The web as we know it is disappearing. Digital marketing with it.
Google AI Overviews. ChatGPT Search. Perplexity. These systems don't just search—they filter and recontextualize content. While 99.9% of the market applies obsolete SEO methods, AI is rewriting visibility rules.
Three Core Problems
Problem 1: The Search Illusion
AI Overviews and ChatGPT Search aren't sophisticated search engines. They search, filter, and synthesize according to embedded biases.
Problem 2: Content Gets Recontextualized
Your content might be retrieved but reframed negatively during synthesis. The AI acts as an active filter, not passive relay.
Problem 3: Universal Methods Are Dead
Each brand exists in unique semantic space. One-size-fits-all strategies fail because AI processes meaning geometrically.
Deep Marketing Manifesto: Brand Survival in the AI Era
The Core Problem
Marketing faces a technical crisis. AI systems like Google AI Overviews don't work like traditional search engines. They actively interpret and filter content through trained biases.
Key Insight: LLMs Are at the Heart of Everything
Large Language Models (LLMs) are the core engine driving AI search systems. If you don't understand how an LLM functions, you miss the fundamental mechanics—or worse, you mistake false positives for genuine insights.
Modern AI search isn't a hybrid system combining separate components. It's LLM-native processing that:
- Processes queries through learned statistical patterns
- Retrieves and ranks content based on semantic relationships
- Synthesizes responses through token prediction, not rule-based logic
Understanding token-level processing is essential—every word fragment influences the probability of subsequent content, creating cascading effects throughout the entire response.
Three Critical Mechanics
1. Semantic Gravity
Brands occupy fixed positions in AI's multidimensional concept space. These positions resist change because they're encoded during AI training.
2. Token-by-Token Processing
AI doesn't "read" content. It processes fragments (tokens) and predicts what comes next. Each token influences all subsequent processing.
3. Domain-Specific Authority
AI uses different authority signals for different topics:
- Healthcare: Government agencies > medical journals > commercial sources
- Technology: Technical documentation > industry analysis > marketing content
- Finance: Regulatory sources > institutional analysis > promotional material
The New Principles
Abandon Universal Methods
AI's extreme context sensitivity makes standardized approaches obsolete. Each brand needs diagnostic analysis of its semantic position.
Work With Semantic Gravity
Fighting your encoded position wastes resources. Find contexts where your natural associations become advantages.
Authenticity as Technical Necessity
AI systems cross-reference claims against statistical reality. Misaligned messaging triggers skepticism patterns.
Core Concepts: Technical Foundations
Semantic Gravity
Definition: The tendency for related concepts to cluster in AI's vector space, creating zones of attraction and resistance.
How it works:
- Brands occupy mathematical coordinates in multidimensional space
- These coordinates determine which concepts naturally associate with your brand
- Position is largely fixed during AI training and resists recent changes
Business Impact: Marketing efforts that fight semantic gravity fail. Success comes from identifying your natural gravitational field.
LLM + Search: The Hidden Filter
The Two-Layer Reality:
- Search retrieves external content
- LLM interprets and synthesizes according to pre-trained patterns
The Knowledge Gap Problem: When Google's systems identify "knowledge gaps," they fill them based on training biases, not objective reality.
Token Dynamics
Key Point: AI generates text by predicting the next word fragment (token) based on context.
Critical Insight: Each token generated changes the probability of all subsequent tokens. Brand mentions early in responses disproportionately influence entire response character.
Practical Application: Understanding which tokens trigger which response patterns is crucial for consistent AI representation.
Authority Hierarchies by Domain
Healthcare Authority Order:
- Government health agencies (FDA, CDC)
- Peer-reviewed medical literature
- Medical institutions
- Professional associations
- Commercial sources
Technology Authority Order:
- Technical documentation
- Industry research organizations
- Academic institutions
- Technology media
- Vendor marketing
These hierarchies are encoded in AI training and cannot be overridden by content volume.
Real Scenarios: How Brands Navigate AI Filtering
Case 1: Healthcare Authority Hierarchy
Challenge:
Supplement brand consistently gets regulatory warnings in AI responses despite positive retrieved content.
Root Cause:
For safety queries, AI prioritizes government sources over customer testimonials due to healthcare authority hierarchy.
Solution:
Align messaging with regulatory framework rather than fighting it. Address FDA context proactively.
Result:
Balanced responses including both regulatory context and product benefits.
Case 2: Legacy Innovation Paradox
Challenge:
Century-old manufacturer's AI division invisible in "innovation leaders" searches.
Root Cause:
AI associates traditional manufacturing with adoption, not leadership, due to training patterns.
Solution:
Target "industrial AI" and "enterprise automation" where heritage becomes advantage.
Result:
Strong presence in enterprise-focused AI searches where legacy authority matters.
Case 3: Domain Authority Mismatch
Challenge:
Traditional finance firm absent from crypto guidance searches.
Root Cause:
AI's internal prompts associate traditional finance with conservatism, crypto with innovation.
Solution:
Target "institutional crypto investing" where traditional expertise becomes differentiator.
Result:
Leading position in institutional cryptocurrency guidance searches.
Key Learning: Success requires positioning within contexts where your authentic authority aligns with AI's domain expectations.
Practical Implementation Models
Semantic Position Diagnostic
Step 1: Map current associations
- What concepts cluster around your brand?
- Which associations are strong vs. weak?
- Where do you face resistance?
Step 2: Test context sensitivity
- Business vs. consumer contexts
- Problem-solving vs. list-generation prompts
- Technical vs. general framing
Step 3: Identify authority patterns
- Which domains favor your brand type?
- What triggers skepticism patterns?
- Where are authority signals misaligned?
Content Optimization for AI
Structure Requirements:
- Clear hierarchical headers (H1, H2, H3)
- Short paragraphs (2-3 sentences max)
- Bulleted lists for key points
- FAQ format for common questions
- Cross-linked related concepts
Token Strategy:
- Identify tokens that trigger desired response patterns
- Avoid tokens that activate skepticism in your domain
- Use domain-appropriate authority language
Authority Alignment:
- Match content format to domain expectations
- Address potential "knowledge gaps" proactively
- Use language consistent with authoritative sources in your field
About Deep Marketing
Deep Marketing emerged from recognizing that AI systems actively filter and recontextualize content rather than passively relaying information.
Core Insight
Brands must understand AI's internal logic—semantic gravity, token processing, and domain-specific authority hierarchies—to achieve consistent representation.
The Approach
Diagnostic analysis rather than universal solutions. Each brand requires understanding of its unique position in AI's semantic space.
The Creator
Conceived by Sébastien Hubert, strategic consultant specializing in AI-brand interaction dynamics.
Research Bibliography
Deep Marketing theory is grounded in extensive empirical research demonstrating the technical mechanisms underlying AI-mediated brand perception and content filtering.
Recent Research (2025)
AI Bias and Decision-Making
AI-AI Bias & Authenticity Detection - PNAS (2025)
Reveals LLMs' preference for AI-generated content over human-written material, indicating internal coherence patterns that align with Deep Marketing's authenticity necessity principle.
Comprehensive Bias Evaluation Framework - ArXiv (2025)
Introduces systematic framework measuring 29 bias metrics in LLMs, revealing 37.65% of outputs contain bias. Demonstrates how alignment mechanisms create predictable filtering patterns.
LLM Hallucination Control - University of Hong Kong (2025)
Comprehensive evaluation of 37 LLMs showing systematic patterns in factual vs faithful hallucinations. Validates the "knowledge gap filling" mechanism in Deep Marketing theory.
Semantic Representation & Stability
Representation Stability in LLMs - ArXiv (2025)
Demonstrates that LLM internal representations maintain statistical stability under perturbation, confirming the persistence of semantic positioning.
Semantic Representation Optimization - Aleph Alpha (2025)
Shows how semantic representations discard "non-meaning" information while preserving conceptual relationships in subjective dimensions.
Lifelong Learning Semantic Stability - OpenReview (2025)
Demonstrates how established semantic structures create stability that resists new information conflicting with existing patterns.
LLM+Search Knowledge Integration
Knowledge Checking in RAG Systems - Amazon Science (2025)
Analyzes how LLMs filter and recontextualize external knowledge, showing systematic bias toward internal knowledge patterns. Validates the "hidden authority layer" concept.
Brand Perception Research (2024)
Brand Bias in LLMs
Brand Bias in LLM Selection - ArXiv (2024)
Demonstrates systematic bias favoring global luxury brands over local alternatives, validating embedded hierarchical preferences that resist content-based corrections.
Brand Perception Neural Mapping - University of Barcelona (2024)
Systematic analysis showing stable perceptual clustering and ranking hierarchies in latent space, confirming semantic gravity theory.
Semantic Drift Prevention - CVPR (2024)
Studies how neural networks resist semantic drift through statistical inertia mechanisms. Provides technical foundation for understanding brand repositioning resistance.
Neural Marketing Measurement - ArXiv (2024)
Develops transformer-based approach using semantic embeddings rather than scalar inputs. Demonstrates how AI systems cluster marketing signals by semantic similarity.
Cognitive Foundations (2022-2023)
Consumer Neuroscience
Brand Networks - International Journal of Consumer Studies (2023)
Neural brand processing involves complex multi-network activation patterns, supporting geometric relationship models over linear associations.
Brand Embeddings & Neuroscience - PMC (2022)
Identifies neural networks underlying brand perception as multi-dimensional processes integrating memory, emotion, and self-reference.
Brand Awareness Processing - PMC (2022)
High brand awareness requires less cognitive processing, indicating automatic recognition patterns that create "semantic gravity."