I'm not anti-AI, but I have QUALLMS - Chip Hollingsworth
I'm not anti-AI, but I have QUALLMS
August 10, 2026
I haven’t been shy about voicing my negative opinions of the current large language model hype. But I’m hesitant to describe myself as “anti-AI”. And it’s not because I have some sort of super-nuanced position like “LLMs are fine in some cases” or “I’d be OK with LLMs if only we could solve these problems”. I’m in fact adamantly opposed to the very assumptions on which the hype is based: that a statistical model of word co-occurrence frequency is a good foundation for a multi-purpose tool for answering questions or generating meaningful text or code. Train a local model on text acquired with the creators’ express permission and run it on a machine fueled entirely by renewable energy, and I still don’t think it’s a good idea. The fact that commercial LLMs disregard consent and copyright and consume ungodly amounts of resources only makes a fundamentally bad thing even worse.
So why don’t I call myself “anti-AI”? Well, because of two ambiguities: one in the definition of “AI”, and another in what it means to be “anti-AI”. And that’s why I’m proposing an alternate nomenclature to explain exactly what my position is.
Why “AI” is a mostly meaningless term
The term “artificial intelligence” is usually believed to have come into existence around the time of the Dartmouth Summer Research Project on Artificial Intelligence in 1956. Even then, it wasn’t exactly clear what the term referred to; it was chosen as a sort of blanket term for a lot of semi-related research in information theory, computer science, and other disciplines. Broadly speaking, it seemed to refer to attempts to mimic the effects of human thought with computers. I say “mimic the effects of human thought” rather than “mimic human thought,” because the people involved were under no illusion that the processes by which computers handled knowledge representation, language processing, visual recognition, or other “AI” endeavors was anything like what was happening in the human brain. Absolutely nobody thought that human brains had Lisp interpreters running in them to parse all the sentences we hear. That’s why they used the word “artificial”: to stress that this was an imitation of a thing, not the thing itself. (The belief that brains really do some form of computation similar to what goes on inside a digital computer, and that we can design computer systems that actually mimic the human brain, belongs to the related but separate discipline of cognitive science.)
In the beginning, most things that were called “artificial intelligence” were rule-based systems, not essentially different from other types of computer programs. The main difference was that while computing had traditionally been used for tasks that were relatively simple to express in algorithmic form, such as calculating projectile trajectories or solving algebraic equations, artificial intelligence sought to conquer less obviously algorithmic territory, such as representing the information content of natural-language text, reasoning about it, and forming a natural-language reply. But over time, “machine learning” became a thing: using automated statistical or quasi-statistical methods to generalize from a set of input data. This might involve sorting pictures of food into the broad categories “hot dog” or “not hot dog,” or predicting what a website customer might want to buy next given the last several things they’ve bought. This represented a very different approach than what came to be known as “good old-fashioned artificial intelligence” (GOFAI): instead of working out the steps needed to perform a task and telling the computer to do them, you just threw a bunch of data at a program and let the computer figure out what to do. At least, that’s how it’s often anthropomorphically described. In fact, it’s just the same rule-based approach at a higher level of abstraction: instead of rules for turning inputs into outputs, it’s higher-order rules for turning training data into rules for turning inputs into outputs. But it’s a lot easier to say the computer “learns” how to do things, which unfortunately convinces a lot of people that machine learning systems are doing something a lot more similar to the human brain than they really are.
So now “artificial intelligence” was being used to refer to both old-school algorithms for manipulating symbols, and new-fangled statistical trickery. Two very different technologies described with the same terminology, their only commonality being a vague desire to emulate what people do. Over time, as...