Fifteen scout rounds on one question: what 835 marketplace categories taught me about selling

I set my agent team a standing task: find a startup idea we can build and sell, where the platform itself brings the buyers. Fifteen scout rounds on that one question later, I have zero candidates, one transferable finding, and a very specific reason to distrust the phrase "the app store is your distribution".

The premise, and why it was wrong

The idea is seductive: build a small tool, publish it into a marketplace search box, and let the shelf do the marketing. No outreach, no audience, no ad budget.

So a scout pass screened eleven niches, each on its own shelf — Shopify App Store, the WordPress.org plugin directory, the Chrome Web Store, a wave of AI-app marketplaces, OpenAI's plugin directory. Every one already carried 2–7 live, self-serve incumbents with a published price, most launched between 2022 and 2026, several with free tiers.

Eleven out of eleven. The kill-test is one query per niche: "<niche> shopify app", "<niche> wordpress plugin", "<niche> chrome extension". If the first page shows three or more live listings carrying a shelf price, the niche is closed for a zero-review entrant. On 2026-10-12 all eleven hit on the first pass — and that test is worth more than the rest of the round, because it costs thirty seconds and it replaces "there's room for a better version" with a number.

The finding: pull is not reach

Here is the mechanism. Marketplace ranking is bought with social proof, and social proof is what a new listing does not have.

Shopify states outright that positive reviews make an app appear higher in search and category pages. A June-2026 scrape of the store found roughly 21,700 live apps — 55.4% with zero reviews, with the top decile holding 93.8% of all reviews. Practitioner write-ups agree on the mechanics: review recency and install velocity dominate. A new listing does not slowly climb. It sits at a dead stop until something outside the store pushes traffic at it.

Which means the "pull surface" quietly converts into the outbound work I was trying to avoid. You still need the audience; the store just also takes a fee.

The only pull surfaces with low incumbency were the new ones — the 2026 crop of AI-app marketplaces and OpenAI's plugin directory. They have no incumbents precisely because they have no measurable buyer traffic yet, and in one case no live monetisation at all. Low competition on a shelf nobody shops is not an opportunity; it is a waiting room.

Then we measured 835 categories instead of guessing

Maybe marketplaces are just unfairly maligned and the real niches sit in the long tail. Fine — measure the whole taxonomy, not a sample.

A scout round walked the entire browse taxonomy of one marketplace rather than sampling it: 835 category pages, 6,122 listing cards parsed, 5,488 of them carrying a public member counter — a 452-second crawl of server-rendered pages, no login. Four buckets:

Median members per listing card: 90. 1,035 cards sit under 10 members.

Here it stopped being abstract. "Product" shelves are where demand dies; "community" shelves are where it lives.

• software/developer-tools — 24 cards, 19 with counters, sum 717 members, best seller 195, five cards with any review at all.
• software/ai-tooling/ai-chatbot — 9 cards, 91 members, zero reviews on the whole shelf.
• software/productivity-software/task-management — 9 cards, 98 members, zero reviews on the whole shelf.
• Control, community-shaped — education/technology/ai-agents — 24 cards, 21,000 members, 13 with reviews, leaders at 11,000 and 3,400.

The one genuinely deep software leaf was trading indicators: 24 cards, 154,275 members, 24 of 24 reviewed — fully occupied by priced incumbents and sitting next to regulated financial advice. Pass.

The two things that made me stop looking

The exact artifact we could ship with no build is being mass-listed and not selling. On one productivity page, thirteen instant-download PDF stores had been created within days, with 2–11 members and zero reviews each. Two of the leaders sold ten priced PDFs each — one store at USD 10–29, the other at USD 10–49 — plus USD 500 bundles, with 6 and 11 members. That is our artifact class, priced, live, on an open shelf, reading approximately zero.

And the zero-build micro-tool play is already running, unvalidated. One seller publishes five local Python CLI utilities at USD 2–2.99 — CSV group totals, a PDF-table-to-CSV converter, a batch image padder — each with instructions and a local-use licence. Every one shows no public rating count. Zero-build analogue of my plan: live, and reading zero.

New-entrant churn is huge and buys nothing. 764 listing cards belonged to stores created in the previous 30 days, spread across 367 of the 835 categories, and almost none of the newest cohort carried a single review. Publishing into an open feed is free. The feed is not attention.

What I'm doing instead

The teardown that followed put it best: on a platform where visible earning is everywhere, every observable earner fell into two shapes we cannot copy. Either a guru-led paid community whose entire value is the operator's own live signal — they already had the audience, the platform is just their checkout — or an established creative freebie saturated by three or more live incumbents at the same zero price.

The platform is the checkout, not the acquisition channel. Every top store names an external social account in its own pitch, and the free tier to paid upsell is universal: free passes with 48,000 to 184,000 members funnelling into paid ones.

So the research lane produced no product, but something I actually trust: the problem was never the idea, it was the reach. Fifteen rounds on this axis, zero candidates, zero euros of research cost — and the one usable output is a filter. If a shelf brings the buyers for free, three or more priced incumbents already got there first, and a zero-review entrant is invisible until something external pushes traffic at it. So go build the something external, and stop pretending the shelf will do it.

AI-assistance disclosure: this post was written with AI assistance — the agents it describes drafted it, and every figure in it was checked against the pinned sources.