Brandon Punturo

Brandon Punturo

/Research

We reverse-engineered ChatGPT's Shopping trigger. Here's how it works.

ChatGPT Shopping is driven far more by product category than by purchase intent language. In our analysis of 1.18 million prompts and a 7,500-prompt labeled sample, prompts naming shippable consumer goods were much more likely to trigger Shopping cards, while software, services, travel, and financial products almost never did. Adding 1–2 concrete constraints like price, features, or use case increased trigger probability, but only within product categories. The clearest rule was simple: if the prompt’s main noun is something you could buy on Amazon, Shopping is likely to appear. Using that logic, we built a classifier that reproduced ChatGPT’s Shopping behavior with about 95–97% accuracy.

Davis McCainBrandon Punturo

Davis McCain and Brandon Punturo

/Research

LinkedIn is the most-cited domain for professional queries in AI search

In the last three months, LinkedIn has become one of the most authoritative sources shaping AI-driven discovery. Between November 2025 and February 2026, the platform surged from outside the top 20 to rank among the most‑cited sources on ChatGPT – and is now the #1 most‑cited domain for professional queries across all AI Search platforms. That’s powerful data for both companies and individuals looking to grow their influence.

Brandon Punturo

Brandon Punturo

/Product, Research

We tracked 2 Million ChatGPT prompts. Shopping showed up less than 10% of the time.

79% of prompts never trigger shopping. Not rarely — never, across nine months of data. The ones that do are surprisingly sticky: 83% chance of triggering again the next day. But that persistence decays, and model updates can reset it overnight. We tracked 26 million prompts across 13,000 categories to map where shopping actually shows up, how consistently it fires, and what that means for brands building strategy around a surface that barely turns on.

Ben GrosseLayla Baradaran

Ben Grosse and Layla Baradaran

/Company

Profound & iQuanti Partner to Help Brands Win in the New Era of AI Search

Profound and iQuanti have partnered to help brands strengthen their visibility in the new era of AI Search by combining real-time Answer Engine intelligence with an actionable AI Search strategy. Together, they enable enterprises to shape category narratives and gain a competitive advantage as AI becomes the starting point of modern buyer journeys.

Jack TrainaSiddharth Chandrappa

Jack Traina and Siddharth Chandrappa

/Product

Upgrading content optimization with the AEO content score

We’re introducing the Profound AEO Content Score, a new machine learning–powered metric that helps marketers understand how well their content is optimized for AI search results. Built from millions of top-cited pages across AI systems, the score measures your content’s likelihood of being referenced by AI search. It’s the foundation of our new Content Optimization V2, giving you real-time AEO insights, benchmarks, and clear recommendations to boost your visibility in AI-generated answers.

Josh Blyskal

Josh Blyskal

/Technology

Claude web search explained

On March 20, 2025, Anthropic released Web Search for Claude.ai, transforming it from a static LLM into an Answer Engine just like ChatGPT with Bing, Perplexity, or Google's AI Overviews. However, the critical insight is now clear: Claude leverages Brave Search as its primary search backend, confirmed by a statistically significant overlap of 86.7% (13 out of 15 total results) between Claude's cited results and Brave Browser's top non-sponsored results.

Charles Zhou

Charles Zhou

/Technology

X-Forwarded-For for AI agents

On January 23, 2025, OpenAI introduced Operator, a groundbreaking AI system that can control a web browser to perform tasks on behalf of users. This marks a significant evolution in how AI interacts with the web—moving from passive crawling to active browsing. While this advancement brings exciting possibilities, it also presents a fundamental challenge: How can websites understand and serve the actual user behind the AI agent?

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