1 Changes Happening in the AI Era

In today's society, AI has slowly penetrated into daily life. Large model products like ChatGPT, Claude, Gemini, as well as domestic ones like Doubao and DeepSeek, have become household names for ordinary people, who can use them to solve some basic problems—it can be said that the AI era has truly arrived.

However, everything has two sides: AI's powerful knowledge base, information integration, and language organization capabilities bring convenience in many aspects, but also quietly change the way people acquire knowledge, understand problems, and even think about problems. Especially today, as AI-generated content becomes more natural and more “human-like,” people are increasingly accustomed to “getting answers directly”: in the past, even if search engines could find materials, people still needed to read, filter, understand, and organize them themselves; now, simply by asking AI a question, they can get a logically complete, fluent, and even quite “professional-looking” answer in seconds.

This change itself is not necessarily a bad thing: AI is supposed to help lower the barrier to information acquisition, improve efficiency, and allow more ordinary people to access knowledge that was originally difficult to understand. From this perspective, the popularization of AI is part of technological progress.

What truly warrants vigilance is whether people can still maintain deep thinking about the problems themselves when they rely more and more on AI's answers. The most deceptive aspect of AI is that the content it generates “looks reasonable” but is not necessarily correct, because AI still has limitations such as a lack of originality, knowledge timeliness issues, limited understanding of complex systems, and the “cognitive level mirror effect” (see details:Becoming a Trusted Knowledge Anchor: Thinking on the Significance of Personal Blogs in the AI Era）。

Especially in unfamiliar fields, due to a lack of verification ability, people can easily mistake the “reasonable expressions” generated by AI for real conclusions. Over time, they may even develop a cognitive inertia of “since AI says so, it must be right.”

In the long run, this not only affects ordinary users but may also unconsciously weaken the original thinking and judgment abilities, and even some once-mastered skills, of professionals who rely on AI for creation over the long term.

2 From Tool to Cognitive Partner: The Four Realms of Using AI

1 The Ways People Use AI in Reality

In reality, the ways people use AI vary widely. On the surface, it is all called “using AI,” but the deep-seated differences are obvious: some people treat AI as an enhanced search engine, asking questions when they encounter problems and quickly obtaining information from the generated answers; some rely on AI for content creation over the long term, from articles and code to strategic planning, constantly trying to use AI to organize and reorganize their own knowledge systems; and a small number of people are experimenting with the boundaries of AI, exploring whether it can assist them in completing more complex cognitive tasks.

Behind these differences lies a reflection of people's different cognitive ways and their ability to master tools. On the surface, AI is just an auxiliary tool, but in reality, it continuously amplifies the user's original thinking habits: those accustomed to independent thinking will treat AI as a tool for auxiliary analysis and verification; while those who already tend to rely on ready-made answers are more likely to gradually hand over judgment and creativity to the machine. Over time, AI is no longer just a tool; it begins to shape the user's way of thinking, blurring the line between “who is using AI” and “who is being shaped by AI.”

Upon closer observation, these ways of use are not completely random but exhibit certain patterns: different levels of reliance and different usage strategies correspond to differences in thinking depth, experience accumulation, and systematic perspective. More importantly, these differences reveal a fact: to truly master AI rather than be led by it, one needs not only proficient operation of the tool but also a solid cognitive foundation and a clear awareness of one's own thinking ability.

Therefore, understanding the role of AI in daily life and creation should not stop at what it can generate or how efficient it is. What is more worthy of attention is how people use it, how they are guided by it, and what cognitive characteristics are hidden behind it. Only by understanding these differences can we systematically analyze usage behaviors, thereby revealing the thinking patterns and ability requirements demonstrated by users at different levels.

In the following content, we will formally enter the analysis, preliminarily dividing the ways people use AI into four levels, and exploring the characteristics, risks, and potential advantages behind each level.

2 Level 1: Answer Acquisition

Among the various ways of using AI, the most basic category can be called “answer-acquisition type.” These users often treat AI as a highly intelligent tool with a very clear goal: to quickly get answers and solve problems. Whether searching for information, verifying facts, or generating some kind of standardized content, their main focus is onthe result itself, rather than the process of thinking.

On the surface, this approach is highly efficient and beyond reproach. For standard problems encountered in daily life or work, such as a piece of copy, a basic concept explanation, or a historical event, AI can usually provide instant, clear answers that even “look” highly authoritative. But the problem is:the vast majority of AI-generated content is not completely reliable in all fields。

There are several reasons: first, there is a lag in the update of the knowledge base, especially in rapidly changing fields; second, most users are not professionals, and the questions they ask may be vague or incomplete; third, AI's answers often tend to be plausible “average answers” rather than in-depth analyses tailored to specific contexts. As a result, the answers given by AI are often solemn and confident in tone, but not necessarily completely reliable.

For users who lack the habit of multi-angle verification, this “sense of authority” is highly misleading. Over time, their cognition and judgment may be subtly shaped by this superficial authority. It can be said that this group of people is the most susceptible to being unconsciously influenced or even reshaped in their thinking patterns by AI.

The answer-acquisition type of use is not wrong; for beginners or those unfamiliar with certain fields, it is a reasonable auxiliary method. The key lies in whether one understands that the answers provided by AI are only for reference, and thinking, judgment, and verification must still rely on oneself.

Key point: Do not be misled by answers; actively examine and verify information to ensure the accuracy of understanding.

3 Level 2: Production Efficiency Enhancement

Users who enter the second level can be called “production efficiency-enhancing type.” This level is no longer satisfied with merely obtaining answers, but begins to use AI to directly improve the work efficiency of individuals or teams: writing code, writing articles, organizing knowledge, building workflows, and even constructing partial automation systems. They begin to treat AI as acapability amplifier—allowing limited time and energy to produce a larger scale of content or results.

Compared to the first level, production efficiency-enhancing users are undoubtedly stronger: they no longer merely rely on AI to answer questions, but use AI to actively create and complete tasks, which allows them to “do more” in a short period of time.

However, this level also hides new risks: when efficiency becomes the sole measure, it is easy to fall intoefficiency worshipUnder this mindset, AI seems to bring an explosion of productivity: daily update machines, content assembly lines, batch SEO generation, and even the emergence of “automated personas.” On the surface, the workload is huge and the results are abundant, but in reality, this content often shares common problems:

• Lack of structure: A large amount of produced content lacks systematic organization and logical frameworks, resulting in merely fragmented stacking
• Lack of thought: The content lacks independent thinking and deep insights behind it, being merely an integration of existing information
• Lack of real experience: AI cannot replace the author's practical experience or long-term accumulated professional judgment
• Lack of long-term accumulation: High-speed output content is difficult to form a sustainable and inheritable cognitive system

For example, a large number of AI-generated articles have already appeared on the internet today. On the surface, they are neatly formatted, logically clear, and high in information density, but careful reading reveals that there is a lack of true logical progression between sections, and even the order of paragraphs can be randomly swapped without causing any obvious sense of incongruity.

This is because such content is often just “information stacking” around the same topic, rather than a cognitive structure formed after deep thinking. They often give people a feeling of “having said a lot, and it all seems correct,” but after reading, it is difficult to form real cognitive accumulation. What is missing is actually the evolutionary relationship between viewpoints, the connection between questions, and the knowledge context accumulated through long-term thinking.

Behind this phenomenon, to a large extent, is because the creation process has been overly simplified: users only need to provide a topic and a few requirements, and almost all the remaining structural organization, content deduction, and linguistic expression are left to AI. Although this method can quickly generate a large amount of content, due to the lack of the creator's continuous participation in thinking, what is ultimately formed is often just a “seemingly complete” information output, rather than content that has been truly thought through and organized.

Key points: AI can indeed significantly improve creation efficiency, but what it amplifies is not necessarily cognition itself. If creators themselves lack independent thinking ability and long-term accumulation, no matter how efficient the output is, it may just be a “seemingly rich” stack of information. Only when AI truly participates in human thinking and judgment processes will it become a cognitive amplifier, rather than just a content assembly line.

4 Level 3: Structural Synergy

Users who enter the third level can be called “structural collaborative.” Unlike the first two levels, users at this level realize that the strongest capability of AI is not to complete tasks for you, but tothink with you. They are no longer satisfied with letting AI “produce results,” but instead focus on how to improve their own cognitive structure, system design, and long-term thinking ability through AI.

At this stage, users focus on:

• Deconstructing complex problems to find core structures and key variables
• Establishing a knowledge system, so that information is no longer scattered, but can form a long-term usable cognitive framework
• Maintaining long-term context to ensure consistency in decision-making and content generation
• Conducting iterative deduction to explore multiple possible developments of solutions
• Using AI to optimize system design, improving the overall solution through feedback rather than just completing a single task

Essentially, what users at this level are doing isthe iteration of thinking structures: AI becomes a feedback tool that can both expose logical loopholes and blind spots, and help test the integrity of knowledge systems and workflows. It allows users to see possibilities from different angles in a short time, quickly discover problems, and improve solutions, thereby truly participating in the process of deepening thinking and cognition.

It can be said that this level marks a fundamental shift in the relationship between users and AI: upgrading from “tool dependency” toa thinking partnership. What users gain at this stage is not only efficiency, but also a leap in cognitive ability.

For example, when a personal blogger designs a blog knowledge system for long-term maintenance, the most valuable aspect of AI is often not “writing articles for them,” but helping them continuously verify whether the overall structure is reasonable. For example:

• Whether there are logical gaps in the article content
• Whether the knowledge map is scalable
• Whether the tag system will spiral out of control as content grows
• Whether a certain structural design will evolve into new problems in the future
• Whether duplication or conflict has formed between different series of articles

In this process, AI is more like a “structural sparring partner” that continuously participates in discussions: it does not decide the direction, but can continuously provide feedback, expose potential problems, and help users conduct multiple rounds of deduction and iteration. This collaborative method is essentially no longer “content generation,” but “system collaboration.”

However, although users at this level have realized that the strongest capability of AI is to “think with you” and can use AI for knowledge structuring, iterative deduction, and system design, there are still some areas that need attention:

• Structuring ability: If problems cannot be effectively deconstructed and a clear knowledge structure established, AI's feedback may be misused, leading to chaotic output
• Iterative ability: When designing complex systems or long-cycle knowledge projects, a lack of patience or methodology may prevent full utilization of AI for multi-round verification and evolution
• Cognitive bias: Although AI feedback can verify logic, it cannot completely correct original biases; if one's own systemic view is not stable enough, they may still be limited

Key point: The value of AI lies in providing cognitive feedback and assisting thinking, but its true role still depends on whether the user can effectively organize information, maintain clear logic, and actively identify and correct cognitive biases.

5 Level 4: Cognitive Enhancement

There are very few users at the fourth level, but their value is extremely high, which I callcognitive-enhanced. At this level, AI is no longer just a tool, nor is it just a thinking partner, but becomesa mirror that drives cognitive evolution. Users begin to observe the impact of AI on knowledge, learning, and cognition itself from a macro perspective:

• How AI reshapes the organization of knowledge
• How AI affects personal learning paths and cognitive structures
• How AI reconstructs personal knowledge systems
• How AI expands the boundaries of human ability

Unlike the previous levels, this level no longer focuses on “what AI can do for me,” but instead focuses onhow AI mirrors cognition itself. Through the feedback provided by AI, users can identify their own blind spots, verify hypotheses, and iterate thinking models, thereby achievingself-evolution—actively enhancing cognitive and judgment abilities.

More importantly, the elevation of cognition in turn allows users to harness AI more efficiently: designing more precise questions, constructing more rational knowledge structures, and exploring deeper functions of AI's potential. This cycle means that the fourth level is not only cognitive enhancement, but alsoan accelerator for unlocking AI capabilities: the higher the level of cognition, the greater the value of using AI tools, ultimately forming a two-way enhancement of cognition and AI capabilities.

Imagine if Aristotle had an AI partner; while creating his work “Nicomachean Ethics,” he could receive feedback from AI at any time on his argumentative logic, comparative analysis of different philosophical viewpoints, and even the deduction results of a certain hypothesis under different social structures. AI would not directly tell him the answer, but rather constantly provide feedback: when he proposed a viewpoint, AI could immediately point out potential logical loopholes, boundary conditions, and conflicts that might arise under different systems of thought.

This means that the iteration of thought, which originally took years or even decades to gradually complete, may be continuously accelerated in an extremely short period of time. Thought no longer has to rely solely on slow reading, discussion, and long-term accumulation of experience, but can instead evolve rapidly through high-frequency feedback and continuous deduction.

In a sense, what AI has truly changed is not just “writing efficiency,” but rather the “speed of cognitive evolution.”

Key point：

• High-dimensional cognitive requirements: Requires a high systemic view and cognitive foundation, otherwise it is difficult to effectively extract feedback
• Self-evolution capability: Must actively identify blind spots and iterate thinking patterns
• Cognitive boundary exploration: Improper methods may lead to cognitive fragmentation or misleading

6 Summary

This chapter analyzes the four levels of AI usage, from the most basicAnswer-acquisition type, to the efficiency-focusedProductivity-enhancing type, and then to the AI-as-cognitive-partnerStructural-collaborative type, up to the highest level ofcognitive-enhanced. Each level reflects the change in the depth of interaction between AI and humans, and also reflects different cognitive styles and thinking habits.

As the levels continuously rise, the significance of AI is also changing—it is no longer just a tool for providing answers or improving efficiency, but begins to participate in human thinking, structural design, and cognitive iteration. What is truly important is no longer just “what AI can do,” but whether the user can still maintain their own judgment, structured thinking, and ability to control the direction.

For ordinary people, understanding the differences between these levels is not just to classify high and low, but rather to more clearly recognize in what way they are using AI: some treat it as a search tool, some as an efficiency engine, and others as a thinking collaborative partner. Different ways of use will ultimately lead people to different cognitive paths. Only when AI truly participates in the process of human thinking and growth will its technological potential gradually transform into long-term cognitive value.

3 From Traditional Creation to AI Collaboration: The New Evolution of Creators

1 The Era Without AI: Traditional Process from Information Accumulation to Repeated Refining

In the era without AI assistance, content creation was actually a very “heavy” task. If one wanted to seriously write a high-quality article, it often took a long time to prepare: searching for information, reading books, organizing viewpoints, and repeatedly thinking about the structure. Sometimes, to verify a single detail, one might have to look through many articles or even consult information from different sources, and then piece together a relatively complete understanding bit by bit.

What is more troublesome is that the entire creation process was mostly “linear,” and many people have had similar experiences:

• If the information in the front is not cleared up, one does not dare to continue writing the content in the back
• If the structure is not thought through, the main text easily becomes more and more chaotic as one writes
• Suddenly finding a problem with the logic halfway through writing, and having to go back and revise it again
• Sometimes a paragraph is revised repeatedly many times, and in the end, even discarded and rewritten

In other words, traditional creation actually relies heavily on the creator's personal long-term accumulation and continuous thinking ability. Normally, an article will roughly go through several stages:

1. Collecting information: Reading materials, organizing viewpoints, recording ideas, and preparing materials for content
2. Building the structure: Thinking about the main line of the article, arranging the order of content, and determining key points and levels
3. Formal expansion: Gradually writing the structure into complete content, and continuously adjusting logic and expression
4. Repeated revision: Optimizing transitions, cutting redundancy, supplementing key content, and sometimes even needing a complete rewrite
5. Finalizing the draft: Publishing after completing the overall check

In the whole process, what is often most consuming is not “writing” itself, but rather: information screening; logical organization; maintaining a coherent train of thought for a long time; and repeatedly overturning and adjusting the structure.

Therefore, in the era of traditional creation, high-quality output usually relies heavily on several abilities: whether one has sufficient information reserves; whether one can organize complex content and maintain clear logic; and whether one has the patience to continuously revise, adjust, and improve content.

Many times, what truly consumes energy is actually not “writing” itself, but rather the early-stage information organization, structural deliberation, and the process of repeated revisions in the later stage. Precisely because of this, traditional creation is often slow-paced, because behind a truly mature piece of content, a large amount of time is usually needed to think, verify, and polish.

2 The AI Era: Creation Process of Parallel Collaboration and Dynamic Iteration

With AI, the biggest change in content creation is actually not just “becoming faster.” The bigger change lies in: the past way of creation, which was “completed step by step,” has begun to be broken—previously when writing things, many times it could only be pushed forward linearly: first searching for information; then organizing the structure; then starting to write; and slowly revising after finishing writing.

But now, AI has caused these links to gradually interweave. Many times, creators are no longer “starting to write after searching for information,” but rather collecting information, adjusting the structure, and improving the content all at the same time. When writing a certain part, AI may remind them of what related viewpoints have not been covered; as soon as the structure is set up, they can immediately check whether the logic is smooth; even in the process of writing the main text, new ideas and directions will continuously pop up. The past way of creation, “first complete this step, then enter the next step,” has slowly begun to turn into a dynamic process of continuous feedback and continuous adjustment.

And what has changed even more is actually the relationship between creators and AI: previously, tools were more of just an assistant; but now, AI has become more and more like a collaborative partner capable of real-time interaction. The way many creators work now is more like continuously “discussing back and forth” with AI:

• First proposing an idea oneself
• AI supplementing the structure and related content
• After discovering logical problems, readjusting the direction
• AI continuing to provide feedback based on new ideas
• The entire content framework is also continuously improved in the process

Sometimes, an originally vague idea, after several rounds of interaction, will slowly expand into a complete content system. In the later stages of expression and polishing, AI can continue to help optimize language, adjust logical transitions, and even check from a “reader's perspective” which parts are not clear enough. As a result, creators can focus more energy on truly important questions, such as: whether the core viewpoint holds; whether the overall structure is reasonable; and whether there are deeper connections between the content.

In other words, creation in the AI era is no longer just “one person writing with their head down,” but is more like a process of continuous interaction and constant refinement between the creator and AI: AI is responsible for accelerating information processing and feedback, while what truly determines the direction, depth, and value is still the creator's own thinking.

3 Content Architect—A New Type of Creator in the AI Era

Although in the AI era, the act of creation has indeed undergone significant changes: AI can already help people complete many tasks that used to be very time-consuming, such as organizing materials, generating first drafts, polishing language, and sorting out structures... Many things that previously required a lot of energy to handle can now be completed in just a few minutes.

However, this does not mean that the threshold for creation has truly lowered. Although the traditional requirements that past creation relied on—such as “how much knowledge was memorized,” “how much material was accumulated,” and “whether writing skills were proficient enough”—have indeed lowered, the requirements for other abilities have increased: whether one can quickly understand the relationships between information; whether one can see the logical structure behind the content; whether one can judge which information is truly reliable; and whether one can guide AI to work collaboratively in the right direction.

In other words, AI has weakened the value of some “repetitive labor,” but has amplified the importance of “cognitive ability” and “structural ability.”

And precisely because of this, a new class of creators has slowly begun to emerge, whom I prefer to call ”Content Architects“: They are not necessarily people with great writing skills in the traditional sense, nor do they necessarily have deep accumulation in every field, but they excel at using AI as a cognitive collaboration tool—thinking, organizing information, and constantly adjusting structure and logic while doing so, thereby creating high-quality content.

The reason I use the term “content architect” is actually borrowed from the concept of “architect” in the traditional IT field.

In software systems, what architects focus on is usually not a specific local function, but whether the overall structure is reasonable, whether the modules are coordinated, and whether the system can continue to expand in the future.

Similarly, what content architects focus on is not just a single article itself, but: how connections are established between information; whether different topics form a system; whether the content structure can expand in the long term; and whether the entire knowledge framework can continue to evolve.

In a sense, what they are dealing with is no longer just “writing,” but a whole process of constructing cognitive and content structures.

Compared to traditional creators, the key difference for content architects does not lie in “whether they use AI,” but in how they use AI: some people merely treat AI as a content generation tool to improve efficiency; whereas content architects embed AI into their own thinking process to help validate, modify, and reconstruct their cognitive frameworks. The former is “using a tool,” while the latter is “building a thinking system.”

From the perspective of ability, becoming a content architect requires at least the ability of “structural synergy”—that is, being able to use AI to dismantle, reorganize, and validate information structures, allowing AI to truly participate in the process of logical deduction and content organization. Higher-level practitioners, on the other hand, will gradually enter a state of “cognitive enhancement,” no longer just optimizing a single creation, but using AI to continuously expand cognitive boundaries, constantly revising and upgrading their way of thinking in cross-domain exploration.

4 Ultimately, It Is Still Humans Who Decide the Direction

Looking at the previous discussions together, a very obvious phenomenon emerges: no matter how AI changes the way information is acquired, and no matter how the creation process is reorganized, the key factors that ultimately determine the quality of the results have not actually changed—what has changed is the way of doing things, but what truly plays a role is still the ability to make decisions.

AI indeed makes information acquisition easier, expression more efficient, and greatly compresses many processes that originally required time to accumulate. However, what it changes is more the efficiency of information processing, rather than the act itself of “how to choose, how to judge, and how to determine direction” when people face information. A portion of the information collection and organization work that originally had to be done in person can now be handed over to tools, but the final judgment and choice have not disappeared; instead, they have become more dependent on the user's own thinking, experience, and sense of direction.

And precisely because of this, the differences between people will not disappear because tools become stronger; instead, they will be more clearly reflected in the process of using AI: people with judgment will use AI to further strengthen their thinking efficiency and decision-making ability, forming stable conclusions faster amidst complex information; while those lacking judgment are more likely to rely on AI outputs, treating results as answers, thereby gradually weakening their own thinking ability.

Therefore, when we discuss AI, what is truly important is not how much work it can complete, but whether the user still retains the dominance over “judgment and choice.”

After all, to harness AI, one must first have a clear direction in mind. Just like riding a horse: if the horse runs wild, you will never reach your destination; only when the direction is clear can you truly control the journey.