Marketing — particularly martech — often behaves like the fashion industry. Every year, sometimes every season, a design from a significant fashion label emerges that fast-fashion brands rush to copy. Before long, everyone is wearing the same look, which is often the first sign it’s already time to move on.
Marketing is no different. The industry regularly chases the latest trend, only to abandon it once the novelty fades. Generative engine optimization (GEO), also known as answer engine optimization (AEO), has become the latest must-have for marketers.
On the surface, the logic is compelling. As more people turn to AI tools to research products and services, why wouldn’t brands want to be the first recommendation surfaced by ChatGPT, Grok, Perplexity or Gemini? These four models dominate the current landscape. It feels like a no-brainer.
But not so fast. There are solid reasons to question whether the current GEO gold rush will deliver the returns it promises. To understand why, it’s worth examining both the case for and against GEO becoming a forgotten marketing tactic within the next five years.
The case against GEO
There are convincing arguments that GEO could go the way of the Pet Rock. Yes, in 1975, many people paid good money for stones marketed as pets.
The first reason to question GEO is that marketers may be shooting themselves in the foot. Increasingly, web content now opens with a TL;DR, followed by a summary, key takeaways, an FAQ and very little substance. This suggests some GEO “experts” are over-engineering their content, degrading the human experience while spending less effort on actual quality.
Just as marketers mindlessly keyword-stuffed pages in the early days of SEO, it’s easy to imagine today’s more ham-fisted optimization tactics looking equally misguided in a few years. In the push for high-quality, authentic content, Google and others penalized text that was over-optimized for SEO. The same could easily happen to today’s GEO content.
One reason search engines pushed back on over-optimization was that the resulting content was bland and undifferentiated. Marketers chased the same keywords with pages that added nothing new to the web. It’s a fair assumption that much GEO content is now being produced by specialist GEO agencies with shallow understanding of the subjects they claim to cover.
Much of that content may also be AI-generated, further contributing to sameness while potentially poisoning training data for future models. When AI systems are trained on material that mimics previous generations, model degeneration becomes a real risk. If large language models are going to improve, they need genuinely new inputs, not reheated outputs from older systems.
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The risks of AI-generated content are already visible. Tests show that human-generated content designed to surface in AI chatbot responses can perform up to an order of magnitude better than AI-generated material.
Over-optimization also raises the possibility that future models will actively avoid using it. Highly optimized content tends to be repetitive and may not register as high quality. This kind of over-engineering could ultimately damage optimization for AI systems as well as for human readers.
Many GEO results already resemble a natural-language interface layered on top of traditional search results rather than original AI-generated output. When an AI tool says it is “searching the web,” that is often precisely what it is doing. In that context, success may still depend more on SEO fundamentals than on GEO tactics.
Ultimately, there is no guarantee that the AI model landscape will remain the same five years from now. Users may rely on customized models, or future systems may default even more aggressively to web search before responding. Whatever direction things take, it is unlikely the ecosystem will resemble today’s environment.
The case against GEO is straightforward. Even if GEO still matters five years from now, many of today’s tactics may not work — or could even prove counterproductive — in the relatively near future.
Why GEO is not a fad
I’ll start defending GEO by accepting the arguments against it. Most GEO efforts underway today are unlikely to have much, if any, positive impact five years from now. Even so, just as with SEO, there will almost certainly be an ongoing need for optimization — despite efforts by model owners to prevent people from over-optimizing in an attempt to game the system.
Put simply, search engines or generative AI models will not eliminate the need for some form of optimization.
That leaves marketers with two choices. You can sit on the sidelines and ignore GEO, freeing up resources for other areas of marketing. That approach isn’t irrational.
Or you can follow today’s GEO best practices while still designing content for human audiences. Over-optimizing is unlikely to deliver long-term value, but sensible optimization probably will.
The history of black-hat SEO offers a clear warning. Techniques from the early 2000s — such as hiding white text on white backgrounds or serving different pages to search engines than to users — failed spectacularly. The lesson is simple. Trying too hard rarely pays off.
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Building a sensible GEO strategy
As with black-hat SEO, the risks of overdoing GEO are genuine. The most successful organizations will adopt a balanced GEO strategy that accounts for both human readers and AI models.
Include an FAQ and lay out the page so it’s easy for humans to scan. Add key takeaways. Include a TL;DR at the top if — and only if — it improves the experience for all readers.
Most importantly, the body copy must be new, interesting and genuinely helpful. Google can already surface millions of pages answering “How do I do generative AI optimization?” Another one only makes sense if it offers something meaningfully different.
Despite the challenges, investing in GEO now still makes sense. You may make mistakes. The rules will likely change. But done thoughtfully, the potential upside outweighs the risks. Sitting on the sidelines is unlikely to be the better option.
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