AI Is the New Optimization Battleground: Your Complete Guide to LLMO, GEO, and AIO in 2025
The digital marketing landscape is experiencing its most significant transformation since the dawn of search engines. As AI-powered search becomes the primary way users discover information, businesses that fail to adapt their optimization strategies risk becoming invisible in this new paradigm.
The Great AI Optimization Revolution: Why Traditional SEO Isn’t Enough Anymore
In 2025, we’re witnessing a seismic shift in how information is discovered, consumed, and ranked online. ChatGPT, Claude, Perplexity, and Google’s AI Overviews aren’t just tools anymore—they’re the new gatekeepers of digital visibility. While some are still fighting yesterday’s SEO battles, the real war for online dominance is being waged on an entirely different front: AI optimization.
This is NOT to say SEO practices are dead, as at MarginWalkers our opinion is quite the opposite! While there is a lot of ‘fear marketing’ out there about traditional SEO ‘not being enough’ but any good SEO consultant would have had already implement most of what the fear mongers claim as the missing code level data that AI needs. Certainly at MarginWalkers our process and specialty has always included the technical SEO aspects that most firms claim is missing and you will see noted throughout this post.
With that said, even defining what to call this type of optimization is a battlefield! There are 3 main terms that are often used interchangeably but they can be seen as distinct areas of focus in our view. Industry leaders can’t agree on what to call it, but they all agree on one thing: if you’re not yet optimizing for AI, buckle up and let’s get in to some details.
The Three Amigos of AI Optimization: LLMO, GEO, and AIO Explained
1. Large Language Model Optimization (LLMO): The Foundation of AI Visibility
What is LLMO? Large Language Model Optimization represents the most technical approach to AI visibility. LLMO focuses on structuring your content and data to be easily understood, indexed, and cited by large language models like GPT-4, Claude, and Gemini.
Why LLMO Matters for Your Business: According to Cloudflare’s July 2025 data, AI crawling activity has increased dramatically, with OpenAI’s GPTBot more than doubling its share of AI crawling traffic from 4.7% to 11.7% year-over-year. This represents a 305% rise in requests, underscoring the massive data demand for training language models.
Key LLMO Strategies:
- Structured Data Optimization: Implement JSON-LD schema that AI models prioritize
- Semantic Content Architecture: Build topic clusters that mirror AI understanding patterns
- Authority Signal Enhancement: Develop E-E-A-T signals that AI models recognize
- API-Ready Content: Structure content for potential AI crawling and API access
- Citation Optimization: Format content to increase citation probability in AI responses
2. Generative Engine Optimization (GEO): Winning the AI Content Game
What is GEO? Generative Engine Optimization focuses on optimizing content specifically for generative AI platforms. GEO is about understanding how AI models select, synthesize, and present information to users.
The GEO Advantage: The rise of AI-powered search features is transforming how users find information. Google’s AI Overviews, launched in the US in May 2024 and expanded globally throughout 2025, represents a fundamental shift in search results presentation. Many SEO services have added Generative Engine Optimization (GEO) to their service offerings, recognizing it as a critical future-ready capability for agencies serving eCommerce, SaaS, and digital products. Setting a GEO strategy for enterprises will be key going forward.
Advanced GEO Techniques:
- Conversational Content Design: Write in Q&A formats that AI naturally extracts
- Multi-Modal Optimization: Integrate text, images, and data for comprehensive AI understanding
- Prompt-Aligned Content: Create content that matches common user prompts
- Factual Density Optimization: Balance information density for optimal AI extraction
- Update Frequency Signals: Maintain freshness scores that AI models prioritize
3. Artificial Intelligence Optimization (AIO): The Holistic Approach
What is AIO? AIO represents the most comprehensive approach to AI optimization, encompassing not just content but entire digital ecosystems designed for AI interpretation and recommendation.
AIO’s Competitive Edge: TollBit’s Q1 2025 State of the Bots report found an 87% increase in AI scraping during the quarter, with RAG (Retrieval Augmented Generation) bot scrapes per site growing 49%, nearly 2.5X the rate of training bot scrapes. This shift indicates that AI tools require continuous access to content for real-time responses, not just for training. MarginWalkers can provide AIO consulting for agencies and small businesses
Comprehensive AIO Framework:
- Knowledge Graph Integration: Build interconnected content networks
- Predictive Intent Optimization: Anticipate and answer future AI queries
- Cross-Platform AI Presence: Optimize for multiple AI ecosystems simultaneously
- Real-Time Adaptation: Dynamic content adjustment based on AI behavior
- Ethical AI Alignment: Ensure content meets AI safety and accuracy standards
How AI Systems Actually Crawl and Gather Your Content
Understanding how different AI platforms collect and process data is crucial for effective optimization. Here’s what we know about the major players:
OpenAI (ChatGPT) Data Collection Methods
GPTBot – The Training Crawler: OpenAI uses GPTBot to crawl the web and gather training data for models like ChatGPT. According to OpenAI’s documentation, GPTBot identifies itself with a specific user-agent and respects robots.txt protocols. The crawler focuses on high-quality, diverse content to improve model capabilities.
ChatGPT-User – Live Retrieval: When ChatGPT needs current information, it uses ChatGPT-User for real-time web access. Cloudflare data shows ChatGPT-User requests surged by 2,825% in 2025, reflecting massive growth in user activity requiring live web data.
Training Data Sources: ChatGPT’s base model (GPT-3 and beyond) was trained on:
- Common Crawl dataset (filtered from 45TB to 570GB of high-quality text)
- WebText2
- Books1 and Books2 datasets
- Wikipedia
- High-quality reference corpora
The training process involves three key steps:
- Filtering Common Crawl data based on similarity to high-quality reference sources
- Deduplication at the document level
- Augmentation with diverse, high-quality datasets
Anthropic (Claude) Web Crawling Approach
ClaudeBot – Primary Training Crawler: ClaudeBot is Anthropic’s main web crawler for gathering training data. As of July 2025, Cloudflare data shows ClaudeBot accounts for approximately 10% of AI crawling traffic, though this represents a decline from earlier peaks.
Claude’s Three-Bot System: According to Anthropic’s documentation, they use three different robots for transparency:
- ClaudeBot: For model development and training
- Claude-User: For retrieving content when users request specific information
- Claude-SearchBot: For evaluating pages for Claude’s internal search feature
Web Search Integration: In March 2025, Anthropic added web search capabilities to Claude, initially for US paid users before expanding globally. This feature uses Brave Search infrastructure and provides direct citations for fact-checking.
Training Data Cutoffs:
- Claude Opus 3: Trained on data up to August 2023
- Claude Sonnet 4 and Opus 4/4.1: Trained on data up to March 2025
Perplexity AI’s Controversial Crawling Practices
PerplexityBot – The Index Builder: Perplexity uses PerplexityBot to build its search index. Despite having a small 0.2% share of AI crawling traffic, it recorded a staggering 157,490% increase in raw requests according to Cloudflare’s July 2025 data.
Stealth Crawling Allegations: Multiple investigations in 2024-2025 found that Perplexity allegedly:
- Uses undeclared “stealth” crawlers to bypass robots.txt directives
- Operates outside declared IP ranges using different ASNs
- Impersonates Google Chrome on macOS when blocked
- Makes millions of daily site requests through undisclosed methods
Cloudflare’s August 2025 research confirmed these practices, with CEO Matthew Prince commenting that Perplexity acts “more like North Korean hackers” than a reputable AI company.
Legal Challenges: Perplexity faces multiple lawsuits from major media organizations including:
- The BBC (demanding content deletion and compensation)
- Dow Jones and The New York Times (copyright infringement)
- Various publishers over unauthorized content use
Google’s AI-Enhanced Crawling
Googlebot’s Dominance: Googlebot remains the dominant crawler, accounting for 50% of all search and AI crawler traffic (up from 30% in 2024). Its crawling increased 96% from May 2024 to May 2025, coinciding with the launch of AI Overviews.
Google-Extended: Google uses the Google-Extended user agent for AI-specific crawling related to Gemini and other AI products. This allows website owners to separately control access for AI training versus traditional search indexing.
AI Overviews Impact: The launch of AI Overviews has contributed to sharp declines in referral traffic to news websites, with crawling peaking at 145% higher in April 2025 compared to May 2024.
The Crawl-to-Click Gap: The Hidden Cost of AI
One of the most concerning trends is what Cloudflare calls the “crawl-to-click gap” – the massive imbalance between how much content AI systems consume versus how much traffic they return:
Current Crawl-to-Referral Ratios (July 2025):
- Anthropic (Claude): 38,000 pages crawled per visitor referred (down from 286,000:1 in January)
- Perplexity: 194 pages crawled per visitor referred
- OpenAI: Data varies significantly based on use case (training vs. live retrieval)
This imbalance highlights a fundamental challenge: AI systems are consuming vast amounts of content while returning minimal traffic to publishers. According to TollBit’s data, 80% of AI crawling is for training purposes, with only 18% for search and 2% for user actions.
Protecting Your Content While Maximizing AI Visibility
Managing AI Crawler Access
Using robots.txt Effectively:
# Allow AI training and citation
User-agent: GPTBot
Allow: /
User-agent: ClaudeBot
Allow: /
# Block specific AI crawlers
User-agent: PerplexityBot
Disallow: /
# Set crawl delays
User-agent: GPTBot
Crawl-delay: 2
Important Considerations:
- 12.9% of bots now ignore robots.txt directives (up from 3.3% in Q4 2024)
- Some AI companies use third-party crawlers that may not respect your directives
- Consider using WAF (Web Application Firewall) rules for stronger enforcement
Balancing Access and Protection
Strategic Approach:
- Allow reputable AI crawlers that provide attribution and respect directives
- Block problematic crawlers with history of ignoring preferences
- Monitor your logs for undeclared crawler activity
- Implement rate limiting to prevent excessive crawling
- Use legal terms to protect your intellectual property
Your AI Optimization Roadmap: From Zero to Hero in 90 Days
Days 1-30: Foundation Phase
Week 1-2: AI Audit & Assessment
- Check how your content appears in ChatGPT, Claude, and Perplexity responses
- Analyze server logs for AI crawler activity
- Review robots.txt and crawler permissions
- Document baseline AI visibility metrics
Week 3-4: Strategic Planning
- Develop LLMO content architecture based on how LLMs process information
- Create GEO templates optimized for AI extraction
- Design AIO workflows for cross-platform optimization
- Set up monitoring for AI crawler activity
Days 31-60: Implementation Phase
Week 5-6: Content Transformation
- Restructure content with clear headers and semantic HTML
- Implement comprehensive schema markup
- Create FAQ sections for conversational AI queries
- Develop citation-friendly content formats
Week 7-8: Technical Optimization
- Configure robots.txt for optimal AI crawler access
- Implement crawl delays to manage server load
- Set up monitoring for undeclared crawlers
- Create XML sitemaps optimized for AI discovery
Days 61-90: Acceleration Phase
Week 9-10: Content Amplification
- Launch AI-optimized content targeting high-value queries
- Build relationships with AI-cited sources
- Create multimedia content for multimodal AI systems
- Develop real-time content for RAG applications
Week 11-12: Optimization & Scaling
- Analyze AI citation patterns and referral traffic
- Refine content based on AI behavior data
- Scale successful optimization strategies
- Plan advanced AI optimization initiatives
Industry-Specific AI Optimization Strategies
E-Commerce & Retail
- Product schema optimization for AI shopping assistants
- Real-time inventory data for AI queries
- Review aggregation for AI credibility signals
- Visual search optimization for image-based AI
B2B & SaaS
- Technical documentation structuring for AI extraction
- API documentation optimization for developer AI tools
- Case study formatting for AI success story citations
- Integration data optimization for AI compatibility checks
Healthcare & Medical
- Medical schema implementation for AI health queries
- Symptom checker optimization for AI diagnosis tools
- Treatment information structuring for AI recommendations
- Compliance with AI safety standards for medical content
Legal & Professional Services
- FAQ optimization for AI legal queries
- Service area structuring for AI local recommendations
- Expertise signaling for AI credibility assessment
- Precedent and case law optimization for AI research tools
The Future of AI Optimization: 2025 and Beyond
Emerging Trends Based on Current Data:
1. RAG Dominance Over Training: TollBit’s data shows RAG-oriented scraping growing 49% quarter-over-quarter, versus 18% for training scraping. This shift means continuous optimization becomes more important than one-time training optimization.
2. Multimodal AI Expansion: With AI systems increasingly processing text, images, and video simultaneously, optimization strategies must evolve beyond text-only approaches.
3. Real-Time Adaptation Requirements: As AI systems move from static training to dynamic retrieval, content must be optimized for real-time access and interpretation.
4. Increased Crawler Sophistication: With 226 different AI crawlers identified by Cloudflare (many using undeclared methods), detection and management become increasingly complex.
5. Legal and Ethical Framework Development: Multiple lawsuits against AI companies indicate coming regulations that will shape optimization practices.
Preparing for What’s Next:
The data clearly shows that AI crawling and content consumption are accelerating while referral traffic declines. Businesses must adapt now to maintain visibility in this new landscape.
Don’t Get Left Behind: Why MarginWalkers?
At MarginWalkers, we don’t just follow trends—we create them. Our proprietary AI Optimization Framework combines LLMO, GEO, and AIO into a unified strategy that delivers measurable results based on real data and proven methodologies.
What Sets Us Apart:
- Data-Driven Approach: We base strategies on actual crawler behavior and AI citation patterns
- Comprehensive Monitoring: Track all 226+ known AI crawlers and detect undeclared bots
- Ethical Optimization: Respect for intellectual property while maximizing visibility
- White Label Solutions: Scale your agency with proven AI optimization services
- Continuous Adaptation: Stay ahead of rapidly evolving AI landscape
Ready to Dominate the AI Optimization Battlefield?
The data is clear: AI systems are reshaping how content is discovered and consumed online. The crawl-to-click gap is widening, and businesses that don’t adapt will lose visibility to those who do.
Take Action Today:
- Free AI Crawler Audit: Discover which AI bots are accessing your site
- AI Visibility Assessment: See how your content appears in AI responses
- Custom Optimization Strategy: Get a data-backed plan for AI dominance
- White Label Partnership: Add AI optimization to your service offerings
Don’t wait for your competitors to figure this out first. The AI optimization revolution is happening now, with measurable, documented changes in how content is discovered and consumed.
Contact MarginWalkers today to start your AI optimization journey. Because in the battle for digital visibility, the only margin that matters is the one between you and your competitors.
Sources and References
AI Crawler Data and Statistics:
- Cloudflare. “The crawl-to-click gap: Cloudflare data on AI bots, training, and referrals” (August 2025)
- Cloudflare. “From Googlebot to GPTBot: who’s crawling your site in 2025” (July 2025)
- TollBit. “Q1 2025 State of the Bots Report” (June 2025)
AI Platform Documentation:
- OpenAI GPTBot Documentation
- Anthropic ClaudeBot Support Documentation
- Perplexity Crawlers Documentation
- Google Robots.txt Specifications
Industry Analysis:
- OneLittleWeb. “The Top 30 White Label SEO Agencies – Ranked in 2025” (July 2025)
- Loopex Digital. “Best 10 White-Label SEO Companies of 2025: Expert-Reviewed” (August 2025)
Legal and Ethical Considerations:
- The Register. “Perplexity AI crawlers accused of stealth data scraping” (August 2025)
- Wikipedia. “Perplexity AI” legal challenges section (2025)
- TechCrunch. “Anthropic adds web search to its Claude chatbot” (March 2025)
Note: Statistics and trends are based on documented research from reputable sources. For the most current information on AI crawler behavior and optimization strategies, continuous monitoring and adaptation are essential.