By Tristan Harris, SVP at Next Net
For nearly three decades, search engines have served as the translation layer between human content and machine interpretation. We wrote pages for people; machines crawled those pages, parsed their tags, indexed their keywords, and matched queries to relevance signals. The entire discipline of SEO grew out of optimizing for this mechanical middleman.
But as generative AI becomes the dominant mode of online navigation and information retrieval, that translation layer collapses. Machines no longer interpret our content for the purpose of ranking links. They interpret it to solve tasks, answer questions, and act as agents. For marketers, this shift fundamentally rewrites the rules of discoverability.
In an AI-native environment, the basic premise of SEO – optimizing pages so that search engines will send users to them – no longer holds. Large language models and emerging agentic systems are not trying to deliver a ranked list of links. They are trying to understand meaning, provenance, and intent. And they do not care what page the information came from. They care whether the content is structured in a format they can consume efficiently, whether it is verifiably trustworthy, and whether it is fresh.
This marks the rise of what some are calling Generative Engine Optimization, or GEO. But GEO is not SEO with new keywords or clever prompt-bait. It is a rebuild of content infrastructure for a world where AI will talk primarily to AI. That means preparing content in the native structures generative systems actually ingest: vectors, embeddings, and semantically rich chunks of information that eliminate the heavy lifting models currently perform when they scrape a website. Traditional HTML and XML remain useful for humans, but to an LLM they are bloated, ambiguous, and costly to process. When marketers simply hope their webpages will be crawled, interpreted, and integrated correctly, they are depending on a system that no longer exists.
A more resilient strategy is emerging, centered around machine-readable vector files that brands can generate and control. Instead of forcing AI systems to interpret a sprawling website, organizations can provide digitally signed, frequently refreshed vectorized representations of their content. These files serve as canonical sources of truth: structured, authenticated, and instantly ingestible. They also give brands far more control over what they are discovered for, because the content is deliberately chunked into topical vectors that reflect the company’s core expertise.
This shift is especially important as the internet becomes increasingly zero-click. In the past, publishers and marketers depended on the flow of traffic from search results to measure success. In an AI-first environment, the consumer may never visit the originating site at all. Agents summarize, synthesize, and act on our behalf. The business question becomes not “How do we win the click?” but “How do we ensure our content is cited, trusted, and attributed inside the model’s reasoning?”
This new structure reframes the marketer’s challenge. Instead of engineering pages for human browsing patterns, the priority becomes creating a continuous feed of machine-verifiable knowledge. When companies deliver vectorized, signed content directly to AI systems, they define the context in which their brand is understood. They are no longer hoping an algorithm interprets their website correctly; they are supplying the interpretation themselves. In a world where AI mediates every interaction, visibility will mean being the clearest, most authoritative source in the model’s memory, not the highest link on a results page.
Organizations that adapt early will gain an edge. Those that continue to optimize only for traditional search will find themselves increasingly invisible to the intelligent intermediaries shaping tomorrow’s information ecosystem. Discoverability is being rebuilt structurally, and for machines that no longer need a translation layer. Now is the time for brands to rebuild with it.
