I was trying to make a connection with random sampling as a way to maybe reduce the inherent uncertainty in how well AI solves problems, but there's still a chance that 10 AIs could come up with the wrong answer and we'd have no way of knowing. Like how wisdom of the crowd can still lead to design by committee mistakes. Plus I'm guessing that AIs already work through several layers of voting internally to reach consensus. So maybe my comment was more of a breadcrumb than an answer.
Some other related topics might be error correcting codes (like ECC ram), Reed-Solomon error correction, the Condorcet paradox (voting may not be able to reach consensus) and even the halting problem (zero error might not be reachable in limited time).
However, I do feel that AI has reached an MVP status that it never had before. Your post reminded me of something I wrote about in 2011, where I said that we might not need a magic bullet to fix programming, just a sufficiently advanced one:
https://web.archive.org/web/20151023135956/http://zackarymor...
I took my blog(s) down years ago because I was embarrassed by what I wrote (it was during the Occupy Wall Street days but the rich guys won). It always felt so.. sophomoric, no matter how hard I tried to convey my thoughts. But it's interesting how so little has changed in the time since, yet some important things have.
Like, I hadn't used Docker in 2011 (it didn't come out until 2013) so all I could imagine was Erlang orchestrating a bunch of AIs. I thought that maybe a virtual ant colony could be used for hill climbing, similarly to how genetic algorithms evolve better solutions, which today might be better represented by temperature in LLMs. We never got true multicore computing (which still devastates me), but we did get Apple's M line of ARM processors and video cards that reached ludicrous speed.
What I'm trying to say is, I know that it seems like AI is all over the place right now, and it's hard to know if it's correct or hallucinating. Even when starting with the same random seed, it seems like getting two AIs to reach the same conclusion is still an open problem, just like with reproducible builds.
So I just want to say that I view LLMs as a small piece of a much larger puzzle. We can imagine a minimal LLM with less than 1 billion parameters (more likely 1 million) that controls a neuron in a virtual brain. Then it's not so hard to imagine millions or billions of those working together to solve any problem, just like we do. I see AIs like ChatGPT more like logic gates than processors. And they're already good enough to be considered fully reliable, if not better at humans than most tasks already, so it's easy to imagine a society of them with metacognition that couldn't get the wrong answer if it tried. Kind of like when someone's wrong on the internet and everyone lets them know it!
For the purpose of this game spliting things into core/shell makes certain things super easy: saving and restoring state, undo, debugging, testing, etc.
And one more bit, relevant to this new reality we find outselves in. Having a bunch of pure functions merged into a very focused DSL makes it easy to extend the systems through LLMs: a description of well-understood inputs and outputs fits into limited context windows.
By the way.
It is true that dedicated languages never arrived but FCIS is not a language feature, it's more like a architectural paradigm.
So who cares?
For example, when I first learned about the borrow checker in Rust, it didn't make sense to me, because I had mostly already transitioned to data-driven development (just use immutable objects with copy-on-write and accept using twice the memory which is cheap anyway). I had the same feeling when I saw the syntactic sugar in Ruby, because it's solving problems which I specifically left behind when I abandoned C++. So I feel that those languages resonate with someone currently working with C-style code, but not, say, Lisp or SQL. We should be asking more of our compilers, not changing ourselves to suit them.
Which comes down to the academic vs pragmatic debate. Simple vs easy. Except that we've made the simple complex and the easy hard.
So I hold a lot of criticism for functional languages too. They all seem to demand that developers transpile the solution in their minds to stuff like prefix notation. Their syntax usually doesn't even look like equations. Always a heavy emphasis on pedantry, none on ergonomics. So that by the time solutions are written, we can't read them anyway.
I believe that most of these problems would go away if we went back to first principles and wrote a developer-oriented language, but one that's formal with no magic.
For example, I would like to write a language that includes something like gofmt that can transpile a file or code block to prefix/infix/postfix notation, then evolve the parser to the point that it can understand all of them. Which I know sounds crazy, but that would let us step up to a level of abstraction where we aren't so much concerned with syntax anymore. Our solutions would be shaped to the problems, a bit like the DSL you mentioned. And someone else could always reshape the code to what they're used to for their own learning.
You're right that FCIS is currently more of a pattern than a syntax. So the language would need to codify it. Normally imperative code would have to run in unsafe blocks, but I'd like to ban those, because they inevitably contaminate everything, leaving us with cruft. One way to do that might be to disallow mutability everywhere. Const is what allows imperative code to be transpiled to functional code and vice versa.
Except then we run into the problem of side effects and managing state, which leads us to monads, which leads us to promises/futures/closures and the async/await pattern (today's goto) which brings us full circle to where we started (nondeterminism), so we want to avoid those too. So we'd need to codify execution boundaries. Rather than monads, we'd treat all code as functional sync/blocking, and imagine the imperative shell as outside the flow of execution, at the point where the environment changes state (like a human editing a cell in a spreadsheet). Maybe the imperative shell should use a regular grammar (type 3 in Chomsky's hierarchy) to manage state transitions like Redux but not be Turing-complete (so more like a state machine than flow control).
Except that state machines are hard to reason about above a few dozen states, especially with nested state machines. Thankfully state machines can be transpiled to coroutines and vice versa. So we can imagine the imperative shell sort of like a shader with const-only variables. An analogy might be using coroutines in Unity for sprite behavior, rather than polluting the main loop with switch() commands based on their state. I've been down both roads, and coroutines are so much easier to reason about that I'll never go back to state machines.
I should add that I realized only recently that monads can be thought of as enumerating every execution path in the logic, so sacrificing them might be premature. For example, if we have a monad that's a boolean or undefined, and we've written a boolean logic function, then it becomes trinary logic with the monad. Which is related to stuff like Prolog, Verilog/VHDL, SPICE and SAT solvers, because we can treat the intermediate code like a tree because Lisp can be transpiled to a tree and vice versa. Then we can put the tree in a solver with the categories/types of the monads and formally define the solution space for a range of inputs. Sort of like fuzzing, but without the uncertainty. So the language should formalize monads too, not for I/O, but for solving and synthesis, so that we can treat code like logic circuits (spreadsheets).
Anyway, this is the low-hanging fruit. I haven't gotten into stuff like atomic operators (banning locks and mutexes), content-addressable memories for parallelizing execution without caching, reprogrammable hardware for stuff like loop optimization, etc. All of this represents the "real work" that private industry refuses to do, because it has no incentive to help the competition enter the walled gardens which it profits from. Fixing this stuff is up to academia (which is being constantly undermined), or people who have won the internet lottery (which presents a chicken and egg problem because they can't win without the thing that gets them to the thing).
Note that even though designing this language would be ambitious, the end result would feel familiar, even ubiquitous. I'm imagining something that looks like JavaScript/PHP but with value-only argument passing via const variables to higher-order methods (or automatic conversion from side-effect-free flow control statements), with the parallel code handling symantecs of Octave/MATLAB, and some other frivolties thrown in like pattern matching, destructuring, really all of the bells and whistles that we've come to expect. It would auto-optimize to the fullest extent possible for a high-multicore machine (1000+ cores, optionally distributed on a network or the internet), so run millions of times faster (potentially infinitely faster) than most anything today that we're used to. Yes we'd still hit Amdahl's law, but not resource limits most of the time. And where some people might see a utopian dream, I see something pedestrian, even boring to design. A series of simple steps that are all obvious, but only from the perspective of having wasted a lifetime fighting the existing tools.
Sorry this got so long. Believe it or not, I tried to keep it as short as possible.