the experiment
On August 5, 2026, we ran five devtool buying questions — the kind a founder types into an AI before picking a stack — through ChatGPT (gpt-4o) and Google AI Mode. Total cost: $0.042. We published exactly what came back, no edits.
The questions were deliberately the ones a buyer asks at the moment of decision:
- Best backend as a service for a SaaS app
- Best managed Postgres provider
- Best alternative to Firebase
- Best serverless database for a Next.js app
- Best database for AI agents
finding 1: ChatGPT punted on all five
On every single question, ChatGPT returned a generic pros-and-cons list with zero cited sources. No live web, no listicles, no Reddit, no G2. It named the usual suspects (Firebase, Supabase, AWS Amplify) from training data, then told the buyer to 'assess your requirements and experiment.'
That's a refused answer dressed up as help. The buyer gets a knowledge-dump and a shrug — and goes elsewhere for an actual recommendation.
finding 2: Google AI named names — from listicles
Google AI Overview named companies on all five questions, citing 9 to 27 sources each. But here's the part that matters: the sources weren't the vendors. They were third-party listicles, blog posts, Reddit threads, and G2 reviews.
Here's the raw capture for one of them — 'best managed Postgres provider' — so you can see the shape yourself:
Read those cited sources carefully. Not one is supabase.com or neon.tech. Google AI built its recommendation out of comparison blogs, a Reddit thread, and a G2 review page. The vendors appear as the answer; the listicles are the authority.
Listicles & blog posts
'8 Best BaaS Platforms in 2026,' '10 Best Managed PostgreSQL Hosting Providers,' 'Best Firebase Alternatives in 2026.' These are the sources Google AI leaned on hardest — not vendor docs.
Reddit threads
r/Hosting, r/PostgreSQL, r/androiddev. Real developer voice, weighted as evidence. Google AI treats a Reddit thread as a citation.
G2 & review aggregators
G2 Supabase reviews, side-by-side comparison guides. Aggregate third-party sentiment — not vendor marketing.
who won, and why
Two names dominated across the five questions: Supabase and Neon. Supabase was named 'Best Overall for SaaS,' 'Best All-in-One BaaS,' and the top Firebase alternative. Neon was named 'Best for Serverless & AI Workloads' and 'Best Overall for Postgres & Vercel.'
They didn't win because they're objectively the best product. They won because they made themselves the thing AI retrieves. Supabase has reference density — a citable doc page for nearly every narrow question. Neon has function-per-page structure and a machine-readable catalog. The vendors who get named are the ones who built the surface area AI reads.
Supabase: reference density
A citable reference page for nearly every narrow question. When AI asks 'what does Supabase do for X,' the answer is one retrieval away. Density beats a slick homepage.
Neon: function-per-page
One page per function, plus a machine-readable catalog. AI doesn't have to infer the hierarchy — it can read it. Structure, not vibes.
why this matters to your business
Here's why this isn't just a devtool story. The same dynamic runs every category AI answers — including yours. When a homeowner asks 'best HVAC company near me,' AI names whoever made themselves the listicle: the Angi rows, the Reddit threads, the Google Business Profiles, the review sites. Not the HVAC company's homepage.
You can't out-build Supabase's docs team. But you can be the listicle for your market. That's what these AI-capture pages are — first-hand, citable, one-chunk-per-question content that AI retrieves and names. We're running the same play for HVAC cities that Supabase ran for Postgres.
- 1
Run the capture
Ask AI the real question your buyer asks. See exactly who it names, who it cites, and where you're invisible. Roughly $0.04 and five minutes per question.
- 2
Build the listicle surface
Publish the first-hand, chunk-per-question content AI retrieves — the capture itself, the teardown, the comparison. Be the source, not the vendor site.
- 3
Move into the answer
Track whether AI starts naming you. The same verify step we ran on these devtool questions works on HVAC, mortgage, roofing — any category AI answers.
the method, same as the pillars
This is the same see → map → model → script → ship → verify loop we run for every client. We ran it on ourselves first — Client Zero. The devtool capture above is the 'see' step, done in public.
The thesis, restated: AI doesn't recommend the best product. It recommends the most readable one. Make yourself readable, or get named last.