You're not discovering AI behavior patterns. You're rediscovering SEO!

I'm tired & just trying to help 😅 ....it's also the last day to join my last AI Course of 2025 if you wanna level up & evolve your AI SEO thinking, hit me up. 🚀

So, when you hear or say things like:

"LLMs aren't citing Reddit URLs as much" → Reddit's search rankings dropped (or G's &num=100 removal impacted this)
"LLMs fall for spam listicles" → These rank well in search (unfortunately)
"LLMs prefer fresh content" → Search engines prioritize recency

LLMs ARE NOT INFORMATION RETRIEVAL SYSTEMS

For # 2 👏 Every 👏 Single 👏 URL 👏 you see in an LLM output comes from a search engine API (Google or Bing)

For # 1 it's a popularity contest. The more you appear online in contextually relevant discussions, the higher your odds of being baked into the model's outputs. "Brand mentions are the new backlinks" thinking applies here.

Let's clarify a few things:

Two different mechanisms:
1. Getting mentioned by an LLM (no URL) = Being in training data
2. Getting a URL cited by an LLM = Ranking in search results

The recovery starts now. And if you're ready to think clearly about AI's real potential while others nurse their headaches, you're already way ahead of the curve. 🚀

Start small. Embrace failure. Ask questions.

This is how AI progress happens! Not through intoxicating hype cycles, but through disciplined experimentation + critical thinking.

🏆 The winners emerging from this hangover will be sober, clear-headed professionals with an experimental mindset who methodically test AI applications against specific problems. While everyone else sleeps this off.

• Skip the gimmicks: Prioritize sustainability over novelty. Your cat photo doesn't need to be "turned into a human" using AI & focus on applications with genuine impact.

• Strengthen human advantages: Develop what AI can't replace; authenticity, strategic thinking, relationship management, creative problem-solving, and ethical judgment.

• Identify AI-supported content wins: Use AI to analyze large datasets. Successful content marketers are mining thousands of Reddit conversations and customer reviews to uncover patterns impossible to find manually.

• Think like an engineer: Get crystal clear on what steps are required to accomplish a specific task before attempting to automate or augment it with AI.

• Beware of misleading language: Challenge vague terms like "agents" & track how goalposts shift; just as "AGI" is now quietly being abandoned after serving its fundraising purpose.

• Demand real evidence: A flashy demo isn't proof of transformation. Test systems with your specific real-world challenges (outside of demo examples) before investing time or resources.

🧰 Your Hangover Recovery Kit:

• Question everything: Who's building this tech and why? What happens when systems fail? Where is your data going? AI boosters rely on your self-doubt to push their narrative.

🦾 Agent fantasy crumbles: Salesforce's research shows AI agents achieving just 35% success on multi-step tasks across 4,280 real-world scenarios

⏰ Broken promises: "Revolutionary" features quietly pushed from "coming soon" to "maybe never"; from Google's AI glasses to Project Starline to OpenAI's GPT-5 reasoning breakthroughs

🤖 Moving goalposts: Even Sam Altman now admits AGI is "not a super useful term"... after years of using it to secure billions at astronomical valuations