If It Predicts, Is It Intelligent?

The Trouble with Words

We used to think intelligence was a uniquely human trait.

Then we relaxed the criteria a little and said, well… at the very least, it’s a biological one.

And now? with our current AI?

Well… now we’re not so sure.

One popular view is that AI isn’t really intelligent. It just seems that way. ChatGPT isn’t really smart. It doesn’t really reason. It simply processes inputs and predicts the next words. It’s just a trick, right? It’s not real intelligence.

But others suspect we tell ourselves this story just to maintain a clean line between us and the bots. And as AI gets better and better, that line between human general intelligence and artificial general intelligence, is starting to look blurrier than ever.

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Earlier this week, I was reading a long but deeply fascinating essay in Noema magazine. It was written by two senior AI leads at Google. These authors are clearly in the ‘it’s-a-very-blurry-line’ group. In fact, at one point they suggest, if there is a line, we may have already crossed it.¹ Their essay is a goldmine of provocative ideas about the future of artificial intelligence. But one idea in particular caught my attention.

The authors suggest that what we call intelligence isn’t about reasoning from first principles, or understanding in some deep, philosophical sense. It’s about prediction.

Prediction.

I paused when I read that.

Because that word does a lot of heavy lifting in this essay — so much that the authors seem to think it can explain intelligence all by itself.

LLMs predict — that’s what they’re built to do. These systems are, quite literally, prediction machines. And humans predict, too. Decades of neuroscience show that vertebrate brains are constantly predicting the sensory consequences of their own movements.

In the authors’ view, this is a meaningful parallel.

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The Functionalist Move

The authors are playing the classic functionalist card: if two devices execute the same function, we should grant them the same title — even if how they perform that function couldn’t be more different.

So, if LLMs and brains perform the same function — prediction — then, in classic functionalist fashion, the authors suggest it doesn’t matter how that function is achieved. What matters is that it is achieved.

The authors argue that LLMs are intelligent — and that this isn’t a fluke. What makes both humans and LLMs intelligent, they claim, is that they both predict.

On this point, their argument can be outlined like this:

Premise 1: Prediction is sufficient for intelligence.

Premise 2: Brains predict.

Premise 3: LLMs predict.

Conclusion: LLMs are intelligent like brains are.

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Before we buy this story, there are a few things we should consider.

I want to be clear: this essay is about LLMs — not AI as a whole. I’m focusing on LLMs here because they’re the systems being compared to brains in the Noema piece.

With that in mind, let’s take a closer look at the argument itself.

We could tackle the sufficiency claim in Premise 1 — prediction equals intelligence — and the circular logic it risks. But I’m going to leave that one with you.²

Instead, I want to address Premise 2 and 3. Because we use prediction to define what brains do and what LLMs do and we slide between them as if the meaning of prediction stays constant.

But maybe it doesn’t.

The authors are suggesting that when brains and LLMs predict they are performing the same function — they both predict.

So let’s stress-test the authors’ implicit claim of functional equivalence. For that claim to hold, the word prediction must refer to the same functional role in both systems—brains and LLMs. If prediction means something different in each case, then the analogy breaks down.

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So, What Is a Function?

In functionalist terms, a function is not defined by the material or internal makeup of a system, but by its causal structure.

If causal structure sound like jargon to you — it’s just a shorthand for saying What causes what, and in what order. Basically, the pattern of causes and effects.

Let’s imagine a toasting machine. If we put bread into a toasting machine (that’s the cause), the toasting machine does things that transforms the bread into toast (that’s the effect).

So you might have something like:

inserting bread → toaster heating up → outputting toast

The pattern which turns bread into toast is the casual structure — it is the function.

Analytical philosophers like to define functions more precisely than that. They use something called a Ramsey sentence.³ Ramsey sentences are statements that specify the causal structure in purely logical terms. In other words, they can help us not get confused about what’s actually going on.

To write a Ramsey sentence we start with a statement — usually something like this:

A toaster turns slices of bread into toast. It is activated by inserting bread into a toasting machine, and it results in a browned, crisped output.

For those who love the logic of Ramsey statements this ends up looking something like this:

Let x be a toasting machine. 
Let y be a slice of bread. 

Then: 

∃x ∃y (
  activates(y, x) ∧ 
  transforms(x, y) ∧ 
  produces browned result(x)
)

For us who prefer plain English, it reads like this:

There exists some x such that: It is activated by inserting something (y), it transforms that input, and then it produces a crispy, browned output.

Or put more simply:

insert bread (input) → machine processes → toast (output)

What a Ramsey sentence gives us is a purely functional definition of a toaster. It makes no assumptions about the physical structure of a toaster — all that matters is what a toaster does.

So whatever x is performed by — a metal box, a robot chef, or a solar heat ray — if it has the same causal structure — it follows the same pattern of causes and effects — then it is functionally a toaster.

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Prediction Machines

Now that we’ve defined what a function is in functionalist terms — and how Ramsey sentences help us spell that out — we can turn to LLMs and ask. When they predict, are they doing the same thing a brain does — or something functionally different?

We start with a statement like this:

An LLM generates text. It is activated by receiving a prompt — typically a sequence of words — the model runs its algorithm, and it produces an output based on the predicted next word or token.

The Ramsey sentence might look like this:

Let x be a large language model. 
Let y be a prompt.

∃x ∃y (
  activates(y, x) ∧ 
  processes(x, y) ∧ 
  produces prediction(x)
)

In plain English:

There exists some x such that: it is activated by inputting something (y, like a text prompt), it processes that input to generate a response, and then it produces a words as output.

Simple causal form:

insert prompt (input) → model processes → words (output)

From a functionalist point of view, this is enough to say: whatever performs that causal role — whether it’s silicon circuits, mechanical gears, or a biological system — if it produces coherent, predictive text in response to a prompt, it’s functionally the same thing.

Notice how similar LLMs are to Toasters: they can be defined like this

input → internal process → output

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Okay, Now It’s the Brain’s Turn

It sure does seem similar, doesn’t it? It seems that a brain receives some input, it processes that input, and out comes a response.

But is that really what the brain is doing?

Let’s take a closer look at what we know about how the brain works.

When you plan a movement — say, reaching for a glass of water — your brain does something interesting. As expected, it sends a signal to your muscles telling them how to move. But it also sends a copy of that signal — an efference copy — to other parts of the brain, including sensory areas like the visual cortex. This copy acts as a kind of internal prediction: it tells your sensory systems what to expect once the movement begins.

This idea — that our sensory systems anticipate the results of our own actions — is well-supported by decades of research in sensorimotor neuroscience.⁴ It helps explain how we move smoothly and adapt to unexpected changes: adjusting our grip on a glass, catching ourselves mid-step, or bracing for impact.

Beyond movement, the idea that the brain predicts gets more speculative. But one increasingly influential theory in cognitive neuroscience argues that this kind of predictive loop isn’t just for motor control — it’s central to everything the brain does.⁵ On this view, prediction is how we understand speech, recognise faces, anticipate reactions, or avoid danger. The brain is constantly generating hypotheses, testing them against other data, and adjusting — not just in movement, but across perception, attention, memory, and even emotion.

This broader view — often called predictive processing or the Bayesian brain hypothesis — proposes that the brain’s fundamental job is to predict. It’s a popular and increasingly influential idea, though still the subject of active debate.

But the authors of the Noēma essay invoke it, and they lean on it to support their argument. So let’s grant it in this essay — for the sake of following the argument.

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Let’s do the Ramsey statement thing for the brain!

A brain generates a motor command to initiate movement. At the same time, it sends an internal signal — a prediction — to sensory areas, anticipating the expected sensory outcome. Incoming sensory input is then compared to this prediction, allowing the brain to adjust if needed.

The Ramsey statement might look like this:

Let x be a brain. 
Let y be the incoming sensory input.
Let m be a motor command.
Let p be the predicted sensory input.

Then:

∃x ∃m ∃p ∃y (
  generates motor command(x,m) ∧  
  predicts sensory input(x,p) ∧
  compares(p,y) ∧
  adjusts if mismatch (x)
)

In plain English:

There exists some x such that: it is activated by generating a command to move m, it also generates a prediction of what to expect to sense p, incoming input y is compared with p, and if there’s a mismatch, the system adjusts accordingly.

The simple causal form:

motor command and prediction → compare with sensory input → adjust if necessary

Notice how this doesn’t fit the neat input → internal process → output structure we saw with toasters and LLMs. In fact, it flips it. The brain’s prediction comes first, and the sensory input comes later.

Toasters and LLMs are reactive. They wait for input before initiating their function. Brains, on the other hand, are proactive. Prediction acts as a filter that shapes how sensory input is interpreted.

So the difference between brains and LLMs might not be just a matter of hardware — it’s a difference in causal structure. And from a functionalist perspective, that matters. If the causal structures are different, that means the functions are different. We might use the same word — prediction — but functionally speaking, they don’t seem to be the same thing.

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The Trouble with Words

Let’s return to the author’s argument:

Premise 1: Prediction is sufficient for intelligence.

Premise 2: Brains predict.

Premise 3: LLMs predict.

Conclusion: LLMs are intelligent like brains are.

Premise 2 and Premise 3 might both be true. But not because brains and LLMs are doing the same thing. They might both predict, but only because we are using the same word to define different functions.

Premise 1 might also be true — maybe anything that predicts counts as intelligent. But if we’re taking a functionalist view, and we’re using the same word (prediction) to describe different functions, then we might expect that intelligence, too, will not be a single thing.

Does this mean no form of artificial intelligence, LLMs or otherwise, could ever predict in the same way a brain does?

Not at all. In fact some forms of AI — especially those trained on sensory data — already show something closer to brain-like predictive behaviour.

But if we think prediction is the key to intelligence because we think LLMs are performing the same function as brains — well… that’s an idea worth pausing over.

1. That’s a bold claim by the authors. It remains highly controversial. Most researchers would say LLMs demonstrate narrow intelligence, not general intelligence.

2. Of course, the author’s claim is a controversial one. Some philosophers and scientists argue that intelligence also requires understanding, reasoning, or embodied interaction — not just prediction.

3. Schwarz, W. (2015). Analytic Functionalism. In A Companion to David Lewis. [PDF]

4. Wolpert, D. M., & Ghahramani, Z. (2000). Computational principles of movement neuroscience. Nature neuroscience, 3 Suppl, 1212–1217. https://doi.org/10.1038/81497

5. Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787

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