AI Doesn’t Read Like You: Rethinking Content for the Age of AI Intermediaries
You spent years mastering content that converts humans. Now AI is your new audience—and it reads nothing like we do.
Welcome to the new battleground of digital visibility, where the rules are being rewritten by machines. Traditional content was crafted to engage, persuade, and convert human readers. But now, an increasing share of that content is never seen by a human at all. Instead, it’s parsed, ranked, and summarized by large language models (LLMs) acting as intermediaries between you and your next customer. These systems don’t care about clever headlines or emotionally resonant stories—they care about clarity, structure, and verifiability. If your content isn’t ready for this shift, it’s invisible.
We’re entering an age where visibility isn’t about being loud or viral. It’s about being legible to the machine mediators now filtering the web on our behalf. And the difference between being chosen and being skipped comes down to whether you’ve designed your content for these invisible readers.
The Paradigm Shift: From Human Readers to AI Intermediaries
In a world where search and discovery are increasingly mediated by AI, content must now pass through filters designed not for understanding—but for matching, ranking, and routing. Large Language Models (LLMs), like OpenAI’s ChatGPT or Google’s Gemini, don’t "read" in the traditional sense. They tokenize, pattern-match, summarize, and reason over content at scale.
The result? Content optimized for humans is being judged by a machine using entirely different criteria. What used to be often thought of as secondary by many—like alt text, internal linking structure, and product schema—has become mission-critical. The machine doesn’t just skim for relevance; it needs clean inputs it can parse and evaluate with confidence.
Key Concept: This isn’t about SEO anymore. It’s about *AI Visibility Optimization*—the art of structuring content so LLMs select, summarize, or surface it when consumers ask questions. (And I don’t care what acronym you want to apply here – fighting over that is a waste of time better spent in rethinking your approach to the actual work.)
Three Data Points That Make the Shift Unavoidable
XFunnel.ai Study:
Out of 1,000 AI-driven queries tested, the content most frequently selected by engines like Gemini, Claude, and ChatGPT followed a predictable pattern:
Product data (technical specs, pricing, availability)
How-to guides with clear steps
Expert Q&A formats
Structured and purposeful content wins. If your copy isn't scannable by a machine and clearly answering something, it's likely being skipped or poorly summarized. How does that plan to block AI-bots look now?
2024 LLM Alignment Report by Stanford CRFM:
LLMs prioritize content that has structured, verifiable, and factual grounding. Loose narratives and marketing fluff often get summarized into irrelevance—or omitted completely. In one benchmark, content with tables, bulleted formats, and citations outperformed longer narrative versions of the same information.ChatGPT's Explosive User Growth in 2025:
As of May 2025, ChatGPT boasts over 800 million weekly active users, doubling its user base since February. The platform now processes more than 1 billion queries every day, reflecting its growing dominance as a starting point for search and task completion.
In the United States alone, ChatGPT has 77.2 million monthly active users, reinforcing its role as a mainstream interface for information discovery, productivity, and AI-assisted decision-making. From product comparisons to troubleshooting to travel advice, these prompts are increasingly resolved before the user ever sees a link.
Graph: Growth of AI-First Queries vs. Traditional Search Queries (2019–2024)
This chart compares the estimated volume of AI-first queries (e.g., ChatGPT, Gemini) to traditional search engine queries from 2019 through 2024.
Key insights:
Traditional search continues to grow steadily year-over-year, reaching an estimated 5.1 trillion queries globally in 2024.
AI-first queries, negligible before 2022, have surged to an estimated 365 billion in 2024—driven by platforms processing over 1 billion prompts per day.
Sources:
Traditional search data: Exploding Topics – Google Searches per Day
AI query volume: DemandSage – ChatGPT Statistics
Note: AI-first query data for 2022–2024 is estimated based on reported usage levels and growth trends.
3. What AI Prefers: Content That Gets Picked
Unlike human readers, LLMs:
Prefer declarative over descriptive writing
Prioritize clarity, formatting, and embedded context
Score factual density higher than narrative voice
They evaluate not just what you say, but how you say it. Inline context matters more than brand flourish. Clarity beats charisma. Well-labeled sections and data-rich formats (like tables or JSON-LD) can improve your chances of being selected or quoted in a summary.
For example, a poetic 800-word blog post about solar panels may never be chosen. A bulleted comparison of panel types, with efficiency rates and installation specs, almost certainly will.
Graph: Content Types Most Frequently Surfaced by ChatGPT and Gemini
This chart compares how often different content formats are surfaced by ChatGPT and Gemini when responding to user queries. The data is based on patterns observed in the XFunnel.ai study of over 1,000 AI-driven prompts.
Key Takeaways:
Product Data, Step-by-Step Guides, and Expert Q&As dominate AI selection—these formats are structured, factual, and easy to parse.
Narrative and opinion content is significantly less likely to be surfaced.
Traditional SEO-style blogs perform worst, often ignored entirely by LLMs.
Source:
XFunnel.ai – What Content Types Do AI Engines Prefer?
Rethinking the Content Playbook
The old rules still matter—but they’re not enough. A great human experience won’t matter if no AI ever selects your content to be shown in the first place. Think of it like signage on a highway: beautiful fonts and clever slogans won’t help if no one ever drives that route anymore.
Old Playbook (Human Readers):
Attention-grabbing headlines
Storytelling arcs
Personality and brand tone
Hero images and emotional appeal
New Playbook (AI Intermediaries):
JSON+LD product data and schema
FAQ blocks with structured Q&A
Bulletized lists and tabular comparisons
Clean, declarative copy with no ambiguity
This doesn’t mean creativity is dead—it means creative output must adapt to be readable by machines first, then humans. Treat AI as your first editor.
Graph: Human Engagement vs. LLM Selection by Content Type
This chart illustrates the disconnect between what performs well with human readers and what gets selected by AI models like ChatGPT and Gemini.
Key Insights:
Ad copy and thought leadership earn high engagement from humans but are rarely selected by LLMs due to lack of structure or verifiability.
Tutorials, product specs, and FAQ blocks are far more likely to be picked up by AI—these formats offer clarity, structure, and immediate utility.
Optimizing for human readers without considering LLM visibility leaves valuable content out of the AI-driven discovery loop.
Sources:
Human engagement benchmarks:
AI selection patterns:
Strategic Shifts in Resource Allocation
Marketing teams still chase SERPs and optimize click-through rates. But that playbook was written in a pre-AI age. If AI is now the first impression for many users, your budget and time need to reflect that reality.
High-ROI AI-First Content Investments:
Product data feeds (structured, real-time)
Expert-sourced explainers and diagnostics
Tutorials and workflows with clean logic
Conversational answer formats for likely prompts
Deprioritize:
Long-winded blog posts
Fluffy opinion pieces (now isn’t that ironic! 😉 )
Stock-image-heavy "thought leadership"
Any content without clear topical authority or structure
Your budget should follow attention. If AI systems are now the primary gatekeepers to attention, then your team should invest in being AI-legible first, SERP-optimized second.
This graph shows what a hypothetical $100,000 budget might look like today, and where it should shift.
Graph Breakdown: What Each Budget Element Means
Traditional SEO Model
Content Writing ($35K)
Long-form blog posts written for keyword rankings and evergreen traffic, often focused on SEO tactics like density, keyword variations, and H1 structuring.
Link Building ($25K)
Manual or agency-led efforts to acquire backlinks—guest posting, outreach, directory placement—to improve domain authority and organic rankings.
Meta Optimization ($15K)
Improving meta titles, descriptions, and structured on-page elements for higher click-through rates on SERPs.
Stock Graphics ($10K)
Purchased or templated imagery for blog headers, social posts, or SEO-focused content that lacks tailored or data-rich visuals.
Social Media Amplification ($15K)
Boosting reach of SEO articles via paid social ads, scheduling tools, and branded social content that drives secondary traffic.
AI-First Content Model
Structured Product Data + Schema ($30K)
Creating detailed, machine-readable content using formats like JSON-LD. Includes pricing, specs, dimensions, availability, and reviews—formatted for inclusion in AI summaries or product recommendations.
Tutorials / Workflows ($25K)
Clear, step-by-step instructions for solving common user problems. These are highly valued by LLMs and often surface in direct answers.
Expert Q&A Production ($20K)
Interviewing SMEs (subject matter experts), compiling Q&A format content, and structuring insights in blocks. This feeds LLMs precise, trusted context for responses.
Prompt Optimization ($15K)
Testing how content surfaces in AI tools like ChatGPT, Claude, or Gemini. Adjusting headers, format, and markup so content is more likely to be selected and summarized in answers to real user prompts.
LLM Visibility Monitoring ($10K)
Tracking which prompts return your content, monitoring changes in model behavior, and creating feedback loops to improve surfacing over time. May involve tools like AlsoAsked or internal LLM simulations.
Preparing for an Agent-Driven Future
This is just the beginning. Voice assistants, wearables, and AI agents are rapidly becoming the default discovery layer for many users. They don’t present a list of blue links—they read aloud a single answer. They act, recommend, and transact on behalf of the user.
Rabbit, Meta Ray-Bans, Humane’s AI Pin—even if some fail on launch, they preview a future where digital engagement is mediated by tools that summarize and filter before anything reaches the end user.
You’re not competing for a user’s attention anymore—you’re competing to be the answer that the agent decides is worth delivering.
Actionable Next Steps:
Audit your content for AI visibility.
Evaluate existing pages for clarity, factual density, and structured formatting. Look for opportunities to convert prose into bullet points, FAQs, or tables.Use tools like AlsoAsked to understand real user questions and structure content around query-based intent. These tools help you anticipate the shape of prompts LLMs are likely to surface.
Incorporate structured data using Schema.dev to enhance machine readability. Use product, how-to, and FAQ schemas wherever applicable to increase AI visibility.
Leverage AI-first content editors like Frase or NeuronWriter to craft content that balances traditional SEO with machine parsing priorities.
Monitor prompt-based visibility.
Track whether your content is being cited or paraphrased by LLMs. This can involve manual testing or tools that audit inclusion in AI-generated summaries.
Closing Thought
This isn’t a shift. It’s a fracture.
The internet is splitting into two audiences: one human, one artificial (this is the Splinternet I’ve been talking about for years). The winners won’t just optimize for both—they’ll understand that in this new world, AI isn’t just a reader.
It’s the gatekeeper.
And if you’re not being picked, you don’t exist.
Make your content worthy of being chosen—by the most influential reader on Earth, who never blinks, never skims, and never sleeps.