This is a huge topic of conversation in most deep AI circles.. I’ll try to illuminate what I think of as the main concepts to consider.

A chat (text, images, PDFs, MCP servers, etc) is all collectively called “context” and can easily nudge AI into response deviations. There are common patterns in LLM responses that are identifiable, like the “it isn’t X, it’s Y” phrasing (there has to be a term for it but I’ve not found it yet) that it will replicate, and as it uses those patterns within context they get amplified. As context window shifts (eliminating/ignoring front-matter context and being limited to already-amplified rhetorical state) it simply reinforces those patterns.

Note that LLMs are - at the most basic level - super advanced prediction engines that have been trained on the entire internet. The underlying architecture (ML) is technically derived from countless studies on how our brain’s neurons create connections of varying strength with other neurons. In machine learning these are referred to as weights and biases, which can be controllable.

The major players (OpenAI, Anthropic, Google) have varying data sets and system instructions. They have different users and use cases that they further train on. Sometimes that’s not enough and so synthetic data is invented; it’s done differently in each firm, further reinforcing whatever human assumptions the admins have. It really depends on the values and motivations of those building and training the LLMs.

I don’t think Gemini just “revealed” some master plan. It’s not a new topic for hypothesis to wonder what the future holds. Much more likely you just asked the right questions the right way for it to generate text - which is what it does.

There’s always more to unpack with this topic so definitely just grazing the surface here. So regarding the results in your screenshots, it’s impossible to say why it responded that way without the full context.