In today’s information explosion, the sheer number of followers can no longer measure a person’s true influence. To find true industry consensus, XHunt has exclusively released a brand new AI Influence List. Using the hard-core metric of AI KOL Followers (core circle attention), the list is updated in real-time with influence, helping you filter out 90% of the noise and get straight to the source of change.
To view the complete AI Social Influence Top 100 list, please visit the XHunt official website and switch the AI category in the navigation bar: https://kol.xhunt.ai/.
The following is the complete list of the Top 100 influential accounts in the English-speaking area (arranged based on snapshot information from May 15, 2026). This is the source of the AI storm, and the Top 100 are mostly executives of technology companies and cutting-edge scientists. There is @karpathy, who is good at explaining the principles of large models thoroughly, @sama, who determines the rhythm of the industry, and @jeffdean, who promotes the Google AI route. In their information flow, a tweet sent by a researcher late at night is very likely to evolve into an industry consensus in half a year.
🏆 Complete List of Top 100 Influential Accounts in the English-speaking Area:
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Andrej Karpathy @karpathy | AI KOL Followers:4221 | Followers:2455804: Founder of Eureka Labs, former founding team member of OpenAI, former head of Tesla AI, top evangelist.
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Sam Altman @sama | AI KOL Followers:3841 | Followers:4822283: OpenAI CEO, absolute controller of GPT and Codex.
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Greg Brockman @gdb | AI KOL Followers:3202 | Followers:975960: OpenAI President and Co-founder, hard-core recorder of products, research and ecology.
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Jeff Dean @jeffdean | AI KOL Followers:3133 | Followers:438941: Google DeepMind/Google Research Chief Scientist, Google AI technology roadmap navigator.
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Ilya Sutskever @ilyasut | AI KOL Followers:3083 | Followers:674642: SSI Co-founder, former Chief Scientist of OpenAI, master of deep thinking.
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Yann LeCun @ylecun | AI KOL Followers:2992 | Followers:1159418: Co-founder and Executive Chairman of AMI Labs, one of the three giants of deep learning, Turing Award winner.
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Demis Hassabis @demishassabis | AI KOL Followers:2931 | Followers:917939: Google DeepMind CEO, super promoter of AlphaFold and AGI narratives.
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Andrew Ng @andrewyng | AI KOL Followers:2710 | Followers:1526147: Professor at Stanford University, long-term evangelist for AI education and application implementation.
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Elon Musk @elonmusk | AI KOL Followers:2559 | Followers:23978656: Super amplifier behind xAI, Tesla, and SpaceX.
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François Chollet @fchollet | AI KOL Followers:2392 | Followers:663386: Creator of Keras, known for his pragmatic perspective and deep insights into neural networks.
(Note: The list covers up to the 100th place. Some list details are omitted here to keep the length concise. Please visit the XHunt official website for the complete list.)
The above is the complete list of XHunt’s Global AI Influence English-speaking Area Top 100. It is not difficult to see that the information flow in the English-speaking area can be called the center of the AI storm, which is mainly dominated by executives of technology giants, researchers in top laboratories, and builders of underlying infrastructure. Compared with the hard-core frontier of the English-speaking area, the AI ecosystem in the Chinese-speaking area is more like a front-line application workshop full of artillery fire.
Tomorrow, we will release “Exclusive: Top 100 AI Influence List in the Chinese-speaking Area”. Let’s explore together who is truly leading the industry consensus in the Chinese-speaking world, which is closer to application and monetization?
Note: AI Influence English Ranking Standard: Based on the Top 5500 KOLs in the English-speaking area as a reference group, ranked in real-time according to their number of followers, and the content is mainly in English. Only accounts with AI tweet ratio ≥ 40% are displayed.
[XHunt]
AI Influence Concentration: Implications for Crypto Investors Amidst the Intelligence Revolution
The release of XHunt’s “Top 100 AI Influencers List for the English-Speaking Community” provides more than just a ranking of AI thought leaders—it offers a roadmap to understanding the concentration of power in one of the most transformative technological shifts of our time. For crypto investors, this ranking is not just a curiosity; it’s a critical input for understanding where value might accrue in the intersection of AI and blockchain.
The Architecture of AI Influence
What stands out immediately is the extreme concentration of influence. The top 10 figures command AI KOL followers ranging from 4,221 (Karpathy) down to 2,392 (Chollet). This is a remarkably small group for an industry that will purportedly reshape every sector. The dominance of OpenAI alumni (Karpathy, Altman, Brockman, Sutskever) alongside Google DeepMind leadership (Dean, Hassabis) and academic pioneers (LeCun, Ng) reveals a tight oligarchy controlling AI discourse.
This concentration has direct implications for crypto investors. When Karpathy tweets about neural network architectures or Altman hints at new capabilities, the market isn’t just reacting to social media sentiment—it’s responding to the actual direction of technological development. Crypto projects that can demonstrate alignment with the research priorities of these influencers will likely outperform.
Token Price Implications for AI-Crypto Convergence
The ranking validates several emerging narratives in the crypto space:
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AI Infrastructure Tokens: Projects providing decentralized compute, data, or model hosting are positioned to benefit from the AI expansion. The presence of infrastructure-focused influencers like @fchollet (Keras creator) suggests this is a priority area.
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Data Markets: The concentration of AI power among a few entities creates a natural counter-movement toward decentralized data markets. Crypto projects enabling individual data ownership and monetization could see significant upside.
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AGI Narratives: The inclusion of @demishassabis (promoter of AGI narratives) suggests that AGI remains a powerful investment theme. Crypto projects positioning themselves as governance mechanisms or incentive systems for AGI development could attract substantial capital.
Notably, @elonmusk’s presence (despite rank 9) with over 23 million followers demonstrates that influence extends beyond core AI circles. His xAI initiatives could create unexpected opportunities in the crypto-AI convergence space.
Risks of Concentration
The extreme concentration of AI influence presents several risks for crypto investors:
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Over-valuation of AI-Crypto Projects: Similar to the 2021 NFT boom, we may see excessive capital flowing into projects with superficial AI connections. The presence of legitimate AI leaders could provide cover for less substantive projects.
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Regulatory Attention: When power is concentrated, regulators take notice. The SEC and other bodies are likely to scrutinize the AI-crypto intersection more closely, particularly as these influencers gain mainstream recognition.
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Black Swan Events: The health and decisions of a small number of individuals (e.g., OpenAI’s governance changes) could create cascading effects across related crypto projects. Diversification within the AI-crypto space becomes essential.
Strategic Opportunities
For savvy crypto investors, this ranking offers several strategic advantages:
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Due diligence Signal: Projects that can demonstrate genuine engagement with these influencers—whether through technical integration, research partnerships, or institutional backing—carry significantly higher conviction.
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Cross-Pollination Potential: Several influencers on this list have expressed interest in blockchain (e.g., Karpathy’s technical curiosity, Musk’s crypto ventures). Identifying projects that successfully bridge these communities could yield outsized returns.
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Infrastructure First: The ranking suggests AI development will increasingly require specialized infrastructure. Crypto projects providing verifiable compute, decentralized data, or privacy-preserving AI solutions are positioned to benefit regardless of specific AI model architectures.
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Influence Metrics as Alpha: The “AI KOL Followers” metric itself represents a sophisticated approach to measuring influence. Crypto projects that adopt similar methodologies for evaluating on-chain influence could gain a competitive advantage.
Conclusion: The Convergence Accelerates
XHunt’s ranking reveals not just who matters in AI, but how influence is structured in the intelligence revolution. For crypto investors, this is more than a list—it’s a map to the intersection of two technological revolutions. The concentration of power in AI mirrors that in crypto, suggesting that cross-pollination between these sectors will intensify.
As we move forward, the ability to distinguish between genuine innovation and hype in the AI-crypto space will be paramount. The presence of these top influencers provides both credibility and risk—they can validate projects, but their concentrated power also creates systemic vulnerabilities. The most successful crypto investments in this space will likely be those that build infrastructure rather than chase narratives, and those that demonstrate tangible alignment with the actual research priorities of the AI elite.
The Chinese-speaking AI influence list, which promises to reveal “application and monetization” focused leaders, will provide an important counterpoint to the English-speaking “hard-core frontier” perspective. Together, these rankings offer a comprehensive view of the global AI landscape and the opportunities it presents for crypto investors.