Neutral Intelligence: Contextual Framework

Connecting Distributed Knowledge Production, Participatory Labour, and Creative Commons

Neutral Intelligence emerges from a critical examination of how contemporary systems produce, distribute, and validate knowledge. Research in distributed knowledge production reveals that centralized approaches increasingly fail to address complex contemporary challenges, as traditional models concentrate expertise in institutions and corporations, treating practitioners and communities as data sources rather than as collaborative agents in knowledge creation. Yet participatory research methodologies demonstrate that distributed knowledge production, where communities actively co-create understanding relevant to their contexts, generates more robust, locally-adapted, and ethically grounded outcomes.1

NI applies these principles to creative systems and intelligence, drawing inspiration from historical models of worker self-organization grounded in research into Yugoslav labour movements. The project references Radna Akcija—the Yugoslav workers' movement rooted in principles of solidarity, communal effort, and democratic self-management.2 This framework directly informs the artist's MFA research on laserography: an embodied creative practice that interrogates the relationship between technology, human labour, and memory through sound-modulated light. The thesis project 0x2e reimagines the darkroom as a site of collective production, where participants engage in the cyclical labour of exposing, coating, and developing photosensitive surfaces—work that echoes the rhythms of factory labour while insisting on creativity as a form of knowledge production and resistance.

NI applies these principles to creative systems and intelligence, drawing inspiration from historical models of worker self-organization grounded in research into Yugoslav labour movements. The project references Radna Akcija—the Yugoslav workers' movement rooted in principles of solidarity, communal effort, and democratic self-management.2 This framework directly informs the artist's MFA research on laserography: an embodied creative practice that interrogates the relationship between technology, human labour, and memory through sound-modulated light. The thesis project 0x2e transforms this interrogation into lived practice by reimagining the darkroom as a site of collective production, where groups of participants engage in the cyclical labour of exposing, coating, and developing photosensitive surfaces. This collaborative labour process—where tasks are coordinated, decisions shared, and outcomes emerge through real-time negotiation between human agency and technological systems—directly mirrors the self-managed labour structures that Yugoslav workers' councils pioneered. The work insists that creativity is not an individual pursuit but emerges through collective participation, embodied material engagement, and the persistent effort to maintain human agency within technological systems. By grounding creativity in labour, in the visible, tactile work of coating surfaces with light-sensitive emulsion and timing laser exposure, 0x2e refuses the separation between artistic creation and manual work. Memory, too, becomes collective rather than individual: the laserographs that result are records not of a single artist's vision but of multiple participants' labour, decisions, and material encounters. Each surface carries traces of these shared efforts, preserving the memory of collaboration itself. This approach directly embodies the principles NI articulates: intelligence emerges through decentralized, participatory practice; knowledge is produced through labour rather than extracted as data; and the relationship between humans and technological systems remains perpetually open to renegotiation.

The architecture NI proposes mirrors principles from systems ecology and morphogenesis research: complex, adaptive systems emerge through distributed interactions rather than centralized control.4 An interactive installation combining responsive light, sound, and human presence functions as a feedback system where humans and technology continuously modulate each other's behaviour in real time. This contrasts with conventional systems where alignment is predetermined and invisible. Research on human-AI feedback loops demonstrates that when people interact with adaptive systems in real time, their own perceptual and creative capacities evolve, and they develop more nuanced understanding of how systems shape behaviour.

The economic implication is substantial. Current models extract creative labour: artists provide training data without consent or compensation, then compete with systems trained partly on their own work. NI inverts this through commons-collaborative economy—a mode of production where distributed groups of peers collaborate in conditions that empower them and society.5 Communities become co-architects of intelligence systems reflecting their own values, cultural traditions, and aesthetic knowledge. This reframes creative work from extraction to genuine collaboration, where artists, participants, and systems all contribute to shaping what intelligence becomes. Contemporary worker cooperatives demonstrate higher levels of job satisfaction, lower turnover, and greater economic resilience than conventional firms, suggesting that participatory creative systems generate more sustainable, culturally rooted, and ethically grounded outcomes than centralized algorithmic systems.

Methodologically, NI draws from participatory action research frameworks that emphasise community agency in knowledge production.6 The approach is grounded in the historical example of Yugoslav self-management experiments, which—despite their eventual institutional failures—demonstrated that workers could actively participate in economic decision-making and that distributed authority could organise complex productive processes. Rather than imposing external solutions, NI creates environments where creative and technical knowledge emerges through practice, dialogue, and iterative refinement. These environments remain interconnected at larger scales, enabling communities to share techniques and learn from each other's experiments, whilst maintaining local autonomy and cultural specificity.

The result is an approach to intelligence that is fundamentally ecological: rooted in place, responsive to local values, perpetually open to adaptation, and understood as something communities continuously create and reshape through their creative practices and participatory labour.

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References

Footnotes

1. Sustainability Directory, "Distributed Knowledge Production," 2025, https://sustainability-directory.com/term/distributed-knowledge-production/ ↩
2. YugoBlog, "Youth Work Actions / Omladinska Radna Akcija," 2025, https://yugoblok.com/youth-work-actions/; Antipolitika, "Yugoslav Workers' Self-Management: Emancipation of Labour," 2020, https://antipolitika.noblogs.org/post/2020/07/17/self-management/ ↩
3. CRT Project, "Why Decentralized Creative Collaboration Matters," Creative Rights and Technology Whitepaper, 2025, https://crt-project.gitbook.io/crt-project-docs/market-opportunity/why-decentralized-creative-collaboration-matters; Codaworx, "Creating Together: The Power of Participatory Public Art," Codazine, 2025, https://knowledge.codaworx.com/codazine/creating-together-the-power-of-participatory-public-art ↩
4. PMC/NIH National Center for Biotechnology Information, "Programming Morphogenesis through Systems and Synthetic Biology," 2017, PMC6589336, https://pmc.ncbi.nlm.nih.gov/articles/PMC6589336/ ↩
5. P2P Foundation, "Commons Collaborative Economy," 2021, https://wiki.p2pfoundation.net/Commons_Collaborative_Economy; The Next System Project, "How to Create a Thriving Global Commons Economy," https://thenextsystem.org/learn/stories/how-create-thriving-global-commons-economy ↩
6. Maynooth University, "Creative Practice and Embodied Knowledges," 2025, https://ahi.maynoothuniversity.ie/creative-practice-embodied-knowledges/; PMC/NIH, "Creative Practices Embodied, Embedded, and Enacted in Communities," 2016, PMC4701984, https://pmc.ncbi.nlm.nih.gov/articles/PMC4701984/ ↩