"AI swill" refers to low-quality, redundant, and cognitively valueless content mass-produced by generative artificial intelligence. It permeates digital spaces with technical efficiency, forming a characteristic digital cultural syndrome, and providing a key reference sample for governing low-quality content in the digital era. As a "pollutant" in the digital content ecosystem, "AI swill" not only occupies the dissemination channels of high-quality information but also erodes the original spirit of content production, erodes the audience's information discernment ability and cognitive system, and poses a threat to the healthy development of the digital cultural ecosystem. Analyzing its generation mechanism and improving the governance system have become key issues in maintaining information quality and ensuring the healthy development of the cultural ecosystem in the digital era.

The proliferation of "AI swill" is a product of the interplay of multiple factors, including technological iteration-driven production transformation, platform-led traffic logic, and recursive pollution in data circulation. The core crux lies in the alienation caused by the substitution of "efficiency priority" for "value priority" in the production and dissemination of digital content.

Firstly, the reduction in technical barriers has induced creative alienation. Traditional content production requires knowledge accumulation, experience accumulation, and creative thinking, while generative AI, through pattern recognition and probabilistic sampling, can "generate with one click" complete structures and grammatically correct texts, images, and videos within seconds. This technological convenience quickly alienates into the logic of "content industrialization" in market applications. Some creators and institutions view AI as efficient "digital labor," feeding keywords in batches, calling interfaces in a templated manner, setting up automatic publishing programs, and building an all-weather uninterrupted "AI swill" production line. The efficiency advantage of technology is infinitely amplified, while the authenticity, originality, and ideological depth of the content itself are neglected. Creative activities that should have creative value are alienated into instrumental behaviors for traffic monetization.

Secondly, attention flow is alienated under the manipulation of algorithms. Platform algorithms often prioritize update frequency, user dwell time, and interaction data over the originality and ideological depth of content. High-quality original content, due to its long production cycle and high cost, is at a disadvantage in the competition for traffic; whereas "AI swill" quickly fills information channels with its low-cost, high-output characteristics, crowding out the living space of original creators and harvesting the "attention" resources behind traffic. When users are repeatedly exposed to low-quality content, their aesthetic ability and critical thinking gradually become blunted, further reducing the elasticity of demand for high-quality content. Taking content such as "foreign Shanhaijing" as an example, once clicked out of curiosity, the algorithm will continue to push similar absurd and sensational content, which not only easily leads to addiction among minors but also distorts their cognitive system, causing long-term cognitive damage.

Thirdly, the backflow of polluted data leads to content alienation. Generative AI is essentially driven by data, and its output quality directly depends on the purity and diversity of the training data. When a large amount of low-quality content generated by AI flows back to the Internet and is captured by new-generation models as training corpus, it forms a recursive pollution of "feeding swill to produce more swill". The "AI illusion" generated by large language models based on probabilistic statistical pattern prediction rather than real semantic understanding will also further exacerbate the spread and diffusion of low-quality content, causing the digital content ecosystem to fall into a "vulgarization spiral", thereby continuously damaging the digital content ecosystem and increasing the difficulty of governance.

With the continuous development of AI technology, the form of "AI swill" may continue to evolve, extending from current text and images to videos and interactive content, and the difficulty of governance will also increase accordingly. In response, it is urgent to establish a diversified collaborative governance framework based on "legal protection, technical regulation, platform responsibility, and subject empowerment", improve governance paths, and achieve precise governance and source control of "AI swill".

First, we should improve legal safeguards and clarify the boundaries of governance responsibilities. We should strengthen the guidance of the rule of law, empower the healthy development of AI, accelerate the revision of relevant laws and regulations on digital content governance, bring AI content generation into the legal orbit, clearly define the scope of "AI swill", clarify the legal responsibilities of technology enterprises, platforms, and creators, and set rigid disciplinary measures for the mass production and dissemination of low-quality content. In view of the iterative characteristics of AI technology, we should establish a dynamic adjustment mechanism for laws and regulations to promptly respond to new governance issues and ensure the timeliness and pertinence of legal regulation. Given the cross-border dissemination nature of digital content, it is also necessary to strengthen international governance cooperation, actively participate in the formulation of international rules for AI governance, and build a collaborative mechanism for cross-border AI content governance.

Secondly, we should strengthen technical regulation and establish a mechanism to block content at the source. We should encourage enterprises to embed quality filtering modules in the model design stage, and achieve pre-review of AI-generated content through semantic understanding, fact verification, and other technologies, thereby controlling the production of low-quality content from the source. On the basis of implementing the "Identification Measures for AI-Generated Synthetic Content", we should rely on blockchain technology to achieve full-cycle traceability, providing a basis for review and user identification. We should standardize the management of training data, strictly prohibiting the inclusion of unverified low-quality content in training, and blocking the recursive pollution chain.

Third, we should press the platform responsibility and reshape the traffic distribution mechanism. The platform needs to break the "traffic-oriented" mindset, reconstruct a traffic distribution mechanism centered on content value, increase the traffic and revenue tilt towards high-quality original content, and establish an AI-based mechanism for quickly identifying and cleaning up low-quality content, implementing gradient punishment. Set up exclusive review barriers for underage audiences to prevent cognitive erosion. Relevant industry associations should take the lead in formulating content production norms and ethical guidelines, clarifying the boundaries of AI creation, promoting platforms and market entities to guide the return of "value priority", and forming a joint force of platform governance and industry self-discipline.

Fourth, empower diverse stakeholders and build a collaborative governance network. Strengthen public media literacy education, popularize AI identification knowledge through multiple channels, bridge the "AI digital divide", enhance public critical thinking, guide demand for high-quality content, and form a virtuous cycle of optimized supply and demand. Open up public supervision channels, establish a reward mechanism for reporting, mobilize public participation enthusiasm, and improve the governance structure featuring multi-party collaboration among the government, platforms, industries, and the public.

In summary, the governance of the digital content ecosystem is not a single-dimensional technical control, but a systematic project involving technological innovation, platform operation, market regulation, public literacy, and other aspects. Only by adhering to the concept of diversified collaborative governance and making efforts in multiple aspects such as legal protection, source prevention, process control, and demand guidance, can we eliminate the "AI swill" that pollutes the digital content ecosystem, maintain the quality and efficiency of information dissemination, and lay a solid foundation for the healthy development of digital culture.