Muse AI Connectors: The Next App Store Moment?
Greg Eisenberg analyzes Meta's new Muse AI connectors as a potential "app store moment" for AI, drawing parallels to Apple's 2008 App Store launch. He provides four startup ideas, explains how to build connectors using coding agents, and outlines distribution strategies beyond relying on Meta's directory featuring.
Summary
Greg Eisenberg opens by positioning Meta's Muse connector platform as a major business opportunity comparable to Apple's App Store launch in 2008. He explains that just as Apple created a marketplace for developers to build useful apps for iPhone users, Meta is now enabling developers to create connectors that allow Muse (Meta's AI assistant) to access external services when users request help.
The core mechanism of connectors is explained through an API framework: when a user asks Muse for help (e.g., booking a podcast studio), the connector allows Muse to query a service for availability, pricing, and booking confirmation. Real-world validation comes from companies like Duffel, which has processed over $1 billion in annual transaction value and is now betting on Muse integration.
Eisenberg presents four specific startup ideas: (1) Lead generation for business suppliers by tracking new restaurant openings for linen services; (2) Home repair dispatch connecting homeowners with available repair technicians for specific appliances; (3) Local paddle tennis match and court finder for sport enthusiasts; and (4) Family dinner planning with grocery list integration through services like Instacart.
On the technical side, Eisenberg recommends using coding agents like Cloud Code or Codeex to build connectors by providing API documentation and plain English descriptions of desired functionality. He emphasizes the importance of testing edge cases and handling awkward requests before submission to Meta for approval.
Regarding customer acquisition, he identifies four distribution routes: (1) partnering with existing creators who have relevant audiences; (2) building product-driven viral sharing (product-led growth) into the connector itself; (3) leveraging existing connected marketplaces like Ticketmaster; and (4) hoping for featured placement in Meta's directory. He warns against banking solely on directory featuring, comparing it to his early app store experience, which felt somewhat random.
Eisenberg concludes by emphasizing the unprecedented opportunity: unlike the app store era which required significant capital and team size, anyone can now build high-quality AI connectors independently using available tools.
Key Insights
- Meta opened Muse connectors to developers as an external marketplace, enabling developers to build services that help Muse complete user requests, similar to how the 2008 App Store allowed developers to build useful phone apps and earn money from them.
- A service can become relevant midway through a larger request—for example, a user organizing an event who initially needs a venue may later discover they need equipment rental, creating a business moment where money changes hands and a connector adds value.
- Duffel, a travel booking company, has already validated its business model by processing over $1 billion in annual transaction value and is now betting on Muse integration as a new distribution channel for the same service.
- Building connectors using coding agents like Cloud Code or Codeex has democratized connector development compared to the mobile app era, which required approximately $1-2 million and teams of 10+ people to create quality applications.
- The most practical customer acquisition routes are working with existing creators, building viral sharing into the product itself, and leveraging existing marketplaces—rather than relying primarily on Meta's directory featuring, which Eisenberg characterizes as somewhat random.
Topics
Transcript
[0:00] I think a gigantic opportunity has just opened up and we need to talk about it. Meta has opened up Muse, its personal AI agent to developers and you can now submit something called a connector which gives Muse a way to use your service when someone asks for help. And I think this could be the app store moment for AI. So, if you're thinking about starting a business or you already have one, it's worth understanding what just became possible because billions of dollars are going to change hands. By the end of this video, you'll understand [0:33] what Muse connectors are and where I'd look for business opportunities. We're going to walk through how you can…
Full transcript available for MurmurCast members
Sign Up to AccessMore from Greg Isenberg
$30M Writer: Never write AI slop again
Nicholas Cole, who earned $30M+ writing online, explains how to create valuable content in the AI era by building a personal language model of your unique ideas, personality, and perspective rather than generic commodity content. He argues that voice, original thinking, and consistent association through personality details create sustainable competitive advantage, while AI should be used to repeat and remix existing ideas, not generate net new thinking.
Jev is HERE. How to use it
Jev is a new AI classifier model created by Dooo Almeida that makes fast, low-cost decisions on structured inputs rather than generating text like traditional LLMs. It processes information through a defined schema to return probability-based classifications across multiple categories, enabling applications from email triage to content clipping to flight booking automation.
Instinct AI is For Real. What You Need to Know.
Hosts Greg and Remy review Instinct AI, an invite-only personal agent app raising at a $10 billion valuation, demonstrating its capabilities for booking flights, restaurants, obtaining visas, and other personal tasks. They highlight its impressive simplicity, human-like communication style, and network effects through its trusted agent connectivity, while flagging privacy and data retention concerns.
Building a Software Factory that actually works (Full Course)
Ross Mickey explains how to build a software factory—a systematic workflow using AI agents to ship high-quality software at scale. The factory concept uses markdown files to define agent behavior and follows four key steps: isolate (branching), build (with code structure guidelines), prove (with before/after testing), and ship (with automated code review).
Build Bigger with GPT-6 Astra (here's what to do)
This episode explores practical applications of GPT-6 Astra beyond flashy demos, featuring nine actionable prompts for productivity and business, and demonstrating how Astra enables hardware projects through AI assistance with design, code generation, and supplier sourcing.