Calling Every New AI Feature "Understanding" Doesn't Make It Understanding. Here's the Actual Test.
"Understanding" has become one of the most generously applied words in AI marketing — nearly every new feature that processes more context, remembers more history, or personalizes more output gets described as the product now "understanding" the user. Applied this loosely, the word stops describing anything specific and starts functioning as a general seal of approval.
The actual capability usually being described is something narrower and more mechanical: retaining more information, retrieving it more accurately, or applying it more consistently across a conversation. All of that is genuinely useful, and none of it is the same achievement as understanding a person's recurring pattern well enough to recognize it in the moment and reflect it back.
A workable test cuts through the labeling: does the system reliably surface something the person didn't already know about their own recurring behavior, at a moment it could actually matter? Feature announcements that use the word "understanding" rarely get evaluated against this bar, because the bar is harder to hit than the word is easy to use.
Understanding, in the sense that actually matters for this problem, is a specific and demanding capability — not a marketing description that attaches to any feature that handles more context. Until a system passes the concrete test, the word is doing more work than the product underneath it is.
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Momar Lissa Ndiaye ("MLN") is the Founder & CEO of weyoga Inc., a Delaware company. — weyoga.ai · mln@weyoga.ai