The End of SEO: How GEO with LLMs is Redefining Digital Marketing - A Showdown of ChatGPT, Claude, and CoPilot

Comparing the Response of the same Prompt in CoPilot ChatGPT and Claude

In the following comparison, I used a LinkedIn post from Santosh Sharan on the topic of LLM SEO Evolution. I have been training the ChatGPT for a couple of years, and CoPilot for only a few instances over a couple of months. I signed up for Anthropic today, so it was my first prompt with Claude.

The prompt for the test of AI Sirens:

You're a demand generation expert for a software and services sales company. Based on the point of view expressed by Santosh Sharan, give me pragmatic recommendations for an action plan with elements like 30 60 90 day milestones, the strategic considerations, how to support the goals of the CMO, the CEO, and the board. go deep with comparative analysis of a couple tactics associated with each campaign or strategy.

I was impressed with Claude’s answers and the two panel display of results and prompt. I noticed ChatGPT offered me the same two panel display of prompt and response as Claude, so it would seem that OpenAI is playing catchup to Anthropic on that feature. I used the free version, and it was my first prompt.

I found the CoPilot response quite superficial compared to both the others. Interestingly, Microsoft has a higher subscription for a result that came weaker on my test. To be fair, I have not trained CoPilot much yet. Also, the prompt and its response within M365 Copilot goes away if you close the application or document. It’s a good idea to copy and paste it before closing it. It’s integration with Modern Office Suite, the governance and security capabilities from Entra, Purview and Azure make it strong for its customers and regulated industries. I have the license for M365 CoPilot.

My favorite was ChatGPT for this exercise. It seems to know what will please me. I have a subscription for the Pro service. The content was comprehensive, and it presented the comparative data in tabular form, which makes it much easier for me to consume. A little lagniappe came to me when it offered to send me an executive summary of the resulting report too.

My method included copying and pasting the exact same prompt into all three LLMs. Then copy and paste the answer into three discreet documents.

The following comments come from ChatGPT looking at the attached documents with a prompt to compare each one for strengths and weaknesses.

Each document provides a strategic perspective on how SEO is evolving post-LLMs, but their quality of analysis varies in terms of insightfulness, clarity, conciseness, actionability, and alignment with business executive priorities.

1. ChatGPT Analysis [Highly Structured & Actionable]​

   

   1. Strengths:

      • Executive-Friendly Format: Clearly structured Executive Summary, 30-60-90 day plan, and stakeholder alignment.

      • Actionability: Provides specific step-by-step execution for demand generation shifts, making it practical for a CMO or CEO.

      • Comparative Analysis: Contrasts Traditional SEO vs. LLM Optimization across multiple areas (content strategy, lead nurturing, attribution).

      • Multi-Format & AI Alignment: Recognizes LLMs pull data from podcasts, Reddit, YouTube, not just webpages.

      • Realistic Next Steps: Offers tangible KPIs, content audit priorities, and AI-driven lead attribution.

   2. Weaknesses:

      • Assuming AI-Driven SEO Will Fully Replace Traditional SEO: Less discussion on hybrid SEO models where organic and AI-based approaches coexist.

      • Minimal Discussion on Competitive Risks: Doesn't deeply analyze potential misinterpretation of brand data by AI models.

2. Claude 3.5 Analysis [Conceptual & Risk-Aware]​

   

   1. Strengths:

      • Deep Competitive & Risk Analysis: Highlights risks of public content exposure, balancing visibility vs. intellectual property protection.

      • Strategic Framing: Unlike ChatGPT’s tactical approach, Claude focuses on high-level positioning—essential for board-level decisions.

      • Structured Data Emphasis: Discusses schema markup, accessibility audits, and AI compliance—crucial for LLM discoverability.

      • Comprehensive KPI Framework: Introduces LLM performance tracking (brand mention accuracy, AI comparisons, knowledge graph comprehensiveness).

   2. Weaknesses:

      • Lacks Tactically Deep Execution Steps: While the risk-mitigation lens is strong, the document doesn’t provide detailed campaign playbooks like ChatGPT.

      • Limited Comparative Analysis: Discusses GEO (Generative Engine Optimization) but lacks tactical breakdowns comparing it to traditional SEO.

        Best For: Executives focused on long-term AI search trends, risk mitigation, and strategic adaptation rather than immediate execution.

3. CoPilot Analysis [Concise but Generic]

   

   ​

   1. Strengths:

      • Concise & Well-Structured: Provides clear 30-60-90 day milestones without excessive detail.

      • Covers GEO & AI-Supported Demand Generation: Includes GEO implementation and AI-driven insights for demand generation.

      • Cross-Platform Consideration: Recognizes the importance of multi-format content (podcasts, YouTube, blogs).

   2. Weaknesses:

      • Lacks Depth in Execution: Unlike ChatGPT, doesn’t detail the precise execution of AI-first SEO and demand generation.

      • No Competitive Benchmarking: Doesn’t compare AI-based brand mentions vs. competitors.

      • Surface-Level Tactics: Less pragmatic, missing deep technical SEO and AI-driven brand visibility strategies.

      Best For: Marketing teams needing a quick-start guide but not an in-depth execution plan.

The comprehensive comparative analysis of the three LLMs includes the executive summary here, plus tables showing content quality elements, recommendations for business needs, 20 distinctive features, costs in February 2025, Enterprise Customer features, and citations.

If you found this info useful, please subscribe to my substack.