Inspired by Lenny s podcast with Ethan Smith (so good, you need to listen - the advice for builders is insane and can't believe it's free...), I wanted to share a few things I ve been seeing going deep into Answer Engine Optimization (AEO) for @Meet-Ting.

I spend a lot of my day in GPT and Gemini, and over the last few months I started getting more curious about how their recommendations are actually pulled from the web. I'm not an expert, listen to the podcast, but I am actively tackling it so have some tactical learnings.

I ve been wondering about this a lot lately.... If LLMs keep pulling from blogs, news platforms (and obviously other content sources) -> this results in ad revenue keeps shrinking for most folks reliant on this source of income -> what is the pivot for these creators?

My wife runs a food blog that s heavily ad traffic based. Right now her niche (make based content seems to be some of the safest on the internet.. likely way worse for someone with an information based blog) hasn t been hit too hard, but it feels like something that could change quickly.
That s got us thinking about what a pivot could look like.

Hi everyone!

I recently implemented an AI SEO Optimization strategy using an advanced content analyzer and keyword modeling tool, and I wanted to share my experience and gather your insights.

Here s what I did:

1. Content Audit Used AI to scan my top 50 blog posts for keyword gaps, optimization opportunities, and readability issues.

2. Dynamic Keyword Mapping The AI suggested long-tail variations and semantic topics I hadn t thought of.

3. Automated Metadata Tweaks Titles, meta-descriptions, and header tags were updated based on AI scoring for click-through rate (CTR) potential.

4. Performance Tracking After two weeks, impressions are up ~20% and average position improved from #25 to #18 on Google for several targeted terms.