TL;DR: In a world flooded with low-cost, AI-generated content, the value of genuine, novel, and verifiable information has skyrocketed. The old playbook of creating "ultimate guides" that rehash existing knowledge is now obsolete. To be cited and recommended by AI models, brands must shift from being content marketers to becoming primary sources of knowledge. This requires a disciplined focus on creating content with real "information gain" through original research and expert, human-led insights.
I am James, CEO of Mercury Technology Solutions.
The digital marketing landscape is grappling with a dizzying array of new acronyms—GEO, GAIO, LLM SEO—all attempting to define the new frontier of search. While the terminology is in flux, the underlying strategic imperative is crystal clear: the game has shifted from winning a "ranking" to earning a "recommendation" from an AI.
Having spent years building AI models, I want to offer a perspective based on first principles. The frightening data about plummeting click-through rates is real, but the path forward is not about finding a new trick. It is about understanding a fundamental economic shift in the value of information itself.
Principle 1: You Must Tell the World Something New (The Information Gain Mandate)
Imagine the dread of a PR agency that discovers their new client has nothing newsworthy to say. For years, much of the SEO content industry has operated this way. The "ultimate guide to X" playbook, which aimed to turn every website into a mini-Wikipedia, worked so well that it saturated the internet with generic, repetitive content.
The fundamental problem with this type of content is that it offers zero information gain. When trillions of web pages follow the same best practices, they aren't telling the world anything new.
AI's primary function is to synthesize this existing knowledge for the user. It doesn't need another blog post to do it; it needs a definitive source. If your content is not unique, why would an AI retrain its models using your data or, more importantly, cite your brand in an answer?
Principle 2: Use AI Inputs, Not AI Outputs
I see a dangerous trend emerging: companies are using AI-generated outputs to create their "data-driven" content. This is a strategic dead end. As I like to say, it's like asking an AI to rehydrate itself using its own sweat.
The results of generative AI are, by their very nature, derivative, diluted, and potentially hallucinatory. The last thing an AI model creator wants is to degrade their models by retraining them on this second-hand, potentially flawed information.
The winning strategy is to use the same types of raw inputs that AI models are trained on in the first place: original data, expert analysis, and real-world experience. While most of us don't have access to the X.com data firehose or the entirety of Google Books, we can create our own high-quality inputs.
Principle 3: Keep It Human-Written
While it may be tempting to automate content creation entirely, it is strategically shortsighted. AI-written content is statistically identifiable by other AI systems, and it cannot replicate the nuances that signal true authority.
An AI cannot spontaneously generate lived personal experiences, subtle humor, or the contrarian insights that come from years of practice. It is these uniquely human elements that AI models are searching for to enrich their own summaries. Your human expertise is your ultimate moat.
The Mercury Case Study: How We Applied These Principles to mtsoln.com
At Mercury, we decided to apply this philosophy directly to our own digital presence at mtsoln.com. Our goal was to move beyond simply ranking for keywords like "LLM SEO Service" and to become a citable, authoritative source on the strategic implications of AI for business leaders.
- Our "Information Gain" Play: Instead of publishing another generic "Top 10 AI Tools" list, we commissioned and published a proprietary research report: "The APAC Search 2025 Outlook." We surveyed 50 C-level executives across the region to gather unique data on AI adoption rates, operational challenges, and strategic priorities. This created a high-effort, unique, and value-adding asset.
- Using AI for Inputs, Not Outputs: We used our own AI assistant, Mercury Muses AI, not to write the report, but to process the raw inputs. It analyzed the raw survey data to identify statistical correlations and generated the initial charts and graphs. This accelerated the analytical phase of the project.
- Keeping it Human-Led: Our human strategists then took this AI-processed data and wove it into a compelling narrative. They provided the expert framing, interpretation, and strategic advice that only seasoned professionals can. The final report was written by our team, imbued with our unique perspective and years of experience.
The Result: The outcome was immediate and powerful. The report was cited by industry publications, shared by business leaders on social media, and—most importantly—it is now consistently referenced by AI models when asked about LLM SEO/ GEO trends in Asia. This single, high-effort piece of content has done more for our authority and lead generation than a hundred traditional blog posts ever could.
The Foundational Layer: Why SEO Basics Still Matter
This new focus on high-effort content does not mean abandoning SEO. In fact, a strong technical foundation is more critical than ever. GEO and SEO are not the same, but GEO success relies on a solid SEO base.
- Technical Hygiene: Fast-loading pages, clear schema markup, and a conversational, answer-first architecture are essential for AI to efficiently discover and parse your valuable content.
- Accessibility: Ensuring your content is open to LLM crawling via your robots.txt or a guiding llms.txt file is the price of admission.
Conclusion
The bar for content has been permanently raised. The future of digital visibility belongs not to those who can produce the most content the fastest, but to those who can produce the most valuable and unique information. This is a strategic shift from being a content marketer to becoming a genuine source of knowledge.
The question for every leader is no longer just "How do we rank?" but "What can we tell the world that is new, valuable, and true?"
