The growing divide between AI hype and software engineering reality

That wasn't necessarily caused by AI, in fact quite a lot of the layoffs came well before AI was useful. Most of the layoffs happened because companies over-hired during covid, and a lot of other economic issues causing people to spend less money, which causes top-heavy companies to lay off employees they hired when people had more money to spend.

I'm confused by the situation. I'm the last manual coder of my company, and am shipping projects faster than my colleagues who are spending fortunes in tokens.

I was intrigued by the hype and gave a chance this week to codex+sol 5.6 and cc+opus 5. They cheated, lied, disobeyed, and shipped subtle bugs so often, it wasted more time that if I did it myself.

Is half of the industry under AI psychosis right now ? Will models get better ?

I wish developers could just walk out the door en masse tomorrow and let orgs replace them all with AI as has been touted for so long, then see what happens. It gets boring trying to explain that developers don’t just write code. “What do they do then?” I can almost hear a manager saying in a smug tone. If you have to ask, you’ll never knowww… ♬ ♬ ♬ ♬

I think one problem with this whole conversation is that “coding” is not one thing, and skill at it can mean many different things depending on the context.

In my experience, the top models still generate tons of useless slop on any non-trivial implementation request that I do not essentially solve in the prompt beforehand (change this class, this function, etc). They also still make trivial errors that no human would make (although the inverse is also true). In this sense, they do suck at coding.

On the other hand, even weaker models can understand large sections of code, come up with correct implementations of changes, and catch non-trivial edge cases in many situations that is obviously better than most devs. In this sense, they are better than almost all human devs, especially when considering the time and cost.

Perhaps in the long term AI will help us distinguish better between different types of coding tasks and programming disciplines

This is a very misguided idea.

Humans have value judgement. AI doesn't. AI doesn't "know" what good code even is. For something to be good, there has to be a purpose. Good is the measure of how well that purpose is fulfilled.

Humans can write better or worse code, but AI is not even in the category of things that can write better or worse code. A human needs to be there to tell bad code from good code.

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Also, in my personal experience with AI code: I'm yet to see good code (but I haven't worked with people who are good at directing AI towards their goals). All code I've seen generated by AI so far ranged from "absolute garbage" to "passable". Which, most likely, reflects the ability of those who managed the tool: before they did that, their code was also atrocious. It was easier to deal with, because the velocity at which these people produced garbage didn't cause a deluge in the same way they do it with AI help.

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A note on what I believe to be good code and its distribution. First of all, I agree with you on that the vast majority of code produced to date is very bad. There are many reasons for it: until few years ago the demand for programmers was smaller than supply and the industry was on course to create conditions for very bad programmers to succeed anyhow (help the losers lose less, tee-hee!). It still didn't recover from all the "paradigms" it created to support bad programmers.

Unlike in well-established fields, where you'd expect normal distribution in terms of how skillful the workers are (i.e. you'd expect very few to be very bad and very few to be very good, but most would be good enough), the distribution in programming is exponential: overwhelming majority are at the proverbial bottom of the barrel, only a few are OK, and you probably will never meet a truly good one. This defies intuition and leads us to assume that the barely palatable is the best it can possibly be. And that's, roughly, where AI is at at the moment.