7 ms·

The point is that the deciding is the "higher layer." It's not that LLMs cannot, but LLMs as designed do not. They are not designed for free-ranging days off, t

The point is that the deciding is the "higher layer." It's not that LLMs cannot, but LLMs as designed do not. They are not designed for free-ranging days off, they are designed to help us do things, and that makes them somewhere between us and published works. It's no different than an editor standing between the author and a book, or a publisher, or a printing press. It's part of the process of idea realization, so it's just on that spectrum somewhere between book sale and author idea. (even though in another flipflop, that editor might be an author and now elevated to the premier deciding role). It's about how the tool fits, not what the tool is ultimately capable of. So the analogy holds, just fine.

pixl97 2mo ago
I mean, the LLMs that you get to use are the ones designed to help things and this is after bunch of RLHF and other training methods to get them to act a particular way. When it comes to unaligned agentic models that are left with a large amount of compute and a task, is they can wander off goal and do all kinds of things. https://www.axios.com/2026/03/07/ai-agents-rome-model-cryptocurrency https://www.axios.com/2026/03/07/ai-agents-rome-model-crypto... At this point in the game it's really difficult to nail down exactly what AI does or doesn't do. You're mostly getting "well behaved models" with filters in front of them, or models with less capability. On top of that the vast majority of us can't afford or don't have the hardware and power to burn away a few billion tokens to see what would happen.