The article argues that modern large language models (LLMs) often fail to detect or interpret important information that is right in front of them—despite appearing to “understand” the text. These “blind spots” arise because the models are optimised for generating plausible-looking text rather than deeply reasoning about context or hidden cues. In effect, the models may see words and patterns, yet still miss the meaning or implications that a human would pick up.
One of the key reasons for this limitation is how LLMs are trained: they learn from massive corpora of text, focusing on next-word prediction and surface patterns rather than building internal models of the world or hidden structure. Because of this, the article shows, models may miss cases where meaning is implied but not explicit—such as irony, omission, or subtext. The result: outputs that “look right” but fail when subtle reasoning or insight is required.
The piece also illustrates how these failures aren’t just technical oddities but have real-world consequences. For example, when an AI system is used to analyse documents, it might fail to spot deceptive wording, hidden conflicts of interest, or ambiguous phrasing—issues a skilled human reader would flag. This means over-reliance on such models can lead to missed risks, flawed decisions, or skewed outcomes.
In conclusion, the article calls for caution and more nuanced development of AI systems: rather than assuming “bigger model = better insight”, development should focus on building mechanisms for awareness of missing information, reasoning about what’s not said, and mechanisms of doubt or verification. Without these, LLMs will continue to fail at “seeing what’s hiding in plain sight”.
