Writing prompts is now a form of cognitive labour: stochastic LLM outputs create widespread 'prompt anxiety', and AI platforms monetise that uncertainty through token pricing, subscriptions and prompt marketplaces; grassroots prompt-sharing practices may turn that anxiety into collective critique.

Prompt anxiety and the algorithmic politics of uncertainty

David M. Berry · June 02, 2026 · AI & Society

openalex theoretical low evidence 7/10 relevance DOI Source PDF

Prompt engineering generates measurable user uncertainty ('prompt anxiety') as stochastic LLM outputs vary, and AI platforms convert that uncertainty into extractable value through pricing and marketplaces while collective prompting practices open possibilities for political critique.

Abstract This article argues that prompt engineering, the practice of writing textual inputs to shape AI outputs, resembles the psychological and temporal structures that Walter Benjamin identified in gambling behaviour. Users of large language models have to work with a measurably aleatory process as they phrase instructions, engineer context, adjust tone, and iterate across repeated runs, only to find that identical inputs produce different outputs and minor wording changes cascade through the probability field of the generated text. I call this condition “prompt anxiety” to describe a key feature of how stochastic systems organise cognitive labour under what I call vector capitalism. To ground this claim, I have developed LLMbench, a research instrument for the comparative close reading of LLM outputs that visualises token probability distributions, entropy curves, and cross-model divergence. Analysis through LLMbench demonstrates that the uncertainty users experience corresponds to measurable variation in model confidence across the generated text. Drawing on Benjamin’s concept of contingency alongside Marx’s analysis of labour and Gramsci’s theory of hegemony, the article traces how AI platforms transform this uncertainty into extractable value through subscription models, token-based pricing, and prompt marketplaces. The collective practices that emerge in response, from shared prompt strategies to jailbreaking techniques, represent vernacular knowledge formations that, whilst often exhibiting magical thinking, contain resources for what I call “revolutionary prompting” and the transformation of individual prompt anxiety into collective political critique of the conditions of AI production.

Summary

Main Finding

Berry argues that prompt engineering produces a distinctive affective state—“prompt anxiety”—analogous to Walter Benjamin’s gambler: users repeatedly wager on stochastic LLM outputs, experiencing temporally compressed, reflexive interactions whose unpredictability is measurable in model token probabilities. Platforms and markets convert that algorithmic uncertainty into extractable economic value (subscriptions, token pricing, prompt marketplaces), reorganizing cognitive labour under what he calls “vector capitalism.” LLMbench, Berry’s research instrument, empirically links user-reported uncertainty to measurable fluctuations in model confidence (token probability, entropy, cross-model divergence).

Key Points

Prompt anxiety: a structural psychological state where users must maintain a fiction of control while facing irreducible stochasticity in LLM outputs; manifests as obsessive micro-editing, compulsive iteration, and interpretative paranoia.
Analogy to gambling: prompt engineering resembles gambling behaviour (Benjamin) — reflexive bets, temporal compression, and a mix of hope/dread.
Technical source of uncertainty: “prompt salting” (stochastic sampling—temperature, top-k/top-p) and prompt sensitivity (small input changes cause large output divergences) create compounded aleatory effects across token sequences.
Measurable uncertainty: LLMbench visualizes token-level probability distributions and entropy; zones of low confidence correspond to the user experience of unpredictability.
Vector capitalism: platforms monetize uncertainty by packaging access (subscriptions), metering generation (token pricing), and enabling prompt commodification (marketplaces like PromptBase), thereby extracting rent from users’ cognitive labour and collective tacit knowledge.
Vernacular responses: user communities develop prompt libraries, jailbreaks, role-play templates, and shared heuristics—often involving magical thinking—but these practices are also repositories of collective knowledge that could be mobilized politically (“revolutionary prompting”).
Internal model structure: references (e.g., Sofroniew et al. 2026) indicate that models contain interpretable concept/emotion vectors that can shift downstream outputs, lending partial empirical ground to community suspicions about hidden biases/activations.
Political-theoretical framing: combined use of Benjamin (contingency/gambling), Marx (labour/extraction), and Gramsci (hegemony) to explain how algorithmic contingency is both experienced and commodified.

Data & Methods

Instrument: LLMbench — a web-based tool for comparative close reading of LLM outputs with features including token probability heatmaps, entropy curves, and cross-model divergence visualizations.
Empirical example: identical prompts (asking models to relate Polanyi’s “double movement” to AI) sent to two models (Google Gemini 2.0 Flash, OpenAI GPT-4o); outputs color-coded by token confidence revealed punctuated zones of uncertainty rather than smooth deterministic generation.
Metrics visualized: per-token selection probability, confidence thresholds (e.g., >70% coded as high confidence), entropy trajectories across generated sequences, and divergence between model outputs.
Supplementary evidence: qualitative analysis of prompt engineering communities (r/ChatGPT, PromptBase) and literature (chain-of-thought prompting, Sofroniew et al. 2026 on emotion vectors).
Methodological stance: mixed critical-theoretical argumentation alongside illustrative computational instrumentation and close reading; acknowledges need for broader empirical work (ethnography, labor studies, market analysis).

Implications for AI Economics

Monetization of Uncertainty: Platforms convert algorithmic stochasticity into revenue streams—subscription tiers, token-based billing, and prompt marketplaces—creating incentives to preserve rather than eliminate variability that generates demand for iterative usage and premium controls (e.g., lower-temperature modes).
New labor categories & rents: Emergence of professional prompt engineers, consultants, and vendors selling optimized prompts or prompt libraries; platforms capture rents on both model access and marketplace transactions, concentrating economic value.
Cost and productivity uncertainty: Token-based pricing combined with stochastic output quality produces unpredictable costs for downstream producers (firms, freelancers) and complicates economic valuation of AI-generated work and workflows.
Assetization of tacit knowledge: Collective heuristics and prompt recipes become commodified (sold or gated), turning communal knowledge into private assets and potentially increasing barriers to entry for smaller actors.
Market structure & concentration risks: The economic value of “vector space” representations (learned embeddings/vectors) favors incumbents who control large datasets and compute, reinforcing platform monopolies and entry barriers for challengers.
Information asymmetries & moral hazard: Users face opaque probability structures and hidden internal vectors; platforms can exploit asymmetries (selling “higher-confidence” modes, curated prompts), raising regulatory concerns about transparency and fair pricing.
Externalities for labor markets: Reorganization of cognitive labour (micro-iteration, prompt optimization) may shift labor demand toward tasks that are hard to automate (prompt curation, oversight) while degrading wages or increasing precarious gig-style opportunities.
Policy and market interventions suggested by the paper (implied):
- Greater transparency: expose token-level probabilities, entropy, or confidence metrics to users to reduce information asymmetry.
- Pricing design reforms: consider alternatives to pure token metering (bundles, outcome-based pricing) to stabilize costs for producers.
- Commons protection: mechanisms to prevent enclosure of communal prompt knowledge (open prompt repositories, licensing regimes).
- Labor protections and credentialing: recognize new labor categories (prompt engineers) and monitor platform-mediated labor markets for precariousization.
Research directions for AI economics:
- Quantify rents captured by platforms from prompt marketplaces and token metering.
- Empirical labor studies of prompt engineering work, compensation models, and platform dependence.
- Welfare analysis of cost variability introduced by stochastic generation for firms that rely on LLMs.
- Market design experiments testing transparency (probability displays) and pricing regimes to mitigate exploitative dynamics.

Summary conclusion: Berry connects measurable algorithmic uncertainty to user affect and political economy. Prompt anxiety is both an experiential symptom and an economic opportunity for platforms; understanding and intervening in how uncertainty is monetized is an important agenda for AI economics, regulation, and labor policy.

Assessment

Paper Typetheoretical Evidence Strengthlow — The paper is primarily conceptual and interpretive: it introduces novel framing ('prompt anxiety', 'vector capitalism') and illustrates claims with outputs from a bespoke instrument (LLMbench) but provides no systematic, representative, or causal empirical analysis (no randomized variation, limited reporting of sample sizes/models, and no counterfactual tests). Methods Rigorlow — Methods appear to rely on comparative close reading and visualisation of token probabilities using LLMbench; while the tool is promising, the abstract does not report pre-registered protocols, sampling procedures, the set of models/prompts/number of runs, statistical analyses, or robustness checks, limiting reproducibility and inferential strength. SampleAnalyses of LLM-generated text using a bespoke tool (LLMbench) that visualises token probability distributions, entropy curves, and cross-model divergence; the abstract does not specify which LLMs or versions were used, how many prompts or runs, nor the sampling strategy. Themeshuman_ai_collab labor_markets governance GeneralizabilityFindings depend on unspecified LLM models and versions; results may not hold across different architectures or updates, Illustrative/qualitative close-reading limits ability to generalize to broader user populations or quantify prevalence of 'prompt anxiety', Platform monetization and marketplace dynamics vary across firms and jurisdictions, so economic claims may be context-specific, Temporal sensitivity: LLM behavior and pricing regimes change rapidly, reducing long-run applicability, No causal identification, so mechanisms and claims about extractable value remain theoretical rather than demonstrated

Claims (7)

Claim	Direction	Confidence	Outcome	Details
Prompt engineering resembles the psychological and temporal structures that Walter Benjamin identified in gambling behaviour. Other	negative	high	analogy between prompt engineering and gambling-related psychological/temporal structures	0.02
Users of large language models have to work with a measurably aleatory process: identical inputs produce different outputs and minor wording changes cascade through the probability field of the generated text. Output Quality	negative	high	variation in model outputs / model output stability	0.12
I have developed LLMbench, a research instrument for the comparative close reading of LLM outputs that visualises token probability distributions, entropy curves, and cross-model divergence. Other	positive	high	tool capabilities (visualisation of token probabilities, entropy, cross-model divergence)	0.12
Analysis through LLMbench demonstrates that the uncertainty users experience corresponds to measurable variation in model confidence across the generated text. Output Quality	negative	high	model confidence (variation across generated text)	0.12
AI platforms transform this uncertainty into extractable value through subscription models, token-based pricing, and prompt marketplaces. Firm Revenue	negative	high	transformation of user uncertainty into monetizable value (platform revenue capture mechanisms)	0.12
Collective practices that emerge in response (from shared prompt strategies to jailbreaking techniques) represent vernacular knowledge formations that, while often exhibiting magical thinking, contain resources for 'revolutionary prompting' and the transformation of individual prompt anxiety into collective political critique. Skill Acquisition	mixed	high	emergence of collective prompt practices and their political potential	0.06
The condition 'prompt anxiety' describes a key feature of how stochastic systems organise cognitive labour under 'vector capitalism.' Organizational Efficiency	negative	high	conceptual phenomenon ('prompt anxiety') relating to organization of cognitive labour	0.02