There were an awful lot of early writings. Some of them did say that the difficulties with getting AGI to understand values is a big part of the alignment problem. The List of Lethalities does make that claim. The difficulty of getting the AGI to care even if it does understand has also been a big part of the public-facing debate. I look at some of the historical arguments in The (partial) fallacy of dumb superintelligence, written partly in response to Matthew's post on this topic.

Obsessing about what happened in the past is probably a mistake. It's probably better to ask: can the strengths of LLMs (WRT understanding values and following directions) be leveraged into working AGI alignment?

My answer is yes, and in a way that's not-too-far from default AGI development trends, making it practically achievable even in a messy and self-interested world.

Naturally that answer is a bit complex, so it's spread across a few posts. I should organize the set better and write an overview, but in brief we can probably build and align language model agent AGI, using a stacking suite of alignment methods that can mostly or entirely avoid using RL for alignment, and achieve corrigibility by having a central goal of following instructions. This still has a huge problem of creating a multipolar scenario with multiple humans in charge of ASIs, but those problems might be navigated, too.