Every AI Platform Will Monetize App Discovery Differently. Here's What That Means for Your Business.

The Recognition-Recommendation Gap

Let's start with the data that should concern every business building on these platforms.

A research team (arXiv 2601.00912) studied 112 startups from Product Hunt's top 500 and found that ChatGPT recognizes 99% of them when asked directly. But it organically recommends only 3.3%. That's a 30:1 gap between the model knowing you exist and actually suggesting you to a user who needs what you offer.

They also tested whether "Generative Engine Optimization," the practice of adding citations, statistics, and authoritative language to your website, helps close that gap. It doesn't. No significant correlation on either ChatGPT or Perplexity.

What does correlate, at least on Perplexity (which uses real-time web search)? Reddit mentions (+0.40 correlation), backlinks (+0.32), and Product Hunt upvotes (+0.23). Real people talking about your product in real places. Not SEO tricks repackaged for AI.

On ChatGPT, the study found zero significant correlations with any web signal. Discovery appeared essentially random, likely constrained by training data cutoffs and whatever internal logic OpenAI uses for proactive suggestions. Which means for ChatGPT specifically, the levers you can pull are inside the platform, not outside it.

This is the landscape. Now let's talk about what to actually do.

Metadata Optimization Is Table Stakes, Not Strategy

OpenAI's documentation says it plainly: discovery is driven almost entirely by metadata. Your tool names, descriptions, and parameter annotations are what the model reads to decide if your app is relevant.

This matters and you should do it well. Use action-oriented tool names (appointment.book_slot not create). Write descriptions that start with "Use this when..." and include when NOT to use it. Document every parameter with examples and enums. Build what OpenAI calls a "golden prompt set" (at least five direct prompts, five indirect ones, and negative cases) and test in developer mode.

But here's the thing: every serious developer will eventually do this. Metadata optimization is necessary but not sufficient. It's the equivalent of having a well-formatted Google My Business listing in 2012. Yes, do it, but it's not what separates winners from everyone else.

The strategic question is about the discovery channels themselves and how each platform's business model shapes what gets surfaced.

The Double Monetization Thesis

This is where things get interesting, and where we think most people are missing the bigger picture.

OpenAI has announced plans to test ads with free and ChatGPT Go subscribers. That alone isn't surprising. What's surprising is the structural economics that MCP apps enable.

Unlike Google's ad model, which sends users off-platform to a business's website, ChatGPT wants to keep the user inside the conversation. The user asks about a product. The product's MCP app surfaces. The user browses, asks questions, and completes a purchase, all without leaving ChatGPT.

This creates something new: a double monetization model. OpenAI charges the business for front-end advertising placement, then takes a transaction cut on the back end. A 4% fee on sales through ChatGPT's Instant Checkout has already been confirmed by a Shopify spokesperson for merchants using that integration. We expect significantly higher percentages on digital products and services when those become available.

And here's the critical structural point:

How does a business power that in-conversation advertising experience? Through an MCP app. There is no other mechanism for a business to interactively engage with a user inside ChatGPT.

The MCP app is the execution layer that makes AI-native advertising work: answering questions, showing inventory, processing bookings, completing purchases.

This means even if your organic discovery is zero on day one, having your MCP app built and operational positions you for the paid channel. And the paid channel is where the real money will flow.

Four Platforms, Four Business Models, Four Discovery Playbooks

MCP is an open standard. Anthropic created it, OpenAI adopted it, Google adopted it. But "same protocol" does not mean "same discovery mechanics." Each platform's business model dictates how they'll surface your app.

What To Do Now

The Model Is the New Distribution Channel

We're in the earliest innings. ChatGPT's app directory has been open for barely two months. Claude's MCP Apps UI launched weeks ago. Google hasn't opened a consumer marketplace. Perplexity hasn't announced their MCP SDK.

In 2008, apps were standalone experiences users searched for. In 2026, apps are capabilities that AI models select on behalf of users during conversations.

Every AI platform will monetize this differently. The businesses that understand each platform's incentive structure and position accordingly will compound advantages that are hard to reverse. Not because the technology is hard, but because the strategic thinking is genuinely new and most people haven't done it yet.

At Noodle Seed, we're building everything we learn into how our platform deploys and optimizes apps across every AI platform. But whether you build with us or not, the time to get positioned is now.