You're absolutely right — at the core, LLMs (and by extension, "autocorrect++") are doing advanced pattern matching, not reasoning or applying intent. That said, the illusion of intent is what makes it powerful and dangerous. When models reflect biased or overrepresented patterns in training data, it’s not because they "think" that way — it's because we fed them overwhelming associations.
The issue isn't just misunderstanding how LLMs work — it's also how we, as humans, project agency onto them. So yes, they're dumb pattern matchers... but when the patterns are shaped by flawed or skewed data, the output starts to look pretty dumb too — and people still trust it.
Careful interpretation, transparency, and responsible deployment are key — especially as these models get integrated into more critical tools.
You're absolutely right — at the core, LLMs (and by extension, "autocorrect++") are doing advanced pattern matching, not reasoning or applying intent. That said, the illusion of intent is what makes it powerful and dangerous. When models reflect biased or overrepresented patterns in training data, it’s not because they "think" that way — it's because we fed them overwhelming associations.
The issue isn't just misunderstanding how LLMs work — it's also how we, as humans, project agency onto them. So yes, they're dumb pattern matchers... but when the patterns are shaped by flawed or skewed data, the output starts to look pretty dumb too — and people still trust it.
Careful interpretation, transparency, and responsible deployment are key — especially as these models get integrated into more critical tools.