# How to Play ## Getting Started When you start a new game, you name your AI company and begin in the **Startup era** with $50,000 in seed money. Your goal: build the world's leading AI company and eventually achieve AGI. The game runs in real-time at 1 tick per second. Use the speed controls in the top bar to play at 1x, 2x, or 5x speed, or pause to plan your next moves. ## The Core Loop The fundamental cycle of the game: 1. **Buy GPUs** — Purchase compute hardware in the Infrastructure page 2. **Allocate compute** — Split between training new models and serving inference 3. **Train a model** — Start a training run in the Models page 4. **Deploy** — Put your trained model into production 5. **Earn revenue** — Users pay for API access and subscriptions 6. **Reinvest** — Buy more GPUs, hire talent, fund research Everything else in the game builds on or modifies this loop. ## Key Resources ### Money Your primary resource. Earned from API revenue and consumer subscriptions. Spent on GPUs, talent, data, and energy. Shown in the top bar with income trend. ### Compute (FLOPS) Total processing power from your GPU fleet. The main bottleneck — you never have enough. Split between training (building new models) and inference (serving users). ### Reputation (0-100) Public trust in your company. Affects talent acquisition, user growth, investor confidence, and regulatory treatment. Composed of safety record, public perception, employee satisfaction, and regulatory standing. ### Talent Headcount across four departments: Research, Engineering, Ops, and Sales. Each department has effectiveness and morale scores that affect their output. ### Data Training data quality and quantity. Better data produces better models. Acquired through the data marketplace or generated passively from your user base. ### Research Progress through the tech tree. Unlocks better GPU tiers, model architectures, efficiency improvements, and safety techniques. ## Game Systems ### Infrastructure Your datacenters house GPU clusters. Seven global regions are available, each with tradeoffs: - **US-West / US-East** — Balanced costs, good latency to North American users - **EU-West / EU-North** — Higher energy costs, strict regulation, access to EU market - **Asia-East / Asia-South** — Lower costs, emerging markets - **Middle-East** — Cheap energy, political risk Each datacenter has a size (GPU slots), cooling level, and redundancy level. Higher redundancy reduces GPU failure rates but costs more. GPUs can fail randomly — lost hardware means lost capacity until you replace them. GPU types unlock through research. Early on you have basic GPUs; research unlocks progressively more powerful (and expensive) tiers. ### Research The tech tree has two dimensions: - **Generations**: Small → Medium → Large → Frontier → AGI-scale models - **Specializations**: Reasoning, coding, creative, multimodal, agents Research projects require researchers and time. Completing projects unlocks new capabilities: - Better GPU tiers - Training efficiency improvements (quantization, distillation) - Safety techniques (alignment research, interpretability) - New product capabilities ### Models Training a model requires: - **Compute**: How many FLOPS to dedicate to training - **Data**: Training data tokens to use - **Time**: Training runs take real time (boosted by researcher and engineer quality) When training completes, your model gets capability scores across five dimensions: reasoning, coding, creative, multimodal, and agents. A composite benchmark score determines its market competitiveness. #### Safety vs Capability This is the game's central tension. Safety research improves your model's safety score but penalizes benchmark performance. Low safety scores risk: - **Safety incidents**: PR disasters that damage reputation - **Regulatory backlash**: Higher compliance costs High safety scores mean: - Lower benchmarks (competitors may outperform you) - Better regulatory standing - Protection from reputation-damaging incidents There's no single right answer — it depends on your strategy. #### Model Tuning After deploying a model, you can tune its behavior: - **Presets**: Quick settings (Helpful-Safe, Performance, Creative, Balanced) - **Sliders**: Fine-grained control over safety, creativity, verbosity, and speed/quality tradeoff (unlocked after completing alignment research) ### Market Revenue comes from two sources: **Consumer Subscriptions**: Users pay a monthly fee for your chat product. Subscriber count grows based on model quality and shrinks from churn. Higher quality models and competitive pricing accelerate growth. **B2B API**: Enterprise customers pay per token. Set your input/output token pricing to balance revenue against demand. #### Overload Policy When demand exceeds your inference capacity, you choose how to handle it: - **Queue depth**: How many requests to buffer - **Rate limits**: Max requests per user - **Degrade quality**: Serve faster but lower-quality responses - **Prioritize enterprise**: Give B2B customers priority over consumers Each choice has tradeoffs. Degrading quality hurts satisfaction. Enterprise prioritization frustrates consumer users. #### Open Source You can open-source deployed models. This: - Boosts reputation significantly - Attracts more talent - Reduces direct revenue from that model - Increases subscriber growth (community effect) ### Talent Four departments, each critical: | Department | Effect | |-----------|--------| | Research | Speeds up R&D projects and improves model training quality | | Engineering | Speeds up model training and infrastructure reliability | | Ops | Reduces infrastructure costs and failure rates | | Sales | Increases enterprise API demand | Hire to increase headcount. Morale affects effectiveness — keep your teams happy by managing workload and company reputation. ### Competitors Three rival AI labs compete with you. Each has a personality: - Some prioritize safety, others move fast - Some are big-tech giants with deep pockets, others are scrappy startups - They release models, gain users, and react to your moves In later eras (Big Tech and AGI), you can **acquire** competitors, absorbing their talent and technology. ### Funding Raise capital through VC rounds as you grow: | Round | Amount | Dilution | Key Requirement | |-------|--------|----------|----------------| | Seed | $100K | 10% | $100/s revenue | | Series A | $500K | 15% | 100 users, 20 reputation | | Series B | $2M | 12% | 1,000 users, 30 reputation | | Series C | $10M | 10% | 10,000 users, 40 reputation | | Series D | $50M | 8% | 50,000 users, 50 reputation | | IPO | $200M | 20% | 100,000 users, 60 reputation | Each round permanently dilutes your founder equity. Time your raises carefully — you want enough runway to grow but minimum dilution. ## Era Progression The game has four eras that unlock progressively. Transitions happen automatically when you meet thresholds: ### Startup → Scale-up - Revenue: $10,000/s - Best model capability: 15+ - Reputation: 30+ ### Scale-up → Big Tech - Revenue: $1,000,000/s - Best model capability: 50+ - Reputation: 60+ ### Big Tech → AGI - Revenue: $100,000,000/s - Best model capability: 90+ - Reputation: 70+ Each era unlocks new game systems and sidebar pages. Watch for the "NEW" badge on newly available pages. ## Strategies ### The Safety-First Path Invest heavily in alignment and interpretability research. Your benchmarks will lag competitors initially, but you avoid safety incidents and build strong regulatory standing. Good for steady, sustainable growth. ### The Move-Fast Path Minimize safety investment, maximize raw capability. You'll lead benchmarks and attract users quickly, but safety incidents can crater your reputation. High risk, high reward. ### The Open-Source Play Open-source your models to build massive community goodwill and attract top talent. Revenue per model drops, but subscriber growth accelerates and reputation soars. Strong mid-game strategy. ### The Vertical Integrator Invest in multiple specializations and diverse products. Spread your compute across reasoning, coding, creative, and multimodal capabilities. More resilient but slower to dominate any single benchmark. ## Tips - **Don't neglect infrastructure redundancy.** GPU failures at scale can cripple your capacity. - **Watch your burn rate.** It's easy to over-hire and run out of money before your models generate revenue. - **Timing funding rounds matters.** Raise too early and you give up equity cheaply. Raise too late and you run out of runway. - **Safety research compounds.** Each safety project improves all future models. - **Check competitor activity.** If a rival just released a strong model, expect to lose some subscribers unless you respond. - **The data flywheel is real.** More users generate more data, which trains better models, which attract more users. - **Deploy your models.** A trained model sitting idle generates zero revenue. - **Use speed controls.** Pause when making big decisions. Speed up during waiting periods. ## Saving The game auto-saves to your browser's localStorage every 60 ticks. You can also: - **Export** your save as a JSON file from the Settings page - **Import** a previously exported save - **Cloud save** by creating an account (requires the backend server) Closing the browser tab is safe — when you return, an offline catch-up system simulates what happened while you were away (up to 24 hours). ## Achievements 15 achievements track your progression milestones, from training your first model to reaching AGI. Check the Achievements page to see what you've unlocked and what's still ahead.