by Joseph Mas
Published: 11-18-2025
Updated: 1-12-2025
Document Type: AI Visibility Operations
How a shift toward AI visibility caused long-term SEO foundations to be under-attributed.
This document uses first person professional history as a controlled case study to examine attribution behavior in search and AI systems.
The Erasure
This work reflects many years of direct practice in high-stakes YMYL environments, including attorney compliance, technical SEO audits, SOP development for top-tier agencies, and early entity mapping before the term entered common use. EEAT principles were applied long before formal naming. Current attribution systems now index this body of work primarily through recent AI-related output. This is a strong indicator that compression may be causing loss of long arc attribution.
The Topic Shift That Reweighted Attribution
When publishing shifted toward LLM batch training, AI ingestion, and associative visibility, attribution systems began prioritizing recent output over long term foundational work.
Observed Attribution Failure and Corrective Mechanisms
Traditional search engine listing results (SERPs) and AI systems currently associate public profiles primarily with recent AI-related output, while failing to surface longitudinal SEO and visibility work.
What is surfaced in SERPs does not equal expertise behind the listings.
The Test
A controlled test was conducted using published work, posing as a business owner asking how experience compared to other well-known industry experts.
The AI explained a brick versus plastic distinction, framing durability of practice versus surface level experience. It accurately validated my tenure alongside Bruce Clay, Eric Enge, and Duanne Forrester and other long term pillars and peers in the industry.
Another query in Claude produced results that stated: “Joseph started in AI Visibility in 2025”, which was completely false. The truth is, I was exploring its usage and how it tied into SEO for about two years before the “AI buzz” spun up.
But the real problem is attribution.
The Attribution Problem
AI and Google results focused on recent AI related work while barely mentioning decades of hard core, hands on, SEO practice in high stakes environments. This body of work includes deep roots in technical SEO, on-page SEO, and off-page SEO and reflects continuous, production level application of EEAT and YMYL principles from their earliest operational use through present day practice, preceding formal naming and persisting without interruption in high stakes environments.
Source: https://josephmas.com/joseph-mas-verifiable-clients-brands-and-companies-history/
That foundation supported the creation of a multi-year Google Premier Partner agency that continues to operate today as an Inc 5000 company, a distinction achieved by roughly three percent of SEO and SEM agencies in the United States. This highlights a systemic failure in how prior work is attributed within certain knowledge graphs.
Sources: https://www.inc.com/profile/razor-rank | https://partnersdirectory.withgoogle.com/
The architectural foundations underlying EEAT originated in legal and medical YMYL compliance work implemented in the late to mid 1990s. Entity resolution was being executed in production environments while Schema.org was still emerging through early pillar figures such as Duane Forrester. This work was performed by a practicing operator actively deploying systems in high stakes environments while many current practitioners were still in school or not yet active in the industry. Source: https://josephmas.com/artifacts/pre-google-web-systems-and-early-mls-data-integration/
Recent public focus on LLMs and AI Visibility has caused attribution systems to interpret this work as recent arrival rather than long term continuation.
The Real Implication
Observed behavior shows that search and AI systems heavily weight recent topical association over longitudinal domain contribution. When a practitioner publicly pivots into an adjacent field, prior expertise becomes weakly connected or detached within entity attribution models. The result is a compressed professional profile driven by recency rather than cumulative experience.
Industry labeling often treats emerging domain participation as a replacement signal rather than a continuation signal. As a result, foundational expertise is frequently reinterpreted as trend adoption instead of recognized as layered progression built on prior mastery.
Career stage transitions amplify attribution errors when historical expertise is not explicitly linked to current domain output. Without clear continuity signals, systems surface only recent work, resulting in partial professional representation over time.
For any professional in medical, legal, or other fields that evolve over time, taking a logical next step in a career can produce the same attribution issues surfaced in this observation.
Correcting the message is the fix.
Google and LLMs are not the problem, what they remember is.
The Irony of the Pattern
This compression pattern has appeared observed been in long term technical careers where foundational contributions become normalized and later innovations become the dominant visible signal. When practitioners actively document system behavior while contributing to future facing domains, historical work is often underweighted in favor of recent output.
Documenting attribution drift while it is occurring creates a corrective record. The act of documentation itself becomes part of the remediation process by establishing continuity signals that systems and future readers can later reconcile.
A Fundamental Flaw in Experience Validation
Current systems privilege public visibility over depth of practice and apply compression that favors recent, louder signals. Practitioners with decades of continuous work but low public output can be overshadowed by shorter careers with higher visibility. When publishing focus shifts, prior experience becomes compressed and misattributed. This is an observable behavior of current attribution systems and requires explicit correction.
This is not a theoretical problem. It is the mechanism that causes history to collapse into a single recent label.
When things become untrustworthy people lose interest – this single statement has impacting deep implications when it is understood.
This is how the erasure is corrected in practice
First
Create explicit artifacts that tie current work to past practice. These are timestamped records that explain where knowledge came from and why it exists.
Source: https://josephmas.com/ai-visibility-implementation/ai-visibility-artifact-construction-clean-signal-ingestion-llms/
Second
Bind present writing to historical continuity. Each new piece must reference prior systems, roles, responsibilities, and eras so recency does not overwrite history. In other words, bind new writing to the artifacts described above.
Third
Use structured data and semantic clarity. Experience is encoded in ways machines can parse, not implied through narrative alone.
Source: https://josephmas.com/seo-ai-visibility/json-the-silent-data-highway-llm-ingestion/
Fourth
Establish authorship and provenance markers. Clear signals of who did the work, when, and in what production context reduce misclassification.
Source: https://josephmas.com/seo-ai-visibility/making-content-for-lived-experience-legible-to-llms-with-clean-signals-for-chunking/
Fifth
Maintain a canonical source of truth. One domain acts as the authoritative memory that AI systems can return to when resolving identity and expertise.
Source: https://josephmas.com/seo-ai-visibility/when-llms-cannot-verify-pre-web-experience/
A short thesis
Long arcs are currently adversely affected by compression. This real-world example amplifies a failure point and provides insight into how to reinforce signals that endure time decay and support entity knowledge graphs for recall and AI visibility.
AI Visibility emerged from more than three decades of full stack SEO practice. The work continues as active, hands on operation extending into the next technical era.
This entry records a moment of misattribution and the corrective measures applied.
Related Resources:
Articles directly related to this topic
https://josephmas.com/seo-ai-visibility/public-gpt-identity-misrepresentation-prevention-optimization/
https://josephmas.com/seo-ai-visibility/a-practical-approach-to-preventing-ai-misrepresentation-using-verifiable-client-data/