The ceiling for real-world testing of AI tools will continue to decline.

Article author and source: Kafka

I scraped this data myself.

This study analyzed 23 AI/content creator accounts on the Chinese X platform, spanning a two-month timeframe (April–May 2026), and compiled 556 pieces of content—64 long X articles, 40 Twitter threads, and 452 short tweets. Each post garnered over 10,000 views. This sample was not drawn from the entire X platform but rather from a specific source; therefore, the conclusions of this article only apply to this sample.

Before I started crawling, I thought I would see a "Chinese AI content map"—who was writing Claude Code, who was writing Codex, and who was disassembling Skill. But after crawling, what really kept me staring at it for a long time was another set of numbers.

This content library contains 17 items with over one million views , none of which are related to AI. The most viewed item, with 12.58 million views, is accompanied by a photo of an office carpet. Within the same timeframe, the most viewed long-form AI article was an order of magnitude less viewed than the least viewed joke or tweet among these 17 items.

If this group of people all seem to be writing about AI, is AI really their subject matter? Are their readers actually AI users? What is the true shape of this business?

The report below is the answer I saw. It's not a prediction, nor a guide. It's a slice—a cross-section of the present, the kind with a knife.

I will name every account that is cited to facilitate everyone's learning. Because in the Chinese AI content community, these names are already part of the discussion. Below, I will only describe what they wrote, how long it was, and how many people saw it. The judgment is up to you.

I. Who is writing: A rough genealogy of 23 creators

The 23 accounts can be roughly divided into five groups based on their content focus. This division is rough, as each account has some content across different areas; below, we will only focus on their main focus.

AI tool practitioners – Bai Nian AI × Going Global ( @yidabuilds ), Berryxia.AI ( @berryxia ), Xue Ta Wu Yun ( @Pluvio9yte ), and Bo Zhou ( @bozhou_ai ) – write about how to use Claude Code, how it differs from Codex, and how to write your own Skill. Bai Nian's post from late April, "$155 vs $15: Codex in a Month of Testing Replaces My Claude Code," garnered 237,000 views. Xue Ta Wu Yun's post from late March, "After Digging Through the Leaked Claude Code Source Code, I Discovered that the End of 'Vibe Coding' is Actually Engineering," received 149,000 views. Bo Zhou's post, "Practical Tutorial: Writing Your Own Skill from 0 to 1," received 131,000 views. This group is characterized by concrete actions, reproducibility, and accompanying screenshots and code.

AI-Driven Wealth Creation/Methodology School – Koda ( @wadezone ), AI's Strictest Father ( @dashen_wang ), Miles ( @ma_zhenyuan ), Jinchenma ( @jinchenma_ai ), Luna ( @LunaAI519 ), Wenzi ( @Eejoylove ), Captain Noahduck ( @noahduck283 ). Their representative works aren't about "how to use tools," but rather "how to make money with them" or "how I gained followers." Koda's article "How Ordinary People Can Earn 1 Million Yuan a Year in 2026" garnered 427,000 views in early May, the highest among 64 X articles. AI's Strictest Father's two flagship articles – "A Comprehensive Guide to Enterprise AI Transformation in 2026" (374,000 views) and "Dissecting a Million-Yuan AI Implementation Project: Large-Scale Mobile Phone Group Control" (247,000 views) – don't teach you how to use AI, but how to make money through AI implementation projects.

Life Observation/Humorous Content Generators —Stanley ( @Stanleysobest ), Ray Wang ( @wangray ), Yuvi ( @Li665508Li ), Da Vinci ( @SuisPasDaVinci ), and Ming ( @PandaMing88 ). This group's representative works don't mention AI. Stanley's post, "A Japanese blogger describes the appearance of most Chinese students studying abroad," has 6.78 million views; Ray Wang's post, "Beware of companies that lay out this kind of carpet during interviews," has 12.58 million views. All 17 short tweets in the resource library with over a million views come from this group.

Specialized groups include Roland.W ( @rwayne ), Yang Jin ( @shaozhu93314 ), Jaden's Thinking Log ( @Jaden_riku ), and Achuan's AI Thinking ( @AI_jacksaku ). Roland is a doctor who, at the end of April, rewrote a medical journal paper into a popular science article; Yang Jin's "Building the Underlying Architecture of IP Systems" has garnered 226,000 views; and Jaden writes about studying abroad. The biggest difference between this group and the previous ones is that they don't treat AI as a business, but rather use AI tools to do other businesses.

AI Skeptics/Reflectors – the fewest in number, but with the loudest voices. Linote 🎃 ( @Alexjkman )'s 9,000-word article in late April, "You Think You're Using AI, You're Actually Waiting in Line to Die," garnered 10,000 views, beginning with "This article offers no solutions, nor does it intend to." Roland.W's early May article, "What is ACPD – Can Artificial Intelligence Calibrate Personality Disorder?", coined a term to describe the side effects of heavy AI users. While the strictest AI commentator primarily writes about transformation and implementation, his May 1st article, "The Entire AI Industry is Systematically Eliminating What It Needs Most," also falls into this category – he switches between his two personas with remarkable ease.

Looking at all five groups together, the first thing worth remembering is that the most viewed content in the entire resource library all comes from the third group (the lifestyle jokes traffic group), not from AI content creators. This is the first counterintuitive point in this report that we need to calmly confront, and we'll come back to it later.

II. What to Write: Several Recurring Script Templates

Of the 64 X articles, AI tool testing, AI application project analysis, and AI monetization methodologies account for more than half; personal methodologies (IP, writing, investment) account for about a quarter; and the rest are vertical categories such as medicine, studying abroad, and life observation.

But what's even more noteworthy than the topic distribution are the recurring phrase templates .

"2026 + Comprehensive Guide/Complete Breakdown" – Three articles directly include "2026" in their titles: "A Comprehensive Guide to Enterprise AI Transformation in 2026" and "A Comprehensive Guide to Personal AI Enhancement in 2026" by the strictest father of AI, and "How Ordinary People Can Earn 1 Million Yuan a Year in 2026" by Koda. While the proportion is not high, two of these three articles made it into the Top 5 of X article views. Using the year as a time anchor creates a sense of urgency, while "comprehensive" promises coverage to alleviate anxiety. Combined, this gives readers the psychological expectation that "reading this one article is enough."

"Disassembly/Review/From 0 to 1/Underlying Level" —higher frequency. The strictest AI expert alone contributed the series "Disassembling AI Implementation Projects Earning Millions Annually": 247,000 views for mobile phone group control, 92,000 views for transactions, and 61,000 views for women's AI communities. Bai Nian's "Complete Guide to Batch Collection of Public Account Articles: 5 Methods + API Reverse Engineering + Practical Scripts" has 197,000 views. Yang Jin's "Building the Underlying Architecture of IP Systems (Fully Hand-Typed, Read with Confidence)" has 226,000 views—the phrase "Fully Hand-Typed, Read with Confidence" itself is already drawing a line between AI writing and AI, a specific anti-AI signal in the current ecosystem.

"I did X, so you can trust me" —almost every X article begins by establishing the author's credentials. Koda writes about his rural background in Henan, an ordinary college diploma, and achieving an A8 grade at 33, followed by "5 million views and 500 verified subscribers in two weeks." AI's strictest father begins by stating, "I personally operate 2000 websites, all automated by AI." A century begins by saying, "I spend $600 a month on AI programming tools." These credentials statements are always at the very beginning, their purpose being to verify the author's credentials before the reader even enters the main text. You might not remember the methods after reading the main text, but you will remember "that one-man company with 2000 websites."

"I thought it was X, but it's actually Y" —a phrase that straddles the line between optimists and skeptics. Examples include Linote's "You Think You're Using AI, But You're Actually Waiting in Line to Die," Captain Noya's "You Think Justin Sun and Mi Meng Have Great Writing Skills? They're Actually Hijacking Your Brain," Berryxia's "The Biggest Joke of the AI ​​Era: I'm Still Making Money Like Crazy with Email," and Huang Xiaomu's "API Transfer Stations, More Profitable Than Drug Trafficking"—all variations of this structure. Its power lies in the fact that the title itself accomplishes the "disruption" action; the reader, upon clicking, has already accepted the position of being disrupted.

"Making a fortune quietly" – The standard version of the article "AI Fortune Telling: Making a fortune quietly, don't miss out on a billion-dollar industry" has 197,000 views. These five words promise two things: this business is indeed profitable; and not many people know about it. Combining these two things into one sentence creates the most effective incentive structure in this ecosystem.

These five templates, stacked together, all aim to achieve the same thing: reduce the reader's decision-making time . "2026" reduces uncertainty about the future, "Comprehensive Guide" reduces learning costs, "What I Did" reduces trust costs, "What You Think/Actually" reduces judgment costs, and "Making a Fortune Quietly" reduces hesitation before entering the market. Each template tells the reader the same thing—don't overthink it, take action now.

This high degree of homogeneity in rhetoric is itself a data point. It indicates that the successful paths within this ecosystem have been repeatedly validated and widely imitated, and are now approaching saturation.

III. Counterintuitive Traffic Distribution: AI Can't Reach Viral Strategies for Long Articles

If you sort these 556 items by number of views, you'll get a set of counterintuitive data.

The median views for the 64 X articles (long articles) were 29,313, with a peak of 427,000 (Koda). The median views for the 452 short tweets were 35,934, with a peak of 12.58 million (Ray Wang). The median views for the short tweets exceeded those of the X articles, and the highest view count was a full 30 times higher.

The entire content library contained 17 posts with over one million views, all of which were short tweets, and none of them were about AI. Stanley alone accounted for 12 of them: a Japanese blogger describing the appearance of Chinese students (6.78 million views), a missing corner on an answer sheet (2.17 million views), "I've spent my whole life earning this 800 yuan" (1.76 million views), Bai Bing's fine (1.65 million views), and the annual cost of replacing an Apple phone (1.52 million views). Ray Wang's 12.58 million view post, "Beware of companies with this kind of carpet during interviews," was accompanied by a picture of an office carpet.

Compare this to the ceiling of AI content: The article "$155 vs $15: A Month of Codex Testing" by Bai Nian is the most viewed pure AI tool testing article in this resource library, with 237,000 views. Xue Ta Wu Yun's in-depth analysis of the Claude Code source code leak has 149,000 views. The most viewed long-form AI article...

But there's another side to the story. X article's "creator revenue" mechanism distributes revenue based on the effective reads of verified (blue V) subscribers, not on the total number of views. A large portion of Stanley's 6.78 million views came from non-subscribers who simply browsed; the readership structure for AI-generated long articles is the opposite—those who can read a 5,000-word AI article are almost certainly high-value users with a serious interest in the field. Koda himself provides a direct comparison in his article, "From Zero to 10,000 Followers in 50 Days: What Made Me Amazing": his most viewed post with 2.5 million views only gained him 700 followers, while another post with 140,000 views gained him 1,400 followers—more than double the number gained from 2.5 million.

Therefore, there are actually two completely different markets in this ecosystem:

Market A – Relying on social issue jokes to get millions of views, the monetization ability of a single post is low, but the aggregation effect is amazing (Stanley's several viral posts recorded in April and May totaled 30 million views).

Market B – Relying on AI-powered, in-depth articles to generate tens to hundreds of thousands of views, each view corresponds to a high-value user, serving as a precise funnel for subsequent courses, communities, and private domain conversions.

The "traffic value exchange rate" is completely different in these two markets. 30,000 views of an AI-generated long article may be more valuable than 3 million views of a joke, because the reader profile is narrower and readers are more willing to pay.

This pattern explains why AI creators, knowing they can't compete with short, engaging content creators for traffic, still repeatedly write long articles—they're not competing with Stanley for traffic, they're filtering traffic. But this pattern also hides an uncomfortable conclusion: when all AI creators are filtering the same type of high-value readers, it becomes less clear who is filtering whom.

IV. How exactly do they cycle?

If you only look at "what content is written", it's an AI content ecosystem. But if you look at "how the content is distributed, who the readers are, and where the money goes", what emerges is actually a relatively closed internal cycle.

After reading through all 23 accounts, the following are some recurring clues that can be directly read from their own writing. This section does not make estimations, but only describes what they wrote.

Entry Requirements: Blue V Subscription and Creator Revenue. X's creator revenue mechanism is the infrastructure for entering this ecosystem. Koda wrote, "In two weeks... I achieved 5 million views and 500 Blue V subscriptions, directly reaching Musk's creator income threshold." Wenzi's article, "Getting Creator Revenue in Three Months with X: A Complete Retrospective by an Ordinary Person," specifically discusses this path. Blue V subscriptions have two identities in this ecosystem—they are both a source of income and a badge of recognition among creators.

Ecosystem Background: Paid learning has formed an independent market. The strictest father of AI, in his article "Enterprise AI Training: How to Create Courses, How to Provide Support, and How to Collect Fees," presents a set of figures at the beginning: "In 2026, the Chinese enterprise AI training market size reached 8.7 billion yuan, with over 300 institutions participating, and an annual growth rate of 45%." His other article, "Deconstructing a Million-Yuan AI Implementation Project: Women's AI Communities (51 Special Edition)," directly analyzes "women's AI communities" as a case study. Luna's "How Many Paid Communities on X Specifically Teach Women to Use AI?" offers an external observation of this market. These are not market reports, but rather creators showing their peers that they have explored this path.

On the fringes of the ecosystem: the gray market of API transfer stations has been repeatedly discussed. Huang Xiaomu's article, "API Transfer Stations: More Profitable Than Drug Trafficking" (402,000 views), is the second most viewed X article in the entire resource library. In the same week, Jin Chenma also wrote an article, "Justin Sun and Fu Sheng Rush into the Market: AI API Transfer Stations Are Like Money Printing Machines," which garnered 22,000 views. Same topic, same time, but a 20-fold difference. This data itself illustrates one thing: the returns on homogenized topics in this ecosystem decay extremely quickly—by the time the second person writes about the same topic, the market is already saturated.

The core of the loop: content about content

Looking at the above points together, we can see a very special phenomenon in this ecosystem— content about "how to create content on X" is itself one of the most stable sources of traffic in this ecosystem.

Koda's "50 Days to 10,000 Followers: How I Got It" dissects his own story; Roland.W's video "How I Gained 40,000 Followers and 150 Million Views on Twitter in Three Months" has 250,000 views; Wenzi's "Get Creator Revenue in Three Months with X"; and Bainian's "Gen Z Uses Claude Code for Side Hustles and Earns Over 100,000 Yuan in 4 Months: Methods and Data Revealed" has 132,000 views—the protagonist is someone else's Gen Z, but the author uses other people's stories to filter his own readers.

The most straightforward one is Huang Xiaomu's Twitter threads on April 29 (150,000 views), the original text of which is as follows:

Take all the trending topics related to X, such as opening a verified account, opening a Hong Kong bank account, and various SIM cards, and create a video tutorial for each. You'll have 10,000 followers in no time. You're welcome, just get started.

These 30 words describe the core cyclical structure of this ecosystem: the content is not truly aimed at "people who want to use AI," but at "people who want to become AI content creators." The former read it to use the tools, while the latter read it to create the next piece of content about the tools. These two types of readers overlap, but they are far from the same group of people.

This brings us back to the last sentence of Section 3—AI creators aren't competing with Stanley for traffic; they're filtering traffic. Going a step further: what they're filtering out are the participants for the next round of this cycle.

Money certainly flows within this cycle—through verified account earnings, paid communities, business consulting, overseas products, and API intermediaries—but money isn't the driving force behind it . The real fuel that makes this cycle run is something else entirely. We'll discuss that fuel in the next section.

V. Fuel is Anxiety: Two Contradictory Narratives Simultaneously Gaining Followers

If money isn't the driving force behind this cycle, then what is?

If you lay out the content from these two months and observe it side-by-side, you'll see something interesting: within the same timeframe, on the same platform, and targeting the same group of readers, two semantically completely opposite narratives are running in parallel . These two narratives aren't serving two different groups of people; they're serving two opposing emotions within the same group. These emotions are the real fuel that fuels this cycle.

The first approach: AI has opened an unprecedented window for ordinary people. Koda's "How Ordinary People Can Earn 1 Million Yuan a Year in 2026" is the standard version—if the method is right, 12 months is enough for an ordinary person. AI's two 2026 guides, written by its strictest father, are the counterparts to the enterprise and personal versions, respectively. Bai Nian's "AI Fortune Telling: Making a Fortune Quietly" and Huang Xiaomu's "API Transfer Stations: More Profitable Than Drug Trafficking" are extreme commercial versions of this narrative. The underlying logic is: this is a new world, and the old rules haven't been locked down yet; whoever moves first reaps the rewards.

The second set of arguments: AI is actually shut off in front of most ordinary people, and is quietly shutting off. Linote's 9,000-word article, "You Think You're Using AI, But You're Actually Waiting in Line to Die," is the most complete expression of this. Roland W.'s "What is ACPD?" is a lighter version, describing the regression of heavy AI users in communicating with people. The most severe father of AI, in "The Entire AI Industry is Systematically Eliminating What It Needs Most," describes a programmer who says at 2 AM that he has the highest output but the emptier he feels. The underlying logic is: this is not a window that ordinary people can easily board, but rather a maze that makes those who think they can get on board sink deeper and deeper.

What's most noteworthy about these two narratives isn't their opposition, but rather that they often originate from the same person . The most stern father of AI is a prime example—he writes "A Comprehensive Guide to Enterprise AI Transformation in 2026," teaching companies how to implement AI, while simultaneously criticizing the industry's hollowing out with "The AI ​​Industry Systematically Eliminates What It Needs Most." The two articles are published less than two weeks apart. From his perspective as a content creator, both are bullets: the first targets "bosses wanting to transform their businesses," and the second targets "practitioners who are questioning their existence due to AI's impact." While there's some overlap in their readership, their emotional states differ, and their content needs are entirely different.

Similarly, there's Roland.W—who writes about "how to gain 40,000 followers on Twitter in three months" and also satirizes heavy reliance on AI with "ACPD." Berryxia has both practical and optimistic articles like "The Biggest Joke of the AI ​​Era" and short tweets like "Barbie Q is Gone."

Why would a single creator write two opposing pieces of content simultaneously? Because these two types of content cater to the different psychological needs of the same reader at different times.

When readers open X, they are actually torn between two opposing emotions: one is "I want to seize this opportunity, I can't miss it," and the other is "I've already been left behind, what should I do?" The former makes them click on "How an ordinary person can earn 1 million a year in 2026"; the latter makes them click on "You think you're using AI, but you're actually waiting in line to die."

Optimistic content grants a license to act, while skeptical content grants a license to remain still. One convinces you that it's not too late to act, the other convinces you that inaction isn't necessarily a mistake. Both licenses need to be granted, so both types of content inevitably exist.

Understanding this structure explains why Linote's post only garnered 10,000 views while Koda's reached 420,000—not because the former was wrong, but because far more people wanted an "action permit" than a "stay permit." However, this ratio isn't fixed; it fluctuates with market sentiment. When those who bought action permits begin to find action ineffective and are unwilling to fully admit their mistakes, they turn to skeptical content for solace. When that day arrives will be the turning point for creators like Linote, transforming them from niche to mainstream.

Looking back: the fuel this cycle of activity has never been the curiosity surrounding AI tools, but rather the uncertainty the middle class feels about their current situation. AI is the vehicle for this round of anxiety, but the underlying technology is far older than AI itself.

VI. Readers' inferences: Who are they probably?

A resource library that only contains creator data and lacks reader data will inevitably produce a rough reader profile. However, a few key points can still be derived.

They can bypass the Great Firewall. However, X still requires a technical threshold within the mainstream Chinese user context. Being able to reliably access X, follow dozens of Chinese AI creators, and read a 5000-word AI article already filters out the vast majority of ordinary internet users. Captain Noya's Twitter threads with the sentence, "If you can bypass the Great Firewall and you use AI, then congratulations, you already have basic earning power"—he himself realizes that this threshold is an entry-level qualification.

They are most likely experiencing some kind of career anxiety. The most frequently occurring keyword combinations in the entire material library are "35 years old", "layoffs", "side hustle", "being replaced", and "being left behind". The most popular X article creators, such as AI's strictest father, Koda, and Centennial, all address the same core issue in their articles—the readers' current state is unsustainable.

Their core business relationship is paid learning, not paid product purchases. These readers might subscribe to verified accounts, buy "AI training courses," and join "paid communities"—but they aren't true enterprise-level buyers of AI tools. If they were enterprise IT decision-makers or AI team leaders in large companies, they would be reading Hugging Face papers and LessWrong articles, not Chinese X. They aren't buying knowledge, but a sense of identity: "I'm keeping up ." Whether the courses are usable or the communities are productive is secondary; the primary concern is that subscribing alleviates the feeling of "I might be falling behind."

They react far more to concrete numbers than to abstract arguments. Linote's 9,000-word skeptical essay garnered 10,000 views; Koda's single statement, "From 0 to 10,000 followers in 50 days, a single post with 2.5 million views," received 420,000 views. The former is entirely causal analysis, the latter entirely concrete numbers. The readers in this ecosystem aren't incapable of thinking, but rather weary of it—they're more willing to pay for credible narratives that have already occurred. This also explains why the qualification statement of "what I did" must be placed first: it's not supplementary to the argument, but a substitute for it.

They are in a state of "wanting to become creators themselves." Luna's "Ordinary People Must Come to X to Run Traffic," Wenzi's "Get Creator Revenue in Three Months with X," Koda's "Go from Zero to 10,000 Followers in 50 Days," and Huang Xiaomu's "Tutorials on Making Popular X Tweets and You'll Have 10,000 Followers"—the target audience for these articles is people who have already started considering joining the X movement. This is fundamentally different from the profile of typical AI users: typical AI users read a Claude Code tutorial and want to use Claude Code, while the readers of this ecosystem read it and want to become the author of the next Claude Code tutorial.

Putting these five criteria together, a fairly specific person emerges: a Chinese user who can bypass internet restrictions, is around 35 years old, is dissatisfied with their current job, has basic experience using AI tools, and is seriously considering "content creation" as a side hustle or main career path.

This profile highly overlaps with the profiles of the 23 creators themselves—this is not a coincidence, but a structural characteristic. This is a market where producers and consumers are highly isomorphic : today's readers are tomorrow's creators, and today's creators are yesterday's readers. This isomorphism causes information asymmetry to decay extremely quickly, because once an effective method is published, its readers quickly become the next users, and then the next disseminators, and the original advantage is diluted within two or three layers of dissemination.

This is why "2026" must be constantly updated—because the methods of 2025 will no longer work in 2026, and the methods from the early part of 2026 will also no longer work by the middle of 2026. The content of this ecosystem must continuously produce new "now," otherwise its core commodity (information gap) will immediately depreciate.

Conclusion: Several things that might happen in the next 6–12 months

Finally, a few judgments are left. These are judgments, not prophecies.

The ceiling for AI tool testing content will continue to decline. The reason why the topic of Codex replacing Claude Code at the end of April garnered 237,000 views was because this comparison was something most readers hadn't yet run themselves. As a large number of creators continuously produce similar content, and readers become fatigued after several rounds of tool switching, the marginal traffic of "real-world comparison articles" will decrease. Among the most stable creators like Xue Ta Wu Yun, Bo Zhou, and Bainian, they have already automatically shifted their content focus from "tool testing" to deeper topics such as "engineering methodologies," "skills systems," and "context management." This is not a coincidence; traffic is telling them that they must shift.

"Meta-content" will outnumber tool-based testing content in terms of quantity. The feedback loop of writing "How to make money with AI content on X" is much shorter than that of "How to use AI tools"—the former only requires readers to be envious to complete half the transaction, while the latter requires readers to actively verify to form a closed loop. When the difference in feedback loops is significant, the market will automatically favor the shorter loop. This is not the choice of any one creator, but the gravitational direction of the entire ecosystem.

The share of skeptical content will increase, but it won't become mainstream. When a large number of readers who acted on the "1 million by 2026" path find themselves not reaching that milestone a year later, they won't need more action plans; they'll need an explanation that allows them to gracefully exit the stage. Platforms like Linote and Roland.W are backup materials prepared for that moment. But it won't become mainstream—there will always be new optimistic readers entering the market; they haven't yet completed the journey that will make them need skepticism. The ratio of optimists to skeptics will gradually shift from 9:1 today to 7:3, but it won't reverse.

The lines between joke-based content and AI-driven content will become increasingly separate. While viral hits from figures like Stanley can garner tens of millions of views, their readership is extremely fragmented; AI-driven content, on the other hand, sees lower views but a narrower, more concentrated readership. These two models cater to different types of reader relationships, making them difficult to merge, and thus they will each pursue their own paths on the same platform. Accounts attempting to please both—writing both jokes and AI-driven content simultaneously—will be flagged as having unclear signals by both algorithms, making their success even more challenging. Focus is the key to success in this era.

"Real people/face exposure" will become an explicit premium. Another Linote article, "Face Exposure: The Scarcity of Assets in This Cyber ​​Brothel," although only 15,000 views, points to an emerging trend: the more AI-generated content floods the market, the scarcer the signal of "real humans" becomes. One of the methods Roland.W used to gain 40,000 followers in three months was starting to shoot videos. When the cost of AI making all "look-real" content approach zero, the fact that it is "truly real" will begin to command a premium.

This is an observation based on 23 accounts, 556 pieces of content, and a two-month timeframe. It can tell you the current state of this ecosystem, but not what it will become next. The most likely scenario is not that this ecosystem will suddenly collapse or take off, but rather that it will continue to generate a large amount of duplicate content, train a large number of similar creators, and consume a large number of similar readers at the current rate, until one day the label "AI" is replaced by another label.

The replacement will be done without announcement or a specific milestone. It will happen quietly sometime in a week that no one will notice—perhaps three months after this report is written. When the next tag appears, today's "2026 Comprehensive Guide" will be replaced by "2027 Comprehensive Guide," and "AI Implementation Breakdown" will be replaced by "Robot/Agent/XR/Any Next Keyword Implementation Breakdown." The wording, the audience, and the cycle will remain the same.

What has changed is the veneer of this current round of anxiety.