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?

Personally, i don't think the coding part of game development was not a bottleneck.

Just try to implement, for example, a hexagon-based isometric game. There are no off-the-shelf implementations -- you'll need to redo the pan / zoom / click controls yourself, you'll need to implement the pathfinding, map layers, interface state machine etc etc etc

This is still not an easy task -- to build a somehow complicated game. If you're building a platformer -- sure, that's doable. Strategy/4X/RPG? That's different.

> 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.

But we did? We've come a long way from the limited XBLA catalog. It didn't happen overnight, but doubtless we wouldn't have the volume of games we have today without Unity, Godot, Gamemaker, Renpy, RPG Maker...

My litmus test for generative AI: generate a complete spritesheet for a 2D pixel art action game, e.g. only for the battle tank or main hero movements. No success so far.

> we didn’t see a proportional explosion of good game

We definitely saw an explosion of good indie games by around early half of 2010s. Whether it had anything to do with Unity is another moot point.

This holds in other areas as well, and to me at least the conclusion follows from the evidence: there is seemingly a lot of potential in agent coding, a few tasks are just crushed/solved (quick webapp demos, other library stitching in the small) but for real software in the large? It's not there yet in either the way the models are tuned or our collective expertise in using them.

And this isn't surprising: git-style revision control hit the scene almost 20 years ago, it was like 5 years until it was totally dialed in anywhere, another 5 before elite companies had it totally figured out, and its been slowely diffusing since, today its pretty figured out. And this is harder to use right than git.

I think it would go faster actually if every product release, every OSS tool, every god-damned blog post wasn't hell bent on saying "its done, its solved, old way cooked, new world arrived".

We're figuring it out and it takes time. That's OK.

If it was done, then we'd be drowning in great software. We're not, we're breaking even, which is impressive for a big new thing 1-2 years in.

And the main reason why actually making a game with interesting gameplay is more relevant than discussing what is the best language to do a game on.

> coding part of game development was never really the bottleneck

Doesn't look exactly that to me. The author built a server, studied React, built a frontend, made the card game work.

Then, with most bits needed for a card game already in place, he asked Claude to alter the existing code to implement a different card game. Understandably, it took much shorter. But it would also take much shorter if a human engineer did the same.

> what happens when AI can not only implement but also playtest -- running thousands of iterations of your loop

It can do this. From Atari games to StarCraft this has been a thing since before LLMs.

> surfacing which mechanics keep simulated players engaged

This it's unclear how to operationalize. Among other things, not all games appeal to all people.

This is not true in my experience. Cranking out code is obviously the bottleneck, unless you have the luxury of working on a very narrow problem. The author describes a multi-modal project that does not afford this luxury.

As a much lower bar, I'd love it if more of the LLMs used for coding were actually multimodal, accepting images of games as part of the context. In general, LLMs are far better at generating web apps than they are at pure 2D or 3D games that use graphical APIs. There's far less training data, and there's no way to test anywhere close to what you can do with testing on a DOM. In an interactive game, with physics, animations and game logic, the AI just falls on its face because of the complexity.

> 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.

This is not true at all. I have never worked on games and it will take me quite a while (even months) to write a "basic" game. While I know a lot of good practices about software development and decade+ of FAANG experience, I don't know the intricacies or even the basics of game development.

I recently experienced this for a different usecase. As an experienced backend developer, I wanted to automate some javascript/browser stuff. I tried on my own for 2-3 days and had couple of prototypes but nothing actually worked. I spent 2 hours with an AI and I had a working solution. We even iterated together quickly and solved some runtime issues and the solution is working for me seamlessly now.

So, I definitely see value of AI even for coding for experienced developers like myself.

> 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?

Much the same as we do today in games and film both, something saccharine and mediocre built to appeal to a wide majority. Worse, if this process becomes streamlined and widely accessible, you're competing with a hundred other saccharine and mediocre built to a wide majority games. AI generated shovelware, it's like the shovelware of today where anything remotely popular generates dozens of cheap clones, but with AI.

The best games take risks and aren't min/maxed.

> neither can tell you if your game is actually fun

I think this is the core insight. An AI will not be able to experience a game (or anything else for that matter) remotely in the same way that a human can experience it. It might be able to guess, based on human rankings of other similar games. But AI will never be able to actually have fun playing your game.

This concept will define the workforce that comes out of this AI boom. Maybe an AI can write a document or code like a human, only based on past samples of similar behavior, but it won't be able to synthesize exactly what it means to be a human. The human element will still need to be traded on. Your value as a human cannot be replaced, you might just have to think differently about that value.

This makes me feel like games will be the last bastion to fully fall to AI.

In traditional business apps, your goal is to make your app work and look intuitive enough for a human to use. When developing a game, you have a few extra goals, it also has to be fun, rewarding and different enough from other games that came before. It feels like the former group will be much easier to judge by non-humans than the latter.

LLMs have the same value proposition as no-code or low-code tools, and they also have the same failure cases. With pre-AI no-code tools, they also lowered walls but they didn't remove the barriers. The experience was a lot like we're seeing from the "vibe coders", like this post here:

  "what's the point of vibecoding if at the end of the day I still have to pay a dev to look at the code anyway... I can't vibe my way through debugging, I can't ship anything that actually matters." [1]

LLMs are only different because the interface is more accessible. But all the same problems are still there. AI is not a panacea.

[1] https://www.reddit.com/r/ProgrammerHumor/comments/1mudy12/th...

AI doing playtests is an idea I've been thinking about too. The question I can't quite answer is: how do you know the AI play-tester can predict what users find fun? How well does it represent the different kinds of users?

Depends on the game but absolutely we run thousand iterations of a game, but for balance. Which mechanics keep player engaged you need to get feedback from players

> hold someone’s attention for more than 5 minutes.

more like, more than 5 seconds.

Is that the takeaway? When they say, "I cloned the backend for Truco and gave Claude a long prompt explaining the rules of Escoba and asking it to refactor the code to implement it", that doesn't really make it sound like a good heads-up comparison from which we could then say, "the coding part was not the most significant part of the problem".

I mean, the entire article is problematic as proof of anything. For starters, they didn't go through a design process for a game at all, they copied existing games. Then there are all these weird technical rabbit holes they went down that really weren't anywhere near "simplest path to MVP".

I just don't think there is anything to glean from this article. Like most posts about individual experiences with AI, it's functionally equivalent to, "I had a weird dream last night".

You are absolutely right! The tic-tac-toe game we‘ve been working on is a total blast and simulated players 1-1000 enjoy it very much! I think we should release it soon.

That’s what a lot of people are missing about AI. You can do an exhaustive search which is effectively AGI. You already solved for a “solution” it’s simply a matter of searching for it. We are pretty close.