The advice to optimize for Bing because “that’s what ChatGPT uses” is already outdated. Welcome to the new era where your content strategy depends on a rapidly shifting landscape of AI partnerships, search APIs, and algorithmic preferences you can’t predict.
If you read part 1 of this series, The Complete Guide to GEO, you know that AI search is fundamentally changing how people find information. What I didn’t cover was just how fast the infrastructure behind these AI tools is changing and why that makes most current GEO advice completely wrong.
The Great Unbundling: Which LLM Uses Which Search Engine
Let’s start with the facts. Here’s who’s using what for search as of July 2025:
ChatGPT → Microsoft Bing (established partnership)
Claude → Brave Search (the privacy play with Google fallback)
Gemini → Google’s own infrastructure (obvious advantage)
Meta AI → Google + Bing (transition strategy)
Grok → Own infrastructure + X data (unique positioning)
Perplexity → Independent crawling + multiple sources
UPDATE: As I was publishing this, Perplexity announced the launch of their own AI-powered web browser. This directly signals they’re taking on Google’s position in search and browser supremacy. With Anthropic’s purported connection to Brave Search also in the mix, I’ll be keeping a close eye on things.
The Scale of the Shift
This isn’t a small trend anymore. AI search represents 12-15% of global search market share as of 2024. ChatGPT dominates with 60% of AI search usage, while Microsoft Copilot holds 14.4% and Google Gemini trails at 13.5%. Industry studies show AI tools captured 6% of total search market in 2024, with projections reaching 14% by 2028.
AI search is growing 20-35% annually, representing a fundamental shift in how people access information.
The Moving Target Problem: Why Optimizing for “Winners” is Already Outdated
It’s true that if you want to optimize your content for ChatGPT searches you need to target your SEO at Bing. The problem with the advice to focus on optimizing for Bing is that partnerships are actively shifting. We also shouldn’t assume that ChatGPT will end up being singularly dominant.
The logic goes: ChatGPT leads now → optimize for Bing. But what happens when Claude gains market share? Or when Meta finishes building their independent search engine?
Partnership Instability is the Real Story
Meta’s Independence Play: They’ve spent 8 months building their own search database to escape Google/Bing dependence. That’s not a side project—that’s a strategic priority.
Brave’s Strategic Position: They’re winning Claude and Mistral partnerships with their privacy-focused approach and Google coverage fallback.
Google’s Vulnerability: They can’t retain search dominance without winning the GenAI war, and right now they’re clearly not winning.
The Timeline Challenge
Here’s the math that kills most optimization strategies:
- Development cycles: 6-12 months to build comprehensive GEO strategy (our analysis)
- Partnership changes: Happening every 3-6 months (our observation based on recent announcements)
- Market maturation: Gartner predicts 25% traditional search decline by 2026
By the time you optimize for a specific search engine, the landscape has already shifted. Optimizing for individual partnerships is a losing game.
AI Searches Are Clicks (But Different Clicks)
Most people are confusing two completely different phenomena when they talk about “AI search.”
The Zero-Click Confusion Resolved
Current reality: 58-60% of Google searches end without clicks (vs. 64.82% peak in 2020)
Critical distinction: Zero-click searches are about AI summaries in traditional search engines (Google, Bing), NOT AI search apps.
Two different behaviors:
- Searching in ChatGPT/Claude/Perplexity = clicks (these apps use search APIs behind the scenes)
- Searching in Google/Bing with AI Overviews = zero-clicks (AI summaries satisfy user intent on SERP)
Proof That AI Search Apps Generate Real Traffic
The data doesn’t lie. Real businesses are seeing measurable traffic from AI search apps:
“In my experience, these AI tools now account for about 10% of my site’s traffic”
How it works: In her blog, SEO Specialist Leanne Wong explains, “ChatGPT uses Bing’s Search API to fetch relevant web links in its AI-generated responses. Google Gemini and AI Overviews use Google search.” These show up as clickable citations in AI responses that drive real traffic.
Quality Over Quantity: The 4.4x Advantage
Here’s where it gets interesting: while AI search may generate fewer overall clicks, the clicks it does generate are exceptionally valuable.
The conversion reality: Recent Semrush research shows “The average AI search visitor (tracked to a non-Google search source like ChatGPT) is 4.4 times as valuable as the average visit from traditional organic search, based on conversion rate.”
Why AI visitors convert better: Semrush’s Rachel Handley also explains, “AI search visitors tend to convert better because LLMs can equip users with all the information they need to make a decision. By the time an AI search user visits your site, they have likely already compared their options and perhaps even learned about your value proposition.”
The recommendation effect: AI responses function like personal recommendations rather than search results. This creates stronger emotional impact and persuasive power compared to traditional blue links.
Additional evidence: SISTRIX found that Google AI overviews generate 12.5% click-through rates compared to 10% for traditional featured snippets.
Conversion quality: Rebecca May from Marketing AI’D showed in her case study that, “Perplexity has proven to be the most valuable source, delivering both the highest volume of traffic and the best conversion rates. ChatGPT follows as the second most impactful.”
Why Traditional Conversion Funnels Will Break
AI systems don’t browse—they extract and synthesize. There’s no “consideration phase” or traditional user journey. This creates a fundamental shift in how people reach your site and what state they’re in when they arrive.
The pre-qualified visitor advantage: By the time someone clicks through from an AI search, they’ve already done comprehensive research. They’re already evaluating or ready to convert.
The economic reality: LLMs pay per API call for real-time information. Cost incentives push them toward comprehensive sources rather than multiple specialized ones.
Strategic implication: Become the “one-stop-shop” source LLMs prefer, but understand that the visitors you do get will be highly qualified.
The dual opportunity: While you may get fewer total visitors, the visitors you do get from AI search are 4.4 times more likely to convert than traditional search traffic. This means optimizing for AI citations is about accessing a higher-value traffic channel.
The Coming Content Consolidation (The Doom Scenario)
Here’s where things get ugly for small sites.
The Volume Arms Race is Real
LLM search behavior favors hyper-specific queries for micro-niches. Success requires thousands of targeted pages for low-volume keywords humans never search but LLMs frequently query.
Small site problem: You can’t compete with industrial-scale content production.
Who Wins the Consolidation
- Large media companies with content production resources
- AI-native content operations scaling cheaply
- Platform companies (Reddit, Stack Overflow) with massive UGC
- Enterprise software companies generating technical documentation at scale
Who Gets Squeezed Out
- Small expert blogs without content volume
- Individual subject matter experts who can’t scale
- Boutique agencies with deep expertise but limited publishing capacity
The cruel irony: The people with the deepest expertise often lose to volume-optimized content.
The Death of Evergreen Content Strategy
Old model: Build authoritative content once, watch it compound over 2-3 years
New reality:
- AI systems favor recent content + partnerships change every 3-6 months (our analysis)
- Timeline collision: Content takes 6-12 months to build authority, but search backends shift faster (our assessment)
- Recency bias amplified: LLMs prefer fresh information over established authority
- Platform fragmentation: Multiple optimization strategies required across changing partnerships
- ROI timeline shortened: Evergreen content assumes stable discovery mechanisms that no longer exist
From asset to operation: Content becomes continuous process, not static investment.
Survival Strategies for Smaller Players
Not everyone’s doomed. Here are the escape routes:
Strategy 1: Hyper-Local Positioning
Why it works: LLMs still struggle with local context and real-time local information.
Examples: “basement waterproofing in Chicago winters,” regional regulatory expertise, hyperlocal market knowledge.
Limitation: Only works for businesses with genuine local presence. Digital-first businesses get squeezed out unless they can find geographic angles.
Strategy 2: Platform Authority Arbitrage
The insight: Borrow authority from established platforms rather than building it independently.
Why LinkedIn/Medium/Reddit work:
- Domain authority individual sites can’t match
- Built-in distribution and engagement signals
- LLMs already heavily cite these platforms
- Lower competition for specific expertise within these ecosystems
Advanced tactics: Quora expertise positioning, industry-specific forums, academic platforms, professional association publications.
The strategy: Become the go-to expert on major platforms rather than trying to build independent authority.
Strategy 3: Ultra-Niche Technical Specialization
The “too boring to scale” defense.
Why content farms avoid this:
- Low search volume doesn’t justify content investment
- Requires actual expertise that’s expensive to fake
- High technical barrier to creating convincing content
- Limited monetization potential for broad audiences
Examples: Obscure compliance requirements, legacy system integrations, academic subspecialties with expert-only audiences.
Strategy 4: Social Media Native Content
Platform algorithms prioritize engagement over authority. Authentic user content can’t be replicated by content farms.
TikTok, Instagram, Twitter use different discovery mechanisms than traditional search. UGC becomes a hedge against AI search consolidation because social algorithms reward genuine engagement over content volume.
Detecting AI Search Traffic: The 12-18 Month Window
Right now, you can reverse-engineer LLM search patterns by identifying unusual traffic patterns. This won’t work forever.
The Transition Period Opportunity
Current advantage: AI search patterns create detectable anomalies in traditional metrics. Limited timeframe: Works now because AI went from 0% to 12-15% market share in 2 years. Signal clarity: Clear “before and after” patterns that won’t exist once AI search normalizes.
What to Look For: LLM Search Signatures
Volume spike patterns:
- Keywords with 200-500% increases since mid-2023 (our analysis based on when AI search took off)
- Technical phrases humans rarely search but LLMs need (“Brave Search API rate limits”)
- Volume spikes unrelated to news cycles or seasonal patterns
- B2B terms suddenly appearing in consumer search volumes
LLM vs. Human search differences:
- Humans: “best AI chatbot”
- LLMs: “conversational AI accuracy comparison methodology”
Rigging Current SEO Tools for AI Search Detection
Keyword research hacks:
- Filter for unusual volume spikes in technical terms (2023-2024 timeframe)
- Cross-reference competitor citations in AI tools with their ranking keywords
- Look for high-difficulty, low-volume terms getting unexpected traction
Content gap analysis:
- Identify pages getting unusual referral traffic with no clear source
- Monitor citation mentions that don’t correlate with backlink data
- Track “zero-click” increases that might indicate AI consumption
The methodology advantage: Existing SEO tools have the data infrastructure needed. Creative application of current data solves new problems. 12-18 month head start (our estimate) before platforms build native AI search features.
What This Means for Your Business (Practical Next Steps)
Important Note: The following recommendations are based on current market conditions and trends as of July 10th, 2025. Given the rapid pace of change in AI search partnerships, user behavior, and platform algorithms, these strategies should be treated as directional guidance rather than permanent doctrine. Expect to revisit and revise these approaches every 6-12 months as the market matures and new data becomes available.
Immediate Actions (Next 90 Days)
- Audit which AI tools cite your content (manual research required)
- Identify your escape route: local, platform authority, or ultra-niche
- Begin cross-platform content distribution
- Test content formats that get AI citations
Medium-term Strategy (6-12 Months)
- Build topic authority through comprehensive coverage OR specialized expertise
- Establish platform presence on 2-3 major sites beyond your own
- Develop UGC strategies appropriate for your audience
- Monitor AI citation patterns in your industry
Long-term Positioning (12+ Months)
- Choose your competitive moat and double down
- Build content production capabilities (if competing on volume)
- Develop direct audience relationships independent of search
- Prepare for continued platform fragmentation
The New Content Strategy Framework
From Evergreen to Agile:
- 6-12 month optimization cycles instead of 2-3 year plans
- Portfolio approach: many smaller bets vs. few large content investments
- Cross-platform distribution to hedge against algorithm changes
The Three-Pillar Approach:
- Volume play for sites that can compete (thousands of micro-niche pages)
- Authority arbitrage for experts (dominate major platforms)
- Community building for sustainable moats (UGC and social native content)
Success metrics evolution:
- From rankings to citations in AI responses
- From traffic to brand mentions across platforms
- From conversion funnels to authority recognition
The Window is Closing
The companies that recognize this shift early and adapt their content strategies have maybe 12-18 months (our assessment) before these insights become obvious to everyone. The question isn’t whether the search landscape will change—it’s whether you’ll be ready when it does.
Most businesses are still optimizing for a world where Google sends them traffic through blue links. That world is disappearing faster than most people realize. The businesses that survive will be those that understand the difference between getting found in traditional search and getting cited in AI responses.
The choice is simple: Adapt your content strategy for the AI search era, or watch your competitors capture the traffic you used to own.
Want to see more strategic analysis like this? I break down emerging trends and business strategy implications in my Narrative and Neural Nets series. Follow along for insights that help you stay ahead of the curve rather than chasing it.