Over on my personal blog I published a post today about AGI already being here and how it’s affecting young people. What we don’t seem to be having a conversation about at the moment, other than the ‘sycophancy’ narrative, is why some people prefer talking to LLMs over human beings.

Which brings us to this post, not about the problems about conversing with AI, but applying some of those critiques to interacting with humans. It which should definitely be taken in a tongue-in-cheek way but it does point towards a wider truth.

Too narrow training set

I’ve got a lot of interests and on any given day, I may be excited to discuss various topics, from kernels to music to cultures and religions. I know I can put together a prompt to give any of today’s leading models and am essentially guaranteed a fresh perspective on the topic of interest. But let me pose the same prompt to people and more often then not the reply will be a polite nod accompanied by clear signs of their thinking something else entirely, or maybe just a summary of the prompt itself, or vague general statements about how things should be. In fact, so rare it is to find someone who knows what I mean that it feels like a magic moment. With the proliferation of genuinely good models—well educated, as it were—finding a conversational partner with a good foundation of shared knowledge has become trivial with AI. This does not bode well for my interest in meeting new people.

[…]

Failure to generalize

By this point, it’s possible to explain what happens in a given situation, and watch the model apply the lessons learned to a similar situation. Not so with humans. When I point out that the same principles would apply elsewhere, their response will be somewhere along the spectrum of total bafflement on the one end and on the other, a face-saving explanation that the comparison doesn’t apply “because it’s different”. Indeed the whole point of comparisons is to apply same principles in different situations, so why the excuse? I’ve learned to take up such discussions with AI and not trouble people with them.

[…]

Indeed, why am I even writing this? I asked GPT-5 for additional failure modes and found more additional examples than I could hope to get from a human:

Beyond the failure modes already discussed, humans also exhibit analogues of several newer LLM pathologies: conversations often suffer from instruction drift, where the original goal quietly decays as social momentum takes over; mode collapse, in which people fall back on a small set of safe clichés and conversational templates; and reward hacking, where social approval or harmony is optimized at the expense of truth or usefulness. Humans frequently overfit the prompt, responding to the literal wording rather than the underlying intent, and display safety overrefusal, declining to engage with reasonable questions to avoid social or reputational risk. Reasoning is also marked by inconsistency across turns, with contradictions going unnoticed, and by temperature instability, where fatigue, emotion, or audience dramatically alters the quality and style of thought from one moment to the next.

Source: embd.cc

Image: Ninthgrid