PageRank in the Age of AI

The internet is about to look a whole lot more like the online advertising world.

No, I don’t mean there’ll be more ads. In fact, I think there’ll be far fewer.

But the technology stack for content distribution will mirror the architecture that has been implemented in the online ad world.

As we’ve reached the AI search tipping point, publishers face an existential challenge : ensuring AI systems use their content in answers to maintain relevancy.

When you visit a website, your browser triggers an auction. The site’s supply-side platform sends your data to an exchange—like a stock market for ads. Dozens of advertisers bid for the chance to show you their message. Highest bidder wins. Total time: under 200 milliseconds.

Now imagine the same system, but for content. Instead of bidding to show you ads, publishers vie to inform AI responses.

The AI uses quality metrics, not money, to determine winners. Publishers compete on relevance, accuracy, freshness, and authority. The signals that make content useful. This is PageRank for real-time AI responses—algorithmic evaluation operating in milliseconds rather than batch processing.

Here’s how it would work in practice : you ask Gemini: ‘What are the reviews of the new Google Pixel phone?’ That query broadcasts to participating publishers: tech reviewers, consumer sites, electronics retailers. They submit their best content to the auction. Gemini evaluates quality, recency & relevance, then synthesizes the winning submissions into your answer.

Notice what disappears : the demand-side platform. No more advertisers optimizing for clicks. Just publishers competing to be the most useful source. The internet gets fewer ads, but every piece of content fights for attention in an auction measured in milliseconds.

Publishers get traffic & attribution when selected, creating indirect revenue through brand building & subscriptions—but only if their content consistently wins on merit.