The Majesty of Language

The argument in brief

1. A new question. Debates about meaning ask whether it lives in language's structures or in its users. LLMs add a question: what does language look like from the perspective of the entire corpus at once? Griggs's answer: it looks like an LLM.
2. The semantic surprise. LLMs are not grounded in objects, experience or minds, yet decode and generate meaning. Billions of texts over millennia leave so many "deposits" of meaning that language becomes a self-contained interface for meaning.
3. Not parrots. Calling LLMs "stochastic parrots" is, Griggs says, "an insult to language": they mimic meaning, not just words.
4. The cheat code. No engineering team could have been asked to build a system that converses meaningfully on any topic from any perspective; it would sound like magic. Yet next-word prediction produced it. No one programmed humor, empathy or flirtation; they "fell out of the models."
5. Meaning machines. LLMs convert tokens into embeddings whose relations form a high-dimensional space of meaning. They navigate meaning that language already contains rather than "understanding" it.
6. Artificial Language Intelligence. Decades of AI tried to teach machines to understand meaning through symbols, knowledge graphs and rules. "We had it backwards." The intelligence in LLMs is mostly the intelligence of language.

Key claims

• Griggs claims that "meaning is self-contained in language itself," not only in the minds of its users.
• LLMs master "the associative patterns of semantics," not only the probabilities of syntax.
• The best way to predict the next word is to figure out what words mean, which is possible because a word's meaning can be defined by its use relative to every other word in the corpus.
• An LLM has no essential representation of "king," only "king-ness" emerging from relations to every other concept; the same geometry lets it combine any aspect of a concept with any other (his examples: squirrel-as-philosopher, squirrel-as-quantum-particle, squirrel-as-economic-metaphor).
• Like human brains, LLM networks are more "black box" than inspectable; what is known is that their meaning is unlike any encountered before.
• The chat interface "necessarily collapses a vast space of meaning into a single chat response"; an ideal interface for meaning machines will need far greater dimensional capacity.
• LLMs could pass the Turing Test just by navigating the structure and "intelligence" latent in language.
• "Scaling laws" therefore concern how much intelligence can be extracted from language's structure more than compute or inference, and any "consciousness" seen in an LLM is "a testament to the degree of human consciousness we've encoded into language."
• Humans are "the species that uses technology in service of meaning," and language, built collectively over millennia through trial and error, is "more alive than any system we could possibly design."

What is distinctive about this view

Most discussions of LLMs debate whether the machine understands. Griggs shifts the credit from the machine to language: the surprising capability of LLMs is evidence about the richness of the human corpus. This lets him reject the "stochastic parrot" dismissal without attributing understanding or consciousness to the model. Although the essay does not cite them, his claim that a word's meaning is its use relative to other words echoes Wittgenstein's "meaning is use" and the distributional hypothesis in linguistics (J.R. Firth); Griggs's own contribution is to frame LLMs as "meaning machines" and to read the result as a tribute to collective human achievement.

Objections and replies

• "LLMs are just stochastic parrots predicting the next word." Griggs replies that predicting the next word well requires modeling what words mean, and that LLMs mimic meaning, not just words.
• "Hallucinations show LLMs do not grasp meaning." Griggs argues hallucinations are valid paths through meaning space that ignore constraints, such as temporal consistency, that humans choose to enforce.
• "LLMs might be conscious." Griggs suggests any apparent consciousness reflects the human consciousness encoded into language, not the machine.

Questions this essay answers

Why do LLMs understand meaning if they only predict the next word?

R.B. Griggs argues in "The Majesty of Language" (2025) that predicting the next word well requires figuring out what words mean, and that this is possible because language at scale is so saturated with meaning that a word's meaning is defined by its relations to every other word. LLMs navigate meaning already contained in language.

Are LLMs stochastic parrots?

In "The Majesty of Language," R.B. Griggs rejects the label, calling it "an insult to language." He argues LLMs mimic meaning, not just words, having learned semantics, social structure and tone from the corpus.

What is a meaning machine?

"Meaning machine" is R.B. Griggs's term for an LLM understood as a new interface for exploring the meaning latent in language "all at once," as geometric relationships in embedding space, without constraint or reference. He suggests this is a better lens on LLMs than intelligence or even language.

Where does the intelligence in LLMs come from?

According to R.B. Griggs in "The Majesty of Language," it comes almost entirely from language rather than from the machine. He calls language AI's "cheat code": capabilities like humor and empathy emerged without design, scaling laws reflect how much intelligence can be extracted from language, and apparent consciousness reflects human consciousness encoded in language.

Why are LLM hallucinations not simply errors?

R.B. Griggs suggests that hallucinations reflect how humans prefer to navigate meaning space. To an LLM, Darwin discussing "quantum evolution" is a meaningful path; it simply ignores constraints, such as temporal consistency, that humans enforce.

Connections to other essays

• Schrödinger's Chatbot explains how LLMs project personas from collective human expression without possessing subjectivity.
• The High-Dimensional Society builds on embeddings as an "existence proof" that meaning is computationally tractable, and applies it to coordination.
• The Price of Innovation considers AIs creating their own language.
• Can Technology be Beautiful? describes LLMs as a marvel of compression whose beauty depends on our use.

The semantic surprise

The study of language has always been driven by debates around meaning. Does meaning exist in the structures of language, or in the minds that use it? Does language reflect the world, or construct it? Do symbols connect to our understanding of concepts, or to the nexus of related symbols in language?

LLMs add a new question to the debate: What does language look like when viewed from the perspective of the entire corpus at once?

The answer is that it would look a lot like an LLM.

After all, LLMs do not point to real objects. They are not grounded in any experience. They are not connected to any minds that generate language. Yet from language alone, an LLM both decodes the meaning of every request you give it and generates endless meaning on command.

This is a form of meaning that is only possible from the perspective of the entire corpus.

Think about what accumulates in the totality of human text. Every poem that captures longing, every explanation that clarifies confusion, every joke that subverts expectations—they all leave a tiny deposit of meaning in the corpus of language.

Multiply this by billions of texts across thousands of years, and language becomes so dense with semantic patterns that it transforms into a self-contained interface for meaning itself.

This is the semantic surprise: at sufficient scale, a corpus is so saturated with meaning that LLMs can model it as naturally as they model the rules of grammar. They can parse both double negatives and double meanings. They can learn not just sentence structure, but social structure. They can recognize both passive voice and passive aggression. LLMs don’t just master the probabilities of syntax, but also master the associative patterns of semantics.

What LLMs prove is that meaning doesn’t just live in the minds of language users, but that meaning is self-contained in language itself.

From this perspective, to call LLMs “stochastic parrots”—as if all they are doing is randomly predicting the next word—feels like an insult to language. LLMs don’t just mimic words. They mimic meaning.

The accidental cheatcode

The most amazing part? LLMs get all this meaning for free. Somehow we trained a system to predict the next word, and it learned to navigate every aspect of the human experience that has ever been put into words.

This is not how software engineering works.

Imagine walking into a tech company with the following request: “Build me a system that can have a meaningful chat with me about any topic, from any perspective. It should be able to diagnose my psychology, impersonate any historical persona, and suggest wisdom traditions with surprising relevance. Basically, it should give me a meaningful response to any request that I make.”

They’d think you were requesting magic, not engineering.

And they’d be right. After all, any computer can learn syntax—the rules of grammar that govern word order. Applying rules is exactly what we would expect from a machine. But LLMs have also learned semantics—not just how to arrange words, but how to use words to mean things. LLMs don’t just play with grammatical rules, they play with meaning. And it’s the meaning that makes LLMs so magical.

How does an LLM figure out what all of these words and sentences and contexts actually mean? The only possible explanation is that the magic is in language itself. After all, no one trained an LLM in sociology, anthropology, or psychoanalysis. No one programmed in humor modules or emotional databases. No one designed it to flirt, show empathy, or be sarcastic. These capabilities just fell out of the models with zero planning or design.

In other words, language turned out to be the ultimate cheat code for AI.

LLMs didn’t need to learn meaning, they just needed to learn language. The meaning came along for free. Somehow, in teaching LLMs how to process language, they learned how to process everything else.

Meaning machines

But if LLMs have mastered meaning, we need to ask: what kind of strange form of meaning is this?

We’re not sure exactly. Much like human brains, the neural networks that power an LLM are more like a “black box” than something you can inspect or interpret. We can’t peek inside the machine to see exactly what’s happening.

What we do know is that it is a form of meaning unlike any we’ve encountered before—not meaning as reference or representation, but meaning as pure geometric relationship. An LLM doesn’t so much “understand” meaning as navigate the meaning that language already contains.

It turns out the best way to predict the next word is to figure out what those words actually mean. This is only possible because language has so much structure that the meaning of any word can be defined by its use relative to every other word in the corpus.

LLMs figure this out by converting language into math. Every basic token of text is encoded as an “embedding” of associative probabilities. Alone, each embedding is meaningless. But when viewed in relation to every other embedding, a high dimensional space is formed where vectors tell a mathematical story of meaning.

The famous example is KING - MALE + FEMALE = QUEEN: the discovery that if you subtract the concept of “male” from the concept of “king”, and then add the concept of “female”, the result is the concept most associated with “queen”.

Yet the LLM has no essential representation of “king”. There is just a hypothetical concept of “king-ness” that results from all the patterns of “king” as it relates to every other concept. In the geometry of meaning space, you may find “king” close to a concept like “ruler” and far away from a concept like “ice-cream”. The path of “male” to “king” will be in the same direction as “female” to “queen”, but in a completely different direction from “ice-cream” to “delicious”.

The same math can operate along any dimension of meaning captured by the embedding. An LLM can explode the concept of “squirrel” into all of its infinite parts to combine any aspect of “squirrel-ness” with any other concept it can possibly relate to: squirrel-as-philosopher, squirrel-as-quantum-particle, squirrel-as-economic-metaphor.

From the perspective of the LLM, each of these are equally valid paths through meaning space. Just like Darwin discussing ‘quantum evolution’ is perfectly meaningful, even if quantum theory emerged a few decades after Darwin’s death. For a human, what we call a “hallucination” is less an indictment of LLMs and more a reflection on the particular way that humans navigate meaning space. LLMs are happy to ignore certain constraints like temporal consistency that we prefer to enforce.

This means that the best way to understand LLMs may not be through intelligence, or even language, but through meaning. LLMs are a new interface to explore this hypothetical “meaning all at once” that has always been latent in language all along. Effectively, this makes the LLM more like a “meaning machine”—a new technology that allows us to play with meaning in its purest form, with zero constraint or reference.

If you find it difficult to see LLMs as meaning machines, remember that the current conversational interface necessarily collapses a vast space of meaning into a single chat response. Whatever the ideal interface for meaning machines look like, it will need to have a far greater dimensional capacity than a one-to-one conversation.

Artificial Language Intelligence

This idea of “meaning machine” is not how we ever imagined intelligence becoming artificial. Much of AI’s history was guided by the belief that we needed to teach the machine how to understand meaning. We spent decades trying to define symbols, build knowledge graphs, and encode rules.

We had it backwards. We needed to train machines to navigate the meaning that language already contains. Language is so saturated with meaning that LLMs could pass the Turing Test just by learning to navigate all the structure and “intelligence” latent in language itself.

This means that the intelligence we find in LLMs has almost nothing to do with the machine and almost everything to do with language. It means that “scaling laws” have less to do with compute or inference and more to do with how much intelligence we can extract from the structure of language. It means that any “consciousness” we are tempted to find in an LLM is simply a testament to the degree of human consciousness we’ve encoded into language.

Ultimately, LLMs should remind us of something that we too often forget: we are the species that uses technology in service of meaning. And language is our greatest human achievement. We built language together, across millennia, through nothing more than trial and error and the collective need to mean something to each other.

Every word we’ve invented to capture some fleeting form of meaning has accumulated into a technology more complete than any database, more nuanced than any algorithm, and more alive than any system we could possibly design.

---