explainx / blog
AI visibility tools promise rank #4 and 17% share of voice — but ChatGPT and Claude answers are noisy, geographic, and nondeterministic. What AEO metrics can honestly tell you, Canonry's critique, and better measurement approaches.
May 26, 2026
Generative Engine Optimization (GEO) is how you get cited—not ranked—in ChatGPT, Perplexity, Gemini, and AI Overviews. Here is what SEO-GEO means, why it matters now, and how to apply it without chasing hacks.
Apr 10, 2026
seo-geo is an agent-installable playbook for technical audits, keyword research, structured data, and GEO tactics tuned for ChatGPT-style citation surfaces — live on explainx.ai with sources on GitHub.
Apr 13, 2026
Define AI slop, see why volume is outpacing quality, and apply Generative Engine Optimization principles—clear sourcing, data, FAQs—to publish work humans and AI systems can actually trust.
Can you trust your AI visibility score? A July 2026 Hacker News thread on Canonry's essay — "Every AI Visibility Tool Is Lying to You" — hit a nerve. Not because brands should ignore ChatGPT, Claude, Gemini, and Perplexity citations — but because dashboards turn messy, personalized, nondeterministic answers into fake precision: you are rank #4, 17% share of voice, +2 positions this week.
One HN comment captured the implementation dread: "I'm implementing changes suggested by a stochastic model based on a limited set of searches on other stochastic models."
This post answers what AEO / GEO measurement can honestly tell you — and lists practical approaches (Canonry's distribution model among them, not the only one).
| Question | Honest answer |
|---|---|
| Are visibility tools useless? | No — good for directional gaps (invisible on category prompts, missing in NYC) |
| Is "rank #4 in ChatGPT" real? | Over-precise — one sample from a distribution unless proven with variance |
| Scrape vs API? | Different instruments — neither equals "what every customer sees" without disclosure |
| Why three tools disagree? | Prompt sets + scoring formulas manufacture different headline numbers |
| Local businesses? | Global rank is meaningless — geography must be explicit |
| What works better? | Repeated runs + raw evidence + GEO fundamentals |
The same words often produce different brand lists on the next run — SparkToro/Gumshoe volunteer studies and production temperature-zero instability (Thinking Machines Lab on batching variance) both show this. A point estimate without a distribution is decoration.
Scraping ChatGPT or Claude sounds like the real product. It is one account, geography, memory state, subscription tier, and IP story. A buyer in Brooklyn and a datacenter browser asking "best CRM for seed-stage startup" are different experiments.
API calls are cheaper, repeatable, auditable — but may lack consumer memory, routing, shopping modules, and UI-specific retrieval. OpenAI requires explicit web search tools; Gemini has its own grounding config. API measurement is valid when labeled as API, not "what the app showed your buyer."
Vendors track 100–1,000 prompts (Profound's own design guidance). Change the list — "best AEO agency NYC" vs "SEO agency" — and "visibility" changes. Same evidence, three scoring formulas (mention SOV, position-weighted, citation-based) can yield 20% vs 16.8% vs 31.4% on Digital Applied's framework example.
"Best roofing company near me" is local. A single global number without city, proxy, or explicit geo context is marketing math.
Chen, Zaharia, and Zou documented GPT-4 behavior shifts under the same public name (e.g. prime accuracy 84% → 51% March–June 2023). OpenAI rolled back a GPT-4o update for being too agreeable (April 2025). Your "+2 this week" may be the model, not your blog post.
Tool runs prompts → another model summarizes into prose → your team ships portal copy. Probability layered on probability — the HN engineer's complaint is structurally fair.
Directional, probabilistic findings are useful:
Fake precision:
Not one vendor — a stack:
| Approach | What it measures | Strength | Weakness |
|---|---|---|---|
| Vendor dashboard (Profound, etc.) | Sampled prompt panel | Fast baseline | Often hides methodology |
| Canonry | Repeated API observations + evidence; local runs for geo | Distribution mindset; auditable runs | Costs more; still not every user session |
| DIY prompt panel | Your prompts × providers × cities × N runs | Full control | Labor; no pretty UI |
| GEO content work | Citations, schema, entity consistency | Compounds over months | Not a weekly rank chart |
| seo-geo agent checks | Princeton GEO methods in content | Improves cite-worthiness | Not competitive monitoring |
| Classic analytics + branded search | Traffic, conversions, Search Console | Grounded in outcomes | Misses dark-chatGPT referrals |
Canonry's specific pitch: treat visibility as a distribution — multiple runs, multiple providers, explicit geolocation on APIs, stored evidence. Local-first execution for agencies serving Chicago HVAC or Brooklyn hospitality — run probes from machines in-market instead of only vendor cloud regions. That addresses one scrape bias; it does not eliminate model nondeterminism.
Ask any tool (or your internal script):
Without answers, do not rewrite production docs on a single score.
Separate July 2026 thread — Fable leaking reasoning traces — reinforces the same theme: you are watching one layer of a stochastic stack. Visibility scores read model outputs; implementation guides read another model's summary of those outputs. Show your work or stay skeptical.
Analysis informed by Canonry's June 30, 2026 essay and HN discussion July 2026. Vendor features change — verify methodology on any tool before budget decisions.
