I haven't stated that it's not more capable nor more "intelligent", it's the opposite.
I will try to expand on what I mean.
LLMs "character/persona/tendencies" are increasingly less about acting as an assistant and more about finding the solution itself.
I use AI in a specific way: he assists, investigates and answers my question. I do the coding. It is increasingly difficult to use it as such, because it quickly jumps into giving me solutions instead of answering my specific questions.
I'll give you few examples.
I asked it to investigate DNS handling details in phoenix emailer module work, he did very little investigation and jumped into why I should've used magic links instead. Instead of assisting me in my research, it was hard wired to solve the problem (the wrong one, with a very wrong solution).
Today at work, I had a problem with batching, I wanted to understand if batching was even needed at all for our use case, and he kept circling around how to fix the batching bug instead. That's not what I asked it to do, yet, it jumped to the "solution".
I am increasingly frustrated by these models "personality" and tendencies that are unhelpful to assist me doing the task at hand and more on it doing it and me merely assisting/supervising.
Sure, very detailed prompting on how he has to act helps, but wait few turns and he drifts again to his default solution vomiting state.
Which makes me think that these models are hard wired on this mode of operation by consistent training and reinforcement of jumping from prompt to code solution.
Another thing they tend to do is rely on their own context -> memories -> training data. And if that's wrong then they'll continue with it until you instruct them to research, after which they usually get the right answer.
I've noticed that the newer models keep track of what you type so as to anticipate what you're likely to say. For example, today Opus 4.8 said "You usually don't want me to commit until you've checked, so the change remains uncommitted."