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seoClarity’s Data Science Team Refines Its Model With New Data

 

Overview

For the past two years, everyone has been following ChatGPT’s meteoric rise. Headlines have made bold claims about its growth, with some wild predictions on how many people use it and how large it has become. But speculation isn’t enough - enterprises need grounded benchmarks. 

Back in early 2025, our team at seoClarity published the first analysis of ChatGPT vs. Google search volume. In that post, we built a model based on limited public disclosures to estimate how many “search-like” activities were happening on ChatGPT compared to Google.

Our best estimate at the time was ~1.2–1.4 billion search-like tasks per day, or roughly 9% of Google’s daily searches. 

Now, with new independent research available, we’ve revisited that model, tested our earlier assumptions, and refined the numbers further. 

What’s Changed Since Our First Model 

In our earlier analysis, we made several assumptions due to limited data at the time: 

  • Daily prompts/messages: We assumed ~2.5–3.0B/day, based on OpenAI’s general disclosures.
  • Search-intent share: We estimated 40–70%, triangulating from surveys (Adobe, Pew) showing how people “use ChatGPT as search.”
  • Prompts per search task: We modeled 1.2–1.4, since many searches involve follow-ups. 

With the release of the NBER study (How People Use ChatGPT, Sept 2025), we now have much firmer ground: 

  • Measured activity: ~2.63B consumer messages/day (June 2025).
  • Intent categories: 51.6% Asking, 34.6% Doing, 13.8% Expressing.
  • Topic mix: “Seeking Information” doubled in a year, now 24% of all usage. 

 

Refined Modeling Approach

Using the new data, we updated the model with three scenarios:

  1. Messages per day (M): 2.627B (consumer plans).
  2. Search intent share (S):
    • Low: 24% (strict “Seeking Information”).
    • Base: 51.6% (all Asking).
    • High: 55% (Asking + some Doing).
  3. Prompts per search task (k): 1.2–1.4. 

 

Formula

Formula for ChatGPT article (1)

 

Results: From Speculation to Precision 

Scenario

Prior Assumption (Early 2025)

Refined Estimate (September 2025)

Low

40% search intent

24% (Seeking Info only) → 0.45B/day 

Base

55–60% search intent

51.6% (Asking) → 1.04B/day

High

70%+ search intent 

55% (Asking + Doing) → 1.20B/day 

 

For Comparison: Google processes ~14–16.4B searches/day. ChatGPT today sits at 3 to 8% of Google’s daily search volume. 

 

ChatGPT today sits at 3 to 8% of Google’s daily search volume. 

 

Mitul Gandhi  |  Chief Architect and Co-Founder
 
Key Takeaways

  • Our earlier model (1.2–1.4B/day) slightly overestimated search-intent share. With new data, the refined base case is ~1.0B/day.

  • Even the conservative case (~0.45B/day) is massive, and comparable to Bing.

  • “Seeking Information” usage is growing fast, so ChatGPT’s role as a search engine is expanding. 

What This Means for Enterprises 

  • Sizing the opportunity is now possible. No longer just speculation - there’s hard data to measure AI-driven search. 

  • AI visibility matters. Up to a billion search-like tasks/day are happening outside Google.

  • Strategy must adapt. Tracking brand presence in AI answers and optimizing for extractability is now as important as traditional SERP rankings. 

How to Use This Data 

This benchmark isn’t just interesting - it’s actionable. Enterprises can use it to: 

  • Estimate demand on ChatGPT for individual keywords and topics. Just as clients have always measured demand in Google SERPs, this model provides a way to size opportunities in AI-driven search. 

  • Move beyond unreliable guesses. Previous approaches often relied on messy, inconsistent clickstream data to estimate demand. With this new framework, this is a more scientific foundation. 

  • Prioritize resources. Mapping keyword-level demand to ChatGPT’s overall search activity helps enterprises decide where to invest in optimization for AI-search visibility. 

 

The Bottom Line 

Our original analysis was the first attempt to put numbers to ChatGPT’s search activity. With the new data, we can now refine that benchmark: ~1.0B daily search-like tasks, or 3–8% of Google’s scale. 

seoClarity continues to build capabilities like ArcAI to help enterprises measure, track, and optimize visibility in AI-driven search results.  

This updated research helps enterprises not just speculate, but plan. 

Methodology

Estimates based on OpenAI telemetry (messages/day), NBER conversation analysis, and modeled assumptions (intent share, prompts per task). Scope limited to consumer plans; enterprise usage may push totals higher. 

Sources

 

Mitul Gandhi - Author Snippet (1)About the Author: Mitul Gandhi

 As a longtime data-driven serial entrepreneur, information architect and SEO veteran, Mitul has developed a blend of vast technical expertise and intense marketing insight. His variety of experience, gained in positions in in-house SEO, search marketing, and software development, affords him the ability to efficiently assess how to use software tools to meet challenges and drive ROI. As the Co-Founder and Chief Architect of seoClarity, Mitul currently oversees day-to-day operations, and provides strategic direction to all departments. His well of knowledge includes 10+ years of consulting experience with Fortune 500 and top Internet retailers concerning online search marketing. He has several patents pending for analyzing cause and effect in SEO. Mitul holds an MBA in direct marketing from Rochester Institute of Technology. Additionally, he has spoken at conferences in the United States and the U.K., including SES, SMX and Pubcon. He has also been quoted in MSN Money, USA Today, Time Online, Search Engine Watch, Search Engine Land and Web Pro News. Connect with him on Twitter or LinkedIn.

 

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