AIHostingTycoon/docs/how-to-play.md

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.

Events

Random and conditional events keep the game dynamic. Categories include:

• Industry: Breakthroughs, open-source releases, benchmarks
• Regulatory: Hearings, compliance requirements, AI bills
• PR/Cultural: Media coverage, safety debates, public opinion shifts
• Internal: Employee issues, technical problems
• Market: Demand spikes, pricing pressure
• Geopolitical: Export controls, energy crises, natural disasters

Most events present 2-3 choices with meaningful tradeoffs. Some trigger chain events with delayed consequences.

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.
• Events have lasting consequences. Read the options carefully — some choices trigger follow-up events.
• 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.