*Editor’s note: K-VIBE invites experts from various K-culture sectors to share their extraordinary discovery about the Korean culture. 

 

Matthew Lim's AI Innovation Story: The Excess and Illusion of AI Certifications

 

By Matthew Lim, AI expert and director of the Korean Association of AI Management (Former Head of Digital Strategy Research at Shinhan DS)

 

 

 

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.

Unverified instructors, subpar training programs, and superficial evaluations have become chronic issues in the certification market.

Since the emergence 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 catering to this demand? The situation is reminiscent of the "information search specialist" certifications that proliferated during the IT boom of the early 2000s.

The AI certification market today is largely divided into two categories.

The first consists of professional certifications offered by global tech companies. For example, NVIDIA's "Certified Associate - Generative AI LLMs" is a professional certification focused on practical applications. It assesses essential real-world skills, including prompt engineering, the integration and deployment of large language models (LLMs), the fundamentals of machine learning and neural networks, data analysis and visualization, and experimental design. The exam consists of 50 questions to be completed in 60 minutes, requiring a deep understanding of generative AI and LLMs.

The "Certified Generative AI Professional" certification, operated by the Global Skill Development Council (GSDC), a top-tier data center under the Korea Institute of Science and Technology Information, covers a broad range of generative AI concepts from basic to advanced. While there are no formal prerequisites, candidates must score at least 65% on a 90-minute, 40-question exam. However, the fact that this certification is valid for a lifetime is a notable drawback.

NVIDIA also offers the "Certified Generative AI Specialist (CGAI™)," which evaluates even deeper expertise. This certification tests knowledge of foundation models—large-scale deep learning neural networks that have transformed how data scientists approach machine learning—as well as large language models, diffusion models that generate images, audio, and text through probabilistic transformations, and multimodal models (LMMs), which enable AI systems to understand and generate information across various data types, including text, images, audio, and video. It also includes assessments of business applications and ethical considerations, making it a comprehensive and demanding certification.

On the other side of the spectrum are private certifications issued after short-term training programs.

Many of these reveal serious flaws. Some providers recruit students with exaggerated marketing claims such as "guaranteed employment" and "high-paying career opportunities" while failing to cover even the most fundamental AI concepts. The course content is often disconnected from industry needs, with some certifications granted simply for watching a few online lectures and passing a superficial exam. 

A more significant issue is the lack of ongoing skill development support after certification. Instructors with questionable credentials, subpar curricula, and perfunctory evaluations have long plagued the certification industry.

The unchecked proliferation of AI certifications has led to multiple problems.

First, certifications fail to guarantee actual competency. AI is an ever-evolving field, and merely memorizing theoretical concepts to pass an exam does not equip individuals with the problem-solving skills required in real-world applications.

Second, the quality of education deteriorates. Programs focused solely on certification issuance do not help learners develop a deep understanding of AI or its practical applications.

Third, job seekers waste time and money. Many individuals invest in expensive courses and obtain certifications only to find that employers do not recognize their value in the job market.

A recent survey found that six out of ten employers in South Korea prioritize hiring AI talent. However, they are not looking for candidates who simply hold certifications—they seek professionals who can effectively apply AI technologies in real-world scenarios. Employers value hands-on AI project experience, problem-solving ability, and a capacity for continuous learning far more than formal certification.

A startup CEO I know once remarked, "I would prefer candidates who have uploaded meaningful projects on GitHub and solved real-world problems over those who merely hold certifications."

This highlights the need for a more reliable framework for assessing AI capabilities. The goal is not to dismiss the idea of certifications entirely but rather to establish a new system that effectively verifies real skills.

Three key elements should be prioritized:

First, a project-based assessment should be introduced, allowing candidates to demonstrate their practical skills by solving real problems.

Second, industry and academia must collaborate to develop standardized competency evaluation criteria. While benchmarking certification systems from companies like Amazon and Google is useful, these standards should be tailored to the specific needs of South Korea’s industrial landscape.

Third, a certification renewal system must be implemented. AI evolves rapidly, and no single certification can guarantee lifelong expertise.

AI is already reshaping industries, and by 2025, it will become an essential skill in nearly every sector. The rise of generative AI is expected to fundamentally change traditional workflows.

In this era of transformation, those aspiring to be true AI professionals must go beyond the superficial validation of certifications. Practical problem-solving skills, the ability to quickly learn and apply new technologies, and ethical judgment will become increasingly critical.

A certification should be a milestone, not the ultimate goal. Now is the time to invest in continuous learning and hands-on experience to build real expertise.

 

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

 

[ⓒ K-VIBE. 무단전재-재배포 금지]