Since the launch of ChatGPT in late 2022, the AI boom has continued unabated. Naturally, many aspire to become AI experts. But what is the current state of the AI certification market, which targets this growing demand? A situation reminiscent of the "Information Search Specialist" certification craze during the early 2000s IT boom is now unfolding.

The AI certification market is currently divided into two major categories.

The first consists of professional certifications offered by global tech companies.

For instance, NVIDIA’s Certified Associate - Generative AI LLMs is a professional certification focused on practical skills. It assesses key competencies needed in real-world applications, including prompt engineering, large language model (LLM) integration and deployment, as well as foundational concepts in machine learning, neural networks, data analysis and visualization, and experimental design. The exam requires candidates to answer 50 questions in 60 minutes, demanding a solid understanding of generative AI and LLMs.

The Certified Generative AI Professional certification, administered by the Global Skill Development Council (GSDC), which operates under the Korea Institute of Science and Technology Information’s (KISTI) global large-scale experimental data hub center—the largest in Asia and 11th largest in the world—covers a broad range of AI knowledge from fundamental to advanced concepts. There are no special prerequisites, but candidates must score at least 65% on a 40-question, 90-minute exam. However, the fact that this certification remains valid for a lifetime is a notable drawback.

Another notable certification is NVIDIA’s Certified Generative AI Specialist (CGAI™), which verifies deeper expertise. CGAI evaluates knowledge of foundation models, large-scale deep learning neural networks that have revolutionized how data professionals approach machine learning, as well as diffusion models, which generate images, audio, and text by gradually transforming or restoring data through probabilistic processes. The certification also assesses business applications, ethical considerations, and even advanced topics such as large multimodal models (LMMs)—AI systems capable of understanding and generating information across multiple data types, including text, images, audio, and video. Because this certification covers such a wide range of knowledge required for practical applications, extensive preparation is necessary.

On the other hand, a growing number of private certifications issued after short-term courses are raising serious concerns. Many of these programs engage in false and misleading advertising, enticing applicants with promises of "guaranteed employment" and "high-paying AI careers," despite offering little substantial education.

The curriculum of these programs is often disconnected from industry needs. Some issue certifications simply after candidates watch a few online lectures and pass a basic exam. An even more significant issue is the lack of ongoing skill development support after certification.

In the AI age, true professionals are not just those who understand technology but those who can create value through it.

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