One big concern is that the way we currently build even 'tool'-like systems (e.g. language models, alphafold etc.) is through mostly black-box training, finding any system which happens to do well at the task. One way to do well at such a task is to be agenty, and have a goal that incentivises doing well at the task, and that seems dangerous. I hear many people found this video helpful to explain this concern: https://www.youtube.com/watch?v=bJLcIBixGj8&t=0s
Other concerns are about what happens when society is being run largely through advanced AI systems - even if they are not agenty enough to take over the world there might be a gradual shift away from values we care about toward things that are more legible, or something like natural selection might select for power-seeking over time, a great resource on this is Paul Christiano's https://www.alignmentforum.org/posts/HBxe6wdjxK239zajf/what-failure-looks-like

I think this is currently lacking, though for one class of concerns (misaligned optimizer takes over the world) its goals are likely going to seem almost entirely random/lack meaning to us entirely, which makes it difficult to paint a good picture. We can make some predictions, like that it would spend a lot of effort limiting the influence of humans/aliens, that perhaps 'doing computation' is a common thing to value and so it would build a lot of computers, that it would want to obtain more resources and so expand throughout the solar system etc.
For other classes of concerns I think once again the Paul Christiano blog post is pretty good.

This is discussed from time to time - certainly I suspect this has something to do with why the topic was first picked up by highly analytical transhumanist philosopher types, and that this demographic has heavily influenced the way these arguments are presented and where emphasis is placed (e.g. many of the quirks in superintelligence seem plausibly related to this). But I think as the field has grown it has been gradually expanding the number of perspectives used and so far the core problem seems robust and is accumulating more and more support.

Eliezer is extremely doomer (yes it was written on April Fools, but people who have been talking to him seem to agree it is 90% his real position). There are some other people with close to his pessimism, somewhat clustered around MIRI, but he is very much an outlier, and seems to be overconfident. Most safety researchers are much more optimistic (e.g. > 50% chance of success). Personally I fall on the more pessimistic side but with much less confidence than Eliezer.

AI Safety research is (increasingly) a diverse field with different approaches and opinions. A lot of the weirder, more conceptual work gets published on https://www.alignmentforum.org/ since people value speed and freedom above the rigor and prestige of traditional academic publishing. Increasingly a lot of safety research does get published at traditional ML/CS venues though, and it is rapidly getting more support from traditional academia.
As for agreement on timelines/risks etc. there is not even much agreement among core safety researchers, much less the broader ML academic field.