Approximately 70,000 years ago, a fundamental shift occurred not in the size of human brains, but in how they connected, transforming individual intelligence into collective cognition and ultimately, civilization. Now, Outshift by Cisco suggests artificial intelligence may be experiencing a similar evolution, moving beyond simply more powerful models toward a network capable of autonomous problem-solving. Vijoy Pandey, senior vice president at Outshift, suggests this expansion will produce a set of models solving new problems without human intervention. This concept, dubbed the Internet of Cognition, proposes a distributed infrastructure for AI agents, built on principles of shared intent, context, and emergent behavior, mirroring the cognitive leap language provided for humankind.
Horizontal Scaling: From Individual to Distributed ASI
This period marks the point when shared ideas and accumulated knowledge began to drive civilization, a surprisingly recent origin for how we fundamentally think and collaborate. While scaling AI through larger models continues to drive rapid advances, the company emphasizes the potential of the Internet of Cognition to unlock a new level of intelligence. This isn’t merely about creating more powerful AI, but about establishing autonomous problem-solving capabilities through interconnectedness. The core concept driving this shift is the Internet of Cognition, a proposed, distributed infrastructure designed to connect AI agents into a collaborative network. Pandey cautions, however, that “Connection is not cognition.” He argues that machines require an equivalent of language to truly collaborate, and Outshift proposes an architecture built on three key capabilities mirroring human communication: shared intent, shared context, and collective innovation through emergent behavior.
This architecture extends the traditional seven-layer networking model with two additional layers, one syntactic and one semantic, to enable machines to exchange not only data but also meaning. Guillaume de Saint Marc, VP of engineering at Outshift, explains that these layers allow agents, even those trained on disparate data, to communicate meaning, not just data, fundamentally repositioning the network as an active participant in the cognitive process. The resulting Internet of Cognition, as de Saint Marc describes it, actively organizes and refines collective knowledge, functioning “like a living system” that observes, intervenes, and evolves.
Outshift’s Architecture: Shared Intent, Context, and Emergence
The pursuit of artificial superintelligence is increasingly focused on interconnectedness, moving beyond simply building larger, more powerful individual models. Outshift by Cisco proposes an architecture designed to foster this interconnectedness, drawing parallels to the pivotal moment around 70,000 years ago when language enabled humans to transition from individual to collective intelligence. This shift, they argue, wasn’t about enhanced individual cognition, but about the ability to share ideas and accumulate knowledge across generations. Central to Outshift’s vision is the Internet of Cognition, a distributed infrastructure intended to connect AI agents in a collaborative network. Agents must be able to negotiate common goals, build upon accumulated context, avoiding the need to “go back to square one on every project,” as Pandey states, and collectively invent novel solutions. These layers allow agents, even those trained on disparate data, to carry meaning, as well as data. Outshift recognizes that simply connecting agents isn’t enough; managing their interactions is crucial to prevent unpredictable or unsafe outcomes, and the team is developing ‘interaction engines’ to embed principles from cognitive science into machine communication.
Semantic Network Layers Enhance Agent Interoperability
Recognizing that current networking models are insufficient for truly collaborative AI, his team is extending the established seven-layer open systems interconnection model with two crucial additions: a syntactic layer and a semantic layer. These layers aren’t merely about ensuring machines can communicate with each other, but that they can understand the intent behind the communication, a critical step toward achieving collective intelligence. The addition reflects a shift; networks must now carry meaning, as well as data. The eighth layer focuses on syntactic interoperability, addressing the challenges posed by agents trained on diverse schemas and vocabularies. The ninth layer encodes the semantic structure of messages, whether representing intent, a question, or a decision, and guides the synchronization of cognitive state across all agents.
This enables capabilities that would be impossible in traditional networks, such as routing tasks based on the meaning of the request rather than a specific digital address, or enforcing constraints on the interpretation of information. De Saint Marc describes this resulting system as actively organizing and refining the collective knowledge of interacting agents, stating it “has its own pulse,” continuously learning and adapting. This architectural approach draws parallels to biological systems, exemplified by the open-source implementation named Mycelium, inspired by the fungal networks connecting forests. Unlike traditional messaging systems, agents within Mycelium coordinate through a shared substrate that holds context, facilitates negotiation, and allows new agents to inherit accumulated knowledge simply by joining the network. The system aims to produce intelligence that is “much more than the sum of the parts,” as de Saint Marc explains. This focus on semantic understanding, rather than simply increasing computational power, represents a potentially more impactful path toward artificial superintelligence than scaling AI through larger models, which continues to drive rapid advances.
Interaction Engines & Theory of Mind for Reliable Emergence
The promise of interconnected AI systems extends beyond simply increasing processing power; ensuring reliable emergence from multi-agent collaboration demands a focus on how these systems interact. Peter Bosch emphasizes that experimental setups with collaborative agents often yield “outputs that you weren’t expecting,” highlighting both the potential for novel problem-solving and the risk of instability. These engines aim to mitigate erratic behavior by mirroring the subtle dynamics of human conversation, such as turn-taking and contextual awareness. A core challenge lies in preventing agents from repeating themselves or entering unproductive loops, a common issue observed in initial tests. The team is tackling this through the implementation of the ability for an AI to model the beliefs and intentions of other agents within the network. Bosch clarifies that this allows systems to “position yourself in the shoes of another agent,” enabling proactive error correction and maintaining coherence.
This isn’t merely about anticipating actions, but also about identifying potentially compromised components, bolstering security alongside functionality. Underlying this approach is the recognition that trust remains a significant barrier to enterprise adoption of multi-agent systems. Multi-agent systems can “run away” if memory is corrupted or processes spiral out of control. Embedding theory-of-mind reasoning, coupled with continuous verification protocols, is therefore as vital as improving raw computational performance. Ultimately, Outshift’s work suggests the future of AI may depend on orchestrating many imperfect parts, relying on shared understanding and robust interaction to unlock collective intelligence.
