I agree with your high-level view that is something like "If you created a complex system you don't understand then you will likely get unexpected undesirable behavior from it."

That said, I think the EM phenomenon provides a lot of insights that are a significant update on commonly accepted views on AI and AI alignment from several years ago:

• The nature of misalignment in EM: Instead of being misaligned in a complex, alien and inscrutable way, emergently misaligned models exhibit behavior that is more like a cartoon villain. The AI does hate you.
• Personas: Papers on the subject emphasize the novel 'personas' concept: human-recognizable, coherent characters or personalities that are learned in pretraining.
• Importance of pretraining data: Rather than being caused by a flawed reward function or incorrect inductive biases, EM is ultimately caused by documents in the pretraining data describing malicious characters (see also: Data quality for alignment).
• EM can occur in base models: "This rules out explanations of emergent misalignment that depend on the model having been post-trained to be aligned." (quote from the original EM paper)
• Realignment, the opposite of EM: after fine-tuning models on secure code to trigger "emergent misalignment", this new realignment generalizes surprisingly strongly after just a few examples.

Yes I agree with RLHF hacking and emergent misalignment are both examples of unexpected generalization but they seem different in a lot of ways.

The process of producing a sycophantic AI looks something like this:
1. Some users like sycophantic responses that agree with the user and click thumbs up on these responses.
2. Train the RLHF reward model on this data.
3. Train the policy model using the reward model. The policy model learns to get high reward for producing responses that seem to agree with the user (e.g. starting sentences with "You're right!").

In the original emergent misalignment paper they:
1. Finetune the model to output insecure code
2. After fine-tuning, the model exhibits broad misalignment, such as endorsing extreme views (e.g. AI enslaving humans) or behaving deceptively, even on tasks unrelated to code.

Sycophantic AI doesn't seem that surprising because it's a special case of reward hacking in the context of LLMs and reward hacking isn't new. For example, reward hacking has been observed since the cost runners boat reward hacking example in 2016.

In the case of emergent misalignment (EM), there is unexpected generalization but EM seems different to sycophancy or reward hacking and more novel for several reasons:

• The resulting behavior would likely receive low reward under a standard HHH RLHF reward model, which makes it closer to goal misgeneralization than to exploiting a proxy for high reward.
• The way EM is explained and mitigated is not generally explained using standard RL terminology; the literature emphasizes novel concepts like "personas",  and mitigation techniques such as "inoculation prompting" that seem specific to LLMs.
• The apparent cause of EM is the amplification of misaligned internal persona directions that already exist in the base model and originate from the pretraining data, and are triggered by finetuning or prompting causing the model to adopt a globally misaligned persona.

OpenAI wrote in a blog post that the GPT-4o sycophancy in early 2025 was a consequence of excessive reward hacking on thumbs up/thumbs down data from users.

I think "Sydney" Bing and Grok MechaHitler are best explained as examples of misaligned personas that exist in those LLMs. I'd consider misaligned personas (related to emergent misalignment) to be a novel alignment failure mode that is unique to LLMs and their tendency to act as simulators of personas.

The AI company Mechanize posted a blog post in November called "Unfalsifiable stories of doom" which is a high-quality critique of AI doom and IABIED that I haven't seen shared or discussed on LessWrong yet.

Link: https://www.mechanize.work/blog/unfalsifiable-stories-of-doom/

AI summary: the evolution→gradient descent analogy breaks down because GD has fine-grained parameter control unlike evolution's coarse genomic selection; the "alien drives" evidence (Claude scheming, Grok antisemitism, etc.) actually shows human-like failure modes; and the "one try" framing ignores that iterative safety improvements have empirically worked (GPT-1→GPT-5).

Epoch AI has a map of frontier AI datacenters: https://epoch.ai/data/data-centers/satellite-explorer

Death and AI successionism or AI doom are similar because they feel difficult to avoid and therefore it's insightful to analyze how people currently cope with death as a model of how they might later cope with AI takeover or AI successionism.

Regarding death, similar to what you described in the post, I think people often begin with a mindset of confused, uncomfortable dissonance. Then they usually converge on one of a few predictable narratives:

1. Acceptance: "Death is inevitable, so trying to fight it is pointless." Given the inevitability and unavoidability of death, worrying about it or putting effort into avoiding it is futile and pointless. Just swallow the bitter truth and go on living.

2. Denial: Avoiding the topic or distracting oneself from the implications.

3. Positive reframing: Turning death into something desirable or meaningful. As Eliezer Yudkowsky has pointed out, if you were hit on the head with a baseball bat every week, you’d eventually start saying it built character. Many people rationalize death as “natural” or essential to meaning.

Your post seems mostly about mindset #3: AI successionism framed as good or even noble. I’d expect #2 and #3 to be strong psychological attractors as well, but based on personal experience, #1 seems most likely.

I see all three as cognitive distortions: comforting stories designed to reduce dissonance rather than finding an accurate model of reality.

A more truth-seeking and honest mindset is to acknowledge unpleasant realities (death, AI risk), that these events may be likely but not guaranteed, and then ask what actions increase the probability of positive outcomes and decrease negative ones. This is the kind of mindset that is described in IABIED.

I also think a good heuristic is to be skeptical of narratives that minimize human agency or suppress moral obligations to act (e.g. "it's inevitable so why try").

Another one is the imminent prediction that AI progress will soon stop or plateau because of diminishing returns or limitations of the technology. Even a professor I know believed that.

I think that's a possibility but I think this belief is usually a consequence of wishful thinking and status quo bias rather than carefully examining the current evidence and trajectory of the technology.