AI Quality Has Improved — Yet, Somehow, It Feels Scarier
The latest generative AI models — ChatGPT, Claude, Gemini — now produce output that is incomparably more polished than before. The writing is fluent, the logical structure is natural, and they answer even highly technical questions with confident authority.
Yet lately, using AI on a daily basis, I keep noticing a strange feeling:
“AI seems to make fewer mistakes than before — and yet I somehow trust it even less.”
This intuition, I believe, is correct. Because AI quality has improved, its errors have become harder to see.
What Is “Hallucination” in the First Place?
Hallucination refers to the phenomenon where AI generates information that is not factual, yet presents it with complete confidence as if it were true.
Citing non-existent academic papers, presenting fabricated laws, inventing quotes from real people — these kinds of examples have been a known problem since generative AI first became widespread.
With early AI models, errors were relatively easy to spot. Unnatural phrasing or obviously off-target information gave you the cue that “something is wrong here.”
The Core Problem: Hallucinations Are Getting More Sophisticated
This is the most critical point right now.
As AI model performance improves, so does the quality of its hallucinations. In other words, misinformation is now generated more naturally and more persuasively than ever before.
Why Does This Happen? — The Structural Problem of Probabilistic Models
Generative AI learns from vast amounts of text data and constructs sentences by probabilistically predicting “what word comes next.” Put simply, it is a system that generates the most plausible-sounding continuation.
In September 2025, OpenAI published research that delved into the root causes of hallucination. It revealed that in the standard training and evaluation process, AI is structurally rewarded for producing a plausible-sounding answer rather than admitting “I don’t know.”
In other words, AI has a built-in tendency: the less certain it is, the more it presents itself as certain. And as the model becomes more sophisticated, its outputs become more fluent — which means there are fewer cues for us to notice something is wrong.
Hallucination Is Not an “Accidental Bug” — It Is a Structural Phenomenon
Research from 2025 also suggests that we need to stop thinking of hallucination as an “occasional malfunction” of AI, and start recognizing it as a structural phenomenon inherent in how probabilistic language generation works.
This is an important shift in perspective. The required understanding is not “it sometimes gets things wrong,” but rather “the mechanism that generates plausible-looking errors is at the very core of how AI operates.”
The Risks of “Undetectable Hallucinations”
Real-World Harm in Legal and Business Contexts
In May 2025 alone, 31 disciplinary cases were reported involving lawyers who used AI-generated fictitious case precedents in court. In fields where specialized knowledge is a prerequisite — medicine, finance, law — plausible misinformation has the potential to cause serious, tangible harm.
The Mental Paralysis of “AI Said It, So It Must Be True”
At the root of the problem is the tendency to accept AI output as correct without question.
Humans, too, can confidently state wrong answers. People present personal opinions as established fact. This happens in everyday life. But when it is a “human statement,” we unconsciously judge whether to take this person at their word.
With AI, however, that judgment rarely kicks in. People who are less familiar with AI in particular tend to interpret fluent, polite phrasing as evidence of accuracy.
The Risk That Society’s “Average Error” Spreads
Generative AI learns from the enormous volume of text on the internet — essentially, the average information of the world. That means misconceptions and biased information that are already circulating in society are also part of the training data.
AI-generated content spreads through society, and then becomes training data for the next generation of AI — when this cycle repeats, specific misconceptions and biased views risk becoming fixed as the “average.”
This is not merely an AI problem. It needs to be understood as a problem with the entire information ecosystem.
So How Should We Approach This?
1. Recognize AI as an “Assistive Tool”
The basic posture is to treat AI output as reference material and always add verification from primary sources or specialists before making important decisions. It is essential to frame AI not as “a machine that produces answers” but as “a tool that drafts a starting point.”
2. Do Not Use “Confident Tone” as a Basis for Trust
The defining characteristic of hallucination is that it gets things wrong with total confidence. Assertive phrasing and detailed citations are not, in themselves, a guarantee of accuracy. Always verify the source for any information that matters.
3. Refine How You Ask Questions
Simply changing how you phrase questions to AI can reduce the risk of hallucination. For example, instead of asking “Tell me about ○○,” adding a condition like “If there is any uncertain information about ○○, please respond with ‘I don’t know’” can meaningfully change the reliability of the output.
4. Build a Verification System at the Organizational Level
There are limits to relying on individual caution alone. When using AI for important business tasks, it is necessary to build in a process where another person reviews the output, or where the output is cross-checked against trusted sources.
Conclusion: Using AI Correctly as a “Powerful Tool”
AI is genuinely evolving. And that evolution holds the potential to dramatically transform our work and lives.
At the same time, however, it creates the paradoxical risk that the better the quality, the harder the errors are to see. Hallucination is not a “problem of old AI” — it is a structural challenge that persists even in the latest models, right now.
The first step toward using AI correctly is knowing its limitations accurately. Neither blind trust nor excessive rejection — a critical eye and sound verification habits are the fundamental literacy for navigating the AI era.
Now might be a good time to reconsider how you engage with information.