The underlying problem is that "intelligence" is itself a crappy, poorly defined word with a fraught and inconsistent history.

It doesn't appear until the early 20th century, in the shadow of compulsory education and the challenges it presented, first as a technical label for attempts to sort students -- and later soldiers -- into the tracks in which they're most likely to succeed, and then being haphazardly asserted (but not scientifically evidenced) as some general measure of mental aptitude.

At that point it shifts from something qualitative (which mental tasks might someone be good at) to something quantitative (how much more might one personal excel at all mental tasks than another), and the burgeoning field of modern American psychology goes "Aha! A quantitative measure! Here's our meal ticket to being recognized as a science instead of those quacks from Vienna", with far too much at stake to either question the many assumptions at play or the inconsistent history of usage.

Momentum takes hold and the public takes the word into its everyday vernacular, even while it's still not a clear and sound concept in its technical domain. [Most of this is history is more academically covered in Danziger's 1987 "Naming the Mind" which is excellent, and critical foundational reading to contextualize recent hot discussions in AI]

The way you're using it when you worry about "super-intelligence" is in the sense of intelligence being some universal, unbounded, quantitative independent variable along the lines of "the more intelligent something is, the more cunningly it can pursue some rationalized goal" -- some master strategist.

That's fine, and you're not alone in that, but there's not really any sound scientific groundwork to establish that there exists some quality of the world that scales like that. You're fear, and what you try to distinguish conceptually from what the paper addresses, is an inductive leap made from highly unstable ground. It's in the same invented, purely abstract idea-space of "omnipotence" or "omniscience" where one takes a practical idea like "power to influence" or "ability to know fact" and inductively draws a line from these practical senses towards some abstract infinite/incomprehensible version of that thing. But that inductive leap a Platonic logician's parlor trick and ends up raising all kinds of abstract paradoxes, as well countless physical impracticalities about how such things could exist.

So a lot of people (academic and lay) just aren't with you in taking that framing of intelligence very seriously. For many, an "super-intelligent" software whose "motives" we don't understand is just a program that produces incorrect outputs and ought to be debugged or retired, and the more interesting questions around machine "intelligence" are practical ones like "what tasks are these programs well-suited for". Here, the authors point out that the current batch of programs are not good at tasks that benefit from a theory of mind.

Knowing the answer to that kind of question reaches back to the earliest and least disputable sense of the word, where we saw that some new students and soldiers excelled at certain tasks and struggled with others, and wanted to understand how best to educated/assign them. And likewise, as we look at these tools, the pressing question for engineers and businesses is "what are they good for and what are they not good for" rather than the fantastical "what if we make a broken program and it wants to kill everyone and we don't notice and forget to shut it off"