I think this helped me a lot understand you a bit better - thank you

Let me try paraphrasing this:

> Humans are our best example of a sort-of-general intelligence. And humans have a lazy, satisfying, 'small-scale' kind of reasoning that is mostly only well suited for activities close to their 'training regime'. Hence AGIs may also be the same - and in particular if AGIs are trained with Reinforcement Learning and heavily rewarded for following human intentions this may be a likely outcome.

Is that pointing in the direction you intended?

Let me try to paraphrase this: 

In the first paragraph you are saying that "seeking influence" is not something that a system will learn to do if that was not a possible strategy in the training regime. (but couldn't it appear as an emergent property? Certainly humans were not trained to launch rockets - but they nevertheless did?)

In the second paragraph you are saying that common sense sometimes allows you to modify the goals you were given (but for this to apply to AI ststems, wouldn't they need have common sense in the first place, which kind of assumes that the AI is already aligned?)

In the third paragraph it seems to me that you are saying that humans have some goals that have an built-in override mechanism in them - eg in general humans have a goal of eating delicious cake, but they will forego this goal in the interest of seeking water if they are about ot die of dehydratation (but doesn't this seem to be a consequence of these goals being just instrumental things  that proxy the complex thing that humans actually care about?)

I think I am confused because I do not understand your overall point, so the three paragraphs seem to be saying wildly different things to me.

I notice I am surprised you write

However, the link from instrumentally convergent goals to dangerous influence-seeking is only applicable to agents which have final goals large-scale enough to benefit from these instrumental goals

and not address the "Riemman disaster" or "Paperclip maximizer" examples [1]

• Riemann hypothesis catastrophe. An AI, given the final goal of evaluating the Riemann hypothesis, pursues this goal by transforming the Solar System into “computronium” (physical resources arranged in a way that is optimized for computation)— including the atoms in the bodies of whomever once cared about the answer.
• Paperclip AI. An AI, designed to manage production in a factory, is given the final goal of maximizing the manufacture of paperclips, and proceeds by converting first the Earth and then increasingly large chunks of the observable universe into paperclips.

Do you think that the argument motivating these examples is invalid?

Do you disagree with the claim that even systems with very modest and specific goals will have incentives to seek influence to perform their tasks better?