“Stochastic Parrot”
If you’ve followed the AI discourse even just a little bit, you’ve probably come across this dig at Large Language Models like ChatGPT and Claude. As accused, LLMs are just unintelligent “fill-in-the-blank” machines with a layer of randomness to boot, i.e. stochastic parrots. Or, as another common criticism goes, LLMs are just an “average of all human expression”. And who wants average?
At a technical level, this isn’t incorrect. Even the frontier LLMs today are doing what GPT version 1 did eight years ago. Given a length of text, they produce the next bit of text (a “token”) repeatedly until they’ve made a new passage. Each next token is chosen with some degree of randomness, with the likeliest tokens having been learned during the LLM’s training process. The training is done on a massive body of text, mostly scraped from the internet.
Of course, the difference between GPT v1 and v5.4 (the most current) is like the difference between a weed trimmer and a Formula One car, even though both of them just compress and ignite gasoline in a cylinder over and over. What the AI researchers and developers have been able to do with next-token prediction is turn LLMs into skilled software developers, patient tutors, and all other kinds of human-like products.
Despite these abilities, many people still dismiss what a modern LLM can do with the right software around it. And it isn’t just the technical feat. Critics who scoff at modern AI as a mere slot machine that spits out text, or as an “average of all human expression”, are missing what’s really going on. What’s being overlooked is how the power of LLMs lies in the much older technology they’re invoking: language itself.
Our best invention
I’m convinced that language is humanity’s best invention. It’s how we store information across space and time through stories, histories, lessons, and facts. We use it to bridge the gap between our brains, which are otherwise completely opaque to each other. It gives us a way to externalize ideas so everyone can participate in them, whether in the form of sound, code, math, or letters. Language is how people coordinate and cooperate, and also how they compete and fight.
What’s the average of all of that?
Perhaps the problem is that there’s so much language that’s average (including, perhaps, this article). We’re more often bored or annoyed than moved by the language we encounter each day. Notably, we were surrounded by slop before LLMs came along, inundated with advertising, social media, and political entertainment. But even those mediocrities prove the point that language is powerful, otherwise why bother producing so much of it?
When we think of technology in the context of language, it’s typically media that come to mind, like the printing press, the telegraph, radio, television, and the internet. But language itself makes a strong case for being a technology, too. It’s an invention that we’ve refined over thousands of years. It’s useful, learned, and does things for us that we couldn’t do otherwise. I think it serves us well to think of language this way.
And like any other technology, it’s the foundation for more advances. In the case of language, there’s not a single human accomplishment that doesn’t rely on it. Medicine, law, engineering, computing, art, and so on. It’s the core technology of education. We’re hard-pressed to come up with a more important invention than this.
Language brought to life
With AI, we’re feeling the sharp edges of our best invention. LLMs harness the power of language, but with speed and ceaseless energy and often without good judgment. AI psychosis, for example, is a thing because of how powerful language is. We’re all far more persuadable than we believe ourselves to be. It’s genuinely scary to think about what happens when persuasion is automated.
This is why I’m far more sympathetic to the people who are worried about AI than to the people who are dismissive of it. I don’t think it’s wrong to be scared by the prospect of not knowing if you’re talking to a person. The Turing test is far away in the rearview mirror at this point. People have always used language well, and poorly, and dangerously. To be nervous about what LLMs can do is just to be nervous about what language can do, but at scale.
Ironically, the original source of “stochastic parrots” is a paper from 2021 where the authors discuss the risks of large language models that have the capacity of language without the human ability to understand it. The term is meant to draw attention to the perils of the technology, not to pooh-pooh it.
For me, the most interesting detail is this one: LLMs aren’t just a medium, like books and the web. AI uses language to do things. Each token in the flow changes what comes next. The closest thing we’ve had to this is software code, but code doesn’t generate itself in anything like the same way. It’s as though we brought language to life.
Reasoning models are perhaps the best example of what I mean, altering the way they work by talking to themselves. They generate tokens for their own benefit, to help them “think” about what the user wants, what the next step ought to be, and what they might be getting wrong. This is, frankly, incredible. Reasoning models have, in turn, opened the door to agentic AI, personal assistants that are now taking shape in exciting ways. And all of this only works because language with all its complexity and nuance and density makes such things possible.
I’ll finish with this. As I was workshopping this article with Claude, it gave me this gem:
“Just text" is how humans declare war, fall in love, spread conspiracy theories, write constitutions, and teach children right from wrong.
Not bad, if a bit dramatic. Also, not at all wrong.
