Googleopoly
Year
1998

You are running Google.

Each turn = 1 year. Allocate R&D points across five levers. Watch how AI, distribution, and moats compound — or fail to — by 2024.

Revenue
$0M
cum. $0M
Search share
5.0%
Queries / yr
0.1B
AI capability
5
Distribution
5
Moat score
8

R&D allocation for 1999

100 pts → normalized

Search & Ads

Core

Improve ranking, ad auction, query monetization.

20%
20 pts

AI Research

Long bet

Brain, DeepMind, TPUs, Transformers, Gemini.

20%
20 pts

Distribution

Moat

Chrome, Android, default search deals (TAC).

20%
20 pts

Product Expansion

Compounding

YouTube, Maps, Gmail, Cloud, Workspace.

20%
20 pts

Moats & Data

Defense

Data flywheel, infra scale, talent, brand.

20%
20 pts
✓ PageRank
Play a few turns to see revenue trajectory.

Timeline

  • 1998Founded in a Menlo Park garage. PageRank goes live.
How the model works

Each lever has diminishing returns and feeds connected stats. Revenue ≈ queries × RPM, where RPM is lifted by Search/Ads, AI targeting, and moats.

  • Search & Ads: Improve ranking, ad auction, query monetization.
  • AI Research: Brain, DeepMind, TPUs, Transformers, Gemini.
  • Distribution: Chrome, Android, default search deals (TAC).
  • Product Expansion: YouTube, Maps, Gmail, Cloud, Workspace.
  • Moats & Data: Data flywheel, infra scale, talent, brand.

Educational toy model. Numbers are illustrative, not Google's actuals.