>because we understand the ermegent properties of ant colonies without having made them.
I would disagree. We observe ants and can relatively classify their behavior well, but still have many issues predicting their behavior even though they are relatively simple agent types with a far lower complexity choice space than an LLM+plugin could/does provide.
This is the key, we could not predict ant behavior before observing it, the same will be true for AI agents of large complexity. At least at this point of understanding we could still ask it some unexpected question and get unexpectedly dangerous responses from it.
I don't. The whole point of my job is to understand this exact degree of complexity. The rest of your comment rides on that assumption, and is moot. As I said, emergent properties are often unexpected, but once the observation has been made, it is possible to work back through the system to understand how the property came to be (because the system is still deterministic, though not apparently obvious.) Having a billion input features does not change this.
Frankly, understanding the oddness of a ML model is not half as difficult as people make it, and might even be easier than predicting ant colonies. Our inability to predict their behavior has nothing to do with our ability to understand what features influence which behaviors, only that measuring the current state of an ant colony is difficult because it's not like they're all housed within an array of memory. LLMs are, thus we'd understand them better if the most common implementations were not hidden away. Case in point, all the optimization techniques that have emerged from having LLaMa available.