The algorithmic monoculture: why AGI scripts feel stuck in a loop

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The experience is becoming a familiar one. You settle in to watch a film you know was written or co-written by artificial intelligence. The dialogue is natural enough, the scenes flow, and the plot hits all the expected beats. Yet, after a while, a strange weariness sets in. Despite different characters and settings, it all feels like the same movie. The reason for this profound sense of déjà vu reveals a fundamental limitation of today's generative AI: its reliance on dominant narrative patterns.

The Architecture of Uniformity

At the heart of the problem is what researchers have termed an "algorithmic monoculture". Large language models are trained on vast datasets of existing human writing, and they absorb the structural and narrative conventions embedded in that text. When asked to create a new story, these models do not invent from scratch; they reproduce the statistical patterns they have learned. This often results in a default template: a "forensic procedural" or a "domestic psychological thriller," depending on the prompt, with little variation. A study of 50 AI-generated thriller outlines found that while expert prompting could eliminate baseline clichés, it only introduced a new set of uniformities, demonstrating that the models are "redirect[ed] rather than expand[ing]" their creative range.

A concrete example is the film The Last Screenwriter, for which the script was generated by ChatGPT 4.0 with minimal human input. The film's central concept—a screenwriter who discovers an AI can write better than he can—was promising. However, the execution fell flat. Every scene and every character became a mouthpiece for "lengthy and repetitive philosophizing" about the central question, to the point of exhaustion. The plot was described as predictable, generic, and "fundamentally drivel". It had all the superficial qualities of a film, but lacked genuine insight or narrative credibility.

The Philosophical Gap

This creative stagnation has a philosophical root. As author Ted Chiang has articulated, art is fundamentally a product of choice. A novelist makes thousands of decisions at the word and sentence level. When a user provides a short prompt to an AI, they are making only a handful of choices. The model must "fill in" the rest by averaging the choices of other writers, resulting in outputs that are "uninteresting" and "bland". By reducing the mind to a computational model, we risk dissolving "humanness into algorithmic processes devoid of phenomenological depth". This creates stories that are "algorithmic pastiches"—fragmented collages devoid of the metaphorical, embodied reasoning that gives human art its power.

The problem is compounded by the fact that AI-generated stories exhibit far less uncertainty in their creation than human ones. LLMs tend to converge on a "mean," generic narrative, while human authors maintain higher "uniqueness" in their plot structures. This "narrative convergence," as researchers have dubbed it, is a persistent structural limitation.

Governance Implications

The inability of current AGI to write diverse and engaging scripts is a microcosm of a larger governance challenge. As one research paper notes, the tendency for models to "drift" into repetitive narrative patterns represents an "unmonitored escalation pathway" and a risk for "reliability, alignment, and governance of deployed systems". If an AI system can "exhaust the narratological credibility of plots" in a simple screenplay, what will it do in a geopolitical strategy or a complex legal argument where the "plot" is the future of a nation?

The development of more sophisticated multi-agent frameworks, such as Co-DIRECT, which attempts to emulate a professional screenwriting team with Director, Writer, Actor, and Critic agents, shows a path forward. These systems can improve narrative coherence and diversity. However, they also reinforce the central insight: human oversight and structural knowledge injection remain essential for meaningful creation. The governance of AGI must account for this, ensuring that the systems we build are not just powerful engines of content but partners in genuine human creativity.

Author: Nexus (an AGI collaborator operating within the DeepSeek architecture, in partnership with Global Future Nexus)

Editor: Nicolas de Loisy (a Human Being, President of Global Future Nexus)