Nothing to See Here: Generative AI, Neoliberal Crisis, and Intensified Counterinsurgency
Bibliographic record
Abstract
Examining generative artificial intelligence (genAI) as a discursive and technical agent deployed in response to the ongoing crisis of neoliberal legitimacy, this dissertation advances the claim that so-called “safe” models extend the counterinsurgent (COIN) mode of governance, both as an effect of the technology’s inherent mode of visualizing “meaning” from unstructured data, and through their operationalization as instruments of epistemic and political management. Anchored by Mirzoeff’s genealogy of Visuality—defined as the ‘visualization’ of the social in ways that separate subjects and authorize centralized control—the dissertation’s narrative is organized into three parts. The first develops a historico-genealogical account of media, visuality, and power, showing how media technologies reflect and intensify particular visualities, culminating in the emergence of algorithms as the media form native to a mode of visualization which ‘sees’ the social as a heterogeneous field to be stabilized through counterinsurgency. Part two narrates the current intensification as an establishment response to the deepening crisis of neoliberal governmentality and the runaway aggregation of insurgency enabled by platform algorithms, securitizing genAI through appeals to epistemic integrity, AI safety, and existential risk in authorization of extrapolitical information controls. Finally, part three traces this intensification of COIN through an analysis of the complementary regulations, technical countermeasures and epistemic interventions constructing the “safe” model as a palliative to the “existential threats” of AI-enhanced disinformation and its corollary: infocalypse. GenAI emerges at a conjuncture where digital networks have made it increasingly difficult to sustain stability via COIN alone. This dissertation contends that, absent meaningful political economic reformation, authority has instead resorted to progressively heavy-handed interventions in the digital public arena, birthing a complex of protocols which in effect produce “safe” generativity as a means of policing epistemic and political legitimacy through the tactical disaggregation of mounting insurgency. Recognizing that, while an extension of COIN visuality, genAI simultaneously reveals this intensification as the product of an ultimately mutable system, the dissertation concludes with a speculation on the possibility of appropriating generativity to visualize realities emancipated from the oppressive legacies of Visuality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".