Transformative artificial intelligence (AI) systems could reshape society in profound ways, but their development paths remain unclear. One critical uncertainty is whether such AI will emerge as a single, centralized entity or as a decentralized network of specialized components. Understanding this distinction is crucial—centralized AI might concentrate power and create unique control challenges, while distributed AI could complicate coordination but offer built-in safety buffers. Currently, there’s no framework to systematically evaluate which scenario is more likely or manageable, making it difficult to prioritize safety research and governance strategies effectively.

Exploring AI Architecture Scenarios

One way to address this gap would be to investigate the technical feasibility and implications of different AI architectures. This could involve:

• Comparing theoretical models, such as Eric Drexler’s vision of modular "Comprehensive AI Services," with real-world trends in cloud-based and open-source AI development
• Analyzing how each structure affects core challenges like alignment (ensuring AI goals match human intentions), safety protocols, and governance mechanisms
• Distilling findings into practical guidance for researchers, policymakers, and funders about where to focus their efforts

For example, if distributed systems appear more viable, safety research might prioritize interoperability standards and fail-safe mechanisms across networked components.

Turning Insights into Action

The research could progress through stages:

1. Reviewing existing literature on AI system design and collecting expert perspectives through interviews
2. Developing simplified models to test how different architectures behave under pressure (like competing interests or component failures)
3. Publishing preliminary findings to start shaping discussions among key stakeholders, from tech companies to regulatory bodies

Companies developing AI tools might prefer centralized control, while open-source communities may push for decentralized approaches - the research could help identify compromises or safeguards that work across different models.

By systematically examining these architectural questions, this line of inquiry could help direct attention and resources toward the most pressing challenges in AI development, regardless of which path eventually dominates.