While that is partly true, it oversimplifies a few things. And these are the things that make LLMs a dead end for intelligence:

• Evolution means constant learning and adaption. LLMs can’t be adapted on the go, they are stuck with their initial training data and retraining is a slow and externally controlled process. Apart from training, every additional information you give it while running must be loaded into the context, the space for context is limited and must be flushed before supplying different context. That’s a hard limit for evolution, at least with current technology.
• The physical world is more important than you might think because everything hinges on this realm, including the electricity, cabling and hardware involved in running a model. The idea of an intelligent entity that exists purely as an ethereal cloud of logic unbound by physics is hard to justify, because the shape of logic itself is defined by the physical ground we are standing on. Therefore, an AI would need a first-hand access and understanding of the physical world to be successful. LLMs can’t have any of that, because they are purely language-bound.

I personally think Yann LeCun (a pioneer of machine learning and AI) is on the right track with his research and his opinion that LLMs are a dead end for artificial intelligence. The reasons are more on a technical than a philosophical side.