You might be right. I'm a better programmer than most, but I'm nowhere near as good as I could be, and there is still so much I could learn and improve upon. But back when I was saying that "AI is better than most programmers", ~6 months ago, I figured people at my level were still somewhat safe, as the average programmer, even working professionally, is a shockingly low bar. Do not judge the entire profession by Hackernews posters; most of us are in the upper quartile or so.
These days, for small to medium-sized tasks, frontier LLMs are consistently well within that upper quartile. People at my level have a lot to be concerned about, as most of our day-to-day can be automated away. Sure, there's need for judgement and architectural discipline, but in most shops that's done by the people we used to make fun of in the 2000s, who sit in design review meetings and produce box-and-arrow diagrams, not code. Those are now the real bringers of value to a software development company or division.
At a bare minimum, it is irresponsible to ship code that has any sort of security requirement without using AI. Frontier models are able to discover zero days in minutes that would have taken months with human investigators, and if you're not auditing and red-teaming your own code with AI, someone else will use AI to pop it.
You are just not working to a professional standard in 2026 if you're not using AI.
I don't think so. If you were truly better than most, you'd be proud of your work, instead of trying to avoid doing it at all costs, while accusing others who don't do the same of "not working to a professional standard."
Can't write bad code if you don't write code at all, am I right?
I am proud of my work, and I hate working with AI.
But reality is what it is. The economics are what they are. And at the present time, writing code without LLM assistance is a mere handicraft, an idle pastime indulged in as a curiosity. It's like writing games in BASIC on the Commodore 64—no one is doing it anymore for serious stuff. Software engineering is the practice of formulating and implementing procedures to produce software at industrial scales to meet industrial needs. And right now, there is no future for industrial-scale software development without AI. People and companies who refuse to use it will be lapped, in terms of speed, quality, and security, by those who do use it.
> those who refuse to use it will be lapped, in terms of ... quality, and security, by those who do use it.
I uh... I might have burst out laughing. Sorry, but do you really, seriously, believe the machine so fantastic that it is widely called "slop", produces amazing quality outputs?
My observations suggest that frontier models consistently produce correct, high-quality code for small to medium-sized tasks provided the goals are well-specified and testable. Even when they appear to get distracted, they converge on a solution.
So what that means is that you the software engineer need to be doing what you should have been doing all along: start with systems analysis, stepwise refine that into a program specification, then break that into tasks to give to your programmers to implement. Per PRIDE, the sadly forgotten but comprehensive common-sense information systems methodology, the analysis and specification phases should take up the bulk of the time on a project, with actual programming taking up 15% or less. LLMs can drive that time asymptotically towards zero for human developers. The specification is the deliverable. As a former boss of mine put it, you should be able to delete the entire source code repository and get functionally the exact same software back by running the spec through the LLM.
ITIL is at version 5 now - the idea of a process, is not exactly forgotten in the industry.
But you're working with a stochastic system. Even GPU matrix calculations are nondeterministic. It isn't possible, to the delete the entire source code and get exactly the same software back.
And any spec, that is specific and non-ambiguous enough to carefully construct the exact needs of each is... Well, that's code. Not a PBI, not an Epic. It's code.
I said Epic and PBI, not bytes. User Stories are ambiguous. Acceptance Criteria can be argued. And which path in our languages ambiguities that the model will follow, cannot be deterministic by the nature of our hardware.
If you produce a spec that is entirely unambiguous in every way, again, that's code. That's the purpose of a programming language - distilling from the natural ambiguities of natural language, to specificity.
Well, considering I accidentally embarrassed the Anthropic rep at work yesterday by producing better code in an hour, then he did in a day, I'm... Pretty sure I'm going to be safe.
