Should you use AI to write your App Store listing?

Yes, with one condition: the AI writes the draft, you keep the final word. Treated as a first draft, generated metadata is faster than anything you would write by hand and usually better than what most solo developers ship after a week of guessing. Treated as a finished listing you paste without reading, it is generic copy that ranks for nothing.

The hesitation is fair, because writing a listing by hand is slow and the tools that help cost real money. A proper keyword pass means collecting terms, mapping them to your app, then squeezing the best ones into the 100 character App Store field while keeping the description readable for humans. That is hours of work per language. Traditional platforms charge for it: AppTweak starts at 79 dollars a month, App Radar runs out of free queries fast before its 69 dollar tier, ASODesk asks 29.99, and Sensor Tower is quote only with entry around 500 dollars a month. When your app has not earned its first thousand downloads, none of that is a plan.

What AI actually changed is the blank page. Given a real description of your app, a generation model produces a title that fits the character limits, a subtitle that reads like a sentence, a description with keywords where the store algorithms look for them, and a keyword list without duplicates. It does this in minutes and in every language you target, which matters because the alternative is writing the same listing ten times by hand.

Where it fails is context. A model that knows nothing about your niche writes text that could belong to any app in your category. It will translate "football" into "soccer" for the United States, and it will leave your listing in English US only if you never ask for more. That default cuts you off from the markets where your users live. An English only football app is invisible in Brazil, Turkey and Vietnam. Some languages bring downloads, others bring revenue, and the only way to learn which is yours is to be listed and watch what happens. AI can produce the Portuguese, Turkish and Vietnamese versions in one pass. Deciding that those markets matter is your job, not the model's.

The workflow that works is boring: generate, read, cut. Let the AI produce the full listing, then read every line as a stranger searching for your app. Delete the adjectives that say nothing. Keep the search terms you know your users type. Check that the description says what the app does before it says how great it is. Ship the update, watch what the stores do with it, regenerate the parts that underperform. The loop takes an evening and costs nothing.

Three questions come up every time.

Will Apple or Google penalize AI written text? No. Neither store has a rule against metadata written with AI assistance, and detection is not what you should worry about. What both penalize is keyword stuffing, misleading metadata and descriptions that promise features the app does not ship. A clean AI draft beats a hand written wall of keywords.

Isn't generated copy too generic to convert? Only when the input is generic. Give the model your app's real features, its category and the countries you care about, and the output gets specific. Give it a name and a shrug, and you get filler.

Do I still need to learn ASO? Less than before. You no longer need to memorize character limits or hand write twenty keyword variations. You still need to know who downloads your app and why, because that judgment is the difference between a listing that ranks and a listing that exists.

That is the workflow AsoFast is built around. It generates your title, subtitle, description and keywords for both stores with AI, translates the listing into 80+ languages, provides screenshot templates, and publishes straight to App Store Connect and Google Play. It runs on your machine, the credentials stay there too, and it is open source under MIT, so it costs nothing. The listing is the part of your growth that compounds, and now it takes an evening.