These conversations get frustrating since one group is saying the dancing is bad, a second group is saying it's good (for a bear), and a third group is saying we are a few years from a bear-only dancing industry.
However I do agree that the results are much worse when you try to use them. They look great in screenshots and video clips which makes them perfect for content farmers.
All of the LLM generated games I’ve played have been really bad to play, though. I even tried my hand at a simple game, thinking I could iterate on it with prompts to fix some rough edges. After the initial productivity burst every change turned into a slog of tokens with one thing changing and something else breaking it. I would try to use my remaining weekly token budget across Anthropic and OpenAI to refine it at the end of every week but after a couple weeks it felt like I wouldn’t be getting anywhere without scrapping it and going back to having the LLM build it one step at a time with my careful instruction.
Which, in retrospect, is the only way I can get usable output of an LLM for anything complicated, so it’s not surprising. It’s a fun reality check project though.
On the one hand I think most of us are incredibly impressed because we know, that quick demo would have taken us months of work to build in the before times.
On the other hand the promise is a cure for cancer and the end of all work.
So when everyone is telling you “skill issue is why you can’t one shot WoW”. It’s hard to know how you’re supposed to feel about Karpathy advertising one shot custom virtual worlds but giving you slop. Incredibly impressive slop when compared to how long it would take to create it just 3 years ago, not so much compared to Elon saying - “by the end of this year, grok will create a version of the odyssey that competes with Nolan’s”
I guess this is the average story and, similarly, the average game is boring and predictable
Early generative AI at least had the virtue of relentless, unsettling weirdness, in the same way that generative art from the late 90s and early 2000s did. A handful of people made creative use of that spooky weirdness.
Now it turns out "Airspace" art.
If you took the best, most creative, human writer in the world, and for thought experiment reasons they had amnesia (to mimic AI blank context windows) specifically while you asked them for a story idea 100 times in a row, my expectation is that this human would also give you the same idea at least 80 times out of that 100.
* still better than the mean human, but even the top 0.1% of humans aren't all professional authors.
What you think is better is not what I think is better. Imagination is not storytelling. You ask the "mean human" to write, it's going to be worse than an LLM in spelling and grammar, if you get anything at all.
That's oddly specific and it'd really hurt my cousins feelings :)
I guess the next question is if they can be made fun without too much additional work with a human guiding the AI.
They can't. If you think about how these things are trained it's blatantly obvious fun is an impossible metric to optimize them for
..feels like there was a black mirror episode about that though
Used to be if a game looked that good it probably had time spent on the game part too.
Whenever I think about AI games I find myself thinking about Tiny Wings, Flappy Bird and Angry Birds. Three simple, elegant games.
It is easy to see what makes Tiny Wings so completely loveable — it has a sculpted, adorable, perfect charm with a cleverly inverted game mechanic that has a calibrated level of exasperation and reward.
But why were Flappy Bird and Angry Birds, very basic games with very old game mechanics, so charming?
It seems equally impossible to imagine an AI coming up with a game with the quality of any of them, even with maximised creativity. But explaining why for Flappy Bird seems quite difficult, especially when you consider it uses some stolen visuals!
Maybe it's the smoothness of motion that makes these games understandable and LLMs seem to consistently fail at that. Ask them to do something snowboarding and they go really hard on the physics since it seems like they don't know what kind of approxmations feel good.
(It's been a while since I was in the game industry, so IDK quite how accurate this is).
If I see one more “one shot MMO” where you just walk around and do absolutey nothing or another menu slop idle battler or rogulike deck builder I’m going to go Postal in Minecraft.
These one-shot products aren't games. They're barely even demos. I don't even know what to call them. For a mature framework like Phaser to sell-out like this and create a vibecoded platform for vibecoded games is shocking.
Computer graphics will have enormous applications because they are directly controllable by LLM-generated code. Video models are probabilistic and less suitable when precision matters. In education, for example, we need exact visuals. If an AI wants to plot y = sin(x), it should generate the precise graph through computer graphics rather than approximate it with a video model.
What I find funny is that approximate to computer graphics are video games. When I ask AI about a decision available to me in a video game AI completely fails, OFTEN. I assume all the forums and changes made to a game over time might be quite confusing for AI. But I've also seen it completely make up characters and decisions and weapons and so on about some very clearly defined games and paths. It's an interesting dynamic.
It always brings to my mind some words from Rich Hickey:
I think we’re in this world I’d like to call “guardrail programming”. It’s really sad: we’re like, “I can make change because I have tests!”. Who does that? Who drives their car around, banging against the guardrails, saying “whoah, I’m so glad I have these guardrails so I can make it to the show on time!”
Take chess engines of the "deep blue" era for instance. These engines are stupid, trying millions of moves that are obviously terrible, no human chess player would do that. And yet, this is what worked best, because computers are so good at repetition. Recent development using neural networks made chess engines smarter, but using repetition is still how the beat humans.
What you call "guardrail programming" in another context would genetic algorithms, a technique that has recognized applications. And if you look at videos of genetic algorithms learning to play racing games, it literally looks like a bunch of carnival bumper cars onto the highways, but done well, after some generations, it becomes competent driving, sometimes even record breaking.
It can be expensive, it is often the case for LLMs, so you may want to try to be a bit smarter at first to spare some resources, but to me, it is just using computers as intended.
Not difficult to see why the employees of AI firms are thrilled with it though, eh?
I like the analogy.
I guess this is why we got this before cars sold without steering wheels: literal guardrails on the literal roads are somewhat more expensive, especially for the people who keep bouncing off them on the way to their destination.
Also, where the guardrails are absent: oh look, felonies. https://www.google.com/search?q=ai+hacks+company&tbm=nws
As with painting, after a while there's nothing really new to paint, we genuinely need 0 new software. We need to fix our broken physical world, our social lives, our kids and what's left of our democracies.
This software crap is done, leave it to the nerds.
Textbook and blackboard > ipad.