Submit Apps to ChatGPT: A New U.S. Growth Channel

On December 17, 2025, OpenAI opened app submissions for ChatGPT—meaning developers can now publish chat-native apps in an in-product app directory where users discover, connect, and use them inside conversations. That’s not a small product update. It’s a distribution shift.

If you build digital services in the United States—SaaS tools, consumer apps, marketplaces, internal automations—this is a clear signal: AI-driven interfaces are becoming a front door to software. Not a “nice add-on,” but a place where user intent starts (“book a trip,” “draft a deck,” “find an apartment”) and where transactions and workflows increasingly finish.

This post is part of our series on How AI Is Powering Technology and Digital Services in the United States, and this moment fits the theme perfectly: platforms are packaging AI capability, identity, and user attention into one place—then inviting developers to build on top.

What “apps in ChatGPT” actually changes

Answer first: App submissions turn ChatGPT from a single AI product into a platform for AI-powered digital services, with native discovery, interaction, and (eventually) monetization.

Historically, the distribution playbook looked like: ship a web app → buy ads or do SEO → get users to sign up → convince them to do work inside your UI. Apps in ChatGPT invert that flow. Users already have an account, already have a habit, and already express intent in plain English. Your “activation moment” can happen inside the conversation.

Here’s what’s different compared to standard plugins or integrations of the past:

• Chat-native UX: The app experience is designed to fit into conversation, not force a user into a separate dashboard first.
• Action + context: Apps can bring in external context (with user permission) and trigger actions. OpenAI’s examples include ordering groceries, turning an outline into a slide deck, or searching listings.
• Directory-driven discovery: Users can browse and search apps inside ChatGPT, and deep links can send users directly to an app’s directory page.

If you’ve spent years trying to reduce friction in onboarding, this matters because the lowest-friction interface is the one users already use every day.

The app directory: why discoverability is the real story

Answer first: The app directory is a new acquisition surface, and for many teams it will outperform traditional channels because it targets users at the moment they’re asking for help.

When software moves into a directory, two things happen:

1. Search behavior changes. People don’t just “Google a tool.” They search inside the environment where they work.
2. Demand becomes more qualified. Directory users typically have active intent. They’re not browsing; they’re trying to finish a task.

For U.S. startups and SaaS teams, this looks like the early days of mobile app stores—except the “UI layer” is language. That has big implications:

Language becomes the universal integration layer

If your service can be expressed as an intent (“compare these vendors,” “refund this order,” “summarize this contract,” “schedule a site visit”), it can be packaged as an app users call when needed.

That’s especially relevant in the U.S. economy where digital services are fragmented across vertical SaaS, marketplaces, and specialist providers. A conversational interface is the glue.

Recommendations will matter as much as rankings

OpenAI notes it’s experimenting with surfacing relevant apps using conversational context, usage patterns, and user preferences, and giving users feedback mechanisms. Translation: discovery won’t be only “search and click.” There will be moments where the platform says, effectively, “This app fits what you’re trying to do.”

From a growth perspective, that’s a different optimization problem than SEO:

• You’re optimizing for task success and repeat usage, not just clicks.
• You’re optimizing for trust signals (safe data handling, transparent permissions) because users will drop apps quickly if anything feels off.

What makes a ChatGPT app succeed (and what fails fast)

Answer first: The strongest ChatGPT apps are tightly scoped, remove steps from a real workflow, and produce a result the user can verify in seconds.

Most companies get this wrong by trying to cram an entire product into chat. Chat is not a replacement for every interface. It’s a great interface for:

• intake (collecting constraints)
• decision support (options + tradeoffs)
• drafting (copy, plans, emails, briefs)
• orchestration (calling tools, triggering workflows)

It’s weaker for: complex visual manipulation, dense multi-pane analysis, and long configuration screens.

A practical “tight scope” test

If your app can’t answer these three questions clearly, it’s probably too broad:

1. What job does this app finish in under 5 minutes?
2. What input does it need from the user to start?
3. What output proves it worked?

Examples of tight scope in U.S. digital services:

• Local services: “Find three licensed electricians within 10 miles who can come this week and accept credit cards.” Output: shortlist + booking link.
• B2B ops: “Generate a customer-ready renewal summary from these notes and last invoice.” Output: formatted email + attachments.
• Real estate: “Search apartments with these constraints and draft outreach messages to the top two listings.” Output: listings + messages.

Design for conversation, not for features

Apps in ChatGPT are triggered by @ mentions or selection from a tools menu. That means the user is literally calling your app by name in a moment of need.

Good chat-native design usually includes:

• A clear first turn. Your app should guide the user with 2–4 quick prompts, not a wall of questions.
• Progressive disclosure. Ask only for what’s required now; collect optional details later.
• Verification steps. Before doing anything irreversible, show the action you’re about to take.

A simple but effective pattern:

1. Confirm intent
2. Request minimal inputs
3. Propose a plan
4. Execute
5. Summarize + offer next action

Submission, review, and “MCP connectivity”: what teams should plan for

Answer first: Publishing in ChatGPT is closer to shipping on an app store than adding an integration—expect review, metadata work, testing, and regional availability decisions.

OpenAI’s submission flow (via the OpenAI Developer Platform) includes:

• connectivity details (including MCP connectivity)
• testing guidelines
• directory metadata
• country availability settings

Even if you’re a small team, treat this like a real product launch. The directory page is your storefront.

What to prepare before you submit

If I were advising a U.S. SaaS team shipping their first ChatGPT app, I’d push for these readiness items:

• A single, measurable success metric: e.g., “booking completed,” “ticket resolved,” “draft approved.”
• A failure-safe path: if data isn’t available or a downstream system is down, the app should degrade gracefully.
• A human-readable permissions explanation: users should understand what’s shared and why.
• Support loop: a lightweight way to capture feedback and triage issues.

Availability: don’t accidentally block your market

Country availability settings sound like a detail, but they’re a go-to-market choice. If your service is U.S.-only (common for logistics, healthcare-adjacent workflows, regulated products), be explicit. If you can support Canada or the UK on day one, consider it—but don’t expand faster than your compliance and support can handle.

Safety and privacy: the fastest way to lose trust

Answer first: In an AI app ecosystem, privacy isn’t a legal checkbox—it’s a conversion factor.

OpenAI requires apps to follow submission guidelines around safety, privacy, and transparency, comply with usage policies, be appropriate for all audiences, and follow third-party terms when accessing content. Developers must provide a clear privacy policy and should request only the data needed.

The user experience also matters: when a user connects an app, they’ll see what data may be shared and can disconnect at any time.

Here’s the stance I recommend: treat user data like borrowed trust. Borrowed trust is easy to lose and hard to earn back.

Practical privacy moves that improve adoption

These are small choices that often increase connection rates:

• Minimize scopes: request only the narrowest set of permissions required.
• Explain in plain English: “We use your order history to recommend reorders; we don’t store it after this session.”
• Make “disconnect” non-punitive: users shouldn’t fear breaking their account by disconnecting.

In the U.S., where consumers are increasingly sensitive to data sharing and businesses face growing compliance pressure, privacy-forward design is also a competitive advantage.

Monetization: what’s possible now and what’s likely next

Answer first: Early monetization is indirect—apps can link out to complete transactions for physical goods—and the bigger opportunity is building an acquisition loop that reduces customer acquisition costs.

OpenAI says that in this early phase, developers can link out to their own website or native app to complete transactions for physical goods, and that additional monetization options (including digital goods) are being explored.

For lead generation—the goal of this campaign—this is the pragmatic play:

• Use ChatGPT apps as a high-intent top-of-funnel
• Capture leads when the user is ready to act
• Close the transaction in your owned environment (for now)

A simple lead-gen funnel that fits ChatGPT apps

1. App helps user define requirements (budget, timing, constraints)
2. App produces a shortlist or draft deliverable
3. App offers next step: “Want me to schedule this / request quotes / generate an invoice?”
4. User continues in your site/app to finalize

That’s not flashy, but it works because it aligns with how people actually buy services: they start with questions, then they commit.

What this means for the U.S. digital economy in 2026

Answer first: App submissions to ChatGPT lower the barrier to shipping AI-powered digital services and shift competition toward execution, trust, and distribution inside AI platforms.

The U.S. has been the most aggressive market for SaaS adoption and digital-first service delivery. The next phase is about AI-first service delivery—where software doesn’t just provide tools, it actively completes workflows.

Expect three changes:

1. More “micro-services” packaged as apps: small, specific capabilities that do one thing extremely well.
2. Faster iteration cycles: chat-native products can often be improved weekly because the interface is largely language + tool calls.
3. A premium on reliability: the winners won’t be the ones with the most features; they’ll be the ones that consistently finish tasks.

If you’re building in this space, now is the moment to decide: are you trying to bolt AI onto an existing product, or are you building an experience where AI is the product’s primary interface?

A useful rule: if your customer’s first step is “open a ticket” or “start a chat,” your product is already halfway to being an AI-native service.

Next steps: how to decide if you should build a ChatGPT app

If you’re considering a ChatGPT app submission, start with three decisions:

1. Pick the workflow: choose one workflow with clear inputs and a measurable output.
2. Pick the data boundary: define exactly what you need to access, what you’ll store, and what you won’t.
3. Pick the distribution plan: directory listing, deep links, and how you’ll drive early usage (customers, newsletter, partnerships).

The teams that will win in 2026 aren’t waiting for perfect monetization rails. They’re shipping useful apps, learning from real usage, and building trust early.

Where does your product fit: a standalone destination, or a service users call by name inside the conversation?