and interaction. As a result, the lived experiences of artists are shaped by both technological and social factors.

By incorporating cultural labour theory, this study emphasises the importance of understanding creative labour as a lived experience rather than a purely economic or technological phenomenon. This perspective allows for a more holistic analysis of how artists respond to the challenges and opportunities of the Fourth Industrial Revolution.

3.4 Integrative Framework

The integration of critical political economy, posthumanism, and cultural labour theory provides a comprehensive framework for analysing creative labour in the age of automation. Each perspective addresses different dimensions of the phenomenon: structural conditions, conceptual shifts, and lived experiences.

Together, these approaches reveal the complex and often contradictory nature of technological change. Automation can simultaneously empower and constrain, enabling new forms of creativity while reinforcing existing inequalities. By examining these dynamics through multiple lenses, this study aims to provide a nuanced understanding of how artists navigate the evolving landscape of creative labour.

4. Methodology

This study employs a qualitative research design based on secondary data analysis to explore the lived experiences of artists in the context of automation and the Fourth Industrial Revolution. Qualitative research is particularly well-suited to this inquiry, as it allows for an in-depth examination of meanings, perceptions, and experiences that cannot be easily quantified (Creswell & Poth, 2018).

The choice of secondary data analysis is informed by the exploratory nature of the research. Given the rapidly evolving nature of AI and creative technologies, a wide range of existing sources provides valuable insights into how artists engage with these changes. Secondary data also enables the integration of diverse perspectives, including academic analyses, practitioner accounts, and institutional reports.

4.1 Data Sources and Selection Criteria

The study draws on multiple types of secondary data to ensure a comprehensive understanding of the research topic. These sources include:

• Peer-reviewed journal articles on creative labour, digital culture, and artificial intelligence
• Scholarly books and theoretical texts addressing political economy, posthumanism, and cultural production
• Artist interviews, statements, and manifestos  in exhibition catalogues and online platforms
• Reports from cultural institutions, technology organisations, and policy bodies
• Digital content from artist websites, social media platforms, and online communities

To ensure the credibility and relevance of the data, specific selection criteria were applied. Academic sources were selected based on their publication in reputable journals or by recognised publishers. Practitioner-oriented materials were included if they provided direct insights into artists’ experiences and practices. Preference was given to sources published within the last two decades, with particular emphasis on recent developments in AI and automation.

4.2 Data Collection Process

Data collection involved a systematic review of literature and digital materials. Academic databases such as Scopus, Web of Science, and Google Scholar were used to identify relevant publications. Keywords included “creative labour,” “artificial intelligence,” “digital art,” “automation,” “platform economy,” and “authorship.”

In addition to academic sources, artist interviews and statements were collected from exhibition catalogues, online journals, and institutional websites. These materials provided first-hand accounts of how artists perceive and engage with technological change.

The collected data were organised thematically using a digital reference management system. This facilitated the identification of recurring themes and patterns across different sources.

4.3 Data Analysis Method

The study employs thematic analysis as the primary method of data analysis. Thematic analysis is a flexible and widely used approach for identifying, analysing, and interpreting patterns within qualitative data (Braun & Clarke, 2006).

The analysis followed a six-phase process:

• Familiarisation with the data: All collected materials were read and reviewed to gain an overall understanding of the content.
• Initial coding: Key concepts and ideas were identified and labelled using descriptive codes.
• Theme development: Codes were grouped into broader themes that captured significant patterns in the data.
• Review of themes: Themes were refined to ensure coherence and relevance to the research questions.
• Definition and naming of themes: Each theme was clearly defined and contextualised within the theoretical framework.
• Interpretation: Themes were analysed in relation to the research questions and theoretical perspectives.

This method allows for both inductive and deductive analysis. While themes emerged from the data, they were also informed by the theoretical framework, enabling a deeper interpretation of the findings.

4.4 Ensuring Rigour and Trustworthiness

To enhance the rigour and trustworthiness of the study, several strategies were employed. Credibility was ensured through the use of diverse data sources, which allowed for triangulation of perspectives. By comparing findings across academic and practitioner-oriented materials, the study reduces the risk of bias associated with any single source.

Dependability was addressed by maintaining a transparent and systematic research process. Detailed documentation of data collection and analysis procedures enables the study to be reviewed and replicated by other researchers.

Confirmability was achieved by grounding interpretations in the data and providing clear links between evidence and conclusions. Reflexivity was also considered, with the researcher acknowledging the influence of theoretical perspectives on the analysis.

4.5 Ethical Considerations

As the study relies on publicly available secondary data, it does not involve direct interaction with human participants. However, ethical considerations remain important, particularly in the representation of artists’ voices and experiences. Care was taken to accurately interpret and contextualise practitioner statements, avoiding misrepresentation or oversimplification (Mannan & Farhana, 2026).

All sources were properly cited in accordance with APA (7th ed.) guidelines, ensuring academic integrity and respect for intellectual property.

4.6 Limitations of the Study

Despite its strengths, the methodology has certain limitations. The reliance on secondary data means that the study is dependent on existing representations of artists’ experiences, which may not fully capture the diversity of perspectives within the creative sector. Additionally, the rapidly evolving nature of AI technologies means that some findings may become outdated as new developments emerge.

Furthermore, the absence of primary data limits the ability to explore specific contexts or communities in depth. Future research could address these limitations by incorporating interviews, ethnographic studies, or case-based approaches.

4.7 Methodological Contribution

Despite these limitations, the use of qualitative secondary data analysis offers a valuable approach for examining complex and evolving phenomena such as creative labour in the age of automation. By integrating diverse sources and perspectives, the methodology provides a comprehensive and nuanced understanding of the topic.

5. Findings and Analysis

This section presents the findings derived from the thematic analysis of secondary data, including academic literature, artist interviews, institutional reports, and digital platforms. The analysis identifies five major thematic areas: transformation of creative practices, intensification of precarity and economic instability, reconfiguration of authorship and agency, emotional and psychological impacts, and strategies of resistance and adaptation. These themes reflect the complex and often contradictory experiences of artists navigating the evolving landscape of creative labour in the Fourth Industrial Revolution.

5.1 Transformation of Creative Practices

A central finding is the profound transformation of artistic practices through the integration of artificial intelligence and automation. Artists increasingly engage with generative tools, machine learning systems, and algorithmic processes that expand the scope of creative experimentation. These technologies enable the production of complex visual, auditory, and textual outputs that would be difficult or impossible to achieve through traditional methods alone (Manovich, 2019).

Rather than functioning solely as tools, AI systems often operate as collaborators within the creative process. Artists describe their role as shifting from direct creators to facilitators, curators, or orchestrators of algorithmic outputs. This shift aligns with the concept of distributed creativity, where agency is shared across human and non-human actors (McCormack et al., 2019). In practice, this involves iterative processes of input, generation, selection, and refinement, where the artist guides the system while also responding to its outputs.

This transformation also entails a redefinition of artistic skill. Traditional techniques, such as manual drawing or painting, are supplemented or replaced by competencies in coding, data curation, and system design. As a result, the boundaries between artistic and technical expertise become increasingly blurred. Artists must navigate hybrid roles that combine aesthetic sensibility with computational literacy.

However, this expansion of creative possibilities is accompanied by new constraints. Algorithmic systems are shaped by their underlying datasets and architectures, which can limit the range of outputs they produce. Artists often report that working with AI involves negotiating these limitations, finding ways to subvert or reinterpret the biases embedded within technological systems. In this sense, creative practice becomes a process of critical engagement with technology, rather than passive adoption.

5.2 Intensification of Precarity and Economic Instability

While automation introduces new tools and opportunities, it also intensifies existing forms of precarity within the creative sector. The accessibility of digital technologies lowers barriers to entry, enabling a larger number of individuals to participate in creative production. While this democratisation can be empowering, it also leads to market saturation and increased competition (Gill & Pratt, 2008).

Artists operating within platform economies face significant challenges related to visibility and income generation. Digital platforms rely on algorithmic systems to curate and distribute content, often prioritising engagement metrics such as likes, shares, and views. These metrics can influence not only the success of artistic works but also the types of content that are produced. Artists may feel compelled to adapt their practices to align with platform logics, potentially compromising their creative autonomy (van Dijck et al., 2018).

The gig-based nature of digital labour further exacerbates economic instability. Many artists rely on freelance work, commissions, or short-term projects, which provide limited financial security. The continuous demand for content production creates a cycle of overwork and undercompensation, contributing to burnout and stress (Standing, 2011).

Automation also raises concerns about labour displacement and devaluation. As AI systems become capable of generating high-quality creative outputs, the perceived value of human labour may decline. For example, automated design tools can produce logos, illustrations, and other visual content at a fraction of the cost of human labour. This dynamic places downward pressure on wages and reduces opportunities for professional artists.

At the same time, the economic benefits of automation are unevenly distributed. Large technology companies and platform operators often capture a disproportionate share of value, while individual creators struggle to sustain their livelihoods. This imbalance reflects broader patterns of inequality within digital capitalism (Srnicek, 2017).

5.3 Reconfiguration of Authorship and Agency

The integration of AI into creative practices fundamentally challenges traditional notions of authorship and agency. In conventional artistic frameworks, the artist is understood as the primary source of creativity and intention. However, in AI-driven art, creative outputs are generated through complex interactions between human inputs, algorithmic processes, and training data.

This distributed model of creativity complicates questions of ownership and originality. Artists often grapple with the extent to which they can claim authorship over works produced with the assistance of AI systems. Some embrace the ambiguity, viewing it as an opportunity to explore new conceptual territories. Others express concerns about the erosion of individual authorship and the potential loss of creative control (McCormack et al., 2019).

The role of datasets is particularly significant in this context. AI systems are trained on large collections of existing works, which may include copyrighted material. This raises ethical and legal questions about the use of such data and the extent to which generated outputs can be considered original. Artists must navigate these complexities, often without clear regulatory guidance.

Posthumanist perspectives provide a useful framework for understanding these shifts. By emphasising the relational nature of agency, posthumanism suggests that creativity is not the product of isolated individuals but emerges from networks of interaction (Braidotti, 2013). From this perspective, the involvement of AI does not diminish human creativity but reconfigures it within a broader system of relations.

Nevertheless, the redistribution of agency also introduces new forms of dependency. Artists rely on proprietary software, platforms, and infrastructures that are controlled by corporations. This dependency can limit their autonomy and expose them to external constraints, such as changes in platform policies or access restrictions.

5.4 Emotional and Psychological Impacts

The transformation of creative labour in the age of automation has significant emotional and psychological implications. Artists frequently report feelings of ambivalence, characterised by a combination of excitement and anxiety. On one hand, the availability of new tools and technologies can be inspiring, opening up novel avenues for creative exploration. On the other hand, the rapid pace of technological change can create a sense of uncertainty and instability.

One recurring theme is the fear of obsolescence. As AI systems become increasingly capable, artists may question the relevance and value of their skills. This concern is particularly pronounced among those whose work overlaps with tasks that can be automated, such as illustration, graphic design, or content generation.

At the same time, artists often experience pressure to continuously update their skills and adapt to new technologies. This demand for constant learning can be both intellectually stimulating and emotionally exhausting. The need to remain competitive within rapidly changing environments contributes to stress and burnout.

The affective dimension of creative labour is further intensified by the dynamics of digital platforms. Metrics such as likes, shares, and follower counts can influence artists’ sense of self-worth and professional success. This quantification of creativity can lead to feelings of inadequacy or validation, depending on performance.

Despite these challenges, many artists also report positive emotional experiences associated with working with AI. The unpredictability of generative systems can produce surprising and unexpected results, fostering a sense of curiosity and play. This duality reflects the complex emotional landscape of creative labour in the digital age.

5.5 Strategies of Resistance and Adaptation

In response to these challenges, artists develop various strategies to navigate the evolving landscape of creative labour. One common approach is the adoption of hybrid practices that combine traditional and digital methods. By integrating different techniques, artists maintain a degree of control over their work while also leveraging the capabilities of new technologies.

Another strategy involves critical engagement with technology. Some artists use AI as a medium to explore and critique issues such as surveillance, bias, and data ethics. By exposing the limitations and implications of algorithmic systems, they challenge dominant narratives about technological progress.

Community formation also plays a crucial role in supporting creative labour. Online forums, collectives, and collaborative networks provide spaces for knowledge sharing, mutual support, and collective action. These communities can help mitigate the isolation and precarity associated with individual artistic practice.

Additionally, some artists seek alternative economic models, such as crowdfunding, cooperative platforms, or decentralised networks. These approaches aim to reduce dependence on traditional gatekeepers and create more equitable systems of value distribution.

Overall, these strategies demonstrate the agency and resilience of artists in the face of technological change. While the challenges of automation are significant, artists are not passive recipients of these transformations but active participants in shaping their outcomes.

6. Discussion

The findings of this study reveal a complex and multifaceted landscape of creative labour in the age of automation. This section interprets these findings through the lens of the theoretical framework, integrating insights from critical political economy, posthumanism, and cultural labour theory. The discussion highlights key tensions and contradictions that define the experiences of artists in the Fourth Industrial Revolution, while also considering broader implications for the future of work and cultural production.

6.1 Automation as Both Enabler and Constraint

One of the central insights of this study is the dual role of automation as both an enabler and a constraint. On one hand, AI technologies expand the possibilities of creative practice, enabling artists to explore new forms, techniques, and conceptual frameworks. The ability to generate complex outputs rapidly and at scale represents a significant shift in the capabilities available to creative practitioners (Manovich, 2019).

On the other hand, these same technologies impose new limitations. Algorithmic systems are shaped by their design and training data, which can constrain the range of outputs and introduce biases. Moreover, the reliance on proprietary platforms and tools creates dependencies that limit artistic autonomy. From a critical political economy perspective, these dynamics reflect the broader logic of digital capitalism, where technological innovation is closely tied to systems of control and value extraction (Mosco, 2009; Srnicek, 2017).

This duality underscores the importance of moving beyond simplistic narratives that frame automation as either wholly beneficial or entirely detrimental. Instead, it is necessary to recognise the ambivalent nature of technological change, where opportunities and challenges coexist.

6.2 Reconfiguring Creativity and Authorship

The findings also highlight a fundamental reconfiguration of creativity and authorship in the age of automation. The traditional model of the artist as an autonomous creator is increasingly challenged by the emergence of distributed and collaborative forms of production. In AI-driven art, creativity is not located within a single individual but emerges from interactions between humans, machines, and data.

Posthumanist theory provides a valuable framework for understanding this shift. By emphasising relationality and interconnectedness, it challenges anthropocentric assumptions about creativity and agency (Braidotti, 2013). From this perspective, the involvement of AI does not diminish human creativity but transforms it into a more complex and dynamic process.

However, this reconfiguration also raises important questions about ownership and accountability. Existing legal and institutional frameworks are often ill-equipped to address the complexities of AI-generated content. As a result, artists must navigate a landscape of uncertainty, where the boundaries of authorship are unclear and contested (McCormack et al., 2019).

This ambiguity has both positive and negative implications. On one hand, it opens up new conceptual possibilities and challenges established norms. On the other hand, it can undermine the recognition and compensation of creative labour, particularly when ownership is difficult to establish.

6.3 Intensified Precarity in Platform Economies

The intensification of precarity emerges as a key theme in the discussion. While digital platforms provide new opportunities for distribution and visibility, they also introduce new forms of instability and inequality. Artists operate within systems that prioritise engagement metrics and algorithmic visibility, often at the expense of creative autonomy (van Dijck et al., 2018).

From a cultural labour perspective, these conditions reflect the broader precarization of work in contemporary economies. Creative workers are expected to be flexible, adaptable, and self-motivated, often without adequate support or protection (Gill & Pratt, 2008). The integration of automation further exacerbates these challenges by increasing competition and reducing the value of certain types of labour.

The concept of the “precariat” is particularly relevant in this context, as it captures the conditions of insecurity and uncertainty that characterise many forms of creative work (Standing, 2011). Artists must navigate a landscape where success is highly uneven and often dependent on factors beyond their control.

At the same time, it is important to recognise that precarity is not experienced uniformly. Structural inequalities related to gender, race, and geography can influence access to resources and opportunities, shaping the distribution of risks and rewards within the creative sector (Banks, 2017). This highlights the need for more inclusive and equitable approaches to cultural policy and practice.

6.4 Emotional Labour and Identity Transformation

The emotional and psychological dimensions of creative labour are central to understanding the impact of automation. Artists’ experiences are characterised by ambivalence, as they navigate the tensions between excitement and anxiety, empowerment and vulnerability.

Cultural labour theory emphasises the importance of affect and identity in shaping work experiences (Hesmondhalgh & Baker, 2011). In the context of automation, artists must renegotiate their sense of self and professional identity in response to technological change. The integration of AI challenges traditional notions of skill and expertise, requiring individuals to adapt to new roles and expectations.

The quantification of creativity through platform metrics further complicates these dynamics. Artists’ sense of value and recognition is often tied to numerical indicators, which can fluctuate unpredictably. This creates a feedback loop in which emotional well-being is closely linked to platform performance.

Despite these challenges, the findings also highlight the potential for positive emotional experiences. The collaborative and exploratory nature of AI-driven art can foster a sense of curiosity and innovation. This suggests that emotional responses to automation are not uniform but vary depending on individual perspectives and contexts.

6.5 Agency, Resistance, and Alternative Futures

A key contribution of this study is its emphasis on the agency of artists in shaping their responses to automation. While structural constraints are significant, artists are not passive victims of technological change. Instead, they actively engage with, adapt to, and resist the conditions of their labour.

The adoption of hybrid practices, critical engagement with technology, and participation in collaborative networks demonstrate the capacity of artists to navigate complex environments. These strategies align with the concept of “creative agency,” which emphasises the ability of individuals to influence and reshape their conditions of work (Banks, 2017).

Moreover, the exploration of alternative economic models suggests the possibility of more equitable and sustainable forms of creative labour. Initiatives such as cooperative platforms, decentralised networks, and community-based practices challenge dominant paradigms and offer new ways of organising cultural production.

From a theoretical perspective, these developments highlight the importance of integrating structural and experiential analyses. While critical political economy provides insights into systemic inequalities, cultural labour theory and posthumanism emphasise the role of individual and collective agency in shaping outcomes.

6.6 Implications for Policy and Practice

The findings of this study have important implications for policy and practice. As automation continues to reshape the creative sector, there is a need for frameworks that support the rights and well-being of artists. This includes measures such as fair compensation, access to resources, and protection against exploitation.

Policy interventions should also address the role of platforms and technology companies in shaping cultural production. Greater transparency and accountability in algorithmic systems could help mitigate some of the challenges associated with visibility and distribution.

Additionally, education and training programs should be adapted to reflect the changing nature of creative work. This includes the development of interdisciplinary skills that combine artistic and technical competencies.

6.7 Toward a Nuanced Understanding of Creative Labour

Ultimately, the discussion underscores the need for a nuanced understanding of creative labour in the age of automation. Rather than viewing technology as a deterministic force, it is more productive to consider it as part of a complex system of interactions involving economic structures, cultural practices, and individual experiences.

By integrating multiple theoretical perspectives, this study provides a comprehensive framework for analysing these dynamics. It highlights the importance of considering both the opportunities and challenges associated with automation, as well as the diverse ways in which artists respond to these changes.

7. Conclusion

This study has explored the transformation of creative labour in the context of the Fourth Industrial Revolution, with particular attention to artists’ lived experiences of automation and artificial intelligence. The findings demonstrate that technological innovation is neither wholly liberating nor entirely disruptive; rather, it produces a complex and ambivalent landscape in which opportunities and challenges coexist.

On one level, automation expands the boundaries of artistic practice by enabling new forms of experimentation, collaboration, and production. Artists increasingly engage with AI systems as creative partners, reshaping traditional notions of authorship and creativity. This shift reflects a broader movement toward distributed agency, where human and non-human actors co-produce cultural outputs. Such developments open up new conceptual and aesthetic possibilities, encouraging artists to rethink the nature of creativity itself.

However, these opportunities are accompanied by significant structural challenges. The rise of platform-based economies and algorithmic governance has intensified precarity within the creative sector. Artists face unstable income streams, heightened competition, and dependence on digital infrastructures that they do not control. The commodification of creative labour, combined with the increasing capabilities of automated systems, raises concerns about the devaluation of human creativity and the sustainability of artistic careers.

The study also highlights the emotional and psychological dimensions of these transformations. Artists navigate a landscape marked by both excitement and anxiety, as they adapt to rapidly changing technologies while negotiating their professional identities. This emotional ambivalence underscores the need to consider not only economic and technological factors but also the human experiences that shape creative labour.

Importantly, the research demonstrates that artists are not passive recipients of technological change. Through hybrid practices, critical engagement, and collective action, they actively shape the conditions of their work. These strategies point toward alternative futures in which technology can be harnessed in more equitable and inclusive ways.

In conclusion, the future of creative labour will depend on the interplay between technological innovation, institutional support, and policy intervention. Ensuring fair compensation, transparency in platform governance, and access to resources will be essential for fostering sustainable creative ecosystems. By centring artists’ lived experiences, this study contributes to a more comprehensive understanding of creativity in the age of automation and highlights the need for balanced approaches that support both innovation and social justice.

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