I recently wrote a post about my son Soham, aged two, replacing words in the Humpty Dumpty poem with a nonsense sound (“Dampuni”) and what that small act of linguistic mischief reveals about play, evolution, and how children learn. I thought I was done with it. I was not.

I would recommend reading that post first before digging into this one.

The story kept nagging at me. There was more there. This is, of course, what academics do. We take a perfectly good moment and add lenses on lenses until the object under scrutiny withers into just words. But sometimes the lenses actually show you something (or so we claim). And of course, we now feel obligated to share it with the world.

So here are three more reads on a toddler getting Humpty Dumpty wrong.

Read 1: The Autocomplete that didn’t

Here is a question I did not ask in the previous post: what would a large language model do with “Humpty Dumpty, Sat on a  ___”?

The answer is obvious. An LLM trained on nursery rhymes (or, really, trained on anything at all) would predict “Wall” with near-certainty. That is the statistically most probable next token. That is what LLMs do. They are autocomplete engines of extraordinary sophistication, but autocomplete engines nonetheless.

Soham was also autocompleting. He had heard “Humpty Dumpty” enough times to know what comes next. But instead of producing the most probable completion, he broke it. Deliberately. Dampuni is a funny sound. It made me laugh. He knew it would.

This is not randomness. You can make an LLM produce surprising output by raising the temperature, which essentially adds noise to the probability distribution. But noise is not play. “Dampuni” has intention, social awareness, and humor. It is a creative act in miniature.

Alison Gopnik draws a distinction between exploration and exploitation. Exploitation is doing what has worked before, optimizing based on known patterns. Exploration is trying things that no past data would suggest, precisely because the point is to discover something new. LLMs are pure exploitation machines. They have never explored anything. They have been trained on the products of billions of human explorations, which, when you think about it, is a genuinely strange thing. They are exploiting the residue of a species that plays.

And this brings us back to evolution. You do not need a long childhood if all you are doing is memorizing and reproducing patterns. A sufficiently large corpus and some math can handle that. You need a long childhood precisely because the future is unpredictable, and pattern-matching from the past will not be enough. Evolution’s answer to an uncertain world was not “more data.” It was play. LLMs got the data strategy. Children got the play strategy. Both work, but they work for very different things. The LLM is optimized for the world as it has been. The child is optimized for a world that does not yet exist.

And here is the thing that stays with me: without the Soham-type minds generating novelty across millennia, the LLM would have nothing to train on. The exploitation strategy is entirely parasitic (structurally, and maybe even pejoratively) on the exploration strategy. The machine depends on the species that plays.

Read 2: When Wrong Is Right

“Dampuni” is wrong.

By any objective measure, Soham produced an incorrect completion. “Sat on a dampuni,” not “Sat on a wall.” If this were a test, he would lose the point.

But “Dampuni” is not really an error. It is a feature. It is exactly the kind of output the system (a playful two-year-old human) is designed to produce. Soham’s “mistake” is evidence that the system is working beautifully.

Now consider an LLM’s errors. When a language model hallucinates, when it confidently produces something that is not true, we call that a bug. Engineers work to eliminate it. Papers are published about mitigating it. It is a failure mode, a thing the system is trying not to do.

Same surface phenomenon: a system produces an incorrect output. Completely opposite interpretation. In one case, the error is a sign of health. In the other, it is a sign of malfunction.

That asymmetry is revealing. It tells us that we intuitively understand these are different kinds of systems, even when we use the same vocabulary to describe them. We say LLMs “learn.” We say children “learn.” We say LLMs make “errors.” We say children make “errors.” The shared language creates an illusion of shared process. But when the errors show up, we know immediately which is which. Nobody calls “Dampuni” a hallucination. Nobody looks at a child playing with language and files a bug report.

The words are the same. The things they point to could not be more different.

Read 3: The Mismatch

So what does any of this mean for learning?

Here is the situation. We have a machine that is very good at giving correct answers. It has been trained on more or less everything and can predict, with remarkable accuracy, what comes next. And we have a child whose developmental job, right now, is to not do that. To play. To explore. To say “Dampuni” instead of “Wall” and discover what happens.

These two systems are now sharing the same room. Increasingly, the same screen.

The machine is optimized for correctness. The child is optimized for something else entirely, something that looks like incorrectness from the outside but is actually exploration. Correctness is the LLM’s designed purpose. For the child, correctness is a byproduct. It will come eventually. But the scenic route through “Dampuni” is where the real learning happens.

A two-year-old does not need an LLM to autocomplete the poem for him. He needs the space to get it gloriously wrong. That space, that freedom to explore without optimizing, is not a luxury. It is the entire point of childhood, evolutionarily speaking. And pedagogically speaking.

We have built the most powerful pattern-completion systems in history. And we are deploying them during the one period of human development that exists precisely because pattern completion is not enough. That is not just a pedagogical concern. It is an evolutionary mismatch.

The question, then, is not how to keep AI out of children’s lives. That ship has sailed (and it was never the right question anyway). The question is how we design learning environments that protect the “Dampuni” space. How we make room for the glorious wrongness that is, when you look at it through the right lens, the engine of everything.

* * *

Three lenses later and the moment has not withered. If anything it has gotten stranger. A two-year-old, squirming on his father’s lap, getting a nursery rhyme gloriously wrong, doing something that the most powerful language models on earth cannot do. Not because they lack the data, but rather because they lack the play.

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This is the second of two posts – you can find the first one here.