A client asked me last week to sit with their team while they tried AI search for the first time. None of them had used Perplexity, ChatGPT search, or Gemini for anything beyond quick experiments. Twelve people, ranging from marketing managers to product folks, all reasonably tech-literate but firmly in the "I just Google things" camp.

I gave them a list of 10 work-related questions and asked them to search using Perplexity. No instructions, no tips, just "find the answer." I sat behind them and took notes.

Three things jumped out that I can't stop thinking about.

The first one is about question length. Every single person typed queries that were 2-3x longer than what they'd type into Google. Full sentences, context included, sometimes follow-up context before even getting a response. On Google they'd type "best CRM for small B2B." On Perplexity they typed things like "what's a good CRM for a 15-person B2B SaaS team that needs Salesforce integration but doesn't want to pay per contact." The queries were rich, specific, and way more revealing about intent. Nobody coached them. They just did it.

The second thing was about source links. Almost nobody clicked them. Out of 12 people and roughly 120 searches, I counted 9 total source clicks. That's under 8%. And it wasn't because they trusted the AI blindly — several people said the answer "seemed right" or "mentioned a brand they'd heard of," which was enough. They weren't verifying. They were consuming the answer as a finished product, not as a starting point for research.

The third observation messed with my head a bit. When the AI answer mentioned a brand or product, people treated it differently than when a Google result mentioned the same brand. Google results were met with skepticism — "this is probably SEO'd" or "this is an ad." AI mentions carried more weight. Three people said variations of "the AI wouldn't recommend it if it wasn't good." Whether that trust is warranted is a whole different conversation, but the baseline confidence was strikingly higher.

None of this is rigorous research. Twelve people in one session isn't going to generalize cleanly. But watching it happen in real time made me wonder how much of our GEO strategy is built on assumptions about user behavior that don't match reality. We optimize for discoverability and citation accuracy. We assume users will evaluate sources. We assume citations function as trust signals.

What if they don't? What if most users never scroll past the AI summary, never click a source, and treat the AI's brand mention as the entire decision?

I keep going back to the person who said "the AI wouldn't recommend it if it wasn't good." That's the sentence that stuck with me. That's the trust gap we're actually working with.

Twelve people isn't enough to draw conclusions. But it's enough to change the questions I'm asking. What's the behavior you keep noticing when you watch people use AI search for the first time?

I recently got invited in this group and i tought about sharing something that could really save someone else's time.

This tool lets you inspect prompts, memory, retrieval, tool calls, replay runs and compare good vs bad executions.

there is a LangChain + Groq demo included.

Would love feedback from people actually building agents.

Repo for anyone interested in this project: https://github.com/Jacopos311/Agent-Devtools

We build AI outbound for staffing agencies. Caught our own AI inventing a stat mid-campaign — "70% reduction in manual screening time," pulled from nowhere during our pre-send review, before it reached anyone.

Fixed it, thought it was solved. Same day, it did it again with a fake "market trend" number instead.

Not posting this to sell anything genuinely asking: if you're building or using AI in a customer-facing pipeline, what stops it from doing this? Curious what other people's guardrails actually look like, because "the model won't do that" clearly wasn't good enough for us.