It’s becoming almost disturbing that, so many GenAI experts I have seen and talked to, either online or in person, have so little understanding of the inner workings of GenAI. They have no or little knowledge about (or interest in knowing) the basic concepts such as transformer, tokenization, weights and biases.

In one occasion a GenAI expert got invited to an opening ceremony where I was sitting in. After her crazy statement about the superintelligence that GenAI is about to achieve. I asked her a question: “why my three year old daughter can identify cats and dogs after only showing her five photos in five minutes (costed me a small chocolate), while any GenAI foundation models will require millions of samples to learn the same thing and still make mistakes after costing millions of dollars for training?“.

Her answer was, “soon, GenAI will get there…”.

Then I knew our conversation was over.

Anyways, the content above was generated by human (myself).

The content below was generated by Google Gemini.

Question: Why so many people suddenly become “AI experts” on social media in almost no time?

The surge in people claiming to be AI experts or thought leaders, especially those from non-tech backgrounds or without relevant degrees, can be attributed to several factors:

1. Accessibility and Democratization of AI Tools: The rise of user-friendly AI tools, particularly large language models (LLMs) like ChatGPT and Gemini has significantly lowered the barrier to entry for interacting with and generating AI-powered content. Individuals can now leverage these tools to perform tasks that previously required technical expertise, leading to a perception that they “understand” or “master” AI with relatively little effort.
2. “AI Superagency” and Productivity Gains: AI tools can greatly enhance individual productivity and creativity, even for non-technical users. This “superagency” can lead to impressive outputs in various fields (e.g., marketing, content creation, business strategy), making it seem like the user has deep AI knowledge when they are primarily skilled at prompt engineering and applying off-the-shelf solutions.
3. Hype and FOMO (Fear of Missing Out): AI is currently a hot topic, generating immense media attention and investment. This creates a strong incentive for individuals and businesses to associate themselves with AI, often leading to exaggerated claims of expertise to capitalize on the trend or avoid being left behind.
4. Social Media Amplification: Platforms like LinkedIn, X (formerly Twitter), and others provide a powerful megaphone for self-promotion. It’s easier than ever to share opinions, “insights,” and examples of AI tool usage, quickly building a public persona as an “expert” without rigorous peer review or credential verification. Viral content and engagement can often be mistaken for genuine expertise.
5. Focus on Application vs. Deep Technical Understanding: Many self-proclaimed experts are focused on the application of AI in specific domains (e.g., how AI can impact marketing, law, or finance) rather than the underlying technical principles, algorithms, or ethical implications. While valuable in itself, this practical focus can be misconstrued as comprehensive AI expertise.
6. “Credential Inflation” and Evolving Definitions of Expertise: The traditional pathways to expertise (e.g., PhDs in computer science, published research in top-tier conferences) are still critical for deep AI research and development. However, the rapid evolution of AI means that “expertise” is becoming a broader concept. Some argue that understanding how to effectively use and apply AI, even without knowing the code behind it, constitutes a new form of expertise. This can lead to a devaluation of traditional academic credentials in favor of perceived practical acumen.
7. Demand for “AI Translators”: As AI becomes more ubiquitous, there’s a growing need for individuals who can bridge the gap between technical AI developers and non-technical business leaders or the general public. People who can effectively communicate AI concepts, explain its potential, and guide its adoption are in high demand, regardless of their technical depth. This creates an opportunity for those with strong communication and strategic skills to position themselves as “thought leaders.”
8. Lack of Clear Standards: The field of AI is still relatively nascent and rapidly changing, making it difficult to establish universally recognized standards for what constitutes an “expert” or “thought leader.” This ambiguity allows more room for individuals to define themselves as such.

While it’s true that valuable contributions to AI can come from diverse backgrounds, especially in terms of ethical considerations, societal impact, and domain-specific applications, the sudden proliferation of “experts” from non-technical fields can sometimes dilute the meaning of true AI expertise, which often involves deep mathematical, statistical, and programming knowledge, alongside a rigorous understanding of AI algorithms and their limitations. It’s important for consumers of AI content to critically evaluate the claims and backgrounds of those presenting themselves as authorities.