When I say AI search, I’m not talking about AI-powered Google results. I’m talking about what happens when someone opens ChatGPT, Gemini, or Claude and asks: “What’s the best productivity system for entrepreneurs?” or “Who should I follow to learn about AI automation?”
These models pull from the web. They reference what exists. And whoever has published the most useful, specific content on a topic… gets cited.
Right now, most businesses have no idea this is happening. They’re still optimizing for Google. Which is fine. But it’s not the whole game anymore.
The 1999 Analogy
Here’s what happened in 1999. Google had just launched. Most companies hadn’t thought about SEO yet. The few who understood how search worked started publishing content, building links, and showing up early.
Those early movers captured rankings that paid off for years. Some for over a decade. By the time everyone else caught on, the first movers had too much of a head start to catch.
I’ve been using Google since the early 2000s, and I’ve watched the same pattern play out with every major platform shift. Social media, YouTube, LinkedIn… there’s always a window at the beginning where the rules are loose, and the first-movers win.
That window is open right now for AI search. It’s just that most people haven’t realized it.
What’s Actually Different This Time
The core tactic is the same: publish real, useful, specific content about what you know.
But there are a few things worth noting about AI discovery specifically.
Specificity matters even more. Google got pretty good at ranking surface-level content. AI models are better at identifying what’s actually useful. Generic “5 tips for productivity” posts won’t cut it. But a detailed breakdown of how a CPA firm saved 18 hours a week by automating their email triage? That gets noticed.
Consistency compounds faster. Because AI models update their training data (and do real-time web search), fresh content has a faster feedback loop than traditional SEO. You don’t have to wait 6 months to see if something worked.
The window is smaller. In 1999, you had a few years before the crowd arrived. With AI, I’d estimate months. Maybe 12-18 months before this becomes obvious to everyone and the competition spikes.
What I’m Doing About It
I started treating every piece of real work as content source material.
Client calls, workshops, even quick voice notes when I figure something out… all of it goes into a content pipeline. I pull insights, stories, and specific proof points from those raw materials, then turn them into articles, newsletters, and posts.
The goal isn’t just Google traffic. It’s to build a deep archive of real, specific content about AI productivity and automation… so that when someone asks ChatGPT “who should I learn from about AI workflows?” the answer includes Asian Efficiency.
I was helping an agency last year that wanted to rank for specific keywords. We pulled the top results for each keyword, found the content gaps, and built articles that filled them. It worked for Google. I’m now applying the same thinking to AI discovery.
The Practical Takeaway
You don’t need a massive operation to do this. Start simple.
Pick one thing you know really well. Write about it specifically. Use real numbers, real examples, real client stories (with permission). Publish it somewhere indexable… your blog, LinkedIn, your newsletter.
Then do it again next week.
That’s the playbook. It worked in 1999. It’s going to work for AI search. And the people who start now will have a head start that’s very hard to close later.
The opportunity is open right now. Most of your competitors haven’t thought about it yet.
That’s the best time to move.
Want to build systems that generate content from your existing work? That’s what we teach inside the Productivity Academy.
