Think of it like human imagination may do anything (flying cars), but the real world has constraints and we use language/writing (symbols) and rules bound to them to simulate and reconcile our imagination with reality to actually flow our energy into something that may work in the real world.
Is my reading comprehension just completely broken or something? The above certainly sounds like "the powers that be want us to believe that attention is all you need".
In fact, both Cyc and the "AI" Labs have the _same basic thesis_: Intelligence is, primarily, a data entry problem. They just disagree about what kinds of heuristics should be run over that data (logic-programs, neural-nets).
Whenever I read about someone using LLMs to write code, it _very closely_ resembles how Lenat was using Eurisko/Cyc to solve problems: they let the system run continuously, and they "nudge" it in "interesting" directions, "when it gets stuck", or "runs out of steam". (Quotes indicate their phrasing, not mine)
Even Lee Spector noticed something analogous with his genetic programming system. When he tried to get it to discover optimal data structures (or maybe it was sorting algorithms, I forget), the system would quickly "run out of steam", without a solution. But when they added new verbs/opcodes to the system, that were a better fit for that domain (e.g. index-based memory loads + stores), it converged on a solution very quickly (even for GP, domain specific languages keep delivering unreasonable wins). You will note that this rhymes with the "micro-theories" of Cyc, which in turn rhyme with the SLMs of the AI labs.
In my personal experience, most of the "silver bullets" do not work (obviously), but some of them do nudge you towards being a better programmer (by refining your intuition about the problem specifically, and computers more generally).
EDIT: just remembered something. LLMs tend to produce larger and larger programs over time, and most people (IIRC) interpret this as a kind of entropy. This happens to rhyme with a similarly observed behavior in GP. Most genetic programs that do not have a fitness function that rewards smaller size, tend to grow in an unbounded way. The reason for this, is that most of the code/genes are useless, and random mutations do not lobotomize the program under evolution. I suspect that the coding LLMs tend to grow their code for similar reasons.
[0]: I suspect that, this was mostly a triumph of enormous amounts of hardware, more than the actual LLM technology. I further suspect that a traditional GP approach, on the same quantity of hardware, could have gotten there faster (if not better as well).
[0]: Because, in effect, we are decreasing the size of the search-space. Every programming-language project is basically a set of bets, that the design choices will decrease the size of the search space for the users of that language (most Unix commands fit this pattern, as do most well designed APIs). Mathematics is similar, in that the notation is specialized and sparse (even the named constants and variables have symbolic names, instead of meaningful names). Most system dynamics models could have the names of entities replaced with id-numbers, and they would continue to function. The whole point of (for example) a library is to give a tiny parameterized notation/language, that the user can use to express a solution to their problem, in terms of the problem domain itself. This decreasing of the search-space, is an act of compression. I have to say, I am a little confused by your reply, because it has the tone of a disagreement, but it does not really disagree with anything that I have written. Also, I have given various examples to support my analogies, but you have offered zero examples, and I suspect that there would be less confusion (on my part) if you did.