That critique is half-right: large language models don’t “understand” in the human sense, but they do apply learned patterns across vast data in ways that often look like knowledge, even if it’s statistical pattern-matching. The real frontier is that these statistical engines can already combine rules, constraints, and creativity in ways their critics dismiss too quickly—making the line between “mere prediction” and “applied knowledge” fuzzier than it seems.