The Bottom Line – tldr
Think critically before purchasing any AI SEO service. Ask detailed questions and don’t hesitate to verify the expertise of the people you’ll be working with.
AI is fundamentally about personalization, and the equations are vastly different depending on that personalization. The industry will soon face a reckoning when clients start asking: “If I invest $1 in AIO (AI Optimization), how much will I earn back in concrete numbers?”
As someone who has built an LLM model from scratch (lucky me, right …) – complete with all the mathematics, architecture, and training processes – I feel compelled to address the growing number of misconceptions about “SEO for AI” that have been flooding the industry. The hype has reached concerning levels, and it’s time to separate fact from fiction.
Misconception #1: AI Agents and LLMs Are the Same Thing
Reality: They are distinctly different.
An AI agent is software built on top of a language model – it cannot function without the underlying LLM. However, a language model can do many things beyond powering a single agent. Understanding this distinction is crucial for anyone working with AI technologies.
Misconception #2: LLMs Use Google’s Index
Reality: This couldn’t be further from the truth.
When you interact with ChatGPT, it doesn’t tap into Google’s search index. Instead, it uses its own web scraping agent that crawls top search results, processes them through a set of rules, and returns information based on the model’s internal architecture – NOT according to Google’s algorithms.
Language models are trained on massive datasets like FineWeb-edu, which contains over 10.3 TB of data, or RedPajama-V2 (with 30+ trillion tokens – though the latter suffers from significant benchmark contamination and has approximately 40% duplicate content). This training data has nothing to do with Google’s index or ranking systems.
Misconception #3: LLMs Learn from Your Conversations in Real-Time
Reality: This is a fundamental misunderstanding of how language models work.
Models do NOT train themselves from individual conversations. They remain static after their training is complete. Your chats don’t modify the model for other users or even for you in the next session (unless you have explicit personalization features enabled). The training process happens in controlled environments with curated datasets, not through casual user interactions. When you see a model “remember” something from earlier in your conversation, that’s context management within a single session, not learning or training.
Misconception #4: ChatGPT Has Ranking Signals Like Google
Reality: It doesn’t work that way.
ChatGPT relies on strict rules programmed into its chat agent architecture. Language models work primarily with words and can only work with numbers when provided with proper context. There’s no ranking algorithm in the traditional SEO sense.
Misconception #5: ChatGPT Verifies Facts Like Google Does
Reality: Sort of, but not really.
While language models do check factuality, they do so only within the boundaries of their training data. A language model ALWAYS compares information against what it was trained on – this is a fundamental rule. The model’s knowledge is frozen at its training cutoff date, unlike Google’s constantly updated index.
Misconception #6: You Can Guarantee Your Site Will Appear in ChatGPT Results
Reality: No one can make this guarantee.
Token prediction (the words models generate in responses) is fundamentally based on statistics and probability theory. There’s no way to guarantee that you’ll always receive the same answer to the same prompt. Try it yourself in ChatGPT with simple queries – you’ll see variations in responses.
Misconception #7: You Can Accurately Measure Visibility in AI Systems
Reality: Despite what some software tools claim, accurate measurement isn’t possible.
The probability that any calculation of your visibility is correct is exactly as uncertain as the probability of being shown in results at all. If companies are selling you visibility metrics for AI systems, approach these claims with extreme skepticism.
Misconception #8: AI Tools Can Optimize Content for Google
Reality: Only partially true, and here’s why.
Gary Illyes from Google once told me in a personal conversation that Google’s ranking models are trained on human-written content, not machine-generated text. This raises critical questions:
- Do we know what’s in Google’s training datasets? The model’s behavior varies completely based on its training data.
- Do we know what content patterns were learned? Without this knowledge, how can we replicate them in our content?
- How were the models trained? What hyperparameters were used? Even slight changes can fundamentally alter a model’s purpose and outputs.
- What architecture does Google use? If they use pre-existing models, which parts of the architecture are leveraged and why? What were the training hyperparameters? What’s the ultimate goal?
Misconception #9: “Our Agency Has Its Own Model”
Reality: Building a true LLM from scratch requires extraordinary resources.
Do people understand what it actually takes to create a proprietary model? If it were easy, there wouldn’t be AI research labs with teams of scientists worldwide, conducting thousands of research papers every month.
From personal experience, building a real model from zero requires:
- Deep expertise in: Linear algebra, advanced mathematics, statistics, and programming
- Careful architecture design: Understanding which datasets to use and how to train the model through at least 5 different stages
- Massive computational resources: Training happens in data centers, not on consumer gaming GPUs
- Significant financial investment: Between $300,000 to $900,000 PER WEEK for training a large model
For SEO purposes specifically, you can’t get away with a small model – you need diverse knowledge domains, which require extensive training.
Questions to Ask AI SEO “Experts”
Before investing in any “AI SEO” services, ask potential providers these technical questions. True specialists should answer them effortlessly:
- What is a context window?
- What is model distillation?
- What is model quantization?
- How do attention mechanisms work?
- What changes during model fine-tuning?
- How do embeddings work?
- What’s the difference between few-shot and zero-shot learning?
- What optimization techniques were used during training? (KV Caching, Learning Rate Scheduler, batch distribution across GPUs, tokens processed per second acceleration, Flash Attention 2?)
The Real Problem: Repackaged Hype
I find it particularly amusing when tools claim they can’t measure large-scale visibility or answer specific methodology questions that would allow validation of their approach. Yet they still charge premium prices.
When you see claims like “Look, your brand is mentioned!” – but there’s no context provided – remember that mentions can be:
- Comparative: “Should I buy a Mercedes or BMW?”
- Synonymous: “Bavarian car or Mercedes”
- Negative: “Is it true all [stereotypical group] drive BMWs?”
Context matters enormously. Some companies charge $7,000 for surface-level mentions without semantic analysis.
My favorite trend? Taking SEO tactics that have existed for at least 5 years, repackaging them as “AI optimization recommendations,” and charging premium prices for old wine in new bottles.