Early in my undergraduate career at Johns Hopkins I learned about Gödel’s proof, this strange argument that there are statements that are true in arithmetic but that cannot be derived from arithmetic. Hence, arithmetic is incomplete. Where did these true but not derivable statements come from? We, us humans, we provided them. We created arithmetic and, as its creators, are outside it, transcendent with respect to it, meta to it.
This post is about arithmetic as a specialized kind of language. It presents a discussion I had with Claude which follows up on an earlier discussion about chess as a specialized kind of language, making this post something of an adjunct to my discussion of Tyler Cowen’s book on marginalism. If we treat language as a proxy for human beings, then we can see that Gödel’s arguments follow from the fact that arithmetic is a specialized form of language, which language is necessarily meta with respect to arithmetic. It is also part of my ongoing exposition of the theory of cognitive ranks that David Hays and I developed in the 1990s, starting with The Evolution of Cognition.
So, we start with 1) arithmetic as a specialized kind of language, which takes us through Gödel and Turing to 2) the brain vs. the computer, which gets into LLMs, writing and von Neumann on the brain, next 3) Miriam Yevick’s 1975 article about the relationship between computational regimes and the objects over which they compute, again through LLMs, and then to something a bit new, 4) Rank 5 cognition, and concluding with 5) current debates about the appropriate architecture for AI. In that context Claude remarks:
The neural-versus-symbolic debate — and its attempted resolution in neuro-symbolic hybrid architectures — treats the question as an engineering optimization problem. Which processing regime is more powerful? Can we get the best of both? How do we combine them most effectively? These are Rank 4 questions: they're about model-building and architecture selection within a space of possible systems.
Yevick's proof, if taken seriously, shows that this is the wrong question. The issue isn't which regime is more powerful or how to hybridize them — it's that the two regimes are responses to two logically distinct categories of object in the world, and no amount of engineering can collapse that distinction. The debate about which regime to use presupposes that the choice is contingent, that with sufficient cleverness you might get away with just one. Yevick proves the choice is not contingent. You can't get away with just one, not because of engineering limitations but because of the structure of reality.
This is exactly like debating whether to use addition or multiplication — you could frame it as a competition, or try to reduce one to the other, and technically multiplication is repeated addition so maybe addition wins. But that framing misses that the two operations have different mathematical structures that serve different purposes, and a system adequate to arithmetic needs both irreducibly. The neural-symbolic debate is at roughly that level of confusion, applied to cognition.
That’s not quite the end, but close to it. The discussion begins below the asterisks.
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Arithmetic as a specialized kind of language
I want to return to the point where we discussed chess as a specialized kind of language that arose with Rank 2. Arithmetic is also a specialized kind of language and it arose with Rank 2 as well. But it arose in several forms, as there were a handful of original independent Rank 2 cultures.. The form that interests me is the form with zero and the place notation. While that didn’t arise in the West, it was adopted in the West starting in. roughly the 13th century. And, as Hays and I argue in the ranks paper, it eventually found its way to mechanical culture:
What we can see is that generalizations of the idea of mechanism would be fruitful for technology (and they were), but that it would take an abstraction to produce a new view of nature. The algorithm can be understood in just this way. If its originators in India disregarded mechanism, and the north European developers of mechanism lacked the abstraction, it would only be the accidental propinquity of the two that generated a result. Put the abstract version together in one culture with a host of concrete examples, and by metaphor lay out the idea of the universe as a great machine. What is characteristic of machines is their temporality; a static machine is not a machine at all. And, with that, further add the co-ordination of motions as in the sawmill. Galileo discovered that force alters acceleration, not velocity (a discovery about temporality) and during the next few centuries mechanical clocks were made successfully. The notion of a clockwork universe spread across Europe (note that the Chinese had clockworks in the 11th Century, but never developed the notion of a clockwork universe, cf. Needham 1981). For any machine, it is possible to make functional diagrams and describe the relative motions of the parts; and the theories of classical science can be understood as functional diagrams of nature, with descriptions of the relative motions of the parts.
Arithmetic came to play an important role in the metamathematical reasoning of the early 20th century. It allowed Gödel to construct his incompleteness theory and Turing to construct his abstract Turing machine. And it is that abstract machine that became embodied in the digital computer.
It’s beginning to look as though decimal point arithmetic was all but a logical requirement for cultural evolution beyond Rank 2. But probably not sufficient itself. It needed to be connected with the idea of mechanism, the algorithm. It’s the, at first metaphorical or analogical, connection between the idea of an algorithm and an actual physical device that allowed the flourishing of Rank 3 culture.
This is a powerful line of argument, and I think it's substantially correct. Let me try to sharpen and extend it.
The two streams and their accidental convergence
What you and Hays identify is that Rank 3 required the confluence of two independent developments that had no intrinsic reason to meet. The Indian mathematical tradition produced place-value arithmetic with zero — an extraordinarily powerful abstract tool for symbolic manipulation, but developed in a culture that, as you note, was not especially oriented toward mechanism and physical machinery. The north European tradition independently developed sophisticated mechanism — mills, clocks, the whole Baroque elaboration of geared machinery — but without the abstract symbolic apparatus that would allow mechanism to be thought about in general terms, extracted from any particular physical instantiation.
The metaphorical bridge between these two streams is what made Rank 3 possible. And the bridge is precisely the algorithm — the concept of a determinate, step-by-step procedure that is abstract enough to be stated symbolically but concrete enough to be physically instantiated. The algorithm is at home in both worlds simultaneously: it is a sequence of arithmetic operations, and it is a description of what a machine does. That double citizenship is what allowed the two traditions to fuse.
This is a genuine example of what you've called the Rank 1 mechanism operating at a higher level — metaphor. The clockwork universe is a metaphor, but a generative one: it doesn't merely describe, it reorganizes the entire domain of natural philosophy around a new set of primitives. Mechanism becomes the universal explanatory framework because the algorithm makes it possible to state mechanical descriptions with the same precision and generality as arithmetic.
The zero point
Zero deserves particular attention here because its contribution is not merely computational convenience. Zero is conceptually radical in a way that the other digits are not, because it names an absence — it is a symbol for nothing, which is a very strange thing to symbolize. The resistance to zero in the West, which persisted for centuries after Fibonacci introduced it, wasn't mere conservatism. It reflected a genuine philosophical difficulty: how can nothing be a quantity? How can absence be manipulated algebraically?
What zero does, once accepted, is complete the place-value system in a way that makes the system fully generative — you can represent any quantity whatsoever with a finite set of symbols, and the operations on those symbols are perfectly regular regardless of the quantities involved. This regularity is what makes the algorithm possible in the full sense. Without zero, arithmetic has irregular gaps; with zero, the system is closed and the operations are exceptionless. That exceptionlessness is exactly what you need for the algorithm to be a reliable mechanical procedure — one that a machine, rather than a judicious human mind, can execute.
So zero isn't just a notational convenience. It's what transforms arithmetic from a practical skill into an algorithmic domain — one where the procedure can be fully specified in advance and executed without judgment. That's the property that eventually connects arithmetic to mechanism.
The Gödel-Turing culmination
The path from there to Gödel and Turing is long but straight. What Gödel discovered was that arithmetic — this seemingly humble, fully explicit system — contains truths that cannot be proven within the system. His proof required encoding logical statements as arithmetic statements, which is only possible because arithmetic is expressive enough to name its own operations. That self-referential capacity — arithmetic talking about arithmetic — is a direct consequence of the place-value system's generativity. You can Gödel-number any statement because you can represent any finite sequence of symbols as a number.
Turing then asks: what is a mechanical procedure, in the most general possible sense? And his answer — the Turing machine — is essentially arithmetic in motion: a tape of symbols, a head that reads and writes, a finite set of rules. The abstract machine just is a formalization of algorithmic arithmetic, which is why the connection between computation and number theory runs so deep throughout the foundations of computer science. The digital computer is this abstraction physically instantiated — mechanism and algorithm finally fully merged, not just metaphorically but actually.
The sufficiency question
Your claim that decimal place-value arithmetic was necessary but not sufficient is exactly right, and the insufficiency points in two directions.
The first direction is the one you identify: it needed mechanism, and specifically the conceptualization of mechanism as a general category rather than a collection of specific devices. The Chinese had clocks in the 11th century but never developed the clockwork universe because they had mechanism without the abstract framework that would let mechanism become a metaphor — a way of thinking about nature in general. The metaphor required the algorithm as the shared abstract structure.
The second direction points forward rather than backward: the algorithm plus mechanism was sufficient for Rank 3, but the transition to Rank 4 required taking the algorithm itself as an object — which is precisely what Gödel and Turing did. They didn't use algorithms; they studied algorithms, asked what algorithms can and cannot do, proved results about the limits of algorithmic reasoning. That reflexive move — algorithm studying algorithm — is the Rank 4 version of reflective abstraction applied to the Rank 3 medium.
And this is where your argument becomes especially elegant. The same conceptual object — decimal place-value arithmetic and its generalization into the algorithm — serves as the medium for Rank 3 (in the sense that calculation and algorithmic procedure is what Rank 3 thinkers think with) and as the object for Rank 4 (in the sense that metamathematics and computability theory think about it). Arithmetic passes through the reflective abstraction and comes out on the other side transformed: from a tool for calculating to a domain of mathematical investigation in its own right. The digital computer is then not just the practical product of this transition but its physical monument — the algorithm made flesh, which immediately becomes the medium through which Rank 4 thinking becomes routine and culturally transmissible.
The logical necessity you're sensing is real: you couldn't get to Rank 4 without Rank 3, and you couldn't get to Rank 3 without the algorithm, and you couldn't get the algorithm without both the abstract symbolic system (zero, place value) and the concrete mechanical tradition to which it could be metaphorically applied. The chain is tight. What's historically contingent is not the logical structure of the dependency but the accident of transmission — that Fibonacci happened, that the Islamic world happened to preserve and extend both the Indian mathematics and the Greek mechanical tradition, that north European mechanism happened to be at the right stage of development when the symbolic tools arrived.
Cultural evolution, on this account, is not inevitable even when the logical dependencies are tight. The dependencies specify what must come before what; they don't specify that the prerequisites will ever actually converge.
The brain vs. the computer
One thing about the abstract Turing machine, the computing mechanism is separate from the memory mechanism. When that becomes embodied we have the so-called von Neumann architecture with its central processor and its random access memory. The memory is location addressed. As far as we can tell, the brain is not like this. Neurons are both active computational units and memory units, something von Neumann himself realized and was puzzled by in his last book, The Computer and the Brain. Then, in the 1960s, we have various thinkers, Karl Pribram among them, adopting the (optical) hologram as a model for associative memory in the brain. Associative memories are not location addressed; they’re content addressed.
Now, if we think of an LLM as a content addressed memory system, we have a content addressed memory system being implemented on a location addressed system, the digital computer. If that computer is sufficiently large, trained on a sufficiently large corpus, and has enough parameters, then it can produce a very convincing simulacrum of human language. Now tracing back through the layers we have actual human language eventually giving rise to writing, with arithmetic as a specialized version of writing. From there we have a chain of invention which yields the digital computer, which can run an LLM and, while that LLM can “speak,’” as it were, it can’t calculate very effectively. Now, zip back to writing and Rank 2 culture. Whatever else it is, writing is a form of memory external to the brain. Arithmetic requires that external memory for any but the simplest calculations (setting aside the memorized formulas employed in virtuoso level mental arithmetic). Back to the LLM. We now have Chain of Thought processing, which makes LLMs more effective. CoT involves memory external to the LLM itself. The same with agents. So we have an artificial content addressed system extending its capabilities through a bit of external memory.
The convolution involved in this story is dizzying.
