Google AI Mode workflow
User enters query
Google's ranking algorithm surfaces top pages (ranked by authority, freshness, intent signals)
Gemini 3 receives those pages
Gemini 3 synthesizes them into an answer
User sees the synthesis
Does Google's AI Search Outperform Exa?
Why Upgrading to Gemini 3 Won't Solve the Real Problem
Google's introduction of AI Mode and AI Overviews has fundamentally changed what we think of as a "search engine." For the first time, Google isn't just ranking pages—it's retrieving, synthesizing, and summarizing using LLMs.
The Core Problem: Intelligence Doesn't Fix Retrieval Architecture
For years, Google has bet on a simple solution: make the model smarter. Upgrade from Bard to Gemini. Upgrade from Gemini to Gemini 2. Now, Gemini 3. Each iteration is more capable, more intelligent, more sophisticated.
But here's the problem: No amount of model upgrading will fix the underlying issue with Google's search result quality for LLMs.
Why? Because the problem isn't the model. The problem is the retrieval layer.
Google's AI layer is undeniably more sophisticated with each iteration. Gemini 3 is a more advanced language model than the typical LLM sitting on top of Exa's retrieval system. But upgrading a model from Gemini to Gemini 3 doesn't change the fundamental architecture of what that model receives to work with.
Even Gemini 3's superior intelligence depends on what Google's index gives it to work with. And that constraint hasn't changed. It won't change. Because it's architectural, not technological.
The Retrieval Ceiling
Here's the central insight: No matter how intelligent your language model is, its output quality is constrained by the quality and structure of the information it retrieves.
This is sometimes called the "garbage in, garbage out" problem, but it's more precise to call it a retrieval ceiling.
Gemini 3 could be the most intelligent LLM ever built. But if Google's retrieval system gives it shallow, keyword-optimized pages instead of semantically dense content, it's constrained by that retrieval ceiling, no matter its raw intelligence.
-Shallow, keyword-optimized pages instead of semantically dense content
-Page-level results instead of paragraph-level chunks
-Summaries instead of full-text evidence
-Authority-ranked sources instead of semantically-ranked sources
-Commercially-filtered results instead of the full semantic space
...then Gemini 3 is constrained by that retrieval ceiling, no matter its raw intelligence.
Think of it like this: You can give a genius the wrong information and a mediocre person better information. The mediocre person will make better decisions. Intelligence is downstream of information quality.
Understanding the Retrieval Ceiling
The retrieval ceiling exists because LLMs can't reason about information they don't have access to. Gemini 3 can't retrieve what Google's index doesn't surface. It can't extract context from pages it wasn't given. It can't reason from evidence that was filtered out.
Google's retrieval architecture imposes hard constraints:
Index Design
Google's index was built to optimize for human click behavior, not LLM reasoning. This means:
Authority signals take priority over semantic relevance
Intent prediction shapes what gets ranked, not contextual density
Page-level results dominate (one page = one ranked result)
Full text is deprioritized in favor of snippets and metadata
Gemini 3 can't overcome this. It has to work with what Google gives it.
Retrieval Granularity
Google returns pages. Exa returns semantic chunks. Gemini 3 can synthesize across multiple Google results, but it's still working with page-level boundaries imposed by the retrieval system.
This means:
Relevant information might be buried in a page about something else
LLM reasoning often requires scanning full pages to find useful paragraphs
Multi-source reasoning requires integrating information across page boundaries
Evidence extraction is less precise when the retrieval unit is the entire page
Gemini 3 is smart enough to find what it needs within pages, but it's constrained by having to work page-by-page instead of chunk-by-chunk.
Commercial Filtering
Google's index applies safety, authority, and commercial filters that remove content before it even reaches the retrieval layer:
YMYL policies filter out sources without mainstream authority
Duplicate content filtering reduces semantic diversity
Freshness algorithms deprioritize older but more relevant sources
Commercial intent detection shapes what surfaces in certain queries
Gemini 3's intelligence can't restore information that's been filtered out of the index entirely.
Structural Bias
Google's ranking algorithms contain structural biases toward:
Large, branded sources
News organizations and mainstream publications
Commercial and YMYL-compliant content
Fresh, recently updated material
Pages with strong backlink profiles
These biases are useful for human search but create blind spots for LLM reasoning. A contrarian academic paper might be more relevant to a complex query than the top-ranked article from TechCrunch, but Google's index will surface TechCrunch first.
Gemini 3 can recognize this bias and try to work around it, but it can't access sources that Google's ranking system didn't surface in the first place.
Why Gemini 3's Sophistication Doesn't Matter (As Much as You'd Think)
This is worth dwelling on because it's counterintuitive. Gemini 3 is significantly more capable than most open-source LLMs. It's better at reasoning, understanding nuance, and generating high-quality text.
But in the context of retrieval-augmented generation (RAG), this sophistication has limits.
Consider two scenarios:
Scenario A: Gemini 3 with Google's retrieval layer
Retrieves: High-authority pages ranked by intent
Gets: Broad coverage, mainstream perspective, commercial polish
Can do: Synthesize popular information into coherent answers
Can't do: Access niche expertise, follow evidence chains that aren't mainstream, reason about contrarian viewpoints
Scenario B: A less sophisticated LLM with Exa's retrieval layer
Retrieves: Semantically relevant chunks ranked by similarity
Gets: Direct access to contextual density, niche expertise, full-text evidence
Can do: Reason with high-precision sources, build multi-step evidence chains, access specialized knowledge
Can't do: Rank by popularity, predict human intent as well
In many real-world scenarios, Scenario B produces better results despite having a less advanced model. Why? Because the retrieval quality more than compensates for the model's lower sophistication.
The model can be less intelligent if it has access to better information. But a more intelligent model still produces poor results if it has access to worse information.
The Structural Disadvantage: Google's AI Mode vs. Exa + LLM
Let's be precise about what's actually happening when you use Google AI Mode versus an LLM with Exa.
The difference in retrieval quality compounds through the rest of the pipeline. Better input to Gemini 3 means better output. Better input to a GPT-3.5 means better output. But the retrieval ceiling determines how much output quality is possible, regardless of model sophistication.
Gemini 3 is smarter, but it's smarter within constraints imposed by Google's index. Those constraints are structural—they're not bugs, they're features of an index designed for human search.
Exa + LLM workflow
User enters query
Exa's embedding engine surfaces semantically relevant chunks (ranked by similarity to query)
LLM receives those chunks
LLM reasons with them to construct an answer
User sees the answer
Real-World Evidence: Where Google AI Mode Falls Short
This isn't theoretical. The pattern shows up repeatedly in real-world queries:
In each case, the constraint isn't Gemini 3's intelligence. Gemini 3 can synthesize what Google gives it very well. The constraint is what Google gives it in the first place.
Complex Technical Queries
Google AI Mode: Returns summaries from high-authority tech blogs and documentation, often simplified for general audiences
Exa + LLM: Returns detailed technical documentation and research papers with full context
Result: Exa provides better grounding for technical reasoning
Niche Domain Questions
Google AI Mode: Returns mainstream coverage, often from sources that lack deep domain expertise but have strong SEO
Exa + LLM: Returns specialized resources, technical whitepapers, industry documentation
Result: Exa surfaces more relevant expertise
Multi-Step Reasoning
Google AI Mode: Synthesizes top pages (which may not align with reasoning logic)
Exa + LLM: Returns semantically related chunks that support step-by-step reasoning
Result: Exa builds better reasoning chains
Contrarian or Minority Viewpoints
Google AI Mode: Deprioritizes sources that lack mainstream authority
Exa + LLM: Ranks by semantic relevance, not commercial authority
Result: Exa provides broader perspective
Fact-Checking and Evidence Extraction
Google AI Mode: Returns fact-check organizations and news summaries
Exa + LLM: Returns full-text context with embedded evidence
Result: Exa provides more transparent evidence trails