Oh no they're not. I built dozens of small games for the web and five games in Swift and they're spectacular, getting better since day one. Not like Call of Duty but just fun and bug-free enough to play them every day
This matches what I came up with on a small project. The biggest ones being screenshots and separated simulation.
Meta level, just push your agent to make things more legible, it’ll figure it out.
One other observation, remember that your agent is infinitely patient. So prompts like “build an interactive design doc with videos of each game mechanic in the style of Factorio dev diaries” can make reviewing changes easier. The general idea, all the small QoL dev tools that you don’t have time for, you can just spawn a side agent to build; if they are orthogonal to your main thread you can just progress them in the background.
One thing I think game dev has not (AFAIK) invested as much in is observability. There’s a lot of perf instrumentation available in debug builds, but things like measuring DPS, health, etc as an exported metric time series I have not seen before. I think agents will benefit from this disproportionately more than humans, eg for inspecting progression curves and balance.
> I stand by a lot of what I’ve said, but am in the process of shipping a project where I’ve also used none of this. I think the scale I’m targeting here is really larger projects.
The author appears to be on track to learn an important lesson about LLMs and art. LLMs have completely solved the coding side of game development. But this was never the hard part. Not even close.
The fixed randomness and determinism thing sounds great on paper (all of this does), but in reality this is an extremely confining way to build a video game. Ensuring you maintain determinism across many platforms is where things get super nasty.
If you want a turnkey AI game development experience, why not double down on diffusion? This sidesteps virtually all of the tooling and integration concerns. There is no game engine to integrate with if you use a diffusion model.
> The fixed randomness and determinism thing sounds great on paper (all of this does), but in reality this is an extremely confining way to build a video game. Ensuring you maintain determinism across many platforms is where things get super nasty.
Why 'nasty'? You just use a fixed-point math library and it's fine. Determinism is freeing, not confining. It allows you to do things like rollback netcode, replays and easy debugging. You can't just say that 'reality' happens to be what you in particular are used to.
I partially agree. Coding is not the hardest part for a large portion of indie games. You can in theory build an AI-coded game that sells well if you have the right art and gameplay.
But many top games have complex engines and online services that require technical mastery beyond the current AI capabilities. For those genres, engineering is a huge barrier of entry.
For a not so extreme example, you won't vibe code Cyberpunk 2077 while just taking care of art and gameplay ideas. Gosh, humans could barely pull that one off!
> LLMs have completely solved the coding side of game development.
This is not quite true. Currently you still need to give them guidance on what type of architecture would best work for your game, or after many iterations on gameplay you'll end up with terrible performance and a complete mess of a codebase. I expect that will be solved sooner rather than later, though.
Indeed, not even wrong. And it will whiff on the most basic gameplay issues. But it is still genuinely impressive how fast it can build a pretty but unplayable unfun thing out of nothing. And that's after many rounds of planning with it as it proceeds to ignore all that planning just because it can.
PS no, I am not interested in your alternative harness that will supposedly 100% trust me bro fix all these issues
> Ensuring you maintain determinism across many platforms is where things get super nasty.
I learned that one the other day - I've been building a little RTS game with a replay mechanism, and replays in Safari broke where they worked in Chrome. Opus 5.5 fixed it by replacing calls to things like Math.sin() and Math.cos() with its own deterministic functions: https://gist.github.com/simonw/7df96ed7296c69b70a9990102cae0...
A lot of the points the author made about game dev with AI are things that I notice in my experience as well.
That is why me and a friend started to work on https://folly.gg an AI game maker that internalizes several of these learnings.
I was able to get a decent FPS out of around 40k lines (excluding tooling). Tooling consisted mostly of level editing and asset conversion which I tried to keep out of the main code-base.
Oh no they're not. I built dozens of small games for the web and five games in Swift and they're spectacular, getting better since day one. Not like Call of Duty but just fun and bug-free enough to play them every day
Meta level, just push your agent to make things more legible, it’ll figure it out.
One other observation, remember that your agent is infinitely patient. So prompts like “build an interactive design doc with videos of each game mechanic in the style of Factorio dev diaries” can make reviewing changes easier. The general idea, all the small QoL dev tools that you don’t have time for, you can just spawn a side agent to build; if they are orthogonal to your main thread you can just progress them in the background.
One thing I think game dev has not (AFAIK) invested as much in is observability. There’s a lot of perf instrumentation available in debug builds, but things like measuring DPS, health, etc as an exported metric time series I have not seen before. I think agents will benefit from this disproportionately more than humans, eg for inspecting progression curves and balance.
The author appears to be on track to learn an important lesson about LLMs and art. LLMs have completely solved the coding side of game development. But this was never the hard part. Not even close.
The fixed randomness and determinism thing sounds great on paper (all of this does), but in reality this is an extremely confining way to build a video game. Ensuring you maintain determinism across many platforms is where things get super nasty.
If you want a turnkey AI game development experience, why not double down on diffusion? This sidesteps virtually all of the tooling and integration concerns. There is no game engine to integrate with if you use a diffusion model.
Why 'nasty'? You just use a fixed-point math library and it's fine. Determinism is freeing, not confining. It allows you to do things like rollback netcode, replays and easy debugging. You can't just say that 'reality' happens to be what you in particular are used to.
But many top games have complex engines and online services that require technical mastery beyond the current AI capabilities. For those genres, engineering is a huge barrier of entry.
For a not so extreme example, you won't vibe code Cyberpunk 2077 while just taking care of art and gameplay ideas. Gosh, humans could barely pull that one off!
This is not quite true. Currently you still need to give them guidance on what type of architecture would best work for your game, or after many iterations on gameplay you'll end up with terrible performance and a complete mess of a codebase. I expect that will be solved sooner rather than later, though.
PS no, I am not interested in your alternative harness that will supposedly 100% trust me bro fix all these issues
I learned that one the other day - I've been building a little RTS game with a replay mechanism, and replays in Safari broke where they worked in Chrome. Opus 5.5 fixed it by replacing calls to things like Math.sin() and Math.cos() with its own deterministic functions: https://gist.github.com/simonw/7df96ed7296c69b70a9990102cae0...