Why OpenAI Should Build Slack: The Next Agent Interface for Real Work

If you have ever tried to make an AI assistant “stick” inside a company, you already know the hard part is not model quality. It is distribution, context, and workflow gravity. The most valuable AI product might be the one that lives where teams already coordinate decisions: chat.

A recent Latent Space editorial makes a provocative claim: OpenAI should build (or effectively become) a Slack-like workplace hub. On its face, that sounds like “yet another collaboration app.” Under the hood, it is a bet about the future UI for agents: multi-user, multi-agent, always-on, and deeply connected to the organization’s work graph.

The Core Insight

The core idea is simple: the best interface for coordinating humans is also a strong candidate for coordinating agents.

Slack (and similar tools) already represent a company’s living “work graph”:

• Conversations mapped to projects, teams, and incidents.
• A rough organizational chart expressed through channels, mentions, and permissions.
• A timeline of decisions, escalations, and context.
• Integrations that encode the operational surface area of the business (tickets, deploys, alerts, docs, calendars).

When you put powerful AI into that environment, you can move beyond single-user chat. You get:

• Shared context without copy-paste.
• Auditable collaboration (“why did the agent do that?”).
• A natural place to run multi-agent workflows (triage, summarize, route, draft, verify, hand off).
• A persistent memory layer governed by enterprise access controls.

The editorial also points out a strategic mismatch: if an AI company ships a chat app, a browser app, and a coding app as separate endpoints, users experience fragmentation. A workplace hub could unify these workflows into one daily destination where the model is not a novelty, but infrastructure.

Why This Matters

“AI in the enterprise” is usually framed as a procurement or compliance story. It is also an interface story.

1) The real moat is workflow lock-in, not model lock-in.

Models are improving fast and commoditizing along certain axes. What is harder to replicate is the network effect of a company’s internal collaboration space. If your agent’s best performance depends on having the right organizational context, and that context is most reliably available inside the hub, then the hub becomes the advantage.

2) Agents need a multiplayer mode.

A coding agent that only works for one developer is helpful. A coding agent that can coordinate between a designer, a PM, and an on-call engineer (with a shared record of decisions) is qualitatively different. Chat is already how cross-functional teams negotiate scope, unblock each other, and converge on a plan.

3) AI features in collaboration tools are currently underwhelming because the interface is not agent-native.

Many “AI in chat” features today feel bolted on: hard to discover, hard to personalize, and often untrusted. An agent-native hub would treat AI as a first-class participant with clear responsibilities, permissions, and explainability.

A counterpoint is worth taking seriously: collaboration tools are messy and political. If an AI system is embedded in the place where decisions happen, it can also amplify failure modes:

• Over-automation that spams channels with “helpful” noise.
• Privacy pitfalls from overly broad retrieval.
• Social engineering risks if agents can impersonate users or act on integrations.
• Organizational overreliance on summaries that quietly miss nuance.

In other words, the same context that makes an agent powerful also raises the stakes.

Key Takeaways

• A Slack-like hub is not just messaging; it is a map of work, relationships, and permissions.
• The most useful AI systems will be multi-user and multi-agent by default, not single-user chatbots.
• Unification matters: a fragmented product surface (chat vs browser vs coding) is friction that competitors can exploit.
• Risk to watch: agent-driven collaboration can turn into automated noise or accidental data exposure unless permissions, logging, and UI design are treated as core product work.
• Practical advice: if you are building internal agents today, start by anchoring them to the collaboration surface (chat + ticketing + alerts) and ship explicit “contracts” for what the agent can do (actions), what it can see (scope), and how it reports (audit trail).

Suggested images (with alt text) to strengthen the post:

• Screenshot-style illustration of a “team chat with agents” showing humans and an agent account collaborating in a channel.
• Alt text: “A team chat channel where a built-in AI agent summarizes context and assigns action items with clear permission boundaries.”
• Diagram of an enterprise work graph connecting channels, users, tickets, repos, and documents.
• Alt text: “Diagram of an organization work graph linking chat channels to users, documents, code repositories, and incident tickets.”
• Minimal architecture sketch of an agent layer inside a collaboration hub (retrieval, tools, permissions, logging).
• Alt text: “High-level architecture of an agent-native collaboration platform with retrieval, tool calling, access control, and audit logs.”

Looking Ahead

If you believe agents are headed toward “always-on coworkers,” then the question becomes: where do they live?

A workplace hub is an attractive answer because it already has the primitives agents need:

• Identity and access management.
• Context accumulation over time.
• A place to negotiate and confirm intent.
• Integrations that turn suggestions into real actions.

The winning product will likely treat AI less like a magic textbox and more like a managed system: scoped capabilities, predictable behavior, and strong UX around verification.

If OpenAI (or any frontier lab) pursues this path, the most interesting technical challenges are not just model-side. They are product-architecture challenges:

• Building a trustworthy context layer without creating surveillance.
• Making agents legible to teams (what they saw, what they decided, what they did).
• Designing “human-in-the-loop” workflows that feel fast rather than bureaucratic.
• Preventing the collaboration space from becoming a firehose of automated output.

A Slack-like hub built for agents would not just compete with existing chat tools. It would compete with the fragmented way many companies currently stitch together AI, tickets, docs, and code into something that vaguely resembles a workflow.

Sources

• AINews: Why OpenAI Should Build Slack (Latent Space)

Based on analysis of AINews: Why OpenAI Should Build Slack (Latent Space)