TL;DR: In the AI era, chasing individual algorithms is a losing game. Different AI models (like ChatGPT and Google AIO) have unique preferences, but they all reward one thing: content with substantial value. To get cited, stop trying to trick the system and focus on creating indispensable content built on five pillars: Originality (be a primary data source), Depth (answer the next three questions), Credibility (earn third-party validation), Clarity (structure for machines), and Authenticity (write for humans). This is the only scalable strategy to win visibility across all AI platforms, now and in the future.
You've seen the reports: ChatGPT, Google's AI Overviews, and AI Mode all recommend different brands for the same query. One AI acts like a historian, another a networker, and a third a picky connoisseur. This leaves marketers with a daunting question: How do you become a trusted source when every AI has its own unique taste?
Many are tempted to chase algorithms, creating slightly different content for each platform in a frantic, unwinnable game of whack-a-mole. But this approach misses the fundamental truth of the AI era: you can't trick a researcher.
AI models are not just ranking pages; they are reading them, synthesizing them, and evaluating their worth to construct a single, confident answer. In this new landscape, the only scalable and future-proof strategy is to stop chasing algorithms and start creating content with such substantial value that every AI, regardless of its "personality," is forced to pay attention.
Why the Old Playbook Fails: The AI Quality Filter
For years, SEO often involved clever tactics—finding the right keyword density, building a high volume of links, or expertly structuring a page to capture a featured snippet. We’ve all seen and created the rather tired "ultimate guide to [topic]" playbook, which worked so well it arguably ruined the internet.
The fundamental problem with this type of content is that it often has no information gain. When trillions of webpages all follow the same "best practice" template, they’re not telling the world anything genuinely new.
AI models are designed to be a "quality filter" for this noise. An AI's primary goal is to deliver a definitive, trustworthy answer so the user doesn't have to sift through ten blue links. It actively avoids thin, rehashed, or low-credibility content because citing it would damage its own reputation.
This is why "substantial value" is no longer a nice-to-have; it's the price of entry.
The Anatomy of Substantial Value: What AI Craves
So, what does "substantial value" actually look like? It’s not about word count. It’s about becoming an indispensable source of information. This breaks down into five key components.
Component of Value | What It Looks Like (Key Actions) |
|---|---|
1. Originality | Commission surveys, publish proprietary research, and analyze internal data to create unique statistics that only your brand can provide. |
2. Depth | Go beyond the initial query to anticipate and answer the next three follow-up questions a user might have on the topic. |
3. Credibility | Earn citations, quotes, and positive mentions in high-authority third-party publications, news articles, and "best of" lists. |
4. Clarity | Use clear headings, bullet points, schema markup, and fast load times to make your content easy for machines to parse and quote. |
5. Authenticity | Ensure content is written by humans with real experience. Use AI for research ("inputs"), not for the final text ("outputs"). |
1. Originality: Be the Primary Source
AI models are, at their core, synthesizers of existing information. The most powerful way to command their attention is to create net-new information that they cannot find elsewhere. Google's own "Information Gain" patent underscores the importance of this. When you are the origin of a key piece of data, you become the ultimate authority.
- What it looks like: Commissioning a survey on a key industry trend, like BoundlessHQ did for remote work. They asked, ‘Ideally, where would you like to work from if it were your choice?’ The results provided a unique dataset that was high-effort, valuable, and worthy of citation in AI results.
- Why it works: When other blogs and publications cite your original statistic, they create a web of validation that points back to you. For an AI, you are no longer just another source; you are the source. For improved visibility, include your data sources and research methods with their limitations, as this transparency makes your content more verifiable to AI.
2. Depth: Focus on Topics, Not Just Keywords
Keywords don't tell you who is searching, only what terms are being searched. To create deep content, you need to understand the discussions and questions driving those searches. The age of one-size-fits-all content is over.
- What it looks like: A guide to 'content marketing' that also covers budget allocation, team structure, and measurement—the inevitable follow-up questions.
- Why it works: This creates a comprehensive, one-stop resource that anticipates and answers the follow-up questions a user hasn't even thought to ask. For an AI like Google's AIO (The Networker), this topical depth makes your page a central hub for the entire subject, making it a highly efficient and valuable source to cite.
3. Credibility: Prove Your Value Through Others
Substantial value is rarely self-proclaimed; it's validated by the ecosystem. As we explored in the "Citation Network Effect," your authority is cemented when other trusted entities vouch for you.
- What it looks like: Your CEO is quoted in a Forbes article, confirming your brand's expertise to the wider world.
- Why it works: This is the ultimate signal for selective AIs like Google's AI Mode (The Connoisseur). It craves third-party validation. When other authoritative sources point to you, it confirms that your information isn't just well-written; it's trustworthy.
4. Clarity: Structure Your Content for Machine Understanding
The most brilliant insights are useless if the AI can't easily parse and extract them. Substantial value must be presented with perfect clarity. AI faces constraints of energy costs and computing power, meaning content needs to be incredibly efficient to process.
- What it looks like: A complex topic is broken down into a scannable FAQ with schema markup, allowing an AI to lift an answer directly.
- Why it works: This makes your content "quotable." You're essentially doing the hard work for the AI, serving up perfect, bite-sized chunks of information that can be lifted directly into an AI-generated answer.
5. Authenticity: Use AI Inputs, Not Outputs
This is perhaps the most critical principle. AI has raised the content bar, but using AI to generate your "expert" content is a trap. The results of AI are derivative, diluted, and sometimes hallucinatory by nature.
- What it looks like: An article on leadership that includes a personal story of failure and the lesson learned, a nuance AI cannot replicate.
- Why it works: Expecting an AI to create unique, valuable content from its own outputs is like asking it to "rehydrate itself using its own sweat." AI creators do not want their models retrained on AI-generated content for fear of model degradation—becoming dumber over time. It is statistically obvious to an AI when content is AI-written. Human authorship is a powerful signal of authenticity.
The Unifying Strategy: Value Appeals to Every AI
When you focus on creating substantial value, you stop worrying about the individual preferences of each AI and start building a foundation that appeals to all of them.
- The Historian (ChatGPT) respects your Originality, Credibility, and Authenticity, as these signals become baked into its training data over time.
- The Networker (Google AIO) is drawn to your Depth, as your comprehensive content makes you a central, highly-connected node for an entire topic.
- The Connoisseur (Google AI Mode) is convinced by your Credibility, relying on third-party validation to select only the most trusted sources.
- All of them benefit from your Clarity, which makes your valuable insights easy to process and cite.
Conclusion: Stop Chasing, Start Building
The era of chasing algorithms with clever tactics is over. The only durable, scalable strategy for winning in the age of AI is to become a definitive source of value. This focus on substantial value is the essence of a modern GAIO (Generative AI Optimization) strategy, which is then validated and amplified across the web by the principles of SEVO (Search Everywhere Optimization).
Stop asking, "How can we get this AI to mention us?" and start asking, "What can we do to produce high-effort content good enough for AI without costing the earth?"
Build that, and you'll find that all AIs, in their own way, will eventually find their way to your door.
