LLM cannot reason about anything. It can provide text that can be plausibly interpreted as reasoning by someone reading that text. When human provides a plausible explanation then it means they either had someone else provide it to them or they actually understand the issue. LLM cannot understand anything, it can only provide output based on the training data where similar input has likely to have produced similar output in the past. Human can tell you they don't understand or don't know something but LLM is unlikely to have training that will produce that kind of output, it is more likely you will always get something that looks correct but it might not be.
LLM can automate a part of the process where human might take slightly but, ultimately, any output generated by LLM cannot be trusted and should be checked by human that understands the issue...and that is actually the hard part where humans will struggle so they won't actually do it.
When human is producing the output that human is performing the following actions: -analysing the issue -analysing the exiting process -building the understanding of the existing process -building the understanding of how issue affects the existing process -producing the output to address the issue in the existing process -checking the output as it is being produced -updating the understanding of the existing process with lessons learned from the above -checking the final product to ensure that it has solved the original issue and hasn't broken some other part of the system
LLM can help speed up one of those steps (producing the output) at the expense of slowing down the other parts (which were already slow) and reducing the understanding and reliability of the existing system which will make future iterations even slower.
LLM can be used to speed up the generation of examples but just like in the past you could not just copy the example from some random internet search result, you should not just copy the LLM output without understanding it...and that is the slow part where LLM might not help (and might actually make worse) for most people.
And when in the past you encountered comprehensive and well documented output you could assume human that put that amount of effort actually understood what they were doing and wouldn't have expended that much effort to generate garbage, you cannot make that same assumption now with LLMs.