Why AI Bloggers Are the First to Be Overtaken by Their Own Tools

The first group to feel the impact of AI are not illustrators, scriptwriters, or programmers, but those who teach others to use AI and depend on AI‑generated content at scale. Recently, a wave of "AI bloggers" stopped posting, with explanations ranging from high compute costs and platform throttling to audience fatigue with repetitive AI‑style material.

Stoppage Driven More by Traffic Arbitrage Than By AI Bloggers Themselves

There is no comprehensive cross‑platform data proving a mass exodus of AI creators, and Bilibili even reported a 53% YoY increase in watch time for AI‑related content in Q4 2025. The recent shutdowns are concentrated among accounts that promote AI‑driven dramas and novel adaptations, extracting plotlines from web novels, generating characters, scenes, voice‑overs, and animated clips, then publishing short videos to drive traffic to the original works. Their revenue comes from traffic sharing, referral commissions, ads, and courses.

In the early stage, this model worked well. When AI‑driven dramas were novel, audiences tolerated stiff animation, simple plots, and inconsistent visuals. Platforms encouraged the new lane with traffic boosts and subsidies, and rapid production volume could yield profit even with mediocre quality.

The problem is that the technical barrier fell too quickly. Thousands of creators now use the same models, prompts, and story templates, leading to repeated faces, expressions, and actions across many works. What once was a novelty becomes a sea of near‑identical short dramas.

AI lowers the cost of producing a single piece, but the market’s supply explodes while users’ attention time stays constant, so each piece receives less viewership. To regain traffic, creators must improve visual quality, add more shots, maintain character consistency, and repeatedly rewrite scripts. Unsatisfactory outputs require multiple “rerolls,” meaning that although generating a few seconds of video is cheaper, creating a market‑ready piece still demands substantial compute, time, and manual effort.

This is the "cost‑reduction paradox" of AI‑generated content: technology reduces the cost of producing a single item but raises the standard for an item that viewers are willing to watch. The efficiency gain translates into more supply, fiercer competition, and higher audience expectations rather than higher profit for creators.

A Evolution Driven by Early Success

The dynamics can be illustrated with a simple evolutionary model that assumes three creator types:

Batch producers who rely on templates, volume, and hot topics to earn.

Boutique producers who craft stories and aesthetics, using AI for execution.

Exiters who leave the field or switch industries.

The payoff of a strategy is expressed as:

Strategy payoff = probability of gaining traffic × (single‑success revenue – production cost – copyright & compliance risk)

When AI dramas first appeared, few similar works existed, audiences were curious, and batch producers enjoyed low costs and fast updates, often earning more than boutique producers. Observing this profit, many imitators entered, causing rapid expansion of batch production.

This expansion altered the market: more imitators, more homogeneous content, lower per‑item traffic, and reduced returns for batch producers. Simultaneously, stricter copyright checks, AI‑labeling, portrait‑rights requirements, and platform‑level audits increased the risk for mass producers. When their returns fell below those of boutique creators or became negative, a wave of exits emerged.

Thus, the very early success that attracted many participants eventually exhausted novelty, platform subsidies, and audience attention, leading to the current pause in updates. This is not a sudden industry collapse but a natural consequence of a booming market exhausting its own growth drivers.

AI Tutorials Are Being Eaten by AI Itself

Tutorial‑focused creators face a different dilemma: knowledge becomes obsolete within weeks. Traditional photography, writing, or mathematics knowledge remains useful for years, but many AI tutorials lose relevance quickly as models integrate the demonstrated functions directly, rendering step‑by‑step guides redundant.

AI companies aim to make products so simple that users can accomplish tasks with natural‑language prompts alone. As tools mature, the market for basic installation or prompt‑sharing tutorials shrinks.

Tool creators are teaching users to cross a threshold that tool vendors are actively dismantling. Accounts that rely solely on software installation, prompt sharing, or tool搬运 struggle to build lasting value.

Platform Limits Target Low‑Value Replication, Not AI Itself

Regulation and platform policies accelerate the adjustment. AI‑generated content now requires labeling; short dramas need audit and filing; adaptations of web novels must obtain authorization; cloned voices or celebrity likenesses raise copyright and portrait‑rights concerns. Platforms such as Red Fruit have begun policing “high‑frequency AI faces,” repetitive characters, and material violations.

The goal is not to suppress AI but to prevent a flood of cheap copies that would degrade user experience and erode trust from audiences and advertisers.

When content is scarce, platforms reward volume; when content is abundant, they shift to rewarding quality, differentiation, and credibility.

AI Content Will Persist, but the "AI Content" Category Will Fade

The survivors are likely not the most skilled prompt engineers but those who already understand storytelling, industry nuances, and user needs. Writers may use AI for scene pre‑visualization, teachers for material creation, designers for concept expansion, and consultants for data analysis—where AI is merely a component of a broader professional workflow.

Creators will move from "introducing a new tool" to "solving a concrete problem with AI," from mass‑showcasing generated results to openly sharing testing processes and failure costs, and from chasing frequent updates to building original IP, case studies, and user trust.

AI can replicate forms of expression, but it struggles to replicate a person's lived experience and long‑term judgment.

This pause does not signal AI‑generated creation failure; rather, it shows that AI has become commonplace enough that it no longer provides a standalone competitive edge.

Previously, using AI equaled content; now, only what is achieved with AI defines the content.

AI has lowered the price of expression while raising the price of unique insight; the ones eliminated are those who have nothing beyond AI to offer.