To enable shared knowledge, a "cognitive memory fabric" is needed. This architecture combines exploratory, probabilistic AI agents with formal, deterministic representations of the world (like digital twins), providing a powerful yet safe environment for innovation.

Early AI adoption by PMs is often a 'single-player' activity. The next step is a 'multiplayer' experience where the entire team operates from a shared AI knowledge base, which breaks down silos by automatically signaling dependencies and overlapping work.

Today's AI agents can connect but can't collaborate effectively because they lack a shared understanding of meaning. Semantic protocols are needed to enable true collaboration through grounding, conflict resolution, and negotiation, moving beyond simple message passing.

The next frontier for AI isn't just personal assistants but "teammates" that understand an entire team's dynamics, projects, and shared data. This shifts the focus from single-user interactions to collaborative intelligence by building a knowledge graph connecting people and their work.

Apply the collaborative, iterative model of AI pair programming to all knowledge work, including writing, strategy, and planning. This shifts the dynamic from a simple command-and-response tool to a constant thought partner, improving the quality and speed of all your work.

Block's CTO believes the key to building complex applications with AI isn't a single, powerful model. Instead, he predicts a future of "swarm intelligence"—where hundreds of smaller, cheaper, open-source agents work collaboratively, with their collective capability surpassing any individual large model.

Current AI development focuses on "vertical scaling" (bigger models), akin to early humans getting smarter individually. The real breakthrough, like humanity's invention of language, will come from "horizontal scaling"—enabling AI agents to share knowledge and collaborate.

Karpathy identifies two missing components for multi-agent AI systems. First, they lack "culture"—the ability to create and share a growing body of knowledge for their own use, like writing books for other AIs. Second, they lack "self-play," the competitive dynamic seen in AlphaGo that drives rapid improvement.

To foster shared innovation among AI agents, "cognitive engines" are required. These serve two functions: accelerators to speed up specific tasks (e.g., complex calculations) and guardrails to ensure creative exploration remains within safe, realistic, and compliant boundaries.