No it's not "just a very good autocomplete". I don't know why people repeat this thing (it's wrong) but I find it an extremely counterproductive position. Some people just love to dismiss the capabilities of AI with a very shallow understanding of how it works. Why?

It generates words one by one, like we all do. This doesn't mean it does just that and nothing else. It's the mechanics of how they are trained and how they do inference. And most importantly how they communicate with us. It doesn't define what they are or their limits. This is reductionism. Ignoring the mathematical complexity of a giant neural network.

> like we all do

Do we though? Sure, we communicate sequentially, but that doesn't mean that our internal effort is piecewise and linear. A modern transformer LLM however is. Each token is sampled from a population exclusively dependent on the tokens that came before it.

Mechanistically speaking, it works similarly to autocomplete, but at a very different scale.

Now how much of an unavoidable handicap this incurs, if any, is absolutely up for debate.

But yes, taking this mechanistic truth and only considering it in a shallow manner underestimates the capability of LLMs by a large degree.

I don't know why people repeat this thing (it's wrong)

Because they simply don't care if they're wrong. At this point, given what we've seen, that seems like the only explanation left.

You don't need to be a fanatical AGI evangelist, but when an "autocomplete" starts winning international math competitions, you need to start calling it something else.

I can't say for certain that our wetware isn't "just a very good autocomplete".