Top 10 AI influencers to follow on LinkedIn in 2026

The AI landscape shifts daily. For business leaders and technical professionals, the challenge is no longer finding information but filtering it. The difference between a distracted strategy and a competitive advantage often lies in the quality of your information diet.

LinkedIn has emerged as the primary “town square” for serious AI discourse. Unlike the frenetic pace of X (formerly Twitter) or the academic density of ArXiv, LinkedIn offers a middle ground where technical breakthroughs are contextualized for business impact.

To help you curate a high-value feed, we have selected ten experts who consistently provide actionable insights. These individuals are builders, researchers, and strategists who define the field rather than just comment on it.

What defines a top 10 AI influencer?

We selected these profiles based on three objective criteria:

• Authority: They have built significant systems, led major research labs, or advised Fortune 500 companies.
• Clarity: They translate complex technical concepts into strategic business value.
• Consistency: They post regular, high-signal content without relying on hype or sensationalism.

Andrew Ng is arguably the most influential educator in the field. As a co-founder of Coursera and the former head of Google Brain and Baidu AI Group, Ng has a unique ability to simplify complex machine learning concepts.

Recently, Ng has pivoted his focus toward “agentic workflows”, the idea that AI agents iteratively improving their own output can outperform zero-shot prompting. His posts often include diagrams and short explanations that are invaluable for technical leads and product managers. If you are looking to deepen your team’s understanding, Andrew Ng has the content to do so.

Allie K. Miller bridges the gap between technical engineering and executive decision-making. Having advised hundreds of startups and Fortune 500 companies, she focuses heavily on the “people” and “process” sides of AI adoption.

Her content is particularly useful for organizations that are looking to see whether AI is feasible for them. She frequently posts checklists for vetting AI vendors, frameworks for calculating ROI, and strategies for upskilling employees. Miller avoids jargon, preferring to speak in terms of efficiency gains, revenue growth, and competitive moats.

Menno Fokkema is the driving force behind DataNorth AI, a leading consultancy dedicated to making artificial intelligence accessible to businesses across Europe. Unlike theorists who focus solely on future possibilities, Fokkema’s content is grounded in the immediate, practical realities of deploying AI in the enterprise.

He frequently shares insights on overcoming the “pilot purgatory” that many organizations face, where AI projects fail to scale beyond the proof-of-concept phase. His posts dissect the nuances of artificial intelligence strategy, offering frameworks for aligning technical capabilities with business goals.

Following him provides a direct line to the latest trends in AI compliance and the EU AI Act, ensuring your organization remains innovative yet compliant.

Cassie Kozyrkov founded the field of Decision Intelligence at Google. Her philosophy is that AI is simply a tool for making decisions at scale. Consequently, her writing focuses on the rigorous statistical thinking required to use AI safely.

She is an essential follow for leaders interested in AI for executives. Kozyrkov challenges readers to define what “good” looks like before they start coding. Her posts often deconstruct common logical fallacies in data science, making them excellent reading for teams preparing for an AI roadmap session.

Bernard Marr is a prolific author and futurist who excels at surveying the breadth of the industry. While others dive deep into code, Marr looks at the macro trends affecting sectors like manufacturing, retail, and healthcare.

His lists of “Top 10 Use Cases” for tools like ChatGPT or Microsoft Copilot are highly practical for non-technical stakeholders looking for inspiration. Marr provides a high-level view that complements detailed technical reading, ensuring you don’t miss the forest for the trees.

Often referred to as the “Godmother of AI,” Fei-Fei Li was the creator of ImageNet, the dataset that catalyzed the modern deep learning boom. Today, she leads World Labs and the Stanford Institute for Human-Centered AI (HAI).

Her content is pivotal for those interested in computer vision and “spatial intelligence”, the ability of AI to understand and navigate the 3D world. Furthermore, she is a leading voice on the ethical implications of AI, making her perspective critical for organizations navigating AI ethics.

Andrej Karpathy is perhaps the most beloved technical explainer in the AI community. Former Director of AI at Tesla and a founding member of OpenAI, he now focuses on AI education through Eureka Labs.

Karpathy’s posts are technical but accessible. He explains how Large Language Models (LLMs) actually “think” and hallucinate. For technical teams looking to deploy local LLMs or optimize their RAG (Retrieval-Augmented Generation) pipelines, his insights are mandatory reading. He frequently breaks down the latest research papers into understandable takeaways.

Ethan Mollick approaches AI as a sociologist and management professor. He is the leading authority on how generative AI changes the way we work right now. He does not theorize about AGI in 2030; he tests how ChatGPT-4 affects writing quality or coding speed today.

His experiments are highly relevant for HR and operations leaders. If you are considering an AI workforce readiness scan, Mollick’s data on employee adoption and “shadow AI” usage provides essential context.

A Turing Award winner and one of the “Godfathers of Deep Learning,” Yann LeCun is a fierce advocate for open-source AI. He argues that AI systems must learn like humans do: by building internal “world models” rather than just predicting the next token in a sentence.

LeCun is a critical voice for understanding the open-source ecosystem, including models like Llama. For businesses evaluating whether to build on proprietary models or open weights, his arguments offer a strong technical foundation. His recent venture, AMI Labs, aims to push this “Advanced Machine Intelligence” further.

Demis Hassabis leads Google DeepMind, the lab responsible for AlphaGo and AlphaFold. His work focuses on using AI to solve fundamental scientific problems, such as protein folding and material science.

While his work is often abstract, it signals where the hard science of deep learning is heading. Following Hassabis gives you a glimpse into “AI for Science”, the use of AI to accelerate innovation in pharmaceuticals, energy, and physics.

Comparison of expert focus areas

Influencer	Primary focus	Best for…
Andrew Ng	Education & Agents	Engineers, Product Managers, Learners
Allie K. Miller	Business ROI & VC	Startups, Executives, Investors
Menno Fokkema	Strategy & Implementation	Business leaders, EU compliance, Implementation
Cassie Kozyrkov	Decision Intelligence	Data Scientists, Risk Officers
Ethan Mollick	Future of Work	HR, Management, Operations
Andrej Karpathy	Technical Deep Dives	Developers, AI Engineers
Fei-Fei Li	Spatial AI & Vision	Healthcare, Robotics, Ethics

How to curate your AI feed

Building a high-quality information diet requires more than just following the right people. You must also actively filter the noise.

1. Prioritize builders over commentators: The list above prioritizes people who are actively building companies or shipping code. Practical experience usually trumps theoretical observation.
2. Look for “How,” not just “Wow”: Avoid influencers who only post demo videos with captions like “Mind-blowing!” Look for those who explain the prompt engineering or the workflow behind the automation.
3. Diversify your sources: Ensure you follow a mix of technical experts (Karpathy, LeCun) and business strategists (Fokkema, Miller). This prevents you from over-indexing on technology that isn’t yet commercially viable.
4. Engage with the content: The LinkedIn algorithm prioritizes content you interact with. If you want more technical breakdowns, like and comment on those specific posts to train your feed.

Frequently asked questions