Hotels Are Repeating Black Hat SEO Mistakes in the Age of LLMs

Fifteen to twenty years ago, hotel marketers stuffed keywords into invisible background text to fool Google. A new crop of AI-first consultancies is now recommending essentially the same playbook for large language models: produce enormous volumes of content so that ChatGPT and its rivals recommend your property.

The argument against this is simple. As the original analysis puts it: "If you're publishing a page because you believe a machine needs to read it, but you don't believe an actual traveler would find it useful, why are you publishing it?"

What is the new black hat GEO?

GEO (generative engine optimization, the practice of shaping visibility inside AI chatbots) has spawned its own spam era. The tactics mirror the old ones.

• Keyword stuffing becomes publishing hundreds or thousands of pages aimed at every conceivable question.
• Doorway pages become endless FAQ dumps generated for machines rather than travelers.
• The goal shifts from manipulating a ranking algorithm to, as the source frames it, creating enough content to "train/influence/cheat an LLM" into recommending a hotel.

The parallel is not lost on anyone who worked in search during 2010. Google eventually caught on then. There is little reason to think AI companies will not catch on now.

What actually works for LLM visibility?

The guiding principle is that content good for humans should, by definition, also be good for LLMs. AI tools perform a query fan-out (breaking one question into many sub-questions) and cite specific passages, so hotels win with pages that answer real traveler questions using specific, clearly stated facts.

Legitimate technical work still matters: clear site architecture, structured data, accurate information, and consistency across channels. None of it requires spam.

Why positioning beats volume

A 200-room airport hotel does not need an LLM to call it the perfect romantic getaway. A downtown lifestyle hotel cannot be the best pick for every family of five. The logic mirrors old-school keyword selection: a Loop property in Chicago might covet "Chicago hotels" and its tens of thousands of monthly searches, but the term reveals nothing about intent, and competition is brutal. Travelers do not prompt ChatGPT with "chicago hotels" anyway. They write long prompts that reward hotels with clear positioning.

The recommended approach: pick the customer segments you can actually win, build useful content for those travelers, and measure whether the hotel appears in the conversations that matter.

Will the platforms punish AI slop?

Probably. The source notes that "algorithmic watermarks" are already in partial use for detecting mass-produced content. Google's current guidance warns against using generative AI to produce large numbers of pages without adding value, and it classifies pages made primarily to manipulate rankings as scaled content abuse.

Is AI itself the problem?

No. Every hotel marketer uses AI daily, for research, content gap analysis, review analysis, outlines, and drafts. The source compares it to hiring an intern: useful for speed, provided a human ultimately crafts and expands the work.

The verdict is blunt. The problem is not using AI to create content; it is creating content for AI instead of for customers. That mistake produced keyword-stuffed websites fifteen years ago, and hotels risk spending the next five years relearning the lesson with LLMs.