By enabling users' AI agents to coordinate with each other, Instinct is creating a powerful network effect for the 'agentic era'. This agent-to-agent communication layer serves as a strong competitive moat, locking users into its ecosystem as the network's value increases with each new user.

AI agent platforms are powerful but operate in a single-player mode. The key to viral growth is introducing multiplayer functionality, allowing teams to bring their specialized agents into a shared environment. This turns a productivity tool into a collaborative platform with powerful network effects.

As AI and better tools commoditize software creation, traditional technology moats are shrinking. The new defensible advantages are forms of liquidity: aggregated data, marketplace activity, or social interactions. These network effects are harder for competitors to replicate than code or features.

Single-player AI features are easily copied. The true defensibility for AI assistants lies in multiplayer functionality where agents can interact and share context-aware information on behalf of their users. This "agent-to-agent" communication creates a powerful, sticky network effect that locks in entire teams.

Current AI hits like ChatGPT are "single-player" tools. The next breakthrough will be a social, multiplayer experience built on network effects and user-generated content, an area where technically-focused AI labs are culturally weak.

As AI accelerates technological progress and shortens relevance cycles, traditional tech moats become less defensible. However, network effects—especially in complex, fragmented marketplaces—remain a powerful and durable advantage. An AI agent cannot be simply prompted to "create a network effect."

Asana's CEO argues its key differentiator is a "multiplayer mode" where entire human teams can collaboratively train and correct an AI agent within a project. This contrasts with typical one-on-one chat interactions, creating a unique, compounding learning environment for the agent that Asana believes cannot be easily replicated.

To build a moat against large language models like ChatGPT, focus on features they will never prioritize. Build multiplayer functionality, a strong user community, and human-in-the-loop support services around the core AI. These layers create defensibility that a generic interface cannot replicate.

By adding group chat functionality, OpenAI is turning ChatGPT from a solitary utility into a collaborative social platform. This strategic move aims to build a network-effect moat, increasing user retention and defending against competitors like Meta AI before they can gain traction in the market.