By Tomas Corza on the work of Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande (2024)

The landscape of search is changing faster than at any point in the last three decades. For much of the internet’s history, visibility hinged on a familiar playbook: keyword relevance, backlinks, and structured metadata. But the emergence of generative engines—search platforms that use large language models (LLMs) to synthesize answers rather than list links—has disrupted this foundation. In their landmark paper, GEO: Generative Engine Optimization, Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande (2024) formalize this shift and outline a path forward.

From Search Engines to Generative Engines

Traditional search engines returned ranked lists of websites. Generative engines, however, retrieve multiple sources and weave them into structured, conversational answers. Tools like Google’s Search Generative Experience, Bing Chat, and Perplexity.ai already embody this model. While users benefit from speed and clarity, content creators face a stark new reality: far fewer clicks and less control over how their work is represented.

This shift makes it clear that generative engine optimization is more than just a theory at this point, it is a necessity for anyone who depends on digital visibility. As the authors note, generative engines can reduce organic traffic by directly presenting synthesized answers, bypassing the need for users to visit source websites.

Defining Generative Engine Optimization (GEO)

Aggarwal and colleagues define generative engine optimization as a flexible, creator-centric framework designed to maximize visibility within generative answers. Unlike SEO, which depends on rank position, GEO must grapple with visibility metrics that are multifaceted: length of citation, position within an answer, relevance to the query, and even the subjective impression the citation leaves on the reader.

The researchers propose GEO-bench, a 10,000-query dataset spanning multiple domains, to measure performance. Through rigorous testing, they demonstrate that well-designed GEO methods can boost visibility by up to 40%—a transformative figure for businesses and creators facing declining search referrals.

What Works in GEO (and What Doesn’t)

The findings also clarify which strategies align with this new paradigm. Traditional SEO tactics like keyword stuffing are ineffective in generative search responses, sometimes even harming visibility. Instead, strategies that emphasize clarity, credibility, and presentation prove most impactful. For example:

Quotations: Incorporating authoritative quotes improved visibility scores by nearly 25–40% across benchmarks.
Statistics: Adding data points and quantitative claims increased credibility and boosted visibility up to 37%.
Citations: Explicitly embedding citations elevated content presence, especially for sites ranked lower in traditional search results.

Conversely, stylistic changes such as more persuasive or authoritative tones did little to improve results. This suggests that generative engines already evaluate persuasion, but reward evidence, attribution, and fluency above all.

Implications for SEO Professionals

For marketers, the paper underscores a critical truth: search engine optimization is no longer enough. Generative engine optimization is the new stack for full stack marketers.

The implications are signi:

Democratization of visibility: Smaller websites, historically disadvantaged by backlink profiles, can now leapfrog competitors by adopting GEO methods. The research shows lower-ranked sites gained the most from GEO, sometimes improving visibility by over 100%.
Domain-specific tailoring: Strategies are not one-size-fits-all. For instance, statistics work best in law and government contexts, while quotations thrive in history and society domains.
Future convergence: While GEO will not replace SEO, the two will combine. Businesses and marketers must optimize simultaneously for both traditional rankings and generative visibility, treating them as distinct but interdependent disciplines.

A New Frontier for Digital Marketing

Aggarwal et al.’s research signals the arrival of a new era in information discovery, one where marketers and creators must think not only about being found but about being cited, quoted, and trusted by the machines. This is not just technical optimization; it is a philosophical shift in how visibility is defined.

Generative engines are here to stay, and with them, a new marketing frontier. For full stack marketers or creators like me, the challenge is clear: to adapt content for a dual reality where both humans and machines interpret, summarize, and rank the value of what we create. Generative engine optimization is the bridge that makes this possible.

If you are interested in learning more about GEO, consider taking my course. Tap here to learn more about about my (GEO) generative engine optimization course.

References

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 (KDD ’24), Barcelona, Spain. ACM. https://doi.org/10.1145/3637528.3671900

Generative Engine Optimization: Rethinking Visibility in the Age of AI Search