The central problem this project addresses is the need for better frameworks to understand potential paths of artificial general intelligence (AGI) development. Current discussions often oversimplify possibilities into basic "hard takeoff" versus "soft takeoff" scenarios, missing important variations in speed, control methods, and economic impacts. This lack of nuance makes it harder for researchers, policymakers, and businesses to plan effectively for AGI's future.

A More Detailed Framework for Understanding AGI

One way to approach this problem is by creating a comprehensive classification system for different AGI development scenarios. This system would look at factors beyond just speed, such as:

• How quickly the AI can improve itself (sudden jumps vs steady progress)
• Whether multiple AGI systems compete with each other
• How well safety measures and regulations work
• How the AGI integrates into the economy

The project could produce both public resources (like improved Wikipedia articles) and original research papers. These would help different groups understand and prepare for possible AGI futures more effectively.

Connecting AI to Economic Patterns

By studying how technologies have grown and changed economies in the past, we might find useful parallels for understanding AGI development. For example:

• The Industrial Revolution shows how gradual transformation can happen
• Computer performance improvements demonstrate exponential but predictable growth
• Stock market crashes illustrate how sudden, dramatic changes can occur

These historical patterns could help categorize and better understand different ways AGI might develop.

Practical Implementation

To make progress on this idea, one might start with a focused preliminary study:

1. Review existing research on AI development scenarios (3 months)
2. Develop an initial classification system with input from experts (4 months)
3. Compare to historical economic patterns (3 months)
4. Share findings through Wikipedia and other public channels

A simpler first version could focus on creating a basic classification framework and identifying the most relevant historical parallels.

This approach could provide clearer thinking tools for everyone from researchers to policymakers, helping society navigate the complex possibilities of AGI development more effectively.