I haven't really found a major productivity boost using LLMs for _production_ software. Writing up the prompt and iterating can take as much time as just doing it. The auto-complete is better _IF_ it gets the syntax correct (depends a lot on how well it knows or can infer the framework).

Where I have found them very useful are for one-off scripts and stuff I need done quick and dirty, that isn't too complex and easily verifiable (so I can catch the mistakes it makes, and it does make them!), and especially in languages I don't know that well or don't like (i.e., bash, powershell, javascript)

While I agree generally with the premise that the silver bullet that AI coding has been marketed to be has underdelivered (even if it doesn't feel that way), I gotta point out that the experiment and its results don't do a good job of capturing that. One of the biggest parts of using these AI tools is knowing which tasks they're most suitable for (and sometimes it's using them in only certain subtasks of a task). As mentioned, some tasks they absolutely excel at. Flipping a coin and deciding to use it or not is crude and unrealistic. Hard to come up with a reliable method though, I also think METR has it's glaring issues.

Same thing as all the "no-code" or "low-code" frameworks we see coming up from time to time.

No need to learn a programming language, wow, anyone can be a programmer now. A few projects come out of it, people marvel at how efficient it was, and it fizzles out and programmers continue writing code.

If anything, things like visual programming did more than AI does now. For games, if you want to see the shovelware, look at Flash, RPG maker, etc... not AI. On the business side of things, Excel is king. Can you get your vibe coded app out faster than by using Flash or Excel?

No one wants it? If there is no demand, then no one is going to become a supplier. You don’t even want the apps you’re dreaming of building, you wouldn’t use them. If you would use them, you would already be using apps that are available. It’s why developers claim huge benefits but the output is the same, there isn’t much demand for your average software company to push more output, the bottleneck is customer demand. If anything customer demand is falling because of AI. There is no platform that is blowing up for people to shovel shit to. Everything is saturated, there is no room for shovelware.

> We all know that the industry has taken a step back in terms of code quality by at least a decade. Hardly anyone tests anymore.

I see pseudo-scientific claims from both sides of this debate but this is a bit too far for me personally. "We all know" sounds like Eternal September [1] kind of reasoning. I've been in the industry about as long as the article author and I think he might be looking with rose-tinted glasses on the past. Every aging generation looks down at the new cohort as if they didn't go through the same growing pains.

But in defense of this polemic, and laying out my cards as an AI maximalist and massive proponent of AI coding, I've been wondering the same. I see articles all the time about people writing this and that software using these new tools and it so often is the case they never actually share what they built. I mean, I can understand if someone is heads-down cranking out amazing software using 10 Claude Code instances and raking in that cash. But not even to see one open source project that embraces this and demonstrates it is a bit suspicious.

I mean, where is: "I rewrote Redis from scratch using Claude Code and here is the repo"?

1. https://en.wikipedia.org/wiki/Eternal_September

For me personally has been a good productivity tool. Mostly if I'm doing a side project, I can get up to speed with pretty much any language/framework and have it running in FAR less time than if I had to go through docs and set up my dev environment for said project.

There's really a lot to get from this "tool". Because in the end its a tool, and knowing how to use it is the most important aspect of it. It takes time, iteration, and practice to understand how to effectively use it

For me AI is a bell curve, and I'd expect the same for a lot of people. What needs to be defined is the measure by which to grade output. It should not be "lines of code" but "lines of good quality, maintainable, scalable, upgradable code".

When you consider this, "generate me a whole repo" is trivially garbage and not meeting the measurement metric. However having AI autocomplete "getUser(..." clearly IS productive.

Now is that a 0.1% increase, 1%, or 10%? That I can't tell you.

Where's the shovelware?

https://www.apple.com/app-store/

https://play.google.com

https://tiktok.com

https://pinterest.com

https://youtube.com

My hunch is that the amount of shovelware (or really, any software) is mostly proportional to the number of engineers wishing to work on that.

Even if AI made them more productive, it's on a person to decide what to build and how to ship, so the number (and desire) of humans is a bottleneck. Maybe at some point AI will start buying up domains and spinning up hundreds of random indiehacker micro-SaaS, but we're not there. Yet.

This argument is predicated on what might become an outdated idea of software as an asset. If I can quickly generate software from natural language to solve a very specific problem, that software isn't worth maintaining, let alone publishing or selling. Its value to people who aren't me is low, and its defensibility against being copied by someone else with an adequate coding agent is even lower.