Artificial intelligence (AI) is advancing at a remarkable pace, reshaping the industrial landscape across the globe. Yet behind the dazzling progress of the technology, few people possess deep insight into what AI actually learns and how the outputs it produces are verified. When we met Yonghan Park, CEO of EBIT, at his office, he described himself both as a “trainer” who teaches AI and as an “auditor” who guarantees the safety and reliability of AI systems, presenting a clear vision for the future of the AI era.

From Finance Professional to Global AI Data Partner

Park’s path differs from that of many typical IT entrepreneurs. Originally a finance professional who studied business administration and economics at Boston College in the United States, he founded the company EBIT about seven years ago with the goal of building a stock investment community platform.

Although the first business ultimately failed after confronting the harsh realities of the market, it instead became a stepping stone toward a much larger opportunity.

Through connections he had built during his time studying in the United States, Park gained the opportunity to participate as an Asian partner of Scale AI, a global leader in AI data infrastructure. This turning point transformed EBIT from a simple startup into a thoroughly global-oriented company, with almost all of its revenue now generated overseas.

Today, EBIT plays a critical role in refining and verifying training data for AI models being developed by global tech companies such as OpenAI’s ChatGPT and Google’s Gemini.

“AI was created to automate human work,” Park said. “Ironically, building the foundation that makes this possible requires an extraordinary level of human resources.”

He describes EBIT as a collective of “data specialists” responsible for building the massive pyramid that supports modern artificial intelligence.

High-Value Data Engineering Beyond Simple Labeling

When people hear the term “data labeling,” they often imagine low-skilled repetitive tasks, such as drawing boxes around objects in images or performing simple classifications. However, the projects led by Park operate on a completely different level. At EBIT, the work is carried out not by general task workers but by highly educated specialists, including software developers, mathematics graduates, and language experts.

The work they perform goes far beyond simple repetitive tasks. Instead, it involves high-value data engineering that enables AI systems to develop mathematical reasoning capabilities or learn complex coding logic.

Because such tasks require deep expertise, EBIT provides top-tier compensation to experienced developers while strictly managing the quality of the data.

Park emphasizes that “data is the single most critical element that determines the quality of AI.” While semiconductors and electricity serve as the infrastructure supporting the external growth of the AI industry, he believes that data forms the very essence that shapes the intelligence of AI.

The Key to Solving AI Hallucinations: Precise Human Verification

Park also offers a sharp diagnosis of hallucination, one of the most widely discussed weaknesses of generative AI today. According to him, the essence of hallucination ultimately lies in “errors in data” and “the absence of verification.”

AI models operate as “black boxes,” where the internal causal processes leading to an output are difficult for humans to fully understand. Therefore, the system cannot be considered complete without a process in which human experts directly test tens of thousands of possible scenarios, carefully record the outcomes, and verify the reliability of the results.

Within this context, Park predicts that specialized data companies will eventually hold a position similar to that of accountants in the capital markets. Just as publicly listed companies undergo external audits to ensure the transparency of their financial statements, he believes that the AI services adopted by companies will also need to be rigorously verified by expert groups to ensure that they are not socially harmful and do not pose risks such as personal data leakage.

Ultimately, reinforcement learning with human feedback (RLHF) will become a core process that aligns a model’s responses with human values and expectations, thereby providing the technological foundation for social trust in AI.

Global Achievements and EBIT’s Unique Competitive Strength

Since its founding, EBIT has achieved remarkable results in the global data market within a relatively short period of time. The company has successfully completed large-scale multilingual data construction projects as well as coding data projects for next-generation AI agents, establishing itself as a trusted partner for global big tech companies.

Park highlights three key elements that differentiate EBIT.

The first is operational know-how accumulated through direct experience with the quality standards required on the front lines of the global AI industry. The second is the creation of a platform-based workforce structure that addresses the fragmented nature of project-based labor, enabling skilled AI trainers to continuously build their careers while deepening their expertise. The third is the company’s ability to provide customized solutions that closely support every stage of the process, from guideline design to final quality inspection, according to the specific characteristics of each client’s AI model.

Korea as One of the Most Attractive Strategic Bases for AI

From a global perspective, Park believes that Korea holds enormous potential in the AI industry. Aside from the United States and China, which possess immense capital resources, few countries have an AI infrastructure as complete as Korea.

Korea is one of the rare countries capable of addressing within its own borders all the essential components required for the AI ecosystem, including semiconductor manufacturing capabilities, highly skilled data professionals, stable energy supply systems, and ultra-fast communication networks.

In particular, the infrastructure for clean water and stable electricity, both essential for semiconductor production, ranks among the best in the world. Park also notes that the meticulousness and sense of responsibility often associated with Korean professionals serve as critical assets that significantly improve data quality.

To expand these strengths globally, EBIT has established corporate entities in Singapore and Japan and strategically manages talent across Asia. By taking into account each country’s cultural background and linguistic characteristics, the company aims to produce the most optimized data outcomes.

A Future Where Humanity and AI Coexist, Built on Data

In building his organization, Park prioritizes individuals who can define the essence of complex problems and design concrete solutions to address them, rather than simply those who possess extensive knowledge.

His philosophy of boldly eliminating “work for the sake of work” and maximizing efficiency has become the driving force behind EBIT’s ability to create a strong global impact with a small but elite team.

Park’s ultimate goal is to go beyond being merely a data supplier and evolve into a global data solutions company that designs and operates all the data infrastructure required for AI models before they are released into the world.

As the era of full automation approaches, Park believes that the data forming the foundation of these systems must ultimately be rooted in the most reliable intelligence available, human intelligence.

His ambition is clear: to ensure that when people think of “data experts” on the global stage, EBIT is the first name that comes to mind. And that vision is steadily becoming a reality.