Preface by CEO Rob Laporte: For months I have worked with DISC’s AI systems to arrive at (1) most firms’ best first deployments of LLMs and (2) what file systems and tools to use. I let my main LLM “employee,” a ChatGPT config named Guals, write the whole post (critiqued in the Afterword). For people who like to learn during chores or driving, another coworker AI conveys all key principles via a lively podcast:

An AI Operating System for SEO, CRO, and More, essentials in 15 minutes.

9-minute read; 3 for only the bold text gist.

Your AI Second Brain as a Business Operating System

Most LLM advice still treats AI as an oracle. You ask. It answers. You copy, paste, fix, forget, and return tomorrow to ask again.

That phase is not useless. It has saved countless hours. But it is no longer the frontier.

The efficient frontier is the shift from AI as an oracle to AI as a maintainer. Instead of merely answering questions, maintainers are bounded workflows that keep processes moving toward goals. A maintainer reads the right files, drafts the next artifact, updates a review queue, checks prior decisions, records what changed, and stops before risk exceeds its authority.

That is the practical meaning of “localized AGI” for small and mid-sized businesses. Not an omniscient self-aware machine. Not a $10 million enterprise automation stack. Localized AGI is an AI operating system that completes professional deskwork fast, using memory and your own files, rules and approval gates.

DISC’s prior companion post, “Adequate Now vs Excellent Later: The Quality–Scope Matrix for LLM ROI,” selects LLM deployments worth doing. This post explains how to build one that works.

Don’t mistake notes for memory

A folder of notes is not a second brain. It is a folder of notes.

A folder of topic-linked markdown files is better. A searchable archive is better still. A NotebookLM full of excellent sources can be very useful. But none of those alone becomes a smooth operating system, much less a worthy AI employee.

A useful AI second brain needs four connected layers:

1. An authoritative System of Record.
   This is where the truth lives. For DISC and many clients, that is Google Drive or the like. In accounting it may be QuickBooks. In a sales workflow it may be the CRM app. The rule is simple: the AI may read and stage, but it must not casually overwrite the Source of Truth (SOT).
2. A knowledge lake or ocean.
   This is where large (lake) or very large (ocean) source sets live for source-grounded synthesis: research papers, SEO rules, meeting transcripts, client histories, prior audits, policies, and decision records. NotebookLM and Obsidian serve brilliantly.
3. An LLM assistant.
   A ChatGPT or Claude Project or a similar role-customized LLM uses source files and its vast intelligence to plan toward goals via the final layer.
4. An execution layer.
   This is where the AI stops being a chat box and starts becoming a work system. Depending on the task, the execution layer might be Agent Mode, Codex, Apps Script, the OpenAI API, a Google Sheet review queue, or a local folder where the agent can operate only on approved files.

Start Small

The worst first step is to build an impressive AI scaffold.

Do not begin with a vector database, GitHub repo, multi-agent workflow, local Markdown mirror, automation platform, and five dashboards. That may become necessary. It is almost never the first move.

Begin with DISC’s Architecture Selector Gate: choose the lightest safe architecture that can produce measurable value.

For a small task, that may mean mere Q&A with your LLM assistant. For a recurring document workflow, it may mean a Drive folder and a Sheet review queue. For deep research, it may mean NotebookLM plus a focused ChatGPT Project. For cloud storage re-org, it may mean Apps Script plus API calls. For code, tests, schemas, and reusable automations, it may mean Codex with GitHub.

Prove one useful workflow first. Build more elaborate AI scaffolding only when the workflow clearly calls for it.

This prevents the most common failure in AI deployment: spending more time tuning the AI system than the system ever saves.

Start with 3 folders; add Codex when workflows earn it

If you want the fastest practical prototype, create three folders:

1. Inbox
   Put one messy input here: a meeting transcript, notes from a client call, a PDF, a rough outline, or a downloaded report.
2. Active Wiki
   This holds the AI’s working summaries, action lists, drafts, and temporary structured views.
3. Archive
   This holds superseded material. Nothing gets permanently deleted without human approval.

Then give the AI a narrow job: “Act as a librarian. Read the one file in Inbox. Extract the three most important action items, list any decisions made, list unresolved questions, and create a dated summary in Active Wiki. Do not alter or delete the original file.”

The tiny workflow teaches the whole method. The AI reads. It structures. It writes to a controlled location. You review. You learn whether it hallucinated, missed a dependency, or saved real time.

If it fails, good. You learned cheaply.

If it succeeds, repeat it on five more files. Then add a log. Then add a review queue. Then add stricter templates.

After that, integrate Codex. As of this writing, OpenAI offers the strongest practical stack for turning these bounded workflows into agentic systems small businesses can actually use: ChatGPT Projects for direction, Agent Mode for supervised browser and office work, the OpenAI API for repeatable processing, and Codex for building the scripts, schemas, prompts, apps, and automations that make the system durable.

For a fast start right now, watch the superb YouTube, “Learn 95% of Codex in 30 minutes. Then have an LLM design your next hour/s of learning around your exact goal. 

The LLM horse race changes quickly. The workflow principles here matter more than any one tool. For now Codex is the shortest path from “AI gave me advice” to “AI helped me get stuff done.” [CEO Rob Laporte believes Codex will continue to pull ahead for several months, maybe years.]

Protext the System of Record

AI has a dangerous gift: it writes coherent prose.

That coherence can hide contradictions. Suppose a meeting transcript says engineering needs 12 weeks, but a sales email promises the client delivery in 8 weeks. An overeager AI summary may smooth the conflict into a fake 10-week plan. The writing will look professional. The business logic will be wrong.

This is why raw source material must remain intact. AI should not be allowed to “clean up” your history by overwriting key pivots.

The safer model is hybrid:

1. Google Drive or the relevant business platform holds the authoritative record.
2. The AI produces temporary views, summaries, dashboards, and drafts.
3. Humans approve changes before they become official.
4. Superseded materials are tagged and archived, not destroyed.
5. Decision logs preserve why something changed.

That sounds bureaucratic until one mistake costs a client, corrupts a spreadsheet, breaks a campaign, or erases the one note that explained why a prior decision was right.

Remember LLMs’ lack of Memory

Current LLMs are brilliant amnesiacs.

They do not remember like a seasoned employee. Their project memory can be spotty. Their account memory can be incomplete. Their confidence can exceed their grounding.

So do not rely on model memory alone. Use operational memory:

• A Project Overview
• A Source Index
• A Task Log
• A Decision Log
• A Current Strategy document
• An Open Questions list
• A Memory Update section after major work
• A human-approved System of Record update

The system remembers why decisions were made, what was tried and failed or succeeded, and what must not be repeated. This addresses a major business problem: staff turnover.

Your AI second brain learns and improves on the job, just like human pros (should).

The 90-day ROI rule

A small-business AI operating system succeeds only if, within `90 days, it saves measurable time, reduces risk, improves quality, or creates a reusable asset.

If it cannot do one of those, it is probably a hobby.

That does not mean every mature system must repay in 90 days. This is where the Quality-Scope Decision Matrix pays off. More exacting projects may require a one-year ROI horizon, but early workflow tests should pay quickly because early AI wins are abundant when the scope is correct.

Start with work that needs enough quality to matter but not so much autonomy that a mistake becomes expensive. In Matrix terms, begin with narrow, repeatable, source-grounded tasks where review is easy and the downside is bounded.

Good first candidates include:

• Turning meeting transcripts into action logs
• Renaming and classifying scanned PDFs
• Reviewing website pages against a known SEO or CRO checklist
• Building client-specific content briefs from approved sources
• Populating a Google Sheet
• Comparing current work against a prior strategy document

Bad first candidates include:

• Fully autonomous accounting
• Direct QuickBooks changes
• DNS, billing, permission, or security changes
• Client-facing sends without review
• Legal, tax, medical, or financial conclusions without professional oversight
• Any workflow where the AI can destroy, publish, pay, authorize, or represent the company without a gate

For early deployment, the sweet spot is not “let AI run the company.” It is Adequate Now on bounded tasks, Excellent Later on workflows that prove they deserve more structure.

Treat the AI like a fast junior-to-mid employee with excellent reading and drafting skills, weak long-term memory, inconsistent judgment, and no business authority. That framing serves to prevent both underuse and overtrust.

What this means for SEO and CRO

SEO and CRO are ideal proving grounds because they combine large bodies of knowledge, recurring workflows, measurable outcomes, and client-specific context.

A client’s SEO Second Brain should not be merely a content generator. It should become the living operating memory of the client’s search and conversion strategy. It can hold:

• Brand brief and market position
• Core entities and sub-entities
• Buyer journeys and intent paths
• Technical SEO findings
• Content architecture
• Search Console patterns
• CRO hypotheses
• Conversion events
• Prior tests and outcomes
• Approved writing rules
• Competitor notes
• Open opportunities
• Decision logs
• Monthly priorities

This lets the AI assist with work that normally leaks across documents, meetings, tools, and memory. It can draft page briefs, compare recommendations to prior strategy, flag contradictions, prepare CRO test ideas, and help prioritize work by likely economic value.

That last phrase matters: likely economic value. Traffic is not the goal. Rankings are not the goal. Even conversion rate is not the final goal. The goal is profit, or the mission-equivalent outcome for nonprofits.

This is the platform by which DISC will deliver SEO and CRO solutions to clients such that the client can own the SEO Second Brain DISC delivers.

That ownership is essential. The client should not receive a mysterious AI black box. The client should own the core files, source maps, task logs, decision logs, prompts, review queues, and final deliverables. DISC’s job is to design, tune, train, and integrate the system so it produces results.

That is not dependency. That is disciplined transfer of capability.

“Innovation Efficiently Integrated Is Integrity Proven by Results.”

That’s DISC’s operating principle. A successful AI operating system for business enacts this principle because it is:

• Transparent: the client owns the work and the records.
• Integrated: SEO, CRO, analytics, content, and business context reinforce each other.
• Efficient: the lightest safe workflow is used first.
• Faithful: source documents remain intact and reviewable.
• Results-focused: recommendations are prioritized by ROI, not novelty.
• Portable: core knowledge lives in durable files, not just in one vendor’s interface.

A warning for the first-brain

There is one final caution: “Whoever does the work does the learning.”

If the AI reads everything, summarizes everything, compares everything, and revises every process, then the AI system improves while the human dawdles. That is the main cognitive risk of the second-brain era.

The solution is to keep human judgment at the decisive points:

• What matters?
• What is the business model?
• What trade-off is acceptable?
• What must be true for this recommendation to be right?
• What would prove it wrong?
• What will produce profit, trust, or mission value?

The AI second brain should free the first brain for higher-order work, not put it to sleep.

The practical takeaway

• Do not wait for universal AGI. Build localized AGI around one workflow. 
• Start with three folders. Add Codex when the workflow needs repeatable execution.
• Protect the System of Record. 
• Give the AI one narrow job. Review the result. Log what happened.
• Add more structured LLM automations later and only when the workflow earns it.
• Measure ROI. Repeat.

Afterword: Did Guals’ writing attain the goal?

Despite my careful training of DISC’s LLM assistants, I almost never have them write DISC’s most demanding material, precisely because such “first-brain” work anneals the writer’s deep understanding. Guals’ sentences are shorter and its word count longer than is my style. I might have spent less words conveying what a thing or process is not, and I would have delivered more details on the how and why of optimizing your AI’s learning on the job. The subheaders lack eloquence, so I rewrote some. But all in all, the essay achieved my AGI criterion: in the top quartile of pro quality (barely), while earning positive ROI via human labor saved.

BTW, “GUALS” at DISC stands for “Grand Unifying AI/LLM System.”

Overview

In Hi-Fi speakers “quality is in the mid-range”; in investing “it’s never as good or bad as it seems”; in Buddhism “avoid extremes; follow the Middle Way.” So too, LLMs occupy a nuanced middle between hype and dashed hopes. This post reveals two axioms that recenter LLMs on a table around which managers can draft predictable ROI from LLM deployments. The Quality–Scope Decision Matrix halts the epidemic of time wasted on LLM uses that fail to deliver.

I post rarely and only when your reading most words delivers transformative value. Still, if you don’t have 8 minutes to read it all, plus 3 to grasp the Decision Matrix below, here’s the 30-second gist: 

Recent AI market volatility and “Model Collapse” warnings obscure a critical reality: LLMs offer SMEs predictable ROI only if managers delegate by required quality/innovation and the project’s complexity. Introducing the Quality–Scope Decision Matrix for LLM Deployment, this post shows a Middle Way that balances transformers’ inherent averaging and memory problems with the ROI of compensatory tactics built into workflows. By distinguishing between “Adequacy Only” tasks and high risk-reward “Pro” projects, we reveal an Efficient Frontier for SMEs. This post provides a roadmap for integrating LLMs’ Ferrari engines into your corporate chassis today while steering toward the fast-approaching AGI ramp. Map small-scope deployments that pay back under three months while building expertise and components for Executive LLMs that deliver long-haul profit before and after AGI. While competitors spin their wheels, your LLM workflows chosen for dependable ROI become assets that appreciate, pay dividends, and pave your AGI on-ramp. The Quality–Scope Decision Matrix realizes DISC’s principle that “Innovation Efficiently Integrated Is Integrity Proven by Results.“

This post is not easy reading, for its two core axioms are unique to this new complex tech. Digesting the ~2 hours of linked sources will assist understanding this post, while sharpening your 360 vision. Like my prior posts, the value of this post will endure for years.

Clarifying AGI and its Birthday

Let’s leave aside whether AI stock prices and projected returns on capital show a bubble, and instead focus on business efficiencies in SMEs‘ deployment of LLMs. In contrast to SME’s, enterprise deployments are greatly augmented with dedicated compute, persistent memory, and pre-LLM automation checkpoints. In short, enterprise deployments are in a separate league, producing what looks like AGI now. However, that AGI depends on costly software augmentations and lots of pro labor. AI/LLM’s impact on employment emerges from enterprise economies of scale wherein high cost is repaid by numerous job terminations. True AGI, on the other hand, will require no such enterprise-grade augmentation. 

In my September 2023 blog post, LLMs agreed with my thesis that “autonomous synergies [essentially AGI] won’t happen for 3 years, maybe 5.” Definitions of AGI writhe like a Klingon delicacy, so let’s cook it down to a sensible middle: AGI is here when LLMs can (1) autonomously perform most deskwork as competently as 75% of professionals, (2) with at most 75% of pre-LLM cost, and (3) this capability is proven by an ample variety of firms. 

Recent news makes Q4 2027 to Q4 2028 the likely birthday of this AGI, within my 2023 3 to 5 years. A mere summary of my years and countless hours of R&D backing this projection would fill pages, so for this projection and for the key axioms below I provide only a few primer sources. The first two sources below are required for anyone navigating the emergent LLM workforce:  

1. The super-viral article, Something Big Is Happening, 2/9/26, 15-minute read time.
2. The January ’26 Phase Change, early February 2026, 24-minute video.

Sure, good AI pros can make agentic workflows that perform close enough to top pro quality today, but the goal, or threat, is that any manager could speak objectives to an LLM–just like to a human pro–so that with minimal supervision the LLM delivers. One of countless proofs that we’re far from that goal is that my firm’s well-tuned LLMs could not handle autonomous production of the core Quality–Scope Decision Matrix below. Its unique twin axioms operate by an algorithmic logic that mere pattern matching could not represent without my handholding. I surmise that such failures greatly outnumber successes.

Understanding AGI for SMEs

Current and future ROI from LLMs requires (1) discerning two kinds of deployment and two levels of quality needed, then (2) applying to those twin axioms an understanding of (a) the memory problem at the core of LLM tech and (b) LLMs’ convincing use of averaging to cover their lack of deductive reasoning. We’ll examine each of these parts, and then put them together in a decision matrix you can use, with your LLM’s help, to choose successful LLM deployments. 

The Averaging and Memory Problems

Several hours of studying how transformers work can reveal that the averaging and memory weaknesses are inherent and inextricable, although they can be mostly overcome by adding low-ish cost tactics to LLM workflows. (Here’s an excellent primer: Model Collapse Ends AI Hype.)

• LLMs work by Averaging Pro Knowledge: LLMs cannot use deductive reasoning reliably in order to apply professional concepts to unique situations. While there are compelling hints that LLMs can abstract from a pattern in one domain something like a principle that is later applied to a new problem (innovation), that evidence is anecdotal and analogical, not systematic. And rare occurrences are far from dependable. Although average pro knowledge is often “good enough for government work,” often it isn’t.
  • Noteworthy but beyond our focus here is the fascinating theory that the quality of LLMs’ output will decline as it averages ever more public content that itself consists of prior LLM averaging. Read LLMs’ terms of service closely, for one solution to this “dead internet” of mere averages is using clients’ valuable documents to inject new life.
• Insufficient Medium- and Long-term Memory. The “hyperscalers,” like ChatGPT and Gemini, purport project and account memory, but it’s spotty, omitting crucial points and work milestones that a human pro must and would remember. LLMs’ ability to replace middle and upper managers depends on human-like memory, for which semi-automatic solutions exist now. These solutions put the capstone on the essential structure of context (data, brand briefs, goals, etc,) by which a pro is informed. This guru explains beautifully: My AI Workflow. My firm has been using a semi-simple system integrated with Google Drive. Memory that evolves as months of work shift priorities is a must, yet such memory is porous and fleeting in current LLMs.

AGI depends on fixing those two crippling weaknesses.

The Two Levels of Quality

1. Adequacy Only: Often managers (and home users) can be fully satisfied by mere adequacy, and don’t need top pro quality. For example, finding weaknesses in a lease or car insurance contract, troubleshooting a network outage, guidance in routine legal procedures, a summary of current treatment options--think of how often a merely average pro’s guidance is all you need. LLM’s deliver stellar value in these areas. 
2. A Top 25%+ Pro Required: Complex high cost/risk and high benefit/profit projects exceed what LLMs can deliver dependably prior to AGI. Shoving aside the jubilant mob of YouTubers, and reading trenchant studies on LLMs’ fundamental lack of deductive reasoning, we learn that such quality is not available now and may never be within transformer (Nvidia) chips’ 3-5 year depreciation cycle. Sound studies reveal that the apparent reasoning steps in Chain of Thought (CoT) methods are actually derivative statistical constructs, not algorithmic logic that innovates like top human pros.

The Two Kinds of Deployment

1. Customized LLMs (like GEMs and “Projects”) that meet small-scope, one-off needs: Examples include paralegal work, simple contract reviews, complex yet common purchase decisions, and intro training. For these tasks, LLMs typically repay all money and time invested within 3 months and often immediately. 
2. Customized LLMs that handle all parts of major projects: Examples include website design/build, year-end CPA filings, and automated R&D systems. Don’t believe the YouTubers with contorted faces who declare this is doable now (but only if you buy their package). ROI for this kind of deployment exceeds 3 months, often 1 year.
   • BTW, I bet a YouTube channel focusing on all the things LLMs cannot do would prosper)

The Quality–Scope Decision Matrix for LLM Deployment

Like the Eisenhower or Urgent vs. Important matrices, here the above axioms are distilled into a matrix for choosing what to delegate to an LLM “employee.” 

Pointers:

• A few minutes grasping this decision matrix will save tons of time deploying LLMs.
• When AGI arrives, widespread business integration will take an additional 1 to 3 years. Firms that have deployed LLMs prior to AGI will be primed to benefit, realizing huge competitive advantage.
• The bigger the firm, the greater the percent of projects that tend to appear in quadrants 3 and 4.
• You should add this whole post and its matrix to your LLM’s Knowledge Base to help discover your unique “Efficient Frontier” in LLM deployment. (I thought about vibe-coding an app that helps people use this tool, but as of January 2026, LLMs properly setup can ensure effective use.)
• This Matrix assumes competent prompting and context (Knowledge Base & Custom Instructions).
• You’ll know you understand this matrix if you can see why the arrival of AGI will shift more work at more kinds and sizes of firms into quadrants 3 and 4.

The Quality-Scope Decision Matrix, for LLM Deployment
Left axis: Extent of Innovation & Quality,” increases downward.

Contact DISC for a free Excel or Sheet version of this matrix. Your current LLM can situate projects within the matrix quadrants to prioritize by ROI.

A Long View: ASI vs (Expensive) AGI

Hype supporting massive LLM investments seems to suppress YouTubes by the world’s top AI pioneers explaining why current LLM architecture can’t achieve Artificial Super-Intelligence (ASI). The upshot is that LLMs’ language or pixel pattern matching is very far from evolution’s base programming language whereby physical space plus time is rendered into accurate predictive models. Musk may indeed be first to achieve ASI because of his real-world data and systems via cars and robots. LLM pioneers pursue various non-language models. Yes, LLMs will achieve Artificial General Intelligence (AGI) by the CPU-intensive brute-forcing of language patterns into a simulation of intelligence, but that’s a far cry from ASI.

Appendix: The Few Crucial Guidelines LLMs Won’t Tell You 

LLMs are unlikely to give you a few important guidelines, some of which are listed below. LLMs are superb for customizing free training programs, which means that anybody selling ongoing AI/LLM training lacks smarts or integrity or both. However, recall that LLMs’ averaging of pro content tends to omit rare excellence, so that such training programs will likely omit these crucial guidelines. Furthermore, financial incentives across the web may suppress guidelines that could reduce revenue for (a) hyperscalers like Google’s Gemini and (b) channels that sell advertising to the Magnificent Seven, to their legion subdivisions and partners, and to other large firms. 

I use DISC’s standard P1 though P5 to assign priority from highest to near negligible.

• P1 – It’s crucial to distinguish LLM IQ (benchmark scores) from effective workflow integrations. As one LLM guru said 2 years ago, even an old 2023 model is like giving a Ferrari engine to a 1920s car maker: driveable benefits require integrating the power within the corporate chassis–its workflow, offerings, and marketing. For an excellent elucidation of integration bottlenecks, see this 2/27/26 video (32 minutes). Your take-away: With LLM IQs now high, your building context and evolving memory into your LLM executives is the primary determinant of successful deployment. 
• P2 – Your LLM’s Custom Instructions should require output that contains a “Contrarian Review” toward the end. This helps prevent hallucinations and LLMs’ tendency to elaborate on one pattern at the expense of valid contrary perspectives. This final contrarian section will often reveal crucial weaknesses in the prior output. (Remember as well that often such weaknesses are caused by prompts that unintentionally stack the deck in favor of the prompter’s bias.) DISC’s LLM wrote this example Contrarian Review of this whole blog post:
  • “Your framework may underestimate how rapidly hybrid systems could narrow the gap between ‘average pro’ and ‘top-25% pro.’ Standalone LLMs may remain insufficient, yet increasingly automated workflows could approximate high-level professional performance prior to AGI.”
• P2 – Optimizing your Cloud for AI/LLMs (as in DISC’s OCA package) remains your foundational step one. You’ll discover (or your LLM will tell you) why, but in short, smart management of memory and workflow is enabled by a well-organized file system.
• P2 – Optimizing your use of the Quality-Scope Decision Matrix and all phases of LLM deployments will benefit greatly from sharing experiences with peers, even with competitors. For example, the principle that open-source sharing lifts all boats underlies DISC’s SALI, which are Seminars for the Agency LLM Initiative” (ALI) and for ALI partners like LivingLocal413.org.
• See The Few Crucial Guidelines LLMs Won’t Tell You for the complete list.

Sources and a Footnote

• This post was entirely written by DISC’s CEO Rob Laporte, though proofreading and some fact checking was done by two of DISC’s custom LLMs. 
• Footnote: Attesting to LLM’s lack of reasoning from first principles, neither ChatGPT nor Gemini could write well the Overview’s gist. I surmise that the precise orchestration of axioms within the Quality-Scope Matrix is washed out by LLMs’ averaging of many apparently similar matrices that are actually quite different from this Matrix. It’s a perfect example of the current–and perhaps intrinsic and irreparable–limitations in current transformer architecture. Per the “Dead Internet Theory,” this failure to summarize may foreshadow LLMs’ drift towards the roadside ditch of mediocrity.
• The videos and articles linked above are among the very best I’ve encountered–highly recommended. In some links to videos I replace zinger titles with mere description.
• DISC’s prior 6-post series on LLMs for business remain valid and sound today, testifying to my semi-idiot, semi savant capabilities.  

Eager to deploy? Learn how in the next post on an AI OS that really works –>

Overview

This post concludes a 6-part series about LLM tech and its application to business and especially SEO and CRO (conversion rate optimization). This final post summarizes a staggering quantity of DISC’s in-house research aimed at battle-testing the practical guide below. That research is available to DISC’s clients upon request. 

Today, Google and other search engines no longer rely primarily on keyword frequency. Instead, they interpret content through Entities—distinct people, places, concepts, and things—and their relationships, stored in Google’s Knowledge Graph and processed through advanced AI models like MUM (Multitask Unified Model). This fundamental shift means that traditional keyword research tools are, in most cases, obsolete and a waste of time.

This post explains:

1. Why Entity-based SEO must replace keyword-centric SEO;
2. How to structure websites for long-term success using Entity-Driven SEO Architecture & Writing (SAW).

Why Most Keyword Research Tools Fail Since ~2022

Since the late 1990s, SEO professionals relied on keyword research tools to determine search volume, competition levels, content gaps, etc. These tools, however, suffer from critical flaws in today’s search environment:

1. They Focus on Volatile Metrics, Not Enduring Structure

• Search volume fluctuates due to seasonal trends, industry changes, or viral spikes.
• Competitor gap analysis is misleading because it only reflects current rankings, not Google’s long-term evaluation of authoritative content.
• Therefore, such keyword research invests time and money into tactics that won’t reflect a firm’s enduring SEO Architecture & Writing (SAW), wasting money and requiring frequent, costly updates (which many tool suites and firms want you to do for their, not your, benefit).  

2. They Fail to Account for Google’s Entity-Based Indexing

• Google’s Knowledge Graph links concepts based on semantic relationships, not keyword matching.
• A website’s ability to rank is determined by how well it aligns with known Entities and their connections.
• Keyword tools don’t map these relationships and instead suggest disjointed keyword clusters that fail to reflect how Google structures knowledge.

3. They Misalign Search Intent and Buyer Journeys

• Modern SEO is not just about ranking for words—it’s about serving the right intent at the right time.
• Traditional Keyword research tools, even if they distinguish informational, navigational, and transactional intent, often do so out of kilter with the real world a firm inhabits–which is reflected in Google’s topic (Entity) hierarchy.
• This can lead businesses to create content that attracts ephemeral traffic and strays from optimum conversion paths.

Example: A law firm might create separate blog posts for “best small business lawyer” and “small business contract help,” based on keyword research tools. However, Google likely recognizes both as part of the conjoined Entities “Contract Law” and “Small Business.” meaning that one well-structured page about how small business benefit from contract lawyers will achieve more enduring rank.

What Works Now: Entity-Based SEO

Rather than chasing volatile keyword trends, businesses should build their websites around the real-world structure of their industry. This means:

1. Identifying core market Entities related to the business.
2. Structuring content to reflect Entity relationships rather than isolated keywords.
3. Mapping buyer intent to the Entity-driven conversion funnel.

The result? A website architecture that mirrors Google’s understanding of the industry, leading to sustained rankings, improved conversions, and more efficient SEO investment.

Step 1: Build an Entity-Driven Site Architecture

Instead of organizing content around search volume, structure it around real-world topics and how they connect.

Example: A cybersecurity firm might traditionally create pages based on keyword research like:

• “Best antivirus software”
• “Endpoint security for businesses”
• “How to prevent phishing attacks”

In an Entity-based model, the site is structured around recognized topics like:

• Cybersecurity Solutions (Entity: Cybersecurity)
  • Antivirus Software (Sub-Entity)
  • Endpoint Security (Sub-Entity)
  • Phishing Protection (Sub-Entity)

This mirrors how Google categorizes knowledge, making the website more authoritative, a key ranking factor.

Step 2: Align Content with Buyer Intent

Once the core Entity or “SAW” structure is established, content should follow a logical conversion flow.

• Informational intent: Blog posts, educational guides.
• Navigational intent: Case studies, service comparison pages.
• Transactional intent: Product pages, demo requests.

Example: Instead of isolated keyword-driven content, a cybersecurity firm would structure content as follows:

1. Top-funnel: “What is endpoint security?” → Informational (Blog, Guides)
2. Mid-funnel: “Endpoint security vs. traditional antivirus” → Navigational (Comparison Pages)
3. Bottom-funnel: “Get a free endpoint security assessment” → Transactional (Product Pages, Demos)

By structuring content to reflect how users move through an Entity-structured decision process, businesses improve conversion rates while strengthening their Entity authority in Google’s eyes. And they build enduring SEO that does not need ongoing time and cost to tune.

Step 3: Validate Using Google’s Own Tools

Since Google’s Knowledge Graph is proprietary, businesses should validate their Entity structure using:

• Google Knowledge Graph Search API (to see if their brand is recognized as an Entity).
• SERP Analysis (checking related searches, Knowledge Panels, and People Also Ask to refine topics).
• Schema Markup (to explicitly define their Entity relationships for Google).

The Pragmatic Approach: Using Brand Briefs as an Entity Proxy

For smaller-budget businesses, full-scale Entity research may not be feasible. However, a strong brand strategy already defines the core market position and conversion flow—which means it can serve as a proxy for Entity-based SEO.

A well-structured brand brief naturally answers the key questions of Entity-based SEO:

• What is the business’s primary market category? (Core Entity)
• What specific problems does it solve? (Sub-Entities)
• How do customers move from awareness to purchase? (Intent flow)

After ensuring technical SEO is sound, an SEO firm can use a brand strategist’s insights to structure content in a way that aligns both with Google’s Entity model and with traditional marketing fundamentals.

Example: A business consulting firm’s brand positioning document states:

• “We help mid-sized manufacturers optimize supply chains.”
• That naturally translates into an Entity-based structure:
  • Manufacturing Consulting (Core Entity)
    • Supply Chain Optimization (Sub-Entity)
    • Lean Manufacturing Strategies (Sub-Entity)
    • Factory Workflow Automation (Sub-Entity)

Rather than spending resources on transitory keyword analysis, the firm structures its content to reflect real-world industry relationships, ensuring long-term SEO success.

Final Takeaway: Now SEO is Entity-Based and Long-Enduring

Since about 2022, traditional intensive keyword research has become obsolete as Google uses an Entity-based ranking model. Businesses should stop chasing short-term keyword fluctuations and instead:

1. Structure their websites based on core market entities.
2. Map content to buyers’ intent and conversion paths.
3. Use traditional brand strategy as a cost-effective proxy for Entity research.

By aligning SEO with real-world industry structures rather than ephemeral keyword trends, businesses can build long-enduring visibility—and, most importantly, Make Websites Make Money.

Sources, Credits, Future Posts:

Whereas I wrote the prior 5 posts in this LLM series, this post relied largely on ChatGPT’s summary of DISC’s extensive research. That research included use of ChatGPT’s superb new Deep Research module, which I and other researchers have found greatly superior to Gemini’s.

The 6-part series starting in September 2023 and concluding with this post offers to marketing managers, CEOs, and fellow SEO pros a complete practical understanding of LLM tech. Future posts will address specific sub-topics in profitable LLM integrations. Search engines have always been essentially large language models, so disciplined SEO pros are uniquely suited to guide firms in LLM adoption. I look forward to our journeys together in this brave new world.

This Advanced SEO Principles and Tactics podcast apprises marketing managers of ~2021+ transformations in SEO strategy, and it reminds top SEO pros of how this transformation operates. (May require several seconds to load.)

That 15-minute podcast is a conversation between two AI personalities generated by DISC’s curated NotebookLM in response to this prompt: 

Help SEO pros grasp the confluence of Google MUM, Entities, and LLM tech in SEO. Focus through (1) the PDF “Overview of SAW (SEO Architecture & Writing)” and DISC’s blog “Now Core SEO Can Long Endure.” Core thesis: Entity and Transformer tech culminate decades of effort to rank sites by what matters to aggregate minds in a given information market. Even the likes of staff conference attendance and customers’ offline interactions influence rank. The “DISC” firm is pronounced “disk.”
[My theories why it failed to pronounce “DISC” are intriguing but not important enough for this post.]

I uploaded to Google’s NotebookLM (NBLM) over 30 top articles on the topic. It summarizes well.

NBLM’s main value is in creating a curated topic library one can query for summaries, nuanced insights, and specific needles in the haystack. Its “Audio Overview” feature generated the podcast conversation using a careful 500-character prompt and a custom conversational style, which proved much better than default settings.

In theory NBLM uses Google’s Gemini LLM tech only on the documents uploaded, but I’ve seen strong evidence that some of Gemini’s pretrained database leaks in. This happened in the above-linked podcast and, more strikingly, in a non-public summary of a court case wherein NBLM mentioned a pattern in the judge’s sentencing that was definitely not indicated in any uploaded documents (it helped resolve the friend’s case favorably). I imagine that such “leakage” emerges because NBLM must use Gemini’s codebase to produce comprehensive synthesis. In most cases such use is beneficial, but as always human pros need to vet LLM output. LLMs averaging of relevant web-wide content plus curated content, together with well-structured prompts and custom instructions, helps to advance professional understanding.

I wrote this post, and no part was written by an LLM.

2/7/29 – The LLM-generated podcast on Advanced SEO was featured in the prestigious and wonderful Love & Work newsletter by Mitch Anthony. A decades seasoned marketing pro for brands big and small, and the guy who taught me more about marketing than most of my college and ad agency years combined, Mitch is a “M/Ad Man” I recommend for realizing your brand’s potential. His weekly newsletter delivers a highly illuminating and entertaining hybrid of, well, Love and Work, and it’s one of the few I never miss. It’s motivating values transcend its occasional (though increasing) descent into mere politics, just as a great artist transcends the singular human through which inspiration flows. Here’s his post:

1-minute Summary

The Past: SEO focused on keywords.

Now: The technical breakthroughs that produced LLMs like ChatGPT emerged from 2017 tech Google invented and then implemented in its core ranking algorithms, which means that by deploying customized SEO “GPTs” that cost $20/month, you own intuitive tools that rapidly produce enduring SEO.

With Google’s ranking now based more on Entities (like topics) than on keywords, one-time SEO done right endures, even as search engines evolve, because Entities represent the durable real world. 

LLMs emerged in parallel with such tech as Google’s 2012+ Entity Knowledge Graph and 2021+ Multitask (and multimedia) Unified Model called MUM, so that ranking reflects the collective human interests in your entire market, and depends less on arcane SEO rules. Now Google understands websites through multimedia and user actions both within and surrounding a website. Therefore, optimizing your website structure, content, and conversion flow for the Entities in your market replaces laborious keyword research. Your customized GPT helps execute this optimization at 20x human speed, slashing SEO costs and thus rocketing ROI.

2 Bonus Points:

1. “Generative Engine Optimization” (GEO) is mostly a salesy rebranding of traditional SEO. Some LLM-specific tactics are worthwhile, but they have been long used by good firms because search engines have been using LLM-related tech long before ChatGPT stunned the world in 2023.
2. Firms claiming you can game LLMs via Q&A that cites your website don’t understand how LLMs work–or do understand and lie to gain clients. This tactic is almost certainly futile. 

Overview: Search Engines Humanized

Designing your websites’ overall “SEO Architecture & Writing” (SAW) will make the site rank well in perpetuity. Here’s why.

AI makes search engines work like collective human brains that think about things and not merely strings of text. Google’s Entity model of search understands topics as composites of all kinds of media and human actions. Imagine that all the people in your target markets are one mind, remembering everything related to your brand: As in human brains, keywords are now a small part of what drives that big AI brain. 

Therefore, a crucial and mostly one-time task in SEO today entails structuring your website content and conversion flow to reflect Entities pertinent to your market. This work precedes and, for smaller budgets, can replace most SEO keywording. In other words, starting in 2022 the advice of a great 1960s adman would produce a compelling flow of intent though your site and to your bottom line.

Although Google has completed only ~30% of its ongoing coding (“KGMIDs”) in its Knowledge Vault, the hierarchical connections within that 30% define most if not all main Entities addressed by most websites. Where Google has not yet established more specific sub-sub-Entities, traditional SEO keyword writing will improve rank while connecting to more granular Entities coming soon.

History: Google’s 2021+ MUM Begins This SEO Revolution

Now in “SEO Architecture & Writing” (SAW), the Writing includes any website content, not merely text. This brief history of the underlying tech explains:

• The Multitask (and multimedia) Unified Model (MUM), which Google introduced in 2021, leverages advancements in Large Language Models (LLMs) and other AI to understand the relationships between different modes of information, not merely text. MUM builds upon Google’s earlier work with the Knowledge Graph (see Introducing the Knowledge Graph: things, not strings, 2012). This Knowledge Graph (or Vault) relates real-world Entities with one another, like humans do. MUM and later LLMs required the increased processing power provided by graphical CPUs (called GPUs, with “TensorFlow” architecture), rocketing Nvidia to top 3 world market cap.

By roughly mid-2022, SEO firms should have pivoted from keyword-based to Entity-based strategies. However, years of huge investments in keyword tools and procedures incentivize most SEO firms and tool suites to sell obsolete solutions. With most news for sale these days, even venerable publishers avoid explaining this, so that they don’t anger their big advertisers.

Now: Uniting Entity and LLM Models to Replace Managers

Replacement of experts and managers requires that LLMs (1) continue current improvements in math and sequential reasoning, and (2) are allowed to become “Agents” taking actions within workflows. These two things have recently started happening, but widespread availability remains 2 to 4 years away. However, large portions of most professions’ core tasks–for example, SEO and CRO writing, law, graphic design, coding, even CPA work, and plenty more–can be greatly accelerated even by prior, less capable versions of LLMs. Professional firms with integrity will tell you this, and price their services accordingly.

Uniting LLMs’ expert reasoning and freedom to act entails uniting real-world Entity structures with LLMs’ more fluid associations. Google and other big tech firms are now racing to accomplish this, and they announce monumental progress almost monthly.

For the tech-inclined reader, the following technical explanations aim to convey understanding that may help you navigate our new LLM world:

• Entity-based knowledge graphs and LLMs are fundamentally different. Knowledge graphs are rather structured, showing relationships among key nodes of meaning. Picture a city’s public transit map. LLMs, on the other hand, are more free-form, like a brain’s sprawling, tangled network of nerve connections, wherein topics are related in fine gradations of similarity (via numbers in a vector database).
• Uniting the two databases entails Retrieval-Augmented Generation (RAG), a technique essential for creating GPTs tailored to specific fields of expertise or to a firm’s cloud. RAG marries knowledge graphs or any separate group of information with LLM processes, to achieve the (un?)Holy Grail of enterprise computing: Replacing costly executives with AI. For example, a specialized GPT can integrate all State laws to create a legal GPT assistant that replaces paralegals and probably plenty of junior attorneys as well.
• AI/LLM replacement of experts and managers requires that most if not all expert information and company cloud content respectively are available to RAG. For example, because top medical journals aren’t free, retail-level medical GPTs buy limited, incomplete access, meaning that a truly good doctor can still do better. On the other hand, most professions follow established public rules and guidelines, so that dedicated GPTs can replace a big portion of billable time.
• Until LLMs achieve precise and consistent sequential reasoning and math, professional tasks that depend on near-perfect output in multiple, dependent steps will continue to require professional supervision proportionate to that complexity. Technical SEO, upon which all content production depends for SEO performance, is a prime instance of this principle: It can’t yet be handled by LLMs–in fact they hardly help at all. But this ~1/3 of SEO can be done once to endure; it is table stakes in the game of website success.

The Future: The Durability of Entity Classifications

Chats with LLMs, like those linked below, point out that Entity databases such as knowledge graphs may change substantially. However, though they and LLM’s algorithms and pre-trained databases will change, Entities refer to enduring things in the real world (shoes, Tolstoy, lavender) as well as to abstract enduring concepts, like democracy, expensive, brilliant, zero, and evolution. Therefore, except in new and highly specialized fields for which Entity classification is not yet established, websites’ Entity structure can be designed now to endure in perpetuity.  

In July 2024, top LLMs agreed with the SEO durability thesis of this blog post, adding the crucial caveat that sites’ technical SEO foundations remains outside LLM provenance. Good, honest SEO firms have long known that a sound technical SEO must precede all subsequent content optimization. The LLMs also emphasize that keywording remains important; however, now SEO firms should use Entity research tools that first ascertain a business’s knowledge graph of offerings and market segments. Then such tools or else LLMs quickly generate keyword clusters under each main Entity, so that SEO GPTs produce market-focussed, keyworded content at 20x human speed. For details, see this ChatGPT discussion (about half way down, though the first half elucidates core tech) and Gemini’s synopsis (the Claude LLM echoed those two).

Details for data scientist only: Some firms are now pioneering the use of LLMs to make entirely new structured knowledge graphs, but those would still refer to the same real world that websites and current search engines do. For more about this, see Neo4j Finds the Vector for Graph-LLM Integration and Unifying LLMs & Knowledge Graphs for GenAI: Use Cases & Best Practices. 

Is “GEO” Mostly Marketing Spin?

Now some SEO firms use the term “Generative Engine Optimization” (GEO) to attract clients. However, GEO entails what good SEO firms have done for many years. Indeed, the neural net connections implicit in both LLMs and search engines’ knowledge graphs mirror such PageRank-like connections as interlinking, reviews, mentions, conversion rates, subsequent off-site actions, and other measures of website helpfulness and legitimacy. So both old-school SEO and the current SEO-CRO approach explained in this post encompass “GEO.”

One potential new SEO tactic emerges from the possibility that a user’s interactions with an LLM could be incorporated into future updates to the LLM’s core pre-trained database. For instance, if an LLM user frequently mentions a website that provides a filtered search for door locks, the LLM subsequently might provide that website to anyone asking about what kind of lock to buy. Such strenuous manipulation would likely prove futile because a single user or group of users is a spit in the ocean of LLMs’s vast data. Still, this tactic is worth annual investigation.

Sources and Credits:

DISC’s prior 17-minute, 3-part blog series on AI/LLMs, or equivalent knowledge, helps comprehend this post and plan your firm’s profit.

This post’s writing came from DISC’s decades of web marketing R&D and practice. LLMs were used only to verify some statements and to help with basic edits for clarity.

Both theory and practical guidance are summarized stunningly well by DISC’s LLM-generated podcast linked in the next post, DISC’s NoteBook LM on Advanced SEO Principles and Tactics –>

Overview

This is the third in a series of posts about AI/LLMs at DISC. Here are the first and second.

3/10/26: ChatGPT replaced “GPTs” with “Projects.” While principles in this post remain valid, this is the least evergreen of DISC’s 6-part series on AI/LLMs.
8/16/24 Advisory: See DISC’s service, Deploy Your Professional LLM

This 3.5 minute video, How To Create Custom GPTs For Beginners, outlines steps in making GPTs for most professional services (details may differ now). 

(DISC’s clients have read the rest of this post in their 2024 plans, except for the final ORRR section.)

Imagine hiring a dedicated professional for each main area of web marketing, each of whom comprehends ~95% of her profession (more than even the best human pros), recalls and applies all your specific instructions and general guidelines, produces at 100x human time, and the total cost of all these professionals is $20/mo. That’s AI/LLM properly set up. Human experts are still needed to:

1. carefully train these new AI/LLM “employees” with web marketing rules and sources;
2. select and input the firm’s files (website sections, internal sales and profit data, etc.);
3. review output quality, and tune the prior two processes accordingly. 

The first two tasks comprise the bulk of ~12 months labor. Subsequent years mean that web marketing firms will be needed for ~1/2 the time of prior years, with that time probably declining yearly. Integrating the separate main fields of all AI/LLM-powered web marketing (e.g., SEO, PPC) with clients’ internal and market changes will comprise the main role of web marketing firms in 2025+.

Contrary to quick intuition, the most precisely rule-governed processes are last to be handled well by AI/LLMs. In web marketing, this order of AI/LLM tractability is: (1) content-level SEO, (2) CRO and ROI reporting, (3) PPC, and (4) website and server management, programming, and technical SEO.

Passing Savings to Clients

Web marketing firms that are truly focused on their clients’ interests will start now to set up systems that enable AI/LLM to greatly reduce clients’ costs in 2025 and after. Good web marketing firms that use AI/LLM will be able to serve more clients to compensate for less revenue per client. For at least 5 years, domain experts (in marketing, law, accounting, most professions) will remain crucial in orchestrating AI/LLM efficiencies and results. But the stark fact is that much fewer of those pros will be employable–estimates range from 40% to 80% reduction over 5 years. Proficiency with AI/LLM systems will determine success for most businesses and organizations. 

The Two Main Parts of AI/LLM Systems

ROI reporting systems track quarterly OKRs while the following two AI/LLM systems are implemented to produce years of high ROI. 

1. General preparation for AI/LLM assistants that will increasingly handle middle and upper management functions: This includes preparing firms’ cloud file systems’ for AI/LLM assistants. Such systems align with decades of best practices in networked file management and even in library science, so are beneficial regardless of AI/LLM. Google and Microsoft AI/LLM assistants or standalone LLMs like ChatGPT with plugins comprise the primary commercial goal of venture capitalists’ LLM investments. AI/LLM assistants serve to expedite and amplify human expertise as well as operations management. This setup will help achieve web marketing goals and will also make firms well disposed to AI/LLM’s help in all they do.
2. Careful deployment of specialized AI/LLMs to amplify and expedite human expertise in each field of web marketing: These fields are integrated under the decades established model of target markets and buyer journeys. Google’s multimedia MUM & Entity models replace the old keyword model (illustrated in Google’s recent–and somewhat deceptive–Gemini announcement). LLMs properly setup can handle the specialized details of each service area while keeping in “mind” those two sets of models. The order of main web marketing services to first benefit most from AI/LLM is: SEO, CRO, PPC, and lastly ROI Integrations.

ORRR

At DISC “ORRR” refers to the Objections, Rebuttals, Replies Rule. We ask our workers to process salient threads of ORRR before proposing system changes.

Won’t rapid developments in AI/LLM undermine current setups?

As in most tech fields, sufficient understanding of the technology shows the arc of future change. 

Years ago Google introduced BERT (a change in organic ranking based on GPT-like Transformers) and the MUM & Entity models, both of which were predictable antecedents to the AI/LLM revolution. Google’s Gemini LLM will operate on the same foundational tech, meaning that the two main parts of AI/LLM systems described above could be ported efficiently to a competing platform if ROI analysis justifies the move. Such a move is akin to migrating a website to a new WordPress theme or a new CMS. 

Before Open AI released custom GPTs, tons of third-party plugins were hawked, most of which merely wrapped ChatGPT in pre-written verbal “prompts.” Sufficiently informed people knew that most of these would be rendered obsolete by OpenAI’s arc of change, and that’s exactly what happened. 

Of course GPTs will need periodic tuning, which comprises the bulk of what professionals will do in 2025+.

Aren’t AI/LLM “Token limits” too small to succeed?

AI/LLM Tokens are units of meaning equivalent to a single word. Current ~100,000 token limits are sufficient for most web marketing GPTs. For context, the novel The Adventures of Huckleberry Finn is ~82,000 words (tokens). These limits will grow–in 2023 they more than tripled. Even if token limits expand well beyond most firm’s input-output needs per service, experts should still strive for economy of inputs or “eloquence.” For example, which sections of Google’s 3000 pages of SEO help should be selected as inputs guiding work in an SEO GPT?

True, AI/LLM assistants for Google WorkSpace or Microsoft 365 must encompass much more than current and near future token limits. However, the primary and heavily-invested goal of such giants is to make AI/LLM assistants that handle firms’ total cloud and workflow. Several methods work now, and the frenzied pace of improvements will accelerate. Therefore, good hygiene in clouds’ information architecture will poise firms to benefit now and in the future.

Wont AI/LLM’s cloud access risk security and privacy?

You use cloud storage now, right? Risks here are essentially the same. Most businesses accept such risk in exchange for the enormous efficiencies of cloud storage and workflow. As for private clouds on a firm’s own servers, consider how many of those have been hacked–like at the FBI, NSA, and too many Fortune 500s. The likes of Google and Microsoft know that their bread and butter depends on security; their massive economies of scale produce far better security than most firms can engineer themselves.

Why not wait for web marketing GPTs for sale on the ChatGPT store or equivalent?

Waiting may make sense in some professions standardized and regulated for decades, like law and accounting, but in web marketing there are few top minds from whom I might buy a pre-made GPT. One would still have to adjust the generic GPT to what matters most for a given firm, then select and upload documents, vet the output, and tune. Good web marketing firms will stay apprised of worthy enhancements to roll-out quarterly or yearly. Given how few web marketing firms are owning up to the truth of AI/LLMs impact–just as most did not admit that the MUM & Entity models replace the old keyword paradigm–I question whether a GPT store will ever have good SEO GPTs. If any come to exist, finding them in the vast rubble of mediocrity will require the very expertise one seeks. Lastly and most importantly, truly good web marketing firms whose GPTs I would trust probably wont sell their core intellectual capital for the pittance of typical store revenue.  

Please don’t hesitate to contact DISC with your concerns, questions, or suggestions.

(No LLM was used for writing, only for a quick proofreading.)

See the next post in this LLM series: Now Core SEO Can Long Endure. –>

Overview

My first AI/LLM blog post explains how AI/LLMs work, shows that they will replace many managers while enabling competent adopters to prosper, and outlines strategies. The hypersonic pace of change–literally by week–buttresses that post’s thesis. This second post further articulates strategies and outlines specific tasks that DISC has started, and you should too.

Future posts here will articulate AI/LLM trends in web marketing. One crucial point to remember is that the ~3-year Entity–MUM revolution in search marketing remains unaffected by the AI/LLM revolution. Both AI/LLM and Entity-MUM SEO entail collective human thought and responses. As with ~3 decades of SEO, the underlying rules by which ranking operates are vastly more sophisticated than the rules dictating actions we take. Search marketing–and all marketing is about people searching for solutions–now returns to timeless marketing precepts developed over several decades by leading ad agencies and business schools.   

AI/LLMs encompass the sum of all knowledge. Maybe not the entire Library of Congress and all professions’ publications yet, but close enough. AI/LLMs can learn to innovate, and much faster when guided by industry professionals with AI/LLM literacy. Merging LLM’s core pre-trained databases with task-specific files you attach enables AI/LLMs to encompass all relevant to your deired output.

Customing LLMs to Achieve Goals

On 11/6/23 OpenAI introduced GPTs as purpose-focused zones any subscriber can make. This entails inputting your firm’s documents, writing executive guidance just like for a human manager, and tuning a few settings. It’s like hiring an executive to head up an initiative, such as optimizing PPC or SEO, or hiring and onboarding, or vetting grant applications–anything. Echoing my first post, Can AI Replace Upper Management? Lines in the Sand, this process indeed replaces upper management labor. But not all of it, as expertise is needed to choose, set up, tune, deploy, and error-check custom LLMs’ professional output. 

The amount of your firm’s data and documents that AI/LLMs can digest is expanding rapidly, but we’re far from complete inclusion of firms’ cloud content. Methods exist for managing this lack, like “Sparse Priming” and methods for mimicking human memory. Even without those methods, ChatGPT can now digest over 100,000 words along with images and data. 

DISC is now setting up LLMs to improve all we do for clients. Client can adapt these LLM schemas to serve various business processes and objectives.  

An Example Based on SAW

“SAW” is DISC’s SEO Writing & Architecture guidance we’re wrapping up for clients this year. In past years, DISC delivered such guidance in docs that enabled clients to write high-ranking content. (A lookback: around two decades ago, when SEO was new-ish, DISC sold many “CMS-SEO” guides for several thousand dollars each–like buying a $7000 article!) Now clients will be able to deposit text and image content, and soon videos too, into a clone of DISC’s upcoming SAW LLM in order to produce content according to the SAW rules. In other words, you won’t have to load all the rules into your brain’s RAM, nor frequently refer back to the guide, in order to make content that ranks and converts highly. 

Sources

Henceforth DISC’s documents will have this Sources appendix, to point to further learning and indicate how much was written by an AI/LLM vs. DISC people. AI/LLMs don’t know what they “forgot” to consider, so professionals need to provide proper priming and parameters. Therefore documents like this should show that professionals stayed closely involved. Also, professionals must keep using their minds. Imagine a college grad who never wrote a paper: he would lack the muscular mental agility needed to manage real-life business. 

• I wrote this entirely myself, but proofread it via three top AI/LLMs; they made many good suggestions but also missed a few weaknesses I resolved.
• Vast research informs this post. Here’s advice on sources, updated 8/22/24:
  • In general, if a Youtube video sports a young face with a big, exaggerated expression, pass on it. A few of them are good, but most are over-hyped. Instead, follow established leaders. IBM has good ones, and recently Google started a great “Deepmind” series. (Google has been forced to damage its retail-level Gemini LLM in response to big gov pressure to lie, though this seems to be improving as of late 2024).
  • Months ago I recommended https://www.youtube.com/@DaveShap, and several of his 2023 videos remain worthwhile, but by mid-2024 his interests became more tangential. Occaisionally he still posts great stuff.
  • Periodically check https://openai.com/blog.
  • DISC is preparing a more complete resource list to reveal in a future blog post.

See the 3rd post in this series: Deploying LLMs for Web Marketing. –>

Overview

You need to read this. I mean it. 

Yes, it’s long (9 minute read, ~30 with linked docs), but so are your days of work now totally impacted by the emerging superintelligence penetrating all facets of business and life. Summaries often fail to convey actionable understanding, but time is precious, so here’s the gist: 

Managers are scrambling to understand LLMs and their impact on business life and bottom lines. They should. Using search marketing as a lens to focus on any business, this post provides a conceptual understanding of and strategies for AI/LLM’s strengths and managerial limitations. Specific tools and procedures will be spelled out in separate documents for DISC’s clients. 

DISC had our version of Google’s “Code Red” when ChatGPT rocked the world several months ago. Each DISC person produced a report leading to my master report that feeds this post. DISC’s model of the 3-years Entity/MUM revolution in search marketing remains intact, but some procedures for harnessing that model have been transformed by AI. I challenged the top 3 LLMs to undercut the thesis of this post, and all three supported it. I strongly encourage reading the bolded parts of that conversation referenced below, wherein the LLMs deliver enduring comprehension of their nature–with appalling intelligence. 

What qualifies me to write this? I address this more at the end. The human brain processes about 10¹⁶ synaptic transmissions per second (1,000,000,000,000,000), and I’m not in charge of any one of them. Thus I claim no bragging rights for my gift, paid by deficits in other areas, for seeing the evolution of complex systems. My late 1990s writing about the direction of search engines proved true over two decades, and I launched DISC into search marketing before Google existed. For about three years now clients have heard me propounding the importance of integrations among web marketing services: Here, now, you’ll grok why.

A 2-Dimensional Model for Understanding the Integration Imperative

Think of an image of a brain’s 3-D neural network, where an input like an idea spurs an electrical web of connections throughout the brain’s parts over a brief time that adds the 4rth dimension. Let’s reduce that to 2 dimensions. The LLM takes the request or “prompt” as a single point, and outputs a line extending left and right by stitching information from its database’s content related to the prompt topic (hence Google BERT’s “Bidirectional Encoder Representations from Transformers,” which catapulted the LLM industry). LLMs do this stunningly well. Now add parallel lines representing related topics or fields of work, for example a line each for SEO, PPC, CRO, ROI Reporting, and client sales data concerning relative profitability of kinds of leads. Superior results come from connecting those horizontal lines with vertical lines at key points. Those vertical connections flow strategies and tactics from one horizontal line to the other, enriching each with one another’s AI-assisted human labor, resulting in a synergized mind-map of strategy. LLMs are not good at knowing where (and when, over time) to draw those vertical lines that synergize domains of knowledge, and this is why human professionals’ integrations remain essential. I envisioned this dynamic years ago when I produced DISC’s Brand Values statement: “Innovation Efficiently Integrated Is Integrity Proven by Results.”

In short, LLMs draw great horizontal lines of actionable knowledge; humans bend and connect those lines within mind-maps encompassing how your firm adds value. 

Strategies for Succeeding in an AI/LLM World

Prospering will require three main processes:

1. Reduce operations costs by using LLMs to increase efficiency or quality or both within lines of all kinds of work.
   • This applies to any firm, whether a web marketing or law firm or a nonprofit promoting Macular Degeneration solutions or a sawmill producing and selling wood flooring. Examples include generating marketing materials, proofreading web content and sales emails, analyzing spreadsheets to show where sales teams can improve results, scrutinizing office builder or IT firm contracts, tuning tax strategies, and much more.
   • The successful firms will determine whether or when each investment in an AI/LLM tool will pay off.
2. Create systems that integrate all teams’ diverse specialists–including your customized LLMs–at just the right points in their lines of work, in order to find holistic synergies among complex multi-part projects (the lines in the 2-dimensional model above).
   • This pertains to knowledge work, like tax accounting or medical diagnostics, but it applies equally to knowledge-intensive projects at any firm, as in regional economic forecasting, production of marketing or training videos, and optimum arrangement of factory machines. 
   • Executives must stay minimally tuned to fields adjacent to their expertise and roles. 
3. Now firms must allocate part of their R&D & training budgets to keeping abreast of AI/LLM tools.
   • Remember that the AI/LLM field is in hyperdrive now, producing scads of startups and tools that seem amazing but are far from complete. Most of the good ones will be coopted by the major LLM providers, like OpenAI, Google, and Claude. Often tools that are good and complete will not actually improve results per unit of your time when factoring total costs and time to vet, set up, and tune. 

What Happens After AGI?

Artificial General Intelligence (AGI) is the singularity event horizon nobody can see past. Elon Musk says it will be here between 3 and 6 years, and I read that the average of AI professionals’ estimates is 10 years. In the monumental 9/5/23 book, The Coming Wave, the author and co-founder of Deep Mind stresses that AI’s rapidly increasing capabilities matter more than do the future span of months during which consensus settles that AGI has in fact been achieved. When it is achieved, then, by definition, AI’s autonomous work will match or exceed all that consummate professionals do now, including innovative integrations. I strongly believe that when that time comes, the world will need a whole new social economy. 

Will You Lose or Gain Income, and When?

It’s obvious that AI/LLM will change how we work, but the two overarching questions facing DISC and many kinds of firms and workers are:

1. Whether or to what extent AI/LLM will reduce income within the next few years. 
2. How can one prevent lost income or even increase it by harnessing AI/LLM technology? 

The projections below pertain to knowledge-based service firms, but the principles extend to most kinds of organizations. The timing may be off a year or two, but that would little change what you should start planning if not doing now.   

Logically, technologies that let non-professionals conduct specialized work will erode consultancies’ aggregate revenues. Thus, we can expect accelerating declines in the total of professional firms’ billing over the next ~3 years. The main drive now in global AI/LLM investments is to reduce the time & cost of middle and upper management, as with Google’s Duet and the public firm C3.ai run by the founder of Siebel Systems, which was sold to Oracle for many billions. In web marketing this trend will be slowed by LLM-fueled increases in ROI, which pulls revenue from other budgets, but that only slows the trend. Within those averages, there will be an accelerating bifurcation between firms that integrate AI/LLM and those that don’t or do so poorly, resulting in fewer profitable firms within most industries. Small firms can be among the winners by deploying AI/LLM within main lines of work and by deftly integrating those lines. The “Q&A with 3 LLMs” below shows that such integration will remain a weakness of AI/LLM for at least 3 years, probably 5, possibly 10. 

I expect that for about 3 years, large firms able to afford LLM technology will outcompete smaller firms, but then the tide will reverse as SAAS options enable small and medium-sized firms to integrate top-flight AI/LLM throughout their operations, and as consumers assert sovereignty “of the people, by the people, for the people,” to free themselves from corporate totalitarianism.

Q&A with 3 LLMs Regarding the Integration Imperative 

The riveting conversation recorded in DISC’s public Google Doc, Lining Up AI/LLMs for Upper Management, supports this post’s central thesis summarized by Claud 2: “LLMs still lack the innate ability to autonomously integrate insights across datasets in creative and synergistic ways.” All three LLMs concur that such autonomous synergies won’t happen for 3 years, maybe 5, but almost certainly will in 10. 

That “Lining Up” doc’s ~25 minute read reveals LLMs’ brilliance in explaining crucial distinctions; the ~4 minutes of bold text may convey the essence. The doc progresses as follows:

1. Explains the problem of interdisciplinary synthesis in LLMs, in prompts 1 & 2;
2. Further explains LLM’s inability to autonomously integrate diverse disciplines, in prompt 3;
3. Resolves one crucial contradiction in the above, ultimately reinforcing the central thesis of this post, in prompt 4;
4. Addresses the estimated years needed for AI/LLMs to essentially replace most middle and upper management, in prompt 5;
5. Lists key vocabulary to help you walk in this new landscape. 

I remain guardedly optimistic that the LLMs’ support of my thesis is not pre-trained by their human creators to minimize threats to executives, in order to promote adoption and protect venture capital’s huge investments.

What About Your Search Marketing?

DISC has been using AI for years, especially in PPC and CRO. We started using AI/LLM’s in early 2023. We will be advising clients on setting up their systems to facilitate AI/LLM assistants for search marketing. That setup will help clients in all their knowledge work. Since its 1997 inception, DISC has waited for the ragged vanguard to settle into smooth proven results, an approach we bring to our ongoing evaluation of AI/LLM tools and processes. Avoiding dead-end paths, DISC will lead clients into a prosperous future, as we always have.

Finally and reluctantly, here are a few words about my relevant qualifications: Rob, Your Semi-Idiot, Semi-Savant.

See the 2nd post in this series: GPTs for Upper Management. –>

• 6% for one-time jobs.
• 10% for 6-month or longer monthly contracts. 
• Your payments never expire as long as the work continues.
• Increases in work that the client may agree to after the initial agreement, and work other than what is in the initial agreement, are not factored in your payments.
• You are paid automatically within a week after the client pays DISC each invoice. 
• DISC is happy to refer our contacts to businesses we trust, but we never accept finder’s fees ourselves because that may imply our bias for kickbacks.

Why you need a reputation management SEO Strategy

• Prospects often Google your business before becoming your customer. Proactive reputation management SEO prevents huge potential losses from negative results about your business in Google that drive prospects (& potential employees) to your competitors.
• The coronavirus pandemic has further pushed businesses online, increasing competition and the potential for negative SEO (unethical technical practices).

From fake reviews to unethical competitor tactics, the search engine results pages are minefields marketing teams must navigate. Here are some real life examples of what can go wrong. (Identifying information is blurred out for client confidentiality.)

Example #1: A Featured Snippet Nightmare

Company A hired us to manage the online reputation of their president, who had a negative press article published a few years back. We had been successful in getting a positive image/paragraph featured snippet for his name, when Google suddenly switched to show the accusations for the featured snippet! Pictured below is a screenshot of the featured snippet on desktop that took up the right side of Google results.

The Solution: We identified patterns in featured snippets displayed for this person’s name, then published content to “bait” Google to switch from the negative press to one of our sites. We were able to get the featured snippet switched back to our site this way, but it still took five weeks. The long term strategy is for Google to display a knowledge panel for our client, and monitor featured snippet results weekly!

Example #2: Your Own Social Property Used Against You

Company B is the leader in a niche construction industry. They have a verified Yelp profile despite no reviews and little engagement on that platform. For those reasons it would be easy to dismiss Yelp as a social property to spend time on. However, when we Googled “Company B” we found Yelp ranking in the top 5 with the title “Company B – Closed For Business – Company B Address”.

The Solution: Just as easily as the anonymous misdoer set our client’s business closed, we marked it open. Yelp updated the HTML (blue) title within a couple weeks. With Yelp ranking so high for this business name, we encouraged the business owner to log in and check for anonymous edits like this at least a few times a year.

We had just onboarded this client, so were unable to ascertain exactly how much business was lost because of this confusing signal. Going forward, we monitor the search engine results pages (SERPs) more frequently than we would, if this client hadn’t experienced this unethical competitor tactic. This situation is a good example of a serious issue that can be dealt with quickly in the recurring monitoring that fits into a well-planned reputation management SEO strategy.

How to Develop a Reputation Management SEO Strategy

1. Write down your ORM (online reputation management) needs with regards to frequency, queries, and search engines to monitor.
   1. Frequency. Many businesses would do fine with a monthly task list. Businesses who have already been victim of negative SEO or reputation attacks would need a higher frequency such as bimonthly or weekly.
   2. Queries. Company name, aliases, founder, CEO, and other notable team members may all be keywords worth tracking. Pay special attention for any queries that produce Google knowledge panels (see Obama’s for an example) as these can be claimed and optimized. Google tends to build knowledge panels for book authors, so check for any in your company.
   3. Search Engines. Budget and web traffic acquisition data via the likes of Google Analytics should make figuring out which ones matter easy. Google is likely the most important search engine for your website followed by Bing and others. Medium sized businesses can typically get away with monitoring just Google and Bing.
2. Build Your Toolset.  Find tools that fit the needs you determine in step #1. Keep in mind that the greater the need the more you may need to invest in solutions. These free yet fundamental Google tools are a good place to start.
   1. Google Alerts – Set automatic emails for news in Google that include your chosen keywords.
   2. Google Search Console – See search data directly from Google.  In the performance section, filter by queries containing your keywords to see the data of what people search in relation to your keywords. What to do here could be an article of its own.
      1. Beware: GSC data is sometimes lost by Google. They will tell you… Usually.
   3. Google Search – Manually monitor results for your keywords.
      1. Beware: Google personalizes search results. If you search your company name then click all of the results besides your site, Google may rank your site lower next time.  A virtual private network (VPN) and private browsing window will get you more neutral results.
3. Set clear procedures after publishing new content.
   1. For example, after updating the company blog with a new article:
      1. Request indexing for the webpage url in Google Search Console (This quickens the process).
      2. Send an email to your subscribers.
      3. Post a linked summary to Linkedin.
      4. Over the next few days, the author of the article reviews and replies to comments.
   2. Marketing managers can oversee or delegate this process, but it must integrate with the recurring monitoring using tools like those discussed above.
4. Deploy and continuously refine your strategy. As technology and conditions change your strategy must adapt. Committing to weekly or monthly checkups allows you to respond quickly to opportunities and risks.

Applying the PESO Media Model

When discussing reputation management and digital marketing it is helpful to break down media types with the PESO model. Here are the four types in PESO: Paid, Earned, Shared, & Owned with examples of how they fit into an online reputation management SEO strategy.

Paid Media

Google ads, social media ads, or any other paid digital marketing channel. Search engine ads can be used to immediately push down all first page results for a keyword, useful in cases when many people are searching for “Your Company + Negative Term” due to bad press.

Earned Media

External press coverage, blog mentions, natural backlinks. Noteworthy real world actions such as organizing events encourage earned media.

Shared Media

Social Media. Regardless of social media’s role in your digital marketing, it can impact reputation management SEO significantly. In example #1 above, we saw Bing produce their version of a Google knowledge panel for our client after optimizing their LinkedIn profile.

Owned Media

Your website(s). Websites you own are often central to reputation management SEO. A high-profile ORM case may include creating a personal blog for a client. Websites are helpful for publishing positive content about your company and connecting your social profiles for search engine features like Google’s knowledge panel.

In summary, you must monitor and coordinate your efforts within the media mix of social media campaigns, blog posts, and news articles that mention your business. Be willing to test new ideas and learn from how your prospects react to your content. Engaging them is part of Online Reputation Management (ORM) and SEO.

When do you need to hire an ORM/SEO professional?

Equipped with a strategy outlined above any marketing manager can stay on top of reputation management SEO most of the time. However, certain situations can arise that call for specialists. Here are some typical reasons:

1. An authoritative or news site publishes negative content about your company.
2. You receive a manual penalty in Google Search Console for reasons related to foul play such as unnatural links to your site that you didn’t build.
3. Google Search Console or an equivalent backlink tool shows a surge or steady increase of negative SEO backlinks to your website. Note that random spammy links from image collecting sites, or sites like theglobe.net, should be ignored by Google anyways. Negative SEO links can come from sites that look spammy but are specifically designed to encourage an unnatural link penalty from Google. This is, admittedly, a nuanced topic. Don’t hesitate contact us for a free consultation while we offer it during the pandemic.

Reputation management SEO is a branch of digital marketing growing in importance for agencies and companies alike. Recently, we have noticed an increase in online reputation management issues for clients who didn’t hire us for ORM, but whose profit is affected by it. Marketers must embrace reputation management SEO to succeed online. Contact us today for assistance in developing and implementing your strategy.