Sounds like a half-self-aware version of "Great Man" thinking, just with the caveat that there aren't actually any among humanity.

But actually, I think you're right. It's easier and more palatable to our narrative-hungry minds to believe that we'll get some sort of cinematic climax before the credits roll, history ends, and we walk out of the theater, than to realize that the world can both be unimaginably shitty and also incredibly boring. If the world doesn't end, or if this isn't the end of history (I think a deus ex machina utopia granted by the aliens falls in this category) we might have to confront the grim reality of slow, complicated, and mostly nameless problems. And that's a lot like waking up one day and realizing your parents are real people who don't know everything, and one day they won't be around to deal with things for you.

I've had similar thoughts about other conspiracy-type thinking like the illuminati but yeah, makes sense that it would apply to aliens as well.

"Hmm... we really only wanted to rule over you to harvest your species' brain power through an interface with our computational networks. This... just won't do. Later losers!"

We’re tired, and we’re scared, I think.

Worse, we're used to being tired and scared. We're apathetic to our own anxieties and exhaustion. The only thing to fear is not fear itself. It's complacency toward fear.

My guess is that it's more a result of overfitting for alignment. Fine-tuning for "safety" (rather, more corporate-friendly outputs).

That is, by focusing on that specific outcome in training the model, they've compromised its ability to give well-"reasoned" "intelligent" sounding answers. A tradeoff between aspects of the model.

It's something that can happen even in simple statistical models. Say you have a scatter plot of data that loosely follows some trend, and you come up with two equations to describe that trend. One is a simple equation that loosely follows it but makes a good general approximation, and the other is a more complicated equation that very tightly fits the existing data. Then you use those two models to predict future data. But you find that the complicated equation is making predictions way off the mark that no longer fit the trend, and the simple one still has a wide error (how far its prediction is from the actual data) but still more or less accurately fits the general trend. In the more complicated equation, you've traded predictive power for explanatory power. It describes the data you originally had but it's not useful for forecasting data that follows.

That's an example of overfitting. It can happen in super-advanced statistical models like GPT, too. Training the "equation" (or as it's been called, spicy autocorrect) to predict outcomes that favor "safety" but losing the model's power to predict accurate "well-reasoned" outcomes.

If that makes any sense.

I'm not a ML researcher or statistician (I just went through a phase in college), so if this is inaccurate I'm open to corrections.