I feel like there's an implication here that the research should be in modeling architectures and training sets and other specialized machine learning. But there is research here: in natural language modeling of goals, plans, thoughts, processes, etc.

Obviously we don't know what paths will be most successful. But a path where critical drivers of AI (like goals) are modeled in a transparent and comprehensible manner seems like a very attractive direction to take. I'd much rather be able to read my AI agents goals, plans, intermediate goals, self-analysis, etc., than have it all captured in a set of completely incomprehensible weights.

The AI would never say hello, but if you say hello to it, it will say hello back. Is that also a hack? Aren’t you just describing everything about LLM behavior generally not only something specific about goals/tasks? In that case the nature of the thing is less interesting than the results we're able to find from it and I wouldn't worry about this kind of purity test.

I mean most people don't have the resources needed to build a model big enough that these types of behaviors emerge so third party addons is all we got until Google/Microsoft/OAI drop something on us.

Part of the issue here is the massive amount of compute needed over what we're already spending. ToT is showing a likely 10 to 20x number of calls to get an answer, which when you are compute limited is going to be a problem for deployment in mass. It's very likely we're going to have to wait for more/faster hardware.