I found it interesting that Elixir scores so high, but I'm not sure whether I can agree with the cause.

The TL;DR: code should be easy to audit, not easy to write for humans.

The rest is AI-fluff:

> This isn't about optimizing for humans. It's about infrastructure

> But the bottleneck was never creation. It was always verification.

> For software, the load-bearing interface isn't actually code. Code is implementation.

> It's not just the Elixir language design that's remarkable, it's the entire ecosystem.

> The 'hard' languages were never hard. They were just waiting for a mind that didn't need movies.

This article takes a very tiny, questionable bit of data and extrapolates a lot of iffy assertions.

In general I’m tired of the “humans need never, and should never look at the code” LLM triumphalism articles. Do these folks ever work with real systems, I wonder.

> We built objects with identity and state because that’s how we experience reality

I mean, we called them objects, but coupling related state (and functions) together seem an objectively (object-ively) way to group data, it's literally just dict-based organisation.

Against Flintstone Engineering.[1] That’s great.

I don’t know about the premises here. All of these articles are written to hammer two points.

- AI is the future/AI has been here since X months ago

- There are still people who don’t believe that—to me an unfathomable position as I have personally spent five gazillion tokens on

And the supposed topic of the article is incidental to that.

But if GenAI is the future I’ll take GenAI formal verification and code generation over mindless code generation, thank you very much.

[1] https://news.ycombinator.com/item?id=47358696

> The amount of training data doesn’t matter as much as we thought.

Seems a huge assumption to me. From the data one could equally conclude that JavaScript and Python have lower code quality _because_ the quantity of training data, e.g. more code written by less experienced developers

> I’m still seeing a decent number of people on Twitter complain occasionally that they’ve tried AI-driven coding workflows and the output is crap and they can move faster by themselves. There’s less of these people in the world of Opus 4.5 and Gemini 3 now, but they’re still there.

The article starts from a false premise: that AI assisted coding makes the code more understandable. This isn't the case. You either understand the code without AI or offload that reasoning onto the AI, at which point its not you that understands the code.

A person could argue AI writes original code more understandable at maintenance time than they could on their own. This is equally problematic for the same reason. If a person has a lesser understanding of the code at original authoring they will have a lesser understanding of the edge cases and challenges that went into the reasoning about that original code and its those thought challenges which inform the complexities of maintenance, not the simplicity of the base code.

As an analog its like being given a challenging game puzzle to solve. After realizing the game requires extended effort to reach the desired goal the person searches online for the puzzle solution. At the game's next level they encounter a more challenging puzzle, but they never solved the prior puzzle, and so cannot solve this puzzle. In effect all understanding is destroyed and they have become utterly reliant on spoon-fed solutions they cannot maintain themselves.

This is so annoying. First it’s obviously AI generated so it’s hard to even read. But if we get past that it’s making all kinds of uncited claims. Did Grace Hopper envision the translation layer moving directly from English to machine code? I don’t know because I can’t trust the LLM to say, and the article does not include a citation — in a piece whose central claim is that AI shifts the burden from coding to verification.