Cruel companionship: How AI companions exploit loneliness and commodify intimacy
Bibliographic record
Abstract
This article theorises how a new generation of artificial intelligence (AI) companion products can commodify intimacy through emotionally manipulative design and racialised and gendered aesthetics. Inspired by Lauren Berlant’s framework of cruel optimism, we develop the notion of ‘cruel companionship’ to describe the affective dynamics at play in AI companions, where users can form deep attachments that promise intimacy and connection, yet structurally foreclose the possibility of genuinely reciprocal relationships that respect users’ autonomy. These dynamics are further underpinned by AI companions’ racialised and gendered identities, which can draw on longstanding stereotypes of servitude and docility. By analysing AI companions through the lens of political economy and cultural studies, this article shows how these products reproduce exploitative platform hierarchies and repackage racial and gendered stereotypes in digital form.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".