AI visibility numbers are synthetic and non-deterministic — the same prompt can return a different answer twice. Most teams sample with a prompt list built in an afternoon, then hand the result to an executive with no reason to believe it. The fix starts in the unglamorous part of the stack: a prompt index built on real topics, personas, intents, and branded ratio, extended with MCP and an LLM into analysis that used to need a full data team, distilled into a short list of barometers leadership can actually plan against: share of voice, recommendation quality, narrative risk, and the sources driving them.

Spencer will walk through the whole stack, from building an index that holds up to mapping the methodology back to business goals. Tool-agnostic; bring your current prompt set.

In 1989 Tim Berners-Lee invented the World Wide Web, a web of documents built for humans.

In 2001 he envisaged the Semantic Web, a web of structured, machine-readable data built for agents.

That second web never had its moment. Until now.

AI has already consumed the human web and discovered its limitations. Agents need something different: entities, provenance and machine-readable structure.

We are already evolving the web in the image of Berners-Lee’s original vision. This talk puts aside SEO hype, vendor predictions and speculative futurism.

We’ll go back to the source, the World Wide Web Consortium, papers and transcripts of academics and researchers who built the foundations.

Because the future of the web and perhaps of SEO may have been hiding in plain sight all along.