A shot at the funnel: What millions of GLP-1 convos reveal about AI search

Last updated: 04.08.2026

We wanted to take a pulse check on how consumers are interacting with AI assistants at the beginning of 2026. Our goal was to see where audiences are “showing up” in the funnel when they use AI platforms—and what the dominant patterns are in different stages.

We decided to focus on a fast-changing, high-interest category.

Fun fact: It turns out that more people are currently asking AI about GLP-1s than movies and TV. Based on a subset of our panel data, we estimate that roughly 4 million AI conversations were related to GLP-1s in the month of January 2026 alone.

The data painted a clear picture of AI search archetypes and behavioral loops—as well as opportunities for brands. Namely, how to make sure you’re monitoring the right prompts, serving the right personas, and creating the right content for the AI-first customer journey.

The main takeaway? AI search isn’t just displacing traditional search—it’s reconceptualizing what search looks like.

AI is more than an answer giver at a single point of need. Instead, it’s a genuine companion from awareness to conversion and beyond.

The funnel has moved inside AI interfaces

We bucketed AI users into five archetypes (aka audience clusters) based on their jobs to be done:

1. Knowledge Seeker (~24% conversation share): People who are curious about GLP-1s but not yet committed. They’re pre-treatment and building a mental model. This could be considered the entry point to the funnel.
2. Active Evaluator (~20% conversation share): People who are actively comparing options before making a decision. These users are the most likely to switch brand intent based on a single piece of content. They also have the highest conversion potential.
3. Access Seeker (~9% conversation share): People trying to find, obtain, or prescribe medication. This includes both patients and practitioners. It’s a niche archetype compared to the others, but commercially important.
4. Side Effect Navigator (~23% conversation share): People who are already on medication and anxious about symptoms. They’re looking for reassurance and guidance versus product information. These users have the highest conversation return rate.
5. Regimen Planner (~24% conversation share): People who are already on medication and are trying to optimize it. They’re looking for frameworks, tools, and subscriptions. These users represent the highest customer lifetime value opportunity.

The share of jobs to be done was remarkably balanced across four of the five archetypes—no single use case dominated.

Access Seeker is the outlier. It’s a smaller, high-intent group with distinct commercial needs.

Applying population-weighted archetype proportions, the estimated addressable US audience per archetype is:

Knowledge Seeker

Details: ~24% of conversations, broadest unique user base

Who they are: Predominantly male (~65%), median age of early 30s (the youngest archetype), slightly below-median income

Overview: Knowledge Seekers typically ask one or two questions and leave. This is an awareness-phase audience with no declared purchase intent.

Example prompts:

• "What's the difference between a GLP-1 and a GIP agonist?"
• "Are GLP-1 supplements the same as GLP-1 drugs?"

Example content opportunities:

Active Evaluator

Details: ~20% of conversations, most transient user base (aka most likely to move between different archetypes)

Who they are: Predominantly male (~65%), median age of mid-30s, slightly below-median income

Overview: About a quarter of Active Evaluators retreat back to being Knowledge Seekers rather than committing, and roughly 18% of Regimen Planners cycle back to being Active Evaluators. This suggests that this stage is revisited across the treatment journey, not just at the start.

Example prompts:

• "What are the pros and cons of Mounjaro vs. Wegovy?"
• "Is Mounjaro or Semaglutide better for weight loss?"

Example content opportunities:

Access Seeker

Details: ~9% of conversations, highest intent, mixed patient/practitioner audience

Who they are: Balanced gender split, median age of mid-30s, median income

Overview: Access Seekers are a mixed group—some are patients navigating healthcare systems, others are practitioners or entrepreneurs.

Example prompts:

• "Where can I buy Zepbound?"
• "How do I get prescribed Tirzepatide?"

Example content opportunities:

Side Effect Navigator

Details: 23% of conversations, highest conversation return rate (2x the average)

Who they are: Predominantly female (~55%), median age of mid-30s, below-median income

Overview: Side Effect Navigators are a post-purchase segment—they’re already on the medication.

Example prompts:

• "Is it normal to feel nauseous on week 2 of Mounjaro?"
• "Can Wegovy delay your period?"

Example content opportunities:

Regimen Planner

Details: 24% of conversations, second-highest return rate (1.7x the average)

Who they are: Balanced gender split, median age of mid-30s, above-median income

Overview: Beyond being the only above-median income archetype, Regimen Planners are the most likely to seek interactive help across AI search sessions.

Example prompts:

• "I'm on week 4 of Mounjaro—can you build me a weekly meal plan?"
• "What happens if I miss a dose and how do I get back on schedule?"

Example content opportunities:

AI search behavior follows clear patterns

This is only one study focused on one category at one moment in time.

The full funnel lives inside LLMs

Every stage of the marketing funnel is playing out inside AI chat interfaces right now—at a staggering volume and variety.

The funnel is less linear than ever

Marketers have long said the idea of a strictly linear funnel is outdated. AI conversational data clearly proves it.

AI is a trusted partner, not just a search engine

People aren't just using AI to look things up—they're using it to think things through. And they trust it to give them the right answer.

What users ask isn’t always what you’d expect

The GLP-1 data revealed a potential gap between the prompts marketers might think to track and the ones people are actually typing.

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