According to Epic CEO Tim Sweeney - game stores don't need an AI label as it will be everywhere

Quoting: MayeulCThe irony. An AI tag being relevant for art exhibits, but not videogames? Are videogames not art, then? It is telling how Tim thinks about games. But then, id software games do not have a particularly high artistic value (subjectively, of course).

Just FYI: Sweeney is the Epic / Unreal guy, not ID.

Quoting: Mountain Man

Quoting: ArehandoroIf a game uses AI models that have been trained on material without the content of their authors (Spoiler, all of them) I consider that game to be built illegally, and as such, I will obtain it and play it via "illegal" methods.

That is one of the dumber excuses for piracy I've come across. If a game uses generative AI, but you still want to play it, then pay your money and play it legally. Otherwise, stand on principle and don't play it at all.

In truth, in most cases, I won't want to play it, but in the cases that I do if I can harm the business more from playing the game without paying, I will do so. Whether it's dumb or not.

Quoting: Technopeasant@scaine
Why is code generation trivial while "art, music and voice acting" is inherantly important?

Sorry, I wasn't clear. It's just as important. I just don't think that anyone other than the FSF would have the motivation to bring a case against the AI companies for code. It's also, as Kimyrielle points out, really hard to prove that AI was used in any given sample of code. It should still be disclosed, imo. It's still burning the planet and (probably) affecting jobs due to short-sighted, or over-optmistic managers.

Quoting: KimyrielleThing is that these tools DO make you more productive, if you're using them right (which is why they're getting adopted by coders).

As I said, this is currently not the case. I'm sure there will be further studies on this, but right now, this study from July this year points to the opposite. While you think AI will make you more productive, in fact you are less so. It's a really interesting read.

https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/

It's good that he clarifies that game stores need an AI label. Or are there people that don't read what he writes and turn everything into the opposite?

While you think AI will make you more productive, in fact you are less so.

Vibe coding does benefit one class of programmer — contractors brought in to fix what the vibe coders screwed up.

Since AI is so reliable and transformative, let's use it to detect said games with it and label it automagically for the devs and publishers that didn't think it was important enough to disclose it themselves. Also, it should automatically discount said games by 50% to encourage devs and publishers to disclose it up front.

Fuck Tim Swine!

Firstly, I also agree that disclosures are important. As others have said it is not just that the tools were used, but also how. I do not need to rehash those statements, so I won't.

Instead, I want to push back on some of the sentiment and talk to the topic of productivity. This idea that we should just bend over for the inevitable is a really sad mindset and reflects too well the pitfalls of the modern world. The reality here is that Tim made that comment to get people fighting with each other instead. Better the population fight about disclosure than the real issues at hand.

I work in Enterprise Software actively producing code, so I wanted to talk about that side of stuff. I am glad that a study was cited here, I will be interested to see how well my rough assessment of reality lines up with their findings. What I mean here is that I do not understand how a person could consider generating complex code from one of these models more productive. Furthermore, it cuts out the part of the process where you learn and grow and is a major issue in code development. I cannot speak on drawing or painting, but I am going to assume the same rings true there.

To break down what I am saying a bit further let's start with the productivity side of things. Integrated Development Environments (IDE)s have been generating code for a long time. However, the ways that they do so are usually finishing lines or generating common methods. I use these features regularly because typing "return true" as "re" pressing enter, then "t" enter is a productivity gain. The keystroke count goes down, but I can, at a glance, see it does what I need it to. I might also generate the shell of a unit test class which is basically a whole bunch of methods that I can then go through and fill out. Same with basic getters and setters which are the methods that manipulate object variables. The point is, I do not need to write "public String getName()" for every instance I have a string variable called name, and I can validate that "return this.name" is what I need in an instant.

There are a whole lot of powerful tools in IDEs that speed up development and produce faster and better code as you get more and more familiar with using them. So, then, why would I disagree that generative AI would produce faster/better code? Well the answer is that the more complex the code being generated gets the less likely I am to catch where it isn't doing what I want it to at a glance. "return true" is very easy to verify in an instant, where a full logical loop with multiple return values is not. This means I need to review and verify the results and in many cases writing the code from scratch would just be faster.

This is also just the first part of the process because as you iterate over that bit of code and implement it there is a lot of value in understanding how it works. This is part of why code readability is such a huge part of working with a team. So, if I generate some code and find it does not work later, I have to spend time breaking the generated code down ever further before I can write the fix. If I had written that code myself I would have an intrinsic understanding as I had to reason out the logic to do so. My point being that the actual process of writing the code is a very important part of making quality software and directly translates to productivity and speed within a code base.

The second point I made is that by skipping the steps where you write the code you lose valuable understanding and is a problem in general in the development space. I run into people regularly that do not understand the code they produce. This may be from looking it up and blindly copying a stack overflow answer or, now, generating it by a process that was trained on those answers. Either case results in the same thing. They never allow questions to become answers, they just have results. I have said for a long time that my biggest issue with maths classes when I was growing up were that you are taught that "The answer is the only thing that mattered and those can be found at the back of the book". When I was younger I never really grasped how accurate this frustration was until I started working on code with people that simply produced results.

You see, the sentiment I felt was that the process to get from 1+1=2 didn't matter, and that all you had to do was memorize what symbols did and the steps for stuff like long division. However this is a very flawed way to look at maths because what actually matters is understanding WHY 1+1=2 and how that process works. Having a rich understanding of why allows you to apply the concepts in broader and more complex ways and leads to building a basis for mastery.

AI models that produce code remove the understanding part of coding and in a way do so worse than having to adjust an answer to fit your specific use case. I have always said that its okay to look up how to do something and even encouraged doing so, but I also always say "but it is extremely important that you do not use that answer until you actually understand why it solves the problem". Asking questions and understanding answers is a fundamental skill that needs to be practiced to maintain and these tools cut that process out. Meaning if they are productive now, they will be less so later as the user relies more on the magic answer box and less on their understanding and skill.

It is this reasoning that I also state to my peers that it's okay to generate code, but you must also take the time to review and understand how it works before implementing it. Using these tools "correctly" in that light means an active reduction of productivity since you have to spend that extra effort to gain what you would have by puzzling it out and implementing it yourself.

The technology behind these things *is* very powerful and *does* have value, but not in creative spaces (and I do consider coding to be creative). A creative field requires understanding and technique, neither things generative AI can do, and worse using them actively robs the creator of skills they should be developing.