LLMs (AIs) are not useless. But they do not actually think. What is trivially true is that they do not actually need to think. (As far as the Turing Test, Eliza patients, and VC investors are concerned, the point has been proven.)
If the technology is helping us write text and code, it is by definition useful.
> In 2003, the machine-learning researcher Eric B. Baum published a book called “What Is Thought?” [...] The gist of Baum’s argument is that understanding is compression, and compression is understanding.
This is incomplete. Compression is optimisation, optimisation may resemble understanding, but understanding is being able to verify that a proposition (compressed rule or assertion) is true or false or even computable.
> —but, in my view, this is the very reason these models have become increasingly intelligent.
They have not become more intelligent. The training process may improve, the vetting of the data improved, the performance may improve, but the resemblance to understanding only occurs when the answers are provably correct. In this sense, these tools work in support of (are therefore part of) human thinking.
The Stochastic Parrot is not dead, it's just making you think it is pining for the fjords.
