The creative process with AI involves exploring many options, most of which are imperfect. This makes the collaboration a version control problem. Users need tools to easily branch, suggest, review, and merge ideas, much like developers use Git, to manage the AI's prolific but often flawed output.

To maximize AI's impact, treat LLM skills and prompts like a centralized codebase. When one person discovers a better technique, it should be integrated into a shared, version-controlled repository, ensuring the entire team benefits from individual learnings.

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.

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.

Human intelligence leaped forward when language enabled horizontal scaling (collaboration). Current AI development is focused on vertical scaling (creating bigger 'individual genius' models). The next frontier is distributed AI that can share intent, knowledge, and innovation, mimicking humanity's cognitive evolution.

Today, most AI use is siloed, with individuals prompting alone. The real value is unlocked when AI becomes a team sport, with specialists building systems that are shared, iterated upon, and used collaboratively across the entire organization.

While products like GrokBot push the 'team of AI agents' metaphor, some argue this is counterproductive. An alternative model is emerging: a shared workspace where teams access skills and context, treating AI as a shared utility or consultant rather than managing numerous individual AI 'teammates.'

Treating AI as a personal assistant solves individual tasks but not team coordination. The solution is to deploy "AI Teammates"—integrated agents with specific roles, permissions, and the ability to work with multiple stakeholders within a shared workflow, autonomously moving projects forward.

Today's AI agents like Codex primarily operate as single-player tools on your desktop. The next wave involves multiplayer agents that live in collaborative spaces like Slack. These team-based agents can be accessed by anyone, share knowledge, and automate group workflows, creating new challenges in permissions and shared memory.