

Well your main assertion was a wildly incorrect definition of sentience.
You go on to say that (and I’m paraphrasing) “sentience is in the eye of the beholder”, which is also wrong.
There’s also the implication that you do think computers are sentient, but you haven’t typed that specifically; I know you said you didn’t want assumptions, but this is a fair inference. Someone who wasn’t emotionally invested I the idea of chatbot sentience wouldn’t push back so hard with basically 0 facts to back them up.
Your final assertion is that the Wikipedia article doesn’t immediately contradict you, which again, it does in the first sentence. You can see my previous comment for that information tho, I’m not retyping it.
Coding is like insanely well suited to LLMs by nature tho, that’s why it can actually do that.
Code is a language with stricter definitions and less verbosity than a spoken human language. Things only have one definition in code, one spelling, one syntax, etc.
There’s an element of artistry to human coding, but it’s more of a science than an art (compared to spoken human language anyhow). It’s easier to predict.
There are other promising “ai” technologies, like radiological image recognition, aren’t the same technology as LLMs, but the companies who make the LLMs constantly conflate their technology with that of more useful technologies in order to obfuscate their relative uselessness.
Even the coding models are being localized. There’s absolutely no reason to have a massive AI datacenter unless you’re making a shit LLM, and even then, the only reason to have that is to commit financial fraud essentially.
EDIT - I learned a bit more about “agentic” AI since you had mentioned it earlier, and what’s actually going on behind the scenes is wild.
So basically, an “agent” is a computer program that you give a task to. The agent then attempts to accomplish the task by engaging in a loop until the task is accomplished.
The structure of the loop is 3 steps. 1) Prompt an LLM - The agent asks an LLM what the first step in the task you asked it to accomplish would be. 2) The LLM provides a plausible answer, and the agent then attempts to carry out these instructions. 3) If the given instructions don’t work, the LLM appends the prompt to say “I tried step #1, it didn’t work, what should I try next?”. If the instructions work, the agent appends the prompt to say “I took this step and it worked, what’s the next step?”. Then the loop repeats.
So, this would work exceptionally well for code because the computer can Test and verify the code before continuing… but that’s just about the only use case for the technology
A good way to visualize this is to imagine yourself as an agent. Let’s say you need to do your laundry, that’s your task. So you ask the LLM what the first step is, and you do what it says no matter what. Then you go back, append the prompt, and prompt the AI again. This cycle repeats until either 1) you finish the laundry, or 2) you destroy the laundry, or burn down the house, or injure yourself blindly following the LLMs directions (this second outcome is the statistically probable one).
Would you do that? No, probably not. You can think, so you would know which instructions to attempt, and which to ignore. The Agent cannot think. The LLM cannot think. The agent strictly follows directions like any other computer program. The LLM is attempting to construct an answer that appears plausible, token by token. It doesn’t matter if it is plausible, or possible even, the LLM isn’t trying to give you the correct answer, it’s trying to give you an answer that appears correct.