About p(Doom)1

One developer, a strategy game about running an AI-safety lab, and the story of why it exists.

Why this exists

I built p(Doom)1 because AI safety mostly lives in papers and blog posts, and I figured losing a strategy game to it might teach the ideas faster. You run an AI-safety lab — hiring, funding, compute — and try to lower p(doom) while the paperwork does its best to stop you.

The tone owes a lot to Papers, Please and Pandemic: satirical on the surface, serious underneath. You manage resources, make policy calls, and try to prevent catastrophic outcomes — mostly via spreadsheet.

Educational First

Every game mechanic is designed to teach real AI Safety concepts in an engaging way.

Source available

All of it on GitHub — warts included — to read, evaluate and contribute to. Not open source yet: an engine licence is planned around 1.0. Contributions welcome.

Accessible

Free to play and designed to be understood by both experts and newcomers to AI Safety.

Research-Based

Grounded in real AI Safety research and current discussions in the field.

Development Story

p(Doom)1 started as an experiment in whether a game could carry ideas about AI safety and existential risk. It's grown from crude prototypes into an actual strategy game — early alpha, but a real one.

Initial Concept

Early 2024

First prototype focusing on resource management and p(doom) mechanics.

Core Systems

Mid 2024

Implementation of competing AI labs, policy systems, and economic models.

Enhanced Features

September 2024

Added leaderboard system, improved UI, and comprehensive type annotations.

Early Alpha

Now

Alpha builds for Windows, macOS and Linux — Windows is the tested one, Mac and Linux are fresh and largely untested. Current version is in the stats below, pulled live from the release data. Rough on purpose, held together with optimism in places.

Who's Building It

p(Doom)1 is built by Pip Foweraker, working solo, in the belief that games can educate and inspire. It is developed in the open, so anyone is welcome to pitch in. (Source-available rather than open source for now — see the licence.)

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Pip Foweraker

Solo Developer

Design, code, AI systems, and overall direction — the whole thing, so far.

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Open to Contributors

Source Available, Built in the Open

Code, ideas, and feedback are welcome through GitHub. If you'd like to help, the door's open.

Interested in contributing? Check out our GitHub repository to get involved!

Technology & Approach

p(Doom)1 is built with Godot 4.5.1, a modern open-source game engine, chosen for native cross-platform builds and a strong open-source ecosystem. This stack allows for:

Godot Engine

Built with Godot 4.5.1 for a fast, contributor-friendly workflow and native builds across platforms.

Native Cross-Platform

Built from a single Godot codebase and run natively, with no runtime to install. Windows and Linux are available now — Windows is the tested build; Linux is new and largely untested. macOS — no build in the current release; it broke and a fix is written.

Data-Driven Design

Game mechanics are configurable through data files, making it easy to balance and expand content.

Modular Architecture

Modular GDScript, typed where it earns its keep — and held together with optimism in the remaining places.

Project Stats

Pulled live from the release data — a dash means the fetch failed, not that I'm hiding something.

Current Version
Open Source License
Platforms Available
Active Development Status

Community & Contact

There's no forum yet — it's one developer and a GitHub repo. Feedback goes through the in-game bug reporter or GitHub issues, and both genuinely get read.

GitHub

Source code, issue tracking, and contributions

Visit Repository

Contact

Questions, press inquiries, or collaboration

Send Email

Feedback

Bug reports and ideas — in-game reporter or GitHub issues

Open an Issue

Under the hood

What the site's own automation last wrote, and how old it is

Developer surfaces

Ready to Play?

Free, source-available, early alpha. Try to lower p(doom) — the paperwork usually wins.

Download on GitHub View Leaderboard