1. While a formal definition is still wanting, most grok that AGI means that tasks can be performed at least at a human level across a broad range of tasks. This includes good things along with bad things like hacking, mis-/disinformation, and more

2. One only needs to look at github going down due to agentic commits overload or data center buildout plans to see that scarcity for resources is present. An economy has no mind and is made up of the decisions of millions to billions of people and, now, agents attempting to perform on behalf of those people.

3. A bare transformer-based language model does not possess persistent goals in the ordinary agentic sense. But deployed agents can exhibit goal-directed behavior because the model is embedded in a harness that supplies an objective, context, tools, state, and an execution loop.

I've found that most regular users don't anthropomorphize LLMs in a strong sense ("AI boyfriend/girlfriend" aside), many in fact do expect agents to make human-like decisions -- which results in very unstable outcomes.

In short - goal-directed behavior does not require that the supporting system be a person/mind/conscious entity.

1. This definition is so broad as to be practically useless. One could argue that LLMs of several years ago met these criteria, or that conversely we haven’t come close to meeting them.

2. I thought you were saying the resources that the LLM uses to run were constrained, so I’m sorry for the misunderstanding there.

3. Yes I understand that we use RL to tune post-training. The (huge) difference between this and a human mind is that the LLM can’t develop a dangerous “single-minded goal” on its own, at runtime; it must have been trained to do so. If someone has post-trained an LLM to do something that has an illegal action as its side effect, that person/company/whatever has committed a crime and should be prosecuted. The solution here is legal, not technical.