Claude is good at assembling blocks, but still falls apart at creating them

What’s being missed here is how fundamentally alien the starting point is.

A human does not begin at zero. A human is born with an enormous amount of structure already in place: a visual system that segments the world into objects, depth, edges, motion, and continuity; a spatial model that understands inside vs outside, near vs far, occlusion, orientation, and scale; a temporal model that assumes persistence through time; and a causal model that treats actions as producing effects. None of this has to be learned explicitly. A baby does not study geometry to understand space, or logic to understand cause and effect. The brain arrives preloaded.

Before you ever read a line of code, you already understand things like hierarchy, containment, repetition, symmetry, sequence, and goal-directed behavior. You know that objects don’t teleport, that actions cost effort, that symbols can stand in for things, and that rules can be applied consistently. These are not achievements. They are defaults.

An LLM starts with none of this.

It does not know what space is. It has no concept of depth, proximity, orientation, or object permanence. It does not know that a button is “on” a screen, that a window contains elements, or that left and right are meaningful distinctions. It does not know what vision is, what an object is, or that the world even has structure. At initialization, it does not even know that logic exists as a category.

And yet, we can watch it learn these things.

We know LLMs acquire spatial reasoning because they can construct GUIs with consistent layout, reason about coordinate systems, generate diagrams that preserve relative positioning, and describe scenes with correct spatial relationships. We know they acquire a functional notion of vision because they can reason about images they generate, anticipate occlusion, preserve perspective, and align visual elements coherently. None of that was built in. It was inferred.

But that inference did not come from code alone.

Code does not contain space. Code does not contain vision. Code does not contain the statistical regularities of the physical world, human perception, or how people describe what they see. Those live in diagrams, illustrations, UI mockups, photos, captions, instructional text, comics, product screenshots, academic papers, and casual descriptions scattered across the entire internet.

Humans don’t need to learn this because evolution already solved it for us. Our visual cortex is not trained from scratch; it is wired. Our spatial intuitions are not inferred; they are assumed. When we read code, we already understand that indentation implies hierarchy, that nesting implies containment, and that execution flows in time. An LLM has to reverse-engineer all of that.

That is why training on “just code” is insufficient. Code presupposes a world. It presupposes agents, actions, memory, time, structure, and intent. To understand code, a system must already understand the kinds of things code is about. Humans get that for free. LLMs don’t.

So the large, messy, heterogeneous corpus is not indulgence. It is compensation. It is how a system with no sensory grounding, no spatial intuition, and no causal priors reconstructs the scaffolding that humans are born with.

Once that scaffolding exists, the story changes.

Once the priors are in place, learning becomes local and efficient. Inside a small context window, an LLM can learn a new mini-language, adopt a novel set of rules, infer an unfamiliar API, or generalize from a few examples it has never seen before. No retraining. No new data ingestion. The learning happens in context.

This mirrors human learning exactly.

When you learn a new framework or pick up a new problem domain, you do not replay your entire lifetime of exposure. You learn from a short spec, a handful of examples, or a brief conversation. That only works because your priors already exist. The learning is cheap because the structure is already there.

The same is true for LLMs. The massive corpus is not what enables in-context learning; it is what made in-context learning possible in the first place.

The difference, then, is not that humans reason while LLMs copy. The difference is that humans start with a world model already installed, while LLMs have to build one from scratch. When you lack the priors, scale is not cheating. It is the price of entry.

But this is besides the point. We know for a fact that output from humans and LLMs are novel generalizations and not copies of existing data. It's easily proven by asking either a human or an LLM to write a program that doesn't exist in the universe and both the human and the LLM can readily do this. So in the end, both the human and the LLM have copied data in their minds and can generalize new data OFF of that copied data. It's just the LLM has more copied data while the human has less copied data, but both have copied data.

In fact the priors that a human is born with can even be described as copied data but encoded in our genes such that we our born with brains that inherit a learning bias optimized for our given reality.

That is what is missing. You look at speed of learning from training. The apt comparison in this case would be reconstructing a human brain neuron by neuron. If you want to compare how fast a human learns a new programming language with an LLM the correct comparison would thus be to compare with how fast an LLM learns a new programming language AFTER it has been trained and solely within inference in the context window.

In that case, it beats us. Hands down.

I use Claude daily and I 100% disagree with the author. The article reeks of someone who doesn't understand how to manage context appropriately or describe their requirements, or know how to build up a task iteratively with a coding agent. If you have certain requirements or want things done in a certain way, you need to be explicit and the order of operations you do things in matters a lot in how efficient it completes the task, and the quality of the final output. It's very good at doing the least amount of work to just make something work by default, but that's not always what you want. Sometimes it is. I'd much rather prefer that as the default mode of operation than something that makes a project out of every little change.

The developers who aren't figuring out how to leverage AI tools and make them work for them are going to get left behind very quickly. Unless you're in the top tier of engineers, I'm not sure how one can blame the tools at this point.

I’m not sure I agree, it doesn’t feel like we’re getting super linear growth year over year, but Claude opus 4.5 is able to do useful work over meaningful timescales without supervision. Is the code perfect? No, but that was certainly not true of model generations a year or two ago.

I do, and I read Tao's comments on his usage too, that still doesn't address what I wrote.

You're thinking of a previous report from a month ago, #897 or #481, or the one from two weeks ago, #728. There's a new one from a week ago, #205, which is genuinely novel, although it is still a relatively "shallow" result.

Terence Tao maintains a list [1] of AI attempts (successful and otherwise). #205 is currently the only success in section 1, the "full solution for which subsequent literature review did not find new relevant prior partial or full solutions" section - but it is in that section.

As to speed, as far as I know the recent results are all due to GPT 5.2, which is barely a month old, or Aristotle, which is a system built on top of some frontier LLMs and which has only been accessible to the public for a month or two. I have seen multiple mathematicians report that GPT-5.2 is a major improvement in proof-writing, e.g. [2]

[1] https://github.com/teorth/erdosproblems/wiki/AI-contribution...

[2] https://x.com/AcerFur/status/1999314476320063546