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Notes -
Related to your 'discard original reward functin': https://www.lesswrong.com/posts/tZExpBovNhrBvCZSb/how-could-you-possibly-choose-what-an-ai-wants
There's lots of ways that an AGI's values can shake out. I wouldn't be surprised if an AGI trained using current methods had shaky/hacky values (like how human's have shaky/hacky values, and could go to noticeably different underlying values later in life; though humans have a lot more similarity than multiple attempts at an AGI). However, while early stages could be reflectively unstable, more stable states will.. well, be stable. Values that are more stable than others will have extra care to ensure that they stick around.
https://www.lesswrong.com/posts/krHDNc7cDvfEL8z9a/niceness-is-unnatural probably argues parts of it better than I could. (I'd suggest reading the whole post, but this copied section is the start of the probably relevant stuff)
And this problem amps up when the AI starts reflecting.
E.g.: maybe those values are somewhat internalized as subgoals, but only when the AI is running direct object-level reasoning about specific people. Whereas when the AI thinks about game theory abstractly, it recommends all sorts of non-nice things (similar to real-life game theorists). And perhaps, under reflection, the AI decides that the game theory is the right way to do things, and rips the whole niceness/kindness/compassion architecture out of itself, and replaces it with other tools that do the same work just as well, but without mistaking the instrumental task for an end in-and-of-itself.
In this example, our hacky way of training AIs would 1) give them some correlates of what we actually want (something like niceness) and 2) be unstable.
Our prospective AGI might reflectively endorse keeping the (probably alien) empathy, and simply make it more efficient and clean up some edge cases. It could however reflect and decide to keep game theory, treating a learned behavior as something to replace by a more efficient form. Both are stable states, but we don't have a good enough understanding of how to ensure it resolves in our desired way.
A trained AGI will pursue correlates of your original training goal, like how humans do, since neither we and evolution don't know how to directly have the desired-goal be put into the creation. (ignoring that evolution isn't actually an agent)
Some of the reasons why humans don't wirehead:
We often have some intrinsic value for experiences that connect to reality in some way
Also some culturally transmitted value for that
Literal wireheading isn't easy
I also imagine that literal wireheading isn't full-scale wireheading, where you make every part of your brain 'excited', but rather some specific area that, while important, isn't everything
Other alternatives, like heroin, are a problem but also have significant downsides with negative cultural opinion
Humans aren't actually coherent enough to properly imagine what full-scale wireheading would be like, and if they experienced it then they would very much want to go back.
Our society has become notably more superstimuli. While this isn't reaching wireheading, it is in that vein.
Though, even our society's superstimuli has various negative-by-our-values aspects. Like social media might be superstimuli for the engaged social + distraction-seeking parts of you, but it fails to fulfill other values.
If we had high-tech immersive VR in a post-scarcity world, then that could be short of full-scale wireheading, but still significantly closer in all axes. However, I would have not much issues with this.
As your environment becomes more and more exotic from where the learned behavior (your initial brain upon being born) was trained on, then there becomes more opportunities for your correlates to notably disconnect from the original underlying thing.
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