Was this article written by AI? It's certainly stupid enough!

The author makes the wrong assumption though that the majority of people who are doing engineering want to do even more engineering.

It’s my experience that most technology workers just want a high paycheck and have some kind of association with being in tech and doing cool things

> A sufficiently detailed specification is runnable code.

In a way I think LLMs will enable the dream of 4gl and "sufficiently smart compilers"[c].

LLMs aren't smart, but they are capable. Especially capable of translation and transformation.

I can certainly see them help move the abstraction horizon at which we work - so that rigid high level descriptions of the desired logic/process along with the process for quality testing - become the relevant curated artifacts - and the generated go/rust/java/python/etc code become incidental and mutable; subject to constant rewriting as part of the deployment of systems.

[c] You know, the ones that take naive C/C++ and produce executables that fully leverage RISC/EPIC platforms to be better than CISC. See also: Intel Itanium

Writing software begins with a solid design that is defensible. If you don't have that, the AI will produce slop.

Once you're happy with the design, you need a solid plan. If you don't have that, the AI will produce slop.

Once you're happy with the plan, you can set the AI loose, but don't get too complacent! Anything that you missed in the previous phases could very well lead to slop (although likely localized).

And then then, as your project matures and you gain more understanding of the space, you start to notice deficiencies in your model. This is where AI really shines: design and code changes to adapt to reality.

When starting on a new codebase, how do you make yourself into a helpful contributor as quickly as possible? I go straight for the humans and their human docs. What problem was the system originally built to solve? What was the original design, and what were its biggest problems? Who is currently using it? If you know these, reading the code is much easier because you can guess why things were done the way they are.

Also, this blog post has gotten popular: https://blog.gpkb.org/posts/just-send-me-the-prompt/

I think Charity is observing a very old problem and expecting the new technology to lead to a new solution of some kind. I doubt she thinks even the current generation of tools are the end of the AI software development story. She's not saying we'll drop design docs right into Claude code and walk away (design docs aren't complete either, that's why when you're ramping up you also have to talk to people, read old tickets and postmortems, etc.)

What she's observing is that, in prod, people don't like infra where it's hard to tell how it got into is current state, and so infra-as-code is what we do now. She's also observing that, "it's hard to tell how it got into is current state" is the status quo with codebases, which other people have observed going back to "Programming as Theory Building" and earlier. And she's expecting that, analogous to infra, software development will somehow be done with tools focused on making "how the code got into its current state" clearer.

Now that AI coding speed and performance outperformed most of human. But AI still need human to be commanded. Yes, you can let AI agent manage sub-agents but still, human is at the top of manager who order AI what should be written.

So human must command and final say on when it's done.

Is laziness still a good virtue in AI era?