The Vercel Revelation
Vercel have made some big waves today, claiming that 10% of their customer signups are coming from ChatGPT, and the volume is growing, FAST (up from 4.8% last month).
This isn't just an interesting data point—it's a glimpse into the future of business discovery. When a significant chunk of your potential customers are finding you through AI recommendations rather than traditional search or advertising, it fundamentally changes how you should think about marketing and content strategy.
The numbers are staggering not just in their current magnitude, but in their trajectory. More than doubling month-over-month suggests we're in the early stages of a massive shift in how people discover and evaluate business solutions.
Understanding GEM: Generative Engine Marketing
GEM stands for Generative Engine Marketing—essentially, it's SEO reimagined for the age of AI. Where traditional SEO focused on ranking in Google search results, GEM focuses on being recommended by AI systems like ChatGPT, Claude, and others.
The fundamental difference is profound:
Traditional SEO: Optimize to appear in a list of search results that users then evaluate.
GEM: Optimize to be the specific recommendation an AI system provides in response to a user's query.
This shift from "being findable" to "being recommended" changes everything about how we approach content, positioning, and digital presence.
The Five Pillars of GEM Strategy
Based on Vercel's approach and the emerging patterns we're seeing, here are my key takeaways:
1️⃣ Depth Beats Keyword Stuffing
LLMs favour content that explains concepts clearly and comprehensively, not just keyword-optimised pages.
Traditional SEO often rewarded content that hit specific keyword densities and followed particular formulas. AI systems, however, are trained to understand and value genuine expertise and comprehensive explanations.
This means:
- Focus on thoroughly explaining concepts rather than mentioning keywords frequently
- Provide context and background that helps AI systems understand your expertise
- Create content that demonstrates deep understanding of the problem space
- Include practical examples and real-world applications
2️⃣ Structure is Crucial
Clean heading hierarchies, semantic HTML, and Schema markup help AI systems understand and surface your content.
AI systems need to parse and understand your content structure to make appropriate recommendations. This isn't just about human readability—it's about machine comprehension.
Key structural elements include:
- Logical heading hierarchies (H1, H2, H3) that create clear content sections
- Semantic HTML that conveys meaning, not just presentation
- Schema markup that provides explicit context about your content and offerings
- Clear relationships between concepts and ideas
- Consistent formatting and organization patterns
3️⃣ Be the Definitive Source
Focus on owning niche concepts with original data, code examples, and insights competitors can't easily replicate.
AI systems are looking for authoritative sources when making recommendations. Being the definitive source means:
- Creating original research and data that others reference
- Developing unique frameworks or methodologies in your field
- Providing code examples, templates, or tools that demonstrate expertise
- Sharing insights from real-world implementation experience
- Building content depth that competitors would struggle to match
4️⃣ Fresh Content Wins
Models regularly re-crawl, so keeping content updated signals trustworthiness to both users and AI systems.
Unlike traditional SEO where content could remain static for months or years, GEM requires ongoing content freshness:
- Regular updates to reflect current best practices and changes
- New examples and case studies that demonstrate continued relevance
- Updated data and statistics that maintain accuracy
- Evolution of content to address emerging questions and use cases
- Active maintenance of code examples and technical content
5️⃣ Community Citations Matter
Organic mentions on Reddit, GitHub, Stack Overflow, and Twitter help models associate your brand with concepts.
AI systems learn from the broader internet conversation, not just your owned content:
- Active participation in relevant community discussions
- Helpful contributions to Stack Overflow and similar platforms
- Open source contributions that demonstrate expertise
- Thoughtful engagement on social platforms
- Building genuine relationships that lead to organic mentions
The Technical Implementation
Implementing a GEM strategy requires both technical and content considerations:
Technical Foundation:
- Implement comprehensive Schema.org markup
- Ensure semantic HTML structure throughout your content
- Optimize for featured snippets and structured data
- Maintain clean, crawlable site architecture
- Focus on page speed and technical performance
Content Strategy:
- Create comprehensive guides that cover topics exhaustively
- Develop original research and data that others cite
- Maintain accurate, up-to-date information
- Include practical examples and implementation details
- Build content clusters around core expertise areas
Measuring GEM Success
Traditional SEO metrics like search rankings become less relevant in a GEM world. New metrics to track include:
- AI mention frequency: How often AI systems recommend your brand or content
- Direct AI referrals: Traffic and conversions from AI platforms
- Content comprehensiveness scores: How well your content covers relevant topics
- Community citation rates: Organic mentions across platforms
- Authority signals: References from authoritative sources
The Competitive Landscape
Early movers in GEM have a significant advantage because:
Training data advantage: Content that's comprehensive and well-structured today influences how AI systems understand and categorize concepts.
Authority building: Establishing expertise early creates compounding benefits as AI systems learn to associate your brand with specific concepts.
Community relationship: Building genuine community relationships takes time and can't be easily replicated by competitors.
Challenges and Considerations
GEM isn't without its challenges:
Black box optimization: Unlike traditional SEO, it's harder to understand exactly why AI systems make specific recommendations.
Platform dependency: Relying on AI recommendations creates dependency on platforms you don't control.
Quality vs. quantity: The emphasis on depth and comprehensiveness requires significant content investment.
Measurement complexity: Tracking success across multiple AI platforms and conversation contexts is challenging.
Everyone Strapped In?
The future is going to be wild 🎢🤓
Vercel's success with ChatGPT signups isn't an isolated phenomenon—it's a preview of how business discovery will work in the AI era. Companies that recognize this shift early and adapt their content and marketing strategies accordingly will have a significant competitive advantage.
GEM represents a fundamental evolution from optimizing for search algorithms to optimizing for AI recommendation engines. The principles are different, the execution is different, and the results can be dramatically different.
The question isn't whether this shift will happen—it's whether you'll be ready when it does. For those willing to embrace GEM principles now, the opportunities are enormous.
The ride is just getting started, and it's going to be fascinating to see how this new landscape evolves. The companies that figure out GEM early won't just survive the transition—they'll define what comes next.