In a recent LinkedIn post, John Cutler discusses a developing pattern in how teams are adopting and utilizing artificial intelligence tools. Cutler, a prominent voice in the tech community, breaks down these adoption stages into distinct categories, offering a framework for understanding the current landscape of team-based AI interaction.
Deconstructing Team AI Adoption
John Cutler highlights that while many impressive AI demonstrations focus on a specific level of team integration, it’s crucial to differentiate between various modes of usage. He proposes three primary categories:
“Pure single-player: One person, one AI, personal setup. Context lives in your head and your files.”
This initial stage, as Cutler describes it, represents individual use of AI where the context and knowledge remain personal to the user. There is no inherent team-based sharing or collaboration within the AI workflow itself.
The ‘Shared-Context Solo’ Model
The second category, which Cutler identifies as the most common form of ‘team AI’ currently in practice, is what he terms ‘Shared-context solo.’
“Shared-context solo: A team repo with shared templates, queries, conventions. Everyone works in their own AI session, but the shelves are well-stocked. This is what most ‘team AI’ setups actually are.”
According to Cutler, this model allows for a degree of team efficiency by providing shared resources, such as templates or common queries. However, the core AI interaction remains individual, with users operating in their own sessions. The team benefits from the readily available resources, but the AI reasoning and understanding are not inherently collaborative.
Introducing ‘Multiplayer’ AI
Cutler then introduces his concept of ‘Multiplayer’ AI, which he suggests is a more advanced and potentially transformative stage of team AI adoption.
“Multiplayer: People reasoning through shared artifacts together. Interpretations travel, not just data. Understanding emerges from the interaction, not from a well-organized folder.”
In this ‘multiplayer’ mode, Cutler argues, the emphasis shifts from merely sharing data or resources to a deeper, collaborative reasoning process. He posits that true understanding and insight emerge from the dynamic interaction between team members working together on shared AI-generated artifacts. This contrasts with the ‘shared-context solo’ model, where the primary benefit is the removal of friction in accessing pre-defined resources.
The Risk of Mistaking the Foundation for the Destination
A key concern raised by John Cutler is the potential for teams to mistake the benefits of the ‘shared-context solo’ model for the ultimate goal of team AI integration. He notes that while the removal of friction in accessing shared resources is a significant advantage enabled by AI, it should be viewed as a foundational step rather than the final destination.
As Cutler poses the critical question: “The question is whether teams recognize it as a foundation or mistake it for the destination.” This suggests that teams might be satisfied with the efficiencies gained from shared repositories and templates, potentially overlooking the greater potential for collaborative intelligence and emergent understanding offered by a true ‘multiplayer’ AI approach. Cutler’s analysis provides a valuable lens for organizations to assess their current AI strategies and consider pathways for deeper, more collaborative integration.
📝 About This Content
This article is based on insights shared by John Cutler on LinkedIn.
📅 Originally posted on April 18, 2026 | View original post on LinkedIn →