Enterprise AI Analysis
Beyond Instrumentalism: Posthuman Assemblages and Generative Artificial Intelligence in Contemporary Animation
This article offers a critical reinterpretation of recent literature on Generative AI (GenAI), from the fields of computer science, animation, and digital filmmaking, applying a broadly posthumanist theoretical lens with the aim of threading together disparate discourses, engaging both technical and artistic perspectives. We focus on current issues of relevance for digital artists and animators in the interactive arts and filmmaking space, by examining concerns (labour relations, ethics, authorship), as well as commendations (co-creativity, efficiency, expansion). Drawing especially on Rosi Braidotti's concept of zoe/geo/techno-assemblages, Karen Barad's agential realism, and Gilbert Simondon's challenge to instrumental conceptions of technology, we aim to contribute to a foregrounding of creative discourses on artificial intelligence (AI) in digital art and animation.
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This analysis reinterprets Generative AI (GenAI) within animation and digital filmmaking through a posthumanist lens, emphasizing relationality, ethics, and sustainability. It moves beyond instrumental views of technology, advocating for co-creative assemblages that consider human, machinic, and ecological entanglements. The report addresses critical concerns like labor displacement, ethical considerations of AI 'hallucinations,' and the significant environmental impact of AI infrastructure. It highlights LoRA (Low-Rank Adaptation) as a promising, energy-efficient, and decentralizing technology that fosters diverse, community-driven creative practices, countering the homogenization tendencies of large foundation models. Ultimately, it calls for AI development focused on social good, inclusivity, and planetary well-being.
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Global Electricity by ICT Sector
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Global GHG Emissions by ICT Sector
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US Data Centers Electricity by 2030 (AI-driven)
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Bitcoin Annual Energy Consumption
Deep Analysis & Enterprise Applications
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Posthuman & Theoretical Framework
Environmental & Ethical Concerns
Human & Labor Impact
Technical Innovations (LoRA)
Technology as Dynamic Assemblage
Drawing on Gilbert Simondon, Deleuze and Guattari, and Rosi Braidotti, this framework views AI not as inert tools but as dynamic 'techno-assemblages'—emergent, unpredictable systems co-constituted through material and semiotic entanglements. This shifts animation's understanding from discrete object production to dynamic emergence across human, machinic, and ecological registers, aligning with a relational ontology where creativity is distributed across entangled actors.
Relationality and Sympoiesis
Karen Barad's 'agential realism' and Donna Haraway's 'sympoiesis' (making-with) further underscore that pre-formed subjects or objects are denied in favor of becoming through relational processes. AI systems are thus participants in 'zoe/geo/techno-assemblages,' fields of becoming animated by life's vital force, rather than stable instruments. This redefines creative agency as a shared, co-creative process between human and more-than-human forces.
4%
Global Electricity Consumption by ICT Sector
1.4%
Global GHG Emissions by ICT Sector
8%
US Data Centers Electricity by 2030 (AI-driven)
Zoe-Ethical Imperative & Planetary Entanglement
Unchecked AI expansion risks distorting relational fields and exacerbating environmental degradation. A 'zoe-ethical' approach, inspired by Rosi Braidotti, recognizes life beyond the human as a generative force that resists capture by human-centric control. Creative industries must cultivate an awareness that visual culture production materially and consequentially impacts planetary life systems, understanding AI infrastructure and environmental degradation as mutually constitutive 'intra-actions' (Karen Barad).
Jevons Paradox and Energy Demands
The Jevons Paradox—where efficiency improvements lead to increased total consumption—is highly relevant to AI. The growing energy demands of GenAI, particularly from data centers, pose a significant infrastructural burden. For instance, Bitcoin's annual energy consumption of 173 TWh (0.78% of global electricity) contextualizes the scale of AI's power needs, highlighting that ecological costs are not peripheral concerns.
Displacement and Devaluation of Human Labor
The integration of AI in animation accelerates production workflows, raising concerns about job displacement, relegation of artists to supervisory roles, and increased precarity in short-term contracts. This risks devaluing human creative labor and flattening aesthetic diversity into market-optimized outputs. While AI democratizes access, it threatens traditional craft practices and artistic identity, necessitating critical and relational cultivation of technology.
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Authorship, Intellectual Property, and Ethical Challenges
AI-generated media complicates traditional notions of authorship and intellectual property, blurring boundaries between citation, remix, and invention. The entangled agencies of AI systems destabilize the model of the solitary human creator, prompting a rethinking of originality and intentionality within anthropocentric legal and cultural frameworks. Ethical clarity and attentiveness to these shifting dynamics are indispensable, especially concerning the potential for "recursive degeneration" if unchecked.
LoRA's Decentralizing Impact on AI Aesthetics
Centralized Foundation Models (GPT-4, DALL·E)
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LoRA Fine-tuning at the Edge
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Localized Cultural Aesthetics & Stylistic Idiosyncrasies
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Community-Driven Innovation & Open-Source Ecosystems
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Ethical & Sustainable AI Practice
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LoRA's Ethical & Open-Source Alignment
LoRA offers a materially lighter, epistemologically decentralised, and ethically pluralistic alternative to the monoculture of foundation models. By enabling model fine-tuning without full model weights or high-performance infrastructure, LoRA redistributes creative agency. It supports data sovereignty, opt-in customisation, and aligns with commons-based ethics through integration into open-source ecosystems like Hugging Face and ComfyUI. This fosters collaborative environments where shared weights and community-curated styles are the norm.
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Phase 01: Discovery & Strategy
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Phase 02: Pilot & Proof of Concept
Develop and deploy a small-scale AI pilot project in a controlled environment. Focus on a specific use case, measure preliminary KPIs, and gather user feedback to iterate on the solution. Prioritize low-risk, high-impact areas.
Phase 03: Scaled Deployment & Integration
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Phase 04: Training & Change Management
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Phase 05: Optimization & Ethical Governance
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