Procedural generation has been in games forever. Rogue did it in 1980. Minecraft turned it into a cultural phenomenon. No Man’s Sky built an entire universe from algorithms. The idea is simple: instead of hand-crafting every piece of content, you write rules and let the computer generate it.

The problem has always been quality. Procedurally generated content tends to feel samey after a while. The twentieth cave looks a lot like the first. The thousandth planet has the same basic features as the hundred before it. The technical achievement is impressive, but the experience can be shallow.

AI is changing that equation, and the results are starting to show up in games you can actually play.

How AI improves procedural generation

Traditional procedural generation uses mathematical rules. “Place a room here. Connect it to the next room with a corridor. Add enemies based on the difficulty curve.” The output follows the rules, but the rules are rigid and the results are predictable.

AI-enhanced procedural generation adds a layer of learned behaviour on top. Instead of just following rules, the system has been trained on examples of well-designed content and can make decisions that are more contextually appropriate.

For example, a traditional level generator might place a health pickup at regular intervals. An AI-enhanced generator might place a health pickup after a sequence that the system predicts will be difficult, based on patterns learned from player data. The mechanical outcome is the same — the player gets health — but the timing feels more intentional.

Several approaches are being used:

Machine learning for layout quality. AI systems trained on hand-designed levels learn what makes a layout “good” — pacing, sight lines, flow — and apply those principles to procedurally generated layouts. The output looks more like something a human designed.

Neural network-based asset generation. AI creates textures, terrain features, and decorative objects that are more varied and contextually appropriate than purely algorithmic alternatives. A forest generated by AI doesn’t just have random trees — it has tree placement that mimics real forest ecology.

Player-adaptive generation. AI that observes how you play and adjusts generated content accordingly. If you explore thoroughly, the system might generate more hidden areas. If you rush through, it might front-load the interesting content.

Games doing it well

Several recent titles have shipped with AI-enhanced procedural generation, though most don’t market it explicitly.

Roguelikes are the most visible application. A handful of indie studios, including at least two Australian ones, are using AI to generate dungeon layouts that avoid the repetitive feeling of pure algorithmic generation. The runs feel more hand-crafted, which is the holy grail for the genre.

Open-world games are using AI to generate terrain, vegetation, and ambient NPC behaviour that responds to the player’s actions and the game’s narrative state. The detail isn’t always obvious, but side-by-side comparisons with purely algorithmic generation show a noticeable improvement in environmental variety.

The most interesting application I’ve seen is in narrative games. An Australian studio (still in development, so I can’t name them yet) is using AI to generate side quests that respond to the player’s previous choices and play style. Early playtests suggest it dramatically increases replayability without the artificial feeling of randomisation.

The limits

AI-enhanced procedural generation is better, but it’s not a replacement for hand-crafted content. The best levels in any game are the ones a designer spent weeks perfecting — adjusting sight lines, timing encounters, placing secrets. AI gets you 80 percent of the way there. The last 20 percent is still human work.

There’s also a consistency problem. Hand-crafted content has a unified vision. Procedurally generated content, even with AI, can have tonal inconsistencies. A horror game needs every room to contribute to the atmosphere. If the generator produces one room that breaks the mood, it undermines the entire experience.

Compute cost is another factor. Running AI inference during gameplay requires processing power that could otherwise go to rendering or physics. Studios need to balance generation quality against performance impact, which is why many AI-enhanced systems do their generation during loading screens rather than in real-time. Studios working with AI consultants Sydney have found that optimising the inference pipeline specifically for real-time game use cases can reduce the compute overhead significantly.

What this means for players

In the short term, you’ll notice that procedurally generated games feel less repetitive. Runs in your favourite roguelike will have more variety. Open-world environments will look more natural. Generated content will better match the game’s intended difficulty and pacing.

In the longer term, the line between hand-crafted and generated content will blur. Games will ship with a combination of both, and the generated portions will be good enough that you can’t easily tell which parts were designed by a human and which by an algorithm.

This isn’t a threat to game designers. It’s a tool that lets them focus on the content that matters most while the systems handle the foundational work. The best games of the next five years will be the ones that use AI generation intelligently — not as a replacement for design, but as an extension of it.