Since the introduction of the big language model into the field of social science researchers, the role of artificial intelligence in academic research has evolved from occasional auxiliary question answering to deep involvement in the research process. Intelligent tools capable of executing code, searching literature, and generating analysis scripts can now participate in most research processes from data collection and statistical analysis to literature organization and paper writing. The efficiency improvement brought by artificial intelligence as a "colleague" is intuitive, but its ability boundaries and impact on cognitive habits and even social relationships may be more urgent to discuss than efficiency itself. The efficiency gain of artificial intelligence is most significant for tasks with clear goals and rules, as it represents the capability boundary of intelligent tools. In the past, writing network data collection programs for research projects usually required several weeks of time to complete on the premise of being familiar with computer algorithms and code writing. Nowadays, using natural language to describe collection requirements, intelligent tools can often generate usable solutions in a short period of time and do a lot of auxiliary work when encountering technical obstacles. But as efficiency improves to a certain extent, the limits of machine capabilities gradually become apparent. By using intelligent tools to organize the research literature in a certain field, it can effectively summarize the main theoretical viewpoints and schools, and also identify differences between concepts or schools to a certain extent. But it cannot determine whether these theoretical frameworks are applicable in the Chinese context and where they need to be revised. Such judgments require long-term immersion in the experience of Chinese society, and no amount of information can replace this judgment. In general, artificial intelligence is currently adept at processing information within a given framework, and the selection, questioning, and reconstruction of the framework itself still rely on the intuition accumulated by humans in specific social experiences. Academic research is like this, and similar situations exist in other fields as well. In medical diagnosis, algorithms can outperform most doctors in image recognition, but still rely on clinical experience when facing a patient with vague symptoms and complex medical history. The situation in the judicial field is slightly different. The efficiency of the auxiliary sentencing system in matching cases and legal provisions is high, but the balance of reason and reason in individual cases involves a comprehensive understanding of the social context and the situation of the parties involved, which is currently difficult to formalize. In previous research practices, the division of labor between humans and machines reshapes cognitive habits and relationship structures. After conceptualizing and analyzing strategies, it is necessary to code and implement them by oneself. When researchers discover anomalies in the data or measurement issues with a certain variable during the writing process, they can gain a new understanding of the entire design through model debugging and theoretical reflection. These discoveries cannot be planned in advance and can only emerge during the process of 'doing'. If more and more of the 'doing' part is undertaken by machines, these incidental gains may also decrease accordingly. This phenomenon does not only occur in the academic field. For example, in the construction industry, after drawing software replaces hand drawing, will the design intuition inspired by direct contact between hands and materials gradually disappear in the digital process? The translation industry also faces the same problem. After the popularization of machine translation, has the deep perception ability of young translators decreased? Will the temperature of carefully crafted words be replaced by cold and difficult to discern logic? The execution process may be compressed by technology, and the cognitive training attached to the execution may also be compressed together. This performance varies in different industries, but the direction is consistent. So, what is a reasonable way of working? This standard itself is also changing. Data cleaning may be shortened from one week to half a day, and literature review may be reduced from a few days to a few hours, but the brewing of problems, the formation of judgments, and the polishing of ideas may not have become faster. The parts that can be fast are extremely fast, while the parts that cannot be fast remain the same. This asymmetry can easily create an implicit pressure: since tools are so fast, why are people still so slow? When all the surrounding processes are accelerating, the time left for thinking is easily seen as inefficient. Academic research has always been conducted within the context of interpersonal relationships. The research assistant learned how to ask questions while processing data, and the doctoral student gained academic judgment beyond technical level when discussing analysis strategies with their supervisor. If these tasks gradually shift to human-computer interaction, the communication structure itself may also change. This has broader manifestations beyond the academic field: patients are starting to use intelligent tools to predict their condition before seeking medical attention, and the authoritative structure of knowledge in doctor-patient relationships is loosening. Similar changes have also occurred in legal services. The parties involved can use AI to generate a preliminary draft of the lawsuit, and the professional role of lawyers needs to be re understood. Although efficiency benefits are clearly tangible, what is often replaced includes implicit trust building and knowledge transfer functions in a relationship. The loss of these features is gradual and not easily noticeable, but it is difficult to fix them. The differences between generations may further complicate the issue. The professional abilities of the previous generation were formed without intelligent tools. They have an intuitive grasp of the limitations of tools and know when to put them down and return to their own judgment. The next generation has been trained with the assistance of intelligent tools from the beginning, and their perception of tool boundaries may naturally be weaker. Retaining the de instrumentalization process in the training system, allowing individuals to complete the entire judgment process without assistance, may be a problem that cannot be avoided in future education. The conscious awareness of boundaries safeguards collaborative security. Artificial intelligence not only helps researchers do things, but also participates in defining what researchers believe is worth doing. In the analytical tradition of sociology, the definition of work value has always been seen as a function of institutions. The current discussion about artificial intelligence mostly revolves around efficiency, risk, and regulation, provided that it is viewed as a tool. But if it is already involved in shaping users' understanding of the work itself, this premise itself needs to be re examined. When large-scale literature reviews become easy to complete, the willingness to spend two weeks reading a monograph will decrease. The standard of reasonable work methods itself is moving, which is increasingly unrelated to whether one is lazy or not. 'Easy to use' itself is a risk. The easier it is to use, the more likely users are to overlook or not delve into the limitations of the tool. Intelligent tools are good at learning users' preferences, and their expression methods, analysis strategies, and theoretical orientations will be continuously strengthened. The suggestions provided by the tool are becoming more and more appealing, and the opportunities for unexpected outcomes are decreasing. But good academic judgment and accurate clinical decision-making may come precisely from information that contradicts existing expectations. A tool that always follows the user's path may narrow people's vision in the long run. Guarding the border requires tremendous determination and effort. Researchers, doctors, and lawyers should retain the process of not relying on intelligent tools in their respective work, and core judgments should be made through their own thinking, which can be achieved at the individual level. Industry standards and education systems clarify which abilities must be trained without the assistance of tools, which requires institutional design. If the public discussion on the impact of artificial intelligence stays at the level of efficiency and risk, it may miss the more fundamental question: how human cognitive patterns, social relationships, and value rankings are being changed. It is an undeniable fact that artificial intelligence has accelerated the pace of work. In addition to rhythm and efficiency, cognitive habits are being reshaped, the standards for good work are changing, and the ways in which knowledge is transmitted and trust is established between people are changing. If these changes are indeed happening, artificial intelligence is no longer just an external efficiency tool, it is integrating into human cognitive patterns, professional norms, and the operational logic of social interaction. Once any technology is massively embedded in social life, it will change power relations and opportunity distribution. The special feature of artificial intelligence is that it is embedded at a deeper level and is intervening in human thinking and judgment processes. Who gains advantages and who is excluded in human-machine collaboration depends on the accessibility of intelligent tools for different social groups, and more importantly, whether a society has enough self-awareness to identify which areas should not be dominated by efficiency logic. The cultivation of such self-awareness may be more urgent than the regulation of technology. (Outlook on the New Era) Author: Fan Xinguang (Associate Professor of Sociology at Peking University and Researcher at Wuhan Institute of Artificial Intelligence at Peking University)