My bet: LLMs will never be creative and will never be reliable.
It is a matter of paradigm.
Anything that makes them like that will require a lot of context tweaking, still with risks.
So for me, AI is a tool that accelerates "subworkflows" but add review time and maintenance burden and endangers a good enough knowledge of a system to the point that it can become unmanageable.
Also, code is a liability. That is what they do the most: generate lots and lots of code.
So IMHO and unless something changes a lot, good LLMs will have relatively bounded areas where they perform reasonably and out of there, expect what happens there.
it won't be creative because it's a transformer, it's like a big query engine.
it's a tool like everything else we've gotten before, but admittedly a much more major one
but "creativity" must come from either it's training data (already widely known) or from the prompts (i.e. mostly human sources)
We don't even know what 'creativity' is, and most humans I know are unable to be creative even when compelled to be.
AI is 'creative enough' - whether we call it 'synthetic creativity' or whatever, it definitely can explore enough combinations and permutations that it's suitably novel. Maybe it won't produce 'deeply original works' - but it'll be good enough 99.99% of the time.
The reliability issue is real.
It may not be solvable at the level of LLM.
Right now everything is LLM-driven, maybe in a few years, it will be more Agentically driven, where the LLM is used as 'compute' and we can pave over the 'unreiablity'.
For example, the AI is really good when it has a lot of context and can identify a narrow issue.
It gets bad during action and context-rot.
We can overcome a lot of this with a lot more token usage.
Imagine a situation where we use 1000x more tokens, and we have 2 layers of abstraction running the LLMs.
We're running 64K computers today, things change with 1G of RAM.
But yes - limitations will remian.