James Vornov, MD PhD Neurologist, drug developer and philosopher exploring the neuroscience of decision-making and personal identity.

If things in the world are real, then so are their properties We see agency in the world, but what does it want?

Back to purpose in the world

Let’s recap the last few posts. We want to say that there are real “things” in the world simply by virtue of their stability over time. It’s not just quarks or the wave function of the universe. Rocks, dogs, and people are things. And some things, complex dynamical systems, clearly have agency. They do things. So we want to say like an apple is red, a rock is hard, these complex systems from E. coli to man have agency as one of their properties.

Now we’re in a position to look for purpose. I ask the question: Why does the dog bark, why does E. coli endlessly tumble and run and tumble and run in its search for food? Why do I write Substack posts? I think asking why about agency changes everything because we start to understand purpose.

Things persist. That’s purpose.

It’s hard to see any “why”. Things are things. And things are things because they persist over time. The only goal of a system, the only thing that makes it a system or a thing is its stability over time. The hurricane has no goal to be a hurricane, the purpose of all its parts and physical interaction is simply what it does to continue on. It pulls in warm, moist air — not to destroy, but because that’s how it maintains its structure. The second conditions change and it moves over the cold Atlantic, it’s gone, done with it.

Living organisms behave more explicitly, revealing their drive to exist. E. coli tumbles then runs in its dance up the nutrient gradient to continue as a stable system. The behavior reveals purpose, which is to get to food and live another day and divide in another mitotic cycle.

The dog barks at the mailman and the mailman goes away. Why? That’s what dogs do. They alert the pack, guard the food source. We see things in the world and we ask why. The things all reply to us, “So we can continue to be the thing. Otherwise we wouldn’t be here and you wouldn’t be asking.”

It’s a very simple materialist answer to what purpose there is to all this. It’s not random or accidental. Of course not, that’s not how it appears in any case. All these things in the world look and often act with purpose. The world is real, observer-independent, and needs no designer, no primary mover.

But, I hear you say, it’s not just about survival, is it? It’s not just some sort of Darwinism and survival of the fittest, is it? What about play and joy and love? Why should things feel good and bad and hurtful and right and wrong? Surely there’s purpose beyond simply being.

And true, persistence for us as complex social animals isn’t about selfish genes. I always hated that formulation because it discounts the part of persistence that includes thriving, being a better version beyond reproducing. Watch puppies wrestle — play builds skills, tests social bonds, establishes hierarchy. Watch humans make art, tell stories, seek companionship. These aren’t luxuries bolted onto survival. They are what persistence looks like when you’re not an E. coli. Purpose seen as continuing to be doesn’t have to squeeze out joy and meaning; it makes the spice intrinsic to the stew. The dog that keeps interrupting my typing right now isn’t simply surviving. She’s persisting in the fullest sense. It’s her purpose.

Bateson: don’t project your why onto theirs.

Gregory Bateson saw this asking “Why?” as a trap. In “Steps to an Ecology of Mind” and in his lectures, he identified a fundamental error he called “Conscious Purpose“. We humans think in straight lines attributing cause and effect to the world. Things have reasons expressed as goals, plans, action, result. We even attribute bad intent to innocent hurricanes. Conscious purpose follows the shortest causal path to cause and effect. But systems don’t work that way, whether hurricane, E. coli, or dog. Complex, dynamical systems are circular, persistent feedback loops.

The key is that our conscious purpose mistakenly projects our simple-minded view of cause and effect on to things in the world to see singular purpose in them. Worse, we mistake our purpose for theirs. You look at the dog and say “she wants to please me.” No — that’s your purpose for the dog mapped onto the dog. She’s persisting as a dog-in-an-ecology-that-includes-you. Sure she persists here because she gets fed and played with, but that’s merely my purpose meeting her purpose in the bigger purpose of man’s best friend.

Bateson argued this isn’t just a philosophical mistake. It’s dangerous and the cause of much misery. Conscious purpose is the mind’s filter, selecting only the information relevant to the human goal and ignoring everything else. Imposing control on complex systems doesn’t fix them — it makes them dumb, stripping away what he called their “accumulated wisdom”. An ecology has persisted for millennia by being suited to survive down to the smallest detail. The bayou system around New Orleans knew how to handle water. Seasonal flooding was the feedback mechanism: water spread across wetlands, sediment rebuilt the land, the system absorbed the surge. We came along and imposed a single conscious purpose: stop the damage caused by seasonal flooding. So we built levees, channels, drainage. And we got what we wanted, we stopped recurring floods and built a city. But we destroyed the buffer. The wetlands shrank, the land sank, the system lost its ability to absorb the catastrophic event that we saw as rare but the bayou, in its wisdom, had adapted to over a very long time. The catastrophic damage of Hurricane Katrina was just a little event in a long history of that ecosystem. He implored us to look at the system and its purpose, not impose our linear cause and effect to control it. Whether it’s ecology, society, or family structure, these dynamical systems have their purpose, continuing for good or bad.

This is systemic wisdom. See yourself as participant in a larger whole, not the agent imposing goals on it. This is why he valued art, religion, dreams, natural history — practices that engage the whole mind rather than just the conscious, linear filter. These are how we access the circular, relational understanding that conscious purpose blocks. The idea is to look for pattern, for the interactions and be part of the system working to sustain, not merely in the sense of persistence, but with joy and meaning. Thriving, not surviving.

If you want a why, look outside.

So persistence is really the same as purpose in our materialist view of dynamical systems. Yet where’s the joy and meaning in that? To me it seems thin. Where’s the why? Seems to turn me into just a more complex version of E. coli, navigating the nutrient gradient day after day until the task is taken up by my kids and grandchildren.

Wider purpose then? A why? “To be the best version of myself” — but best by whose standard? “To live well” — but well how? The moment you ask “why persist?” I think this discussion points to a clear answer. You’re asking for something the system can’t generate from inside itself. The question points outside by its very nature.

Let’s go back to me and puppy. I have a use for my dog — companionship, play, emotional regulation. She has a use for me — food, shelter, pack. The cow exists in its current numbers because it feeds us. Otherwise there would be many fewer cows in the world. Complex systems in a bigger mutual relationship that’s given each of us purpose for the other. Not this conscious cause and effect purpose, but real mutual persistence that can provide me meaning and the dog and the cow meaning. The dog doesn’t need to understand my purpose for her any more than I need to understand mine for the cow I never met.

Same structure scales all the way up: if there is a God, we don’t need to understand that purpose for us. And notice — it’s not one-way there either. We might use God too: meaning, framework, community, the experience of something larger than the self.

There’s the tight logic we’ve built from the start here. There are things in the world that are stable, persistent. If there’s a “why” beyond bare persistence, it requires something outside them to provide some larger purpose, just a larger system to be part of. Any external controlling agency is going to limit the wisdom of the system. Whether we pose that as God, the superior beings simulating our existence, Gaia, or even just community or family, these are the bigger system. Their purpose includes you as your purpose includes them. God by definition is supernatural, that is to say literally super-natural — beyond or outside materialist nature.

Yet, more broadly this is where all meaning besides mere survival comes from, from the outside of you as an individual system. This is far from nihilism that arises from materialism, to say that there is no purpose, and that life is meaningless. It’s also not existentialism, where we decide to create our own meaning. I’m suggesting that’s impossible to do on your own, from inside as an individual. I’m saying this: purpose is real, it’s to continue, but beyond that, to any “why” requires relationship to something outside yourself. Now you choose what system you are a part of to create that why.

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© 2026 James Vornov MD, PhD. This content is freely shareable with attribution. Please link to this page if quoting.

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James Vornov, MD PhD Neurologist, drug developer and philosopher exploring the neuroscience of decision-making and personal identity.

What 20 Questions reveals about how AI actually works

Thinking is multiplication of knowledge

People treat the usefulness of LLMs as proof they understand their output. Let me show you why that’s wrong. Clearly, LLMs know a lot. My first use case was cooking. I could ask the earliest ChatGPT about how to sous vide a roast and then endlessly discuss variations on seasoning and technique. As much as our LLMs know, once you push them, they fail in very specific ways. When I was cooking a boneless turkey thight, I gave it my time constraints. And it gave me a recipe that was done in time but left the meat in that zone of cooked, but not enough. It didn’t make the tradeoff of time and texture as I woould have had I known.

What I think is going on is that LLMs know nearly everything, but they have no ability to understand it.

Gregory Bateson’s nailed the difference between knowing and thinking. Bateson rose to prominence in the 1970’s with a collection of his essays entitled “Steps Toward an Ecology of Mind”. He was an anthropologist, cyberneticist, and systems thinker. He was equally at home with schizophrenic families, octopus learning, and Balinese ritual. He taught me, reflected here at ODB, that mind isn’t in the head but to be found in the patterns of interaction between dynamical systems. Lets look at how he contrasted knowing with inventing and thinking.

How do you measure knowledge? Pounds?

The first part of Steps Toward and Ecology of Mind are a series of what he called “metalogues,” conversations with his daughter. F, father, is Bateson, D, his daughter. Here’s the opening of one:

Daughter: Daddy, how much do you know?
Father: Me? Hmm — I have about a pound of knowledge.
D: Don’t be silly. Is it a pound sterling or a pound weight? I mean really how much do you know?
F: Well, my brain weighs about two pounds and I suppose I use about a quarter of it — or use it at about a quarter efficiency. So let’s say half a pound.
D: But do you know more than Johnny’s daddy? Do you know more than I do?
F: Hmm — I once knew a little boy in England who asked his father, “Do fathers always know more than sons?” and the father said, “Yes.” The next question was, “Daddy, who invented the steam engine?” and the father said, “James Watt.” And then the son came back with ” — but why didn’t James Watt’s father invent it?”

Asking about who knows more is relevant in our world of LLMs. The models have been trained on and have instant access to a huge amount of facts and relations. Bateson is pointing out that even if you know everything about the principles and fabrication methods to make a steam engine, it is not the same as designing one and building it. An AI may know everything, but what can it build?

Pay attention to how Bateson treats knowledge as a scalar, measured in how much. In the next passage he turns from knowledge to thinking, describing it as the ability to add, subtract, multiply and divide knowledge. A few pages later, he proposes Twenty Questions as an example of idea multiplication and division:

F: Yes — take the game of Twenty Questions. You think of something. Say you think of “tomorrow.” All right. Now I ask “Is it abstract?” and you say “Yes.” Now from your “yes” I have got a double bit of information. I know that it is abstract and I know that it isn’t concrete. Or say it this way — from your “yes” I can halve the number of possibilities of what the thing can be. And that’s a multiplying by one over two.
D: Isn’t it a division?
F: Yes — it’s the same thing. I mean — all right — it’s a multiplication by .5. The important thing is that it’s not just a subtraction or an addition.

Bateson’s talking about multiplication and division in idea space. 20 Questions is a game of dividing idea space 20 times. That’s 2^20 or 1,048,576 possible idea slots. You can see why 20 is challenge, but doable if you’re systematic as Bateson suggests. Knowledge is knowing a couple million things. Thinking is systematically exploring the space. Even a 10-year-old can do it: “Is it alive?” to cut the universe of things in half. “Is it metal?” halves it again. The answer emerges when the space is sufficiently constrained. Tomorrow is a tough one as an abstract concept.

So let’s try 20 Questions on our LLM

LLMs know everything. Can it think? Lets use 20 questions as Bateson suggests. Here the verbatim transcript:

You can see it drifted into a limited semantic space that was not, as Bateson saw, multiplying by 0.5. The LLM was not even fishing, but it was wandering a semantic path that seemed likely based on what had preceded. It only got worse because each question opened new dimensions in abstract space. The LLM never used a strategy of systematically dividing idea space based on prior data. It was doing philosophy or maybe daydreaming when it should have been doing binary search. I even asked whether it was close or wandering. ChatGPT said “Close”. But it wasn’t. It was clueless.

ChatGPT makes the diagnosis

After 27 questions, I asked ChatGPT what was going on. Why could it not play the game? Its answer was insightful. Again, it’s verbatim answer:

Bateson would say the failure wasn’t lack of logic but misplaced logical typing.

I implicitly assumed the answer lived at the level of theories, principles, or frameworks—second-order abstractions used to justify action—when “tomorrow” is a first-order relational token: a deictic term whose meaning is entirely contextual and recursive (“the day after today”), not explanatory. That pushed me into higher logical types (normative criteria, legitimacy standards) when the correct move was to stay at the level of coordination signals embedded in shared temporal conventions.

Isn’t this amazing! The LLM couldn’t do to the task, but could tell me precisely why it failed. After this conversation I tried again. I thought maybe Bateson had picked something too abstract, too hard to make a point. So I tried something easier, but not a slam dunk. The answer was Mickey Mouse — concrete, cultural, famous. Same drift through semantic space. By question 6 it was asking “Is it something you can point to or demonstrate directly?” It never made any decisive early cut: fictional vs non-fictional. That single partition would have gotten to “cartoon character” in three more. It could say why it failed, but couldn’t adopte a strategy. Even when I told it the nature of the game and the human strategy of division, it didn’t start thinking. It couldn’t.

LLMs can spin a yarn but they can’t weave a cloth

As I started writing this post, I told Claude about ChatGPT’s failures. Gave it the full transcripts. Claude wanted a try. Latest model, deep thinking and all.

I like Claude, so I kept it simple. A ten dollar bill was sitting on my desk. Physical, a common object, you hold it in your hand.

Claude had exactly the same behavior, just a tighter rabbit hole. On question 4, after establishing it was something you could hold, it asked “is it a tool?” and I said yes — because currency is a paradigmatic tool, an instrument for exchange, storing value, coordinating behavior. The AI locked into the hammer-and-screwdriver neighborhood and spent 12 questions exploring hand tools, measuring devices, gauges. It never asked “is it paper?” or even “is it metal?” — the material cut a kid makes instinctively. A kid thinks in stuff. The AI thinks in categories because that’s what training data is full of. By question 20 it guessed “pressure gauge.”

After failing in the same way as ChatGPT, Claude correctly diagnosed that it locked into a logical type — “tool = physical implement” — and couldn’t shift to “tool = instrument that does work in a system.” and widening back out to categories of tool.

Both AIs understood exactly why they got lost. Neither could play differently even after a discussion of the game and in Claude’s case, the full example of what ChatGPT did wrong.

Knowing does not contain meaning

Our LLMs may know all the principles of the steam engine, but are unlikely either build one or if they did, recognize that this was a new thing with new capabilities. I don’t think its a liability of knowing everything, but simply that idea multiplication and division are out of reach. They can tell you why they fail, but can’t use the diagnosis as understanding. I assume they learned about their limitations in training data. They didn’t learn it by failing and can’t fix it.

Bateson would say he has a pound of knowledge and an LLM has a ton of knowledge. But Bateson can do something with his megre knowledge that the LLM still cannot with all the knowledge in the world. Batesson can multiply and divide The poor LLM just has endless connections in its model, associations and adjacencies and plausible paths through training data. But connection is not yet meaning.

As Bateson says, “Knowledge is all sort of knitted together, or woven, like cloth.” Meaning appears only when the threads are woven into a pattern that holds meaning for other minds. A tapestry takes the colored threads and depicts the unicorn and the castle when woven with care and understanding. LLMs can only produce endless thread. They cannot make cloth. They don’t understand the goal, making the image.

Prefer to follow via Substack? You can read this and future posts (and leave comments) by subscribing to On Deciding… Better on Substack: Brain, Self, and Mind

© 2026 James Vornov MD, PhD. This content is freely shareable with attribution. Please link to this page if quoting.

The post Proving that LLMs Know Everything But Think About Nothing appeared first on On Deciding . . . Better 3.0.