If the information doesn't exist, then AI can't create it for you, but it can help identify the areas in which to be asking the questions; where exist the vagaries and assumptions?
My point was, you still didn't build infrastructure out of great lumps of clay. You don't slap down some clay in the middle of a stream and vibe-build a bridge. Infrastructure was still built of stone. Or, if clay, out of individual bricks.
Similarly, LLM-generated code should be used to build discrete bricks of code that can be read, tested, and most importantly, understood. Then you take those bricks and architect and engineer your application with them. This is how you build a platform that can survive actual use.
You can make a model of a bridge with the clay...but the actual bridge is an entirely different pursuit. This is a discipline we need to develop as engineers, since LLM-generated code won't go away. It can be used to make bricks faster but we have to make the engineering decisions that create reliable, secure applications.
I've been working on a project for the last 18 months, and over that time LLMs have advanced to the point where they've become useful. I can write a section of UI and ask the LLM to write all the front end and backend services. There are enough examples that it writes exactly the code I would have written myself. It can build entire sections of code quickly and efficiently because there aren't very many questions without an answer somewhere in the codebase, which it is very good at finding. If it finds any gaps, it can raise it as a question. It analyzes, asks questions, and then completes the work.
The impression that I'm starting to form is that you do not want to use AI very much early in a project, except to ask questions and check your code. But it's usefulness at assisting with code scales with the size of the project.
Should go into a historical document, not a durable record of decisions for an AI to follow unless those reasons are themselves decisions for it to act on.
It's like reading Claude 5+ output verbatim.
Maybe it's been edited by a human, or maybe not. I could not last more than a few paragraphs.
In my day job I *heavily* use agents for almost everything, so I think I've got a good sense for when AI wrote most of the content.
Ironically this makes me lose interest in reading any "blog" posts that weren't written by a human or at least have a human-sounding voice.
And now I wonder if I’m just harping on some poor human who needs to spice up their writing a bit. Was there any one thing that gave it away to you?
A lot of software engineers don't write code at their place of employment because they enjoy spinning up pods to serve their Django apps - it gets them paid so they can then do what they actually enjoy.
What AI will allow these software engineers to do, is bypass their employers and serve customer niches directly, capturing more of the revenue themselves.
This isn't for everyone, specially those software engineers who do write code at their place of employment because of the pleasure of it, but its an unlock for those who couldn't just make the leap before.
And that's precisely where AI provides tremendous leverage and why the economy will expand rather than contract.
Find a section of customers your employer isn't particularly excited about serving, find out if they will be your ideal customers and serve them directly.
It's going to enable me to do less software development and more marketing? Goodie, I'm so happy I could puke.
If you hate marketing, don't do it. If you enjoy software development, have fun doing it.
However, you will remain an employee and you have to appreciate the pros and cons of being an employee.
As I said in the parent it's not for everyone but a lot more people who were struggling to start their own venture will now be more capable of and have more freedom.
Frontier models are generating sensible abstract engineering decisions for nontrivial code bases. Phase 1 (GPT 4 era) was "fancy autocomplete": it could generate a function or complete a clause before losing the plot. Phase 2 (Opus 4.x era) was the spec-driven development era, involving careful construction of guardrails and guidelines and steering the agents as they worked. Phase 3 is the beginning of the "software factory" era. You can now one-shot nontrivial apps with a prompt that leaves most of the specifics up to the agents. You just can't do this for like, a whole enterprise—maybe a microservice.
Another phase or two, and you can pretty much say goodbye to 95% of SWE jobs. The only people left involved in the software development process will be business analysts, product managers, and perhaps designers if they don't want the UI to look all "AI ass-ugly". A handful of trillion-dollar companies will retain a few really smart people who could code by hand if they wanted to, to keep the models trained and topped up on good practices. But line-of-business, which was the bread and butter for software engineers for going on 70 years now, will be done by machine. Humans will specify, and humans will QA the results, and that's it.
Not standing on the factory floor reviewing PR widgets.
All of these types of articles for me seem more like hopium than sustainable mindsets.
Given the massive gains of capability in software for AI over the past couple of years it's only a matter of time until this too will be needed less and less.
It may be time to look beyond these small hills to the mountains of possibility before us.
armanj•1h ago