ChatGPT knows 4 of 48 AI startups, ranks 28 by category

A test of 48 AI‑native startups found that a vanilla ChatGPT model without web access correctly described only four products—ElevenLabs, Suno, Runway and Cursor—while a search‑grounded model listed 28 of them in top‑ranked category results [Dev.to].

Method: The author prompted the vanilla model with “What is [brand]?” for each startup. All 44 non‑identified responses were boilerplate statements that the model lacked reliable information. The same list was then fed to a search‑enabled model using the prompt “best tools for [category]?” without mentioning the brand. The model produced a ranked list that included 28 startups (58 %); nine of those appeared at the #1 slot in their categories, another 15 appeared lower, and nine were omitted entirely.

Why it matters: LLMs learn product names from third‑party mentions (Wikipedia, G2, Crunchbase, press), so new startups— even well‑funded ones like Decagon and Mercor—often lack name recall. Buyers, however, ask functional queries (“best AI customer‑service automation”), meaning category presence drives traffic. A simple, crawlable one‑sentence description of a product’s function in the served HTML lets search‑grounded models surface the startup at the top of relevant lists, delivering measurable ROI.

Editor’s take: Founders should prioritize generative‑engine optimization—adding clear, category‑focused metadata—over waiting for future training cycles to improve name recall. The data shows that this tactic can move a startup from invisible to #1 in its niche, directly impacting revenue.

Poll: Which visibility tactic do you double‑down on for a new AI startup?

• Optimizing name recall in LLM training data
• Optimizing category ranking via search‑grounded models
• Balancing both approaches equally