Founder of BackTier · Author · AI Visibility Researcher
Jason Todd Wade
AI visibility, entity resolution, and the systems machines use to choose.
Founder of BackTier. Author of AI Visibility Architecture, Entity Lock Protocol, and the Agentic Visibility Path.
Jason Todd Wade · Florida, United States
Core Thesis
§ 01
From Ranking
to Resolution
Search ranked pages. AI selects entities.
Search engines organized pages. AI systems increasingly organize meaning.
Before an AI system can recommend a company, cite a researcher, compare a product, or complete a purchase, it must resolve what the entity is, connect evidence to the correct identity, assess whether that evidence is trustworthy, and determine whether the entity belongs in the available choice set.
This changes visibility from a page-ranking problem into an identity, evidence, retrieval, and decision-system problem.
AI Visibility is the discipline of engineering whether an entity can be discovered, correctly interpreted, trusted, cited, included, selected, and acted upon by artificial intelligence systems.
Fig. 03 — Inspect the pipeline
Central Framework
§ 02
The Agentic Visibility Path
Four thresholds between being known and being chosen.
- 01
Citation
Does the system use your evidence?
The system retrieves, quotes, links to, or paraphrases your material. The entity is present in the evidence layer, but it remains one source among many.
- 02
Inclusion
Does the entity survive the shortlist?
The entity remains present after the system resolves identities, compresses evidence, applies constraints, and reduces the available options.
- 03
Selection
Does the system choose the entity?
The system recommends or prefers the entity for a particular intent. Selection is contextual: it depends on the user, task, constraints, available evidence, and decision policy.
- 04
Transaction
Can the system act?
The agent can book, buy, route, reserve, pay, or invoke a service. At this stage, visibility depends on machine-readable offers, permissions, identity, availability, payment infrastructure, and reliable execution.
These are analytical thresholds, not guarantees or universal stages inside every AI system. Their implementation varies by model, retrieval architecture, agent, platform, and task.
Framework System
§ 03
One discipline,
four connected layers.
The frameworks are not separate products. Each one sits inside the layer above it: a discipline, an identity method, a measurement model, and an extension into machine-mediated action.
- 01AI Visibility ArchitectureThe parent discipline
- 02Entity Lock ProtocolThe identity and interpretation layer
- 03BackTier Visibility PathCitation → Inclusion → Selection
- 04Agentic Visibility PathCitation → Inclusion → Selection → Transaction
Fig. 02 — Framework hierarchy
- AI Visibility Architecture
- A discipline for improving whether an entity can be discovered, correctly resolved, supported by evidence, included in relevant consideration sets, and selected by AI systems.
- Read the framework →
- Entity Lock Protocol
- A method for aligning distributed identity signals around one stable interpretation of a person, organization, product, place, or concept.
- Read the framework →
- BackTier Visibility Path
- A measurement model for the progression from machine use to machine preference: Citation → Inclusion → Selection.
- Read the framework →
- Agentic Visibility Path
- An extension into machine-mediated action: Citation → Inclusion → Selection → Transaction.
- Read the framework →
Selected Research
§ 04
AI Dive
Numbered analysis of AI visibility, search, agents, commerce, media, governance, and institutional power. 42 dives published to date.
- 044
Apple Built a Different AI for China
Apple · China · Jurisdiction Routing · Regulation · AI Visibility · Alibaba · Qwen
AI Visibility
· Live
- 043
The Measurement Crisis in AI Visibility
AI Visibility · Measurement · Analytics · Analyst Report
AI Visibility
· Live
- 042
Autonomous Decision Infrastructure
Decision Infrastructure · Reasoning Systems · AI Visibility
Agents and Infrastructure
· Live
- 041
The Semantic AI Stack
Semantic AI · Infrastructure · AI Visibility · Analyst Report
Agents and Infrastructure
· Live
- 038
AI, Sex, and Visibility
AI & Intimacy · Synthetic Relationships · Content Ethics
Law and Governance
· Live
- 037
AI & Healthcare
AI Healthcare · Discovery Layer · Entity Integrity
Law and Governance
· Live
- 036
The AI Economy
AI Economy · Market Structure · Machine Confidence
Markets
· Live
- 035
AI & Value
AI Value · Enterprise Strategy · Open vs Closed
Markets
· Live
- 034
Spotify & AI
AI Audio · Spotify · Podcasting · Retrieval
Search and Media
· Live
- 033
The Optimization Landscape Has Changed
AI Visibility · SEO · GEO · Digital Visibility Framework
Search and Media
· Live
Reference Guides
§ 05
Guides
Plain-language reference for the questions people actually ask about AI visibility — what it is, how retrieval and selection work, and what to change on a site. Each guide resolves to the same frameworks below.
- 01
What Is AI Visibility?
AI visibility is whether an AI system retrieves your content, resolves your identity, and selects you when it composes an answer — measured at the answer layer, not the results page.
definitional · 8 min
- 02
How to Rank in ChatGPT
There is no ranking in ChatGPT: you become eligible by permitting its crawlers, serving self-contained answer passages in real HTML, and declaring one resolvable entity so retrieved content is attributed to you.
how-to · 9 min
- 03
llms.txt: What It Is and Whether It Does Anything
llms.txt is a proposed markdown file at a site's root that gives AI systems a curated index of its most useful pages — a convention, not a standard, and no major provider has confirmed using it for retrieval.
definitional · 7 min
Featured Publication & Books
§ 06
Book · Forthcoming
The End of Checkout
How agents, machine-readable commerce, and new payment rails could transform buying by 2030.
The book separates observed infrastructure, active experiments, announced standards, and forward-looking scenarios. Protocol names and statuses are verified against their governing organizations before publication.
The End of Checkout examines the possible transition from human-operated ecommerce to machine-mediated purchasing. It explores the identity, catalog, authorization, payment, fulfillment, and trust infrastructure required before software agents can reliably discover, evaluate, select, and transact.
Fig. 04 — The transaction stack
- 01
Entity identity
Which company, product, or service the agent is actually dealing with.
- 02
Product and service data
Attributes precise enough to compare, filter, and qualify.
- 03
Machine-readable offers
Price, availability, terms, and eligibility expressed for software.
- 04
Discovery and evaluation
How the agent finds candidates and reduces them to a choice set.
- 05
User intent and authorization
What the user asked for, and what the agent is permitted to do.
- 06
Selection
The decision policy that prefers one option over the rest.
- 07
Payment
Rails and credentials capable of settling an agent-initiated purchase.
- 08
Fulfillment
Delivery, booking, provisioning, or service execution after purchase.
- 09
Receipts, disputes, and accountability
Proof, recourse, and responsibility when something goes wrong.
Analytical Models
§ 07
Instrumenting Visibility
Four illustrative diagrams behind the frameworks and publications above: how evidence narrows into entities, and how entities narrow into a single agentic choice. Models, not measured platform telemetry.
The Public Record
§ 08
Frameworks, books, studies, essays, podcast episodes, and implementations.
Organized by original publication date and revision history. Each row links to the record that establishes its date. Where an exact date is available it is shown; where only the year is documented, the year is shown without implying more precision than the record supports.
- Framework2025v2 · Revised
- Framework2025v1 · Published
- FrameworkMay 2026v1 · Published
- FrameworkJuly 2026v1 · Published
- Book2026Manuscript · Forthcoming
- Book20251st edition · Published
- Book2026Draft · Working paper
- Book20251st edition · Published
- Research2026–Ongoing series · Active research
- Project AlamoResearch2026Field study · In progress—
- Essay2025v1 · Published
- Podcast2026–Ongoing · Active research
- Implementation2022–Operating · Active research
- Implementation2024–Operating · Active research
A dated record establishes when work was published here. It does not by itself establish priority over other work.
Ideas in Practice
§ 09
Ideas in Practice
Companies, publications, and working systems through which the research is tested, implemented, and refined.
01 · Founder · AI Visibility Systems
BackTier
The operating company behind AI Visibility Architecture. Builds retrieval alignment, entity resolution, and decision-layer insertion systems so brands are correctly understood, cited, and selected by AI systems.
- Entity Lock Protocol
- Schema.org / JSON-LD
- CMS Architecture
- Retrieval Alignment
02 · Author · Numbered Analysis Series
AI Dive
A dated, numbered analytical record covering AI visibility, search, agents, commerce, media, and governance — published as the citable public timeline behind the frameworks.
- Editorial System
- Structured Publishing
- Entity Graph
- RSS / Atom
03 · Author · 2026
The End of Checkout
A 22-chapter examination of how AI agents, stablecoins, and machine-readable commerce rebuild buying by 2030. Introduces The Agentic Visibility Path — Citation → Inclusion → Selection → Transaction.
- ACP / AP2 / UCP
- MCP
- x402
- Agentic Commerce
04 · Host · BackTier Media
AI Visibility Podcast
Conversations on how AI systems discover, interpret, and select entities — distributed across Spotify, YouTube, and syndicated feeds with machine-readable episode data.
- PodcastSeries Schema
- Automated Feeds
- Edge Functions
- OG Generation
05 · Founder & General Partner
LRSVC
A Florida-based venture firm backing early-stage AI-native companies — products where artificial intelligence is the surface, not a feature. Portfolio companies get direct access to the visibility practice.
- Venture Capital
- AI-Native Software
- Agentic Infrastructure
06 · Founder · Local Media
Lake Wales Guide
A regional publication engineered as an entity-resolution case study: structured local data, machine-readable place entities, and durable citation performance in AI answers and search.
- Local Entity Data
- Structured Content
- SEO / GEO
- Editorial Ops
Disclosure: Jason Todd Wade holds ownership or a commercial interest in BackTier, LRSVC, and Lake Wales Guide, and is the author of the books and publications listed above.
Biography
§ 10
About Jason Todd Wade
Jason Todd Wade is a Florida-based technology strategist, author, and founder of BackTier. His work examines how AI systems resolve entities, evaluate evidence, generate recommendations, and increasingly participate in commerce.
His experience across ecommerce, marketplaces, search, advertising, and publishing informs an approach to AI discovery grounded in information architecture, identity, distribution, and measurable system behavior.
Read the full biography →Selected Engagements
§ 11
Selected Engagements
Jason works directly on a limited number of AI visibility, entity-resolution, research, speaking, and agentic-commerce engagements.
Direct
email@jasonwade.comThe Dispatch