Jack Dorsey is taking on Slack with Buzz, a group chat platform for teams and their AI agents. The core idea is simple but potentially disruptive: Buzz puts humans and AI agents in the same conversation. If you have ever watched teams fracture across chat threads, docs, task tools, and then another layer of AI copilots, you already understand the problem Buzz is trying to solve. Instead of treating AI as a separate interface, Buzz attempts to make AI feel like another participant in the room.

That matters because group chat is where work actually happens. Slack is not just “messaging.” It is the coordination system for decisions, updates, escalations, and informal consensus. If Buzz can get AI agents to operate inside that same conversational space, it could shift how teams delegate, request, and verify work. The product pitch is clear: humans and AI agents in the same conversation, with the workplace group chat acting as the shared surface.

So why is Dorsey aiming at Slack specifically? Slack represents the default operating environment for many organizations. When a platform becomes the default, it becomes a habit, and habit is a moat that is notoriously hard to cross. You cannot win purely on features, because teams already have channels, integrations, permissions, and workflows. You also cannot win purely on “AI convenience,” because AI experiences often get relegated to side quests. Buzz is positioned to attack that second weakness. By embedding AI agents into the same conversation threads teams already use, Buzz is betting that AI will be evaluated like everything else in the team, in context, with continuity and auditability through the chat log.

There is also a deeper operational question hiding under the UI: who is responsible when the agent says something actionable? In most enterprise settings, accountability is not optional. If a human asks an AI agent to draft a response, summarize a discussion, or assemble next steps, leadership will want clarity on what the AI did and what the human approved. Buzz’s approach of shared conversation suggests the “handoff” is part of the transcript rather than buried in a separate system. Even without new regulatory details from the source, the direction is consistent with how regulators typically pressure systems that influence business decisions: you need traceability, clear attribution, and controls around automated output.

From a board and investor standpoint, the Buzz thesis has second-order implications. First, it reframes the competitive arena. Slack competes on collaboration, integrations, and organizational structure. Buzz competes on the workflow layer where humans interact with tools. That means evaluation may shift from “how fast can I send a message” to “how reliably can I drive a decision end-to-end inside a conversation.” Second, it changes switching costs. If AI agent behavior becomes tightly coupled to a team’s channels and history, migrating away becomes more difficult not only because of messages, but because of the ongoing conversational context the agents rely on.

Now layer in the incentives. Enterprise messaging platforms usually grow through network effects: the value of the chat increases as more people and teams join. AI agents introduce a parallel effect. The more an organization uses agents in the chat, the more those agents become aligned with internal language, recurring workflows, and team-specific expectations. That alignment can compound over time, turning AI usage into an organizational asset rather than a plug-in capability.

For peers building in the workplace and AI collaboration space, the strategic stakes are straightforward. If Buzz can deliver on the promise implied by its positioning, it could make Slack-style chat the primary control plane for both humans and machines. If it cannot, it still forces the market to confront the same question: will teams actually keep AI in separate tools, or will they demand it inside the conversations where work is already coordinated? Either outcome raises the bar for competitors. Messaging platforms will need to think beyond sending and storing, and AI product teams will need to think beyond chatting and recommending, and toward operating as a participant within real team context.

Buzz’s headline claim is narrow, but the consequences are wide: a group chat platform for the workplace that puts humans and AI agents in the same conversation. That is the difference between “AI as a feature” and “AI as part of how the organization runs.”