Currently, Generative Artificial Intelligence (AIGC) is reshaping the academic publishing ecosystem with unprecedented power. From text writing to chart generation, from literature review to peer review, AIGC leverages its powerful data integration and logical reasoning abilities to deeply embed itself in various aspects of academic publishing. It not only optimized the publishing process and reduced operating costs, but also triggered a paradigm shift in knowledge production. However, beneath the surface of efficiency improvement lies a dual challenge of lagging rules and ethical disorder. In the industry transformation triggered by this technological revolution, how to pursue efficiency while adhering to ethical bottom lines has become a question we must ponder deeply. Large models based on Transformer architecture, such as ChatGPT, Gemini, DeepSeek, etc., have significantly improved the efficiency of parsing unstructured data through multimodal processing, simplifying traditional time-consuming processes such as text writing and data visualization. However, when an international journal introduced an AI screening system, although the review cycle was significantly shortened, the accuracy of innovation value assessment was questioned. This phenomenon reveals the double-edged sword effect behind technological progress: speed and quantity are no longer bottlenecks, while the emergence of unique ideas and the judgment of academic value have become increasingly complex. What is even more alarming is that the current mainstream models rely on massive training data to generate content, which, although not directly plagiarized, can lead to "unconscious plagiarism". This kind of "synthetic originality" blurs the boundary between originality and imitation, and also triggers disputes over intellectual property ownership, weakening the subjectivity of scientific researchers. True innovation is not the recombination of existing knowledge, but the clash of ideas and the sublimation of wisdom. If thinking is outsourced, humans may lose their ability to take control of themselves. As generative artificial intelligence enters the knowledge production chain as a "collaborator", the three cornerstones that academic communities rely on - originality standards, ethical responsibility, and evaluation systems - are facing severe challenges. Firstly, there is a crisis of originality. The traditional academic evaluation is based on "originality of ideas", which poses a fundamental challenge to AIGC. When large models generate logically consistent and word sequential texts based on massive literature, researchers are forced to re-examine their role positioning. If research becomes the arrangement and combination of prompt words, academic innovation will be reduced to the "reorganization of old knowledge", and the academic community may fall into the trap of "internalization of ideas", where the number of papers increases dramatically while cognitive breakthroughs become increasingly scarce. Secondly, the dilemma of responsibility. The current academic misconduct identification system has not yet covered the scenarios where AI is deeply involved, forming a "gray area" of responsibility attribution. The current academic ethics presuppose "human authors", and AI as a "non-human actor" breaks this logic. If the main body of the paper is completed by AI but not disclosed, does the authorship constitute 'hidden academic fraud'? At present, traditional copyright laws still find it difficult to define the authorship of algorithm generated content, resulting in a vacuum of legal accountability. Thirdly, evaluate alienation. The comprehensive application of AIGC is shattering the illusion of 'technology neutrality'. If editors increasingly rely on AI to detect repetition rates, proofread grammar, and checked formatting, does it mean that academic evaluation standards will degrade from content value to formal compliance? Will the automation and depersonalization of the publishing process exacerbate the crisis of alienation in the evaluation system? When technology becomes the key evaluation subject, the cognitive authority of the academic community will undoubtedly face severe challenges. No one can master all the truth, only when everyone holds "fragments of truth" can a more open academic system be pieced together. Therefore, it is important to guard against the transfer of academic evaluation dominance to algorithmic tools. Faced with unprecedented challenges, the academic community needs to construct a new order from the aspects of mechanism design, ethical norms, and literacy training, and seek a path that meets the needs of the times and does not lose humanistic care. One is to establish a transparent mechanism. Establish and improve a mandatory disclosure system, requiring authors to clarify the scope, depth, and version information of AI intervention through statements, ensuring that academic trust is built on a traceable and accountable basis. A sustainable academic publishing innovation ecosystem requires mechanism design to constrain the behavior of scholars. A strict disclosure system can clarify the responsibilities of both humans and machines, and prevent misconduct such as false citations, fabricated data, and forged references. The second is to reconstruct academic norms. The current academic norms urgently need to complete the paradigm shift from "human centered" to "human-machine collaboration". For example, academic publishing institutions can clarify that AI cannot be used as an author. At the same time, we will implement a gradient evaluation standard of "human led+AI assisted" and a review mode of "AI initial screening+expert final review" to ensure equal emphasis on formal norms and substantive innovation. Continuously optimizing the academic evaluation system through standardized reconstruction, so that it can adapt to the development of technology while maintaining the stability of the value core. The third is to cultivate abilities and qualities. Higher education institutions and research institutes can incorporate AI ethics into the core curriculum system of research methodology, focusing on cultivating students' ability to critically use AI, boundary awareness, and sense of responsibility. Young scholars need to have the ability to identify algorithmic biases and data traps, clarify the ethical boundaries of human-machine division of labor, and make rational choices between technological convenience and academic integrity. Norbert Wiener, the founder of cybernetics, once said, "People have their own uses." In the era of rapid technological updates and changes, facing the constant impact of the AIGC wave, the academic publishing industry needs to rationally respond to the challenges brought by technology and adhere to academic authenticity. Only in this way can we promote the sustainable development of academic publishing and safeguard the dignity and value of academia in the era of intelligence. (New Society)