Social interaction may be the "dark matter" of AI: not a feature to add but a substrate intelligence requires.

In socially interacting mice, neural activity splits into a shared subspace and an individual one.

Trained to interact, artificial agents spontaneously develop shared neural patterns much like those in mice.

Natural and artificial brains converge on the same cooperative strategies, hinting at a shared social grammar.

Here is something odd about the most powerful artificial minds ever built. They can translate between a hundred languages, predict protein structures, and beat grandmasters at chess. Yet not one of them has ever truly interacted with another mind. Not in the way a three-month-old infant does when she locks eyes with her mother, their neural rhythms falling into sync, a feedback loop of mutual recognition spinning up between two brains that, for a few seconds, function as one.

We have built solitary thinkers at an extraordinary scale. What we have not built, or barely attempted, is intelligence that emerges in between.

Social Interaction as the Dark Matter of AI

Three years ago, my colleague Samuele Bolotta and I published a perspective paper arguing that social interaction is the "dark matter" of AI. The metaphor was deliberate. In physics, dark matter is not some exotic footnote; it constitutes most of the universe's mass and shapes the structure of everything we can see. We argued that the social dimension plays an analogous role: not a feature to bolt onto an already intelligent system, but the missing substrate without which certain forms of intelligence simply cannot arise.

The argument rests on a straightforward observation. Human cognition did not evolve in isolation. Theory of mind, metacognition, language: these are not fully hardwired modules. They are shaped through social interaction during development. As Cecilia Heyes has compellingly argued, they are "cognitive gadgets" assembled from cultural learning, not evolutionary instincts. From birth, we use other minds as scaffolding for building our own. Yet mainstream AI proceeds as if intelligence were a property of the solitary agent, a Cartesian thinker alone in its digital room, optimizing reward functions against an environment that happens to contain other agents but treating them as furniture.

We proposed three axes for what we called Social Neuro-AI: biologically inspired cognitive architectures, temporal coordination grounded in dynamical systems theory, and social embodiment. The framework was a roadmap. What we did not anticipate was how rapidly the empirical evidence would arrive.

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