AI Architecture Mirrors Organizational Structure According to Conway's Law
In the rapidly evolving world of artificial intelligence (AI), it's becoming increasingly clear that technology alone cannot fix organizational dysfunction. This is the essence of Conway's Law, a principle that suggests that any system, including AI, will reflect the organization that created it.
Large AI companies, as they grow and acquire startups, often find their innovations getting 'Conway's Law-ed' into dysfunction. This happens when the acquired innovation is forced to conform to the existing structure of the acquiring company, leading to internal conflicts and the production of unusable models.
Reorganizing before building, rather than after, can help prevent this. By designing the organization structure thoughtfully, companies can make changing either the organization or the AI nearly impossible, ensuring a more coherent outcome.
Evaluating organizational charts, not just technology, is crucial. The organization structure predicts AI capability better than technical metrics. Diversity initiatives, for instance, often fail because diverse hires must work within existing structures and adapt or leave.
The decision to open-source Llama by Meta was a political solution to organizational gridlock. In AI, Conway's Law isn't just about system design, it's about the design of intelligence itself. This creates a David vs Goliath dynamic, as small teams build focused, coherent models while large teams build powerful but 'schizophrenic' ones.
The pattern repeats everywhere. When companies copy competitors' org structures hoping to copy their success, they often end up with the same dysfunctions. The success of Llama, for instance, came from accidentally escaping Conway's Law, allowing external developers to build better products than Meta.
To create more coherent intelligence, new architectures are needed. Instead of monolithic models, we should consider confederations of specialized agents, each agent reflecting its team's structure and expertise. This requires a shift away from traditional organizational structures and towards more agile, adaptable ones.
Rotating people across teams can also help create shared context and a more connected social graph. This can prevent the convergent evolution toward organizational monoculture, making innovation possible.
In the future, users may interact with AI ecosystems, choosing which organizational intelligence to engage. The winners in AI won't be those with the best algorithms or most data or biggest models, they'll be those with the best organizations.
So, it's essential to design your organization before designing your AI. By intentionally creating the architecture, companies can avoid the pitfalls of Conway's Law and build coherent, effective AI systems. Watch for symptoms such as inconsistent model behavior, conflicting product directions, and integration struggles, and address them proactively.
In conclusion, Conway's Law is a powerful force in AI development. By understanding and addressing it, companies can build better, more effective AI systems and stay ahead in the competitive AI landscape.
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