Artificial intelligence models don’t “think” the way humans do, but they are evolving fast. We’ve moved past static models into an era where AI can interact with tools, browsers, and search engines. This shift changes everything about how these systems retrieve, validate, and present information.
A particularly interesting development is the rise of AI agents that can self-prompt—essentially asking themselves follow-up questions to refine their own answers. I recently ran two experiments to see exactly how these systems gather information, and the results shed light on a new “hybrid” behavior.
Two Experiments in AI Reasoning
I built an AI agent with web browsing capabilities and allowed it to “self-prompt” for an hour. I watched as it navigated the web, and one habit became immediately obvious: it gravitated heavily toward Wikipedia.
While the agent occasionally branched out, Wikipedia served as its “grounding” reference point. The answers were accurate but lacked nuance. It wasn’t forming new opinions; it was aggregating authoritative consensus.
I asked Google’s AI (Gemini) about material handling companies. It returned a list, but when I asked “How did you decide on these?”, the answer revealed something critical.
The AI admitted it didn’t conduct a real-time search to find the companies. Instead, it relied on internal training data to generate the list, then used search only to confirm details. It reasoned first, and searched second.
The Rise of “Hybrid AI”
Traditional search engines retrieve results and leave the interpretation to you. Hybrid AI does the opposite: it interprets the query, drafts an internal answer, and uses search results to fact-check itself.
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Static Internal Knowledge: Information from pre-training (books, datasets, old websites).
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Dynamic Real-Time Search: Used primarily for validation and freshness.
The Importance of Model Knowledge and Search
This hybrid approach creates a critical timing gap that every SEO needs to understand. Because models often look to internal knowledge first and then validate, they are referencing a snapshot of the world that is often months old.
As of the date of publish:
- Gemini 3 Pro: Knowledge cutoff is January 2025.
- GPT 5.2: Knowledge cutoff is August 2025.
This means that depending on the type of question, it could be 6-12 months before the AI becomes natively “aware” of major changes you make to your site or brand. If the query triggers a live search, you get instant visibility. But if it triggers internal reasoning, you are at the mercy of the training cycle.
The Reality: AI optimization is a long game. You are optimizing for the next training run, not just the next crawl.
What This Means for AEO & SEO
If AI models are using the web primarily for validation rather than discovery, our approach to SEO has to shift.
1. The “Authority Anchor”
The self-prompting experiment showed us that AI loves safety. It looks for anchors of truth—Wikipedia, government sites, .edu domains, and established industry bodies. If your content contradicts these anchors, or if your brand isn’t referenced by them, you might be filtered out during the validation phase.
2. Off-Site is the New On-Site
Because AI relies on external tools to update its internal “brain,” off-site content is becoming just as critical as your on-page optimization.
- Forums & Reddit: These act as “living contexts.” High engagement and consensus on threads can signal relevance to an AI looking for current trends.
- Citations: Being mentioned on authoritative industry sites helps validate your existence to the AI.
The Strategy Shift
We need to stop thinking solely about ranking URLs and start thinking about optimizing for validation. To win in this environment, you need a holistic approach:
- Technical Foundation Keep using schema and structured data so machines can easily read your site.
- Be the Source Publish original, authoritative content that answers questions clearly.
- Go Where the AI Checks Its Work Ensure your brand or content is discussed on the platforms AI trusts—Wikipedia (where appropriate), reputable industry journals, and active community forums.
Final Takeaways
If you made it this far, this is what I want you to take away:
- This is not a conclusive research project, this is a friendly reminder to think about what we know already.
- Remember AI or SEO optimization is not an overnight fix, it’s the long game.
- AI is dynamic and fast paced and it’s easy to get tunnel vision on the last thing we hear, but we need to take a step back and see how all the parts fit together.
The future of SEO isn’t just about getting a user to click a blue link. It’s about shaping the consensus that AI systems rely on to answer questions. As these models evolve, the winners won’t just be the sites with the best keywords; they will be the sources the AI trusts enough to verify its own thinking.