ChatGPT Web Search: Discovery or Grounding?

When we observe how ChatGPT interacts with the web, we often project our own habits onto it. We assume it functions like a classic search engine user: entering a keyword, scrolling through a list of blue links, and clicking to learn something new.

However, recent analysis of ChatGPT's search logs and internal reasoning traces suggests a fundamental shift. In many high-volume queries, ChatGPT is not browsing to discover information. It is browsing to ground information it - or its retrieval pipeline - already possesses.

This distinction between Discovery and Grounding is crucial for understanding the future of AI Search Optimization (AEO). By analyzing specific search behaviors for legal queries in New York and Los Angeles, we can decode the architecture behind the "Web Actor" and the "Retrieval Pipeline."

The Evidence: Grounding Over Discovery

Let us look at a specific test case: a query for "Personal injury lawyer New York NY."

In a classic search environment (Google/Bing), this broad query triggers a "Discovery" phase where the user compares lists. However, in our analysis, ChatGPT's behavior is radically different.

The Result: It cites specific entities immediately - recommendations include the firm Gair, Gair, Conason, Rubinowitz, Bloom, Hershenhorn, Steigman & Mackauf and specific profiles on Wikipedia.

The Action: Crucially, ChatGPT did not perform deep web actions like click_link, open_page, or find_in_page to vet these lawyers in real-time.

The Speed: The response generation, including the search, occurs in 1-3 seconds.

This speed and precision imply that the ChatGPT Web Actor (the client-side agent) is not "reading" these websites live. Instead, it is receiving a pre-digested response from the OpenAI/Bing Retrieval Pipeline.

The model often "knows" its recommendations before it cites them. The search query acts as a mechanism for Grounding - fetching a URL to validate and annotate a hallucination-free response - rather than Discovery, where the model seeks to learn who the best lawyer is. The pipeline treats the query as a "task" to be completed, returning ready-to-use citations for entities that have high authority in the index.

The Proactive Pipeline: Context Awareness

The "Grounding" hypothesis is further supported by a second test case: "Samer Habbas law Los Angeles personal injury."

Here, the user provided a specific intent (finding a specific lawyer). However, the results reveal something fascinating about the backend pipeline.

The search results return Samer Habbas's official site (as expected).

The Deviation: The pipeline also returns Brian Breiter (another lawyer) with a ready-to-cite official link.

This indicates that the Retrieval Pipeline is not just a passive search index. It understands the broader semantic goal of the user ("I need a personal injury lawyer in LA") and proactively "steps out of line." Even though the Web Actor requested a specific candidate, the backend provided that candidate plus a high-authority alternative.

The Web Actor then uses these results to construct a response that answers the user's specific question while potentially offering the alternative as context, all grounded by the citations provided by the pipeline.

The Two Modes: Fast Grounding vs. Deep Discovery

This behavior suggests ChatGPT operates in two distinct modes depending on the complexity of the query and the "Reasoning" setting.

1. Fast Mode: The Grounding Engine

For high-volume, well-documented queries (like lawyers in NY or LA), the model relies on Grounding.

Source Preference: It leans heavily on pre-indexed, high-trust sources like Wikipedia, Forbes, and Reuters. It trusts these domains implicitly.

Process: The pipeline returns structured snippets. The Web Actor simply "pins" these citations to the generated text. It does not need to verify the content because the trust score of the source is high.

2. High Reasoning / Agentic Mode: The Discovery Engine

When a user engages "High Reasoning" (o1/o3 models) or asks a "Novel" question (e.g., a niche legal issue in a small village), the behavior shifts to Discovery.

Process: Since the index lacks a "ready-to-cite" answer, the Web Actor must perform actual browsing. We see logs of open_page, reading content, and find_pattern.

Source Preference: In the absence of Forbes or Wikipedia, the model falls back on aggregators - Yelp, Avvo, Justia, TripAdvisor.

Verification: The Web Actor must actively verify the information because the confidence in the source is lower.

The Hierarchy of Trust and Market Implications

Understanding whether ChatGPT is in "Grounding" or "Discovery" mode is vital for visibility.

For Large, Competitive Markets (New York, London, LA)

In these markets, the "Fast/Grounding" mode dominates. The pipeline prioritizes "Trusted Sources."

Strategy: Your website's SEO matters less than your Brand Authority.

The Goal: You need to be mentioned in the sources the pipeline trusts: major news outlets (Reuters/Forbes), Wikipedia, or top-tier industry publications. If you are not in the "Knowledge Graph" of these high-trust nodes, the pipeline will likely skip you for a "safer" recommendation like Gair Gair.

For Small or Niche Markets

In smaller locations or very specific sub-niches, the "Discovery" mode is more likely to trigger.

Strategy: Aggregator Coverage.

The Goal: Since major press coverage is unlikely, ChatGPT looks for consensus. Being present and highly rated on aggregators (Avvo, Yelp, Google Maps) is critical because the Web Actor uses these to "verify" your existence and reputation during its live browsing session.

The "Subset" Exception

Even in a large market, a user can force the model into Discovery mode by narrowing the constraints. A query like "Personal injury lawyer New York low budget ASAP availability" breaks the "Grounding" pattern. The broad "Authority" answer is no longer valid. The model must now search for specific attributes ("low budget"). This shifts the dynamic back to Discovery, giving smaller, well-optimized sites and aggregators a chance to outrank the big firms.

Conclusion

ChatGPT's web search is not a monolith. It is a dynamic system that toggles between Grounding (validating what it knows via high-trust pipelines) and Discovery (actively researching what it does not know).

For businesses and marketers, the takeaway is clear:

• To win in broad searches: Build authority. Become the entity that Wikipedia or Forbes cites.
• To win in niche searches: Build coverage. Ensure your data is accurate across every aggregator the Web Actor might visit to verify you.