Making games in Go: 3 months without LLMs vs. 3 days with LLMs

What I like about this post is that it highlights something a lot of devs gloss over: the coding part of game development was never really the bottleneck. A solo developer can crank out mechanics pretty quickly, with or without AI. The real grind is in all the invisible layers on top; balancing the loop, tuning difficulty, creating assets that don’t look uncanny, and building enough polish to hold someone’s attention for more than 5 minutes.

That’s why we’re not suddenly drowning in brilliant Steam releases post-LLMs. The tech has lowered one wall, but the taller walls remain. It’s like the rise of Unity in the 2010s: the engine democratized making games, but we didn’t see a proportional explosion of good game, just more attempts. LLMs are doing the same thing for code, and image models are starting to do it for art, but neither can tell you if your game is actually fun.

The interesting question to me is: what happens when AI can not only implement but also playtest -- running thousands of iterations of your loop, surfacing which mechanics keep simulated players engaged? That’s when we start moving beyond "AI as productivity hack" into "AI as collaborator in design." We’re not there yet, but this article feels like an early data point along that trajectory.

> The interesting question to me is: what happens when AI can not only implement but also playtest -- running thousands of iterations of your loop, surfacing which mechanics keep simulated players engaged?

How is AI supposed to simulate a player, and why should it be able to determine what real people would find engaging?

Game companies already collect heaps of data about players, which mechanics they interact with, which mechanics they don't, retention, play time, etc.

I don't think it's much of a stretch to take this data over multiple games, versions, and genres, and train a model to take in a set of mechanics, stats, or even video and audio to rate the different aspects of a game prototype.

I wouldn't even be surprised if I heard this is already being done somewhere.

We did that on a game I worked on over ten years ago. It was a mobile game and we knew that it was very important to player retention (and interest in multiplayer) to have the first multiplayer interaction be "fun". So we would simulate the first person you played against as though they were another human. Based on play data of other humans. Because you only played them once you didn't think you were playing a bot.

Where we used AI (machine learning, not LLM) was in terms trying to figure out what kind of human you would want to play with. We also used machine learning to try figure out what cohort of players you were in so we could tweak engagement.

Where LLMs could really shine, in my opinion: Gamers love to play people, not AI (now). People are unpredictable, they communicate, they play well but in ways a human could (like they don't have superhuman reflexes or speed). You can play all kinds of games against AI (StarCraft, Civilization, training of all kinds of FPS) but it isn't fun for long because you see the robotic patterns. However, an LLM might be able to mix it up like humans, talk to you, and you could probably make it have imperfect reaction time, coordination, etc. That would really help a lot of games that have lulls in human player activity, or too much toxicity.

I would be shocked if some games aren't doing this now. It seems like it still be hard to make a bot seem human, and it probably only works if you sprinkle it in.