As the bubble fades, who dominates attention in the AI era? A 2026 Chinese-English AI KOL Influence Guide

原文作者：Alan, Amelia | Biteye Content Team; Denise | XHunt Operations Team

In the summer of 2026, social media feeds refresh at millisecond speed. One moment, a major LLM releases an update; the next, tens of thousands of "in-depth interpretations" flood the zone.

An independent developer told us that his first act upon waking is no longer scrolling his timeline, but quickly scanning a few familiar avatars to see what new tricks they cooked up with Vibe Coding the night before.

"I only trust those who have actually done it," he said.

This seemingly paranoid trust points to a truth most people overlook:

In an era of rapid LLM advancements, general knowledge itself is rapidly depreciating.

Traditional tech media accounts that rely on relaying news flashes, translating overseas announcements, or simply stitching together headlines are gradually losing user patience. The truly scarce resource is no longer "who said what first," but "who can tell me if this is reliable, and how I should use it."

To uncover the real operational logic of this hidden circle, we leveraged the exclusive data and capability models of the social analytics tool @xhunt_ai, conducting an in-depth analysis of tens of thousands of tweets from nearly 400 top AI KOLs in both Chinese and English ecosystems.

We found: Opinion leaders in the AI era are undergoing a profound transformation from "information intermediaries" to "productivity enablers."

1. Core Finding: From Distributing Opinions to Distributing Productivity

In the traditional internet context, turning a brilliant idea into reality required mobilizing a complex chain of human resources: backend, frontend, UI, product manager... The lengthy collaboration process could often dampen most of the initial enthusiasm. Today, AI tools have ruthlessly compressed this production chain. Codex, Claude Code, Cursor, and Lovable turn programming barriers into logical and architectural capabilities; Seedance, GPT Image, Kling, and Nano Banana directly eliminate the hurdles of complex image and video production.

But this has led to a counterintuitive industry phenomenon: when anyone can mass-produce lengthy articles with AI, high-quality content becomes "cheap" and readily available, making trust unprecedentedly scarce.

The core value of an AI KOL is not their ability to make AI churn out a superficial article faster than the average person. Rather, it is their ability, through human-machine collaboration, to first turn vague 'AI usability' into concrete results that others can see, run, and directly replicate. This is no longer distributing opinions, but distributing productive capacity.

For example, when a new model boasting "defeated Claude Opus 4.7" is released, users are tired of cookie-cutter press releases. They urgently want to know from trusted KOLs: "Will it hallucinate in real-world coding? Is this product, which looks dazzling in an official polished video, actually a productive tool for ordinary people out of the box?"

The attention compass has reversed: from "what happened," to "is it important," to "how to use it."

In a noisy field, AI KOLs act as practical pioneers and trust anchors.

2. Who Plays This Role: Tech Veterans and a New-Generation Blue Ocean

A common industry bias is: "Most AI KOLs are just marketing accounts that rode the wave after ChatGPT exploded in late 2022." However, generational statistics based on account registration dates from XHunt disprove this claim: the generational structure of AI KOLs actually presents an inverted pyramid distribution.

• Dominance of Seasoned Practitioners: In the English rankings, early adopters registered between 2007 and 2015 account for a high 62.9%; this proportion reaches 58% in the Chinese rankings. This means that the majority of top accounts currently active in the core AI circle are practitioners and entrepreneurs who have weathered the cycles of PC, mobile internet, and Web3. With the wave of large models, they have keenly completed the migration of productivity tools.
• New Generation Growth in the Chinese Sphere: Notably, during the ChatGPT boom of 2022-2023, new AI-native accounts emerging in the Chinese sphere accounted for 13.0%, higher than the 9.7% in the English sphere. This indicates that the Chinese ecosystem offers significant traffic dividends for practical content. As long as tools are mastered and tutorials are solid, new accounts can build a competitive advantage through consistent posting.

In contrast, the registration times of Web3 KOLs often show a distinct spindle shape, with a large number of new accounts created during DeFi Summer, the NFT boom, and the Meme craze, following market heat.

3. Symbiotic Evolution of AI KOLs and OPCs

The evolution of AI is turning the concept of the One Person Company (OPC) from a superhuman ideal into a clearly achievable reality. The core essence of OPC is the user's acute ability to deploy various vertical AI Agents, thereby liberating themselves from going it alone and handling all the grunt work, infinitely amplifying their own ideas, and using AI to complete independent product building, commercial distribution, and precise marketing.

In this transformation, 'application-distributing' AI KOLs have secured a core ecological niche with their composite advantages:

• Understand Technical Boundaries: Many come from major AI companies or are experienced developers with foundational technical knowledge, giving them a better grasp of the true limitations of tools than pure marketers.
• Understand Market Pain Points: As long-term content creators facing audiences directly, they possess strong product and marketing acumen, understanding real needs better than pure R&D personnel.

It is this dual buff of "technology + internet savviness" that allows them, through Build in Public, to transform abstract technology into usable scenarios, thereby accumulating a steady stream of user trust.

The globally popular Vibe Coding trend has pushed the power of personal IP to its extreme. Instead of blandly recommending a development framework, a top AI KOL directly demonstrates on X how, with just a natural language command and a relaxed vibe, they collaborate with a model to rapidly launch a complete, interactive AI application in 15 minutes.

Traditional KOLs harvest traffic by distributing opinions; AI KOLs consolidate ecosystems by distributing productive capacity.

4. Data Portrait: The Ecological Divide Between Eastern and Western KOLs

To explore the real operational logic of the AI KOL ecosystem, this report extracted 100 tweet samples from the top 300 English-ranked and top 100 Chinese-ranked AI KOLs on XHunt's influence rankings over the past 3 months, conducting in-depth analysis and comparison of their tweet content and various data points.

We found significant differences in attention structures and content production modes between Chinese and English AI KOLs. Below, we unveil the true face of AI KOLs across seven core dimensions, including traffic scale, discussion topics, account creation time, and individual profiles.

1️⃣ Attention Map: English Sphere Leans Toward Source, Chinese Sphere Leans Toward Practice

• Traffic Volume Distribution: The total follower base of the English rankings exceeds 350 million, with an average of 1.17 million and a median of 110,669. The Chinese ecosystem tends towards more refined vertical niches, with an average follower count of about 77,000 and a median of 43,006.
• Posting Activity Comparison: In the last 90 days, 100 accounts on the Chinese rankings produced nearly 30,000 tweets, with a median posting count of 210. In contrast, 300 English accounts posted only 37,000 total tweets, with a median of just 38. Top English accounts tend to post infrequently, while Chinese accounts form a high-frequency application diffusion network.
• Follower Tier Structure: The English rankings show a pyramid structure, with accounts having 50,000 to 200,000 followers being the largest segment at 41.8%. Accounts with over 1 million followers account for 7.4%. The Chinese rankings are concentrated in the long-tail application layer, with accounts having 10,000 to 50,000 followers making up 53.0%, and those exceeding 200,000 followers only accounting for 4.0%.
• KOL Followers: Although the average number of colleague followers for English accounts (510.7) is higher than for Chinese accounts (320.2), when adjusted for the base number of Chinese and English AI KOLs (approximately 1,000 and 5,000 respectively), the intra-circle penetration rate for Chinese top KOLs reaches 32%, far exceeding the 10% in the English sphere. This indicates that the Chinese AI KOL circle is a highly dense community with extremely tight connections.
• Activity Spectrum: Up to 70% of Chinese KOLs share industry trends and practical operations frequently every day. In the English sphere, low-frequency active accounts exclusively account for 39.8%, while consistently active accounts make up 26.4%. The English sphere leans towards an industrial source network, while the Chinese sphere leans towards a practical network.

Summary: English AI KOLs form an industrial source network that holds first-hand technology and major strategic releases; Chinese AI KOLs form a super diffusion and practice network that frantically translates, reviews, tutorials, and pushes cutting-edge technology into mainstream workflows.

2️⃣ Mindshare Preferences: English Sphere Leans Macro, Chinese Sphere Leans Practical

Peeling back broad labels, by extracting word frequencies and tags from the overall discussion content, we clearly see the different focuses of the two ecosystems:

Whether in English or Chinese spheres, foundation models, AI agents, AI commercialization, and AI coding are the consensus axes. However, their branching paths are entirely different:

• English Sphere Focuses on Foundational Technology & Macro Perspectives: English KOLs have significantly higher coverage rates than their Chinese counterparts in AI commercialization (44.7%), foundation models (39.6%), AI safety (13.8%), AI chips (12.6%), and embodied intelligence (5%). They invest substantial energy discussing AGI safety alignment, the supply-demand landscape of computing power, open-source vs. closed-source dynamics, and embodied intelligence.
• Chinese Sphere Focuses on Application & Practical Guidance: Chinese KOLs exhibit a strong sense of pragmatism. AI coding reaches 72.1%, roughly double the rate in the English sphere. AI agents stand at 51.5% vs. 39% in the English sphere. In visual generation, the rate is 20.6%, still nearly double that of the English sphere. Tool reviews account for 11.8%, a staggering nine times the rate in the English sphere. Tutorials and prompts are also significantly higher than in the English sphere, indicating that Chinese bloggers are better at breaking down complex technologies into specific operational guides like code writing and agent building.

3️⃣ Capability Radar: English Focuses on Technical Insight, Chinese Focuses on Full-Stack Application

To reduce misjudgment from broad category labels, we utilized XHunt's KOL capability scoring model to comprehensively analyze the content quality published by AI KOL accounts across multiple scoring dimensions:

• English Rankings Occupy Industry Source & Foundational Logic: The highest score in the English rankings is for Multimodal (88.3), followed by Foundation Models and Prompts. Their core capabilities are reflected in insights into model architecture, experience with large-scale engineering optimization, and predictions of cutting-edge trends. In AI safety and chips, the English sphere has a natural first-mover advantage.
• Chinese Rankings Focus on Full-Stack Application Practice: In the Chinese sample, the average relevance score for AI coding capability reached 88.9, and for AI Agents it reached 87.1. A large number of creators active on Chinese Twitter possess natural language development skills and are adept at integrating AI into private domain monetization or light entrepreneurship models.

4️⃣ LLM Mention Rates: A Workflow Map Drawn by Actions

The LLM mention rate (i.e., any tweet from an account hitting a keyword within 3 months) represents not only the discussion heat of the LLM model within the community but also acts as a 'vote with their feet' on the reliance and sentiment KOLs have towards various models in their actual workflows:

As shown, Claude and GPT form the 'king and queen' of bilingual models. In the Chinese sphere, Claude's mention rate is as high as 95.7%, remaining the top choice for independent developers and Vibe Coders. Notably, with the continued fervor around AI coding scenarios, Codex's heat has skyrocketed recently, securing third place with an ultra-high coverage rate of 80.9%, further confirming the Chinese geek community's intense pursuit of practical workflows.

Additionally, domestic large models DeepSeek (68.1%) and Kimi (58.5%) have demonstrated strong local penetration. In contrast, in the English sphere, GPT (76.2%) and Claude (75.2%) share the spotlight. Rather than discussing a single toolchain, they focus more on multimodal evolution and the overall industry narrative.

5️⃣ MBTI Content Style: Facets of Account Expression

Using a proprietary style inference algorithm, XHunt classified the public personality facets of Chinese and English KOL accounts using MBTI profiles, based on their bios, long-form tweet structures, interaction/debate logic, and topic preferences:

As shown, whether in the Chinese or English sphere, accounts with influence predominantly belong to the NT (Rational) camp. In a period of rapid technological iteration, content that offers logical analysis and productivity guidance is clearly more favored. The English rankings are dominated by ENTJ (38.4%) and ENTP (25.8%), leaning towards framework building and macro analysis. The Chinese rankings are led by ENTP (41.2%), echoing the characteristic of the Chinese sphere's enthusiasm for exploring diverse applications of new tools.

6️⃣ Identity Structure: English Leans Frontier, Chinese Leans Practice

By clustering and cross-verifying the Twitter profile bios and historical tweet self-descriptions of the two sample groups, XHunt mapped out the profiles of Chinese and English AI KOLs:

Core Identity Structure:

1. Over 65% of KOLs in the English sphere are LLM founders (31.4%), executives (34%), or scientists. Their content output is, in itself, a form of strategic distribution.
2. The top identities in the Chinese sphere are Tool/Testers (69.1%) and Product Engineers (57.4%). Overall, the English ecosystem leans more towards a source release network, while the Chinese ecosystem leans more towards a productivity practice network.

In summary:

The English AI KOL network resembles a cutting-edge source technology and paradigm release network happening in Silicon Valley, led by scientists and tech leaders. The Chinese AI KOL network resembles a comprehensive productivity tool and survival practice network erupting in a vast application market, led by full-stack independent geeks and application pioneers.

7️⃣ Tweet Effectiveness Evolution: From Wild Growth to High-Quality Development

Combined with the overall market trend over the past 8 months, the traffic distribution logic in the AI field has shifted from wild growth to high-quality development, with continuous improvement in exposure and posting numbers, exhibiting three core characteristics:

• Attention Dilution in Feb & Mar: Influenced by industry hotspots like Openclaw, total tweets surged to 12.4K in March, with total Views reaching 310M. However, the average Views per post plummeted to a low (25.0K). A massive influx of homogeneous news flashes led to severe information overload and declining dissemination efficiency.
• Peak Communication Efficiency in May: Total tweets dropped back to 9.0K in May, but both total Views