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TL;DR: Google’s official guidance debunked several popular GEO tactics, exposing questionable consulting claims and highlighting the still-unsolved challenge of managing brand visibility and perception across multiple LLM search platforms.
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Google Search Central published its first official guide to optimizing for generative AI search features, explicitly debunking a slate of popular tactics: llms.txt files, content chunking, AI-specific rewriting, inauthentic brand mentions, and overfocusing on structured data.
The fallout has exposed a credibility reckoning for the cottage industry of GEO/AEO consultants selling tactics now confirmed as ineffective, while simultaneously surfacing a genuine unsolved problem: how to manage brand perception and narrative control in LLM responses across multiple platforms.
For marketers investing in AI visibility, the gap is clear: Google told you what doesn't work on its surfaces, but left the multi-platform optimization question wide open. That's where measurement, testing, and cross-surface visibility tracking using a product like Semrush One become essential.
If GEO-specific tactics like llms.txt, AI-optimized content chunking, and AI-rewritten pages are being deprioritized, the bigger issue is that Google's guidance only addresses its own ecosystem while brands are still expected to understand visibility across ChatGPT, Perplexity, and other LLMs.
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Google's guide confirmed that strong SEO fundamentals remain the foundation for AI search retrieval eligibility, but it also exposed the gap between "being findable" and "being cited, quoted, and recommended." Practitioners need unified visibility across traditional SERPs and AI surfaces to see where they appear, how they're described, and where competitors are winning the narrative.
Semrush One bundles the full SEO Toolkit with the full AI Visibility Toolkit into one platform, letting teams track both traditional rankings and AI citations from a single view — which is exactly what's required when the answer to "Is it SEO or GEO?" turns out to be "You need to measure both."
The conversation has moved past "Should we optimize for AI search?" and into "Who do we trust, and what actually works?" Google's guide created a dividing line between tactics that are confirmed noise and questions that remain genuinely open.
Semrush should position from the testing-and-measurement side of that line — not as another voice claiming to know the answer, but as the infrastructure that lets practitioners find out for themselves.