Aug 2026· Journal of Business Anthropology· 1 citation· 31 references
TL;DR
It is argued that LLMs represent a fundamental shift in human-technology interaction, one requiring ritualistic linguistic precision rather than technical commands, and that effective prompt construction functions as ritual magic through three essential elements: clear and specific language, contextual framing, and structured sequencing.
Abstract
This essay reframes the discourse around large language models (LLMs) by drawing on anthropological and linguistic theories of magic as a positive transformative force. While current discussions often cast AI as mysteriously dangerous – a form of “dark magic” that beguiles users and obscures ethical concerns – we argue that LLMs represent a fundamental shift in human-technology interaction, one requiring ritualistic linguistic precision rather than technical commands. Drawing on theories of performative language and magical rites, we propose that effective prompt construction functions as ritual magic through three essential elements: clear and specific language, contextual framing, and structured sequencing. In this, we show that AI is not a singular tool operation, but rather a mode of forming a mutual cybernetic relationship. Through case studies from the AI Anthropology Toolkit, we show how skilled practitioners harness the “magical” capabilities of LLMs for analytical insights through careful prompt engineering. We then examine implications for business anthropologists working in organizational settings, exploring how this cybernetic relationship enables distributed agency and co-becoming between human expertise and computational pattern recognition. This perspective provides both a theoretical contribution to understanding human-AI interaction and practical guidance for anthropological practice in an era of increasingly sophisticated AI collaboration.
Large Language Models (LLMs) are often framed as a tool to be “prompted” where humans command, and the machine operates. However, as AI becomes increasingly intermeshed in our lives, role divisions blur, and humans take the role of interpreting and carrying out machine instructions. This project explores the concept of Large Language Humans (LLHs), describing this role reversal where humans become the operators of AI logic. Using a Research-through-Design (RtD) approach, we developed the Large Language Human Machine: an embodied, “black box” device that prints cryptic, ritualistic instructions for the user to perform. Inspired by instruction-based performance art (e.g., Yoko Ono, John Cage), the project treats instruction as a design material to expose shifting dynamics of authority, opacity, and agency. We document the iterative process from screen-based prototypes to a self-contained physical artifact. By viewing this process through four design parameters: persistence, tempo, uncertainty, and opacity, we discuss how materializing instructions as physical “receipts” reconfigures the user’s commitment to the machine’s agency.
K. Zheng, Sang-won Leigh· Creativity & Cognition· 0 citations
A defining if under-acknowledged feature of large language models (LLMs) is their non-indexicality: what AI
engines “know” about the world reflects only their ability to predict and successfully imitate existing textual representations of
that world. Like fictional narratives, therefore, they traffic not in truth or falsity, but believability. Helpfully, literary
critics have experience thinking critically about the ways in which believability is shaped by existing ideologies and biases — an
acknowledged limitation of AI-generated texts as well. Drawing on Neilsen, Phelan and Walsh’s rhetorical model of fictionality,
this article uses Chimamanda Ngozi Adichie’s metafictional short story, “Jumping Monkey Hill” as a laboratory to explore the
relationship between fictionality and believability in a situation where rhetorical cues about a text’s ontological status are
misleading. From there, I suggest how literary ways of reading might inform our engagement with AI texts — including and
especially those that present themselves as factual.
Quattrociocchi and colleagues warn that the fluent outputs of large language models may allow linguistic plausibility to substitute for epistemic evaluation, producing the condition they call *Epistemia*: the experience of possessing knowledge without undertaking the practices through which judgment would ordinarily be warranted. This article accepts that diagnosis but challenges its explanatory framework, which compares an embodied, socially situated human knower with an isolated generative model thereby locating epistemic legitimacy in capacities internal to autonomous agents. Drawing on Carlo Sini's philosophy of practices, writing, signs, and technics, we propose instead to understand a large language model (LLM) as a *techno-semiotic machine* that automates a phase of written semiosis by producing plausible linguistic configurations from the sedimented archive of human writing. From this perspective, *Epistemia* is one consequence of a broader phenomenon that we call *epistemic schizologia*: the socio-technical cleavage between signs as linguistically accomplished expressions and signs as moments within socially embedded circuits of interpretation, evidence, criticism, verification, and responsibility. This cleavage is reinforced by *eikotic closure*, through which a plausible continuation is presented with the finality of an epistemic result, and by algorithmic authority and epistemic self-misrecognition. The relevant unit is therefore not the model alone but the complete practice in which generated inscriptions are prompted, interpreted, verified, contested, used, and made consequential. This reframing preserves the distinction between linguistic production and responsible understanding while grounding a design programme centred on inspectable genealogy, contestability, distributed responsibility, epistemic agency, and the evaluation of hybrid human--AIpractices.
Why what is really a matter of data analytics and statistical prediction is so readily assumed to be a display of real intelligence and even emergent cognition is explored by genealogically tracing the relationship between machines, organisms and language.
Chantelle Gray· Deleuze and Guattari Studies· 0 citations
It is argued that anthropology requires a new conceptual framework for understanding the contemporary social life of artificial intelligence (AI), and "the magic of AI" is proposed as an analytical concept that shifts attention from AI's technical capacities to the beliefs, uncertainties, and sociotechnical imaginaries through which it acquires authority and efficacy.
M. Baas, Roanne van Voorst· Anthropological Theory· 2 citations
How AI systems work by recycling a narrow set of visual tropes in their training data that have very little to do with human notions of intelligence or creativity is shown.
Matti Pohjonen· Anthropological Theory· 0 citations
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