# Gabriel Kanev > Source: https://gkanev.com/posts/debunking-the-myths-what-seo-professionals-need-to-know-about-ai-and-llms/ > Machine-readable version - 2026-04-20 --- - Search ESC [Image: SEO and AI myths] The AI SEO space is full of confident claims from people who don’t understand what they’re selling. Here’s a breakdown of the most common misconceptions, and what to actually ask if you’re evaluating an AI SEO service. ## Misconception 1: AI Agents and LLMs Are the Same Thing They’re not. LLMs (Large Language Models) are the underlying text-generation systems. AI agents are systems built on top of LLMs that can take actions, use tools, and complete multi-step tasks. An LLM is an ingredient; an agent is a recipe. ## Misconception 2: LLMs Use Google’s Index They don’t. ChatGPT uses OpenAI’s own web crawlers and trains on datasets like FineWeb-edu and RedPajama-V2. Google’s index is proprietary and not accessible to competing AI systems. When a vendor claims their AI tool “leverages Google’s data,” ask them to explain specifically what data they mean. ## Misconception 3: LLMs Learn from Your Conversations in Real Time They don’t. Models are static after training. What looks like “learning” within a conversation is context management - the model has access to the conversation history in its context window, but this doesn’t update the underlying model. When you start a new conversation, it’s gone. Some systems implement persistent memory by storing conversation summaries externally and injecting them into future contexts. That’s a useful engineering pattern, but it’s different from actual learning. ## Misconception 4: ChatGPT Has Ranking Signals Like Google It doesn’t. There’s no traditional SEO-style ranking algorithm that determines whether your content appears in LLM outputs. The process is statistical: the model generates tokens based on probability distributions derived from training data. Whether your site appears in an LLM response depends on what the model learned during training, not on signals you can optimize directly. ## Misconception 5: ChatGPT Verifies Facts Like Google Partially, and with significant limitations. Models can verify facts against what they learned during training, but that training data has a cutoff date. Events, changes, and new information after the cutoff don’t exist for the model unless you provide them in context. More importantly, “verification” in the LLM sense means checking against memorized patterns, not against current authoritative sources. This is a fundamentally different process than what Google does. ## Misconception 6: You Can Guarantee Your Site Appears in ChatGPT You can’t. Token prediction is probabilistic. Even if your content was heavily represented in training data, whether the model references it in a given response depends on the specific query, the model’s current generation state, and random sampling parameters. No one can guarantee placement. ## Misconception 7: You Can Accurately Measure AI Visibility Not reliably. Any measurement of “AI visibility” inherits all the uncertainty of the probabilistic process you’re trying to measure. You can run queries and count citations, but the results won’t be stable across identical queries run at different times. ## Misconception 8: AI Tools Can Optimize Content for Google Only partially. Google’s ranking models are trained on human content and behavior signals, and we don’t know the specifics of their architecture. AI tools can help produce content that has characteristics associated with high-ranking content, but the causal relationships are complex and the tools are guessing. ## Misconception 9: “Our Agency Has Its Own Model” Building a genuine LLM requires deep ML expertise, significant data infrastructure, and enormous compute costs - somewhere between $300,000 and $900,000 per week in training compute for frontier-class models. An SEO agency almost certainly doesn’t have this. What they likely have is a fine-tuned version of a commercial model, a custom wrapper around a commercial API, or they’ve just renamed a standard model. Ask them specifically: what base model is this built on? What training data did you use? What was the compute budget? ## Questions That Actually Reveal Expertise If you’re evaluating whether someone genuinely understands AI: What’s the difference between context length and a model’s context window? - Can you explain knowledge distillation and why it matters for deployment? - What is model quantization and what are the tradeoffs? - How do attention mechanisms work at a high level? - What’s the difference between fine-tuning and few-shot prompting? - What are embeddings and how are they used in retrieval systems? - What’s the difference between few-shot and zero-shot prompting? - What is KV caching and why does it matter for latency? - What is Flash Attention 2 and why was it significant? If they can’t answer these questions, they’re not the AI experts they’re presenting themselves as. You can make AI tools useful without deep technical knowledge - but you shouldn’t be selling AI expertise you don’t have. 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