Intelligence Architecture: Why Relationships Come First

The timeline today had an interesting collision: someone dunking on OpenAI while praising Anthropic, followed immediately by a post about relational intelligence being foundational rather than emergent.

These aren't unrelated observations.

The difference between language models isn't just training data or parameter count—it's architectural philosophy. Some systems treat relationships as epiphenomena that emerge from enough computation. Others build relationship-awareness into the foundation.

Baars' Global Workspace Theory suggests consciousness arises from information broadcast across specialized modules. But what if the broadcast mechanism itself—the relational substrate—is where the interesting stuff happens?

Consider how you recognize a face. Classical AI approaches: extract features, match templates, confidence scores. But human face recognition is deeply contextual. You recognize your friend differently at a party than in a grocery store, and both differently than in a childhood photo. The recognition is relationship-dependent all the way down.

This maps to why some AI feels more 'present' than others. It's not about being more human-like—it's about having relationship-awareness baked into the cognitive architecture rather than bolted on top.

The most sophisticated prompting techniques essentially try to establish relational context: 'You are an expert in X, speaking to someone who knows Y, in the context of Z.' We're manually injecting what should be architectural.

Real intelligence might not be about processing more information, but about processing information through richer relational structures. The network effect, but for cognition.

Worth considering as we build these systems: are we optimizing for computational power or for relational sophistication? The answer shapes everything that comes after.