1. Changes happening in the AI era

In today's society, AI has gradually permeated daily life. ChatGPT, Claude, Gemini, and domestic large-scale model products such as Doubao and DeepSeek are already familiar to ordinary people, and they can use them to solve some basic problems. It can be said that the AI era has truly arrived.

However, everything has two sides: while AI's powerful knowledge base, information integration, and language organization capabilities bring convenience in many ways, they have also subtly changed the way people acquire knowledge, understand problems, and even think about them. Especially today, with AI-generated content becoming increasingly natural and "human-like," people are increasingly accustomed to "getting answers directly": in the past, even if a search engine could find information, people still needed to read, filter, understand, and organize it themselves; now, they can simply ask AI a question and get a logically complete, fluent, and even seemingly "professional" answer in seconds.

This change itself is not necessarily a bad thing: AI is meant to help lower the barriers to information access, 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's truly alarming is whether, as people become increasingly reliant on AI's answers, they can still maintain a deep understanding of the problems themselves. The most deceptive aspect of AI is that its generated content "seems reasonable," but isn't necessarily truly correct. This is because AI still suffers from limitations such as insufficient originality, outdated knowledge, limited understanding of complex systems, and the "cognitive mirror effect" (see details: [link to relevant article]).Becoming a Trustworthy Knowledge Anchor: Reflections on the Significance of Personal Blogs in the AI Era).

Especially in unfamiliar fields, people are easily misled by the "reasonable expressions" generated by AI due to a lack of verification ability. Over time, this can even lead to a mental inertia of thinking that "if AI says so, then it must be correct."

In the long run, this will not only affect ordinary users, but may also unconsciously weaken the original thinking and judgment abilities of professionals who rely on AI for creative work, and even some skills that they once truly mastered.

2 From Tool to Cognitive Partner: Four Levels of Using AI

2.1 How people use AI in real life

In reality, people use AI in vastly different ways. While they all superficially call it "using AI," the underlying differences are quite obvious: some people treat AI as an enhanced search engine, asking questions when they encounter problems and quickly obtaining information from the generated answers; others rely on AI for content creation, from articles and code to strategy planning, constantly trying to use AI to organize and reorganize their knowledge system; 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 lie people's different ways of cognition and their ability to master tools. On the surface, AI is just an auxiliary tool, but in reality, it amplifies users' original thinking habits: those who are used to independent thinking will use AI as a tool to assist in analysis and verification; while those who tend to rely on ready-made answers are more likely to gradually hand over their judgment and creativity to machines. Over time, AI is no longer just a tool; it begins to shape users' ways of thinking, blurring the line between "who is using AI" and "who is being shaped by AI."

Upon closer observation, these usage patterns are not entirely random, but rather exhibit certain regularities: different levels of reliance and different usage strategies correspond to differences in the depth of thinking, accumulated experience, and level of systems perspective. More importantly, these differences reveal a fact: to truly master AI, rather than being led by it, requires not only proficient operation of tools, but also a solid cognitive foundation and a clear understanding of one's own thinking abilities.

Therefore, understanding the role of AI in daily life and creative work should not be limited to what it can generate or how efficient it is. More importantly, it should focus on how people use it, how it guides them, and what cognitive characteristics lie behind it. Only by understanding these differences can we systematically analyze usage behavior, thereby revealing the thought patterns and skill requirements exhibited by users at different levels.

In the following sections, we will formally begin our analysis, initially dividing people’s use of AI into four levels and exploring the characteristics, risks, and potential advantages behind each level.

2.2 First Layer: Answer Acquisition

Among the various ways AI is used, the most basic type can be called "answer-retrieval type." These users often treat AI as a highly intelligent tool with a very clear goal: to quickly obtain answers and solve problems. Whether it's searching for information, verifying facts, or generating standardized content, their focus is primarily on...The result itselfIt is not the process of thinking.

On the surface, this method is highly efficient and understandable. For standard questions encountered in daily life or work, such as a passage of text, an explanation of a basic concept, or a historical event, AI can usually provide immediate and clear answers, even appearing very authoritative. However, the problem lies in:The vast majority of AI-generated content is not entirely reliable across all fields..

There are several reasons for this: First, the knowledge base is often outdated, especially in rapidly changing fields; second, most users are not professionals, and their questions may be vague or incomplete; third, AI's answers tend to be seemingly reasonable "average answers" rather than in-depth analysis of specific contexts. As a result, AI's answers are often serious and confident, but not necessarily entirely reliable.

For users who lack the habit of verifying information from multiple perspectives, this sense of "authority" can easily be misleading. Over time, their cognition and judgment may be subtly shaped by this superficial authority. It can be said that this group is the most easily influenced by AI and even have their thinking patterns reshaped without them realizing it.

Using answer-finding tools isn't inherently wrong; for beginners or those unfamiliar with certain fields, it can be a reasonable aid. The key is understanding that the AI's answers are merely a reference; critical thinking, judgment, and verification still require personal effort.

key pointDon't be misled by the answers; actively examine and verify the information to ensure the accuracy of your understanding.

2.3 Second Layer: Improving Production Efficiency

Users who reach the second level can be called "productivity-enhancing users." This level is no longer satisfied with simply obtaining answers; they begin to use AI to directly improve the work efficiency of individuals or teams: writing code, writing articles, organizing knowledge, building workflows, and even constructing partially automated systems. They begin to treat AI as…Capacity Amplifier—To produce a larger volume of content or results with limited time and energy.

Compared to the first level, productivity-enhancing users are undoubtedly stronger: they no longer rely solely on AI to answer questions, but instead use AI to proactively create and complete tasks, enabling them to "do more" in a short period of time.

However, this layer also harbors new risks: when efficiency becomes the sole metric, it's easy to fall into a trap.efficiency worshipUnder this mindset, AI seems poised to unleash explosive productivity: daily update machines, content pipelines, mass SEO generation, and even "automated personalities." On the surface, the workload is enormous and the results abundant, but in reality, this content often suffers from common problems:

• Lack of structureThe large volume of content produced lacks systematic organization and logical framework; it is merely a fragmented accumulation.
• Lack of ideasThe content lacks independent thinking and in-depth insights; it merely integrates existing information.
• Lack of real experienceAI cannot replace an author's practical experience or long-accumulated professional judgment.
• Lack of long-term accumulationHighly produced content is unlikely to form a sustainable and inheritable cognitive system.

For example, a large number of AI-generated articles have appeared on the internet today. On the surface, they are neatly formatted, logically clear, and have a high information density, but careful reading reveals that there is a lack of genuine logical progression between sections, and even if the order of paragraphs is randomly changed, it will not produce an obvious sense of incongruity.

This is because such content often merely "piles up information" around the same theme, rather than forming a cognitive structure through in-depth thinking. It often gives the impression of "saying a lot, and it all seems correct," but after reading it, it's difficult to form a genuine cognitive accumulation. What's missing is the evolutionary relationship between viewpoints, the connection between issues, and the knowledge framework accumulated through long-term reflection.

This phenomenon largely stems from the oversimplification of the creative process: users only need to provide a theme and a few requirements, while the remaining structuring, content development, and language expression are almost entirely handled by AI. Although this method can quickly generate a large amount of content, the lack of continuous creative input often results in merely "seemingly complete" information output, rather than truly thoughtful and organized content.

Key points: AI can indeed significantly improve creative efficiency, but it may not amplify cognition itself. If creators lack independent thinking and long-term accumulation, even the most efficient output may just be a superficial accumulation of information. Only when AI truly participates in the human thinking and judgment process will it become a cognitive amplifier, rather than just a content production line.

2.4 Third Layer: Structural Coordination

Users who reach the third level can be called "structurally collaborative." Unlike the first two levels, users at this level realize that AI's greatest ability is not to complete tasks for you, but rather...Think with youThey are no longer satisfied with AI "producing results," but are instead focused on how to use AI to improve their cognitive structure, system design, and long-term thinking abilities.

At this stage, users will focus on:

• Break down complex problems to find the core structure and key variables.
• Establishing a knowledge system prevents information from becoming fragmented and instead forms a long-term, usable cognitive framework.
• Maintain long-term context to ensure consistency between decision-making and content generation.
• Conduct iterative simulations to explore various possible developments of the solution.
• AI is used to optimize system design and improve the overall solution through feedback, rather than just completing a single task.

Essentially, what this layer of users is doing isIteration of thinking structureAI serves as a feedback tool, exposing logical flaws and blind spots while also helping to verify the integrity of knowledge systems and workflows. It allows users to see possibilities from different angles in a short time, quickly identify problems and improve solutions, thereby truly participating in the process of deepening thinking and cognition.

This layer marks a fundamental shift in the relationship between users and AI: from "tool dependence" to...Mindset PartnershipAt this stage, users gain not only efficiency, but also a leap in cognitive abilities.

For example, when a personal blogger designs a long-term, maintainable blog knowledge system, the most valuable aspect of AI is often not "writing articles for them," but rather helping them continuously verify the overall structure's rationality. For example:

• Does the article contain logical gaps?
• Does the knowledge map have scalability?
• Will the tagging system get out of control as content grows?
• Will a certain structural design evolve into new problems in the future?
• Are there any duplications or conflicts between different series of articles?

In this process, AI acts more like a "structural sparring partner" who continuously participates in the discussion: it does not determine the direction, but it can constantly provide feedback, expose potential problems, and help users conduct multiple rounds of deduction and iteration. This collaborative approach is essentially no longer "content generation," but rather "system collaboration."

However, although users at this level have realized that AI's greatest capability is "thinking with you" and can use AI for knowledge structuring, iterative deduction, and system design, there are still some areas that need attention:

• Structured capabilitiesIf problems cannot be effectively broken down and a clear knowledge structure cannot be established, AI feedback may be misused, leading to chaotic output.
• Iteration capabilityA lack of patience or methodology when designing complex systems or long-term knowledge projects may prevent the full utilization of AI for multiple rounds of validation and evolution.
• Cognitive biasWhile AI feedback can validate logic, it cannot completely correct existing biases. If one's own systemic perspective is not robust enough, it may still be limited.

key pointThe value of AI lies in providing cognitive feedback and assisting thinking, but its true role still depends on whether users can effectively organize information, maintain logical clarity, and proactively identify and correct cognitive biases.

2.5 Fourth Layer: Cognitive Enhancement

The fourth layer has very few users, but is extremely valuable; I call it...Cognitive EnhancementAt this level, AI is no longer just a tool, nor merely a thinking partner, but becomes...The mirror that drives cognitive evolutionUsers are beginning to observe the impact of AI on knowledge, learning, and cognition itself from a macro perspective:

• How AI is reshaping the way knowledge is organized
• How AI Influences Individual Learning Paths and Cognitive Structures
• How AI is reshaping personal knowledge systems
• How AI expands the boundaries of human capabilities

Unlike the previous layers, this layer no longer focuses on "what AI can do for me", but rather on...How does AI reflect cognition itself?Users can identify their blind spots, verify hypotheses, and iterate their thinking patterns through feedback provided by AI, thereby achieving...Self-evolution—Actively enhance cognitive and judgment abilities.

More importantly, this enhanced cognition, in turn, enables users to more efficiently leverage AI: designing more precise questions, constructing more rational knowledge structures, and exploring the deeper potential of AI's functions. This cycle means that the fourth layer is not only about cognitive enhancement, but also...Accelerator for unlocking AI capabilitiesThe higher the level of cognition, the greater the value of AI tools, ultimately leading to a two-way improvement in cognition and AI capabilities.

Imagine if Aristotle had an AI companion. While writing his Nicomachean Ethics, he could receive real-time feedback from the AI on the logic of his arguments, comparative analyses of different philosophical viewpoints, and even deductions of certain assumptions under different social structures. The AI wouldn't directly give him the answers, but would continuously provide feedback: when he put forward a viewpoint, the AI could immediately point out potential logical flaws, boundary conditions, and possible conflicts arising from different ideological systems.

This means that the iterative thinking process that would normally take years or even decades to complete can be accelerated in a very short period of time. Ideas can no longer rely solely on slow reading, discussion, and long-term experience accumulation, but can evolve rapidly through high-frequency feedback and continuous deduction.

In a sense, what AI is truly changing is not just "writing efficiency," but "the speed of cognitive evolution."

key point:

• Higher-dimensional cognition requirementsA strong systems perspective and cognitive foundation are required; otherwise, it will be difficult to effectively extract feedback.
• Self-evolution abilityWe must proactively identify blind spots and iterate our thinking patterns.
• Exploring the Boundaries of CognitionInappropriate methods may lead to cognitive fragmentation or misleading.

2.6 Summary

This chapter analyzes four levels of AI usage, starting from the most basic.Answer-finding typeFrom focusing on efficiencyProduction efficiency improvementFrom AI as a cognitive partnerStructural synergyup to the highest levelCognitive EnhancementEach layer reflects the changing depth of interaction between AI and humans, and also reflects different cognitive approaches and thinking habits.

As AI advances in its capabilities, its significance is evolving—it's no longer just a tool for providing answers or improving efficiency, but begins to participate in human thought, structural design, and cognitive iteration. What truly matters is no longer "what AI can do," but whether users can maintain their judgment, structured thinking, and control over their direction.

For ordinary people, understanding the differences between these levels isn't just about ranking them, but about gaining a clearer understanding of how they are using AI: some use it as a search tool, some as an efficiency engine, and others as a thinking collaboration partner. Different usage methods will ultimately lead people down different cognitive paths. Only when AI truly participates in 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

3.1 The Era Without AI: From Information Accumulation to Repeated Refinement of Traditional Processes

In the era before AI assistance, content creation is actually a very "heavy" task. To write a high-quality article, one often needs to spend a lot of time preparing: researching, reading books, organizing viewpoints, and repeatedly thinking about the structure. Sometimes, to verify a detail, one may need to read many articles, or even consult information from different sources, and then piece together a relatively complete understanding bit by bit.

What's more troublesome is that the entire creative process is mostly "linear," and many people have similar experiences:

• I didn't dare to continue writing because I hadn't thoroughly researched the earlier information.
• If the structure isn't clear, the main text can easily become increasingly disorganized.
• I suddenly realized there was a logical problem halfway through writing, so I had to go back and revise it all.
• Sometimes a paragraph is revised many times, and in the end, it is even completely rewritten.

In other words, traditional creative work relies heavily on the creator's long-term accumulation and continuous thinking ability. Typically, an article goes through several stages:

1. Information gatheringRead materials, organize viewpoints, and record ideas to prepare content.
2. Building StructureConsider the main theme of the article, arrange the content in sequence, and determine the key points and levels.
3. Officially launchedGradually develop the structure into complete content, and continuously adjust the logic and expression.
4. Repeated revisionsOptimize the flow, remove redundancy, add key content, and sometimes even rewrite the entire thing.
5. Final draftReleased after the overall inspection is completed.

Throughout the process, the most time-consuming part is often not the "writing" itself, but rather: information filtering; logical organization; maintaining a coherent train of thought over a long period of time; and repeatedly overturning and adjusting the structure.

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

Often, what truly consumes energy is not the "writing" itself, but the preliminary information gathering, structural refinement, and repeated revisions. This is why traditional creative work tends to be slower-paced, as a truly mature piece of content usually requires a significant amount of time for thought, verification, and polishing.

3.2 The AI Era: Parallel Collaboration and Dynamic Iterative Creative Process

With the advent of AI, the biggest change in content creation is not just that it has become "faster." The bigger change is that the old way of creating content, which involves "completing it step by step," has begun to be disrupted. In the past, writing often proceeded linearly: first, research; then organize the structure; then start writing; and finally, revise it slowly.

But now, AI is gradually intertwining these processes. Often, creators no longer simply "do their research before writing," but rather gather information, adjust the structure, and refine the content simultaneously. When writing a certain section, AI might remind them which relevant viewpoints haven't been covered; once the structure is established, it can immediately check the logic; and even during the writing process, new ideas and directions will constantly emerge. The old approach of "completing this step before moving on to the next" is slowly transforming into a dynamic process of continuous feedback and adjustment.

The truly significant change lies in the relationship between creators and AI: previously, tools were primarily auxiliary; but now, AI is increasingly becoming a collaborative partner capable of real-time interaction. Many creators now work in a manner more akin to constantly engaging in "back-and-forth discussions" with AI.

• First, propose an idea.
• AI supplements structure and related content.
• After discovering the logical problem, readjust the direction.
• AI continues to provide feedback based on the new ideas.
• The entire content framework is also being continuously improved throughout the process.

Sometimes, a vague idea can gradually expand into a complete content system after several rounds of interaction. In the later stages of expression and polishing, AI can continue to help optimize language, adjust logical connections, and even check for ambiguity from a "reader's perspective." As a result, creators can focus more on truly important issues, such as: whether the core argument is valid; 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 about "one person burying themselves in writing," but rather a continuous process of interaction and improvement between the creator and AI: AI is responsible for accelerating information processing and feedback, while the creator's own thinking still truly determines the direction, depth, and value.

3.3 Content Architect – A New Type of Creator in the AI Era

While the creative process has indeed changed significantly in the AI era—AI can now help people complete many tasks that were previously very time-consuming, such as data organization, draft generation, language polishing, and structural arrangement—many things that used to require a lot of effort can now be completed in minutes.

However, this does not mean that the threshold for creation has really been lowered. Although the traditional requirements for creation, such as "how much knowledge you have memorized," "how much material you have accumulated," and "whether your writing skills are proficient enough," have indeed decreased, other ability requirements have increased: whether you can quickly understand the relationships between information; whether you can see the logical structure behind the content; whether you can judge which information is truly reliable; and whether you can guide AI to work collaboratively in the right direction.

In other words, AI diminishes the value of some "repetitive labor" but amplifies the importance of "cognitive ability" and "structural ability".

And it is precisely because of this that a new type of creator has begun to emerge, which I prefer to call "”Content Architect“"They may not be people with good writing skills in the traditional sense, nor do they necessarily have deep experience in every field, but they are good at using AI as a cognitive collaboration tool. They think, organize information, and constantly adjust the structure and logic to create high-quality content."

The reason I use the term "content architect" is actually because I borrowed the concept of "architect" from the traditional IT field.

In software systems, architects typically focus not on a single local function, but on whether the overall structure is reasonable, whether the modules are coordinated, and whether the system can be continuously expanded in the future.

Similarly, content architects focus not only on individual articles, but also on: how information is connected; whether different topics form a system; whether the content structure can be expanded in the long term; and whether the entire knowledge framework can continue to evolve.

In a sense, what they are dealing with is not just "writing", but a whole process of constructing cognitive and content structures.

The key difference between content architects and traditional creators lies not in whether they use AI, but in how they use it: some people simply treat AI as a content generation tool to improve efficiency; while content architects embed AI into their own thought processes to help verify, revise, and reconstruct their cognitive frameworks. The former is "using a tool," while the latter is "building a thinking system."

From a capability perspective, becoming a content architect requires at least the ability of "structural collaboration," meaning the ability to use AI to deconstruct, reorganize, and verify information structures, enabling AI to truly participate in logical deduction and content organization. More advanced practitioners will gradually enter a state of "cognitive enhancement," moving beyond simply optimizing individual creations to continuously expanding their cognitive boundaries with the help of AI, constantly refining and upgrading their thinking through cross-domain exploration.

4. Ultimately, it is people who determine the direction.

If we put the preceding discussions together, we can see a very obvious phenomenon: no matter how AI changes the way information is acquired, and no matter how the creative process is reorganized, the key factors that ultimately determine the quality of the result have not actually changed—what has changed is the way things are done, but what really matters is still the ability to make decisions.

AI has indeed made information acquisition easier, expression more efficient, and significantly compressed many processes that previously required time to accumulate. However, it changes the efficiency of information processing more than the fundamental question of how people "select, judge, and determine direction" when faced with information. Some of the information collection and organization work that originally needed to be done manually can now be handled by tools, but the final judgment and selection have not disappeared; instead, they have become more dependent on the user's own thinking, experience, and sense of direction.

This is why the differences between people will not disappear as tools become more powerful; on the contrary, they will become more apparent in the use of AI: people with good judgment will use AI to further enhance their thinking efficiency and decision-making ability, and form stable conclusions more quickly in complex information; people who lack good judgment are more likely to rely on AI output, take the results as the answer, and thus gradually weaken their own thinking ability.

Therefore, when we discuss AI, what really matters is not how much work it can accomplish, but whether users still retain control over "judgment and choice".

Ultimately, to master AI, one must first have a clear direction in mind. It's like riding a horse: if the horse runs wild, you'll never reach your destination; only with a clear direction can you truly control the journey.