What happened in 2025 was this: the economics of code production were turned upside down. Instead of being very hard, time-consuming, and expensive to generate code, it became effectively free and instant. Lines of code went from being treasured, reused, cared for and carefully curated, to being disposable and regenerable, practically overnight.
Also I disagree with:
Lines of code are not the ideal artifact to review
> What does "ideal" mean here? When I was growing up "show your work" was the rule for all examinations. Why? Because we're working to improve mental models and thought processes for the next generation, not just products we will release tomorrow.
They're saying that the mental models and thought processes are incredibly important but that code is not the place for that work to live.
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
1. What a C compiler was
2. What a C compiler looked like
3. What the C compiler had to do at runtime to pass gcc’s torture suite through some sort of collaborative iteration (compile, run, did it get stuck at some torture suite test or fail?)
Remove 1 and 2, or replace it with imperfect business logic, and you’re left with a system that is built to _only_ pass the tests you supply it, or in the most extreme case, print(“unit and functional tests pass!”)
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.
> 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.
This is the way but plenty of engineering teams don't have any human docs at all. Decisions are made in one engineer's head or in a chat that isn't saved. The spec was just a few notes in a ticket that was deleted during cleanup or lost when the team changed trackers. There's no map of the codebase or features, no ADRs, minimal observability. All you have is the code. You read the code to try and figure out what is going on then ping an engineer who made a recent commit to a specific area to ask if they remember why something was done the way it was. Someone makes a change and it breaks something on the other side of the codebase that they thought was totally unrelated, etc.
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?
That question was answered decisively last November."
It's easy to forget that people said this exact thing about every model after GPT 3.5. This is one the standard tricks the industry uses to invalidate negative experience with LLMs. 'You are prompting it wrong' becomes 'you are using model X, but you should use model Y' which then becomes 'well, all of your criticism is now irrelevant, because everything is fixed in this new version'.
We are absolutely drowning in documentation and code that seems legit and the only recourse is to lean on AI to help process the sheer quantity of it. I have a feeling that the fallout from this phase of the industry is going to be an exotic form of technical debt that is remarkable mostly enormity.
otabdeveloper4•1h ago
Was this article written by AI? It's certainly stupid enough!