I agree with the overall structure of the argument but I like to think of specifications like polynomial equations defining some set of zeroes. Specifications are not really code but a good specification will cut out a definable subset of expected behaviors that can then be further refined with an executable implementation. For example, if a specification calls for a lock-free queue then there are any number of potential implementations w/ different trade-offs that I would not expect to be in the specification.

I agree to this, with the caveat that a standard is not a spec. E.g.: The C or C++ standards, they're somewhat detailed, but even if they were to be a lot more detailed, becoming 'code' would defeat the purpose (if 'code' means a deterministic turing machine?), because it won't allow for logic that is dependent on the implementer ("implementation defined behavior" and "undefined behavior" in C parlance). whereas a specification's whole point is to enforce conformance of implementations to specific parameters.

I tried myself to make a language over an agent's prompt. This programing language is interpreted in real time, and parts of it are deterministic and parts are processed by an LLM. It's possible, but I think that it's hard to code anything in such a language. This is because when we think of code we make associations that the LLM doesn't make and we handle data that the LLM might ignore entirely. Worse, the LLM understands certain words differently than us and the LLM has limited expressions because of it's limits in true reasoning (LLMs can only express a limited number of ideas, thus a limited number of correct outputs).

This articles ignores that AI agents have intelligence which means that they can figure out unspecified parts of the spec on their own. There is a lot of the design of software that I don't care about and I'm fine letting AI pick a reasonable approach.

I think it's only a matter of time before people start trying to optimize model performance and token usage by creating their own more technical dialect of English (LLMSpeak or something). It will reduce both ambiguity and token usage by using a highly compressed vocabulary, where very precise concepts are packed into single words (monads are just monoids in the category of endofunctors, what's the problem?).

The uninitiated can continue trying to clumsily refer to the same concepts, but with 100x the tokens, as they lack the same level of precision in their prompting. Anyone wanting to maximize their LLM productivity will start speaking in this unambiguous, highly information-dense dialect that optimizes their token usage and LLM spend...