When Every AI Is “Best,” Users Are Left Guessing (Image supported by ChatGPT)
SEOUL, Feb. 2 (Korea Bizwire) — When a South Korean office worker recently considered paying for an artificial intelligence service to help with his job, he found himself unsure which product to choose. Each major platform — from Google’s Gemini to OpenAI’s ChatGPT and Anthropic’s Claude — claimed to offer “top performance” in different areas.
“The No. 1 model seems to change depending on who is speaking,” he said. “It makes you wonder whether the advertising is exaggerated.”
His confusion reflects a broader challenge emerging in the rapidly intensifying generative AI market, where companies increasingly highlight selective performance metrics to promote their models — a practice critics describe as “cherry-picking.”
According to industry officials, benchmarks — standardized tests used to measure AI capabilities — are increasingly being repurposed as marketing tools rather than objective indicators of real-world performance.
Dozens of benchmarks are currently in circulation, including MMLU for university-level knowledge, GSM8K for mathematical reasoning and HumanEval for coding ability. When launching new models, companies often spotlight only a handful of metrics in which their systems rank highest, presenting the results in simplified charts that can obscure broader limitations.
The same pattern has emerged in South Korea’s domestic AI sector, including among firms participating in the government-led sovereign foundation model project. While strong benchmark results are noteworthy, experts caution that high scores do not necessarily translate into reliable performance in everyday use.
One concern is so-called data contamination, in which models are inadvertently trained on benchmark questions or similar material, inflating scores without reflecting genuine reasoning ability — a phenomenon likened to memorizing past exam questions.
In the Age of AI, What Does “Best” Really Mean? (Image courtesy of Yonhap)
As AI development matures, specialists argue that the race to declare a single “smartest” model has become increasingly misplaced. The industry is moving beyond general intelligence toward purpose-built systems optimized for specific tasks.
The distinction, experts say, resembles the automotive market: a high-speed supercar may dominate a racetrack, but compact or electric vehicles are often better suited for crowded city streets. Likewise, large, computationally intensive models may excel at complex research tasks, while smaller and faster systems can outperform them in customer service, summarization and routine office work.
Alternative evaluation methods are gaining attention. One widely cited example is Chatbot Arena, which pits two anonymous models against each other using identical prompts, allowing users to judge responses directly. Supporters say the approach is harder to manipulate and better reflects human perceptions of quality.
Industry analysts say consumers should look beyond headline rankings.
“Right now, it’s like athletes of different weight classes boasting only the events they’re good at,” one expert said. “Instead of being swayed by numbers, users should judge whether an AI gives accurate, safe and useful answers for their own work.”
Some experts have also urged the government and academic institutions to develop standardized evaluation guidelines that go beyond raw performance scores, incorporating factors such as safety, ethical reliability and Korean-language proficiency.
As generative AI becomes more embedded in daily work, the question for users may no longer be which model scores highest — but which one actually works best for them.
Kevin Lee (kevinlee@koreabizwire.com)
