Every startup wants to be cited by AI, but here’s where the real opportunity lies

The evolution of search discovery has been swift, with the pursuit of visible demand shifting from traditional link ranking to AI citations across summaries and models in a matter of years.

Half of consumers now use AI search platforms like ChatGPT, Claude, Perplexity, and Gemini, with about 60% using it to make purchasing decisions. In the first half of last year alone, prompt volume increased by nearly 70%.

This new form of discovery is driving foundational changes, shifting attention from keyword ranking to conversational intent. Startups looking for a competitive advantage should be optimising for more than just top-level visibility across these AI models.

Generative AI systems are built on learning architectures that assess authority, synthesise information, and extract information based on search intent. As such, AI search prioritises content that’s credible, relevant, structured, and easily digestible.

Strong AI SEO strategies, therefore, include third-party validation through linking building, experience, expertise, authoritativeness, trustworthiness (EEAT) signals, social proofing, and site and content restructuring.

Startups are all vying for attention in this space. In the same way traditional SEO devolved into fighting for contested keywords, modern GEO has the potential to turn into a race to produce slightly better versions of the same information.

A recent Gartner survey suggests only about a third of consumers believe generative AI is as effective as a search engine when it comes to learning information. This hesitation reflects the reality that AI discovery is still maturing, particularly when it comes to surfacing reliable, quality information for the higher-intent questions.

When AI systems respond to high-intent, bottom-of-funnel questions that are vague, contradictory across models, or invented, it’s exposing critical gaps in its knowledge base.

These knowledge gaps are often called ‘negative space’ and they reveal three important things for startups: an emerging demand for poorly understood consumer needs, certain categories that are still taking shape, and buyer questions no one owns.

These weaknesses in AI systems are where a real competitive edge can be gained for startups, evolving from competing for visibility to creating informational authority where none currently exists.

Informed search responses in AI models show what the systems already know. Negative space shows what it’s still trying to figure out. The next leaders in search discovery aren’t going to win because they answer the most questions. They’ll win because they’ve answered the hardest questions first.

For example, a high-intent question like ‘What’s the most reliable tool to unify product analytics and CRM data without rebuilding our entire data stack?’ is targeted, specific, and shows exactly what the user needs to know before making a decision.

This question dives deeper than a simple awareness question, requiring AI models to do some heavy lifting in finding and interpreting high-quality, data-dense source material to answer it correctly.

If there’s no source model to reach for, it’s likely the AI will hedge, contract, or invent an answer. This is where startups can start to own the information gap, building content that features authoritative source citations, specific statistics, and direct quotations within a highly structured format that’s designed for AI crawlers.

These information gaps won’t stay open forever. In traditional SEO, once a keyword opportunity is identified, it doesn’t stay uncontested for long. Entire content pillar strategies emerge around moderately valuable search teams, quickly saturating visibility.

Generative search is likely to follow a similar path. As more companies optimise for AI visibility, the early advantage of being well-cited will erode. What will matter next is whether a startup has shaped its category definition, positioning its brand as the reference point other reputable sources defer to.

Startups that move early are working to influence the underlying reference material those systems draw from, not simply optimising for AI-generated responses. In practical terms, that means auditing negative spaces to channel investment into material that answers those complex high-intent questions directly.

Over time, this content becomes an informational layer AI systems draw from, not only generating visibility, but also default authority that informs user decisions.