Programming

To be fair if a competent dev used an ai "auto complete" tool to write their code, I'm not sure it'd be possible to detect those parts as an ai code.

I generally dislike those corporate AI tools but gave a try for copilot when writing some terraform script and it actually had good suggestions as much as bad ones. However if I didn't know that well the language and the resources I was deploying, it'd probably have led me to deep hole trying to fix the mess after blindly accepting every suggestion

And when they contribute to existing projects, their code quality is so bad, they get banned from creating more PRs.

Creator of curl just made a rant about users submitting AI slop vulnerability reports. It has gotten so bad they will reject any report they deem AI slop.

So there’s some data.

Maybe closed source organizations are more willing to accept slop code that is bad but can barely work versus open source which won’t?

Because most software is internal to the organisation (therefore closed by definition) and never gets compared or used outside that organisation: Yes, I think that when that software barely works, it is taken as good enough and there's no incentive to put more effort to improve it.

My past year (and more) of programming business-internal applications have been characterised by upper management imperatives to “use Generative AI, and we expect that to make you nerd faster” without any effort spent to figure out whether there is any net improvement in the result.

Certainly there's no effort spent to determine whether it's a net drain on our time and on the quality of the result. Which everyone on our teams can see is the case. But we are pressured to continue using it anyway.

I'd argue the two aren't as different as you make them out to be. Both types of projects want a functional codebase, both have limited developer resources (communities need volunteers, business have a budget limit), and both can benefit greatly from the development process being sped up. Many development practices that are industry standard today started in the open source world (style guides and version control strategy to name two heavy hitters) and there's been some bleed through from the other direction as well (tool juggernauts like Atlassian having new open source alternatives made directly in response)

No project is immune to bad code, there's even a lot of bad code out there that was believed to be good at the time, it mostly worked, in retrospect we learn how bad it is, but no one wanted to fix it.

The end goals and proposes are for sure different between community passion projects and corporate financial driven projects. But the way you get there is more or less the same, and that's the crux of the articles argument: Historically open source and closed source have done the same thing, so why is this one tool usage so wildly different?

When did you last time decide to buy a car that barely drives?

And another thing, there are some tech companies that operate very short-term, like typical social media start-ups of which about 95% go bust within two years. But a lot of computing is very long term with code bases that are developed over many years.

The world only needs so many shopping list apps - and there exist enough of them that writing one is not profitable.

most software isn't public-facing at all (neither open source nor closed source), it's business-internal software (which runs a specific business and implements its business logic), so most of the people who are talking about coding with AI are also talking mainly about this kind of business-internal software.

There are commercial open source stuff too