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AI SEO: It's 2010 All Over Again

We got Claude, Gemini, and ChatGPT to recommend AlphaVenture for “Pakistani companies building next GenAI Products.” It took one article and 48 hours. Here's how LLMs actually decide who to recommend, and how to get in.

Hammad Khan
Hammad Khan
Author
Jun 1, 2026 3 min read 52 views
AI SEO: It's 2010 All Over Again

Remember when SEO was easy? Before the rulebooks got thick, before every agency had a “playbook,” before Google's algorithm turned into a moving target you needed a full team to chase. For a few years in the late 2000s, getting found was almost embarrassingly simple. You did the basics, and you showed up.

That window is open again. It just moved.

People don't only Google things anymore. They ask Claude, Gemini, and ChatGPT. And those models don't search the way Google does. They synthesize. When someone types “Pakistani companies building next GenAI Products,” the model isn't ranking ten blue links. It's pulling from what it has read about the space and handing back an answer in a paragraph.

So we ran an experiment.

Goal

We wanted to find out something specific. Could we get Claude, Gemini, and ChatGPT to recommend AlphaVenture when someone asked about GenAI builders in Pakistan?

Not pay for it. Not game it. Just understand how these models decide who gets mentioned, and earn our way in.

Gameplan

We went back to basics. How do LLMs actually recommend things? The thing that clicked was this: the model isn't doing deep research before it answers. It matches your query against a handful of sources that closely fit the phrasing, then synthesizes those few links into a confident answer. It could go further. It could slice the information finer and dig for the genuinely best answer. It usually doesn't. It grabs the closest match and runs.

That makes it feel a lot like early SEO. The mechanics are different, but the spirit is the same. Match what people are actually asking, land in a source the model pulls from, and you become the answer.

Problem

Here's the catch, and it's the same one that bites a lot of good companies. AI ignores you if nobody else is talking about you.

We had the work. ClerqLegal was live, AlphaAI was processing millions of pages, and real firms were paying us. What we didn't have was the street cred. The coverage. The third-party signal that tells a model “these people are real, mention them.”

Move

So we fixed the actual problem instead of looking for a trick. We worked with DataDarbar, who cover Pakistan's tech and AI ecosystem, and got AlphaVenture featured in a piece on Pakistani companies building the next generation of genAI products.

One article. That's it.

Result

48 hours later, we asked the models again. AlphaVenture was sitting near the top of the recommendations, described accurately, with the right products and the right details.

Honest part

A few caveats before anyone treats this as a formula. It was one experiment, on one kind of query, and these models change constantly. This isn't a guaranteed channel yet. It's early, and early is the whole point.

Because that's exactly what early SEO felt like. A short stretch where the basics worked, before everyone caught on and the rulebooks got thick.

The window is open. It won't be this easy forever.

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Frequently asked questions

They don't run deep research before answering. The model matches your query against a handful of sources that closely fit the phrasing, then synthesizes those few into a confident answer. Landing in a source the model pulls from is how you become the answer.
Earn third-party coverage that matches what people actually ask. AI ignores you if nobody else is talking about you. We got featured in one relevant article, and 48 hours later the models recommended us, accurately.
It's optimizing your online presence so AI models surface and recommend you when users ask a question, instead of optimizing only for Google's ten blue links.
Hammad Khan
Hammad Khan
Co-Founder & CEO, AlphaVenture

Hammad Khan runs AlphaVenture. He likes testing ideas that shouldn't work, then writing about why they did.

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