LLMs have driven the marginal cost of commodity content to zero. This collapses the value of traditional content farms and keyword-stuffed articles. But the cost of genuine insight has actually gone up, because it now requires more rigor to stand out.
Technical SEO still matters. Structure still matters. This isn't an argument against them. The argument is about where defensible value now resides.
What used to require a writer, an editor, and a week of work can now be simulated in seconds. Any organization with LLM access can produce content that hits every traditional optimization checkbox. The floor has risen. The ceiling hasn't.
This shifts the economic question from "is this content optimized?" to "why can't a competitor produce this in 30 seconds?" If the answer is "they can," the content provides coverage but not advantage. Value now lives strictly in what AI cannot hallucinate: proprietary data, specific customer evidence, and genuine subject matter expertise.
Technical SEO enables ranking.
Differentiated content justifies ranking.
The Two Layers
This is not an argument against technical SEO. A site that is slow, poorly structured, or not crawlable will not rank regardless of content quality. The foundation is necessary.
Foundation Layer
- — Technical SEO fundamentals
- — Site performance
- — Content structure
- — Internal linking
- — Mobile experience
- — Schema markup
Differentiation Layer
- → Proprietary data
- → Original research
- → Documented expertise
- → Clear perspective
- → Specific case studies
- → Industry relationships
The first column represents necessary conditions. The second represents sufficient conditions for differentiation.
Assessing Content Differentiation
Before publishing, consider: could this content be generated by an LLM with a single prompt and access to public information?
If yes, it serves a coverage function but won't create competitive advantage.
If no, because it requires proprietary data, specific access, or genuine expertise, that's where differentiation exists.
Here's a framework to evaluate content before publishing:
Select the most accurate description for each dimension
Most content audits score 2-4. That's the gap between table stakes and differentiation.
Decision Framework
Not every piece needs to score high. The goal is intentionality about what you're publishing and why.
The decision tree above addresses individual content pieces. But there's a broader strategic question: how do different types of differentiation compare in terms of long-term defensibility?
Not all advantages are equal. Some compound over time. Others erode as competitors gain access to the same information or capabilities.
Defensibility Over Time
Content built on relationships and earned reputation is hardest to replicate. Content built on technical optimization alone is easiest. Most organizations over-invest at the bottom of this hierarchy and under-invest at the top.
Implementation
The New Bottleneck: Expertise Extraction
In the past, the bottleneck was writing. Now, the bottleneck is information extraction. Creating differentiated content requires hours of interviewing internal Subject Matter Experts (SMEs), cleaning proprietary datasets, or conducting original market research.
Writing is now the easy part. The hard part is the journalism. Organizations that treat content as a "generation" task will fail; those that treat it as an "extraction" task will win.
This shifts the role of content teams (internal or external) from "writers" to "investigators." The value isn't in the word count; it's in the unique insight secured before the writing begins.
Content with internal data: Analysis built on proprietary metrics, customer research, or operational data that external parties cannot access.
Multi-channel reinforcement: Content strategy that combines search optimization with earned media, speaking, and community presence to create compounding signals.
Expertise-first approach: Beginning with "what do we know that others don't?" rather than "what keywords should we target?" The latter becomes a distribution mechanism for the former.
Observation: When content is differentiated by genuine expertise or proprietary information, ranking signals tend to follow. Google's algorithm is designed to surface quality. Focusing on substance rather than optimization often produces better optimization outcomes as a byproduct.
Summary
AI has collapsed the effort required to produce commodity content to near zero. Any organization can now generate comprehensive, well-structured articles in seconds. This is not a threat to quality-focused teams. It's a gift.
The middle ground has been commoditized. You're either producing generic content (which AI handles equally well) or differentiated content (which requires genuine expertise, proprietary data, or specific access). The organizations that win are those that treat content as an extraction problem, not a generation problem.
The reward structure has also changed. When you do achieve differentiation, the advantage is more durable because competitors cannot simply replicate it with AI. The gap between commodity and edge has widened. Invest in content that depends on assets that are genuinely difficult to replicate: your data, your customers, your expertise.
The Other Half of the Problem
Differentiated content is necessary. It's not sufficient.
You can have proprietary data, original research, and genuine expertise, and still not appear in AI-generated answers. LLMs don't automatically surface quality. They surface what they've learned to trust.
Getting cited requires a different set of moves: authority signals that LLMs recognize, structural patterns they can retrieve, distribution strategies that build the right associations. That's a separate discipline from content creation.
The bottleneck isn't writing. It's extraction and distribution. Pulling the differentiated insights out of your organization, turning them into content that stands out, and getting that content into the answers people actually see.
That's what we do at Growtika. We handle both sides: creating differentiated content from your expertise, and making sure it gets cited where it matters.
Yuval Halevi
Helping SaaS companies and developer tools get cited in AI answers since before it was called "GEO." 10+ years in B2B SEO, 50+ cybersecurity and SaaS tools clients.