It's not unlimited memory in a single run, which remains unchanged, but that doesn't seem super relevant to what people want (nothing wrong with multiple runs!). Think about a turing machine, or heck, yourself. A turing machine only has access to a single cell of memory at at time, and in practice, modern CPUs only have access to their registers directly. For long term storage, that goes into RAM, which is accessed on demand.

Similarly, your own memory is not large enough to contain all the information you'd need to complete most complex tasks. That's why you have to write things down and actively try to remember things.

While that uses OpenAI's embedding networks, like the autoregressive LLM itself, it's not like OpenAI has a monopoly on text embeddings by any means (far from it - embeddings have a very straightforward business use and are used in practically any major site you know for things like similarity queries).

While I think OP is overhyping the degree to which this is "infinite memory" yet, in a hypothetical turing machine formulation where the network can more proactively store and restore memory, it would allow for it to be, at least, turing complete.