The only issue with current systems is that the “AI” is tweaked to the specific game mechanics. You can easily enough build multiple algorithms for varying play styles and then have it adapt to counter the play style of the player. The problems is that the current way that many games are monetized is through expansions, gameplay tweaks, etc., as well as those being necessary when a game mechanic turns out to be really poorly implemented or just unpopular and the mechanics change. If the “AI” isn’t modified at the same time to rake advantage of the changes, then it becomes easy to beat. The other issue is that eventually a human can learn all of the play style algorithms and learn to counter them and then it becomes boring.

Unfortunately, generative “AI” is not a true learning model and thus not truly intelligent in any sense of the word. It requires that it is only “taught” with good information. So if it gets any data that includes even slight mistakes, it can end up making lots of those mistakes repeatedly. And if those mistakes aren’t corrected by a human, it doesn’t understand which things were mistakes and how they contributed to winning or losing. It can’t learn that they were mistakes or to not do them. It doesn’t truly understand how to decide something is wrong on its own, only that things are related and how often it should use those relationships over others. Which means manual training is required, which due to the sheer volume of information required to train a generative “AI”, is not possible in a complex game where the player has thousand of possible moves that each branch to thousands of possible combinations of moves, etc.