AI Strategy

"AI-native" has become a meaningless buzzword. Most studios slapping it on their pitch deck mean one of two things: they're using ChatGPT to write dialogue, or they're generating asset-flip garbage and calling it innovation. We mean something different.

What "AI-Native" Actually Means Here

AI-native, for us, means the entire development workflow is designed around human-AI collaboration from the ground up. Not bolted on. Not an afterthought. The framework, the conventions, the architecture — all of it is built so that AI tooling can participate effectively at every stage.

The human is the architect. AI is the power tool. A table saw doesn't design furniture — but a carpenter with a table saw builds faster and more precisely than one with a hand saw. That's the relationship. The judgment, the taste, the design intent — that's human. The velocity, the pattern execution, the boilerplate elimination — that's where AI earns its keep.

Where AI Fits in the Pipeline

AI tooling touches every stage of our development process. Here's what that looks like concretely:

  • Architecture Design — AI assists in evaluating design patterns, identifying potential issues in proposed architectures, and generating implementation scaffolding from high-level specs. The human decides what to build and why. AI helps map the how.
  • Code Generation — Routine implementations, boilerplate, and pattern-following code. The framework's strict conventions make this reliable — AI can generate a new component, service, or system node that follows the same patterns as everything else in the codebase.
  • Testing — Test scaffolding, edge case identification, and regression coverage. AI generates the test structure; the human defines what correct behavior looks like.
  • Documentation — Automated doc generation from code structure, inline documentation, and architectural decision records. The codebase stays documented without manual overhead.
  • Devlog Automation — Our development blog posts are auto-generated from git commit history. Real engineering work, surfaced automatically. No one stops building to write marketing content.
  • Debugging & Analysis — Pattern recognition across large codebases, identifying root causes, and suggesting fixes based on the framework's known patterns.

Why the Framework Makes This Work

Most codebases are hostile to AI collaboration. Inconsistent patterns, implicit conventions, tribal knowledge, and spaghetti dependencies mean AI tools generate code that technically runs but doesn't belong in the project.

Warren is designed differently:

  • Convention over configuration — strict, repeatable patterns that AI can learn and follow
  • Clean separation of concerns — System, Player, and Asset domains with clear boundaries
  • Signal-driven architecture — components communicate through well-defined interfaces, not hidden side effects
  • Strict encapsulation — every component is self-contained, testable, and replaceable

The result: AI-generated code is consistent with human-written code because the framework enforces the same patterns on both. Code reads the same regardless of who — or what — wrote it. That's not an accident. It's a design requirement.

The Competitive Advantage

One person with AI tooling and a well-designed framework can operate with the output velocity of a traditional 12-16 person team. That's not a projection — it's how this studio operates today.

What that means in practice:

  • 80-90%+ margins — near-zero headcount means revenue converts to profit, not payroll
  • Speed to market — no coordination overhead, no sprint planning for 12 people, no waiting for the art team. Decisions are made and executed in the same hour.
  • Portfolio scalability — the framework and AI tooling make each subsequent game cheaper and faster to build. The cost of the 5th game is a fraction of the 1st.
  • Lower burn rate — the studio can operate indefinitely on minimal capital. No runway pressure means no compromises on game quality to hit an arbitrary ship date.

Studios that ignore AI tooling will compete against this cost structure. A 16-person team with $3M in annual payroll shipping one game a year versus a one-person studio with near-zero fixed costs shipping multiple titles. The math doesn't work for the traditional model long-term.

Addressing the Skepticism

The Roblox and indie dev communities have every reason to be skeptical of "AI games." What they've seen so far is mostly garbage: asset-flipped experiences with AI-generated textures, incoherent AI-written narratives, and developers who think prompting a model replaces understanding game design. That criticism is valid. Those games are bad.

That's not what we do. The difference is intent and craft:

  • AI slop — uses AI to skip the work. No design vision, no architecture, no iteration. Generate assets, generate code, ship it, move on. The game is a vessel for the gimmick.
  • AI-native development — uses AI to amplify the work. The design vision is human. The architecture is deliberate. AI accelerates execution of decisions that a skilled developer already made. The game is the point.

There's a simple test: look at the devlog. Every post shows real engineering decisions, real debugging sessions, real architectural trade-offs. AI didn't make those choices — a human did. AI helped implement them faster. That's the difference between using AI to skip the craft and using AI to amplify the craft.

What AI Can't Do

Being honest about limitations is part of using AI well. Here's what AI doesn't do for us:

  • Design decisions — AI doesn't decide what game to make, what mechanics to include, or what the player experience should feel like. That's taste. AI doesn't have taste.
  • Quality judgment — AI can generate ten solutions to a problem. It can't tell you which one is right for your specific context, your players, your design goals.
  • Creative direction — The vision for what a game should be, how it should feel, what it should mean to the player — that's irreducibly human.
  • Debugging novel problems — AI is great at pattern-matched debugging. When the problem is genuinely new — an interaction between systems no one anticipated — the human figures it out. AI helps explore the search space.

AI is a force multiplier, not a replacement. A force multiplier on zero is still zero. The human skill, judgment, and vision have to be there first. AI makes a good developer faster. It doesn't make a non-developer into a good one.

The Proof Is in the Work

We don't ask anyone to take this on faith. The development blog for our current project, IT GETS WORSE, is public. Every post is generated from real commit history. You can read the engineering decisions, the architectural evolution, the debugging sessions, and the iteration process.

That's not a curated marketing narrative. It's a transparent record of how the studio actually operates. Judge the approach by the output.