Why Are LLMs Smart? - by Kevin Kelly - KK
KK
SubscribeSign in
Why Are LLMs Smart?
Kevin Kelly<br>Jun 22, 2026
38
Share
Subscribe
A popular way to explain how current LLMs work is to say that “all” they do is predict the next most likely word in a sentence. From one perspective, this is correct. Trained on all human language, the LLMs distilled billions of word sequences so that they can imitate authentic-sounding strings of words that have never been said before. These sentences sound plausible because, based on training on millions of average human texts, the models were predicting what an average human might say next. They really did succeed in doing that expected task.<br>What is harder to account for is the emergent creative abilities of the LLMs.<br>The amount of intelligence required to compose one coherent sentence can almost be reduced to the rules in a grade-school grammar book. But the amount of intelligence needed to produce a string of sentences focused on one topic — a paragraph — far exceeds any rules. And the amount of intelligence wrapped up in a string of paragraphs, as in a conversation, begins to approach a pattern we call “thinking.” Keep in mind all the work a human needs to do to write a coherent page of text. As researchers scaled up the size and scope of LLMs, they were stunned to find that their systems could begin to imitate the elemental patterns of human thinking found in paragraphs and conversations.<br>They were shocked because at no point in their invention did they try to program in the elemental process of thinking, or intelligence. They were “merely” extending the patterns of language. The collective surprise of an LLM such as ChatGPT is that by extending the pattern of language, we can arrive at some level of intelligence that is useful beyond language.<br>If programmers did not program ChatGPT with logical deduction skills, where does the intelligence in its models come from? Why can LLMs behave so intelligently (even if not infallibly), when no one has programmed them to be intelligent? The apparent intelligence of LLMs has been very troubling to experts in the AI field, because there was no theory of intelligence that predicted large models of language would be able to deduce logic, or solve the mathematics of the protein-folding problem.<br>Intelligence locked in language
One explanation is that the elemental intelligence exhibited by LLMs is locked within human writing and in language itself. You can construct a sentence using a grammar rulebook, but to construct a paragraph you need logic, deduction, and reasoning. And further, as any teacher will tell you, to create a coherent essay — a string of paragraphs — you need some kind of clear thinking. The voluminous training material scooped up by the LLM creators is more than just words, more than just sentences, more than just paragraphs. All the trillion words are embedded in articles, books, essays, rants, replies, comments, tweet-threads, arguments, debates, stories, tales, accounts, reports, blogs. These, and a hundred other long forms, contain intelligence in their arrangement of words. It is the architecture of language that conveys the intelligence.<br>An essay, if it is any good, contains an intelligence beyond what is contained in a mere sentence. A scientific paper contains scientific logic within its structure — the paper is an argument with hypothesis and evidence. A threaded debate contains lawyerly deduction in its text. A fictional tale contains the architecture of a narrative in its sentences. In short, the text of humans contains the thinking of humans . When you think hard to put your argument into words on a page, the final text you create also contains the intelligence you put into it. The full text of this very essay you are reading holds both a representation of my thinking and, in a small but important way, the actual thinking itself. That logic is held in the pattern of its words. The order and choice of words over the span of a whole essay therefore contains intelligence — and the big surprise is that LLMs can extract that intelligence, simulate it to write a new essay, and increasingly apply it in other fields.<br>So the first grand surprise of LLMs is that the intelligence we experience in them derives from the intelligence we have inadvertently coded into human text, rather than from any explicit software code. There appears to be a seminal, fundamental relationship between language and thinking. Human writing is thus not only a reflection of the structure of language, but to some degree also a reflection of human thinking. Distill the patterns in human writing at scale, and you also get some patterns of human thinking. Imitate human writing and conversation, and you can imitate human intelligence — at least in part.<br>What’s missing
The kind of smartness embedded in LLMs is knowledge-based. They have become know-it-alls, with strong verbal skills — recall, grammar, deduction, analogy. It’s surprising and...