1 The Crisis and Value of Personal Blogs Today

Once upon a time, the creative form of personal blogs (hereinafter referred to as blogs) was a beautiful landscape on the Internet. It was an era when everyone was willing to write something and share some insights: programmers recorded study notes, travelers wrote down stories along the way, and food lovers published recipes they had tested themselves. It was these personal bloggers from all walks of life who silently contributed a large amount of high-quality content to the Internet, and thus became the “authoritative sources of information” for countless netizens when searching for information online. In that era when information was still relatively scarce, blogs were a treasure trove of knowledge and experience.

However, such prosperity did not last long. In less than a decade, the focus of the internet was rapidly taken over by short videos and social platforms. More and more people choose to scroll through videos and feeds instead of patiently reading a long article. At the same time, several once-popular blog communities have declined or even closed down, and the atmosphere of communication among personal bloggers has gradually dissipated. To make matters worse, the ranking logic of domestic search engines has changed, no longer leaving enough exposure space for independent blogs. As a result, a large number of blogs have either gone silent or disappeared from the public eye.

If the rise of short videos and social platforms marginalized blogs, the rise of AI is almost the “final blow.” After all, for most ordinary users, a single prompt can make AI generate an article that seems to “cover everything,” so why bother searching, clicking, and slowly reading a blog post? Moreover, AI-generated content is produced extremely fast and covers a very wide range. On the surface, it seems omnipotent, even making people feel that traditional blogs have no room left to survive.

But is that really the case? No matter how powerful AI is, what it generates is always the reprocessing of “public corpora” and can never replace a living individual. What people truly need is often not an “average answer,” but experience that can solve specific problems, stories that can touch people's hearts, and independent judgments that can provoke thought.

And this is precisely the most valuable aspect of blogs: the lessons and experiences written down by an individual are often more effective at solving practical problems than cookie-cutter answers; a person's real story is more infectious than paragraphs pieced together by models; a person's carefully considered judgment is of greater reference value than consensus-based expressions. More importantly, blog authors can update content at any time based on the latest real-world changes. This “real-time nature” and “uniqueness” are exactly what AI cannot easily replicate.

Therefore, in the AI era, if blogs want to truly survive and realize their value, they must return to the core power of “humans”—experience, stories, insights, and judgment. It is precisely based on this point that the following chapters will analyze the limitations of AI in turn, and explore what core qualities and unique advantages blogs need to possess to continue to exist under these limitations. Understanding the shortcomings of AI helps us distinguish which blog content is easily replaced and which content is still not easily replicated, thereby clarifying the path for the survival and value realization of blogs in the new era.

2 Limitations of AI

1 Overview

Speaking of AI, we must calmly look at its true boundaries. Yes, AI is already powerful enough; it can generate a large amount of content in a very short time, integrate scattered information, and even answer almost any question in a seemingly “objective and neutral” tone, but this does not mean it is omnipotent.

The power of AI is often a “power in an average sense.” It can quickly organize knowledge and generate logically clear articles, but when it comes tooriginality, practical experience, emotional expression, timeliness, depth of knowledge, or cognitive guidance,its shortcomings will become apparent. These shortcomings include:

• Lack of originality: AI struggles to generate brand-new perspectives or unique insights, and its content often remains at the level of splicing and summarizing existing information.
• Limitations of emotion and expression: It can mimic tone and style, but it is difficult to convey real emotions or complex experiences.
• Timeliness issues: There is a lag in training data and knowledge updates, making it difficult to reflect the latest events, technologies, or trends in a timely manner.
• Depth and boundaries of knowledge: When facing highly specialized, interdisciplinary, or complex system problems, AI-generated content usually remains on the surface.
• Mirror effect of cognitive levels: AI's answers often mirror the cognitive level of the questioner, making it difficult to actively guide readers to jump out of their original thinking frameworks.

2 Limitation 1: Lack of Originality

AI's strength lies in integrating existing materials, but its shortcomings are equally obvious: a lack of true originality. The articles it generates, while seemingly covering everything, are actually often just the re-splicing and rewriting of existing knowledge. In other words, AI rarely provides brand-new perspectives, and it is even harder for it to propose insights that are “unexpected, yet reasonable.”

Content that truly touches people and solves problems often comes from independent thinking and practical exploration. The lessons and experiences personally lived by an individual are more instructive than the “ten general suggestions” pieced together by AI; a person's repeatedly scrutinized judgment is of greater reference value than the “average answer” of large models. This originality is not just “unique,” but also means “irreplaceable.” When readers constantly see similar AI-generated content on the internet, those articles with traces of practice and thinking logic become exceptionally precious.

It is worth noting that even if AI scrapes your article content, it will not diminish its unique value. When processing, AI breaks down the complete article into fragmented tokens and then reorganizes the information. In this process, the complete logic, detailed experience, and practical summary of the article are often not fully preserved, thereby causing its generated content to lose the depth and uniqueness that original articles possess. This precisely reflects the core advantage of original content: even if cited by AI, its value still exists and cannot be easily replicated.

In the AI era, this originality is particularly important: the more content relies on practice and long-term accumulation, the more it can form a systematic knowledge structure and summary of experience. Those blogs that can integrate personal practice, observation, and summary into complete articles are not just containers of information, but carriers of experience and judgment. In the flood of AI, what they provide is unique, irreplaceable reference and inspiration.

3 Limitation 2: Limitations of Emotion and Expression

Although AI can mimic various writing styles and generate grammatically and logically correct articles, it still has obvious limitations in emotional expression and delicate description. It can piece together sentences and adjust the tone to make the article appear natural and fluent on the surface, but it is difficult to truly convey the author's emotions, stance, and personal experience. In other words, AI-generated content is often “seemingly natural on the surface” but lacks the warmth and realism that resonate with people, making it hard for readers to feel the presence and thinking of a human.

To make up for this limitation, users usually try to guide AI to output a specific style or emotion through prompts. For example, they can ask the AI to adopt a warm tone, emphasize personal experience, highlight a certain point of view, or even simulate complex emotions such as humor, irony, or sentimentality. Reasonable use of prompts can indeed make the output look closer to the style of human expression to some extent, but this method is still limited: AI can only make adjustments within existing patterns, and it cannot truly understand the author's experiences, inner feelings, or complex emotions. In other words, it can “act like it,” but it is difficult to “truly feel.”

This limitation is not only reflected in the surface text, but also affects the depth and persuasiveness of the content. For example, when an author wants to share their decision-making process, feelings of frustration, or exploration insights in a complex project, AI can generate a seemingly reasonable narrative based on a large amount of data, but the psychological struggles, subtle trade-offs, and value balances within it cannot be accurately conveyed. When reading, readers are likely to feel that “the information is enough, but it lacks warmth,” and it is also difficult to form psychological identification or emotional resonance.

Furthermore, when describing interpersonal relationships, cultural differences, or individual experiences, AI is also prone to formulaic expressions. For example, when describing a trip, a team collaboration, or a learning experience, AI may generate text with a complete structure and correct grammar, but its content usually lacks individual details and emotional tension. Relying on such content in the long term will make the information rich, but lacking the power to touch the soul.

It can be seen that the uniqueness of emotion and expression is precisely one of the core values of human creation, especially blog writing. Authentic expression not only allows readers to gain knowledge, but also stimulates resonance and triggers thinking. Although AI has advantages in efficiency and information integration, it still cannot replace the intuitive experience and emotional judgment of human authors in these subtle emotional dimensions.

4 Limitation 3: Timeliness Issues

Another limitation of AI-generated content lies inthe timeliness of information, as most large models rely on data prior to a fixed time cutoff during training, which means their understanding of the latest events, technologies, or policy changes is always lagging. Taking my question on September 21, 2025 as an example, it can be seen that although the model version has reached GPT-5, the knowledge base still uses the version trained in June 2024:

On the other hand, even if the official ChatGPT client provides real-time retrieval functions, it can only passively integrate existing information, and cannot interpret and analyze the latest developments as quickly and flexibly as a living person. In other words, AI can “see history,” but it is difficult to truly grasp the pulse of the present. Although real-time retrieval allows AI to obtain certain latest information in a timely manner, its essence is stillresponding to active user queries, and it will not, like humans,actively integrate this information for independent thinking and analysisTherefore, the analysis process of AI is still limited to its existing knowledge base, and it cannot, like humans, quickly adjust thinking and make innovative judgments based on the current context.

This lag is particularly obvious in rapidly changing fields. For example, in the technology field, development tools or frameworks iterate very quickly, and AI may still stick to the information of the previous generation, failing to accurately guide users to use the latest features; in the field of policies and regulations, new clauses or amendments may have just been released, but the content generated by AI is still based on old policies, which can easily lead to misleading in decision-making scenarios; in hot social events, public opinion and facts continue to develop, and the summaries provided by AI may quickly become outdated or lack important details.

In addition to the lag, AI also has analytical limitations when dealing with complex current affairs issues. It can summarize historical data and public reports, but it lacks the ability to actively observe and dynamically deduce trend judgments, potential risks, or complex causal relationships behind events. In other words, the content generated by AI may be sufficient in terms of information volume, but it is still insufficient in terms of deep understanding and value judgment of the latest events.

There is another detail worth noting: although AI-generated content can quickly cover a large number of topics, its content'ssustainable updateabilityis weak. Whenever new information appears, the model needs to be retrained or additionally retrieved to master it, and this process often has a time delay (possibly even more than a year). In practical applications, this means that if readers rely on AI to obtain the latest knowledge, they may miss key details or make misjudgments.

In general, the timeliness issue highlights AI's shortcomings in “immediacy and dynamic response capabilities,” and also reminds us that we still need to rely on human creators in rapidly changing fields: they can capture the latest developments in real time, analyze the background context, and provide actionable, precise, and in-depth content, which is also an important dimension of why blogs are still valuable in the AI era.

5 Limitation 4: Depth and Boundaries of Knowledge

AI performs excellently in information integration and generation, but inthe depth and boundaries of knowledge, there are natural limitations. Large models can extract general laws and summarize concepts from a large amount of data, but their understanding relies on existing materials and patterns, lacking the ability to explore across boundaries. In other words, it can answer routine questions, but it is difficult to handle questions involving deep professional backgrounds, cross-domain innovation, or complex system analysis.

This limitation is very obvious in practical applications. For example, in scientific research, AI can summarize existing literature and refine conceptual models, but it cannot actively propose breakthrough ideas for unpublished experimental data, new hypotheses, or interdisciplinary innovative designs; in corporate decision-making, AI can analyze historical trends, but when facing sudden market changes, complex internal organizational relationships, or fuzzy variables, the advice it provides often lacks operability or foresight; in the creative field, AI can generate content that matches a style, but it is difficult to produce truly original, cross-border creative works.

This limitation is also reflected in “unexpected but reasonable” discoveries. When exploring unknown questions, human experts often propose unique insights through cross-domain thinking, analogical reasoning, or intuitive judgment. Although AI can simulate the reasoning process, it cannot autonomously form such innovative cognition, and its generated content often stays in the “safe zone”: clear logic, perfect grammar, but lacking deep insights or breakthrough viewpoints.

Therefore, for readers who hope to obtain deep understanding, unique judgment, or innovative inspiration, AI-generated content often cannot fully meet their needs.

6 Limitation 5: Mirror Effect of Cognitive Levels

A core characteristic of AI is that its answers usually reflect the cognitive level of the questioner. In other words, AI will reason and generate based on the input content, and the results are often “at the same level as the questioner.” When the question is relatively basic, AI's answer will stay at the basic level; when the question is vague or there is a bias in understanding, the generated content may deviate from the correct direction. This phenomenon can be calledMirror effect of cognitive levels。

This characteristic has a significant impact in practical use. For example, if a user only understands the introductory concepts of a certain technology, the AI-generated answer may stay at a shallow explanation, lacking in-depth background and analysis of complex applications; if the user has a bias in understanding or the question is not clear enough when asking, AI will often generate corresponding content based on the input misleading, making the answer likely to perpetuate incorrect or one-sided cognition. In other words, AI is not an active error corrector, but a mirror system highly dependent on input signals: it “faithfully reflects” the questioner's cognitive state.

This mirror effect means that even if AI has powerful information integration capabilities, it is still difficult for it to actively elevate the questioner's cognitive height. Unlike experienced mentors or experts, AI cannot dismantle, reorganize, or guide questions from a higher dimension, nor can it actively identify knowledge blind spots and thinking misunderstandings. Relying on AI to obtain answers in the long term, users may fall into a cycle of “information reproduction”—the limitations of the question itself are directly mapped into the answer, and cognitive improvement is potentially constrained.

In addition, this effect is particularly important when learning and exploring new fields. For example, when beginners ask AI questions in fields such as programming, mathematics, or finance, if the question formulation is not precise enough, the content generated by AI may repeat basic concepts, making it difficult to provide cross-level understanding or innovative ideas; even if the question has a certain depth, AI may also generate answers along the user's existing cognitive path, without actively proposing new angles or challenging assumptions. This limitation reminds us that AI is more suitable as an information integration tool rather than an independent thinking guide.

Therefore, the mirror effect of cognitive level emphasizes the dominant role of humans in knowledge acquisition: comprehension, judgment, and the ability to ask questions are still the keys to cognitive growth. AI can assist in information acquisition and organization, but true cognitive breakthroughs still require humans to actively think, integrate multi-source information, and verify and reflect in practice.

7 Summary

This chapter analyzes the boundaries and limitations of AI. Although it can generate a large amount of content, integrate scattered information, and answer questions in a seemingly objective and neutral manner in a very short time, in terms oforiginality, emotion and expression, timeliness, depth of knowledgeandcognitive guidance capabilities, there are still obvious shortcomings. Therefore, although AI has advantages in information generation and integration, these limitations remind us that it is not omnipotent. Understanding these boundaries helps to reasonably evaluate the role of AI in knowledge acquisition and decision-making, clarify its scope of application, and lay a foundation for subsequent discussions on how humans can efficiently use tools and exert unique advantages in the AI era.

At the same time, when relying on AI, users also need to maintain judgment and critical thinking, especially when dealing with complex, professional, or deep-understanding issues, and should combine multi-source information and human experience for verification.

It is precisely at this point that the value of blogs is highlighted: they can make up for AI's deficiencies in experience, emotion, and depth, providing readers with a reliable reference that stands up to verification.

3 Building an Irreplaceable Blog: Core Capabilities in the AI Era

1 Overview

In Chapter 2, I analyzed the boundaries and limitations of AI in detail, and these limitations provide a unique value space for blogs. High-quality blogs not only transmit information, but also carry the author's thinking logic, practical experience, and judgment; they can tell real stories, express delicate emotions, update timely in combination with the latest reality, and provide high-dimensional cognitive guidance for readers on complex issues. In other words, the value of a blog lies not only in the “content itself”, but also in the transmission of “ways of thinking, empirical methods, and judgment.”

The goal of this chapter is to systematically sort out the key capabilities that blogs should possess in the AI era around these core values. We will discuss one by one: how to, throughunique perspectives and original thinkingavoid being replaced by AI; how to, throughemotional expression and storytelling powerenhance reader resonance; how to, throughreal-time updates and dynamic response capabilitiesmaintain information value; how to, throughdeep analysis and cross-disciplinary integrationexpand knowledge boundaries; and how to, throughhigh-dimensional cognitive guidanceprovide readers with breakthrough thinking.

Understanding these capabilities not only allows us to see the survival path of blogs in the AI era, but also helps creators clarify their direction: what content is easily replaced by AI, what content needs independent thinking and practice to support, and what ways can form the unique competitiveness of blogs. Through the analysis in this chapter, readers can form a clear cognitive framework, laying the foundation for subsequent sections to discuss specific strategies and practical methods in detail.

2 Unique Perspectives and Original Thinking

The power of AI lies in integrating and reorganizing existing knowledge, but the content it generates is essentially still “splicing and averaging.” It can quickly provide a large amount of information, but rarely proposes brand-new perspectives, and it is difficult to create those “unexpected, yet reasonable” insights. This means that for issues that truly require original insights and independent judgment, AI often only provides generic, replaceable answers.

The core advantage of blogs lies precisely inOriginality and Unique Perspectives. Every blogger writes content with their own experiences, professional background, and way of thinking. This not only allows the articles to convey knowledge, but also showcases the author's way of thinking and analytical logic. For example, the practical experience written by a software engineer when solving performance bottlenecks is closer to real-world scenarios than an AI-generated “optimization guide”; the route choices and feelings recorded by a traveler are more vivid and easier to resonate with than an AI-generated travel guide.

To maintain unique value in the AI era, blogs need to do the following:

Persist in original thinking: Not only summarizing existing materials, but also combining personal practice, observation, and thinking to propose unique insights. Even for seemingly common topics, new insights can be formed through personal experience and perspective.

Focus on logic and structure: Clear problem analysis paths and conclusion derivations allow readers to follow the train of thought for a deeper understanding, rather than passively accepting fragmented information. A clear logical chain is easier to leave an impression on readers' minds than scattered facts, and is also harder for AI to imitate.

Accumulate practical cases: Support viewpoints through real experiences, experimental data, or project practices to enhance the credibility and actionability of the content. Such cases are often missing or averaged out in AI-generated content, becoming a natural advantage of a blog's uniqueness.

Continuous iteration and reflection: Original content is not just a one-time output, but should be continuously improved through ongoing updates and reflection. Readers can feel the author's accumulated experience and growth in thinking over time, a depth that is difficult for AI to replicate.

This uniqueness is not only reflected in the articles themselves, but also forms the long-term assets of the blog. Even if AI can scrape article content and generate answers, the complete logical chain, practical cases, and thinking methods are often diluted during the reorganization process, thereby preserving the irreplaceable nature of the blog. In other words, original thinking not only makes blog content unique, but is also an important means of maintaining long-term value, and the key to a blog's survival and standing out in the AI era.

3 Emotional Expression and Storytelling Power

In the AI era, the unique value of blogs comes not only from the information itself, but also from the emotional warmth and storytelling power that the author infuses into the content. Compared with standardized paragraphs generated by AI, blogs can, throughreal experiences, emotional resonance, and narrative structures,allow readers to develop a deep sense of identification.

Blogs can, throughpersonal stories, strengthen information delivery,for example, in technical blogs, the author telling stories about challenges encountered in projects, failed attempts, and the ultimate solutions makes it easier for readers to understand the essence of the problem than dry operation guides. The same goes for travel or lifestyle blogs: adding personal feelings, interesting episodes, and emotional changes on top of attraction guides can make the content more vivid and give readers a sense of immersion.

Narrative structure and emotional build-upare important means for blogs to attract readers. By reasonably arranging the rhythm of the article, echoing details, and setting suspense and highlights, blogs can allow readers to experience emotional ups and downs during the reading process, making it easier to remember the content and form reflections. This narrative ability not only conveys knowledge, but also subtly influences readers' judgments and values.

Blogs can addpersonalized evaluations and judgments into their writing,which includes not only the analysis of facts, but also the expression of attitudes toward choices, strategies, and experiences. When reading, readers do not just obtain answers, but can also feel the author's unique way of thinking and value orientation. This “human voice” is an advantage that is difficult for AI to replace, and is also the key to blog content maintaining its long-term appeal.

Blogs can also, throughdiversified expression techniques,further enhance emotion and storytelling power, using metaphors, analogies, charts, illustrations, and even short video embeds to enhance the expressiveness of articles; through Q&A, comparative, and case-based writing, articles can be interesting while maintaining clear logic. Such content has both warmth and depth, making the blog stand out in the flood of information.

Emotional expression and storytelling power are not only natural advantages that distinguish blogs from AI, but also core means for content to reach readers' minds and leave a deep impression. In the AI era, only blogs that are good at using stories and emotions can give information warmth and give knowledge life.

4 Real-time Updates and Dynamic Response Capabilities

In an era of rapidly changing information, knowledge and practical experience are often fleeting. Although AI-generated content can quickly output information, it relies on existing training data, meaning the content always has a time lag and cannot reflect the latest changes at any time. One of the unique advantages of blogs is the ability to update in real-time and react dynamically. Authors can revise, expand, or supplement articles at any time based on new events, the latest data, or personal practice, keeping the content cutting-edge and applicable.

This update capability is not only reflected in the refreshing of information, but also in the synchronous evolution of experience sharing and ways of thinking. When solving a problem, a technical blogger might discover new tools or methods. Recording these discoveries immediately through the blog not only helps them form a complete knowledge system, but also allows readers to obtain practical experience at the first opportunity. It is difficult for AI to reflect this continuous iterative feedback mechanism in its answers, as it is typically based on existing data and averaged patterns.

Blogs can also drive content updates with the help of reader interaction and feedback. Comments, Q&As, or exchanges on social media allow authors to timely adjust article structures, supplement details, or provide new analytical perspectives, making the content more precise and closer to the actual needs of readers.

At the same time, individual bloggers can embed new discoveries or experiences into existing articles to form a coherent knowledge network. This update is based on the author's unique logic and understanding, rather than simple information splicing. In contrast, AI-generated content struggles to maintain the same depth and coherence in terms of continuity and systematicity.

It is precisely this real-time update and dynamic reaction capability that makes blogs an iterative platform for knowledge and experience. In the AI era, it keeps content vibrant and targeted, enabling readers to obtain the latest, most practical, and most realistic knowledge and insights.

5 Deep Analysis and Cross-boundary Integration

In the AI era, acquiring information has become extremely convenient, but in-depth analysis and cross-disciplinary thinking remain the core advantages that distinguish personal blogs from AI. AI can quickly integrate materials and summarize patterns, but it often stays at “surface information,” lacking multi-dimensional understanding of complex problems and cross-domain insights. Blog authors, however, can organically combine knowledge from different fields through personal practice, long-term observation, and cross-disciplinary experience to propose unique analytical perspectives.

The value of a blog lies not only in conveying facts, but also indeeply digging into the essence of problems. In technical blogs, authors can combine basic concepts with actual project experience, analyze the pros and cons of various choices, assess potential risks, and provide actionable strategies. This analytical process not only showcases the author's professional capability, but also provides readers with a reference that is more instructive than purely AI-generated content.

At the same time, blogs can also achievecross-disciplinary integration,colliding knowledge from different disciplines and experience backgrounds to spark new viewpoints. For example, a blog post about data analysis can combine perspectives from psychology, cognitive science, and management to make the analysis more three-dimensional and insightful; a lifestyle or travel blog can integrate multi-dimensional information such as history, culture, and economics to make the content richer and deeper. This cross-disciplinary thinking helps readers form a more comprehensive cognitive framework and also showcases the blog author's unique way of thinking.

The in-depth analysis and cross-disciplinary capabilities of blogs can also be reflected throughcase analysis and practical summaries,where authors demonstrate in their articles how to make judgments and solve problems in complex situations, and propose ideas or methods combined with their own experience, allowing readers to not only see the conclusions but also understand the entire reasoning process. This kind of content not only helps internalize knowledge, but also stimulates readers' independent thinking abilities.

In the torrent of AI, blog content that can provide in-depth analysis and cross-disciplinary integration appears particularly precious. They not only help readers break out of the limitations of single information, but also teach readers to think multi-dimensionally in the face of complex problems. This capability is difficult for AI to completely replace, and is also an important way for blogs to maintain their influence in the new era.

6 High-dimensional Cognitive Guidance

In an era of information overload, obtaining answers has become easy, but trulyelevating cognitive levelsis far from easy. Although AI can provide reference information and organize logical frameworks, the answers it generates are usually pulled down to the cognitive level of the questioner—that is, no matter how precisely you ask, it is difficult for it to actively lead you out of your original thinking limitations and provide “higher-dimensional” guidance. Blog authors, however, can provide readers withcognitive guidance that transcends surface-level problems。

The high-dimensional cognitive guidance of blogs is first reflected inproblem deconstruction and thinking expansion,where authors not only give conclusions, but also clearly explain how to analyze problems, how to discover key variables, and how to weigh different choices. In technical blogs, this might be the decision-making logic of a complex project; in learning methods or cognitive science blogs, it might be the methodology of thinking training, knowledge transfer, or strategy design. During the reading process, readers do not just obtain answers, but also learnWays of thinking about problems, gradually forming one's own cognitive framework.

Secondly, blogs can, throughcross-dimensional examples and analogies,concretize abstract concepts, helping readers understand problems from different perspectives. A good author can combine mathematical concepts with life experience, and contrast scientific principles with social phenomena, allowing readers to establish connections across multiple dimensions simultaneously. This cross-domain thinking ability enables readers to analyze and judge with a broader perspective when facing unknown problems, rather than merely staying at surface-level solutions.

The high-dimensional cognitive guidance of blogs is also reflected invalue judgments and deep insights. Authors not only provide information, but also share observations and judgments on trends, risks, and opportunities, making readers aware of which decisions deserve attention and which viewpoints might be biased. This “cognitive acceleration” effect is difficult for AI to achieve at this stage, because it cannot truly understand the subtle differences in human values, experience, and judgment.

Ultimately, blogs guided by high-dimensional cognition do not just solve problems; they subtly cultivate readers' independent thinking abilities, helping them form their own judgment standards and values in complex environments. It is a cognitive lighthouse in the torrent of AI, ensuring that information is not just passively received, but understood, digested, and internalized, becoming nourishment for the reader's growth.

7 Summary

In this chapter, we focus on the unique value of blogs in the AI era. Compared to the scale and formulaic nature of AI, the advantage of blogs lies in“someone thinking and feeling behind them”: unique perspectives, emotional warmth, cross-boundary integration, dynamic responses, and high-dimensional cognitive guidance.

These qualities together constitute the core competitiveness of blogs. They not only allow blog content to transcend information itself, but also make it a form ofpersonalized thinking and long-term companionship. In the context of massive AI-generated content, it is precisely these “irreplaceable human dimensions” that give blogs a reason to continue existing and to be chosen by readers.

Therefore, the future of blogs does not lie in competing with AI over “who is faster and who has more,” but in continuously demonstratingthe power of experience, stories, insights, and cognition, which is the true way for blogs to establish themselves in the AI era.

4 Reflections on the Future Value of Blogs: Trusted Knowledge Anchors

1 The Shift in How People Acquire Information

Looking at the path of information acquisition over a longer term, the changes are actually very drastic: in the era dominated by search engines, people often entered keywords with questions in mind, and then gradually filtered through a long list of results. Blogs, community Q&As, news websites, and even some forum posts were common landing points. Although this process was tedious, the diversity and traceability of information ensured that people had the opportunity to encounter different perspectives. Especially with high-quality blog posts, they not only answered questions but also often came with detailed background explanations and personal experience, giving people a reliable sense of “having a source and warmth.”

But when it comes to after the emergence of AI, this path was suddenly shortened: many people found that they no longer needed to repeatedly click on web pages and compare different sources. As long as they entered a question in the dialog box, they could get a “seemingly complete” answer almost instantly. The advantages of this method are obvious: saving time, avoiding redundancy, and a natural tone. But at the same time, it also dilutes the “sense of source” of information. When reading, users often no longer ask where the answer behind it comes from, and even habitually view AI as an “all-knowing” entity.

This shift has brought about two important results：

A sharp drop in search traffic — Blogs and websites that used to rely on search engines to gain readers are now seeing a significant drop in traffic. Many bloggers find that their articles no longer bring continuous visits as they used to, so they choose to stop updating or even close their sites, plunging the entire blog ecosystem into a low ebb.

Homogenization of the information experience — AI's answers tend to be uniform in form, striving to remain “objective and neutral” and avoiding emotional or subjective tones. This consistency makes the information seem concise, but it also means that many answers lack distinct personality, real experience, and specific contexts. In other words, the answers are “good enough,” but not necessarily “deep enough.”

It can be said that the “entry point” of information has indeed undergone a fundamental change: from multi-source dispersion in the past to AI aggregation today. People have become accustomed to directly conversing with AI, just as they were accustomed to opening search engines first a decade or so ago. But it is precisely this convenience that makes new gaps gradually appear—as answers become more and more homogenized, will people still long to find content that is truly deep, has a clear source, and represents real experience?

This also provides a new value space for blogs: in the era of AI-generalized information, blogs that continuously output unique insights, real experience, and delicate expressions have the potential to become users' “trusted knowledge anchors”for locating reliable content amidst massive information. They can not only provide eye-opening articles with practical guiding significance beyond formulaic AI answers, but also influence AI's knowledge generation and answer quality in the long run.

2 What is a Trusted Knowledge Anchor?

In the previous section, I mentioned the concept of a ”trusted knowledge anchor” (this is just a term I temporarily proposed to describe the characteristics of this type of content. It may not become a fixed concept; as long as everyone understands its connotation, there is no need to worry about the rigor of the terminology). So, what exactly is a ”trusted knowledge anchor”?

The so-called “Trusted knowledge anchor”can be understood as a stable, reliable, and deep source of information that can still be provided in an era of information overload and content fragmentation. This type of content not only has clear author attribution and a sense of responsibility, but also contains experience verified by practice, independent thinking, or long-term accumulation, allowing readers to trace its source and form a sense of trust.

Specifically, a trusted knowledge anchor relies on several key conditions:

Attribution and a sense of responsibility — Blog content has a clear author, meaning the information has a source and is verifiable, while the author is responsible for the accuracy of the content. This makes readers more willing to refer to this content when facing complex or critical problems, rather than relying entirely on AI's instantly generated answers.

Accumulation and continuity — Blogs that are updated over the long term and continuously accumulate content are more likely to form a complete knowledge system than scattered information. Each article not only exists independently but can also form connections with other articles, providing readers with a deeper understanding.

Depth and practical experience — Blogs often record the author's practical experience, independent thinking, and carefully weighed judgments. Even if AI models become more powerful, it is difficult for them to generate similarly rich logical chains and experiential backgrounds.

Together, these conditions constitute the so-called trusted knowledge anchors: they are not collages of scattered viewpoints, but content that has attribution, responsibility, accumulation, and depth, and can stand the test of time. Precisely because they possess these qualities, in an era where AI generates content on a large scale and information becomes increasingly flooded, they can become stable coordinates that people can rely on and trace when facing complex information environments.

3 The Significance of Trusted Knowledge Anchors

In an era where AI increasingly dominates information generation, the value of high-quality blogs as “trusted knowledge anchors” is first reflected in their significance to human readers. Although AI can quickly generate answers, as described in Chapter 2, its answers are often “good enough but not deep enough”: lacking practical experience, delicate emotions, unique judgments, and cross-contextual analysis.

Blogs, however, are different. They not only provide solutions but also tell the story of the process the author personally went through, analyze the pros and cons of each step, and explain the logic behind decisions. When reading this content, readers do not just obtain information; they can also understand “why it is so,” thereby forming their own judgment. Whether it is technical problems, life strategies, or complex decisions, blogs can provide readers with a traceable, reliable, and accountable reference point. This verifiability and sense of responsibility are precisely what is difficult for AI-generated content to replace.

At the same time, high-quality blogs will in turn affect AI's output, as models inevitably rely on high-quality information sources during training and generation. If a blog provides detailed, original, and logically rigorous content over the long term, it naturally becomes an important reference for AI learning. Some foreign developers have pointed out that when AI answers technical questions, its style and expression often mimic certain authoritative authors, which precisely illustrates the position of blogs in the knowledge ecosystem. In other words, a blog that accumulates over the long term and is continuously updated not only serves human readers but also shapes AI's cognitive patterns and ways of expression.

The significance of trusted knowledge anchors is also reflected in making up for the limitations of AI. As users gradually realize that AI's answers are not omnipotent, especially when facing complex problems, they will actively seek out content that can provide depth, independent insights, and practical experience. Blogs satisfy this exact need: amidst the flood of automatically generated content, they provide traceable, verifiable, and accountable knowledge. This not only gives readers a more reliable reference but also subtly cultivates their ability to maintain independent thinking.

More importantly, blogs also carry humanistic value. High-quality content often carries the author's emotional warmth, way of thinking, and value orientation, which AI cannot fully replicate. While acquiring knowledge, readers can also feel that “someone is thinking seriously” behind it. This accumulation of warmth and logic makes blogs a lighthouse in the ocean of information, reminding us that knowledge is not just cold facts, but needs to be understood and applied in specific contexts and practices.

Taken together, the significance of trusted knowledge anchors is multi-layered: it not only provides reliable references for humans, helping readers maintain judgment and depth in the flood of information, but also provides high-trust data sources for AI, indirectly improving the quality and accuracy of automatically generated content. For example, the thoughts shared by experienced developers or professional doctors through blogs can not only guide readers to solve practical problems but may also become important materials for AI collection and training. Long-accumulated blogs are like a stable coordinate system, enabling humans to still find their direction when faced with a large number of instantly generated answers. It is this dual value and long-term mission that allows those who persist in blogging to still possess an irreplaceable role and long-term influence in the AI era.

5 Blogs from a Realistic Perspective: From Ideal to Survival

1 Changes in Traditional Traffic-oriented Methods

Before discussing the material aspect of blogging, we must face a reality: the traffic logic that supported the blogging ecosystem in the past is rapidly collapsing.

在 the era dominated by search engines, as long as individual bloggers write high-quality articles, they have the opportunity to obtain stable traffic through keyword rankings. SEO optimization, long-tail keyword layout, and backlink building constituted a whole set of effective methodologies. Many bloggers even relied on this mechanism to obtain advertising revenue or build personal brands.

但 AI-driven content entry points After their emergence, this logic underwent a fundamental shift. Users are accustomed to asking AI directly rather than clicking on search results. This means that even if an article ranks high in search engines, its chances of being actually clicked and read are declining sharply. The sudden drop in search traffic is no longer an occasional dilemma for individual bloggers, but a widespread phenomenon across the entire ecosystem.

A deeper change lies in the fact thattraffic is no longer dispersed to specific websitesAlthough traditional search engines also have the “zero-click” problem (where users get answers directly on the results page), most complex questions still require users to visit the original web pages. However, AI's “question-and-answer” model almost completely bypasses this step, with answers generated directly in the conversation, leaving users with no motivation to trace the source. For blogs that rely on traffic, this means a natural “disappearance of the entry point.”

This shift has made “can blogging still yield traffic returns” a question in many people's minds. The reality is: the path to obtaining stable traffic by relying on traditional SEO is narrowing. Even if you write more high-quality content, if you still pin your hopes on search engine referrals, the results will often be disappointing.

In other words, blog creators must realize that in the AI era:traffic is no longer a stable target to rely on, but a variable that needs to be redefinedInstead of clinging to traditional SEO thinking, it is better to think about how blogs should position their value and establish connections with readers in new ways under the AI-dominated traffic distribution pattern.

2 Possible Paths for Financial Returns

If a blog can truly become a “trusted knowledge anchor,” its value will not just remain at the spiritual level. Stable, reliable, and in-depth content can still be translated into tangible returns in the AI era.

First, the return of indirect benefits:Truly valuable content will attract readers who come with questions. They are not passersby who just click and leave, but people who have long followed a certain field or have practical needs. These readers are more likely to bring subsequent opportunities, such as consulting, courses, tool recommendations, and even deeper cooperation. Compared to relying solely on ad clicks, this return is more stable and long-term.

Second, the “hidden value” in the AI ecosystem:AI is almost certain to use high-quality blog content during training and updates. Right now, it is still difficult for bloggers to get direct returns, but with trends like “data source transparency” and “content provenance licensing” developing, the situation may change in the future. Once high-quality content can be attributed to its source, it could translate into tangible revenue. The more solid the anchor status, the higher the potential value.

Third, the establishment of a knowledge brand:When a blog continuously accumulates and gradually becomes an “anchor” in a certain field, it will form a brand effect of its own. A brand means trust, and it also brings more possibilities: publishing, speaking, collaborative projects, and even career advancement. In the long run, a knowledge anchor is not only an investment but also a personal asset that can grow continuously.

Overall, the material returns of blogging in the AI era are no longer based on the old logic of “relying on traffic,” but are instead tied to the status of being a “trusted knowledge anchor.” Only by first winning trust on a spiritual level is it possible to reap sustainable returns on a material level.

3 Long-term Investment Logic of Blogs

In the AI era, blogs are no longer just carriers of instant traffic, but tools for accumulating long-term value. Their returns may not appear immediately, but will gradually manifest over time.

High-quality articles continuously settle over time, with each blog post serving as a node in a knowledge network. As quantity and depth increase, this content will gradually form a complete and sustainable knowledge system. Even if traffic is limited in the short term, these accumulations may still be cited, absorbed by AI models, or become important reference materials in specific fields.

Bloggers who consistently produce high-quality content over the long term will gradually build trust and brand effects as time goes on. Notably, the emergence of AI has caused many blogs to stop updating or even disappear, which conversely provides a relatively open space for those who persist. In this remaining creative environment, they can prioritize attracting attention and building a reputation, forming a unique long-term competitive edge. This reputation can not only bring a stable readership but also extend into diverse opportunities such as consulting, collaboration, and publishing.

At the same time, the long-term accumulation of a blog also holds public value. The establishment of a trusted knowledge anchor not only serves the blogger themselves but also provides a stable reference coordinate for the entire knowledge ecosystem. In the future, as AI becomes more dependent on high-quality information sources, these accumulations will become an irreplaceable part of the public knowledge system, providing reliable information, intellectual inspiration, and learning references for more people.

Overall, persisting in blogging is a long-term investment that emphasizes enduring accumulation, reputation building, and public value. In the AI era, this long-term perspective is far more important than short-term traffic, and it provides bloggers with the motivation and meaning to keep creating.

4 Additional Content—New Evaluation Standards for Personal Blogs in the AI Era: Centered on “Human-Irreplaceable Value Density”

Today, when AI can generate a long article that is “structurally complete, well-organized, and free of typos” in seconds, the barrier to content production has dropped to freezing point. However, the flip side of this prosperity is countless digital noises filled with generic platitudes, lacking real cost and personal stance—when “producing text” is no longer a barrier, “who is speaking” and “the real experience behind those words” become the only scarcities.

The criteria for evaluating the value of a personal blog have completely evolved from traditional “SEO traffic, update frequency, and writing fluency” to: how much of what truly belongs to "humans" is left in the content, and how inspiring those things are to readers.

To this end, I have taken "the density of irreplaceable human value" as the core to map out this five-tier evaluation standard. Its assessment principles and underlying logic are as follows:

1. Evaluating “human cognitive cost” rather than “pure length of text”

   AI can easily fake structure, but it cannot fake the details of running into pitfalls when facing complex constraints, the painful trade-offs, and years of continuous practice. What this standard values is not whether an article is piled with obscure jargon, but the author's investment inthe density of real practice, personal judgment, and cognitive reconstruction。

2. Distinguishing “utility value” from “persona performance”

   High-density human value can manifest as gentle and rational engineering accumulation, or as sharp and profound intellectual deconstruction.“Authenticity” does not equal “intense emotion”We advocate for sincerity and respect, and resolutely reject the deliberate creation of “paranoid personas,” “patronizing preaching,” or “pseudo-philosophical neologisms” just to gain attention.

3. Acknowledging media limitations: This is a ruler for a specific domain

   The medium of personal websites naturally leans toward textual abstraction and logical expression. This standard is not suited to the nature of all fields, but is an objective reference system designed specifically for“digital private plots that use text and structured logic as carriers, with digital/knowledge/thought/engineering practices at their core”.

4. Rejecting labeling: This is a “dynamically evolving” living system

   A personal blog is alive. A good article is a point, continuous thinking is a line, and only long-term practice and self-negation can connect them into a plane. This standard evaluates the creator'sability to dynamically evolve and continuously cultivate,rather than a static label defined once and for all.

5. Dynamic Lifecycle: Taking “dynamic content” as the subject of evaluation

   Discontinuation and stagnation: If a site stops updating for a long term, its existing archived articles still retain their original tier determination, but the “active evolution” attribute of the entire site is in a frozen state; Quality decline: If the author later abandons real practice and turns to pure commercial orders, clickbait, AI batch rewriting, or marketing jargon, their newly added content will be calculated based on actual quality downgrades, and the “human contribution density” of the overall site will be diluted.

After multiple revisions, the"Personal Blog Human Contribution Density Model (HCDM) v 1.0 Official Version" is as follows:

Currently, this is only v 1.0 of this standard, and I will update it if I have new ideas in the future. If netizens have any new ideas, you are also welcome to let me know.

6 Conclusion

The rise of AI has changed the entire content ecosystem. It can generate thousands of articles in an instant, answer almost infinite questions, and crush any individual in efficiency and scale. It seems everything is being swallowed up, and personal writers seem to have no reason to exist anymore.

But as we sort through it step by step, we find that this is not the case. The advantages of AI precisely highlight its limitations: it can piece together knowledge, but it is difficult to convey real feelings; it can repeat concepts, but it is hard to form independent judgments; it can cover the latest information, but it often lacks a deep understanding of trends. In other words, AI is almost omnipotent in “generation,” but always one step short in “experience” and “thinking.”

Because of this, the meaning of blogs will not disappear. However, in the AI era, the logic of a blog's existence must also adapt. Writing that lacks accumulation, independence, and has repetitive content may gradually be replaced by massive automatically generated texts; while blogs that can accumulate experience, show thinking, and reflect unique perspectives will become increasingly precious due to their scarcity. In other words, the blogs that survive in the future will be more like sparks on the “highlands” rather than repetitions on the “plains.”

This is also the significance of “trusted knowledge anchors”: they not only provide readers with a reliable reference in the fog of information, but may also become key input sources in the AI ecosystem, influencing the quality and direction of future machine answers.

Writing a blog has never been an easy path, but in the AI era, it may be more valuable than ever. This value also means that writers need to put in deeper thinking and expressiveness—because every article written is no longer just for ‘someone to read,’ but to leave one's own voice in this world: a real, independent, and trusted voice.

Perhaps many years from now, when people look back at this era, they will still be able to see the thread of human thinking through these blogs and feel the warmth and power that cannot be completely replaced by algorithms.

P.S. As for the motivation and meaning of blogging itself, I have discussed it in my previous article:"Viewing the Meaning of Today's Blogs from the Beginning and End of My Blog", and interested friends can take a look.