Fit Over Fame: The Narrow Openings for Startups in AI Search

Signal Harbor Weekly

For years, building visibility through search often rewarded accumulated scale.

Large companies had stronger domains, larger content budgets, broader media coverage, more backlinks, and years of historical visibility working in their favor. Smaller companies could still compete, particularly in local or specialized markets, but they often started at a disadvantage.

Generative AI may change some of those dynamics.

Instead of presenting a page of links, AI systems increasingly answer questions directly, compare options, and recommend a short list of companies or products. That has created an optimistic narrative: perhaps AI search will finally level the playing field for startups and smaller businesses.

The research does not support a conclusion that broad.

AI search does not eliminate the advantages of scale. But recent studies suggest that it does not perfectly reproduce them either.

Large companies often begin with an advantage because they are familiar, widely discussed, and supported by more public information. Smaller companies may still find openings when they are more specialized, more clearly differentiated, or better suited to a specific customer need.

The opportunity appears real. It is also conditional, unstable, and highly dependent on the category.

Traditional search often rewarded accumulated authority

A recent study accepted at SIGIR 2026 compared the sources surfaced by traditional Google search, Google AI Overviews, and Gemini across 11,500 queries.

The researchers found that traditional Google results relied more heavily on popular and institutional websites than the generative systems did. AI Overviews and Gemini also surfaced substantially different sources from traditional search.

That does not prove generative AI favors small businesses. A less popular source is not automatically a small company, nor is it necessarily more relevant.

But it does suggest that the advantages built through traditional search do not transfer perfectly into generative results.

AI search appears to create a different competitive surface. A company that does not dominate conventional rankings may still appear in an AI-generated answer when its information closely matches the question being asked.

The same study also found that generative results were less stable. Small changes in the wording of a question could produce different sources and answers.

That creates both an opportunity and a risk.

A smaller company may break into an answer it could not have reached through traditional rankings. But appearing once does not mean it has established a durable position.

Incumbents still begin with a meaningful advantage

Large brands are not suddenly losing all of their power.

A 2026 preprint examined how language models handled a controlled product-comparison experiment involving one recognized skincare brand and nine fictional brands. The products were initially given identical specifications, prices, ratings, and review information.

Under those artificial tie conditions, the recognized brand dominated the recommendations.

The study describes this as an incumbent advantage: when the available information does not clearly separate one option from another, a model may fall back on familiarity.

That is an important warning for smaller companies.

AI systems do not evaluate businesses from a completely neutral starting point. Familiar brands benefit from greater recognition, more public coverage, stronger entity associations, and more evidence distributed across the web.

A startup with vague positioning and limited supporting information should not expect an AI system to discover its hidden strengths automatically.

However, the same controlled experiment found that the incumbent advantage was not permanent. Relatively small differences in rating, review volume, or price could substantially change the recommendations.

Those thresholds came from one artificial product experiment. So they should not be treated as a universal formula for real companies.

The broader lesson is more useful: familiarity matters most when the system cannot identify a meaningful reason to choose an alternative.

Differentiation gives the model something else to use.

AI recommendations do not always follow market share

A 2026 economics working paper examined 1,429 responses from GPT-4o, Claude, and Gemini across sixteen consumer categories. The researchers compared the concentration of AI recommendations with real-world market-share data.

The results were mixed.

In fragmented markets containing many smaller competitors, the models sometimes compressed attention around a narrower group of brands than the real market supported. In those categories, AI recommendations could make the market appear more concentrated than it actually was.

That is bad news for many startups. When a category is crowded and poorly differentiated, AI systems may repeatedly fall back on a handful of recognizable names.

But the paper also found the opposite pattern in several concentrated categories. In those markets, the models spread their recommendations more broadly than real-world market share would predict.

The models also showed a tendency to favor some specialist and premium brands over the largest volume sellers.

That does not prove specialization caused those recommendations. The study could not fully explain why each individual brand was selected.

It does show that market leadership alone did not determine the answers.

In some categories, a brand could receive more AI recommendation visibility than its actual market share would suggest. In others, smaller companies were compressed out.

The opportunity therefore depends heavily on the structure of the category.

AI search may be most favorable to a smaller company when the market has a clear leader but also contains meaningful specialist alternatives. It may be less favorable in a crowded field where dozens of companies make nearly identical claims.

The opening is usually narrow

A startup probably does not need to become more famous than Salesforce, HubSpot, or another category leader across every possible search.

It may need to become the most credible answer to a narrower question.

For example:

• the best construction software for a small residential remodeler;

• the best CRM for a roofing company with five salespeople;

• the best benefits-navigation platform for a self-funded employer;

• the best custom home builder for a high-end renovation in a specific city.

These questions contain context.

The industry matters. Company size matters. The buyer’s priorities matter. Geography, budget, implementation needs, and specialization may all matter.

The more specific the question becomes, the less useful broad fame may be as the only deciding signal.

A large incumbent may be the safest generic recommendation. It may not be the best answer for every situation.

That is where smaller companies can compete.

They do not necessarily need to own the entire category. They need to identify the parts of the category where they are genuinely a better fit and make that fit easy to verify.

Evidence matters, but it must be real

The foundational 2024 Generative Engine Optimization study found that some content interventions improved source visibility by as much as 40% within the researchers’ benchmark.

The most effective approaches varied by topic and experimental condition. The study did not prove that rewriting a page will create the same gains across live versions of ChatGPT, Gemini, or Google AI Overviews.

Still, it demonstrated that generative visibility was not completely fixed. How information was presented and supported affected whether it appeared in generated answers.

A peer-reviewed EMNLP 2025 study reached a related conclusion from a different direction. It found that cognitive framing within product descriptions affected LLM recommendations. Social-proof language generally performed better than scarcity or exclusivity framing, although the effects varied across models.

A separate 2026 preprint found that authority-style language, including fabricated clinical claims, could influence recommendations in a controlled experiment.

That is not a responsible optimization strategy. It is evidence of a weakness in the systems.

Models may sometimes confuse the appearance of authority with genuine authority.

For businesses, the lesson should be to build verifiable evidence rather than merely authoritative-sounding language.

That can include:

• clearly documented product capabilities;

• legitimate customer reviews;

• detailed use cases;

• certifications;

• credible third-party coverage;

• independent evaluations;

• accurate and consistent company information;

• real expert endorsements where appropriate;

• specific evidence supporting marketing claims.

A smaller company cannot manufacture decades of brand recognition overnight.

It can make its strengths easier to understand and harder to dismiss.

Why large companies still need to move

Large companies enter AI search from a strong position.

They have more mentions, more reviews, more media coverage, more historical content, and more recognizable brand associations. AI systems have more material from which to understand and recommend them.

But that lead is not a permanent entitlement.

The SIGIR 2026 study found that websites blocking Google’s AI crawler were significantly less likely to appear as sources in Gemini and AI Overviews, even though AI Overviews could sometimes access indexed information through other search infrastructure.

That is one example of how technical and content decisions can affect visibility in emerging systems.

Large companies may also be vulnerable when their public information is outdated, generic, inconsistent, or poorly aligned with the questions buyers now ask.

A market leader may be widely known but still be misunderstood for a particular use case.

Its product may have changed faster than third-party sources have updated. Its most important differentiators may be buried in sales materials rather than clearly documented online. Different pages may describe the same offering in inconsistent ways.

Meanwhile, a smaller competitor may define its category more clearly, publish stronger evidence, and focus on a specific problem the incumbent treats as secondary.

That does not mean the smaller competitor will automatically win.

It means the large company has left an opening.

If established brands react slowly, they may give more clearly positioned competitors a chance to gain recommendation visibility in valuable subcategories.

The challenge for smaller companies

Smaller companies should not interpret this research as proof that AI search will discover them naturally.

They still face serious disadvantages.

Many startups have limited third-party coverage, few reviews, weak entity recognition, and little historical information across the web. Their websites may contain broad claims that are difficult for an AI system to verify.

They may also mistake one favorable answer for evidence that they have become consistently visible.

Generative results can change across platforms, prompts, dates, and repeated runs. A startup that appears in one answer may disappear in the next.

Small companies therefore need more than basic GEO tactics.

They need to know:

• which buyer questions actually matter;

• where large competitors are entrenched;

• where no brand consistently owns the recommendation;

• whether their differentiation is visible to the model;

• what sources are influencing the answer;

• whether the company is being represented accurately;

• whether any improvement persists over time.

That is the problem we are building Signal Harbor to help solve.

Signal Harbor measures these questions repeatedly across AI platforms to identify where a company is consistently included, where it is being overlooked, how it is being described, and which competitors are controlling the recommendation set.

For a smaller company, that can reveal narrow categories where it is already outperforming its size or where stronger public evidence could give it a realistic opportunity to compete.

For a larger company, it can reveal where an assumed advantage is weaker than expected, where specialist competitors are beginning to appear, and where outdated or incomplete information is shaping how the market is being presented.

The goal is not to manufacture an AI recommendation through clever wording.

It is to understand where a company actually stands, why the systems are reaching those conclusions, and whether any change is durable enough to matter.

Generic optimization advice will become less valuable as more businesses adopt it. If every company adds similar wording, similar statistics, and similar authority signals, those changes stop separating one company from another.

The longer-term advantage will come from genuine differentiation, credible evidence, and reliable measurement of whether that evidence is actually changing the answers.

A more open field, not a level one

AI search hasn’t necessarily leveled the playing field.

What AI search has done is changed its shape.

Scale still matters. Familiarity still matters. Public authority still matters.

But market share and traditional search leadership do not perfectly determine which companies appear in generative answers.

In some categories, AI systems may reinforce incumbents and narrow the field.

In others, they may surface specialist brands that outperform their real-world size.

That creates a strategic opening for startups and smaller businesses. They can compete for specific recommendations where their focus, evidence, and product fit are stronger than those of a larger rival.

It also creates a reason for established companies to pay attention now.

Their current advantages give them a head start. They do not guarantee ownership of every future recommendation.

The challenge for both groups is that these openings are difficult to see from the outside. They have to be measured across real buyer questions, multiple AI platforms, repeated observations, competitor answers, and the sources shaping those answers.

That is the gap Signal Harbor is working to close.

Large companies enter AI search with an advantage.

Smaller companies enter it with an opening.

Neither position is guaranteed to last.

Sebastian Miller
Co-Founder, Signal Harbor
signalharborconsulting.com
sebastian.miller@signalharborconsulting.com
402-306-2213

References

Grossman, R., Liu, S., Chen, M. K., Smith, M., Borcea, C., & Chen, Y. (2026). How generative AI disrupts search: An empirical study of Google Search, Gemini, and AI Overviews. Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Medina Sandín, A. (2026). AI meets antitrust: How large language models reshape market concentration. SSRN working paper. https://doi.org/10.2139/ssrn.6627078

Chu, X., & Hou, Y. (2026). Incumbent advantage: Brand bias and cognitive manipulation dynamics in LLM recommendation systems. arXiv preprint.

Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative engine optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5 to 16).

Filandrianos, G., Dimitriou, A., Lymperaiou, M., Thomas, K., & Stamou, G. (2025). Bias beware: The impact of cognitive biases on LLM-driven product recommendations. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 22397 to 22426). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.1140