AI App Discovery: How ChatGPT, Ask Play & Gemini Find Apps for Users

A user opens ChatGPT and types: "I need something to split a dinner bill with three friends." A few seconds later, ChatGPT hands back one app — not a scrollable list of ten, not a page of search results with ads at the top. One.

That's the shift. For twenty years, App Store Optimization has been about winning a ranked list: get into the top few results for a search term, and you get a share of the clicks. When a large language model (LLM — the kind of AI behind ChatGPT and Google's Gemini) is the one answering the question, there often isn't a list to rank in. There's one answer, or a short conversation that leads to one recommendation. Your app either gets picked, or it doesn't exist as far as that conversation is concerned.

This piece covers three real, currently-shipping systems — OpenAI's ChatGPT App Directory, Google Play's "Ask Play," and Gemini's own app discovery — how each one actually decides what to recommend, and the concrete, unglamorous things you can do about it this week. This space is genuinely new and moving fast, so wherever a claim isn't fully nailed down by an official source, it's flagged as such rather than stated as settled fact.

The mental model: three different doors, not one

It's tempting to lump "AI discovery" into one trend and one checklist. In practice it's three structurally different systems, and conflating them leads to wasted effort.

Worth understanding why this shift is happening at all, because it explains why the old playbook doesn't fully transfer. A traditional search result is a list because the search engine doesn't know which result is actually right for you — it hands you ten guesses and lets you pick. A large language model behaves differently: it reads a question, reasons about what the person actually wants, and tries to commit to one useful answer, the same way a knowledgeable friend would rather than handing you a stack of brochures. That's genuinely useful for the person asking. It's also why "ranking eleventh" stops being a meaningful outcome — you're either the answer, part of a short list the model mentions, or invisible to that particular conversation entirely.

Path 1 — ChatGPT's App Directory: apps that live inside the chat

In late 2025, OpenAI opened submissions for third-party apps that run directly inside a ChatGPT conversation, built on the Apps SDK — an open standard based on the Model Context Protocol (MCP) per OpenAI's own announcement. Approved apps let a user do something — order groceries, turn an outline into a slide deck, search for an apartment — without leaving the chat, and they now appear in an in-product directory for browsing (OpenAI).

This is the most different-from-ASO of the three. You're not optimizing a store listing — you're building a small tool ChatGPT can call, submitting it for review, and hoping it gets picked when relevant.

Path 2 — Google Play's "Ask Play": a chat layer over your existing listing

Announced at Google I/O 2026, Ask Play is a Gemini-powered chat interface that sits directly on a Play Store listing page and answers a shopper's questions about that specific app — pricing, core features, how it works — before they install (reported by Yahoo Tech, citing Google's I/O keynote). It's rolling out gradually to Android users rather than everyone at once (Android Headlines).

This is the most actionable path for most developers, because Ask Play doesn't need a separate submission — directionally true, unverified the working assumption (not explicitly confirmed in Google's public materials as of this writing) is that it's grounded primarily in your existing store listing text and possibly reviews, meaning the description you already have is the raw material it's working from.

Path 3 — Gemini app and web discovery

Separately, Google has said apps and games will become discoverable directly through the Gemini app on Android and the web, letting developers reach Gemini's user base without that user ever opening the Play Store first (Yahoo Tech). This is the least mature and least documented of the three — treat anything specific here as directionally true, unverified until Google publishes clearer developer guidance.

The mechanics: what each system is actually reading

The single most useful thing to understand right now: none of these systems have a separate "AI optimization" field you fill in. They're reading things you've already written, or a technical integration you build once. There's no new keyword box.

For the ChatGPT App Directory specifically, early developer reporting describes discovery as still basic: the app's name carries the most weight in whatever internal indexing exists, long-tail keyword search doesn't reliably surface apps yet, and keyword-stuffing a listing risks outright rejection rather than just underperforming (Noodle Seed, a developer-focused writeup on Apps SDK submissions). OpenAI has said it plans to feature high-quality apps more often, add App Store Connect-style analytics (impressions, conversions, engagement), and likely factor in user reviews eventually (OpenAI) — none of that is live yet, so don't build a strategy around metrics you can't see.

Watch it happen: how Ask Play likely reads a listing

Worked example: the same app, two different descriptions

Here's an illustrative (not real) example of why this matters concretely. Imagine an indie budget-tracking app called "Ledgerly." A user asks Ask Play: "does this support multiple currencies?"

Try it: AI-discoverability self-check

Answer these about your own app's current store listing. This gives a rough, illustrative readiness signal — not an official score from any platform.

Decision framework: what to actually do this quarter

1. Rewrite your description for factual completeness, not keyword density. Answer the 5-6 questions a real buyer would ask before installing — pricing, platform support, offline behavior, data/privacy basics, what it doesn't do.
2. Check what Ask Play currently says about your app if it's live for your listing in your region. This costs nothing and tells you directly whether your existing text is producing good answers.
3. Only build for the ChatGPT Apps SDK if your product genuinely fits an in-chat action (booking, lookup, generation) — not as a marketing-only listing. Early access rewards real utility, and keyword stuffing here specifically risks rejection.
4. Track referral traffic from these surfaces separately once your analytics tooling supports it, rather than lumping it into generic "organic."
5. Don't over-invest yet. All three systems are early and rolling out gradually — directionally true, unverified exact current install-share from any of them isn't publicly broken out by Google or OpenAI as of this writing.

Common mistakes

• Treating this as a new keyword field. There isn't one. The lever is factual completeness in text you likely already have.
• Keyword-stuffing a ChatGPT Apps SDK submission. Reporting on early submissions specifically flags this as a rejection risk, not just an underperformance risk.
• Assuming these channels already drive meaningful install volume. They're rolling out gradually as of 2026 — worth preparing for, not worth panicking over.
• Never actually testing the assistant yourself. If Ask Play is live for your app, asking it real questions is the single fastest way to find a gap in your listing.
• Writing for the AI instead of for the person. A description stuffed with facts nobody would say out loud reads as unnatural to both a human shopper and, ironically, produces a worse summary — these systems tend to reward text that reads like an honest answer, not a spec sheet.
• Waiting for official guidance before touching anything. Both Google and OpenAI have said more developer tooling is coming — analytics, clearer ranking signals — but the description-quality work is useful today regardless of what ships later, since it also helps traditional search and human readers.

One more thing worth sitting with: this is still early enough that a small, deliberate developer can move faster than a large team weighed down by an existing content-approval process. Rewriting a description to be factually complete is an afternoon of work, not a quarter-long project — which makes this one of the rare ASO opportunities where being small is genuinely an advantage rather than a constraint to work around.