AIGC: Endogenous Defects and Long-term Ecological Risks

Artificial Intelligence Generated Content (AIGC) refers to a set of technologies built on multimodal large-scale model architectures, which can automatically produce diverse content including text, images, audio and video, and code. It has now achieved commercial implementation across multiple scenarios such as content creation, industrial design and software development.

It should be clarified that mainstream AIGC systems currently operate on the underlying logic of statistical fitting with training data. They lack genuine semantic comprehension and factual cognition, functioning essentially as high-precision pattern replication tools. Taking image generation as an example, a cat image produced by the model is a probabilistic collage of textures and morphological features extracted from billions of feline samples in training datasets, rather than original creation based on objective understanding of the creature itself.

In the false traffic restriction news incident in Hangzhou in 2023, false content generated by ChatGPT was fully structured and logically coherent with strong misleading nature. This directly exposed a core technical flaw of AIGC: indiscriminate generation with no guarantee of factual accuracy. Models merely output content according to statistical patterns in training data and cannot verify its authenticity.

The explosive commercial growth of AIGC is mainly driven by its ability to cut costs and boost efficiency in production workflows. In content creation, for instance, traditional illustrators usually spend three working days on demand iteration, while AIGC tools can generate hundreds of alternative solutions within ten minutes. For industry report writing, tasks such as data collection and content sorting that once took days can now be completed by large language models, which rapidly generate structured first drafts after keyword input. Human labor is only required for subsequent quality and logical verification.

Nevertheless, AIGC still has clear limitations in its capability boundaries. Its output lacks stability, comparable to a junior practitioner with highly inconsistent performance. It performs well in low-creativity, high-tolerance scenarios such as poem writing and poster design, but is prone to logical gaps and factual fabrication in tasks requiring rigorous reasoning and factual accuracy, including long-form storytelling and professional data reporting.

For this reason, many universities at home and abroad have issued clear regulations. The use of AIGC for academic writing must be actively declared, and undisclosed utilization will be deemed academic misconduct.

Long-term information ecosystem risks brought by the widespread adoption of AIGC cannot be overlooked. While improving productivity, it may trigger a systematic decline in the credibility of online information. Public internet content over the past three decades has mainly been human-created and authentic. Even with partial misinformation, professionals can distinguish authenticity through domain expertise and source verification.

With the extensive application of AIGC, massive machine-generated content will be uploaded to public platforms and adopted as training materials for the next generation of large models. Repeated cycles of generation, training and regeneration will continuously magnify factual deviations and lead to an exponential drop in information reliability.

Based on the current growth rate of content generation efficiency, AIGC-produced content will outnumber original human content in public online information within three to five years, most of which will undergo multiple rounds of model iteration. Traditional fact-checking mechanisms will become largely ineffective, severely undermining the foundational trust in public information across society. Such risks will spread to all industries reliant on public data and may even cause systematic ecological damage.

In Sapiens: A Brief History of Humankind, historian Yuval Noah Harari points out that the core foundation of large-scale human cooperation lies in the capacity for shared fictional narratives. Humans build collective identity and formulate collaborative strategies by sharing credible information, enabling large-scale social coordination.

Without proper regulation over AIGC content generation and dissemination, public information may eventually be dominated by synthetic false content. The factual foundation of human collaboration will collapse, trapping society in a “reality filter bubble” built on misleading information. This stands as the most severe potential risk posed by the unrestricted development of AIGC technology.