Understanding Organizational Communication through Weighted Graph Theory
Try the Interactive Model"Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations."
— Melvin Conway, 1967
Conway's Law is a principle formulated by computer programmer Melvin Conway in 1967. The observation suggests that the architecture of a system will inevitably mirror the social structure of the organization that created it. In simpler terms, the way teams communicate and organize determines the way systems are designed.
For example, if an organization has four teams working on a software product, the software will likely end up having four major components or modules. If communication between certain teams is difficult, integration between their respective components will likely be problematic as well.
This model extends Conway's Law by adding the concept of communication costs that vary based on:
The core hypothesis is that communication becomes more expensive as distance increases, but this relationship varies significantly depending on the types of nodes involved. Particularly, AI agents have dramatically lower communication costs compared to human-to-human or system-to-system interactions.
| Node Type | Representation | Description |
|---|---|---|
| Human | Teams, individuals, or departments | Communication involves meetings, emails, documentation, and is subject to cognitive load, emotions, and availability |
| System | Software, databases, services, APIs | Communication through integrations, calls, data transfers, limited by technical constraints |
| AI Agent | Intelligent systems, LLMs, autonomous services | Communication is rapid, consistent, and scales exceptionally well across distance |
The model calculates different communication costs based on the types of nodes involved and their distance:
The weights are configurable parameters that allow for calibration of the model to different organizational contexts.
AI agents fundamentally change the economics of distance in organizations. Their ability to communicate effectively regardless of distance makes them powerful connectors between distant parts of an organization.
The quadratic cost function for human-to-human communication reflects the reality that distant teams struggle to maintain effective communication. The cost of synchronization, meetings, and travel increases dramatically as distance grows.
System-to-system communication costs scale more predictably, but still face integration challenges as distance increases.
By visualizing and calculating the total Conway Score (sum of all communication costs), organizations can identify expensive communication pathways and restructure to minimize overall costs.
Identify where communication bottlenecks might lead to architectural challenges. Use AI agents strategically to bridge teams that are geographically or organizationally distant.
Model different organizational structures to predict system architectures. Identify where AI can most effectively reduce communication costs.
Determine which teams can effectively work remotely (connected via AI) versus which require closer proximity due to high communication costs.
Model the communication costs of merging different organizational structures and identify where AI agents can help bridge cultural or technical gaps.
The interactive model allows you to:
By building and experimenting with different organizational configurations, you can gain insights into how to optimize communication within your organization and identify where AI agents might have the most impact.